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transformers | transformers-main/src/transformers/data/metrics/squad_metrics.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Very heavily inspired by the official evaluation script for SQuAD version 2.0 which was modified by XLNet authors to
update `find_best_threshold` scripts for SQuAD V2.0
In addition to basic functionality, we also compute additional statistics and plot precision-recall curves if an
additional na_prob.json file is provided. This file is expected to map question ID's to the model's predicted
probability that a question is unanswerable.
"""
import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
return re.sub(regex, " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def get_tokens(s):
if not s:
return []
return normalize_answer(s).split()
def compute_exact(a_gold, a_pred):
return int(normalize_answer(a_gold) == normalize_answer(a_pred))
def compute_f1(a_gold, a_pred):
gold_toks = get_tokens(a_gold)
pred_toks = get_tokens(a_pred)
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if len(gold_toks) == 0 or len(pred_toks) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def get_raw_scores(examples, preds):
"""
Computes the exact and f1 scores from the examples and the model predictions
"""
exact_scores = {}
f1_scores = {}
for example in examples:
qas_id = example.qas_id
gold_answers = [answer["text"] for answer in example.answers if normalize_answer(answer["text"])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = [""]
if qas_id not in preds:
print(f"Missing prediction for {qas_id}")
continue
prediction = preds[qas_id]
exact_scores[qas_id] = max(compute_exact(a, prediction) for a in gold_answers)
f1_scores[qas_id] = max(compute_f1(a, prediction) for a in gold_answers)
return exact_scores, f1_scores
def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
new_scores = {}
for qid, s in scores.items():
pred_na = na_probs[qid] > na_prob_thresh
if pred_na:
new_scores[qid] = float(not qid_to_has_ans[qid])
else:
new_scores[qid] = s
return new_scores
def make_eval_dict(exact_scores, f1_scores, qid_list=None):
if not qid_list:
total = len(exact_scores)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values()) / total),
("f1", 100.0 * sum(f1_scores.values()) / total),
("total", total),
]
)
else:
total = len(qid_list)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total),
("f1", 100.0 * sum(f1_scores[k] for k in qid_list) / total),
("total", total),
]
)
def merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval[f"{prefix}_{k}"] = new_eval[k]
def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
has_ans_score, has_ans_cnt = 0, 0
for qid in qid_list:
if not qid_to_has_ans[qid]:
continue
has_ans_cnt += 1
if qid not in scores:
continue
has_ans_score += scores[qid]
return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval["best_exact"] = best_exact
main_eval["best_exact_thresh"] = exact_thresh
main_eval["best_f1"] = best_f1
main_eval["best_f1_thresh"] = f1_thresh
main_eval["has_ans_exact"] = has_ans_exact
main_eval["has_ans_f1"] = has_ans_f1
def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for _, qid in enumerate(qid_list):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
return 100.0 * best_score / len(scores), best_thresh
def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval["best_exact"] = best_exact
main_eval["best_exact_thresh"] = exact_thresh
main_eval["best_f1"] = best_f1
main_eval["best_f1_thresh"] = f1_thresh
def squad_evaluate(examples, preds, no_answer_probs=None, no_answer_probability_threshold=1.0):
qas_id_to_has_answer = {example.qas_id: bool(example.answers) for example in examples}
has_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if has_answer]
no_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if not has_answer]
if no_answer_probs is None:
no_answer_probs = {k: 0.0 for k in preds}
exact, f1 = get_raw_scores(examples, preds)
exact_threshold = apply_no_ans_threshold(
exact, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold
)
f1_threshold = apply_no_ans_threshold(f1, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold)
evaluation = make_eval_dict(exact_threshold, f1_threshold)
if has_answer_qids:
has_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=has_answer_qids)
merge_eval(evaluation, has_ans_eval, "HasAns")
if no_answer_qids:
no_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=no_answer_qids)
merge_eval(evaluation, no_ans_eval, "NoAns")
if no_answer_probs:
find_all_best_thresh(evaluation, preds, exact, f1, no_answer_probs, qas_id_to_has_answer)
return evaluation
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heuristic between
# `pred_text` and `orig_text` to get a character-to-character alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for i, c in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose_logging:
logger.info(f"Unable to find text: '{pred_text}' in '{orig_text}'")
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose_logging:
logger.info(f"Length not equal after stripping spaces: '{orig_ns_text}' vs '{tok_ns_text}'")
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for i, tok_index in tok_ns_to_s_map.items():
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose_logging:
logger.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose_logging:
logger.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position : (orig_end_position + 1)]
return output_text
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
def compute_predictions_logits(
all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
verbose_logging,
version_2_with_negative,
null_score_diff_threshold,
tokenizer,
):
"""Write final predictions to the json file and log-odds of null if needed."""
if output_prediction_file:
logger.info(f"Writing predictions to: {output_prediction_file}")
if output_nbest_file:
logger.info(f"Writing nbest to: {output_nbest_file}")
if output_null_log_odds_file and version_2_with_negative:
logger.info(f"Writing null_log_odds to: {output_null_log_odds_file}")
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_logit", "end_logit"]
)
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for example_index, example in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min null score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for feature_index, feature in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
feature_null_score = result.start_logits[0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index],
)
)
if version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(
feature_index=min_null_feature_index,
start_index=0,
end_index=0,
start_logit=null_start_logit,
end_logit=null_end_logit,
)
)
prelim_predictions = sorted(prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"]
)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
# tok_text = " ".join(tok_tokens)
#
# # De-tokenize WordPieces that have been split off.
# tok_text = tok_text.replace(" ##", "")
# tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(_NbestPrediction(text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit))
# if we didn't include the empty option in the n-best, include it
if version_2_with_negative:
if "" not in seen_predictions:
nbest.append(_NbestPrediction(text="", start_logit=null_start_logit, end_logit=null_end_logit))
# In very rare edge cases we could only have single null prediction.
# So we just create a nonce prediction in this case to avoid failure.
if len(nbest) == 1:
nbest.insert(0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
if len(nbest) < 1:
raise ValueError("No valid predictions")
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for i, entry in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
if len(nbest_json) < 1:
raise ValueError("No valid predictions")
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
if output_prediction_file:
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
if output_nbest_file:
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if output_null_log_odds_file and version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
def compute_predictions_log_probs(
all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
version_2_with_negative,
tokenizer,
verbose_logging,
):
"""
XLNet write prediction logic (more complex than Bert's). Write final predictions to the json file and log-odds of
null if needed.
Requires utils_squad_evaluate.py
"""
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_log_prob", "end_log_prob"]
)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_log_prob", "end_log_prob"]
)
logger.info(f"Writing predictions to: {output_prediction_file}")
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for example_index, example in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
for feature_index, feature in enumerate(features):
result = unique_id_to_result[feature.unique_id]
cur_null_score = result.cls_logits
# if we could have irrelevant answers, get the min score of irrelevant
score_null = min(score_null, cur_null_score)
for i in range(start_n_top):
for j in range(end_n_top):
start_log_prob = result.start_logits[i]
start_index = result.start_top_index[i]
j_index = i * end_n_top + j
end_log_prob = result.end_logits[j_index]
end_index = result.end_top_index[j_index]
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= feature.paragraph_len - 1:
continue
if end_index >= feature.paragraph_len - 1:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_log_prob=start_log_prob,
end_log_prob=end_log_prob,
)
)
prelim_predictions = sorted(
prelim_predictions, key=lambda x: (x.start_log_prob + x.end_log_prob), reverse=True
)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
# XLNet un-tokenizer
# Let's keep it simple for now and see if we need all this later.
#
# tok_start_to_orig_index = feature.tok_start_to_orig_index
# tok_end_to_orig_index = feature.tok_end_to_orig_index
# start_orig_pos = tok_start_to_orig_index[pred.start_index]
# end_orig_pos = tok_end_to_orig_index[pred.end_index]
# paragraph_text = example.paragraph_text
# final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
# Previously used Bert untokenizer
tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
if hasattr(tokenizer, "do_lower_case"):
do_lower_case = tokenizer.do_lower_case
else:
do_lower_case = tokenizer.do_lowercase_and_remove_accent
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(text=final_text, start_log_prob=pred.start_log_prob, end_log_prob=pred.end_log_prob)
)
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(_NbestPrediction(text="", start_log_prob=-1e6, end_log_prob=-1e6))
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_log_prob + entry.end_log_prob)
if not best_non_null_entry:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for i, entry in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_log_prob"] = entry.start_log_prob
output["end_log_prob"] = entry.end_log_prob
nbest_json.append(output)
if len(nbest_json) < 1:
raise ValueError("No valid predictions")
if best_non_null_entry is None:
raise ValueError("No valid predictions")
score_diff = score_null
scores_diff_json[example.qas_id] = score_diff
# note(zhiliny): always predict best_non_null_entry
# and the evaluation script will search for the best threshold
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
| 29,698 | 37.026889 | 117 | py |
transformers | transformers-main/src/transformers/data/metrics/__init__.py | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import f1_score, matthews_corrcoef
DEPRECATION_WARNING = (
"This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
"library. You can have a look at this example script for pointers: "
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)
def simple_accuracy(preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_backends(simple_accuracy, "sklearn")
return (preds == labels).mean()
def acc_and_f1(preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_backends(acc_and_f1, "sklearn")
acc = simple_accuracy(preds, labels)
f1 = f1_score(y_true=labels, y_pred=preds)
return {
"acc": acc,
"f1": f1,
"acc_and_f1": (acc + f1) / 2,
}
def pearson_and_spearman(preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_backends(pearson_and_spearman, "sklearn")
pearson_corr = pearsonr(preds, labels)[0]
spearman_corr = spearmanr(preds, labels)[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def glue_compute_metrics(task_name, preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_backends(glue_compute_metrics, "sklearn")
assert len(preds) == len(labels), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
if task_name == "cola":
return {"mcc": matthews_corrcoef(labels, preds)}
elif task_name == "sst-2":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mrpc":
return acc_and_f1(preds, labels)
elif task_name == "sts-b":
return pearson_and_spearman(preds, labels)
elif task_name == "qqp":
return acc_and_f1(preds, labels)
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(preds, labels)}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(preds, labels)}
elif task_name == "qnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "rte":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "wnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "hans":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)
def xnli_compute_metrics(task_name, preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_backends(xnli_compute_metrics, "sklearn")
if len(preds) != len(labels):
raise ValueError(f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}")
if task_name == "xnli":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)
| 3,629 | 35.666667 | 118 | py |
transformers | transformers-main/src/transformers/data/datasets/glue.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
logger = logging.get_logger(__name__)
@dataclass
class GlueDataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command
line.
"""
task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys())})
data_dir: str = field(
metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
)
max_seq_length: int = field(
default=128,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
def __post_init__(self):
self.task_name = self.task_name.lower()
class Split(Enum):
train = "train"
dev = "dev"
test = "test"
class GlueDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
args: GlueDataTrainingArguments
output_mode: str
features: List[InputFeatures]
def __init__(
self,
args: GlueDataTrainingArguments,
tokenizer: PreTrainedTokenizerBase,
limit_length: Optional[int] = None,
mode: Union[str, Split] = Split.train,
cache_dir: Optional[str] = None,
):
warnings.warn(
"This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
"library. You can have a look at this example script for pointers: "
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py",
FutureWarning,
)
self.args = args
self.processor = glue_processors[args.task_name]()
self.output_mode = glue_output_modes[args.task_name]
if isinstance(mode, str):
try:
mode = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name")
# Load data features from cache or dataset file
cached_features_file = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,
f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}",
)
label_list = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
label_list[1], label_list[2] = label_list[2], label_list[1]
self.label_list = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not args.overwrite_cache:
start = time.time()
self.features = torch.load(cached_features_file)
logger.info(
f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
)
else:
logger.info(f"Creating features from dataset file at {args.data_dir}")
if mode == Split.dev:
examples = self.processor.get_dev_examples(args.data_dir)
elif mode == Split.test:
examples = self.processor.get_test_examples(args.data_dir)
else:
examples = self.processor.get_train_examples(args.data_dir)
if limit_length is not None:
examples = examples[:limit_length]
self.features = glue_convert_examples_to_features(
examples,
tokenizer,
max_length=args.max_seq_length,
label_list=label_list,
output_mode=self.output_mode,
)
start = time.time()
torch.save(self.features, cached_features_file)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
)
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> InputFeatures:
return self.features[i]
def get_labels(self):
return self.label_list
| 6,160 | 37.030864 | 119 | py |
transformers | transformers-main/src/transformers/data/datasets/squad.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features
logger = logging.get_logger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class SquadDataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
model_type: str = field(
default=None, metadata={"help": "Model type selected in the list: " + ", ".join(MODEL_TYPES)}
)
data_dir: str = field(
default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
)
max_seq_length: int = field(
default=128,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
doc_stride: int = field(
default=128,
metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
)
max_query_length: int = field(
default=64,
metadata={
"help": (
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
)
},
)
max_answer_length: int = field(
default=30,
metadata={
"help": (
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
version_2_with_negative: bool = field(
default=False, metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}
)
null_score_diff_threshold: float = field(
default=0.0, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
)
n_best_size: int = field(
default=20, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
)
lang_id: int = field(
default=0,
metadata={
"help": (
"language id of input for language-specific xlm models (see"
" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
)
},
)
threads: int = field(default=1, metadata={"help": "multiple threads for converting example to features"})
class Split(Enum):
train = "train"
dev = "dev"
class SquadDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
args: SquadDataTrainingArguments
features: List[SquadFeatures]
mode: Split
is_language_sensitive: bool
def __init__(
self,
args: SquadDataTrainingArguments,
tokenizer: PreTrainedTokenizer,
limit_length: Optional[int] = None,
mode: Union[str, Split] = Split.train,
is_language_sensitive: Optional[bool] = False,
cache_dir: Optional[str] = None,
dataset_format: Optional[str] = "pt",
):
self.args = args
self.is_language_sensitive = is_language_sensitive
self.processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
if isinstance(mode, str):
try:
mode = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name")
self.mode = mode
# Load data features from cache or dataset file
version_tag = "v2" if args.version_2_with_negative else "v1"
cached_features_file = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,
f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}",
)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not args.overwrite_cache:
start = time.time()
self.old_features = torch.load(cached_features_file)
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
self.features = self.old_features["features"]
self.dataset = self.old_features.get("dataset", None)
self.examples = self.old_features.get("examples", None)
logger.info(
f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
)
if self.dataset is None or self.examples is None:
logger.warning(
f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
" future run"
)
else:
if mode == Split.dev:
self.examples = self.processor.get_dev_examples(args.data_dir)
else:
self.examples = self.processor.get_train_examples(args.data_dir)
self.features, self.dataset = squad_convert_examples_to_features(
examples=self.examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=mode == Split.train,
threads=args.threads,
return_dataset=dataset_format,
)
start = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples},
cached_features_file,
)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
)
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
# Convert to Tensors and build dataset
feature = self.features[i]
input_ids = torch.tensor(feature.input_ids, dtype=torch.long)
attention_mask = torch.tensor(feature.attention_mask, dtype=torch.long)
token_type_ids = torch.tensor(feature.token_type_ids, dtype=torch.long)
cls_index = torch.tensor(feature.cls_index, dtype=torch.long)
p_mask = torch.tensor(feature.p_mask, dtype=torch.float)
is_impossible = torch.tensor(feature.is_impossible, dtype=torch.float)
inputs = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask})
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible})
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape, dtype=torch.int64) * self.args.lang_id)})
if self.mode == Split.train:
start_positions = torch.tensor(feature.start_position, dtype=torch.long)
end_positions = torch.tensor(feature.end_position, dtype=torch.long)
inputs.update({"start_positions": start_positions, "end_positions": end_positions})
return inputs
| 9,219 | 39.086957 | 118 | py |
transformers | transformers-main/src/transformers/data/datasets/language_modeling.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import pickle
import random
import time
import warnings
from typing import Dict, List, Optional
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
DEPRECATION_WARNING = (
"This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
"library. You can have a look at this example script for pointers: {0}"
)
class TextDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(
self,
tokenizer: PreTrainedTokenizer,
file_path: str,
block_size: int,
overwrite_cache=False,
cache_dir: Optional[str] = None,
):
warnings.warn(
DEPRECATION_WARNING.format(
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py"
),
FutureWarning,
)
if os.path.isfile(file_path) is False:
raise ValueError(f"Input file path {file_path} not found")
block_size = block_size - tokenizer.num_special_tokens_to_add(pair=False)
directory, filename = os.path.split(file_path)
cached_features_file = os.path.join(
cache_dir if cache_dir is not None else directory,
f"cached_lm_{tokenizer.__class__.__name__}_{block_size}_{filename}",
)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not overwrite_cache:
start = time.time()
with open(cached_features_file, "rb") as handle:
self.examples = pickle.load(handle)
logger.info(
f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
)
else:
logger.info(f"Creating features from dataset file at {directory}")
self.examples = []
with open(file_path, encoding="utf-8") as f:
text = f.read()
tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
for i in range(0, len(tokenized_text) - block_size + 1, block_size): # Truncate in block of block_size
self.examples.append(
tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size])
)
# Note that we are losing the last truncated example here for the sake of simplicity (no padding)
# If your dataset is small, first you should look for a bigger one :-) and second you
# can change this behavior by adding (model specific) padding.
start = time.time()
with open(cached_features_file, "wb") as handle:
pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
logger.info(
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
)
def __len__(self):
return len(self.examples)
def __getitem__(self, i) -> torch.Tensor:
return torch.tensor(self.examples[i], dtype=torch.long)
class LineByLineTextDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):
warnings.warn(
DEPRECATION_WARNING.format(
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py"
),
FutureWarning,
)
if os.path.isfile(file_path) is False:
raise ValueError(f"Input file path {file_path} not found")
# Here, we do not cache the features, operating under the assumption
# that we will soon use fast multithreaded tokenizers from the
# `tokenizers` repo everywhere =)
logger.info(f"Creating features from dataset file at {file_path}")
with open(file_path, encoding="utf-8") as f:
lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)
self.examples = batch_encoding["input_ids"]
self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]
def __len__(self):
return len(self.examples)
def __getitem__(self, i) -> Dict[str, torch.tensor]:
return self.examples[i]
class LineByLineWithRefDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, ref_path: str):
warnings.warn(
DEPRECATION_WARNING.format(
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm_wwm.py"
),
FutureWarning,
)
if os.path.isfile(file_path) is False:
raise ValueError(f"Input file path {file_path} not found")
if os.path.isfile(ref_path) is False:
raise ValueError(f"Ref file path {file_path} not found")
# Here, we do not cache the features, operating under the assumption
# that we will soon use fast multithreaded tokenizers from the
# `tokenizers` repo everywhere =)
logger.info(f"Creating features from dataset file at {file_path}")
logger.info(f"Use ref segment results at {ref_path}")
with open(file_path, encoding="utf-8") as f:
data = f.readlines() # use this method to avoid delimiter '\u2029' to split a line
data = [line.strip() for line in data if len(line) > 0 and not line.isspace()]
# Get ref inf from file
with open(ref_path, encoding="utf-8") as f:
ref = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
if len(data) != len(ref):
raise ValueError(
f"Length of Input file should be equal to Ref file. But the length of {file_path} is {len(data)} "
f"while length of {ref_path} is {len(ref)}"
)
batch_encoding = tokenizer(data, add_special_tokens=True, truncation=True, max_length=block_size)
self.examples = batch_encoding["input_ids"]
self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]
n = len(self.examples)
for i in range(n):
self.examples[i]["chinese_ref"] = torch.tensor(ref[i], dtype=torch.long)
def __len__(self):
return len(self.examples)
def __getitem__(self, i) -> Dict[str, torch.tensor]:
return self.examples[i]
class LineByLineWithSOPTextDataset(Dataset):
"""
Dataset for sentence order prediction task, prepare sentence pairs for SOP task
"""
def __init__(self, tokenizer: PreTrainedTokenizer, file_dir: str, block_size: int):
warnings.warn(
DEPRECATION_WARNING.format(
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py"
),
FutureWarning,
)
if os.path.isdir(file_dir) is False:
raise ValueError(f"{file_dir} is not a directory")
logger.info(f"Creating features from dataset file folder at {file_dir}")
self.examples = []
# TODO: randomness could apply a random seed, ex. rng = random.Random(random_seed)
# file path looks like ./dataset/wiki_1, ./dataset/wiki_2
for file_name in os.listdir(file_dir):
file_path = os.path.join(file_dir, file_name)
if os.path.isfile(file_path) is False:
raise ValueError(f"{file_path} is not a file")
article_open = False
with open(file_path, encoding="utf-8") as f:
original_lines = f.readlines()
article_lines = []
for line in original_lines:
if "<doc id=" in line:
article_open = True
elif "</doc>" in line:
article_open = False
document = [
tokenizer.convert_tokens_to_ids(tokenizer.tokenize(line))
for line in article_lines[1:]
if (len(line) > 0 and not line.isspace())
]
examples = self.create_examples_from_document(document, block_size, tokenizer)
self.examples.extend(examples)
article_lines = []
else:
if article_open:
article_lines.append(line)
logger.info("Dataset parse finished.")
def create_examples_from_document(self, document, block_size, tokenizer, short_seq_prob=0.1):
"""Creates examples for a single document."""
# Account for special tokens
max_num_tokens = block_size - tokenizer.num_special_tokens_to_add(pair=True)
# We *usually* want to fill up the entire sequence since we are padding
# to `block_size` anyways, so short sequences are generally wasted
# computation. However, we *sometimes*
# (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
# sequences to minimize the mismatch between pretraining and fine-tuning.
# The `target_seq_length` is just a rough target however, whereas
# `block_size` is a hard limit.
target_seq_length = max_num_tokens
if random.random() < short_seq_prob:
target_seq_length = random.randint(2, max_num_tokens)
# We DON'T just concatenate all of the tokens from a document into a long
# sequence and choose an arbitrary split point because this would make the
# next sentence prediction task too easy. Instead, we split the input into
# segments "A" and "B" based on the actual "sentences" provided by the user
# input.
examples = []
current_chunk = [] # a buffer stored current working segments
current_length = 0
i = 0
while i < len(document):
segment = document[i] # get a segment
if not segment:
i += 1
continue
current_chunk.append(segment) # add a segment to current chunk
current_length += len(segment) # overall token length
# if current length goes to the target length or reaches the end of file, start building token a and b
if i == len(document) - 1 or current_length >= target_seq_length:
if current_chunk:
# `a_end` is how many segments from `current_chunk` go into the `A` (first) sentence.
a_end = 1
# if current chunk has more than 2 sentences, pick part of it `A` (first) sentence
if len(current_chunk) >= 2:
a_end = random.randint(1, len(current_chunk) - 1)
# token a
tokens_a = []
for j in range(a_end):
tokens_a.extend(current_chunk[j])
# token b
tokens_b = []
for j in range(a_end, len(current_chunk)):
tokens_b.extend(current_chunk[j])
if len(tokens_a) == 0 or len(tokens_b) == 0:
continue
# switch tokens_a and tokens_b randomly
if random.random() < 0.5:
is_next = False
tokens_a, tokens_b = tokens_b, tokens_a
else:
is_next = True
def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens):
"""Truncates a pair of sequences to a maximum sequence length."""
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_num_tokens:
break
trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
if not (len(trunc_tokens) >= 1):
raise ValueError("Sequence length to be truncated must be no less than one")
# We want to sometimes truncate from the front and sometimes from the
# back to add more randomness and avoid biases.
if random.random() < 0.5:
del trunc_tokens[0]
else:
trunc_tokens.pop()
truncate_seq_pair(tokens_a, tokens_b, max_num_tokens)
if not (len(tokens_a) >= 1):
raise ValueError(f"Length of sequence a is {len(tokens_a)} which must be no less than 1")
if not (len(tokens_b) >= 1):
raise ValueError(f"Length of sequence b is {len(tokens_b)} which must be no less than 1")
# add special tokens
input_ids = tokenizer.build_inputs_with_special_tokens(tokens_a, tokens_b)
# add token type ids, 0 for sentence a, 1 for sentence b
token_type_ids = tokenizer.create_token_type_ids_from_sequences(tokens_a, tokens_b)
example = {
"input_ids": torch.tensor(input_ids, dtype=torch.long),
"token_type_ids": torch.tensor(token_type_ids, dtype=torch.long),
"sentence_order_label": torch.tensor(0 if is_next else 1, dtype=torch.long),
}
examples.append(example)
current_chunk = [] # clear current chunk
current_length = 0 # reset current text length
i += 1 # go to next line
return examples
def __len__(self):
return len(self.examples)
def __getitem__(self, i) -> Dict[str, torch.tensor]:
return self.examples[i]
class TextDatasetForNextSentencePrediction(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(
self,
tokenizer: PreTrainedTokenizer,
file_path: str,
block_size: int,
overwrite_cache=False,
short_seq_probability=0.1,
nsp_probability=0.5,
):
warnings.warn(
DEPRECATION_WARNING.format(
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py"
),
FutureWarning,
)
if not os.path.isfile(file_path):
raise ValueError(f"Input file path {file_path} not found")
self.short_seq_probability = short_seq_probability
self.nsp_probability = nsp_probability
directory, filename = os.path.split(file_path)
cached_features_file = os.path.join(
directory,
f"cached_nsp_{tokenizer.__class__.__name__}_{block_size}_{filename}",
)
self.tokenizer = tokenizer
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
# Input file format:
# (1) One sentence per line. These should ideally be actual sentences, not
# entire paragraphs or arbitrary spans of text. (Because we use the
# sentence boundaries for the "next sentence prediction" task).
# (2) Blank lines between documents. Document boundaries are needed so
# that the "next sentence prediction" task doesn't span between documents.
#
# Example:
# I am very happy.
# Here is the second sentence.
#
# A new document.
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not overwrite_cache:
start = time.time()
with open(cached_features_file, "rb") as handle:
self.examples = pickle.load(handle)
logger.info(
f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
)
else:
logger.info(f"Creating features from dataset file at {directory}")
self.documents = [[]]
with open(file_path, encoding="utf-8") as f:
while True:
line = f.readline()
if not line:
break
line = line.strip()
# Empty lines are used as document delimiters
if not line and len(self.documents[-1]) != 0:
self.documents.append([])
tokens = tokenizer.tokenize(line)
tokens = tokenizer.convert_tokens_to_ids(tokens)
if tokens:
self.documents[-1].append(tokens)
logger.info(f"Creating examples from {len(self.documents)} documents.")
self.examples = []
for doc_index, document in enumerate(self.documents):
self.create_examples_from_document(document, doc_index, block_size)
start = time.time()
with open(cached_features_file, "wb") as handle:
pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
logger.info(
f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
)
def create_examples_from_document(self, document: List[List[int]], doc_index: int, block_size: int):
"""Creates examples for a single document."""
max_num_tokens = block_size - self.tokenizer.num_special_tokens_to_add(pair=True)
# We *usually* want to fill up the entire sequence since we are padding
# to `block_size` anyways, so short sequences are generally wasted
# computation. However, we *sometimes*
# (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
# sequences to minimize the mismatch between pretraining and fine-tuning.
# The `target_seq_length` is just a rough target however, whereas
# `block_size` is a hard limit.
target_seq_length = max_num_tokens
if random.random() < self.short_seq_probability:
target_seq_length = random.randint(2, max_num_tokens)
current_chunk = [] # a buffer stored current working segments
current_length = 0
i = 0
while i < len(document):
segment = document[i]
current_chunk.append(segment)
current_length += len(segment)
if i == len(document) - 1 or current_length >= target_seq_length:
if current_chunk:
# `a_end` is how many segments from `current_chunk` go into the `A`
# (first) sentence.
a_end = 1
if len(current_chunk) >= 2:
a_end = random.randint(1, len(current_chunk) - 1)
tokens_a = []
for j in range(a_end):
tokens_a.extend(current_chunk[j])
tokens_b = []
if len(current_chunk) == 1 or random.random() < self.nsp_probability:
is_random_next = True
target_b_length = target_seq_length - len(tokens_a)
# This should rarely go for more than one iteration for large
# corpora. However, just to be careful, we try to make sure that
# the random document is not the same as the document
# we're processing.
for _ in range(10):
random_document_index = random.randint(0, len(self.documents) - 1)
if random_document_index != doc_index:
break
random_document = self.documents[random_document_index]
random_start = random.randint(0, len(random_document) - 1)
for j in range(random_start, len(random_document)):
tokens_b.extend(random_document[j])
if len(tokens_b) >= target_b_length:
break
# We didn't actually use these segments so we "put them back" so
# they don't go to waste.
num_unused_segments = len(current_chunk) - a_end
i -= num_unused_segments
# Actual next
else:
is_random_next = False
for j in range(a_end, len(current_chunk)):
tokens_b.extend(current_chunk[j])
if not (len(tokens_a) >= 1):
raise ValueError(f"Length of sequence a is {len(tokens_a)} which must be no less than 1")
if not (len(tokens_b) >= 1):
raise ValueError(f"Length of sequence b is {len(tokens_b)} which must be no less than 1")
# add special tokens
input_ids = self.tokenizer.build_inputs_with_special_tokens(tokens_a, tokens_b)
# add token type ids, 0 for sentence a, 1 for sentence b
token_type_ids = self.tokenizer.create_token_type_ids_from_sequences(tokens_a, tokens_b)
example = {
"input_ids": torch.tensor(input_ids, dtype=torch.long),
"token_type_ids": torch.tensor(token_type_ids, dtype=torch.long),
"next_sentence_label": torch.tensor(1 if is_random_next else 0, dtype=torch.long),
}
self.examples.append(example)
current_chunk = []
current_length = 0
i += 1
def __len__(self):
return len(self.examples)
def __getitem__(self, i):
return self.examples[i]
| 23,718 | 43.66855 | 121 | py |
transformers | transformers-main/src/transformers/data/datasets/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 909 | 36.916667 | 74 | py |
transformers | transformers-main/src/transformers/data/processors/glue.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" GLUE processors and helpers"""
import os
import warnings
from dataclasses import asdict
from enum import Enum
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_tf_available, logging
from .utils import DataProcessor, InputExample, InputFeatures
if is_tf_available():
import tensorflow as tf
logger = logging.get_logger(__name__)
DEPRECATION_WARNING = (
"This {0} will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
"library. You can have a look at this example script for pointers: "
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)
def glue_convert_examples_to_features(
examples: Union[List[InputExample], "tf.data.Dataset"],
tokenizer: PreTrainedTokenizer,
max_length: Optional[int] = None,
task=None,
label_list=None,
output_mode=None,
):
"""
Loads a data file into a list of `InputFeatures`
Args:
examples: List of `InputExamples` or `tf.data.Dataset` containing the examples.
tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length. Defaults to the tokenizer's max_len
task: GLUE task
label_list: List of labels. Can be obtained from the processor using the `processor.get_labels()` method
output_mode: String indicating the output mode. Either `regression` or `classification`
Returns:
If the `examples` input is a `tf.data.Dataset`, will return a `tf.data.Dataset` containing the task-specific
features. If the input is a list of `InputExamples`, will return a list of task-specific `InputFeatures` which
can be fed to the model.
"""
warnings.warn(DEPRECATION_WARNING.format("function"), FutureWarning)
if is_tf_available() and isinstance(examples, tf.data.Dataset):
if task is None:
raise ValueError("When calling glue_convert_examples_to_features from TF, the task parameter is required.")
return _tf_glue_convert_examples_to_features(examples, tokenizer, max_length=max_length, task=task)
return _glue_convert_examples_to_features(
examples, tokenizer, max_length=max_length, task=task, label_list=label_list, output_mode=output_mode
)
if is_tf_available():
def _tf_glue_convert_examples_to_features(
examples: tf.data.Dataset,
tokenizer: PreTrainedTokenizer,
task=str,
max_length: Optional[int] = None,
) -> tf.data.Dataset:
"""
Returns:
A `tf.data.Dataset` containing the task-specific features.
"""
processor = glue_processors[task]()
examples = [processor.tfds_map(processor.get_example_from_tensor_dict(example)) for example in examples]
features = glue_convert_examples_to_features(examples, tokenizer, max_length=max_length, task=task)
label_type = tf.float32 if task == "sts-b" else tf.int64
def gen():
for ex in features:
d = {k: v for k, v in asdict(ex).items() if v is not None}
label = d.pop("label")
yield (d, label)
input_names = tokenizer.model_input_names
return tf.data.Dataset.from_generator(
gen,
({k: tf.int32 for k in input_names}, label_type),
({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),
)
def _glue_convert_examples_to_features(
examples: List[InputExample],
tokenizer: PreTrainedTokenizer,
max_length: Optional[int] = None,
task=None,
label_list=None,
output_mode=None,
):
if max_length is None:
max_length = tokenizer.model_max_length
if task is not None:
processor = glue_processors[task]()
if label_list is None:
label_list = processor.get_labels()
logger.info(f"Using label list {label_list} for task {task}")
if output_mode is None:
output_mode = glue_output_modes[task]
logger.info(f"Using output mode {output_mode} for task {task}")
label_map = {label: i for i, label in enumerate(label_list)}
def label_from_example(example: InputExample) -> Union[int, float, None]:
if example.label is None:
return None
if output_mode == "classification":
return label_map[example.label]
elif output_mode == "regression":
return float(example.label)
raise KeyError(output_mode)
labels = [label_from_example(example) for example in examples]
batch_encoding = tokenizer(
[(example.text_a, example.text_b) for example in examples],
max_length=max_length,
padding="max_length",
truncation=True,
)
features = []
for i in range(len(examples)):
inputs = {k: batch_encoding[k][i] for k in batch_encoding}
feature = InputFeatures(**inputs, label=labels[i])
features.append(feature)
for i, example in enumerate(examples[:5]):
logger.info("*** Example ***")
logger.info(f"guid: {example.guid}")
logger.info(f"features: {features[i]}")
return features
class OutputMode(Enum):
classification = "classification"
regression = "regression"
class MrpcProcessor(DataProcessor):
"""Processor for the MRPC data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence1"].numpy().decode("utf-8"),
tensor_dict["sentence2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
logger.info(f"LOOKING AT {os.path.join(data_dir, 'train.tsv')}")
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{i}"
text_a = line[3]
text_b = line[4]
label = None if set_type == "test" else line[0]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class MnliProcessor(DataProcessor):
"""Processor for the MultiNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["premise"].numpy().decode("utf-8"),
tensor_dict["hypothesis"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test_matched")
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[8]
text_b = line[9]
label = None if set_type.startswith("test") else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class MnliMismatchedProcessor(MnliProcessor):
"""Processor for the MultiNLI Mismatched data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")), "dev_mismatched")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_mismatched.tsv")), "test_mismatched")
class ColaProcessor(DataProcessor):
"""Processor for the CoLA data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence"].numpy().decode("utf-8"),
None,
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
test_mode = set_type == "test"
if test_mode:
lines = lines[1:]
text_index = 1 if test_mode else 3
examples = []
for i, line in enumerate(lines):
guid = f"{set_type}-{i}"
text_a = line[text_index]
label = None if test_mode else line[1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class Sst2Processor(DataProcessor):
"""Processor for the SST-2 data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence"].numpy().decode("utf-8"),
None,
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
text_index = 1 if set_type == "test" else 0
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{i}"
text_a = line[text_index]
label = None if set_type == "test" else line[1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class StsbProcessor(DataProcessor):
"""Processor for the STS-B data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence1"].numpy().decode("utf-8"),
tensor_dict["sentence2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return [None]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[7]
text_b = line[8]
label = None if set_type == "test" else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class QqpProcessor(DataProcessor):
"""Processor for the QQP data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["question1"].numpy().decode("utf-8"),
tensor_dict["question2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
test_mode = set_type == "test"
q1_index = 1 if test_mode else 3
q2_index = 2 if test_mode else 4
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
try:
text_a = line[q1_index]
text_b = line[q2_index]
label = None if test_mode else line[5]
except IndexError:
continue
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class QnliProcessor(DataProcessor):
"""Processor for the QNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["question"].numpy().decode("utf-8"),
tensor_dict["sentence"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["entailment", "not_entailment"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[1]
text_b = line[2]
label = None if set_type == "test" else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class RteProcessor(DataProcessor):
"""Processor for the RTE data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence1"].numpy().decode("utf-8"),
tensor_dict["sentence2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["entailment", "not_entailment"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[1]
text_b = line[2]
label = None if set_type == "test" else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class WnliProcessor(DataProcessor):
"""Processor for the WNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence1"].numpy().decode("utf-8"),
tensor_dict["sentence2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[1]
text_b = line[2]
label = None if set_type == "test" else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
glue_tasks_num_labels = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
"sst-2": 2,
"sts-b": 1,
"qqp": 2,
"qnli": 2,
"rte": 2,
"wnli": 2,
}
glue_processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mnli-mm": MnliMismatchedProcessor,
"mrpc": MrpcProcessor,
"sst-2": Sst2Processor,
"sts-b": StsbProcessor,
"qqp": QqpProcessor,
"qnli": QnliProcessor,
"rte": RteProcessor,
"wnli": WnliProcessor,
}
glue_output_modes = {
"cola": "classification",
"mnli": "classification",
"mnli-mm": "classification",
"mrpc": "classification",
"sst-2": "classification",
"sts-b": "regression",
"qqp": "classification",
"qnli": "classification",
"rte": "classification",
"wnli": "classification",
}
| 23,216 | 35.051242 | 119 | py |
transformers | transformers-main/src/transformers/data/processors/squad.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from ...models.bert.tokenization_bert import whitespace_tokenize
from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
from ...utils import is_tf_available, is_torch_available, logging
from .utils import DataProcessor
# Store the tokenizers which insert 2 separators tokens
MULTI_SEP_TOKENS_TOKENIZERS_SET = {"roberta", "camembert", "bart", "mpnet"}
if is_torch_available():
import torch
from torch.utils.data import TensorDataset
if is_tf_available():
import tensorflow as tf
logger = logging.get_logger(__name__)
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
for new_start in range(input_start, input_end + 1):
for new_end in range(input_end, new_start - 1, -1):
text_span = " ".join(doc_tokens[new_start : (new_end + 1)])
if text_span == tok_answer_text:
return (new_start, new_end)
return (input_start, input_end)
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
best_score = None
best_span_index = None
for span_index, doc_span in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
def _new_check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# if len(doc_spans) == 1:
# return True
best_score = None
best_span_index = None
for span_index, doc_span in enumerate(doc_spans):
end = doc_span["start"] + doc_span["length"] - 1
if position < doc_span["start"]:
continue
if position > end:
continue
num_left_context = position - doc_span["start"]
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span["length"]
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
def _is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False
def squad_convert_example_to_features(
example, max_seq_length, doc_stride, max_query_length, padding_strategy, is_training
):
features = []
if is_training and not example.is_impossible:
# Get start and end position
start_position = example.start_position
end_position = example.end_position
# If the answer cannot be found in the text, then skip this example.
actual_text = " ".join(example.doc_tokens[start_position : (end_position + 1)])
cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
if actual_text.find(cleaned_answer_text) == -1:
logger.warning(f"Could not find answer: '{actual_text}' vs. '{cleaned_answer_text}'")
return []
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for i, token in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
if tokenizer.__class__.__name__ in [
"RobertaTokenizer",
"LongformerTokenizer",
"BartTokenizer",
"RobertaTokenizerFast",
"LongformerTokenizerFast",
"BartTokenizerFast",
]:
sub_tokens = tokenizer.tokenize(token, add_prefix_space=True)
else:
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text
)
spans = []
truncated_query = tokenizer.encode(
example.question_text, add_special_tokens=False, truncation=True, max_length=max_query_length
)
# Tokenizers who insert 2 SEP tokens in-between <context> & <question> need to have special handling
# in the way they compute mask of added tokens.
tokenizer_type = type(tokenizer).__name__.replace("Tokenizer", "").lower()
sequence_added_tokens = (
tokenizer.model_max_length - tokenizer.max_len_single_sentence + 1
if tokenizer_type in MULTI_SEP_TOKENS_TOKENIZERS_SET
else tokenizer.model_max_length - tokenizer.max_len_single_sentence
)
sequence_pair_added_tokens = tokenizer.model_max_length - tokenizer.max_len_sentences_pair
span_doc_tokens = all_doc_tokens
while len(spans) * doc_stride < len(all_doc_tokens):
# Define the side we want to truncate / pad and the text/pair sorting
if tokenizer.padding_side == "right":
texts = truncated_query
pairs = span_doc_tokens
truncation = TruncationStrategy.ONLY_SECOND.value
else:
texts = span_doc_tokens
pairs = truncated_query
truncation = TruncationStrategy.ONLY_FIRST.value
encoded_dict = tokenizer.encode_plus( # TODO(thom) update this logic
texts,
pairs,
truncation=truncation,
padding=padding_strategy,
max_length=max_seq_length,
return_overflowing_tokens=True,
stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
return_token_type_ids=True,
)
paragraph_len = min(
len(all_doc_tokens) - len(spans) * doc_stride,
max_seq_length - len(truncated_query) - sequence_pair_added_tokens,
)
if tokenizer.pad_token_id in encoded_dict["input_ids"]:
if tokenizer.padding_side == "right":
non_padded_ids = encoded_dict["input_ids"][: encoded_dict["input_ids"].index(tokenizer.pad_token_id)]
else:
last_padding_id_position = (
len(encoded_dict["input_ids"]) - 1 - encoded_dict["input_ids"][::-1].index(tokenizer.pad_token_id)
)
non_padded_ids = encoded_dict["input_ids"][last_padding_id_position + 1 :]
else:
non_padded_ids = encoded_dict["input_ids"]
tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
token_to_orig_map = {}
for i in range(paragraph_len):
index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i
token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i]
encoded_dict["paragraph_len"] = paragraph_len
encoded_dict["tokens"] = tokens
encoded_dict["token_to_orig_map"] = token_to_orig_map
encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
encoded_dict["token_is_max_context"] = {}
encoded_dict["start"] = len(spans) * doc_stride
encoded_dict["length"] = paragraph_len
spans.append(encoded_dict)
if "overflowing_tokens" not in encoded_dict or (
"overflowing_tokens" in encoded_dict and len(encoded_dict["overflowing_tokens"]) == 0
):
break
span_doc_tokens = encoded_dict["overflowing_tokens"]
for doc_span_index in range(len(spans)):
for j in range(spans[doc_span_index]["paragraph_len"]):
is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
index = (
j
if tokenizer.padding_side == "left"
else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
)
spans[doc_span_index]["token_is_max_context"][index] = is_max_context
for span in spans:
# Identify the position of the CLS token
cls_index = span["input_ids"].index(tokenizer.cls_token_id)
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
# Original TF implementation also keep the classification token (set to 0)
p_mask = np.ones_like(span["token_type_ids"])
if tokenizer.padding_side == "right":
p_mask[len(truncated_query) + sequence_added_tokens :] = 0
else:
p_mask[-len(span["tokens"]) : -(len(truncated_query) + sequence_added_tokens)] = 0
pad_token_indices = np.where(span["input_ids"] == tokenizer.pad_token_id)
special_token_indices = np.asarray(
tokenizer.get_special_tokens_mask(span["input_ids"], already_has_special_tokens=True)
).nonzero()
p_mask[pad_token_indices] = 1
p_mask[special_token_indices] = 1
# Set the cls index to 0: the CLS index can be used for impossible answers
p_mask[cls_index] = 0
span_is_impossible = example.is_impossible
start_position = 0
end_position = 0
if is_training and not span_is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = span["start"]
doc_end = span["start"] + span["length"] - 1
out_of_span = False
if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
start_position = cls_index
end_position = cls_index
span_is_impossible = True
else:
if tokenizer.padding_side == "left":
doc_offset = 0
else:
doc_offset = len(truncated_query) + sequence_added_tokens
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
features.append(
SquadFeatures(
span["input_ids"],
span["attention_mask"],
span["token_type_ids"],
cls_index,
p_mask.tolist(),
example_index=0, # Can not set unique_id and example_index here. They will be set after multiple processing.
unique_id=0,
paragraph_len=span["paragraph_len"],
token_is_max_context=span["token_is_max_context"],
tokens=span["tokens"],
token_to_orig_map=span["token_to_orig_map"],
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible,
qas_id=example.qas_id,
)
)
return features
def squad_convert_example_to_features_init(tokenizer_for_convert: PreTrainedTokenizerBase):
global tokenizer
tokenizer = tokenizer_for_convert
def squad_convert_examples_to_features(
examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
padding_strategy="max_length",
return_dataset=False,
threads=1,
tqdm_enabled=True,
):
"""
Converts a list of examples into a list of features that can be directly given as input to a model. It is
model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
Args:
examples: list of [`~data.processors.squad.SquadExample`]
tokenizer: an instance of a child of [`PreTrainedTokenizer`]
max_seq_length: The maximum sequence length of the inputs.
doc_stride: The stride used when the context is too large and is split across several features.
max_query_length: The maximum length of the query.
is_training: whether to create features for model evaluation or model training.
padding_strategy: Default to "max_length". Which padding strategy to use
return_dataset: Default False. Either 'pt' or 'tf'.
if 'pt': returns a torch.data.TensorDataset, if 'tf': returns a tf.data.Dataset
threads: multiple processing threads.
Returns:
list of [`~data.processors.squad.SquadFeatures`]
Example:
```python
processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=not evaluate,
)
```"""
# Defining helper methods
features = []
threads = min(threads, cpu_count())
with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:
annotate_ = partial(
squad_convert_example_to_features,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
padding_strategy=padding_strategy,
is_training=is_training,
)
features = list(
tqdm(
p.imap(annotate_, examples, chunksize=32),
total=len(examples),
desc="convert squad examples to features",
disable=not tqdm_enabled,
)
)
new_features = []
unique_id = 1000000000
example_index = 0
for example_features in tqdm(
features, total=len(features), desc="add example index and unique id", disable=not tqdm_enabled
):
if not example_features:
continue
for example_feature in example_features:
example_feature.example_index = example_index
example_feature.unique_id = unique_id
new_features.append(example_feature)
unique_id += 1
example_index += 1
features = new_features
del new_features
if return_dataset == "pt":
if not is_torch_available():
raise RuntimeError("PyTorch must be installed to return a PyTorch dataset.")
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_attention_masks = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
all_is_impossible = torch.tensor([f.is_impossible for f in features], dtype=torch.float)
if not is_training:
all_feature_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
dataset = TensorDataset(
all_input_ids, all_attention_masks, all_token_type_ids, all_feature_index, all_cls_index, all_p_mask
)
else:
all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
dataset = TensorDataset(
all_input_ids,
all_attention_masks,
all_token_type_ids,
all_start_positions,
all_end_positions,
all_cls_index,
all_p_mask,
all_is_impossible,
)
return features, dataset
elif return_dataset == "tf":
if not is_tf_available():
raise RuntimeError("TensorFlow must be installed to return a TensorFlow dataset.")
def gen():
for i, ex in enumerate(features):
if ex.token_type_ids is None:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"feature_index": i,
"qas_id": ex.qas_id,
},
{
"start_positions": ex.start_position,
"end_positions": ex.end_position,
"cls_index": ex.cls_index,
"p_mask": ex.p_mask,
"is_impossible": ex.is_impossible,
},
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
"feature_index": i,
"qas_id": ex.qas_id,
},
{
"start_positions": ex.start_position,
"end_positions": ex.end_position,
"cls_index": ex.cls_index,
"p_mask": ex.p_mask,
"is_impossible": ex.is_impossible,
},
)
# Why have we split the batch into a tuple? PyTorch just has a list of tensors.
if "token_type_ids" in tokenizer.model_input_names:
train_types = (
{
"input_ids": tf.int32,
"attention_mask": tf.int32,
"token_type_ids": tf.int32,
"feature_index": tf.int64,
"qas_id": tf.string,
},
{
"start_positions": tf.int64,
"end_positions": tf.int64,
"cls_index": tf.int64,
"p_mask": tf.int32,
"is_impossible": tf.int32,
},
)
train_shapes = (
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"token_type_ids": tf.TensorShape([None]),
"feature_index": tf.TensorShape([]),
"qas_id": tf.TensorShape([]),
},
{
"start_positions": tf.TensorShape([]),
"end_positions": tf.TensorShape([]),
"cls_index": tf.TensorShape([]),
"p_mask": tf.TensorShape([None]),
"is_impossible": tf.TensorShape([]),
},
)
else:
train_types = (
{"input_ids": tf.int32, "attention_mask": tf.int32, "feature_index": tf.int64, "qas_id": tf.string},
{
"start_positions": tf.int64,
"end_positions": tf.int64,
"cls_index": tf.int64,
"p_mask": tf.int32,
"is_impossible": tf.int32,
},
)
train_shapes = (
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"feature_index": tf.TensorShape([]),
"qas_id": tf.TensorShape([]),
},
{
"start_positions": tf.TensorShape([]),
"end_positions": tf.TensorShape([]),
"cls_index": tf.TensorShape([]),
"p_mask": tf.TensorShape([None]),
"is_impossible": tf.TensorShape([]),
},
)
return tf.data.Dataset.from_generator(gen, train_types, train_shapes)
else:
return features
class SquadProcessor(DataProcessor):
"""
Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
version 2.0 of SQuAD, respectively.
"""
train_file = None
dev_file = None
def _get_example_from_tensor_dict(self, tensor_dict, evaluate=False):
if not evaluate:
answer = tensor_dict["answers"]["text"][0].numpy().decode("utf-8")
answer_start = tensor_dict["answers"]["answer_start"][0].numpy()
answers = []
else:
answers = [
{"answer_start": start.numpy(), "text": text.numpy().decode("utf-8")}
for start, text in zip(tensor_dict["answers"]["answer_start"], tensor_dict["answers"]["text"])
]
answer = None
answer_start = None
return SquadExample(
qas_id=tensor_dict["id"].numpy().decode("utf-8"),
question_text=tensor_dict["question"].numpy().decode("utf-8"),
context_text=tensor_dict["context"].numpy().decode("utf-8"),
answer_text=answer,
start_position_character=answer_start,
title=tensor_dict["title"].numpy().decode("utf-8"),
answers=answers,
)
def get_examples_from_dataset(self, dataset, evaluate=False):
"""
Creates a list of [`~data.processors.squad.SquadExample`] using a TFDS dataset.
Args:
dataset: The tfds dataset loaded from *tensorflow_datasets.load("squad")*
evaluate: Boolean specifying if in evaluation mode or in training mode
Returns:
List of SquadExample
Examples:
```python
>>> import tensorflow_datasets as tfds
>>> dataset = tfds.load("squad")
>>> training_examples = get_examples_from_dataset(dataset, evaluate=False)
>>> evaluation_examples = get_examples_from_dataset(dataset, evaluate=True)
```"""
if evaluate:
dataset = dataset["validation"]
else:
dataset = dataset["train"]
examples = []
for tensor_dict in tqdm(dataset):
examples.append(self._get_example_from_tensor_dict(tensor_dict, evaluate=evaluate))
return examples
def get_train_examples(self, data_dir, filename=None):
"""
Returns the training examples from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the training file has a different name than the original one
which is `train-v1.1.json` and `train-v2.0.json` for squad versions 1.1 and 2.0 respectively.
"""
if data_dir is None:
data_dir = ""
if self.train_file is None:
raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
with open(
os.path.join(data_dir, self.train_file if filename is None else filename), "r", encoding="utf-8"
) as reader:
input_data = json.load(reader)["data"]
return self._create_examples(input_data, "train")
def get_dev_examples(self, data_dir, filename=None):
"""
Returns the evaluation example from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the evaluation file has a different name than the original one
which is `dev-v1.1.json` and `dev-v2.0.json` for squad versions 1.1 and 2.0 respectively.
"""
if data_dir is None:
data_dir = ""
if self.dev_file is None:
raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
with open(
os.path.join(data_dir, self.dev_file if filename is None else filename), "r", encoding="utf-8"
) as reader:
input_data = json.load(reader)["data"]
return self._create_examples(input_data, "dev")
def _create_examples(self, input_data, set_type):
is_training = set_type == "train"
examples = []
for entry in tqdm(input_data):
title = entry["title"]
for paragraph in entry["paragraphs"]:
context_text = paragraph["context"]
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position_character = None
answer_text = None
answers = []
is_impossible = qa.get("is_impossible", False)
if not is_impossible:
if is_training:
answer = qa["answers"][0]
answer_text = answer["text"]
start_position_character = answer["answer_start"]
else:
answers = qa["answers"]
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
context_text=context_text,
answer_text=answer_text,
start_position_character=start_position_character,
title=title,
is_impossible=is_impossible,
answers=answers,
)
examples.append(example)
return examples
class SquadV1Processor(SquadProcessor):
train_file = "train-v1.1.json"
dev_file = "dev-v1.1.json"
class SquadV2Processor(SquadProcessor):
train_file = "train-v2.0.json"
dev_file = "dev-v2.0.json"
class SquadExample:
"""
A single training/test example for the Squad dataset, as loaded from disk.
Args:
qas_id: The example's unique identifier
question_text: The question string
context_text: The context string
answer_text: The answer string
start_position_character: The character position of the start of the answer
title: The title of the example
answers: None by default, this is used during evaluation. Holds answers as well as their start positions.
is_impossible: False by default, set to True if the example has no possible answer.
"""
def __init__(
self,
qas_id,
question_text,
context_text,
answer_text,
start_position_character,
title,
answers=[],
is_impossible=False,
):
self.qas_id = qas_id
self.question_text = question_text
self.context_text = context_text
self.answer_text = answer_text
self.title = title
self.is_impossible = is_impossible
self.answers = answers
self.start_position, self.end_position = 0, 0
doc_tokens = []
char_to_word_offset = []
prev_is_whitespace = True
# Split on whitespace so that different tokens may be attributed to their original position.
for c in self.context_text:
if _is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
else:
doc_tokens[-1] += c
prev_is_whitespace = False
char_to_word_offset.append(len(doc_tokens) - 1)
self.doc_tokens = doc_tokens
self.char_to_word_offset = char_to_word_offset
# Start and end positions only has a value during evaluation.
if start_position_character is not None and not is_impossible:
self.start_position = char_to_word_offset[start_position_character]
self.end_position = char_to_word_offset[
min(start_position_character + len(answer_text) - 1, len(char_to_word_offset) - 1)
]
class SquadFeatures:
"""
Single squad example features to be fed to a model. Those features are model-specific and can be crafted from
[`~data.processors.squad.SquadExample`] using the
:method:*~transformers.data.processors.squad.squad_convert_examples_to_features* method.
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
token_type_ids: Segment token indices to indicate first and second portions of the inputs.
cls_index: the index of the CLS token.
p_mask: Mask identifying tokens that can be answers vs. tokens that cannot.
Mask with 1 for tokens than cannot be in the answer and 0 for token that can be in an answer
example_index: the index of the example
unique_id: The unique Feature identifier
paragraph_len: The length of the context
token_is_max_context:
List of booleans identifying which tokens have their maximum context in this feature object. If a token
does not have their maximum context in this feature object, it means that another feature object has more
information related to that token and should be prioritized over this feature for that token.
tokens: list of tokens corresponding to the input ids
token_to_orig_map: mapping between the tokens and the original text, needed in order to identify the answer.
start_position: start of the answer token index
end_position: end of the answer token index
encoding: optionally store the BatchEncoding with the fast-tokenizer alignment methods.
"""
def __init__(
self,
input_ids,
attention_mask,
token_type_ids,
cls_index,
p_mask,
example_index,
unique_id,
paragraph_len,
token_is_max_context,
tokens,
token_to_orig_map,
start_position,
end_position,
is_impossible,
qas_id: str = None,
encoding: BatchEncoding = None,
):
self.input_ids = input_ids
self.attention_mask = attention_mask
self.token_type_ids = token_type_ids
self.cls_index = cls_index
self.p_mask = p_mask
self.example_index = example_index
self.unique_id = unique_id
self.paragraph_len = paragraph_len
self.token_is_max_context = token_is_max_context
self.tokens = tokens
self.token_to_orig_map = token_to_orig_map
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
self.qas_id = qas_id
self.encoding = encoding
class SquadResult:
"""
Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
Args:
unique_id: The unique identifier corresponding to that example.
start_logits: The logits corresponding to the start of the answer
end_logits: The logits corresponding to the end of the answer
"""
def __init__(self, unique_id, start_logits, end_logits, start_top_index=None, end_top_index=None, cls_logits=None):
self.start_logits = start_logits
self.end_logits = end_logits
self.unique_id = unique_id
if start_top_index:
self.start_top_index = start_top_index
self.end_top_index = end_top_index
self.cls_logits = cls_logits
| 33,153 | 38.189125 | 125 | py |
transformers | transformers-main/src/transformers/data/processors/utils.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union
from ...utils import is_tf_available, is_torch_available, logging
logger = logging.get_logger(__name__)
@dataclass
class InputExample:
"""
A single training/test example for simple sequence classification.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
guid: str
text_a: str
text_b: Optional[str] = None
label: Optional[str] = None
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(dataclasses.asdict(self), indent=2) + "\n"
@dataclass(frozen=True)
class InputFeatures:
"""
A single set of features of data. Property names are the same names as the corresponding inputs to a model.
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
Mask values selected in `[0, 1]`: Usually `1` for tokens that are NOT MASKED, `0` for MASKED (padded)
tokens.
token_type_ids: (Optional) Segment token indices to indicate first and second
portions of the inputs. Only some models use them.
label: (Optional) Label corresponding to the input. Int for classification problems,
float for regression problems.
"""
input_ids: List[int]
attention_mask: Optional[List[int]] = None
token_type_ids: Optional[List[int]] = None
label: Optional[Union[int, float]] = None
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(dataclasses.asdict(self)) + "\n"
class DataProcessor:
"""Base class for data converters for sequence classification data sets."""
def get_example_from_tensor_dict(self, tensor_dict):
"""
Gets an example from a dict with tensorflow tensors.
Args:
tensor_dict: Keys and values should match the corresponding Glue
tensorflow_dataset examples.
"""
raise NotImplementedError()
def get_train_examples(self, data_dir):
"""Gets a collection of [`InputExample`] for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of [`InputExample`] for the dev set."""
raise NotImplementedError()
def get_test_examples(self, data_dir):
"""Gets a collection of [`InputExample`] for the test set."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
def tfds_map(self, example):
"""
Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
examples to the correct format.
"""
if len(self.get_labels()) > 1:
example.label = self.get_labels()[int(example.label)]
return example
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r", encoding="utf-8-sig") as f:
return list(csv.reader(f, delimiter="\t", quotechar=quotechar))
class SingleSentenceClassificationProcessor(DataProcessor):
"""Generic processor for a single sentence classification data set."""
def __init__(self, labels=None, examples=None, mode="classification", verbose=False):
self.labels = [] if labels is None else labels
self.examples = [] if examples is None else examples
self.mode = mode
self.verbose = verbose
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
if isinstance(idx, slice):
return SingleSentenceClassificationProcessor(labels=self.labels, examples=self.examples[idx])
return self.examples[idx]
@classmethod
def create_from_csv(
cls, file_name, split_name="", column_label=0, column_text=1, column_id=None, skip_first_row=False, **kwargs
):
processor = cls(**kwargs)
processor.add_examples_from_csv(
file_name,
split_name=split_name,
column_label=column_label,
column_text=column_text,
column_id=column_id,
skip_first_row=skip_first_row,
overwrite_labels=True,
overwrite_examples=True,
)
return processor
@classmethod
def create_from_examples(cls, texts_or_text_and_labels, labels=None, **kwargs):
processor = cls(**kwargs)
processor.add_examples(texts_or_text_and_labels, labels=labels)
return processor
def add_examples_from_csv(
self,
file_name,
split_name="",
column_label=0,
column_text=1,
column_id=None,
skip_first_row=False,
overwrite_labels=False,
overwrite_examples=False,
):
lines = self._read_tsv(file_name)
if skip_first_row:
lines = lines[1:]
texts = []
labels = []
ids = []
for i, line in enumerate(lines):
texts.append(line[column_text])
labels.append(line[column_label])
if column_id is not None:
ids.append(line[column_id])
else:
guid = f"{split_name}-{i}" if split_name else str(i)
ids.append(guid)
return self.add_examples(
texts, labels, ids, overwrite_labels=overwrite_labels, overwrite_examples=overwrite_examples
)
def add_examples(
self, texts_or_text_and_labels, labels=None, ids=None, overwrite_labels=False, overwrite_examples=False
):
if labels is not None and len(texts_or_text_and_labels) != len(labels):
raise ValueError(
f"Text and labels have mismatched lengths {len(texts_or_text_and_labels)} and {len(labels)}"
)
if ids is not None and len(texts_or_text_and_labels) != len(ids):
raise ValueError(f"Text and ids have mismatched lengths {len(texts_or_text_and_labels)} and {len(ids)}")
if ids is None:
ids = [None] * len(texts_or_text_and_labels)
if labels is None:
labels = [None] * len(texts_or_text_and_labels)
examples = []
added_labels = set()
for text_or_text_and_label, label, guid in zip(texts_or_text_and_labels, labels, ids):
if isinstance(text_or_text_and_label, (tuple, list)) and label is None:
text, label = text_or_text_and_label
else:
text = text_or_text_and_label
added_labels.add(label)
examples.append(InputExample(guid=guid, text_a=text, text_b=None, label=label))
# Update examples
if overwrite_examples:
self.examples = examples
else:
self.examples.extend(examples)
# Update labels
if overwrite_labels:
self.labels = list(added_labels)
else:
self.labels = list(set(self.labels).union(added_labels))
return self.examples
def get_features(
self,
tokenizer,
max_length=None,
pad_on_left=False,
pad_token=0,
mask_padding_with_zero=True,
return_tensors=None,
):
"""
Convert examples in a list of `InputFeatures`
Args:
tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length
pad_on_left: If set to `True`, the examples will be padded on the left rather than on the right (default)
pad_token: Padding token
mask_padding_with_zero: If set to `True`, the attention mask will be filled by `1` for actual values
and by `0` for padded values. If set to `False`, inverts it (`1` for padded values, `0` for actual
values)
Returns:
If the `examples` input is a `tf.data.Dataset`, will return a `tf.data.Dataset` containing the
task-specific features. If the input is a list of `InputExamples`, will return a list of task-specific
`InputFeatures` which can be fed to the model.
"""
if max_length is None:
max_length = tokenizer.max_len
label_map = {label: i for i, label in enumerate(self.labels)}
all_input_ids = []
for ex_index, example in enumerate(self.examples):
if ex_index % 10000 == 0:
logger.info(f"Tokenizing example {ex_index}")
input_ids = tokenizer.encode(
example.text_a,
add_special_tokens=True,
max_length=min(max_length, tokenizer.max_len),
)
all_input_ids.append(input_ids)
batch_length = max(len(input_ids) for input_ids in all_input_ids)
features = []
for ex_index, (input_ids, example) in enumerate(zip(all_input_ids, self.examples)):
if ex_index % 10000 == 0:
logger.info(f"Writing example {ex_index}/{len(self.examples)}")
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length.
padding_length = batch_length - len(input_ids)
if pad_on_left:
input_ids = ([pad_token] * padding_length) + input_ids
attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
else:
input_ids = input_ids + ([pad_token] * padding_length)
attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
if len(input_ids) != batch_length:
raise ValueError(f"Error with input length {len(input_ids)} vs {batch_length}")
if len(attention_mask) != batch_length:
raise ValueError(f"Error with input length {len(attention_mask)} vs {batch_length}")
if self.mode == "classification":
label = label_map[example.label]
elif self.mode == "regression":
label = float(example.label)
else:
raise ValueError(self.mode)
if ex_index < 5 and self.verbose:
logger.info("*** Example ***")
logger.info(f"guid: {example.guid}")
logger.info(f"input_ids: {' '.join([str(x) for x in input_ids])}")
logger.info(f"attention_mask: {' '.join([str(x) for x in attention_mask])}")
logger.info(f"label: {example.label} (id = {label})")
features.append(InputFeatures(input_ids=input_ids, attention_mask=attention_mask, label=label))
if return_tensors is None:
return features
elif return_tensors == "tf":
if not is_tf_available():
raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
import tensorflow as tf
def gen():
for ex in features:
yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
dataset = tf.data.Dataset.from_generator(
gen,
({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64),
({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([])),
)
return dataset
elif return_tensors == "pt":
if not is_torch_available():
raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
import torch
from torch.utils.data import TensorDataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
if self.mode == "classification":
all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
elif self.mode == "regression":
all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_attention_mask, all_labels)
return dataset
else:
raise ValueError("return_tensors should be one of 'tf' or 'pt'")
| 13,829 | 38.514286 | 118 | py |
transformers | transformers-main/src/transformers/data/processors/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 1,014 | 52.421053 | 118 | py |
transformers | transformers-main/src/transformers/data/processors/xnli.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" XNLI utils (dataset loading and evaluation)"""
import os
from ...utils import logging
from .utils import DataProcessor, InputExample
logger = logging.get_logger(__name__)
class XnliProcessor(DataProcessor):
"""
Processor for the XNLI dataset. Adapted from
https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/run_classifier.py#L207
"""
def __init__(self, language, train_language=None):
self.language = language
self.train_language = train_language
def get_train_examples(self, data_dir):
"""See base class."""
lg = self.language if self.train_language is None else self.train_language
lines = self._read_tsv(os.path.join(data_dir, f"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"))
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"train-{i}"
text_a = line[0]
text_b = line[1]
label = "contradiction" if line[2] == "contradictory" else line[2]
if not isinstance(text_a, str):
raise ValueError(f"Training input {text_a} is not a string")
if not isinstance(text_b, str):
raise ValueError(f"Training input {text_b} is not a string")
if not isinstance(label, str):
raise ValueError(f"Training label {label} is not a string")
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_test_examples(self, data_dir):
"""See base class."""
lines = self._read_tsv(os.path.join(data_dir, "XNLI-1.0/xnli.test.tsv"))
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
language = line[0]
if language != self.language:
continue
guid = f"test-{i}"
text_a = line[6]
text_b = line[7]
label = line[1]
if not isinstance(text_a, str):
raise ValueError(f"Training input {text_a} is not a string")
if not isinstance(text_b, str):
raise ValueError(f"Training input {text_b} is not a string")
if not isinstance(label, str):
raise ValueError(f"Training label {label} is not a string")
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
xnli_processors = {
"xnli": XnliProcessor,
}
xnli_output_modes = {
"xnli": "classification",
}
xnli_tasks_num_labels = {
"xnli": 3,
}
| 3,489 | 34.612245 | 112 | py |
transformers | transformers-main/src/transformers/pipelines/image_segmentation.py | from typing import Any, Dict, List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import (
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
)
logger = logging.get_logger(__name__)
Prediction = Dict[str, Any]
Predictions = List[Prediction]
@add_end_docstrings(PIPELINE_INIT_ARGS)
class ImageSegmentationPipeline(Pipeline):
"""
Image segmentation pipeline using any `AutoModelForXXXSegmentation`. This pipeline predicts masks of objects and
their classes.
Example:
```python
>>> from transformers import pipeline
>>> segmenter = pipeline(model="facebook/detr-resnet-50-panoptic")
>>> segments = segmenter("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
>>> len(segments)
2
>>> segments[0]["label"]
'bird'
>>> segments[1]["label"]
'bird'
>>> type(segments[0]["mask"]) # This is a black and white mask showing where is the bird on the original image.
<class 'PIL.Image.Image'>
>>> segments[0]["mask"].size
(768, 512)
```
This image segmentation pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"image-segmentation"`.
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=image-segmentation).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.framework == "tf":
raise ValueError(f"The {self.__class__} is only available in PyTorch.")
requires_backends(self, "vision")
self.check_model_type(
dict(
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
+ MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
+ MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
+ MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()
)
)
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
postprocess_kwargs = {}
if "subtask" in kwargs:
postprocess_kwargs["subtask"] = kwargs["subtask"]
preprocess_kwargs["subtask"] = kwargs["subtask"]
if "threshold" in kwargs:
postprocess_kwargs["threshold"] = kwargs["threshold"]
if "mask_threshold" in kwargs:
postprocess_kwargs["mask_threshold"] = kwargs["mask_threshold"]
if "overlap_mask_area_threshold" in kwargs:
postprocess_kwargs["overlap_mask_area_threshold"] = kwargs["overlap_mask_area_threshold"]
return preprocess_kwargs, {}, postprocess_kwargs
def __call__(self, images, **kwargs) -> Union[Predictions, List[Prediction]]:
"""
Perform segmentation (detect masks & classes) in the image(s) passed as inputs.
Args:
images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
The pipeline handles three types of images:
- A string containing an HTTP(S) link pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the
same format: all as HTTP(S) links, all as local paths, or all as PIL images.
subtask (`str`, *optional*):
Segmentation task to be performed, choose [`semantic`, `instance` and `panoptic`] depending on model
capabilities. If not set, the pipeline will attempt tp resolve in the following order:
`panoptic`, `instance`, `semantic`.
threshold (`float`, *optional*, defaults to 0.9):
Probability threshold to filter out predicted masks.
mask_threshold (`float`, *optional*, defaults to 0.5):
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (`float`, *optional*, defaults to 0.5):
Mask overlap threshold to eliminate small, disconnected segments.
Return:
A dictionary or a list of dictionaries containing the result. If the input is a single image, will return a
list of dictionaries, if the input is a list of several images, will return a list of list of dictionaries
corresponding to each image.
The dictionaries contain the mask, label and score (where applicable) of each detected object and contains
the following keys:
- **label** (`str`) -- The class label identified by the model.
- **mask** (`PIL.Image`) -- A binary mask of the detected object as a Pil Image of shape (width, height) of
the original image. Returns a mask filled with zeros if no object is found.
- **score** (*optional* `float`) -- Optionally, when the model is capable of estimating a confidence of the
"object" described by the label and the mask.
"""
return super().__call__(images, **kwargs)
def preprocess(self, image, subtask=None):
image = load_image(image)
target_size = [(image.height, image.width)]
if self.model.config.__class__.__name__ == "OneFormerConfig":
if subtask is None:
kwargs = {}
else:
kwargs = {"task_inputs": [subtask]}
inputs = self.image_processor(images=[image], return_tensors="pt", **kwargs)
inputs["task_inputs"] = self.tokenizer(
inputs["task_inputs"],
padding="max_length",
max_length=self.model.config.task_seq_len,
return_tensors=self.framework,
)["input_ids"]
else:
inputs = self.image_processor(images=[image], return_tensors="pt")
inputs["target_size"] = target_size
return inputs
def _forward(self, model_inputs):
target_size = model_inputs.pop("target_size")
model_outputs = self.model(**model_inputs)
model_outputs["target_size"] = target_size
return model_outputs
def postprocess(
self, model_outputs, subtask=None, threshold=0.9, mask_threshold=0.5, overlap_mask_area_threshold=0.5
):
fn = None
if subtask in {"panoptic", None} and hasattr(self.image_processor, "post_process_panoptic_segmentation"):
fn = self.image_processor.post_process_panoptic_segmentation
elif subtask in {"instance", None} and hasattr(self.image_processor, "post_process_instance_segmentation"):
fn = self.image_processor.post_process_instance_segmentation
if fn is not None:
outputs = fn(
model_outputs,
threshold=threshold,
mask_threshold=mask_threshold,
overlap_mask_area_threshold=overlap_mask_area_threshold,
target_sizes=model_outputs["target_size"],
)[0]
annotation = []
segmentation = outputs["segmentation"]
for segment in outputs["segments_info"]:
mask = (segmentation == segment["id"]) * 255
mask = Image.fromarray(mask.numpy().astype(np.uint8), mode="L")
label = self.model.config.id2label[segment["label_id"]]
score = segment["score"]
annotation.append({"score": score, "label": label, "mask": mask})
elif subtask in {"semantic", None} and hasattr(self.image_processor, "post_process_semantic_segmentation"):
outputs = self.image_processor.post_process_semantic_segmentation(
model_outputs, target_sizes=model_outputs["target_size"]
)[0]
annotation = []
segmentation = outputs.numpy()
labels = np.unique(segmentation)
for label in labels:
mask = (segmentation == label) * 255
mask = Image.fromarray(mask.astype(np.uint8), mode="L")
label = self.model.config.id2label[label]
annotation.append({"score": None, "label": label, "mask": mask})
else:
raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model)}")
return annotation
| 8,731 | 40.580952 | 119 | py |
transformers | transformers-main/src/transformers/pipelines/base.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import csv
import importlib
import json
import os
import pickle
import sys
import types
import warnings
from abc import ABC, abstractmethod
from collections import UserDict
from contextlib import contextmanager
from os.path import abspath, exists
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
from packaging import version
from ..dynamic_module_utils import custom_object_save
from ..feature_extraction_utils import PreTrainedFeatureExtractor
from ..image_processing_utils import BaseImageProcessor
from ..modelcard import ModelCard
from ..models.auto.configuration_auto import AutoConfig
from ..tokenization_utils import PreTrainedTokenizer
from ..utils import ModelOutput, add_end_docstrings, infer_framework, is_tf_available, is_torch_available, logging
GenericTensor = Union[List["GenericTensor"], "torch.Tensor", "tf.Tensor"]
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TFAutoModel
if is_torch_available():
import torch
from torch.utils.data import DataLoader, Dataset
from ..models.auto.modeling_auto import AutoModel
# Re-export for backward compatibility
from .pt_utils import KeyDataset
else:
Dataset = None
KeyDataset = None
if TYPE_CHECKING:
from ..modeling_tf_utils import TFPreTrainedModel
from ..modeling_utils import PreTrainedModel
logger = logging.get_logger(__name__)
def no_collate_fn(items):
if len(items) != 1:
raise ValueError("This collate_fn is meant to be used with batch_size=1")
return items[0]
def _pad(items, key, padding_value, padding_side):
batch_size = len(items)
if isinstance(items[0][key], torch.Tensor):
# Others include `attention_mask` etc...
shape = items[0][key].shape
dim = len(shape)
if key in ["pixel_values", "image"]:
# This is probable image so padding shouldn't be necessary
# B, C, H, W
return torch.cat([item[key] for item in items], dim=0)
elif dim == 4 and key == "input_features":
# this is probably a mel spectrogram batched
return torch.cat([item[key] for item in items], dim=0)
max_length = max(item[key].shape[1] for item in items)
min_length = min(item[key].shape[1] for item in items)
dtype = items[0][key].dtype
if dim == 2:
if max_length == min_length:
# Bypass for `ImageGPT` which doesn't provide a padding value, yet
# we can consistently pad since the size should be matching
return torch.cat([item[key] for item in items], dim=0)
tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
elif dim == 3:
tensor = torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype) + padding_value
elif dim == 4:
tensor = torch.zeros((batch_size, max_length, shape[-2], shape[-1]), dtype=dtype) + padding_value
for i, item in enumerate(items):
if dim == 2:
if padding_side == "left":
tensor[i, -len(item[key][0]) :] = item[key][0].clone()
else:
tensor[i, : len(item[key][0])] = item[key][0].clone()
elif dim == 3:
if padding_side == "left":
tensor[i, -len(item[key][0]) :, :] = item[key][0].clone()
else:
tensor[i, : len(item[key][0]), :] = item[key][0].clone()
elif dim == 4:
if padding_side == "left":
tensor[i, -len(item[key][0]) :, :, :] = item[key][0].clone()
else:
tensor[i, : len(item[key][0]), :, :] = item[key][0].clone()
return tensor
else:
return [item[key] for item in items]
def pad_collate_fn(tokenizer, feature_extractor):
# Tokenizer
t_padding_side = None
# Feature extractor
f_padding_side = None
if tokenizer is None and feature_extractor is None:
raise ValueError("Pipeline without tokenizer or feature_extractor cannot do batching")
if tokenizer is not None:
if tokenizer.pad_token_id is None:
raise ValueError(
"Pipeline with tokenizer without pad_token cannot do batching. You can try to set it with "
"`pipe.tokenizer.pad_token_id = model.config.eos_token_id`."
)
else:
t_padding_value = tokenizer.pad_token_id
t_padding_side = tokenizer.padding_side
if feature_extractor is not None:
# Feature extractor can be images, where no padding is expected
f_padding_value = getattr(feature_extractor, "padding_value", None)
f_padding_side = getattr(feature_extractor, "padding_side", None)
if t_padding_side is not None and f_padding_side is not None and t_padding_side != f_padding_side:
raise ValueError(
f"The feature extractor, and tokenizer don't agree on padding side {t_padding_side} != {f_padding_side}"
)
padding_side = "right"
if t_padding_side is not None:
padding_side = t_padding_side
if f_padding_side is not None:
padding_side = f_padding_side
def inner(items):
keys = set(items[0].keys())
for item in items:
if set(item.keys()) != keys:
raise ValueError(
f"The elements of the batch contain different keys. Cannot batch them ({set(item.keys())} !="
f" {keys})"
)
# input_values, input_pixels, input_ids, ...
padded = {}
for key in keys:
if key in {"input_ids"}:
# ImageGPT uses a feature extractor
if tokenizer is None and feature_extractor is not None:
_padding_value = f_padding_value
else:
_padding_value = t_padding_value
elif key in {"input_values", "pixel_values", "input_features"}:
_padding_value = f_padding_value
elif key in {"p_mask", "special_tokens_mask"}:
_padding_value = 1
elif key in {"attention_mask", "token_type_ids"}:
_padding_value = 0
else:
# This is likely another random key maybe even user provided
_padding_value = 0
padded[key] = _pad(items, key, _padding_value, padding_side)
return padded
return inner
def infer_framework_load_model(
model,
config: AutoConfig,
model_classes: Optional[Dict[str, Tuple[type]]] = None,
task: Optional[str] = None,
framework: Optional[str] = None,
**model_kwargs,
):
"""
Select framework (TensorFlow or PyTorch) to use from the `model` passed. Returns a tuple (framework, model).
If `model` is instantiated, this function will just infer the framework from the model class. Otherwise `model` is
actually a checkpoint name and this method will try to instantiate it using `model_classes`. Since we don't want to
instantiate the model twice, this model is returned for use by the pipeline.
If both frameworks are installed and available for `model`, PyTorch is selected.
Args:
model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel`]):
The model to infer the framework from. If `str`, a checkpoint name. The model to infer the framewrok from.
config ([`AutoConfig`]):
The config associated with the model to help using the correct class
model_classes (dictionary `str` to `type`, *optional*):
A mapping framework to class.
task (`str`):
The task defining which pipeline will be returned.
model_kwargs:
Additional dictionary of keyword arguments passed along to the model's `from_pretrained(...,
**model_kwargs)` function.
Returns:
`Tuple`: A tuple framework, model.
"""
if not is_tf_available() and not is_torch_available():
raise RuntimeError(
"At least one of TensorFlow 2.0 or PyTorch should be installed. "
"To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ "
"To install PyTorch, read the instructions at https://pytorch.org/."
)
if isinstance(model, str):
model_kwargs["_from_pipeline"] = task
class_tuple = ()
look_pt = is_torch_available() and framework in {"pt", None}
look_tf = is_tf_available() and framework in {"tf", None}
if model_classes:
if look_pt:
class_tuple = class_tuple + model_classes.get("pt", (AutoModel,))
if look_tf:
class_tuple = class_tuple + model_classes.get("tf", (TFAutoModel,))
if config.architectures:
classes = []
for architecture in config.architectures:
transformers_module = importlib.import_module("transformers")
if look_pt:
_class = getattr(transformers_module, architecture, None)
if _class is not None:
classes.append(_class)
if look_tf:
_class = getattr(transformers_module, f"TF{architecture}", None)
if _class is not None:
classes.append(_class)
class_tuple = class_tuple + tuple(classes)
if len(class_tuple) == 0:
raise ValueError(f"Pipeline cannot infer suitable model classes from {model}")
for model_class in class_tuple:
kwargs = model_kwargs.copy()
if framework == "pt" and model.endswith(".h5"):
kwargs["from_tf"] = True
logger.warning(
"Model might be a TensorFlow model (ending with `.h5`) but TensorFlow is not available. "
"Trying to load the model with PyTorch."
)
elif framework == "tf" and model.endswith(".bin"):
kwargs["from_pt"] = True
logger.warning(
"Model might be a PyTorch model (ending with `.bin`) but PyTorch is not available. "
"Trying to load the model with Tensorflow."
)
try:
model = model_class.from_pretrained(model, **kwargs)
if hasattr(model, "eval"):
model = model.eval()
# Stop loading on the first successful load.
break
except (OSError, ValueError):
continue
if isinstance(model, str):
raise ValueError(f"Could not load model {model} with any of the following classes: {class_tuple}.")
if framework is None:
framework = infer_framework(model.__class__)
return framework, model
def infer_framework_from_model(
model,
model_classes: Optional[Dict[str, Tuple[type]]] = None,
task: Optional[str] = None,
framework: Optional[str] = None,
**model_kwargs,
):
"""
Select framework (TensorFlow or PyTorch) to use from the `model` passed. Returns a tuple (framework, model).
If `model` is instantiated, this function will just infer the framework from the model class. Otherwise `model` is
actually a checkpoint name and this method will try to instantiate it using `model_classes`. Since we don't want to
instantiate the model twice, this model is returned for use by the pipeline.
If both frameworks are installed and available for `model`, PyTorch is selected.
Args:
model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel`]):
The model to infer the framework from. If `str`, a checkpoint name. The model to infer the framewrok from.
model_classes (dictionary `str` to `type`, *optional*):
A mapping framework to class.
task (`str`):
The task defining which pipeline will be returned.
model_kwargs:
Additional dictionary of keyword arguments passed along to the model's `from_pretrained(...,
**model_kwargs)` function.
Returns:
`Tuple`: A tuple framework, model.
"""
if isinstance(model, str):
config = AutoConfig.from_pretrained(model, _from_pipeline=task, **model_kwargs)
else:
config = model.config
return infer_framework_load_model(
model, config, model_classes=model_classes, _from_pipeline=task, task=task, framework=framework, **model_kwargs
)
def get_framework(model, revision: Optional[str] = None):
"""
Select framework (TensorFlow or PyTorch) to use.
Args:
model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel`]):
If both frameworks are installed, picks the one corresponding to the model passed (either a model class or
the model name). If no specific model is provided, defaults to using PyTorch.
"""
warnings.warn(
"`get_framework` is deprecated and will be removed in v5, use `infer_framework_from_model` instead.",
FutureWarning,
)
if not is_tf_available() and not is_torch_available():
raise RuntimeError(
"At least one of TensorFlow 2.0 or PyTorch should be installed. "
"To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ "
"To install PyTorch, read the instructions at https://pytorch.org/."
)
if isinstance(model, str):
if is_torch_available() and not is_tf_available():
model = AutoModel.from_pretrained(model, revision=revision)
elif is_tf_available() and not is_torch_available():
model = TFAutoModel.from_pretrained(model, revision=revision)
else:
try:
model = AutoModel.from_pretrained(model, revision=revision)
except OSError:
model = TFAutoModel.from_pretrained(model, revision=revision)
framework = infer_framework(model.__class__)
return framework
def get_default_model_and_revision(
targeted_task: Dict, framework: Optional[str], task_options: Optional[Any]
) -> Union[str, Tuple[str, str]]:
"""
Select a default model to use for a given task. Defaults to pytorch if ambiguous.
Args:
targeted_task (`Dict` ):
Dictionary representing the given task, that should contain default models
framework (`str`, None)
"pt", "tf" or None, representing a specific framework if it was specified, or None if we don't know yet.
task_options (`Any`, None)
Any further value required by the task to get fully specified, for instance (SRC, TGT) languages for
translation task.
Returns
`str` The model string representing the default model for this pipeline
"""
if is_torch_available() and not is_tf_available():
framework = "pt"
elif is_tf_available() and not is_torch_available():
framework = "tf"
defaults = targeted_task["default"]
if task_options:
if task_options not in defaults:
raise ValueError(f"The task does not provide any default models for options {task_options}")
default_models = defaults[task_options]["model"]
elif "model" in defaults:
default_models = targeted_task["default"]["model"]
else:
# XXX This error message needs to be updated to be more generic if more tasks are going to become
# parametrized
raise ValueError('The task defaults can\'t be correctly selected. You probably meant "translation_XX_to_YY"')
if framework is None:
framework = "pt"
return default_models[framework]
class PipelineException(Exception):
"""
Raised by a [`Pipeline`] when handling __call__.
Args:
task (`str`): The task of the pipeline.
model (`str`): The model used by the pipeline.
reason (`str`): The error message to display.
"""
def __init__(self, task: str, model: str, reason: str):
super().__init__(reason)
self.task = task
self.model = model
class ArgumentHandler(ABC):
"""
Base interface for handling arguments for each [`~pipelines.Pipeline`].
"""
@abstractmethod
def __call__(self, *args, **kwargs):
raise NotImplementedError()
class PipelineDataFormat:
"""
Base class for all the pipeline supported data format both for reading and writing. Supported data formats
currently includes:
- JSON
- CSV
- stdin/stdout (pipe)
`PipelineDataFormat` also includes some utilities to work with multi-columns like mapping from datasets columns to
pipelines keyword arguments through the `dataset_kwarg_1=dataset_column_1` format.
Args:
output_path (`str`, *optional*): Where to save the outgoing data.
input_path (`str`, *optional*): Where to look for the input data.
column (`str`, *optional*): The column to read.
overwrite (`bool`, *optional*, defaults to `False`):
Whether or not to overwrite the `output_path`.
"""
SUPPORTED_FORMATS = ["json", "csv", "pipe"]
def __init__(
self,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite: bool = False,
):
self.output_path = output_path
self.input_path = input_path
self.column = column.split(",") if column is not None else [""]
self.is_multi_columns = len(self.column) > 1
if self.is_multi_columns:
self.column = [tuple(c.split("=")) if "=" in c else (c, c) for c in self.column]
if output_path is not None and not overwrite:
if exists(abspath(self.output_path)):
raise OSError(f"{self.output_path} already exists on disk")
if input_path is not None:
if not exists(abspath(self.input_path)):
raise OSError(f"{self.input_path} doesnt exist on disk")
@abstractmethod
def __iter__(self):
raise NotImplementedError()
@abstractmethod
def save(self, data: Union[dict, List[dict]]):
"""
Save the provided data object with the representation for the current [`~pipelines.PipelineDataFormat`].
Args:
data (`dict` or list of `dict`): The data to store.
"""
raise NotImplementedError()
def save_binary(self, data: Union[dict, List[dict]]) -> str:
"""
Save the provided data object as a pickle-formatted binary data on the disk.
Args:
data (`dict` or list of `dict`): The data to store.
Returns:
`str`: Path where the data has been saved.
"""
path, _ = os.path.splitext(self.output_path)
binary_path = os.path.extsep.join((path, "pickle"))
with open(binary_path, "wb+") as f_output:
pickle.dump(data, f_output)
return binary_path
@staticmethod
def from_str(
format: str,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite=False,
) -> "PipelineDataFormat":
"""
Creates an instance of the right subclass of [`~pipelines.PipelineDataFormat`] depending on `format`.
Args:
format (`str`):
The format of the desired pipeline. Acceptable values are `"json"`, `"csv"` or `"pipe"`.
output_path (`str`, *optional*):
Where to save the outgoing data.
input_path (`str`, *optional*):
Where to look for the input data.
column (`str`, *optional*):
The column to read.
overwrite (`bool`, *optional*, defaults to `False`):
Whether or not to overwrite the `output_path`.
Returns:
[`~pipelines.PipelineDataFormat`]: The proper data format.
"""
if format == "json":
return JsonPipelineDataFormat(output_path, input_path, column, overwrite=overwrite)
elif format == "csv":
return CsvPipelineDataFormat(output_path, input_path, column, overwrite=overwrite)
elif format == "pipe":
return PipedPipelineDataFormat(output_path, input_path, column, overwrite=overwrite)
else:
raise KeyError(f"Unknown reader {format} (Available reader are json/csv/pipe)")
class CsvPipelineDataFormat(PipelineDataFormat):
"""
Support for pipelines using CSV data format.
Args:
output_path (`str`, *optional*): Where to save the outgoing data.
input_path (`str`, *optional*): Where to look for the input data.
column (`str`, *optional*): The column to read.
overwrite (`bool`, *optional*, defaults to `False`):
Whether or not to overwrite the `output_path`.
"""
def __init__(
self,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite=False,
):
super().__init__(output_path, input_path, column, overwrite=overwrite)
def __iter__(self):
with open(self.input_path, "r") as f:
reader = csv.DictReader(f)
for row in reader:
if self.is_multi_columns:
yield {k: row[c] for k, c in self.column}
else:
yield row[self.column[0]]
def save(self, data: List[dict]):
"""
Save the provided data object with the representation for the current [`~pipelines.PipelineDataFormat`].
Args:
data (`List[dict]`): The data to store.
"""
with open(self.output_path, "w") as f:
if len(data) > 0:
writer = csv.DictWriter(f, list(data[0].keys()))
writer.writeheader()
writer.writerows(data)
class JsonPipelineDataFormat(PipelineDataFormat):
"""
Support for pipelines using JSON file format.
Args:
output_path (`str`, *optional*): Where to save the outgoing data.
input_path (`str`, *optional*): Where to look for the input data.
column (`str`, *optional*): The column to read.
overwrite (`bool`, *optional*, defaults to `False`):
Whether or not to overwrite the `output_path`.
"""
def __init__(
self,
output_path: Optional[str],
input_path: Optional[str],
column: Optional[str],
overwrite=False,
):
super().__init__(output_path, input_path, column, overwrite=overwrite)
with open(input_path, "r") as f:
self._entries = json.load(f)
def __iter__(self):
for entry in self._entries:
if self.is_multi_columns:
yield {k: entry[c] for k, c in self.column}
else:
yield entry[self.column[0]]
def save(self, data: dict):
"""
Save the provided data object in a json file.
Args:
data (`dict`): The data to store.
"""
with open(self.output_path, "w") as f:
json.dump(data, f)
class PipedPipelineDataFormat(PipelineDataFormat):
"""
Read data from piped input to the python process. For multi columns data, columns should separated by \t
If columns are provided, then the output will be a dictionary with {column_x: value_x}
Args:
output_path (`str`, *optional*): Where to save the outgoing data.
input_path (`str`, *optional*): Where to look for the input data.
column (`str`, *optional*): The column to read.
overwrite (`bool`, *optional*, defaults to `False`):
Whether or not to overwrite the `output_path`.
"""
def __iter__(self):
for line in sys.stdin:
# Split for multi-columns
if "\t" in line:
line = line.split("\t")
if self.column:
# Dictionary to map arguments
yield {kwargs: l for (kwargs, _), l in zip(self.column, line)}
else:
yield tuple(line)
# No dictionary to map arguments
else:
yield line
def save(self, data: dict):
"""
Print the data.
Args:
data (`dict`): The data to store.
"""
print(data)
def save_binary(self, data: Union[dict, List[dict]]) -> str:
if self.output_path is None:
raise KeyError(
"When using piped input on pipeline outputting large object requires an output file path. "
"Please provide such output path through --output argument."
)
return super().save_binary(data)
class _ScikitCompat(ABC):
"""
Interface layer for the Scikit and Keras compatibility.
"""
@abstractmethod
def transform(self, X):
raise NotImplementedError()
@abstractmethod
def predict(self, X):
raise NotImplementedError()
PIPELINE_INIT_ARGS = r"""
Arguments:
model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
[`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
tokenizer ([`PreTrainedTokenizer`]):
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
[`PreTrainedTokenizer`].
modelcard (`str` or [`ModelCard`], *optional*):
Model card attributed to the model for this pipeline.
framework (`str`, *optional*):
The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
installed.
If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
provided.
task (`str`, defaults to `""`):
A task-identifier for the pipeline.
num_workers (`int`, *optional*, defaults to 8):
When the pipeline will use *DataLoader* (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.
batch_size (`int`, *optional*, defaults to 1):
When the pipeline will use *DataLoader* (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read [Batching with
pipelines](https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching) .
args_parser ([`~pipelines.ArgumentHandler`], *optional*):
Reference to the object in charge of parsing supplied pipeline parameters.
device (`int`, *optional*, defaults to -1):
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id. You can pass native `torch.device` or a `str` too.
binary_output (`bool`, *optional*, defaults to `False`):
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.
"""
if is_torch_available():
from transformers.pipelines.pt_utils import (
PipelineChunkIterator,
PipelineDataset,
PipelineIterator,
PipelinePackIterator,
)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class Pipeline(_ScikitCompat):
"""
The Pipeline class is the class from which all pipelines inherit. Refer to this class for methods shared across
different pipelines.
Base class implementing pipelined operations. Pipeline workflow is defined as a sequence of the following
operations:
Input -> Tokenization -> Model Inference -> Post-Processing (task dependent) -> Output
Pipeline supports running on CPU or GPU through the device argument (see below).
Some pipeline, like for instance [`FeatureExtractionPipeline`] (`'feature-extraction'`) output large tensor object
as nested-lists. In order to avoid dumping such large structure as textual data we provide the `binary_output`
constructor argument. If set to `True`, the output will be stored in the pickle format.
"""
default_input_names = None
def __init__(
self,
model: Union["PreTrainedModel", "TFPreTrainedModel"],
tokenizer: Optional[PreTrainedTokenizer] = None,
feature_extractor: Optional[PreTrainedFeatureExtractor] = None,
image_processor: Optional[BaseImageProcessor] = None,
modelcard: Optional[ModelCard] = None,
framework: Optional[str] = None,
task: str = "",
args_parser: ArgumentHandler = None,
device: Union[int, "torch.device"] = None,
torch_dtype: Optional[Union[str, "torch.dtype"]] = None,
binary_output: bool = False,
**kwargs,
):
if framework is None:
framework, model = infer_framework_load_model(model, config=model.config)
self.task = task
self.model = model
self.tokenizer = tokenizer
self.feature_extractor = feature_extractor
self.image_processor = image_processor
self.modelcard = modelcard
self.framework = framework
if self.framework == "pt" and device is not None and not (isinstance(device, int) and device < 0):
self.model.to(device)
if device is None:
# `accelerate` device map
hf_device_map = getattr(self.model, "hf_device_map", None)
if hf_device_map is not None:
# Take the first device used by `accelerate`.
device = next(iter(hf_device_map.values()))
else:
device = -1
if is_torch_available() and self.framework == "pt":
if isinstance(device, torch.device):
self.device = device
elif isinstance(device, str):
self.device = torch.device(device)
elif device < 0:
self.device = torch.device("cpu")
else:
self.device = torch.device(f"cuda:{device}")
else:
self.device = device if device is not None else -1
self.torch_dtype = torch_dtype
self.binary_output = binary_output
# Update config and generation_config with task specific parameters
task_specific_params = self.model.config.task_specific_params
if task_specific_params is not None and task in task_specific_params:
self.model.config.update(task_specific_params.get(task))
if self.model.can_generate():
self.model.generation_config.update(**task_specific_params.get(task))
self.call_count = 0
self._batch_size = kwargs.pop("batch_size", None)
self._num_workers = kwargs.pop("num_workers", None)
self._preprocess_params, self._forward_params, self._postprocess_params = self._sanitize_parameters(**kwargs)
if self.image_processor is None and self.feature_extractor is not None:
if isinstance(self.feature_extractor, BaseImageProcessor):
# Backward compatible change, if users called
# ImageSegmentationPipeline(.., feature_extractor=MyFeatureExtractor())
# then we should keep working
self.image_processor = self.feature_extractor
def save_pretrained(self, save_directory: str, safe_serialization: bool = False):
"""
Save the pipeline's model and tokenizer.
Args:
save_directory (`str`):
A path to the directory where to saved. It will be created if it doesn't exist.
safe_serialization (`str`):
Whether to save the model using `safetensors` or the traditional way for PyTorch or Tensorflow
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
os.makedirs(save_directory, exist_ok=True)
if hasattr(self, "_registered_impl"):
# Add info to the config
pipeline_info = self._registered_impl.copy()
custom_pipelines = {}
for task, info in pipeline_info.items():
if info["impl"] != self.__class__:
continue
info = info.copy()
module_name = info["impl"].__module__
last_module = module_name.split(".")[-1]
# Change classes into their names/full names
info["impl"] = f"{last_module}.{info['impl'].__name__}"
info["pt"] = tuple(c.__name__ for c in info["pt"])
info["tf"] = tuple(c.__name__ for c in info["tf"])
custom_pipelines[task] = info
self.model.config.custom_pipelines = custom_pipelines
# Save the pipeline custom code
custom_object_save(self, save_directory)
self.model.save_pretrained(save_directory, safe_serialization=safe_serialization)
if self.tokenizer is not None:
self.tokenizer.save_pretrained(save_directory)
if self.feature_extractor is not None:
self.feature_extractor.save_pretrained(save_directory)
if self.modelcard is not None:
self.modelcard.save_pretrained(save_directory)
def transform(self, X):
"""
Scikit / Keras interface to transformers' pipelines. This method will forward to __call__().
"""
return self(X)
def predict(self, X):
"""
Scikit / Keras interface to transformers' pipelines. This method will forward to __call__().
"""
return self(X)
@contextmanager
def device_placement(self):
"""
Context Manager allowing tensor allocation on the user-specified device in framework agnostic way.
Returns:
Context manager
Examples:
```python
# Explicitly ask for tensor allocation on CUDA device :0
pipe = pipeline(..., device=0)
with pipe.device_placement():
# Every framework specific tensor allocation will be done on the request device
output = pipe(...)
```"""
if self.framework == "tf":
with tf.device("/CPU:0" if self.device == -1 else f"/device:GPU:{self.device}"):
yield
else:
if self.device.type == "cuda":
with torch.cuda.device(self.device):
yield
else:
yield
def ensure_tensor_on_device(self, **inputs):
"""
Ensure PyTorch tensors are on the specified device.
Args:
inputs (keyword arguments that should be `torch.Tensor`, the rest is ignored):
The tensors to place on `self.device`.
Recursive on lists **only**.
Return:
`Dict[str, torch.Tensor]`: The same as `inputs` but on the proper device.
"""
return self._ensure_tensor_on_device(inputs, self.device)
def _ensure_tensor_on_device(self, inputs, device):
if isinstance(inputs, ModelOutput):
return ModelOutput(
{name: self._ensure_tensor_on_device(tensor, device) for name, tensor in inputs.items()}
)
elif isinstance(inputs, dict):
return {name: self._ensure_tensor_on_device(tensor, device) for name, tensor in inputs.items()}
elif isinstance(inputs, UserDict):
return UserDict({name: self._ensure_tensor_on_device(tensor, device) for name, tensor in inputs.items()})
elif isinstance(inputs, list):
return [self._ensure_tensor_on_device(item, device) for item in inputs]
elif isinstance(inputs, tuple):
return tuple([self._ensure_tensor_on_device(item, device) for item in inputs])
elif isinstance(inputs, torch.Tensor):
if device == torch.device("cpu") and inputs.dtype in {torch.float16, torch.bfloat16}:
inputs = inputs.float()
return inputs.to(device)
else:
return inputs
def check_model_type(self, supported_models: Union[List[str], dict]):
"""
Check if the model class is in supported by the pipeline.
Args:
supported_models (`List[str]` or `dict`):
The list of models supported by the pipeline, or a dictionary with model class values.
"""
if not isinstance(supported_models, list): # Create from a model mapping
supported_models_names = []
for config, model in supported_models.items():
# Mapping can now contain tuples of models for the same configuration.
if isinstance(model, tuple):
supported_models_names.extend([_model.__name__ for _model in model])
else:
supported_models_names.append(model.__name__)
supported_models = supported_models_names
if self.model.__class__.__name__ not in supported_models:
logger.error(
f"The model '{self.model.__class__.__name__}' is not supported for {self.task}. Supported models are"
f" {supported_models}."
)
@abstractmethod
def _sanitize_parameters(self, **pipeline_parameters):
"""
_sanitize_parameters will be called with any excessive named arguments from either `__init__` or `__call__`
methods. It should return 3 dictionnaries of the resolved parameters used by the various `preprocess`,
`forward` and `postprocess` methods. Do not fill dictionnaries if the caller didn't specify a kwargs. This
let's you keep defaults in function signatures, which is more "natural".
It is not meant to be called directly, it will be automatically called and the final parameters resolved by
`__init__` and `__call__`
"""
raise NotImplementedError("_sanitize_parameters not implemented")
@abstractmethod
def preprocess(self, input_: Any, **preprocess_parameters: Dict) -> Dict[str, GenericTensor]:
"""
Preprocess will take the `input_` of a specific pipeline and return a dictionary of everything necessary for
`_forward` to run properly. It should contain at least one tensor, but might have arbitrary other items.
"""
raise NotImplementedError("preprocess not implemented")
@abstractmethod
def _forward(self, input_tensors: Dict[str, GenericTensor], **forward_parameters: Dict) -> ModelOutput:
"""
_forward will receive the prepared dictionary from `preprocess` and run it on the model. This method might
involve the GPU or the CPU and should be agnostic to it. Isolating this function is the reason for `preprocess`
and `postprocess` to exist, so that the hot path, this method generally can run as fast as possible.
It is not meant to be called directly, `forward` is preferred. It is basically the same but contains additional
code surrounding `_forward` making sure tensors and models are on the same device, disabling the training part
of the code (leading to faster inference).
"""
raise NotImplementedError("_forward not implemented")
@abstractmethod
def postprocess(self, model_outputs: ModelOutput, **postprocess_parameters: Dict) -> Any:
"""
Postprocess will receive the raw outputs of the `_forward` method, generally tensors, and reformat them into
something more friendly. Generally it will output a list or a dict or results (containing just strings and
numbers).
"""
raise NotImplementedError("postprocess not implemented")
def get_inference_context(self):
inference_context = (
torch.inference_mode
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.9.0")
else torch.no_grad
)
return inference_context
def forward(self, model_inputs, **forward_params):
with self.device_placement():
if self.framework == "tf":
model_inputs["training"] = False
model_outputs = self._forward(model_inputs, **forward_params)
elif self.framework == "pt":
inference_context = self.get_inference_context()
with inference_context():
model_inputs = self._ensure_tensor_on_device(model_inputs, device=self.device)
model_outputs = self._forward(model_inputs, **forward_params)
model_outputs = self._ensure_tensor_on_device(model_outputs, device=torch.device("cpu"))
else:
raise ValueError(f"Framework {self.framework} is not supported")
return model_outputs
def get_iterator(
self, inputs, num_workers: int, batch_size: int, preprocess_params, forward_params, postprocess_params
):
if isinstance(inputs, collections.abc.Sized):
dataset = PipelineDataset(inputs, self.preprocess, preprocess_params)
else:
if num_workers > 1:
logger.warning(
"For iterable dataset using num_workers>1 is likely to result"
" in errors since everything is iterable, setting `num_workers=1`"
" to guarantee correctness."
)
num_workers = 1
dataset = PipelineIterator(inputs, self.preprocess, preprocess_params)
if "TOKENIZERS_PARALLELISM" not in os.environ:
logger.info("Disabling tokenizer parallelism, we're using DataLoader multithreading already")
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# TODO hack by collating feature_extractor and image_processor
feature_extractor = self.feature_extractor if self.feature_extractor is not None else self.image_processor
collate_fn = no_collate_fn if batch_size == 1 else pad_collate_fn(self.tokenizer, feature_extractor)
dataloader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, collate_fn=collate_fn)
model_iterator = PipelineIterator(dataloader, self.forward, forward_params, loader_batch_size=batch_size)
final_iterator = PipelineIterator(model_iterator, self.postprocess, postprocess_params)
return final_iterator
def __call__(self, inputs, *args, num_workers=None, batch_size=None, **kwargs):
if args:
logger.warning(f"Ignoring args : {args}")
if num_workers is None:
if self._num_workers is None:
num_workers = 0
else:
num_workers = self._num_workers
if batch_size is None:
if self._batch_size is None:
batch_size = 1
else:
batch_size = self._batch_size
preprocess_params, forward_params, postprocess_params = self._sanitize_parameters(**kwargs)
# Fuse __init__ params and __call__ params without modifying the __init__ ones.
preprocess_params = {**self._preprocess_params, **preprocess_params}
forward_params = {**self._forward_params, **forward_params}
postprocess_params = {**self._postprocess_params, **postprocess_params}
self.call_count += 1
if self.call_count > 10 and self.framework == "pt" and self.device.type == "cuda":
warnings.warn(
"You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a"
" dataset",
UserWarning,
)
is_dataset = Dataset is not None and isinstance(inputs, Dataset)
is_generator = isinstance(inputs, types.GeneratorType)
is_list = isinstance(inputs, list)
is_iterable = is_dataset or is_generator or is_list
# TODO make the get_iterator work also for `tf` (and `flax`).
can_use_iterator = self.framework == "pt" and (is_dataset or is_generator or is_list)
if is_list:
if can_use_iterator:
final_iterator = self.get_iterator(
inputs, num_workers, batch_size, preprocess_params, forward_params, postprocess_params
)
outputs = list(final_iterator)
return outputs
else:
return self.run_multi(inputs, preprocess_params, forward_params, postprocess_params)
elif can_use_iterator:
return self.get_iterator(
inputs, num_workers, batch_size, preprocess_params, forward_params, postprocess_params
)
elif is_iterable:
return self.iterate(inputs, preprocess_params, forward_params, postprocess_params)
elif self.framework == "pt" and isinstance(self, ChunkPipeline):
return next(
iter(
self.get_iterator(
[inputs], num_workers, batch_size, preprocess_params, forward_params, postprocess_params
)
)
)
else:
return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
def run_multi(self, inputs, preprocess_params, forward_params, postprocess_params):
return [self.run_single(item, preprocess_params, forward_params, postprocess_params) for item in inputs]
def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
model_inputs = self.preprocess(inputs, **preprocess_params)
model_outputs = self.forward(model_inputs, **forward_params)
outputs = self.postprocess(model_outputs, **postprocess_params)
return outputs
def iterate(self, inputs, preprocess_params, forward_params, postprocess_params):
# This function should become `get_iterator` again, this is a temporary
# easy solution.
for input_ in inputs:
yield self.run_single(input_, preprocess_params, forward_params, postprocess_params)
class ChunkPipeline(Pipeline):
def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
all_outputs = []
for model_inputs in self.preprocess(inputs, **preprocess_params):
model_outputs = self.forward(model_inputs, **forward_params)
all_outputs.append(model_outputs)
outputs = self.postprocess(all_outputs, **postprocess_params)
return outputs
def get_iterator(
self, inputs, num_workers: int, batch_size: int, preprocess_params, forward_params, postprocess_params
):
if "TOKENIZERS_PARALLELISM" not in os.environ:
logger.info("Disabling tokenizer parallelism, we're using DataLoader multithreading already")
os.environ["TOKENIZERS_PARALLELISM"] = "false"
if num_workers > 1:
logger.warning(
"For ChunkPipeline using num_workers>0 is likely to result in errors since everything is iterable,"
" setting `num_workers=1` to guarantee correctness."
)
num_workers = 1
dataset = PipelineChunkIterator(inputs, self.preprocess, preprocess_params)
# TODO hack by collating feature_extractor and image_processor
feature_extractor = self.feature_extractor if self.feature_extractor is not None else self.image_processor
collate_fn = no_collate_fn if batch_size == 1 else pad_collate_fn(self.tokenizer, feature_extractor)
dataloader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, collate_fn=collate_fn)
model_iterator = PipelinePackIterator(dataloader, self.forward, forward_params, loader_batch_size=batch_size)
final_iterator = PipelineIterator(model_iterator, self.postprocess, postprocess_params)
return final_iterator
class PipelineRegistry:
def __init__(self, supported_tasks: Dict[str, Any], task_aliases: Dict[str, str]) -> None:
self.supported_tasks = supported_tasks
self.task_aliases = task_aliases
def get_supported_tasks(self) -> List[str]:
supported_task = list(self.supported_tasks.keys()) + list(self.task_aliases.keys())
supported_task.sort()
return supported_task
def check_task(self, task: str) -> Tuple[str, Dict, Any]:
if task in self.task_aliases:
task = self.task_aliases[task]
if task in self.supported_tasks:
targeted_task = self.supported_tasks[task]
return task, targeted_task, None
if task.startswith("translation"):
tokens = task.split("_")
if len(tokens) == 4 and tokens[0] == "translation" and tokens[2] == "to":
targeted_task = self.supported_tasks["translation"]
task = "translation"
return task, targeted_task, (tokens[1], tokens[3])
raise KeyError(f"Invalid translation task {task}, use 'translation_XX_to_YY' format")
raise KeyError(
f"Unknown task {task}, available tasks are {self.get_supported_tasks() + ['translation_XX_to_YY']}"
)
def register_pipeline(
self,
task: str,
pipeline_class: type,
pt_model: Optional[Union[type, Tuple[type]]] = None,
tf_model: Optional[Union[type, Tuple[type]]] = None,
default: Optional[Dict] = None,
type: Optional[str] = None,
) -> None:
if task in self.supported_tasks:
logger.warning(f"{task} is already registered. Overwriting pipeline for task {task}...")
if pt_model is None:
pt_model = ()
elif not isinstance(pt_model, tuple):
pt_model = (pt_model,)
if tf_model is None:
tf_model = ()
elif not isinstance(tf_model, tuple):
tf_model = (tf_model,)
task_impl = {"impl": pipeline_class, "pt": pt_model, "tf": tf_model}
if default is not None:
if "model" not in default and ("pt" in default or "tf" in default):
default = {"model": default}
task_impl["default"] = default
if type is not None:
task_impl["type"] = type
self.supported_tasks[task] = task_impl
pipeline_class._registered_impl = {task: task_impl}
def to_dict(self):
return self.supported_tasks
| 51,438 | 40.550081 | 119 | py |
transformers | transformers-main/src/transformers/pipelines/zero_shot_object_detection.py | from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class ZeroShotObjectDetectionPipeline(ChunkPipeline):
"""
Zero shot object detection pipeline using `OwlViTForObjectDetection`. This pipeline predicts bounding boxes of
objects when you provide an image and a set of `candidate_labels`.
Example:
```python
>>> from transformers import pipeline
>>> detector = pipeline(model="google/owlvit-base-patch32", task="zero-shot-object-detection")
>>> detector(
... "http://images.cocodataset.org/val2017/000000039769.jpg",
... candidate_labels=["cat", "couch"],
... )
[{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.254, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}]
>>> detector(
... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png",
... candidate_labels=["head", "bird"],
... )
[{'score': 0.119, 'label': 'bird', 'box': {'xmin': 71, 'ymin': 170, 'xmax': 410, 'ymax': 508}}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This object detection pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"zero-shot-object-detection"`.
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=zero-shot-object-detection).
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
if self.framework == "tf":
raise ValueError(f"The {self.__class__} is only available in PyTorch.")
requires_backends(self, "vision")
self.check_model_type(MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING)
def __call__(
self,
image: Union[str, "Image.Image", List[Dict[str, Any]]],
candidate_labels: Union[str, List[str]] = None,
**kwargs,
):
"""
Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
Args:
image (`str`, `PIL.Image` or `List[Dict[str, Any]]`):
The pipeline handles three types of images:
- A string containing an http url pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
You can use this parameter to send directly a list of images, or a dataset or a generator like so:
```python
>>> from transformers import pipeline
>>> detector = pipeline(model="google/owlvit-base-patch32", task="zero-shot-object-detection")
>>> detector(
... [
... {
... "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
... "candidate_labels": ["cat", "couch"],
... },
... {
... "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
... "candidate_labels": ["cat", "couch"],
... },
... ]
... )
[[{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.25, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}], [{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.254, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}]]
```
candidate_labels (`str` or `List[str]` or `List[List[str]]`):
What the model should recognize in the image.
threshold (`float`, *optional*, defaults to 0.1):
The probability necessary to make a prediction.
top_k (`int`, *optional*, defaults to None):
The number of top predictions that will be returned by the pipeline. If the provided number is `None`
or higher than the number of predictions available, it will default to the number of predictions.
Return:
A list of lists containing prediction results, one list per input image. Each list contains dictionaries
with the following keys:
- **label** (`str`) -- Text query corresponding to the found object.
- **score** (`float`) -- Score corresponding to the object (between 0 and 1).
- **box** (`Dict[str,int]`) -- Bounding box of the detected object in image's original size. It is a
dictionary with `x_min`, `x_max`, `y_min`, `y_max` keys.
"""
if "text_queries" in kwargs:
candidate_labels = kwargs.pop("text_queries")
if isinstance(image, (str, Image.Image)):
inputs = {"image": image, "candidate_labels": candidate_labels}
else:
inputs = image
results = super().__call__(inputs, **kwargs)
return results
def _sanitize_parameters(self, **kwargs):
postprocess_params = {}
if "threshold" in kwargs:
postprocess_params["threshold"] = kwargs["threshold"]
if "top_k" in kwargs:
postprocess_params["top_k"] = kwargs["top_k"]
return {}, {}, postprocess_params
def preprocess(self, inputs):
image = load_image(inputs["image"])
candidate_labels = inputs["candidate_labels"]
if isinstance(candidate_labels, str):
candidate_labels = candidate_labels.split(",")
target_size = torch.tensor([[image.height, image.width]], dtype=torch.int32)
for i, candidate_label in enumerate(candidate_labels):
text_inputs = self.tokenizer(candidate_label, return_tensors=self.framework)
image_features = self.image_processor(image, return_tensors=self.framework)
yield {
"is_last": i == len(candidate_labels) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def _forward(self, model_inputs):
target_size = model_inputs.pop("target_size")
candidate_label = model_inputs.pop("candidate_label")
is_last = model_inputs.pop("is_last")
outputs = self.model(**model_inputs)
model_outputs = {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
return model_outputs
def postprocess(self, model_outputs, threshold=0.1, top_k=None):
results = []
for model_output in model_outputs:
label = model_output["candidate_label"]
model_output = BaseModelOutput(model_output)
outputs = self.image_processor.post_process_object_detection(
outputs=model_output, threshold=threshold, target_sizes=model_output["target_size"]
)[0]
for index in outputs["scores"].nonzero():
score = outputs["scores"][index].item()
box = self._get_bounding_box(outputs["boxes"][index][0])
result = {"score": score, "label": label, "box": box}
results.append(result)
results = sorted(results, key=lambda x: x["score"], reverse=True)
if top_k:
results = results[:top_k]
return results
def _get_bounding_box(self, box: "torch.Tensor") -> Dict[str, int]:
"""
Turns list [xmin, xmax, ymin, ymax] into dict { "xmin": xmin, ... }
Args:
box (`torch.Tensor`): Tensor containing the coordinates in corners format.
Returns:
bbox (`Dict[str, int]`): Dict containing the coordinates in corners format.
"""
if self.framework != "pt":
raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
xmin, ymin, xmax, ymax = box.int().tolist()
bbox = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 9,029 | 41.59434 | 577 | py |
transformers | transformers-main/src/transformers/pipelines/image_to_text.py | from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class ImageToTextPipeline(Pipeline):
"""
Image To Text pipeline using a `AutoModelForVision2Seq`. This pipeline predicts a caption for a given image.
Example:
```python
>>> from transformers import pipeline
>>> captioner = pipeline(model="ydshieh/vit-gpt2-coco-en")
>>> captioner("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
[{'generated_text': 'two birds are standing next to each other '}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This image to text pipeline can currently be loaded from pipeline() using the following task identifier:
"image-to-text".
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?pipeline_tag=image-to-text).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
requires_backends(self, "vision")
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING
)
def _sanitize_parameters(self, max_new_tokens=None, generate_kwargs=None, prompt=None):
forward_kwargs = {}
preprocess_params = {}
if prompt is not None:
preprocess_params["prompt"] = prompt
if generate_kwargs is not None:
forward_kwargs["generate_kwargs"] = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
forward_kwargs["generate_kwargs"] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
" please use only one"
)
forward_kwargs["generate_kwargs"]["max_new_tokens"] = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs):
"""
Assign labels to the image(s) passed as inputs.
Args:
images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
The pipeline handles three types of images:
- A string containing a HTTP(s) link pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
The pipeline accepts either a single image or a batch of images.
max_new_tokens (`int`, *optional*):
The amount of maximum tokens to generate. By default it will use `generate` default.
generate_kwargs (`Dict`, *optional*):
Pass it to send all of these arguments directly to `generate` allowing full control of this function.
Return:
A list or a list of list of `dict`: Each result comes as a dictionary with the following key:
- **generated_text** (`str`) -- The generated text.
"""
return super().__call__(images, **kwargs)
def preprocess(self, image, prompt=None):
image = load_image(image)
if prompt is not None:
if not isinstance(prompt, str):
raise ValueError(
f"Received an invalid text input, got - {type(prompt)} - but expected a single string. "
"Note also that one single text can be provided for conditional image to text generation."
)
model_type = self.model.config.model_type
if model_type == "git":
model_inputs = self.image_processor(images=image, return_tensors=self.framework)
input_ids = self.tokenizer(text=prompt, add_special_tokens=False).input_ids
input_ids = [self.tokenizer.cls_token_id] + input_ids
input_ids = torch.tensor(input_ids).unsqueeze(0)
model_inputs.update({"input_ids": input_ids})
elif model_type == "pix2struct":
model_inputs = self.image_processor(images=image, header_text=prompt, return_tensors=self.framework)
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
model_inputs = self.image_processor(images=image, return_tensors=self.framework)
text_inputs = self.tokenizer(prompt, return_tensors=self.framework)
model_inputs.update(text_inputs)
else:
raise ValueError(f"Model type {model_type} does not support conditional text generation")
else:
model_inputs = self.image_processor(images=image, return_tensors=self.framework)
if self.model.config.model_type == "git" and prompt is None:
model_inputs["input_ids"] = None
return model_inputs
def _forward(self, model_inputs, generate_kwargs=None):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["input_ids"], list)
and all(x is None for x in model_inputs["input_ids"])
):
model_inputs["input_ids"] = None
if generate_kwargs is None:
generate_kwargs = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
inputs = model_inputs.pop(self.model.main_input_name)
model_outputs = self.model.generate(inputs, **model_inputs, **generate_kwargs)
return model_outputs
def postprocess(self, model_outputs):
records = []
for output_ids in model_outputs:
record = {
"generated_text": self.tokenizer.decode(
output_ids,
skip_special_tokens=True,
)
}
records.append(record)
return records
| 7,138 | 39.106742 | 117 | py |
transformers | transformers-main/src/transformers/pipelines/automatic_speech_recognition.py | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from typing import TYPE_CHECKING, Dict, Optional, Union
import numpy as np
import requests
from ..utils import is_torch_available, is_torchaudio_available, logging
from .audio_utils import ffmpeg_read
from .base import ChunkPipeline
if TYPE_CHECKING:
from pyctcdecode import BeamSearchDecoderCTC
from ..feature_extraction_sequence_utils import SequenceFeatureExtractor
logger = logging.get_logger(__name__)
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_CTC_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
def rescale_stride(stride, ratio):
"""
Rescales the stride values from audio space to tokens/logits space.
(160_000, 16_000, 16_000) -> (2000, 200, 200) for instance.
"""
# Shape is [B, SEQ] for tokens
# [B, SEQ, V] for logits
new_strides = []
for input_n, left, right in stride:
token_n = int(round(input_n * ratio))
left = int(round(left / input_n * token_n))
right = int(round(right / input_n * token_n))
new_stride = (token_n, left, right)
new_strides.append(new_stride)
return new_strides
def chunk_iter(inputs, feature_extractor, chunk_len, stride_left, stride_right, rescale=True, dtype=None):
inputs_len = inputs.shape[0]
step = chunk_len - stride_left - stride_right
for chunk_start_idx in range(0, inputs_len, step):
chunk_end_idx = chunk_start_idx + chunk_len
chunk = inputs[chunk_start_idx:chunk_end_idx]
processed = feature_extractor(chunk, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt")
if dtype is not None:
processed = processed.to(dtype=dtype)
_stride_left = 0 if chunk_start_idx == 0 else stride_left
# all right strides must be full, otherwise it is the last item
is_last = chunk_end_idx > inputs_len if stride_right > 0 else chunk_end_idx >= inputs_len
_stride_right = 0 if is_last else stride_right
chunk_len = chunk.shape[0]
stride = (chunk_len, _stride_left, _stride_right)
if "input_features" in processed:
processed_len = processed["input_features"].shape[-1]
elif "input_values" in processed:
processed_len = processed["input_values"].shape[-1]
if processed_len != chunk.shape[-1] and rescale:
ratio = processed_len / chunk_len
stride = rescale_stride([stride], ratio)[0]
if chunk.shape[0] > _stride_left:
yield {"is_last": is_last, "stride": stride, **processed}
if is_last:
break
def _fast_find_longest_common_sequence(sequence_left, sequence_right):
seq_len_left = len(sequence_left)
seq_len_right = len(sequence_right)
counter = [[0] * (seq_len_right + 1) for _ in range(seq_len_left + 1)]
longest = 0
for i in range(seq_len_left):
for j in range(seq_len_right):
if sequence_left[i] == sequence_right[j]:
previous_counter = counter[i][j] + 1
counter[i + 1][j + 1] = previous_counter
if previous_counter > longest:
longest = previous_counter
counter = np.array(counter)
# we return the idx of the first element of the longest common sequence in the left sequence
index_left = np.argwhere(counter == longest)[-1][0] - longest if longest != 0 else -1
index_right = np.argwhere(counter == longest)[-1][1] - longest if longest != 0 else -1
return index_left, index_right, longest
def _find_longest_common_sequence(sequences, tokenizer):
# TODO Use a faster algorithm this can probably be done in O(n)
# using suffix array.
# It might be tedious to do because of fault tolerance.
# We actually have a really good property which is that the total sequence
# MUST be those subsequences in order.
# Also the algorithm should be more tolerant to errors.
sequence = [tok_id for tok_id in sequences[0][0].tolist() if tok_id not in tokenizer.all_special_ids]
for new_seq in sequences[1:]:
new_sequence = [tok_id for tok_id in new_seq[0].tolist() if tok_id not in tokenizer.all_special_ids]
index = 0
max_ = 0.0
for i in range(1, len(new_sequence) + 1):
# epsilon to favor long perfect matches
eps = i / 10000.0
matches = np.sum(np.array(sequence[-i:]) == np.array(new_sequence[:i]))
matching = matches / i + eps
if matches > 1 and matching > max_:
index = i
max_ = matching
sequence.extend(new_sequence[index:])
return np.array(sequence)
class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
"""
Pipeline that aims at extracting spoken text contained within some audio.
The input can be either a raw waveform or a audio file. In case of the audio file, ffmpeg should be installed for
to support multiple audio formats
Example:
```python
>>> from transformers import pipeline
>>> transcriber = pipeline(model="openai/whisper-base")
>>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
{'text': ' He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered flour-fatten sauce.'}
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
Arguments:
model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
[`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
tokenizer ([`PreTrainedTokenizer`]):
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
[`PreTrainedTokenizer`].
feature_extractor ([`SequenceFeatureExtractor`]):
The feature extractor that will be used by the pipeline to encode waveform for the model.
chunk_length_s (`float`, *optional*, defaults to 0):
The input length for in each chunk. If `chunk_length_s = 0` then chunking is disabled (default). Only
available for CTC models, e.g. [`Wav2Vec2ForCTC`].
<Tip>
For more information on how to effectively use `chunk_length_s`, please have a look at the [ASR chunking
blog post](https://huggingface.co/blog/asr-chunking).
</Tip>
stride_length_s (`float`, *optional*, defaults to `chunk_length_s / 6`):
The length of stride on the left and right of each chunk. Used only with `chunk_length_s > 0`. This enables
the model to *see* more context and infer letters better than without this context but the pipeline
discards the stride bits at the end to make the final reconstitution as perfect as possible.
<Tip>
For more information on how to effectively use `stride_length_s`, please have a look at the [ASR chunking
blog post](https://huggingface.co/blog/asr-chunking).
</Tip>
framework (`str`, *optional*):
The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
installed. If no framework is specified, will default to the one currently installed. If no framework is
specified and both frameworks are installed, will default to the framework of the `model`, or to PyTorch if
no model is provided.
device (Union[`int`, `torch.device`], *optional*):
Device ordinal for CPU/GPU supports. Setting this to `None` will leverage CPU, a positive will run the
model on the associated CUDA device id.
decoder (`pyctcdecode.BeamSearchDecoderCTC`, *optional*):
[PyCTCDecode's
BeamSearchDecoderCTC](https://github.com/kensho-technologies/pyctcdecode/blob/2fd33dc37c4111417e08d89ccd23d28e9b308d19/pyctcdecode/decoder.py#L180)
can be passed for language model boosted decoding. See [`Wav2Vec2ProcessorWithLM`] for more information.
"""
def __init__(
self,
feature_extractor: Union["SequenceFeatureExtractor", str],
*,
decoder: Optional[Union["BeamSearchDecoderCTC", str]] = None,
**kwargs,
):
super().__init__(**kwargs)
self.feature_extractor = feature_extractor
if self.model.config.model_type == "whisper":
self.type = "seq2seq_whisper"
elif self.model.__class__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.values():
self.type = "seq2seq"
elif (
feature_extractor._processor_class
and feature_extractor._processor_class.endswith("WithLM")
and decoder is not None
):
self.decoder = decoder
self.type = "ctc_with_lm"
else:
self.type = "ctc"
if self.framework == "tf":
raise ValueError("The AutomaticSpeechRecognitionPipeline is only available in PyTorch.")
self.check_model_type(dict(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.items() + MODEL_FOR_CTC_MAPPING.items()))
def __call__(
self,
inputs: Union[np.ndarray, bytes, str],
**kwargs,
):
"""
Transcribe the audio sequence(s) given as inputs to text. See the [`AutomaticSpeechRecognitionPipeline`]
documentation for more information.
Args:
inputs (`np.ndarray` or `bytes` or `str` or `dict`):
The inputs is either :
- `str` that is the filename of the audio file, the file will be read at the correct sampling rate
to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system.
- `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the
same way.
- (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
Raw audio at the correct sampling rate (no further check will be done)
- `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this
pipeline do the resampling. The dict must be in the format `{"sampling_rate": int, "raw":
np.array}` with optionally a `"stride": (left: int, right: int)` than can ask the pipeline to
treat the first `left` samples and last `right` samples to be ignored in decoding (but used at
inference to provide more context to the model). Only use `stride` with CTC models.
return_timestamps (*optional*, `str`):
Only available for pure CTC models. If set to `"char"`, the pipeline will return timestamps along the
text for every character in the text. For instance if you get `[{"text": "h", "timestamp": (0.5, 0.6)},
{"text": "i", "timestamp": (0.7, 0.9)}]`, then it means the model predicts that the letter "h" was
pronounced after `0.5` and before `0.6` seconds. If set to `"word"`, the pipeline will return
timestamps along the text for every word in the text. For instance if you get `[{"text": "hi ",
"timestamp": (0.5, 0.9)}, {"text": "there", "timestamp": (1.0, 1.5)}]`, then it means the model
predicts that the word "hi" was pronounced after `0.5` and before `0.9` seconds.
generate_kwargs (`dict`, *optional*):
The dictionary of ad-hoc parametrization of `generate_config` to be used for the generation call. For a
complete overview of generate, check the [following
guide](https://huggingface.co/docs/transformers/en/main_classes/text_generation).
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
Return:
`Dict`: A dictionary with the following keys:
- **text** (`str` ) -- The recognized text.
- **chunks** (*optional(, `List[Dict]`)
When using `return_timestamps`, the `chunks` will become a list containing all the various text
chunks identified by the model, *e.g.* `[{"text": "hi ", "timestamp": (0.5, 0.9)}, {"text":
"there", "timestamp": (1.0, 1.5)}]`. The original full text can roughly be recovered by doing
`"".join(chunk["text"] for chunk in output["chunks"])`.
"""
return super().__call__(inputs, **kwargs)
def _sanitize_parameters(
self,
chunk_length_s=None,
stride_length_s=None,
ignore_warning=None,
decoder_kwargs=None,
return_timestamps=None,
return_language=None,
generate_kwargs=None,
max_new_tokens=None,
):
# No parameters on this pipeline right now
preprocess_params = {}
if chunk_length_s is not None:
preprocess_params["chunk_length_s"] = chunk_length_s
if stride_length_s is not None:
preprocess_params["stride_length_s"] = stride_length_s
if ignore_warning is not None:
preprocess_params["ignore_warning"] = ignore_warning
forward_params = defaultdict(dict)
if max_new_tokens is not None:
forward_params["generate_kwargs"]["max_new_tokens"] = max_new_tokens
if generate_kwargs is not None:
if max_new_tokens is not None and "max_new_tokens" in generate_kwargs:
raise ValueError(
"`max_new_tokens` is defined both as an argument and inside `generate_kwargs` argument, please use"
" only 1 version"
)
forward_params["generate_kwargs"].update(generate_kwargs)
postprocess_params = {}
if decoder_kwargs is not None:
postprocess_params["decoder_kwargs"] = decoder_kwargs
if return_timestamps is not None:
forward_params["return_timestamps"] = return_timestamps
postprocess_params["return_timestamps"] = return_timestamps
if return_language is not None:
postprocess_params["return_language"] = return_language
return preprocess_params, forward_params, postprocess_params
def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None, ignore_warning=False):
if isinstance(inputs, str):
if inputs.startswith("http://") or inputs.startswith("https://"):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
inputs = requests.get(inputs).content
else:
with open(inputs, "rb") as f:
inputs = f.read()
if isinstance(inputs, bytes):
inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
stride = None
extra = {}
if isinstance(inputs, dict):
stride = inputs.pop("stride", None)
# Accepting `"array"` which is the key defined in `datasets` for
# better integration
if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
raise ValueError(
"When passing a dictionary to AutomaticSpeechRecognitionPipeline, the dict needs to contain a "
'"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
"containing the sampling_rate associated with that array"
)
_inputs = inputs.pop("raw", None)
if _inputs is None:
# Remove path which will not be used from `datasets`.
inputs.pop("path", None)
_inputs = inputs.pop("array", None)
in_sampling_rate = inputs.pop("sampling_rate")
extra = inputs
inputs = _inputs
if in_sampling_rate != self.feature_extractor.sampling_rate:
import torch
if is_torchaudio_available():
from torchaudio import functional as F
else:
raise ImportError(
"torchaudio is required to resample audio samples in AutomaticSpeechRecognitionPipeline. "
"The torchaudio package can be installed through: `pip install torchaudio`."
)
inputs = F.resample(
torch.from_numpy(inputs), in_sampling_rate, self.feature_extractor.sampling_rate
).numpy()
ratio = self.feature_extractor.sampling_rate / in_sampling_rate
else:
ratio = 1
if stride is not None:
if stride[0] + stride[1] > inputs.shape[0]:
raise ValueError("Stride is too large for input")
# Stride needs to get the chunk length here, it's going to get
# swallowed by the `feature_extractor` later, and then batching
# can add extra data in the inputs, so we need to keep track
# of the original length in the stride so we can cut properly.
stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio)))
if not isinstance(inputs, np.ndarray):
raise ValueError(f"We expect a numpy ndarray as input, got `{type(inputs)}`")
if len(inputs.shape) != 1:
raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline")
if chunk_length_s:
if self.type == "seq2seq" and not ignore_warning:
logger.warning(
"Using `chunk_length_s` is very experimental with seq2seq models. The results will not necessarily"
" be entirely accurate and will have caveats. More information:"
" https://github.com/huggingface/transformers/pull/20104. Ignore this warning with pipeline(...,"
" ignore_warning=True)"
)
self._preprocess_params["ignore_warning"] = True
if stride_length_s is None:
stride_length_s = chunk_length_s / 6
if isinstance(stride_length_s, (int, float)):
stride_length_s = [stride_length_s, stride_length_s]
# XXX: Carefuly, this variable will not exist in `seq2seq` setting.
# Currently chunking is not possible at this level for `seq2seq` so
# it's ok.
align_to = getattr(self.model.config, "inputs_to_logits_ratio", 1)
chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate / align_to) * align_to)
stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate / align_to) * align_to)
stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate / align_to) * align_to)
if chunk_len < stride_left + stride_right:
raise ValueError("Chunk length must be superior to stride length")
rescale = self.type != "seq2seq_whisper"
# make sure that
for item in chunk_iter(
inputs, self.feature_extractor, chunk_len, stride_left, stride_right, rescale, self.torch_dtype
):
yield item
else:
processed = self.feature_extractor(
inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
)
if self.torch_dtype is not None:
processed = processed.to(dtype=self.torch_dtype)
if stride is not None:
if self.type == "seq2seq":
raise ValueError("Stride is only usable with CTC models, try removing it !")
processed["stride"] = stride
yield {"is_last": True, **processed, **extra}
def _forward(self, model_inputs, return_timestamps=False, generate_kwargs=None):
if generate_kwargs is None:
generate_kwargs = {}
if return_timestamps and self.type == "seq2seq_whisper":
generate_kwargs["return_timestamps"] = return_timestamps
if return_timestamps == "word":
generate_kwargs["return_token_timestamps"] = True
is_last = model_inputs.pop("is_last")
if self.type in {"seq2seq", "seq2seq_whisper"}:
encoder = self.model.get_encoder()
# Consume values so we can let extra information flow freely through
# the pipeline (important for `partial` in microphone)
if "input_features" in model_inputs:
inputs = model_inputs.pop("input_features")
elif "input_values" in model_inputs:
inputs = model_inputs.pop("input_values")
else:
raise ValueError(
"Seq2Seq speech recognition model requires either a "
f"`input_features` or `input_values` key, but only has {model_inputs.keys()}"
)
# we need to pass `processed.get("attention_mask")` here since audio encoder
# attention mask length is different from expected text decoder `encoder_attention_mask` length
# `generate` magic to create the mask automatically won't work, we basically need to help
# it here.
attention_mask = model_inputs.pop("attention_mask", None)
tokens = self.model.generate(
encoder_outputs=encoder(inputs, attention_mask=attention_mask),
attention_mask=attention_mask,
**generate_kwargs,
)
if return_timestamps == "word" and self.type == "seq2seq_whisper":
out = {"tokens": tokens["sequences"], "token_timestamps": tokens["token_timestamps"]}
else:
out = {"tokens": tokens}
if self.type == "seq2seq_whisper":
stride = model_inputs.pop("stride", None)
if stride is not None:
out["stride"] = stride
else:
stride = model_inputs.pop("stride", None)
input_values = model_inputs.pop("input_values")
attention_mask = model_inputs.pop("attention_mask", None)
outputs = self.model(input_values=input_values, attention_mask=attention_mask)
logits = outputs.logits
if self.type == "ctc_with_lm":
out = {"logits": logits}
else:
out = {"tokens": logits.argmax(dim=-1)}
if stride is not None:
# Send stride to `postprocess`.
# it needs to be handled there where
# the pieces are to be concatenated.
ratio = 1 / self.model.config.inputs_to_logits_ratio
if isinstance(stride, tuple):
out["stride"] = rescale_stride([stride], ratio)[0]
else:
out["stride"] = rescale_stride(stride, ratio)
# Leftover
extra = model_inputs
return {"is_last": is_last, **out, **extra}
def postprocess(
self, model_outputs, decoder_kwargs: Optional[Dict] = None, return_timestamps=None, return_language=None
):
# Optional return types
optional = {}
if return_timestamps and self.type == "seq2seq":
raise ValueError("We cannot return_timestamps yet on non-ctc models apart from Whisper !")
if return_timestamps == "char" and self.type == "ctc_with_lm":
raise ValueError("CTC with LM cannot return `char` timestamps, only `word`")
if return_timestamps == "char" and self.type == "seq2seq_whisper":
raise ValueError("Whisper cannot return `char` timestamps, use `True` or `word` instead.")
if return_language is not None and self.type != "seq2seq_whisper":
raise ValueError("Only whisper can return language for now.")
final_items = []
key = "logits" if self.type == "ctc_with_lm" else "tokens"
stride = None
for outputs in model_outputs:
items = outputs[key].numpy()
stride = outputs.get("stride", None)
if stride is not None and self.type in {"ctc", "ctc_with_lm"}:
total_n, left, right = stride
# Total_n might be < logits.shape[1]
# because of padding, that's why
# we need to reconstruct this information
# This won't work with left padding (which doesn't exist right now)
right_n = total_n - right
items = items[:, left:right_n]
final_items.append(items)
if stride and self.type == "seq2seq":
items = _find_longest_common_sequence(final_items, self.tokenizer)
elif self.type == "seq2seq_whisper":
time_precision = self.feature_extractor.chunk_length / self.model.config.max_source_positions
# Send the chunking back to seconds, it's easier to handle in whisper
sampling_rate = self.feature_extractor.sampling_rate
for output in model_outputs:
if "stride" in output:
chunk_len, stride_left, stride_right = output["stride"]
# Go back in seconds
chunk_len /= sampling_rate
stride_left /= sampling_rate
stride_right /= sampling_rate
output["stride"] = chunk_len, stride_left, stride_right
text, optional = self.tokenizer._decode_asr(
model_outputs,
return_timestamps=return_timestamps,
return_language=return_language,
time_precision=time_precision,
)
else:
items = np.concatenate(final_items, axis=1)
items = items.squeeze(0)
if self.type == "ctc_with_lm":
if decoder_kwargs is None:
decoder_kwargs = {}
beams = self.decoder.decode_beams(items, **decoder_kwargs)
text = beams[0][0]
if return_timestamps:
# Simply cast from pyctcdecode format to wav2vec2 format to leverage
# pre-existing code later
chunk_offset = beams[0][2]
offsets = []
for word, (start_offset, end_offset) in chunk_offset:
offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset})
elif self.type != "seq2seq_whisper":
skip_special_tokens = self.type != "ctc"
text = self.tokenizer.decode(items, skip_special_tokens=skip_special_tokens)
if return_timestamps:
offsets = self.tokenizer.decode(
items, skip_special_tokens=skip_special_tokens, output_char_offsets=True
)["char_offsets"]
if return_timestamps == "word":
offsets = self.tokenizer._get_word_offsets(offsets, self.tokenizer.replace_word_delimiter_char)
if return_timestamps and self.type not in {"seq2seq", "seq2seq_whisper"}:
chunks = []
for item in offsets:
start = item["start_offset"] * self.model.config.inputs_to_logits_ratio
start /= self.feature_extractor.sampling_rate
stop = item["end_offset"] * self.model.config.inputs_to_logits_ratio
stop /= self.feature_extractor.sampling_rate
chunks.append({"text": item[return_timestamps], "timestamp": (start, stop)})
optional["chunks"] = chunks
extra = defaultdict(list)
for output in model_outputs:
output.pop("tokens", None)
output.pop("logits", None)
output.pop("is_last", None)
output.pop("stride", None)
output.pop("token_timestamps", None)
for k, v in output.items():
extra[k].append(v)
return {"text": text, **optional, **extra}
def _find_timestamp_sequence(sequences, tokenizer, feature_extractor, max_source_positions):
"""
Computes the final sequences by merging the end of the nth sequence with the beginning of the n+1th sequence. Since
`WhisperForConditionalGeneration` produces the timestamps pairwise, we filter the consecutive timestamps and only
iterate over them. We keep track of the `time` which indicates the actual starting time of the chunk that is
processed. We need to make sure to offset the timestamps tokens by the `time` in order for the tokenizer to
properly compute the final `offset`.
"""
# index of the first timestamp token
timestamp_begin = tokenizer.convert_tokens_to_ids("<|notimestamps|>") + 1
items = []
# approximation of the token to time ratio : ~0.2seconds
time_precision = feature_extractor.chunk_length / max_source_positions
time = 0
for seq_idx, item in enumerate(sequences):
sequence, stride = item
if isinstance(sequence, list):
sequence = np.array(sequence)
chunk_len, stride_left, stride_right = stride
sequence = sequence.squeeze(0)
# get rid of the `forced_decoder_idx` that are use to parametrize the generation
begin_idx = np.where(sequence == timestamp_begin)[0][0] if timestamp_begin in sequence else 0
sequence = sequence[begin_idx:]
timestamp_tokens = sequence >= timestamp_begin
if seq_idx != 0 and sum(timestamp_tokens) > 0:
consecutive = np.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + 1
last_timestamp = np.where(timestamp_tokens)[0][-1]
consecutive = np.append(consecutive, last_timestamp) if last_timestamp not in consecutive else consecutive
time -= stride_left + stride_right
offset = int((time / feature_extractor.sampling_rate) / time_precision)
overlap_time = int((stride_left / feature_extractor.sampling_rate) / time_precision)
# relevant timestamps are in the overlapping part
relevant_timestamp = np.where(sequence[consecutive] >= timestamp_begin + overlap_time)[0]
if relevant_timestamp.shape[0] > 0:
relevant_timestamp = (
consecutive[relevant_timestamp[0] - 1] if relevant_timestamp[0] > 0 else consecutive[0]
)
# if a big stride is used, we need to check some of the previous items for the best overlap
best_match = 0
sliced_sequence = []
for idx, previous_sequence in enumerate(reversed(items)):
previous_tokens = previous_sequence[1:-1]
if previous_sequence[0] < (timestamp_begin + offset - overlap_time) and idx != 0:
break # the previous sequence is too far in the past
if len(previous_tokens) > 0:
# find the longest common sequence between the overlapping parts
index_left, index_right, match_length = _fast_find_longest_common_sequence(
sequence[1:relevant_timestamp], previous_tokens
)
# don't do anything if only 1 token was matched
if match_length > 1 and match_length > best_match:
best_match = match_length
best_idx = idx
end_of_curr_sequence_idx = (
np.where(sequence[index_left + 1 :] >= timestamp_begin)[0][0] + 1
)
end_of_curr_sequence_idx = end_of_curr_sequence_idx + 1 + index_left
# if all the tokens are matched, suffix
if index_left == 0 and match_length == len(previous_tokens):
sliced_sequence = np.insert(
sequence[index_left + 1 : end_of_curr_sequence_idx], 0, previous_sequence[0]
)
sliced_sequence[-1] = previous_sequence[-1]
# if part of the previous sequence is not taken
elif index_left >= 0:
sliced_sequence = sequence[index_left + 1 : end_of_curr_sequence_idx]
# let's insert the missing part of the previous sequence
previous_slice = (
previous_sequence[: index_right + 1] if index_right > 0 else [previous_sequence[0]]
)
sliced_sequence = np.insert(sliced_sequence, 0, previous_slice)
sliced_sequence[-1] += offset
if len(sliced_sequence) > 0:
items[len(items) - best_idx - 1] = sliced_sequence
items = items[: len(items) - best_idx]
sequence = sequence[end_of_curr_sequence_idx:]
# sequence might have changed
timestamp_tokens = sequence >= timestamp_begin
consecutive = np.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + 1
if sum(timestamp_tokens) > 0:
last_timestamp = np.where(timestamp_tokens)[0][-1]
consecutive = (
np.append(consecutive, last_timestamp + 1) if last_timestamp not in consecutive else consecutive
)
if len(consecutive) > 0:
last_slice = 0
for current_slice in consecutive:
actual_offset = items[-1][-1] if seq_idx != 0 or last_slice != 0 else sequence[0]
sliced_tokens = sequence[last_slice:current_slice]
duration = sliced_tokens[-1] - sliced_tokens[0]
sliced_tokens[0] = actual_offset
sliced_tokens[-1] = actual_offset + duration
items.append(sliced_tokens)
last_slice = current_slice
time += chunk_len
result = []
for i in range(len(items)):
result += items[i].tolist()
return result
| 35,296 | 49.496423 | 176 | py |
transformers | transformers-main/src/transformers/pipelines/token_classification.py | import types
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
from ..models.bert.tokenization_bert import BasicTokenizer
from ..utils import (
ExplicitEnum,
add_end_docstrings,
is_tf_available,
is_torch_available,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline, Dataset
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
class TokenClassificationArgumentHandler(ArgumentHandler):
"""
Handles arguments for token classification.
"""
def __call__(self, inputs: Union[str, List[str]], **kwargs):
if inputs is not None and isinstance(inputs, (list, tuple)) and len(inputs) > 0:
inputs = list(inputs)
batch_size = len(inputs)
elif isinstance(inputs, str):
inputs = [inputs]
batch_size = 1
elif Dataset is not None and isinstance(inputs, Dataset) or isinstance(inputs, types.GeneratorType):
return inputs, None
else:
raise ValueError("At least one input is required.")
offset_mapping = kwargs.get("offset_mapping")
if offset_mapping:
if isinstance(offset_mapping, list) and isinstance(offset_mapping[0], tuple):
offset_mapping = [offset_mapping]
if len(offset_mapping) != batch_size:
raise ValueError("offset_mapping should have the same batch size as the input")
return inputs, offset_mapping
class AggregationStrategy(ExplicitEnum):
"""All the valid aggregation strategies for TokenClassificationPipeline"""
NONE = "none"
SIMPLE = "simple"
FIRST = "first"
AVERAGE = "average"
MAX = "max"
@add_end_docstrings(
PIPELINE_INIT_ARGS,
r"""
ignore_labels (`List[str]`, defaults to `["O"]`):
A list of labels to ignore.
grouped_entities (`bool`, *optional*, defaults to `False`):
DEPRECATED, use `aggregation_strategy` instead. Whether or not to group the tokens corresponding to the
same entity together in the predictions or not.
stride (`int`, *optional*):
If stride is provided, the pipeline is applied on all the text. The text is split into chunks of size
model_max_length. Works only with fast tokenizers and `aggregation_strategy` different from `NONE`. The
value of this argument defines the number of overlapping tokens between chunks. In other words, the model
will shift forward by `tokenizer.model_max_length - stride` tokens each step.
aggregation_strategy (`str`, *optional*, defaults to `"none"`):
The strategy to fuse (or not) tokens based on the model prediction.
- "none" : Will simply not do any aggregation and simply return raw results from the model
- "simple" : Will attempt to group entities following the default schema. (A, B-TAG), (B, I-TAG), (C,
I-TAG), (D, B-TAG2) (E, B-TAG2) will end up being [{"word": ABC, "entity": "TAG"}, {"word": "D",
"entity": "TAG2"}, {"word": "E", "entity": "TAG2"}] Notice that two consecutive B tags will end up as
different entities. On word based languages, we might end up splitting words undesirably : Imagine
Microsoft being tagged as [{"word": "Micro", "entity": "ENTERPRISE"}, {"word": "soft", "entity":
"NAME"}]. Look for FIRST, MAX, AVERAGE for ways to mitigate that and disambiguate words (on languages
that support that meaning, which is basically tokens separated by a space). These mitigations will
only work on real words, "New york" might still be tagged with two different entities.
- "first" : (works only on word based models) Will use the `SIMPLE` strategy except that words, cannot
end up with different tags. Words will simply use the tag of the first token of the word when there
is ambiguity.
- "average" : (works only on word based models) Will use the `SIMPLE` strategy except that words,
cannot end up with different tags. scores will be averaged first across tokens, and then the maximum
label is applied.
- "max" : (works only on word based models) Will use the `SIMPLE` strategy except that words, cannot
end up with different tags. Word entity will simply be the token with the maximum score.
""",
)
class TokenClassificationPipeline(ChunkPipeline):
"""
Named Entity Recognition pipeline using any `ModelForTokenClassification`. See the [named entity recognition
examples](../task_summary#named-entity-recognition) for more information.
Example:
```python
>>> from transformers import pipeline
>>> token_classifier = pipeline(model="Jean-Baptiste/camembert-ner", aggregation_strategy="simple")
>>> sentence = "Je m'appelle jean-baptiste et je vis à montréal"
>>> tokens = token_classifier(sentence)
>>> tokens
[{'entity_group': 'PER', 'score': 0.9931, 'word': 'jean-baptiste', 'start': 12, 'end': 26}, {'entity_group': 'LOC', 'score': 0.998, 'word': 'montréal', 'start': 38, 'end': 47}]
>>> token = tokens[0]
>>> # Start and end provide an easy way to highlight words in the original text.
>>> sentence[token["start"] : token["end"]]
' jean-baptiste'
>>> # Some models use the same idea to do part of speech.
>>> syntaxer = pipeline(model="vblagoje/bert-english-uncased-finetuned-pos", aggregation_strategy="simple")
>>> syntaxer("My name is Sarah and I live in London")
[{'entity_group': 'PRON', 'score': 0.999, 'word': 'my', 'start': 0, 'end': 2}, {'entity_group': 'NOUN', 'score': 0.997, 'word': 'name', 'start': 3, 'end': 7}, {'entity_group': 'AUX', 'score': 0.994, 'word': 'is', 'start': 8, 'end': 10}, {'entity_group': 'PROPN', 'score': 0.999, 'word': 'sarah', 'start': 11, 'end': 16}, {'entity_group': 'CCONJ', 'score': 0.999, 'word': 'and', 'start': 17, 'end': 20}, {'entity_group': 'PRON', 'score': 0.999, 'word': 'i', 'start': 21, 'end': 22}, {'entity_group': 'VERB', 'score': 0.998, 'word': 'live', 'start': 23, 'end': 27}, {'entity_group': 'ADP', 'score': 0.999, 'word': 'in', 'start': 28, 'end': 30}, {'entity_group': 'PROPN', 'score': 0.999, 'word': 'london', 'start': 31, 'end': 37}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This token recognition pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"ner"` (for predicting the classes of tokens in a sequence: person, organisation, location or miscellaneous).
The models that this pipeline can use are models that have been fine-tuned on a token classification task. See the
up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=token-classification).
"""
default_input_names = "sequences"
def __init__(self, args_parser=TokenClassificationArgumentHandler(), *args, **kwargs):
super().__init__(*args, **kwargs)
self.check_model_type(
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
)
self._basic_tokenizer = BasicTokenizer(do_lower_case=False)
self._args_parser = args_parser
def _sanitize_parameters(
self,
ignore_labels=None,
grouped_entities: Optional[bool] = None,
ignore_subwords: Optional[bool] = None,
aggregation_strategy: Optional[AggregationStrategy] = None,
offset_mapping: Optional[List[Tuple[int, int]]] = None,
stride: Optional[int] = None,
):
preprocess_params = {}
if offset_mapping is not None:
preprocess_params["offset_mapping"] = offset_mapping
postprocess_params = {}
if grouped_entities is not None or ignore_subwords is not None:
if grouped_entities and ignore_subwords:
aggregation_strategy = AggregationStrategy.FIRST
elif grouped_entities and not ignore_subwords:
aggregation_strategy = AggregationStrategy.SIMPLE
else:
aggregation_strategy = AggregationStrategy.NONE
if grouped_entities is not None:
warnings.warn(
"`grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to"
f' `aggregation_strategy="{aggregation_strategy}"` instead.'
)
if ignore_subwords is not None:
warnings.warn(
"`ignore_subwords` is deprecated and will be removed in version v5.0.0, defaulted to"
f' `aggregation_strategy="{aggregation_strategy}"` instead.'
)
if aggregation_strategy is not None:
if isinstance(aggregation_strategy, str):
aggregation_strategy = AggregationStrategy[aggregation_strategy.upper()]
if (
aggregation_strategy
in {AggregationStrategy.FIRST, AggregationStrategy.MAX, AggregationStrategy.AVERAGE}
and not self.tokenizer.is_fast
):
raise ValueError(
"Slow tokenizers cannot handle subwords. Please set the `aggregation_strategy` option"
' to `"simple"` or use a fast tokenizer.'
)
postprocess_params["aggregation_strategy"] = aggregation_strategy
if ignore_labels is not None:
postprocess_params["ignore_labels"] = ignore_labels
if stride is not None:
if stride >= self.tokenizer.model_max_length:
raise ValueError(
"`stride` must be less than `tokenizer.model_max_length` (or even lower if the tokenizer adds special tokens)"
)
if aggregation_strategy == AggregationStrategy.NONE:
raise ValueError(
"`stride` was provided to process all the text but `aggregation_strategy="
f'"{aggregation_strategy}"`, please select another one instead.'
)
else:
if self.tokenizer.is_fast:
tokenizer_params = {
"return_overflowing_tokens": True,
"padding": True,
"stride": stride,
}
preprocess_params["tokenizer_params"] = tokenizer_params
else:
raise ValueError(
"`stride` was provided to process all the text but you're using a slow tokenizer."
" Please use a fast tokenizer."
)
return preprocess_params, {}, postprocess_params
def __call__(self, inputs: Union[str, List[str]], **kwargs):
"""
Classify each token of the text(s) given as inputs.
Args:
inputs (`str` or `List[str]`):
One or several texts (or one list of texts) for token classification.
Return:
A list or a list of list of `dict`: Each result comes as a list of dictionaries (one for each token in the
corresponding input, or each entity if this pipeline was instantiated with an aggregation_strategy) with
the following keys:
- **word** (`str`) -- The token/word classified. This is obtained by decoding the selected tokens. If you
want to have the exact string in the original sentence, use `start` and `end`.
- **score** (`float`) -- The corresponding probability for `entity`.
- **entity** (`str`) -- The entity predicted for that token/word (it is named *entity_group* when
*aggregation_strategy* is not `"none"`.
- **index** (`int`, only present when `aggregation_strategy="none"`) -- The index of the corresponding
token in the sentence.
- **start** (`int`, *optional*) -- The index of the start of the corresponding entity in the sentence. Only
exists if the offsets are available within the tokenizer
- **end** (`int`, *optional*) -- The index of the end of the corresponding entity in the sentence. Only
exists if the offsets are available within the tokenizer
"""
_inputs, offset_mapping = self._args_parser(inputs, **kwargs)
if offset_mapping:
kwargs["offset_mapping"] = offset_mapping
return super().__call__(inputs, **kwargs)
def preprocess(self, sentence, offset_mapping=None, **preprocess_params):
tokenizer_params = preprocess_params.pop("tokenizer_params", {})
truncation = True if self.tokenizer.model_max_length and self.tokenizer.model_max_length > 0 else False
inputs = self.tokenizer(
sentence,
return_tensors=self.framework,
truncation=truncation,
return_special_tokens_mask=True,
return_offsets_mapping=self.tokenizer.is_fast,
**tokenizer_params,
)
inputs.pop("overflow_to_sample_mapping", None)
num_chunks = len(inputs["input_ids"])
for i in range(num_chunks):
if self.framework == "tf":
model_inputs = {k: tf.expand_dims(v[i], 0) for k, v in inputs.items()}
else:
model_inputs = {k: v[i].unsqueeze(0) for k, v in inputs.items()}
if offset_mapping is not None:
model_inputs["offset_mapping"] = offset_mapping
model_inputs["sentence"] = sentence if i == 0 else None
model_inputs["is_last"] = i == num_chunks - 1
yield model_inputs
def _forward(self, model_inputs):
# Forward
special_tokens_mask = model_inputs.pop("special_tokens_mask")
offset_mapping = model_inputs.pop("offset_mapping", None)
sentence = model_inputs.pop("sentence")
is_last = model_inputs.pop("is_last")
if self.framework == "tf":
logits = self.model(**model_inputs)[0]
else:
output = self.model(**model_inputs)
logits = output["logits"] if isinstance(output, dict) else output[0]
return {
"logits": logits,
"special_tokens_mask": special_tokens_mask,
"offset_mapping": offset_mapping,
"sentence": sentence,
"is_last": is_last,
**model_inputs,
}
def postprocess(self, all_outputs, aggregation_strategy=AggregationStrategy.NONE, ignore_labels=None):
if ignore_labels is None:
ignore_labels = ["O"]
all_entities = []
for model_outputs in all_outputs:
logits = model_outputs["logits"][0].numpy()
sentence = all_outputs[0]["sentence"]
input_ids = model_outputs["input_ids"][0]
offset_mapping = (
model_outputs["offset_mapping"][0] if model_outputs["offset_mapping"] is not None else None
)
special_tokens_mask = model_outputs["special_tokens_mask"][0].numpy()
maxes = np.max(logits, axis=-1, keepdims=True)
shifted_exp = np.exp(logits - maxes)
scores = shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
if self.framework == "tf":
input_ids = input_ids.numpy()
offset_mapping = offset_mapping.numpy() if offset_mapping is not None else None
pre_entities = self.gather_pre_entities(
sentence, input_ids, scores, offset_mapping, special_tokens_mask, aggregation_strategy
)
grouped_entities = self.aggregate(pre_entities, aggregation_strategy)
# Filter anything that is in self.ignore_labels
entities = [
entity
for entity in grouped_entities
if entity.get("entity", None) not in ignore_labels
and entity.get("entity_group", None) not in ignore_labels
]
all_entities.extend(entities)
num_chunks = len(all_outputs)
if num_chunks > 1:
all_entities = self.aggregate_overlapping_entities(all_entities)
return all_entities
def aggregate_overlapping_entities(self, entities):
if len(entities) == 0:
return entities
entities = sorted(entities, key=lambda x: x["start"])
aggregated_entities = []
previous_entity = entities[0]
for entity in entities:
if previous_entity["start"] <= entity["start"] < previous_entity["end"]:
current_length = entity["end"] - entity["start"]
previous_length = previous_entity["end"] - previous_entity["start"]
if current_length > previous_length:
previous_entity = entity
elif current_length == previous_length and entity["score"] > previous_entity["score"]:
previous_entity = entity
else:
aggregated_entities.append(previous_entity)
previous_entity = entity
aggregated_entities.append(previous_entity)
return aggregated_entities
def gather_pre_entities(
self,
sentence: str,
input_ids: np.ndarray,
scores: np.ndarray,
offset_mapping: Optional[List[Tuple[int, int]]],
special_tokens_mask: np.ndarray,
aggregation_strategy: AggregationStrategy,
) -> List[dict]:
"""Fuse various numpy arrays into dicts with all the information needed for aggregation"""
pre_entities = []
for idx, token_scores in enumerate(scores):
# Filter special_tokens
if special_tokens_mask[idx]:
continue
word = self.tokenizer.convert_ids_to_tokens(int(input_ids[idx]))
if offset_mapping is not None:
start_ind, end_ind = offset_mapping[idx]
if not isinstance(start_ind, int):
if self.framework == "pt":
start_ind = start_ind.item()
end_ind = end_ind.item()
word_ref = sentence[start_ind:end_ind]
if getattr(self.tokenizer, "_tokenizer", None) and getattr(
self.tokenizer._tokenizer.model, "continuing_subword_prefix", None
):
# This is a BPE, word aware tokenizer, there is a correct way
# to fuse tokens
is_subword = len(word) != len(word_ref)
else:
# This is a fallback heuristic. This will fail most likely on any kind of text + punctuation mixtures that will be considered "words". Non word aware models cannot do better than this unfortunately.
if aggregation_strategy in {
AggregationStrategy.FIRST,
AggregationStrategy.AVERAGE,
AggregationStrategy.MAX,
}:
warnings.warn(
"Tokenizer does not support real words, using fallback heuristic",
UserWarning,
)
is_subword = start_ind > 0 and " " not in sentence[start_ind - 1 : start_ind + 1]
if int(input_ids[idx]) == self.tokenizer.unk_token_id:
word = word_ref
is_subword = False
else:
start_ind = None
end_ind = None
is_subword = False
pre_entity = {
"word": word,
"scores": token_scores,
"start": start_ind,
"end": end_ind,
"index": idx,
"is_subword": is_subword,
}
pre_entities.append(pre_entity)
return pre_entities
def aggregate(self, pre_entities: List[dict], aggregation_strategy: AggregationStrategy) -> List[dict]:
if aggregation_strategy in {AggregationStrategy.NONE, AggregationStrategy.SIMPLE}:
entities = []
for pre_entity in pre_entities:
entity_idx = pre_entity["scores"].argmax()
score = pre_entity["scores"][entity_idx]
entity = {
"entity": self.model.config.id2label[entity_idx],
"score": score,
"index": pre_entity["index"],
"word": pre_entity["word"],
"start": pre_entity["start"],
"end": pre_entity["end"],
}
entities.append(entity)
else:
entities = self.aggregate_words(pre_entities, aggregation_strategy)
if aggregation_strategy == AggregationStrategy.NONE:
return entities
return self.group_entities(entities)
def aggregate_word(self, entities: List[dict], aggregation_strategy: AggregationStrategy) -> dict:
word = self.tokenizer.convert_tokens_to_string([entity["word"] for entity in entities])
if aggregation_strategy == AggregationStrategy.FIRST:
scores = entities[0]["scores"]
idx = scores.argmax()
score = scores[idx]
entity = self.model.config.id2label[idx]
elif aggregation_strategy == AggregationStrategy.MAX:
max_entity = max(entities, key=lambda entity: entity["scores"].max())
scores = max_entity["scores"]
idx = scores.argmax()
score = scores[idx]
entity = self.model.config.id2label[idx]
elif aggregation_strategy == AggregationStrategy.AVERAGE:
scores = np.stack([entity["scores"] for entity in entities])
average_scores = np.nanmean(scores, axis=0)
entity_idx = average_scores.argmax()
entity = self.model.config.id2label[entity_idx]
score = average_scores[entity_idx]
else:
raise ValueError("Invalid aggregation_strategy")
new_entity = {
"entity": entity,
"score": score,
"word": word,
"start": entities[0]["start"],
"end": entities[-1]["end"],
}
return new_entity
def aggregate_words(self, entities: List[dict], aggregation_strategy: AggregationStrategy) -> List[dict]:
"""
Override tokens from a given word that disagree to force agreement on word boundaries.
Example: micro|soft| com|pany| B-ENT I-NAME I-ENT I-ENT will be rewritten with first strategy as microsoft|
company| B-ENT I-ENT
"""
if aggregation_strategy in {
AggregationStrategy.NONE,
AggregationStrategy.SIMPLE,
}:
raise ValueError("NONE and SIMPLE strategies are invalid for word aggregation")
word_entities = []
word_group = None
for entity in entities:
if word_group is None:
word_group = [entity]
elif entity["is_subword"]:
word_group.append(entity)
else:
word_entities.append(self.aggregate_word(word_group, aggregation_strategy))
word_group = [entity]
# Last item
if word_group is not None:
word_entities.append(self.aggregate_word(word_group, aggregation_strategy))
return word_entities
def group_sub_entities(self, entities: List[dict]) -> dict:
"""
Group together the adjacent tokens with the same entity predicted.
Args:
entities (`dict`): The entities predicted by the pipeline.
"""
# Get the first entity in the entity group
entity = entities[0]["entity"].split("-")[-1]
scores = np.nanmean([entity["score"] for entity in entities])
tokens = [entity["word"] for entity in entities]
entity_group = {
"entity_group": entity,
"score": np.mean(scores),
"word": self.tokenizer.convert_tokens_to_string(tokens),
"start": entities[0]["start"],
"end": entities[-1]["end"],
}
return entity_group
def get_tag(self, entity_name: str) -> Tuple[str, str]:
if entity_name.startswith("B-"):
bi = "B"
tag = entity_name[2:]
elif entity_name.startswith("I-"):
bi = "I"
tag = entity_name[2:]
else:
# It's not in B-, I- format
# Default to I- for continuation.
bi = "I"
tag = entity_name
return bi, tag
def group_entities(self, entities: List[dict]) -> List[dict]:
"""
Find and group together the adjacent tokens with the same entity predicted.
Args:
entities (`dict`): The entities predicted by the pipeline.
"""
entity_groups = []
entity_group_disagg = []
for entity in entities:
if not entity_group_disagg:
entity_group_disagg.append(entity)
continue
# If the current entity is similar and adjacent to the previous entity,
# append it to the disaggregated entity group
# The split is meant to account for the "B" and "I" prefixes
# Shouldn't merge if both entities are B-type
bi, tag = self.get_tag(entity["entity"])
last_bi, last_tag = self.get_tag(entity_group_disagg[-1]["entity"])
if tag == last_tag and bi != "B":
# Modify subword type to be previous_type
entity_group_disagg.append(entity)
else:
# If the current entity is different from the previous entity
# aggregate the disaggregated entity group
entity_groups.append(self.group_sub_entities(entity_group_disagg))
entity_group_disagg = [entity]
if entity_group_disagg:
# it's the last entity, add it to the entity groups
entity_groups.append(self.group_sub_entities(entity_group_disagg))
return entity_groups
NerPipeline = TokenClassificationPipeline
| 26,656 | 45.603147 | 731 | py |
transformers | transformers-main/src/transformers/pipelines/video_classification.py | from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class VideoClassificationPipeline(Pipeline):
"""
Video classification pipeline using any `AutoModelForVideoClassification`. This pipeline predicts the class of a
video.
This video classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"video-classification"`.
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=video-classification).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
requires_backends(self, "decord")
self.check_model_type(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING)
def _sanitize_parameters(self, top_k=None, num_frames=None, frame_sampling_rate=None):
preprocess_params = {}
if frame_sampling_rate is not None:
preprocess_params["frame_sampling_rate"] = frame_sampling_rate
if num_frames is not None:
preprocess_params["num_frames"] = num_frames
postprocess_params = {}
if top_k is not None:
postprocess_params["top_k"] = top_k
return preprocess_params, {}, postprocess_params
def __call__(self, videos: Union[str, List[str]], **kwargs):
"""
Assign labels to the video(s) passed as inputs.
Args:
videos (`str`, `List[str]`):
The pipeline handles three types of videos:
- A string containing a http link pointing to a video
- A string containing a local path to a video
The pipeline accepts either a single video or a batch of videos, which must then be passed as a string.
Videos in a batch must all be in the same format: all as http links or all as local paths.
top_k (`int`, *optional*, defaults to 5):
The number of top labels that will be returned by the pipeline. If the provided number is higher than
the number of labels available in the model configuration, it will default to the number of labels.
num_frames (`int`, *optional*, defaults to `self.model.config.num_frames`):
The number of frames sampled from the video to run the classification on. If not provided, will default
to the number of frames specified in the model configuration.
frame_sampling_rate (`int`, *optional*, defaults to 1):
The sampling rate used to select frames from the video. If not provided, will default to 1, i.e. every
frame will be used.
Return:
A dictionary or a list of dictionaries containing result. If the input is a single video, will return a
dictionary, if the input is a list of several videos, will return a list of dictionaries corresponding to
the videos.
The dictionaries contain the following keys:
- **label** (`str`) -- The label identified by the model.
- **score** (`int`) -- The score attributed by the model for that label.
"""
return super().__call__(videos, **kwargs)
def preprocess(self, video, num_frames=None, frame_sampling_rate=1):
if num_frames is None:
num_frames = self.model.config.num_frames
if video.startswith("http://") or video.startswith("https://"):
video = BytesIO(requests.get(video).content)
videoreader = VideoReader(video)
videoreader.seek(0)
start_idx = 0
end_idx = num_frames * frame_sampling_rate - 1
indices = np.linspace(start_idx, end_idx, num=num_frames, dtype=np.int64)
video = videoreader.get_batch(indices).asnumpy()
video = list(video)
model_inputs = self.image_processor(video, return_tensors=self.framework)
return model_inputs
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs, top_k=5):
if top_k > self.model.config.num_labels:
top_k = self.model.config.num_labels
if self.framework == "pt":
probs = model_outputs.logits.softmax(-1)[0]
scores, ids = probs.topk(top_k)
else:
raise ValueError(f"Unsupported framework: {self.framework}")
scores = scores.tolist()
ids = ids.tolist()
return [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
| 5,008 | 39.723577 | 119 | py |
transformers | transformers-main/src/transformers/pipelines/object_detection.py | from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
logger = logging.get_logger(__name__)
Prediction = Dict[str, Any]
Predictions = List[Prediction]
@add_end_docstrings(PIPELINE_INIT_ARGS)
class ObjectDetectionPipeline(Pipeline):
"""
Object detection pipeline using any `AutoModelForObjectDetection`. This pipeline predicts bounding boxes of objects
and their classes.
Example:
```python
>>> from transformers import pipeline
>>> detector = pipeline(model="facebook/detr-resnet-50")
>>> detector("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
[{'score': 0.997, 'label': 'bird', 'box': {'xmin': 69, 'ymin': 171, 'xmax': 396, 'ymax': 507}}, {'score': 0.999, 'label': 'bird', 'box': {'xmin': 398, 'ymin': 105, 'xmax': 767, 'ymax': 507}}]
>>> # x, y are expressed relative to the top left hand corner.
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This object detection pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"object-detection"`.
See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=object-detection).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.framework == "tf":
raise ValueError(f"The {self.__class__} is only available in PyTorch.")
requires_backends(self, "vision")
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items())
)
def _sanitize_parameters(self, **kwargs):
postprocess_kwargs = {}
if "threshold" in kwargs:
postprocess_kwargs["threshold"] = kwargs["threshold"]
return {}, {}, postprocess_kwargs
def __call__(self, *args, **kwargs) -> Union[Predictions, List[Prediction]]:
"""
Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
Args:
images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
The pipeline handles three types of images:
- A string containing an HTTP(S) link pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the
same format: all as HTTP(S) links, all as local paths, or all as PIL images.
threshold (`float`, *optional*, defaults to 0.9):
The probability necessary to make a prediction.
Return:
A list of dictionaries or a list of list of dictionaries containing the result. If the input is a single
image, will return a list of dictionaries, if the input is a list of several images, will return a list of
list of dictionaries corresponding to each image.
The dictionaries contain the following keys:
- **label** (`str`) -- The class label identified by the model.
- **score** (`float`) -- The score attributed by the model for that label.
- **box** (`List[Dict[str, int]]`) -- The bounding box of detected object in image's original size.
"""
return super().__call__(*args, **kwargs)
def preprocess(self, image):
image = load_image(image)
target_size = torch.IntTensor([[image.height, image.width]])
inputs = self.image_processor(images=[image], return_tensors="pt")
if self.tokenizer is not None:
inputs = self.tokenizer(text=inputs["words"], boxes=inputs["boxes"], return_tensors="pt")
inputs["target_size"] = target_size
return inputs
def _forward(self, model_inputs):
target_size = model_inputs.pop("target_size")
outputs = self.model(**model_inputs)
model_outputs = outputs.__class__({"target_size": target_size, **outputs})
if self.tokenizer is not None:
model_outputs["bbox"] = model_inputs["bbox"]
return model_outputs
def postprocess(self, model_outputs, threshold=0.9):
target_size = model_outputs["target_size"]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
height, width = target_size[0].tolist()
def unnormalize(bbox):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
]
)
)
scores, classes = model_outputs["logits"].squeeze(0).softmax(dim=-1).max(dim=-1)
labels = [self.model.config.id2label[prediction] for prediction in classes.tolist()]
boxes = [unnormalize(bbox) for bbox in model_outputs["bbox"].squeeze(0)]
keys = ["score", "label", "box"]
annotation = [dict(zip(keys, vals)) for vals in zip(scores.tolist(), labels, boxes) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
raw_annotations = self.image_processor.post_process_object_detection(model_outputs, threshold, target_size)
raw_annotation = raw_annotations[0]
scores = raw_annotation["scores"]
labels = raw_annotation["labels"]
boxes = raw_annotation["boxes"]
raw_annotation["scores"] = scores.tolist()
raw_annotation["labels"] = [self.model.config.id2label[label.item()] for label in labels]
raw_annotation["boxes"] = [self._get_bounding_box(box) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
keys = ["score", "label", "box"]
annotation = [
dict(zip(keys, vals))
for vals in zip(raw_annotation["scores"], raw_annotation["labels"], raw_annotation["boxes"])
]
return annotation
def _get_bounding_box(self, box: "torch.Tensor") -> Dict[str, int]:
"""
Turns list [xmin, xmax, ymin, ymax] into dict { "xmin": xmin, ... }
Args:
box (`torch.Tensor`): Tensor containing the coordinates in corners format.
Returns:
bbox (`Dict[str, int]`): Dict containing the coordinates in corners format.
"""
if self.framework != "pt":
raise ValueError("The ObjectDetectionPipeline is only available in PyTorch.")
xmin, ymin, xmax, ymax = box.int().tolist()
bbox = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 7,441 | 40.575419 | 195 | py |
transformers | transformers-main/src/transformers/pipelines/zero_shot_image_classification.py | from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class ZeroShotImageClassificationPipeline(Pipeline):
"""
Zero shot image classification pipeline using `CLIPModel`. This pipeline predicts the class of an image when you
provide an image and a set of `candidate_labels`.
Example:
```python
>>> from transformers import pipeline
>>> classifier = pipeline(model="openai/clip-vit-large-patch14")
>>> classifier(
... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png",
... candidate_labels=["animals", "humans", "landscape"],
... )
[{'score': 0.965, 'label': 'animals'}, {'score': 0.03, 'label': 'humans'}, {'score': 0.005, 'label': 'landscape'}]
>>> classifier(
... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png",
... candidate_labels=["black and white", "photorealist", "painting"],
... )
[{'score': 0.996, 'label': 'black and white'}, {'score': 0.003, 'label': 'photorealist'}, {'score': 0.0, 'label': 'painting'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"zero-shot-image-classification"`.
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=zero-shot-image-classification).
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
requires_backends(self, "vision")
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
)
def __call__(self, images: Union[str, List[str], "Image", List["Image"]], **kwargs):
"""
Assign labels to the image(s) passed as inputs.
Args:
images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
The pipeline handles three types of images:
- A string containing a http link pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
candidate_labels (`List[str]`):
The candidate labels for this image
hypothesis_template (`str`, *optional*, defaults to `"This is a photo of {}"`):
The sentence used in cunjunction with *candidate_labels* to attempt the image classification by
replacing the placeholder with the candidate_labels. Then likelihood is estimated by using
logits_per_image
Return:
A list of dictionaries containing result, one dictionary per proposed label. The dictionaries contain the
following keys:
- **label** (`str`) -- The label identified by the model. It is one of the suggested `candidate_label`.
- **score** (`float`) -- The score attributed by the model for that label (between 0 and 1).
"""
return super().__call__(images, **kwargs)
def _sanitize_parameters(self, **kwargs):
preprocess_params = {}
if "candidate_labels" in kwargs:
preprocess_params["candidate_labels"] = kwargs["candidate_labels"]
if "hypothesis_template" in kwargs:
preprocess_params["hypothesis_template"] = kwargs["hypothesis_template"]
return preprocess_params, {}, {}
def preprocess(self, image, candidate_labels=None, hypothesis_template="This is a photo of {}."):
image = load_image(image)
inputs = self.image_processor(images=[image], return_tensors=self.framework)
inputs["candidate_labels"] = candidate_labels
sequences = [hypothesis_template.format(x) for x in candidate_labels]
text_inputs = self.tokenizer(sequences, return_tensors=self.framework, padding=True)
inputs["text_inputs"] = [text_inputs]
return inputs
def _forward(self, model_inputs):
candidate_labels = model_inputs.pop("candidate_labels")
text_inputs = model_inputs.pop("text_inputs")
if isinstance(text_inputs[0], UserDict):
text_inputs = text_inputs[0]
else:
# Batching case.
text_inputs = text_inputs[0][0]
outputs = self.model(**text_inputs, **model_inputs)
model_outputs = {
"candidate_labels": candidate_labels,
"logits": outputs.logits_per_image,
}
return model_outputs
def postprocess(self, model_outputs):
candidate_labels = model_outputs.pop("candidate_labels")
logits = model_outputs["logits"][0]
if self.framework == "pt":
probs = logits.softmax(dim=-1).squeeze(-1)
scores = probs.tolist()
if not isinstance(scores, list):
scores = [scores]
elif self.framework == "tf":
probs = stable_softmax(logits, axis=-1)
scores = probs.numpy().tolist()
else:
raise ValueError(f"Unsupported framework: {self.framework}")
result = [
{"score": score, "label": candidate_label}
for score, candidate_label in sorted(zip(scores, candidate_labels), key=lambda x: -x[0])
]
return result
| 6,075 | 37.700637 | 130 | py |
transformers | transformers-main/src/transformers/pipelines/mask_generation.py | from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class MaskGenerationPipeline(ChunkPipeline):
"""
Automatic mask generation for images using `SamForMaskGeneration`. This pipeline predicts binary masks for an
image, given an image. It is a `ChunkPipeline` because you can seperate the points in a mini-batch in order to
avoid OOM issues. Use the `points_per_batch` argument to control the number of points that will be processed at the
same time. Default is `64`.
The pipeline works in 3 steps:
1. `preprocess`: A grid of 1024 points evenly separated is generated along with bounding boxes and point
labels.
For more details on how the points and bounding boxes are created, check the `_generate_crop_boxes`
function. The image is also preprocessed using the `image_processor`. This function `yields` a minibatch of
`points_per_batch`.
2. `forward`: feeds the outputs of `preprocess` to the model. The image embedding is computed only once.
Calls both `self.model.get_image_embeddings` and makes sure that the gradients are not computed, and the
tensors and models are on the same device.
3. `postprocess`: The most important part of the automatic mask generation happens here. Three steps
are induced:
- image_processor.postprocess_masks (run on each minibatch loop): takes in the raw output masks,
resizes them according
to the image size, and transforms there to binary masks.
- image_processor.filter_masks (on each minibatch loop): uses both `pred_iou_thresh` and
`stability_scores`. Also
applies a variety of filters based on non maximum suppression to remove bad masks.
- image_processor.postprocess_masks_for_amg applies the NSM on the mask to only keep relevant ones.
Arguments:
model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
[`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
tokenizer ([`PreTrainedTokenizer`]):
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
[`PreTrainedTokenizer`].
feature_extractor ([`SequenceFeatureExtractor`]):
The feature extractor that will be used by the pipeline to encode the input.
points_per_batch (*optional*, int, default to 64):
Sets the number of points run simultaneously by the model. Higher numbers may be faster but use more GPU
memory.
output_bboxes_mask (`bool`, *optional*, default to `False`):
Whether or not to output the bounding box predictions.
output_rle_masks (`bool`, *optional*, default to `False`):
Whether or not to output the masks in `RLE` format
Example:
```python
>>> from transformers import pipeline
>>> generator = pipeline(model="facebook/sam-vit-base", task="mask-generation")
>>> outputs = generator(
... "http://images.cocodataset.org/val2017/000000039769.jpg",
... )
>>> outputs = generator(
... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", points_per_batch=128
... )
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This segmentation pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"mask-generation"`.
See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=mask-generation).
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
requires_backends(self, "vision")
requires_backends(self, "torch")
if self.framework != "pt":
raise ValueError(f"The {self.__class__} is only available in PyTorch.")
self.check_model_type(MODEL_FOR_MASK_GENERATION_MAPPING)
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
postprocess_kwargs = {}
forward_params = {}
# preprocess args
if "points_per_batch" in kwargs:
preprocess_kwargs["points_per_batch"] = kwargs["points_per_batch"]
if "points_per_crop" in kwargs:
preprocess_kwargs["points_per_crop"] = kwargs["points_per_crop"]
if "crops_n_layers" in kwargs:
preprocess_kwargs["crops_n_layers"] = kwargs["crops_n_layers"]
if "crop_overlap_ratio" in kwargs:
preprocess_kwargs["crop_overlap_ratio"] = kwargs["crop_overlap_ratio"]
if "crop_n_points_downscale_factor" in kwargs:
preprocess_kwargs["crop_n_points_downscale_factor"] = kwargs["crop_n_points_downscale_factor"]
# postprocess args
if "pred_iou_thresh" in kwargs:
forward_params["pred_iou_thresh"] = kwargs["pred_iou_thresh"]
if "stability_score_offset" in kwargs:
forward_params["stability_score_offset"] = kwargs["stability_score_offset"]
if "mask_threshold" in kwargs:
forward_params["mask_threshold"] = kwargs["mask_threshold"]
if "stability_score_thresh" in kwargs:
forward_params["stability_score_thresh"] = kwargs["stability_score_thresh"]
if "crops_nms_thresh" in kwargs:
postprocess_kwargs["crops_nms_thresh"] = kwargs["crops_nms_thresh"]
if "output_rle_mask" in kwargs:
postprocess_kwargs["output_rle_mask"] = kwargs["output_rle_mask"]
if "output_bboxes_mask" in kwargs:
postprocess_kwargs["output_bboxes_mask"] = kwargs["output_bboxes_mask"]
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__(self, image, *args, num_workers=None, batch_size=None, **kwargs):
"""
Generates binary segmentation masks
Args:
inputs (`np.ndarray` or `bytes` or `str` or `dict`):
Image or list of images.
mask_threshold (`float`, *optional*, defaults to 0.0):
Threshold to use when turning the predicted masks into binary values.
pred_iou_thresh (`float`, *optional*, defaults to 0.88):
A filtering threshold in `[0,1]` applied on the model's predicted mask quality.
stability_score_thresh (`float`, *optional*, defaults to 0.95):
A filtering threshold in `[0,1]`, using the stability of the mask under changes to the cutoff used to
binarize the model's mask predictions.
stability_score_offset (`int`, *optional*, defaults to 1):
The amount to shift the cutoff when calculated the stability score.
crops_nms_thresh (`float`, *optional*, defaults to 0.7):
The box IoU cutoff used by non-maximal suppression to filter duplicate masks.
crops_n_layers (`int`, *optional*, defaults to 0):
If `crops_n_layers>0`, mask prediction will be run again on crops of the image. Sets the number of
layers to run, where each layer has 2**i_layer number of image crops.
crop_overlap_ratio (`float`, *optional*, defaults to `512 / 1500`):
Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (`int`, *optional*, defaults to `1`):
The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
Return:
`Dict`: A dictionary with the following keys:
- **mask** (`PIL.Image`) -- A binary mask of the detected object as a PIL Image of shape `(width,
height)` of the original image. Returns a mask filled with zeros if no object is found.
- **score** (*optional* `float`) -- Optionally, when the model is capable of estimating a confidence of
the "object" described by the label and the mask.
"""
return super().__call__(image, *args, num_workers=num_workers, batch_size=batch_size, **kwargs)
def preprocess(
self,
image,
points_per_batch=64,
crops_n_layers: int = 0,
crop_overlap_ratio: float = 512 / 1500,
points_per_crop: Optional[int] = 32,
crop_n_points_downscale_factor: Optional[int] = 1,
):
image = load_image(image)
target_size = self.image_processor.size["longest_edge"]
crop_boxes, grid_points, cropped_images, input_labels = self.image_processor.generate_crop_boxes(
image, target_size, crops_n_layers, crop_overlap_ratio, points_per_crop, crop_n_points_downscale_factor
)
model_inputs = self.image_processor(images=cropped_images, return_tensors="pt")
with self.device_placement():
if self.framework == "pt":
inference_context = self.get_inference_context()
with inference_context():
model_inputs = self._ensure_tensor_on_device(model_inputs, device=self.device)
image_embeddings = self.model.get_image_embeddings(model_inputs.pop("pixel_values"))
model_inputs["image_embeddings"] = image_embeddings
n_points = grid_points.shape[1]
points_per_batch = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
"Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
"To return all points at once, set points_per_batch to None"
)
for i in range(0, n_points, points_per_batch):
batched_points = grid_points[:, i : i + points_per_batch, :, :]
labels = input_labels[:, i : i + points_per_batch]
is_last = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def _forward(
self,
model_inputs,
pred_iou_thresh=0.88,
stability_score_thresh=0.95,
mask_threshold=0,
stability_score_offset=1,
):
input_boxes = model_inputs.pop("input_boxes")
is_last = model_inputs.pop("is_last")
original_sizes = model_inputs.pop("original_sizes").tolist()
reshaped_input_sizes = model_inputs.pop("reshaped_input_sizes").tolist()
model_outputs = self.model(**model_inputs)
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
low_resolution_masks = model_outputs["pred_masks"]
masks = self.image_processor.post_process_masks(
low_resolution_masks, original_sizes, reshaped_input_sizes, mask_threshold, binarize=False
)
iou_scores = model_outputs["iou_scores"]
masks, iou_scores, boxes = self.image_processor.filter_masks(
masks[0],
iou_scores[0],
original_sizes[0],
input_boxes[0],
pred_iou_thresh,
stability_score_thresh,
mask_threshold,
stability_score_offset,
)
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def postprocess(
self,
model_outputs,
output_rle_mask=False,
output_bboxes_mask=False,
crops_nms_thresh=0.7,
):
all_scores = []
all_masks = []
all_boxes = []
for model_output in model_outputs:
all_scores.append(model_output.pop("iou_scores"))
all_masks.extend(model_output.pop("masks"))
all_boxes.append(model_output.pop("boxes"))
all_scores = torch.cat(all_scores)
all_boxes = torch.cat(all_boxes)
output_masks, iou_scores, rle_mask, bounding_boxes = self.image_processor.post_process_for_mask_generation(
all_masks, all_scores, all_boxes, crops_nms_thresh
)
extra = defaultdict(list)
for output in model_outputs:
for k, v in output.items():
extra[k].append(v)
optional = {}
if output_rle_mask:
optional["rle_mask"] = rle_mask
if output_bboxes_mask:
optional["bounding_boxes"] = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 13,291 | 45.313589 | 119 | py |
transformers | transformers-main/src/transformers/pipelines/pt_utils.py | import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class PipelineDataset(Dataset):
def __init__(self, dataset, process, params):
self.dataset = dataset
self.process = process
self.params = params
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
item = self.dataset[i]
processed = self.process(item, **self.params)
return processed
class PipelineIterator(IterableDataset):
def __init__(self, loader, infer, params, loader_batch_size=None):
"""
Roughly equivalent to
```
for item in loader:
yield infer(item, **params)
```
Arguments:
loader (`torch.utils.data.DataLoader` or any iterator):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `infer` along with every item
loader_batch_size (`int`, *optional*):
If specified, the items of `loader` are supposed to come as batch, and are loader_batched here
making it roughly behave as
```
for items in loader:
for i in loader_batch_size:
item = items[i]
yield infer(item, **params)
```"""
self.loader = loader
self.infer = infer
self.params = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
loader_batch_size = None
self.loader_batch_size = loader_batch_size
# Internal bookkeeping
self._loader_batch_index = None
self._loader_batch_data = None
def __len__(self):
return len(self.loader)
def __iter__(self):
self.iterator = iter(self.loader)
return self
def loader_batch_item(self):
"""
Return item located at `loader_batch_index` within the current `loader_batch_data`.
"""
if isinstance(self._loader_batch_data, torch.Tensor):
# Batch data is simple tensor, just fetch the slice
result = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
loader_batched = {}
for k, element in self._loader_batch_data.items():
if isinstance(element, ModelOutput):
# Convert ModelOutput to tuple first
element = element.to_tuple()
if isinstance(element[0], torch.Tensor):
loader_batched[k] = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0], np.ndarray):
loader_batched[k] = tuple(np.expand_dims(el[self._loader_batch_index], 0) for el in element)
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(element, tuple):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0], torch.Tensor):
loader_batched[k] = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0], np.ndarray):
loader_batched[k] = tuple(np.expand_dims(el[self._loader_batch_index], 0) for el in element)
continue
if element is None:
# This can happen for optional data that get passed around
loader_batched[k] = None
elif isinstance(element[self._loader_batch_index], torch.Tensor):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
loader_batched[k] = element[self._loader_batch_index].unsqueeze(0)
elif isinstance(element[self._loader_batch_index], np.ndarray):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
loader_batched[k] = np.expand_dims(element[self._loader_batch_index], 0)
else:
# This is typically a list, so no need to `unsqueeze`.
loader_batched[k] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
result = self._loader_batch_data.__class__(loader_batched)
self._loader_batch_index += 1
return result
def __next__(self):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
item = next(self.iterator)
processed = self.infer(item, **self.params)
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(processed, torch.Tensor):
first_tensor = processed
else:
key = list(processed.keys())[0]
first_tensor = processed[key]
if isinstance(first_tensor, list):
observed_batch_size = len(first_tensor)
else:
observed_batch_size = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
self.loader_batch_size = observed_batch_size
# Setting internal index to unwrap the batch
self._loader_batch_data = processed
self._loader_batch_index = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class PipelineChunkIterator(PipelineIterator):
def __init__(self, loader, infer, params, loader_batch_size=None):
"""
Roughly equivalent to
```
for iterator in loader:
for item in iterator:
yield infer(item, **params)
```
Arguments:
loader (`torch.utils.data.DataLoader` or any iterator):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `infer` along with every item
"""
super().__init__(loader, infer, params)
def __iter__(self):
self.iterator = iter(self.loader)
self.subiterator = None
return self
def __next__(self):
if self.subiterator is None:
"Subiterator None means we haven't started a `preprocess` iterator. so start it"
self.subiterator = self.infer(next(self.iterator), **self.params)
try:
# Try to return next item
processed = next(self.subiterator)
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
self.subiterator = self.infer(next(self.iterator), **self.params)
processed = next(self.subiterator)
return processed
class PipelinePackIterator(PipelineIterator):
"""
Roughly equivalent to
```
packed = []
for item in loader:
packed.append(item)
if item["is_last"]:
yield packed
packed = []
```
but it also handles cases where `item` are batched (meaning it's a dict of Tensor with first dimension > 1. In
that case it does
```
packed = []
for batch in loader:
# item is batched
for item in batch:
packed.append(item)
if item["is_last"]:
yield packed
packed = []
```
Arguments:
loader (`torch.utils.data.DataLoader` or any iterator):
The iterator that will be used to apply `infer` on.
infer (any function):
The function to apply of each element of `loader`.
params (`dict`):
The parameters passed to `infer` along with every item
loader_batch_size (`int`, *optional*):
If specified, the items of `loader` are supposed to come as batch, and are loader_batched here making
it roughly behave as
```
for items in loader:
for i in loader_batch_size:
item = items[i]
yield infer(item, **params)
```"""
def __iter__(self):
self.iterator = iter(self.loader)
return self
def __next__(self):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
is_last = False
accumulator = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
item = self.loader_batch_item()
is_last = item.pop("is_last")
accumulator.append(item)
if is_last:
return accumulator
while not is_last:
processed = self.infer(next(self.iterator), **self.params)
if self.loader_batch_size is not None:
if isinstance(processed, torch.Tensor):
first_tensor = processed
else:
key = list(processed.keys())[0]
first_tensor = processed[key]
if isinstance(first_tensor, list):
observed_batch_size = len(first_tensor)
else:
observed_batch_size = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
self.loader_batch_size = observed_batch_size
self._loader_batch_data = processed
self._loader_batch_index = 0
while self._loader_batch_index < self.loader_batch_size:
item = self.loader_batch_item()
is_last = item.pop("is_last")
accumulator.append(item)
if is_last:
return accumulator
else:
item = processed
is_last = item.pop("is_last")
accumulator.append(item)
return accumulator
class KeyDataset(Dataset):
def __init__(self, dataset: Dataset, key: str):
self.dataset = dataset
self.key = key
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
return self.dataset[i][self.key]
class KeyPairDataset(Dataset):
def __init__(self, dataset: Dataset, key1: str, key2: str):
self.dataset = dataset
self.key1 = key1
self.key2 = key2
def __len__(self):
return len(self.dataset)
def __getitem__(self, i):
return {"text": self.dataset[i][self.key1], "text_pair": self.dataset[i][self.key2]}
| 12,613 | 38.54232 | 118 | py |
transformers | transformers-main/src/transformers/pipelines/text_classification.py | import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def sigmoid(_outputs):
return 1.0 / (1.0 + np.exp(-_outputs))
def softmax(_outputs):
maxes = np.max(_outputs, axis=-1, keepdims=True)
shifted_exp = np.exp(_outputs - maxes)
return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
class ClassificationFunction(ExplicitEnum):
SIGMOID = "sigmoid"
SOFTMAX = "softmax"
NONE = "none"
@add_end_docstrings(
PIPELINE_INIT_ARGS,
r"""
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `"default"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `"sigmoid"`: Applies the sigmoid function on the output.
- `"softmax"`: Applies the softmax function on the output.
- `"none"`: Does not apply any function on the output.
""",
)
class TextClassificationPipeline(Pipeline):
"""
Text classification pipeline using any `ModelForSequenceClassification`. See the [sequence classification
examples](../task_summary#sequence-classification) for more information.
Example:
```python
>>> from transformers import pipeline
>>> classifier = pipeline(model="distilbert-base-uncased-finetuned-sst-2-english")
>>> classifier("This movie is disgustingly good !")
[{'label': 'POSITIVE', 'score': 1.0}]
>>> classifier("Director tried too much.")
[{'label': 'NEGATIVE', 'score': 0.996}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This text classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"sentiment-analysis"` (for classifying sequences according to positive or negative sentiments).
If multiple classification labels are available (`model.config.num_labels >= 2`), the pipeline will run a softmax
over the results. If there is a single label, the pipeline will run a sigmoid over the result.
The models that this pipeline can use are models that have been fine-tuned on a sequence classification task. See
the up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=text-classification).
"""
return_all_scores = False
function_to_apply = ClassificationFunction.NONE
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
)
def _sanitize_parameters(self, return_all_scores=None, function_to_apply=None, top_k="", **tokenizer_kwargs):
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
preprocess_params = tokenizer_kwargs
postprocess_params = {}
if hasattr(self.model.config, "return_all_scores") and return_all_scores is None:
return_all_scores = self.model.config.return_all_scores
if isinstance(top_k, int) or top_k is None:
postprocess_params["top_k"] = top_k
postprocess_params["_legacy"] = False
elif return_all_scores is not None:
warnings.warn(
"`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"
" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.",
UserWarning,
)
if return_all_scores:
postprocess_params["top_k"] = None
else:
postprocess_params["top_k"] = 1
if isinstance(function_to_apply, str):
function_to_apply = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
postprocess_params["function_to_apply"] = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__(self, *args, **kwargs):
"""
Classify the text(s) given as inputs.
Args:
args (`str` or `List[str]` or `Dict[str]`, or `List[Dict[str]]`):
One or several texts to classify. In order to use text pairs for your classification, you can send a
dictionary containing `{"text", "text_pair"}` keys, or a list of those.
top_k (`int`, *optional*, defaults to `1`):
How many results to return.
function_to_apply (`str`, *optional*, defaults to `"default"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different
values:
If this argument is not specified, then it will apply the following functions according to the number
of labels:
- If the model has a single label, will apply the sigmoid function on the output.
- If the model has several labels, will apply the softmax function on the output.
Possible values are:
- `"sigmoid"`: Applies the sigmoid function on the output.
- `"softmax"`: Applies the softmax function on the output.
- `"none"`: Does not apply any function on the output.
Return:
A list or a list of list of `dict`: Each result comes as list of dictionaries with the following keys:
- **label** (`str`) -- The label predicted.
- **score** (`float`) -- The corresponding probability.
If `top_k` is used, one such dictionary is returned per label.
"""
result = super().__call__(*args, **kwargs)
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
_legacy = "top_k" not in kwargs
if isinstance(args[0], str) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def preprocess(self, inputs, **tokenizer_kwargs) -> Dict[str, GenericTensor]:
return_tensors = self.framework
if isinstance(inputs, dict):
return self.tokenizer(**inputs, return_tensors=return_tensors, **tokenizer_kwargs)
elif isinstance(inputs, list) and len(inputs) == 1 and isinstance(inputs[0], list) and len(inputs[0]) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0], text_pair=inputs[0][1], return_tensors=return_tensors, **tokenizer_kwargs
)
elif isinstance(inputs, list):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'
)
return self.tokenizer(inputs, return_tensors=return_tensors, **tokenizer_kwargs)
def _forward(self, model_inputs):
return self.model(**model_inputs)
def postprocess(self, model_outputs, function_to_apply=None, top_k=1, _legacy=True):
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
function_to_apply = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
function_to_apply = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config, "function_to_apply") and function_to_apply is None:
function_to_apply = self.model.config.function_to_apply
else:
function_to_apply = ClassificationFunction.NONE
outputs = model_outputs["logits"][0]
outputs = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
scores = sigmoid(outputs)
elif function_to_apply == ClassificationFunction.SOFTMAX:
scores = softmax(outputs)
elif function_to_apply == ClassificationFunction.NONE:
scores = outputs
else:
raise ValueError(f"Unrecognized `function_to_apply` argument: {function_to_apply}")
if top_k == 1 and _legacy:
return {"label": self.model.config.id2label[scores.argmax().item()], "score": scores.max().item()}
dict_scores = [
{"label": self.model.config.id2label[i], "score": score.item()} for i, score in enumerate(scores)
]
if not _legacy:
dict_scores.sort(key=lambda x: x["score"], reverse=True)
if top_k is not None:
dict_scores = dict_scores[:top_k]
return dict_scores
| 10,043 | 44.243243 | 119 | py |
transformers | transformers-main/src/transformers/pipelines/depth_estimation.py | from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class DepthEstimationPipeline(Pipeline):
"""
Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image.
Example:
```python
>>> from transformers import pipeline
>>> depth_estimator = pipeline(task="depth-estimation", model="Intel/dpt-large")
>>> output = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg")
>>> # This is a tensor with the values being the depth expressed in meters for each pixel
>>> output["predicted_depth"].shape
torch.Size([1, 384, 384])
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This depth estimation pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"depth-estimation"`.
See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=depth-estimation).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
requires_backends(self, "vision")
self.check_model_type(MODEL_FOR_DEPTH_ESTIMATION_MAPPING)
def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs):
"""
Assign labels to the image(s) passed as inputs.
Args:
images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
The pipeline handles three types of images:
- A string containing a http link pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
images.
top_k (`int`, *optional*, defaults to 5):
The number of top labels that will be returned by the pipeline. If the provided number is higher than
the number of labels available in the model configuration, it will default to the number of labels.
Return:
A dictionary or a list of dictionaries containing result. If the input is a single image, will return a
dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to
the images.
The dictionaries contain the following keys:
- **label** (`str`) -- The label identified by the model.
- **score** (`int`) -- The score attributed by the model for that label.
"""
return super().__call__(images, **kwargs)
def _sanitize_parameters(self, **kwargs):
return {}, {}, {}
def preprocess(self, image):
image = load_image(image)
self.image_size = image.size
model_inputs = self.image_processor(images=image, return_tensors=self.framework)
return model_inputs
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs):
predicted_depth = model_outputs.predicted_depth
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1), size=self.image_size[::-1], mode="bicubic", align_corners=False
)
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
output_dict = {}
output_dict["predicted_depth"] = predicted_depth
output_dict["depth"] = depth
return output_dict
| 4,258 | 38.073394 | 119 | py |
transformers | transformers-main/src/transformers/pipelines/fill_mask.py | from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
@add_end_docstrings(
PIPELINE_INIT_ARGS,
r"""
top_k (`int`, defaults to 5):
The number of predictions to return.
targets (`str` or `List[str]`, *optional*):
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).
""",
)
class FillMaskPipeline(Pipeline):
"""
Masked language modeling prediction pipeline using any `ModelWithLMHead`. See the [masked language modeling
examples](../task_summary#masked-language-modeling) for more information.
Example:
```python
>>> from transformers import pipeline
>>> fill_masker = pipeline(model="bert-base-uncased")
>>> fill_masker("This is a simple [MASK].")
[{'score': 0.042, 'token': 3291, 'token_str': 'problem', 'sequence': 'this is a simple problem.'}, {'score': 0.031, 'token': 3160, 'token_str': 'question', 'sequence': 'this is a simple question.'}, {'score': 0.03, 'token': 8522, 'token_str': 'equation', 'sequence': 'this is a simple equation.'}, {'score': 0.027, 'token': 2028, 'token_str': 'one', 'sequence': 'this is a simple one.'}, {'score': 0.024, 'token': 3627, 'token_str': 'rule', 'sequence': 'this is a simple rule.'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This mask filling pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"fill-mask"`.
The models that this pipeline can use are models that have been trained with a masked language modeling objective,
which includes the bi-directional models in the library. See the up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=fill-mask).
<Tip>
This pipeline only works for inputs with exactly one token masked. Experimental: We added support for multiple
masks. The returned values are raw model output, and correspond to disjoint probabilities where one might expect
joint probabilities (See [discussion](https://github.com/huggingface/transformers/pull/10222)).
</Tip>"""
def get_masked_index(self, input_ids: GenericTensor) -> np.ndarray:
if self.framework == "tf":
masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy()
elif self.framework == "pt":
masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False)
else:
raise ValueError("Unsupported framework")
return masked_index
def _ensure_exactly_one_mask_token(self, input_ids: GenericTensor) -> np.ndarray:
masked_index = self.get_masked_index(input_ids)
numel = np.prod(masked_index.shape)
if numel < 1:
raise PipelineException(
"fill-mask",
self.model.base_model_prefix,
f"No mask_token ({self.tokenizer.mask_token}) found on the input",
)
def ensure_exactly_one_mask_token(self, model_inputs: GenericTensor):
if isinstance(model_inputs, list):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input["input_ids"][0])
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(input_ids)
def preprocess(self, inputs, return_tensors=None, **preprocess_parameters) -> Dict[str, GenericTensor]:
if return_tensors is None:
return_tensors = self.framework
model_inputs = self.tokenizer(inputs, return_tensors=return_tensors)
self.ensure_exactly_one_mask_token(model_inputs)
return model_inputs
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
model_outputs["input_ids"] = model_inputs["input_ids"]
return model_outputs
def postprocess(self, model_outputs, top_k=5, target_ids=None):
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
top_k = target_ids.shape[0]
input_ids = model_outputs["input_ids"][0]
outputs = model_outputs["logits"]
if self.framework == "tf":
masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy()[:, 0]
outputs = outputs.numpy()
logits = outputs[0, masked_index, :]
probs = stable_softmax(logits, axis=-1)
if target_ids is not None:
probs = tf.gather_nd(tf.squeeze(probs, 0), target_ids.reshape(-1, 1))
probs = tf.expand_dims(probs, 0)
topk = tf.math.top_k(probs, k=top_k)
values, predictions = topk.values.numpy(), topk.indices.numpy()
else:
masked_index = torch.nonzero(input_ids == self.tokenizer.mask_token_id, as_tuple=False).squeeze(-1)
# Fill mask pipeline supports only one ${mask_token} per sample
logits = outputs[0, masked_index, :]
probs = logits.softmax(dim=-1)
if target_ids is not None:
probs = probs[..., target_ids]
values, predictions = probs.topk(top_k)
result = []
single_mask = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist(), predictions.tolist())):
row = []
for v, p in zip(_values, _predictions):
# Copy is important since we're going to modify this array in place
tokens = input_ids.numpy().copy()
if target_ids is not None:
p = target_ids[p].tolist()
tokens[masked_index[i]] = p
# Filter padding out:
tokens = tokens[np.where(tokens != self.tokenizer.pad_token_id)]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
sequence = self.tokenizer.decode(tokens, skip_special_tokens=single_mask)
proposition = {"score": v, "token": p, "token_str": self.tokenizer.decode([p]), "sequence": sequence}
row.append(proposition)
result.append(row)
if single_mask:
return result[0]
return result
def get_target_ids(self, targets, top_k=None):
if isinstance(targets, str):
targets = [targets]
try:
vocab = self.tokenizer.get_vocab()
except Exception:
vocab = {}
target_ids = []
for target in targets:
id_ = vocab.get(target, None)
if id_ is None:
input_ids = self.tokenizer(
target,
add_special_tokens=False,
return_attention_mask=False,
return_token_type_ids=False,
max_length=1,
truncation=True,
)["input_ids"]
if len(input_ids) == 0:
logger.warning(
f"The specified target token `{target}` does not exist in the model vocabulary. "
"We cannot replace it with anything meaningful, ignoring it"
)
continue
id_ = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
f"The specified target token `{target}` does not exist in the model vocabulary. "
f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_)}`."
)
target_ids.append(id_)
target_ids = list(set(target_ids))
if len(target_ids) == 0:
raise ValueError("At least one target must be provided when passed.")
target_ids = np.array(target_ids)
return target_ids
def _sanitize_parameters(self, top_k=None, targets=None):
postprocess_params = {}
if targets is not None:
target_ids = self.get_target_ids(targets, top_k)
postprocess_params["target_ids"] = target_ids
if top_k is not None:
postprocess_params["top_k"] = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
"fill-mask", self.model.base_model_prefix, "The tokenizer does not define a `mask_token`."
)
return {}, {}, postprocess_params
def __call__(self, inputs, *args, **kwargs):
"""
Fill the masked token in the text(s) given as inputs.
Args:
args (`str` or `List[str]`):
One or several texts (or one list of prompts) with masked tokens.
targets (`str` or `List[str]`, *optional*):
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first
resulting token will be used (with a warning, and that might be slower).
top_k (`int`, *optional*):
When passed, overrides the number of predictions to return.
Return:
A list or a list of list of `dict`: Each result comes as list of dictionaries with the following keys:
- **sequence** (`str`) -- The corresponding input with the mask token prediction.
- **score** (`float`) -- The corresponding probability.
- **token** (`int`) -- The predicted token id (to replace the masked one).
- **token_str** (`str`) -- The predicted token (to replace the masked one).
"""
outputs = super().__call__(inputs, **kwargs)
if isinstance(inputs, list) and len(inputs) == 1:
return outputs[0]
return outputs
| 10,691 | 43 | 483 | py |
transformers | transformers-main/src/transformers/pipelines/question_answering.py | import types
import warnings
from collections.abc import Iterable
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
import numpy as np
from ..data import SquadExample, SquadFeatures, squad_convert_examples_to_features
from ..modelcard import ModelCard
from ..tokenization_utils import PreTrainedTokenizer
from ..utils import (
PaddingStrategy,
add_end_docstrings,
is_tf_available,
is_tokenizers_available,
is_torch_available,
logging,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
logger = logging.get_logger(__name__)
if TYPE_CHECKING:
from ..modeling_tf_utils import TFPreTrainedModel
from ..modeling_utils import PreTrainedModel
if is_tokenizers_available():
import tokenizers
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING
Dataset = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
from ..models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
def decode_spans(
start: np.ndarray, end: np.ndarray, topk: int, max_answer_len: int, undesired_tokens: np.ndarray
) -> Tuple:
"""
Take the output of any `ModelForQuestionAnswering` and will generate probabilities for each span to be the actual
answer.
In addition, it filters out some unwanted/impossible cases like answer len being greater than max_answer_len or
answer end position being before the starting position. The method supports output the k-best answer through the
topk argument.
Args:
start (`np.ndarray`): Individual start probabilities for each token.
end (`np.ndarray`): Individual end probabilities for each token.
topk (`int`): Indicates how many possible answer span(s) to extract from the model output.
max_answer_len (`int`): Maximum size of the answer to extract from the model's output.
undesired_tokens (`np.ndarray`): Mask determining tokens that can be part of the answer
"""
# Ensure we have batch axis
if start.ndim == 1:
start = start[None]
if end.ndim == 1:
end = end[None]
# Compute the score of each tuple(start, end) to be the real answer
outer = np.matmul(np.expand_dims(start, -1), np.expand_dims(end, 1))
# Remove candidate with end < start and end - start > max_answer_len
candidates = np.tril(np.triu(outer), max_answer_len - 1)
# Inspired by Chen & al. (https://github.com/facebookresearch/DrQA)
scores_flat = candidates.flatten()
if topk == 1:
idx_sort = [np.argmax(scores_flat)]
elif len(scores_flat) < topk:
idx_sort = np.argsort(-scores_flat)
else:
idx = np.argpartition(-scores_flat, topk)[0:topk]
idx_sort = idx[np.argsort(-scores_flat[idx])]
starts, ends = np.unravel_index(idx_sort, candidates.shape)[1:]
desired_spans = np.isin(starts, undesired_tokens.nonzero()) & np.isin(ends, undesired_tokens.nonzero())
starts = starts[desired_spans]
ends = ends[desired_spans]
scores = candidates[0, starts, ends]
return starts, ends, scores
def select_starts_ends(
start,
end,
p_mask,
attention_mask,
min_null_score=1000000,
top_k=1,
handle_impossible_answer=False,
max_answer_len=15,
):
"""
Takes the raw output of any `ModelForQuestionAnswering` and first normalizes its outputs and then uses
`decode_spans()` to generate probabilities for each span to be the actual answer.
Args:
start (`np.ndarray`): Individual start logits for each token.
end (`np.ndarray`): Individual end logits for each token.
p_mask (`np.ndarray`): A mask with 1 for values that cannot be in the answer
attention_mask (`np.ndarray`): The attention mask generated by the tokenizer
min_null_score(`float`): The minimum null (empty) answer score seen so far.
topk (`int`): Indicates how many possible answer span(s) to extract from the model output.
handle_impossible_answer(`bool`): Whether to allow null (empty) answers
max_answer_len (`int`): Maximum size of the answer to extract from the model's output.
"""
# Ensure padded tokens & question tokens cannot belong to the set of candidate answers.
undesired_tokens = np.abs(np.array(p_mask) - 1)
if attention_mask is not None:
undesired_tokens = undesired_tokens & attention_mask
# Generate mask
undesired_tokens_mask = undesired_tokens == 0.0
# Make sure non-context indexes in the tensor cannot contribute to the softmax
start = np.where(undesired_tokens_mask, -10000.0, start)
end = np.where(undesired_tokens_mask, -10000.0, end)
# Normalize logits and spans to retrieve the answer
start = np.exp(start - start.max(axis=-1, keepdims=True))
start = start / start.sum()
end = np.exp(end - end.max(axis=-1, keepdims=True))
end = end / end.sum()
if handle_impossible_answer:
min_null_score = min(min_null_score, (start[0, 0] * end[0, 0]).item())
# Mask CLS
start[0, 0] = end[0, 0] = 0.0
starts, ends, scores = decode_spans(start, end, top_k, max_answer_len, undesired_tokens)
return starts, ends, scores, min_null_score
class QuestionAnsweringArgumentHandler(ArgumentHandler):
"""
QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question & context) to be mapped to
internal [`SquadExample`].
QuestionAnsweringArgumentHandler manages all the possible to create a [`SquadExample`] from the command-line
supplied arguments.
"""
def normalize(self, item):
if isinstance(item, SquadExample):
return item
elif isinstance(item, dict):
for k in ["question", "context"]:
if k not in item:
raise KeyError("You need to provide a dictionary with keys {question:..., context:...}")
elif item[k] is None:
raise ValueError(f"`{k}` cannot be None")
elif isinstance(item[k], str) and len(item[k]) == 0:
raise ValueError(f"`{k}` cannot be empty")
return QuestionAnsweringPipeline.create_sample(**item)
raise ValueError(f"{item} argument needs to be of type (SquadExample, dict)")
def __call__(self, *args, **kwargs):
# Detect where the actual inputs are
if args is not None and len(args) > 0:
if len(args) == 1:
inputs = args[0]
elif len(args) == 2 and {type(el) for el in args} == {str}:
inputs = [{"question": args[0], "context": args[1]}]
else:
inputs = list(args)
# Generic compatibility with sklearn and Keras
# Batched data
elif "X" in kwargs:
inputs = kwargs["X"]
elif "data" in kwargs:
inputs = kwargs["data"]
elif "question" in kwargs and "context" in kwargs:
if isinstance(kwargs["question"], list) and isinstance(kwargs["context"], str):
inputs = [{"question": Q, "context": kwargs["context"]} for Q in kwargs["question"]]
elif isinstance(kwargs["question"], list) and isinstance(kwargs["context"], list):
if len(kwargs["question"]) != len(kwargs["context"]):
raise ValueError("Questions and contexts don't have the same lengths")
inputs = [{"question": Q, "context": C} for Q, C in zip(kwargs["question"], kwargs["context"])]
elif isinstance(kwargs["question"], str) and isinstance(kwargs["context"], str):
inputs = [{"question": kwargs["question"], "context": kwargs["context"]}]
else:
raise ValueError("Arguments can't be understood")
else:
raise ValueError(f"Unknown arguments {kwargs}")
# When user is sending a generator we need to trust it's a valid example
generator_types = (types.GeneratorType, Dataset) if Dataset is not None else (types.GeneratorType,)
if isinstance(inputs, generator_types):
return inputs
# Normalize inputs
if isinstance(inputs, dict):
inputs = [inputs]
elif isinstance(inputs, Iterable):
# Copy to avoid overriding arguments
inputs = list(inputs)
else:
raise ValueError(f"Invalid arguments {kwargs}")
for i, item in enumerate(inputs):
inputs[i] = self.normalize(item)
return inputs
@add_end_docstrings(PIPELINE_INIT_ARGS)
class QuestionAnsweringPipeline(ChunkPipeline):
"""
Question Answering pipeline using any `ModelForQuestionAnswering`. See the [question answering
examples](../task_summary#question-answering) for more information.
Example:
```python
>>> from transformers import pipeline
>>> oracle = pipeline(model="deepset/roberta-base-squad2")
>>> oracle(question="Where do I live?", context="My name is Wolfgang and I live in Berlin")
{'score': 0.9191, 'start': 34, 'end': 40, 'answer': 'Berlin'}
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This question answering pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"question-answering"`.
The models that this pipeline can use are models that have been fine-tuned on a question answering task. See the
up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=question-answering).
"""
default_input_names = "question,context"
handle_impossible_answer = False
def __init__(
self,
model: Union["PreTrainedModel", "TFPreTrainedModel"],
tokenizer: PreTrainedTokenizer,
modelcard: Optional[ModelCard] = None,
framework: Optional[str] = None,
task: str = "",
**kwargs,
):
super().__init__(
model=model,
tokenizer=tokenizer,
modelcard=modelcard,
framework=framework,
task=task,
**kwargs,
)
self._args_parser = QuestionAnsweringArgumentHandler()
self.check_model_type(
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING if self.framework == "tf" else MODEL_FOR_QUESTION_ANSWERING_MAPPING
)
@staticmethod
def create_sample(
question: Union[str, List[str]], context: Union[str, List[str]]
) -> Union[SquadExample, List[SquadExample]]:
"""
QuestionAnsweringPipeline leverages the [`SquadExample`] internally. This helper method encapsulate all the
logic for converting question(s) and context(s) to [`SquadExample`].
We currently support extractive question answering.
Arguments:
question (`str` or `List[str]`): The question(s) asked.
context (`str` or `List[str]`): The context(s) in which we will look for the answer.
Returns:
One or a list of [`SquadExample`]: The corresponding [`SquadExample`] grouping question and context.
"""
if isinstance(question, list):
return [SquadExample(None, q, c, None, None, None) for q, c in zip(question, context)]
else:
return SquadExample(None, question, context, None, None, None)
def _sanitize_parameters(
self,
padding=None,
topk=None,
top_k=None,
doc_stride=None,
max_answer_len=None,
max_seq_len=None,
max_question_len=None,
handle_impossible_answer=None,
align_to_words=None,
**kwargs,
):
# Set defaults values
preprocess_params = {}
if padding is not None:
preprocess_params["padding"] = padding
if doc_stride is not None:
preprocess_params["doc_stride"] = doc_stride
if max_question_len is not None:
preprocess_params["max_question_len"] = max_question_len
if max_seq_len is not None:
preprocess_params["max_seq_len"] = max_seq_len
postprocess_params = {}
if topk is not None and top_k is None:
warnings.warn("topk parameter is deprecated, use top_k instead", UserWarning)
top_k = topk
if top_k is not None:
if top_k < 1:
raise ValueError(f"top_k parameter should be >= 1 (got {top_k})")
postprocess_params["top_k"] = top_k
if max_answer_len is not None:
if max_answer_len < 1:
raise ValueError(f"max_answer_len parameter should be >= 1 (got {max_answer_len}")
if max_answer_len is not None:
postprocess_params["max_answer_len"] = max_answer_len
if handle_impossible_answer is not None:
postprocess_params["handle_impossible_answer"] = handle_impossible_answer
if align_to_words is not None:
postprocess_params["align_to_words"] = align_to_words
return preprocess_params, {}, postprocess_params
def __call__(self, *args, **kwargs):
"""
Answer the question(s) given as inputs by using the context(s).
Args:
args ([`SquadExample`] or a list of [`SquadExample`]):
One or several [`SquadExample`] containing the question and context.
X ([`SquadExample`] or a list of [`SquadExample`], *optional*):
One or several [`SquadExample`] containing the question and context (will be treated the same way as if
passed as the first positional argument).
data ([`SquadExample`] or a list of [`SquadExample`], *optional*):
One or several [`SquadExample`] containing the question and context (will be treated the same way as if
passed as the first positional argument).
question (`str` or `List[str]`):
One or several question(s) (must be used in conjunction with the `context` argument).
context (`str` or `List[str]`):
One or several context(s) associated with the question(s) (must be used in conjunction with the
`question` argument).
topk (`int`, *optional*, defaults to 1):
The number of answers to return (will be chosen by order of likelihood). Note that we return less than
topk answers if there are not enough options available within the context.
doc_stride (`int`, *optional*, defaults to 128):
If the context is too long to fit with the question for the model, it will be split in several chunks
with some overlap. This argument controls the size of that overlap.
max_answer_len (`int`, *optional*, defaults to 15):
The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
max_seq_len (`int`, *optional*, defaults to 384):
The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
model. The context will be split in several chunks (using `doc_stride` as overlap) if needed.
max_question_len (`int`, *optional*, defaults to 64):
The maximum length of the question after tokenization. It will be truncated if needed.
handle_impossible_answer (`bool`, *optional*, defaults to `False`):
Whether or not we accept impossible as an answer.
align_to_words (`bool`, *optional*, defaults to `True`):
Attempts to align the answer to real words. Improves quality on space separated langages. Might hurt on
non-space-separated languages (like Japanese or Chinese)
Return:
A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys:
- **score** (`float`) -- The probability associated to the answer.
- **start** (`int`) -- The character start index of the answer (in the tokenized version of the input).
- **end** (`int`) -- The character end index of the answer (in the tokenized version of the input).
- **answer** (`str`) -- The answer to the question.
"""
# Convert inputs to features
examples = self._args_parser(*args, **kwargs)
if isinstance(examples, (list, tuple)) and len(examples) == 1:
return super().__call__(examples[0], **kwargs)
return super().__call__(examples, **kwargs)
def preprocess(self, example, padding="do_not_pad", doc_stride=None, max_question_len=64, max_seq_len=None):
# XXX: This is specal, args_parser will not handle anything generator or dataset like
# For those we expect user to send a simple valid example either directly as a SquadExample or simple dict.
# So we still need a little sanitation here.
if isinstance(example, dict):
example = SquadExample(None, example["question"], example["context"], None, None, None)
if max_seq_len is None:
max_seq_len = min(self.tokenizer.model_max_length, 384)
if doc_stride is None:
doc_stride = min(max_seq_len // 2, 128)
if doc_stride > max_seq_len:
raise ValueError(f"`doc_stride` ({doc_stride}) is larger than `max_seq_len` ({max_seq_len})")
if not self.tokenizer.is_fast:
features = squad_convert_examples_to_features(
examples=[example],
tokenizer=self.tokenizer,
max_seq_length=max_seq_len,
doc_stride=doc_stride,
max_query_length=max_question_len,
padding_strategy=PaddingStrategy.MAX_LENGTH,
is_training=False,
tqdm_enabled=False,
)
else:
# Define the side we want to truncate / pad and the text/pair sorting
question_first = self.tokenizer.padding_side == "right"
encoded_inputs = self.tokenizer(
text=example.question_text if question_first else example.context_text,
text_pair=example.context_text if question_first else example.question_text,
padding=padding,
truncation="only_second" if question_first else "only_first",
max_length=max_seq_len,
stride=doc_stride,
return_token_type_ids=True,
return_overflowing_tokens=True,
return_offsets_mapping=True,
return_special_tokens_mask=True,
)
# When the input is too long, it's converted in a batch of inputs with overflowing tokens
# and a stride of overlap between the inputs. If a batch of inputs is given, a special output
# "overflow_to_sample_mapping" indicate which member of the encoded batch belong to which original batch sample.
# Here we tokenize examples one-by-one so we don't need to use "overflow_to_sample_mapping".
# "num_span" is the number of output samples generated from the overflowing tokens.
num_spans = len(encoded_inputs["input_ids"])
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
# We put 0 on the tokens from the context and 1 everywhere else (question and special tokens)
p_mask = [
[tok != 1 if question_first else 0 for tok in encoded_inputs.sequence_ids(span_id)]
for span_id in range(num_spans)
]
features = []
for span_idx in range(num_spans):
input_ids_span_idx = encoded_inputs["input_ids"][span_idx]
attention_mask_span_idx = (
encoded_inputs["attention_mask"][span_idx] if "attention_mask" in encoded_inputs else None
)
token_type_ids_span_idx = (
encoded_inputs["token_type_ids"][span_idx] if "token_type_ids" in encoded_inputs else None
)
# keep the cls_token unmasked (some models use it to indicate unanswerable questions)
if self.tokenizer.cls_token_id is not None:
cls_indices = np.nonzero(np.array(input_ids_span_idx) == self.tokenizer.cls_token_id)[0]
for cls_index in cls_indices:
p_mask[span_idx][cls_index] = 0
submask = p_mask[span_idx]
features.append(
SquadFeatures(
input_ids=input_ids_span_idx,
attention_mask=attention_mask_span_idx,
token_type_ids=token_type_ids_span_idx,
p_mask=submask,
encoding=encoded_inputs[span_idx],
# We don't use the rest of the values - and actually
# for Fast tokenizer we could totally avoid using SquadFeatures and SquadExample
cls_index=None,
token_to_orig_map={},
example_index=0,
unique_id=0,
paragraph_len=0,
token_is_max_context=0,
tokens=[],
start_position=0,
end_position=0,
is_impossible=False,
qas_id=None,
)
)
for i, feature in enumerate(features):
fw_args = {}
others = {}
model_input_names = self.tokenizer.model_input_names + ["p_mask", "token_type_ids"]
for k, v in feature.__dict__.items():
if k in model_input_names:
if self.framework == "tf":
tensor = tf.constant(v)
if tensor.dtype == tf.int64:
tensor = tf.cast(tensor, tf.int32)
fw_args[k] = tf.expand_dims(tensor, 0)
elif self.framework == "pt":
tensor = torch.tensor(v)
if tensor.dtype == torch.int32:
tensor = tensor.long()
fw_args[k] = tensor.unsqueeze(0)
else:
others[k] = v
is_last = i == len(features) - 1
yield {"example": example, "is_last": is_last, **fw_args, **others}
def _forward(self, inputs):
example = inputs["example"]
model_inputs = {k: inputs[k] for k in self.tokenizer.model_input_names}
output = self.model(**model_inputs)
if isinstance(output, dict):
return {"start": output["start_logits"], "end": output["end_logits"], "example": example, **inputs}
else:
start, end = output[:2]
return {"start": start, "end": end, "example": example, **inputs}
def postprocess(
self,
model_outputs,
top_k=1,
handle_impossible_answer=False,
max_answer_len=15,
align_to_words=True,
):
min_null_score = 1000000 # large and positive
answers = []
for output in model_outputs:
start_ = output["start"]
end_ = output["end"]
example = output["example"]
p_mask = output["p_mask"]
attention_mask = (
output["attention_mask"].numpy() if output.get("attention_mask", None) is not None else None
)
starts, ends, scores, min_null_score = select_starts_ends(
start_, end_, p_mask, attention_mask, min_null_score, top_k, handle_impossible_answer, max_answer_len
)
if not self.tokenizer.is_fast:
char_to_word = np.array(example.char_to_word_offset)
# Convert the answer (tokens) back to the original text
# Score: score from the model
# Start: Index of the first character of the answer in the context string
# End: Index of the character following the last character of the answer in the context string
# Answer: Plain text of the answer
for s, e, score in zip(starts, ends, scores):
token_to_orig_map = output["token_to_orig_map"]
answers.append(
{
"score": score.item(),
"start": np.where(char_to_word == token_to_orig_map[s])[0][0].item(),
"end": np.where(char_to_word == token_to_orig_map[e])[0][-1].item(),
"answer": " ".join(example.doc_tokens[token_to_orig_map[s] : token_to_orig_map[e] + 1]),
}
)
else:
# Convert the answer (tokens) back to the original text
# Score: score from the model
# Start: Index of the first character of the answer in the context string
# End: Index of the character following the last character of the answer in the context string
# Answer: Plain text of the answer
question_first = bool(self.tokenizer.padding_side == "right")
enc = output["encoding"]
# Encoding was *not* padded, input_ids *might*.
# It doesn't make a difference unless we're padding on
# the left hand side, since now we have different offsets
# everywhere.
if self.tokenizer.padding_side == "left":
offset = (output["input_ids"] == self.tokenizer.pad_token_id).numpy().sum()
else:
offset = 0
# Sometimes the max probability token is in the middle of a word so:
# - we start by finding the right word containing the token with `token_to_word`
# - then we convert this word in a character span with `word_to_chars`
sequence_index = 1 if question_first else 0
for s, e, score in zip(starts, ends, scores):
s = s - offset
e = e - offset
start_index, end_index = self.get_indices(enc, s, e, sequence_index, align_to_words)
answers.append(
{
"score": score.item(),
"start": start_index,
"end": end_index,
"answer": example.context_text[start_index:end_index],
}
)
if handle_impossible_answer:
answers.append({"score": min_null_score, "start": 0, "end": 0, "answer": ""})
answers = sorted(answers, key=lambda x: x["score"], reverse=True)[:top_k]
if len(answers) == 1:
return answers[0]
return answers
def get_indices(
self, enc: "tokenizers.Encoding", s: int, e: int, sequence_index: int, align_to_words: bool
) -> Tuple[int, int]:
if align_to_words:
try:
start_word = enc.token_to_word(s)
end_word = enc.token_to_word(e)
start_index = enc.word_to_chars(start_word, sequence_index=sequence_index)[0]
end_index = enc.word_to_chars(end_word, sequence_index=sequence_index)[1]
except Exception:
# Some tokenizers don't really handle words. Keep to offsets then.
start_index = enc.offsets[s][0]
end_index = enc.offsets[e][1]
else:
start_index = enc.offsets[s][0]
end_index = enc.offsets[e][1]
return start_index, end_index
def span_to_answer(self, text: str, start: int, end: int) -> Dict[str, Union[str, int]]:
"""
When decoding from token probabilities, this method maps token indexes to actual word in the initial context.
Args:
text (`str`): The actual context to extract the answer from.
start (`int`): The answer starting token index.
end (`int`): The answer end token index.
Returns:
Dictionary like `{'answer': str, 'start': int, 'end': int}`
"""
words = []
token_idx = char_start_idx = char_end_idx = chars_idx = 0
for i, word in enumerate(text.split(" ")):
token = self.tokenizer.tokenize(word)
# Append words if they are in the span
if start <= token_idx <= end:
if token_idx == start:
char_start_idx = chars_idx
if token_idx == end:
char_end_idx = chars_idx + len(word)
words += [word]
# Stop if we went over the end of the answer
if token_idx > end:
break
# Append the subtokenization length to the running index
token_idx += len(token)
chars_idx += len(word) + 1
# Join text with spaces
return {
"answer": " ".join(words),
"start": max(0, char_start_idx),
"end": min(len(text), char_end_idx),
}
| 29,474 | 43.323308 | 124 | py |
transformers | transformers-main/src/transformers/pipelines/document_question_answering.py | # Copyright 2022 The Impira Team and the HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from typing import List, Optional, Tuple, Union
import numpy as np
from ..utils import (
ExplicitEnum,
add_end_docstrings,
is_pytesseract_available,
is_torch_available,
is_vision_available,
logging,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
from .question_answering import select_starts_ends
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
TESSERACT_LOADED = False
if is_pytesseract_available():
TESSERACT_LOADED = True
import pytesseract
logger = logging.get_logger(__name__)
# normalize_bbox() and apply_tesseract() are derived from apply_tesseract in models/layoutlmv3/feature_extraction_layoutlmv3.py.
# However, because the pipeline may evolve from what layoutlmv3 currently does, it's copied (vs. imported) to avoid creating an
# unnecessary dependency.
def normalize_box(box, width, height):
return [
int(1000 * (box[0] / width)),
int(1000 * (box[1] / height)),
int(1000 * (box[2] / width)),
int(1000 * (box[3] / height)),
]
def apply_tesseract(image: "Image.Image", lang: Optional[str], tesseract_config: Optional[str]):
"""Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes."""
# apply OCR
data = pytesseract.image_to_data(image, lang=lang, output_type="dict", config=tesseract_config)
words, left, top, width, height = data["text"], data["left"], data["top"], data["width"], data["height"]
# filter empty words and corresponding coordinates
irrelevant_indices = [idx for idx, word in enumerate(words) if not word.strip()]
words = [word for idx, word in enumerate(words) if idx not in irrelevant_indices]
left = [coord for idx, coord in enumerate(left) if idx not in irrelevant_indices]
top = [coord for idx, coord in enumerate(top) if idx not in irrelevant_indices]
width = [coord for idx, coord in enumerate(width) if idx not in irrelevant_indices]
height = [coord for idx, coord in enumerate(height) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
actual_boxes = []
for x, y, w, h in zip(left, top, width, height):
actual_box = [x, y, x + w, y + h]
actual_boxes.append(actual_box)
image_width, image_height = image.size
# finally, normalize the bounding boxes
normalized_boxes = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(box, image_width, image_height))
if len(words) != len(normalized_boxes):
raise ValueError("Not as many words as there are bounding boxes")
return words, normalized_boxes
class ModelType(ExplicitEnum):
LayoutLM = "layoutlm"
LayoutLMv2andv3 = "layoutlmv2andv3"
VisionEncoderDecoder = "vision_encoder_decoder"
@add_end_docstrings(PIPELINE_INIT_ARGS)
class DocumentQuestionAnsweringPipeline(ChunkPipeline):
# TODO: Update task_summary docs to include an example with document QA and then update the first sentence
"""
Document Question Answering pipeline using any `AutoModelForDocumentQuestionAnswering`. The inputs/outputs are
similar to the (extractive) question answering pipeline; however, the pipeline takes an image (and optional OCR'd
words/boxes) as input instead of text context.
Example:
```python
>>> from transformers import pipeline
>>> document_qa = pipeline(model="impira/layoutlm-document-qa")
>>> document_qa(
... image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png",
... question="What is the invoice number?",
... )
[{'score': 0.425, 'answer': 'us-001', 'start': 16, 'end': 16}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This document question answering pipeline can currently be loaded from [`pipeline`] using the following task
identifier: `"document-question-answering"`.
The models that this pipeline can use are models that have been fine-tuned on a document question answering task.
See the up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=document-question-answering).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.tokenizer is not None and not self.tokenizer.__class__.__name__.endswith("Fast"):
raise ValueError(
"`DocumentQuestionAnsweringPipeline` requires a fast tokenizer, but a slow tokenizer "
f"(`{self.tokenizer.__class__.__name__}`) is provided."
)
if self.model.config.__class__.__name__ == "VisionEncoderDecoderConfig":
self.model_type = ModelType.VisionEncoderDecoder
if self.model.config.encoder.model_type != "donut-swin":
raise ValueError("Currently, the only supported VisionEncoderDecoder model is Donut")
else:
self.check_model_type(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING)
if self.model.config.__class__.__name__ == "LayoutLMConfig":
self.model_type = ModelType.LayoutLM
else:
self.model_type = ModelType.LayoutLMv2andv3
def _sanitize_parameters(
self,
padding=None,
doc_stride=None,
max_question_len=None,
lang: Optional[str] = None,
tesseract_config: Optional[str] = None,
max_answer_len=None,
max_seq_len=None,
top_k=None,
handle_impossible_answer=None,
**kwargs,
):
preprocess_params, postprocess_params = {}, {}
if padding is not None:
preprocess_params["padding"] = padding
if doc_stride is not None:
preprocess_params["doc_stride"] = doc_stride
if max_question_len is not None:
preprocess_params["max_question_len"] = max_question_len
if max_seq_len is not None:
preprocess_params["max_seq_len"] = max_seq_len
if lang is not None:
preprocess_params["lang"] = lang
if tesseract_config is not None:
preprocess_params["tesseract_config"] = tesseract_config
if top_k is not None:
if top_k < 1:
raise ValueError(f"top_k parameter should be >= 1 (got {top_k})")
postprocess_params["top_k"] = top_k
if max_answer_len is not None:
if max_answer_len < 1:
raise ValueError(f"max_answer_len parameter should be >= 1 (got {max_answer_len}")
postprocess_params["max_answer_len"] = max_answer_len
if handle_impossible_answer is not None:
postprocess_params["handle_impossible_answer"] = handle_impossible_answer
return preprocess_params, {}, postprocess_params
def __call__(
self,
image: Union["Image.Image", str],
question: Optional[str] = None,
word_boxes: Tuple[str, List[float]] = None,
**kwargs,
):
"""
Answer the question(s) given as inputs by using the document(s). A document is defined as an image and an
optional list of (word, box) tuples which represent the text in the document. If the `word_boxes` are not
provided, it will use the Tesseract OCR engine (if available) to extract the words and boxes automatically for
LayoutLM-like models which require them as input. For Donut, no OCR is run.
You can invoke the pipeline several ways:
- `pipeline(image=image, question=question)`
- `pipeline(image=image, question=question, word_boxes=word_boxes)`
- `pipeline([{"image": image, "question": question}])`
- `pipeline([{"image": image, "question": question, "word_boxes": word_boxes}])`
Args:
image (`str` or `PIL.Image`):
The pipeline handles three types of images:
- A string containing a http link pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
The pipeline accepts either a single image or a batch of images. If given a single image, it can be
broadcasted to multiple questions.
question (`str`):
A question to ask of the document.
word_boxes (`List[str, Tuple[float, float, float, float]]`, *optional*):
A list of words and bounding boxes (normalized 0->1000). If you provide this optional input, then the
pipeline will use these words and boxes instead of running OCR on the image to derive them for models
that need them (e.g. LayoutLM). This allows you to reuse OCR'd results across many invocations of the
pipeline without having to re-run it each time.
top_k (`int`, *optional*, defaults to 1):
The number of answers to return (will be chosen by order of likelihood). Note that we return less than
top_k answers if there are not enough options available within the context.
doc_stride (`int`, *optional*, defaults to 128):
If the words in the document are too long to fit with the question for the model, it will be split in
several chunks with some overlap. This argument controls the size of that overlap.
max_answer_len (`int`, *optional*, defaults to 15):
The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
max_seq_len (`int`, *optional*, defaults to 384):
The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
model. The context will be split in several chunks (using `doc_stride` as overlap) if needed.
max_question_len (`int`, *optional*, defaults to 64):
The maximum length of the question after tokenization. It will be truncated if needed.
handle_impossible_answer (`bool`, *optional*, defaults to `False`):
Whether or not we accept impossible as an answer.
lang (`str`, *optional*):
Language to use while running OCR. Defaults to english.
tesseract_config (`str`, *optional*):
Additional flags to pass to tesseract while running OCR.
Return:
A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys:
- **score** (`float`) -- The probability associated to the answer.
- **start** (`int`) -- The start word index of the answer (in the OCR'd version of the input or provided
`word_boxes`).
- **end** (`int`) -- The end word index of the answer (in the OCR'd version of the input or provided
`word_boxes`).
- **answer** (`str`) -- The answer to the question.
- **words** (`list[int]`) -- The index of each word/box pair that is in the answer
"""
if isinstance(question, str):
inputs = {"question": question, "image": image}
if word_boxes is not None:
inputs["word_boxes"] = word_boxes
else:
inputs = image
return super().__call__(inputs, **kwargs)
def preprocess(
self,
input,
padding="do_not_pad",
doc_stride=None,
max_seq_len=None,
word_boxes: Tuple[str, List[float]] = None,
lang=None,
tesseract_config="",
):
# NOTE: This code mirrors the code in question answering and will be implemented in a follow up PR
# to support documents with enough tokens that overflow the model's window
if max_seq_len is None:
max_seq_len = self.tokenizer.model_max_length
if doc_stride is None:
doc_stride = min(max_seq_len // 2, 256)
image = None
image_features = {}
if input.get("image", None) is not None:
image = load_image(input["image"])
if self.image_processor is not None:
image_features.update(self.image_processor(images=image, return_tensors=self.framework))
elif self.feature_extractor is not None:
image_features.update(self.feature_extractor(images=image, return_tensors=self.framework))
elif self.model_type == ModelType.VisionEncoderDecoder:
raise ValueError("If you are using a VisionEncoderDecoderModel, you must provide a feature extractor")
words, boxes = None, None
if not self.model_type == ModelType.VisionEncoderDecoder:
if "word_boxes" in input:
words = [x[0] for x in input["word_boxes"]]
boxes = [x[1] for x in input["word_boxes"]]
elif "words" in image_features and "boxes" in image_features:
words = image_features.pop("words")[0]
boxes = image_features.pop("boxes")[0]
elif image is not None:
if not TESSERACT_LOADED:
raise ValueError(
"If you provide an image without word_boxes, then the pipeline will run OCR using Tesseract,"
" but pytesseract is not available"
)
if TESSERACT_LOADED:
words, boxes = apply_tesseract(image, lang=lang, tesseract_config=tesseract_config)
else:
raise ValueError(
"You must provide an image or word_boxes. If you provide an image, the pipeline will automatically"
" run OCR to derive words and boxes"
)
if self.tokenizer.padding_side != "right":
raise ValueError(
"Document question answering only supports tokenizers whose padding side is 'right', not"
f" {self.tokenizer.padding_side}"
)
if self.model_type == ModelType.VisionEncoderDecoder:
task_prompt = f'<s_docvqa><s_question>{input["question"]}</s_question><s_answer>'
# Adapted from https://huggingface.co/spaces/nielsr/donut-docvqa/blob/main/app.py
encoding = {
"inputs": image_features["pixel_values"],
"decoder_input_ids": self.tokenizer(
task_prompt, add_special_tokens=False, return_tensors=self.framework
).input_ids,
"return_dict_in_generate": True,
}
yield {
**encoding,
"p_mask": None,
"word_ids": None,
"words": None,
"output_attentions": True,
"is_last": True,
}
else:
tokenizer_kwargs = {}
if self.model_type == ModelType.LayoutLM:
tokenizer_kwargs["text"] = input["question"].split()
tokenizer_kwargs["text_pair"] = words
tokenizer_kwargs["is_split_into_words"] = True
else:
tokenizer_kwargs["text"] = [input["question"]]
tokenizer_kwargs["text_pair"] = [words]
tokenizer_kwargs["boxes"] = [boxes]
encoding = self.tokenizer(
padding=padding,
max_length=max_seq_len,
stride=doc_stride,
return_token_type_ids=True,
truncation="only_second",
return_overflowing_tokens=True,
**tokenizer_kwargs,
)
# TODO: check why slower `LayoutLMTokenizer` and `LayoutLMv2Tokenizer` don't have this key in outputs
# FIXME: ydshieh and/or Narsil
encoding.pop("overflow_to_sample_mapping", None) # We do not use this
num_spans = len(encoding["input_ids"])
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
# We put 0 on the tokens from the context and 1 everywhere else (question and special tokens)
# This logic mirrors the logic in the question_answering pipeline
p_mask = [[tok != 1 for tok in encoding.sequence_ids(span_id)] for span_id in range(num_spans)]
for span_idx in range(num_spans):
if self.framework == "pt":
span_encoding = {k: torch.tensor(v[span_idx : span_idx + 1]) for (k, v) in encoding.items()}
if "pixel_values" in image_features:
span_encoding["image"] = image_features["pixel_values"]
else:
raise ValueError("Unsupported: Tensorflow preprocessing for DocumentQuestionAnsweringPipeline")
input_ids_span_idx = encoding["input_ids"][span_idx]
# keep the cls_token unmasked (some models use it to indicate unanswerable questions)
if self.tokenizer.cls_token_id is not None:
cls_indices = np.nonzero(np.array(input_ids_span_idx) == self.tokenizer.cls_token_id)[0]
for cls_index in cls_indices:
p_mask[span_idx][cls_index] = 0
# For each span, place a bounding box [0,0,0,0] for question and CLS tokens, [1000,1000,1000,1000]
# for SEP tokens, and the word's bounding box for words in the original document.
if "boxes" not in tokenizer_kwargs:
bbox = []
for input_id, sequence_id, word_id in zip(
encoding.input_ids[span_idx],
encoding.sequence_ids(span_idx),
encoding.word_ids(span_idx),
):
if sequence_id == 1:
bbox.append(boxes[word_id])
elif input_id == self.tokenizer.sep_token_id:
bbox.append([1000] * 4)
else:
bbox.append([0] * 4)
if self.framework == "pt":
span_encoding["bbox"] = torch.tensor(bbox).unsqueeze(0)
elif self.framework == "tf":
raise ValueError("Unsupported: Tensorflow preprocessing for DocumentQuestionAnsweringPipeline")
yield {
**span_encoding,
"p_mask": p_mask[span_idx],
"word_ids": encoding.word_ids(span_idx),
"words": words,
"is_last": span_idx == num_spans - 1,
}
def _forward(self, model_inputs):
p_mask = model_inputs.pop("p_mask", None)
word_ids = model_inputs.pop("word_ids", None)
words = model_inputs.pop("words", None)
is_last = model_inputs.pop("is_last", False)
if self.model_type == ModelType.VisionEncoderDecoder:
model_outputs = self.model.generate(**model_inputs)
else:
model_outputs = self.model(**model_inputs)
model_outputs = dict(model_outputs.items())
model_outputs["p_mask"] = p_mask
model_outputs["word_ids"] = word_ids
model_outputs["words"] = words
model_outputs["attention_mask"] = model_inputs.get("attention_mask", None)
model_outputs["is_last"] = is_last
return model_outputs
def postprocess(self, model_outputs, top_k=1, **kwargs):
if self.model_type == ModelType.VisionEncoderDecoder:
answers = [self.postprocess_encoder_decoder_single(o) for o in model_outputs]
else:
answers = self.postprocess_extractive_qa(model_outputs, top_k=top_k, **kwargs)
answers = sorted(answers, key=lambda x: x.get("score", 0), reverse=True)[:top_k]
return answers
def postprocess_encoder_decoder_single(self, model_outputs, **kwargs):
sequence = self.tokenizer.batch_decode(model_outputs["sequences"])[0]
# TODO: A lot of this logic is specific to Donut and should probably be handled in the tokenizer
# (see https://github.com/huggingface/transformers/pull/18414/files#r961747408 for more context).
sequence = sequence.replace(self.tokenizer.eos_token, "").replace(self.tokenizer.pad_token, "")
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token
ret = {
"answer": None,
}
answer = re.search(r"<s_answer>(.*)</s_answer>", sequence)
if answer is not None:
ret["answer"] = answer.group(1).strip()
return ret
def postprocess_extractive_qa(
self, model_outputs, top_k=1, handle_impossible_answer=False, max_answer_len=15, **kwargs
):
min_null_score = 1000000 # large and positive
answers = []
for output in model_outputs:
words = output["words"]
starts, ends, scores, min_null_score = select_starts_ends(
start=output["start_logits"],
end=output["end_logits"],
p_mask=output["p_mask"],
attention_mask=output["attention_mask"].numpy()
if output.get("attention_mask", None) is not None
else None,
min_null_score=min_null_score,
top_k=top_k,
handle_impossible_answer=handle_impossible_answer,
max_answer_len=max_answer_len,
)
word_ids = output["word_ids"]
for start, end, score in zip(starts, ends, scores):
word_start, word_end = word_ids[start], word_ids[end]
if word_start is not None and word_end is not None:
answers.append(
{
"score": float(score),
"answer": " ".join(words[word_start : word_end + 1]),
"start": word_start,
"end": word_end,
}
)
if handle_impossible_answer:
answers.append({"score": min_null_score, "answer": "", "start": 0, "end": 0})
return answers
| 23,079 | 45.532258 | 128 | py |
transformers | transformers-main/src/transformers/pipelines/audio_classification.py | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import subprocess
from typing import Union
import numpy as np
import requests
from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
logger = logging.get_logger(__name__)
def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
"""
Helper function to read an audio file through ffmpeg.
"""
ar = f"{sampling_rate}"
ac = "1"
format_for_conversion = "f32le"
ffmpeg_command = [
"ffmpeg",
"-i",
"pipe:0",
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
try:
ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
except FileNotFoundError:
raise ValueError("ffmpeg was not found but is required to load audio files from filename")
output_stream = ffmpeg_process.communicate(bpayload)
out_bytes = output_stream[0]
audio = np.frombuffer(out_bytes, np.float32)
if audio.shape[0] == 0:
raise ValueError("Malformed soundfile")
return audio
@add_end_docstrings(PIPELINE_INIT_ARGS)
class AudioClassificationPipeline(Pipeline):
"""
Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a
raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio
formats.
Example:
```python
>>> from transformers import pipeline
>>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks")
>>> classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
[{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"audio-classification"`.
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=audio-classification).
"""
def __init__(self, *args, **kwargs):
# Default, might be overriden by the model.config.
kwargs["top_k"] = 5
super().__init__(*args, **kwargs)
if self.framework != "pt":
raise ValueError(f"The {self.__class__} is only available in PyTorch.")
self.check_model_type(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING)
def __call__(
self,
inputs: Union[np.ndarray, bytes, str],
**kwargs,
):
"""
Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more
information.
Args:
inputs (`np.ndarray` or `bytes` or `str` or `dict`):
The inputs is either :
- `str` that is the filename of the audio file, the file will be read at the correct sampling rate
to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system.
- `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the
same way.
- (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
Raw audio at the correct sampling rate (no further check will be done)
- `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this
pipeline do the resampling. The dict must be either be in the format `{"sampling_rate": int,
"raw": np.array}`, or `{"sampling_rate": int, "array": np.array}`, where the key `"raw"` or
`"array"` is used to denote the raw audio waveform.
top_k (`int`, *optional*, defaults to None):
The number of top labels that will be returned by the pipeline. If the provided number is `None` or
higher than the number of labels available in the model configuration, it will default to the number of
labels.
Return:
A list of `dict` with the following keys:
- **label** (`str`) -- The label predicted.
- **score** (`float`) -- The corresponding probability.
"""
return super().__call__(inputs, **kwargs)
def _sanitize_parameters(self, top_k=None, **kwargs):
# No parameters on this pipeline right now
postprocess_params = {}
if top_k is not None:
if top_k > self.model.config.num_labels:
top_k = self.model.config.num_labels
postprocess_params["top_k"] = top_k
return {}, {}, postprocess_params
def preprocess(self, inputs):
if isinstance(inputs, str):
if inputs.startswith("http://") or inputs.startswith("https://"):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
inputs = requests.get(inputs).content
else:
with open(inputs, "rb") as f:
inputs = f.read()
if isinstance(inputs, bytes):
inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
if isinstance(inputs, dict):
# Accepting `"array"` which is the key defined in `datasets` for
# better integration
if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
raise ValueError(
"When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
'"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
"containing the sampling_rate associated with that array"
)
_inputs = inputs.pop("raw", None)
if _inputs is None:
# Remove path which will not be used from `datasets`.
inputs.pop("path", None)
_inputs = inputs.pop("array", None)
in_sampling_rate = inputs.pop("sampling_rate")
inputs = _inputs
if in_sampling_rate != self.feature_extractor.sampling_rate:
import torch
if is_torchaudio_available():
from torchaudio import functional as F
else:
raise ImportError(
"torchaudio is required to resample audio samples in AudioClassificationPipeline. "
"The torchaudio package can be installed through: `pip install torchaudio`."
)
inputs = F.resample(
torch.from_numpy(inputs), in_sampling_rate, self.feature_extractor.sampling_rate
).numpy()
if not isinstance(inputs, np.ndarray):
raise ValueError("We expect a numpy ndarray as input")
if len(inputs.shape) != 1:
raise ValueError("We expect a single channel audio input for AudioClassificationPipeline")
processed = self.feature_extractor(
inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
)
return processed
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs, top_k=5):
probs = model_outputs.logits[0].softmax(-1)
scores, ids = probs.topk(top_k)
scores = scores.tolist()
ids = ids.tolist()
labels = [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
return labels
| 8,769 | 39.601852 | 177 | py |
transformers | transformers-main/src/transformers/pipelines/text2text_generation.py | import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
logger = logging.get_logger(__name__)
class ReturnType(enum.Enum):
TENSORS = 0
TEXT = 1
@add_end_docstrings(PIPELINE_INIT_ARGS)
class Text2TextGenerationPipeline(Pipeline):
"""
Pipeline for text to text generation using seq2seq models.
Example:
```python
>>> from transformers import pipeline
>>> generator = pipeline(model="mrm8488/t5-base-finetuned-question-generation-ap")
>>> generator(
... "answer: Manuel context: Manuel has created RuPERTa-base with the support of HF-Transformers and Google"
... )
[{'generated_text': 'question: Who created the RuPERTa-base?'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This Text2TextGenerationPipeline pipeline can currently be loaded from [`pipeline`] using the following task
identifier: `"text2text-generation"`.
The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=text2text-generation). For a list of available
parameters, see the [following
documentation](https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.generation.GenerationMixin.generate)
Usage:
```python
text2text_generator = pipeline("text2text-generation")
text2text_generator("question: What is 42 ? context: 42 is the answer to life, the universe and everything")
```"""
# Used in the return key of the pipeline.
return_name = "generated"
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == "tf"
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
)
def _sanitize_parameters(
self,
return_tensors=None,
return_text=None,
return_type=None,
clean_up_tokenization_spaces=None,
truncation=None,
stop_sequence=None,
**generate_kwargs,
):
preprocess_params = {}
if truncation is not None:
preprocess_params["truncation"] = truncation
forward_params = generate_kwargs
postprocess_params = {}
if return_tensors is not None and return_type is None:
return_type = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
postprocess_params["return_type"] = return_type
if clean_up_tokenization_spaces is not None:
postprocess_params["clean_up_tokenization_spaces"] = clean_up_tokenization_spaces
if stop_sequence is not None:
stop_sequence_ids = self.tokenizer.encode(stop_sequence, add_special_tokens=False)
if len(stop_sequence_ids) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim."
)
generate_kwargs["eos_token_id"] = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def check_inputs(self, input_length: int, min_length: int, max_length: int):
"""
Checks whether there might be something wrong with given input with regard to the model.
"""
return True
def _parse_and_tokenize(self, *args, truncation):
prefix = self.model.config.prefix if self.model.config.prefix is not None else ""
if isinstance(args[0], list):
if self.tokenizer.pad_token_id is None:
raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input")
args = ([prefix + arg for arg in args[0]],)
padding = True
elif isinstance(args[0], str):
args = (prefix + args[0],)
padding = False
else:
raise ValueError(
f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"
)
inputs = self.tokenizer(*args, padding=padding, truncation=truncation, return_tensors=self.framework)
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__(self, *args, **kwargs):
r"""
Generate the output text(s) using text(s) given as inputs.
Args:
args (`str` or `List[str]`):
Input text for the encoder.
return_tensors (`bool`, *optional*, defaults to `False`):
Whether or not to include the tensors of predictions (as token indices) in the outputs.
return_text (`bool`, *optional*, defaults to `True`):
Whether or not to include the decoded texts in the outputs.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to clean up the potential extra spaces in the text output.
truncation (`TruncationStrategy`, *optional*, defaults to `TruncationStrategy.DO_NOT_TRUNCATE`):
The truncation strategy for the tokenization within the pipeline. `TruncationStrategy.DO_NOT_TRUNCATE`
(default) will never truncate, but it is sometimes desirable to truncate the input to fit the model's
max_length instead of throwing an error down the line.
generate_kwargs:
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework [here](./model#generative-models)).
Return:
A list or a list of list of `dict`: Each result comes as a dictionary with the following keys:
- **generated_text** (`str`, present when `return_text=True`) -- The generated text.
- **generated_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token
ids of the generated text.
"""
result = super().__call__(*args, **kwargs)
if (
isinstance(args[0], list)
and all(isinstance(el, str) for el in args[0])
and all(len(res) == 1 for res in result)
):
return [res[0] for res in result]
return result
def preprocess(self, inputs, truncation=TruncationStrategy.DO_NOT_TRUNCATE, **kwargs):
inputs = self._parse_and_tokenize(inputs, truncation=truncation, **kwargs)
return inputs
def _forward(self, model_inputs, **generate_kwargs):
if self.framework == "pt":
in_b, input_length = model_inputs["input_ids"].shape
elif self.framework == "tf":
in_b, input_length = tf.shape(model_inputs["input_ids"]).numpy()
generate_kwargs["min_length"] = generate_kwargs.get("min_length", self.model.config.min_length)
generate_kwargs["max_length"] = generate_kwargs.get("max_length", self.model.config.max_length)
self.check_inputs(input_length, generate_kwargs["min_length"], generate_kwargs["max_length"])
output_ids = self.model.generate(**model_inputs, **generate_kwargs)
out_b = output_ids.shape[0]
if self.framework == "pt":
output_ids = output_ids.reshape(in_b, out_b // in_b, *output_ids.shape[1:])
elif self.framework == "tf":
output_ids = tf.reshape(output_ids, (in_b, out_b // in_b, *output_ids.shape[1:]))
return {"output_ids": output_ids}
def postprocess(self, model_outputs, return_type=ReturnType.TEXT, clean_up_tokenization_spaces=False):
records = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
record = {f"{self.return_name}_token_ids": output_ids}
elif return_type == ReturnType.TEXT:
record = {
f"{self.return_name}_text": self.tokenizer.decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
)
}
records.append(record)
return records
@add_end_docstrings(PIPELINE_INIT_ARGS)
class SummarizationPipeline(Text2TextGenerationPipeline):
"""
Summarize news articles and other documents.
This summarizing pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"summarization"`.
The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is
currently, '*bart-large-cnn*', '*t5-small*', '*t5-base*', '*t5-large*', '*t5-3b*', '*t5-11b*'. See the up-to-date
list of available models on [huggingface.co/models](https://huggingface.co/models?filter=summarization). For a list
of available parameters, see the [following
documentation](https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.generation.GenerationMixin.generate)
Usage:
```python
# use bart in pytorch
summarizer = pipeline("summarization")
summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)
# use t5 in tf
summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)
```"""
# Used in the return key of the pipeline.
return_name = "summary"
def __call__(self, *args, **kwargs):
r"""
Summarize the text(s) given as inputs.
Args:
documents (*str* or `List[str]`):
One or several articles (or one list of articles) to summarize.
return_text (`bool`, *optional*, defaults to `True`):
Whether or not to include the decoded texts in the outputs
return_tensors (`bool`, *optional*, defaults to `False`):
Whether or not to include the tensors of predictions (as token indices) in the outputs.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs:
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework [here](./model#generative-models)).
Return:
A list or a list of list of `dict`: Each result comes as a dictionary with the following keys:
- **summary_text** (`str`, present when `return_text=True`) -- The summary of the corresponding input.
- **summary_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token
ids of the summary.
"""
return super().__call__(*args, **kwargs)
def check_inputs(self, input_length: int, min_length: int, max_length: int) -> bool:
"""
Checks whether there might be something wrong with given input with regard to the model.
"""
if max_length < min_length:
logger.warning(f"Your min_length={min_length} must be inferior than your max_length={max_length}.")
if input_length < max_length:
logger.warning(
f"Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is "
"a summarization task, where outputs shorter than the input are typically wanted, you might "
f"consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"
)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class TranslationPipeline(Text2TextGenerationPipeline):
"""
Translates from one language to another.
This translation pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"translation_xx_to_yy"`.
The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=translation).
For a list of available parameters, see the [following
documentation](https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.generation.GenerationMixin.generate)
Usage:
```python
en_fr_translator = pipeline("translation_en_to_fr")
en_fr_translator("How old are you?")
```"""
# Used in the return key of the pipeline.
return_name = "translation"
def check_inputs(self, input_length: int, min_length: int, max_length: int):
if input_length > 0.9 * max_length:
logger.warning(
f"Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider "
"increasing your max_length manually, e.g. translator('...', max_length=400)"
)
return True
def preprocess(self, *args, truncation=TruncationStrategy.DO_NOT_TRUNCATE, src_lang=None, tgt_lang=None):
if getattr(self.tokenizer, "_build_translation_inputs", None):
return self.tokenizer._build_translation_inputs(
*args, return_tensors=self.framework, truncation=truncation, src_lang=src_lang, tgt_lang=tgt_lang
)
else:
return super()._parse_and_tokenize(*args, truncation=truncation)
def _sanitize_parameters(self, src_lang=None, tgt_lang=None, **kwargs):
preprocess_params, forward_params, postprocess_params = super()._sanitize_parameters(**kwargs)
if src_lang is not None:
preprocess_params["src_lang"] = src_lang
if tgt_lang is not None:
preprocess_params["tgt_lang"] = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
task = kwargs.get("task", self.task)
items = task.split("_")
if task and len(items) == 4:
# translation, XX, to YY
preprocess_params["src_lang"] = items[1]
preprocess_params["tgt_lang"] = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__(self, *args, **kwargs):
r"""
Translate the text(s) given as inputs.
Args:
args (`str` or `List[str]`):
Texts to be translated.
return_tensors (`bool`, *optional*, defaults to `False`):
Whether or not to include the tensors of predictions (as token indices) in the outputs.
return_text (`bool`, *optional*, defaults to `True`):
Whether or not to include the decoded texts in the outputs.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to clean up the potential extra spaces in the text output.
src_lang (`str`, *optional*):
The language of the input. Might be required for multilingual models. Will not have any effect for
single pair translation models
tgt_lang (`str`, *optional*):
The language of the desired output. Might be required for multilingual models. Will not have any effect
for single pair translation models
generate_kwargs:
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework [here](./model#generative-models)).
Return:
A list or a list of list of `dict`: Each result comes as a dictionary with the following keys:
- **translation_text** (`str`, present when `return_text=True`) -- The translation.
- **translation_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The
token ids of the translation.
"""
return super().__call__(*args, **kwargs)
| 16,875 | 44.858696 | 141 | py |
transformers | transformers-main/src/transformers/pipelines/conversational.py | import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class Conversation:
"""
Utility class containing a conversation and its history. This class is meant to be used as an input to the
[`ConversationalPipeline`]. The conversation contains several utility functions to manage the addition of new user
inputs and generated model responses. A conversation needs to contain an unprocessed user input before being passed
to the [`ConversationalPipeline`]. This user input is either created when the class is instantiated, or by calling
`conversational_pipeline.append_response("input")` after a conversation turn.
Arguments:
text (`str`, *optional*):
The initial user input to start the conversation. If not provided, a user input needs to be provided
manually using the [`~Conversation.add_user_input`] method before the conversation can begin.
conversation_id (`uuid.UUID`, *optional*):
Unique identifier for the conversation. If not provided, a random UUID4 id will be assigned to the
conversation.
past_user_inputs (`List[str]`, *optional*):
Eventual past history of the conversation of the user. You don't need to pass it manually if you use the
pipeline interactively but if you want to recreate history you need to set both `past_user_inputs` and
`generated_responses` with equal length lists of strings
generated_responses (`List[str]`, *optional*):
Eventual past history of the conversation of the model. You don't need to pass it manually if you use the
pipeline interactively but if you want to recreate history you need to set both `past_user_inputs` and
`generated_responses` with equal length lists of strings
Usage:
```python
conversation = Conversation("Going to the movies tonight - any suggestions?")
# Steps usually performed by the model when generating a response:
# 1. Mark the user input as processed (moved to the history)
conversation.mark_processed()
# 2. Append a mode response
conversation.append_response("The Big lebowski.")
conversation.add_user_input("Is it good?")
```"""
def __init__(
self, text: str = None, conversation_id: uuid.UUID = None, past_user_inputs=None, generated_responses=None
):
if not conversation_id:
conversation_id = uuid.uuid4()
if past_user_inputs is None:
past_user_inputs = []
if generated_responses is None:
generated_responses = []
self.uuid: uuid.UUID = conversation_id
self.past_user_inputs: List[str] = past_user_inputs
self.generated_responses: List[str] = generated_responses
self.new_user_input: Optional[str] = text
def __eq__(self, other):
if not isinstance(other, Conversation):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def add_user_input(self, text: str, overwrite: bool = False):
"""
Add a user input to the conversation for the next round. This populates the internal `new_user_input` field.
Args:
text (`str`): The user input for the next conversation round.
overwrite (`bool`, *optional*, defaults to `False`):
Whether or not existing and unprocessed user input should be overwritten when this function is called.
"""
if self.new_user_input:
if overwrite:
logger.warning(
f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
f'with: "{text}".'
)
self.new_user_input = text
else:
logger.warning(
f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'
)
else:
self.new_user_input = text
def mark_processed(self):
"""
Mark the conversation as processed (moves the content of `new_user_input` to `past_user_inputs`) and empties
the `new_user_input` field.
"""
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input)
self.new_user_input = None
def append_response(self, response: str):
"""
Append a response to the list of generated responses.
Args:
response (`str`): The model generated response.
"""
self.generated_responses.append(response)
def iter_texts(self):
"""
Iterates over all blobs of the conversation.
Returns: Iterator of (is_user, text_chunk) in chronological order of the conversation. `is_user` is a `bool`,
`text_chunks` is a `str`.
"""
for user_input, generated_response in zip(self.past_user_inputs, self.generated_responses):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__(self):
"""
Generates a string representation of the conversation.
Return:
`str`:
Example: Conversation id: 7d15686b-dc94-49f2-9c4b-c9eac6a1f114 user >> Going to the movies tonight - any
suggestions? bot >> The Big Lebowski
"""
output = f"Conversation id: {self.uuid} \n"
for is_user, text in self.iter_texts():
name = "user" if is_user else "bot"
output += f"{name} >> {text} \n"
return output
@add_end_docstrings(
PIPELINE_INIT_ARGS,
r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""",
)
class ConversationalPipeline(Pipeline):
"""
Multi-turn conversational pipeline.
Example:
```python
>>> from transformers import pipeline, Conversation
>>> chatbot = pipeline(model="microsoft/DialoGPT-medium")
>>> conversation = Conversation("Going to the movies tonight - any suggestions?")
>>> conversation = chatbot(conversation)
>>> conversation.generated_responses[-1]
'The Big Lebowski'
>>> conversation.add_user_input("Is it an action movie?")
>>> conversation = chatbot(conversation)
>>> conversation.generated_responses[-1]
"It's a comedy."
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This conversational pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"conversational"`.
The models that this pipeline can use are models that have been fine-tuned on a multi-turn conversational task,
currently: *'microsoft/DialoGPT-small'*, *'microsoft/DialoGPT-medium'*, *'microsoft/DialoGPT-large'*. See the
up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=conversational).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.tokenizer.pad_token_id is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
def _sanitize_parameters(
self, min_length_for_response=None, minimum_tokens=None, clean_up_tokenization_spaces=None, **generate_kwargs
):
preprocess_params = {}
forward_params = {}
postprocess_params = {}
if min_length_for_response is not None:
preprocess_params["min_length_for_response"] = min_length_for_response
if minimum_tokens is not None:
forward_params["minimum_tokens"] = minimum_tokens
if "max_length" in generate_kwargs:
forward_params["max_length"] = generate_kwargs["max_length"]
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
postprocess_params["clean_up_tokenization_spaces"] = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(generate_kwargs)
return preprocess_params, forward_params, postprocess_params
def __call__(self, conversations: Union[Conversation, List[Conversation]], num_workers=0, **kwargs):
r"""
Generate responses for the conversation(s) given as inputs.
Args:
conversations (a [`Conversation`] or a list of [`Conversation`]):
Conversations to generate responses for.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs:
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework [here](./model#generative-models)).
Returns:
[`Conversation`] or a list of [`Conversation`]: Conversation(s) with updated generated responses for those
containing a new user input.
"""
# XXX: num_workers==0 is required to be backward compatible
# Otherwise the threads will require a Conversation copy.
# This will definitely hinder performance on GPU, but has to be opted
# in because of this BC change.
outputs = super().__call__(conversations, num_workers=num_workers, **kwargs)
if isinstance(outputs, list) and len(outputs) == 1:
return outputs[0]
return outputs
def preprocess(self, conversation: Conversation, min_length_for_response=32) -> Dict[str, Any]:
if not isinstance(conversation, Conversation):
raise ValueError("ConversationalPipeline, expects Conversation as inputs")
if conversation.new_user_input is None:
raise ValueError(
f"Conversation with UUID {type(conversation.uuid)} does not contain new user input to process. "
"Add user inputs with the conversation's `add_user_input` method"
)
if hasattr(self.tokenizer, "_build_conversation_input_ids"):
input_ids = self.tokenizer._build_conversation_input_ids(conversation)
else:
# If the tokenizer cannot handle conversations, we default to only the old version
input_ids = self._legacy_parse_and_tokenize(conversation)
if self.framework == "pt":
input_ids = torch.LongTensor([input_ids])
elif self.framework == "tf":
input_ids = tf.constant([input_ids])
return {"input_ids": input_ids, "conversation": conversation}
def _forward(self, model_inputs, minimum_tokens=10, **generate_kwargs):
max_length = generate_kwargs.get("max_length", self.model.config.max_length)
n = model_inputs["input_ids"].shape[1]
if max_length - minimum_tokens < n:
logger.warning(f"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})")
trim = max_length - minimum_tokens
model_inputs["input_ids"] = model_inputs["input_ids"][:, -trim:]
if "attention_mask" in model_inputs:
model_inputs["attention_mask"] = model_inputs["attention_mask"][:, -trim:]
conversation = model_inputs.pop("conversation")
generate_kwargs["max_length"] = max_length
output_ids = self.model.generate(**model_inputs, **generate_kwargs)
if self.model.config.is_encoder_decoder:
start_position = 1
else:
start_position = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def postprocess(self, model_outputs, clean_up_tokenization_spaces=True):
output_ids = model_outputs["output_ids"]
answer = self.tokenizer.decode(
output_ids[0],
skip_special_tokens=True,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
)
conversation = model_outputs["conversation"]
conversation.mark_processed()
conversation.append_response(answer)
return conversation
def _legacy_parse_and_tokenize(self, conversation: Conversation) -> Dict:
eos_token_id = self.tokenizer.eos_token_id
input_ids = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(text, add_special_tokens=False) + [eos_token_id])
else:
input_ids.extend(self.tokenizer.encode(text, add_special_tokens=False))
if len(input_ids) > self.tokenizer.model_max_length:
input_ids = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 13,594 | 42.713826 | 119 | py |
transformers | transformers-main/src/transformers/pipelines/table_question_answering.py | import collections
import types
import numpy as np
from ..utils import (
add_end_docstrings,
is_tensorflow_probability_available,
is_tf_available,
is_torch_available,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException
if is_torch_available():
import torch
from ..models.auto.modeling_auto import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
)
if is_tf_available() and is_tensorflow_probability_available():
import tensorflow as tf
import tensorflow_probability as tfp
from ..models.auto.modeling_tf_auto import (
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
)
class TableQuestionAnsweringArgumentHandler(ArgumentHandler):
"""
Handles arguments for the TableQuestionAnsweringPipeline
"""
def __call__(self, table=None, query=None, **kwargs):
# Returns tqa_pipeline_inputs of shape:
# [
# {"table": pd.DataFrame, "query": List[str]},
# ...,
# {"table": pd.DataFrame, "query" : List[str]}
# ]
requires_backends(self, "pandas")
import pandas as pd
if table is None:
raise ValueError("Keyword argument `table` cannot be None.")
elif query is None:
if isinstance(table, dict) and table.get("query") is not None and table.get("table") is not None:
tqa_pipeline_inputs = [table]
elif isinstance(table, list) and len(table) > 0:
if not all(isinstance(d, dict) for d in table):
raise ValueError(
f"Keyword argument `table` should be a list of dict, but is {(type(d) for d in table)}"
)
if table[0].get("query") is not None and table[0].get("table") is not None:
tqa_pipeline_inputs = table
else:
raise ValueError(
"If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"
f" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."
)
elif Dataset is not None and isinstance(table, Dataset) or isinstance(table, types.GeneratorType):
return table
else:
raise ValueError(
"Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
f"is {type(table)})"
)
else:
tqa_pipeline_inputs = [{"table": table, "query": query}]
for tqa_pipeline_input in tqa_pipeline_inputs:
if not isinstance(tqa_pipeline_input["table"], pd.DataFrame):
if tqa_pipeline_input["table"] is None:
raise ValueError("Table cannot be None.")
tqa_pipeline_input["table"] = pd.DataFrame(tqa_pipeline_input["table"])
return tqa_pipeline_inputs
@add_end_docstrings(PIPELINE_INIT_ARGS)
class TableQuestionAnsweringPipeline(Pipeline):
"""
Table Question Answering pipeline using a `ModelForTableQuestionAnswering`. This pipeline is only available in
PyTorch.
Example:
```python
>>> from transformers import pipeline
>>> oracle = pipeline(model="google/tapas-base-finetuned-wtq")
>>> table = {
... "Repository": ["Transformers", "Datasets", "Tokenizers"],
... "Stars": ["36542", "4512", "3934"],
... "Contributors": ["651", "77", "34"],
... "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
... }
>>> oracle(query="How many stars does the transformers repository have?", table=table)
{'answer': 'AVERAGE > 36542', 'coordinates': [(0, 1)], 'cells': ['36542'], 'aggregator': 'AVERAGE'}
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This tabular question answering pipeline can currently be loaded from [`pipeline`] using the following task
identifier: `"table-question-answering"`.
The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task.
See the up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=table-question-answering).
"""
default_input_names = "table,query"
def __init__(self, args_parser=TableQuestionAnsweringArgumentHandler(), *args, **kwargs):
super().__init__(*args, **kwargs)
self._args_parser = args_parser
self.check_model_type(
dict(
TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
+ TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()
)
if self.framework == "tf"
else dict(
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()
)
)
self.aggregate = bool(getattr(self.model.config, "aggregation_labels", None)) and bool(
getattr(self.model.config, "num_aggregation_labels", None)
)
self.type = "tapas" if hasattr(self.model.config, "aggregation_labels") else None
def batch_inference(self, **inputs):
return self.model(**inputs)
def sequential_inference(self, **inputs):
"""
Inference used for models that need to process sequences in a sequential fashion, like the SQA models which
handle conversational query related to a table.
"""
if self.framework == "pt":
all_logits = []
all_aggregations = []
prev_answers = None
batch_size = inputs["input_ids"].shape[0]
input_ids = inputs["input_ids"].to(self.device)
attention_mask = inputs["attention_mask"].to(self.device)
token_type_ids = inputs["token_type_ids"].to(self.device)
token_type_ids_example = None
for index in range(batch_size):
# If sequences have already been processed, the token type IDs will be created according to the previous
# answer.
if prev_answers is not None:
prev_labels_example = token_type_ids_example[:, 3] # shape (seq_len,)
model_labels = np.zeros_like(prev_labels_example.cpu().numpy()) # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
for i in range(model_labels.shape[0]):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col_id = token_type_ids_example[:, 1].tolist()[i] - 1
row_id = token_type_ids_example[:, 2].tolist()[i] - 1
if row_id >= 0 and col_id >= 0 and segment_id == 1:
model_labels[i] = int(prev_answers[(col_id, row_id)])
token_type_ids_example[:, 3] = torch.from_numpy(model_labels).type(torch.long).to(self.device)
input_ids_example = input_ids[index]
attention_mask_example = attention_mask[index] # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
outputs = self.model(
input_ids=input_ids_example.unsqueeze(0),
attention_mask=attention_mask_example.unsqueeze(0),
token_type_ids=token_type_ids_example.unsqueeze(0),
)
logits = outputs.logits
if self.aggregate:
all_aggregations.append(outputs.logits_aggregation)
all_logits.append(logits)
dist_per_token = torch.distributions.Bernoulli(logits=logits)
probabilities = dist_per_token.probs * attention_mask_example.type(torch.float32).to(
dist_per_token.probs.device
)
coords_to_probs = collections.defaultdict(list)
for i, p in enumerate(probabilities.squeeze().tolist()):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col = token_type_ids_example[:, 1].tolist()[i] - 1
row = token_type_ids_example[:, 2].tolist()[i] - 1
if col >= 0 and row >= 0 and segment_id == 1:
coords_to_probs[(col, row)].append(p)
prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}
logits_batch = torch.cat(tuple(all_logits), 0)
return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(all_aggregations), 0))
else:
all_logits = []
all_aggregations = []
prev_answers = None
batch_size = inputs["input_ids"].shape[0]
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
token_type_ids = inputs["token_type_ids"].numpy()
token_type_ids_example = None
for index in range(batch_size):
# If sequences have already been processed, the token type IDs will be created according to the previous
# answer.
if prev_answers is not None:
prev_labels_example = token_type_ids_example[:, 3] # shape (seq_len,)
model_labels = np.zeros_like(prev_labels_example, dtype=np.int32) # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
for i in range(model_labels.shape[0]):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col_id = token_type_ids_example[:, 1].tolist()[i] - 1
row_id = token_type_ids_example[:, 2].tolist()[i] - 1
if row_id >= 0 and col_id >= 0 and segment_id == 1:
model_labels[i] = int(prev_answers[(col_id, row_id)])
token_type_ids_example[:, 3] = model_labels
input_ids_example = input_ids[index]
attention_mask_example = attention_mask[index] # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
outputs = self.model(
input_ids=np.expand_dims(input_ids_example, axis=0),
attention_mask=np.expand_dims(attention_mask_example, axis=0),
token_type_ids=np.expand_dims(token_type_ids_example, axis=0),
)
logits = outputs.logits
if self.aggregate:
all_aggregations.append(outputs.logits_aggregation)
all_logits.append(logits)
dist_per_token = tfp.distributions.Bernoulli(logits=logits)
probabilities = dist_per_token.probs_parameter() * tf.cast(attention_mask_example, tf.float32)
coords_to_probs = collections.defaultdict(list)
token_type_ids_example = token_type_ids_example
for i, p in enumerate(tf.squeeze(probabilities).numpy().tolist()):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col = token_type_ids_example[:, 1].tolist()[i] - 1
row = token_type_ids_example[:, 2].tolist()[i] - 1
if col >= 0 and row >= 0 and segment_id == 1:
coords_to_probs[(col, row)].append(p)
prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}
logits_batch = tf.concat(tuple(all_logits), 0)
return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(all_aggregations), 0))
def __call__(self, *args, **kwargs):
r"""
Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below:
- `pipeline(table, query)`
- `pipeline(table, [query])`
- `pipeline(table=table, query=query)`
- `pipeline(table=table, query=[query])`
- `pipeline({"table": table, "query": query})`
- `pipeline({"table": table, "query": [query]})`
- `pipeline([{"table": table, "query": query}, {"table": table, "query": query}])`
The `table` argument should be a dict or a DataFrame built from that dict, containing the whole table:
Example:
```python
data = {
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
}
```
This dictionary can be passed in as such, or can be converted to a pandas DataFrame:
Example:
```python
import pandas as pd
table = pd.DataFrame.from_dict(data)
```
Args:
table (`pd.DataFrame` or `Dict`):
Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values.
See above for an example of dictionary.
query (`str` or `List[str]`):
Query or list of queries that will be sent to the model alongside the table.
sequential (`bool`, *optional*, defaults to `False`):
Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
inference to be done sequentially to extract relations within sequences, given their conversational
nature.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`TapasTruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length`
or to the maximum acceptable input length for the model if that argument is not provided. This will
truncate row by row, removing rows from the table.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
Return:
A dictionary or a list of dictionaries containing results: Each result is a dictionary with the following
keys:
- **answer** (`str`) -- The answer of the query given the table. If there is an aggregator, the answer will
be preceded by `AGGREGATOR >`.
- **coordinates** (`List[Tuple[int, int]]`) -- Coordinates of the cells of the answers.
- **cells** (`List[str]`) -- List of strings made up of the answer cell values.
- **aggregator** (`str`) -- If the model has an aggregator, this returns the aggregator.
"""
pipeline_inputs = self._args_parser(*args, **kwargs)
results = super().__call__(pipeline_inputs, **kwargs)
if len(results) == 1:
return results[0]
return results
def _sanitize_parameters(self, sequential=None, padding=None, truncation=None, **kwargs):
preprocess_params = {}
if padding is not None:
preprocess_params["padding"] = padding
if truncation is not None:
preprocess_params["truncation"] = truncation
forward_params = {}
if sequential is not None:
forward_params["sequential"] = sequential
return preprocess_params, forward_params, {}
def preprocess(self, pipeline_input, sequential=None, padding=True, truncation=None):
if truncation is None:
if self.type == "tapas":
truncation = "drop_rows_to_fit"
else:
truncation = "do_not_truncate"
table, query = pipeline_input["table"], pipeline_input["query"]
if table.empty:
raise ValueError("table is empty")
if query is None or query == "":
raise ValueError("query is empty")
inputs = self.tokenizer(table, query, return_tensors=self.framework, truncation=truncation, padding=padding)
inputs["table"] = table
return inputs
def _forward(self, model_inputs, sequential=False):
table = model_inputs.pop("table")
if self.type == "tapas":
if sequential:
outputs = self.sequential_inference(**model_inputs)
else:
outputs = self.batch_inference(**model_inputs)
else:
outputs = self.model.generate(**model_inputs)
model_outputs = {"model_inputs": model_inputs, "table": table, "outputs": outputs}
return model_outputs
def postprocess(self, model_outputs):
inputs = model_outputs["model_inputs"]
table = model_outputs["table"]
outputs = model_outputs["outputs"]
if self.type == "tapas":
if self.aggregate:
logits, logits_agg = outputs[:2]
predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits, logits_agg)
answer_coordinates_batch, agg_predictions = predictions
aggregators = {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(agg_predictions)}
no_agg_label_index = self.model.config.no_aggregation_label_index
aggregators_prefix = {
i: aggregators[i] + " > " for i, pred in enumerate(agg_predictions) if pred != no_agg_label_index
}
else:
logits = outputs[0]
predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits)
answer_coordinates_batch = predictions[0]
aggregators = {}
aggregators_prefix = {}
answers = []
for index, coordinates in enumerate(answer_coordinates_batch):
cells = [table.iat[coordinate] for coordinate in coordinates]
aggregator = aggregators.get(index, "")
aggregator_prefix = aggregators_prefix.get(index, "")
answer = {
"answer": aggregator_prefix + ", ".join(cells),
"coordinates": coordinates,
"cells": [table.iat[coordinate] for coordinate in coordinates],
}
if aggregator:
answer["aggregator"] = aggregator
answers.append(answer)
if len(answer) == 0:
raise PipelineException("Empty answer")
else:
answers = [{"answer": answer} for answer in self.tokenizer.batch_decode(outputs, skip_special_tokens=True)]
return answers if len(answers) > 1 else answers[0]
| 19,892 | 44.521739 | 120 | py |
transformers | transformers-main/src/transformers/pipelines/zero_shot_classification.py | from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
logger = logging.get_logger(__name__)
class ZeroShotClassificationArgumentHandler(ArgumentHandler):
"""
Handles arguments for zero-shot for text classification by turning each possible label into an NLI
premise/hypothesis pair.
"""
def _parse_labels(self, labels):
if isinstance(labels, str):
labels = [label.strip() for label in labels.split(",") if label.strip()]
return labels
def __call__(self, sequences, labels, hypothesis_template):
if len(labels) == 0 or len(sequences) == 0:
raise ValueError("You must include at least one label and at least one sequence.")
if hypothesis_template.format(labels[0]) == hypothesis_template:
raise ValueError(
(
'The provided hypothesis_template "{}" was not able to be formatted with the target labels. '
"Make sure the passed template includes formatting syntax such as {{}} where the label should go."
).format(hypothesis_template)
)
if isinstance(sequences, str):
sequences = [sequences]
sequence_pairs = []
for sequence in sequences:
sequence_pairs.extend([[sequence, hypothesis_template.format(label)] for label in labels])
return sequence_pairs, sequences
@add_end_docstrings(PIPELINE_INIT_ARGS)
class ZeroShotClassificationPipeline(ChunkPipeline):
"""
NLI-based zero-shot classification pipeline using a `ModelForSequenceClassification` trained on NLI (natural
language inference) tasks. Equivalent of `text-classification` pipelines, but these models don't require a
hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is
**much** more flexible.
Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis
pair and passed to the pretrained model. Then, the logit for *entailment* is taken as the logit for the candidate
label being valid. Any NLI model can be used, but the id of the *entailment* label must be included in the model
config's :attr:*~transformers.PretrainedConfig.label2id*.
Example:
```python
>>> from transformers import pipeline
>>> oracle = pipeline(model="facebook/bart-large-mnli")
>>> oracle(
... "I have a problem with my iphone that needs to be resolved asap!!",
... candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
... )
{'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.504, 0.479, 0.013, 0.003, 0.002]}
>>> oracle(
... "I have a problem with my iphone that needs to be resolved asap!!",
... candidate_labels=["english", "german"],
... )
{'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['english', 'german'], 'scores': [0.814, 0.186]}
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This NLI pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"zero-shot-classification"`.
The models that this pipeline can use are models that have been fine-tuned on an NLI task. See the up-to-date list
of available models on [huggingface.co/models](https://huggingface.co/models?search=nli).
"""
def __init__(self, args_parser=ZeroShotClassificationArgumentHandler(), *args, **kwargs):
self._args_parser = args_parser
super().__init__(*args, **kwargs)
if self.entailment_id == -1:
logger.warning(
"Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
"-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."
)
@property
def entailment_id(self):
for label, ind in self.model.config.label2id.items():
if label.lower().startswith("entail"):
return ind
return -1
def _parse_and_tokenize(
self, sequence_pairs, padding=True, add_special_tokens=True, truncation=TruncationStrategy.ONLY_FIRST, **kwargs
):
"""
Parse arguments and tokenize only_first so that hypothesis (label) is not truncated
"""
return_tensors = self.framework
if self.tokenizer.pad_token is None:
# Override for tokenizers not supporting padding
logger.error(
"Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
" `pad_token=eos_token`"
)
self.tokenizer.pad_token = self.tokenizer.eos_token
try:
inputs = self.tokenizer(
sequence_pairs,
add_special_tokens=add_special_tokens,
return_tensors=return_tensors,
padding=padding,
truncation=truncation,
)
except Exception as e:
if "too short" in str(e):
# tokenizers might yell that we want to truncate
# to a value that is not even reached by the input.
# In that case we don't want to truncate.
# It seems there's not a really better way to catch that
# exception.
inputs = self.tokenizer(
sequence_pairs,
add_special_tokens=add_special_tokens,
return_tensors=return_tensors,
padding=padding,
truncation=TruncationStrategy.DO_NOT_TRUNCATE,
)
else:
raise e
return inputs
def _sanitize_parameters(self, **kwargs):
if kwargs.get("multi_class", None) is not None:
kwargs["multi_label"] = kwargs["multi_class"]
logger.warning(
"The `multi_class` argument has been deprecated and renamed to `multi_label`. "
"`multi_class` will be removed in a future version of Transformers."
)
preprocess_params = {}
if "candidate_labels" in kwargs:
preprocess_params["candidate_labels"] = self._args_parser._parse_labels(kwargs["candidate_labels"])
if "hypothesis_template" in kwargs:
preprocess_params["hypothesis_template"] = kwargs["hypothesis_template"]
postprocess_params = {}
if "multi_label" in kwargs:
postprocess_params["multi_label"] = kwargs["multi_label"]
return preprocess_params, {}, postprocess_params
def __call__(
self,
sequences: Union[str, List[str]],
*args,
**kwargs,
):
"""
Classify the sequence(s) given as inputs. See the [`ZeroShotClassificationPipeline`] documentation for more
information.
Args:
sequences (`str` or `List[str]`):
The sequence(s) to classify, will be truncated if the model input is too large.
candidate_labels (`str` or `List[str]`):
The set of possible class labels to classify each sequence into. Can be a single label, a string of
comma-separated labels, or a list of labels.
hypothesis_template (`str`, *optional*, defaults to `"This example is {}."`):
The template used to turn each label into an NLI-style hypothesis. This template must include a {} or
similar syntax for the candidate label to be inserted into the template. For example, the default
template is `"This example is {}."` With the candidate label `"sports"`, this would be fed into the
model like `"<cls> sequence to classify <sep> This example is sports . <sep>"`. The default template
works well in many cases, but it may be worthwhile to experiment with different templates depending on
the task setting.
multi_label (`bool`, *optional*, defaults to `False`):
Whether or not multiple candidate labels can be true. If `False`, the scores are normalized such that
the sum of the label likelihoods for each sequence is 1. If `True`, the labels are considered
independent and probabilities are normalized for each candidate by doing a softmax of the entailment
score vs. the contradiction score.
Return:
A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys:
- **sequence** (`str`) -- The sequence for which this is the output.
- **labels** (`List[str]`) -- The labels sorted by order of likelihood.
- **scores** (`List[float]`) -- The probabilities for each of the labels.
"""
if len(args) == 0:
pass
elif len(args) == 1 and "candidate_labels" not in kwargs:
kwargs["candidate_labels"] = args[0]
else:
raise ValueError(f"Unable to understand extra arguments {args}")
return super().__call__(sequences, **kwargs)
def preprocess(self, inputs, candidate_labels=None, hypothesis_template="This example is {}."):
sequence_pairs, sequences = self._args_parser(inputs, candidate_labels, hypothesis_template)
for i, (candidate_label, sequence_pair) in enumerate(zip(candidate_labels, sequence_pairs)):
model_input = self._parse_and_tokenize([sequence_pair])
yield {
"candidate_label": candidate_label,
"sequence": sequences[0],
"is_last": i == len(candidate_labels) - 1,
**model_input,
}
def _forward(self, inputs):
candidate_label = inputs["candidate_label"]
sequence = inputs["sequence"]
model_inputs = {k: inputs[k] for k in self.tokenizer.model_input_names}
outputs = self.model(**model_inputs)
model_outputs = {
"candidate_label": candidate_label,
"sequence": sequence,
"is_last": inputs["is_last"],
**outputs,
}
return model_outputs
def postprocess(self, model_outputs, multi_label=False):
candidate_labels = [outputs["candidate_label"] for outputs in model_outputs]
sequences = [outputs["sequence"] for outputs in model_outputs]
logits = np.concatenate([output["logits"].numpy() for output in model_outputs])
N = logits.shape[0]
n = len(candidate_labels)
num_sequences = N // n
reshaped_outputs = logits.reshape((num_sequences, n, -1))
if multi_label or len(candidate_labels) == 1:
# softmax over the entailment vs. contradiction dim for each label independently
entailment_id = self.entailment_id
contradiction_id = -1 if entailment_id == 0 else 0
entail_contr_logits = reshaped_outputs[..., [contradiction_id, entailment_id]]
scores = np.exp(entail_contr_logits) / np.exp(entail_contr_logits).sum(-1, keepdims=True)
scores = scores[..., 1]
else:
# softmax the "entailment" logits over all candidate labels
entail_logits = reshaped_outputs[..., self.entailment_id]
scores = np.exp(entail_logits) / np.exp(entail_logits).sum(-1, keepdims=True)
top_inds = list(reversed(scores[0].argsort()))
return {
"sequence": sequences[0],
"labels": [candidate_labels[i] for i in top_inds],
"scores": scores[0, top_inds].tolist(),
}
| 11,983 | 44.915709 | 198 | py |
transformers | transformers-main/src/transformers/pipelines/__init__.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import json
import os
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
from huggingface_hub import model_info
from numpy import isin
from ..configuration_utils import PretrainedConfig
from ..dynamic_module_utils import get_class_from_dynamic_module
from ..feature_extraction_utils import PreTrainedFeatureExtractor
from ..image_processing_utils import BaseImageProcessor
from ..models.auto.configuration_auto import AutoConfig
from ..models.auto.feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
from ..models.auto.image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
from ..models.auto.modeling_auto import AutoModelForDepthEstimation
from ..models.auto.tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
from ..tokenization_utils import PreTrainedTokenizer
from ..utils import (
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
is_kenlm_available,
is_offline_mode,
is_pyctcdecode_available,
is_tf_available,
is_torch_available,
logging,
)
from .audio_classification import AudioClassificationPipeline
from .automatic_speech_recognition import AutomaticSpeechRecognitionPipeline
from .base import (
ArgumentHandler,
CsvPipelineDataFormat,
JsonPipelineDataFormat,
PipedPipelineDataFormat,
Pipeline,
PipelineDataFormat,
PipelineException,
PipelineRegistry,
get_default_model_and_revision,
infer_framework_load_model,
)
from .conversational import Conversation, ConversationalPipeline
from .depth_estimation import DepthEstimationPipeline
from .document_question_answering import DocumentQuestionAnsweringPipeline
from .feature_extraction import FeatureExtractionPipeline
from .fill_mask import FillMaskPipeline
from .image_classification import ImageClassificationPipeline
from .image_segmentation import ImageSegmentationPipeline
from .image_to_text import ImageToTextPipeline
from .mask_generation import MaskGenerationPipeline
from .object_detection import ObjectDetectionPipeline
from .question_answering import QuestionAnsweringArgumentHandler, QuestionAnsweringPipeline
from .table_question_answering import TableQuestionAnsweringArgumentHandler, TableQuestionAnsweringPipeline
from .text2text_generation import SummarizationPipeline, Text2TextGenerationPipeline, TranslationPipeline
from .text_classification import TextClassificationPipeline
from .text_generation import TextGenerationPipeline
from .token_classification import (
AggregationStrategy,
NerPipeline,
TokenClassificationArgumentHandler,
TokenClassificationPipeline,
)
from .video_classification import VideoClassificationPipeline
from .visual_question_answering import VisualQuestionAnsweringPipeline
from .zero_shot_audio_classification import ZeroShotAudioClassificationPipeline
from .zero_shot_classification import ZeroShotClassificationArgumentHandler, ZeroShotClassificationPipeline
from .zero_shot_image_classification import ZeroShotImageClassificationPipeline
from .zero_shot_object_detection import ZeroShotObjectDetectionPipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import (
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_WITH_LM_HEAD_MAPPING,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForImageClassification,
TFAutoModelForMaskedLM,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeq2SeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelForVision2Seq,
TFAutoModelForZeroShotImageClassification,
)
if is_torch_available():
import torch
from ..models.auto.modeling_auto import (
MODEL_FOR_MASKED_LM_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
AutoModel,
AutoModelForAudioClassification,
AutoModelForCausalLM,
AutoModelForCTC,
AutoModelForDocumentQuestionAnswering,
AutoModelForImageClassification,
AutoModelForImageSegmentation,
AutoModelForMaskedLM,
AutoModelForMaskGeneration,
AutoModelForObjectDetection,
AutoModelForQuestionAnswering,
AutoModelForSemanticSegmentation,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoModelForSpeechSeq2Seq,
AutoModelForTableQuestionAnswering,
AutoModelForTokenClassification,
AutoModelForVideoClassification,
AutoModelForVision2Seq,
AutoModelForVisualQuestionAnswering,
AutoModelForZeroShotImageClassification,
AutoModelForZeroShotObjectDetection,
)
if TYPE_CHECKING:
from ..modeling_tf_utils import TFPreTrainedModel
from ..modeling_utils import PreTrainedModel
from ..tokenization_utils_fast import PreTrainedTokenizerFast
logger = logging.get_logger(__name__)
# Register all the supported tasks here
TASK_ALIASES = {
"sentiment-analysis": "text-classification",
"ner": "token-classification",
"vqa": "visual-question-answering",
}
SUPPORTED_TASKS = {
"audio-classification": {
"impl": AudioClassificationPipeline,
"tf": (),
"pt": (AutoModelForAudioClassification,) if is_torch_available() else (),
"default": {"model": {"pt": ("superb/wav2vec2-base-superb-ks", "372e048")}},
"type": "audio",
},
"automatic-speech-recognition": {
"impl": AutomaticSpeechRecognitionPipeline,
"tf": (),
"pt": (AutoModelForCTC, AutoModelForSpeechSeq2Seq) if is_torch_available() else (),
"default": {"model": {"pt": ("facebook/wav2vec2-base-960h", "55bb623")}},
"type": "multimodal",
},
"feature-extraction": {
"impl": FeatureExtractionPipeline,
"tf": (TFAutoModel,) if is_tf_available() else (),
"pt": (AutoModel,) if is_torch_available() else (),
"default": {"model": {"pt": ("distilbert-base-cased", "935ac13"), "tf": ("distilbert-base-cased", "935ac13")}},
"type": "multimodal",
},
"text-classification": {
"impl": TextClassificationPipeline,
"tf": (TFAutoModelForSequenceClassification,) if is_tf_available() else (),
"pt": (AutoModelForSequenceClassification,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("distilbert-base-uncased-finetuned-sst-2-english", "af0f99b"),
"tf": ("distilbert-base-uncased-finetuned-sst-2-english", "af0f99b"),
},
},
"type": "text",
},
"token-classification": {
"impl": TokenClassificationPipeline,
"tf": (TFAutoModelForTokenClassification,) if is_tf_available() else (),
"pt": (AutoModelForTokenClassification,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("dbmdz/bert-large-cased-finetuned-conll03-english", "f2482bf"),
"tf": ("dbmdz/bert-large-cased-finetuned-conll03-english", "f2482bf"),
},
},
"type": "text",
},
"question-answering": {
"impl": QuestionAnsweringPipeline,
"tf": (TFAutoModelForQuestionAnswering,) if is_tf_available() else (),
"pt": (AutoModelForQuestionAnswering,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("distilbert-base-cased-distilled-squad", "626af31"),
"tf": ("distilbert-base-cased-distilled-squad", "626af31"),
},
},
"type": "text",
},
"table-question-answering": {
"impl": TableQuestionAnsweringPipeline,
"pt": (AutoModelForTableQuestionAnswering,) if is_torch_available() else (),
"tf": (TFAutoModelForTableQuestionAnswering,) if is_tf_available() else (),
"default": {
"model": {
"pt": ("google/tapas-base-finetuned-wtq", "69ceee2"),
"tf": ("google/tapas-base-finetuned-wtq", "69ceee2"),
},
},
"type": "text",
},
"visual-question-answering": {
"impl": VisualQuestionAnsweringPipeline,
"pt": (AutoModelForVisualQuestionAnswering,) if is_torch_available() else (),
"tf": (),
"default": {
"model": {"pt": ("dandelin/vilt-b32-finetuned-vqa", "4355f59")},
},
"type": "multimodal",
},
"document-question-answering": {
"impl": DocumentQuestionAnsweringPipeline,
"pt": (AutoModelForDocumentQuestionAnswering,) if is_torch_available() else (),
"tf": (),
"default": {
"model": {"pt": ("impira/layoutlm-document-qa", "52e01b3")},
},
"type": "multimodal",
},
"fill-mask": {
"impl": FillMaskPipeline,
"tf": (TFAutoModelForMaskedLM,) if is_tf_available() else (),
"pt": (AutoModelForMaskedLM,) if is_torch_available() else (),
"default": {"model": {"pt": ("distilroberta-base", "ec58a5b"), "tf": ("distilroberta-base", "ec58a5b")}},
"type": "text",
},
"summarization": {
"impl": SummarizationPipeline,
"tf": (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),
"pt": (AutoModelForSeq2SeqLM,) if is_torch_available() else (),
"default": {"model": {"pt": ("sshleifer/distilbart-cnn-12-6", "a4f8f3e"), "tf": ("t5-small", "d769bba")}},
"type": "text",
},
# This task is a special case as it's parametrized by SRC, TGT languages.
"translation": {
"impl": TranslationPipeline,
"tf": (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),
"pt": (AutoModelForSeq2SeqLM,) if is_torch_available() else (),
"default": {
("en", "fr"): {"model": {"pt": ("t5-base", "686f1db"), "tf": ("t5-base", "686f1db")}},
("en", "de"): {"model": {"pt": ("t5-base", "686f1db"), "tf": ("t5-base", "686f1db")}},
("en", "ro"): {"model": {"pt": ("t5-base", "686f1db"), "tf": ("t5-base", "686f1db")}},
},
"type": "text",
},
"text2text-generation": {
"impl": Text2TextGenerationPipeline,
"tf": (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),
"pt": (AutoModelForSeq2SeqLM,) if is_torch_available() else (),
"default": {"model": {"pt": ("t5-base", "686f1db"), "tf": ("t5-base", "686f1db")}},
"type": "text",
},
"text-generation": {
"impl": TextGenerationPipeline,
"tf": (TFAutoModelForCausalLM,) if is_tf_available() else (),
"pt": (AutoModelForCausalLM,) if is_torch_available() else (),
"default": {"model": {"pt": ("gpt2", "6c0e608"), "tf": ("gpt2", "6c0e608")}},
"type": "text",
},
"zero-shot-classification": {
"impl": ZeroShotClassificationPipeline,
"tf": (TFAutoModelForSequenceClassification,) if is_tf_available() else (),
"pt": (AutoModelForSequenceClassification,) if is_torch_available() else (),
"default": {
"model": {"pt": ("facebook/bart-large-mnli", "c626438"), "tf": ("roberta-large-mnli", "130fb28")},
"config": {"pt": ("facebook/bart-large-mnli", "c626438"), "tf": ("roberta-large-mnli", "130fb28")},
},
"type": "text",
},
"zero-shot-image-classification": {
"impl": ZeroShotImageClassificationPipeline,
"tf": (TFAutoModelForZeroShotImageClassification,) if is_tf_available() else (),
"pt": (AutoModelForZeroShotImageClassification,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("openai/clip-vit-base-patch32", "f4881ba"),
"tf": ("openai/clip-vit-base-patch32", "f4881ba"),
}
},
"type": "multimodal",
},
"zero-shot-audio-classification": {
"impl": ZeroShotAudioClassificationPipeline,
"tf": (),
"pt": (AutoModel,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("laion/clap-htsat-fused", "973b6e5"),
}
},
"type": "multimodal",
},
"conversational": {
"impl": ConversationalPipeline,
"tf": (TFAutoModelForSeq2SeqLM, TFAutoModelForCausalLM) if is_tf_available() else (),
"pt": (AutoModelForSeq2SeqLM, AutoModelForCausalLM) if is_torch_available() else (),
"default": {
"model": {"pt": ("microsoft/DialoGPT-medium", "8bada3b"), "tf": ("microsoft/DialoGPT-medium", "8bada3b")}
},
"type": "text",
},
"image-classification": {
"impl": ImageClassificationPipeline,
"tf": (TFAutoModelForImageClassification,) if is_tf_available() else (),
"pt": (AutoModelForImageClassification,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("google/vit-base-patch16-224", "5dca96d"),
"tf": ("google/vit-base-patch16-224", "5dca96d"),
}
},
"type": "image",
},
"image-segmentation": {
"impl": ImageSegmentationPipeline,
"tf": (),
"pt": (AutoModelForImageSegmentation, AutoModelForSemanticSegmentation) if is_torch_available() else (),
"default": {"model": {"pt": ("facebook/detr-resnet-50-panoptic", "fc15262")}},
"type": "multimodal",
},
"image-to-text": {
"impl": ImageToTextPipeline,
"tf": (TFAutoModelForVision2Seq,) if is_tf_available() else (),
"pt": (AutoModelForVision2Seq,) if is_torch_available() else (),
"default": {
"model": {
"pt": ("ydshieh/vit-gpt2-coco-en", "65636df"),
"tf": ("ydshieh/vit-gpt2-coco-en", "65636df"),
}
},
"type": "multimodal",
},
"object-detection": {
"impl": ObjectDetectionPipeline,
"tf": (),
"pt": (AutoModelForObjectDetection,) if is_torch_available() else (),
"default": {"model": {"pt": ("facebook/detr-resnet-50", "2729413")}},
"type": "multimodal",
},
"zero-shot-object-detection": {
"impl": ZeroShotObjectDetectionPipeline,
"tf": (),
"pt": (AutoModelForZeroShotObjectDetection,) if is_torch_available() else (),
"default": {"model": {"pt": ("google/owlvit-base-patch32", "17740e1")}},
"type": "multimodal",
},
"depth-estimation": {
"impl": DepthEstimationPipeline,
"tf": (),
"pt": (AutoModelForDepthEstimation,) if is_torch_available() else (),
"default": {"model": {"pt": ("Intel/dpt-large", "e93beec")}},
"type": "image",
},
"video-classification": {
"impl": VideoClassificationPipeline,
"tf": (),
"pt": (AutoModelForVideoClassification,) if is_torch_available() else (),
"default": {"model": {"pt": ("MCG-NJU/videomae-base-finetuned-kinetics", "4800870")}},
"type": "video",
},
"mask-generation": {
"impl": MaskGenerationPipeline,
"tf": (),
"pt": (AutoModelForMaskGeneration,) if is_torch_available() else (),
"default": {"model": {"pt": ("facebook/sam-vit-huge", "997b15")}},
"type": "multimodal",
},
}
NO_FEATURE_EXTRACTOR_TASKS = set()
NO_IMAGE_PROCESSOR_TASKS = set()
NO_TOKENIZER_TASKS = set()
# Those model configs are special, they are generic over their task, meaning
# any tokenizer/feature_extractor might be use for a given model so we cannot
# use the statically defined TOKENIZER_MAPPING and FEATURE_EXTRACTOR_MAPPING to
# see if the model defines such objects or not.
MULTI_MODEL_CONFIGS = {"SpeechEncoderDecoderConfig", "VisionEncoderDecoderConfig", "VisionTextDualEncoderConfig"}
for task, values in SUPPORTED_TASKS.items():
if values["type"] == "text":
NO_FEATURE_EXTRACTOR_TASKS.add(task)
NO_IMAGE_PROCESSOR_TASKS.add(task)
elif values["type"] in {"image", "video"}:
NO_TOKENIZER_TASKS.add(task)
elif values["type"] in {"audio"}:
NO_TOKENIZER_TASKS.add(task)
NO_IMAGE_PROCESSOR_TASKS.add(task)
elif values["type"] != "multimodal":
raise ValueError(f"SUPPORTED_TASK {task} contains invalid type {values['type']}")
PIPELINE_REGISTRY = PipelineRegistry(supported_tasks=SUPPORTED_TASKS, task_aliases=TASK_ALIASES)
def get_supported_tasks() -> List[str]:
"""
Returns a list of supported task strings.
"""
return PIPELINE_REGISTRY.get_supported_tasks()
def get_task(model: str, use_auth_token: Optional[str] = None) -> str:
if is_offline_mode():
raise RuntimeError("You cannot infer task automatically within `pipeline` when using offline mode")
try:
info = model_info(model, token=use_auth_token)
except Exception as e:
raise RuntimeError(f"Instantiating a pipeline without a task set raised an error: {e}")
if not info.pipeline_tag:
raise RuntimeError(
f"The model {model} does not seem to have a correct `pipeline_tag` set to infer the task automatically"
)
if getattr(info, "library_name", "transformers") != "transformers":
raise RuntimeError(f"This model is meant to be used with {info.library_name} not with transformers")
task = info.pipeline_tag
return task
def check_task(task: str) -> Tuple[str, Dict, Any]:
"""
Checks an incoming task string, to validate it's correct and return the default Pipeline and Model classes, and
default models if they exist.
Args:
task (`str`):
The task defining which pipeline will be returned. Currently accepted tasks are:
- `"audio-classification"`
- `"automatic-speech-recognition"`
- `"conversational"`
- `"depth-estimation"`
- `"document-question-answering"`
- `"feature-extraction"`
- `"fill-mask"`
- `"image-classification"`
- `"image-segmentation"`
- `"image-to-text"`
- `"object-detection"`
- `"question-answering"`
- `"summarization"`
- `"table-question-answering"`
- `"text2text-generation"`
- `"text-classification"` (alias `"sentiment-analysis"` available)
- `"text-generation"`
- `"token-classification"` (alias `"ner"` available)
- `"translation"`
- `"translation_xx_to_yy"`
- `"video-classification"`
- `"visual-question-answering"`
- `"zero-shot-classification"`
- `"zero-shot-image-classification"`
- `"zero-shot-object-detection"`
Returns:
(normalized_task: `str`, task_defaults: `dict`, task_options: (`tuple`, None)) The normalized task name
(removed alias and options). The actual dictionary required to initialize the pipeline and some extra task
options for parametrized tasks like "translation_XX_to_YY"
"""
return PIPELINE_REGISTRY.check_task(task)
def clean_custom_task(task_info):
import transformers
if "impl" not in task_info:
raise RuntimeError("This model introduces a custom pipeline without specifying its implementation.")
pt_class_names = task_info.get("pt", ())
if isinstance(pt_class_names, str):
pt_class_names = [pt_class_names]
task_info["pt"] = tuple(getattr(transformers, c) for c in pt_class_names)
tf_class_names = task_info.get("tf", ())
if isinstance(tf_class_names, str):
tf_class_names = [tf_class_names]
task_info["tf"] = tuple(getattr(transformers, c) for c in tf_class_names)
return task_info, None
def pipeline(
task: str = None,
model: Optional[Union[str, "PreTrainedModel", "TFPreTrainedModel"]] = None,
config: Optional[Union[str, PretrainedConfig]] = None,
tokenizer: Optional[Union[str, PreTrainedTokenizer, "PreTrainedTokenizerFast"]] = None,
feature_extractor: Optional[Union[str, PreTrainedFeatureExtractor]] = None,
image_processor: Optional[Union[str, BaseImageProcessor]] = None,
framework: Optional[str] = None,
revision: Optional[str] = None,
use_fast: bool = True,
use_auth_token: Optional[Union[str, bool]] = None,
device: Optional[Union[int, str, "torch.device"]] = None,
device_map=None,
torch_dtype=None,
trust_remote_code: Optional[bool] = None,
model_kwargs: Dict[str, Any] = None,
pipeline_class: Optional[Any] = None,
**kwargs,
) -> Pipeline:
"""
Utility factory method to build a [`Pipeline`].
Pipelines are made of:
- A [tokenizer](tokenizer) in charge of mapping raw textual input to token.
- A [model](model) to make predictions from the inputs.
- Some (optional) post processing for enhancing model's output.
Args:
task (`str`):
The task defining which pipeline will be returned. Currently accepted tasks are:
- `"audio-classification"`: will return a [`AudioClassificationPipeline`].
- `"automatic-speech-recognition"`: will return a [`AutomaticSpeechRecognitionPipeline`].
- `"conversational"`: will return a [`ConversationalPipeline`].
- `"depth-estimation"`: will return a [`DepthEstimationPipeline`].
- `"document-question-answering"`: will return a [`DocumentQuestionAnsweringPipeline`].
- `"feature-extraction"`: will return a [`FeatureExtractionPipeline`].
- `"fill-mask"`: will return a [`FillMaskPipeline`]:.
- `"image-classification"`: will return a [`ImageClassificationPipeline`].
- `"image-segmentation"`: will return a [`ImageSegmentationPipeline`].
- `"image-to-text"`: will return a [`ImageToTextPipeline`].
- `"mask-generation"`: will return a [`MaskGenerationPipeline`].
- `"object-detection"`: will return a [`ObjectDetectionPipeline`].
- `"question-answering"`: will return a [`QuestionAnsweringPipeline`].
- `"summarization"`: will return a [`SummarizationPipeline`].
- `"table-question-answering"`: will return a [`TableQuestionAnsweringPipeline`].
- `"text2text-generation"`: will return a [`Text2TextGenerationPipeline`].
- `"text-classification"` (alias `"sentiment-analysis"` available): will return a
[`TextClassificationPipeline`].
- `"text-generation"`: will return a [`TextGenerationPipeline`]:.
- `"token-classification"` (alias `"ner"` available): will return a [`TokenClassificationPipeline`].
- `"translation"`: will return a [`TranslationPipeline`].
- `"translation_xx_to_yy"`: will return a [`TranslationPipeline`].
- `"video-classification"`: will return a [`VideoClassificationPipeline`].
- `"visual-question-answering"`: will return a [`VisualQuestionAnsweringPipeline`].
- `"zero-shot-classification"`: will return a [`ZeroShotClassificationPipeline`].
- `"zero-shot-image-classification"`: will return a [`ZeroShotImageClassificationPipeline`].
- `"zero-shot-audio-classification"`: will return a [`ZeroShotAudioClassificationPipeline`].
- `"zero-shot-object-detection"`: will return a [`ZeroShotObjectDetectionPipeline`].
model (`str` or [`PreTrainedModel`] or [`TFPreTrainedModel`], *optional*):
The model that will be used by the pipeline to make predictions. This can be a model identifier or an
actual instance of a pretrained model inheriting from [`PreTrainedModel`] (for PyTorch) or
[`TFPreTrainedModel`] (for TensorFlow).
If not provided, the default for the `task` will be loaded.
config (`str` or [`PretrainedConfig`], *optional*):
The configuration that will be used by the pipeline to instantiate the model. This can be a model
identifier or an actual pretrained model configuration inheriting from [`PretrainedConfig`].
If not provided, the default configuration file for the requested model will be used. That means that if
`model` is given, its default configuration will be used. However, if `model` is not supplied, this
`task`'s default model's config is used instead.
tokenizer (`str` or [`PreTrainedTokenizer`], *optional*):
The tokenizer that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained tokenizer inheriting from [`PreTrainedTokenizer`].
If not provided, the default tokenizer for the given `model` will be loaded (if it is a string). If `model`
is not specified or not a string, then the default tokenizer for `config` is loaded (if it is a string).
However, if `config` is also not given or not a string, then the default tokenizer for the given `task`
will be loaded.
feature_extractor (`str` or [`PreTrainedFeatureExtractor`], *optional*):
The feature extractor that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained feature extractor inheriting from [`PreTrainedFeatureExtractor`].
Feature extractors are used for non-NLP models, such as Speech or Vision models as well as multi-modal
models. Multi-modal models will also require a tokenizer to be passed.
If not provided, the default feature extractor for the given `model` will be loaded (if it is a string). If
`model` is not specified or not a string, then the default feature extractor for `config` is loaded (if it
is a string). However, if `config` is also not given or not a string, then the default feature extractor
for the given `task` will be loaded.
framework (`str`, *optional*):
The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
installed.
If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
provided.
revision (`str`, *optional*, defaults to `"main"`):
When passing a task name or a string model identifier: The specific model version to use. It can be a
branch name, a tag name, or a commit id, since we use a git-based system for storing models and other
artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
use_fast (`bool`, *optional*, defaults to `True`):
Whether or not to use a Fast tokenizer if possible (a [`PreTrainedTokenizerFast`]).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
device (`int` or `str` or `torch.device`):
Defines the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank like `1`) on which this
pipeline will be allocated.
device_map (`str` or `Dict[str, Union[int, str, torch.device]`, *optional*):
Sent directly as `model_kwargs` (just a simpler shortcut). When `accelerate` library is present, set
`device_map="auto"` to compute the most optimized `device_map` automatically (see
[here](https://huggingface.co/docs/accelerate/main/en/package_reference/big_modeling#accelerate.cpu_offload)
for more information).
<Tip warning={true}>
Do not use `device_map` AND `device` at the same time as they will conflict
</Tip>
torch_dtype (`str` or `torch.dtype`, *optional*):
Sent directly as `model_kwargs` (just a simpler shortcut) to use the available precision for this model
(`torch.float16`, `torch.bfloat16`, ... or `"auto"`).
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom code defined on the Hub in their own modeling, configuration,
tokenization or even pipeline files. This option should only be set to `True` for repositories you trust
and in which you have read the code, as it will execute code present on the Hub on your local machine.
model_kwargs (`Dict[str, Any]`, *optional*):
Additional dictionary of keyword arguments passed along to the model's `from_pretrained(...,
**model_kwargs)` function.
kwargs (`Dict[str, Any]`, *optional*):
Additional keyword arguments passed along to the specific pipeline init (see the documentation for the
corresponding pipeline class for possible values).
Returns:
[`Pipeline`]: A suitable pipeline for the task.
Examples:
```python
>>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
>>> # Sentiment analysis pipeline
>>> analyzer = pipeline("sentiment-analysis")
>>> # Question answering pipeline, specifying the checkpoint identifier
>>> oracle = pipeline(
... "question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="bert-base-cased"
... )
>>> # Named entity recognition pipeline, passing in a specific model and tokenizer
>>> model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
>>> recognizer = pipeline("ner", model=model, tokenizer=tokenizer)
```"""
if model_kwargs is None:
model_kwargs = {}
# Make sure we only pass use_auth_token once as a kwarg (it used to be possible to pass it in model_kwargs,
# this is to keep BC).
use_auth_token = model_kwargs.pop("use_auth_token", use_auth_token)
hub_kwargs = {
"revision": revision,
"use_auth_token": use_auth_token,
"trust_remote_code": trust_remote_code,
"_commit_hash": None,
}
if task is None and model is None:
raise RuntimeError(
"Impossible to instantiate a pipeline without either a task or a model "
"being specified. "
"Please provide a task class or a model"
)
if model is None and tokenizer is not None:
raise RuntimeError(
"Impossible to instantiate a pipeline with tokenizer specified but not the model as the provided tokenizer"
" may not be compatible with the default model. Please provide a PreTrainedModel class or a"
" path/identifier to a pretrained model when providing tokenizer."
)
if model is None and feature_extractor is not None:
raise RuntimeError(
"Impossible to instantiate a pipeline with feature_extractor specified but not the model as the provided"
" feature_extractor may not be compatible with the default model. Please provide a PreTrainedModel class"
" or a path/identifier to a pretrained model when providing feature_extractor."
)
if isinstance(model, Path):
model = str(model)
# Config is the primordial information item.
# Instantiate config if needed
if isinstance(config, str):
config = AutoConfig.from_pretrained(config, _from_pipeline=task, **hub_kwargs, **model_kwargs)
hub_kwargs["_commit_hash"] = config._commit_hash
elif config is None and isinstance(model, str):
config = AutoConfig.from_pretrained(model, _from_pipeline=task, **hub_kwargs, **model_kwargs)
hub_kwargs["_commit_hash"] = config._commit_hash
custom_tasks = {}
if config is not None and len(getattr(config, "custom_pipelines", {})) > 0:
custom_tasks = config.custom_pipelines
if task is None and trust_remote_code is not False:
if len(custom_tasks) == 1:
task = list(custom_tasks.keys())[0]
else:
raise RuntimeError(
"We can't infer the task automatically for this model as there are multiple tasks available. Pick "
f"one in {', '.join(custom_tasks.keys())}"
)
if task is None and model is not None:
if not isinstance(model, str):
raise RuntimeError(
"Inferring the task automatically requires to check the hub with a model_id defined as a `str`."
f"{model} is not a valid model_id."
)
task = get_task(model, use_auth_token)
# Retrieve the task
if task in custom_tasks:
normalized_task = task
targeted_task, task_options = clean_custom_task(custom_tasks[task])
if pipeline_class is None:
if not trust_remote_code:
raise ValueError(
"Loading this pipeline requires you to execute the code in the pipeline file in that"
" repo on your local machine. Make sure you have read the code there to avoid malicious use, then"
" set the option `trust_remote_code=True` to remove this error."
)
class_ref = targeted_task["impl"]
pipeline_class = get_class_from_dynamic_module(
class_ref, model, revision=revision, use_auth_token=use_auth_token
)
else:
normalized_task, targeted_task, task_options = check_task(task)
if pipeline_class is None:
pipeline_class = targeted_task["impl"]
# Use default model/config/tokenizer for the task if no model is provided
if model is None:
# At that point framework might still be undetermined
model, default_revision = get_default_model_and_revision(targeted_task, framework, task_options)
revision = revision if revision is not None else default_revision
logger.warning(
f"No model was supplied, defaulted to {model} and revision"
f" {revision} ({HUGGINGFACE_CO_RESOLVE_ENDPOINT}/{model}).\n"
"Using a pipeline without specifying a model name and revision in production is not recommended."
)
if config is None and isinstance(model, str):
config = AutoConfig.from_pretrained(model, _from_pipeline=task, **hub_kwargs, **model_kwargs)
hub_kwargs["_commit_hash"] = config._commit_hash
if device_map is not None:
if "device_map" in model_kwargs:
raise ValueError(
'You cannot use both `pipeline(... device_map=..., model_kwargs={"device_map":...})` as those'
" arguments might conflict, use only one.)"
)
if device is not None:
logger.warning(
"Both `device` and `device_map` are specified. `device` will override `device_map`. You"
" will most likely encounter unexpected behavior. Please remove `device` and keep `device_map`."
)
model_kwargs["device_map"] = device_map
if torch_dtype is not None:
if "torch_dtype" in model_kwargs:
raise ValueError(
'You cannot use both `pipeline(... torch_dtype=..., model_kwargs={"torch_dtype":...})` as those'
" arguments might conflict, use only one.)"
)
model_kwargs["torch_dtype"] = torch_dtype
model_name = model if isinstance(model, str) else None
# Load the correct model if possible
# Infer the framework from the model if not already defined
if isinstance(model, str) or framework is None:
model_classes = {"tf": targeted_task["tf"], "pt": targeted_task["pt"]}
framework, model = infer_framework_load_model(
model,
model_classes=model_classes,
config=config,
framework=framework,
task=task,
**hub_kwargs,
**model_kwargs,
)
model_config = model.config
hub_kwargs["_commit_hash"] = model.config._commit_hash
load_tokenizer = type(model_config) in TOKENIZER_MAPPING or model_config.tokenizer_class is not None
load_feature_extractor = type(model_config) in FEATURE_EXTRACTOR_MAPPING or feature_extractor is not None
load_image_processor = type(model_config) in IMAGE_PROCESSOR_MAPPING or image_processor is not None
# If `model` (instance of `PretrainedModel` instead of `str`) is passed (and/or same for config), while
# `image_processor` or `feature_extractor` is `None`, the loading will fail. This happens particularly for some
# vision tasks when calling `pipeline()` with `model` and only one of the `image_processor` and `feature_extractor`.
# TODO: we need to make `NO_IMAGE_PROCESSOR_TASKS` and `NO_FEATURE_EXTRACTOR_TASKS` more robust to avoid such issue.
# This block is only temporarily to make CI green.
if load_image_processor and load_feature_extractor:
load_feature_extractor = False
if (
tokenizer is None
and not load_tokenizer
and normalized_task not in NO_TOKENIZER_TASKS
# Using class name to avoid importing the real class.
and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
):
# This is a special category of models, that are fusions of multiple models
# so the model_config might not define a tokenizer, but it seems to be
# necessary for the task, so we're force-trying to load it.
load_tokenizer = True
if (
image_processor is None
and not load_image_processor
and normalized_task not in NO_IMAGE_PROCESSOR_TASKS
# Using class name to avoid importing the real class.
and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
and normalized_task != "automatic-speech-recognition"
):
# This is a special category of models, that are fusions of multiple models
# so the model_config might not define a tokenizer, but it seems to be
# necessary for the task, so we're force-trying to load it.
load_image_processor = True
if (
feature_extractor is None
and not load_feature_extractor
and normalized_task not in NO_FEATURE_EXTRACTOR_TASKS
# Using class name to avoid importing the real class.
and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
):
# This is a special category of models, that are fusions of multiple models
# so the model_config might not define a tokenizer, but it seems to be
# necessary for the task, so we're force-trying to load it.
load_feature_extractor = True
if task in NO_TOKENIZER_TASKS:
# These will never require a tokenizer.
# the model on the other hand might have a tokenizer, but
# the files could be missing from the hub, instead of failing
# on such repos, we just force to not load it.
load_tokenizer = False
if task in NO_FEATURE_EXTRACTOR_TASKS:
load_feature_extractor = False
if task in NO_IMAGE_PROCESSOR_TASKS:
load_image_processor = False
if load_tokenizer:
# Try to infer tokenizer from model or config name (if provided as str)
if tokenizer is None:
if isinstance(model_name, str):
tokenizer = model_name
elif isinstance(config, str):
tokenizer = config
else:
# Impossible to guess what is the right tokenizer here
raise Exception(
"Impossible to guess which tokenizer to use. "
"Please provide a PreTrainedTokenizer class or a path/identifier to a pretrained tokenizer."
)
# Instantiate tokenizer if needed
if isinstance(tokenizer, (str, tuple)):
if isinstance(tokenizer, tuple):
# For tuple we have (tokenizer name, {kwargs})
use_fast = tokenizer[1].pop("use_fast", use_fast)
tokenizer_identifier = tokenizer[0]
tokenizer_kwargs = tokenizer[1]
else:
tokenizer_identifier = tokenizer
tokenizer_kwargs = model_kwargs.copy()
tokenizer_kwargs.pop("torch_dtype", None)
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_identifier, use_fast=use_fast, _from_pipeline=task, **hub_kwargs, **tokenizer_kwargs
)
if load_image_processor:
# Try to infer image processor from model or config name (if provided as str)
if image_processor is None:
if isinstance(model_name, str):
image_processor = model_name
elif isinstance(config, str):
image_processor = config
# Backward compatibility, as `feature_extractor` used to be the name
# for `ImageProcessor`.
elif feature_extractor is not None and isinstance(feature_extractor, BaseImageProcessor):
image_processor = feature_extractor
else:
# Impossible to guess what is the right image_processor here
raise Exception(
"Impossible to guess which image processor to use. "
"Please provide a PreTrainedImageProcessor class or a path/identifier "
"to a pretrained image processor."
)
# Instantiate image_processor if needed
if isinstance(image_processor, (str, tuple)):
image_processor = AutoImageProcessor.from_pretrained(
image_processor, _from_pipeline=task, **hub_kwargs, **model_kwargs
)
if load_feature_extractor:
# Try to infer feature extractor from model or config name (if provided as str)
if feature_extractor is None:
if isinstance(model_name, str):
feature_extractor = model_name
elif isinstance(config, str):
feature_extractor = config
else:
# Impossible to guess what is the right feature_extractor here
raise Exception(
"Impossible to guess which feature extractor to use. "
"Please provide a PreTrainedFeatureExtractor class or a path/identifier "
"to a pretrained feature extractor."
)
# Instantiate feature_extractor if needed
if isinstance(feature_extractor, (str, tuple)):
feature_extractor = AutoFeatureExtractor.from_pretrained(
feature_extractor, _from_pipeline=task, **hub_kwargs, **model_kwargs
)
if (
feature_extractor._processor_class
and feature_extractor._processor_class.endswith("WithLM")
and isinstance(model_name, str)
):
try:
import kenlm # to trigger `ImportError` if not installed
from pyctcdecode import BeamSearchDecoderCTC
if os.path.isdir(model_name) or os.path.isfile(model_name):
decoder = BeamSearchDecoderCTC.load_from_dir(model_name)
else:
language_model_glob = os.path.join(
BeamSearchDecoderCTC._LANGUAGE_MODEL_SERIALIZED_DIRECTORY, "*"
)
alphabet_filename = BeamSearchDecoderCTC._ALPHABET_SERIALIZED_FILENAME
allow_patterns = [language_model_glob, alphabet_filename]
decoder = BeamSearchDecoderCTC.load_from_hf_hub(model_name, allow_patterns=allow_patterns)
kwargs["decoder"] = decoder
except ImportError as e:
logger.warning(f"Could not load the `decoder` for {model_name}. Defaulting to raw CTC. Error: {e}")
if not is_kenlm_available():
logger.warning("Try to install `kenlm`: `pip install kenlm")
if not is_pyctcdecode_available():
logger.warning("Try to install `pyctcdecode`: `pip install pyctcdecode")
if task == "translation" and model.config.task_specific_params:
for key in model.config.task_specific_params:
if key.startswith("translation"):
task = key
warnings.warn(
f'"translation" task was used, instead of "translation_XX_to_YY", defaulting to "{task}"',
UserWarning,
)
break
if tokenizer is not None:
kwargs["tokenizer"] = tokenizer
if feature_extractor is not None:
kwargs["feature_extractor"] = feature_extractor
if torch_dtype is not None:
kwargs["torch_dtype"] = torch_dtype
if image_processor is not None:
kwargs["image_processor"] = image_processor
if device is not None:
kwargs["device"] = device
return pipeline_class(model=model, framework=framework, task=task, **kwargs)
| 46,315 | 45.831143 | 120 | py |
transformers | transformers-main/src/transformers/pipelines/audio_utils.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
"""
Helper function to read an audio file through ffmpeg.
"""
ar = f"{sampling_rate}"
ac = "1"
format_for_conversion = "f32le"
ffmpeg_command = [
"ffmpeg",
"-i",
"pipe:0",
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
try:
with subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) as ffmpeg_process:
output_stream = ffmpeg_process.communicate(bpayload)
except FileNotFoundError as error:
raise ValueError("ffmpeg was not found but is required to load audio files from filename") from error
out_bytes = output_stream[0]
audio = np.frombuffer(out_bytes, np.float32)
if audio.shape[0] == 0:
raise ValueError("Malformed soundfile")
return audio
def ffmpeg_microphone(
sampling_rate: int,
chunk_length_s: float,
format_for_conversion: str = "f32le",
):
"""
Helper function ro read raw microphone data.
"""
ar = f"{sampling_rate}"
ac = "1"
if format_for_conversion == "s16le":
size_of_sample = 2
elif format_for_conversion == "f32le":
size_of_sample = 4
else:
raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`")
system = platform.system()
if system == "Linux":
format_ = "alsa"
input_ = "default"
elif system == "Darwin":
format_ = "avfoundation"
input_ = ":0"
elif system == "Windows":
format_ = "dshow"
input_ = "default"
ffmpeg_command = [
"ffmpeg",
"-f",
format_,
"-i",
input_,
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-fflags",
"nobuffer",
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
chunk_len = int(round(sampling_rate * chunk_length_s)) * size_of_sample
iterator = _ffmpeg_stream(ffmpeg_command, chunk_len)
for item in iterator:
yield item
def ffmpeg_microphone_live(
sampling_rate: int,
chunk_length_s: float,
stream_chunk_s: Optional[int] = None,
stride_length_s: Optional[Union[Tuple[float, float], float]] = None,
format_for_conversion: str = "f32le",
):
"""
Helper function to read audio from the microphone file through ffmpeg. This will output `partial` overlapping
chunks starting from `stream_chunk_s` (if it is defined) until `chunk_length_s` is reached. It will make use of
striding to avoid errors on the "sides" of the various chunks.
Arguments:
sampling_rate (`int`):
The sampling_rate to use when reading the data from the microphone. Try using the model's sampling_rate to
avoid resampling later.
chunk_length_s (`float` or `int`):
The length of the maximum chunk of audio to be sent returned. This includes the eventual striding.
stream_chunk_s (`float` or `int`)
The length of the minimal temporary audio to be returned.
stride_length_s (`float` or `int` or `(float, float)`, *optional*, defaults to `None`)
The length of the striding to be used. Stride is used to provide context to a model on the (left, right) of
an audio sample but without using that part to actually make the prediction. Setting this does not change
the length of the chunk.
format_for_conversion (`str`, defalts to `f32le`)
The name of the format of the audio samples to be returned by ffmpeg. The standard is `f32le`, `s16le`
could also be used.
Return:
A generator yielding dictionaries of the following form
`{"sampling_rate": int, "raw": np.array(), "partial" bool}` With optionnally a `"stride" (int, int)` key if
`stride_length_s` is defined.
`stride` and `raw` are all expressed in `samples`, and `partial` is a boolean saying if the current yield item
is a whole chunk, or a partial temporary result to be later replaced by another larger chunk.
"""
if stream_chunk_s is not None:
chunk_s = stream_chunk_s
else:
chunk_s = chunk_length_s
microphone = ffmpeg_microphone(sampling_rate, chunk_s, format_for_conversion=format_for_conversion)
if format_for_conversion == "s16le":
dtype = np.int16
size_of_sample = 2
elif format_for_conversion == "f32le":
dtype = np.float32
size_of_sample = 4
else:
raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`")
if stride_length_s is None:
stride_length_s = chunk_length_s / 6
chunk_len = int(round(sampling_rate * chunk_length_s)) * size_of_sample
if isinstance(stride_length_s, (int, float)):
stride_length_s = [stride_length_s, stride_length_s]
stride_left = int(round(sampling_rate * stride_length_s[0])) * size_of_sample
stride_right = int(round(sampling_rate * stride_length_s[1])) * size_of_sample
audio_time = datetime.datetime.now()
delta = datetime.timedelta(seconds=chunk_s)
for item in chunk_bytes_iter(microphone, chunk_len, stride=(stride_left, stride_right), stream=True):
# Put everything back in numpy scale
item["raw"] = np.frombuffer(item["raw"], dtype=dtype)
item["stride"] = (
item["stride"][0] // size_of_sample,
item["stride"][1] // size_of_sample,
)
item["sampling_rate"] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def chunk_bytes_iter(iterator, chunk_len: int, stride: Tuple[int, int], stream: bool = False):
"""
Reads raw bytes from an iterator and does chunks of length `chunk_len`. Optionally adds `stride` to each chunks to
get overlaps. `stream` is used to return partial results even if a full `chunk_len` is not yet available.
"""
acc = b""
stride_left, stride_right = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"
)
_stride_left = 0
for raw in iterator:
acc += raw
if stream and len(acc) < chunk_len:
stride = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(acc) >= chunk_len:
# We are flushing the accumulator
stride = (_stride_left, stride_right)
item = {"raw": acc[:chunk_len], "stride": stride}
if stream:
item["partial"] = False
yield item
_stride_left = stride_left
acc = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(acc) > stride_left:
item = {"raw": acc, "stride": (_stride_left, 0)}
if stream:
item["partial"] = False
yield item
def _ffmpeg_stream(ffmpeg_command, buflen: int):
"""
Internal function to create the generator of data through ffmpeg
"""
bufsize = 2**24 # 16Mo
try:
with subprocess.Popen(ffmpeg_command, stdout=subprocess.PIPE, bufsize=bufsize) as ffmpeg_process:
while True:
raw = ffmpeg_process.stdout.read(buflen)
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError("ffmpeg was not found but is required to stream audio files from filename") from error
| 8,094 | 34.977778 | 119 | py |
transformers | transformers-main/src/transformers/pipelines/visual_question_answering.py | from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class VisualQuestionAnsweringPipeline(Pipeline):
"""
Visual Question Answering pipeline using a `AutoModelForVisualQuestionAnswering`. This pipeline is currently only
available in PyTorch.
Example:
```python
>>> from transformers import pipeline
>>> oracle = pipeline(model="dandelin/vilt-b32-finetuned-vqa")
>>> image_url = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/lena.png"
>>> oracle(question="What is she wearing ?", image=image_url)
[{'score': 0.948, 'answer': 'hat'}, {'score': 0.009, 'answer': 'fedora'}, {'score': 0.003, 'answer': 'clothes'}, {'score': 0.003, 'answer': 'sun hat'}, {'score': 0.002, 'answer': 'nothing'}]
>>> oracle(question="What is she wearing ?", image=image_url, top_k=1)
[{'score': 0.948, 'answer': 'hat'}]
>>> oracle(question="Is this a person ?", image=image_url, top_k=1)
[{'score': 0.993, 'answer': 'yes'}]
>>> oracle(question="Is this a man ?", image=image_url, top_k=1)
[{'score': 0.996, 'answer': 'no'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This visual question answering pipeline can currently be loaded from [`pipeline`] using the following task
identifiers: `"visual-question-answering", "vqa"`.
The models that this pipeline can use are models that have been fine-tuned on a visual question answering task. See
the up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=visual-question-answering).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.check_model_type(MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING)
def _sanitize_parameters(self, top_k=None, padding=None, truncation=None, **kwargs):
preprocess_params, postprocess_params = {}, {}
if padding is not None:
preprocess_params["padding"] = padding
if truncation is not None:
preprocess_params["truncation"] = truncation
if top_k is not None:
postprocess_params["top_k"] = top_k
return preprocess_params, {}, postprocess_params
def __call__(self, image: Union["Image.Image", str], question: str = None, **kwargs):
r"""
Answers open-ended questions about images. The pipeline accepts several types of inputs which are detailed
below:
- `pipeline(image=image, question=question)`
- `pipeline({"image": image, "question": question})`
- `pipeline([{"image": image, "question": question}])`
- `pipeline([{"image": image, "question": question}, {"image": image, "question": question}])`
Args:
image (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
The pipeline handles three types of images:
- A string containing a http link pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
The pipeline accepts either a single image or a batch of images. If given a single image, it can be
broadcasted to multiple questions.
question (`str`, `List[str]`):
The question(s) asked. If given a single question, it can be broadcasted to multiple images.
top_k (`int`, *optional*, defaults to 5):
The number of top labels that will be returned by the pipeline. If the provided number is higher than
the number of labels available in the model configuration, it will default to the number of labels.
Return:
A dictionary or a list of dictionaries containing the result. The dictionaries contain the following keys:
- **label** (`str`) -- The label identified by the model.
- **score** (`int`) -- The score attributed by the model for that label.
"""
if isinstance(image, (Image.Image, str)) and isinstance(question, str):
inputs = {"image": image, "question": question}
else:
"""
Supports the following format
- {"image": image, "question": question}
- [{"image": image, "question": question}]
- Generator and datasets
"""
inputs = image
results = super().__call__(inputs, **kwargs)
return results
def preprocess(self, inputs, padding=False, truncation=False):
image = load_image(inputs["image"])
model_inputs = self.tokenizer(
inputs["question"], return_tensors=self.framework, padding=padding, truncation=truncation
)
image_features = self.image_processor(images=image, return_tensors=self.framework)
model_inputs.update(image_features)
return model_inputs
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs, top_k=5):
if top_k > self.model.config.num_labels:
top_k = self.model.config.num_labels
if self.framework == "pt":
probs = model_outputs.logits.sigmoid()[0]
scores, ids = probs.topk(top_k)
else:
raise ValueError(f"Unsupported framework: {self.framework}")
scores = scores.tolist()
ids = ids.tolist()
return [{"score": score, "answer": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
| 5,968 | 42.253623 | 194 | py |
transformers | transformers-main/src/transformers/pipelines/text_generation.py | import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class ReturnType(enum.Enum):
TENSORS = 0
NEW_TEXT = 1
FULL_TEXT = 2
@add_end_docstrings(PIPELINE_INIT_ARGS)
class TextGenerationPipeline(Pipeline):
"""
Language generation pipeline using any `ModelWithLMHead`. This pipeline predicts the words that will follow a
specified text prompt.
Example:
```python
>>> from transformers import pipeline
>>> generator = pipeline(model="gpt2")
>>> generator("I can't believe you did such a ", do_sample=False)
[{'generated_text': "I can't believe you did such a icky thing to me. I'm so sorry. I'm so sorry. I'm so sorry. I'm so sorry. I'm so sorry. I'm so sorry. I'm so sorry. I"}]
>>> # These parameters will return suggestions, and only the newly created text making it easier for prompting suggestions.
>>> outputs = generator("My tart needs some", num_return_sequences=4, return_full_text=False)
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This language generation pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"text-generation"`.
The models that this pipeline can use are models that have been trained with an autoregressive language modeling
objective, which includes the uni-directional models in the library (e.g. gpt2). See the list of available models
on [huggingface.co/models](https://huggingface.co/models?filter=text-generation).
"""
# Prefix text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
XL_PREFIX = """
In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
begging for his blessing. <eod> </s> <eos>
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING
)
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
prefix = None
if self.model.config.prefix is not None:
prefix = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
prefix = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
preprocess_params, forward_params, _ = self._sanitize_parameters(prefix=prefix, **self._forward_params)
self._preprocess_params = {**self._preprocess_params, **preprocess_params}
self._forward_params = {**self._forward_params, **forward_params}
def _sanitize_parameters(
self,
return_full_text=None,
return_tensors=None,
return_text=None,
return_type=None,
clean_up_tokenization_spaces=None,
prefix=None,
handle_long_generation=None,
stop_sequence=None,
**generate_kwargs,
):
preprocess_params = {}
if prefix is not None:
preprocess_params["prefix"] = prefix
if prefix:
prefix_inputs = self.tokenizer(
prefix, padding=False, add_special_tokens=False, return_tensors=self.framework
)
generate_kwargs["prefix_length"] = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f"{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"
" [None, 'hole']"
)
preprocess_params["handle_long_generation"] = handle_long_generation
preprocess_params.update(generate_kwargs)
forward_params = generate_kwargs
postprocess_params = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`")
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`")
return_type = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`")
return_type = ReturnType.TENSORS
if return_type is not None:
postprocess_params["return_type"] = return_type
if clean_up_tokenization_spaces is not None:
postprocess_params["clean_up_tokenization_spaces"] = clean_up_tokenization_spaces
if stop_sequence is not None:
stop_sequence_ids = self.tokenizer.encode(stop_sequence, add_special_tokens=False)
if len(stop_sequence_ids) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim."
)
generate_kwargs["eos_token_id"] = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
# overriding _parse_and_tokenize to allow for unusual language-modeling tokenizer arguments
def _parse_and_tokenize(self, *args, **kwargs):
"""
Parse arguments and tokenize
"""
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True})
return super()._parse_and_tokenize(*args, **kwargs)
def __call__(self, text_inputs, **kwargs):
"""
Complete the prompt(s) given as inputs.
Args:
args (`str` or `List[str]`):
One or several prompts (or one list of prompts) to complete.
return_tensors (`bool`, *optional*, defaults to `False`):
Whether or not to return the tensors of predictions (as token indices) in the outputs. If set to
`True`, the decoded text is not returned.
return_text (`bool`, *optional*, defaults to `True`):
Whether or not to return the decoded texts in the outputs.
return_full_text (`bool`, *optional*, defaults to `True`):
If set to `False` only added text is returned, otherwise the full text is returned. Only meaningful if
*return_text* is set to True.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to clean up the potential extra spaces in the text output.
prefix (`str`, *optional*):
Prefix added to prompt.
handle_long_generation (`str`, *optional*):
By default, this pipelines does not handle long generation (ones that exceed in one form or the other
the model maximum length). There is no perfect way to adress this (more info
:https://github.com/huggingface/transformers/issues/14033#issuecomment-948385227). This provides common
strategies to work around that problem depending on your use case.
- `None` : default strategy where nothing in particular happens
- `"hole"`: Truncates left of input, and leaves a gap wide enough to let generation happen (might
truncate a lot of the prompt and not suitable when generation exceed the model capacity)
generate_kwargs:
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework [here](./model#generative-models)).
Return:
A list or a list of list of `dict`: Returns one of the following dictionaries (cannot return a combination
of both `generated_text` and `generated_token_ids`):
- **generated_text** (`str`, present when `return_text=True`) -- The generated text.
- **generated_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token
ids of the generated text.
"""
return super().__call__(text_inputs, **kwargs)
def preprocess(self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs):
inputs = self.tokenizer(
prefix + prompt_text, padding=False, add_special_tokens=False, return_tensors=self.framework
)
inputs["prompt_text"] = prompt_text
if handle_long_generation == "hole":
cur_len = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
new_tokens = generate_kwargs["max_new_tokens"]
else:
new_tokens = generate_kwargs.get("max_length", self.model.config.max_length) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected")
if cur_len + new_tokens > self.tokenizer.model_max_length:
keep_length = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length"
)
inputs["input_ids"] = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
inputs["attention_mask"] = inputs["attention_mask"][:, -keep_length:]
return inputs
def _forward(self, model_inputs, **generate_kwargs):
input_ids = model_inputs["input_ids"]
attention_mask = model_inputs.get("attention_mask", None)
# Allow empty prompts
if input_ids.shape[1] == 0:
input_ids = None
attention_mask = None
in_b = 1
else:
in_b = input_ids.shape[0]
prompt_text = model_inputs.pop("prompt_text")
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
prefix_length = generate_kwargs.pop("prefix_length", 0)
if prefix_length > 0:
has_max_new_tokens = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
generate_kwargs["max_length"] = generate_kwargs.get("max_length") or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
has_min_new_tokens = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
generated_sequence = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs)
out_b = generated_sequence.shape[0]
if self.framework == "pt":
generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:])
elif self.framework == "tf":
generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:]))
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def postprocess(self, model_outputs, return_type=ReturnType.FULL_TEXT, clean_up_tokenization_spaces=True):
generated_sequence = model_outputs["generated_sequence"][0]
input_ids = model_outputs["input_ids"]
prompt_text = model_outputs["prompt_text"]
generated_sequence = generated_sequence.numpy().tolist()
records = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
record = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
text = self.tokenizer.decode(
sequence,
skip_special_tokens=True,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
)
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
prompt_length = 0
else:
prompt_length = len(
self.tokenizer.decode(
input_ids[0],
skip_special_tokens=True,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
)
)
if return_type == ReturnType.FULL_TEXT:
all_text = prompt_text + text[prompt_length:]
else:
all_text = text[prompt_length:]
record = {"generated_text": all_text}
records.append(record)
return records
| 15,205 | 48.530945 | 176 | py |
transformers | transformers-main/src/transformers/pipelines/feature_extraction.py | from typing import Dict
from .base import GenericTensor, Pipeline
# Can't use @add_end_docstrings(PIPELINE_INIT_ARGS) here because this one does not accept `binary_output`
class FeatureExtractionPipeline(Pipeline):
"""
Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base
transformer, which can be used as features in downstream tasks.
Example:
```python
>>> from transformers import pipeline
>>> extractor = pipeline(model="bert-base-uncased", task="feature-extraction")
>>> result = extractor("This is a simple test.", return_tensors=True)
>>> result.shape # This is a tensor of shape [1, sequence_lenth, hidden_dimension] representing the input string.
torch.Size([1, 8, 768])
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier:
`"feature-extraction"`.
All models may be used for this pipeline. See a list of all models, including community-contributed models on
[huggingface.co/models](https://huggingface.co/models).
Arguments:
model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
[`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
tokenizer ([`PreTrainedTokenizer`]):
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
[`PreTrainedTokenizer`].
modelcard (`str` or [`ModelCard`], *optional*):
Model card attributed to the model for this pipeline.
framework (`str`, *optional*):
The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
installed.
If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
provided.
return_tensors (`bool`, *optional*):
If `True`, returns a tensor according to the specified framework, otherwise returns a list.
task (`str`, defaults to `""`):
A task-identifier for the pipeline.
args_parser ([`~pipelines.ArgumentHandler`], *optional*):
Reference to the object in charge of parsing supplied pipeline parameters.
device (`int`, *optional*, defaults to -1):
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.
tokenize_kwargs (`dict`, *optional*):
Additional dictionary of keyword arguments passed along to the tokenizer.
"""
def _sanitize_parameters(self, truncation=None, tokenize_kwargs=None, return_tensors=None, **kwargs):
if tokenize_kwargs is None:
tokenize_kwargs = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
"truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"
)
tokenize_kwargs["truncation"] = truncation
preprocess_params = tokenize_kwargs
postprocess_params = {}
if return_tensors is not None:
postprocess_params["return_tensors"] = return_tensors
return preprocess_params, {}, postprocess_params
def preprocess(self, inputs, **tokenize_kwargs) -> Dict[str, GenericTensor]:
return_tensors = self.framework
model_inputs = self.tokenizer(inputs, return_tensors=return_tensors, **tokenize_kwargs)
return model_inputs
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs, return_tensors=False):
# [0] is the first available tensor, logits or last_hidden_state.
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__(self, *args, **kwargs):
"""
Extract the features of the input(s).
Args:
args (`str` or `List[str]`): One or several texts (or one list of texts) to get the features of.
Return:
A nested list of `float`: The features computed by the model.
"""
return super().__call__(*args, **kwargs)
| 4,822 | 43.657407 | 119 | py |
transformers | transformers-main/src/transformers/pipelines/image_classification.py | from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class ImageClassificationPipeline(Pipeline):
"""
Image classification pipeline using any `AutoModelForImageClassification`. This pipeline predicts the class of an
image.
Example:
```python
>>> from transformers import pipeline
>>> classifier = pipeline(model="microsoft/beit-base-patch16-224-pt22k-ft22k")
>>> classifier("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
[{'score': 0.442, 'label': 'macaw'}, {'score': 0.088, 'label': 'popinjay'}, {'score': 0.075, 'label': 'parrot'}, {'score': 0.073, 'label': 'parodist, lampooner'}, {'score': 0.046, 'label': 'poll, poll_parrot'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"image-classification"`.
See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=image-classification).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
requires_backends(self, "vision")
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
)
def _sanitize_parameters(self, top_k=None):
postprocess_params = {}
if top_k is not None:
postprocess_params["top_k"] = top_k
return {}, {}, postprocess_params
def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs):
"""
Assign labels to the image(s) passed as inputs.
Args:
images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
The pipeline handles three types of images:
- A string containing a http link pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
images.
top_k (`int`, *optional*, defaults to 5):
The number of top labels that will be returned by the pipeline. If the provided number is higher than
the number of labels available in the model configuration, it will default to the number of labels.
Return:
A dictionary or a list of dictionaries containing result. If the input is a single image, will return a
dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to
the images.
The dictionaries contain the following keys:
- **label** (`str`) -- The label identified by the model.
- **score** (`int`) -- The score attributed by the model for that label.
"""
return super().__call__(images, **kwargs)
def preprocess(self, image):
image = load_image(image)
model_inputs = self.image_processor(images=image, return_tensors=self.framework)
return model_inputs
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs, top_k=5):
if top_k > self.model.config.num_labels:
top_k = self.model.config.num_labels
if self.framework == "pt":
probs = model_outputs.logits.softmax(-1)[0]
scores, ids = probs.topk(top_k)
elif self.framework == "tf":
probs = stable_softmax(model_outputs.logits, axis=-1)[0]
topk = tf.math.top_k(probs, k=top_k)
scores, ids = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f"Unsupported framework: {self.framework}")
scores = scores.tolist()
ids = ids.tolist()
return [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
| 4,940 | 37.601563 | 214 | py |
transformers | transformers-main/src/transformers/pipelines/zero_shot_audio_classification.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class ZeroShotAudioClassificationPipeline(Pipeline):
"""
Zero shot audio classification pipeline using `ClapModel`. This pipeline predicts the class of an audio when you
provide an audio and a set of `candidate_labels`.
Example:
```python
>>> from transformers import pipeline
>>> from datasets import load_dataset
>>> dataset = load_dataset("ashraq/esc50")
>>> audio = next(iter(dataset["train"]["audio"]))["array"]
>>> classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-unfused")
>>> classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
[{'score': 0.9996, 'label': 'Sound of a dog'}, {'score': 0.0004, 'label': 'Sound of vaccum cleaner'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This audio
classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"zero-shot-audio-classification"`. See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=zero-shot-audio-classification).
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
if self.framework != "pt":
raise ValueError(f"The {self.__class__} is only available in PyTorch.")
# No specific FOR_XXX available yet
def __call__(self, audios: Union[np.ndarray, bytes, str], **kwargs):
"""
Assign labels to the audio(s) passed as inputs.
Args:
audios (`str`, `List[str]`, `np.array` or `List[np.array]`):
The pipeline handles three types of inputs:
- A string containing a http link pointing to an audio
- A string containing a local path to an audio
- An audio loaded in numpy
candidate_labels (`List[str]`):
The candidate labels for this audio
hypothesis_template (`str`, *optional*, defaults to `"This is a sound of {}"`):
The sentence used in cunjunction with *candidate_labels* to attempt the audio classification by
replacing the placeholder with the candidate_labels. Then likelihood is estimated by using
logits_per_audio
Return:
A list of dictionaries containing result, one dictionary per proposed label. The dictionaries contain the
following keys:
- **label** (`str`) -- The label identified by the model. It is one of the suggested `candidate_label`.
- **score** (`float`) -- The score attributed by the model for that label (between 0 and 1).
"""
return super().__call__(audios, **kwargs)
def _sanitize_parameters(self, **kwargs):
preprocess_params = {}
if "candidate_labels" in kwargs:
preprocess_params["candidate_labels"] = kwargs["candidate_labels"]
if "hypothesis_template" in kwargs:
preprocess_params["hypothesis_template"] = kwargs["hypothesis_template"]
return preprocess_params, {}, {}
def preprocess(self, audio, candidate_labels=None, hypothesis_template="This is a sound of {}."):
if isinstance(audio, str):
if audio.startswith("http://") or audio.startswith("https://"):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
audio = requests.get(audio).content
else:
with open(audio, "rb") as f:
audio = f.read()
if isinstance(audio, bytes):
audio = ffmpeg_read(audio, self.feature_extractor.sampling_rate)
if not isinstance(audio, np.ndarray):
raise ValueError("We expect a numpy ndarray as input")
if len(audio.shape) != 1:
raise ValueError("We expect a single channel audio input for ZeroShotAudioClassificationPipeline")
inputs = self.feature_extractor(
[audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
)
inputs["candidate_labels"] = candidate_labels
sequences = [hypothesis_template.format(x) for x in candidate_labels]
text_inputs = self.tokenizer(sequences, return_tensors=self.framework, padding=True)
inputs["text_inputs"] = [text_inputs]
return inputs
def _forward(self, model_inputs):
candidate_labels = model_inputs.pop("candidate_labels")
text_inputs = model_inputs.pop("text_inputs")
if isinstance(text_inputs[0], UserDict):
text_inputs = text_inputs[0]
else:
# Batching case.
text_inputs = text_inputs[0][0]
outputs = self.model(**text_inputs, **model_inputs)
model_outputs = {
"candidate_labels": candidate_labels,
"logits": outputs.logits_per_audio,
}
return model_outputs
def postprocess(self, model_outputs):
candidate_labels = model_outputs.pop("candidate_labels")
logits = model_outputs["logits"][0]
if self.framework == "pt":
probs = logits.softmax(dim=0)
scores = probs.tolist()
else:
raise ValueError("`tf` framework not supported.")
result = [
{"score": score, "label": candidate_label}
for score, candidate_label in sorted(zip(scores, candidate_labels), key=lambda x: -x[0])
]
return result
| 6,501 | 40.679487 | 117 | py |
transformers | transformers-main/src/transformers/generation/flax_utils.py | # coding=utf-8
# Copyright 2021 The Google AI Flax Team Authors, and The HuggingFace Inc. team.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
import warnings
from functools import partial
from typing import Any, Dict, Optional, Union
import flax
import jax
import jax.numpy as jnp
import numpy as np
from jax import lax
from ..models.auto import (
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
)
from ..utils import ModelOutput, logging
from .configuration_utils import GenerationConfig
from .flax_logits_process import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxForceTokensLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxSuppressTokensAtBeginLogitsProcessor,
FlaxSuppressTokensLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
logger = logging.get_logger(__name__)
@flax.struct.dataclass
class FlaxGreedySearchOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
"""
sequences: jnp.ndarray = None
@flax.struct.dataclass
class FlaxSampleOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using sampling.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
"""
sequences: jnp.ndarray = None
@flax.struct.dataclass
class FlaxBeamSearchOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
scores (`jnp.ndarray` of shape `(batch_size,)`):
The scores (log probabilities) of the generated sequences.
"""
sequences: jnp.ndarray = None
scores: jnp.ndarray = None
@flax.struct.dataclass
class GreedyState:
cur_len: jnp.ndarray
sequences: jnp.ndarray
running_token: jnp.ndarray
is_sent_finished: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray]
@flax.struct.dataclass
class SampleState:
cur_len: jnp.ndarray
sequences: jnp.ndarray
running_token: jnp.ndarray
is_sent_finished: jnp.ndarray
prng_key: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray]
@flax.struct.dataclass
class BeamSearchState:
cur_len: jnp.ndarray
running_sequences: jnp.ndarray
running_scores: jnp.ndarray
sequences: jnp.ndarray
scores: jnp.ndarray
is_sent_finished: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray]
class FlaxGenerationMixin:
"""
A class containing all functions for auto-regressive text generation, to be used as a mixin in
[`FlaxPreTrainedModel`].
The class exposes [`~generation.FlaxGenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.FlaxGenerationMixin._greedy_search`] if `num_beams=1` and
`do_sample=False`
- *multinomial sampling* by calling [`~generation.FlaxGenerationMixin._sample`] if `num_beams=1` and
`do_sample=True`
- *beam-search decoding* by calling [`~generation.FlaxGenerationMixin._beam_search`] if `num_beams>1` and
`do_sample=False`
You do not need to call any of the above methods directly. Pass custom parameter values to 'generate' instead. To
learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
"""
def prepare_inputs_for_generation(self, *args, **kwargs):
raise NotImplementedError(
"A model class needs to define a `prepare_inputs_for_generation` method in order to use `generate`."
)
@staticmethod
def _run_loop_in_debug(cond_fn, body_fn, init_state):
"""
Run generation in untraced mode. This should only be used for debugging purposes.
"""
state = init_state
while cond_fn(state):
state = body_fn(state)
return state
def _prepare_encoder_decoder_kwargs_for_generation(self, input_ids, params, model_kwargs):
encoder_kwargs = {
argument: value
for argument, value in model_kwargs.items()
if not (argument.startswith("decoder_") or argument.startswith("cross_attn"))
}
model_kwargs["encoder_outputs"] = self.encode(input_ids, params=params, return_dict=True, **encoder_kwargs)
return model_kwargs
def _prepare_decoder_input_ids_for_generation(
self,
batch_size: int,
decoder_start_token_id: int = None,
bos_token_id: int = None,
model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
) -> jnp.ndarray:
if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
# Only use this arg if not None, otherwise just remove from model_kwargs
decoder_input_ids = model_kwargs.pop("decoder_input_ids")
if decoder_input_ids is not None:
return decoder_input_ids
decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
return jnp.array(decoder_start_token_id, dtype="i4").reshape(1, -1).repeat(batch_size, axis=0)
def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
# retrieve decoder_start_token_id for encoder-decoder models
# fall back to bos_token_id if necessary
decoder_start_token_id = (
decoder_start_token_id
if decoder_start_token_id is not None
else self.generation_config.decoder_start_token_id
)
bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
if decoder_start_token_id is not None:
return decoder_start_token_id
elif (
hasattr(self.config, "decoder")
and hasattr(self.config.decoder, "decoder_start_token_id")
and self.config.decoder.decoder_start_token_id is not None
):
return self.config.decoder.decoder_start_token_id
elif bos_token_id is not None:
return bos_token_id
elif (
hasattr(self.config, "decoder")
and hasattr(self.config.decoder, "bos_token_id")
and self.config.decoder.bos_token_id is not None
):
return self.config.decoder.bos_token_id
raise ValueError(
"`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
)
@staticmethod
def _expand_to_num_beams(tensor, num_beams):
return jnp.broadcast_to(tensor[:, None], (tensor.shape[0], num_beams) + tensor.shape[1:])
def _adapt_logits_for_beam_search(self, logits):
"""
This function can be overwritten in the specific modeling_flax_<model-name>.py classes to allow for custom beam
search behavior. Note that the only model that overwrites this method is [`~transformes.FlaxMarianMTModel`].
"""
return logits
def _validate_model_class(self):
"""
Confirms that the model class is compatible with generation. If not, raises an exception that points to the
right class to use.
"""
if not self.can_generate():
generate_compatible_mappings = [
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
]
generate_compatible_classes = set()
for model_mapping in generate_compatible_mappings:
supported_models = model_mapping.get(type(self.config), default=None)
if supported_models is not None:
generate_compatible_classes.add(supported_models.__name__)
exception_message = (
f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
"it doesn't have a language model head."
)
if generate_compatible_classes:
exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
raise TypeError(exception_message)
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
"""Validates model kwargs for generation. Generate argument typos will also be caught here."""
unused_model_args = []
model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
# `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
# `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
if "kwargs" in model_args or "model_kwargs" in model_args:
model_args |= set(inspect.signature(self.__call__).parameters)
for key, value in model_kwargs.items():
if value is not None and key not in model_args:
unused_model_args.append(key)
if unused_model_args:
raise ValueError(
f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
" generate arguments will also show up in this list)"
)
def generate(
self,
input_ids: jnp.ndarray,
generation_config: Optional[GenerationConfig] = None,
prng_key: Optional[jnp.ndarray] = None,
trace: bool = True,
params: Optional[Dict[str, jnp.ndarray]] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
**kwargs,
):
r"""
Generates sequences of token ids for models with a language modeling head.
Parameters:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call. `**kwargs`
passed to generate matching the attributes of `generation_config` will override them. If
`generation_config` is not provided, the default will be used, which had the following loading
priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
default values, whose documentation should be checked to parameterize generation.
trace (`bool`, *optional*, defaults to `True`):
Whether to trace generation. Setting `trace=False` should only be used for debugging and will lead to a
considerably slower runtime.
params (`Dict[str, jnp.ndarray]`, *optional*):
Optionally the model parameters can be passed. Can be useful for parallelized generation.
logits_processor (`FlaxLogitsProcessorList `, *optional*):
Custom logits processors that complement the default logits processors built from arguments and
generation config. If a logit processor is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
kwargs (`Dict[str, Any]`, *optional*):
Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
Return:
[`~utils.ModelOutput`].
"""
# Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
self._validate_model_class()
# priority: `generation_config` argument > `model.generation_config` (the default generation config)
if generation_config is None:
# legacy: users may modify the model configuration to control generation -- update the generation config
# model attribute accordingly, if it was created from the model config
if self.generation_config._from_model_config:
new_generation_config = GenerationConfig.from_model_config(self.config)
if new_generation_config != self.generation_config:
warnings.warn(
"You have modified the pretrained model configuration to control generation. This is a"
" deprecated strategy to control generation and will be removed soon, in a future version."
" Please use a generation configuration file (see"
" https://huggingface.co/docs/transformers/main_classes/text_generation )"
)
self.generation_config = new_generation_config
generation_config = self.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
generation_config.validate()
self._validate_model_kwargs(model_kwargs.copy())
logits_processor = logits_processor if logits_processor is not None else FlaxLogitsProcessorList()
# set init values
prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0)
if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
if model_kwargs.get("attention_mask") is None:
logger.warning(
"The attention mask and the pad token id were not set. As a consequence, you may observe "
"unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
)
eos_token_id = generation_config.eos_token_id
if isinstance(eos_token_id, list):
eos_token_id = eos_token_id[0]
logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
generation_config.pad_token_id = eos_token_id
if generation_config.decoder_start_token_id is None and self.config.is_encoder_decoder:
raise ValueError("`decoder_start_token_id` has to be defined for encoder-decoder generation.")
# decoder-only models should use left-padding for generation (can't be checked with `trace=True`)
if not self.config.is_encoder_decoder and not trace:
if (
generation_config.pad_token_id is not None
and jnp.sum(input_ids[:, -1] == generation_config.pad_token_id) > 0
):
logger.warning(
"A decoder-only architecture is being used, but right-padding was detected! For correct "
"generation results, please set `padding_side='left'` when initializing the tokenizer."
)
batch_size = input_ids.shape[0]
if self.config.is_encoder_decoder:
# add encoder_outputs to model_kwargs
if model_kwargs.get("encoder_outputs") is None:
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, params, model_kwargs)
# prepare decoder_input_ids for generation
input_ids = self._prepare_decoder_input_ids_for_generation(
batch_size,
decoder_start_token_id=generation_config.decoder_start_token_id,
bos_token_id=generation_config.bos_token_id,
model_kwargs=model_kwargs,
)
# Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None:
warnings.warn(
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
" recommend using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif generation_config.max_new_tokens is not None:
if not has_default_max_length:
logger.warning(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
raise ValueError(
f"Unfeasable length constraints: the minimum length ({generation_config.min_length}) is larger than"
f" the maximum length ({generation_config.max_length})"
)
if input_ids_seq_length >= generation_config.max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
" increasing`max_new_tokens`."
)
logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
logits_processor=logits_processor,
)
if not generation_config.do_sample and generation_config.num_beams == 1:
return self._greedy_search(
input_ids,
generation_config.max_length,
generation_config.pad_token_id,
generation_config.eos_token_id,
logits_processor=logits_processor,
trace=trace,
params=params,
model_kwargs=model_kwargs,
)
elif generation_config.do_sample and generation_config.num_beams == 1:
logits_warper = self._get_logits_warper(generation_config=generation_config)
return self._sample(
input_ids,
generation_config.max_length,
generation_config.pad_token_id,
generation_config.eos_token_id,
prng_key,
logits_warper=logits_warper,
logits_processor=logits_processor,
trace=trace,
params=params,
model_kwargs=model_kwargs,
)
elif not generation_config.do_sample and generation_config.num_beams > 1:
# broadcast input_ids & encoder_outputs
input_ids = self._expand_to_num_beams(input_ids, num_beams=generation_config.num_beams)
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = self._expand_to_num_beams(
model_kwargs["encoder_outputs"]["last_hidden_state"], num_beams=generation_config.num_beams
)
for kwarg in ["attention_mask", "decoder_attention_mask"]:
if kwarg in model_kwargs:
model_kwargs[kwarg] = self._expand_to_num_beams(
model_kwargs[kwarg], num_beams=generation_config.num_beams
)
return self._beam_search(
input_ids,
generation_config.max_length,
generation_config.pad_token_id,
generation_config.eos_token_id,
length_penalty=generation_config.length_penalty,
early_stopping=generation_config.early_stopping,
logits_processor=logits_processor,
trace=trace,
params=params,
num_return_sequences=generation_config.num_return_sequences,
model_kwargs=model_kwargs,
)
else:
raise NotImplementedError("`Beam sampling is currently not implemented.")
def _get_logits_warper(self, generation_config: GenerationConfig) -> FlaxLogitsProcessorList:
"""
This class returns a [`FlaxLogitsProcessorList`] list object that contains all relevant [`FlaxLogitsWarper`]
instances used for multinomial sampling.
"""
warpers = FlaxLogitsProcessorList()
if generation_config.temperature is not None and generation_config.temperature != 1.0:
warpers.append(FlaxTemperatureLogitsWarper(generation_config.temperature))
if generation_config.top_k is not None and generation_config.top_k != 0:
warpers.append(FlaxTopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=1))
if generation_config.top_p is not None and generation_config.top_p < 1.0:
warpers.append(FlaxTopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=1))
return warpers
def _get_logits_processor(
self,
generation_config: GenerationConfig,
input_ids_seq_length: int,
logits_processor: Optional[FlaxLogitsProcessorList],
) -> FlaxLogitsProcessorList:
"""
This class returns a [`FlaxLogitsProcessorList`] list object that contains all relevant [`FlaxLogitsProcessor`]
instances used to modify the scores of the language model head.
"""
processors = FlaxLogitsProcessorList()
if (
generation_config.min_length is not None
and generation_config.eos_token_id is not None
and generation_config.min_length > -1
):
processors.append(
FlaxMinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id)
)
if generation_config.forced_bos_token_id is not None:
processors.append(FlaxForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id))
if generation_config.forced_eos_token_id is not None:
processors.append(
FlaxForcedEOSTokenLogitsProcessor(generation_config.max_length, generation_config.forced_eos_token_id)
)
if generation_config.suppress_tokens is not None:
processors.append(FlaxSuppressTokensLogitsProcessor(generation_config.suppress_tokens))
if generation_config.begin_suppress_tokens is not None:
begin_index = input_ids_seq_length
begin_index = (
begin_index
if (input_ids_seq_length > 1 or generation_config.forced_bos_token_id is None)
else begin_index + 1
)
if generation_config.forced_decoder_ids is not None and len(generation_config.forced_decoder_ids) > 0:
# generation starts after the last token that is forced
begin_index += generation_config.forced_decoder_ids[-1][0]
processors.append(
FlaxSuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index)
)
if generation_config.forced_decoder_ids is not None:
forced_decoder_ids = [
[input_ids_seq_length + i[0] - 1, i[1]] for i in generation_config.forced_decoder_ids
]
processors.append(FlaxForceTokensLogitsProcessor(forced_decoder_ids))
processors = self._merge_criteria_processor_list(processors, logits_processor)
return processors
def _merge_criteria_processor_list(
self,
default_list: FlaxLogitsProcessorList,
custom_list: FlaxLogitsProcessorList,
) -> FlaxLogitsProcessorList:
if len(custom_list) == 0:
return default_list
for default in default_list:
for custom in custom_list:
if type(custom) is type(default):
object_type = "logits processor"
raise ValueError(
f"A custom {object_type} of type {type(custom)} with values {custom} has been passed to"
f" `generate`, but it has already been created with the values {default}. {default} has been"
" created by passing the corresponding arguments to generate or by the model's config default"
f" values. If you just want to change the default values of {object_type} consider passing"
f" them as arguments to `generate` instead of using a custom {object_type}."
)
default_list.extend(custom_list)
return default_list
def _greedy_search(
self,
input_ids: None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
trace: bool = True,
params: Optional[Dict[str, jnp.ndarray]] = None,
model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
):
# init values
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
batch_size, cur_len = input_ids.shape
eos_token_id = jnp.array(eos_token_id, dtype=jnp.int32 if eos_token_id is not None else None)
pad_token_id = jnp.array(pad_token_id, dtype=jnp.int32)
cur_len = jnp.array(cur_len)
# per batch-item holding current token in loop.
sequences = jnp.full((batch_size, max_length), pad_token_id, dtype=jnp.int32)
sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0))
# per batch-item state bit indicating if sentence has finished.
is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_)
# For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
# and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
model = self.decode if self.config.is_encoder_decoder else self
# initialize model specific kwargs
model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs)
# initialize state
state = GreedyState(
cur_len=cur_len,
sequences=sequences,
running_token=input_ids,
is_sent_finished=is_sent_finished,
model_kwargs=model_kwargs,
)
def greedy_search_cond_fn(state):
"""state termination condition fn."""
has_reached_max_length = state.cur_len == max_length
all_sequence_finished = jnp.all(state.is_sent_finished)
finish_generation = jnp.logical_or(has_reached_max_length, all_sequence_finished)
return ~finish_generation
def greedy_search_body_fn(state):
"""state update fn."""
model_outputs = model(state.running_token, params=params, **state.model_kwargs)
logits = model_outputs.logits[:, -1]
# apply min_length, ...
logits = logits_processor(state.sequences, logits, state.cur_len)
next_token = jnp.argmax(logits, axis=-1)
next_token = next_token * ~state.is_sent_finished + pad_token_id * state.is_sent_finished
next_is_sent_finished = state.is_sent_finished | (next_token == eos_token_id)
next_token = next_token[:, None]
next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len))
next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
return GreedyState(
cur_len=state.cur_len + 1,
sequences=next_sequences,
running_token=next_token,
is_sent_finished=next_is_sent_finished,
model_kwargs=next_model_kwargs,
)
# The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU
if input_ids.shape[1] > 1:
state = greedy_search_body_fn(state)
if not trace:
state = self._run_loop_in_debug(greedy_search_cond_fn, greedy_search_body_fn, state)
else:
state = lax.while_loop(greedy_search_cond_fn, greedy_search_body_fn, state)
return FlaxGreedySearchOutput(sequences=state.sequences)
def _sample(
self,
input_ids: None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
prng_key: Optional[jnp.ndarray] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
logits_warper: Optional[FlaxLogitsProcessorList] = None,
trace: bool = True,
params: Optional[Dict[str, jnp.ndarray]] = None,
model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
):
# init values
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0)
batch_size, cur_len = input_ids.shape
eos_token_id = jnp.array(eos_token_id, dtype=jnp.int32 if eos_token_id is not None else None)
pad_token_id = jnp.array(pad_token_id, dtype=jnp.int32)
cur_len = jnp.array(cur_len)
# per batch-item holding current token in loop.
sequences = jnp.full((batch_size, max_length), pad_token_id, dtype=jnp.int32)
sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0))
# per batch-item state bit indicating if sentence has finished.
is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_)
# For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
# and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
model = self.decode if self.config.is_encoder_decoder else self
# initialize model specific kwargs
model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs)
# initialize state
state = SampleState(
cur_len=cur_len,
sequences=sequences,
running_token=input_ids,
is_sent_finished=is_sent_finished,
prng_key=prng_key,
model_kwargs=model_kwargs,
)
def sample_search_cond_fn(state):
"""state termination condition fn."""
has_reached_max_length = state.cur_len == max_length
all_sequence_finished = jnp.all(state.is_sent_finished)
finish_generation = jnp.logical_or(has_reached_max_length, all_sequence_finished)
return ~finish_generation
def sample_search_body_fn(state):
"""state update fn."""
prng_key, prng_key_next = jax.random.split(state.prng_key)
model_outputs = model(state.running_token, params=params, **state.model_kwargs)
logits = model_outputs.logits[:, -1]
# apply min_length, ...
logits = logits_processor(state.sequences, logits, state.cur_len)
# apply top_p, top_k, temperature
logits = logits_warper(logits, logits, state.cur_len)
next_token = jax.random.categorical(prng_key, logits, axis=-1)
next_is_sent_finished = state.is_sent_finished | (next_token == eos_token_id)
next_token = next_token * ~next_is_sent_finished + pad_token_id * next_is_sent_finished
next_token = next_token[:, None]
next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len))
next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
return SampleState(
cur_len=state.cur_len + 1,
sequences=next_sequences,
running_token=next_token,
is_sent_finished=next_is_sent_finished,
model_kwargs=next_model_kwargs,
prng_key=prng_key_next,
)
# The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU
if input_ids.shape[1] > 1:
state = sample_search_body_fn(state)
if not trace:
state = self._run_loop_in_debug(sample_search_cond_fn, sample_search_body_fn, state)
else:
state = lax.while_loop(sample_search_cond_fn, sample_search_body_fn, state)
return FlaxSampleOutput(sequences=state.sequences)
def _beam_search(
self,
input_ids: None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
length_penalty: Optional[float] = None,
early_stopping: Optional[Union[bool, str]] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
trace: bool = True,
params: Optional[Dict[str, jnp.ndarray]] = None,
num_return_sequences: Optional[int] = None,
model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
):
"""
This beam search function is heavily inspired by Flax's official example:
https://github.com/google/flax/blob/main/examples/wmt/decode.py
"""
def flatten_beam_dim(tensor):
"""Flattens the first two dimensions of a non-scalar array."""
# ignore scalars (e.g. cache index)
if tensor.ndim == 0:
return tensor
return tensor.reshape((tensor.shape[0] * tensor.shape[1],) + tensor.shape[2:])
def unflatten_beam_dim(tensor, batch_size, num_beams):
"""Unflattens the first, flat batch*beam dimension of a non-scalar array."""
# ignore scalars (e.g. cache index)
if tensor.ndim == 0:
return tensor
return tensor.reshape((batch_size, num_beams) + tensor.shape[1:])
def gather_beams(nested, beam_indices, batch_size, new_num_beams):
"""
Gathers the beam slices indexed by beam_indices into new beam array.
"""
batch_indices = jnp.reshape(
jnp.arange(batch_size * new_num_beams) // new_num_beams, (batch_size, new_num_beams)
)
def gather_fn(tensor):
# ignore scalars (e.g. cache index)
if tensor.ndim == 0:
return tensor
else:
return tensor[batch_indices, beam_indices]
return jax.tree_util.tree_map(gather_fn, nested)
# init values
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
length_penalty = length_penalty if length_penalty is not None else self.generation_config.length_penalty
early_stopping = early_stopping if early_stopping is not None else self.generation_config.early_stopping
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.generation_config.num_return_sequences
)
batch_size, num_beams, cur_len = input_ids.shape
eos_token_id = jnp.array(eos_token_id, dtype=jnp.int32 if eos_token_id is not None else None)
pad_token_id = jnp.array(pad_token_id, dtype=jnp.int32)
cur_len = jnp.array(cur_len)
# per batch,beam-item holding current token in loop.
sequences = jnp.full((batch_size, num_beams, max_length), pad_token_id, dtype=jnp.int32)
running_sequences = jnp.full((batch_size, num_beams, max_length), pad_token_id, dtype=jnp.int32)
running_sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0, 0))
# per batch,beam-item state bit indicating if sentence has finished.
is_sent_finished = jnp.zeros((batch_size, num_beams), dtype=jnp.bool_)
# per batch,beam-item score, logprobs
running_scores = jnp.tile(jnp.array([0.0] + [np.array(-1.0e7)] * (num_beams - 1)), [batch_size, 1])
scores = jnp.ones((batch_size, num_beams)) * np.array(-1.0e7)
# For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
# and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
model = self.decode if self.config.is_encoder_decoder else self
# flatten beam dim
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = flatten_beam_dim(
model_kwargs["encoder_outputs"]["last_hidden_state"]
)
for kwarg in ["attention_mask", "decoder_attention_mask"]:
if kwarg in model_kwargs:
model_kwargs[kwarg] = flatten_beam_dim(model_kwargs[kwarg])
# initialize model specific kwargs
model_kwargs = self.prepare_inputs_for_generation(flatten_beam_dim(input_ids), max_length, **model_kwargs)
# initialize state
state = BeamSearchState(
cur_len=cur_len,
running_sequences=running_sequences,
running_scores=running_scores,
sequences=sequences,
scores=scores,
is_sent_finished=is_sent_finished,
model_kwargs=model_kwargs,
)
def beam_search_cond_fn(state):
"""beam search state termination condition fn."""
# 1. is less than max length?
not_max_length_yet = state.cur_len < max_length
# 2. can the new beams still improve?
# early_stopping == False -> apply heuristic = always get the best score from `cur_len`. See the discussion
# below for more details.
# https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
# early_stopping == "never" -> compute the best score from max_length or cur_len, depending on the sign of
# length_penalty. Positive length_penalty favors longer sequences, thus we use max_length there.
if early_stopping == "never" and length_penalty > 0.0:
best_running_score = state.running_scores[:, :1] / (max_length**length_penalty)
else:
best_running_score = state.running_scores[:, :1] / (state.cur_len**length_penalty)
worst_finished_score = jnp.where(
state.is_sent_finished, jnp.min(state.scores, axis=1, keepdims=True), np.array(-1.0e7)
)
improvement_still_possible = jnp.any(best_running_score > worst_finished_score)
# 3. is there still a beam that has not finished?
still_open_beam = ~(jnp.all(state.is_sent_finished) & (early_stopping is True))
return not_max_length_yet & still_open_beam & improvement_still_possible
def beam_search_body_fn(state, input_ids_length=1):
"""beam search state update fn."""
# 1. Forward current tokens
# Collect the current position slice along length to feed the fast
# autoregressive decoder model. Flatten the beam dimension into batch
# dimension for feeding into the model.
# unflatten beam dimension
# Unflatten beam dimension in attention cache arrays
input_token = flatten_beam_dim(
lax.dynamic_slice(
state.running_sequences,
(0, 0, state.cur_len - input_ids_length),
(batch_size, num_beams, input_ids_length),
)
)
model_outputs = model(input_token, params=params, **state.model_kwargs)
logits = unflatten_beam_dim(model_outputs.logits[:, -1], batch_size, num_beams)
cache = jax.tree_util.tree_map(
lambda tensor: unflatten_beam_dim(tensor, batch_size, num_beams), model_outputs.past_key_values
)
# adapt logits for FlaxMarianMTModel
logits = self._adapt_logits_for_beam_search(logits)
# 2. Compute log probs
# get log probabilities from logits,
# process logits with processors (*e.g.* min_length, ...), and
# add new logprobs to existing running logprobs scores.
log_probs = jax.nn.log_softmax(logits)
log_probs = logits_processor(
flatten_beam_dim(running_sequences), flatten_beam_dim(log_probs), state.cur_len
)
log_probs = unflatten_beam_dim(log_probs, batch_size, num_beams)
log_probs = log_probs + jnp.expand_dims(state.running_scores, axis=2)
vocab_size = log_probs.shape[2]
log_probs = log_probs.reshape((batch_size, num_beams * vocab_size))
# 3. Retrieve top-K
# Each item in batch has num_beams * vocab_size candidate sequences.
# For each item, get the top 2*k candidates with the highest log-
# probabilities. We gather the top 2*K beams here so that even if the best
# K sequences reach EOS simultaneously, we have another K sequences
# remaining to continue the live beam search.
# Gather the top 2*K scores from _all_ beams.
# Gather 2*k top beams.
# Recover the beam index by floor division.
# Recover token id by modulo division and expand Id array for broadcasting.
# Update sequences for the 2*K top-k new sequences.
beams_to_keep = 2 * num_beams
topk_log_probs, topk_indices = lax.top_k(log_probs, k=beams_to_keep)
topk_beam_indices = topk_indices // vocab_size
topk_running_sequences = gather_beams(
state.running_sequences, topk_beam_indices, batch_size, beams_to_keep
)
topk_ids = jnp.expand_dims(topk_indices % vocab_size, axis=2)
topk_sequences = lax.dynamic_update_slice(topk_running_sequences, topk_ids, (0, 0, state.cur_len))
# 4. Check which sequences have ended
# Update current sequences:
# Did any of these sequences reach an end marker?
# To prevent these just finished sequences from being added to the current sequences
# set of active beam search sequences, set their log probs to a very large
# negative value.
did_topk_just_finished = topk_sequences[:, :, state.cur_len] == eos_token_id
running_topk_log_probs = topk_log_probs + did_topk_just_finished * np.array(-1.0e7)
# 5. Get running sequences scores for next
# Determine the top k beam indices (from top 2*k beams) from log probs
# and gather top k beams (from top 2*k beams).
next_topk_indices = lax.top_k(running_topk_log_probs, k=num_beams)[1]
next_running_sequences, next_running_scores = gather_beams(
[topk_sequences, running_topk_log_probs], next_topk_indices, batch_size, num_beams
)
# 6. Process topk logits
# Further process log probs:
# - add length penalty
# - make sure no scores can be added anymore if beam is full
# - make sure still running sequences cannot be chosen as finalized beam
topk_log_probs = topk_log_probs / (state.cur_len**length_penalty)
beams_in_batch_are_full = jnp.broadcast_to(
state.is_sent_finished.all(axis=-1, keepdims=True), did_topk_just_finished.shape
) & (early_stopping is True)
add_penalty = ~did_topk_just_finished | beams_in_batch_are_full
topk_log_probs += add_penalty * np.array(-1.0e7)
# 7. Get scores, sequences, is sentence finished for next.
# Combine sequences, scores, and flags along the beam dimension and compare
# new finished sequence scores to existing finished scores and select the
# best from the new set of beams
merged_sequences = jnp.concatenate([state.sequences, topk_sequences], axis=1)
merged_scores = jnp.concatenate([state.scores, topk_log_probs], axis=1)
merged_is_sent_finished = jnp.concatenate([state.is_sent_finished, did_topk_just_finished], axis=1)
topk_merged_indices = lax.top_k(merged_scores, k=num_beams)[1]
next_sequences, next_scores, next_is_sent_finished = gather_beams(
[merged_sequences, merged_scores, merged_is_sent_finished], topk_merged_indices, batch_size, num_beams
)
# 8. Update model kwargs.
# Determine the top k beam indices from the original set of all beams.
# With these, gather the top k beam-associated caches.
next_running_indices = gather_beams(topk_beam_indices, next_topk_indices, batch_size, num_beams)
next_cache = gather_beams(cache, next_running_indices, batch_size, num_beams)
model_outputs["past_key_values"] = jax.tree_util.tree_map(lambda x: flatten_beam_dim(x), next_cache)
next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
return BeamSearchState(
cur_len=state.cur_len + 1,
running_scores=next_running_scores,
running_sequences=next_running_sequences,
scores=next_scores,
sequences=next_sequences,
is_sent_finished=next_is_sent_finished,
model_kwargs=next_model_kwargs,
)
# The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU
if input_ids.shape[-1] > 1:
state = partial(beam_search_body_fn, input_ids_length=input_ids.shape[-1])(state)
if not trace:
state = self._run_loop_in_debug(beam_search_cond_fn, beam_search_body_fn, state)
else:
state = lax.while_loop(beam_search_cond_fn, beam_search_body_fn, state)
# Account for the edge-case where there are no finished sequences for a
# particular batch item. If so, return running sequences for that batch item.
none_finished = jnp.any(state.is_sent_finished, axis=1)
sequences = jnp.where(none_finished[:, None, None], state.sequences, state.running_sequences)
scores = jnp.where(none_finished[:, None], state.scores, state.running_scores)
# Take best beams for each batch (the score is sorted in descending order)
sequences = flatten_beam_dim(sequences[:, :num_return_sequences, :])
scores = flatten_beam_dim(scores[:, :num_return_sequences])
return FlaxBeamSearchOutput(sequences=sequences, scores=scores)
| 49,096 | 47.659068 | 119 | py |
transformers | transformers-main/src/transformers/generation/tf_utils.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
import warnings
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from tensorflow.compiler.tf2xla.python.xla import dynamic_update_slice
from ..modeling_tf_outputs import TFCausalLMOutputWithPast, TFSeq2SeqLMOutput
from ..models.auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
)
from ..tf_utils import shape_list, stable_softmax
from ..utils import ModelOutput, logging
from .configuration_utils import GenerationConfig
from .tf_logits_process import (
TFForcedBOSTokenLogitsProcessor,
TFForcedEOSTokenLogitsProcessor,
TFForceTokensLogitsProcessor,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
TFNoBadWordsLogitsProcessor,
TFNoRepeatNGramLogitsProcessor,
TFRepetitionPenaltyLogitsProcessor,
TFSuppressTokensAtBeginLogitsProcessor,
TFSuppressTokensLogitsProcessor,
TFTemperatureLogitsWarper,
TFTopKLogitsWarper,
TFTopPLogitsWarper,
)
logger = logging.get_logger(__name__)
@dataclass
class TFGreedySearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFGreedySearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using sampling.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(num_return_sequences*batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(num_return_sequences*batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size*num_return_sequences,
num_heads, sequence_length, sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size*num_return_sequences, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_return_sequences, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam search.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
beam_indices (`tf.Tensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `tf.Tensor` of shape
`(batch_size*num_return_sequences, sequence_length)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
beam_indices: Optional[tf.Tensor] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. `Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
beam_indices (`tf.Tensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `tf.Tensor` of shape
`(batch_size*num_return_sequences, sequence_length)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, num_heads, generated_length,
sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
beam_indices: Optional[tf.Tensor] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam sample.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
beam_indices (`tf.Tensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `tf.Tensor` of shape
`(batch_size*num_return_sequences, sequence_length)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
beam_indices: Optional[tf.Tensor] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFBeamSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_beams, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`tf.Tensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
beam_indices (`tf.Tensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `tf.Tensor` of shape
`(batch_size*num_return_sequences, sequence_length)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size*num_beams, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
sequences_scores: Optional[tf.Tensor] = None
scores: Optional[Tuple[tf.Tensor]] = None
beam_indices: Optional[tf.Tensor] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFContrastiveSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using contrastive search.
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
@dataclass
class TFContrastiveSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using contrastive search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: tf.Tensor = None
scores: Optional[Tuple[tf.Tensor]] = None
encoder_attentions: Optional[Tuple[tf.Tensor]] = None
encoder_hidden_states: Optional[Tuple[tf.Tensor]] = None
decoder_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
cross_attentions: Optional[Tuple[Tuple[tf.Tensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[tf.Tensor]]] = None
TFGreedySearchOutput = Union[TFGreedySearchEncoderDecoderOutput, TFGreedySearchDecoderOnlyOutput]
TFSampleOutput = Union[TFSampleEncoderDecoderOutput, TFSampleDecoderOnlyOutput]
TFBeamSearchOutput = Union[TFBeamSearchEncoderDecoderOutput, TFBeamSearchDecoderOnlyOutput]
TFBeamSampleOutput = Union[TFBeamSampleEncoderDecoderOutput, TFBeamSampleDecoderOnlyOutput]
TFContrastiveSearchOutput = Union[TFContrastiveSearchEncoderDecoderOutput, TFContrastiveSearchDecoderOnlyOutput]
TFGenerateOutput = Union[
TFGreedySearchOutput, TFSampleOutput, TFBeamSearchOutput, TFBeamSampleOutput, TFContrastiveSearchOutput
]
class TFGenerationMixin:
"""
A class containing all of the functions supporting generation, to be used as a mixin in [`TFPreTrainedModel`].
The class exposes [`~generation.TFGenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.TFGenerationMixin.greedy_search`] if `num_beams=1` and
`do_sample=False`
- *contrastive search* by calling [`~generation.TFGenerationMixin.contrastive_search`] if `penalty_alpha>0` and
`top_k>1`
- *multinomial sampling* by calling [`~generation.TFGenerationMixin.sample`] if `num_beams=1` and
`do_sample=True`
- *beam-search decoding* by calling [`~generation.TFGenerationMixin.beam_search`] if `num_beams>1`
You do not need to call any of the above methods directly. Pass custom parameter values to 'generate' instead. To
learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
"""
_seed_generator = None
@property
def seed_generator(self):
warnings.warn("`seed_generator` is deprecated and will be removed in a future version.", UserWarning)
if self._seed_generator is None:
self._seed_generator = tf.random.Generator.from_non_deterministic_state()
return self._seed_generator
supports_xla_generation = True
def prepare_inputs_for_generation(self, *args, **kwargs):
raise NotImplementedError(
"A model class needs to define a `prepare_inputs_for_generation` method in order to use `generate`."
)
def adjust_logits_during_generation(
self, logits, cur_len, max_length, forced_bos_token_id, forced_eos_token_id, **kwargs
):
"""
Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
"""
vocab_size = getattr(self.config, "vocab_size", None)
if vocab_size is None and self.config.is_encoder_decoder:
decoder_config = getattr(self.config, "decoder", None)
if decoder_config is not None:
vocab_size = getattr(self.config.decoder, "vocab_size", None)
if cur_len == 1 and forced_bos_token_id is not None:
vocab_range = tf.constant(range(vocab_size))
return tf.where(vocab_range != forced_bos_token_id, -1e8, logits)
elif cur_len == max_length - 1 and forced_eos_token_id is not None:
vocab_range = tf.constant(range(vocab_size))
return tf.where(vocab_range != forced_eos_token_id, -1e8, logits)
else:
return logits
def compute_transition_scores(
self,
sequences: tf.Tensor,
scores: Tuple[tf.Tensor],
beam_indices: Optional[tf.Tensor] = None,
normalize_logits: bool = False,
) -> tf.Tensor:
"""
Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was
used). This is a convenient method to quicky obtain the scores of the selected tokens at generation time.
Parameters:
sequences (`tf.Tensor`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or
shorter if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)`):
Transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens Tuple of
`tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token), with each
tensor of shape `(batch_size*num_beams, config.vocab_size)`.
beam_indices (`tf.Tensor`, *optional*):
Beam indices of generated token id at each generation step. `tf.Tensor` of shape
`(batch_size*num_return_sequences, sequence_length)`. Only required if a `num_beams>1` at
generate-time.
normalize_logits (`bool`, *optional*, defaults to `False`):
Whether to normalize the logits (which, for legacy reasons, may be unnormalized).
Return:
`tf.Tensor`: A `tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)` containing
the transition scores (logits)
Examples:
```python
>>> from transformers import GPT2Tokenizer, TFAutoModelForCausalLM
>>> import numpy as np
>>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
>>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")
>>> tokenizer.pad_token_id = tokenizer.eos_token_id
>>> inputs = tokenizer(["Today is"], return_tensors="tf")
>>> # Example 1: Print the scores for each token generated with Greedy Search
>>> outputs = model.generate(**inputs, max_new_tokens=5, return_dict_in_generate=True, output_scores=True)
>>> transition_scores = model.compute_transition_scores(
... outputs.sequences, outputs.scores, normalize_logits=True
... )
>>> # input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for
>>> # encoder-decoder models, like BART or T5.
>>> input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
>>> generated_tokens = outputs.sequences[:, input_length:]
>>> for tok, score in zip(generated_tokens[0], transition_scores[0]):
... # | token | token string | logits | probability
... print(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
| 262 | the | -1.413 | 24.33%
| 1110 | day | -2.609 | 7.36%
| 618 | when | -2.009 | 13.41%
| 356 | we | -1.859 | 15.58%
| 460 | can | -2.508 | 8.14%
>>> # Example 2: Reconstruct the sequence scores from Beam Search
>>> outputs = model.generate(
... **inputs,
... max_new_tokens=5,
... num_beams=4,
... num_return_sequences=4,
... return_dict_in_generate=True,
... output_scores=True,
... )
>>> transition_scores = model.compute_transition_scores(
... outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False
... )
>>> # If you sum the generated tokens' scores and apply the length penalty, you'll get the sequence scores.
>>> # Tip: recomputing the scores is only guaranteed to match with `normalize_logits=False`. Depending on the
>>> # use case, you might want to recompute it with `normalize_logits=True`.
>>> output_length = input_length + np.sum(transition_scores.numpy() < 0, axis=1)
>>> length_penalty = model.generation_config.length_penalty
>>> reconstructed_scores = np.sum(transition_scores, axis=1) / (output_length**length_penalty)
>>> print(np.allclose(outputs.sequences_scores, reconstructed_scores))
True
```"""
# 1. In absence of `beam_indices`, we can assume that we come from e.g. greedy search, which is equivalent
# to a beam search approach were the first (and only) beam is always selected
if beam_indices is None:
beam_indices = tf.tile(tf.expand_dims(tf.range(scores[0].shape[0]), axis=1), [1, len(scores)])
# 2. reshape scores as [batch_size, vocab_size, # generation steps] with # generation steps being
# seq_len - input_length
scores = tf.transpose(tf.reshape(tf.stack(scores), (len(scores), -1)), (1, 0))
scores = tf.reshape(scores, (-1, self.config.vocab_size, scores.shape[-1]))
# 3. Optionally normalize the logits (across the vocab dimension)
if normalize_logits:
scores = tf.nn.log_softmax(scores, axis=1)
# 4. cut beam_indices to longest beam length
beam_indices_mask = beam_indices < 0
max_beam_length = tf.math.reduce_max(
tf.math.reduce_sum((1 - tf.cast(beam_indices_mask, dtype=tf.int32)), axis=-1)
)
beam_indices = beam_indices[:, -max_beam_length:]
beam_indices_mask = beam_indices_mask[:, -max_beam_length:]
# 5. Set indices of beams that finished early to 0; such indices will be masked correctly afterwards
beam_indices = tf.where(beam_indices_mask, 0, beam_indices)
# 6. Define which indices contributed to scores
cut_idx = sequences.shape[-1] - max_beam_length
token_indices = sequences[:, cut_idx:]
gen_step_idx = tf.broadcast_to(tf.range(scores.shape[-1]), token_indices.shape)
indices = tf.stack([beam_indices, token_indices, gen_step_idx], axis=-1)
# 7. Compute scores
transition_scores = tf.gather_nd(scores, indices)
# 8. Mask out transition_scores of beams that stopped early
transition_scores = tf.where(beam_indices_mask, 0, transition_scores)
return transition_scores
def _validate_model_class(self):
"""
Confirms that the model class is compatible with generation. If not, raises an exception that points to the
right class to use.
"""
if not self.can_generate():
generate_compatible_mappings = [
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
]
generate_compatible_classes = set()
for model_mapping in generate_compatible_mappings:
supported_models = model_mapping.get(type(self.config), default=None)
if supported_models is not None:
generate_compatible_classes.add(supported_models.__name__)
exception_message = (
f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
"it doesn't have a language model head."
)
if generate_compatible_classes:
exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
raise TypeError(exception_message)
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
"""Validates model kwargs for generation. Generate argument typos will also be caught here."""
# Excludes arguments that are handled before calling any model function
if self.config.is_encoder_decoder:
for key in ["decoder_input_ids"]:
model_kwargs.pop(key, None)
unused_model_args = []
model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
# `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
# `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
if "kwargs" in model_args or "model_kwargs" in model_args:
model_args |= set(inspect.signature(self.call).parameters)
for key, value in model_kwargs.items():
if value is not None and key not in model_args:
unused_model_args.append(key)
if unused_model_args:
raise ValueError(
f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
" generate arguments will also show up in this list)"
)
def generate(
self,
inputs: Optional[tf.Tensor] = None,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[TFLogitsProcessorList] = None,
seed=None,
**kwargs,
) -> Union[TFGenerateOutput, tf.Tensor]:
r"""
Generates sequences of token ids for models with a language modeling head.
<Tip warning={true}>
Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
model's default generation configuration. You can override any `generation_config` by passing the corresponding
parameters to generate, e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
For an overview of generation strategies and code examples, check out the [following
guide](../generation_strategies).
</Tip>
Parameters:
inputs (`tf.Tensor` of varying shape depending on the modality, *optional*):
The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
`input_ids`, `input_values`, `input_features`, or `pixel_values`.
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call. `**kwargs`
passed to generate matching the attributes of `generation_config` will override them. If
`generation_config` is not provided, the default will be used, which had the following loading
priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
default values, whose documentation should be checked to parameterize generation.
logits_processor (`LogitsProcessorList`, *optional*):
Custom logits processors that complement the default logits processors built from arguments and
generation config. If a logit processor is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
seed (`List[int]`, *optional*):
Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the
`seed` argument from stateless functions in `tf.random`.
kwargs (`Dict[str, Any]`, *optional*):
Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
Return:
[`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when
`config.return_dict_in_generate=True`) or a `tf.Tensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchDecoderOnlyOutput`],
- [`~generation.TFSampleDecoderOnlyOutput`],
- [`~generation.TFBeamSearchDecoderOnlyOutput`],
- [`~generation.TFBeamSampleDecoderOnlyOutput`]
If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.TFGreedySearchEncoderDecoderOutput`],
- [`~generation.TFSampleEncoderDecoderOutput`],
- [`~generation.TFBeamSearchEncoderDecoderOutput`],
- [`~generation.TFBeamSampleEncoderDecoderOutput`]
"""
# 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
self._validate_model_class()
# priority: `generation_config` argument > `model.generation_config` (the default generation config)
if generation_config is None:
# legacy: users may modify the model configuration to control generation -- update the generation config
# model attribute accordingly, if it was created from the model config
if self.generation_config._from_model_config:
new_generation_config = GenerationConfig.from_model_config(self.config)
if new_generation_config != self.generation_config:
warnings.warn(
"You have modified the pretrained model configuration to control generation. This is a"
" deprecated strategy to control generation and will be removed soon, in a future version."
" Please use a generation configuration file (see"
" https://huggingface.co/docs/transformers/main_classes/text_generation )"
)
self.generation_config = new_generation_config
generation_config = self.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
generation_config.validate()
self._validate_model_kwargs(model_kwargs.copy())
# 2. Cast input dtypes to tf.int32 unless they're floats (which happens for some image models)
if inputs is not None:
if isinstance(inputs, tf.Tensor) and inputs.dtype.is_floating:
pass
elif isinstance(inputs, np.ndarray) and np.issubdtype(inputs.dtype, np.floating):
pass
else:
inputs = tf.cast(inputs, tf.int32)
if model_kwargs.get("attention_mask") is not None:
model_kwargs["attention_mask"] = tf.cast(model_kwargs["attention_mask"], tf.int32)
if "decoder_input_ids" in model_kwargs:
if (
isinstance(model_kwargs["decoder_input_ids"], tf.Tensor)
and model_kwargs["decoder_input_ids"].dtype.is_floating
):
pass
elif isinstance(model_kwargs["decoder_input_ids"], np.ndarray) and np.issubdtype(
model_kwargs["decoder_input_ids"].dtype, np.floating
):
pass
else:
model_kwargs["decoder_input_ids"] = tf.cast(model_kwargs["decoder_input_ids"], tf.int32)
# 3. Set generation parameters if not already defined
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
if model_kwargs.get("attention_mask") is None:
logger.warning(
"The attention mask and the pad token id were not set. As a consequence, you may observe "
"unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
)
eos_token_id = generation_config.eos_token_id
if isinstance(eos_token_id, list):
eos_token_id = eos_token_id[0]
logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
generation_config.pad_token_id = eos_token_id
use_xla = not tf.executing_eagerly()
if use_xla and not self.supports_xla_generation:
raise ValueError(
"The selected model does not support Graph mode nor XLA generation (e.g. from tf.function())"
)
# 4. Define model inputs
inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
inputs, generation_config.bos_token_id, model_kwargs
)
# inputs_ids now has to be defined and cannot be None anymore
batch_size = shape_list(inputs_tensor)[0]
# 5. Prepare other model kwargs
model_kwargs["output_attentions"] = generation_config.output_attentions
model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
model_kwargs["use_cache"] = generation_config.use_cache
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.call).parameters.keys())
requires_attention_mask = "encoder_outputs" not in model_kwargs
if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
)
# decoder-only models should use left-padding for generation
if not self.config.is_encoder_decoder:
if generation_config.pad_token_id is not None and tf.math.reduce_any(
inputs_tensor[:, -1] == generation_config.pad_token_id
):
logger.warning(
"A decoder-only architecture is being used, but right-padding was detected! For correct "
"generation results, please set `padding_side='left'` when initializing the tokenizer."
)
if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
# if model is encoder decoder encoder_outputs are created and added to `model_kwargs`
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
inputs_tensor, model_kwargs, model_input_name
)
# 6. Prepare model inputs which will be used for auto-regressive generation
if self.config.is_encoder_decoder:
input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
batch_size=batch_size,
model_input_name=model_input_name,
model_kwargs=model_kwargs,
decoder_start_token_id=generation_config.decoder_start_token_id,
bos_token_id=generation_config.bos_token_id,
)
else:
input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
# 7. Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = shape_list(input_ids)[-1]
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None:
warnings.warn(
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
" recommend using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif generation_config.max_new_tokens is not None:
if not has_default_max_length:
logger.warning(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
# If the input length is a tensor (i.e. dynamic length), skip length checks
if not isinstance(input_ids_seq_length, tf.Tensor):
if (
generation_config.min_length is not None
and generation_config.min_length > generation_config.max_length
):
raise ValueError(
f"Unfeasable length constraints: the minimum length ({generation_config.min_length}) is larger"
f" than the maximum length ({generation_config.max_length})"
)
if input_ids_seq_length >= generation_config.max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
" increasing`max_new_tokens`."
)
# 8. determine generation mode
is_contrastive_search_gen_mode = (
generation_config.top_k is not None
and generation_config.top_k > 1
and generation_config.do_sample is False
and generation_config.penalty_alpha is not None
and generation_config.penalty_alpha > 0
)
is_greedy_gen_mode = (
not is_contrastive_search_gen_mode
and (generation_config.num_beams == 1)
and generation_config.do_sample is False
)
is_beam_gen_mode = (
not is_contrastive_search_gen_mode
and (generation_config.num_beams > 1)
and generation_config.do_sample is False
)
is_sample_gen_mode = (generation_config.num_beams == 1) and generation_config.do_sample is True
is_beam_sample_gen_mode = (generation_config.num_beams > 1) and generation_config.do_sample is True
# 9. prepare distribution pre_processing samplers
logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
logits_processor=logits_processor,
)
# 10. go into different generation modes
if is_greedy_gen_mode:
if generation_config.num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
" greedy search."
)
# 11. run greedy search
return self.greedy_search(
input_ids,
max_length=generation_config.max_length,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
logits_processor=logits_processor,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
**model_kwargs,
)
elif is_contrastive_search_gen_mode:
if generation_config.num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
" contrastive search."
)
# 11. run contrastive search
return self.contrastive_search(
input_ids,
top_k=generation_config.top_k,
penalty_alpha=generation_config.penalty_alpha,
logits_processor=logits_processor,
max_length=generation_config.max_length,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
**model_kwargs,
)
elif is_sample_gen_mode:
# 11. prepare logits warper
logits_warper = self._get_logits_warper(generation_config=generation_config)
# 12. expand input_ids with `num_return_sequences` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_return_sequences,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run sample
return self.sample(
input_ids,
logits_processor=logits_processor,
logits_warper=logits_warper,
max_length=generation_config.max_length,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
seed=seed,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
**model_kwargs,
)
elif is_beam_gen_mode:
if generation_config.num_beams < generation_config.num_return_sequences:
raise ValueError(
"Beam search decoding cannot return more sequences than it has beams. Please set num_beams >="
f" num_return_sequences, got {generation_config.num_beams} and"
f" {generation_config.num_return_sequences} (respectivelly)"
)
# 11. broadcast inputs to the desired number of beams
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
expand_in_new_axis=True,
**model_kwargs,
)
# 12. run beam search
return self.beam_search(
input_ids,
max_length=generation_config.max_length,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
length_penalty=generation_config.length_penalty,
early_stopping=generation_config.early_stopping,
logits_processor=logits_processor,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
num_return_sequences=generation_config.num_return_sequences,
**model_kwargs,
)
elif is_beam_sample_gen_mode:
if generation_config.num_beams < generation_config.num_return_sequences:
raise ValueError(
"Beam search decoding cannot return more sequences than it has beams. Please set num_beams >="
f" num_return_sequences, got {generation_config.num_beams} and"
f" {generation_config.num_return_sequences} (respectivelly)"
)
# 11. prepare logits warper
logits_warper = self._get_logits_warper(generation_config=generation_config)
# 12. broadcast inputs to the desired number of beams
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
expand_in_new_axis=True,
**model_kwargs,
)
# 13. run beam sample (beam search with sampling)
return self.beam_search(
input_ids,
do_sample=True,
max_length=generation_config.max_length,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
length_penalty=generation_config.length_penalty,
early_stopping=generation_config.early_stopping,
logits_processor=logits_processor,
logits_warper=logits_warper,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
num_return_sequences=generation_config.num_return_sequences,
**model_kwargs,
)
def _prepare_attention_mask_for_generation(
self,
inputs: tf.Tensor,
pad_token_id: Optional[int],
eos_token_id: Optional[int],
) -> tf.Tensor:
is_input_ids = len(inputs.shape) == 2 and inputs.dtype in (tf.int32, tf.int64)
is_pad_token_in_inputs = (pad_token_id is not None) and tf.math.reduce_any(inputs == pad_token_id)
is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (pad_token_id != eos_token_id)
# Check if input is input_ids and padded -> only then is attention_mask defined
if is_input_ids and is_pad_token_in_inputs and is_pad_token_not_equal_to_eos_token_id:
return tf.cast(tf.math.not_equal(inputs, pad_token_id), dtype=tf.int32)
else:
return tf.ones(inputs.shape[:2], dtype=tf.int32)
def _prepare_encoder_decoder_kwargs_for_generation(
self, inputs_tensor: tf.Tensor, model_kwargs, model_input_name: Optional[str] = None
) -> Dict[str, Any]:
# 1. get encoder and store encoder outputs
encoder = self.get_encoder()
# 2. prepare encoder args and encoder kwargs from model kwargs
irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
encoder_kwargs = {
argument: value
for argument, value in model_kwargs.items()
if not any(argument.startswith(p) for p in irrelevant_prefix)
}
encoder_signature = set(inspect.signature(encoder.call).parameters)
encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature
if not encoder_accepts_wildcard:
encoder_kwargs = {
argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
}
# 3. vision models don't use `attention_mask`.
encoder_kwargs["return_dict"] = True
encoder_kwargs[model_input_name] = inputs_tensor
if model_input_name != self.main_input_name: # in Keras, the first input must always be passed
encoder_kwargs[self.main_input_name] = None
encoder_outputs = encoder(**encoder_kwargs)
model_kwargs["encoder_outputs"] = encoder_outputs
return model_kwargs
def _prepare_decoder_input_ids_for_generation(
self,
batch_size: int,
model_input_name: str,
model_kwargs: Dict[str, tf.Tensor],
decoder_start_token_id: int = None,
bos_token_id: int = None,
) -> Tuple[tf.Tensor, Dict[str, tf.Tensor]]:
"""Prepares `decoder_input_ids` for generation with encoder-decoder models"""
# 1. Check whether the user has defined `decoder_input_ids` manually. To facilitate in terms of input naming,
# we also allow the user to pass it under `input_ids`, if the encoder does not use it as the main input.
if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
decoder_input_ids = model_kwargs.pop("decoder_input_ids")
elif "input_ids" in model_kwargs and model_input_name != "input_ids":
decoder_input_ids = model_kwargs.pop("input_ids")
else:
decoder_input_ids = None
# 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
decoder_input_ids_start = tf.ones((batch_size, 1), dtype=tf.int32) * decoder_start_token_id
# no user input -> use decoder_start_token_id as decoder_input_ids
if decoder_input_ids is None:
decoder_input_ids = decoder_input_ids_start
# user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
# decoder_attention_mask if provided)
elif tf.reduce_all(decoder_input_ids[:, 0] != decoder_start_token_id):
decoder_input_ids = tf.concat([decoder_input_ids_start, decoder_input_ids], axis=-1)
if "decoder_attention_mask" in model_kwargs:
decoder_attention_mask = model_kwargs["decoder_attention_mask"]
decoder_attention_mask = tf.concat(
(tf.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
axis=-1,
)
model_kwargs["decoder_attention_mask"] = decoder_attention_mask
return decoder_input_ids, model_kwargs
def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
# retrieve decoder_start_token_id for encoder-decoder models
# fall back to bos_token_id if necessary
decoder_start_token_id = (
decoder_start_token_id
if decoder_start_token_id is not None
else self.generation_config.decoder_start_token_id
)
bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
if decoder_start_token_id is not None:
return decoder_start_token_id
elif bos_token_id is not None:
return bos_token_id
raise ValueError(
"`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
)
@staticmethod
def _expand_inputs_for_generation(
expand_size: int = 1,
is_encoder_decoder: bool = False,
input_ids: Optional[tf.Tensor] = None,
expand_in_new_axis: bool = False,
**model_kwargs,
) -> Tuple[tf.Tensor, Dict[str, Any]]:
"""
Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...] or [batch_size, expand_size, ...],
depending on `expand_in_new_axis`. Beam-based approaches expect this function to be used with
`expand_in_new_axis=True`
"""
def _expand_tensor(tensor: tf.Tensor):
if expand_in_new_axis:
shape = shape_list(tensor)
return tf.broadcast_to(tensor[:, None], (shape[0], expand_size) + tuple(shape[1:]))
else:
return tf.repeat(tensor, expand_size, axis=0)
def _expand_dict_for_generation(dict_to_expand):
for key in dict_to_expand:
if dict_to_expand[key] is not None and isinstance(dict_to_expand[key], tf.Tensor):
dict_to_expand[key] = _expand_tensor(dict_to_expand[key])
return dict_to_expand
if input_ids is not None:
input_ids = _expand_tensor(input_ids)
model_kwargs = _expand_dict_for_generation(model_kwargs)
if is_encoder_decoder:
if model_kwargs.get("encoder_outputs") is None:
raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])
return input_ids, model_kwargs
def _prepare_model_inputs(
self,
inputs: Optional[tf.Tensor] = None,
bos_token_id: Optional[int] = None,
model_kwargs: Optional[Dict[str, tf.Tensor]] = None,
) -> Tuple[tf.Tensor, Optional[str], Dict[str, tf.Tensor]]:
"""
This function extracts the model-specific `inputs` for generation.
"""
# 1. retrieve all kwargs that are non-None or non-model input related.
# some encoder-decoder models have different names for model and encoder
if (
self.config.is_encoder_decoder
and hasattr(self, "encoder")
and hasattr(self.encoder, "main_input_name")
and self.encoder.main_input_name != self.main_input_name
):
input_name = self.encoder.main_input_name
else:
input_name = self.main_input_name
model_kwargs = {k: v for k, v in model_kwargs.items() if v is not None or k != input_name}
# 2. check whether model_input_name is passed as kwarg
# if yes and `inputs` is None use kwarg inputs
inputs_kwarg = model_kwargs.pop(input_name, None)
if inputs_kwarg is not None and inputs is not None:
raise ValueError(
f"`inputs`: {inputs}` were passed alongside {input_name} which is not allowed."
f"Make sure to either pass {inputs} or {input_name}=..."
)
elif inputs_kwarg is not None:
inputs = inputs_kwarg
# 3. In the presence of `inputs_embeds` for text models:
# - decoder-only models should complain if the user attempts to pass `inputs_embeds`, but the model
# doesn't have its forwarding implemented. `inputs_embeds` is kept in `model_kwargs` and can coexist with
# input_ids (`inputs_embeds` will be used in the 1st generation step, as opposed to `input_ids`)
# - encoder-decoder models should complain if the user attempts to pass `inputs_embeds` and `input_ids`, and
# pull the former to inputs. It will be used in place of `input_ids` to get the encoder hidden states.
if input_name == "input_ids" and "inputs_embeds" in model_kwargs:
if not self.config.is_encoder_decoder:
has_inputs_embeds_forwarding = "inputs_embeds" in set(
inspect.signature(self.prepare_inputs_for_generation).parameters.keys()
)
if not has_inputs_embeds_forwarding:
raise ValueError(
f"You passed `inputs_embeds` to `.generate()`, but the model class {self.__class__.__name__} "
"doesn't have its forwarding implemented. See the GPT2 implementation for an example "
"(https://github.com/huggingface/transformers/pull/21405), and feel free to open a PR with it!"
)
# In this case, `input_ids` is moved to the `model_kwargs`, so a few automations (like the creation of
# the attention mask) can rely on the actual model input.
model_kwargs["input_ids"] = self._maybe_initialize_input_ids_for_generation(
inputs, bos_token_id, model_kwargs=model_kwargs
)
else:
if inputs is not None:
raise ValueError("You passed `inputs_embeds` and `input_ids` to `.generate()`. Please pick one.")
inputs, input_name = model_kwargs["inputs_embeds"], "inputs_embeds"
# 4. if `inputs` is still None, try to create `input_ids` from BOS token
inputs = self._maybe_initialize_input_ids_for_generation(inputs, bos_token_id, model_kwargs)
return inputs, input_name, model_kwargs
def _maybe_initialize_input_ids_for_generation(
self,
inputs: Optional[tf.Tensor] = None,
bos_token_id: Optional[int] = None,
model_kwargs: Optional[Dict[str, tf.Tensor]] = None,
) -> tf.Tensor:
"""Initializes input ids for generation, if necessary."""
if inputs is not None:
return inputs
encoder_outputs = model_kwargs.get("encoder_outputs")
if self.config.is_encoder_decoder and encoder_outputs is not None:
# make dummy input_ids with value -100, as a sanity check ensuring that they won't be used for encoding
shape = encoder_outputs.last_hidden_state.shape[:-1]
return tf.ones(shape, dtype=tf.int32) * -100
if bos_token_id is None:
raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.")
# If there is some tensor in `model_kwargs`, we can infer the batch size from it. This is helpful with
# soft-prompting or in multimodal implementations built on top of decoder-only language models.
batch_size = 1
for value in model_kwargs.values():
if isinstance(value, tf.Tensor):
batch_size = value.shape[0]
break
return tf.ones((batch_size, 1), dtype=tf.int32) * bos_token_id
@staticmethod
def _extract_past_from_model_output(outputs: ModelOutput):
past_key_values = None
if "past_key_values" in outputs:
past_key_values = outputs.past_key_values
elif "mems" in outputs:
past_key_values = outputs.mems
elif "past_buckets_states" in outputs:
past_key_values = outputs.past_buckets_states
return past_key_values
def _update_model_kwargs_for_generation(
self, outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False
) -> Dict[str, Any]:
# update past_key_values
model_kwargs["past_key_values"] = self._extract_past_from_model_output(outputs)
# update attention mask
if not is_encoder_decoder:
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = tf.concat(
[attention_mask, tf.ones((shape_list(attention_mask)[0], 1), dtype=tf.int32)], axis=-1
)
return model_kwargs
def _update_model_kwargs_for_xla_generation(
self,
model_outputs: ModelOutput,
model_kwargs: Dict[str, Any],
cur_len: int,
max_length: int,
batch_size: int,
is_encoder_decoder: bool = False,
batch_axis: int = 0,
):
def _initialize_attention(model_kwargs, num_padding_values, is_encoder_decoder):
"""initializes the appropriate attention mask -- encoder-decoder models use `decoder_attention_mask`"""
if is_encoder_decoder:
# One 1 for decoder_start_token_id, 0s for the currently-unfilled locations in the past_key_values tensor,
# 1s for the actual input_ids
decoder_attention_mask = tf.concat(
[
tf.ones((batch_size, 1), dtype=tf.int32),
tf.zeros((batch_size, num_padding_values), dtype=tf.int32),
tf.ones((batch_size, 1), dtype=tf.int32),
],
axis=1,
)
mask = {"decoder_attention_mask": decoder_attention_mask}
else:
attention_mask = model_kwargs.pop("attention_mask")
# 0s for the currently-unfilled locations in the past_key_values tensor, 1s for the actual input_ids
attention_mask = tf.concat(
[
attention_mask,
tf.zeros((batch_size, num_padding_values), dtype=attention_mask.dtype),
tf.ones((batch_size, 1), dtype=attention_mask.dtype),
],
axis=1,
)
mask = {"attention_mask": attention_mask}
return mask
def _update_attention(model_kwargs, new_past_index, is_encoder_decoder):
"""updates the appropriate attention mask -- encoder-decoder models use `decoder_attention_mask`"""
update_start = tf.constant([0, 1], dtype=tf.int32) * new_past_index
if is_encoder_decoder:
decoder_attention_mask = model_kwargs.pop("decoder_attention_mask")
decoder_attention_mask_update_slice = tf.ones((batch_size, 1), dtype=decoder_attention_mask.dtype)
decoder_attention_mask = dynamic_update_slice(
decoder_attention_mask, decoder_attention_mask_update_slice, update_start
)
mask = {"decoder_attention_mask": decoder_attention_mask}
else:
attention_mask = model_kwargs.pop("attention_mask")
attention_mask_update_slice = tf.ones((batch_size, 1), dtype=attention_mask.dtype)
attention_mask = dynamic_update_slice(attention_mask, attention_mask_update_slice, update_start)
mask = {"attention_mask": attention_mask}
return mask
def _initialize_past(past_key_values, num_padding_values, batch_axis):
"""initialize past_key_values with zeros -- the structure depends on `batch_axis`"""
if batch_axis == 0:
padding_values = tf.constant([[0, 0], [0, 0], [0, num_padding_values], [0, 0]], dtype=tf.int32)
new_past = ()
for past_layer in past_key_values:
new_past_layer = list(past_layer)
for i in range(len(new_past_layer[:2])):
new_past_layer[i] = tf.pad(past_layer[i], padding_values)
new_past += (tuple(new_past_layer),)
else:
padding_values = tf.scatter_nd(indices=[[3, 1]], updates=[num_padding_values], shape=(5, 2))
new_past = list(past_key_values)
for i in range(len(past_key_values)):
new_past[i] = tf.pad(past_key_values[i], padding_values)
return new_past
def _update_past(past_key_values, new_past_index, batch_axis):
if batch_axis == 0:
slice_start_base = tf.constant([0, 0, 1, 0])
new_past = ()
for past_layer in past_key_values:
new_past_layer = list(past_layer)
for i in range(len(new_past_layer[:2])):
update_slice = past_layer[i][:, :, -1:]
# Write the last slice to the first open location in the padded past_key_values array
# and then truncate the last slice off the array
new_past_layer[i] = dynamic_update_slice(
past_layer[i][:, :, :-1], update_slice, slice_start_base * new_past_index
)
new_past += (tuple(new_past_layer),)
else:
slice_start_base = tf.constant([0, 0, 0, 1, 0])
new_past = [None for _ in range(len(past_key_values))]
for i in range(len(past_key_values)):
update_slice = past_key_values[i][:, :, :, -1:]
# Write the last slice to the first open location in the padded past_key_values array
# and then truncate the last slice off the array
new_past[i] = dynamic_update_slice(
past_key_values[i][:, :, :, :-1], update_slice, slice_start_base * new_past_index
)
return new_past
past_key_values = self._extract_past_from_model_output(model_outputs)
if past_key_values is None:
raise ValueError(
"No known `past_key_values variable` found in model outputs (model outputs keys:"
f" {list(model_outputs.keys())})"
)
is_past_initialized = model_kwargs.pop("past_key_values", None) is not None
if not is_past_initialized:
# The padded version of `past_key_values` has a length of `max_length - 1`, as `past_key_values` holds information relative to
# previous autoregressive generation steps (step 0 has no past_key_values, step 1 has 1 past_key_values value, ..., the last step
# has `max_length - 1` past_key_values values).
num_padding_values = max_length - cur_len - 1
mask = _initialize_attention(model_kwargs, num_padding_values, is_encoder_decoder)
new_past = _initialize_past(past_key_values, num_padding_values, batch_axis)
else:
# The new index of past_key_values to be filled corresponds to the current length of the sequence, with two
# subtractions: -1 because past_key_values holds information regarding previous generation steps (read comment above)
# and -1 again because in an array the index is the length of the array minus 1.
new_past_index = cur_len - 2
mask = _update_attention(model_kwargs, new_past_index, is_encoder_decoder)
new_past = _update_past(past_key_values, new_past_index, batch_axis)
# sets the updated variables (mask and past_key_values)
model_kwargs.update(mask)
model_kwargs["past_key_values"] = tuple(new_past)
return model_kwargs
def _get_logits_warper(
self,
generation_config: GenerationConfig,
) -> TFLogitsProcessorList:
"""
This class returns a [`TFLogitsProcessorList`] list object that contains all relevant [`TFLogitsWarper`]
instances used for multinomial sampling.
"""
# instantiate warpers list
warpers = TFLogitsProcessorList()
# the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
# all samplers can be found in `generation_utils_samplers.py`
if generation_config.temperature is not None and generation_config.temperature != 1.0:
warpers.append(TFTemperatureLogitsWarper(generation_config.temperature))
if generation_config.top_k is not None and generation_config.top_k != 0:
warpers.append(TFTopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=1))
if generation_config.top_p is not None and generation_config.top_p < 1.0:
warpers.append(TFTopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=1))
return warpers
def _get_logits_processor(
self,
generation_config: GenerationConfig,
input_ids_seq_length: int,
logits_processor: Optional[TFLogitsProcessorList],
) -> TFLogitsProcessorList:
"""
This class returns a [`TFLogitsProcessorList`] list object that contains all relevant [`TFLogitsProcessor`]
instances used to modify the scores of the language model head.
"""
processors = TFLogitsProcessorList()
# instantiate processors list
if generation_config.repetition_penalty is not None and generation_config.repetition_penalty != 1.0:
processors.append(TFRepetitionPenaltyLogitsProcessor(penalty=generation_config.repetition_penalty))
if generation_config.no_repeat_ngram_size is not None and generation_config.no_repeat_ngram_size > 0:
processors.append(TFNoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
if generation_config.bad_words_ids is not None:
processors.append(
TFNoBadWordsLogitsProcessor(generation_config.bad_words_ids, generation_config.eos_token_id)
)
if (
generation_config.min_length is not None
and generation_config.eos_token_id is not None
and generation_config.min_length > 0
):
processors.append(TFMinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id))
if generation_config.forced_bos_token_id is not None:
processors.append(TFForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id))
if generation_config.forced_eos_token_id is not None:
processors.append(
TFForcedEOSTokenLogitsProcessor(generation_config.max_length, generation_config.forced_eos_token_id)
)
if generation_config.suppress_tokens is not None:
processors.append(TFSuppressTokensLogitsProcessor(generation_config.suppress_tokens))
if generation_config.begin_suppress_tokens is not None:
begin_index = input_ids_seq_length
begin_index = (
begin_index
if (input_ids_seq_length > 1 or generation_config.forced_bos_token_id is None)
else begin_index + 1
)
if generation_config.forced_decoder_ids is not None:
begin_index += generation_config.forced_decoder_ids[-1][
0
] # generation starts after the last token that is forced
processors.append(
TFSuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index)
)
if generation_config.forced_decoder_ids is not None:
processors.append(TFForceTokensLogitsProcessor(generation_config.forced_decoder_ids))
processors = self._merge_criteria_processor_list(processors, logits_processor)
return processors
def _merge_criteria_processor_list(
self,
default_list: TFLogitsProcessorList,
custom_list: TFLogitsProcessorList,
) -> TFLogitsProcessorList:
if len(custom_list) == 0:
return default_list
for default in default_list:
for custom in custom_list:
if type(custom) is type(default):
object_type = "logits processor"
raise ValueError(
f"A custom {object_type} of type {type(custom)} with values {custom} has been passed to"
f" `generate`, but it has already been created with the values {default}. {default} has been"
" created by passing the corresponding arguments to generate or by the model's config default"
f" values. If you just want to change the default values of {object_type} consider passing"
f" them as arguments to `generate` instead of using a custom {object_type}."
)
default_list.extend(custom_list)
return default_list
def greedy_search(
self,
input_ids: tf.Tensor,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
logits_processor: Optional[TFLogitsProcessorList] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFGreedySearchOutput, tf.Tensor]:
r"""
Generates sequences for models with a language modeling head using greedy decoding.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `call` function of the model. If
model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFGreedySearchDecoderOnlyOutput`], [`~generation.TFGreedySearchEncoderDecoderOutput`] or
`tf.Tensor`: A `tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFGreedySearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.TFGreedySearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... TFAutoModelForCausalLM,
... TFLogitsProcessorList,
... TFMinLengthLogitsProcessor,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a PAD token
>>> model.generation_config.pad_token_id = model.generation_config.eos_token_id
>>> input_prompt = "Today is a beautiful day, and"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [
... TFMinLengthLogitsProcessor(15, eos_token_id=model.generation_config.eos_token_id),
... ]
... )
>>> outputs = model.greedy_search(input_ids, logits_processor=logits_processor)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
["Today is a beautiful day, and I'm so happy to be here. I'm so happy to"]
```"""
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
use_cache = model_kwargs.pop("use_cache", self.generation_config.use_cache)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any(model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")) else 0
# some models, like XLNet, need more than the last token in the presence of past_key_values
needs_full_input = "use_mems" in set(inspect.signature(self.prepare_inputs_for_generation).parameters.keys())
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, cur_len = shape_list(input_ids)
# initialize `generated` (`input_ids` padded with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,), dtype=tf.bool)
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define condition fn
def greedy_search_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
"""state termination condition fn."""
return ~tf.reduce_all(finished_sequences)
# define condition fn
def greedy_search_body_fn(generated, finished_sequences, cur_len, model_kwargs):
"""state update fn."""
if model_kwargs.get("past_key_values") is None or needs_full_input:
input_ids = generated[:, :cur_len]
else:
input_ids = tf.expand_dims(generated[:, cur_len - 1], -1)
model_inputs = self.prepare_inputs_for_generation(input_ids, use_cache=use_cache, **model_kwargs)
# forward pass to get next token logits
model_outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
next_token_logits = model_outputs.logits[:, -1]
# pre-process distribution
next_tokens_scores = logits_processor(generated, next_token_logits, cur_len)
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(next_tokens_scores)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(model_outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# argmax
next_tokens = tf.argmax(next_tokens_scores, axis=-1, output_type=tf.int32)
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pad_token_id * (1 - unfinished_seq)
next_token_is_eos = tf.math.reduce_any(
tf.equal(
tf.broadcast_to(next_tokens, (len(eos_token_id), batch_size)), tf.expand_dims(eos_token_id, -1)
),
axis=0,
)
finished_sequences = finished_sequences | next_token_is_eos
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1
# update model_kwargs
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_size,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
model_outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if we don't cache past_key_values key values we need the whole input
if model_kwargs.get("past_key_values", None) is None:
# let's throw out `past_key_values` since we don't want `None` tensors
model_kwargs.pop("past_key_values", None)
return generated, finished_sequences, cur_len, model_kwargs
# 5. run generation
# 1st generation step has to be run before to initialize `past_key_values`
generated, finished_sequences, cur_len, model_kwargs = greedy_search_body_fn(
generated, finished_sequences, cur_len, model_kwargs
)
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
maximum_iterations = max_length - cur_len
generated, _, cur_len, _ = tf.while_loop(
greedy_search_cond_fn,
greedy_search_body_fn,
(generated, finished_sequences, cur_len, model_kwargs),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
if not use_xla:
# cut for backward compatibility
generated = generated[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
scores = tuple(scores) if scores is not None else None
decoder_attentions = tuple(decoder_attentions) if decoder_attentions is not None else None
cross_attentions = tuple(cross_attentions) if cross_attentions is not None else None
decoder_hidden_states = tuple(decoder_hidden_states) if decoder_hidden_states is not None else None
return TFGreedySearchEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFGreedySearchDecoderOnlyOutput(
sequences=generated,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return generated
def sample(
self,
input_ids: tf.Tensor,
logits_processor: Optional[TFLogitsProcessorList] = None,
logits_warper: Optional[TFLogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
seed: Optional[Tuple[int, int]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFSampleOutput, tf.Tensor]:
r"""
Generates sequences for models with a language modeling head using multinomial sampling.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsWarper`]
used to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
seed (`List[int]`, *optional*):
Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the
`seed` argument from stateless functions in `tf.random`.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific kwargs will be forwarded to the `call` function of the model. If model is an
encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFSampleDecoderOnlyOutput`], [`~generation.TFSampleEncoderDecoderOutput`] or `tf.Tensor`: A
`tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFSampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.TFSampleEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import (
... AutoTokenizer,
... TFAutoModelForCausalLM,
... TFLogitsProcessorList,
... TFMinLengthLogitsProcessor,
... TFTopKLogitsWarper,
... TFTemperatureLogitsWarper,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = TFAutoModelForCausalLM.from_pretrained("gpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
>>> model.generation_config.pad_token_id = model.generation_config.eos_token_id
>>> input_prompt = "Today is a beautiful day, and"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf").input_ids
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [
... TFMinLengthLogitsProcessor(15, eos_token_id=model.generation_config.eos_token_id),
... ]
... )
>>> # instantiate logits processors
>>> logits_warper = TFLogitsProcessorList(
... [
... TFTopKLogitsWarper(50),
... TFTemperatureLogitsWarper(0.7),
... ]
... )
>>> tf.random.set_seed(0)
>>> outputs = model.sample(input_ids, logits_processor=logits_processor, logits_warper=logits_warper)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Today is a beautiful day, and I love my country. But when I look at Donald Trump,']
```"""
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
use_cache = model_kwargs.pop("use_cache", self.generation_config.use_cache)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any(model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")) else 0
# some models, like XLNet, need more than the last token in the presence of past_key_values
needs_full_input = "use_mems" in set(inspect.signature(self.prepare_inputs_for_generation).parameters.keys())
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, cur_len = shape_list(input_ids)
# initialize `generated` (pre-populated with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,), dtype=tf.bool)
# 4. define "xla-compile-able" stop-condition and auto-regressive function
def sample_cond_fn(generated, finished_sequences, cur_len, model_kwargs):
return ~tf.reduce_all(finished_sequences)
def sample_body_fn(generated, finished_sequences, cur_len, model_kwargs):
if model_kwargs.get("past_key_values") is None or needs_full_input:
input_ids = generated[:, :cur_len]
else:
input_ids = tf.expand_dims(generated[:, cur_len - 1], -1)
model_inputs = self.prepare_inputs_for_generation(input_ids, use_cache=use_cache, **model_kwargs)
# forward pass to get next token logits
model_outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
next_token_logits = model_outputs.logits[:, -1]
# pre-process distribution
next_tokens_scores = logits_processor(generated, next_token_logits, cur_len)
next_tokens_scores = logits_warper(generated, next_tokens_scores, cur_len)
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(next_tokens_scores)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(model_outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# sample
if seed is not None:
sample_seed = seed
else:
sample_seed = tf.experimental.numpy.random.randint(tf.int32.min, tf.int32.max, (2,), dtype=tf.int32)
next_tokens = tf.squeeze(
tf.random.stateless_categorical(
logits=next_tokens_scores, num_samples=1, seed=sample_seed, dtype=tf.int32
),
axis=1,
)
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pad_token_id * (1 - unfinished_seq)
next_token_is_eos = tf.math.reduce_any(
tf.equal(
tf.broadcast_to(next_tokens, (len(eos_token_id), batch_size)), tf.expand_dims(eos_token_id, -1)
),
axis=0,
)
finished_sequences = finished_sequences | next_token_is_eos
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1
# update model_kwargs
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_size,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
model_outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if we don't cache past_key_values key values we need the whole input
if model_kwargs.get("past_key_values", None) is None:
# let's throw out `past_key_values` since we don't want `None` tensors
model_kwargs.pop("past_key_values", None)
return generated, finished_sequences, cur_len, model_kwargs
# 5. run generation
# 1st generation step has to be run before to initialize `past_key_values`
generated, finished_sequences, cur_len, model_kwargs = sample_body_fn(
generated, finished_sequences, cur_len, model_kwargs
)
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
maximum_iterations = max_length - cur_len
generated, _, cur_len, _ = tf.while_loop(
sample_cond_fn,
sample_body_fn,
(generated, finished_sequences, cur_len, model_kwargs),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
if not use_xla:
# cut for backward compatibility
generated = generated[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
scores = tuple(scores) if scores is not None else None
decoder_attentions = tuple(decoder_attentions) if decoder_attentions is not None else None
cross_attentions = tuple(cross_attentions) if cross_attentions is not None else None
decoder_hidden_states = tuple(decoder_hidden_states) if decoder_hidden_states is not None else None
return TFSampleEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFSampleDecoderOnlyOutput(
sequences=generated,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return generated
@staticmethod
def _gather_beams(nested, beam_indices, batch_axis=0):
"""Gathers the beam slices indexed by beam_indices into new beam array."""
def gather_fn(tensor):
if batch_axis > 0:
# pushes all dimentions before the batch to the end, so we get (batch, beam_id, ...)
perm = tf.concat((tf.range(tf.rank(tensor))[batch_axis:], tf.range(batch_axis)), axis=0)
tensor = tf.transpose(tensor, perm=perm)
gathered_tensor = tf.gather(params=tensor, indices=beam_indices, axis=1, batch_dims=1)
if batch_axis > 0:
# transposes back to the original dimensions
perm = tf.concat((tf.range(tf.rank(tensor))[batch_axis:], tf.range(batch_axis)), axis=0)
perm = tf.math.invert_permutation(perm)
gathered_tensor = tf.transpose(gathered_tensor, perm=perm)
return gathered_tensor
return tf.nest.map_structure(gather_fn, nested)
def beam_search(
self,
input_ids: tf.Tensor,
do_sample: bool = False,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
length_penalty: Optional[float] = None,
early_stopping: Optional[Union[bool, str]] = None,
logits_processor: Optional[TFLogitsProcessorList] = None,
logits_warper: Optional[TFLogitsProcessorList] = None,
num_return_sequences: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFBeamSearchOutput, TFBeamSampleOutput, tf.Tensor]:
r"""
Generates sequences for models with a language modeling head using beam search. If `do_sample` is `False`, uses
a greedy approach, otherwise does multinomial sampling without replacement.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent
to the sequence length, which in turn is used to divide the score of the sequence. Since the score is
the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences,
while `length_penalty` < 0.0 encourages shorter sequences.
early_stopping (`bool` or `str`, *optional*, defaults to `False`):
Controls the stopping condition for beam-based methods, like beam-search. It accepts the following
values: `True`, where the generation stops as soon as there are `num_beams` complete candidates;
`False`, where an heuristic is applied and the generation stops when is it very unlikely to find better
candidates; `"never"`, where the beam search procedure only stops when there cannot be better
candidates (canonical beam search algorithm).
logits_processor (`[TFLogitsProcessorList]`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsWarper`]
used to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
num_return_sequences(`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific kwargs will be forwarded to the `call` function of the model. If model is an
encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFBeamSearchDecoderOnlyOutput`], [`~generation.TFBeamSearchEncoderDecoderOutput`] or
`tf.Tensor`: A `tf.Tensor` containing the generated tokens (default behaviour) or a
[`~generation.TFBeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.TFBeamSearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... TFAutoModelForSeq2SeqLM,
... TFLogitsProcessorList,
... TFMinLengthLogitsProcessor,
... )
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> encoder_input_str = "translate English to German: How old are you?"
>>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="tf").input_ids
>>> # lets run beam search using 3 beams
>>> num_beams = 3
>>> # define decoder start token ids
>>> input_ids = tf.ones((1, num_beams, 1), dtype=tf.int32)
>>> input_ids = input_ids * model.generation_config.decoder_start_token_id
>>> # add encoder_outputs to model keyword arguments
>>> encoder_outputs = model.get_encoder()(encoder_input_ids, return_dict=True)
>>> encoder_outputs.last_hidden_state = tf.repeat(
... tf.expand_dims(encoder_outputs.last_hidden_state, axis=0), num_beams, axis=1
... )
>>> model_kwargs = {"encoder_outputs": encoder_outputs}
>>> # instantiate logits processors
>>> logits_processor = TFLogitsProcessorList(
... [TFMinLengthLogitsProcessor(5, eos_token_id=model.generation_config.eos_token_id)]
... )
>>> outputs = model.beam_search(input_ids, logits_processor=logits_processor, **model_kwargs)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Wie alt bist du?']
```"""
def flatten_beam_dim(tensor, batch_axis=0):
"""Flattens the first two dimensions of a non-scalar array."""
shape = shape_list(tensor)
return tf.reshape(
tensor,
shape[:batch_axis] + [shape[batch_axis] * shape[batch_axis + 1]] + shape[batch_axis + 2 :],
)
def unflatten_beam_dim(tensor, num_beams, batch_axis=0):
"""Unflattens the first, flat batch*beam dimension of a non-scalar array."""
shape = shape_list(tensor)
return tf.reshape(tensor, shape[:batch_axis] + [-1, num_beams] + shape[batch_axis + 1 :])
# 1. init beam_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.generation_config.num_return_sequences
)
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
length_penalty = length_penalty if length_penalty is not None else self.generation_config.length_penalty
early_stopping = early_stopping if early_stopping is not None else self.generation_config.early_stopping
use_cache = model_kwargs.pop("use_cache", self.generation_config.use_cache)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any(model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")) else 0
# some models, like XLNet, need more than the last token in the presence of past_key_values
needs_full_input = "use_mems" in set(inspect.signature(self.prepare_inputs_for_generation).parameters.keys())
# 2. init `attentions`, `hidden_states`, and `scores` tuples
all_scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, num_beams, cur_len = shape_list(input_ids)
# per batch, beam-item holding current token in loop, pre-populated with `pad_token_id`
input_ids_padding = tf.ones((batch_size, num_beams, max_length - cur_len), dtype=tf.int32) * (
pad_token_id or 0
)
running_sequences = tf.concat([input_ids, input_ids_padding], axis=-1)
sequences = tf.ones((batch_size, num_beams, max_length), dtype=tf.int32) * (pad_token_id or 0)
# per batch,beam-item state bit indicating if sentence has finished.
is_sent_finished = tf.zeros((batch_size, num_beams), dtype=tf.bool)
# per batch, beam-item score, logprobs
running_scores = tf.tile(
tf.expand_dims(tf.convert_to_tensor([0.0] + [-1.0e9] * (num_beams - 1)), axis=0), [batch_size, 1]
)
scores = tf.ones((batch_size, num_beams)) * -1.0e9
# per batch beam indices
running_beam_indices = tf.ones((batch_size, num_beams, max_length), dtype=tf.int32) * -1
beam_indices = tf.ones((batch_size, num_beams, max_length), dtype=tf.int32) * -1
# flatten beam dim
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = flatten_beam_dim(
model_kwargs["encoder_outputs"]["last_hidden_state"]
)
if "attention_mask" in model_kwargs:
model_kwargs["attention_mask"] = flatten_beam_dim(model_kwargs["attention_mask"])
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define stop-condition and auto-regressive function
def beam_search_cond_fn(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
sequences,
scores,
beam_indices,
is_sent_finished,
model_kwargs,
):
"""
Beam Search termination condition function -- halts the generation loop if any of these conditions becomes
False
"""
# 1. is less than max length?
not_max_length_yet = cur_len < max_length
# 2. can the new beams still improve?
# early_stopping == False -> apply heuristic = always get the best score from `cur_len`. See the discussion
# below for more details.
# https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
# early_stopping == "never" -> compute the best score from max_length or cur_len, depending on the sign of
# length_penalty. Positive length_penalty favors longer sequences, thus we use max_length there.
if early_stopping == "never" and length_penalty > 0.0:
best_running_score = running_scores[:, :1] / (max_length**length_penalty)
else:
best_running_score = running_scores[:, :1] / (tf.cast(cur_len, dtype=tf.float32) ** length_penalty)
worst_finished_score = tf.where(
is_sent_finished, tf.math.reduce_min(scores, axis=1, keepdims=True), -1.0e9
)
improvement_still_possible = tf.math.reduce_any(best_running_score > worst_finished_score)
# 3. is there still a beam that has not finished?
still_open_beam = ~(tf.math.reduce_all(is_sent_finished) & (early_stopping is True))
return not_max_length_yet & still_open_beam & improvement_still_possible
def beam_search_body_fn(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
sequences,
scores,
beam_indices,
is_sent_finished,
model_kwargs,
):
"""
Beam Search iterative update function -- each iteration adds a new token and updates the best sequences
seen so far
"""
# 1. Forward current tokens
if model_kwargs.get("past_key_values") is None or needs_full_input:
input_ids = running_sequences[:, :, :cur_len]
else:
input_ids = tf.expand_dims(running_sequences[:, :, cur_len - 1], -1)
model_inputs = self.prepare_inputs_for_generation(
flatten_beam_dim(input_ids), use_cache=use_cache, **model_kwargs
)
model_outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
logits = unflatten_beam_dim(model_outputs.logits[:, -1], num_beams)
# 2. Compute log probs
# get log probabilities from logits, process logits with processors (*e.g.* min_length, ...), and
# add new logprobs to existing running logprobs scores.
log_probs = tf.nn.log_softmax(logits)
log_probs = logits_processor(flatten_beam_dim(running_sequences), flatten_beam_dim(log_probs), cur_len)
log_probs = unflatten_beam_dim(log_probs, num_beams)
log_probs_processed = log_probs
log_probs = log_probs + tf.expand_dims(running_scores, axis=2)
if do_sample:
# Note: logits warpers are intentionally applied after adding running beam scores. On some logits
# warpers (like top_p) this is indiferent, but on others (like temperature) it is not. For reference,
# see https://github.com/huggingface/transformers/pull/5420#discussion_r449779867
log_probs = logits_warper(flatten_beam_dim(running_sequences), flatten_beam_dim(log_probs), cur_len)
log_probs = unflatten_beam_dim(log_probs, num_beams)
vocab_size = log_probs.shape[2]
log_probs = tf.reshape(log_probs, (batch_size, num_beams * vocab_size))
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
all_scores.append(
logits_warper(
flatten_beam_dim(running_sequences), flatten_beam_dim(log_probs_processed), cur_len
)
)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(model_outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(model_outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(model_outputs.hidden_states)
# 3. Retrieve top-K
# Each item in batch has num_beams * vocab_size candidate sequences. For each item, get the top 2*k
# candidates with the highest log-probabilities. We gather the top 2*K beams here so that even if the
# best K sequences reach EOS simultaneously, we have another K sequences remaining to continue the live
# beam search.
# Gather the top 2*K scores from _all_ beams.
# Gather 2*k top beams.
# Recover the beam index by floor division.
# Recover token id by modulo division and expand Id array for broadcasting.
# Update sequences for the 2*K top-k new sequences.
beams_to_keep = 2 * num_beams
if do_sample:
topk_indices = sample_without_replacement(log_probs, beams_to_keep)
topk_log_probs = tf.gather(log_probs, topk_indices, axis=1, batch_dims=1)
else:
topk_log_probs, topk_indices = tf.math.top_k(log_probs, k=beams_to_keep)
topk_current_beam_indices = topk_indices // vocab_size
topk_running_beam_indices = self._gather_beams(running_beam_indices, topk_current_beam_indices)
topk_running_sequences = self._gather_beams(running_sequences, topk_current_beam_indices)
topk_ids = topk_indices % vocab_size
# writes the new token
indices_batch = tf.repeat(tf.range(batch_size), [beams_to_keep])
indices_beam = tf.tile(tf.range(beams_to_keep), [batch_size])
update_indices = tf.stack(
[indices_batch, indices_beam, tf.broadcast_to(cur_len, [batch_size * beams_to_keep])], axis=-1
)
topk_sequences = tf.tensor_scatter_nd_update(
tensor=topk_running_sequences,
indices=update_indices,
updates=tf.reshape(topk_ids, [batch_size * beams_to_keep]),
)
# we want to store the beam indices with batch information -> real beam index = beam index % num beams
batch_modified_indices = topk_current_beam_indices + tf.broadcast_to(
tf.expand_dims(tf.range(batch_size) * num_beams, axis=1), topk_current_beam_indices.shape
)
topk_beam_indices = tf.tensor_scatter_nd_update(
tensor=topk_running_beam_indices,
indices=update_indices,
updates=tf.reshape(batch_modified_indices, [batch_size * beams_to_keep]),
)
# 4. Check which sequences have ended
# Update current sequences: Did the top `num_beams` sequences reach an end marker?
# To prevent these just finished sequences from being added to the current sequences
# set of active beam search sequences, set their log probs to a very large negative value.
if eos_token_id is None:
eos_in_next_token = tf.zeros(topk_sequences[:, :, cur_len].shape, dtype=tf.bool)
else:
eos_in_next_token = tf.math.reduce_any(
tf.equal(
tf.broadcast_to(
topk_sequences[:, :, cur_len], [len(eos_token_id)] + topk_sequences[:, :, cur_len].shape
),
tf.expand_dims(tf.expand_dims(eos_token_id, -1), -1),
),
axis=0,
)
did_topk_just_finished = eos_in_next_token & tf.broadcast_to(
tf.concat((tf.ones((num_beams), dtype=tf.bool), tf.zeros((num_beams), dtype=tf.bool)), axis=0),
shape_list(eos_in_next_token),
)
# non-top `num_beams` eos tokens can't be used to finish a beam, but the others can't be used in the next
# running sentences either
running_topk_log_probs = topk_log_probs + tf.cast(eos_in_next_token, tf.float32) * -1.0e9
# 5. Get running sequences scores for next
# Determine the top k beam indices (from top 2*k beams) from log probs and gather top k beams
# (from top 2*k beams).
next_topk_indices = tf.math.top_k(running_topk_log_probs, k=num_beams)[1]
next_running_sequences, next_running_scores, next_running_beam_indices = self._gather_beams(
[topk_sequences, running_topk_log_probs, topk_beam_indices], next_topk_indices
)
# 6. Process topk logits
# Further process log probs:
# - add length penalty
# - make sure no scores can be added anymore if beam is full
# - make sure still running sequences cannot be chosen as finalized beam
topk_log_probs = topk_log_probs / (tf.cast(cur_len, dtype=tf.float32) ** length_penalty)
beams_in_batch_are_full = tf.broadcast_to(
tf.math.reduce_all(is_sent_finished, axis=-1, keepdims=True), shape_list(did_topk_just_finished)
) & (early_stopping is True)
add_penalty = ~did_topk_just_finished | beams_in_batch_are_full
topk_log_probs += tf.cast(add_penalty, tf.float32) * -1.0e9
# 7. Get scores, sequences, is sentence finished for next.
# Combine sequences, scores, and flags along the beam dimension and compare new finished sequence scores
# to existing finished scores and select the best from the new set of beams
merged_sequences = tf.concat([sequences, topk_sequences], axis=1)
merged_scores = tf.concat([scores, topk_log_probs], axis=1)
merged_beams = tf.concat([beam_indices, topk_beam_indices], axis=1)
merged_is_sent_finished = tf.concat([is_sent_finished, did_topk_just_finished], axis=1)
topk_merged_indices = tf.math.top_k(merged_scores, k=num_beams)[1]
next_sequences, next_scores, next_beam_indices, next_is_sent_finished = self._gather_beams(
[merged_sequences, merged_scores, merged_beams, merged_is_sent_finished], topk_merged_indices
)
# 8. Prepare data for the next iteration
# Determine the top k beam indices from the original set of all beams. With these, gather the top k
# beam-associated caches.
cur_len = cur_len + 1
if "past_key_values" in model_outputs:
cache = tf.nest.map_structure(
lambda tensor: unflatten_beam_dim(tensor, num_beams, batch_axis=cache_batch_axis),
model_outputs.past_key_values,
)
next_running_indices = self._gather_beams(topk_current_beam_indices, next_topk_indices)
next_cache = self._gather_beams(cache, next_running_indices, batch_axis=cache_batch_axis)
model_outputs["past_key_values"] = tf.nest.map_structure(
lambda tensor: flatten_beam_dim(tensor, batch_axis=cache_batch_axis), next_cache
)
if use_xla:
next_model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=model_outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=(batch_size * num_beams),
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
next_model_kwargs = self._update_model_kwargs_for_generation(
model_outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if we don't cache past_key_values key values we need the whole input
if model_kwargs.get("past_key_values", None) is None:
# let's throw out `past_key_values` since we don't want `None` tensors
model_kwargs.pop("past_key_values", None)
return (
cur_len,
next_running_sequences,
next_running_scores,
next_running_beam_indices,
next_sequences,
next_scores,
next_beam_indices,
next_is_sent_finished,
next_model_kwargs,
)
# 5. run generation
# 1st generation step has to be run before to initialize `past_key_values` (if active)
(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
sequences,
scores,
beam_indices,
is_sent_finished,
model_kwargs,
) = beam_search_body_fn(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
sequences,
scores,
beam_indices,
is_sent_finished,
model_kwargs,
)
# 2-to-n generation steps can then be run in autoregressive fashion (only in case 1st generation step does
# NOT yield EOS token though)
maximum_iterations = max_length - cur_len
(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
sequences,
scores,
beam_indices,
is_sent_finished,
_,
) = tf.while_loop(
beam_search_cond_fn,
beam_search_body_fn,
(
cur_len,
running_sequences,
running_scores,
running_beam_indices,
sequences,
scores,
beam_indices,
is_sent_finished,
model_kwargs,
),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
# Account for the edge-case where there are no finished sequences for a particular batch item. If so, return
# running sequences for that batch item.
none_finished = tf.math.reduce_any(is_sent_finished, axis=1)
sequences = tf.where(none_finished[:, None, None], sequences, running_sequences)
beam_indices = tf.where(none_finished[:, None, None], beam_indices, running_beam_indices)
# Apply the length penalty so that running scores match the finalized scores if they are used
running_scores = running_scores / (tf.cast(cur_len, dtype=tf.float32) ** length_penalty)
scores = tf.where(none_finished[:, None], scores, running_scores)
# Take best beams for each batch (the score is sorted in descending order)
sequences = flatten_beam_dim(sequences[:, :num_return_sequences, :])
scores = flatten_beam_dim(scores[:, :num_return_sequences])
beam_indices = flatten_beam_dim(beam_indices[:, :num_return_sequences, :])
if not use_xla:
# Cut for backward compatibility
sequences = sequences[:, :cur_len]
beam_indices = beam_indices[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
output_cls = TFBeamSampleEncoderDecoderOutput if do_sample else TFBeamSearchEncoderDecoderOutput
return output_cls(
sequences=sequences,
sequences_scores=scores,
scores=all_scores,
beam_indices=beam_indices,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
output_cls = TFBeamSampleDecoderOnlyOutput if do_sample else TFBeamSearchDecoderOnlyOutput
return output_cls(
sequences=sequences,
sequences_scores=scores,
scores=all_scores,
beam_indices=beam_indices,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return sequences
def contrastive_search(
self,
input_ids: tf.Tensor,
top_k: Optional[int] = 1,
penalty_alpha: Optional[float] = 0,
logits_processor: Optional[TFLogitsProcessorList] = None,
logits_warper: Optional[TFLogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
**model_kwargs,
) -> Union[TFContrastiveSearchOutput, tf.Tensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **contrastive search** and can
be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
Parameters:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
top_k (`int`, *optional*, defaults to 1):
The size of the candidate set that is used to re-rank for contrastive search
penalty_alpha (`float`, *optional*, defaults to 0):
The degeneration penalty for contrastive search; activate when it is larger than 0
logits_processor (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`TFLogitsProcessorList`, *optional*):
An instance of [`TFLogitsProcessorList`]. List of instances of class derived from [`TFLogitsWarper`]
used to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `call` function of the model. If
model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.TFContrastiveSearchDecoderOnlyOutput`],
[`~generation.TFContrastiveSearchEncoderDecoderOutput`] or `tf.Tensor`: A `tf.Tensor` containing the
generated tokens (default behaviour) or a [`~generation.TFContrastiveySearchDecoderOnlyOutput`] if
`model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a
[`~generation.TFContrastiveSearchEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import AutoTokenizer, TFAutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
>>> model = TFAutoModelForCausalLM.from_pretrained("facebook/opt-125m")
>>> # set pad_token_id to eos_token_id because OPT does not have a PAD token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "DeepMind Company is"
>>> input_ids = tokenizer(input_prompt, return_tensors="tf")
>>> outputs = model.contrastive_search(**input_ids, penalty_alpha=0.6, top_k=4, max_length=64)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['DeepMind Company is a company that focuses on the development and commercialization of artificial intelligence (AI). DeepMind’s mission is to help people understand and solve problems that are difficult to solve in the world today.\n\nIn this post, we talk about the benefits of deep learning in business and how it']
```"""
def gather_best_candidate(nested, selected_idx_stacked, batch_axis=0):
"""Gathers the slices indexed by selected_idx_stacked from a potentially nested structure of tensors."""
def gather_fn(tensor):
gathered_tensor = tf.gather(params=tensor, indices=selected_idx_stacked, axis=batch_axis)
return gathered_tensor
return tf.nest.map_structure(gather_fn, nested)
# 1. init greedy_search values
logits_processor = logits_processor if logits_processor is not None else TFLogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else TFLogitsProcessorList()
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
use_cache = True # In contrastive search, we always use cache
model_kwargs.pop("use_cache", None)
use_xla = not tf.executing_eagerly()
# TODO (Joao): fix cache format or find programatic way to detect cache index
# GPT2 and other models has a slightly different cache structure, with a different batch axis
model_name = str(self.decoder) if "EncoderDecoder" in str(self) else str(self)
cache_batch_axis = 1 if any(model_prefix in model_name for model_prefix in ("TFGPT2", "TFCTRL")) else 0
# 2. init `attentions`, `hidden_states`, and `scores` tuples
scores = [] if (return_dict_in_generate and output_scores) else None
decoder_attentions = [] if (return_dict_in_generate and output_attentions) else None
cross_attentions = [] if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = [] if (return_dict_in_generate and output_hidden_states) else None
# 3. init tensors to use for "xla-compileable" generate function
batch_size, cur_len = shape_list(input_ids)
# initialize `generated` (`input_ids` padded with `pad_token_id`), `finished_sequences`
input_ids_padding = tf.ones((batch_size, max_length - cur_len), dtype=tf.int32) * (pad_token_id or 0)
generated = tf.concat([input_ids, input_ids_padding], axis=-1)
finished_sequences = tf.zeros((batch_size,), dtype=tf.bool)
# 4. define "xla-compile-able" stop-condition and auto-regressive function
# define condition fn
def contrastive_search_cond_fn(
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
):
"""state termination condition fn."""
return ~tf.reduce_all(finished_sequences)
# define condition fn
def contrastive_search_body_fn(
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
):
"""state update fn."""
# if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
# (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
if model_kwargs.get("past_key_values") is None:
# prepare inputs
model_inputs = self.prepare_inputs_for_generation(
generated[:, :cur_len], use_cache=use_cache, **model_kwargs
)
# encode the given prefix and prepare model inputs; encoder-decoder model process the prefix and save
# the `encoder_outputs`
outputs = self(
**model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
)
# last decoder hidden states will be used to compute the degeneration penalty (cosine similarity with
# previous tokens)
if self.config.is_encoder_decoder:
last_hidden_states = outputs.decoder_hidden_states[-1]
else:
last_hidden_states = outputs.hidden_states[-1]
# XLA: last_hidden_states normally grows at each step, but in XLA it is padded so as to be used across
# iterations (with fixed shapes)
if use_xla:
last_hidden_states = tf.pad(last_hidden_states, [[0, 0], [0, max_length - cur_len], [0, 0]])
# next logit for contrastive search to select top-k candidate tokens
logit_for_next_step = outputs.logits[:, -1, :]
if use_xla:
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=outputs,
model_kwargs=model_kwargs,
cur_len=cur_len,
max_length=max_length,
batch_size=batch_size,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# Expands model inputs top_k times, for batched forward passes (akin to beam search).
_, model_kwargs = self._expand_inputs_for_generation(
expand_size=top_k, is_encoder_decoder=self.config.is_encoder_decoder, **model_kwargs
)
past_key_values = model_kwargs.get("past_key_values")
if past_key_values is None:
raise ValueError(
f"{self.__class__.__name__} does not support caching and therefore **can't** be used "
"for contrastive search."
)
elif (
not isinstance(past_key_values[0], (tuple, tf.Tensor))
or past_key_values[0][0].shape[0] != batch_size
):
raise ValueError(
f"{self.__class__.__name__} does not have a standard cache format and therefore **can't** be "
"used for contrastive search without further modifications."
)
else:
logit_for_next_step = next_step_cached_variables["logit_for_next_step"]
last_hidden_states = next_step_cached_variables["last_hidden_states"]
outputs = next_step_cached_variables["outputs"]
# contrastive_search main logic start:
# contrastive search decoding consists of two steps: (1) candidate tokens recall; (2) candidate re-rank by
# degeneration penalty
logit_for_next_step = logits_processor(generated, logit_for_next_step, cur_len)
logit_for_next_step = logits_warper(generated, logit_for_next_step, cur_len)
next_probs = stable_softmax(logit_for_next_step, axis=-1)
top_k_probs, top_k_ids = tf.math.top_k(next_probs, k=top_k)
# Store scores, attentions and hidden_states when required
if not use_xla and return_dict_in_generate:
if output_scores:
scores.append(logit_for_next_step)
if output_attentions and self.config.is_encoder_decoder:
decoder_attentions.append(outputs.decoder_attentions)
elif output_attentions and not self.config.is_encoder_decoder:
decoder_attentions.append(outputs.attentions)
if self.config.is_encoder_decoder:
cross_attentions.append(outputs.cross_attentions)
if output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(outputs.decoder_hidden_states)
elif output_hidden_states and self.config.is_encoder_decoder:
decoder_hidden_states.append(outputs.hidden_states)
# Replicates the new past_key_values to match the `top_k` candidates
model_kwargs["past_key_values"] = tf.nest.map_structure(
lambda tensor: tf.repeat(tensor, top_k, axis=cache_batch_axis), model_kwargs["past_key_values"]
)
# compute the candidate tokens by the language model and collects their hidden_states
next_model_inputs = self.prepare_inputs_for_generation(
tf.reshape(top_k_ids, [-1, 1]), use_cache=use_cache, **model_kwargs
)
outputs = self(
**next_model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
)
next_past_key_values = self._extract_past_from_model_output(outputs)
logits = outputs.logits[:, -1, :]
# name is different for encoder-decoder and decoder-only models
if self.config.is_encoder_decoder:
next_hidden = outputs.decoder_hidden_states[-1]
full_hidden_states = outputs.decoder_hidden_states
else:
next_hidden = outputs.hidden_states[-1]
full_hidden_states = outputs.hidden_states
context_hidden = tf.repeat(last_hidden_states[:, :cur_len, :], top_k, axis=0)
# compute the degeneration penalty and re-rank the candidates based on the degeneration penalty and the
# model confidence
selected_idx = _ranking_fast(context_hidden, next_hidden, top_k_probs, penalty_alpha, top_k)
# converts indices to a dimension of top_k to the stacked top_k * batch_size dimension, for indexing
# without a need to reshape on tensors that have these two dimensions stacked
selected_idx_stacked = selected_idx + tf.range(selected_idx.shape[0], dtype=tf.int64) * top_k
# prepare for the next step: (1) next token_id; (2) past_key_values; (3) last_hidden_states for computing
# the degeneration penalty; (4) logits for selecting next top-k candidates; (5) selected tokens scores
# (model confidence minus degeneration penalty); (6) decoder hidden_states
next_tokens = tf.gather(top_k_ids, selected_idx, axis=1, batch_dims=1)
next_hidden = gather_best_candidate(next_hidden, selected_idx_stacked)
# XLA: last_hidden_states normally grows at each step, but in XLA it is padded so as to be used across
# iterations (with fixed shapes)
if use_xla:
last_hidden_states = dynamic_update_slice(last_hidden_states, next_hidden, [0, cur_len, 0])
else:
last_hidden_states = tf.concat([last_hidden_states, next_hidden], axis=1)
next_decoder_hidden_states = gather_best_candidate(full_hidden_states, selected_idx_stacked)
next_past_key_values = gather_best_candidate(
next_past_key_values, selected_idx_stacked, batch_axis=cache_batch_axis
)
logit_for_next_step = gather_best_candidate(logits, selected_idx_stacked)
# Rebuilds the relevant parts of the model output for the selected token, for use in the next iteration
if self.config.is_encoder_decoder:
next_step_cross_attentions = ()
next_step_decoder_attentions = ()
if output_attentions:
next_step_cross_attentions = gather_best_candidate(outputs.cross_attentions, selected_idx_stacked)
next_step_decoder_attentions = gather_best_candidate(
outputs.decoder_attentions, selected_idx_stacked
)
outputs = TFSeq2SeqLMOutput(
past_key_values=next_past_key_values,
decoder_hidden_states=next_decoder_hidden_states,
decoder_attentions=next_step_decoder_attentions or None,
cross_attentions=next_step_cross_attentions or None,
)
else:
next_step_attentions = ()
if output_attentions:
next_step_attentions = gather_best_candidate(outputs.attentions, selected_idx_stacked)
outputs = TFCausalLMOutputWithPast(
past_key_values=next_past_key_values,
hidden_states=next_decoder_hidden_states,
attentions=next_step_attentions or None,
)
# contrastive_search main logic end
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
unfinished_seq = 1 - tf.cast(finished_sequences, tf.int32)
next_tokens = next_tokens * unfinished_seq + pad_token_id * (1 - unfinished_seq)
next_token_is_eos = tf.math.reduce_any(
tf.equal(
tf.broadcast_to(next_tokens, (len(eos_token_id), batch_size)), tf.expand_dims(eos_token_id, -1)
),
axis=0,
)
finished_sequences = finished_sequences | next_token_is_eos
# update `generated` and `cur_len`
update_indices = tf.stack([tf.range(batch_size), tf.broadcast_to(cur_len, [batch_size])], axis=-1)
generated = tf.tensor_scatter_nd_update(tensor=generated, indices=update_indices, updates=next_tokens)
cur_len += 1
if use_xla:
# NOTE: 1) relative to other generation strategies, contrastive search is always running forward
# passes one step ahead -- hence the `cur_len=cur_len + 1`; 2) the attention mask here is expanded from
# [batch_size, ...] to [batch_size*top_k, ...] -- hence the `batch_size=batch_size * top_k`
model_kwargs = self._update_model_kwargs_for_xla_generation(
model_outputs=outputs,
model_kwargs=model_kwargs,
cur_len=cur_len + 1,
max_length=max_length,
batch_size=batch_size * top_k,
is_encoder_decoder=self.config.is_encoder_decoder,
batch_axis=cache_batch_axis,
)
else:
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
next_step_cached_variables = {
"logit_for_next_step": logit_for_next_step,
"last_hidden_states": last_hidden_states,
"outputs": outputs,
}
return generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables
# 5. run generation
# 1st generation step has to be run before to initialize `past_key_values`
generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables = contrastive_search_body_fn(
generated, finished_sequences, cur_len, model_kwargs, None
)
# 2-to-n generation steps can then be run in autoregressive fashion
# only in case 1st generation step does NOT yield EOS token though
maximum_iterations = max_length - cur_len
generated, _, cur_len, _, _ = tf.while_loop(
contrastive_search_cond_fn,
contrastive_search_body_fn,
(generated, finished_sequences, cur_len, model_kwargs, next_step_cached_variables),
maximum_iterations=maximum_iterations,
)
# 6. prepare outputs
if not use_xla:
# cut for backward compatibility
generated = generated[:, :cur_len]
if return_dict_in_generate:
if self.config.is_encoder_decoder:
# if model is an encoder-decoder, retrieve encoder attention weights
# and hidden states
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
scores = tuple(scores) if scores is not None else None
decoder_attentions = tuple(decoder_attentions) if decoder_attentions is not None else None
cross_attentions = tuple(cross_attentions) if cross_attentions is not None else None
decoder_hidden_states = tuple(decoder_hidden_states) if decoder_hidden_states is not None else None
return TFContrastiveSearchEncoderDecoderOutput(
sequences=generated,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return TFContrastiveSearchDecoderOnlyOutput(
sequences=generated,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return generated
def tf_top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
"""
Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (batch size, vocabulary size)
top_k (`int`, *optional*, defaults to 0):
If > 0, only keep the top k tokens with highest probability (top-k filtering)
top_p (`float`, *optional*, defaults to 1.0):
If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimumber of tokens we keep per batch example in the output.
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
logits_shape = shape_list(logits)
if top_k > 0:
top_k = min(max(top_k, min_tokens_to_keep), logits_shape[-1]) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < tf.math.top_k(logits, k=top_k)[0][..., -1, None]
logits = tf.where(indices_to_remove, filter_value, logits)
if top_p < 1.0:
sorted_indices = tf.argsort(logits, direction="DESCENDING")
sorted_logits = tf.gather(
logits, sorted_indices, axis=-1, batch_dims=1
) # expects logits to be of dim (batch_size, vocab_size)
cumulative_probs = tf.math.cumsum(stable_softmax(sorted_logits, axis=-1), axis=-1)
# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs > top_p
if min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
sorted_indices_to_remove = tf.concat(
[
tf.zeros_like(sorted_indices_to_remove[:, :min_tokens_to_keep]),
sorted_indices_to_remove[:, min_tokens_to_keep:],
],
-1,
)
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove = tf.concat(
[tf.zeros_like(sorted_indices_to_remove[:, :1]), sorted_indices_to_remove[:, :-1]],
-1,
)
# scatter sorted tensors to original indexing
indices_to_remove = scatter_values_on_batch_indices(sorted_indices_to_remove, sorted_indices)
logits = tf.where(indices_to_remove, filter_value, logits)
return logits
def scatter_values_on_batch_indices(values, batch_indices):
shape = shape_list(batch_indices)
# broadcast batch dim to shape
broad_casted_batch_dims = tf.reshape(tf.broadcast_to(tf.expand_dims(tf.range(shape[0]), axis=-1), shape), [1, -1])
# transform batch_indices to pair_indices
pair_indices = tf.transpose(tf.concat([broad_casted_batch_dims, tf.reshape(batch_indices, [1, -1])], 0))
# scatter values to pair indices
return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), shape)
def sample_without_replacement(logits, num_samples):
"""
categorical sampling without replacement is currently not implemented the gumbel-max trick will do for now see
https://github.com/tensorflow/tensorflow/issues/9260 for more info
"""
z = -tf.math.log(-tf.math.log(tf.random.uniform(shape_list(logits), 0, 1)))
_, indices = tf.nn.top_k(logits + z, num_samples)
return indices
def _ranking_fast(
context_hidden: tf.Tensor,
next_hidden: tf.Tensor,
next_top_k_probs: tf.Tensor,
alpha: float,
beam_width: int,
) -> tf.Tensor:
"""
Reranks the top_k candidates based on a degeneration penalty (cosine similarity with previous tokens), as described
in the paper "A Contrastive Framework for Neural Text Generation". Returns the index of the best candidate for each
row in the batch.
"""
norm_context_hidden = context_hidden / tf.norm(context_hidden, axis=2, keepdims=True)
norm_next_hidden = next_hidden / tf.norm(next_hidden, axis=2, keepdims=True)
cosine_matrix = tf.squeeze(tf.linalg.matmul(norm_context_hidden, norm_next_hidden, transpose_b=True), axis=-1)
degeneration_penalty = tf.reduce_max(cosine_matrix, axis=-1)
next_top_k_probs = tf.reshape(next_top_k_probs, shape=[-1])
contrastive_score = (1.0 - alpha) * next_top_k_probs - alpha * degeneration_penalty
contrastive_score = tf.reshape(contrastive_score, shape=[-1, beam_width])
selected_idx = tf.argmax(contrastive_score, axis=1)
return selected_idx
| 177,962 | 55.03369 | 327 | py |
transformers | transformers-main/src/transformers/generation/configuration_utils.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Generation configuration class and utilities."""
import copy
import json
import os
import warnings
from typing import Any, Dict, Optional, Union
from .. import __version__
from ..configuration_utils import PretrainedConfig
from ..utils import (
GENERATION_CONFIG_NAME,
PushToHubMixin,
cached_file,
download_url,
extract_commit_hash,
is_remote_url,
logging,
)
logger = logging.get_logger(__name__)
class GenerationConfig(PushToHubMixin):
r"""
Class that holds a configuration for a generation task. A `generate` call supports the following generation methods
for text-decoder, text-to-text, speech-to-text, and vision-to-text models:
- *greedy decoding* by calling [`~generation.GenerationMixin.greedy_search`] if `num_beams=1` and
`do_sample=False`
- *contrastive search* by calling [`~generation.GenerationMixin.contrastive_search`] if `penalty_alpha>0.`
and `top_k>1`
- *multinomial sampling* by calling [`~generation.GenerationMixin.sample`] if `num_beams=1` and
`do_sample=True`
- *beam-search decoding* by calling [`~generation.GenerationMixin.beam_search`] if `num_beams>1` and
`do_sample=False`
- *beam-search multinomial sampling* by calling [`~generation.GenerationMixin.beam_sample`] if
`num_beams>1` and `do_sample=True`
- *diverse beam-search decoding* by calling [`~generation.GenerationMixin.group_beam_search`], if
`num_beams>1` and `num_beam_groups>1`
- *constrained beam-search decoding* by calling [`~generation.GenerationMixin.constrained_beam_search`], if
`constraints!=None` or `force_words_ids!=None`
- *assisted decoding* by calling [`~generation.GenerationMixin.assisted_decoding`], if
`assistant_model` is passed to `.generate()`
You do not need to call any of the above methods directly. Pass custom parameter values to '.generate()'. To learn
more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
Arg:
> Parameters that control the length of the output
max_length (`int`, *optional*, defaults to 20):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. Its effect is overridden by `max_new_tokens`, if also set.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
min_length (`int`, *optional*, defaults to 0):
The minimum length of the sequence to be generated. Corresponds to the length of the input prompt +
`min_new_tokens`. Its effect is overridden by `min_new_tokens`, if also set.
min_new_tokens (`int`, *optional*):
The minimum numbers of tokens to generate, ignoring the number of tokens in the prompt.
early_stopping (`bool` or `str`, *optional*, defaults to `False`):
Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values:
`True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an
heuristic is applied and the generation stops when is it very unlikely to find better candidates;
`"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical
beam search algorithm).
max_time(`float`, *optional*):
The maximum amount of time you allow the computation to run for in seconds. generation will still finish
the current pass after allocated time has been passed.
> Parameters that control the generation strategy used
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
num_beams (`int`, *optional*, defaults to 1):
Number of beams for beam search. 1 means no beam search.
num_beam_groups (`int`, *optional*, defaults to 1):
Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
[this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
penalty_alpha (`float`, *optional*):
The values balance the model confidence and the degeneration penalty in contrastive search decoding.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.
> Parameters for manipulation of the model output logits
temperature (`float`, *optional*, defaults to 1.0):
The value used to modulate the next token probabilities.
top_k (`int`, *optional*, defaults to 50):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`, *optional*, defaults to 1.0):
If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to
`top_p` or higher are kept for generation.
typical_p (`float`, *optional*, defaults to 1.0):
Local typicality measures how similar the conditional probability of predicting a target token next is to
the expected conditional probability of predicting a random token next, given the partial text already
generated. If set to float < 1, the smallest set of the most locally typical tokens with probabilities that
add up to `typical_p` or higher are kept for generation. See [this
paper](https://arxiv.org/pdf/2202.00666.pdf) for more details.
epsilon_cutoff (`float`, *optional*, defaults to 0.0):
If set to float strictly between 0 and 1, only tokens with a conditional probability greater than
`epsilon_cutoff` will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on the
size of the model. See [Truncation Sampling as Language Model
Desmoothing](https://arxiv.org/abs/2210.15191) for more details.
eta_cutoff (`float`, *optional*, defaults to 0.0):
Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly between
0 and 1, a token is only considered if it is greater than either `eta_cutoff` or `sqrt(eta_cutoff) *
exp(-entropy(softmax(next_token_logits)))`. The latter term is intuitively the expected next token
probability, scaled by `sqrt(eta_cutoff)`. In the paper, suggested values range from 3e-4 to 2e-3,
depending on the size of the model. See [Truncation Sampling as Language Model
Desmoothing](https://arxiv.org/abs/2210.15191) for more details.
diversity_penalty (`float`, *optional*, defaults to 0.0):
This value is subtracted from a beam's score if it generates a token same as any beam from other group at a
particular time. Note that `diversity_penalty` is only effective if `group beam search` is enabled.
repetition_penalty (`float`, *optional*, defaults to 1.0):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
encoder_repetition_penalty (`float`, *optional*, defaults to 1.0):
The paramater for encoder_repetition_penalty. An exponential penalty on sequences that are not in the
original input. 1.0 means no penalty.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
`length_penalty` < 0.0 encourages shorter sequences.
no_repeat_ngram_size (`int`, *optional*, defaults to 0):
If set to int > 0, all ngrams of that size can only occur once.
bad_words_ids(`List[List[int]]`, *optional*):
List of list of token ids that are not allowed to be generated. Check
[`~generation.NoBadWordsLogitsProcessor`] for further documentation and examples.
force_words_ids(`List[List[int]]` or `List[List[List[int]]]`, *optional*):
List of token ids that must be generated. If given a `List[List[int]]`, this is treated as a simple list of
words that must be included, the opposite to `bad_words_ids`. If given `List[List[List[int]]]`, this
triggers a [disjunctive constraint](https://github.com/huggingface/transformers/issues/14081), where one
can allow different forms of each word.
renormalize_logits (`bool`, *optional*, defaults to `False`):
Whether to renormalize the logits after applying all the logits processors or warpers (including the custom
ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the score logits
are normalized but some logit processors or warpers break the normalization.
constraints (`List[Constraint]`, *optional*):
Custom constraints that can be added to the generation to ensure that the output will contain the use of
certain tokens as defined by `Constraint` objects, in the most sensible way possible.
forced_bos_token_id (`int`, *optional*, defaults to `model.config.forced_bos_token_id`):
The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful for
multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be the target
language token.
forced_eos_token_id (`Union[int, List[int]]`, *optional*, defaults to `model.config.forced_eos_token_id`):
The id of the token to force as the last generated token when `max_length` is reached. Optionally, use a
list to set multiple *end-of-sequence* tokens.
remove_invalid_values (`bool`, *optional*, defaults to `model.config.remove_invalid_values`):
Whether to remove possible *nan* and *inf* outputs of the model to prevent the generation method to crash.
Note that using `remove_invalid_values` can slow down generation.
exponential_decay_length_penalty (`tuple(int, float)`, *optional*):
This Tuple adds an exponentially increasing length penalty, after a certain amount of tokens have been
generated. The tuple shall consist of: `(start_index, decay_factor)` where `start_index` indicates where
penalty starts and `decay_factor` represents the factor of exponential decay
suppress_tokens (`List[int]`, *optional*):
A list of tokens that will be suppressed at generation. The `SupressTokens` logit processor will set their
log probs to `-inf` so that they are not sampled.
begin_suppress_tokens (`List[int]`, *optional*):
A list of tokens that will be suppressed at the beginning of the generation. The `SupressBeginTokens` logit
processor will set their log probs to `-inf` so that they are not sampled.
forced_decoder_ids (`List[List[int]]`, *optional*):
A list of pairs of integers which indicates a mapping from generation indices to token indices that will be
forced before sampling. For example, `[[1, 123]]` means the second generated token will always be a token
of index 123.
sequence_bias (`Dict[Tuple[int], float]`, *optional*)):
Dictionary that maps a sequence of tokens to its bias term. Positive biases increase the odds of the
sequence being selected, while negative biases do the opposite. Check
[`~generation.SequenceBiasLogitsProcessor`] for further documentation and examples.
guidance_scale (`float`, *optional*):
The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`.
Higher guidance scale encourages the model to generate samples that are more closely linked to the input
prompt, usually at the expense of poorer quality.
> Parameters that define the output variables of `generate`
num_return_sequences(`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
> Special tokens that can be used at generation time
pad_token_id (`int`, *optional*):
The id of the *padding* token.
bos_token_id (`int`, *optional*):
The id of the *beginning-of-sequence* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
> Generation parameters exclusive to encoder-decoder models
encoder_no_repeat_ngram_size (`int`, *optional*, defaults to 0):
If set to int > 0, all ngrams of that size that occur in the `encoder_input_ids` cannot occur in the
`decoder_input_ids`.
decoder_start_token_id (`int`, *optional*):
If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token.
> Wild card
generation_kwargs:
Additional generation kwargs will be forwarded to the `generate` function of the model. Kwargs that are not
present in `generate`'s signature will be used in the model forward pass.
"""
def __init__(self, **kwargs):
# Parameters that control the length of the output
self.max_length = kwargs.pop("max_length", 20)
self.max_new_tokens = kwargs.pop("max_new_tokens", None)
self.min_length = kwargs.pop("min_length", 0)
self.min_new_tokens = kwargs.pop("min_new_tokens", None)
self.early_stopping = kwargs.pop("early_stopping", False)
self.max_time = kwargs.pop("max_time", None)
# Parameters that control the generation strategy used
self.do_sample = kwargs.pop("do_sample", False)
self.num_beams = kwargs.pop("num_beams", 1)
self.num_beam_groups = kwargs.pop("num_beam_groups", 1)
self.penalty_alpha = kwargs.pop("penalty_alpha", None)
self.use_cache = kwargs.pop("use_cache", True)
# Parameters for manipulation of the model output logits
self.temperature = kwargs.pop("temperature", 1.0)
self.top_k = kwargs.pop("top_k", 50)
self.top_p = kwargs.pop("top_p", 1.0)
self.typical_p = kwargs.pop("typical_p", 1.0)
self.epsilon_cutoff = kwargs.pop("epsilon_cutoff", 0.0)
self.eta_cutoff = kwargs.pop("eta_cutoff", 0.0)
self.diversity_penalty = kwargs.pop("diversity_penalty", 0.0)
self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0)
self.encoder_repetition_penalty = kwargs.pop("encoder_repetition_penalty", 1.0)
self.length_penalty = kwargs.pop("length_penalty", 1.0)
self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
self.bad_words_ids = kwargs.pop("bad_words_ids", None)
self.force_words_ids = kwargs.pop("force_words_ids", None)
self.renormalize_logits = kwargs.pop("renormalize_logits", False)
self.constraints = kwargs.pop("constraints", None)
self.forced_bos_token_id = kwargs.pop("forced_bos_token_id", None)
self.forced_eos_token_id = kwargs.pop("forced_eos_token_id", None)
self.remove_invalid_values = kwargs.pop("remove_invalid_values", False)
self.exponential_decay_length_penalty = kwargs.pop("exponential_decay_length_penalty", None)
self.suppress_tokens = kwargs.pop("suppress_tokens", None)
self.begin_suppress_tokens = kwargs.pop("begin_suppress_tokens", None)
self.forced_decoder_ids = kwargs.pop("forced_decoder_ids", None)
self.sequence_bias = kwargs.pop("sequence_bias", None)
self.guidance_scale = kwargs.pop("guidance_scale", None)
# Parameters that define the output variables of `generate`
self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
self.output_attentions = kwargs.pop("output_attentions", False)
self.output_hidden_states = kwargs.pop("output_hidden_states", False)
self.output_scores = kwargs.pop("output_scores", False)
self.return_dict_in_generate = kwargs.pop("return_dict_in_generate", False)
# Special tokens that can be used at generation time
self.pad_token_id = kwargs.pop("pad_token_id", None)
self.bos_token_id = kwargs.pop("bos_token_id", None)
self.eos_token_id = kwargs.pop("eos_token_id", None)
# Generation parameters exclusive to encoder-decoder models
self.encoder_no_repeat_ngram_size = kwargs.pop("encoder_no_repeat_ngram_size", 0)
self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
# Wild card
self.generation_kwargs = kwargs.pop("generation_kwargs", {})
# The remaining attributes do not parametrize `.generate()`, but are informative and/or used by the the hub
# interface.
self._from_model_config = kwargs.pop("_from_model_config", False)
self._commit_hash = kwargs.pop("_commit_hash", None)
self.transformers_version = kwargs.pop("transformers_version", __version__)
# Additional attributes without default values
if not self._from_model_config:
# we don't want to copy values from the model config if we're initializing a `GenerationConfig` from a
# model's default configuration file
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
# Validate the values of the attributes
self.validate()
def __eq__(self, other):
if not isinstance(other, GenerationConfig):
return False
self_dict = self.__dict__.copy()
other_dict = other.__dict__.copy()
# ignore metadata
for metadata_field in ("_from_model_config", "_commit_hash", "transformers_version"):
self_dict.pop(metadata_field, None)
other_dict.pop(metadata_field, None)
return self_dict == other_dict
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
def validate(self):
"""
Validates the values of the attributes of the GenerationConfig instance, and raises a `ValueError` if any of
the values are invalid.
"""
if self.early_stopping not in {True, False, "never"}:
raise ValueError(f"`early_stopping` must be a boolean or 'never', but is {self.early_stopping}.")
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
config_file_name: Optional[Union[str, os.PathLike]] = None,
push_to_hub: bool = False,
**kwargs,
):
r"""
Save a generation configuration object to the directory `save_directory`, so that it can be re-loaded using the
[`~GenerationConfig.from_pretrained`] class method.
Args:
save_directory (`str` or `os.PathLike`):
Directory where the configuration JSON file will be saved (will be created if it does not exist).
config_file_name (`str` or `os.PathLike`, *optional*, defaults to `"generation_config.json"`):
Name of the generation configuration JSON file to be saved in `save_directory`.
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs (`Dict[str, Any]`, *optional*):
Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
config_file_name = config_file_name if config_file_name is not None else GENERATION_CONFIG_NAME
if os.path.isfile(save_directory):
raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = self._create_repo(repo_id, **kwargs)
files_timestamps = self._get_files_timestamps(save_directory)
output_config_file = os.path.join(save_directory, config_file_name)
self.to_json_file(output_config_file, use_diff=True)
logger.info(f"Configuration saved in {output_config_file}")
if push_to_hub:
self._upload_modified_files(
save_directory,
repo_id,
files_timestamps,
commit_message=commit_message,
token=kwargs.get("use_auth_token"),
)
@classmethod
def from_pretrained(
cls,
pretrained_model_name: Union[str, os.PathLike],
config_file_name: Optional[Union[str, os.PathLike]] = None,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = None,
revision: str = "main",
**kwargs,
) -> "GenerationConfig":
r"""
Instantiate a [`GenerationConfig`] from a generation configuration file.
Args:
pretrained_model_name (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
config_file_name (`str` or `os.PathLike`, *optional*, defaults to `"generation_config.json"`):
Name of the generation configuration JSON file to be loaded from `pretrained_model_name`.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if
they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
<Tip>
To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>".
</Tip>
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
If `False`, then this function returns just the final configuration object.
If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of `kwargs` which has not been used to update `config` and is otherwise ignored.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.
kwargs (`Dict[str, Any]`, *optional*):
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
by the `return_unused_kwargs` keyword parameter.
Returns:
[`GenerationConfig`]: The configuration object instantiated from this pretrained model.
Examples:
```python
>>> from transformers import GenerationConfig
>>> # Download configuration from huggingface.co and cache.
>>> generation_config = GenerationConfig.from_pretrained("gpt2")
>>> # E.g. config was saved using *save_pretrained('./test/saved_model/')*
>>> generation_config.save_pretrained("./test/saved_model/")
>>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/")
>>> # You can also specify configuration names to your generation configuration file
>>> generation_config.save_pretrained("./test/saved_model/", config_file_name="my_configuration.json")
>>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/", "my_configuration.json")
>>> # If you'd like to try a minor variation to an existing configuration, you can also pass generation
>>> # arguments to `.from_pretrained()`. Be mindful that typos and unused arguments will be ignored
>>> generation_config, unused_kwargs = GenerationConfig.from_pretrained(
... "gpt2", top_k=1, foo=False, return_unused_kwargs=True
... )
>>> generation_config.top_k
1
>>> unused_kwargs
{'foo': False}
```"""
config_file_name = config_file_name if config_file_name is not None else GENERATION_CONFIG_NAME
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
use_auth_token = kwargs.pop("use_auth_token", None)
subfolder = kwargs.pop("subfolder", "")
from_pipeline = kwargs.pop("_from_pipeline", None)
from_auto_class = kwargs.pop("_from_auto", False)
commit_hash = kwargs.pop("_commit_hash", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.", FutureWarning
)
if token is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
token = use_auth_token
user_agent = {"file_type": "config", "from_auto_class": from_auto_class}
if from_pipeline is not None:
user_agent["using_pipeline"] = from_pipeline
config_path = os.path.join(pretrained_model_name, config_file_name)
config_path = str(config_path)
is_local = os.path.exists(config_path)
if os.path.isfile(os.path.join(subfolder, config_path)):
# Special case when config_path is a local file
resolved_config_file = config_path
is_local = True
elif is_remote_url(config_path):
configuration_file = config_path
resolved_config_file = download_url(config_path)
else:
configuration_file = config_file_name
try:
# Load from local folder or from cache or download from model Hub and cache
resolved_config_file = cached_file(
pretrained_model_name,
configuration_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=token,
user_agent=user_agent,
revision=revision,
subfolder=subfolder,
_commit_hash=commit_hash,
)
commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
except EnvironmentError:
# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
# the original exception.
raise
except Exception:
# For any other exception, we throw a generic error.
raise EnvironmentError(
f"Can't load the configuration of '{pretrained_model_name}'. If you were trying to load it"
" from 'https://huggingface.co/models', make sure you don't have a local directory with the same"
f" name. Otherwise, make sure '{pretrained_model_name}' is the correct path to a directory"
f" containing a {configuration_file} file"
)
try:
# Load config dict
config_dict = cls._dict_from_json_file(resolved_config_file)
config_dict["_commit_hash"] = commit_hash
except (json.JSONDecodeError, UnicodeDecodeError):
raise EnvironmentError(
f"It looks like the config file at '{resolved_config_file}' is not a valid JSON file."
)
if is_local:
logger.info(f"loading configuration file {resolved_config_file}")
else:
logger.info(f"loading configuration file {configuration_file} from cache at {resolved_config_file}")
return cls.from_dict(config_dict, **kwargs)
@classmethod
def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
@classmethod
def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "GenerationConfig":
"""
Instantiates a [`GenerationConfig`] from a Python dictionary of parameters.
Args:
config_dict (`Dict[str, Any]`):
Dictionary that will be used to instantiate the configuration object.
kwargs (`Dict[str, Any]`):
Additional parameters from which to initialize the configuration object.
Returns:
[`GenerationConfig`]: The configuration object instantiated from those parameters.
"""
return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
# Those arguments may be passed along for our internal telemetry.
# We remove them so they don't appear in `return_unused_kwargs`.
kwargs.pop("_from_auto", None)
kwargs.pop("_from_pipeline", None)
# The commit hash might have been updated in the `config_dict`, we don't want the kwargs to erase that update.
if "_commit_hash" in kwargs and "_commit_hash" in config_dict:
kwargs["_commit_hash"] = config_dict["_commit_hash"]
# The line below allows model-specific config to be loaded as well through kwargs, with safety checks.
# See https://github.com/huggingface/transformers/pull/21269
config = cls(**{**config_dict, **kwargs})
unused_kwargs = config.update(**kwargs)
logger.info(f"Generate config {config}")
if return_unused_kwargs:
return config, unused_kwargs
else:
return config
def dict_torch_dtype_to_str(self, d: Dict[str, Any]) -> None:
"""
Checks whether the passed dictionary and its nested dicts have a *torch_dtype* key and if it's not None,
converts torch.dtype to a string of just the type. For example, `torch.float32` get converted into *"float32"*
string, which can then be stored in the json format.
"""
if d.get("torch_dtype", None) is not None and not isinstance(d["torch_dtype"], str):
d["torch_dtype"] = str(d["torch_dtype"]).split(".")[1]
for value in d.values():
if isinstance(value, dict):
self.dict_torch_dtype_to_str(value)
def to_diff_dict(self) -> Dict[str, Any]:
"""
Removes all attributes from config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.
Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance,
"""
config_dict = self.to_dict()
# get the default config dict
default_config_dict = GenerationConfig().to_dict()
serializable_config_dict = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if key not in default_config_dict or key == "transformers_version" or value != default_config_dict[key]:
serializable_config_dict[key] = value
self.dict_torch_dtype_to_str(serializable_config_dict)
return serializable_config_dict
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary.
Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
"""
output = copy.deepcopy(self.__dict__)
if "_commit_hash" in output:
del output["_commit_hash"]
# Transformers version when serializing this file
output["transformers_version"] = __version__
self.dict_torch_dtype_to_str(output)
return output
def to_json_string(self, use_diff: bool = True) -> str:
"""
Serializes this instance to a JSON string.
Args:
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default `GenerationConfig()`
is serialized to JSON string.
Returns:
`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
if use_diff is True:
config_dict = self.to_diff_dict()
else:
config_dict = self.to_dict()
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: Union[str, os.PathLike], use_diff: bool = True):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default `GenerationConfig()`
is serialized to JSON file.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string(use_diff=use_diff))
@classmethod
def from_model_config(cls, model_config: PretrainedConfig) -> "GenerationConfig":
"""
Instantiates a [`GenerationConfig`] from a [`PretrainedConfig`]. This function is useful to convert legacy
[`PretrainedConfig`] objects, which may contain generation parameters, into a stand-alone [`GenerationConfig`].
Args:
model_config (`PretrainedConfig`):
The model config that will be used to instantiate the generation config.
Returns:
[`GenerationConfig`]: The configuration object instantiated from those parameters.
"""
config_dict = model_config.to_dict()
config_dict.pop("_from_model_config", None)
config = cls.from_dict(config_dict, return_unused_kwargs=False, _from_model_config=True)
# Special case: some models have generation attributes set in the decoder. Use them if still unset in the
# generation config.
for decoder_name in ("decoder", "generator", "text_config"):
if decoder_name in config_dict:
default_generation_config = GenerationConfig()
decoder_config = config_dict[decoder_name]
for attr in config.to_dict().keys():
if attr in decoder_config and getattr(config, attr) == getattr(default_generation_config, attr):
setattr(config, attr, decoder_config[attr])
return config
def update(self, **kwargs):
"""
Updates attributes of this class instance with attributes from `kwargs` if they match existing atributtes,
returning all the unused kwargs.
Args:
kwargs (`Dict[str, Any]`):
Dictionary of attributes to tentatively update this class.
Returns:
`Dict[str, Any]`: Dictionary containing all the key-value pairs that were not used to update the instance.
"""
to_remove = []
for key, value in kwargs.items():
if hasattr(self, key):
setattr(self, key, value)
to_remove.append(key)
# remove all the attributes that were updated, without modifying the input dict
unused_kwargs = {key: value for key, value in kwargs.items() if key not in to_remove}
return unused_kwargs
| 40,270 | 53.493911 | 119 | py |
transformers | transformers-main/src/transformers/generation/streamers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class BaseStreamer:
"""
Base class from which `.generate()` streamers should inherit.
"""
def put(self, value):
"""Function that is called by `.generate()` to push new tokens"""
raise NotImplementedError()
def end(self):
"""Function that is called by `.generate()` to signal the end of generation"""
raise NotImplementedError()
class TextStreamer(BaseStreamer):
"""
Simple text streamer that prints the token(s) to stdout as soon as entire words are formed.
<Tip warning={true}>
The API for the streamer classes is still under development and may change in the future.
</Tip>
Parameters:
tokenizer (`AutoTokenizer`):
The tokenized used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
>>> tok = AutoTokenizer.from_pretrained("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextStreamer(tok)
>>> # Despite returning the usual output, the streamer will also print the generated text to stdout.
>>> _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20)
An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,
```
"""
def __init__(self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, **decode_kwargs):
self.tokenizer = tokenizer
self.skip_prompt = skip_prompt
self.decode_kwargs = decode_kwargs
# variables used in the streaming process
self.token_cache = []
self.print_len = 0
self.next_tokens_are_prompt = True
def put(self, value):
"""
Receives tokens, decodes them, and prints them to stdout as soon as they form entire words.
"""
if len(value.shape) > 1 and value.shape[0] > 1:
raise ValueError("TextStreamer only supports batch size 1")
elif len(value.shape) > 1:
value = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
self.next_tokens_are_prompt = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist())
text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs)
# After the symbol for a new line, we flush the cache.
if text.endswith("\n"):
printable_text = text[self.print_len :]
self.token_cache = []
self.print_len = 0
# If the last token is a CJK character, we print the characters.
elif len(text) > 0 and self._is_chinese_char(ord(text[-1])):
printable_text = text[self.print_len :]
self.print_len += len(printable_text)
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
printable_text = text[self.print_len : text.rfind(" ") + 1]
self.print_len += len(printable_text)
self.on_finalized_text(printable_text)
def end(self):
"""Flushes any remaining cache and prints a newline to stdout."""
# Flush the cache, if it exists
if len(self.token_cache) > 0:
text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs)
printable_text = text[self.print_len :]
self.token_cache = []
self.print_len = 0
else:
printable_text = ""
self.next_tokens_are_prompt = True
self.on_finalized_text(printable_text, stream_end=True)
def on_finalized_text(self, text: str, stream_end: bool = False):
"""Prints the new text to stdout. If the stream is ending, also prints a newline."""
print(text, flush=True, end="" if not stream_end else None)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
class TextIteratorStreamer(TextStreamer):
"""
Streamer that stores print-ready text in a queue, to be used by a downstream application as an iterator. This is
useful for applications that benefit from acessing the generated text in a non-blocking way (e.g. in an interactive
Gradio demo).
<Tip warning={true}>
The API for the streamer classes is still under development and may change in the future.
</Tip>
Parameters:
tokenizer (`AutoTokenizer`):
The tokenized used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
timeout (`float`, *optional*):
The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions
in `.generate()`, when it is called in a separate thread.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
>>> from threading import Thread
>>> tok = AutoTokenizer.from_pretrained("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextIteratorStreamer(tok)
>>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
>>> generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)
>>> thread = Thread(target=model.generate, kwargs=generation_kwargs)
>>> thread.start()
>>> generated_text = ""
>>> for new_text in streamer:
... generated_text += new_text
>>> generated_text
'An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,'
```
"""
def __init__(
self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, timeout: Optional[float] = None, **decode_kwargs
):
super().__init__(tokenizer, skip_prompt, **decode_kwargs)
self.text_queue = Queue()
self.stop_signal = None
self.timeout = timeout
def on_finalized_text(self, text: str, stream_end: bool = False):
"""Put the new text in the queue. If the stream is ending, also put a stop signal in the queue."""
self.text_queue.put(text, timeout=self.timeout)
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout)
def __iter__(self):
return self
def __next__(self):
value = self.text_queue.get(timeout=self.timeout)
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 9,145 | 39.114035 | 119 | py |
transformers | transformers-main/src/transformers/generation/utils.py | # coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
import warnings
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.distributed as dist
from torch import nn
from ..deepspeed import is_deepspeed_zero3_enabled
from ..modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
from ..models.auto import (
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
MODEL_FOR_VISION_2_SEQ_MAPPING,
)
from ..utils import ModelOutput, logging
from .beam_constraints import DisjunctiveConstraint, PhrasalConstraint
from .beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
from .configuration_utils import GenerationConfig
from .logits_process import (
ClassifierFreeGuidanceLogitsProcessor,
EncoderNoRepeatNGramLogitsProcessor,
EncoderRepetitionPenaltyLogitsProcessor,
EpsilonLogitsWarper,
EtaLogitsWarper,
ExponentialDecayLengthPenalty,
ForcedBOSTokenLogitsProcessor,
ForcedEOSTokenLogitsProcessor,
ForceTokensLogitsProcessor,
HammingDiversityLogitsProcessor,
InfNanRemoveLogitsProcessor,
LogitNormalization,
LogitsProcessorList,
MinLengthLogitsProcessor,
MinNewTokensLengthLogitsProcessor,
NoBadWordsLogitsProcessor,
NoRepeatNGramLogitsProcessor,
PrefixConstrainedLogitsProcessor,
RepetitionPenaltyLogitsProcessor,
SequenceBiasLogitsProcessor,
SuppressTokensAtBeginLogitsProcessor,
SuppressTokensLogitsProcessor,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
TypicalLogitsWarper,
)
from .stopping_criteria import (
MaxLengthCriteria,
MaxTimeCriteria,
StoppingCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from .streamers import BaseStreamer
logger = logging.get_logger(__name__)
@dataclass
class GreedySearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
scores: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class ContrastiveSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using contrastive search.
Args:
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
scores: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class ContrastiveSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using contrastive search.
Args:
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when
`config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is
passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
scores: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class GreedySearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
scores: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class SampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using sampling.
Args:
sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(num_return_sequences*batch_size, num_heads, generated_length,
sequence_length)`.
hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(num_return_sequences*batch_size, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
scores: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class SampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape
`(batch_size*num_return_sequences, num_heads, sequence_length, sequence_length)`.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size*num_return_sequences, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_return_sequences, num_heads, generated_length,
sequence_length)`.
cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
scores: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class BeamSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam search.
Args:
sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
`(batch_size*num_return_sequences, sequence_length)`.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
sequences_scores: Optional[torch.FloatTensor] = None
scores: Optional[Tuple[torch.FloatTensor]] = None
beam_indices: Optional[torch.LongTensor] = None
attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class BeamSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
`(batch_size*num_return_sequences, sequence_length)`.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, num_heads, generated_length,
sequence_length)`.
cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
sequences_scores: Optional[torch.FloatTensor] = None
scores: Optional[Tuple[torch.FloatTensor]] = None
beam_indices: Optional[torch.LongTensor] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class BeamSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam sample.
Args:
sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`torch.FloatTensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
`(batch_size*num_return_sequences, sequence_length)`.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
sequences_scores: Optional[torch.FloatTensor] = None
scores: Optional[Tuple[torch.FloatTensor]] = None
beam_indices: Optional[torch.LongTensor] = None
attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class BeamSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
Args:
sequences (`torch.LongTensor` of shape `(batch_size*num_beams, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
if all batches finished early due to the `eos_token_id`.
sequences_scores (`torch.FloatTensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Final beam scores of the generated `sequences`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`).
beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
`(batch_size*num_return_sequences, sequence_length)`.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`.
encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size*num_beams, sequence_length, hidden_size)`.
decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
"""
sequences: torch.LongTensor = None
sequences_scores: Optional[torch.FloatTensor] = None
scores: Optional[Tuple[torch.FloatTensor]] = None
beam_indices: Optional[torch.LongTensor] = None
encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
GreedySearchOutput = Union[GreedySearchEncoderDecoderOutput, GreedySearchDecoderOnlyOutput]
SampleOutput = Union[SampleEncoderDecoderOutput, SampleDecoderOnlyOutput]
BeamSearchOutput = Union[BeamSearchEncoderDecoderOutput, BeamSearchDecoderOnlyOutput]
BeamSampleOutput = Union[BeamSampleEncoderDecoderOutput, BeamSampleDecoderOnlyOutput]
ContrastiveSearchOutput = Union[ContrastiveSearchEncoderDecoderOutput, ContrastiveSearchDecoderOnlyOutput]
GenerateOutput = Union[GreedySearchOutput, SampleOutput, BeamSearchOutput, BeamSampleOutput, ContrastiveSearchOutput]
class GenerationMixin:
"""
A class containing all functions for auto-regressive text generation, to be used as a mixin in [`PreTrainedModel`].
The class exposes [`~generation.GenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.GenerationMixin.greedy_search`] if `num_beams=1` and
`do_sample=False`
- *contrastive search* by calling [`~generation.GenerationMixin.contrastive_search`] if `penalty_alpha>0` and
`top_k>1`
- *multinomial sampling* by calling [`~generation.GenerationMixin.sample`] if `num_beams=1` and
`do_sample=True`
- *beam-search decoding* by calling [`~generation.GenerationMixin.beam_search`] if `num_beams>1` and
`do_sample=False`
- *beam-search multinomial sampling* by calling [`~generation.GenerationMixin.beam_sample`] if `num_beams>1`
and `do_sample=True`
- *diverse beam-search decoding* by calling [`~generation.GenerationMixin.group_beam_search`], if `num_beams>1`
and `num_beam_groups>1`
- *constrained beam-search decoding* by calling [`~generation.GenerationMixin.constrained_beam_search`], if
`constraints!=None` or `force_words_ids!=None`
You do not need to call any of the above methods directly. Pass custom parameter values to 'generate' instead. To
learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
"""
def prepare_inputs_for_generation(self, *args, **kwargs):
raise NotImplementedError(
"A model class needs to define a `prepare_inputs_for_generation` method in order to use `.generate()`."
)
def _prepare_model_inputs(
self,
inputs: Optional[torch.Tensor] = None,
bos_token_id: Optional[int] = None,
model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
) -> Tuple[torch.Tensor, Optional[str], Dict[str, torch.Tensor]]:
"""
This function extracts the model-specific `inputs` for generation.
"""
# 1. retrieve all kwargs that are non-None or non-model input related.
# some encoder-decoder models have different names for model and encoder
if (
self.config.is_encoder_decoder
and hasattr(self, "encoder")
and self.encoder.main_input_name != self.main_input_name
):
input_name = self.encoder.main_input_name
else:
input_name = self.main_input_name
model_kwargs = {k: v for k, v in model_kwargs.items() if v is not None or k != input_name}
# 2. check whether model_input_name is passed as kwarg
# if yes and `inputs` is None use kwarg inputs
inputs_kwarg = model_kwargs.pop(input_name, None)
if inputs_kwarg is not None and inputs is not None:
raise ValueError(
f"`inputs`: {inputs}` were passed alongside {input_name} which is not allowed."
f"Make sure to either pass {inputs} or {input_name}=..."
)
elif inputs_kwarg is not None:
inputs = inputs_kwarg
# 3. In the presence of `inputs_embeds` for text models:
# - decoder-only models should complain if the user attempts to pass `inputs_embeds`, but the model
# doesn't have its forwarding implemented. `inputs_embeds` is kept in `model_kwargs` and can coexist with
# input_ids (`inputs_embeds` will be used in the 1st generation step, as opposed to `input_ids`)
# - encoder-decoder models should complain if the user attempts to pass `inputs_embeds` and `input_ids`, and
# pull the former to inputs. It will be used in place of `input_ids` to get the encoder hidden states.
if input_name == "input_ids" and "inputs_embeds" in model_kwargs:
if not self.config.is_encoder_decoder:
has_inputs_embeds_forwarding = "inputs_embeds" in set(
inspect.signature(self.prepare_inputs_for_generation).parameters.keys()
)
if not has_inputs_embeds_forwarding:
raise ValueError(
f"You passed `inputs_embeds` to `.generate()`, but the model class {self.__class__.__name__} "
"doesn't have its forwarding implemented. See the GPT2 implementation for an example "
"(https://github.com/huggingface/transformers/pull/21405), and feel free to open a PR with it!"
)
# In this case, `input_ids` is moved to the `model_kwargs`, so a few automations (like the creation of
# the attention mask) can rely on the actual model input.
model_kwargs["input_ids"] = self._maybe_initialize_input_ids_for_generation(
inputs, bos_token_id, model_kwargs=model_kwargs
)
else:
if inputs is not None:
raise ValueError("You passed `inputs_embeds` and `input_ids` to `.generate()`. Please pick one.")
inputs, input_name = model_kwargs["inputs_embeds"], "inputs_embeds"
# 4. if `inputs` is still None, try to create `input_ids` from BOS token
inputs = self._maybe_initialize_input_ids_for_generation(inputs, bos_token_id, model_kwargs)
return inputs, input_name, model_kwargs
def adjust_logits_during_generation(self, logits: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
"""
Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
"""
return logits
def _maybe_initialize_input_ids_for_generation(
self,
inputs: Optional[torch.Tensor] = None,
bos_token_id: Optional[int] = None,
model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
) -> torch.LongTensor:
"""Initializes input ids for generation, if necessary."""
if inputs is not None:
return inputs
encoder_outputs = model_kwargs.get("encoder_outputs")
if self.config.is_encoder_decoder and encoder_outputs is not None:
# make dummy input_ids with value -100, as a sanity check ensuring that they won't be used for encoding
shape = encoder_outputs.last_hidden_state.size()[:-1]
return torch.ones(shape, dtype=torch.long, device=self.device) * -100
if bos_token_id is None:
raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.")
# If there is some tensor in `model_kwargs`, we can infer the batch size from it. This is helpful with
# soft-prompting or in multimodal implementations built on top of decoder-only language models.
batch_size = 1
for value in model_kwargs.values():
if isinstance(value, torch.Tensor):
batch_size = value.shape[0]
break
return torch.ones((batch_size, 1), dtype=torch.long, device=self.device) * bos_token_id
def _prepare_attention_mask_for_generation(
self,
inputs: torch.Tensor,
pad_token_id: Optional[int],
eos_token_id: Optional[Union[int, List[int]]],
) -> torch.LongTensor:
is_input_ids = len(inputs.shape) == 2 and inputs.dtype in [torch.int, torch.long]
is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs)
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (pad_token_id not in eos_token_id)
# Check if input is input_ids and padded -> only then is attention_mask defined
if is_input_ids and is_pad_token_in_inputs and is_pad_token_not_equal_to_eos_token_id:
return inputs.ne(pad_token_id).long()
else:
return torch.ones(inputs.shape[:2], dtype=torch.long, device=inputs.device)
def _prepare_encoder_decoder_kwargs_for_generation(
self, inputs_tensor: torch.Tensor, model_kwargs, model_input_name: Optional[str] = None
) -> Dict[str, Any]:
# 1. get encoder
encoder = self.get_encoder()
# Compatibility with Accelerate big model inference: we need the encoder to outputs stuff on the same device
# as the inputs.
if hasattr(encoder, "_hf_hook"):
encoder._hf_hook.io_same_device = True
# 2. Prepare encoder args and encoder kwargs from model kwargs.
irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
encoder_kwargs = {
argument: value
for argument, value in model_kwargs.items()
if not any(argument.startswith(p) for p in irrelevant_prefix)
}
encoder_signature = set(inspect.signature(encoder.forward).parameters)
encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature
if not encoder_accepts_wildcard:
encoder_kwargs = {
argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
}
# 3. make sure that encoder returns `ModelOutput`
model_input_name = model_input_name if model_input_name is not None else self.main_input_name
encoder_kwargs["return_dict"] = True
encoder_kwargs[model_input_name] = inputs_tensor
model_kwargs["encoder_outputs"]: ModelOutput = encoder(**encoder_kwargs)
return model_kwargs
def _prepare_decoder_input_ids_for_generation(
self,
batch_size: int,
model_input_name: str,
model_kwargs: Dict[str, torch.Tensor],
decoder_start_token_id: int = None,
bos_token_id: int = None,
device: torch.device = None,
) -> Tuple[torch.LongTensor, Dict[str, torch.Tensor]]:
"""Prepares `decoder_input_ids` for generation with encoder-decoder models"""
# 1. Check whether the user has defined `decoder_input_ids` manually. To facilitate in terms of input naming,
# we also allow the user to pass it under `input_ids`, if the encoder does not use it as the main input.
if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
decoder_input_ids = model_kwargs.pop("decoder_input_ids")
elif "input_ids" in model_kwargs and model_input_name != "input_ids":
decoder_input_ids = model_kwargs.pop("input_ids")
else:
decoder_input_ids = None
# 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
if device is None:
device = self.device
decoder_input_ids_start = torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_token_id
# no user input -> use decoder_start_token_id as decoder_input_ids
if decoder_input_ids is None:
decoder_input_ids = decoder_input_ids_start
# exception: Donut checkpoints have task-specific decoder starts and don't expect a BOS token
elif self.config.model_type == "vision-encoder-decoder" and "donut" in self.name_or_path.lower():
pass
# user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
# decoder_attention_mask if provided)
elif (decoder_input_ids[:, 0] != decoder_start_token_id).all().item():
decoder_input_ids = torch.cat([decoder_input_ids_start, decoder_input_ids], dim=-1)
if "decoder_attention_mask" in model_kwargs:
decoder_attention_mask = model_kwargs["decoder_attention_mask"]
decoder_attention_mask = torch.cat(
(torch.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
dim=-1,
)
model_kwargs["decoder_attention_mask"] = decoder_attention_mask
return decoder_input_ids, model_kwargs
def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
decoder_start_token_id = (
decoder_start_token_id
if decoder_start_token_id is not None
else self.generation_config.decoder_start_token_id
)
bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
if decoder_start_token_id is not None:
return decoder_start_token_id
elif bos_token_id is not None:
return bos_token_id
raise ValueError(
"`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
)
@staticmethod
def _expand_inputs_for_generation(
expand_size: int = 1,
is_encoder_decoder: bool = False,
input_ids: Optional[torch.LongTensor] = None,
**model_kwargs,
) -> Tuple[torch.LongTensor, Dict[str, Any]]:
"""Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...]"""
def _expand_dict_for_generation(dict_to_expand):
for key in dict_to_expand:
if dict_to_expand[key] is not None and isinstance(dict_to_expand[key], torch.Tensor):
dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0)
return dict_to_expand
if input_ids is not None:
input_ids = input_ids.repeat_interleave(expand_size, dim=0)
model_kwargs = _expand_dict_for_generation(model_kwargs)
if is_encoder_decoder:
if model_kwargs.get("encoder_outputs") is None:
raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])
return input_ids, model_kwargs
def _extract_past_from_model_output(self, outputs: ModelOutput, standardize_cache_format: bool = False):
past_key_values = None
if "past_key_values" in outputs:
past_key_values = outputs.past_key_values
elif "mems" in outputs:
past_key_values = outputs.mems
elif "past_buckets_states" in outputs:
past_key_values = outputs.past_buckets_states
# Bloom fix: standardizes the cache format when requested
if standardize_cache_format and hasattr(self, "_convert_to_standard_cache"):
batch_size = outputs.logits.shape[0]
past_key_values = self._convert_to_standard_cache(past_key_values, batch_size=batch_size)
return past_key_values
def _update_model_kwargs_for_generation(
self,
outputs: ModelOutput,
model_kwargs: Dict[str, Any],
is_encoder_decoder: bool = False,
standardize_cache_format: bool = False,
) -> Dict[str, Any]:
# update past_key_values
model_kwargs["past_key_values"] = self._extract_past_from_model_output(
outputs, standardize_cache_format=standardize_cache_format
)
if getattr(outputs, "state", None) is not None:
model_kwargs["state"] = outputs.state
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
if not is_encoder_decoder:
# update attention mask
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
)
else:
# update decoder attention mask
if "decoder_attention_mask" in model_kwargs:
decoder_attention_mask = model_kwargs["decoder_attention_mask"]
model_kwargs["decoder_attention_mask"] = torch.cat(
[decoder_attention_mask, decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1))],
dim=-1,
)
return model_kwargs
def _reorder_cache(self, past_key_values, beam_idx):
raise NotImplementedError(
f"Make sure that a `_reorder_cache` function is correctly implemented in {self.__class__.__module__} to"
f" enable beam search for {self.__class__}"
)
def _get_logits_warper(
self,
generation_config: GenerationConfig,
) -> LogitsProcessorList:
"""
This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsWarper`] instances
used for multinomial sampling.
"""
# instantiate warpers list
warpers = LogitsProcessorList()
# the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
# all samplers can be found in `generation_utils_samplers.py`
if generation_config.temperature is not None and generation_config.temperature != 1.0:
warpers.append(TemperatureLogitsWarper(generation_config.temperature))
min_tokens_to_keep = 2 if generation_config.num_beams > 1 else 1
if generation_config.top_k is not None and generation_config.top_k != 0:
warpers.append(TopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=min_tokens_to_keep))
if generation_config.top_p is not None and generation_config.top_p < 1.0:
warpers.append(TopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=min_tokens_to_keep))
if generation_config.typical_p is not None and generation_config.typical_p < 1.0:
warpers.append(
TypicalLogitsWarper(mass=generation_config.typical_p, min_tokens_to_keep=min_tokens_to_keep)
)
if generation_config.epsilon_cutoff is not None and 0.0 < generation_config.epsilon_cutoff < 1.0:
warpers.append(
EpsilonLogitsWarper(epsilon=generation_config.epsilon_cutoff, min_tokens_to_keep=min_tokens_to_keep)
)
if generation_config.eta_cutoff is not None and 0.0 < generation_config.eta_cutoff < 1.0:
warpers.append(
EtaLogitsWarper(epsilon=generation_config.eta_cutoff, min_tokens_to_keep=min_tokens_to_keep)
)
# `LogitNormalization` should always be the last logit processor, when present
if generation_config.renormalize_logits is True:
warpers.append(LogitNormalization())
return warpers
def _get_logits_processor(
self,
generation_config: GenerationConfig,
input_ids_seq_length: int,
encoder_input_ids: torch.LongTensor,
prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]],
logits_processor: Optional[LogitsProcessorList],
) -> LogitsProcessorList:
"""
This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsProcessor`]
instances used to modify the scores of the language model head.
"""
# instantiate processors list
processors = LogitsProcessorList()
if generation_config.sequence_bias is not None:
processors.append(SequenceBiasLogitsProcessor(sequence_bias=generation_config.sequence_bias))
if generation_config.diversity_penalty is not None and generation_config.diversity_penalty > 0.0:
processors.append(
HammingDiversityLogitsProcessor(
diversity_penalty=generation_config.diversity_penalty,
num_beams=generation_config.num_beams,
num_beam_groups=generation_config.num_beam_groups,
)
)
if (
generation_config.encoder_repetition_penalty is not None
and generation_config.encoder_repetition_penalty != 1.0
):
processors.append(
EncoderRepetitionPenaltyLogitsProcessor(
penalty=generation_config.encoder_repetition_penalty, encoder_input_ids=encoder_input_ids
)
)
if generation_config.repetition_penalty is not None and generation_config.repetition_penalty != 1.0:
processors.append(RepetitionPenaltyLogitsProcessor(penalty=generation_config.repetition_penalty))
if generation_config.no_repeat_ngram_size is not None and generation_config.no_repeat_ngram_size > 0:
processors.append(NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
if (
generation_config.encoder_no_repeat_ngram_size is not None
and generation_config.encoder_no_repeat_ngram_size > 0
):
if self.config.is_encoder_decoder:
processors.append(
EncoderNoRepeatNGramLogitsProcessor(
generation_config.encoder_no_repeat_ngram_size, encoder_input_ids
)
)
else:
raise ValueError(
"It's impossible to use `encoder_no_repeat_ngram_size` with decoder-only architecture"
)
if generation_config.bad_words_ids is not None:
processors.append(
NoBadWordsLogitsProcessor(generation_config.bad_words_ids, generation_config.eos_token_id)
)
if (
generation_config.min_length is not None
and generation_config.eos_token_id is not None
and generation_config.min_length > 0
):
processors.append(MinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id))
if (
generation_config.min_new_tokens is not None
and generation_config.eos_token_id is not None
and generation_config.min_new_tokens > 0
):
processors.append(
MinNewTokensLengthLogitsProcessor(
input_ids_seq_length, generation_config.min_new_tokens, generation_config.eos_token_id
)
)
if prefix_allowed_tokens_fn is not None:
processors.append(
PrefixConstrainedLogitsProcessor(
prefix_allowed_tokens_fn, generation_config.num_beams // generation_config.num_beam_groups
)
)
if generation_config.forced_bos_token_id is not None:
processors.append(ForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id))
if generation_config.forced_eos_token_id is not None:
processors.append(
ForcedEOSTokenLogitsProcessor(generation_config.max_length, generation_config.forced_eos_token_id)
)
if generation_config.remove_invalid_values is True:
processors.append(InfNanRemoveLogitsProcessor())
if generation_config.exponential_decay_length_penalty is not None:
processors.append(
ExponentialDecayLengthPenalty(
generation_config.exponential_decay_length_penalty,
generation_config.eos_token_id,
input_ids_seq_length,
)
)
if generation_config.suppress_tokens is not None:
processors.append(SuppressTokensLogitsProcessor(generation_config.suppress_tokens))
if generation_config.begin_suppress_tokens is not None:
begin_index = input_ids_seq_length
begin_index = (
begin_index
if (input_ids_seq_length > 1 or generation_config.forced_bos_token_id is None)
else begin_index + 1
)
if generation_config.forced_decoder_ids is not None:
# generation starts after the last token that is forced
begin_index += generation_config.forced_decoder_ids[-1][0]
processors.append(
SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index)
)
if generation_config.forced_decoder_ids is not None:
processors.append(ForceTokensLogitsProcessor(generation_config.forced_decoder_ids))
if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1:
processors.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale))
processors = self._merge_criteria_processor_list(processors, logits_processor)
# `LogitNormalization` should always be the last logit processor, when present
if generation_config.renormalize_logits is True:
processors.append(LogitNormalization())
return processors
def _get_stopping_criteria(
self, generation_config: GenerationConfig, stopping_criteria: Optional[StoppingCriteriaList]
) -> StoppingCriteriaList:
criteria = StoppingCriteriaList()
if generation_config.max_length is not None:
max_position_embeddings = getattr(self.config, "max_position_embeddings", None)
criteria.append(
MaxLengthCriteria(
max_length=generation_config.max_length,
max_position_embeddings=max_position_embeddings,
)
)
if generation_config.max_time is not None:
criteria.append(MaxTimeCriteria(max_time=generation_config.max_time))
criteria = self._merge_criteria_processor_list(criteria, stopping_criteria)
return criteria
def _merge_criteria_processor_list(
self,
default_list: Union[LogitsProcessorList, StoppingCriteriaList],
custom_list: Union[LogitsProcessorList, StoppingCriteriaList],
) -> Union[LogitsProcessorList, StoppingCriteriaList]:
if len(custom_list) == 0:
return default_list
for default in default_list:
for custom in custom_list:
if type(custom) is type(default):
object_type = "stopping criteria" if isinstance(custom, StoppingCriteria) else "logits processor"
raise ValueError(
f"A custom {object_type} of type {type(custom)} with values {custom} has been passed to"
f" `.generate()`, but it has already been created with the values {default}. {default} has been"
" created by passing the corresponding arguments to generate or by the model's config default"
f" values. If you just want to change the default values of {object_type} consider passing"
f" them as arguments to `.generate()` instead of using a custom {object_type}."
)
default_list.extend(custom_list)
return default_list
def compute_transition_scores(
self,
sequences: torch.Tensor,
scores: Tuple[torch.Tensor],
beam_indices: Optional[torch.Tensor] = None,
normalize_logits: bool = False,
) -> torch.Tensor:
"""
Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was
used). This is a convenient method to quicky obtain the scores of the selected tokens at generation time.
Parameters:
sequences (`torch.LongTensor`):
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or
shorter if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)`):
Transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens Tuple of
`torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with
each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
beam_indices (`torch.LongTensor`, *optional*):
Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
`(batch_size*num_return_sequences, sequence_length)`. Only required if a `num_beams>1` at
generate-time.
normalize_logits (`bool`, *optional*, defaults to `False`):
Whether to normalize the logits (which, for legacy reasons, may be unnormalized).
Return:
`torch.Tensor`: A `torch.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)` containing
the transition scores (logits)
Examples:
```python
>>> from transformers import GPT2Tokenizer, AutoModelForCausalLM
>>> import numpy as np
>>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> tokenizer.pad_token_id = tokenizer.eos_token_id
>>> inputs = tokenizer(["Today is"], return_tensors="pt")
>>> # Example 1: Print the scores for each token generated with Greedy Search
>>> outputs = model.generate(**inputs, max_new_tokens=5, return_dict_in_generate=True, output_scores=True)
>>> transition_scores = model.compute_transition_scores(
... outputs.sequences, outputs.scores, normalize_logits=True
... )
>>> # input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for
>>> # encoder-decoder models, like BART or T5.
>>> input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
>>> generated_tokens = outputs.sequences[:, input_length:]
>>> for tok, score in zip(generated_tokens[0], transition_scores[0]):
... # | token | token string | logits | probability
... print(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
| 262 | the | -1.414 | 24.33%
| 1110 | day | -2.609 | 7.36%
| 618 | when | -2.010 | 13.40%
| 356 | we | -1.859 | 15.58%
| 460 | can | -2.508 | 8.14%
>>> # Example 2: Reconstruct the sequence scores from Beam Search
>>> outputs = model.generate(
... **inputs,
... max_new_tokens=5,
... num_beams=4,
... num_return_sequences=4,
... return_dict_in_generate=True,
... output_scores=True,
... )
>>> transition_scores = model.compute_transition_scores(
... outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False
... )
>>> # If you sum the generated tokens' scores and apply the length penalty, you'll get the sequence scores.
>>> # Tip: recomputing the scores is only guaranteed to match with `normalize_logits=False`. Depending on the
>>> # use case, you might want to recompute it with `normalize_logits=True`.
>>> output_length = input_length + np.sum(transition_scores.numpy() < 0, axis=1)
>>> length_penalty = model.generation_config.length_penalty
>>> reconstructed_scores = transition_scores.sum(axis=1) / (output_length**length_penalty)
>>> print(np.allclose(outputs.sequences_scores, reconstructed_scores))
True
```"""
# 1. In absence of `beam_indices`, we can assume that we come from e.g. greedy search, which is equivalent
# to a beam search approach were the first (and only) beam is always selected
if beam_indices is None:
beam_indices = torch.arange(scores[0].shape[0]).view(-1, 1).to(sequences.device)
beam_indices = beam_indices.expand(-1, len(scores))
# 2. reshape scores as [batch_size*vocab_size, # generation steps] with # generation steps being
# seq_len - input_length
scores = torch.stack(scores).reshape(len(scores), -1).transpose(0, 1)
# 3. Optionally normalize the logits (across the vocab dimension)
if normalize_logits:
scores = scores.reshape(-1, self.config.vocab_size, scores.shape[-1])
scores = torch.nn.functional.log_softmax(scores, dim=1)
scores = scores.reshape(-1, scores.shape[-1])
# 4. cut beam_indices to longest beam length
beam_indices_mask = beam_indices < 0
max_beam_length = (1 - beam_indices_mask.long()).sum(-1).max()
beam_indices = beam_indices.clone()[:, :max_beam_length]
beam_indices_mask = beam_indices_mask[:, :max_beam_length]
# 5. Set indices of beams that finished early to 0; such indices will be masked correctly afterwards
beam_indices[beam_indices_mask] = 0
# 6. multiply beam_indices with vocab size to gather correctly from scores
beam_sequence_indices = beam_indices * self.config.vocab_size
# 7. Define which indices contributed to scores
cut_idx = sequences.shape[-1] - max_beam_length
indices = sequences[:, cut_idx:] + beam_sequence_indices
# 8. Compute scores
transition_scores = scores.gather(0, indices)
# 9. Mask out transition_scores of beams that stopped early
transition_scores[beam_indices_mask] = 0
return transition_scores
def _validate_model_class(self):
"""
Confirms that the model class is compatible with generation. If not, raises an exception that points to the
right class to use.
"""
if not self.can_generate():
generate_compatible_mappings = [
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
MODEL_FOR_VISION_2_SEQ_MAPPING,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
]
generate_compatible_classes = set()
for model_mapping in generate_compatible_mappings:
supported_models = model_mapping.get(type(self.config), default=None)
if supported_models is not None:
generate_compatible_classes.add(supported_models.__name__)
exception_message = (
f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
"it doesn't have a language model head."
)
if generate_compatible_classes:
exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
raise TypeError(exception_message)
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
"""Validates model kwargs for generation. Generate argument typos will also be caught here."""
# Excludes arguments that are handled before calling any model function
if self.config.is_encoder_decoder:
for key in ["decoder_input_ids"]:
model_kwargs.pop(key, None)
unused_model_args = []
model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
# `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
# `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
if "kwargs" in model_args or "model_kwargs" in model_args:
model_args |= set(inspect.signature(self.forward).parameters)
for key, value in model_kwargs.items():
if value is not None and key not in model_args:
unused_model_args.append(key)
if unused_model_args:
raise ValueError(
f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
" generate arguments will also show up in this list)"
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
synced_gpus: Optional[bool] = None,
assistant_model: Optional["PreTrainedModel"] = None,
streamer: Optional["BaseStreamer"] = None,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head.
<Tip warning={true}>
Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
model's default generation configuration. You can override any `generation_config` by passing the corresponding
parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
For an overview of generation strategies and code examples, check out the [following
guide](../generation_strategies).
</Tip>
Parameters:
inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
`input_ids`, `input_values`, `input_features`, or `pixel_values`.
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call. `**kwargs`
passed to generate matching the attributes of `generation_config` will override them. If
`generation_config` is not provided, the default will be used, which had the following loading
priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
default values, whose documentation should be checked to parameterize generation.
logits_processor (`LogitsProcessorList`, *optional*):
Custom logits processors that complement the default logits processors built from arguments and
generation config. If a logit processor is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
stopping_criteria (`StoppingCriteriaList`, *optional*):
Custom stopping criteria that complement the default stopping criteria built from arguments and a
generation config. If a stopping criteria is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*):
If provided, this function constraints the beam search to allowed tokens only at each step. If not
provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
`input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful
for constrained generation conditioned on the prefix, as described in [Autoregressive Entity
Retrieval](https://arxiv.org/abs/2010.00904).
synced_gpus (`bool`, *optional*):
Whether to continue running the while loop until max_length. Unless overridden this flag will be set to
`True` under DeepSpeed ZeRO Stage 3 multiple GPUs environment to avoid hanging if one GPU finished
generating before other GPUs. Otherwise it'll be set to `False`.
assistant_model (`PreTrainedModel`, *optional*):
An assistant model that can be used to accelerate generation. The assistant model must have the exact
same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistent model
is much faster than running generation with the model you're calling generate from. As such, the
assistant model should be much smaller.
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
kwargs (`Dict[str, Any]`, *optional*):
Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
Return:
[`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.GreedySearchDecoderOnlyOutput`],
- [`~generation.SampleDecoderOnlyOutput`],
- [`~generation.BeamSearchDecoderOnlyOutput`],
- [`~generation.BeamSampleDecoderOnlyOutput`]
If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[`~utils.ModelOutput`] types are:
- [`~generation.GreedySearchEncoderDecoderOutput`],
- [`~generation.SampleEncoderDecoderOutput`],
- [`~generation.BeamSearchEncoderDecoderOutput`],
- [`~generation.BeamSampleEncoderDecoderOutput`]
"""
if synced_gpus is None:
if is_deepspeed_zero3_enabled() and dist.get_world_size() > 1:
synced_gpus = True
else:
synced_gpus = False
# 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
self._validate_model_class()
# priority: `generation_config` argument > `model.generation_config` (the default generation config)
if generation_config is None:
# legacy: users may modify the model configuration to control generation -- update the generation config
# model attribute accordingly, if it was created from the model config
if self.generation_config._from_model_config:
new_generation_config = GenerationConfig.from_model_config(self.config)
if new_generation_config != self.generation_config:
warnings.warn(
"You have modified the pretrained model configuration to control generation. This is a"
" deprecated strategy to control generation and will be removed soon, in a future version."
" Please use a generation configuration file (see"
" https://huggingface.co/docs/transformers/main_classes/text_generation )"
)
self.generation_config = new_generation_config
generation_config = self.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
generation_config.validate()
self._validate_model_kwargs(model_kwargs.copy())
# 2. Set generation parameters if not already defined
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
if model_kwargs.get("attention_mask", None) is None:
logger.warning(
"The attention mask and the pad token id were not set. As a consequence, you may observe "
"unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
)
eos_token_id = generation_config.eos_token_id
if isinstance(eos_token_id, list):
eos_token_id = eos_token_id[0]
logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
generation_config.pad_token_id = eos_token_id
# 3. Define model inputs
# inputs_tensor has to be defined
# model_input_name is defined if model-specific keyword input is passed
# otherwise model_input_name is None
# all model-specific keyword inputs are removed from `model_kwargs`
inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
inputs, generation_config.bos_token_id, model_kwargs
)
batch_size = inputs_tensor.shape[0]
# 4. Define other model kwargs
model_kwargs["output_attentions"] = generation_config.output_attentions
model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
# decoder-only models with inputs_embeds forwarding must use caching (otherwise we can't detect whether we are
# generating the first new token or not, and we only want to use the embeddings for the first new token)
if not self.config.is_encoder_decoder and model_input_name == "inputs_embeds":
model_kwargs["use_cache"] = True
else:
model_kwargs["use_cache"] = generation_config.use_cache
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
requires_attention_mask = "encoder_outputs" not in model_kwargs
if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
)
# decoder-only models should use left-padding for generation
if not self.config.is_encoder_decoder:
# If `input_ids` was given, check if the last id in any sequence is `pad_token_id`
# Note: If using, `inputs_embeds` this check does not work, because we want to be more hands-off.
if (
generation_config.pad_token_id is not None
and len(inputs_tensor.shape) == 2
and torch.sum(inputs_tensor[:, -1] == generation_config.pad_token_id) > 0
):
logger.warning(
"A decoder-only architecture is being used, but right-padding was detected! For correct "
"generation results, please set `padding_side='left'` when initializing the tokenizer."
)
if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
# if model is encoder decoder encoder_outputs are created
# and added to `model_kwargs`
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
inputs_tensor, model_kwargs, model_input_name
)
# 5. Prepare `input_ids` which will be used for auto-regressive generation
if self.config.is_encoder_decoder:
input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
batch_size=batch_size,
model_input_name=model_input_name,
model_kwargs=model_kwargs,
decoder_start_token_id=generation_config.decoder_start_token_id,
bos_token_id=generation_config.bos_token_id,
device=inputs_tensor.device,
)
else:
input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
if streamer is not None:
streamer.put(input_ids.cpu())
# 6. Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None:
warnings.warn(
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
" recommend using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif generation_config.max_new_tokens is not None:
if not has_default_max_length:
logger.warning(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
raise ValueError(
f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
f" the maximum length ({generation_config.max_length})"
)
if input_ids_seq_length >= generation_config.max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
" increasing `max_new_tokens`."
)
# 7. determine generation mode
is_constraint_gen_mode = (
generation_config.constraints is not None or generation_config.force_words_ids is not None
)
is_contrastive_search_gen_mode = (
(generation_config.num_beams == 1)
and generation_config.top_k is not None
and generation_config.top_k > 1
and generation_config.do_sample is False
and generation_config.penalty_alpha is not None
and generation_config.penalty_alpha > 0
)
is_greedy_gen_mode = (
(generation_config.num_beams == 1)
and (generation_config.num_beam_groups == 1)
and generation_config.do_sample is False
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
is_sample_gen_mode = (
(generation_config.num_beams == 1)
and (generation_config.num_beam_groups == 1)
and generation_config.do_sample is True
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
is_beam_gen_mode = (
(generation_config.num_beams > 1)
and (generation_config.num_beam_groups == 1)
and generation_config.do_sample is False
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
is_beam_sample_gen_mode = (
(generation_config.num_beams > 1)
and (generation_config.num_beam_groups == 1)
and generation_config.do_sample is True
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
is_group_beam_gen_mode = (
(generation_config.num_beams > 1)
and (generation_config.num_beam_groups > 1)
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
is_assisted_gen_mode = False
if assistant_model is not None:
if not (is_greedy_gen_mode or is_sample_gen_mode):
raise ValueError(
"You've set `assistant_model`, which triggers assisted generate. Currently, assisted generate "
"is only supported with Greedy Search and Sample."
)
is_assisted_gen_mode = True
if generation_config.num_beam_groups > generation_config.num_beams:
raise ValueError("`num_beam_groups` has to be smaller or equal to `num_beams`")
if is_group_beam_gen_mode and generation_config.do_sample is True:
raise ValueError(
"Diverse beam search cannot be used in sampling mode. Make sure that `do_sample` is set to `False`."
)
if streamer is not None and (generation_config.num_beams > 1):
raise ValueError(
"`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
)
if self.device.type != input_ids.device.type:
warnings.warn(
"You are calling .generate() with the `input_ids` being on a device type different"
f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
" Please make sure that you have put `input_ids` to the"
f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
" running `.generate()`.",
UserWarning,
)
# 8. prepare distribution pre_processing samplers
logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
encoder_input_ids=inputs_tensor,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
logits_processor=logits_processor,
)
# 9. prepare stopping criteria
stopping_criteria = self._get_stopping_criteria(
generation_config=generation_config, stopping_criteria=stopping_criteria
)
# 10. go into different generation modes
if is_assisted_gen_mode:
if generation_config.num_return_sequences > 1:
raise ValueError(
"num_return_sequences has to be 1 when doing assisted generate, "
f"but is {generation_config.num_return_sequences}."
)
if batch_size > 1:
raise ValueError("assisted generate is only supported for batch_size = 1")
if not model_kwargs["use_cache"]:
raise ValueError("assisted generate requires `use_cache=True`")
# 11. If the assistant model is an encoder-decoder, prepare its encoder outputs
if assistant_model.config.is_encoder_decoder:
assistant_model_kwargs = copy.deepcopy(model_kwargs)
inputs_tensor, model_input_name, assistant_model_kwargs = assistant_model._prepare_model_inputs(
inputs_tensor, assistant_model.generation_config.bos_token_id, assistant_model_kwargs
)
assistant_model_kwargs = assistant_model._prepare_encoder_decoder_kwargs_for_generation(
inputs_tensor, assistant_model_kwargs, model_input_name
)
model_kwargs["assistant_encoder_outputs"] = assistant_model_kwargs["encoder_outputs"]
# 12. run assisted generate
return self.assisted_decoding(
input_ids,
assistant_model=assistant_model,
do_sample=generation_config.do_sample,
logits_processor=logits_processor,
logits_warper=self._get_logits_warper(generation_config) if generation_config.do_sample else None,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
**model_kwargs,
)
if is_greedy_gen_mode:
if generation_config.num_return_sequences > 1:
raise ValueError(
"num_return_sequences has to be 1 when doing greedy search, "
f"but is {generation_config.num_return_sequences}."
)
# 11. run greedy search
return self.greedy_search(
input_ids,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
**model_kwargs,
)
elif is_contrastive_search_gen_mode:
if generation_config.num_return_sequences > 1:
raise ValueError(
"num_return_sequences has to be 1 when doing contrastive search, "
f"but is {generation_config.num_return_sequences}."
)
if not model_kwargs["use_cache"]:
raise ValueError("Contrastive search requires `use_cache=True`")
return self.contrastive_search(
input_ids,
top_k=generation_config.top_k,
penalty_alpha=generation_config.penalty_alpha,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
**model_kwargs,
)
elif is_sample_gen_mode:
# 11. prepare logits warper
logits_warper = self._get_logits_warper(generation_config)
# 12. expand input_ids with `num_return_sequences` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_return_sequences,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run sample
return self.sample(
input_ids,
logits_processor=logits_processor,
logits_warper=logits_warper,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
**model_kwargs,
)
elif is_beam_gen_mode:
if generation_config.num_return_sequences > generation_config.num_beams:
raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
if stopping_criteria.max_length is None:
raise ValueError("`max_length` needs to be a stopping_criteria for now.")
# 11. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
max_length=generation_config.max_length,
)
# 12. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run beam search
return self.beam_search(
input_ids,
beam_scorer,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
**model_kwargs,
)
elif is_beam_sample_gen_mode:
# 11. prepare logits warper
logits_warper = self._get_logits_warper(generation_config)
if stopping_criteria.max_length is None:
raise ValueError("`max_length` needs to be a stopping_criteria for now.")
# 12. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size * generation_config.num_return_sequences,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
max_length=generation_config.max_length,
)
# 13. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams * generation_config.num_return_sequences,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 14. run beam sample
return self.beam_sample(
input_ids,
beam_scorer,
logits_processor=logits_processor,
logits_warper=logits_warper,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
**model_kwargs,
)
elif is_group_beam_gen_mode:
if generation_config.num_return_sequences > generation_config.num_beams:
raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
if generation_config.num_beams % generation_config.num_beam_groups != 0:
raise ValueError("`num_beams` should be divisible by `num_beam_groups` for group beam search.")
if generation_config.diversity_penalty == 0.0:
raise ValueError(
"`diversity_penalty` should be greater than `0.0`, otherwise your beam groups will be identical."
)
if stopping_criteria.max_length is None:
raise ValueError("`max_length` needs to be a stopping_criteria for now.")
has_default_typical_p = kwargs.get("typical_p") is None and generation_config.typical_p == 1.0
if not has_default_typical_p:
raise ValueError("Decoder argument `typical_p` is not supported with beam groups.")
# 11. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
num_beam_groups=generation_config.num_beam_groups,
max_length=generation_config.max_length,
)
# 12. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run beam search
return self.group_beam_search(
input_ids,
beam_scorer,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
**model_kwargs,
)
elif is_constraint_gen_mode:
if generation_config.num_return_sequences > generation_config.num_beams:
raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
if stopping_criteria.max_length is None:
raise ValueError("`max_length` needs to be a stopping_criteria for now.")
if generation_config.num_beams <= 1:
raise ValueError("`num_beams` needs to be greater than 1 for constrained generation.")
if generation_config.do_sample:
raise ValueError("`do_sample` needs to be false for constrained generation.")
if generation_config.num_beam_groups is not None and generation_config.num_beam_groups > 1:
raise ValueError("`num_beam_groups` not supported yet for constrained generation.")
final_constraints = []
if generation_config.constraints is not None:
final_constraints = generation_config.constraints
if generation_config.force_words_ids is not None:
def typeerror():
raise ValueError(
"`force_words_ids` has to either be a `List[List[List[int]]]` or `List[List[int]]`"
f"of positive integers, but is {generation_config.force_words_ids}."
)
if (
not isinstance(generation_config.force_words_ids, list)
or len(generation_config.force_words_ids) == 0
):
typeerror()
for word_ids in generation_config.force_words_ids:
if isinstance(word_ids[0], list):
if not isinstance(word_ids, list) or len(word_ids) == 0:
typeerror()
if any(not isinstance(token_ids, list) for token_ids in word_ids):
typeerror()
if any(
any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
for token_ids in word_ids
):
typeerror()
constraint = DisjunctiveConstraint(word_ids)
else:
if not isinstance(word_ids, list) or len(word_ids) == 0:
typeerror()
if any((not isinstance(token_id, int) or token_id < 0) for token_id in word_ids):
typeerror()
constraint = PhrasalConstraint(word_ids)
final_constraints.append(constraint)
# 11. prepare beam search scorer
constrained_beam_scorer = ConstrainedBeamSearchScorer(
constraints=final_constraints,
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
max_length=generation_config.max_length,
)
# 12. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run beam search
return self.constrained_beam_search(
input_ids,
constrained_beam_scorer=constrained_beam_scorer,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
**model_kwargs,
)
@torch.no_grad()
def contrastive_search(
self,
input_ids: torch.LongTensor,
top_k: Optional[int] = 1,
penalty_alpha: Optional[float] = 0,
logits_processor: Optional[LogitsProcessorList] = None,
logits_warper: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
streamer: Optional["BaseStreamer"] = None,
**model_kwargs,
) -> Union[ContrastiveSearchOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **contrastive search** and can
be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.contrastive_search`] directly. Use
generate() instead. For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
top_k (`int`, *optional*, defaults to 1):
The size of the candidate set that is used to re-rank for contrastive search
penalty_alpha (`float`, *optional*, defaults to 0):
The degeneration penalty for contrastive search; activate when it is larger than 0
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.ContrastiveSearchDecoderOnlyOutput`], [`~generation.ContrastiveSearchEncoderDecoderOutput`]
or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.ContrastiveSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.ContrastiveSearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForCausalLM,
... StoppingCriteriaList,
... MaxLengthCriteria,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m")
>>> # set pad_token_id to eos_token_id because OPT does not have a PAD token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "DeepMind Company is"
>>> input_ids = tokenizer(input_prompt, return_tensors="pt")
>>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=64)])
>>> outputs = model.contrastive_search(
... **input_ids, penalty_alpha=0.6, top_k=4, stopping_criteria=stopping_criteria
... )
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['DeepMind Company is a company that focuses on the development and commercialization of artificial intelligence (AI). DeepMind’s mission is to help people understand and solve problems that are difficult to solve in the world today.\n\nIn this post, we talk about the benefits of deep learning in business and how it']
```"""
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
# keep track of which sequences are already finished
unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
this_peer_finished = False # used by synced_gpus only
batch_size = input_ids.shape[0]
while True:
if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
# send 0.0 if we finished, 1.0 otherwise
dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
# did all peers finish? the reduced sum will be 0.0 then
if this_peer_finished_flag.item() == 0.0:
break
# if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
# (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
if model_kwargs.get("past_key_values") is None:
# prepare inputs
model_kwargs["use_cache"] = True
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# encode the given prefix and prepare model inputs; encoder-decoder model process the prefix and save
# the `encoder_outputs`
outputs = self(
**model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
)
# last decoder hidden states will be used to compute the degeneration penalty (cosine similarity with
# previous tokens)
if self.config.is_encoder_decoder:
last_hidden_states = outputs.decoder_hidden_states[-1]
else:
last_hidden_states = outputs.hidden_states[-1]
# next logit for contrastive search to select top-k candidate tokens
logit_for_next_step = outputs.logits[:, -1, :]
model_kwargs = self._update_model_kwargs_for_generation(
outputs,
model_kwargs,
is_encoder_decoder=self.config.is_encoder_decoder,
standardize_cache_format=True,
)
# Expands model inputs top_k times, for batched forward passes (akin to beam search).
_, model_kwargs = self._expand_inputs_for_generation(
expand_size=top_k, is_encoder_decoder=self.config.is_encoder_decoder, **model_kwargs
)
past_key_values = model_kwargs.get("past_key_values")
if past_key_values is None:
raise ValueError(
f"{self.__class__.__name__} does not support caching and therefore **can't** be used "
"for contrastive search."
)
elif (
not isinstance(past_key_values[0], (tuple, torch.Tensor))
or past_key_values[0][0].shape[0] != batch_size
):
raise ValueError(
f"{self.__class__.__name__} does not have a standard cache format and therefore **can't** be "
"used for contrastive search without further modifications."
)
# contrastive_search main logic start:
# contrastive search decoding consists of two steps: (1) candidate tokens recall; (2) candidate re-rank by
# degeneration penalty
logit_for_next_step = logits_processor(input_ids, logit_for_next_step)
logit_for_next_step = logits_warper(input_ids, logit_for_next_step)
next_probs = nn.functional.softmax(logit_for_next_step, dim=-1)
top_k_probs, top_k_ids = torch.topk(next_probs, dim=-1, k=top_k)
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (logit_for_next_step,)
if output_attentions:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# Replicates the new past_key_values to match the `top_k` candidates
new_key_values = []
for layer in model_kwargs["past_key_values"]:
items = []
# item is either the key or the value matrix
for item in layer:
items.append(item.repeat_interleave(top_k, dim=0))
new_key_values.append(items)
model_kwargs["past_key_values"] = new_key_values
# compute the candidate tokens by the language model and collects their hidden_states
next_model_inputs = self.prepare_inputs_for_generation(top_k_ids.view(-1, 1), **model_kwargs)
outputs = self(
**next_model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
)
next_past_key_values = self._extract_past_from_model_output(outputs, standardize_cache_format=True)
logits = outputs.logits[:, -1, :]
# name is different for encoder-decoder and decoder-only models
if self.config.is_encoder_decoder:
next_hidden = outputs.decoder_hidden_states[-1]
full_hidden_states = outputs.decoder_hidden_states
else:
next_hidden = outputs.hidden_states[-1]
full_hidden_states = outputs.hidden_states
context_hidden = last_hidden_states.repeat_interleave(top_k, dim=0)
# compute the degeneration penalty and re-rank the candidates based on the degeneration penalty and the
# model confidence. Keeping `selected_idx` on CPU enables multi-device contrastive search and doesn't
# introduce (noticeable) slowdowns on single-device runs.
selected_idx = _ranking_fast(context_hidden, next_hidden, top_k_probs, penalty_alpha, top_k)
selected_idx = selected_idx.to("cpu")
# prepare for the next step: (1) next token_id; (2) past_key_values; (3) last_hidden_states for computing
# the degeneration penalty; (4) logits for selecting next top-k candidates; (5) selected tokens scores
# (model confidence minus degeneration penalty); (6) decoder hidden_states
next_tokens = top_k_ids[range(len(top_k_ids)), selected_idx]
next_hidden = torch.stack(torch.split(next_hidden.squeeze(dim=1), top_k))
next_hidden = next_hidden[range(batch_size), selected_idx, :]
last_hidden_states = torch.cat([last_hidden_states, next_hidden.unsqueeze(1)], dim=1)
next_decoder_hidden_states = ()
for layer in full_hidden_states:
layer = torch.stack(torch.split(layer, top_k))[range(batch_size), selected_idx, :]
next_decoder_hidden_states += (layer,)
# select the past_key_value
new_key_values = ()
for layer in next_past_key_values:
items = ()
# item is either the key or the value matrix
for item in layer:
item = torch.stack(torch.split(item, top_k, dim=0)) # [B, K, num_head, seq_len, esz]
item = item[range(batch_size), selected_idx, ...] # [B, num_head, seq_len, esz]
items += (item,)
new_key_values += (items,)
next_past_key_values = new_key_values
logit_for_next_step = torch.stack(torch.split(logits, top_k))[range(batch_size), selected_idx, :]
# Rebuilds the relevant parts of the model output for the selected token, for use in the next iteration
if self.config.is_encoder_decoder:
next_step_cross_attentions = ()
next_step_decoder_attentions = ()
if output_attentions:
for layer in outputs.cross_attentions:
layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
next_step_cross_attentions += (layer,)
for layer in outputs.decoder_attentions:
layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
next_step_decoder_attentions += (layer,)
outputs = Seq2SeqLMOutput(
past_key_values=next_past_key_values,
decoder_hidden_states=next_decoder_hidden_states,
decoder_attentions=next_step_decoder_attentions or None,
cross_attentions=next_step_cross_attentions or None,
)
else:
next_step_attentions = ()
if output_attentions:
for layer in outputs.attentions:
layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
next_step_attentions += (layer,)
outputs = CausalLMOutputWithPast(
past_key_values=next_past_key_values,
hidden_states=next_decoder_hidden_states,
attentions=next_step_attentions or None,
)
# contrastive_search main logic end
if synced_gpus and this_peer_finished:
continue # don't waste resources running the code we don't need
# finished sentences should have their next token be a padding token
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
if streamer is not None:
streamer.put(next_tokens.cpu())
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if eos_token was found in one sentence, set sentence to finished
if eos_token_id_tensor is not None:
unfinished_sequences = unfinished_sequences.mul(
next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
)
# stop when each sentence is finished
if unfinished_sequences.max() == 0:
this_peer_finished = True
# stop if we exceed the maximum length
if stopping_criteria(input_ids, scores):
this_peer_finished = True
if this_peer_finished and not synced_gpus:
break
if streamer is not None:
streamer.end()
if return_dict_in_generate:
if self.config.is_encoder_decoder:
return ContrastiveSearchEncoderDecoderOutput(
sequences=input_ids,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return ContrastiveSearchDecoderOnlyOutput(
sequences=input_ids,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return input_ids
def greedy_search(
self,
input_ids: torch.LongTensor,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
streamer: Optional["BaseStreamer"] = None,
**model_kwargs,
) -> Union[GreedySearchOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **greedy decoding** and can be
used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.greedy_search`] directly. Use generate()
instead. For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
max_length (`int`, *optional*, defaults to 20):
**DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.GreedySearchDecoderOnlyOutput`], [`~generation.GreedySearchEncoderDecoderOutput`] or
`torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.GreedySearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.GreedySearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForCausalLM,
... LogitsProcessorList,
... MinLengthLogitsProcessor,
... StoppingCriteriaList,
... MaxLengthCriteria,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a PAD token
>>> model.generation_config.pad_token_id = model.generation_config.eos_token_id
>>> input_prompt = "It might be possible to"
>>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
>>> # instantiate logits processors
>>> logits_processor = LogitsProcessorList(
... [
... MinLengthLogitsProcessor(10, eos_token_id=model.generation_config.eos_token_id),
... ]
... )
>>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
>>> outputs = model.greedy_search(
... input_ids, logits_processor=logits_processor, stopping_criteria=stopping_criteria
... )
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
["It might be possible to get a better understanding of the nature of the problem, but it's not"]
```"""
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if max_length is not None:
warnings.warn(
"`max_length` is deprecated in this function, use"
" `stopping_criteria=StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])` instead.",
UserWarning,
)
stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
# keep track of which sequences are already finished
unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
this_peer_finished = False # used by synced_gpus only
while True:
if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
# send 0.0 if we finished, 1.0 otherwise
dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
# did all peers finish? the reduced sum will be 0.0 then
if this_peer_finished_flag.item() == 0.0:
break
# prepare model inputs
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if synced_gpus and this_peer_finished:
continue # don't waste resources running the code we don't need
next_token_logits = outputs.logits[:, -1, :]
# pre-process distribution
next_tokens_scores = logits_processor(input_ids, next_token_logits)
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (next_tokens_scores,)
if output_attentions:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# argmax
next_tokens = torch.argmax(next_tokens_scores, dim=-1)
# finished sentences should have their next token be a padding token
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
if streamer is not None:
streamer.put(next_tokens.cpu())
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if eos_token was found in one sentence, set sentence to finished
if eos_token_id_tensor is not None:
unfinished_sequences = unfinished_sequences.mul(
next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
)
# stop when each sentence is finished
if unfinished_sequences.max() == 0:
this_peer_finished = True
# stop if we exceed the maximum length
if stopping_criteria(input_ids, scores):
this_peer_finished = True
if this_peer_finished and not synced_gpus:
break
if streamer is not None:
streamer.end()
if return_dict_in_generate:
if self.config.is_encoder_decoder:
return GreedySearchEncoderDecoderOutput(
sequences=input_ids,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return GreedySearchDecoderOnlyOutput(
sequences=input_ids,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return input_ids
def sample(
self,
input_ids: torch.LongTensor,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_warper: Optional[LogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
streamer: Optional["BaseStreamer"] = None,
**model_kwargs,
) -> Union[SampleOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **multinomial sampling** and
can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.sample`] directly. Use generate() instead.
For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
logits_warper (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
**DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
model_kwargs:
Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.SampleDecoderOnlyOutput`], [`~generation.SampleEncoderDecoderOutput`] or `torch.LongTensor`:
A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.SampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.SampleEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForCausalLM,
... LogitsProcessorList,
... MinLengthLogitsProcessor,
... TopKLogitsWarper,
... TemperatureLogitsWarper,
... StoppingCriteriaList,
... MaxLengthCriteria,
... )
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> model.generation_config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "Today is a beautiful day, and"
>>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
>>> # instantiate logits processors
>>> logits_processor = LogitsProcessorList(
... [
... MinLengthLogitsProcessor(15, eos_token_id=model.generation_config.eos_token_id),
... ]
... )
>>> # instantiate logits processors
>>> logits_warper = LogitsProcessorList(
... [
... TopKLogitsWarper(50),
... TemperatureLogitsWarper(0.7),
... ]
... )
>>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
>>> torch.manual_seed(0) # doctest: +IGNORE_RESULT
>>> outputs = model.sample(
... input_ids,
... logits_processor=logits_processor,
... logits_warper=logits_warper,
... stopping_criteria=stopping_criteria,
... )
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Today is a beautiful day, and we must do everything possible to make it a day of celebration.']
```"""
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if max_length is not None:
warnings.warn(
"`max_length` is deprecated in this function, use"
" `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
UserWarning,
)
stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
# keep track of which sequences are already finished
unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
this_peer_finished = False # used by synced_gpus only
# auto-regressive generation
while True:
if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
# send 0.0 if we finished, 1.0 otherwise
dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
# did all peers finish? the reduced sum will be 0.0 then
if this_peer_finished_flag.item() == 0.0:
break
# prepare model inputs
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if synced_gpus and this_peer_finished:
continue # don't waste resources running the code we don't need
next_token_logits = outputs.logits[:, -1, :]
# pre-process distribution
next_token_scores = logits_processor(input_ids, next_token_logits)
next_token_scores = logits_warper(input_ids, next_token_scores)
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (next_token_scores,)
if output_attentions:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# sample
probs = nn.functional.softmax(next_token_scores, dim=-1)
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
# finished sentences should have their next token be a padding token
if eos_token_id is not None:
if pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
if streamer is not None:
streamer.put(next_tokens.cpu())
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if eos_token was found in one sentence, set sentence to finished
if eos_token_id_tensor is not None:
unfinished_sequences = unfinished_sequences.mul(
next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
)
# stop when each sentence is finished
if unfinished_sequences.max() == 0:
this_peer_finished = True
# stop if we exceed the maximum length
if stopping_criteria(input_ids, scores):
this_peer_finished = True
if this_peer_finished and not synced_gpus:
break
if streamer is not None:
streamer.end()
if return_dict_in_generate:
if self.config.is_encoder_decoder:
return SampleEncoderDecoderOutput(
sequences=input_ids,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return SampleDecoderOnlyOutput(
sequences=input_ids,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return input_ids
def beam_search(
self,
input_ids: torch.LongTensor,
beam_scorer: BeamScorer,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
**model_kwargs,
) -> Union[BeamSearchOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **beam search decoding** and
can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.beam_search`] directly. Use generate()
instead. For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
beam_scorer (`BeamScorer`):
An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
max_length (`int`, *optional*, defaults to 20):
**DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs:
Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`generation.BeamSearchDecoderOnlyOutput`], [`~generation.BeamSearchEncoderDecoderOutput`] or
`torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.BeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.BeamSearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForSeq2SeqLM,
... LogitsProcessorList,
... MinLengthLogitsProcessor,
... BeamSearchScorer,
... )
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> encoder_input_str = "translate English to German: How old are you?"
>>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
>>> # lets run beam search using 3 beams
>>> num_beams = 3
>>> # define decoder start token ids
>>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
>>> input_ids = input_ids * model.config.decoder_start_token_id
>>> # add encoder_outputs to model keyword arguments
>>> model_kwargs = {
... "encoder_outputs": model.get_encoder()(
... encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
... )
... }
>>> # instantiate beam scorer
>>> beam_scorer = BeamSearchScorer(
... batch_size=1,
... num_beams=num_beams,
... device=model.device,
... )
>>> # instantiate logits processors
>>> logits_processor = LogitsProcessorList(
... [
... MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
... ]
... )
>>> outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Wie alt bist du?']
```"""
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if max_length is not None:
warnings.warn(
"`max_length` is deprecated in this function, use"
" `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
UserWarning,
)
stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
if len(stopping_criteria) == 0:
warnings.warn("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
batch_size = len(beam_scorer._beam_hyps)
num_beams = beam_scorer.num_beams
batch_beam_size, cur_len = input_ids.shape
if num_beams * batch_size != batch_beam_size:
raise ValueError(
f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
)
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
beam_indices = (
tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
)
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
# initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
# of the first beam are considered to avoid sampling the exact same tokens across all beams.
beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
beam_scores[:, 1:] = -1e9
beam_scores = beam_scores.view((batch_size * num_beams,))
this_peer_finished = False # used by synced_gpus only
while True:
if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
# send 0.0 if we finished, 1.0 otherwise
dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
# did all peers finish? the reduced sum will be 0.0 then
if this_peer_finished_flag.item() == 0.0:
break
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if synced_gpus and this_peer_finished:
cur_len = cur_len + 1
continue # don't waste resources running the code we don't need
next_token_logits = outputs.logits[:, -1, :]
# hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
# cannot be generated both before and after the `nn.functional.log_softmax` operation.
next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
next_token_scores = nn.functional.log_softmax(
next_token_logits, dim=-1
) # (batch_size * num_beams, vocab_size)
next_token_scores_processed = logits_processor(input_ids, next_token_scores)
next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(next_token_scores)
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (next_token_scores_processed,)
if output_attentions:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# reshape for beam search
vocab_size = next_token_scores.shape[-1]
next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
# Sample 2 next tokens for each beam (so we have some spare tokens and match output of beam search)
next_token_scores, next_tokens = torch.topk(
next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True
)
next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
next_tokens = next_tokens % vocab_size
# stateless
beam_outputs = beam_scorer.process(
input_ids,
next_token_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
beam_indices=beam_indices,
)
beam_scores = beam_outputs["next_beam_scores"]
beam_next_tokens = beam_outputs["next_beam_tokens"]
beam_idx = beam_outputs["next_beam_indices"]
input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
if model_kwargs["past_key_values"] is not None:
model_kwargs["past_key_values"] = self._reorder_cache(model_kwargs["past_key_values"], beam_idx)
if return_dict_in_generate and output_scores:
beam_indices = tuple((beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices))))
# increase cur_len
cur_len = cur_len + 1
if beam_scorer.is_done or stopping_criteria(input_ids, scores):
if not synced_gpus:
break
else:
this_peer_finished = True
sequence_outputs = beam_scorer.finalize(
input_ids,
beam_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
max_length=stopping_criteria.max_length,
beam_indices=beam_indices,
)
if return_dict_in_generate:
if not output_scores:
sequence_outputs["sequence_scores"] = None
if self.config.is_encoder_decoder:
return BeamSearchEncoderDecoderOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
beam_indices=sequence_outputs["beam_indices"],
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return BeamSearchDecoderOnlyOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
beam_indices=sequence_outputs["beam_indices"],
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return sequence_outputs["sequences"]
def beam_sample(
self,
input_ids: torch.LongTensor,
beam_scorer: BeamScorer,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_warper: Optional[LogitsProcessorList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
**model_kwargs,
) -> Union[BeamSampleOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **beam search multinomial
sampling** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.beam_sample`] directly. Use generate()
instead. For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
beam_scorer (`BeamScorer`):
A derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
logits_warper (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
**DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs:
Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.BeamSampleDecoderOnlyOutput`], [`~generation.BeamSampleEncoderDecoderOutput`] or
`torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.BeamSampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.BeamSampleEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForSeq2SeqLM,
... LogitsProcessorList,
... MinLengthLogitsProcessor,
... TopKLogitsWarper,
... TemperatureLogitsWarper,
... BeamSearchScorer,
... )
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> encoder_input_str = "translate English to German: How old are you?"
>>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
>>> # lets run beam search using 3 beams
>>> num_beams = 3
>>> # define decoder start token ids
>>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
>>> input_ids = input_ids * model.config.decoder_start_token_id
>>> # add encoder_outputs to model keyword arguments
>>> model_kwargs = {
... "encoder_outputs": model.get_encoder()(
... encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
... )
... }
>>> # instantiate beam scorer
>>> beam_scorer = BeamSearchScorer(
... batch_size=1,
... max_length=model.config.max_length,
... num_beams=num_beams,
... device=model.device,
... )
>>> # instantiate logits processors
>>> logits_processor = LogitsProcessorList(
... [MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id)]
... )
>>> # instantiate logits processors
>>> logits_warper = LogitsProcessorList(
... [
... TopKLogitsWarper(50),
... TemperatureLogitsWarper(0.7),
... ]
... )
>>> outputs = model.beam_sample(
... input_ids, beam_scorer, logits_processor=logits_processor, logits_warper=logits_warper, **model_kwargs
... )
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Wie alt bist du?']
```"""
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if max_length is not None:
warnings.warn(
"`max_length` is deprecated in this function, use"
" `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
UserWarning,
)
stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
batch_size = len(beam_scorer._beam_hyps)
num_beams = beam_scorer.num_beams
batch_beam_size, cur_len = input_ids.shape
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
beam_indices = (
tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
)
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
beam_scores = beam_scores.view((batch_size * num_beams,))
this_peer_finished = False # used by synced_gpus only
while True:
if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
# send 0.0 if we finished, 1.0 otherwise
dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
# did all peers finish? the reduced sum will be 0.0 then
if this_peer_finished_flag.item() == 0.0:
break
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if synced_gpus and this_peer_finished:
cur_len = cur_len + 1
continue # don't waste resources running the code we don't need
next_token_logits = outputs.logits[:, -1, :]
# hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
# cannot be generated both before and after the `nn.functional.log_softmax` operation.
next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
next_token_scores = nn.functional.log_softmax(
next_token_logits, dim=-1
) # (batch_size * num_beams, vocab_size)
next_token_scores_processed = logits_processor(input_ids, next_token_scores)
next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(next_token_scores)
# Note: logits warpers are intentionally applied after adding running beam scores. On some logits warpers
# (like top_p) this is indiferent, but on others (like temperature) it is not. For reference, see
# https://github.com/huggingface/transformers/pull/5420#discussion_r449779867
next_token_scores = logits_warper(input_ids, next_token_scores)
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (logits_warper(input_ids, next_token_scores_processed),)
if output_attentions:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# reshape for beam search
vocab_size = next_token_scores.shape[-1]
next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
probs = nn.functional.softmax(next_token_scores, dim=-1)
next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)
next_token_scores = torch.gather(next_token_scores, -1, next_tokens)
next_token_scores, _indices = torch.sort(next_token_scores, descending=True, dim=1)
next_tokens = torch.gather(next_tokens, -1, _indices)
next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
next_tokens = next_tokens % vocab_size
# stateless
beam_outputs = beam_scorer.process(
input_ids,
next_token_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
beam_indices=beam_indices,
)
beam_scores = beam_outputs["next_beam_scores"]
beam_next_tokens = beam_outputs["next_beam_tokens"]
beam_idx = beam_outputs["next_beam_indices"]
input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
if model_kwargs["past_key_values"] is not None:
model_kwargs["past_key_values"] = self._reorder_cache(model_kwargs["past_key_values"], beam_idx)
if return_dict_in_generate and output_scores:
beam_indices = tuple((beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices))))
# increase cur_len
cur_len = cur_len + 1
if beam_scorer.is_done or stopping_criteria(input_ids, scores):
if not synced_gpus:
break
else:
this_peer_finished = True
sequence_outputs = beam_scorer.finalize(
input_ids,
beam_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
max_length=stopping_criteria.max_length,
beam_indices=beam_indices,
)
if return_dict_in_generate:
if not output_scores:
sequence_outputs["sequence_scores"] = None
if self.config.is_encoder_decoder:
return BeamSampleEncoderDecoderOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
beam_indices=sequence_outputs["beam_indices"],
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return BeamSampleDecoderOnlyOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
beam_indices=sequence_outputs["beam_indices"],
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return sequence_outputs["sequences"]
def group_beam_search(
self,
input_ids: torch.LongTensor,
beam_scorer: BeamScorer,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
**model_kwargs,
):
r"""
Generates sequences of token ids for models with a language modeling head using **diverse beam search
decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.group_beam_search`] directly. Use
generate() instead. For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
beam_scorer (`BeamScorer`):
An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
max_length (`int`, *optional*, defaults to 20):
**DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs:
Additional model specific kwargs that will be forwarded to the `forward` function of the model. If
model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.BeamSearchDecoderOnlyOutput`], [`~generation.BeamSearchEncoderDecoderOutput`] or
`torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.BeamSearchDecoderOnlyOutput`] if [`~generation.BeamSearchDecoderOnlyOutput`] if
`model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a
[`~generation.BeamSearchEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForSeq2SeqLM,
... LogitsProcessorList,
... MinLengthLogitsProcessor,
... HammingDiversityLogitsProcessor,
... BeamSearchScorer,
... )
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> encoder_input_str = "translate English to German: How old are you?"
>>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
>>> # lets run diverse beam search using 6 beams
>>> num_beams = 6
>>> # define decoder start token ids
>>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
>>> input_ids = input_ids * model.config.decoder_start_token_id
>>> # add encoder_outputs to model keyword arguments
>>> model_kwargs = {
... "encoder_outputs": model.get_encoder()(
... encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
... )
... }
>>> # instantiate beam scorer
>>> beam_scorer = BeamSearchScorer(
... batch_size=1,
... max_length=model.config.max_length,
... num_beams=num_beams,
... device=model.device,
... num_beam_groups=3,
... )
>>> # instantiate logits processors
>>> logits_processor = LogitsProcessorList(
... [
... HammingDiversityLogitsProcessor(5.5, num_beams=6, num_beam_groups=3),
... MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
... ]
... )
>>> outputs = model.group_beam_search(
... input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs
... )
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Wie alt bist du?']
```"""
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if max_length is not None:
warnings.warn(
"`max_length` is deprecated in this function, use"
" `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
UserWarning,
)
stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
num_beams = beam_scorer.num_beams
num_beam_groups = beam_scorer.num_beam_groups
num_sub_beams = num_beams // num_beam_groups
batch_size = len(beam_scorer._beam_hyps) // num_beam_groups
device = input_ids.device
batch_beam_size, cur_len = input_ids.shape
if return_dict_in_generate and output_scores:
beam_indices = [tuple(() for _ in range(num_sub_beams * batch_size)) for _ in range(num_beam_groups)]
else:
beam_indices = None
if num_beams * batch_size != batch_beam_size:
raise ValueError(
f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
)
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
# initialise score of first beam of each group with 0 and the rest with -1e9. This ensures that the beams in
# the same group don't produce same tokens everytime.
beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
beam_scores[:, ::num_sub_beams] = 0
beam_scores = beam_scores.view((batch_size * num_beams,))
this_peer_finished = False # used by synced_gpus only
while True:
if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
# send 0.0 if we finished, 1.0 otherwise
dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
# did all peers finish? the reduced sum will be 0.0 then
if this_peer_finished_flag.item() == 0.0:
break
# predicted tokens in cur_len step
current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)
# indices which will form the beams in the next time step
reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)
# do one decoder step on all beams of all sentences in batch
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if synced_gpus and this_peer_finished:
cur_len = cur_len + 1
continue # don't waste resources running the code we don't need
if output_scores:
processed_score = torch.zeros_like(outputs.logits[:, -1, :])
for beam_group_idx in range(num_beam_groups):
group_start_idx = beam_group_idx * num_sub_beams
group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
group_size = group_end_idx - group_start_idx
# indices of beams of current group among all sentences in batch
batch_group_indices = []
for batch_idx in range(batch_size):
batch_group_indices.extend(
[batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
)
group_input_ids = input_ids[batch_group_indices]
# select outputs of beams of current group only
next_token_logits = outputs.logits[batch_group_indices, -1, :]
# hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
# cannot be generated both before and after the `nn.functional.log_softmax` operation.
next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
next_token_scores = nn.functional.log_softmax(
next_token_logits, dim=-1
) # (batch_size * group_size, vocab_size)
vocab_size = next_token_scores.shape[-1]
next_token_scores_processed = logits_processor(
group_input_ids, next_token_scores, current_tokens=current_tokens, beam_group_idx=beam_group_idx
)
next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
next_token_scores = next_token_scores.expand_as(next_token_scores_processed)
if output_scores:
processed_score[batch_group_indices] = next_token_scores_processed
# reshape for beam search
next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)
# Sample 2 next tokens for each beam (so we have some spare tokens and match output of beam search)
next_token_scores, next_tokens = torch.topk(
next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True
)
next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
next_tokens = next_tokens % vocab_size
# stateless
process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
beam_outputs = beam_scorer.process(
group_input_ids,
next_token_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
beam_indices=process_beam_indices,
group_index=beam_group_idx,
)
beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"]
beam_next_tokens = beam_outputs["next_beam_tokens"]
beam_idx = beam_outputs["next_beam_indices"]
if return_dict_in_generate and output_scores:
beam_indices[beam_group_idx] = tuple(
beam_indices[beam_group_idx][beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices[0]))
)
input_ids[batch_group_indices] = group_input_ids[beam_idx]
group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
current_tokens[batch_group_indices] = group_input_ids[:, -1]
# (beam_idx // group_size) -> batch_idx
# (beam_idx % group_size) -> offset of idx inside the group
reordering_indices[batch_group_indices] = (
num_beams * torch.div(beam_idx, group_size, rounding_mode="floor")
+ group_start_idx
+ (beam_idx % group_size)
)
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (processed_score,)
if output_attentions:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
if model_kwargs["past_key_values"] is not None:
model_kwargs["past_key_values"] = self._reorder_cache(
model_kwargs["past_key_values"], reordering_indices
)
# increase cur_len
cur_len = cur_len + 1
if beam_scorer.is_done or stopping_criteria(input_ids, scores):
if not synced_gpus:
break
else:
this_peer_finished = True
final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
sequence_outputs = beam_scorer.finalize(
input_ids,
beam_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
max_length=stopping_criteria.max_length,
beam_indices=final_beam_indices,
)
if return_dict_in_generate:
if not output_scores:
sequence_outputs["sequence_scores"] = None
if self.config.is_encoder_decoder:
return BeamSearchEncoderDecoderOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
beam_indices=sequence_outputs["beam_indices"],
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return BeamSearchDecoderOnlyOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
beam_indices=sequence_outputs["beam_indices"],
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return sequence_outputs["sequences"]
def constrained_beam_search(
self,
input_ids: torch.LongTensor,
constrained_beam_scorer: ConstrainedBeamSearchScorer,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: Optional[bool] = None,
**model_kwargs,
) -> Union[BeamSearchOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **constrained beam search
decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.constrained_beam_search`] directly. Use
generate() instead. For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
constrained_beam_scorer (`ConstrainedBeamSearchScorer`):
A derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
sorted during generation, while satisfying a list of positive constraints. For more information, the
documentation of [`ConstrainedBeamSearchScorer`] should be read.
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
logits_warper (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
max_length (`int`, *optional*, defaults to 20):
**DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs:
Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`generation.BeamSearchDecoderOnlyOutput`], [`~generation.BeamSearchEncoderDecoderOutput`] or
`torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.BeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.BeamSearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForSeq2SeqLM,
... LogitsProcessorList,
... MinLengthLogitsProcessor,
... ConstrainedBeamSearchScorer,
... PhrasalConstraint,
... )
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> encoder_input_str = "translate English to German: How old are you?"
>>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
>>> # lets run beam search using 3 beams
>>> num_beams = 3
>>> # define decoder start token ids
>>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
>>> input_ids = input_ids * model.config.decoder_start_token_id
>>> # add encoder_outputs to model keyword arguments
>>> model_kwargs = {
... "encoder_outputs": model.get_encoder()(
... encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
... )
... }
>>> constraint_str = "Sie"
>>> constraint_token_ids = tokenizer.encode(constraint_str)[:-1] # slice to remove eos token
>>> constraints = [PhrasalConstraint(token_ids=constraint_token_ids)]
>>> # instantiate beam scorer
>>> beam_scorer = ConstrainedBeamSearchScorer(
... batch_size=1, num_beams=num_beams, device=model.device, constraints=constraints
... )
>>> # instantiate logits processors
>>> logits_processor = LogitsProcessorList(
... [
... MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
... ]
... )
>>> outputs = model.constrained_beam_search(
... input_ids, beam_scorer, constraints=constraints, logits_processor=logits_processor, **model_kwargs
... )
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Wie alt sind Sie?']
```"""
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if max_length is not None:
warnings.warn(
"`max_length` is deprecated in this function, use"
" `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
UserWarning,
)
stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
if len(stopping_criteria) == 0:
warnings.warn("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
batch_size = len(constrained_beam_scorer._beam_hyps)
num_beams = constrained_beam_scorer.num_beams
batch_beam_size, cur_len = input_ids.shape
if num_beams * batch_size != batch_beam_size:
raise ValueError(
f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
)
# initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
# of the first beam are considered to avoid sampling the exact same tokens across all beams.
beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
beam_scores[:, 1:] = -1e9
beam_scores = beam_scores.view((batch_size * num_beams,))
this_peer_finished = False # used by synced_gpus only
while True:
if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
# send 0.0 if we finished, 1.0 otherwise
dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
# did all peers finish? the reduced sum will be 0.0 then
if this_peer_finished_flag.item() == 0.0:
break
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if synced_gpus and this_peer_finished:
cur_len = cur_len + 1
continue # don't waste resources running the code we don't need
next_token_logits = outputs.logits[:, -1, :]
# hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
# cannot be generated both before and after the `nn.functional.log_softmax` operation.
next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
next_token_scores = nn.functional.log_softmax(
next_token_logits, dim=-1
) # (batch_size * num_beams, vocab_size)
next_token_scores_processed = logits_processor(input_ids, next_token_scores)
next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(next_token_scores)
scores_for_all_vocab = next_token_scores.clone()
# Store scores, attentions and hidden_states when required
if return_dict_in_generate:
if output_scores:
scores += (next_token_scores,)
if output_attentions:
decoder_attentions += (
(outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
)
if self.config.is_encoder_decoder:
cross_attentions += (outputs.cross_attentions,)
if output_hidden_states:
decoder_hidden_states += (
(outputs.decoder_hidden_states,)
if self.config.is_encoder_decoder
else (outputs.hidden_states,)
)
# reshape for beam search
vocab_size = next_token_scores.shape[-1]
next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
# Sample 2 next tokens for each beam (so we have some spare tokens and match output of beam search)
next_token_scores, next_tokens = torch.topk(
next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True
)
next_indices = (next_tokens / vocab_size).long()
next_tokens = next_tokens % vocab_size
# stateless
beam_outputs = constrained_beam_scorer.process(
input_ids,
next_token_scores,
next_tokens,
next_indices,
scores_for_all_vocab,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
)
beam_scores = beam_outputs["next_beam_scores"]
beam_next_tokens = beam_outputs["next_beam_tokens"]
beam_idx = beam_outputs["next_beam_indices"]
input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
if model_kwargs["past_key_values"] is not None:
model_kwargs["past_key_values"] = self._reorder_cache(model_kwargs["past_key_values"], beam_idx)
# increase cur_len
cur_len = cur_len + 1
if constrained_beam_scorer.is_done or stopping_criteria(input_ids, scores):
if not synced_gpus:
break
else:
this_peer_finished = True
sequence_outputs = constrained_beam_scorer.finalize(
input_ids,
beam_scores,
next_tokens,
next_indices,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
max_length=stopping_criteria.max_length,
)
if return_dict_in_generate:
if not output_scores:
sequence_outputs["sequence_scores"] = None
if self.config.is_encoder_decoder:
return BeamSearchEncoderDecoderOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return BeamSearchDecoderOnlyOutput(
sequences=sequence_outputs["sequences"],
sequences_scores=sequence_outputs["sequence_scores"],
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return sequence_outputs["sequences"]
def assisted_decoding(
self,
input_ids: torch.LongTensor,
assistant_model: "PreTrainedModel",
do_sample: bool = False,
logits_processor: Optional[LogitsProcessorList] = None,
logits_warper: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: bool = False,
streamer: Optional["BaseStreamer"] = None,
**model_kwargs,
):
r"""
Generates sequences of token ids for models with a language modeling head using **greedy decoding** or
**sample** (depending on `do_sample`), assisted by a smaller model. Can be used for text-decoder, text-to-text,
speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.assisted_decoding`] directly. Use
generate() instead. For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
assistant_model (`PreTrainedModel`, *optional*):
An assistant model that can be used to accelerate generation. The assistant model must have the exact
same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistent model
is much faster than running generation with the model you're calling generate from. As such, the
assistant model should be much smaller.
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`~generation.GreedySearchDecoderOnlyOutput`], [`~generation.GreedySearchEncoderDecoderOutput`] or
`torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
[`~generation.GreedySearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`~generation.GreedySearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForCausalLM,
... LogitsProcessorList,
... MinLengthLogitsProcessor,
... StoppingCriteriaList,
... MaxLengthCriteria,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> assistant_model = AutoModelForCausalLM.from_pretrained("distilgpt2")
>>> # set pad_token_id to eos_token_id because GPT2 does not have a PAD token
>>> model.generation_config.pad_token_id = model.generation_config.eos_token_id
>>> input_prompt = "It might be possible to"
>>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
>>> # instantiate logits processors
>>> logits_processor = LogitsProcessorList(
... [
... MinLengthLogitsProcessor(10, eos_token_id=model.generation_config.eos_token_id),
... ]
... )
>>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
>>> outputs = model.assisted_decoding(
... input_ids,
... assistant_model=assistant_model,
... logits_processor=logits_processor,
... stopping_criteria=stopping_criteria,
... )
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
["It might be possible to get a better understanding of the nature of the problem, but it's not"]
```"""
# Assistant: initialize assistant-related variables
if not hasattr(assistant_model, "max_assistant_tokens"):
assistant_model.max_assistant_tokens = 5 # this value, which will be updated, persists across calls
# init values
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
if eos_token_id is not None and pad_token_id is None:
raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
output_attentions = (
output_attentions if output_attentions is not None else self.generation_config.output_attentions
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
)
return_dict_in_generate = (
return_dict_in_generate
if return_dict_in_generate is not None
else self.generation_config.return_dict_in_generate
)
# init attention / hidden states / scores tuples
scores = () if (return_dict_in_generate and output_scores) else None
decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
cross_attentions = () if (return_dict_in_generate and output_attentions) else None
decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
# if model is an encoder-decoder, retrieve encoder attention weights and hidden states
if return_dict_in_generate and self.config.is_encoder_decoder:
encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
encoder_hidden_states = (
model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
)
# keep track of which sequences are already finished
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
# other auxiliary variables
max_len = stopping_criteria[0].max_length
assistant_kv_indexing = (
1
if "bloom" in assistant_model.__class__.__name__.lower()
or (
assistant_model.config.architectures is not None
and "bloom" in assistant_model.config.architectures[0].lower()
)
else 0
)
this_peer_finished = False # used by synced_gpus only
while True:
if synced_gpus:
# Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
# The following logic allows an early break if all peers finished generating their sequence
this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
# send 0.0 if we finished, 1.0 otherwise
dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
# did all peers finish? the reduced sum will be 0.0 then
if this_peer_finished_flag.item() == 0.0:
break
# Assistant: main logic start
cur_len = input_ids.shape[-1]
# 1. Forecast next N tokens using the assistant model. This `for` block can be replaced with a
# `.generate()` call if we decide to add `past_key_values` as a possible output of generate, as we
# need access to the assistant cache to secure strong speedups.
candidate_input_ids = input_ids
for _ in range(int(assistant_model.max_assistant_tokens)):
# 1.1. use the assistant model to obtain the next candidate logits
if "assistant_past_key_values" in model_kwargs:
prev_seq_len = model_kwargs["assistant_past_key_values"][0][assistant_kv_indexing].shape[-2]
# `new_token_len` can be 1 or 2 (next token in assistant + last token picked by the larger model)
new_token_len = candidate_input_ids.shape[1] - prev_seq_len
assist_inputs = candidate_input_ids[:, -new_token_len:]
assist_attn = torch.ones_like(candidate_input_ids)
# TODO (joao): make it compatible with models that use unconventional fwd pass logic, like blip2
if assistant_model.config.is_encoder_decoder:
assistant_model_outputs = assistant_model(
decoder_input_ids=assist_inputs,
decoder_attention_mask=assist_attn,
past_key_values=model_kwargs["assistant_past_key_values"],
encoder_outputs=model_kwargs["assistant_encoder_outputs"],
)
else:
assistant_model_outputs = assistant_model(
assist_inputs,
attention_mask=assist_attn,
past_key_values=model_kwargs["assistant_past_key_values"],
)
else:
if assistant_model.config.is_encoder_decoder:
assistant_model_outputs = assistant_model(
decoder_input_ids=candidate_input_ids,
encoder_outputs=model_kwargs["assistant_encoder_outputs"],
)
else:
assistant_model_outputs = assistant_model(candidate_input_ids)
# 1.2. greedily select the next candidate token
model_kwargs["assistant_past_key_values"] = assistant_model_outputs.past_key_values
if len(logits_processor) > 0:
assistant_model_outputs.logits[:, -1, :] = logits_processor(
candidate_input_ids, assistant_model_outputs.logits[:, -1, :]
)
new_token = assistant_model_outputs.logits[:, -1, :].argmax(dim=-1)
candidate_input_ids = torch.cat((candidate_input_ids, new_token[:, None]), dim=-1)
# 1.3. stop assistant generation on EOS
if eos_token_id_tensor is not None:
last_assistant_token_is_eos = new_token.tile(eos_token_id_tensor.shape[0], 1)
last_assistant_token_is_eos = (
~last_assistant_token_is_eos.ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0).bool()
)
if last_assistant_token_is_eos:
break
else:
last_assistant_token_is_eos = False
candidate_length = candidate_input_ids.shape[1] - input_ids.shape[1]
# 2. Use the original model to obtain the next token logits given the candidate sequence. We obtain
# `candidate_length + 1` relevant logits from this process: in the event that all candidates are correct,
# we use this forward pass to also pick the subsequent logits in the original model.
# 2.1. Run a forward pass on the candidate sequence
if "past_key_values" in model_kwargs:
model_attn = torch.ones_like(candidate_input_ids)
model_input_ids = candidate_input_ids[:, -candidate_length - 1 :]
if self.config.is_encoder_decoder:
outputs = self(
decoder_input_ids=model_input_ids,
decoder_attention_mask=model_attn,
past_key_values=model_kwargs["past_key_values"],
encoder_outputs=model_kwargs["encoder_outputs"],
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=True,
)
else:
outputs = self(
model_input_ids,
attention_mask=model_attn,
past_key_values=model_kwargs["past_key_values"],
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=True,
)
else:
if self.config.is_encoder_decoder:
outputs = self(
decoder_input_ids=candidate_input_ids,
encoder_outputs=model_kwargs["encoder_outputs"],
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=True,
)
else:
outputs = self(
candidate_input_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=True,
)
# 2.2. Process the new logits
new_logits = outputs.logits[:, -candidate_length - 1 :] # excludes the input prompt if present
if len(logits_processor) > 0:
for i in range(candidate_length):
new_logits[:, i, :] = logits_processor(candidate_input_ids[:, : cur_len + i], new_logits[:, i, :])
if len(logits_warper) > 0:
for i in range(candidate_length):
new_logits[:, i, :] = logits_warper(candidate_input_ids[:, : cur_len + i], new_logits[:, i, :])
# 3. Obtain the next tokens from the original model logits.
if do_sample:
probs = new_logits[:, -candidate_length - 1 :, :].softmax(dim=-1)
selected_tokens = torch.multinomial(probs[0, :, :], num_samples=1).squeeze(1)[None, :]
else:
selected_tokens = new_logits[:, -candidate_length - 1 :, :].argmax(dim=-1)
# 4. Compare the argmax from the original model logits with the assistant forecasted tokens. We can keep
# the assistant forecasted tokens until the first mismatch, or until the max length is reached.
candidate_new_tokens = candidate_input_ids[:, -candidate_length:]
n_matches = ((~(candidate_new_tokens == selected_tokens[:, :-1])).cumsum(dim=-1) < 1).sum()
# 5. Update variables according to the number of matching assistant tokens. Remember: the token generated
# by the model after the last candidate match is also valid, as it is generated from a correct sequence.
# Because of this last token, assisted generation search reduces to a normal greedy search/sample if there
# is no match.
# 5.1. Ensure we don't generate beyond max_len or an EOS token
if last_assistant_token_is_eos and n_matches == candidate_length:
n_matches -= 1
n_matches = min(n_matches, max_len - cur_len - 1)
# 5.2. Get the valid continuation, after the matching tokens
valid_tokens = selected_tokens[:, : n_matches + 1]
input_ids = torch.cat((input_ids, valid_tokens), dim=-1)
if streamer is not None:
streamer.put(valid_tokens.cpu())
new_cur_len = input_ids.shape[-1]
# 5.3. Discard past key values relative to unused assistant tokens
new_cache_size = new_cur_len - 1
outputs.past_key_values = _crop_past_key_values(self, outputs.past_key_values, new_cache_size)
model_kwargs["assistant_past_key_values"] = _crop_past_key_values(
assistant_model, model_kwargs["assistant_past_key_values"], new_cache_size - 1
) # the assistant does not have the token after the last match, hence the -1
# 6. Adjust the max number of assistant tokens to use in the next iteration. This is a simple heuristic,
# probably can be improved -- we want to balance the benefits of getting assistant tokens correct with the
# cost of forecasting incorrect assistant tokens.
if n_matches == int(assistant_model.max_assistant_tokens):
assistant_model.max_assistant_tokens += 2.0
else:
assistant_model.max_assistant_tokens = max(1.0, assistant_model.max_assistant_tokens - 1.0)
# Assistant: main logic end
if synced_gpus and this_peer_finished:
continue # don't waste resources running the code we don't need
# Store scores, attentions and hidden_states when required
# Assistant: modified to append one tuple element per token, as in the other generation methods.
if return_dict_in_generate:
if output_scores:
scores += tuple(new_logits[:, i, :] for i in range(n_matches + 1))
if "past_key_values" not in model_kwargs:
added_len = new_cur_len
else:
added_len = n_matches + 1
if output_attentions:
if self.config.is_encoder_decoder:
cross_attentions = _split_model_outputs(
cross_attentions, outputs.cross_attentions, cur_len, added_len
)
decoder_attentions = _split_model_outputs(
decoder_attentions,
outputs.decoder_attentions,
cur_len,
added_len,
is_decoder_attention=True,
)
else:
decoder_attentions = _split_model_outputs(
decoder_attentions,
outputs.attentions,
cur_len,
added_len,
is_decoder_attention=True,
)
if output_hidden_states:
if self.config.is_encoder_decoder:
decoder_hidden_states = _split_model_outputs(
decoder_hidden_states, outputs.decoder_hidden_states, cur_len, added_len
)
else:
decoder_hidden_states = _split_model_outputs(
decoder_hidden_states, outputs.hidden_states, cur_len, added_len
)
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
# if eos_token was found in one sentence, set sentence to finished
if eos_token_id_tensor is not None:
unfinished_sequences = unfinished_sequences.mul(
input_ids[:, -1]
.tile(eos_token_id_tensor.shape[0], 1)
.ne(eos_token_id_tensor.unsqueeze(1))
.prod(dim=0)
)
# stop when each sentence is finished
if unfinished_sequences.max() == 0:
this_peer_finished = True
# stop if we exceed the maximum length
if stopping_criteria(input_ids, scores):
this_peer_finished = True
if this_peer_finished and not synced_gpus:
break
if streamer is not None:
streamer.end()
if return_dict_in_generate:
if self.config.is_encoder_decoder:
return GreedySearchEncoderDecoderOutput(
sequences=input_ids,
scores=scores,
encoder_attentions=encoder_attentions,
encoder_hidden_states=encoder_hidden_states,
decoder_attentions=decoder_attentions,
cross_attentions=cross_attentions,
decoder_hidden_states=decoder_hidden_states,
)
else:
return GreedySearchDecoderOnlyOutput(
sequences=input_ids,
scores=scores,
attentions=decoder_attentions,
hidden_states=decoder_hidden_states,
)
else:
return input_ids
def _crop_past_key_values(model, past_key_values, maximum_length):
"""Crops the past key values up to a certain maximum length."""
new_past = []
if model.config.is_encoder_decoder:
for idx in range(len(past_key_values)):
new_past.append(
(
past_key_values[idx][0][:, :, :maximum_length, :],
past_key_values[idx][1][:, :, :maximum_length, :],
past_key_values[idx][2],
past_key_values[idx][3],
)
)
past_key_values = tuple(new_past)
# bloom is special
elif "bloom" in model.__class__.__name__.lower() or (
model.config.architectures is not None and "bloom" in model.config.architectures[0].lower()
):
for idx in range(len(past_key_values)):
new_past.append(
(
past_key_values[idx][0][:, :, :maximum_length],
past_key_values[idx][1][:, :maximum_length, :],
)
)
past_key_values = tuple(new_past)
# gptbigcode is too
elif "gptbigcode" in model.__class__.__name__.lower() or (
model.config.architectures is not None and "gptbigcode" in model.config.architectures[0].lower()
):
if model.config.multi_query:
for idx in range(len(past_key_values)):
past_key_values[idx] = past_key_values[idx][:, :maximum_length, :]
else:
for idx in range(len(past_key_values)):
past_key_values[idx] = past_key_values[idx][:, :, :maximum_length, :]
else:
for idx in range(len(past_key_values)):
new_past.append(
(
past_key_values[idx][0][:, :, :maximum_length, :],
past_key_values[idx][1][:, :, :maximum_length, :],
)
)
past_key_values = tuple(new_past)
return past_key_values
def _split_model_outputs(outputs, new_outputs, cur_len, added_len, is_decoder_attention=False):
"""
Given the (decoder/cross attentions)/(decoder hidden states) for multiple generated tokens, splits it into a tuple
where each member corresponds to a single generated token.
"""
# Retrocompatibility: in our generation functions, the first iteration includes the attention/hidden states for the
# prompt.
if len(outputs) == 0:
new_tuple = ()
for layer in new_outputs:
last_dim_size = cur_len if is_decoder_attention else layer.shape[-1]
new_tuple += (layer[..., :cur_len, :last_dim_size],)
outputs += (new_tuple,)
# The first iteration contains the prompt + 1 generated token, let's update the length variables accordingly
cur_len += 1
added_len -= cur_len
for i in range(added_len):
new_tuple = ()
for layer in new_outputs:
last_dim_size = cur_len + i if is_decoder_attention else layer.shape[-1]
new_tuple += (layer[..., i : i + 1, :last_dim_size],)
outputs += (new_tuple,)
return outputs
def top_k_top_p_filtering(
logits: torch.FloatTensor,
top_k: int = 0,
top_p: float = 1.0,
filter_value: float = -float("Inf"),
min_tokens_to_keep: int = 1,
) -> torch.FloatTensor:
"""
Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (batch size, vocabulary size)
top_k (`int`, *optional*, defaults to 0):
If > 0, only keep the top k tokens with highest probability (top-k filtering)
top_p (`float`, *optional*, defaults to 1.0):
If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimumber of tokens we keep per batch example in the output.
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
if top_k > 0:
logits = TopKLogitsWarper(top_k=top_k, filter_value=filter_value, min_tokens_to_keep=min_tokens_to_keep)(
None, logits
)
if 0 <= top_p <= 1.0:
logits = TopPLogitsWarper(top_p=top_p, filter_value=filter_value, min_tokens_to_keep=min_tokens_to_keep)(
None, logits
)
return logits
def _ranking_fast(
context_hidden: torch.FloatTensor,
next_hidden: torch.FloatTensor,
next_top_k_probs: torch.FloatTensor,
alpha: float,
beam_width: int,
) -> torch.FloatTensor:
"""
Reranks the top_k candidates based on a degeneration penalty (cosine similarity with previous tokens), as described
in the paper "A Contrastive Framework for Neural Text Generation". Returns the index of the best candidate for each
row in the batch.
"""
norm_context_hidden = context_hidden / context_hidden.norm(dim=2, keepdim=True)
norm_next_hidden = next_hidden / next_hidden.norm(dim=2, keepdim=True)
cosine_matrix = torch.matmul(norm_context_hidden, norm_next_hidden.transpose(1, 2)).squeeze(-1) # [B*K, S]
degeneration_penalty, _ = torch.max(cosine_matrix, dim=-1) # [B*K]
next_top_k_probs = next_top_k_probs.view(-1) # [B*K]
contrastive_score = (1.0 - alpha) * next_top_k_probs - alpha * degeneration_penalty
contrastive_score = torch.stack(torch.split(contrastive_score, beam_width)) # [B, K]
_, selected_idx = contrastive_score.max(dim=-1) # [B]
return selected_idx
| 252,409 | 53.130388 | 327 | py |
transformers | transformers-main/src/transformers/generation/beam_search.py | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
from collections import UserDict
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from ..utils import add_start_docstrings
from .beam_constraints import Constraint, ConstraintListState
PROCESS_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size * num_beams, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using any class inheriting from [`PreTrainedTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
next_scores (`torch.FloatTensor` of shape `(batch_size, 2 * num_beams)`):
Current scores of the top `2 * num_beams` non-finished beam hypotheses.
next_tokens (`torch.LongTensor` of shape `(batch_size, 2 * num_beams)`):
`input_ids` of the tokens corresponding to the top `2 * num_beams` non-finished beam hypotheses.
next_indices (`torch.LongTensor` of shape `(batch_size, 2 * num_beams)`):
Beam indices indicating to which beam hypothesis the `next_tokens` correspond.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
beam_indices (`torch.LongTensor]`, *optional*):
Beam indices indicating to which beam hypothesis each token correspond.
group_index (`int`, *optional*):
The index of the group of beams. Used with [`~PreTrainedModel.group_beam_search`].
Return:
`UserDict`: A dictionary composed of the fields as defined above:
- **next_beam_scores** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Updated scores of all
non-finished beams.
- **next_beam_tokens** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Next tokens to be added
to the non-finished beam_hypotheses.
- **next_beam_indices** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Beam indices
indicating to which beam the next tokens shall be added.
"""
FINALIZE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size * num_beams, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using any class inheriting from [`PreTrainedTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
final_beam_scores (`torch.FloatTensor` of shape `(batch_size * num_beams)`):
The final scores of all non-finished beams.
final_beam_tokens (`torch.FloatTensor` of shape `(batch_size * num_beams)`):
The last tokens to be added to the non-finished beam_hypotheses.
final_beam_indices (`torch.FloatTensor` of shape `(batch_size * num_beams)`):
The beam indices indicating to which beam the `final_beam_tokens` shall be added.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
Return:
`torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`: The generated sequences.
The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early
due to the `eos_token_id`.
"""
class BeamScorer(ABC):
"""
Abstract base class for all beam scorers that are used for [`~PreTrainedModel.beam_search`] and
[`~PreTrainedModel.beam_sample`].
"""
@abstractmethod
@add_start_docstrings(PROCESS_INPUTS_DOCSTRING)
def process(
self,
input_ids: torch.LongTensor,
next_scores: torch.FloatTensor,
next_tokens: torch.LongTensor,
next_indices: torch.LongTensor,
**kwargs,
) -> Tuple[torch.Tensor]:
raise NotImplementedError("This is an abstract method.")
@abstractmethod
@add_start_docstrings(FINALIZE_INPUTS_DOCSTRING)
def finalize(
self,
input_ids: torch.LongTensor,
next_scores: torch.FloatTensor,
next_tokens: torch.LongTensor,
next_indices: torch.LongTensor,
max_length: int,
**kwargs,
) -> torch.LongTensor:
raise NotImplementedError("This is an abstract method.")
class BeamSearchScorer(BeamScorer):
r"""
[`BeamScorer`] implementing standard beam search decoding.
Adapted in part from [Facebook's XLM beam search
code](https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529).
Reference for the diverse beam search algorithm and implementation [Ashwin Kalyan's DBS
implementation](https://github.com/ashwinkalyan/dbs/blob/master/dbs/beam_utils.lua)
Args:
batch_size (`int`):
Batch Size of `input_ids` for which standard beam search decoding is run in parallel.
num_beams (`int`):
Number of beams for beam search.
device (`torch.device`):
Defines the device type (*e.g.*, `"cpu"` or `"cuda"`) on which this instance of `BeamSearchScorer` will be
allocated.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
`length_penalty` < 0.0 encourages shorter sequences.
do_early_stopping (`bool` or `str`, *optional*, defaults to `False`):
Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values:
`True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an
heuristic is applied and the generation stops when is it very unlikely to find better candidates;
`"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical
beam search algorithm).
num_beam_hyps_to_keep (`int`, *optional*, defaults to 1):
The number of beam hypotheses that shall be returned upon calling
[`~transformer.BeamSearchScorer.finalize`].
num_beam_groups (`int`):
Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
max_length (`int`, *optional*):
The maximum length of the sequence to be generated.
"""
def __init__(
self,
batch_size: int,
num_beams: int,
device: torch.device,
length_penalty: Optional[float] = 1.0,
do_early_stopping: Optional[Union[bool, str]] = False,
num_beam_hyps_to_keep: Optional[int] = 1,
num_beam_groups: Optional[int] = 1,
max_length: Optional[int] = None,
):
self.num_beams = num_beams
self.device = device
self.length_penalty = length_penalty
self.do_early_stopping = do_early_stopping
self.num_beam_hyps_to_keep = num_beam_hyps_to_keep
self.num_beam_groups = num_beam_groups
self.group_size = self.num_beams // self.num_beam_groups
self._is_init = False
# self._beam_hyps[i*self.num_beam_groups+j] is the beam_hyps of the j-th group in the i-th mini-batch.
# If group_beam_search is not used, the list consists of `batch_size` beam_hyps.
self._beam_hyps = [
BeamHypotheses(
num_beams=self.group_size,
length_penalty=self.length_penalty,
early_stopping=self.do_early_stopping,
max_length=max_length,
)
for _ in range(batch_size * self.num_beam_groups)
]
# self._done[i*self.num_beam_groups+j] indicates whether the generation of the beam_hyps of the j-th group
# in the i-th mini-batch is complete.
self._done = torch.tensor(
[False for _ in range(batch_size * self.num_beam_groups)], dtype=torch.bool, device=self.device
)
if not isinstance(num_beams, int) or num_beams <= 1:
raise ValueError(
f"`num_beams` has to be an integer strictly greater than 1, but is {num_beams}. For `num_beams` == 1,"
" one should make use of `greedy_search` instead."
)
if not isinstance(num_beam_groups, int) or (num_beam_groups > num_beams) or (num_beams % num_beam_groups != 0):
raise ValueError(
"`num_beam_groups` has to be an integer smaller or equal than `num_beams` and `num_beams` has to be"
f" divisible by `num_beam_groups`, but is {num_beam_groups} with `num_beams` being {num_beams}."
)
@property
def is_done(self) -> bool:
return self._done.all()
def process(
self,
input_ids: torch.LongTensor,
next_scores: torch.FloatTensor,
next_tokens: torch.LongTensor,
next_indices: torch.LongTensor,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
beam_indices: Optional[torch.LongTensor] = None,
group_index: Optional[int] = 0,
) -> Dict[str, torch.Tensor]:
cur_len = input_ids.shape[-1] + 1 # add up to the length which the next_scores is calculated on
batch_size = len(self._beam_hyps) // self.num_beam_groups
if not (batch_size == (input_ids.shape[0] // self.group_size)):
if self.num_beam_groups > 1:
raise ValueError(
f"A group beam size of {input_ids.shape[0]} is used as the input, but a group beam "
f"size of {self.group_size} is expected by the beam scorer."
)
else:
raise ValueError(
f"A beam size of {input_ids.shape[0]} is used as the input, but a beam size of "
f"{self.group_size} is expected by the beam scorer."
)
device = input_ids.device
next_beam_scores = torch.zeros((batch_size, self.group_size), dtype=next_scores.dtype, device=device)
next_beam_tokens = torch.zeros((batch_size, self.group_size), dtype=next_tokens.dtype, device=device)
next_beam_indices = torch.zeros((batch_size, self.group_size), dtype=next_indices.dtype, device=device)
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
for batch_idx in range(batch_size):
batch_group_idx = batch_idx * self.num_beam_groups + group_index
if self._done[batch_group_idx]:
if self.num_beams < len(self._beam_hyps[batch_group_idx]):
raise ValueError(f"Batch can only be done if at least {self.num_beams} beams have been generated")
if eos_token_id is None or pad_token_id is None:
raise ValueError("Generated beams >= num_beams -> eos_token_id and pad_token have to be defined")
# pad the batch
next_beam_scores[batch_idx, :] = 0
next_beam_tokens[batch_idx, :] = pad_token_id
next_beam_indices[batch_idx, :] = 0
continue
# next tokens for this sentence
beam_idx = 0
for beam_token_rank, (next_token, next_score, next_index) in enumerate(
zip(next_tokens[batch_idx], next_scores[batch_idx], next_indices[batch_idx])
):
batch_beam_idx = batch_idx * self.group_size + next_index
# add to generated hypotheses if end of sentence
if (eos_token_id is not None) and (next_token.item() in eos_token_id):
# if beam_token does not belong to top num_beams tokens, it should not be added
is_beam_token_worse_than_top_num_beams = beam_token_rank >= self.group_size
if is_beam_token_worse_than_top_num_beams:
continue
if beam_indices is not None:
beam_index = beam_indices[batch_beam_idx]
beam_index = beam_index + (batch_beam_idx,)
else:
beam_index = None
self._beam_hyps[batch_group_idx].add(
input_ids[batch_beam_idx].clone(),
next_score.item(),
beam_indices=beam_index,
)
else:
# add next predicted token since it is not eos_token
next_beam_scores[batch_idx, beam_idx] = next_score
next_beam_tokens[batch_idx, beam_idx] = next_token
next_beam_indices[batch_idx, beam_idx] = batch_beam_idx
beam_idx += 1
# once the beam for next step is full, don't add more tokens to it.
if beam_idx == self.group_size:
break
if beam_idx < self.group_size:
raise ValueError(
f"At most {self.group_size} tokens in {next_tokens[batch_idx]} can be equal to `eos_token_id:"
f" {eos_token_id}`. Make sure {next_tokens[batch_idx]} are corrected."
)
# Check if we are done so that we can save a pad step if all(done)
self._done[batch_group_idx] = self._done[batch_group_idx] or self._beam_hyps[batch_group_idx].is_done(
next_scores[batch_idx].max().item(), cur_len
)
return UserDict(
{
"next_beam_scores": next_beam_scores.view(-1),
"next_beam_tokens": next_beam_tokens.view(-1),
"next_beam_indices": next_beam_indices.view(-1),
}
)
def finalize(
self,
input_ids: torch.LongTensor,
final_beam_scores: torch.FloatTensor,
final_beam_tokens: torch.LongTensor,
final_beam_indices: torch.LongTensor,
max_length: int,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
beam_indices: Optional[torch.LongTensor] = None,
) -> Tuple[torch.LongTensor]:
batch_size = len(self._beam_hyps) // self.num_beam_groups
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
# finalize all open beam hypotheses and add to generated hypotheses
for batch_group_idx, beam_hyp in enumerate(self._beam_hyps):
if self._done[batch_group_idx]:
continue
# all open beam hypotheses are added to the beam hypothesis
# beam hypothesis class automatically keeps the best beams
for index_per_group in range(self.group_size):
batch_beam_idx = batch_group_idx * self.group_size + index_per_group
final_score = final_beam_scores[batch_beam_idx].item()
final_tokens = input_ids[batch_beam_idx]
beam_index = beam_indices[batch_beam_idx] if beam_indices is not None else None
beam_hyp.add(final_tokens, final_score, beam_indices=beam_index)
# select the best hypotheses
sent_lengths = input_ids.new(batch_size * self.num_beam_hyps_to_keep)
best = []
best_indices = []
best_scores = torch.zeros(batch_size * self.num_beam_hyps_to_keep, device=self.device, dtype=torch.float32)
# retrieve best hypotheses
for i in range(batch_size):
beam_hyps_in_batch = self._beam_hyps[i * self.num_beam_groups : (i + 1) * self.num_beam_groups]
candidate_beams = [beam for beam_hyp in beam_hyps_in_batch for beam in beam_hyp.beams]
sorted_hyps = sorted(candidate_beams, key=lambda x: x[0])
for j in range(self.num_beam_hyps_to_keep):
best_hyp_tuple = sorted_hyps.pop()
best_score = best_hyp_tuple[0]
best_hyp = best_hyp_tuple[1]
best_index = best_hyp_tuple[2]
sent_lengths[self.num_beam_hyps_to_keep * i + j] = len(best_hyp)
# append hyp to lists
best.append(best_hyp)
# append indices to list
best_indices.append(best_index)
best_scores[i * self.num_beam_hyps_to_keep + j] = best_score
# prepare for adding eos
sent_lengths_max = sent_lengths.max().item() + 1
sent_max_len = min(sent_lengths_max, max_length) if max_length is not None else sent_lengths_max
decoded: torch.LongTensor = input_ids.new(batch_size * self.num_beam_hyps_to_keep, sent_max_len)
if len(best_indices) > 0 and best_indices[0] is not None:
indices: torch.LongTensor = input_ids.new(batch_size * self.num_beam_hyps_to_keep, sent_max_len)
else:
indices = None
# shorter batches are padded if needed
if sent_lengths.min().item() != sent_lengths.max().item():
if pad_token_id is None:
raise ValueError("`pad_token_id` has to be defined")
decoded.fill_(pad_token_id)
if indices is not None:
indices.fill_(-1)
# fill with hypotheses and eos_token_id if the latter fits in
for i, (hypo, best_idx) in enumerate(zip(best, best_indices)):
decoded[i, : sent_lengths[i]] = hypo
if indices is not None:
indices[i, : len(best_idx)] = torch.tensor(best_idx)
if sent_lengths[i] < sent_max_len:
# inserting only the first eos_token_id
decoded[i, sent_lengths[i]] = eos_token_id[0]
return UserDict(
{
"sequences": decoded,
"sequence_scores": best_scores,
"beam_indices": indices,
}
)
class ConstrainedBeamSearchScorer(BeamScorer):
r"""
[`BeamScorer`] implementing constrained beam search decoding.
Args:
batch_size (`int`):
Batch Size of `input_ids` for which standard beam search decoding is run in parallel.
num_beams (`int`):
Number of beams for beam search.
constraints (`List[Constraint]`):
A list of positive constraints represented as `Constraint` objects that must be fulfilled in the generation
output. For more information, the documentation of [`Constraint`] should be read.
device (`torch.device`):
Defines the device type (*e.g.*, `"cpu"` or `"cuda"`) on which this instance of `BeamSearchScorer` will be
allocated.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
`length_penalty` < 0.0 encourages shorter sequences.
do_early_stopping (`bool` or `str`, *optional*, defaults to `False`):
Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values:
`True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an
heuristic is applied and the generation stops when is it very unlikely to find better candidates;
`"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical
beam search algorithm).
num_beam_hyps_to_keep (`int`, *optional*, defaults to 1):
The number of beam hypotheses that shall be returned upon calling
[`~transformer.BeamSearchScorer.finalize`].
num_beam_groups (`int`):
Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
max_length (`int`, *optional*):
The maximum length of the sequence to be generated.
"""
def __init__(
self,
batch_size: int,
num_beams: int,
constraints: List[Constraint],
device: torch.device,
length_penalty: Optional[float] = 1.0,
do_early_stopping: Optional[Union[bool, str]] = False,
num_beam_hyps_to_keep: Optional[int] = 1,
num_beam_groups: Optional[int] = 1,
max_length: Optional[int] = None,
):
self.num_beams = num_beams
self.device = device
self.length_penalty = length_penalty
self.do_early_stopping = do_early_stopping
self.num_beam_hyps_to_keep = num_beam_hyps_to_keep
self.num_beam_groups = num_beam_groups
self.group_size = self.num_beams // self.num_beam_groups
self.constraints = constraints
self._is_init = False
self._beam_hyps = [
BeamHypotheses(
num_beams=self.num_beams,
length_penalty=self.length_penalty,
early_stopping=self.do_early_stopping,
max_length=max_length,
)
for _ in range(batch_size)
]
self._done = torch.tensor([False for _ in range(batch_size)], dtype=torch.bool, device=self.device)
if not isinstance(num_beams, int) or num_beams <= 1:
raise ValueError(
f"`num_beams` has to be an integer strictly greater than 1, but is {num_beams}. For `num_beams` == 1,"
" one should make use of `greedy_search` instead."
)
if not isinstance(num_beam_groups, int) or (num_beam_groups > num_beams) or (num_beams % num_beam_groups != 0):
raise ValueError(
"`num_beam_groups` has to be an integer smaller or equal than `num_beams` and `num_beams` has to be"
f" divisible by `num_beam_groups`, but is {num_beam_groups} with `num_beams` being {num_beams}."
)
@property
def is_done(self) -> bool:
return self._done.all()
def make_constraint_states(self, n):
return [ConstraintListState([constraint.copy() for constraint in self.constraints]) for _ in range(n)]
def check_completes_constraints(self, sequence):
new_state = self.make_constraint_states(1)[0]
new_state.reset(sequence)
return new_state.completed
def process(
self,
input_ids: torch.LongTensor,
next_scores: torch.FloatTensor,
next_tokens: torch.LongTensor,
next_indices: torch.LongTensor,
scores_for_all_vocab: torch.FloatTensor,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
) -> Tuple[torch.Tensor]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size * num_beams, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using any class inheriting from [`PreTrainedTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
next_scores (`torch.FloatTensor` of shape `(batch_size, 2 * num_beams)`):
Current scores of the top `2 * num_beams` non-finished beam hypotheses.
next_tokens (`torch.LongTensor` of shape `(batch_size, 2 * num_beams)`):
`input_ids` of the tokens corresponding to the top `2 * num_beams` non-finished beam hypotheses.
next_indices (`torch.LongTensor` of shape `(batch_size, 2 * num_beams)`):
Beam indices indicating to which beam hypothesis the `next_tokens` correspond.
scores_for_all_vocab (`torch.FloatTensor` of shape `(batch_size * num_beams, sequence_length)`):
The scores of all tokens in the vocabulary for each of the beam hypotheses.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
Return:
`UserDict`: A dictionary composed of the fields as defined above:
- **next_beam_scores** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Updated scores of
all
non-finished beams.
- **next_beam_tokens** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Next tokens to be
added
to the non-finished beam_hypotheses.
- **next_beam_indices** (`torch.FloatTensor` of shape `(batch_size * num_beams)`) -- Beam indices
indicating to which beam the next tokens shall be added.
"""
cur_len = input_ids.shape[-1] + 1 # add up to the length which the next_scores is calculated on
batch_size = len(self._beam_hyps)
if not (batch_size == (input_ids.shape[0] // self.group_size)):
if self.num_beam_groups > 1:
raise ValueError(
f"A group beam size of {input_ids.shape[0]} is used as the input, but a group beam "
f"size of {self.group_size} is expected by the beam scorer."
)
else:
raise ValueError(
f"A beam size of {input_ids.shape[0]} is used as the input, but a beam size of "
f"{self.group_size} is expected by the beam scorer."
)
device = input_ids.device
next_beam_scores = torch.zeros((batch_size, self.group_size), dtype=next_scores.dtype, device=device)
next_beam_tokens = torch.zeros((batch_size, self.group_size), dtype=next_tokens.dtype, device=device)
next_beam_indices = torch.zeros((batch_size, self.group_size), dtype=next_indices.dtype, device=device)
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
for batch_idx, beam_hyp in enumerate(self._beam_hyps):
if self._done[batch_idx]:
if self.num_beams < len(beam_hyp):
raise ValueError(f"Batch can only be done if at least {self.num_beams} beams have been generated")
if eos_token_id is None or pad_token_id is None:
raise ValueError("Generated beams >= num_beams -> eos_token_id and pad_token have to be defined")
# pad the batch
next_beam_scores[batch_idx, :] = 0
next_beam_tokens[batch_idx, :] = pad_token_id
next_beam_indices[batch_idx, :] = 0
continue
# next tokens for this sentence.
beam_idx = 0
for beam_token_rank, (next_token, next_score, next_index) in enumerate(
zip(next_tokens[batch_idx], next_scores[batch_idx], next_indices[batch_idx])
):
batch_beam_idx = batch_idx * self.group_size + next_index
# add to generated hypotheses if end of sentence
if (eos_token_id is not None) and (next_token.item() in eos_token_id):
# if beam_token does not belong to top num_beams tokens, it should not be added
is_beam_token_worse_than_top_num_beams = beam_token_rank >= self.group_size
if is_beam_token_worse_than_top_num_beams:
continue
completes_constraint = self.check_completes_constraints(input_ids[batch_beam_idx].cpu().tolist())
if completes_constraint:
beam_hyp.add(
input_ids[batch_beam_idx].clone(),
next_score.item(),
)
else:
# add next predicted token since it is not eos_token
next_beam_scores[batch_idx, beam_idx] = next_score
next_beam_tokens[batch_idx, beam_idx] = next_token
next_beam_indices[batch_idx, beam_idx] = batch_beam_idx
beam_idx += 1
# once the beam for next step is full, don't add more tokens to it.
if beam_idx == self.group_size:
break
new_scores, new_tokens, new_indices = self.step_sentence_constraint(
batch_idx,
input_ids,
scores_for_all_vocab,
next_beam_scores[batch_idx],
next_beam_tokens[batch_idx],
next_beam_indices[batch_idx],
)
next_beam_scores[batch_idx] = new_scores
next_beam_tokens[batch_idx] = new_tokens
next_beam_indices[batch_idx] = new_indices
if beam_idx < self.group_size:
raise ValueError(
f"At most {self.group_size} tokens in {next_tokens[batch_idx]} can be equal to `eos_token_id:"
f" {eos_token_id}`. Make sure {next_tokens[batch_idx]} are corrected."
)
# Check if we are done so that we can save a pad step if all(done)
self._done[batch_idx] = self._done[batch_idx] or beam_hyp.is_done(
next_scores[batch_idx].max().item(), cur_len
)
return UserDict(
{
"next_beam_scores": next_beam_scores.view(-1),
"next_beam_tokens": next_beam_tokens.view(-1),
"next_beam_indices": next_beam_indices.view(-1),
}
)
def step_sentence_constraint(
self,
batch_idx: int,
input_ids: torch.LongTensor,
vocab_scores: torch.FloatTensor,
sent_beam_scores: torch.FloatTensor,
sent_beam_tokens: torch.LongTensor,
sent_beam_indices: torch.LongTensor,
push_progress: bool = False,
):
# sent_beam_tokens are the next {num_beams} number of tokens that are under consideration for this beam
# (candidate next tokens)
# 1. Adding "advance_tokens"
# using ConstraintStateList.advance(), we propose new tokens to be added into this "candidate list" that will
# advance us in fulfilling the constraints.
# 2. Selecting best candidates such that we end up with highest probable candidates
# that fulfill our constraints.
orig_len = sent_beam_indices.size(0)
device = sent_beam_indices.device
# initialize states
topk_contraint_states = self.make_constraint_states(orig_len)
advance_constraint_states = self.make_constraint_states(orig_len)
sidx, eidx = batch_idx * orig_len, (batch_idx + 1) * orig_len
this_batch_input_ids = input_ids[sidx:eidx]
this_batch_token_scores = vocab_scores[sidx:eidx]
full_hypotheses = torch.cat((input_ids[sent_beam_indices], sent_beam_tokens.unsqueeze(-1)), dim=-1)
# need to make new hypothesis that advance the constraints
track_new = {
"new_seqs": full_hypotheses.tolist(),
"new_states": [],
"new_indices": [],
"new_tokens": [],
"new_scores": [],
}
for seq_idx, pre_seq in enumerate(this_batch_input_ids):
# pre_seq = ith sequence generated before this step.
# input_ids -> (topk) generic beam search best model next tokens
# -> (advance) constraints forcing the next token
# either way, we need to sort them into "banks" later, so store a "ConstraintListState" for all types of
# hypotheses.
topk_state = topk_contraint_states[seq_idx]
topk_state.reset(full_hypotheses[seq_idx].cpu().tolist())
advance_state = advance_constraint_states[seq_idx]
advance_state.reset(pre_seq.cpu().tolist())
if not advance_state.completed:
advance_tokens = torch.LongTensor(advance_state.advance()).to(device)
for advance_token in advance_tokens:
# since adding each `advance_token` leads to a different hypothesis, create new state instance.
new_state = advance_state.copy(stateful=True)
new_state.add(advance_token.cpu().tolist())
advance_seq = torch.cat((pre_seq, advance_token.unsqueeze(0)), -1).cpu().tolist()
if advance_seq not in track_new["new_seqs"]:
# prevent duplicates, which are basically bound to happen in this process.
track_new["new_seqs"].append(advance_seq)
track_new["new_indices"].append(sidx + seq_idx) # idx -> global idx across all the batches
track_new["new_tokens"].append(advance_token)
track_new["new_scores"].append(this_batch_token_scores[seq_idx].take(advance_token))
track_new["new_states"].append(new_state)
elif push_progress:
# Basically, `sent_beam_indices` often chooses very little among `input_ids` the generated sequences that
# actually fulfill our constraints. For example, let constraints == ["loves pies"] and
# pre_seq_1 = "The child loves pies and" pre_seq_2 = "The child plays in the playground and"
# Without this step, if `sent_beam_indices` is something like [1,1], then
# 1. `pre_seq_1` won't be added to the list of (topk) hypothesis since it's not in the indices and
# 2. it won't be added to the list of (advance) hypothesis since it's completed already. (this is
# the else part of `if constraints_completed[seq_idx]`)
# 3. it ends up simply getting removed from consideration.
# #3 might be fine and actually desired, since it's likely that it's a low-probability output anyways,
# especially if it's not in the list of `sent_beam_indices`. But this often leads to lengthened beam
# search times, since completed sequences keep getting removed after all this effort for constrained
# generation.
# Here, we basically take `pre_seq_1` and to "push" it into the considered list of hypotheses, by simply
# appending the next likely token in the vocabulary and adding it to the list of hypotheses.
new_score, new_token = torch.max(this_batch_token_scores[seq_idx], 0) # some next probable token
advance_seq = torch.cat((pre_seq, new_token.unsqueeze(0)), -1)
advance_state = advance_constraint_states[seq_idx]
advance_seq = advance_seq.cpu().tolist()
advance_state.reset(advance_seq)
if advance_seq not in track_new["new_seqs"]:
# but still don't want to have duplicates
track_new["new_seqs"].append(advance_seq)
track_new["new_indices"].append(seq_idx)
track_new["new_tokens"].append(new_token)
track_new["new_scores"].append(new_score)
track_new["new_states"].append(advance_state)
if len(track_new["new_indices"]) > 0:
new_indices = torch.tensor(track_new["new_indices"]).to(device)
new_tokens = torch.stack(track_new["new_tokens"]).to(device)
new_scores = torch.stack(track_new["new_scores"]).to(device)
all_states = topk_contraint_states + track_new["new_states"]
all_tokens = torch.cat((sent_beam_tokens, new_tokens), -1)
all_scores = torch.cat((sent_beam_scores, new_scores), -1)
all_banks = torch.tensor([one.get_bank() for one in all_states]).to(device)
zipped = all_banks * 100 + all_scores
indices = zipped.sort(descending=True).indices
sorted_banks = all_banks[indices]
# Then we end up with {sorted among bank C}, {sorted among bank C-1}, ..., {sorted among bank 0}
counter = -1
cur_bank = sorted_banks[0]
increments = []
for bank in sorted_banks:
if bank == cur_bank:
counter += 1
else:
counter = 0
cur_bank = bank
increments.append(counter)
rearrangers = torch.tensor(np.argsort(increments, kind="mergesort"))
indices = indices[rearrangers][:orig_len]
sent_beam_scores = all_scores[indices]
sent_beam_tokens = all_tokens[indices]
sent_beam_indices = torch.cat((sent_beam_indices, new_indices))[indices]
return sent_beam_scores, sent_beam_tokens, sent_beam_indices
def finalize(
self,
input_ids: torch.LongTensor,
final_beam_scores: torch.FloatTensor,
final_beam_tokens: torch.LongTensor,
final_beam_indices: torch.LongTensor,
max_length: int,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
) -> Tuple[torch.LongTensor]:
batch_size = len(self._beam_hyps)
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
# finalize all open beam hypotheses and add to generated hypotheses
for batch_idx, beam_hyp in enumerate(self._beam_hyps):
if self._done[batch_idx]:
continue
# all open beam hypotheses are added to the beam hypothesis
# beam hypothesis class automatically keeps the best beams
ids_collect = []
for beam_id in range(self.num_beams):
batch_beam_idx = batch_idx * self.num_beams + beam_id
final_score = final_beam_scores[batch_beam_idx].item()
final_tokens = input_ids[batch_beam_idx]
completes_constraint = self.check_completes_constraints(final_tokens.cpu().tolist())
if completes_constraint:
beam_hyp.add(final_tokens, final_score)
ids_collect.append(beam_id)
# due to overly complex constraints or other factors, sometimes we can't gaurantee a successful
# generation. In these cases we simply return the highest scoring outputs.
if len(ids_collect) < self.num_beam_hyps_to_keep:
for beam_id in range(self.num_beams):
if beam_id not in ids_collect:
batch_beam_idx = batch_idx * self.num_beams + beam_id
final_score = final_beam_scores[batch_beam_idx].item()
final_tokens = input_ids[batch_beam_idx]
beam_hyp.add(final_tokens, final_score)
if len(ids_collect) >= self.num_beam_hyps_to_keep:
break
# select the best hypotheses
sent_lengths = input_ids.new(batch_size * self.num_beam_hyps_to_keep)
best = []
best_scores = torch.zeros(batch_size * self.num_beam_hyps_to_keep, device=self.device, dtype=torch.float32)
# retrieve best hypotheses
for i, beam_hyp in enumerate(self._beam_hyps):
sorted_hyps = sorted(beam_hyp.beams, key=lambda x: x[0])
for j in range(self.num_beam_hyps_to_keep):
best_hyp_tuple = sorted_hyps.pop()
best_score = best_hyp_tuple[0]
best_hyp = best_hyp_tuple[1]
sent_lengths[self.num_beam_hyps_to_keep * i + j] = len(best_hyp)
# append to lists
best.append(best_hyp)
best_scores[i * self.num_beam_hyps_to_keep + j] = best_score
# prepare for adding eos
sent_lengths_max = sent_lengths.max().item() + 1
sent_max_len = min(sent_lengths_max, max_length) if max_length is not None else sent_lengths_max
decoded: torch.LongTensor = input_ids.new(batch_size * self.num_beam_hyps_to_keep, sent_max_len)
# shorter batches are padded if needed
if sent_lengths.min().item() != sent_lengths.max().item():
if pad_token_id is None:
raise ValueError("`pad_token_id` has to be defined")
decoded.fill_(pad_token_id)
# fill with hypotheses and eos_token_id if the latter fits in
for i, hypo in enumerate(best):
decoded[i, : sent_lengths[i]] = hypo
if sent_lengths[i] < sent_max_len:
# inserting only the first eos_token_id
decoded[i, sent_lengths[i]] = eos_token_id[0]
return UserDict(
{
"sequences": decoded,
"sequence_scores": best_scores,
}
)
class BeamHypotheses:
def __init__(self, num_beams: int, length_penalty: float, early_stopping: bool, max_length: Optional[int] = None):
"""
Initialize n-best list of hypotheses.
"""
self.length_penalty = length_penalty
self.early_stopping = early_stopping
self.max_length = max_length
self.num_beams = num_beams
self.beams = []
self.worst_score = 1e9
if not isinstance(self.early_stopping, bool) and self.max_length is None:
raise ValueError(
"When `do_early_stopping` is set to a string, `max_length` must be defined. Ensure it is passed to the"
" BeamScorer class instance at initialization time."
)
def __len__(self):
"""
Number of hypotheses in the list.
"""
return len(self.beams)
def add(self, hyp: torch.LongTensor, sum_logprobs: float, beam_indices: Optional[torch.LongTensor] = None):
"""
Add a new hypothesis to the list.
"""
score = sum_logprobs / (hyp.shape[-1] ** self.length_penalty)
if len(self) < self.num_beams or score > self.worst_score:
self.beams.append((score, hyp, beam_indices))
if len(self) > self.num_beams:
sorted_next_scores = sorted([(s, idx) for idx, (s, _, _) in enumerate(self.beams)])
del self.beams[sorted_next_scores[0][1]]
self.worst_score = sorted_next_scores[1][0]
else:
self.worst_score = min(score, self.worst_score)
def is_done(self, best_sum_logprobs: float, cur_len: int) -> bool:
"""
If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst
one in the heap, then we are done with this sentence.
"""
if len(self) < self.num_beams:
return False
# `True`: stop as soon as at least `num_beams` hypotheses are finished
if self.early_stopping is True:
return True
# `False`: heuristic -- compute best possible score from `cur_len`, even though it is not entirely accurate
# when `length_penalty` is positive. See the discussion below for more details.
# https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
elif self.early_stopping is False:
highest_attainable_score = best_sum_logprobs / cur_len**self.length_penalty
ret = self.worst_score >= highest_attainable_score
return ret
# `"never"`: compute the best possible score, depending on the signal of `length_penalty`
else:
# `length_penalty` > 0.0 -> max denominator is obtaned from `max_length`, not from `cur_len` -> min
# abs(`highest_attainable_score`) is obtained -> `highest_attainable_score` is negative, hence we obtain
# its max this way
if self.length_penalty > 0.0:
highest_attainable_score = best_sum_logprobs / self.max_length**self.length_penalty
# the opposite logic applies here (max `highest_attainable_score` from `cur_len`)
else:
highest_attainable_score = best_sum_logprobs / cur_len**self.length_penalty
ret = self.worst_score >= highest_attainable_score
return ret
| 45,955 | 47.527983 | 127 | py |
transformers | transformers-main/src/transformers/generation/__init__.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
_import_structure = {
"configuration_utils": ["GenerationConfig"],
"streamers": ["TextIteratorStreamer", "TextStreamer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["beam_constraints"] = [
"Constraint",
"ConstraintListState",
"DisjunctiveConstraint",
"PhrasalConstraint",
]
_import_structure["beam_search"] = [
"BeamHypotheses",
"BeamScorer",
"BeamSearchScorer",
"ConstrainedBeamSearchScorer",
]
_import_structure["logits_process"] = [
"EpsilonLogitsWarper",
"EtaLogitsWarper",
"ForcedBOSTokenLogitsProcessor",
"ForcedEOSTokenLogitsProcessor",
"HammingDiversityLogitsProcessor",
"InfNanRemoveLogitsProcessor",
"LogitsProcessor",
"LogitsProcessorList",
"LogitsWarper",
"MinLengthLogitsProcessor",
"MinNewTokensLengthLogitsProcessor",
"NoBadWordsLogitsProcessor",
"NoRepeatNGramLogitsProcessor",
"PrefixConstrainedLogitsProcessor",
"RepetitionPenaltyLogitsProcessor",
"SequenceBiasLogitsProcessor",
"EncoderRepetitionPenaltyLogitsProcessor",
"TemperatureLogitsWarper",
"TopKLogitsWarper",
"TopPLogitsWarper",
"TypicalLogitsWarper",
"EncoderNoRepeatNGramLogitsProcessor",
"ExponentialDecayLengthPenalty",
"LogitNormalization",
]
_import_structure["stopping_criteria"] = [
"MaxNewTokensCriteria",
"MaxLengthCriteria",
"MaxTimeCriteria",
"StoppingCriteria",
"StoppingCriteriaList",
"validate_stopping_criteria",
]
_import_structure["utils"] = [
"GenerationMixin",
"top_k_top_p_filtering",
"GreedySearchEncoderDecoderOutput",
"GreedySearchDecoderOnlyOutput",
"SampleEncoderDecoderOutput",
"SampleDecoderOnlyOutput",
"BeamSearchEncoderDecoderOutput",
"BeamSearchDecoderOnlyOutput",
"BeamSampleEncoderDecoderOutput",
"BeamSampleDecoderOnlyOutput",
"ContrastiveSearchEncoderDecoderOutput",
"ContrastiveSearchDecoderOnlyOutput",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tf_logits_process"] = [
"TFForcedBOSTokenLogitsProcessor",
"TFForcedEOSTokenLogitsProcessor",
"TFLogitsProcessor",
"TFLogitsProcessorList",
"TFLogitsWarper",
"TFMinLengthLogitsProcessor",
"TFNoBadWordsLogitsProcessor",
"TFNoRepeatNGramLogitsProcessor",
"TFRepetitionPenaltyLogitsProcessor",
"TFTemperatureLogitsWarper",
"TFTopKLogitsWarper",
"TFTopPLogitsWarper",
"TFForceTokensLogitsProcessor",
"TFSuppressTokensAtBeginLogitsProcessor",
"TFSuppressTokensLogitsProcessor",
]
_import_structure["tf_utils"] = [
"TFGenerationMixin",
"tf_top_k_top_p_filtering",
"TFGreedySearchDecoderOnlyOutput",
"TFGreedySearchEncoderDecoderOutput",
"TFSampleEncoderDecoderOutput",
"TFSampleDecoderOnlyOutput",
"TFBeamSearchEncoderDecoderOutput",
"TFBeamSearchDecoderOnlyOutput",
"TFBeamSampleEncoderDecoderOutput",
"TFBeamSampleDecoderOnlyOutput",
"TFContrastiveSearchEncoderDecoderOutput",
"TFContrastiveSearchDecoderOnlyOutput",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["flax_logits_process"] = [
"FlaxForcedBOSTokenLogitsProcessor",
"FlaxForcedEOSTokenLogitsProcessor",
"FlaxLogitsProcessor",
"FlaxLogitsProcessorList",
"FlaxLogitsWarper",
"FlaxMinLengthLogitsProcessor",
"FlaxTemperatureLogitsWarper",
"FlaxTopKLogitsWarper",
"FlaxTopPLogitsWarper",
]
_import_structure["flax_utils"] = [
"FlaxGenerationMixin",
"FlaxGreedySearchOutput",
"FlaxSampleOutput",
"FlaxBeamSearchOutput",
]
if TYPE_CHECKING:
from .configuration_utils import GenerationConfig
from .streamers import TextIteratorStreamer, TextStreamer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
from .logits_process import (
EncoderNoRepeatNGramLogitsProcessor,
EncoderRepetitionPenaltyLogitsProcessor,
EpsilonLogitsWarper,
EtaLogitsWarper,
ExponentialDecayLengthPenalty,
ForcedBOSTokenLogitsProcessor,
ForcedEOSTokenLogitsProcessor,
HammingDiversityLogitsProcessor,
InfNanRemoveLogitsProcessor,
LogitNormalization,
LogitsProcessor,
LogitsProcessorList,
LogitsWarper,
MinLengthLogitsProcessor,
MinNewTokensLengthLogitsProcessor,
NoBadWordsLogitsProcessor,
NoRepeatNGramLogitsProcessor,
PrefixConstrainedLogitsProcessor,
RepetitionPenaltyLogitsProcessor,
SequenceBiasLogitsProcessor,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
TypicalLogitsWarper,
)
from .stopping_criteria import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
from .utils import (
BeamSampleDecoderOnlyOutput,
BeamSampleEncoderDecoderOutput,
BeamSearchDecoderOnlyOutput,
BeamSearchEncoderDecoderOutput,
ContrastiveSearchDecoderOnlyOutput,
ContrastiveSearchEncoderDecoderOutput,
GenerationMixin,
GreedySearchDecoderOnlyOutput,
GreedySearchEncoderDecoderOutput,
SampleDecoderOnlyOutput,
SampleEncoderDecoderOutput,
top_k_top_p_filtering,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tf_logits_process import (
TFForcedBOSTokenLogitsProcessor,
TFForcedEOSTokenLogitsProcessor,
TFForceTokensLogitsProcessor,
TFLogitsProcessor,
TFLogitsProcessorList,
TFLogitsWarper,
TFMinLengthLogitsProcessor,
TFNoBadWordsLogitsProcessor,
TFNoRepeatNGramLogitsProcessor,
TFRepetitionPenaltyLogitsProcessor,
TFSuppressTokensAtBeginLogitsProcessor,
TFSuppressTokensLogitsProcessor,
TFTemperatureLogitsWarper,
TFTopKLogitsWarper,
TFTopPLogitsWarper,
)
from .tf_utils import (
TFBeamSampleDecoderOnlyOutput,
TFBeamSampleEncoderDecoderOutput,
TFBeamSearchDecoderOnlyOutput,
TFBeamSearchEncoderDecoderOutput,
TFContrastiveSearchDecoderOnlyOutput,
TFContrastiveSearchEncoderDecoderOutput,
TFGenerationMixin,
TFGreedySearchDecoderOnlyOutput,
TFGreedySearchEncoderDecoderOutput,
TFSampleDecoderOnlyOutput,
TFSampleEncoderDecoderOutput,
tf_top_k_top_p_filtering,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .flax_logits_process import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessor,
FlaxLogitsProcessorList,
FlaxLogitsWarper,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 9,515 | 33.603636 | 119 | py |
transformers | transformers-main/src/transformers/generation/stopping_criteria.py | import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
logger = logging.get_logger(__name__)
STOPPING_CRITERIA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs (`Dict[str, Any]`, *optional*):
Additional stopping criteria specific kwargs.
Return:
`bool`. `False` indicates we should continue, `True` indicates we should stop.
"""
class StoppingCriteria(ABC):
"""Abstract base class for all stopping criteria that can be applied during generation."""
@add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
raise NotImplementedError("StoppingCriteria needs to be subclassed")
class MaxLengthCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the full generated number of tokens exceeds `max_length`. Keep
in mind for decoder-only type of transformers, this will include the initial prompted tokens.
Args:
max_length (`int`):
The maximum length that the output sequence can have in number of tokens.
max_position_embeddings (`int`, `optional`):
The maximum model length, as defined by the model's `config.max_position_embeddings` attribute.
"""
def __init__(self, max_length: int, max_position_embeddings: Optional[int] = None):
self.max_length = max_length
self.max_position_embeddings = max_position_embeddings
@add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
cur_len = input_ids.shape[-1]
is_done = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
"This is a friendly reminder - the current text generation call will exceed the model's predefined "
f"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe "
"exceptions, performance degradation, or nothing at all."
)
return is_done
class MaxNewTokensCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the generated number of tokens exceeds `max_new_tokens`. Keep in
mind for decoder-only type of transformers, this will **not** include the initial prompted tokens. This is very
close to `MaxLengthCriteria` but ignores the number of initial tokens.
Args:
start_length (`int`):
The number of initial tokens.
max_new_tokens (`int`):
The maximum number of tokens to generate.
"""
def __init__(self, start_length: int, max_new_tokens: int):
warnings.warn(
"The class `MaxNewTokensCriteria` is deprecated. "
f"Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` "
"with `max_length = start_length + max_new_tokens` instead.",
FutureWarning,
)
self.start_length = start_length
self.max_new_tokens = max_new_tokens
self.max_length = start_length + max_new_tokens
@add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return input_ids.shape[-1] >= self.max_length
class MaxTimeCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the full generation exceeds some amount of time. By default, the
time will start being counted when you initialize this function. You can override this by passing an
`initial_time`.
Args:
max_time (`float`):
The maximum allowed time in seconds for the generation.
initial_time (`float`, *optional*, defaults to `time.time()`):
The start of the generation allowed time.
"""
def __init__(self, max_time: float, initial_timestamp: Optional[float] = None):
self.max_time = max_time
self.initial_timestamp = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return time.time() - self.initial_timestamp > self.max_time
class StoppingCriteriaList(list):
@add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return any(criteria(input_ids, scores) for criteria in self)
@property
def max_length(self) -> Optional[int]:
for stopping_criterium in self:
if isinstance(stopping_criterium, MaxLengthCriteria):
return stopping_criterium.max_length
elif isinstance(stopping_criterium, MaxNewTokensCriteria):
return stopping_criterium.max_length
return None
def validate_stopping_criteria(stopping_criteria: StoppingCriteriaList, max_length: int) -> StoppingCriteriaList:
stopping_max_length = stopping_criteria.max_length
new_stopping_criteria = deepcopy(stopping_criteria)
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter", UserWarning)
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=max_length))
return new_stopping_criteria
| 6,373 | 42.360544 | 119 | py |
transformers | transformers-main/src/transformers/generation/flax_logits_process.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
logger = get_logger(__name__)
LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
"""
class FlaxLogitsProcessor:
"""Abstract base class for all logit processors that can be applied during generation."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray) -> jnp.ndarray:
"""Flax method for processing logits."""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
class FlaxLogitsWarper:
"""Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray) -> jnp.ndarray:
"""Flax method for warping logits."""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
class FlaxLogitsProcessorList(list):
"""
This class can be used to create a list of [`FlaxLogitsProcessor`] or [`FlaxLogitsWarper`] to subsequently process
a `scores` input tensor. This class inherits from list and adds a specific *__call__* method to apply each
[`FlaxLogitsProcessor`] or [`FlaxLogitsWarper`] to the inputs.
"""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int, **kwargs) -> jnp.ndarray:
for processor in self:
function_args = inspect.signature(processor.__call__).parameters
if len(function_args) > 3:
if not all(arg in kwargs for arg in list(function_args.keys())[2:]):
raise ValueError(
f"Make sure that all the required parameters: {list(function_args.keys())} for "
f"{processor.__class__} are passed to the logits processor."
)
scores = processor(input_ids, scores, cur_len, **kwargs)
else:
scores = processor(input_ids, scores, cur_len)
return scores
class FlaxTemperatureLogitsWarper(FlaxLogitsWarper):
r"""
[`FlaxLogitsWarper`] for temperature (exponential scaling output probability distribution).
Args:
temperature (`float`):
The value used to module the logits distribution.
"""
def __init__(self, temperature: float):
if not isinstance(temperature, float) or not (temperature > 0):
raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}")
self.temperature = temperature
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
scores = scores / self.temperature
return scores
class FlaxTopPLogitsWarper(FlaxLogitsWarper):
"""
[`FlaxLogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
Args:
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(self, top_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
if not isinstance(top_p, float) or (top_p < 0 or top_p > 1.0):
raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}")
if not isinstance(min_tokens_to_keep, int) or (min_tokens_to_keep < 1):
raise ValueError(f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}")
self.top_p = top_p
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
topk_scores, topk_indices = lax.top_k(scores, scores.shape[-1])
mask_scores = jnp.full_like(scores, self.filter_value)
cumulative_probs = jax.nn.softmax(topk_scores, axis=-1).cumsum(axis=-1)
score_mask = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
score_mask = jnp.roll(score_mask, 1)
score_mask |= score_mask.at[:, 0].set(True)
# min tokens to keep
score_mask = score_mask.at[:, : self.min_tokens_to_keep].set(True)
topk_next_scores = jnp.where(score_mask, topk_scores, mask_scores)
next_scores = jax.lax.sort_key_val(topk_indices, topk_next_scores)[-1]
return next_scores
class FlaxTopKLogitsWarper(FlaxLogitsWarper):
r"""
[`FlaxLogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
Args:
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(self, top_k: int, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
if not isinstance(top_k, int) or top_k <= 0:
raise ValueError(f"`top_k` has to be a strictly positive integer, but is {top_k}")
self.top_k = max(top_k, min_tokens_to_keep)
self.filter_value = filter_value
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
batch_size, vocab_size = scores.shape
next_scores_flat = jnp.full(batch_size * vocab_size, self.filter_value)
topk = min(self.top_k, scores.shape[-1]) # Safety check
topk_scores, topk_indices = lax.top_k(scores, topk)
shift = jnp.broadcast_to((jnp.arange(batch_size) * vocab_size)[:, None], (batch_size, topk)).flatten()
topk_scores_flat = topk_scores.flatten()
topk_indices_flat = topk_indices.flatten() + shift
next_scores_flat = next_scores_flat.at[topk_indices_flat].set(topk_scores_flat)
next_scores = next_scores_flat.reshape(batch_size, vocab_size)
return next_scores
class FlaxForcedBOSTokenLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that enforces the specified token as the first generated token.
Args:
bos_token_id (`int`):
The id of the token to force as the first generated token.
"""
def __init__(self, bos_token_id: int):
self.bos_token_id = bos_token_id
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
new_scores = jnp.full(scores.shape, -float("inf"))
apply_penalty = 1 - jnp.bool_(cur_len - 1)
scores = jnp.where(apply_penalty, new_scores.at[:, self.bos_token_id].set(0), scores)
return scores
class FlaxForcedEOSTokenLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that enforces the specified token as the last generated token when `max_length` is reached.
Args:
max_length (`int`):
The maximum length of the sequence to be generated.
eos_token_id (`int`):
The id of the token to force as the last generated token when `max_length` is reached.
"""
def __init__(self, max_length: int, eos_token_id: int):
self.max_length = max_length
self.eos_token_id = eos_token_id
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
new_scores = jnp.full(scores.shape, -float("inf"))
apply_penalty = 1 - jnp.bool_(cur_len - self.max_length + 1)
scores = jnp.where(apply_penalty, new_scores.at[:, self.eos_token_id].set(0), scores)
return scores
class FlaxMinLengthLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] enforcing a min-length by setting EOS probability to 0.
Args:
min_length (`int`):
The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
eos_token_id (`int`):
The id of the *end-of-sequence* token.
"""
def __init__(self, min_length: int, eos_token_id: int):
if not isinstance(min_length, int) or min_length < 0:
raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}")
if not isinstance(eos_token_id, int) or eos_token_id < 0:
raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}")
self.min_length = min_length
self.eos_token_id = eos_token_id
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
# create boolean flag to decide if min length penalty should be applied
apply_penalty = 1 - jnp.clip(cur_len - self.min_length, 0, 1)
scores = jnp.where(apply_penalty, scores.at[:, self.eos_token_id].set(-float("inf")), scores)
return scores
class FlaxSuppressTokensAtBeginLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] supressing a list of tokens as soon as the `generate` function starts generating using
`begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` are not sampled at the
begining of the generation.
Args:
begin_suppress_tokens (`List[int]`):
Tokens to not sample.
begin_index (`int`):
Index where the tokens are suppressed.
"""
def __init__(self, begin_suppress_tokens, begin_index):
self.begin_suppress_tokens = list(begin_suppress_tokens)
self.begin_index = begin_index
def __call__(self, input_ids, scores, cur_len: int):
apply_penalty = 1 - jnp.bool_(cur_len - self.begin_index)
scores = jnp.where(apply_penalty, scores.at[:, self.begin_suppress_tokens].set(-float("inf")), scores)
return scores
class FlaxSuppressTokensLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] suppressing a list of tokens at each decoding step. The processor will set their log probs
to be `-inf` so they are not sampled.
Args:
suppress_tokens (`list`):
Tokens to not sample.
"""
def __init__(self, suppress_tokens: list):
self.suppress_tokens = list(suppress_tokens)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
scores = scores.at[..., self.suppress_tokens].set(-float("inf"))
return scores
class FlaxForceTokensLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that takes a list of pairs of integers which indicates a mapping from generation indices to
token indices that will be forced before sampling. The processor will set their log probs to 0 and all other tokens
to `-inf` so that they are sampled at their corresponding index.
Args:
force_token_map (`list`):
Map giving token ids and indices where they will be forced to be sampled.
"""
def __init__(self, force_token_map):
force_token_map = dict(force_token_map)
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
force_token_array = jnp.ones((max(force_token_map.keys()) + 1), dtype=jnp.int32) * -1
for index, token in force_token_map.items():
if token is not None:
force_token_array = force_token_array.at[index].set(token)
self.force_token_array = jnp.int32(force_token_array)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
def _force_token(generation_idx):
batch_size = scores.shape[0]
current_token = self.force_token_array[generation_idx]
new_scores = jnp.ones_like(scores, dtype=scores.dtype) * -float("inf")
updates = jnp.zeros((batch_size, 1), dtype=scores.dtype)
new_scores = lax.dynamic_update_slice(new_scores, updates, (0, current_token))
return new_scores
scores = lax.cond(
cur_len >= self.force_token_array.shape[0],
# If the current length is geq than the length of force_token_array, the processor does nothing.
lambda: scores,
# Otherwise, it may force a certain token.
lambda: lax.cond(
self.force_token_array[cur_len] >= 0,
# Only valid (positive) tokens are forced
lambda: _force_token(cur_len),
# Otherwise, the processor does nothing.
lambda: scores,
),
)
return scores
class FlaxWhisperTimeStampLogitsProcessor(FlaxLogitsProcessor):
r"""
Whisper specific Processor. This processor can be used to force a list of tokens. The processor will set their log
probs to `inf` so that they are sampled at their corresponding index.
Args:
generate_config (`GenerateConfig`):
The generate config used to generate the output. The following parameters are required:
eos_token_id (`int`, *optional*, defaults to 50257):
The id of the *end-of-sequence* token.
no_timestamps_token_id (`int`, *optional*, defaults to 50363):
The id of the `"<|notimestamps|>"` token.
max_initial_timestamp_index (`int`, *optional*, defaults to 1):
Used to set the maximum value of the initial timestamp. This is used to prevent the model from
predicting timestamps that are too far in the future.
"""
def __init__(self, generate_config, model_config, decoder_input_length):
self.eos_token_id = generate_config.eos_token_id
self.no_timestamps_token_id = generate_config.no_timestamps_token_id
self.timestamp_begin = generate_config.no_timestamps_token_id + 1
self.begin_index = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(generate_config, "max_initial_timestamp_index"):
self.max_initial_timestamp_index = generate_config.max_initial_timestamp_index
else:
self.max_initial_timestamp_index = model_config.vocab_size
if self.max_initial_timestamp_index is None:
self.max_initial_timestamp_index = model_config.vocab_size
def __call__(self, input_ids, scores, cur_len):
# suppress <|notimestamps|> which is handled by without_timestamps
scores = scores.at[:, self.no_timestamps_token_id].set(-float("inf"))
def handle_pairs(input_ids_k, scores_k):
last_was_timestamp = jnp.where((cur_len - self.begin_index) >= 1, True, False)
last_was_timestamp = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin,
True and last_was_timestamp,
False,
)
penultimate_was_timestamp = jnp.where((cur_len - self.begin_index) < 2, True, False)
penultimate_was_timestamp = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin,
True,
penultimate_was_timestamp,
)
return jnp.where(
last_was_timestamp,
jnp.where(
penultimate_was_timestamp > 0,
scores_k.at[self.timestamp_begin :].set(-float("inf")),
scores_k.at[: self.eos_token_id].set(-float("inf")),
),
scores_k,
)
scores = jax.vmap(handle_pairs)(input_ids, scores)
apply_max_initial_timestamp = jnp.where(cur_len == self.begin_index, True, False)
apply_max_initial_timestamp = jnp.where(
self.max_initial_timestamp_index is not None,
True and apply_max_initial_timestamp,
False,
)
last_allowed = self.timestamp_begin + self.max_initial_timestamp_index
scores = jnp.where(
apply_max_initial_timestamp,
scores.at[:, last_allowed + 1 :].set(-float("inf")),
scores,
)
# if sum of probability over timestamps is above any other token, sample timestamp
logprobs = jax.nn.log_softmax(scores, axis=-1)
def handle_cumulative_probs(logprobs_k, scores_k):
timestamp_logprob = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :], axis=-1)
max_text_token_logprob = jnp.max(logprobs_k[: self.timestamp_begin])
return jnp.where(
timestamp_logprob > max_text_token_logprob,
scores_k.at[: self.timestamp_begin].set(-float("inf")),
scores_k,
)
scores = jax.vmap(handle_cumulative_probs)(logprobs, scores)
return scores
| 19,218 | 40.962882 | 119 | py |
transformers | transformers-main/src/transformers/generation/logits_process.py | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import math
from typing import Callable, Dict, Iterable, List, Tuple, Union
import numpy as np
import torch
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
logger = get_logger(__name__)
LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
Return:
`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
"""
class LogitsProcessor:
"""Abstract base class for all logit processors that can be applied during generation."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
class LogitsWarper:
"""Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
class LogitsProcessorList(list):
"""
This class can be used to create a list of [`LogitsProcessor`] or [`LogitsWarper`] to subsequently process a
`scores` input tensor. This class inherits from list and adds a specific *__call__* method to apply each
[`LogitsProcessor`] or [`LogitsWarper`] to the inputs.
"""
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using
beam search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional kwargs that are specific to a logits processor.
Return:
`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`:
The processed prediction scores.
"""
for processor in self:
function_args = inspect.signature(processor.__call__).parameters
if len(function_args) > 2:
if not all(arg in kwargs for arg in list(function_args.keys())[2:]):
raise ValueError(
f"Make sure that all the required parameters: {list(function_args.keys())} for "
f"{processor.__class__} are passed to the logits processor."
)
scores = processor(input_ids, scores, **kwargs)
else:
scores = processor(input_ids, scores)
return scores
class MinLengthLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing a min-length by setting EOS probability to 0.
Args:
min_length (`int`):
The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
eos_token_id (`Union[int, List[int]]`):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
"""
def __init__(self, min_length: int, eos_token_id: Union[int, List[int]]):
if not isinstance(min_length, int) or min_length < 0:
raise ValueError(f"`min_length` has to be a non-negative integer, but is {min_length}")
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
if not all(isinstance(i, int) for i in eos_token_id) or any(i < 0 for i in eos_token_id):
logger.warning(f"`eos_token_id` has to be a list of positive integers, but is {eos_token_id}")
self.min_length = min_length
self.eos_token_id = eos_token_id
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
cur_len = input_ids.shape[-1]
if cur_len < self.min_length:
for i in self.eos_token_id:
scores[:, i] = -float("inf")
return scores
class MinNewTokensLengthLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing a min-length of new tokens by setting EOS (End-Of-Sequence) token probability to 0.
Args:
prompt_length_to_skip (`int`):
The input tokens length.
min_new_tokens (`int`):
The minimum *new* tokens length below which the score of `eos_token_id` is set to `-float("Inf")`.
eos_token_id (`Union[int, List[int]]`):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
"""
def __init__(self, prompt_length_to_skip: int, min_new_tokens: int, eos_token_id: Union[int, List[int]]):
for arg_name, arg_value in [
("prompt_length_to_skip", prompt_length_to_skip),
("min_new_tokens", min_new_tokens),
]:
if not isinstance(arg_value, int) or arg_value < 0:
raise ValueError(f"`{arg_name}` has to be a positive integer, but is {arg_value}")
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
if not all(isinstance(i, int) for i in eos_token_id) or any(i < 0 for i in eos_token_id):
logger.warning(f"`eos_token_id` has to be a list of positive integers, but is {eos_token_id}")
self.prompt_length_to_skip = prompt_length_to_skip
self.min_new_tokens = min_new_tokens
self.eos_token_id = eos_token_id
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
new_tokens_length = input_ids.shape[-1] - self.prompt_length_to_skip
if new_tokens_length < self.min_new_tokens:
for i in self.eos_token_id:
scores[:, i] = -float("inf")
return scores
class TemperatureLogitsWarper(LogitsWarper):
r"""
[`LogitsWarper`] for temperature (exponential scaling output probability distribution).
Args:
temperature (`float`):
The value used to module the logits distribution.
"""
def __init__(self, temperature: float):
if not isinstance(temperature, float) or not (temperature > 0):
raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}")
self.temperature = temperature
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
scores = scores / self.temperature
return scores
class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing an exponential penalty on repeated sequences.
Args:
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
"""
def __init__(self, penalty: float):
if not isinstance(penalty, float) or not (penalty > 0):
raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
self.penalty = penalty
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
score = torch.gather(scores, 1, input_ids)
# if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
score = torch.where(score < 0, score * self.penalty, score / self.penalty)
scores.scatter_(1, input_ids, score)
return scores
class EncoderRepetitionPenaltyLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing an exponential penalty on tokens that are not in the original input.
Args:
hallucination_penalty (`float`):
The parameter for hallucination penalty. 1.0 means no penalty.
encoder_input_ids (`torch.LongTensor`):
The encoder_input_ids that should not be repeated within the decoder ids.
"""
def __init__(self, penalty: float, encoder_input_ids: torch.LongTensor):
if not isinstance(penalty, float) or not (penalty > 0):
raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
self.penalty = 1 / penalty
self.encoder_input_ids = encoder_input_ids
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
score = torch.gather(scores, 1, self.encoder_input_ids)
# if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
score = torch.where(score < 0, score * self.penalty, score / self.penalty)
scores.scatter_(1, self.encoder_input_ids, score)
return scores
class TopPLogitsWarper(LogitsWarper):
"""
[`LogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
Args:
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(self, top_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
top_p = float(top_p)
if top_p < 0 or top_p > 1.0:
raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}")
if not isinstance(min_tokens_to_keep, int) or (min_tokens_to_keep < 1):
raise ValueError(f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}")
self.top_p = top_p
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
sorted_logits, sorted_indices = torch.sort(scores, descending=False)
cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
# Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs <= (1 - self.top_p)
# Keep at least min_tokens_to_keep
sorted_indices_to_remove[..., -self.min_tokens_to_keep :] = 0
# scatter sorted tensors to original indexing
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
scores = scores.masked_fill(indices_to_remove, self.filter_value)
return scores
class TopKLogitsWarper(LogitsWarper):
r"""
[`LogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
Args:
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(self, top_k: int, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
if not isinstance(top_k, int) or top_k <= 0:
raise ValueError(f"`top_k` has to be a strictly positive integer, but is {top_k}")
self.top_k = max(top_k, min_tokens_to_keep)
self.filter_value = filter_value
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
top_k = min(self.top_k, scores.size(-1)) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = scores < torch.topk(scores, top_k)[0][..., -1, None]
scores = scores.masked_fill(indices_to_remove, self.filter_value)
return scores
class TypicalLogitsWarper(LogitsWarper):
r"""
[`LogitsWarper`] that performs typical decoding. See [Typical Decoding for Natural Language
Generation](https://arxiv.org/abs/2202.00666) for more information.
Args:
mass (`float`):
Value of typical_p between 0 and 1 inclusive, defaults to 0.9.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(self, mass: float = 0.9, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
mass = float(mass)
if not (mass > 0 and mass < 1):
raise ValueError(f"`typical_p` has to be a float > 0 and < 1, but is {mass}")
if not isinstance(min_tokens_to_keep, int) or (min_tokens_to_keep < 1):
raise ValueError(f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}")
self.filter_value = filter_value
self.mass = mass
self.min_tokens_to_keep = min_tokens_to_keep
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# calculate entropy
normalized = torch.nn.functional.log_softmax(scores, dim=-1)
p = torch.exp(normalized)
ent = -(normalized * p).nansum(-1, keepdim=True)
# shift and sort
shifted_scores = torch.abs((-normalized) - ent)
sorted_scores, sorted_indices = torch.sort(shifted_scores, descending=False)
sorted_logits = scores.gather(-1, sorted_indices)
cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
# Remove tokens with cumulative mass above the threshold
last_ind = (cumulative_probs < self.mass).sum(dim=1)
last_ind[last_ind < 0] = 0
sorted_indices_to_remove = sorted_scores > sorted_scores.gather(1, last_ind.view(-1, 1))
sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
scores = scores.masked_fill(indices_to_remove, self.filter_value)
return scores
class EpsilonLogitsWarper(LogitsWarper):
r"""
[`LogitsWarper`] that performs epsilon-sampling, i.e. restricting to tokens with `prob >= epsilon`. Takes the
largest min_tokens_to_keep tokens if no tokens satisfy this constraint. See [Truncation Sampling as Language Model
Desmoothing](https://arxiv.org/abs/2210.15191) for more information.
Args:
epsilon (`float`):
If set to > 0, only the most tokens with probabilities `epsilon` or higher are kept for generation.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(self, epsilon: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
epsilon = float(epsilon)
if epsilon <= 0 or epsilon >= 1:
raise ValueError(f"`epsilon_cutoff` has to be a float > 0 and < 1, but is {epsilon}")
min_tokens_to_keep = int(min_tokens_to_keep)
if min_tokens_to_keep < 1:
raise ValueError(
f"`min_tokens_to_keep` has to be a strictly positive integer, but is {min_tokens_to_keep}"
)
self.epsilon = epsilon
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# Determine which indices to remove
probabilities = scores.softmax(dim=-1)
indices_to_remove = probabilities < self.epsilon
# Keep the words with the 'min_tokens_to_keep'-highest probabilities
top_k = min(self.min_tokens_to_keep, scores.size(-1)) # Safety check
indices_to_remove = indices_to_remove & (scores < torch.topk(scores, top_k)[0][..., -1, None])
scores = scores.masked_fill(indices_to_remove, self.filter_value)
return scores
class EtaLogitsWarper(LogitsWarper):
r"""
[`LogitsWarper`] that performs eta-sampling, i.e. calculates a dynamic cutoff `eta := min(epsilon, sqrt(epsilon,
e^-entropy(probabilities)))` and restricts to tokens with `prob >= eta`. Takes the largest min_tokens_to_keep
tokens if no tokens satisfy this constraint. See [Truncation Sampling as Language Model
Desmoothing](https://arxiv.org/abs/2210.15191) for more information.
Args:
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered."""
def __init__(self, epsilon: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
epsilon = float(epsilon)
if epsilon <= 0 or epsilon >= 1:
raise ValueError(f"`eta_cutoff` has to be a float > 0 and < 1, but is {epsilon}")
min_tokens_to_keep = int(min_tokens_to_keep)
if min_tokens_to_keep < 1:
raise ValueError(
f"`min_tokens_to_keep` has to be a strictly positive integer, but is {min_tokens_to_keep}"
)
self.epsilon = torch.tensor(epsilon)
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# Calculate the adaptive cutoff
probabilities = scores.softmax(dim=-1)
entropy = torch.distributions.Categorical(logits=scores).entropy()
eta = torch.min(self.epsilon, torch.sqrt(self.epsilon) * torch.exp(-entropy))[..., None]
indices_to_remove = probabilities < eta
# Keep the words with the 'min_tokens_to_keep'-highest probabilities
top_k = min(self.min_tokens_to_keep, scores.size(-1)) # Safety check
indices_to_remove = indices_to_remove & (scores < torch.topk(scores, top_k)[0][..., -1, None])
scores = scores.masked_fill(indices_to_remove, self.filter_value)
return scores
def _get_ngrams(ngram_size: int, prev_input_ids: torch.Tensor, num_hypos: int):
generated_ngrams = [{} for _ in range(num_hypos)]
for idx in range(num_hypos):
gen_tokens = prev_input_ids[idx].tolist()
generated_ngram = generated_ngrams[idx]
for ngram in zip(*[gen_tokens[i:] for i in range(ngram_size)]):
prev_ngram_tuple = tuple(ngram[:-1])
generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
return generated_ngrams
def _get_generated_ngrams(banned_ngrams, prev_input_ids, ngram_size, cur_len):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = cur_len + 1 - ngram_size
ngram_idx = tuple(prev_input_ids[start_idx:cur_len].tolist())
return banned_ngrams.get(ngram_idx, [])
def _calc_banned_ngram_tokens(
ngram_size: int, prev_input_ids: torch.Tensor, num_hypos: int, cur_len: int
) -> List[Iterable[int]]:
"""Copied from fairseq for no_repeat_ngram in beam_search"""
if cur_len + 1 < ngram_size:
# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
return [[] for _ in range(num_hypos)]
generated_ngrams = _get_ngrams(ngram_size, prev_input_ids, num_hypos)
banned_tokens = [
_get_generated_ngrams(generated_ngrams[hypo_idx], prev_input_ids[hypo_idx], ngram_size, cur_len)
for hypo_idx in range(num_hypos)
]
return banned_tokens
class NoRepeatNGramLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces no repetition of n-grams. See
[Fairseq](https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345).
Args:
ngram_size (`int`):
All ngrams of size `ngram_size` can only occur once.
"""
def __init__(self, ngram_size: int):
if not isinstance(ngram_size, int) or ngram_size <= 0:
raise ValueError(f"`ngram_size` has to be a strictly positive integer, but is {ngram_size}")
self.ngram_size = ngram_size
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
num_batch_hypotheses = scores.shape[0]
cur_len = input_ids.shape[-1]
banned_batch_tokens = _calc_banned_ngram_tokens(self.ngram_size, input_ids, num_batch_hypotheses, cur_len)
for i, banned_tokens in enumerate(banned_batch_tokens):
scores[i, banned_tokens] = -float("inf")
return scores
class EncoderNoRepeatNGramLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces no repetition of encoder input ids n-grams for the decoder ids. See
[ParlAI](https://github.com/facebookresearch/ParlAI/blob/master/parlai/core/torch_generator_agent.py#L1350).
Args:
encoder_ngram_size (`int`):
All ngrams of size `ngram_size` can only occur within the encoder input ids.
encoder_input_ids (`int`):
The encoder_input_ids that should not be repeated within the decoder ids.
"""
def __init__(self, encoder_ngram_size: int, encoder_input_ids: torch.LongTensor):
if not isinstance(encoder_ngram_size, int) or encoder_ngram_size <= 0:
raise ValueError(
f"`encoder_ngram_size` has to be a strictly positive integer, but is {encoder_ngram_size}"
)
self.ngram_size = encoder_ngram_size
if len(encoder_input_ids.shape) == 1:
encoder_input_ids = encoder_input_ids.unsqueeze(0)
self.batch_size = encoder_input_ids.shape[0]
self.generated_ngrams = _get_ngrams(encoder_ngram_size, encoder_input_ids, self.batch_size)
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# B x num_beams
num_hypos = scores.shape[0]
num_beams = num_hypos // self.batch_size
cur_len = input_ids.shape[-1]
banned_batch_tokens = [
_get_generated_ngrams(
self.generated_ngrams[hypo_idx // num_beams], input_ids[hypo_idx], self.ngram_size, cur_len
)
for hypo_idx in range(num_hypos)
]
for i, banned_tokens in enumerate(banned_batch_tokens):
scores[i, banned_tokens] = -float("inf")
return scores
class SequenceBiasLogitsProcessor(LogitsProcessor):
"""
[`LogitsProcessor`] that applies an additive bias on sequences. The bias is applied to the last token of a sequence
when the next generated token can complete it. Consequently, to take the most of biasing sequences with more than
one token, consider using beam methods (to gracefully work around partially completed sequences that have a
negative bias) and applying the bias to their prefixes (to ensure the bias is applied earlier).
<Tip>
In order to get the token ids of the sequences that you want to bias, make sure to set `add_prefix_space=True` when
initializing the tokenizer, and use `tokenizer(bad_words, add_special_tokens=False).input_ids`. The
`add_prefix_space` argument is only supported for some slow tokenizers, as fast tokenizers' prefixing behaviours
come from `pre tokenizers`. Read more [here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
</Tip>
Args:
sequence_bias (`Dict[Tuple[int], float]`):
Dictionary that maps a sequence of tokens to its bias term. Positive biases increase the odds of the
sequence being selected, while negative biases do the opposite. If a sequence has a length of 1, its bias
will always be applied. Otherwise, the bias will only be applied if the sequence in question is about to be
completed (in the token selection step after this processor is applied).
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> inputs = tokenizer(["The full name of Donald is Donald"], return_tensors="pt")
>>> summary_ids = model.generate(inputs["input_ids"], max_new_tokens=4)
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True)[0])
The full name of Donald is Donald J. Trump Jr
>>> # Now let's control generation through a bias. Please note that the tokenizer is initialized differently!
>>> tokenizer_with_prefix_space = AutoTokenizer.from_pretrained("gpt2", add_prefix_space=True)
>>> def get_tokens_as_tuple(word):
... return tuple(tokenizer_with_prefix_space([word], add_special_tokens=False).input_ids[0])
>>> # If we add a negative bias without beam search, it may become "stuck" in a prefix without good continuations
>>> sequence_bias = {get_tokens_as_tuple("Trump"): -10.0}
>>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, sequence_bias=sequence_bias)
>>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
The full name of Donald is Donald J. Donald,
>>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=4, sequence_bias=sequence_bias)
>>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
The full name of Donald is Donald Rumsfeld,
>>> # We can also add a positive bias to nudge the model towards specific tokens or continuations
>>> sequence_bias = {get_tokens_as_tuple("Donald Duck"): 10.0}
>>> biased_ids = model.generate(inputs["input_ids"], max_new_tokens=4, num_beams=4, sequence_bias=sequence_bias)
>>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
The full name of Donald is Donald Duck.
```
"""
def __init__(self, sequence_bias: Dict[Tuple[int], float]):
self.sequence_bias = sequence_bias
self._validate_arguments()
# Bias variables that will be populated on the first call (for retrocompatibility purposes, the vocabulary size
# is infered in the first usage, which inhibits initializing here)
self.sequences_length_greater_than_1 = []
self.length_1_bias = None
self.length_greather_than_1_bias = None
self.prepared_bias_variables = False
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# 1 - Prepares the bias tensors. This is only needed the first time the logit processor is called.
if not self.prepared_bias_variables:
self._prepare_bias_variables(scores)
# 2 - prepares an empty bias to add
bias = torch.zeros_like(scores)
# 3 - include the bias from length = 1
bias += self.length_1_bias
# 4 - include the bias from length > 1, after determining which biased sequences may be completed.
# `matching_mask` is a (batch_size, vocab_size) boolean mask that is True for all tokens whose corresponding
# bias should be applied. The bias is applied on the last token of the sequence, if (and only if) the sequence
# may become complete this iteration.
matching_mask = torch.zeros_like(scores, dtype=torch.bool)
for sequence_ids in self.sequences_length_greater_than_1:
if len(sequence_ids) > input_ids.shape[1]: # the sequence is longer than the context, ignore
continue
prefix_length = len(sequence_ids) - 1
last_token = sequence_ids[-1]
matching_rows = torch.eq(
input_ids[:, -prefix_length:],
torch.tensor(sequence_ids[:-1], dtype=input_ids.dtype, device=input_ids.device),
).prod(dim=1)
matching_mask[:, last_token] |= matching_rows.bool()
bias += torch.where(
matching_mask,
self.length_greather_than_1_bias,
torch.tensor(0.0, device=self.length_greather_than_1_bias.device),
)
# 5 - apply the bias to the scores
scores = scores + bias
return scores
def _prepare_bias_variables(self, scores: torch.FloatTensor):
vocabulary_size = scores.shape[-1]
sequence_bias = self.sequence_bias
tokens_with_bias = []
# Check biased tokens out of bounds
invalid_biases = []
for sequence_ids in sequence_bias:
for token_id in sequence_ids:
if token_id >= vocabulary_size:
invalid_biases.append(token_id)
if len(invalid_biases) > 0:
raise ValueError(
f"The model vocabulary size is {vocabulary_size}, but the following tokens were being biased: "
f"{invalid_biases}"
)
# Precompute the bias tensors to be applied. Sequences of length 1 are kept separately, as they can be applied
# with simpler logic.
self.length_1_bias = torch.zeros((vocabulary_size,), dtype=torch.float).to(scores.device)
self.length_greather_than_1_bias = torch.zeros((vocabulary_size,), dtype=torch.float).to(scores.device)
for sequence_ids, bias in sequence_bias.items():
if len(sequence_ids) == 1:
self.length_1_bias[sequence_ids[-1]] = bias
else:
self.sequences_length_greater_than_1.append(sequence_ids)
if self.length_greather_than_1_bias[sequence_ids[-1]] != 0.0:
raise ValueError(
"Setting a bias on sequences that share a common token termination is not yet supported. "
"Please open an issue if you see this error message (after checking that it doesn't already "
"exist)."
)
self.length_greather_than_1_bias[sequence_ids[-1]] = bias
tokens_with_bias.append(sequence_ids[-1])
self.prepared_bias_variables = True
def _validate_arguments(self):
sequence_bias = self.sequence_bias
if not isinstance(sequence_bias, dict) or len(sequence_bias) == 0:
raise ValueError(f"`sequence_bias` has to be a non-empty dictionary, but is {sequence_bias}.")
if any(not isinstance(sequence_ids, tuple) for sequence_ids in sequence_bias.keys()):
raise ValueError(f"`sequence_bias` has to be a dict with tuples as keys, but is {sequence_bias}.")
if any(
any((not isinstance(token_id, (int, np.integer)) or token_id < 0) for token_id in sequence_ids)
or len(sequence_ids) == 0
for sequence_ids in sequence_bias.keys()
):
raise ValueError(
f"Each key in `sequence_bias` has to be a non-empty tuple of positive integers, but is "
f"{sequence_bias}."
)
if any(not isinstance(bias, float) for bias in sequence_bias.values()):
raise ValueError(f"`sequence_bias` has to be a dict with floats as values, but is {sequence_bias}.")
class NoBadWordsLogitsProcessor(SequenceBiasLogitsProcessor):
"""
[`LogitsProcessor`] that enforces that specified sequences will never be selected.
<Tip>
In order to get the token ids of the words that should not appear in the generated text, make sure to set
`add_prefix_space=True` when initializing the tokenizer, and use `tokenizer(bad_words,
add_special_tokens=False).input_ids`. The `add_prefix_space` argument is only supported for some slow tokenizers,
as fast tokenizers' prefixing behaviours come from `pre tokenizers`. Read more
[here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
</Tip>
Args:
bad_words_ids (`List[List[int]]`):
List of list of token ids that are not allowed to be generated.
eos_token_id (`Union[int, List[int]]`):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
"""
def __init__(self, bad_words_ids: List[List[int]], eos_token_id: Union[int, List[int]]):
self.bad_word_ids = bad_words_ids
self._validate_arguments()
# Filter EOS token from bad_words_ids
if eos_token_id is None:
eos_token_id = []
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
bad_words_ids = list(
filter(lambda bad_token_seq: all(bad_token_seq != [i] for i in eos_token_id), bad_words_ids)
)
# Forbidding a sequence is equivalent to setting its bias to -inf
sequence_bias = {tuple(sequence): float("-inf") for sequence in bad_words_ids}
super().__init__(sequence_bias=sequence_bias)
def _validate_arguments(self):
bad_words_ids = self.bad_word_ids
if not isinstance(bad_words_ids, list) or len(bad_words_ids) == 0:
raise ValueError(f"`bad_words_ids` has to be a non-empty list, but is {bad_words_ids}.")
if any(not isinstance(bad_word_ids, list) for bad_word_ids in bad_words_ids):
raise ValueError(f"`bad_words_ids` has to be a list of lists, but is {bad_words_ids}.")
if any(
any((not isinstance(token_id, (int, np.integer)) or token_id < 0) for token_id in bad_word_ids)
for bad_word_ids in bad_words_ids
):
raise ValueError(
f"Each list in `bad_words_ids` has to be a list of positive integers, but is {bad_words_ids}."
)
class PrefixConstrainedLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces constrained generation and is useful for prefix-conditioned constrained
generation. See [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904) for more information.
Args:
prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`):
This function constraints the beam search to allowed tokens only at each step. This function takes 2
arguments `inputs_ids` and the batch ID `batch_id`. It has to return a list with the allowed tokens for the
next generation step conditioned on the previously generated tokens `inputs_ids` and the batch ID
`batch_id`.
"""
def __init__(self, prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]], num_beams: int):
self._prefix_allowed_tokens_fn = prefix_allowed_tokens_fn
self._num_beams = num_beams
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
mask = torch.full_like(scores, -math.inf)
for batch_id, beam_sent in enumerate(input_ids.view(-1, self._num_beams, input_ids.shape[-1])):
for beam_id, sent in enumerate(beam_sent):
mask[batch_id * self._num_beams + beam_id, self._prefix_allowed_tokens_fn(batch_id, sent)] = 0
return scores + mask
class HammingDiversityLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces diverse beam search. Note that this logits processor is only effective for
[`PreTrainedModel.group_beam_search`]. See [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
Models](https://arxiv.org/pdf/1610.02424.pdf) for more details.
Args:
diversity_penalty (`float`):
This value is subtracted from a beam's score if it generates a token same as any beam from other group at a
particular time. Note that `diversity_penalty` is only effective if `group beam search` is enabled.
num_beams (`int`):
Number of beams used for group beam search. See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more
details.
num_beam_groups (`int`):
Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
See [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
"""
def __init__(self, diversity_penalty: float, num_beams: int, num_beam_groups: int):
if not isinstance(diversity_penalty, float) or (not diversity_penalty > 0.0):
raise ValueError("`diversity_penalty` should be a float strictly larger than 0.")
self._diversity_penalty = diversity_penalty
if not isinstance(num_beams, int) or num_beams < 2:
raise ValueError("`num_beams` should be an integer strictly larger than 1.")
self._num_beams = num_beams
if not isinstance(num_beam_groups, int) or num_beam_groups < 2:
raise ValueError("`num_beam_groups` should be an integer strictly larger than 1.")
if num_beam_groups > num_beams:
raise ValueError("`beam_groups` has to be smaller or equal to `num_beams`.")
self._num_sub_beams = num_beams // num_beam_groups
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
current_tokens: torch.LongTensor,
beam_group_idx: int,
) -> torch.FloatTensor:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using
beam search or log softmax for each vocabulary token when using beam search
current_tokens (`torch.LongTensor` of shape `(batch_size)`):
Indices of input sequence tokens in the vocabulary, corresponding to the tokens selected by the other
beam groups in the current generation step.
beam_group_idx (`int`):
The index of the beam group currently being processed.
Return:
`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`:
The processed prediction scores.
"""
# hamming diversity: penalise using same token in current group which was used in previous groups at
# the same time step
batch_size = current_tokens.shape[0] // self._num_beams
group_start_idx = beam_group_idx * self._num_sub_beams
group_end_idx = min(group_start_idx + self._num_sub_beams, self._num_beams)
group_size = group_end_idx - group_start_idx
vocab_size = scores.shape[-1]
if group_start_idx == 0:
return scores
for batch_idx in range(batch_size):
# predicted tokens of last time step of previous groups
previous_group_tokens = current_tokens[
batch_idx * self._num_beams : batch_idx * self._num_beams + group_start_idx
]
token_frequency = torch.bincount(previous_group_tokens, minlength=vocab_size).to(scores.device)
scores[batch_idx * group_size : (batch_idx + 1) * group_size] -= self._diversity_penalty * token_frequency
return scores
class ForcedBOSTokenLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces the specified token as the first generated token.
Args:
bos_token_id (`int`):
The id of the token to force as the first generated token.
"""
def __init__(self, bos_token_id: int):
self.bos_token_id = bos_token_id
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
cur_len = input_ids.shape[-1]
if cur_len == 1:
num_tokens = scores.shape[1]
scores[:, [i for i in range(num_tokens) if i != self.bos_token_id]] = -float("inf")
scores[:, self.bos_token_id] = 0
return scores
class ForcedEOSTokenLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces the specified token as the last generated token when `max_length` is reached.
Args:
max_length (`int`):
The maximum length of the sequence to be generated.
eos_token_id (`Union[int, List[int]]`):
The id of the token to force as the last generated token when `max_length` is reached. Optionally, use a
list to set multiple *end-of-sequence* tokens.
"""
def __init__(self, max_length: int, eos_token_id: Union[int, List[int]]):
self.max_length = max_length
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
self.eos_token_id = eos_token_id
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
cur_len = input_ids.shape[-1]
if cur_len == self.max_length - 1:
num_tokens = scores.shape[1]
scores[:, [i for i in range(num_tokens) if i not in self.eos_token_id]] = -float("inf")
for i in self.eos_token_id:
scores[:, i] = 0
return scores
class InfNanRemoveLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that removes all `nan` and `inf` values to avoid the generation method to fail. Note that using
the logits processor should only be used if necessary since it can slow down the generation method.
"""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# set all nan values to 0.0
scores[scores != scores] = 0.0
# set all inf values to max possible value
scores[scores == float("inf")] = torch.finfo(scores.dtype).max
return scores
class ExponentialDecayLengthPenalty(LogitsProcessor):
r"""
[`LogitsProcessor`] that exponentially increases the score of the eos_token_id after regulation_start has been
reached.
Args:
exponential_decay_length_penalty (`tuple(int, float)`):
This tuple shall consist of: `(start_index, decay_factor)` where `start_index` indicates where penalty
starts and `decay_factor` represents the factor of exponential decay
eos_token_id (`Union[int, List[int]]`):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
input_ids_seq_length (`int`):
The length of the input sequence.
"""
def __init__(
self,
exponential_decay_length_penalty: Tuple[int, float],
eos_token_id: Union[int, List[int]],
input_ids_seq_length: int,
):
self.regulation_start = exponential_decay_length_penalty[0] + input_ids_seq_length
self.regulation_factor = exponential_decay_length_penalty[1]
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
self.eos_token_id = eos_token_id
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
cur_len = input_ids.shape[-1]
if cur_len > self.regulation_start:
for i in self.eos_token_id:
scores[:, i] = scores[:, i] * pow(self.regulation_factor, cur_len - self.regulation_start)
return scores
class LogitNormalization(LogitsProcessor, LogitsWarper):
r"""
[`LogitsWarper`] and [`LogitsProcessor`] for normalizing the scores using log-softmax. It's important to normalize
the scores during beam search, after applying the logits processors or warpers, since the search algorithm used in
this library doesn't do it (it only does it before, but they may need re-normalization) but it still supposes that
the scores are normalized when comparing the hypotheses.
"""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
scores = scores.log_softmax(dim=-1)
return scores
class SuppressTokensAtBeginLogitsProcessor(LogitsProcessor):
r"""
[`SuppressTokensAtBeginLogitsProcessor`] supresses a list of tokens as soon as the `generate` function starts
generating using `begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` at not
sampled at the begining of the generation.
"""
def __init__(self, begin_suppress_tokens, begin_index):
self.begin_suppress_tokens = list(begin_suppress_tokens)
self.begin_index = begin_index
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
if input_ids.shape[1] == self.begin_index:
scores[:, self.begin_suppress_tokens] = -float("inf")
return scores
class SuppressTokensLogitsProcessor(LogitsProcessor):
r"""This processor can be used to suppress a list of tokens. The processor will set their log probs to `-inf` so that they
are not sampled."""
def __init__(self, suppress_tokens):
self.suppress_tokens = list(suppress_tokens)
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
scores[:, self.suppress_tokens] = -float("inf")
return scores
class ForceTokensLogitsProcessor(LogitsProcessor):
r"""This processor takes a list of pairs of integers which indicates a mapping from generation indices to token
indices that will be forced before sampling. The processor will set their log probs to `inf` so that they are
sampled at their corresponding index."""
def __init__(self, force_token_map: List[List[int]]):
self.force_token_map = dict(force_token_map)
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
generation_idx = input_ids.shape[-1]
current_token = self.force_token_map.get(generation_idx, None)
if current_token is not None:
scores[:, :] = -float("inf")
scores[:, current_token] = 0
return scores
class WhisperTimeStampLogitsProcessor(LogitsProcessor):
r"""
Whisper specific Processor. This processor can be used to force a list of tokens. The processor will set their log
probs to `inf` so that they are sampled at their corresponding index.
Args:
generate_config (`GenerateConfig`):
The generate config used to generate the output. The following parameters are required:
eos_token_id (`int`, *optional*, defaults to 50257):
The id of the *end-of-sequence* token.
no_timestamps_token_id (`int`, *optional*, defaults to 50363):
The id of the `"<|notimestamps|>"` token.
max_initial_timestamp_index (`int`, *optional*, defaults to 1):
Used to set the maximum value of the initial timestamp. This is used to prevent the model from
predicting timestamps that are too far in the future.
"""
def __init__(self, generate_config): # support for the kwargs
self.eos_token_id = generate_config.eos_token_id
self.no_timestamps_token_id = generate_config.no_timestamps_token_id
self.timestamp_begin = generate_config.no_timestamps_token_id + 1
self.begin_index = len(generate_config.forced_decoder_ids) + 2
if generate_config.forced_decoder_ids[-1][1] == self.no_timestamps_token_id:
self.begin_index -= 1
self.max_initial_timestamp_index = generate_config.max_initial_timestamp_index
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# suppress <|notimestamps|> which is handled by without_timestamps
scores[:, self.no_timestamps_token_id] = -float("inf")
if input_ids.shape[1] == self.begin_index - 1:
scores[:, :] = -float("inf")
scores[:, self.timestamp_begin] = 0
return scores
# timestamps have to appear in pairs, except directly before eos_token; mask logits accordingly
for k in range(input_ids.shape[0]):
seq = list(input_ids[k, self.begin_index :].tolist())
last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.timestamp_begin
penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.timestamp_begin
if last_was_timestamp:
if penultimate_was_timestamp: # has to be non-timestamp
scores[k, self.timestamp_begin :] = -float("inf")
else: # cannot be normal text tokens
scores[k, : self.eos_token_id] = -float("inf")
# apply the `max_initial_timestamp` option
if input_ids.shape[1] == self.begin_index and self.max_initial_timestamp_index is not None:
last_allowed = self.timestamp_begin + self.max_initial_timestamp_index
scores[:, last_allowed + 1 :] = -float("inf")
# if sum of probability over timestamps is above any other token, sample timestamp
logprobs = torch.nn.functional.log_softmax(scores.float(), dim=-1)
for k in range(input_ids.shape[0]):
timestamp_logprob = logprobs[k, self.timestamp_begin :].logsumexp(dim=-1)
max_text_token_logprob = logprobs[k, : self.timestamp_begin].max()
if timestamp_logprob > max_text_token_logprob:
scores[k, : self.timestamp_begin] = -float("inf")
return scores
class ClassifierFreeGuidanceLogitsProcessor(LogitsProcessor):
r"""Logits processor for classifier free guidance (CFG). The scores are split over the batch dimension,
where the first half correspond to the conditional logits (predicted from the input prompt) and the second half
correspond to the unconditional logits (predicted from an empty or 'null' prompt). The processor computes a
weighted average across the conditional and unconditional logits, parameterised by the `guidance_scale`.
Args:
guidance_scale (float):
The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`.
Higher guidance scale encourages the model to generate samples that are more closely linked to the input
prompt, usually at the expense of poorer quality.
"""
def __init__(self, guidance_scale):
if guidance_scale > 1:
self.guidance_scale = guidance_scale
else:
raise ValueError(
"Require guidance scale >1 to use the classifier free guidance processor, got guidance scale "
f"{guidance_scale}."
)
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# simple check to make sure we have compatible batch sizes between our
# logits scores (cond + uncond) and input ids (cond only)
if scores.shape[0] != 2 * input_ids.shape[0]:
raise ValueError(
f"Logits should have twice the batch size of the input ids, the first half of batches corresponding to "
f"the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got "
f"batch size {scores.shape[0]} for the logits and {input_ids.shape[0]} for the input ids."
)
unguided_bsz = scores.shape[0] // 2
cond_logits, uncond_logits = scores.split(unguided_bsz, dim=0)
scores = uncond_logits + (cond_logits - uncond_logits) * self.guidance_scale
return scores
class AlternatingCodebooksLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing alternated generation between the two codebooks of [`Bark`]'s fine submodel.
Args:
input_start_len (`int`):
The length of the initial input sequence.
semantic_vocab_size (`int`):
Vocabulary size of the semantic part, i.e number of tokens associated to the semantic vocabulary.
codebook_size (`int`):
Number of tokens associated to the codebook.
"""
def __init__(self, input_start_len: int, semantic_vocab_size: int, codebook_size: int):
if not isinstance(input_start_len, int) or input_start_len < 0:
raise ValueError(f"`input_starting_length` has to be a non-negative integer, but is {input_start_len}")
self.input_start_len = input_start_len
self.semantic_vocab_size = semantic_vocab_size
self.codebook_size = codebook_size
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
curr_len = input_ids.shape[-1]
# even -> first codebook, odd -> second codebook
is_first_codebook = ((curr_len - self.input_start_len) % 2) == 0
if is_first_codebook:
scores[:, : self.semantic_vocab_size] = -float("inf")
scores[:, self.semantic_vocab_size + self.codebook_size :] = -float("inf")
else:
scores[:, : self.semantic_vocab_size + self.codebook_size] = -float("inf")
return scores
| 56,335 | 46.460826 | 131 | py |
transformers | transformers-main/src/transformers/generation/tf_logits_process.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import List, Tuple
import numpy as np
import tensorflow as tf
from ..tf_utils import stable_softmax
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
logger = get_logger(__name__)
TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`tf.Tensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search.
cur_len (`int`):
The current length of valid input sequence tokens. In the TF implementation, the input_ids' sequence length
is the maximum length generate can produce, and we need to know which of its tokens are valid.
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`tf.Tensor` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
"""
class TFLogitsProcessor:
"""Abstract base class for all logit processors that can be applied during generation."""
@add_start_docstrings(TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
"""TF method for processing logits."""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
class TFLogitsWarper:
"""Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."""
@add_start_docstrings(TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
"""TF method for warping logits."""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
class TFLogitsProcessorList(list):
"""
This class can be used to create a list of [`TFLogitsProcessor`] to subsequently process a `scores` input tensor.
This class inherits from list and adds a specific *__call__* method to apply each [`TFLogitsProcessor`] to the
inputs.
"""
@add_start_docstrings(TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int, **kwargs) -> tf.Tensor:
for processor in self:
function_args = inspect.signature(processor.__call__).parameters
if len(function_args) > 3:
if not all(arg in kwargs for arg in list(function_args.keys())[2:]):
raise ValueError(
f"Make sure that all the required parameters: {list(function_args.keys())} for "
f"{processor.__class__} are passed to the logits processor."
)
scores = processor(input_ids, scores, cur_len, **kwargs)
else:
scores = processor(input_ids, scores, cur_len)
return scores
class TFTemperatureLogitsWarper(TFLogitsWarper):
r"""
[`TFLogitsWarper`] for temperature (exponential scaling output probability distribution).
Args:
temperature (`float`):
The value used to module the logits distribution.
"""
def __init__(self, temperature: float):
if not isinstance(temperature, float) or not (temperature > 0):
raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}")
self.temperature = temperature
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
scores = scores / self.temperature
return scores
class TFTopKLogitsWarper(TFLogitsWarper):
r"""
[`TFLogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
Args:
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(self, top_k: int, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
if not isinstance(top_k, int) or top_k <= 0:
raise ValueError(f"`top_k` has to be a strictly positive integer, but is {top_k}")
self.top_k = max(top_k, min_tokens_to_keep)
self.filter_value = filter_value
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
top_k = min(self.top_k, scores.shape[-1]) # Safety check
# Boolean mask containing all tokens with a probability less than the last token of the top-k
indices_to_remove = scores < tf.math.top_k(scores, k=top_k)[0][..., -1:]
next_scores = tf.where(indices_to_remove, self.filter_value, scores)
return next_scores
class TFTopPLogitsWarper(TFLogitsWarper):
"""
[`TFLogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to <= prob_cut_off.
Args:
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
filter_value (`float`, *optional*, defaults to `-float("Inf")`):
All filtered values will be set to this float value.
min_tokens_to_keep (`int`, *optional*, defaults to 1):
Minimum number of tokens that cannot be filtered.
"""
def __init__(self, top_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
if not isinstance(top_p, float) or (top_p < 0 or top_p > 1.0):
raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}")
if not isinstance(min_tokens_to_keep, int) or (min_tokens_to_keep < 1):
raise ValueError(f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}")
self.top_p = top_p
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
topk_scores, topk_indices = tf.math.top_k(scores, scores.shape[-1])
mask_scores = tf.fill(scores.shape, self.filter_value)
cumulative_probs = tf.math.cumsum(stable_softmax(topk_scores, axis=-1), axis=-1)
score_mask = cumulative_probs < self.top_p
# Also include the token that is higher than top_p (the first false = shift and insert a True on the left)
score_mask = tf.concat((tf.ones([score_mask.shape[0], 1], dtype=tf.bool), score_mask[:, :-1]), axis=-1)
# Ensure min tokens to keep
score_mask = tf.concat(
(
tf.ones([score_mask.shape[0], self.min_tokens_to_keep], dtype=tf.bool),
score_mask[:, self.min_tokens_to_keep :],
),
axis=-1,
)
# Mask the values that do not fit the criteria
topk_next_scores = tf.where(score_mask, topk_scores, mask_scores)
# Undo the topk sorting: converts the 2D matrix of per-row original indices of shape (batch_size, vocab_size)
# to a 3D tensor of shape (batch_size, vocab_size, 2) containing the original score coordinate, from which we
# can scatter (i.e. `scatter_indices[row, col, :]` is a tensor containing `[row, topk_indices[row, col]]`)
scatter_rows = tf.tile(tf.expand_dims(tf.range(topk_indices.shape[0]), axis=-1), [1, topk_indices.shape[-1]])
scatter_indices = tf.stack((scatter_rows, topk_indices), axis=-1)
next_scores = tf.scatter_nd(scatter_indices, topk_next_scores, shape=topk_next_scores.shape)
return next_scores
class TFMinLengthLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] enforcing a min-length by setting EOS probability to 0.
Args:
min_length (`int`):
The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
eos_token_id (`int`):
The id of the *end-of-sequence* token.
"""
def __init__(self, min_length: int, eos_token_id: int):
if not isinstance(min_length, int) or min_length < 0:
raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}")
if not isinstance(eos_token_id, int) or eos_token_id < 0:
raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}")
self.min_length = min_length
self.eos_token_id = eos_token_id
def _apply_eos_token_mask(self, scores: tf.Tensor) -> tf.Tensor:
eos_token_id_mask = tf.range(scores.shape[-1]) == self.eos_token_id
scores = tf.where(eos_token_id_mask, float("-inf"), scores)
return scores
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
# applies eos token masking if the first argument is true
scores = tf.cond(
tf.less(cur_len, self.min_length),
lambda: self._apply_eos_token_mask(scores),
lambda: tf.identity(scores),
)
return scores
class TFRepetitionPenaltyLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] enforcing an exponential penalty on repeated sequences.
Args:
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
"""
def __init__(self, penalty: float):
if not isinstance(penalty, float) or not (penalty > 0):
raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
self.penalty = penalty
def _create_score_penalties(self, input_ids: tf.Tensor, logits: tf.Tensor) -> tf.Tensor:
# We want to populate the penalties in the positions of `input_ids`. Since XLA can't handle shapes unknown
# before runtime, `tf.unique` can't be used. Therefore, we may have redundant updates, when a given row has
# the same token multiple times.
# Gathers the penalties to apply
logit_penalties = tf.gather(logits, input_ids, axis=1, batch_dims=1)
logit_penalties = tf.where(logit_penalties > 0, 1 / self.penalty, logit_penalties)
logit_penalties = tf.where(logit_penalties < 0, self.penalty, logit_penalties)
# Scatters the penalties
token_penalties = tf.ones(logits.shape)
batch_size = input_ids.shape[0]
seq_len = tf.shape(input_ids)[1] # the sequence length has dynamic size, hence the dynamic shape
indexable_prev_input_ids = tf.concat(
(
tf.expand_dims(tf.repeat(tf.range(batch_size), seq_len), axis=-1),
tf.expand_dims(tf.reshape(input_ids, [-1]), axis=-1),
),
axis=1,
)
token_penalties = tf.tensor_scatter_nd_update(
token_penalties, indices=indexable_prev_input_ids, updates=tf.reshape(logit_penalties, [-1])
)
return token_penalties
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
score_penalties = self._create_score_penalties(input_ids[:, :cur_len], scores)
scores = tf.math.multiply(scores, score_penalties)
return scores
class TFNoBadWordsLogitsProcessor(TFLogitsProcessor):
"""
[`TFLogitsProcessor`] that enforces that specified sequences will never be sampled.
Args:
bad_words_ids (`List[List[int]]`):
List of list of token ids that are not allowed to be generated. In order to get the tokens of the words
that should not appear in the generated text, make sure to set `add_prefix_space=True` when initializing
the tokenizer, and use `tokenizer(bad_words, add_special_tokens=False).input_ids`. The `add_prefix_space`
argument is only supported for some slow tokenizers, as fast tokenizers' prefixing behaviours come from
`pre tokenizers`. Read more [here](https://huggingface.co/docs/tokenizers/api/pre-tokenizers).
eos_token_id (`int`):
The id of the *end-of-sequence* token.
"""
def __init__(self, bad_words_ids: List[List[int]], eos_token_id: int):
if not isinstance(bad_words_ids, List) or len(bad_words_ids) == 0:
raise ValueError(f"`bad_words_ids` has to be a non-empty list, but is {bad_words_ids}.")
if any(not isinstance(bad_word_ids, list) for bad_word_ids in bad_words_ids):
raise ValueError(f"`bad_words_ids` has to be a list of lists, but is {bad_words_ids}.")
if any(
any((not isinstance(token_id, (int, np.integer)) or token_id < 0) for token_id in bad_word_ids)
for bad_word_ids in bad_words_ids
):
raise ValueError(
f"Each list in `bad_words_ids` has to be a list of positive integers, but is {bad_words_ids}."
)
# stores the information about bad words in three tensors:
# 1. a rectangular tensor with the forbidden sequences (padded with `-1`), for full data comparisons
self.bad_word_seqs_ids = tf.ragged.constant(bad_words_ids).to_tensor(default_value=-1)
# 2. a tensor with the unpadded length of each forbidden sequence, for quick length comparisons
bad_word_seqs_len = [len(bad_words) for bad_words in bad_words_ids]
if any(word_len == 0 for word_len in bad_word_seqs_len):
raise ValueError(f"Banned words token sequences {bad_words_ids} cannot have an empty list")
self.bad_word_seqs_len = tf.convert_to_tensor(bad_word_seqs_len, dtype=tf.int32)
# 3. a tensor containing the last token for each sequence, for easy access to the tokens that may be banned
self.seq_forbidden_tokens = tf.convert_to_tensor([bad_words[-1] for bad_words in bad_words_ids])
def _calc_row_banned_bad_tokens(self, row_input_ids: tf.Tensor) -> tf.Tensor:
def _tokens_match(bad_word_seq_number):
def _len_one():
# If the bad sequence only has one token, always mask it
return tf.cond(
tf.math.equal(self.bad_word_seqs_len[bad_word_seq_number], 1),
lambda: tf.ones((), dtype=tf.bool),
_len_greater_than_cur_len,
)
def _len_greater_than_cur_len():
# Otherwise, if the bad sequence is longer than the current length they can't ever match
return tf.cond(
tf.math.greater(self.bad_word_seqs_len[bad_word_seq_number], tf.shape(row_input_ids)[0]),
lambda: tf.zeros((), dtype=tf.bool),
_match_found,
)
def _match_found():
# Finaly, runs the actual comparison. Can only be called if the previous comparisons do not yield
# an answer (otherwise we get indexing exceptions)
compare_len = self.bad_word_seqs_len[bad_word_seq_number] - 1
return tf.cond(
tf.math.reduce_all(
tf.math.equal(
row_input_ids[-compare_len:], self.bad_word_seqs_ids[bad_word_seq_number, :compare_len]
)
),
lambda: tf.ones((), dtype=tf.bool),
lambda: tf.zeros((), dtype=tf.bool),
)
match = _len_one()
return match
# Compares the current row against all bad word sequences, obtaining a mask with the matches.
match_mask = tf.map_fn(_tokens_match, tf.range(self.bad_word_seqs_ids.shape[0]), fn_output_signature=tf.bool)
row_banned_tokens = self.seq_forbidden_tokens[match_mask]
return row_banned_tokens
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
# We want to mask some banned tokens, at a score level. Since the banned tokens depend on the previous
# `input_ids`, they may have a different length for each row, and they may even be empty for some rows.
# To remain simple and XLA-compatible, we work on a per-row fashion.
# TODO (Joao): this function might trigger XLA retracing as `cur_len` increases. Fix it if it becomes
# a frequent choke point. (make `cur_len` a tensor?)
def _get_row_updated_score(row_inputs: Tuple[tf.Tensor]) -> tf.Tensor:
row_input_ids, row_score = row_inputs
banned_tokens = self._calc_row_banned_bad_tokens(row_input_ids[:cur_len])
banned_tokens_mask = tf.scatter_nd(
indices=tf.expand_dims(banned_tokens, axis=-1),
updates=tf.ones_like(banned_tokens, dtype=tf.bool),
shape=row_score.shape,
)
row_score = tf.where(banned_tokens_mask, -float("inf"), row_score)
return row_score
scores = tf.map_fn(_get_row_updated_score, (input_ids, scores), fn_output_signature=tf.float32)
return scores
class TFNoRepeatNGramLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] that enforces no repetition of n-grams. See
[Fairseq](https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345).
Args:
ngram_size (`int`):
All ngrams of size `ngram_size` can only occur once.
"""
def __init__(self, ngram_size: int):
if not isinstance(ngram_size, int) or ngram_size <= 0:
raise ValueError(f"`ngram_size` has to be a strictly positive integer, but is {ngram_size}")
self.ngram_size = ngram_size
def calc_banned_ngram_tokens(self, input_ids, num_hypos, cur_len):
# Copied from fairseq for no_repeat_ngram in beam_search
if cur_len + 1 < self.ngram_size:
# return no banned tokens if we haven't generated ngram_size tokens yet
return [[] for _ in range(num_hypos)]
generated_ngrams = [{} for _ in range(num_hypos)]
prev_input_ids = input_ids[:, :cur_len]
for idx in range(num_hypos):
gen_tokens = prev_input_ids[idx].numpy().tolist()
generated_ngram = generated_ngrams[idx]
for ngram in zip(*[gen_tokens[i:] for i in range(self.ngram_size)]):
prev_ngram_tuple = tuple(ngram[:-1])
generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
def _get_generated_ngrams(hypo_idx):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = cur_len + 1 - self.ngram_size
ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].numpy().tolist())
return generated_ngrams[hypo_idx].get(ngram_idx, [])
banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
return banned_tokens
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
# TODO (joao): enable XLA on this logits processor. See discussion and attempts in
# https://github.com/huggingface/transformers/pull/16974
if not tf.executing_eagerly():
raise NotImplementedError("TFNoRepeatNGramLogitsProcessor is only implemented for eager execution.")
batch_size, vocab_size = scores.shape
banned_tokens = self.calc_banned_ngram_tokens(input_ids, batch_size, cur_len)
# create banned_tokens boolean mask
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
scores = tf.where(tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf"), scores)
return scores
class TFForcedBOSTokenLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] that enforces the specified token as the first generated token.
Args:
bos_token_id (`int`):
The id of the token to force as the first generated token.
"""
def __init__(self, bos_token_id: int):
if bos_token_id < 0:
raise ValueError(f"The forced bos token id must be a non-negative integer, got {bos_token_id}")
self.bos_token_id = bos_token_id
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
if cur_len == 1:
batch_size, num_tokens = scores.shape
# sets the score to 0 in the bos_token_id column
scores = tf.zeros((batch_size, 1))
# sets the score to -inf everywhere else
if self.bos_token_id > 0:
scores = tf.concat((tf.broadcast_to(-float("inf"), (batch_size, self.bos_token_id)), scores), axis=-1)
if self.bos_token_id < (num_tokens - 1):
scores = tf.concat(
(scores, tf.broadcast_to(-float("inf"), (batch_size, (num_tokens - 1) - self.bos_token_id))),
axis=-1,
)
return scores
class TFForcedEOSTokenLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] that enforces the specified token as the last generated token when `max_length` is reached.
Args:
max_length (`int`):
The maximum length of the sequence to be generated.
eos_token_id (`int`):
The id of the token to force as the last generated token when `max_length` is reached.
"""
def __init__(self, max_length: int, eos_token_id: int):
self.max_length = max_length
if eos_token_id < 0:
raise ValueError(f"The forced eos token id must be a non-negative integer, got {eos_token_id}")
self.eos_token_id = eos_token_id
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
if cur_len == self.max_length - 1:
batch_size, num_tokens = scores.shape
# sets the score to 0 in the eos_token_id column
scores = tf.zeros((batch_size, 1))
# sets the score to -inf everywhere else
if self.eos_token_id > 0:
scores = tf.concat((tf.broadcast_to(-float("inf"), (batch_size, self.eos_token_id)), scores), axis=-1)
if self.eos_token_id < (num_tokens - 1):
scores = tf.concat(
(scores, tf.broadcast_to(-float("inf"), (batch_size, (num_tokens - 1) - self.eos_token_id))),
axis=-1,
)
return scores
class TFSuppressTokensAtBeginLogitsProcessor(TFLogitsProcessor):
r"""
[`TFSuppressTokensAtBeginLogitsProcessor`] suppresses a list of tokens as soon as the `generate` function starts
generating using `begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` at not
sampled at the begining of the generation.
"""
def __init__(self, begin_suppress_tokens, begin_index):
self.begin_suppress_tokens = list(begin_suppress_tokens)
self.begin_index = begin_index
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
scores = tf.cond(
tf.equal(cur_len, self.begin_index),
lambda: tf.tensor_scatter_nd_update(
scores,
indices=[[i, token] for i in range(scores.shape[0]) for token in self.begin_suppress_tokens],
updates=[-float("inf") for _ in range(scores.shape[0] * len(self.begin_suppress_tokens))],
),
lambda: scores,
)
return scores
class TFSuppressTokensLogitsProcessor(TFLogitsProcessor):
r"""This processor can be used to suppress a list of tokens. The processor will set their log probs to `-inf` so that they
are not sampled."""
def __init__(self, suppress_tokens):
self.suppress_tokens = list(suppress_tokens)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
scores = tf.tensor_scatter_nd_update(
scores,
indices=[[i, token] for i in range(scores.shape[0]) for token in self.suppress_tokens],
updates=[-float("inf") for _ in range(scores.shape[0] * len(self.suppress_tokens))],
)
return scores
class TFForceTokensLogitsProcessor(TFLogitsProcessor):
r"""This processor takes a list of pairs of integers which indicates a mapping from generation indices to token
indices that will be forced before sampling. The processor will set their log probs to `0` and all other tokens to
`-inf` so that they are sampled at their corresponding index."""
def __init__(self, force_token_map: List[List[int]]):
force_token_map = dict(force_token_map)
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have an negative value.
force_token_array = np.ones((max(force_token_map.keys()) + 1), dtype=np.int32) * -1
for index, token in force_token_map.items():
if token is not None:
force_token_array[index] = token
self.force_token_array = tf.convert_to_tensor(force_token_array, dtype=tf.int32)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
def _force_token(generation_idx):
batch_size = scores.shape[0]
current_token = self.force_token_array[generation_idx]
new_scores = tf.ones_like(scores, dtype=scores.dtype) * -float("inf")
indices = tf.stack((tf.range(batch_size), tf.tile([current_token], [batch_size])), axis=1)
updates = tf.zeros((batch_size,), dtype=scores.dtype)
new_scores = tf.tensor_scatter_nd_update(new_scores, indices, updates)
return new_scores
scores = tf.cond(
tf.greater_equal(cur_len, tf.shape(self.force_token_array)[0]),
# If the current length is geq than the length of force_token_array, the processor does nothing.
lambda: tf.identity(scores),
# Otherwise, it may force a certain token.
lambda: tf.cond(
tf.greater_equal(self.force_token_array[cur_len], 0),
# Only valid (positive) tokens are forced
lambda: _force_token(cur_len),
# Otherwise, the processor does nothing.
lambda: scores,
),
)
return scores
| 28,136 | 46.528716 | 131 | py |
transformers | transformers-main/src/transformers/generation/beam_constraints.py | from abc import ABC, abstractmethod
from typing import List, Optional
class Constraint(ABC):
r"""Abstract base class for all constraints that can be applied during generation.
It must define how the constraint can be satisfied.
All classes that inherit Constraint must follow the requirement that
```py
completed = False
while not completed:
_, completed = constraint.update(constraint.advance())
```
will always terminate (halt).
"""
def __init__(self):
# test for the above condition
self.test()
def test(self):
"""
Tests whether this constraint has been properly defined.
"""
counter = 0
completed = False
while not completed:
if counter == 1:
self.reset()
advance = self.advance()
if not self.does_advance(advance):
raise Exception(
"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."
)
stepped, completed, reset = self.update(advance)
counter += 1
if counter > 10000:
raise Exception("update() does not fulfill the constraint.")
if self.remaining() != 0:
raise Exception("Custom Constraint is not defined correctly.")
@abstractmethod
def advance(self):
"""
When called, returns the token that would take this constraint one step closer to being fulfilled.
Return:
token_ids(`torch.tensor`): Must be a tensor of a list of indexable tokens, not some integer.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
@abstractmethod
def does_advance(self, token_id: int):
"""
Reads in a token and returns whether it creates progress.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
@abstractmethod
def update(self, token_id: int):
"""
Reads in a token and returns booleans that indicate the progress made by it. This function will update the
state of this object unlikes `does_advance(self, token_id: int)`.
This isn't to test whether a certain token will advance the progress; it's to update its state as if it has
been generated. This becomes important if token_id != desired token (refer to else statement in
PhrasalConstraint)
Args:
token_id(`int`):
The id of a newly generated token in the beam search.
Return:
stepped(`bool`):
Whether this constraint has become one step closer to being fulfuilled.
completed(`bool`):
Whether this constraint has been completely fulfilled by this token being generated.
reset (`bool`):
Whether this constraint has reset its progress by this token being generated.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
@abstractmethod
def reset(self):
"""
Resets the state of this constraint to its initialization. We would call this in cases where the fulfillment of
a constraint is abrupted by an unwanted token.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
@abstractmethod
def remaining(self):
"""
Returns the number of remaining steps of `advance()` in order to complete this constraint.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
@abstractmethod
def copy(self, stateful=False):
"""
Creates a new instance of this constraint.
Args:
stateful(`bool`): Whether to not only copy the constraint for new instance, but also its state.
Return:
constraint(`Constraint`): The same constraint as the one being called from.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
class PhrasalConstraint(Constraint):
r"""
[`Constraint`] enforcing that an ordered sequence of tokens is included in the output.
Args:
token_ids (`List[int]`):
The id of the token that must be generated by the output.
"""
def __init__(self, token_ids: List[int]):
super(Constraint, self).__init__()
if not isinstance(token_ids, list) or len(token_ids) == 0:
raise ValueError(f"`token_ids` has to be a non-empty list, but is {token_ids}.")
if any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids):
raise ValueError(f"Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.")
self.token_ids = token_ids
self.seqlen = len(self.token_ids)
self.fulfilled_idx = -1 # the index of the currently fulfilled step
self.completed = False
def advance(self):
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def does_advance(self, token_id: int):
if not isinstance(token_id, int):
raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(token_id)}")
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def update(self, token_id: int):
if not isinstance(token_id, int):
raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(token_id)}")
stepped = False
completed = False
reset = False
if self.does_advance(token_id):
self.fulfilled_idx += 1
stepped = True
if self.fulfilled_idx == (self.seqlen - 1):
completed = True
self.completed = completed
else:
# failed to make progress.
reset = True
self.reset()
return stepped, completed, reset
def reset(self):
self.completed = False
self.fulfilled_idx = 0
def remaining(self):
return self.seqlen - (self.fulfilled_idx + 1)
def copy(self, stateful=False):
new_constraint = PhrasalConstraint(self.token_ids)
if stateful:
new_constraint.seq_len = self.seqlen
new_constraint.fulfilled_idx = self.fulfilled_idx
new_constraint.completed = self.completed
return new_constraint
class DisjunctiveTrie:
def __init__(self, nested_token_ids: List[List[int]], no_subsets=True):
r"""
A helper class that builds a trie with the words represented in `nested_token_ids`.
"""
self.max_height = max([len(one) for one in nested_token_ids])
root = {}
for token_ids in nested_token_ids:
level = root
for tidx, token_id in enumerate(token_ids):
if token_id not in level:
level[token_id] = {}
level = level[token_id]
if no_subsets and self.has_subsets(root, nested_token_ids):
raise ValueError(
"Each list in `nested_token_ids` can't be a complete subset of another list, but is"
f" {nested_token_ids}."
)
self.trie = root
def next_tokens(self, current_seq):
"""
The next possible tokens that will progress the trie, given the current sequence of tokens in `current_seq`.
"""
start = self.trie
for current_token in current_seq:
start = start[current_token]
next_tokens = list(start.keys())
return next_tokens
def reached_leaf(self, current_seq):
next_tokens = self.next_tokens(current_seq)
return len(next_tokens) == 0
def count_leaves(self, root):
next_nodes = list(root.values())
if len(next_nodes) == 0:
return 1
else:
return sum([self.count_leaves(nn) for nn in next_nodes])
def has_subsets(self, trie, nested_token_ids):
"""
Returns whether # of leaves == # of words. Otherwise some word is a subset of another.
"""
leaf_count = self.count_leaves(trie)
return len(nested_token_ids) != leaf_count
class DisjunctiveConstraint(Constraint):
r"""
A special [`Constraint`] that is fulfilled by fulfilling just one of several constraints.
Args:
nested_token_ids (`List[List[int]]`): a list of words, where each word is a list of ids. This constraint
is fulfilled by generating just one from the list of words.
"""
def __init__(self, nested_token_ids: List[List[int]]):
super(Constraint, self).__init__()
if not isinstance(nested_token_ids, list) or len(nested_token_ids) == 0:
raise ValueError(f"`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.")
if any(not isinstance(token_ids, list) for token_ids in nested_token_ids):
raise ValueError(f"`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.")
if any(
any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
for token_ids in nested_token_ids
):
raise ValueError(
f"Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."
)
self.trie = DisjunctiveTrie(nested_token_ids)
self.token_ids = nested_token_ids
self.seqlen = self.trie.max_height
self.current_seq = []
self.completed = False
def advance(self):
token_list = self.trie.next_tokens(self.current_seq)
if len(token_list) == 0:
return None
else:
return token_list
def does_advance(self, token_id: int):
if not isinstance(token_id, int):
raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(token_id)}")
next_tokens = self.trie.next_tokens(self.current_seq)
return token_id in next_tokens
def update(self, token_id: int):
if not isinstance(token_id, int):
raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(token_id)}")
stepped = False
completed = False
reset = False
if self.does_advance(token_id):
self.current_seq.append(token_id)
stepped = True
else:
reset = True
self.reset()
completed = self.trie.reached_leaf(self.current_seq)
self.completed = completed
return stepped, completed, reset
def reset(self):
self.completed = False
self.current_seq = []
def remaining(self):
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq)
def copy(self, stateful=False):
new_constraint = DisjunctiveConstraint(self.token_ids)
if stateful:
new_constraint.seq_len = self.seqlen
new_constraint.current_seq = self.current_seq
new_constraint.completed = self.completed
return new_constraint
class ConstraintListState:
r"""
A class for beam scorers to track its progress through a list of constraints.
Args:
constraints (`List[Constraint]`):
A list of [`Constraint`] objects that must be fulfilled by the beam scorer.
"""
def __init__(self, constraints: List[Constraint]):
self.constraints = constraints
# max # of steps required to fulfill a given constraint
self.max_seqlen = max([c.seqlen for c in constraints])
self.n_constraints = len(constraints)
self.completed = False
self.init_state()
def init_state(self):
self.complete_constraints = []
self.inprogress_constraint = None
self.pending_constraints = [constraint.copy(stateful=False) for constraint in self.constraints]
def get_bank(self):
add = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints) * self.max_seqlen) + add
def advance(self):
"""The list of tokens to generate such that we can make progress.
By "list" we don't mean the list of token that will fully fulfill a constraint.
Given constraints `c_i = {t_ij | j == # of tokens}`, If we're not in the middle of progressing through a
specific constraint `c_i`, we return:
`[t_k1 for k in indices of unfulfilled constraints]`
If we are in the middle of a constraint, then we return:
`[t_ij]`, where `i` is the index of the inprogress constraint, `j` is the next step for the constraint.
Though we don't care which constraint is fulfilled first, if we are in the progress of fulfilling a constraint,
that's the only one we'll return.
"""
token_list = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
advance = constraint.advance()
if isinstance(advance, int):
token_list.append(advance)
elif isinstance(advance, list):
token_list.extend(advance)
else:
advance = self.inprogress_constraint.advance()
if isinstance(advance, int):
token_list.append(advance)
elif isinstance(advance, list):
token_list.extend(advance)
if len(token_list) == 0:
return None
else:
return token_list
def reset(self, token_ids: Optional[List[int]]):
"""
token_ids: the tokens generated thus far to reset the state of the progress through constraints.
"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
complete, stepped = self.add(token)
# the entire list of constraints are fulfilled
if self.completed:
break
def add(self, token_id: int):
if not isinstance(token_id, int):
raise ValueError(f"`token_id` should be an `int`, but is `{token_id}`.")
complete, stepped = False, False
if self.completed:
complete = True
stepped = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
stepped, complete, reset = self.inprogress_constraint.update(token_id)
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=False))
self.inprogress_constraint = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint)
self.inprogress_constraint = None
if len(self.pending_constraints) == 0:
# we're done!
self.completed = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints):
if pending_constraint.does_advance(token_id):
stepped, complete, reset = pending_constraint.update(token_id)
if not stepped:
raise Exception(
"`constraint.update(token_id)` is not yielding incremental progress, "
"even though `constraint.does_advance(token_id)` is true."
)
if complete:
self.complete_constraints.append(pending_constraint)
self.inprogress_constraint = None
if not complete and stepped:
self.inprogress_constraint = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
self.pending_constraints = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
self.completed = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def copy(self, stateful=True):
new_state = ConstraintListState(self.constraints) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
new_state.complete_constraints = [
constraint.copy(stateful=True) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
new_state.inprogress_constraint = self.inprogress_constraint.copy(stateful=True)
new_state.pending_constraints = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 19,089 | 35.641075 | 121 | py |
transformers | transformers-main/.circleci/create_circleci_config.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import copy
import glob
import os
import random
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
import yaml
COMMON_ENV_VARIABLES = {
"OMP_NUM_THREADS": 1,
"TRANSFORMERS_IS_CI": True,
"PYTEST_TIMEOUT": 120,
"RUN_PIPELINE_TESTS": False,
"RUN_PT_TF_CROSS_TESTS": False,
"RUN_PT_FLAX_CROSS_TESTS": False,
}
COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "dist": "loadfile", "s": None}
DEFAULT_DOCKER_IMAGE = [{"image": "cimg/python:3.8.12"}]
class EmptyJob:
job_name = "empty"
def to_dict(self):
return {
"working_directory": "~/transformers",
"docker": copy.deepcopy(DEFAULT_DOCKER_IMAGE),
"steps":["checkout"],
}
@dataclass
class CircleCIJob:
name: str
additional_env: Dict[str, Any] = None
cache_name: str = None
cache_version: str = "0.7"
docker_image: List[Dict[str, str]] = None
install_steps: List[str] = None
marker: Optional[str] = None
parallelism: Optional[int] = 1
pytest_num_workers: int = 8
pytest_options: Dict[str, Any] = None
resource_class: Optional[str] = "xlarge"
tests_to_run: Optional[List[str]] = None
working_directory: str = "~/transformers"
# This should be only used for doctest job!
command_timeout: Optional[int] = None
def __post_init__(self):
# Deal with defaults for mutable attributes.
if self.additional_env is None:
self.additional_env = {}
if self.cache_name is None:
self.cache_name = self.name
if self.docker_image is None:
# Let's avoid changing the default list and make a copy.
self.docker_image = copy.deepcopy(DEFAULT_DOCKER_IMAGE)
if self.install_steps is None:
self.install_steps = []
if self.pytest_options is None:
self.pytest_options = {}
if isinstance(self.tests_to_run, str):
self.tests_to_run = [self.tests_to_run]
if self.parallelism is None:
self.parallelism = 1
def to_dict(self):
env = COMMON_ENV_VARIABLES.copy()
env.update(self.additional_env)
job = {
"working_directory": self.working_directory,
"docker": self.docker_image,
"environment": env,
}
if self.resource_class is not None:
job["resource_class"] = self.resource_class
if self.parallelism is not None:
job["parallelism"] = self.parallelism
steps = [
"checkout",
{"attach_workspace": {"at": "~/transformers/test_preparation"}},
{
"restore_cache": {
"keys": [
f"v{self.cache_version}-{self.cache_name}-pip-" + '{{ checksum "setup.py" }}',
f"v{self.cache_version}-{self.cache_name}-pip-",
]
}
},
{
"restore_cache": {
"keys": [
f"v{self.cache_version}-{self.cache_name}-site-packages-" + '{{ checksum "setup.py" }}',
f"v{self.cache_version}-{self.cache_name}-site-packages-",
]
}
},
]
steps.extend([{"run": l} for l in self.install_steps])
steps.append(
{
"save_cache": {
"key": f"v{self.cache_version}-{self.cache_name}-pip-" + '{{ checksum "setup.py" }}',
"paths": ["~/.cache/pip"],
}
}
)
steps.append(
{
"save_cache": {
"key": f"v{self.cache_version}-{self.cache_name}-site-packages-" + '{{ checksum "setup.py" }}',
"paths": ["~/.pyenv/versions/"],
}
}
)
steps.append({"run": {"name": "Show installed libraries and their versions", "command": "pip freeze | tee installed.txt"}})
steps.append({"store_artifacts": {"path": "~/transformers/installed.txt"}})
all_options = {**COMMON_PYTEST_OPTIONS, **self.pytest_options}
pytest_flags = [f"--{key}={value}" if (value is not None or key in ["doctest-modules"]) else f"-{key}" for key, value in all_options.items()]
pytest_flags.append(
f"--make-reports={self.name}" if "examples" in self.name else f"--make-reports=tests_{self.name}"
)
test_command = ""
if self.command_timeout:
test_command = f"timeout {self.command_timeout} "
test_command += f"python -m pytest -n {self.pytest_num_workers} " + " ".join(pytest_flags)
if self.parallelism == 1:
if self.tests_to_run is None:
test_command += " << pipeline.parameters.tests_to_run >>"
else:
test_command += " " + " ".join(self.tests_to_run)
else:
# We need explicit list instead of `pipeline.parameters.tests_to_run` (only available at job runtime)
tests = self.tests_to_run
if tests is None:
folder = os.environ["test_preparation_dir"]
test_file = os.path.join(folder, "filtered_test_list.txt")
if os.path.exists(test_file):
with open(test_file) as f:
tests = f.read().split(" ")
# expand the test list
if tests == ["tests"]:
tests = [os.path.join("tests", x) for x in os.listdir("tests")]
expanded_tests = []
for test in tests:
if test.endswith(".py"):
expanded_tests.append(test)
elif test == "tests/models":
expanded_tests.extend([os.path.join(test, x) for x in os.listdir(test)])
elif test == "tests/pipelines":
expanded_tests.extend([os.path.join(test, x) for x in os.listdir(test)])
else:
expanded_tests.append(test)
# Avoid long tests always being collected together
random.shuffle(expanded_tests)
tests = " ".join(expanded_tests)
# Each executor to run ~10 tests
n_executors = max(len(tests) // 10, 1)
# Avoid empty test list on some executor(s) or launching too many executors
if n_executors > self.parallelism:
n_executors = self.parallelism
job["parallelism"] = n_executors
# Need to be newline separated for the command `circleci tests split` below
command = f'echo {tests} | tr " " "\\n" >> tests.txt'
steps.append({"run": {"name": "Get tests", "command": command}})
command = 'TESTS=$(circleci tests split tests.txt) && echo $TESTS > splitted_tests.txt'
steps.append({"run": {"name": "Split tests", "command": command}})
steps.append({"store_artifacts": {"path": "~/transformers/tests.txt"}})
steps.append({"store_artifacts": {"path": "~/transformers/splitted_tests.txt"}})
test_command = ""
if self.timeout:
test_command = f"timeout {self.timeout} "
test_command += f"python -m pytest -n {self.pytest_num_workers} " + " ".join(pytest_flags)
test_command += " $(cat splitted_tests.txt)"
if self.marker is not None:
test_command += f" -m {self.marker}"
if self.name == "pr_documentation_tests":
# can't use ` | tee tee tests_output.txt` as usual
test_command += " > tests_output.txt"
# Save the return code, so we can check if it is timeout in the next step.
test_command += '; touch "$?".txt'
# Never fail the test step for the doctest job. We will check the results in the next step, and fail that
# step instead if the actual test failures are found. This is to avoid the timeout being reported as test
# failure.
test_command = f"({test_command}) || true"
else:
test_command += " | tee tests_output.txt"
steps.append({"run": {"name": "Run tests", "command": test_command}})
# return code `124` means the previous (pytest run) step is timeout
if self.name == "pr_documentation_tests":
checkout_doctest_command = 'if [ -s reports/tests_pr_documentation_tests/failures_short.txt ]; '
checkout_doctest_command += 'then echo "some test failed"; '
checkout_doctest_command += 'cat reports/tests_pr_documentation_tests/failures_short.txt; '
checkout_doctest_command += 'cat reports/tests_pr_documentation_tests/summary_short.txt; exit -1; '
checkout_doctest_command += 'elif [ -s reports/tests_pr_documentation_tests/stats.txt ]; then echo "All tests pass!"; '
checkout_doctest_command += 'elif [ -f 124.txt ]; then echo "doctest timeout!"; else echo "other fatal error)"; exit -1; fi;'
steps.append({"run": {"name": "Check doctest results", "command": checkout_doctest_command}})
steps.append({"store_artifacts": {"path": "~/transformers/tests_output.txt"}})
steps.append({"store_artifacts": {"path": "~/transformers/reports"}})
job["steps"] = steps
return job
@property
def job_name(self):
return self.name if "examples" in self.name else f"tests_{self.name}"
# JOBS
torch_and_tf_job = CircleCIJob(
"torch_and_tf",
additional_env={"RUN_PT_TF_CROSS_TESTS": True},
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng git-lfs cmake",
"git lfs install",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[sklearn,tf-cpu,torch,testing,sentencepiece,torch-speech,vision]",
"pip install -U --upgrade-strategy eager tensorflow_probability",
"pip install -U --upgrade-strategy eager git+https://github.com/huggingface/accelerate",
],
marker="is_pt_tf_cross_test",
pytest_options={"rA": None, "durations": 0},
)
torch_and_flax_job = CircleCIJob(
"torch_and_flax",
additional_env={"RUN_PT_FLAX_CROSS_TESTS": True},
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng",
"pip install -U --upgrade-strategy eager --upgrade pip",
"pip install -U --upgrade-strategy eager .[sklearn,flax,torch,testing,sentencepiece,torch-speech,vision]",
"pip install -U --upgrade-strategy eager git+https://github.com/huggingface/accelerate",
],
marker="is_pt_flax_cross_test",
pytest_options={"rA": None, "durations": 0},
)
torch_job = CircleCIJob(
"torch",
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng time",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]",
"pip install -U --upgrade-strategy eager git+https://github.com/huggingface/accelerate",
],
parallelism=1,
pytest_num_workers=3,
)
tf_job = CircleCIJob(
"tf",
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng cmake",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]",
"pip install -U --upgrade-strategy eager tensorflow_probability",
],
parallelism=1,
pytest_num_workers=6,
pytest_options={"rA": None},
)
flax_job = CircleCIJob(
"flax",
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[flax,testing,sentencepiece,flax-speech,vision]",
],
parallelism=1,
pytest_options={"rA": None},
)
pipelines_torch_job = CircleCIJob(
"pipelines_torch",
additional_env={"RUN_PIPELINE_TESTS": True},
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm,video]",
],
pytest_options={"rA": None},
marker="is_pipeline_test",
)
pipelines_tf_job = CircleCIJob(
"pipelines_tf",
additional_env={"RUN_PIPELINE_TESTS": True},
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y cmake",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[sklearn,tf-cpu,testing,sentencepiece,vision]",
"pip install -U --upgrade-strategy eager tensorflow_probability",
],
pytest_options={"rA": None},
marker="is_pipeline_test",
)
custom_tokenizers_job = CircleCIJob(
"custom_tokenizers",
additional_env={"RUN_CUSTOM_TOKENIZERS": True},
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y cmake",
{
"name": "install jumanpp",
"command":
"wget https://github.com/ku-nlp/jumanpp/releases/download/v2.0.0-rc3/jumanpp-2.0.0-rc3.tar.xz\n"
"tar xvf jumanpp-2.0.0-rc3.tar.xz\n"
"mkdir jumanpp-2.0.0-rc3/bld\n"
"cd jumanpp-2.0.0-rc3/bld\n"
"sudo cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local\n"
"sudo make install\n",
},
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]",
"python -m unidic download",
],
parallelism=None,
resource_class=None,
tests_to_run=[
"./tests/models/bert_japanese/test_tokenization_bert_japanese.py",
"./tests/models/openai/test_tokenization_openai.py",
"./tests/models/clip/test_tokenization_clip.py",
],
)
examples_torch_job = CircleCIJob(
"examples_torch",
cache_name="torch_examples",
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[sklearn,torch,sentencepiece,testing,torch-speech]",
"pip install -U --upgrade-strategy eager -r examples/pytorch/_tests_requirements.txt",
],
)
examples_tensorflow_job = CircleCIJob(
"examples_tensorflow",
cache_name="tensorflow_examples",
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y cmake",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[sklearn,tensorflow,sentencepiece,testing]",
"pip install -U --upgrade-strategy eager -r examples/tensorflow/_tests_requirements.txt",
],
)
examples_flax_job = CircleCIJob(
"examples_flax",
cache_name="flax_examples",
install_steps=[
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[flax,testing,sentencepiece]",
"pip install -U --upgrade-strategy eager -r examples/flax/_tests_requirements.txt",
],
)
hub_job = CircleCIJob(
"hub",
additional_env={"HUGGINGFACE_CO_STAGING": True},
install_steps=[
"sudo apt-get -y update && sudo apt-get install git-lfs",
'git config --global user.email "ci@dummy.com"',
'git config --global user.name "ci"',
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[torch,sentencepiece,testing,vision]",
],
marker="is_staging_test",
pytest_num_workers=1,
)
onnx_job = CircleCIJob(
"onnx",
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y cmake",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[torch,tf,testing,sentencepiece,onnxruntime,vision,rjieba]",
],
pytest_options={"k onnx": None},
pytest_num_workers=1,
)
exotic_models_job = CircleCIJob(
"exotic_models",
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[torch,testing,vision]",
"pip install -U --upgrade-strategy eager torchvision",
"pip install -U --upgrade-strategy eager scipy",
"pip install -U --upgrade-strategy eager 'git+https://github.com/facebookresearch/detectron2.git'",
"sudo apt install tesseract-ocr",
"pip install -U --upgrade-strategy eager pytesseract",
"pip install -U --upgrade-strategy eager natten",
# TODO (ydshieh): Remove this line once `https://github.com/facebookresearch/detectron2/issues/5010` is resolved
'pip install -U --upgrade-strategy eager "Pillow<10.0.0"',
],
tests_to_run=[
"tests/models/*layoutlmv*",
"tests/models/*nat",
"tests/models/deta",
],
pytest_num_workers=1,
pytest_options={"durations": 100},
)
repo_utils_job = CircleCIJob(
"repo_utils",
install_steps=[
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager .[quality,testing,torch]",
],
parallelism=None,
pytest_num_workers=1,
resource_class="large",
tests_to_run="tests/repo_utils",
)
# We also include a `dummy.py` file in the files to be doc-tested to prevent edge case failure. Otherwise, the pytest
# hangs forever during test collection while showing `collecting 0 items / 21 errors`. (To see this, we have to remove
# the bash output redirection.)
py_command = 'from utils.tests_fetcher import get_doctest_files; to_test = get_doctest_files() + ["dummy.py"]; to_test = " ".join(to_test); print(to_test)'
py_command = f"$(python3 -c '{py_command}')"
command = f'echo "{py_command}" > pr_documentation_tests_temp.txt'
doc_test_job = CircleCIJob(
"pr_documentation_tests",
additional_env={"TRANSFORMERS_VERBOSITY": "error", "DATASETS_VERBOSITY": "error", "SKIP_CUDA_DOCTEST": "1"},
install_steps=[
"sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng time ffmpeg",
"pip install --upgrade --upgrade-strategy eager pip",
"pip install -U --upgrade-strategy eager -e .[dev]",
"pip install -U --upgrade-strategy eager git+https://github.com/huggingface/accelerate",
"pip install --upgrade --upgrade-strategy eager pytest pytest-sugar",
"pip install -U --upgrade-strategy eager natten",
"find -name __pycache__ -delete",
"find . -name \*.pyc -delete",
# Add an empty file to keep the test step running correctly even no file is selected to be tested.
"touch dummy.py",
{
"name": "Get files to test",
"command": command,
},
{
"name": "Show information in `Get files to test`",
"command":
"cat pr_documentation_tests_temp.txt"
},
{
"name": "Get the last line in `pr_documentation_tests.txt`",
"command":
"tail -n1 pr_documentation_tests_temp.txt | tee pr_documentation_tests.txt"
},
],
tests_to_run="$(cat pr_documentation_tests.txt)", # noqa
pytest_options={"-doctest-modules": None, "doctest-glob": "*.md", "dist": "loadfile", "rvsA": None},
command_timeout=1200, # test cannot run longer than 1200 seconds
pytest_num_workers=1,
)
REGULAR_TESTS = [
torch_and_tf_job,
torch_and_flax_job,
torch_job,
tf_job,
flax_job,
custom_tokenizers_job,
hub_job,
onnx_job,
exotic_models_job,
]
EXAMPLES_TESTS = [
examples_torch_job,
examples_tensorflow_job,
examples_flax_job,
]
PIPELINE_TESTS = [
pipelines_torch_job,
pipelines_tf_job,
]
REPO_UTIL_TESTS = [repo_utils_job]
DOC_TESTS = [doc_test_job]
def create_circleci_config(folder=None):
if folder is None:
folder = os.getcwd()
# Used in CircleCIJob.to_dict() to expand the test list (for using parallelism)
os.environ["test_preparation_dir"] = folder
jobs = []
all_test_file = os.path.join(folder, "test_list.txt")
if os.path.exists(all_test_file):
with open(all_test_file) as f:
all_test_list = f.read()
else:
all_test_list = []
if len(all_test_list) > 0:
jobs.extend(PIPELINE_TESTS)
test_file = os.path.join(folder, "filtered_test_list.txt")
if os.path.exists(test_file):
with open(test_file) as f:
test_list = f.read()
else:
test_list = []
if len(test_list) > 0:
jobs.extend(REGULAR_TESTS)
extended_tests_to_run = set(test_list.split())
# Extend the test files for cross test jobs
for job in jobs:
if job.job_name in ["tests_torch_and_tf", "tests_torch_and_flax"]:
for test_path in copy.copy(extended_tests_to_run):
dir_path, fn = os.path.split(test_path)
if fn.startswith("test_modeling_tf_"):
fn = fn.replace("test_modeling_tf_", "test_modeling_")
elif fn.startswith("test_modeling_flax_"):
fn = fn.replace("test_modeling_flax_", "test_modeling_")
else:
if job.job_name == "test_torch_and_tf":
fn = fn.replace("test_modeling_", "test_modeling_tf_")
elif job.job_name == "test_torch_and_flax":
fn = fn.replace("test_modeling_", "test_modeling_flax_")
new_test_file = str(os.path.join(dir_path, fn))
if os.path.isfile(new_test_file):
if new_test_file not in extended_tests_to_run:
extended_tests_to_run.add(new_test_file)
extended_tests_to_run = sorted(extended_tests_to_run)
for job in jobs:
if job.job_name in ["tests_torch_and_tf", "tests_torch_and_flax"]:
job.tests_to_run = extended_tests_to_run
fn = "filtered_test_list_cross_tests.txt"
f_path = os.path.join(folder, fn)
with open(f_path, "w") as fp:
fp.write(" ".join(extended_tests_to_run))
example_file = os.path.join(folder, "examples_test_list.txt")
if os.path.exists(example_file) and os.path.getsize(example_file) > 0:
with open(example_file, "r", encoding="utf-8") as f:
example_tests = f.read()
for job in EXAMPLES_TESTS:
framework = job.name.replace("examples_", "").replace("torch", "pytorch")
if example_tests == "all":
job.tests_to_run = [f"examples/{framework}"]
else:
job.tests_to_run = [f for f in example_tests.split(" ") if f.startswith(f"examples/{framework}")]
if len(job.tests_to_run) > 0:
jobs.append(job)
doctest_file = os.path.join(folder, "doctest_list.txt")
if os.path.exists(doctest_file):
with open(doctest_file) as f:
doctest_list = f.read()
else:
doctest_list = []
if len(doctest_list) > 0:
jobs.extend(DOC_TESTS)
repo_util_file = os.path.join(folder, "test_repo_utils.txt")
if os.path.exists(repo_util_file) and os.path.getsize(repo_util_file) > 0:
jobs.extend(REPO_UTIL_TESTS)
if len(jobs) == 0:
jobs = [EmptyJob()]
config = {"version": "2.1"}
config["parameters"] = {
# Only used to accept the parameters from the trigger
"nightly": {"type": "boolean", "default": False},
"tests_to_run": {"type": "string", "default": test_list},
}
config["jobs"] = {j.job_name: j.to_dict() for j in jobs}
config["workflows"] = {"version": 2, "run_tests": {"jobs": [j.job_name for j in jobs]}}
with open(os.path.join(folder, "generated_config.yml"), "w") as f:
f.write(yaml.dump(config, indent=2, width=1000000, sort_keys=False))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--fetcher_folder", type=str, default=None, help="Only test that all tests and modules are accounted for."
)
args = parser.parse_args()
create_circleci_config(args.fetcher_folder)
| 25,350 | 39.239683 | 155 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for {{cookiecutter.modelname}}."""
{%- if cookiecutter.tokenizer_type == "Based on BERT" %}
from ...utils import logging
from ..bert.tokenization_bert import BertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"{{cookiecutter.checkpoint_identifier}}": {"do_lower_case": False},
}
class {{cookiecutter.camelcase_modelname}}Tokenizer(BertTokenizer):
r"""
Construct a {{cookiecutter.modelname}} tokenizer.
[`~{{cookiecutter.camelcase_modelname}}Tokenizer`] is identical to [`BertTokenizer`] and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
{%- elif cookiecutter.tokenizer_type == "Based on BART" %}
from ...utils import logging
from ..bart.tokenization_bart import BartTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.json",
},
"merges_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/merges.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 1024,
}
class {{cookiecutter.camelcase_modelname}}Tokenizer(BartTokenizer):
"""
Construct a {{cookiecutter.modelname}} tokenizer.
[`~{{cookiecutter.camelcase_modelname}}Tokenizer`] is identical to [`BartTokenizer`] and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BartTokenizer`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
{%- elif cookiecutter.tokenizer_type == "Standalone" %}
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 1024,
}
class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
"""
Construct a {{cookiecutter.modelname}} tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
**kwargs
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
""" Initialisation """
@property
def vocab_size(self):
""" Returns vocab size """
def get_vocab(self):
""" Returns vocab as a dict """
def _tokenize(self, text):
""" Returns a tokenized string. """
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
def save_vocabulary(self, save_directory):
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A {{cookiecutter.modelname}} sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
text = " " + text
return (text, kwargs)
class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
merges_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
trim_offsets=True,
**kwargs
):
super().__init__(
ByteLevelBPETokenizer(
vocab_file=vocab_file,
merges_file=merges_file,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
),
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
**kwargs,
)
self.add_prefix_space = add_prefix_space
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
if token_ids_1 is None:
return output
return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
{% endif %}
| 12,042 | 35.165165 | 138 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_fast_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for {{cookiecutter.modelname}}."""
{%- if cookiecutter.tokenizer_type == "Based on BERT" %}
from ...utils import logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"{{cookiecutter.checkpoint_identifier}}": {"do_lower_case": False},
}
class {{cookiecutter.camelcase_modelname}}TokenizerFast(BertTokenizerFast):
r"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
[`~{{cookiecutter.camelcase_modelname}}TokenizerFast`] is identical to [`BertTokenizerFast`] and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BertTokenizerFast`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
{%- elif cookiecutter.tokenizer_type == "Based on BART" %}
from ...utils import logging
from ..bart.tokenization_bart_fast import BartTokenizerFast
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.json",
},
"merges_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/merges.txt",
},
"tokenizer_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 1024,
}
class {{cookiecutter.camelcase_modelname}}TokenizerFast(BartTokenizerFast):
r"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
[`~{{cookiecutter.camelcase_modelname}}TokenizerFast`] is identical to [`BartTokenizerFast`] and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BartTokenizerFast`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
{%- elif cookiecutter.tokenizer_type == "Standalone" %}
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
},
"tokenizer_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"{{cookiecutter.checkpoint_identifier}}": 1024,
}
class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
def __init__(
self,
vocab_file,
merges_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
trim_offsets=True,
**kwargs
):
super().__init__(
ByteLevelBPETokenizer(
vocab_file=vocab_file,
merges_file=merges_file,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
),
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
**kwargs,
)
self.add_prefix_space = add_prefix_space
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
if token_ids_1 is None:
return output
return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
{% endif %}
| 7,417 | 35.722772 | 142 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/to_replace_{{cookiecutter.lowercase_modelname}}.py | ## Copyright 2022 The HuggingFace Team. All rights reserved.
##
## Licensed under the Apache License, Version 2.0 (the "License");
## you may not use this file except in compliance with the License.
## You may obtain a copy of the License at
##
## http://www.apache.org/licenses/LICENSE-2.0
##
## Unless required by applicable law or agreed to in writing, software
## distributed under the License is distributed on an "AS IS" BASIS,
## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
## See the License for the specific language governing permissions and
## limitations under the License.
## This file is made so that specific statements may be copied inside existing files. This is useful to copy
## import statements in __init__.py, or to complete model lists in the AUTO files.
##
## It is to be used as such:
## Put '# To replace in: "FILE_PATH"' in order to indicate the contents will be copied in the file at path FILE_PATH
## Put '# Below: "STATEMENT"' in order to copy the contents below **the first occurence** of that line in the file at FILE_PATH
## Put '# Replace with:' followed by the lines containing the content to define the content
## End a statement with '# End.'. If starting a new statement without redefining the FILE_PATH, it will continue pasting
## content in that file.
##
## Put '## COMMENT' to comment on the file.
# To replace in: "src/transformers/__init__.py"
# Below: " # PyTorch models structure" if generating PyTorch
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST",
"{{cookiecutter.camelcase_modelname}}ForMaskedLM",
"{{cookiecutter.camelcase_modelname}}ForCausalLM",
"{{cookiecutter.camelcase_modelname}}ForMultipleChoice",
"{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"{{cookiecutter.camelcase_modelname}}ForTokenClassification",
"{{cookiecutter.camelcase_modelname}}Layer",
"{{cookiecutter.camelcase_modelname}}Model",
"{{cookiecutter.camelcase_modelname}}PreTrainedModel",
"load_tf_weights_in_{{cookiecutter.lowercase_modelname}}",
]
)
{% else %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST",
"{{cookiecutter.camelcase_modelname}}ForCausalLM",
"{{cookiecutter.camelcase_modelname}}ForConditionalGeneration",
"{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"{{cookiecutter.camelcase_modelname}}Model",
"{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% endif -%}
# End.
# Below: " # TensorFlow models structure" if generating TensorFlow
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST",
"TF{{cookiecutter.camelcase_modelname}}ForMaskedLM",
"TF{{cookiecutter.camelcase_modelname}}ForCausalLM",
"TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice",
"TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"TF{{cookiecutter.camelcase_modelname}}ForTokenClassification",
"TF{{cookiecutter.camelcase_modelname}}Layer",
"TF{{cookiecutter.camelcase_modelname}}Model",
"TF{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% else %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration",
"TF{{cookiecutter.camelcase_modelname}}Model",
"TF{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% endif -%}
# End.
# Below: " # Flax models structure" if generating Flax
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM",
"Flax{{cookiecutter.camelcase_modelname}}ForCausalLM",
"Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice",
"Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification",
"Flax{{cookiecutter.camelcase_modelname}}Layer",
"Flax{{cookiecutter.camelcase_modelname}}Model",
"Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% else %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration",
"Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"Flax{{cookiecutter.camelcase_modelname}}Model",
"Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% endif -%}
# End.
# Below: " # Fast tokenizers structure"
# Replace with:
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].append("{{cookiecutter.camelcase_modelname}}TokenizerFast")
# End.
# Below: " # Models"
# Replace with:
"models.{{cookiecutter.lowercase_modelname}}": ["{{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP", "{{cookiecutter.camelcase_modelname}}Config", "{{cookiecutter.camelcase_modelname}}Tokenizer"],
# End.
# To replace in: "src/transformers/__init__.py"
# Below: " # PyTorch model imports" if generating PyTorch
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
from .models.{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
{{cookiecutter.camelcase_modelname}}ForMaskedLM,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}ForTokenClassification,
{{cookiecutter.camelcase_modelname}}Layer,
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}PreTrainedModel,
load_tf_weights_in_{{cookiecutter.lowercase_modelname}},
)
{% else %}
from .models.{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% endif -%}
# End.
# Below: " # TensorFlow model imports" if generating TensorFlow
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
from .models.{{cookiecutter.lowercase_modelname}} import (
TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
TF{{cookiecutter.camelcase_modelname}}Layer,
TF{{cookiecutter.camelcase_modelname}}Model,
TF{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% else %}
from .models.{{cookiecutter.lowercase_modelname}} import (
TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
TF{{cookiecutter.camelcase_modelname}}Model,
TF{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% endif -%}
# End.
# Below: " # Flax model imports" if generating Flax
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
from .models.{{cookiecutter.lowercase_modelname}} import (
Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM,
Flax{{cookiecutter.camelcase_modelname}}ForCausalLM,
Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification,
Flax{{cookiecutter.camelcase_modelname}}Layer,
Flax{{cookiecutter.camelcase_modelname}}Model,
Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% else %}
from .models.{{cookiecutter.lowercase_modelname}} import (
Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}Model,
Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% endif -%}
# End.
# Below: " # Fast tokenizers imports"
# Replace with:
from .models.{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}TokenizerFast
# End.
# Below: " from .models.albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig"
# Replace with:
from .models.{{cookiecutter.lowercase_modelname}} import {{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP, {{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}Tokenizer
# End.
# To replace in: "src/transformers/models/__init__.py"
# Below: "from . import ("
# Replace with:
{{cookiecutter.lowercase_modelname}},
# End.
# To replace in: "src/transformers/models/auto/configuration_auto.py"
# Below: "# Add configs here"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}Config"),
# End.
# Below: "# Add archive maps here"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP"),
# End.
# Below: "# Add full (and cased) model names here"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}"),
# End.
# To replace in: "src/transformers/models/auto/modeling_auto.py" if generating PyTorch
# Below: "# Base model mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}Model"),
# End.
# Below: "# Model with LM heads mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# Below: "# Model for Causal LM mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForCausalLM"),
# End.
# Below: "# Model for Masked LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Sequence Classification mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForSequenceClassification"),
# End.
# Below: "# Model for Question Answering mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"),
# End.
# Below: "# Model for Token Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForTokenClassification"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Multiple Choice mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForMultipleChoice"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Seq2Seq Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else %}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# To replace in: "src/transformers/models/auto/modeling_tf_auto.py" if generating TensorFlow
# Below: "# Base model mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}Model"),
# End.
# Below: "# Model with LM heads mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# Below: "# Model for Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForCausalLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Masked LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Sequence Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Question Answering mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Token Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForTokenClassification"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Multiple Choice mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Seq2Seq Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else %}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# To replace in: "src/transformers/models/auto/modeling_flax_auto.py" if generating Flax
# Below: "# Base model mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}Model"),
# End.
# Below: "# Model for Masked LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# Below: "# Model for Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForCausalLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Masked LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Sequence Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification"),
{% endif -%}
# End.
# Below: "# Model for Question Answering mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"),
{% endif -%}
# End.
# Below: "# Model for Token Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Multiple Choice mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Seq2Seq Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else %}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# To replace in: "utils/check_repo.py" if generating PyTorch
# Below: "models to ignore for model xxx mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else -%}
"{{cookiecutter.camelcase_modelname}}Encoder",
"{{cookiecutter.camelcase_modelname}}Decoder",
"{{cookiecutter.camelcase_modelname}}DecoderWrapper",
{% endif -%}
# End.
# Below: "models to ignore for not tested"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else -%}
"{{cookiecutter.camelcase_modelname}}Encoder", # Building part of bigger (tested) model.
"{{cookiecutter.camelcase_modelname}}Decoder", # Building part of bigger (tested) model.
"{{cookiecutter.camelcase_modelname}}DecoderWrapper", # Building part of bigger (tested) model.
{% endif -%}
# End.
| 19,719 | 40.691332 | 218 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_flax_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
{% if cookiecutter.is_encoder_decoder_model == "False" %}
import unittest
from transformers import is_flax_available, {{cookiecutter.camelcase_modelname}}Config
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import numpy as np
from transformers import (
Flax{{cookiecutter.camelcase_modelname}}ForCausalLM,
Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM,
Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification,
Flax{{cookiecutter.camelcase_modelname}}Model,
)
class Flax{{cookiecutter.camelcase_modelname}}ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_mask = True
self.use_token_type_ids = True
self.use_labels = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 5
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = {{cookiecutter.camelcase_modelname}}Config(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = Flax{{cookiecutter.camelcase_modelname}}Model(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
inputs = [input_ids, input_mask]
result = model(*inputs)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_lm_head(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = Flax{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
prediction_scores = model(**inputs)["logits"]
self.parent.assertListEqual(
list(prediction_scores.shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(**inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(**inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice(config=config)
multiple_choice_inputs_ids = np.tile(np.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = np.tile(np.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = np.tile(np.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(**inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(**inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(**inputs)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_flax
class Flax{{cookiecutter.camelcase_modelname}}ModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
Flax{{cookiecutter.camelcase_modelname}}Model,
Flax{{cookiecutter.camelcase_modelname}}ForCausalLM,
Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification,
Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
)
if is_flax_available()
else ()
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = Flax{{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = Flax{{cookiecutter.camelcase_modelname}}Model.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
self.assertIsNotNone(model)
def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
"""If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if _assert_tensors_equal(a, b, atol=atol):
return True
raise
except Exception:
if len(prefix) > 0:
prefix = f"{prefix}: "
raise AssertionError(f"{prefix}{a} != {b}")
@require_flax
class Flax{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
input_ids = np.array([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
# TODO Replace vocab size
vocab_size = 32000
expected_shape = [1, 6, vocab_size]
self.assertEqual(output.shape, expected_shape)
print(output[:, :3, :3])
# TODO Replace values below with what was printed above.
expected_slice = np.array(
[
[
[-0.05243197, -0.04498899, 0.05512108],
[-0.07444685, -0.01064632, 0.04352357],
[-0.05020351, 0.05530146, 0.00700043],
]
]
)
_assert_tensors_equal(output[:, :3, :3], expected_slice, atol=1e-4)
{% else %}
import unittest
from transformers import (
is_flax_available,
{{cookiecutter.camelcase_modelname}}Config,
{{cookiecutter.camelcase_modelname}}Tokenizer,
)
from transformers.testing_utils import require_sentencepiece, require_flax, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import numpy as np
import jax.numpy as jnp
from transformers import (
Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}Model,
)
@require_flax
class Flax{{cookiecutter.camelcase_modelname}}ModelTester:
config_cls = {{cookiecutter.camelcase_modelname}}Config
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size).clip(3, self.vocab_size)
eos_tensor = np.expand_dims(np.array([self.eos_token_id] * self.batch_size), 1)
input_ids = np.concatenate([input_ids, eos_tensor], axis=1)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.config_cls(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_ids=[2],
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.pad_token_id,
**self.config_updates,
)
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def check_use_cache_forward(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=outputs_cache.past_key_values,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
decoder_attention_mask_cache = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
],
axis=-1,
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask_cache,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
past_key_values=outputs_cache.past_key_values,
decoder_attention_mask=decoder_attention_mask_cache,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
):
if attention_mask is None:
attention_mask = np.not_equal(input_ids, config.pad_token_id).astype(np.int8)
if decoder_attention_mask is None:
decoder_attention_mask = np.concatenate([np.ones(decoder_input_ids[:, :1].shape, dtype=np.int8), np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id).astype(np.int8)], axis=-1)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class Flax{{cookiecutter.camelcase_modelname}}ModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}Model,
) if is_flax_available()
else ()
)
all_generative_model_classes = (Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,) if is_flax_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = Flax{{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config)
def test_config(self):
self.config_tester.run_common_tests()
def test_use_cache_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
def test_use_cache_forward_with_attn_mask(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
"""If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if _assert_tensors_equal(a, b, atol=atol):
return True
raise
except Exception:
if len(prefix) > 0:
prefix = f"{prefix}: "
raise AssertionError(f"{prefix}{a} != {b}")
def _long_tensor(tok_lst):
return np.array(tok_lst, dtype=np.int32)
TOLERANCE = 1e-4
@slow
@require_sentencepiece
@require_tokenizers
@require_flax
class Flax{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
def test_inference_no_head(self):
model = Flax{{cookiecutter.camelcase_modelname}}Model.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
# change to intended input here
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
output = model(**inputs_dict)[0]
expected_shape = (1, 11, 1024)
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = np.array(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]],
)
_assert_tensors_equal(output[:, :3, :3], expected_slice, atol=TOLERANCE)
def test_inference_with_head(self):
model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
# change to intended input here
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
output = model(**inputs_dict)[0]
expected_shape = (1, 11, 1024)
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = np.array(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]],
)
_assert_tensors_equal(output[:, :3, :3], expected_slice, atol=TOLERANCE)
def test_seq_to_seq_generation(self):
hf = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
tok = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
batch_input = [
# string 1,
# string 2,
# string 3,
# string 4,
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
dct = tok.batch_encode_plus(
batch_input,
max_length=512,
padding="max_length",
truncation_strategy="only_first",
truncation=True,
return_tensors="np",
)
hypotheses_batch = hf.generate(
input_ids=dct["input_ids"],
attention_mask=dct["attention_mask"],
num_beams=2,
)
EXPECTED = [
# here expected 1,
# here expected 2,
# here expected 3,
# here expected 4,
]
generated = tok.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
)
assert generated == EXPECTED
{%- endif %}
| 26,929 | 39.19403 | 191 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_tf_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
{% if cookiecutter.is_encoder_decoder_model == "False" %}
import unittest
from transformers import is_tf_available, {{cookiecutter.camelcase_modelname}}Config
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import (
TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
TF{{cookiecutter.camelcase_modelname}}Model,
)
class TF{{cookiecutter.camelcase_modelname}}ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_mask = True
self.use_token_type_ids = True
self.use_labels = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 5
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = {{cookiecutter.camelcase_modelname}}Config(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TF{{cookiecutter.camelcase_modelname}}Model(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_causal_lm_base_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}Model(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TF{{cookiecutter.camelcase_modelname}}Model(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
# Also check the case where encoder outputs are not passed
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_causal_lm_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
prediction_scores = model(inputs)["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_causal_lm_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
prediction_scores = result["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_causal_lm_model_past(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs.past_key_values
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and attn_mask
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0]
output_from_past = model(
next_tokens, past_key_values=past_key_values, output_hidden_states=True
).hidden_states[0]
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
def create_and_check_causal_lm_model_past_with_attn_mask(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
# create attention mask
half_seq_length = self.seq_length // 2
attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
# first forward pass
outputs = model(input_ids, attention_mask=attn_mask, use_cache=True)
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
past_key_values = outputs.past_key_values
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
condition = tf.transpose(
tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
)
input_ids = tf.where(condition, random_other_next_tokens, input_ids)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
attn_mask = tf.concat(
[attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
axis=1,
)
output_from_no_past = model(
next_input_ids,
attention_mask=attn_mask,
output_hidden_states=True,
).hidden_states[0]
output_from_past = model(
next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True
).hidden_states[0]
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
def create_and_check_causal_lm_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
input_ids = input_ids[:1, :]
input_mask = input_mask[:1, :]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
past_key_values = outputs.past_key_values
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
output_hidden_states=True,
).hidden_states[0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
).hidden_states[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
input_ids = input_ids[:1, :]
input_mask = input_mask[:1, :]
encoder_hidden_states = encoder_hidden_states[:1, :, :]
encoder_attention_mask = encoder_attention_mask[:1, :]
self.batch_size = 1
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
).hidden_states[0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
).hidden_states[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
TF{{cookiecutter.camelcase_modelname}}Model,
TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
)
if is_tf_available()
else ()
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
"""Test the base model"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="Template classes interact badly with this test.")
def test_keras_fit(self):
pass
def test_causal_lm_base_model(self):
"""Test the base model of the causal LM model
is_deocder=True, no cross_attention, no encoder outputs
"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs)
def test_model_as_decoder(self):
"""Test the base model as a decoder (of an encoder-decoder architecture)
is_deocder=True + cross_attention + pass encoder outputs
"""
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm(self):
"""Test the causal LM model"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model(*config_and_inputs)
def test_causal_lm_model_as_decoder(self):
"""Test the causal LM model as a decoder"""
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs)
def test_causal_lm_model_past(self):
"""Test causal LM model with `past_key_values`"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs)
def test_causal_lm_model_past_with_attn_mask(self):
"""Test the causal LM model with `past_key_values` and `attention_mask`"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)
def test_causal_lm_model_past_with_large_inputs(self):
"""Test the causal LM model with `past_key_values` and a longer decoder sequence length"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
"""Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention"""
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
self.assertIsNotNone(model)
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
# TODO Replace vocab size
vocab_size = 32000
expected_shape = [1, 6, vocab_size]
self.assertEqual(output.shape, expected_shape)
print(output[:, :3, :3])
# TODO Replace values below with what was printed above.
expected_slice = tf.constant(
[
[
[-0.05243197, -0.04498899, 0.05512108],
[-0.07444685, -0.01064632, 0.04352357],
[-0.05020351, 0.05530146, 0.00700043],
]
]
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
{% else %}
import unittest
from transformers import (
is_tf_available,
{{cookiecutter.camelcase_modelname}}Config,
{{cookiecutter.camelcase_modelname}}Tokenizer,
)
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
if is_tf_available():
import tensorflow as tf
from transformers import (
TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
TF{{cookiecutter.camelcase_modelname}}Model,
)
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelTester:
config_cls = {{cookiecutter.camelcase_modelname}}Config
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
input_ids = tf.concat([input_ids, eos_tensor], axis=1)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.config_cls(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_ids=[2],
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.pad_token_id,
**self.config_updates,
)
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = TF{{cookiecutter.camelcase_modelname}}Model(config=config).get_decoder()
input_ids = inputs_dict["input_ids"]
input_ids = input_ids[:1, :]
attention_mask = inputs_dict["attention_mask"][:1, :]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
):
if attention_mask is None:
attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int32)
if decoder_attention_mask is None:
decoder_attention_mask = tf.concat([tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int32), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int32)], axis=-1)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, TF{{cookiecutter.camelcase_modelname}}Model) if is_tf_available() else ()
all_generative_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,) if is_tf_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
@unittest.skip(reason="Template classes interact badly with this test.")
def test_keras_fit(self):
pass
def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
"""If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if tf.debugging.assert_near(a, b, atol=atol):
return True
raise
except Exception:
if len(prefix) > 0:
prefix = f"{prefix}: "
raise AssertionError(f"{prefix}{a} != {b}")
def _long_tensor(tok_lst):
return tf.constant(tok_lst, dtype=tf.int32)
TOLERANCE = 1e-4
@slow
@require_sentencepiece
@require_tokenizers
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
def test_inference_no_head(self):
model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
# change to intended input here
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
output = model(**inputs_dict)[0]
expected_shape = (1, 11, 1024)
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = tf.Tensor(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]],
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
def test_inference_with_head(self):
model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
# change to intended input here
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
output = model(**inputs_dict)[0]
expected_shape = (1, 11, 1024)
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = tf.Tensor(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]],
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
def test_seq_to_seq_generation(self):
hf = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
tok = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
batch_input = [
# string 1,
# string 2,
# string 3,
# string 4,
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
dct = tok.batch_encode_plus(
batch_input,
max_length=512,
padding="max_length",
truncation_strategy="only_first",
truncation=True,
return_tensors="tf",
)
hypotheses_batch = hf.generate(
input_ids=dct["input_ids"],
attention_mask=dct["attention_mask"],
num_beams=2,
)
EXPECTED = [
# here expected 1,
# here expected 2,
# here expected 3,
# here expected 4,
]
generated = tok.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
)
assert generated == EXPECTED
{%- endif %}
| 38,379 | 38.485597 | 195 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch {{cookiecutter.modelname}} model. """
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
import unittest
from ...test_modeling_common import floats_tensor
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers import {{cookiecutter.camelcase_modelname}}Config
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForMaskedLM,
{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}ForTokenClassification,
{{cookiecutter.camelcase_modelname}}Model,
)
from transformers.models.{{cookiecutter.lowercase_modelname}}.modeling_{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class {{cookiecutter.camelcase_modelname}}ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return {{cookiecutter.camelcase_modelname}}Config(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = {{cookiecutter.camelcase_modelname}}Model(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = {{cookiecutter.camelcase_modelname}}Model(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = {{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = {{cookiecutter.camelcase_modelname}}ForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = {{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = {{cookiecutter.camelcase_modelname}}ForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = {{cookiecutter.camelcase_modelname}}ForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = {{cookiecutter.camelcase_modelname}}ForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = {{cookiecutter.camelcase_modelname}}ForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class {{cookiecutter.camelcase_modelname}}ModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}ForMaskedLM,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}ForTokenClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = ({{cookiecutter.camelcase_modelname}}ForCausalLM,) if is_torch_available() else ()
def setUp(self):
self.model_tester = {{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
@slow
def test_model_from_pretrained(self):
for model_name in {{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = {{cookiecutter.camelcase_modelname}}Model.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class {{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = {{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
# TODO Replace vocab size
vocab_size = 32000
expected_shape = torch.Size((1, 6, vocab_size))
self.assertEqual(output.shape, expected_shape)
# TODO Replace values below with what was printed above.
expected_slice = torch.tensor(
[[[-0.0483, 0.1188, -0.0313], [-0.0606, 0.1435, 0.0199], [-0.0235, 0.1519, 0.0175]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
{% else -%}
import copy
import tempfile
import unittest
from transformers import is_torch_available
from transformers.utils import cached_property
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import (
{{cookiecutter.camelcase_modelname}}Config,
{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}Tokenizer,
)
from transformers.models.{{cookiecutter.lowercase_modelname}}.modeling_{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.camelcase_modelname}}Decoder,
{{cookiecutter.camelcase_modelname}}Encoder,
)
def prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
@require_torch
class {{cookiecutter.camelcase_modelname}}ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
3,
)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = {{cookiecutter.camelcase_modelname}}Config(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = {{cookiecutter.camelcase_modelname}}Model(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = {{cookiecutter.camelcase_modelname}}Model(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = {{cookiecutter.camelcase_modelname}}Encoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = {{cookiecutter.camelcase_modelname}}Decoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class {{cookiecutter.camelcase_modelname}}ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
({{cookiecutter.camelcase_modelname}}Model, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.camelcase_modelname}}ForSequenceClassification, {{cookiecutter.camelcase_modelname}}ForQuestionAnswering)
if is_torch_available()
else ()
)
all_generative_model_classes = ({{cookiecutter.camelcase_modelname}}ForConditionalGeneration,) if is_torch_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_missing_keys = False
def setUp(self):
self.model_tester = {{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
# {{cookiecutter.camelcase_modelname}}ForSequenceClassification does not support inputs_embeds
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in ({{cookiecutter.camelcase_modelname}}Model, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.camelcase_modelname}}ForQuestionAnswering):
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
if not self.is_encoder_decoder:
input_ids = inputs["input_ids"]
del inputs["input_ids"]
else:
encoder_input_ids = inputs["input_ids"]
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
del inputs["input_ids"]
inputs.pop("decoder_input_ids", None)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs["inputs_embeds"] = wte(input_ids)
else:
inputs["inputs_embeds"] = wte(encoder_input_ids)
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
model(**inputs)[0]
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def assert_tensors_close(a, b, atol=1e-12, prefix=""):
"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
if a.numel() > 100:
msg = f"tensor values are {pct_different:.1%} percent different."
else:
msg = f"{a} != {b}"
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
TOLERANCE = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class {{cookiecutter.camelcase_modelname}}ModelIntegrationTests(unittest.TestCase):
@cached_property
def default_tokenizer(self):
return {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
def test_inference_no_head(self):
model = {{cookiecutter.camelcase_modelname}}Model.from_pretrained('{{cookiecutter.checkpoint_identifier}}').to(torch_device)
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[2, 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
with torch.no_grad():
output = model(**inputs_dict)[0]
expected_shape = torch.Size((1, 11, 1024))
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = torch.tensor(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
def test_inference_head(self):
model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}').to(torch_device)
# change to intended input
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
with torch.no_grad():
output = model(**inputs_dict)[0]
expected_shape = torch.Size((1, 11, model.config.vocab_size))
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = torch.tensor(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
def test_seq_to_seq_generation(self):
hf = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}').to(torch_device)
tok = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
batch_input = [
# string 1,
# string 2,
# string 3,
# string 4,
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
dct = tok.batch_encode_plus(
batch_input,
max_length=512,
padding="max_length",
truncation_strategy="only_first",
truncation=True,
return_tensors="pt",
)
hypotheses_batch = hf.generate(
input_ids=dct["input_ids"].to(torch_device),
attention_mask=dct["attention_mask"].to(torch_device),
num_beams=2,
)
EXPECTED = [
# here expected 1,
# here expected 2,
# here expected 3,
# here expected 4,
]
generated = tok.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
)
assert generated == EXPECTED
class {{cookiecutter.camelcase_modelname}}StandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=4,
encoder_attention_heads=4,
decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = {{cookiecutter.camelcase_modelname}}Config(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = {{cookiecutter.camelcase_modelname}}Decoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = {{cookiecutter.camelcase_modelname}}Decoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class {{cookiecutter.camelcase_modelname}}StandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = ({{cookiecutter.camelcase_modelname}}Decoder, {{cookiecutter.camelcase_modelname}}ForCausalLM) if is_torch_available() else ()
all_generative_model_classes = ({{cookiecutter.camelcase_modelname}}ForCausalLM,) if is_torch_available() else ()
test_pruning = False
is_encoder_decoder = False
def setUp(
self,
):
self.model_tester = {{cookiecutter.camelcase_modelname}}StandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
return
{% endif -%}
| 44,199 | 40.269841 | 234 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/configuration_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" {{cookiecutter.modelname}} model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
{{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/config.json",
# See all {{cookiecutter.modelname}} models at https://huggingface.co/models?filter={{cookiecutter.lowercase_modelname}}
}
class {{cookiecutter.camelcase_modelname}}Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~{{cookiecutter.camelcase_modelname}}Model`].
It is used to instantiate an {{cookiecutter.modelname}} model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the {{cookiecutter.modelname}} [{{cookiecutter.checkpoint_identifier}}](https://huggingface.co/{{cookiecutter.checkpoint_identifier}}) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used
to control the model outputs. Read the documentation from [`PretrainedConfig`]
for more information.
Args:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the {{cookiecutter.modelname}} model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~{{cookiecutter.camelcase_modelname}}Model`] or
[`~TF{{cookiecutter.camelcase_modelname}}Model`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler.
If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`~{{cookiecutter.camelcase_modelname}}Model`] or
[`~TF{{cookiecutter.camelcase_modelname}}Model`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
{% else -%}
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the {{cookiecutter.modelname}} model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~{{cookiecutter.camelcase_modelname}}Model`] or
[`~TF{{cookiecutter.camelcase_modelname}}Model`].
d_model (`int`, *optional*, defaults to 1024):
Dimension of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
`"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see
https://arxiv.org/abs/1909.11556) for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see
https://arxiv.org/abs/1909.11556) for more details.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
{% endif -%}
Example:
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Model, {{cookiecutter.camelcase_modelname}}Config
>>> # Initializing a {{cookiecutter.modelname}} {{cookiecutter.checkpoint_identifier}} style configuration
>>> configuration = {{cookiecutter.camelcase_modelname}}Config()
>>> # Initializing a model from the {{cookiecutter.checkpoint_identifier}} style configuration
>>> model = {{cookiecutter.camelcase_modelname}}Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "{{cookiecutter.lowercase_modelname}}"
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else -%}
keys_to_ignore_at_inference = ["past_key_values"]
{% endif -%}
{% if cookiecutter.is_encoder_decoder_model == "False" %}
{%- else %}
attribute_map = {
"num_attention_heads": "encoder_attention_heads",
"hidden_size": "d_model"
}
{%- endif %}
def __init__(
self,
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
use_cache=True,
{% else -%}
vocab_size=50265,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
use_cache=True,
is_encoder_decoder=True,
activation_function="gelu",
d_model=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
decoder_start_token_id=2,
classifier_dropout=0.0,
scale_embedding=False,
{% endif -%}
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.type_vocab_size = type_vocab_size
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
{% else -%}
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
{% endif -%}
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else -%}
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
{% endif -%}
**kwargs
)
| 12,026 | 48.904564 | 152 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 {{cookiecutter.modelname}} model. """
{% if cookiecutter.is_encoder_decoder_model == "False" %}
import math
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...utils import (
DUMMY_INPUTS,
MULTIPLE_CHOICE_DUMMY_INPUTS,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
)
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFCausalLMOutputWithCrossAttentions,
TFMaskedLMOutput,
TFMultipleChoiceModelOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFMaskedLanguageModelingLoss,
TFModelInputType,
TFMultipleChoiceLoss,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFSequenceSummary,
TFTokenClassificationLoss,
get_initializer,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import logging
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}"
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [
"{{cookiecutter.checkpoint_identifier}}",
# See all {{cookiecutter.modelname}} models at https://huggingface.co/models?filter={{cookiecutter.lowercase_modelname}}
]
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.type_vocab_size = config.type_vocab_size
self.hidden_size = config.hidden_size
self.max_position_embeddings = config.max_position_embeddings
self.initializer_range = config.initializer_range
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def build(self, input_shape: tf.TensorShape):
with tf.name_scope("word_embeddings"):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
super().build(input_shape)
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
past_key_values_length=0,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
"""
assert not (input_ids is None and inputs_embeds is None)
if input_ids is not None:
check_embeddings_within_bounds(input_ids, self.vocab_size)
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
if position_ids is None:
position_ids = tf.expand_dims(
tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0
)
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
final_embeddings = inputs_embeds + position_embeds + token_type_embeds
final_embeddings = self.LayerNorm(inputs=final_embeddings)
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
return final_embeddings
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number "
f"of attention heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.query = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = tf.keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(inputs=hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size)
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
key_layer = tf.concat([past_key_value[0], key_layer], axis=2)
value_layer = tf.concat([past_key_value[1], value_layer], axis=2)
else:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TF{{cookiecutter.camelcase_modelname}}Model call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
attention_probs = stable_softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}SelfOutput(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Attention(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.self_attention = TF{{cookiecutter.camelcase_modelname}}SelfAttention(config, name="self")
self.dense_output = TF{{cookiecutter.camelcase_modelname}}SelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
self_outputs = self.self_attention(
hidden_states=input_tensor,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = self.dense_output(
hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
)
# add attentions (possibly with past_key_value) if we output them
outputs = (attention_output,) + self_outputs[1:]
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Intermediate(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Output(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Layer(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.attention = TF{{cookiecutter.camelcase_modelname}}Attention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = TF{{cookiecutter.camelcase_modelname}}Attention(config, name="crossattention")
self.intermediate = TF{{cookiecutter.camelcase_modelname}}Intermediate(config, name="intermediate")
self.bert_output = TF{{cookiecutter.camelcase_modelname}}Output(config, name="output")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_value: Tuple[tf.Tensor] | None,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
input_tensor=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=self_attn_past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers "
"by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
input_tensor=attention_output,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
training=training,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
intermediate_output = self.intermediate(hidden_states=attention_output)
layer_output = self.bert_output(
hidden_states=intermediate_output, input_tensor=attention_output, training=training
)
outputs = (layer_output,) + outputs # add attentions if we output them
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TF{{cookiecutter.camelcase_modelname}}Layer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_values: Tuple[Tuple[tf.Tensor]] | None,
use_cache: Optional[bool],
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
past_key_value = past_key_values[i] if past_key_values is not None else None
layer_outputs = layer_module(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if self.config.add_cross_attention and encoder_hidden_states is not None:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
if isinstance(config.hidden_act, str):
self.transform_act_fn = get_tf_activation(config.hidden_act)
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(inputs=hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.transform = TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(config, name="transform")
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape: tf.TensorShape):
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
super().build(input_shape)
def get_output_embeddings(self) -> tf.keras.layers.Layer:
return self.input_embeddings
def set_output_embeddings(self, value: tf.Variable):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self) -> Dict[str, tf.Variable]:
return {"bias": self.bias}
def set_bias(self, value: tf.Variable):
self.bias = value["bias"]
self.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.transform(hidden_states=hidden_states)
seq_length = shape_list(hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->{{cookiecutter.camelcase_modelname}}
class TF{{cookiecutter.camelcase_modelname}}MLMHead(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
prediction_scores = self.predictions(hidden_states=sequence_output)
return prediction_scores
@keras_serializable
class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
config_class = {{cookiecutter.camelcase_modelname}}Config
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.is_decoder = config.is_decoder
self.embeddings = TF{{cookiecutter.camelcase_modelname}}Embeddings(config, name="embeddings")
self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, name="encoder")
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.embeddings
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
def set_input_embeddings(self, value: tf.Variable):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
if not self.config.is_decoder:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
if past_key_values is None:
past_key_values_length = 0
past_key_values = [None] * len(self.encoder.layer)
else:
past_key_values_length = shape_list(past_key_values[0][0])[-2]
if attention_mask is None:
attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1)
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
training=training,
)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask_shape = shape_list(attention_mask)
mask_seq_length = seq_length + past_key_values_length
# Copied from `modeling_tf_t5.py`
# Provided a padding mask of dimensions [batch_size, mask_seq_length]
# - if the model is a decoder, apply a causal mask in addition to the padding mask
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
if self.is_decoder:
seq_ids = tf.range(mask_seq_length)
causal_mask = tf.less_equal(
tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)),
seq_ids[None, :, None],
)
causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype)
extended_attention_mask = causal_mask * attention_mask[:, None, :]
attention_mask_shape = shape_list(extended_attention_mask)
extended_attention_mask = tf.reshape(
extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2])
)
if past_key_values[0] is not None:
# attention_mask needs to be sliced to the shape `[batch_size, 1, from_seq_length - cached_seq_length, to_seq_length]
extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
else:
extended_attention_mask = tf.reshape(
attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1])
)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
# Copied from `modeling_tf_t5.py` with -1e9 -> -10000
if self.is_decoder and encoder_attention_mask is not None:
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
encoder_attention_mask = tf.cast(
encoder_attention_mask, dtype=extended_attention_mask.dtype
)
num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
if num_dims_encoder_attention_mask == 3:
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
if num_dims_encoder_attention_mask == 2:
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
# tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (
sequence_output,
) + encoder_outputs[1:]
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=sequence_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
"""An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = {{cookiecutter.camelcase_modelname}}Config
base_model_prefix = "{{cookiecutter.lowercase_modelname}}"
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass.
Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general
usage and behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating
your own layers or models with the Keras `Functional` API, there are three possibilities you
can use to gather all the input Tensors in the first positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with (subclassing)[https://keras.io/guides/making_new_layers_and_models_via_subclassing/]
then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python
function!
</Tip>
Args:
config ([`~{{cookiecutter.camelcase_modelname}}Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
Args:
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]`, `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See
[`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare {{cookiecutter.modelname}} Model transformer outputing raw hidden-states without any specific head on top.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
@unpack_inputs
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
r"""
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)`
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up
decoding (see `past_key_values`). Set to `False` during training, `True` during generation
"""
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
@add_start_docstrings("""{{cookiecutter.modelname}} Model with a `language modeling` head on top. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING)
class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TF{{cookiecutter.camelcase_modelname}}ForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, input_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
@unpack_inputs
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
prediction_scores = self.mlm(sequence_output=sequence_output, training=training)
loss = (
None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores)
)
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model with a `language modeling` head on top for CLM fine-tuning. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
)
class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if not config.is_decoder:
logger.warning("If you want to use `TF{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, input_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
def prepare_inputs_for_generation(self, inputs, past_key_values=None, attention_mask=None, **model_kwargs):
# cut decoder_input_ids if past is used
if past_key_values:
inputs = tf.expand_dims(inputs[:, -1], -1)
return {
"input_ids": inputs,
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"use_cache": model_kwargs["use_cache"],
}
@unpack_inputs
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]:
r"""
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)`
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up
decoding (see `past_key_values`). Set to `False` during training, `True` during generation
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`.
"""
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
logits = self.mlm(sequence_output=sequence_output, training=training)
loss = None
if labels is not None:
# shift labels to the left and cut last logit token
shifted_logits = logits[:, :-1]
labels = labels[:, 1:]
loss = self.hf_compute_loss(labels=labels, logits=shifted_logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFCausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
class TF{{cookiecutter.camelcase_modelname}}ClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.out_proj = tf.keras.layers.Dense(
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
)
if isinstance(config.hidden_act, str):
self.classifier_act_fn = get_tf_activation(config.hidden_act)
else:
self.classifier_act_fn = config.hidden_act
def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS])
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.classifier_act_fn(hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = self.out_proj(hidden_states)
return hidden_states
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model transformer with a sequence classification/regression head on top
e.g., for GLUE tasks. """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.classifier = TF{{cookiecutter.camelcase_modelname}}ClassificationHead(config, name="classifier")
@unpack_inputs
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
logits = self.classifier(hidden_states=outputs[0], training=training)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.sequence_summary = TFSequenceSummary(
config, config.initializer_range, name="sequence_summary"
)
self.classifier = tf.keras.layers.Dense(
units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@unpack_inputs
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
if input_ids is not None:
num_choices = shape_list(input_ids)[1]
seq_length = shape_list(input_ids)[2]
else:
num_choices = shape_list(inputs_embeds)[1]
seq_length = shape_list(inputs_embeds)[2]
flat_input_ids = (
tf.reshape(tensor=input_ids, shape=(-1, seq_length)) if input_ids is not None else None
)
flat_attention_mask = (
tf.reshape(tensor=attention_mask, shape=(-1, seq_length))
if attention_mask is not None
else None
)
flat_token_type_ids = (
tf.reshape(tensor=token_type_ids, shape=(-1, seq_length))
if token_type_ids is not None
else None
)
flat_position_ids = (
tf.reshape(tensor=position_ids, shape=(-1, seq_length))
if position_ids is not None
else None
)
flat_inputs_embeds = (
tf.reshape(
tensor=inputs_embeds, shape=(-1, seq_length, shape_list(inputs_embeds)[3])
)
if inputs_embeds is not None
else None
)
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids=flat_input_ids,
attention_mask=flat_attention_mask,
token_type_ids=flat_token_type_ids,
position_ids=flat_position_ids,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
logits = self.sequence_summary(inputs=outputs[0], training=training)
logits = self.classifier(inputs=logits)
reshaped_logits = tf.reshape(tensor=logits, shape=(-1, num_choices))
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=reshaped_logits)
if not return_dict:
output = (reshaped_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFMultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@unpack_inputs
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
sequence_output = self.dropout(inputs=sequence_output, training=training)
logits = self.classifier(inputs=sequence_output)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
self.qa_outputs = tf.keras.layers.Dense(
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
@unpack_inputs
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
start_positions: np.ndarray | tf.Tensor | None = None,
end_positions: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
"""
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
logits = self.qa_outputs(inputs=sequence_output)
start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
start_logits = tf.squeeze(input=start_logits, axis=-1)
end_logits = tf.squeeze(input=end_logits, axis=-1)
loss = None
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
{% else %}
import random
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
# Public API
from ...modeling_tf_utils import (
DUMMY_INPUTS,
TFPreTrainedModel,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import ContextManagers, logging
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}"
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
_TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer"
LARGE_NEGATIVE = -1e8
# Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right
def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
start_tokens = tf.fill((shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype))
shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids = tf.where(
shifted_input_ids == -100,
tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)),
shifted_input_ids,
)
# "Verify that `labels` has only positive values and -100"
assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=shifted_input_ids.dtype))
# Make sure the assertion op is called by wrapping the result in an identity no-op
with tf.control_dependencies([assert_gte0]):
shifted_input_ids = tf.identity(shifted_input_ids)
return shifted_input_ids
def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
mask_cond = tf.range(shape_list(mask)[-1])
mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
if past_key_values_length > 0:
mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
class TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(tf.keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
super().__init__(num_embeddings, embedding_dim, **kwargs)
def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
"""Input is expected to be of size [bsz x seqlen]."""
seq_len = input_shape[1]
position_ids = tf.range(seq_len, delta=1, name="range")
position_ids += past_key_values_length
return super().call(tf.cast(position_ids, dtype=tf.int32))
class TF{{cookiecutter.camelcase_modelname}}Attention(tf.keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = tf.keras.layers.Dropout(dropout)
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.is_decoder = is_decoder
self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
def call(
self,
hidden_states: tf.Tensor,
key_value_states: tf.Tensor | None = None,
past_key_value: Tuple[Tuple[tf.Tensor]] | None = None,
attention_mask: tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
training=False,
) -> Tuple[tf.Tensor, tf.Tensor | None]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = shape_list(hidden_states)
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = tf.concat([past_key_value[0], key_states], axis=2)
value_states = tf.concat([past_key_value[1], value_states], axis=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
key_states = tf.reshape(key_states, proj_shape)
value_states = tf.reshape(value_states, proj_shape)
src_len = shape_list(key_states)[1]
attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
tf.debugging.assert_equal(
shape_list(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {shape_list(attn_weights)}",
)
if attention_mask is not None:
tf.debugging.assert_equal(
shape_list(attention_mask),
[bsz, 1, tgt_len, src_len],
message=f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}",
)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = stable_softmax(attn_weights, axis=-1)
if layer_head_mask is not None:
tf.debugging.assert_equal(
shape_list(layer_head_mask),
[self.num_heads],
message=f"Head mask for a single layer should be of size {(self.num_heads)}, but is {shape_list(layer_head_mask)}",
)
attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
attn_weights, (bsz, self.num_heads, tgt_len, src_len)
)
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_probs = self.dropout(attn_weights, training=training)
attn_output = tf.matmul(attn_probs, value_states)
tf.debugging.assert_equal(
shape_list(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {shape_list(attn_output)}",
)
attn_output = tf.transpose(
tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
)
attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
attn_output = self.out_proj(attn_output)
attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
return attn_output, attn_weights, past_key_value
class TF{{cookiecutter.camelcase_modelname}}EncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TF{{cookiecutter.camelcase_modelname}}Attention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training=False):
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
attention_mask (`tf.Tensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
*(encoder_attention_heads,)*
"""
residual = hidden_states
hidden_states, self_attn_weights, _ = self.self_attn(
hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask
)
tf.debugging.assert_equal(
shape_list(hidden_states),
shape_list(residual),
message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
return hidden_states, self_attn_weights
class TF{{cookiecutter.camelcase_modelname}}DecoderLayer(tf.keras.layers.Layer):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TF{{cookiecutter.camelcase_modelname}}Attention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
name="self_attn",
is_decoder=True,
)
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation_fn = get_tf_activation(config.activation_function)
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout)
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TF{{cookiecutter.camelcase_modelname}}Attention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
is_decoder=True,
)
self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
def call(
self,
hidden_states,
attention_mask: tf.Tensor | None = None,
encoder_hidden_states: tf.Tensor | None = None,
encoder_attention_mask: tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
cross_attn_layer_head_mask: tf.Tensor | None = None,
past_key_value: Tuple[tf.Tensor] | None = None,
training=False,
) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
attention_mask (`tf.Tensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
encoder_hidden_states (`tf.Tensor`): cross attention input to the layer of shape *(batch, seq_len, embed_dim)*
encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
*(decoder_attention_heads,)*
cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module.
*(decoder_attention_heads,)*
past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states
"""
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
return (
hidden_states,
self_attn_weights,
cross_attn_weights,
present_key_value,
)
class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
config_class = {{cookiecutter.camelcase_modelname}}Config
base_model_prefix = "model"
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use
it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
and behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating
your own layers or models with the Keras `Functional` API, there are three possibilities you
can use to gather all the input Tensors in the first positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with (subclassing)[https://keras.io/guides/making_new_layers_and_models_via_subclassing/]
then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python
function!
</Tip>
Args:
config ([`~{{cookiecutter.camelcase_modelname}}Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the
model weights.
"""
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
Args:
input_ids (`tf.Tensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
{{cookiecutter.camelcase_modelname}} uses the `eos_token_id` as the starting token for
`decoder_input_ids` generation. If `past_key_values` is used, optionally only the last
`decoder_input_ids` have to be input (see `past_key_values`).
For translation and summarization training, `decoder_input_ids` should be provided. If no
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to
the right for denoising pre-training following the paper.
decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tf.FloatTensor`, *optional*):
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)`
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up
decoding (see `past_key_values`). Set to `False` during training, `True` during generation
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@keras_serializable
class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
config_class = {{cookiecutter.camelcase_modelname}}Config
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TF{{cookiecutter.camelcase_modelname}}EncoderLayer`].
Args:
config: {{cookiecutter.camelcase_modelname}}Config
"""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.layerdrop = config.encoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.embed_tokens = embed_tokens
self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.layers = [TF{{cookiecutter.camelcase_modelname}}EncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
@unpack_inputs
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded
representation. This is useful if you want more control over how to convert `input_ids` indices
into associated vectors than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
in the config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
# if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name
# scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope`
# is used with a name ending in `/`, that name replaces the current name scope.
# (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0)
context = []
if hasattr(self.embed_tokens, "load_weight_prefix"):
context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/"))
with ContextManagers(context):
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
# check attention mask and invert
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
tf.debugging.assert_equal(
shape_list(head_mask)[0],
len(self.layers),
message=f"The head_mask should be specified for {len(self.layers)} layers, but it is for {shape_list(head_mask)[0]}.",
)
# encoder layers
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if training and (dropout_probability < self.layerdrop): # skip the layer
continue
hidden_states, attn = encoder_layer(
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
)
if output_attentions:
all_attentions += (attn,)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
@keras_serializable
class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
config_class = {{cookiecutter.camelcase_modelname}}Config
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TF{{cookiecutter.camelcase_modelname}}DecoderLayer`]
Args:
config: {{cookiecutter.camelcase_modelname}}Config
embed_tokens: output embedding
"""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.padding_idx = config.pad_token_id
self.embed_tokens = embed_tokens
self.layerdrop = config.decoder_layerdrop
self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
self.layers = [TF{{cookiecutter.camelcase_modelname}}DecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
self.dropout = tf.keras.layers.Dropout(config.dropout)
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
@unpack_inputs
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
r"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
decoding.
If `past_key_values` are used, the user can optionally input only the last
`decoder_input_ids` (those that don't have their past key value states given to this model) of
shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size,
sequence_length)`.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices
into associated vectors than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
in the config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
past_key_values_length = (
shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
)
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
if inputs_embeds is None:
# if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name
# scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope`
# is used with a name ending in `/`, that name replaces the current name scope.
# (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0)
context = []
if hasattr(self.embed_tokens, "load_weight_prefix"):
context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/"))
with ContextManagers(context):
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
inputs_embeds = self.embed_tokens(input_ids)
hidden_states = inputs_embeds
attention_mask, combined_attention_mask = self.compute_combined_attns_mask(
input_ids, attention_mask, input_shape, past_key_values_length
)
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
hidden_states = self.layernorm_embedding(hidden_states + positions)
hidden_states = self.dropout(hidden_states, training=training)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None
present_key_values = () if use_cache else None
# check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
if attn_mask is not None:
tf.debugging.assert_equal(
shape_list(attn_mask)[0],
len(self.layers),
message=f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for {shape_list(attn_mask)[0]}.",
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if training and (dropout_probability < self.layerdrop):
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
hidden_states,
attention_mask=combined_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=head_mask[idx] if head_mask is not None else None,
cross_attn_layer_head_mask=cross_attn_head_mask[idx]
if cross_attn_head_mask is not None
else None,
past_key_value=past_key_value,
)
if use_cache:
present_key_values += (present_key_value,)
if output_attentions:
all_self_attns += (layer_self_attn,)
if encoder_hidden_states is not None:
all_cross_attns += (layer_cross_attn,)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
else:
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attns,
)
@tf.function
def compute_combined_attns_mask(self, input_ids, attention_mask, input_shape, past_key_values_length):
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
else:
combined_attention_mask = _expand_mask(
tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
)
if attention_mask is None and input_ids is not None and input_shape[-1] > 1:
attention_mask = tf.cast(
tf.math.not_equal(input_ids, self.config.pad_token_id), input_ids.dtype
)
attention_mask = tf.concat(
[
tf.ones((input_shape[0], past_key_values_length), dtype=attention_mask.dtype),
attention_mask,
],
axis=-1,
)
else:
attention_mask = tf.ones((input_shape[0], input_shape[1] + past_key_values_length))
return attention_mask, combined_attention_mask
@keras_serializable
class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
config_class = {{cookiecutter.camelcase_modelname}}Config
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = tf.keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_model,
embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=self.config.init_std),
name="model.shared"
)
# Additional attribute to specify the expected name scope of the layer (for loading/storing weights)
self.shared.load_weight_prefix = "model.shared"
self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, self.shared, name="encoder")
self.decoder = TF{{cookiecutter.camelcase_modelname}}Decoder(config, self.shared, name="decoder")
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
@unpack_inputs
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs
):
if decoder_input_ids is None and decoder_inputs_embeds is None:
use_cache = False
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput):
encoder_outputs = TFBaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
elif not return_dict and not isinstance(encoder_outputs, tuple):
encoder_outputs = encoder_outputs.to_tuple()
decoder_outputs = self.decoder(
decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return TFSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The bare {{cookiecutter.uppercase_modelname}} Model outputting raw hidden-states without any specific head on top.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
return self.model.decoder
@unpack_inputs
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs
):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
# Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
class BiasLayer(tf.keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `tf.keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(name=name, **kwargs)
# Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of
# "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see:
# https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214
self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable)
def call(self, x):
return x + self.bias
@add_start_docstrings(
"The {{cookiecutter.uppercase_modelname}} Model with a language modeling head. Can be used for summarization.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="model")
self.use_cache = config.use_cache
# final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency.
self.bias_layer = BiasLayer(
name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
)
def get_decoder(self):
return self.model.decoder
def get_encoder(self):
return self.model.encoder
def get_bias(self):
return {"final_logits_bias": self.bias_layer.bias}
def set_bias(self, value):
# Replaces the existing layers containing bias for correct (de)serialization.
vocab_size = value["final_logits_bias"].shape[-1]
self.bias_layer = BiasLayer(
name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False
)
self.bias_layer.bias.assign(value["final_logits_bias"])
def get_output_embeddings(self):
return self.get_input_embeddings()
def set_output_embeddings(self, value):
self.set_input_embeddings(value)
@unpack_inputs
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs: Optional[TFBaseModelOutput] = None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
):
"""
Returns:
Examples:
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> import tensorflow as tf
>>> mname = '{{cookiecutter.checkpoint_identifier}}'
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained(mname)
>>> TXT = "My friends are <mask> but they eat too many carbs."
>>> model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained(mname)
>>> batch = tokenizer([TXT], return_tensors='tf')
>>> logits = model(inputs=batch.input_ids).logits
>>> probs = tf.nn.softmax(logits[0])
>>> # probs[5] is associated with the mask token
```"""
if labels is not None:
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training
)
lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True)
lm_logits = self.bias_layer(lm_logits)
masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return TFSeq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values, # index 1 of d outputs
decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs
decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs
cross_attentions=outputs.cross_attentions, # index 4 of d outputs
encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs
encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out
encoder_attentions=outputs.encoder_attentions, # 2 of e out
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs
):
# cut decoder_input_ids if past is used
if past_key_values is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
return {
"input_ids": None, # needs to be passed to make Keras.layer.__call__ happy
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
def hf_compute_loss(self, labels, logits):
"""CrossEntropyLoss that ignores pad tokens"""
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True,
reduction=tf.keras.losses.Reduction.NONE,
)
melted_labels = tf.reshape(labels, (-1,))
active_loss = tf.not_equal(melted_labels, self.config.pad_token_id)
reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
labels = tf.boolean_mask(melted_labels, active_loss)
return loss_fn(labels, reduced_logits)
{% endif -%}
| 135,870 | 46.960113 | 221 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/__init__.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule, OptionalDependencyNotAvailable, is_tokenizers_available
{%- if "TensorFlow" in cookiecutter.generate_tensorflow_pytorch_and_flax %}
from ...utils import is_tf_available
{% endif %}
{%- if "PyTorch" in cookiecutter.generate_tensorflow_pytorch_and_flax %}
from ...utils import is_torch_available
{% endif %}
{%- if "Flax" in cookiecutter.generate_tensorflow_pytorch_and_flax %}
from ...utils import is_flax_available
{% endif %}
_import_structure = {
"configuration_{{cookiecutter.lowercase_modelname}}": ["{{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP", "{{cookiecutter.camelcase_modelname}}Config"],
"tokenization_{{cookiecutter.lowercase_modelname}}": ["{{cookiecutter.camelcase_modelname}}Tokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_{{cookiecutter.lowercase_modelname}}_fast"] = ["{{cookiecutter.camelcase_modelname}}TokenizerFast"]
{%- if "PyTorch" in cookiecutter.generate_tensorflow_pytorch_and_flax %}
{% if cookiecutter.is_encoder_decoder_model == "False" %}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_{{cookiecutter.lowercase_modelname}}"] = [
"{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST",
"{{cookiecutter.camelcase_modelname}}ForMaskedLM",
"{{cookiecutter.camelcase_modelname}}ForCausalLM",
"{{cookiecutter.camelcase_modelname}}ForMultipleChoice",
"{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"{{cookiecutter.camelcase_modelname}}ForTokenClassification",
"{{cookiecutter.camelcase_modelname}}Layer",
"{{cookiecutter.camelcase_modelname}}Model",
"{{cookiecutter.camelcase_modelname}}PreTrainedModel",
"load_tf_weights_in_{{cookiecutter.lowercase_modelname}}",
]
{% else %}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_{{cookiecutter.lowercase_modelname}}"] = [
"{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST",
"{{cookiecutter.camelcase_modelname}}ForConditionalGeneration",
"{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"{{cookiecutter.camelcase_modelname}}ForCausalLM",
"{{cookiecutter.camelcase_modelname}}Model",
"{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
{% endif %}
{% endif %}
{%- if "TensorFlow" in cookiecutter.generate_tensorflow_pytorch_and_flax %}
{% if cookiecutter.is_encoder_decoder_model == "False" %}
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_{{cookiecutter.lowercase_modelname}}"] = [
"TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST",
"TF{{cookiecutter.camelcase_modelname}}ForMaskedLM",
"TF{{cookiecutter.camelcase_modelname}}ForCausalLM",
"TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice",
"TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"TF{{cookiecutter.camelcase_modelname}}ForTokenClassification",
"TF{{cookiecutter.camelcase_modelname}}Layer",
"TF{{cookiecutter.camelcase_modelname}}Model",
"TF{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
{% else %}
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_tf_{{cookiecutter.lowercase_modelname}}"] = [
"TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration",
"TF{{cookiecutter.camelcase_modelname}}Model",
"TF{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
{% endif %}
{% endif %}
{%- if "Flax" in cookiecutter.generate_tensorflow_pytorch_and_flax %}
{% if cookiecutter.is_encoder_decoder_model == "False" %}
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_{{cookiecutter.lowercase_modelname}}"] = [
"Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM",
"Flax{{cookiecutter.camelcase_modelname}}ForCausalLM",
"Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice",
"Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification",
"Flax{{cookiecutter.camelcase_modelname}}Layer",
"Flax{{cookiecutter.camelcase_modelname}}Model",
"Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
{% else %}
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_flax_{{cookiecutter.lowercase_modelname}}"] = [
"Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration",
"Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"Flax{{cookiecutter.camelcase_modelname}}Model",
"Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
{% endif %}
{% endif %}
if TYPE_CHECKING:
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP, {{cookiecutter.camelcase_modelname}}Config
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_{{cookiecutter.lowercase_modelname}}_fast import {{cookiecutter.camelcase_modelname}}TokenizerFast
{%- if "PyTorch" in cookiecutter.generate_tensorflow_pytorch_and_flax %}
{% if cookiecutter.is_encoder_decoder_model == "False" %}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
{{cookiecutter.camelcase_modelname}}ForMaskedLM,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}ForTokenClassification,
{{cookiecutter.camelcase_modelname}}Layer,
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}PreTrainedModel,
load_tf_weights_in_{{cookiecutter.lowercase_modelname}},
)
{% else %}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% endif %}
{% endif %}
{%- if "TensorFlow" in cookiecutter.generate_tensorflow_pytorch_and_flax %}
{% if cookiecutter.is_encoder_decoder_model == "False" %}
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_{{cookiecutter.lowercase_modelname}} import (
TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
TF{{cookiecutter.camelcase_modelname}}Layer,
TF{{cookiecutter.camelcase_modelname}}Model,
TF{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% else %}
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_{{cookiecutter.lowercase_modelname}} import (
TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
TF{{cookiecutter.camelcase_modelname}}Model,
TF{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% endif %}
{% endif %}
{%- if "Flax" in cookiecutter.generate_tensorflow_pytorch_and_flax %}
{% if cookiecutter.is_encoder_decoder_model == "False" %}
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_{{cookiecutter.lowercase_modelname}} import (
Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM,
Flax{{cookiecutter.camelcase_modelname}}ForCausalLM,
Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification,
Flax{{cookiecutter.camelcase_modelname}}Layer,
Flax{{cookiecutter.camelcase_modelname}}Model,
Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% else %}
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_{{cookiecutter.lowercase_modelname}} import (
Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}Model,
Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% endif %}
{% endif %}
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12,270 | 41.756098 | 178 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_flax_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Flax {{cookiecutter.modelname}} model. """
{% if cookiecutter.is_encoder_decoder_model == "False" %}
from typing import Callable, Optional, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, unfreeze, freeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.traverse_util import flatten_dict, unflatten_dict
from flax.linen.attention import dot_product_attention_weights
from jax import lax
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_flax_outputs import (
FlaxBaseModelOutputWithPastAndCrossAttentions,
FlaxBaseModelOutputWithPoolingAndCrossAttentions,
FlaxCausalLMOutput,
FlaxCausalLMOutputWithCrossAttentions,
FlaxMaskedLMOutput,
FlaxMultipleChoiceModelOutput,
FlaxQuestionAnsweringModelOutput,
FlaxSequenceClassifierOutput,
FlaxTokenClassifierOutput,
)
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_call_sample_docstring,
overwrite_call_docstring,
)
from ...utils import logging
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}"
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
_TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer"
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading, saving and converting weights from
PyTorch models)
This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module
and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`~{{cookiecutter.uppercase_modelname}}Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the
model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on
GPUs) and `jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see
[`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`].
"""
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
Args:
input_ids (`numpy.ndarray` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`~{{cookiecutter.uppercase_modelname}}ConfiTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`numpy.ndarray` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`numpy.ndarray` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.
head_mask (`numpy.ndarray` of shape `({0})`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
remat = nn_partitioning.remat
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}Embeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.word_embeddings = nn.Embed(
self.config.vocab_size,
self.config.hidden_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
)
self.position_embeddings = nn.Embed(
self.config.max_position_embeddings,
self.config.hidden_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
)
self.token_type_embeddings = nn.Embed(
self.config.type_vocab_size,
self.config.hidden_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
)
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
# Embed
inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
position_embeds = self.position_embeddings(position_ids.astype("i4"))
token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))
# Sum all embeddings
hidden_states = inputs_embeds + token_type_embeddings + position_embeds
# Layer Norm
hidden_states = self.LayerNorm(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
return hidden_states
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}SelfAttention(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
causal: bool = False
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.head_dim = self.config.hidden_size // self.config.num_attention_heads
if self.config.hidden_size % self.config.num_attention_heads != 0:
raise ValueError(
"`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads`\
: {self.config.num_attention_heads}"
)
self.query = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
)
self.key = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
)
self.value = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
)
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,))
@nn.compact
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
"""
# detect if we're initializing by absence of existing cache data.
is_initialized = self.has_variable("cache", "cached_key")
cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update_slice(cached_key.value, key, indices)
value = lax.dynamic_update_slice(cached_value.value, value, indices)
cached_key.value = key
cached_value.value = value
num_updated_cache_vectors = query.shape[1]
cache_index.value = cache_index.value + num_updated_cache_vectors
# causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
pad_mask = jnp.broadcast_to(
jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask
def __call__(
self,
hidden_states,
attention_mask,
layer_head_mask,
key_value_states: Optional[jnp.array] = None,
init_cache: bool = False,
deterministic=True,
output_attentions: bool = False,
):
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
batch_size = hidden_states.shape[0]
# get query proj
query_states = self.query(hidden_states)
# get key, value proj
if is_cross_attention:
# cross_attentions
key_states = self.key(key_value_states)
value_states = self.value(key_value_states)
else:
# self_attention
key_states = self.key(hidden_states)
value_states = self.value(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
# handle cache prepare causal attention mask
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_mask = lax.dynamic_slice(
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
)
else:
causal_mask = self.causal_mask[:, :, :query_length, :key_length]
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask = causal_mask
elif attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
key_states, value_states, attention_mask = self._concatenate_to_cache(
key_states, value_states, query_states, attention_mask
)
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
else:
attention_bias = None
dropout_rng = None
if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.config.attention_probs_dropout_prob,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
# Mask heads if we want to
if layer_head_mask is not None:
attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
return outputs
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}SelfOutput(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}Attention(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
causal: bool = False
dtype: jnp.dtype = jnp.float32
def setup(self):
self.self = Flax{{cookiecutter.camelcase_modelname}}SelfAttention(self.config, dtype=self.dtype)
self.output = Flax{{cookiecutter.camelcase_modelname}}SelfOutput(self.config, dtype=self.dtype)
def __call__(
self,
hidden_states,
attention_mask,
layer_head_mask,
key_value_states=None,
init_cache=False,
deterministic=True,
output_attentions: bool = False,
):
# Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
# FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
# with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
attn_outputs = self.self(
hidden_states,
attention_mask,
layer_head_mask=layer_head_mask,
key_value_states=key_value_states,
init_cache=init_cache,
deterministic=deterministic,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0]
hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_outputs[1],)
return outputs
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}Intermediate(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.activation = ACT2FN[self.config.hidden_act]
def __call__(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}Output(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
def __call__(self, hidden_states, attention_output, deterministic: bool = True):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = self.LayerNorm(hidden_states + attention_output)
return hidden_states
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}Layer(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = Flax{{cookiecutter.camelcase_modelname}}Attention(self.config, dtype=self.dtype)
self.intermediate = Flax{{cookiecutter.camelcase_modelname}}Intermediate(self.config, dtype=self.dtype)
self.output = Flax{{cookiecutter.camelcase_modelname}}Output(self.config, dtype=self.dtype)
if self.config.add_cross_attention:
self.crossattention = Flax{{cookiecutter.camelcase_modelname}}Attention(self.config, causal=False, dtype=self.dtype)
def __call__(
self,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
output_attentions: bool = False,
):
# Self Attention
attention_outputs = self.attention(
hidden_states,
attention_mask,
layer_head_mask=layer_head_mask,
init_cache=init_cache,
deterministic=deterministic,
output_attentions=output_attentions,
)
attention_output = attention_outputs[0]
# Cross-Attention Block
if encoder_hidden_states is not None:
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask=encoder_attention_mask,
layer_head_mask=layer_head_mask,
key_value_states=encoder_hidden_states,
deterministic=deterministic,
output_attentions=output_attentions,
)
attention_output = cross_attention_outputs[0]
hidden_states = self.intermediate(attention_output)
hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)
outputs = (hidden_states,)
if output_attentions:
outputs += (attention_outputs[1],)
if encoder_hidden_states is not None:
outputs += (cross_attention_outputs[1],)
return outputs
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}LayerCollection(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
Flax{{cookiecutter.camelcase_modelname}}CheckpointLayer = remat(Flax{{cookiecutter.camelcase_modelname}}Layer, static_argnums=(5, 6, 7))
self.layers = [
Flax{{cookiecutter.camelcase_modelname}}CheckpointLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.num_hidden_layers)
]
else:
self.layers = [
Flax{{cookiecutter.camelcase_modelname}}Layer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
# Check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.shape[0] != (len(self.layers)):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for \
{head_mask.shape[0]}."
)
for i, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = layer(
hidden_states,
attention_mask,
head_mask[i] if head_mask is not None else None,
encoder_hidden_states,
encoder_attention_mask,
init_cache,
deterministic,
output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states,)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}Encoder(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.layer = Flax{{cookiecutter.camelcase_modelname}}LayerCollection(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
def __call__(
self,
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
return self.layer(
hidden_states,
attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
init_cache=init_cache,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}Pooler(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.dtype,
)
def __call__(self, hidden_states):
cls_hidden_state = hidden_states[:, 0]
cls_hidden_state = self.dense(cls_hidden_state)
return nn.tanh(cls_hidden_state)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPredictionHeadTransform with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
self.activation = ACT2FN[self.config.hidden_act]
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
def __call__(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.activation(hidden_states)
return self.LayerNorm(hidden_states)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLMPredictionHead with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}LMPredictionHead(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.transform = Flax{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(self.config, dtype=self.dtype)
self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False)
self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))
def __call__(self, hidden_states, shared_embedding=None):
hidden_states = self.transform(hidden_states)
if shared_embedding is not None:
hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
hidden_states = self.decoder(hidden_states)
hidden_states += self.bias
return hidden_states
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOnlyMLMHead with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.predictions = Flax{{cookiecutter.camelcase_modelname}}LMPredictionHead(self.config, dtype=self.dtype)
def __call__(self, hidden_states, shared_embedding=None):
hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding)
return hidden_states
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOnlyNSPHead with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}OnlyNSPHead(nn.Module):
dtype: jnp.dtype = jnp.float32
def setup(self):
self.seq_relationship = nn.Dense(2, dtype=self.dtype)
def __call__(self, pooled_output):
return self.seq_relationship(pooled_output)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainingHeads with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}PreTrainingHeads(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.predictions = Flax{{cookiecutter.camelcase_modelname}}LMPredictionHead(self.config, dtype=self.dtype)
self.seq_relationship = nn.Dense(2, dtype=self.dtype)
def __call__(self, hidden_states, pooled_output, shared_embedding=None):
prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = {{cookiecutter.camelcase_modelname}}Config
base_model_prefix = "{{cookiecutter.lowercase_modelname}}"
module_class: nn.Module = None
def __init__(
self,
config: {{cookiecutter.camelcase_modelname}}Config,
input_shape: Tuple = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
gradient_checkpointing: bool = False,
**kwargs
):
module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing
def enable_gradient_checkpointing(self):
self._module = self.module_class(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=True,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.init_weights with Bert->{{cookiecutter.camelcase_modelname}}
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
token_type_ids = jnp.zeros_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
attention_mask = jnp.ones_like(input_ids)
head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
if self.config.add_cross_attention:
encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
encoder_attention_mask = attention_mask
module_init_outputs = self.module.init(
rngs,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
return_dict=False,
)
else:
module_init_outputs = self.module.init(
rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False
)
random_params = module_init_outputs["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.init_cache with Bert->{{cookiecutter.camelcase_modelname}}
def init_cache(self, batch_size, max_length):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
"""
# init input variables to retrieve cache
input_ids = jnp.ones((batch_size, max_length))
attention_mask = jnp.ones_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
init_variables = self.module.init(
jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
)
return unfreeze(init_variables["cache"])
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.__call__ with Bert->{{cookiecutter.camelcase_modelname}}
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
past_key_values: dict = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# init input tensors if not passed
if token_type_ids is None:
token_type_ids = jnp.zeros_like(input_ids)
if position_ids is None:
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if head_mask is None:
head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
if self.config.add_cross_attention:
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
# down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
# changed by FlaxBertAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
outputs = self.module.apply(
inputs,
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
token_type_ids=jnp.array(token_type_ids, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
head_mask=jnp.array(head_mask, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
deterministic=not train,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
rngs=rngs,
mutable=mutable,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past_key_values = outputs
outputs["past_key_values"] = unfreeze(past_key_values["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past_key_values = outputs
outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
else:
outputs = self.module.apply(
inputs,
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
token_type_ids=jnp.array(token_type_ids, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
head_mask=jnp.array(head_mask, dtype="i4"),
deterministic=not train,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
rngs=rngs,
)
return outputs
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->{{cookiecutter.camelcase_modelname}}
class Flax{{cookiecutter.camelcase_modelname}}Module(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
add_pooling_layer: bool = True
gradient_checkpointing: bool = False
def setup(self):
self.embeddings = Flax{{cookiecutter.camelcase_modelname}}Embeddings(self.config, dtype=self.dtype)
self.encoder = Flax{{cookiecutter.camelcase_modelname}}Encoder(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
self.pooler = Flax{{cookiecutter.camelcase_modelname}}Pooler(self.config, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
head_mask: Optional[jnp.ndarray] = None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# make sure `token_type_ids` is correctly initialized when not passed
if token_type_ids is None:
token_type_ids = jnp.zeros_like(input_ids)
# make sure `position_ids` is correctly initialized when not passed
if position_ids is None:
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
hidden_states = self.embeddings(
input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic
)
outputs = self.encoder(
hidden_states,
attention_mask,
head_mask=head_mask,
deterministic=deterministic,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
pooled = self.pooler(hidden_states) if self.add_pooling_layer else None
if not return_dict:
# if pooled is None, don't return it
if pooled is None:
return (hidden_states,) + outputs[1:]
return (hidden_states, pooled) + outputs[1:]
return FlaxBaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=hidden_states,
pooler_output=pooled,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
add_start_docstrings(
"The bare {{cookiecutter.camelcase_modelname}} Model transformer outputting raw hidden-states without any specific head on top.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class Flax{{cookiecutter.camelcase_modelname}}Model(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}Module
class Flax{{cookiecutter.camelcase_modelname}}ForMaskedLMModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
self.cls = Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(config=self.config, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.{{cookiecutter.lowercase_modelname}}.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
else:
shared_embedding = None
# Compute the prediction scores
logits = self.cls(hidden_states, shared_embedding=shared_embedding)
if not return_dict:
return (logits,) + outputs[1:]
return FlaxCausalLMOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings("""{{cookiecutter.camelcase_modelname}} Model with a `language modeling` head on top for MLM training. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING)
class Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForMaskedLMModule
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC
)
class Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
self.cls = Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(config=self.config, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.{{cookiecutter.lowercase_modelname}}.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
else:
shared_embedding = None
# Compute the prediction scores
logits = self.cls(hidden_states, shared_embedding=shared_embedding)
if not return_dict:
return (logits,) + outputs[1:]
return FlaxCausalLMOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings("""{{cookiecutter.camelcase_modelname}} Model with a `language modeling` head on top for CLM training. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING)
class Flax{{cookiecutter.camelcase_modelname}}ForCausalLM(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutput, _CONFIG_FOR_DOC
)
class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
self.classifier = nn.Dense(
self.config.num_labels,
dtype=self.dtype,
)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, deterministic=deterministic)
logits = self.classifier(pooled_output)
if not return_dict:
return (logits,) + outputs[2:]
return FlaxSequenceClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
{{cookiecutter.camelcase_modelname}} Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
_TOKENIZER_FOR_DOC,
_CHECKPOINT_FOR_DOC,
FlaxSequenceClassifierOutput,
_CONFIG_FOR_DOC,
)
class Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoiceModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
self.classifier = nn.Dense(1, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
num_choices = input_ids.shape[1]
input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None
attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None
token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None
# Model
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, deterministic=deterministic)
logits = self.classifier(pooled_output)
reshaped_logits = logits.reshape(-1, num_choices)
if not return_dict:
return (reshaped_logits,) + outputs[2:]
return FlaxMultipleChoiceModelOutput(
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
{{cookiecutter.camelcase_modelname}} Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoiceModule
overwrite_call_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice, {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC
)
class Flax{{cookiecutter.camelcase_modelname}}ForTokenClassificationModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
logits = self.classifier(hidden_states)
if not return_dict:
return (logits,) + outputs[1:]
return FlaxTokenClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
{{cookiecutter.camelcase_modelname}} Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForTokenClassificationModule
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC
)
class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype, add_pooling_layer=False, gradient_checkpointing=self.gradient_checkpointing)
self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.qa_outputs(hidden_states)
start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
if not return_dict:
return (start_logits, end_logits) + outputs[1:]
return FlaxQuestionAnsweringModelOutput(
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
{{cookiecutter.camelcase_modelname}} Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
_TOKENIZER_FOR_DOC,
_CHECKPOINT_FOR_DOC,
FlaxQuestionAnsweringModelOutput,
_CONFIG_FOR_DOC,
)
class Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.{{cookiecutter.lowercase_modelname}} = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, add_pooling_layer=False, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
self.cls = Flax{{cookiecutter.camelcase_modelname}}OnlyMLMHead(config=self.config, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
token_type_ids: Optional[jnp.ndarray] = None,
head_mask: Optional[jnp.ndarray] = None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask,
token_type_ids,
position_ids,
head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
init_cache=init_cache,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.{{cookiecutter.lowercase_modelname}}.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
else:
shared_embedding = None
# Compute the prediction scores
logits = self.cls(hidden_states, shared_embedding=shared_embedding)
if not return_dict:
return (logits,) + outputs[1:]
return FlaxCausalLMOutputWithCrossAttentions(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
@add_start_docstrings(
"""
{{cookiecutter.camelcase_modelname}} Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
autoregressive tasks.
""",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class Flax{{cookiecutter.camelcase_modelname}}ForCausalLM(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values = self.init_cache(batch_size, max_length)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyway.
# Thus, we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if attention_mask is not None:
position_ids = attention_mask.cumsum(axis=-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
else:
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
return {
"past_key_values": past_key_values,
"attention_mask": extended_attention_mask,
"position_ids": position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
return model_kwargs
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForCausalLM,
_TOKENIZER_FOR_DOC,
_CHECKPOINT_FOR_DOC,
FlaxCausalLMOutputWithCrossAttentions,
_CONFIG_FOR_DOC,
)
{# encoder_decoder #}
{% else %}
import math
import random
from functools import partial
from typing import Callable, Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, unfreeze, freeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...utils import add_start_docstrings, replace_return_docstrings
from ...modeling_flax_outputs import (
FlaxBaseModelOutput,
FlaxBaseModelOutputWithPastAndCrossAttentions,
FlaxCausalLMOutputWithCrossAttentions,
FlaxSeq2SeqLMOutput,
FlaxSeq2SeqModelOutput,
FlaxSeq2SeqQuestionAnsweringModelOutput,
FlaxSeq2SeqSequenceClassifierOutput,
)
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_call_sample_docstring,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import logging
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}"
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
_TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer"
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax
Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`~{{cookiecutter.camelcase_modelname}}Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the
model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on
GPUs) and `jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see
[`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`].
"""
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
For translation and summarization training, `decoder_input_ids` should be provided. If no
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to
the right for denoising pre-training following the paper.
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will
also be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
{{cookiecutter.uppercase_modelname}}_ENCODE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
{{cookiecutter.uppercase_modelname}}_DECODE_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
For translation and summarization training, `decoder_input_ids` should be provided. If no
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to
the right for denoising pre-training following the paper.
encoder_outputs (`tuple(tuple(jnp.ndarray)`):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`,
*optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
cross-attention of the decoder.
encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will
also be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
"""
Shift input ids one token to the right.
"""
shifted_input_ids = jnp.roll(input_ids, 1, axis=-1)
shifted_input_ids = shifted_input_ids.at[(..., 0)].set(decoder_start_token_id)
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
return shifted_input_ids
class Flax{{cookiecutter.camelcase_modelname}}Attention(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
assert (
self.head_dim * self.num_heads == self.embed_dim
), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
dense = partial(
nn.Dense,
self.embed_dim,
use_bias=self.bias,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
self.out_proj = dense()
self.dropout_layer = nn.Dropout(rate=self.dropout)
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
"""
# detect if we're initializing by absence of existing cache data.
is_initialized = self.has_variable("cache", "cached_key")
cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update_slice(cached_key.value, key, indices)
value = lax.dynamic_update_slice(cached_value.value, value, indices)
cached_key.value = key
cached_value.value = value
num_updated_cache_vectors = query.shape[1]
cache_index.value = cache_index.value + num_updated_cache_vectors
# causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
pad_mask = jnp.broadcast_to(
jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
batch_size = hidden_states.shape[0]
# get query proj
query_states = self.q_proj(hidden_states)
# get key, value proj
if is_cross_attention:
# cross_attentions
key_states = self.k_proj(key_value_states)
value_states = self.v_proj(key_value_states)
else:
# self_attention
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
# handle cache prepare causal attention mask
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_mask = lax.dynamic_slice(
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
)
else:
causal_mask = self.causal_mask[:, :, :query_length, :key_length]
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask = causal_mask
elif attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
key_states, value_states, attention_mask = self._concatenate_to_cache(
key_states, value_states, query_states, attention_mask
)
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
else:
attention_bias = None
dropout_rng = None
if not deterministic and self.dropout > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.dropout,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
class Flax{{cookiecutter.camelcase_modelname}}EncoderLayer(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = Flax{{cookiecutter.camelcase_modelname}}Attention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.encoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype
)
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.fc1 = nn.Dense(
self.config.encoder_ffn_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.fc2 = nn.Dense(
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class Flax{{cookiecutter.camelcase_modelname}}EncoderLayerCollection(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
Flax{{cookiecutter.camelcase_modelname}}EncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.encoder_layers)
]
self.layerdrop = self.config.encoder_layerdrop
def __call__(
self,
hidden_states,
attention_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for encoder_layer in self.layers:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.layerdrop): # skip the layer
layer_outputs = (None, None)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
output_attentions,
deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
class Flax{{cookiecutter.camelcase_modelname}}DecoderLayer(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = Flax{{cookiecutter.camelcase_modelname}}Attention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
causal=True,
dtype=self.dtype,
)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype)
self.encoder_attn = Flax{{cookiecutter.camelcase_modelname}}Attention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
)
self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype)
self.fc1 = nn.Dense(
self.config.decoder_ffn_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.fc2 = nn.Dense(
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
class Flax{{cookiecutter.camelcase_modelname}}DecoderLayerCollection(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
Flax{{cookiecutter.camelcase_modelname}}DecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.decoder_layers)
]
self.layerdrop = self.config.decoder_layerdrop
def __call__(
self,
hidden_states,
attention_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.layerdrop):
layer_outputs = (None, None, None)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
init_cache=init_cache,
output_attentions=output_attentions,
deterministic=deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
class Flax{{cookiecutter.camelcase_modelname}}ClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
config: {{cookiecutter.camelcase_modelname}}Config
inner_dim: int
num_classes: int
pooler_dropout: float
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(
self.inner_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.dropout = nn.Dropout(rate=self.pooler_dropout)
self.out_proj = nn.Dense(
self.num_classes,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
def __call__(self, hidden_states: jnp.ndarray, deterministic: bool):
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = self.dense(hidden_states)
hidden_states = jnp.tanh(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = self.out_proj(hidden_states)
return hidden_states
class Flax{{cookiecutter.camelcase_modelname}}Encoder(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
embed_tokens: Optional[nn.Embed] = None
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.padding_idx = self.config.pad_token_id
self.max_source_positions = self.config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0
if self.embed_tokens is None:
self.embed_tokens = nn.Embed(
self.config.vocab_size,
embed_dim,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
)
# {{cookiecutter.camelcase_modelname}} is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjust num_embeddings appropriately. Other models don't have this hack
self.offset = 2
self.embed_positions = nn.Embed(
self.config.max_position_embeddings + self.offset,
embed_dim,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
)
self.layers = Flax{{cookiecutter.camelcase_modelname}}EncoderLayerCollection(self.config, self.dtype)
self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
input_shape = input_ids.shape
input_ids = input_ids.reshape(-1, input_shape[-1])
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(position_ids + self.offset)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return outputs
return FlaxBaseModelOutput(
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class Flax{{cookiecutter.camelcase_modelname}}Decoder(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
embed_tokens: Optional[nn.Embed] = None
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.padding_idx = self.config.pad_token_id
self.max_target_positions = self.config.max_position_embeddings
self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0
if self.embed_tokens is None:
self.embed_tokens = nn.Embed(
self.config.vocab_size,
embed_dim,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
)
# {{cookiecutter.camelcase_modelname}} is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjust num_embeddings appropriately. Other models don't have this hack
self.offset = 2
self.embed_positions = nn.Embed(
self.config.max_position_embeddings + self.offset,
embed_dim,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
)
self.layers = Flax{{cookiecutter.camelcase_modelname}}DecoderLayerCollection(self.config, self.dtype)
self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
input_shape = input_ids.shape
input_ids = input_ids.reshape(-1, input_shape[-1])
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
# embed positions
positions = self.embed_positions(position_ids + self.offset)
hidden_states = inputs_embeds + positions
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return outputs
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
class Flax{{cookiecutter.camelcase_modelname}}Module(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.init_std),
)
self.encoder = Flax{{cookiecutter.camelcase_modelname}}Encoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
self.decoder = Flax{{cookiecutter.camelcase_modelname}}Decoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return FlaxSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
class Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel(FlaxPreTrainedModel):
config_class = {{cookiecutter.camelcase_modelname}}Config
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: {{cookiecutter.camelcase_modelname}}Config,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
# make sure initialization pass will work for Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule
input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
attention_mask = jnp.ones_like(input_ids)
decoder_input_ids = input_ids
decoder_attention_mask = jnp.ones_like(input_ids)
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def init_cache(self, batch_size, max_length, encoder_outputs):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`,
*optional*: `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the
encoder. Used in the cross-attention of the decoder.
"""
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
)
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
init_variables = self.module.init(
jax.random.PRNGKey(0),
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
init_cache=True,
method=_decoder_forward, # we only need to call the decoder to init the cache
)
return unfreeze(init_variables["cache"])
@add_start_docstrings({{cookiecutter.uppercase_modelname}}_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class={{cookiecutter.camelcase_modelname}}Config)
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
>>> encoder_outputs = model.encode(**inputs)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if position_ids is None:
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(input_ids, attention_mask, position_ids, **kwargs)
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
method=_encoder_forward,
)
@add_start_docstrings({{cookiecutter.uppercase_modelname}}_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class={{cookiecutter.camelcase_modelname}}Config)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by Flax{{cookiecutter.camelcase_modelname}}Attention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_input_ids: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# prepare encoder inputs
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if position_ids is None:
batch_size, sequence_length = input_ids.shape
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
# prepare decoder inputs
if decoder_input_ids is None:
decoder_input_ids = shift_tokens_right(
input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
if decoder_position_ids is None:
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
position_ids=jnp.array(position_ids, dtype="i4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
)
@add_start_docstrings(
"The bare {{cookiecutter.camelcase_modelname}} Model transformer outputting raw hidden-states without any specific head on top.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class Flax{{cookiecutter.camelcase_modelname}}Model(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = Flax{{cookiecutter.camelcase_modelname}}Module
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}Model, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC
)
class Flax{{cookiecutter.camelcase_modelname}}ForConditionalGenerationModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.model.shared.num_embeddings,
use_bias=False,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings))
def _get_encoder_module(self):
return self.model.encoder
def _get_decoder_module(self):
return self.model.decoder
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
position_ids=position_ids,
decoder_position_ids=decoder_position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.model.variables["params"]["shared"]["embedding"]
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
lm_logits += self.final_logits_bias.astype(self.dtype)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return output
return FlaxSeq2SeqLMOutput(
logits=lm_logits,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings(
"The {{cookiecutter.uppercase_modelname}} Model with a language modeling head. Can be used for summarization.", {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
)
class Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings({{cookiecutter.uppercase_modelname}}_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class={{cookiecutter.camelcase_modelname}}Config)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
deterministic: bool = True,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by Flax{{cookiecutter.camelcase_modelname}}Attention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
outputs = decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = module.model.variables["params"]["shared"]["embedding"]
lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = module.lm_head(hidden_states)
lm_logits += module.final_logits_bias.astype(self.dtype)
return lm_logits, outputs
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
if past_key_values is None:
lm_logits, decoder_outputs = outputs
else:
(lm_logits, decoder_outputs), past = outputs
if return_dict:
outputs = FlaxCausalLMOutputWithCrossAttentions(
logits=lm_logits,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
)
else:
outputs = (lm_logits,) + decoder_outputs[1:]
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jnp.DeviceArray] = None,
decoder_attention_mask: Optional[jnp.DeviceArray] = None,
encoder_outputs=None,
**kwargs
):
# initializing the cache
batch_size, seq_length = decoder_input_ids.shape
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if decoder_attention_mask is not None:
position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
else:
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
"decoder_position_ids": position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
return model_kwargs
FLAX_{{cookiecutter.uppercase_modelname}}_CONDITIONAL_GENERATION_DOCSTRING = """
Returns:
Summarization example:
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='np')
>>> # Generate Summary
>>> summary_ids = model.generate(inputs['input_ids']).sequences
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
```
Mask filling example:
```python
>>> import jax
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> TXT = "My friends are <mask> but they eat too many carbs."
>>> input_ids = tokenizer([TXT], return_tensors='np')['input_ids']
>>> logits = model(input_ids).logits
>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
>>> probs = jax.nn.softmax(logits[0, masked_index], axis=0)
>>> values, predictions = jax.lax.top_k(probs, k=1)
>>> tokenizer.decode(predictions).split()
```
"""
overwrite_call_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING + FLAX_{{cookiecutter.uppercase_modelname}}_CONDITIONAL_GENERATION_DOCSTRING
)
append_replace_return_docstrings(
Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
)
class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
num_labels: Optional[int] = None
def setup(self):
self.model = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype)
self.classification_head = Flax{{cookiecutter.camelcase_modelname}}ClassificationHead(
config=self.config,
inner_dim=self.config.d_model,
num_classes=self.num_labels if self.num_labels is not None else self.config.num_labels,
pooler_dropout=self.config.classifier_dropout,
)
def _get_encoder_module(self):
return self.model.encoder
def _get_decoder_module(self):
return self.model.decoder
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
position_ids=position_ids,
decoder_position_ids=decoder_position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
hidden_states = outputs[0] # last hidden state
eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0)
# The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation
if type(eos_mask) != jax.interpreters.partial_eval.DynamicJaxprTracer:
if len(jnp.unique(eos_mask.sum(1))) > 1:
raise ValueError("All examples must have the same number of <eos> tokens.")
if any(eos_mask.sum(1) == 0):
raise ValueError("There are missing <eos> tokens in input_ids")
# Ensure to keep 1 only for the last <eos> token for each example
eos_mask_noised = eos_mask + jnp.arange(eos_mask.shape[1]) * 1e-6
eos_mask = jnp.where(eos_mask_noised == eos_mask_noised.max(1).reshape(-1, 1), 1, 0)
sentence_representation = jnp.einsum("ijk, ij -> ijk", hidden_states, eos_mask).sum(1)
logits = self.classification_head(sentence_representation, deterministic=deterministic)
if not return_dict:
output = (logits,) + outputs[1:]
return output
return FlaxSeq2SeqSequenceClassifierOutput(
logits=logits,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings(
"""
{{cookiecutter.camelcase_modelname}} model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
tasks.
""",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule
dtype = jnp.float32
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
_TOKENIZER_FOR_DOC,
_CHECKPOINT_FOR_DOC,
FlaxSeq2SeqSequenceClassifierOutput,
_CONFIG_FOR_DOC,
)
class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule(nn.Module):
config: {{cookiecutter.camelcase_modelname}}Config
dtype: jnp.dtype = jnp.float32
num_labels = 2
def setup(self):
self.model = Flax{{cookiecutter.camelcase_modelname}}Module(config=self.config, dtype=self.dtype)
self.qa_outputs = nn.Dense(
self.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
def _get_encoder_module(self):
return self.model.encoder
def _get_decoder_module(self):
return self.model.decoder
def __call__(
self,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
position_ids,
decoder_position_ids,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
position_ids=position_ids,
decoder_position_ids=decoder_position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = jnp.split(logits, logits.shape[-1], axis=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
if not return_dict:
output = (start_logits, end_logits) + outputs[1:]
return output
return FlaxSeq2SeqQuestionAnsweringModelOutput(
start_logits=start_logits,
end_logits=end_logits,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings(
"""
{{cookiecutter.uppercase_modelname}} Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel):
module_class = Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnsweringModule
dtype = jnp.float32
append_call_sample_docstring(
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
_TOKENIZER_FOR_DOC,
_CHECKPOINT_FOR_DOC,
FlaxSeq2SeqQuestionAnsweringModelOutput,
_CONFIG_FOR_DOC,
)
{% endif -%}
| 139,390 | 42.008639 | 213 | py |
transformers | transformers-main/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch {{cookiecutter.modelname}} model. """
{% if cookiecutter.is_encoder_decoder_model == "False" %}
import math
import os
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from typing import Optional, Tuple, Union
from ...activations import ACT2FN
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel, SequenceSummary
from ...pytorch_utils import (
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
prune_linear_layer,
)
from ...utils import logging
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}"
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [
"{{cookiecutter.checkpoint_identifier}}",
# See all {{cookiecutter.modelname}} models at https://huggingface.co/models?filter={{cookiecutter.lowercase_modelname}}
]
def load_tf_weights_in_{{cookiecutter.lowercase_modelname}}(model, config, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scope_names = re.split(r"_(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "squad":
pointer = getattr(pointer, "classifier")
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
assert (
pointer.shape == array.shape
), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}Embeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
persistent=False,
)
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}SelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(config, "position_embedding_type", "absolute")
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in {{cookiecutter.camelcase_modelname}}Model forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}SelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}Attention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = {{cookiecutter.camelcase_modelname}}SelfAttention(config, position_embedding_type=position_embedding_type)
self.output = {{cookiecutter.camelcase_modelname}}SelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}Intermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}Output(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}Layer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = {{cookiecutter.camelcase_modelname}}Attention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
self.crossattention = {{cookiecutter.camelcase_modelname}}Attention(config, position_embedding_type="absolute")
self.intermediate = {{cookiecutter.camelcase_modelname}}Intermediate(config)
self.output = {{cookiecutter.camelcase_modelname}}Output(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
assert hasattr(
self, "crossattention"
), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}Encoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([{{cookiecutter.camelcase_modelname}}Layer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
if self.gradient_checkpointing and self.training and use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, past_key_value, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}PredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}LMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = {{cookiecutter.camelcase_modelname}}PredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}OnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = {{cookiecutter.camelcase_modelname}}LMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = {{cookiecutter.camelcase_modelname}}Config
load_tf_weights = load_tf_weights_in_{{cookiecutter.lowercase_modelname}}
base_model_prefix = "{{cookiecutter.lowercase_modelname}}"
supports_gradient_checkpointing = True
_keys_to_ignore_on_load_missing = [r"position_ids"]
def _init_weights(self, module):
""" Initialize the weights """
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, {{cookiecutter.camelcase_modelname}}Encoder):
module.gradient_checkpointing = value
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
usage and behavior.
Parameters:
config ([`~{{cookiecutter.camelcase_modelname}}Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`{{cookiecutter.camelcase_modelname}}Tokenizer`].
See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert *input_ids* indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare {{cookiecutter.modelname}} Model transformer outputting raw hidden-states without any specific head on top.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelname}}PreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well
as a decoder, in which case a layer of cross-attention is added between
the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani,
Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the
`is_decoder` argument of the configuration set to `True`.
To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder`
argument and `add_cross_attention` set to `True`; an
`encoder_hidden_states` is then expected as an input to the forward pass.
"""
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = {{cookiecutter.camelcase_modelname}}Embeddings(config)
self.encoder = {{cookiecutter.camelcase_modelname}}Encoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask
is used in the cross-attention if the model is configured as a decoder.
Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)`
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up
decoding (see `past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=sequence_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings("""{{cookiecutter.modelname}} Model with a `language modeling` head on top. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING)
class {{cookiecutter.camelcase_modelname}}ForMaskedLM({{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `{{cookiecutter.camelcase_modelname}}ForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
self.cls = {{cookiecutter.camelcase_modelname}}OnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss.
Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring)
Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels
in `[0, ..., config.vocab_size]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
effective_batch_size = input_shape[0]
# add a dummy token
assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
dummy_token = torch.full(
(effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
)
input_ids = torch.cat([input_ids, dummy_token], dim=1)
return {"input_ids": input_ids, "attention_mask": attention_mask}
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model with a `language modeling` head on top for CLM fine-tuning. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
)
class {{cookiecutter.camelcase_modelname}}ForCausalLM({{cookiecutter.camelcase_modelname}}PreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
self.cls = {{cookiecutter.camelcase_modelname}}OnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2
tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional
tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two
additional tensors are only required when the model is used as a decoder in a Sequence to Sequence
model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential
decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)`
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up
decoding (see `past_key_values`).
Returns:
Example:
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForCausalLM, {{cookiecutter.camelcase_modelname}}Config
>>> import torch
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> config = {{cookiecutter.camelcase_modelname}}Config.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
>>> config.is_decoder = True
>>> model = {{cookiecutter.camelcase_modelname}}ForCausalLM.from_pretrained('{{cookiecutter.checkpoint_identifier}}', config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[1:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
# cut decoder_input_ids if past is used
if past_key_values is not None:
input_ids = input_ids[:, -1:]
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],)
return reordered_past
class {{cookiecutter.camelcase_modelname}}ClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
self.config = config
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = ACT2FN[self.config.hidden_act](x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class {{cookiecutter.camelcase_modelname}}ForSequenceClassification({{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
self.classifier = {{cookiecutter.camelcase_modelname}}ClassificationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss.
Indices should be in `[0, ..., config.num_labels - 1]`.
If `config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class {{cookiecutter.camelcase_modelname}}ForMultipleChoice({{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
self.sequence_summary = SequenceSummary(config)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss.
Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension
of the input tensors. (See `input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
pooled_output = self.sequence_summary(sequence_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class {{cookiecutter.camelcase_modelname}}ForTokenClassification({{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss.
Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""{{cookiecutter.modelname}} Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """,
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class {{cookiecutter.camelcase_modelname}}ForQuestionAnswering({{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.{{cookiecutter.lowercase_modelname}}(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
{% else %}
import math
import copy
from typing import Optional, Tuple, List, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...utils import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
Seq2SeqQuestionAnsweringModelOutput,
Seq2SeqSequenceClassifierOutput,
CausalLMOutputWithCrossAttentions
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}"
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config"
{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [
"{{cookiecutter.checkpoint_identifier}}",
# See all {{cookiecutter.modelname}} models at https://huggingface.co/models?filter={{cookiecutter.lowercase_modelname}}
]
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min)
mask_cond = torch.arange(mask.size(-1))
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
def _expand_mask(
mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None
):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
class {{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
)
return super().forward(positions)
class {{cookiecutter.camelcase_modelname}}Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert (
self.head_dim * num_heads == self.embed_dim
), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads})."
self.scaling = self.head_dim ** -0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class {{cookiecutter.camelcase_modelname}}EncoderLayer(nn.Module):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = {{cookiecutter.camelcase_modelname}}Attention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
output_attentions: bool = False,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
attention_mask (`torch.FloatTensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
*(config.encoder_attention_heads,)*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
if hidden_states.dtype == torch.float16 and (torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class {{cookiecutter.camelcase_modelname}}DecoderLayer(nn.Module):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = {{cookiecutter.camelcase_modelname}}Attention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = {{cookiecutter.camelcase_modelname}}Attention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
attention_mask (`torch.FloatTensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape *(batch, seq_len, embed_dim)*
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
*(encoder_attention_heads,)*.
cross_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size *(decoder_attention_heads,)*.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
# Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}ClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim: int,
inner_dim: int,
num_classes: int,
pooler_dropout: float,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, hidden_states: torch.Tensor):
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
config_class = {{cookiecutter.camelcase_modelname}}Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, ({{cookiecutter.camelcase_modelname}}Decoder, {{cookiecutter.camelcase_modelname}}Encoder)):
module.gradient_checkpointing = value
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic
methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
pruning heads etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.
Parameters:
config ([`~{{cookiecutter.camelcase_modelname}}Config`]):
Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model
weights.
"""
{{cookiecutter.uppercase_modelname}}_GENERATION_EXAMPLE = r"""
Summarization example:
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
>>> # Generate Summary
>>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5)
>>> print(tokenizer.decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
```
"""
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Provide for translation and summarization training. By default, the model will create this tensor by
shifting the `input_ids` to the right, following the paper.
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will
also be used by default.
If you want to change padding behavior, you should read [`modeling_{{cookiecutter.lowercase_modelname}}._prepare_decoder_attention_mask`] and
modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`,
*optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors
of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)`
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds`
have to be input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds`
takes the value of `inputs_embeds`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up
decoding (see `past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
{{cookiecutter.uppercase_modelname}}_STANDALONE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`ProphetNetTokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class {{cookiecutter.camelcase_modelname}}Encoder({{cookiecutter.camelcase_modelname}}PreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`{{cookiecutter.camelcase_modelname}}EncoderLayer`].
Args:
config: {{cookiecutter.camelcase_modelname}}Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
self.embed_positions = {{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([{{cookiecutter.camelcase_modelname}}EncoderLayer(config) for _ in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(embed_dim)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded
representation. This is useful if you want more control over how to convert `input_ids` indices
into associated vectors than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layers)
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = torch.randn([])
if self.training and (dropout_probability < self.layerdrop): # skip the layer
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class {{cookiecutter.camelcase_modelname}}Decoder({{cookiecutter.camelcase_modelname}}PreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`{{cookiecutter.camelcase_modelname}}DecoderLayer`]
Args:
config: {{cookiecutter.camelcase_modelname}}Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
self.embed_positions = {{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList([{{cookiecutter.camelcase_modelname}}DecoderLayer(config) for _ in range(config.decoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length
).to(self.device)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2
tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional
tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential
decoding.
If `past_key_values` are used, the user can optionally input only the last
`decoder_input_ids` (those that don't have their past key value states given to this model) of
shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size,
sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices
into associated vectors than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
attention_mask = self._prepare_decoder_attention_mask(attention_mask, input_shape, inputs_embeds, past_key_values_length)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
hidden_states = inputs_embeds + positions
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# decoder layers
if self.gradient_checkpointing and self.training and use_cache:
logger.warning("`use_cache = True` is incompatible with gradient checkpointing`. Setting `use_cache = False`...")
use_cache = False
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
assert attn_mask.size()[0] == (
len(self.layers)
), f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = torch.randn([])
if self.training and (dropout_probability < self.layerdrop):
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, output_attentions, use_cache)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"The bare {{cookiecutter.modelname}} Model outputting raw hidden-states without any specific head on top.",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
self.encoder = {{cookiecutter.camelcase_modelname}}Encoder(config, self.shared)
self.decoder = {{cookiecutter.camelcase_modelname}}Decoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"The {{cookiecutter.modelname}} Model with a language modeling head. Can be used for summarization.", {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
)
class {{cookiecutter.camelcase_modelname}}ForConditionalGeneration({{cookiecutter.camelcase_modelname}}PreTrainedModel):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
r"final_logits_bias",
r"encoder\.version",
r"decoder\.version",
r"lm_head\.weight",
]
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config):
super().__init__(config)
self.model = {{cookiecutter.camelcase_modelname}}Model(config)
self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens)
self._resize_final_logits_bias(new_num_tokens)
return new_embeddings
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings({{cookiecutter.uppercase_modelname}}_GENERATION_EXAMPLE)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Conditional generation example:
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> TXT = "My friends are <mask> but they eat too many carbs."
>>> model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
>>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
>>> logits = model(input_ids).logits
>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
>>> probs = logits[0, masked_index].softmax(dim=0)
>>> values, predictions = probs.topk(5)
>>> tokenizer.decode(predictions).split()
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs
):
# cut decoder_input_ids if past is used
if past_key_values is not None:
decoder_input_ids = decoder_input_ids[:, -1:]
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
@add_start_docstrings(
"""
{{cookiecutter.camelcase_modelname}} model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
tasks.
""",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class {{cookiecutter.camelcase_modelname}}ForSequenceClassification({{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
super().__init__(config, **kwargs)
self.model = {{cookiecutter.camelcase_modelname}}Model(config)
self.classification_head = {{cookiecutter.camelcase_modelname}}ClassificationHead(
config.d_model,
config.d_model,
config.num_labels,
config.classifier_dropout,
)
self.model._init_weights(self.classification_head.dense)
self.model._init_weights(self.classification_head.out_proj)
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
if input_ids is None and inputs_embeds is not None:
raise NotImplementedError(
f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0] # last hidden state
eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device)
if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
raise ValueError("All examples must have the same number of <eos> tokens.")
sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
:, -1, :
]
logits = self.classification_head(sentence_representation)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.config.num_labels == 1:
self.config.problem_type = "regression"
elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.config.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return Seq2SeqSequenceClassifierOutput(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings(
"""
{{cookiecutter.modelname}} Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
{{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
)
class {{cookiecutter.camelcase_modelname}}ForQuestionAnswering({{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.model = {{cookiecutter.camelcase_modelname}}Model(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.model._init_weights(self.qa_outputs)
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
start_positions=None,
end_positions=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if start_positions is not None and end_positions is not None:
use_cache = False
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (
start_logits,
end_logits,
) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return Seq2SeqQuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}DecoderWrapper({{cookiecutter.camelcase_modelname}}PreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = {{cookiecutter.camelcase_modelname}}Decoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->{{cookiecutter.camelcase_modelname}}
class {{cookiecutter.camelcase_modelname}}ForCausalLM({{cookiecutter.camelcase_modelname}}PreTrainedModel):
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = {{cookiecutter.camelcase_modelname}}DecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)`
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up
decoding (see `past_key_values`).
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Returns:
Example:
```python
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForCausalLM
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('facebook/bart-large')
>>> model = {{cookiecutter.camelcase_modelname}}ForCausalLM.from_pretrained('facebook/bart-large', add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
```
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs):
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_ids.shape)
if past_key_values:
input_ids = input_ids[:, -1:]
# first step, decoder_cached_states are empty
return {
"input_ids": input_ids, # encoder_outputs is defined. input_ids not needed
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"use_cache": use_cache,
}
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
{% endif -%}
| 154,748 | 45.345912 | 390 | py |
transformers | transformers-main/templates/adding_a_missing_tokenization_test/cookiecutter-template-{{cookiecutter.modelname}}/test_tokenization_{{cookiecutter.lowercase_modelname}}.py | # coding=utf-8
# Copyright 2022 {{cookiecutter.authors}}. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the {{cookiecutter.modelname}} tokenizer. """
import unittest
{% if cookiecutter.has_slow_class == "True" and cookiecutter.has_fast_class == "True" -%}
from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}TokenizerFast
{% elif cookiecutter.has_slow_class == "True" -%}
from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer
{% elif cookiecutter.has_fast_class == "True" -%}
from transformers import {{cookiecutter.camelcase_modelname}}TokenizerFast
{% endif -%}
{% if cookiecutter.has_fast_class == "True" and cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%}
from transformers.testing_utils import require_sentencepiece, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_sentencepiece
@require_tokenizers
{% elif cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%}
from transformers.testing_utils import require_sentencepiece
from ...test_tokenization_common import TokenizerTesterMixin
@require_sentencepiece
{% elif cookiecutter.has_fast_class == "True" -%}
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
{% else -%}
from ...test_tokenization_common import TokenizerTesterMixin
{% endif -%}
class {{cookiecutter.camelcase_modelname}}TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
{% if cookiecutter.has_slow_class == "True" -%}
tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
test_slow_tokenizer = True
{% else -%}
tokenizer_class = None
test_slow_tokenizer = False
{% endif -%}
{% if cookiecutter.has_fast_class == "True" -%}
rust_tokenizer_class = {{cookiecutter.camelcase_modelname}}TokenizerFast
test_rust_tokenizer = True
{% else -%}
rust_tokenizer_class = None
test_rust_tokenizer = False
{% endif -%}
{% if cookiecutter.slow_tokenizer_use_sentencepiece == "True" -%}
test_sentencepiece = True
{% endif -%}
# TODO: Check in `TokenizerTesterMixin` if other attributes need to be changed
def setUp(self):
super().setUp()
raise NotImplementedError(
"Here you have to implement the saving of a toy tokenizer in "
"`self.tmpdirname`."
)
# TODO: add tests with hard-coded target values | 3,032 | 37.884615 | 121 | py |
transformers | transformers-main/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning a 🤗 Transformers model on {{cookiecutter.example_name}}.
"""
# You can also adapt this script on your own {{cookiecutter.example_name}} task. Pointers for this are left as comments.
{%- if cookiecutter.with_trainer == "True" %}
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional, List
import datasets
import torch
from datasets import load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
AutoConfig,
{{cookiecutter.model_class}},
AutoTokenizer,
DataCollatorWithPadding,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import send_example_telemetry
logger = logging.getLogger(__name__)
{%- if cookiecutter.can_train_from_scratch == "True" %}
# You should update this to your particular problem to have better documentation of `model_type`
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": "The model checkpoint for weights initialization."
"Don't set if you want to train a model from scratch."
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
{%- elif cookiecutter.can_train_from_scratch == "False" %}
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
},
)
{% endif %}
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
test_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input test data file to predict the label on (a text file)."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
def __post_init__(self):
if (
self.dataset_name is None
and self.train_file is None
and self.validation_file is None
and self.test_file is None
):
raise ValueError("Need either a dataset name or a training/validation/test file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
if self.test_file is not None:
extension = self.test_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`test_file` should be a csv, a json or a txt file."
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_{{cookiecutter.example_shortcut}}", model_args, data_args)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
if extension == "txt":
extension = "text"
raw_datasets = load_dataset(extension, data_files=data_files)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
{%- if cookiecutter.can_train_from_scratch == "True" %}
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.model_name_or_path:
model = {{cookiecutter.model_class}}.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
logger.info("Training new model from scratch")
model = {{cookiecutter.model_class}}.from_config(config)
model.resize_token_embeddings(len(tokenizer))
{%- elif cookiecutter.can_train_from_scratch == "False" %}
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
# num_labels=num_labels, Uncomment if you have a certain number of labels
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
{% endif %}
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = raw_datasets["train"].column_names
elif training_args.do_eval:
column_names = raw_datasets["validation"].column_names
elif training_args.do_predict:
column_names = raw_datasets["test"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], padding="max_length", truncation=True)
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = raw_datasets["train"]
if data_args.max_train_samples is not None:
# Select Sample from Dataset
train_dataset = train_dataset.select(range(data_args.max_train_samples))
# tokenize train dataset in batch
with training_args.main_process_first(desc="train dataset map tokenization"):
train_dataset = train_dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
)
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = raw_datasets["validation"]
# Selecting samples from dataset
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
# tokenize validation dataset
with training_args.main_process_first(desc="validation dataset map tokenization"):
eval_dataset = eval_dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
)
if training_args.do_predict:
if "test" not in raw_datasets:
raise ValueError("--do_predict requires a test dataset")
predict_dataset = raw_datasets["test"]
# Selecting samples from dataset
if data_args.max_predict_samples is not None:
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
# tokenize predict dataset
with training_args.main_process_first(desc="prediction dataset map tokenization"):
predict_dataset = predict_dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
)
# Data collator
data_collator=default_data_collator if not training_args.fp16 else DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=data_collator,
)
# Training
if training_args.do_train:
{%- if cookiecutter.can_train_from_scratch == "False" %}
if last_checkpoint is not None:
checkpoint = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path):
checkpoint = model_args.model_name_or_path
else:
checkpoint = None
{%- elif cookiecutter.can_train_from_scratch == "True" %}
if last_checkpoint is not None:
checkpoint = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
checkpoint = model_args.model_name_or_path
else:
checkpoint = None
{% endif %}
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(predict_dataset)
max_predict_samples = data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
# write custom code for saving predictions according to task
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
{%- elif cookiecutter.with_trainer == "False" %}
import argparse
import logging
import math
import os
import random
import datasets
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import transformers
from accelerate import Accelerator
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
AdamW,
AutoConfig,
{{cookiecutter.model_class}},
AutoTokenizer,
DataCollatorWithPadding,
PretrainedConfig,
SchedulerType,
default_data_collator,
get_scheduler,
set_seed,
)
from transformers.utils import send_example_telemetry
logger = logging.getLogger(__name__)
{%- if cookiecutter.can_train_from_scratch == "True" %}
# You should update this to your particular problem to have better documentation of `model_type`
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
{% endif %}
def parse_args():
parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help= "The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
"--train_file", type=str, default=None, help="A csv or a json file containing the training data."
)
parser.add_argument(
"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
)
parser.add_argument(
"--max_length",
type=int,
default=128,
help=(
"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
" sequences shorter will be padded if `--pad_to_max_lengh` is passed."
),
)
parser.add_argument(
"--pad_to_max_length",
action="store_true",
help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
)
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
required=True,
)
parser.add_argument(
"--config_name",
type=str,
default=None,
help="Pretrained config name or path if not the same as model_name",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--use_slow_tokenizer",
action="store_true",
help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=8,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=8,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="linear",
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
{%- if cookiecutter.can_train_from_scratch == "True" %}
parser.add_argument(
"--model_type",
type=str,
default=None,
help="Model type to use if training from scratch.",
choices=MODEL_TYPES,
)
{% endif %}
args = parser.parse_args()
# Sanity checks
if args.task_name is None and args.train_file is None and args.validation_file is None:
raise ValueError("Need either a task name or a training/validation file.")
else:
if args.train_file is not None:
extension = args.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if args.validation_file is not None:
extension = args.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
return args
def main():
args = parse_args()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_{{cookiecutter.example_shortcut}", args)
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
accelerator = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
else:
data_files = {}
if args.train_file is not None:
data_files["train"] = args.train_file
if args.validation_file is not None:
data_files["validation"] = args.validation_file
extension = args.train_file.split(".")[-1]
raw_datasets = load_dataset(extension, data_files=data_files)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
{%- if cookiecutter.can_train_from_scratch == "True" %}
if model_args.config_name:
config = AutoConfig.from_pretrained(args.model_name_or_path)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(args.model_name_or_path)
else:
config = CONFIG_MAPPING[args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.model_name_or_path:
model = {{cookiecutter.model_class}}.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
)
else:
logger.info("Training new model from scratch")
model = {{cookiecutter.model_class}}.from_config(config)
model.resize_token_embeddings(len(tokenizer))
{%- elif cookiecutter.can_train_from_scratch == "False" %}
config = AutoConfig.from_pretrained(
args.config_name if model_args.config_name else args.model_name_or_path,
# num_labels=num_labels, Uncomment if you have a certain number of labels
finetuning_task=data_args.task_name,
)
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name if model_args.tokenizer_name else args.model_name_or_path,
use_fast=not args.use_slow_tokenizer,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
)
{% endif %}
# Preprocessing the datasets.
# First we tokenize all the texts.
column_names = raw_datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
padding = "max_length" if args.pad_to_max_length else False
def tokenize_function(examples):
result = tokenizer(examples[text_column_name], padding=padding, max_length=args.max_length, truncation=True)
if "label" in examples:
result["labels"] = examples["label"]
return result
processed_datasets = raw_datasets.map(
preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names
)
train_dataset = processed_datasets["train"]
eval_dataset = processed_datasets["validation"]
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
# DataLoaders creation:
if args.pad_to_max_length:
# If padding was already done ot max length, we use the default data collator that will just convert everything
# to tensors.
data_collator = default_data_collator
else:
# Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
# the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
# of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None))
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
)
eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
# Optimizer
# Split weights in two groups, one with weight decay and the other not.
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader
)
# Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
# shorter in multiprocess)
# Scheduler and math around the number of training steps.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
else:
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=args.max_train_steps,
)
# TODO Get the proper metric function
# metric = load_metric(xxx)
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
completed_steps = 0
for epoch in range(args.num_train_epochs):
model.train()
for step, batch in enumerate(train_dataloader):
outputs = model(**batch)
loss = outputs.loss
loss = loss / args.gradient_accumulation_steps
accelerator.backward(loss)
if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
completed_steps += 1
if completed_steps >= args.max_train_steps:
break
model.eval()
for step, batch in enumerate(eval_dataloader):
with torch.no_grad():
outputs = model(**batch)
predictions = outputs.logits.argmax(dim=-1)
metric.add_batch(
predictions=accelerator.gather(predictions),
references=accelerator.gather(batch["labels"]),
)
eval_metric = metric.compute()
logger.info(f"epoch {epoch}: {eval_metric}")
if args.output_dir is not None:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
if __name__ == "__main__":
main()
{% endif %}
| 37,654 | 40.56181 | 130 | py |
transformers | transformers-main/scripts/stale.py | # Copyright 2021 The HuggingFace Team, the AllenNLP library authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Script to close stale issue. Taken in part from the AllenNLP repository.
https://github.com/allenai/allennlp.
"""
from datetime import datetime as dt
import os
from github import Github
LABELS_TO_EXEMPT = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def main():
g = Github(os.environ["GITHUB_TOKEN"])
repo = g.get_repo("huggingface/transformers")
open_issues = repo.get_issues(state="open")
for issue in open_issues:
comments = sorted([comment for comment in issue.get_comments()], key=lambda i: i.created_at, reverse=True)
last_comment = comments[0] if len(comments) > 0 else None
if (
last_comment is not None and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="closed")
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored."
)
if __name__ == "__main__":
main()
| 2,654 | 38.626866 | 115 | py |
transformers | transformers-main/scripts/check_tokenizers.py | from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from transformers.utils import logging
logging.set_verbosity_info()
TOKENIZER_CLASSES = {
name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
}
dataset = datasets.load_dataset("xnli", split="test+validation")
total = 0
perfect = 0
imperfect = 0
wrong = 0
def check_diff(spm_diff, tok_diff, slow, fast):
if spm_diff == list(reversed(tok_diff)):
# AAA -> AA+A vs A+AA case.
return True
elif len(spm_diff) == len(tok_diff) and fast.decode(spm_diff) == fast.decode(tok_diff):
# Second order OK
# Barrich -> Barr + ich vs Bar + rich
return True
spm_reencoded = slow.encode(slow.decode(spm_diff))
tok_reencoded = fast.encode(fast.decode(spm_diff))
if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
# Type 3 error.
# Snehagatha ->
# Sne, h, aga, th, a
# Sne, ha, gat, ha
# Encoding the wrong with sp does not even recover what spm gave us
# It fits tokenizer however...
return True
return False
def check_LTR_mark(line, idx, fast):
enc = fast.encode_plus(line)[0]
offsets = enc.offsets
curr, prev = offsets[idx], offsets[idx - 1]
if curr is not None and line[curr[0] : curr[1]] == "\u200f":
return True
if prev is not None and line[prev[0] : prev[1]] == "\u200f":
return True
def check_details(line, spm_ids, tok_ids, slow, fast):
# Encoding can be the same with same result AAA -> A + AA vs AA + A
# We can check that we use at least exactly the same number of tokens.
for i, (spm_id, tok_id) in enumerate(zip(spm_ids, tok_ids)):
if spm_id != tok_id:
break
first = i
for i, (spm_id, tok_id) in enumerate(zip(reversed(spm_ids), reversed(tok_ids))):
if spm_id != tok_id:
break
last = len(spm_ids) - i
spm_diff = spm_ids[first:last]
tok_diff = tok_ids[first:last]
if check_diff(spm_diff, tok_diff, slow, fast):
return True
if check_LTR_mark(line, first, fast):
return True
if last - first > 5:
# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
spms = Counter(spm_ids[first:last])
toks = Counter(tok_ids[first:last])
removable_tokens = {spm_ for (spm_, si) in spms.items() if toks.get(spm_, 0) == si}
min_width = 3
for i in range(last - first - min_width):
if all(spm_ids[first + i + j] in removable_tokens for j in range(min_width)):
possible_matches = [
k
for k in range(last - first - min_width)
if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
]
for j in possible_matches:
if check_diff(spm_ids[first : first + i], tok_ids[first : first + j], sp, tok) and check_details(
line,
spm_ids[first + i : last],
tok_ids[first + j : last],
slow,
fast,
):
return True
print(f"Spm: {[fast.decode([spm_ids[i]]) for i in range(first, last)]}")
try:
print(f"Tok: {[fast.decode([tok_ids[i]]) for i in range(first, last)]}")
except Exception:
pass
ok_start = fast.decode(spm_ids[:first])
ok_end = fast.decode(spm_ids[last:])
wrong = fast.decode(spm_ids[first:last])
print()
print(wrong)
return False
def test_string(slow, fast, text):
global perfect
global imperfect
global wrong
global total
slow_ids = slow.encode(text)
fast_ids = fast.encode(text)
skip_assert = False
total += 1
if slow_ids != fast_ids:
if check_details(text, slow_ids, fast_ids, slow, fast):
skip_assert = True
imperfect += 1
else:
wrong += 1
else:
perfect += 1
if total % 10000 == 0:
print(f"({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})")
if skip_assert:
return
assert (
slow_ids == fast_ids
), f"line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(text)}\n{fast.tokenize(text)}"
def test_tokenizer(slow, fast):
global batch_total
for i in range(len(dataset)):
# premise, all languages
for text in dataset[i]["premise"].values():
test_string(slow, fast, text)
# hypothesis, all languages
for text in dataset[i]["hypothesis"]["translation"]:
test_string(slow, fast, text)
if __name__ == "__main__":
for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
checkpoint_names = list(slow_class.max_model_input_sizes.keys())
for checkpoint in checkpoint_names:
imperfect = 0
perfect = 0
wrong = 0
total = 0
print(f"========================== Checking {name}: {checkpoint} ==========================")
slow = slow_class.from_pretrained(checkpoint, force_download=True)
fast = fast_class.from_pretrained(checkpoint, force_download=True)
test_tokenizer(slow, fast)
print(f"Accuracy {perfect * 100 / total:.2f}")
| 5,547 | 31.635294 | 117 | py |
transformers | transformers-main/scripts/distributed/torch-distributed-gpu-test.py | #!/usr/bin/env python
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def printflock(*msgs):
"""solves multi-process interleaved print problem"""
with open(__file__, "r") as fh:
fcntl.flock(fh, fcntl.LOCK_EX)
try:
print(*msgs)
finally:
fcntl.flock(fh, fcntl.LOCK_UN)
local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
device = torch.device("cuda", local_rank)
hostname = socket.gethostname()
gpu = f"[{hostname}-{local_rank}]"
try:
# test distributed
dist.init_process_group("nccl")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
rank = dist.get_rank()
world_size = dist.get_world_size()
printflock(f"{gpu} is OK (global rank: {rank}/{world_size})")
dist.barrier()
if rank == 0:
printflock(f"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")
except Exception:
printflock(f"{gpu} is broken")
raise
| 3,009 | 31.365591 | 109 | py |
transformers | transformers-main/scripts/pegasus/build_test_sample_spm_no_bos.py | #!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus
# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt
# 3. build
import sentencepiece as spm
# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train('--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2 --eos_id=1 --vocab_size=1000')
# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/
| 1,252 | 35.852941 | 148 | py |
transformers | transformers-main/scripts/fsmt/gen-card-facebook-wmt19.py | #!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def write_model_card(model_card_dir, src_lang, tgt_lang):
texts = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
scores = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
pair = f"{src_lang}-{tgt_lang}"
readme = f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
{pair} | {scores[pair][0]} | {scores[pair][1]}
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{{...,
year={{2020}},
title={{Facebook FAIR's WMT19 News Translation Task Submission}},
author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
booktitle={{Proc. of WMT}},
}}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
"""
os.makedirs(model_card_dir, exist_ok=True)
path = os.path.join(model_card_dir, "README.md")
print(f"Generating {path}")
with open(path, "w", encoding="utf-8") as f:
f.write(readme)
# make sure we are under the root of the project
repo_dir = Path(__file__).resolve().parent.parent.parent
model_cards_dir = repo_dir / "model_cards"
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
base, src_lang, tgt_lang = model_name.split("-")
model_card_dir = model_cards_dir / "facebook" / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 5,476 | 32.193939 | 270 | py |
transformers | transformers-main/scripts/fsmt/gen-card-allenai-wmt19.py | #!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Usage:
# ./gen-card-allenai-wmt19.py
import os
from pathlib import Path
def write_model_card(model_card_dir, src_lang, tgt_lang, model_name):
texts = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, nicht wahr?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
scores = {
"wmt19-de-en-6-6-base": [0, 38.37],
"wmt19-de-en-6-6-big": [0, 39.90],
}
pair = f"{src_lang}-{tgt_lang}"
readme = f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
2 models are available:
* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
## Training data
Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
## Eval results
Here are the BLEU scores:
model | transformers
-------|---------
{model_name} | {scores[model_name][1]}
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```
@misc{{kasai2020deep,
title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
year={{2020}},
eprint={{2006.10369}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}}
}}
```
"""
model_card_dir.mkdir(parents=True, exist_ok=True)
path = os.path.join(model_card_dir, "README.md")
print(f"Generating {path}")
with open(path, "w", encoding="utf-8") as f:
f.write(readme)
# make sure we are under the root of the project
repo_dir = Path(__file__).resolve().parent.parent.parent
model_cards_dir = repo_dir / "model_cards"
for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
model_card_dir = model_cards_dir / "allenai" / model_name
write_model_card(model_card_dir, src_lang="de", tgt_lang="en", model_name=model_name)
| 4,456 | 28.130719 | 270 | py |
transformers | transformers-main/scripts/fsmt/fsmt-make-tiny-model.py | #!/usr/bin/env python
# coding: utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
mname = "facebook/wmt19-en-de"
tokenizer = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
config = FSMTConfig.from_pretrained(mname)
config.update(dict(
d_model=4,
encoder_layers=1, decoder_layers=1,
encoder_ffn_dim=4, decoder_ffn_dim=4,
encoder_attention_heads=1, decoder_attention_heads=1))
tiny_model = FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")
# Test
batch = tokenizer(["Making tiny model"], return_tensors="pt")
outputs = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
mname_tiny = "tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 2,179 | 35.333333 | 114 | py |
transformers | transformers-main/scripts/fsmt/fsmt-make-super-tiny-model.py | #!/usr/bin/env python
# coding: utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
mname_tiny = "tiny-wmt19-en-ru"
# Build
# borrowed from a test
vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "w</w>", "r</w>", "t</w>", "lo", "low", "er</w>", "low</w>", "lowest</w>", "newer</w>", "wider</w>", "<unk>", ]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
with tempfile.TemporaryDirectory() as tmpdirname:
build_dir = Path(tmpdirname)
src_vocab_file = build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
tgt_vocab_file = build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
merges_file = build_dir / VOCAB_FILES_NAMES["merges_file"]
with open(src_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens))
with open(merges_file, "w") as fp : fp.write("\n".join(merges))
tokenizer = FSMTTokenizer(
langs=["en", "ru"],
src_vocab_size = len(vocab),
tgt_vocab_size = len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
config = FSMTConfig(
langs=['ru', 'en'],
src_vocab_size=1000, tgt_vocab_size=1000,
d_model=4,
encoder_layers=1, decoder_layers=1,
encoder_ffn_dim=4, decoder_ffn_dim=4,
encoder_attention_heads=1, decoder_attention_heads=1,
)
tiny_model = FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")
# Test
batch = tokenizer(["Making tiny model"], return_tensors="pt")
outputs = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 3,263 | 36.090909 | 171 | py |
transformers | transformers-main/scripts/fsmt/gen-card-allenai-wmt16.py | #!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def write_model_card(model_card_dir, src_lang, tgt_lang, model_name):
texts = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, nicht wahr?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
scores = {
"wmt16-en-de-dist-12-1": [28.3, 27.52],
"wmt16-en-de-dist-6-1": [27.4, 27.11],
"wmt16-en-de-12-1": [26.9, 25.75],
}
pair = f"{src_lang}-{tgt_lang}"
readme = f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 models are available:
* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
## Training data
Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
## Eval results
Here are the BLEU scores:
model | fairseq | transformers
-------|---------|----------
{model_name} | {scores[model_name][0]} | {scores[model_name][1]}
The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
## Data Sources
- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)
### BibTeX entry and citation info
```
@misc{{kasai2020deep,
title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
year={{2020}},
eprint={{2006.10369}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}}
}}
```
"""
model_card_dir.mkdir(parents=True, exist_ok=True)
path = os.path.join(model_card_dir, "README.md")
print(f"Generating {path}")
with open(path, "w", encoding="utf-8") as f:
f.write(readme)
# make sure we are under the root of the project
repo_dir = Path(__file__).resolve().parent.parent.parent
model_cards_dir = repo_dir / "model_cards"
for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
model_card_dir = model_cards_dir / "allenai" / model_name
write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)
| 4,874 | 30.25 | 270 | py |
transformers | transformers-main/scripts/benchmark/trainer-benchmark.py | #!/usr/bin/env python
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
nan = float("nan")
class Tee:
"""
A helper class to tee print's output into a file.
Usage:
sys.stdout = Tee(filename)
"""
def __init__(self, filename):
self.stdout = sys.stdout
self.file = open(filename, "a")
def __getattr__(self, attr):
return getattr(self.stdout, attr)
def write(self, msg):
self.stdout.write(msg)
# strip tqdm codes
self.file.write(re.sub(r"^.*\r", "", msg, 0, re.M))
def get_original_command(max_width=80, full_python_path=False):
"""
Return the original command line string that can be replayed nicely and wrapped for 80 char width.
Args:
max_width (`int`, `optional`, defaults to 80):
The width to wrap for.
full_python_path (`bool`, `optional`, defaults to `False`):
Whether to replicate the full path or just the last segment (i.e. `python`).
"""
cmd = []
# deal with critical env vars
env_keys = ["CUDA_VISIBLE_DEVICES"]
for key in env_keys:
val = os.environ.get(key, None)
if val is not None:
cmd.append(f"{key}={val}")
# python executable (not always needed if the script is executable)
python = sys.executable if full_python_path else sys.executable.split("/")[-1]
cmd.append(python)
# now the normal args
cmd += list(map(shlex.quote, sys.argv))
# split up into up to MAX_WIDTH lines with shell multi-line escapes
lines = []
current_line = ""
while len(cmd) > 0:
current_line += f"{cmd.pop(0)} "
if len(cmd) == 0 or len(current_line) + len(cmd[0]) + 1 > max_width - 1:
lines.append(current_line)
current_line = ""
return "\\\n".join(lines)
def get_base_command(args, output_dir):
# unwrap multi-line input
args.base_cmd = re.sub(r"[\\\n]+", " ", args.base_cmd)
# remove --output_dir if any and set our own
args.base_cmd = re.sub("--output_dir\s+[^\s]+", "", args.base_cmd)
args.base_cmd += f" --output_dir {output_dir}"
# ensure we have --overwrite_output_dir
args.base_cmd = re.sub("--overwrite_output_dir\s+", "", args.base_cmd)
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd)
def process_run_single(id, cmd, variation, output_dir, target_metric_key, metric_keys, verbose):
# Enable to debug everything but the run itself, to do it fast and see the progress.
# This is useful for debugging the output formatting quickly - we can remove it later once
# everybody is happy with the output
if 0:
import random
from time import sleep
sleep(0)
return dict(
{k: random.uniform(0, 100) for k in metric_keys},
**{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222])},
)
result = subprocess.run(cmd, capture_output=True, text=True)
if verbose:
print("STDOUT", result.stdout)
print("STDERR", result.stderr)
# save the streams
prefix = variation.replace(" ", "-")
with open(Path(output_dir) / f"log.{prefix}.stdout.txt", "w") as f:
f.write(result.stdout)
with open(Path(output_dir) / f"log.{prefix}.stderr.txt", "w") as f:
f.write(result.stderr)
if result.returncode != 0:
if verbose:
print("failed")
return {target_metric_key: nan}
with io.open(f"{output_dir}/all_results.json", "r", encoding="utf-8") as f:
metrics = json.load(f)
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def process_run(
id,
cmd,
variation_key,
variation,
longest_variation_len,
target_metric_key,
report_metric_keys,
repeat_times,
output_dir,
verbose,
):
results = []
metrics = []
preamble = f"{id}: {variation:<{longest_variation_len}}"
outcome = f"{preamble}: "
metric_keys = set(report_metric_keys + [target_metric_key])
for i in tqdm(range(repeat_times), desc=preamble, leave=False):
single_run_metrics = process_run_single(
id, cmd, variation, output_dir, target_metric_key, metric_keys, verbose
)
result = single_run_metrics[target_metric_key]
if not math.isnan(result):
metrics.append(single_run_metrics)
results.append(result)
outcome += "✓"
else:
outcome += "✘"
outcome = f"\33[2K\r{outcome}"
if len(metrics) > 0:
mean_metrics = {k: fmean([x[k] for x in metrics]) for k in metrics[0].keys()}
mean_target = round(mean_metrics[target_metric_key], 2)
results_str = f"{outcome} {mean_target}"
if len(metrics) > 1:
results_str += f" {tuple(round(x, 2) for x in results)}"
print(results_str)
mean_metrics[variation_key] = variation
return mean_metrics
else:
print(outcome)
return {variation_key: variation, target_metric_key: nan}
def get_versions():
properties = torch.cuda.get_device_properties(torch.device("cuda"))
return f"""
Datetime : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
Software:
transformers: {transformers.__version__}
torch : {torch.__version__}
cuda : {torch.version.cuda}
python : {platform.python_version()}
Hardware:
{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB
"""
def process_results(results, target_metric_key, report_metric_keys, base_variation, output_dir):
df = pd.DataFrame(results)
variation_key = "variation"
diff_key = "diff_%"
sentinel_value = nan
if base_variation is not None and len(df[df[variation_key] == base_variation]):
# this may still return nan
sentinel_value = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(sentinel_value):
# as a fallback, use the minimal value as the sentinel
sentinel_value = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(sentinel_value):
df[diff_key] = df.apply(
lambda r: round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value)
if not math.isnan(r[target_metric_key])
else 0,
axis="columns",
)
# re-order columns
cols = [variation_key, target_metric_key, diff_key, *report_metric_keys]
df = df.reindex(cols, axis="columns") # reorder cols
# capitalize
df = df.rename(str.capitalize, axis="columns")
# make the cols as narrow as possible
df_github = df.rename(lambda c: c.replace("_", "<br>"), axis="columns")
df_console = df.rename(lambda c: c.replace("_", "\n"), axis="columns")
report = ["", "Copy between the cut-here-lines and paste as is to github or a forum"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=False, floatfmt=".2f")]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=False, floatfmt=".2f")]
print("\n\n".join(report))
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--base-cmd",
default=None,
type=str,
required=True,
help="Base cmd",
)
parser.add_argument(
"--variations",
default=None,
type=str,
nargs="+",
required=True,
help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'",
)
parser.add_argument(
"--base-variation",
default=None,
type=str,
help="Baseline variation to compare to. if None the minimal target value will be used to compare against",
)
parser.add_argument(
"--target-metric-key",
default=None,
type=str,
required=True,
help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second",
)
parser.add_argument(
"--report-metric-keys",
default="",
type=str,
help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples",
)
parser.add_argument(
"--repeat-times",
default=1,
type=int,
help="How many times to re-run each variation - an average will be reported",
)
parser.add_argument(
"--output_dir",
default="output_benchmark",
type=str,
help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked",
)
parser.add_argument(
"--verbose",
default=False,
action="store_true",
help="Whether to show the outputs of each run or just the benchmark progress",
)
args = parser.parse_args()
output_dir = args.output_dir
Path(output_dir).mkdir(exist_ok=True)
base_cmd = get_base_command(args, output_dir)
# split each dimension into its --foo variations
dims = [list(map(str.strip, re.split(r"\|", x))) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
variations = list(map(str.strip, map(" ".join, itertools.product(*dims))))
longest_variation_len = max(len(x) for x in variations)
# split wanted keys
report_metric_keys = args.report_metric_keys.split()
# capture prints into a log file for convenience
report_fn = f"benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.txt"
print(f"\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt")
print(f"and this script's output is also piped into {report_fn}")
sys.stdout = Tee(report_fn)
print(f"\n*** Running {len(variations)} benchmarks:")
print(f"Base command: {' '.join(base_cmd)}")
variation_key = "variation"
results = []
for id, variation in enumerate(tqdm(variations, desc="Total completion: ", leave=False)):
cmd = base_cmd + variation.split()
results.append(
process_run(
id + 1,
cmd,
variation_key,
variation,
longest_variation_len,
args.target_metric_key,
report_metric_keys,
args.repeat_times,
output_dir,
args.verbose,
)
)
process_results(results, args.target_metric_key, report_metric_keys, args.base_variation, output_dir)
if __name__ == "__main__":
main()
| 15,565 | 33.668151 | 189 | py |
transformers | transformers-main/tests/test_modeling_utils.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import json
import os
import os.path
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from pytest import mark
from requests.exceptions import HTTPError
from transformers import (
AutoConfig,
AutoModel,
PretrainedConfig,
is_torch_available,
logging,
)
from transformers.testing_utils import (
TOKEN,
USER,
CaptureLogger,
TestCasePlus,
is_staging_test,
require_accelerate,
require_safetensors,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_usr_bin_time,
slow,
)
from transformers.utils import (
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402
if is_torch_available():
import torch
from test_module.custom_modeling import CustomModel, NoSuperInitModel
from torch import nn
from transformers import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoModelForCausalLM,
AutoTokenizer,
BertConfig,
BertModel,
CLIPTextModel,
PreTrainedModel,
T5Config,
T5ForConditionalGeneration,
)
from transformers.modeling_utils import shard_checkpoint
# Fake pretrained models for tests
class BaseModel(PreTrainedModel):
base_model_prefix = "base"
config_class = PretrainedConfig
def __init__(self, config):
super().__init__(config)
self.linear = nn.Linear(5, 5)
self.linear_2 = nn.Linear(5, 5)
def forward(self, x):
return self.linear_2(self.linear(x))
class BaseModelWithTiedWeights(PreTrainedModel):
config_class = PretrainedConfig
def __init__(self, config):
super().__init__(config)
self.linear = nn.Linear(5, 5)
self.linear_2 = nn.Linear(5, 5)
def forward(self, x):
return self.linear_2(self.linear(x))
def tie_weights(self):
self.linear_2.weight = self.linear.weight
class ModelWithHead(PreTrainedModel):
base_model_prefix = "base"
config_class = PretrainedConfig
def _init_weights(self, module):
pass
def __init__(self, config):
super().__init__(config)
self.base = BaseModel(config)
# linear is a common name between Base and Head on purpose.
self.linear = nn.Linear(5, 5)
self.linear2 = nn.Linear(5, 5)
def forward(self, x):
return self.linear2(self.linear(self.base(x)))
class ModelWithHeadAndTiedWeights(PreTrainedModel):
base_model_prefix = "base"
config_class = PretrainedConfig
def _init_weights(self, module):
pass
def __init__(self, config):
super().__init__(config)
self.base = BaseModel(config)
self.decoder = nn.Linear(5, 5)
def forward(self, x):
return self.decoder(self.base(x))
def tie_weights(self):
self.decoder.weight = self.base.linear.weight
TINY_T5 = "patrickvonplaten/t5-tiny-random"
TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
def check_models_equal(model1, model2):
models_are_equal = True
for model1_p, model2_p in zip(model1.parameters(), model2.parameters()):
if model1_p.data.ne(model2_p.data).sum() > 0:
models_are_equal = False
return models_are_equal
@require_torch
class ModelUtilsTest(TestCasePlus):
@slow
def test_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)
model = BertModel.from_pretrained(model_name)
model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, PreTrainedModel)
self.assertEqual(len(loading_info["missing_keys"]), 0)
self.assertEqual(len(loading_info["unexpected_keys"]), 8)
self.assertEqual(len(loading_info["mismatched_keys"]), 0)
self.assertEqual(len(loading_info["error_msgs"]), 0)
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# Not sure this is the intended behavior. TODO fix Lysandre & Thom
config.name_or_path = model_name
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)
def test_model_from_pretrained_subfolder(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
model = BertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder))
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(tmp_dir)
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_subfolder_sharded(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
model = BertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(tmp_dir)
model_loaded = BertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_hub_subfolder(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(model_id)
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_hub_subfolder_sharded(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(OSError):
_ = BertModel.from_pretrained(model_id)
model = BertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_with_different_pretrained_model_name(self):
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
self.assertIsNotNone(model)
logger = logging.get_logger("transformers.configuration_utils")
with CaptureLogger(logger) as cl:
BertModel.from_pretrained(TINY_T5)
self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)
def test_model_from_config_torch_dtype(self):
# test that the model can be instantiated with dtype of user's choice - as long as it's a
# float dtype. To make it happen config.torch_dtype needs to be set before instantiating the
# model from the config object.
config = T5Config.from_pretrained(TINY_T5)
model = AutoModel.from_config(config)
# XXX: isn't supported
# model = T5ForConditionalGeneration.from_config(config)
self.assertEqual(model.dtype, torch.float32)
model = AutoModel.from_config(config, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# torch.set_default_dtype() supports only float dtypes, so will fail with non-float type
with self.assertRaises(ValueError):
model = AutoModel.from_config(config, torch_dtype=torch.int64)
def test_model_from_pretrained_torch_dtype(self):
# test that the model can be instantiated with dtype of either
# 1. explicit from_pretrained's torch_dtype argument
# 2. via autodiscovery by looking at model weights (torch_dtype="auto")
# so if a model.half() was saved, we want it to be instantiated as such.
#
# test an explicit model class, but also AutoModel separately as the latter goes through a different code path
model_path = self.get_auto_remove_tmp_dir()
# baseline - we know TINY_T5 is fp32 model
model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
self.assertEqual(model.dtype, torch.float32)
def remove_torch_dtype(model_path):
file = f"{model_path}/config.json"
with open(file, "r", encoding="utf-8") as f:
s = json.load(f)
s.pop("torch_dtype")
with open(file, "w", encoding="utf-8") as f:
json.dump(s, f)
# test the default fp32 save_pretrained => from_pretrained cycle
model.save_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
self.assertEqual(model.dtype, torch.float32)
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
# 2. test torch_dtype="auto" via auto-derivation
# now remove the torch_dtype entry from config.json and try "auto" again which should
# perform auto-derivation from weights
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
# test forced loading in fp16 (even though the weights are in fp32)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test fp16 save_pretrained, loaded with auto-detection
model = model.half()
model.save_pretrained(model_path)
# 1. test torch_dtype="auto" via `config.torch_dtype`
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.config.torch_dtype, torch.float16)
self.assertEqual(model.dtype, torch.float16)
# tests `config.torch_dtype` saving
with open(f"{model_path}/config.json") as f:
config_dict = json.load(f)
self.assertEqual(config_dict["torch_dtype"], "float16")
# 2. test torch_dtype="auto" via auto-derivation
# now same with using config info
remove_torch_dtype(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float16)
# 3. now retest that AutoModel behaves the same wrt torch_dtype="auto" as T5ForConditionalGeneration
model = AutoModel.from_pretrained(model_path, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float16)
# test fp16 save_pretrained, loaded with the explicit fp16
model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test AutoModel separately as it goes through a different path
# test auto-detection - as currently TINY_T5 doesn't have torch_dtype entry
model = AutoModel.from_pretrained(TINY_T5, torch_dtype="auto")
# test that the config object didn't get polluted with torch_dtype="auto"
# there was a bug that after this call we ended up with config.torch_dtype=="auto"
self.assertNotEqual(model.config.torch_dtype, "auto")
# now test the outcome
self.assertEqual(model.dtype, torch.float32)
model = AutoModel.from_pretrained(TINY_T5, torch_dtype=torch.float16)
self.assertEqual(model.dtype, torch.float16)
# test model whose first param is not of a floating type, but int
model = AutoModel.from_pretrained(TINY_BERT_FOR_TOKEN_CLASSIFICATION, torch_dtype="auto")
self.assertEqual(model.dtype, torch.float32)
def test_no_super_init_config_and_model(self):
config = NoSuperInitConfig(attribute=32)
model = NoSuperInitModel(config)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
new_model = NoSuperInitModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_shard_checkpoint(self):
# This is the model we will use, total size 340,000 bytes.
model = torch.nn.Sequential(
torch.nn.Linear(100, 200, bias=False), # size 80,000
torch.nn.Linear(200, 200, bias=False), # size 160,000
torch.nn.Linear(200, 100, bias=False), # size 80,000
torch.nn.Linear(100, 50, bias=False), # size 20,000
)
state_dict = model.state_dict()
with self.subTest("No shard when max size is bigger than model size"):
shards, index = shard_checkpoint(state_dict)
self.assertIsNone(index)
self.assertDictEqual(shards, {WEIGHTS_NAME: state_dict})
with self.subTest("Test sharding, no weights bigger than max size"):
shards, index = shard_checkpoint(state_dict, max_shard_size="300kB")
# Split is first two layers then last two.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"0.weight": "pytorch_model-00001-of-00002.bin",
"1.weight": "pytorch_model-00001-of-00002.bin",
"2.weight": "pytorch_model-00002-of-00002.bin",
"3.weight": "pytorch_model-00002-of-00002.bin",
},
},
)
shard1 = {"0.weight": state_dict["0.weight"], "1.weight": state_dict["1.weight"]}
shard2 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
self.assertDictEqual(
shards, {"pytorch_model-00001-of-00002.bin": shard1, "pytorch_model-00002-of-00002.bin": shard2}
)
with self.subTest("Test sharding with weights bigger than max size"):
shards, index = shard_checkpoint(state_dict, max_shard_size="100kB")
# Split is first layer, second layer then last 2.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"0.weight": "pytorch_model-00001-of-00003.bin",
"1.weight": "pytorch_model-00002-of-00003.bin",
"2.weight": "pytorch_model-00003-of-00003.bin",
"3.weight": "pytorch_model-00003-of-00003.bin",
},
},
)
shard1 = {"0.weight": state_dict["0.weight"]}
shard2 = {"1.weight": state_dict["1.weight"]}
shard3 = {"2.weight": state_dict["2.weight"], "3.weight": state_dict["3.weight"]}
self.assertDictEqual(
shards,
{
"pytorch_model-00001-of-00003.bin": shard1,
"pytorch_model-00002-of-00003.bin": shard2,
"pytorch_model-00003-of-00003.bin": shard3,
},
)
def test_checkpoint_sharding_local(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
# We use the same folder for various sizes to make sure a new save erases the old checkpoint.
for max_size in ["50kB", "50kiB", "100kB", "100kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
# Get each shard file and its size
shard_to_size = {}
for shard in os.listdir(tmp_dir):
if shard.endswith(".bin"):
shard_file = os.path.join(tmp_dir, shard)
shard_to_size[shard_file] = os.path.getsize(shard_file)
index_file = os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)
# Check there is an index but no regular weight file
self.assertTrue(os.path.isfile(index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
# Check a file is bigger than max_size only when it has a single weight
for shard_file, size in shard_to_size.items():
if max_size.endswith("kiB"):
max_size_int = int(max_size[:-3]) * 2**10
else:
max_size_int = int(max_size[:-2]) * 10**3
# Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
# the size asked for (since we count parameters)
if size >= max_size_int + 50000:
state_dict = torch.load(shard_file)
self.assertEqual(len(state_dict), 1)
# Check the index and the shard files found match
with open(index_file, "r", encoding="utf-8") as f:
index = json.loads(f.read())
all_shards = set(index["weight_map"].values())
shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".bin")}
self.assertSetEqual(all_shards, shards_found)
# Finally, check the model can be reloaded
new_model = BertModel.from_pretrained(tmp_dir)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_sharding_from_hub(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
# the model above is the same as the model below, just a sharded version.
ref_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.parameters(), ref_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2")
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_local_sharded(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB")
weights_index_name = ".".join(WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
for i in range(1, 5):
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00005"] + ["bin"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", safe_serialization=True)
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["safetensors"])
weights_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_checkpoint_variant_local_sharded_safe(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, variant="v2", max_shard_size="50kB", safe_serialization=True)
weights_index_name = ".".join(SAFE_WEIGHTS_INDEX_NAME.split(".")[:-1] + ["v2"] + ["json"])
weights_index_file = os.path.join(tmp_dir, weights_index_name)
self.assertTrue(os.path.isfile(weights_index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
for i in range(1, 5):
weights_name = ".".join(SAFE_WEIGHTS_NAME.split(".")[:-1] + [f"v2-0000{i}-of-00005"] + ["safetensors"])
weights_name_file = os.path.join(tmp_dir, weights_name)
self.assertTrue(os.path.isfile(weights_name_file))
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(tmp_dir)
new_model = BertModel.from_pretrained(tmp_dir, variant="v2")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_checkpoint_variant_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
def test_checkpoint_variant_hub_sharded(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
@require_safetensors
def test_checkpoint_variant_hub_sharded_safe(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertRaises(EnvironmentError):
_ = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir
)
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant-sharded-safe", cache_dir=tmp_dir, variant="v2"
)
self.assertIsNotNone(model)
def test_checkpoint_variant_save_load(self):
with tempfile.TemporaryDirectory() as tmp_dir:
model = BertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert-variant", cache_dir=tmp_dir, variant="v2"
)
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
model.save_pretrained(tmp_dir, variant="v2")
# saving will create a variant checkpoint
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name)))
model.save_pretrained(tmp_dir)
# saving shouldn't delete variant checkpoints
weights_name = ".".join(WEIGHTS_NAME.split(".")[:-1] + ["v2"] + ["bin"])
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, weights_name)))
# there should be a normal checkpoint
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
self.assertIsNotNone(model)
@require_accelerate
@mark.accelerate_tests
def test_from_pretrained_low_cpu_mem_usage_functional(self):
# test that we can use `from_pretrained(..., low_cpu_mem_usage=True)` with normal and
# sharded models
mnames = [
"hf-internal-testing/tiny-random-bert-sharded",
"hf-internal-testing/tiny-random-bert",
]
for mname in mnames:
_ = BertModel.from_pretrained(mname, low_cpu_mem_usage=True)
@require_usr_bin_time
@require_accelerate
@mark.accelerate_tests
def test_from_pretrained_low_cpu_mem_usage_measured(self):
# test that `from_pretrained(..., low_cpu_mem_usage=True)` uses less cpu memory than default
mname = "bert-base-cased"
preamble = "from transformers import AutoModel"
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=False)'
max_rss_normal = self.python_one_liner_max_rss(one_liner_str)
# print(f"{max_rss_normal=}")
one_liner_str = f'{preamble}; AutoModel.from_pretrained("{mname}", low_cpu_mem_usage=True)'
max_rss_low_mem = self.python_one_liner_max_rss(one_liner_str)
# print(f"{max_rss_low_mem=}")
diff_bytes = max_rss_normal - max_rss_low_mem
diff_percent = diff_bytes / max_rss_low_mem
# print(f"{diff_bytes=}, {diff_percent=}")
# ideally we would compare that the diff is close to ~1x checkpoint size in bytes, but
# measuring cpu memory on linux is very tricky and inconsistent, so instead let's check that
# it's at least 15% less cpu memory consumed
self.assertGreater(
diff_percent,
0.15,
"should use less CPU memory for low_cpu_mem_usage=True, "
f"but got max_rss_normal={max_rss_normal} and max_rss_low_mem={max_rss_low_mem}",
)
# if you want to compare things manually, let's first look at the size of the model in bytes
# model = BertModel.from_pretrained(mname, low_cpu_mem_usage=False)
# total_numel = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values())
# total_bytes = total_numel * 4 # 420MB
# Now the diff_bytes should be very close to total_bytes, but the reports are inconsistent.
# The easiest way to test this is to switch the model and torch.load to do all the work on
# gpu - that way one can measure exactly the total and peak memory used. Perhaps once we add
# functionality to load models directly on gpu, this test can be rewritten to use torch's
# cuda memory tracking and then we should be able to do a much more precise test.
@require_accelerate
@mark.accelerate_tests
@require_torch_multi_gpu
@slow
def test_model_parallelism_gpt2(self):
device_map = {"transformer.wte": 0, "transformer.wpe": 0, "lm_head": 0, "transformer.ln_f": 1}
for i in range(12):
device_map[f"transformer.h.{i}"] = 0 if i <= 5 else 1
model = AutoModelForCausalLM.from_pretrained("gpt2", device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
inputs = tokenizer("Hello, my name is", return_tensors="pt")
output = model.generate(inputs["input_ids"].to(0))
text_output = tokenizer.decode(output[0].tolist())
self.assertEqual(text_output, "Hello, my name is John. I'm a writer, and I'm a writer. I'm")
@require_accelerate
@mark.accelerate_tests
@require_torch_gpu
def test_from_pretrained_disk_offload_task_model(self):
model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-gpt2")
device_map = {
"transformer.wte": 0,
"transformer.wpe": 0,
"transformer.h.0": "cpu",
"transformer.h.1": "cpu",
"transformer.h.2": "cpu",
"transformer.h.3": "disk",
"transformer.h.4": "disk",
"transformer.ln_f": 0,
"lm_head": 0,
}
with tempfile.TemporaryDirectory() as tmp_dir:
inputs = torch.tensor([[1, 2, 3]]).to(0)
model.save_pretrained(tmp_dir)
new_model = AutoModelForCausalLM.from_pretrained(tmp_dir).to(0)
outputs1 = new_model.to(0)(inputs)
offload_folder = os.path.join(tmp_dir, "offload")
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
tmp_dir, device_map=device_map, offload_folder=offload_folder
)
outputs2 = new_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
# With state dict temp offload
offload_folder = os.path.join(tmp_dir, "offload")
new_model_with_offload = AutoModelForCausalLM.from_pretrained(
tmp_dir,
device_map=device_map,
offload_folder=offload_folder,
offload_state_dict=True,
)
outputs2 = new_model_with_offload(inputs)
self.assertTrue(torch.allclose(outputs1.logits.cpu(), outputs2.logits.cpu()))
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def test_load_from_one_file(self):
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", f
)
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = BertModel.from_pretrained(tmp_file, config=config)
finally:
os.remove(tmp_file)
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = BertModel.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/pytorch_model.bin", config=config
)
@require_safetensors
def test_use_safetensors(self):
# test nice error message if no safetensor files available
with self.assertRaises(OSError) as env_error:
AutoModel.from_pretrained("hf-internal-testing/tiny-random-RobertaModel", use_safetensors=True)
self.assertTrue(
"model.safetensors or model.safetensors.index.json and thus cannot be loaded with `safetensors`"
in str(env_error.exception)
)
# test that error if only safetensors is available
with self.assertRaises(OSError) as env_error:
BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors", use_safetensors=False)
self.assertTrue("does not appear to have a file named pytorch_model.bin" in str(env_error.exception))
# test that only safetensors if both available and use_safetensors=False
with tempfile.TemporaryDirectory() as tmp_dir:
CLIPTextModel.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all",
subfolder="text_encoder",
use_safetensors=False,
cache_dir=tmp_dir,
)
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*"))
self.assertTrue(any(f.endswith("bin") for f in all_downloaded_files))
self.assertFalse(any(f.endswith("safetensors") for f in all_downloaded_files))
# test that no safetensors if both available and use_safetensors=True
with tempfile.TemporaryDirectory() as tmp_dir:
CLIPTextModel.from_pretrained(
"hf-internal-testing/diffusers-stable-diffusion-tiny-all",
subfolder="text_encoder",
use_safetensors=True,
cache_dir=tmp_dir,
)
all_downloaded_files = glob.glob(os.path.join(tmp_dir, "*", "snapshots", "*", "*", "*"))
self.assertTrue(any(f.endswith("safetensors") for f in all_downloaded_files))
self.assertFalse(any(f.endswith("bin") for f in all_downloaded_files))
@require_safetensors
def test_safetensors_save_and_load(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
# No pytorch_model.bin file, only a model.safetensors
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
new_model = BertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_load_from_hub(self):
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors")
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Check models are equal
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_save_and_load_sharded(self):
model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True, max_shard_size="100kB")
# No pytorch_model.bin index file, only a model.safetensors index
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_INDEX_NAME)))
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))
# No regular weights file
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
new_model = BertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
@require_safetensors
def test_safetensors_load_from_hub_sharded(self):
safetensors_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded-safetensors")
pytorch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
# Check models are equal
for p1, p2 in zip(safetensors_model.parameters(), pytorch_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
def test_base_model_to_head_model_load(self):
base_model = BaseModel(PretrainedConfig())
with tempfile.TemporaryDirectory() as tmp_dir:
base_model.save_pretrained(tmp_dir)
# Can load a base model in a model with head
model = ModelWithHead.from_pretrained(tmp_dir)
for p1, p2 in zip(model.base.parameters(), base_model.parameters()):
self.assertTrue(torch.allclose(p1, p2))
# It doesn't work if the state dict has a mix of keys of the head and base without prefix though.
base_state_dict = base_model.state_dict()
head_state_dict = model.state_dict()
base_state_dict["linear2.weight"] = head_state_dict["linear2.weight"]
base_state_dict["linear2.bias"] = head_state_dict["linear2.bias"]
torch.save(base_state_dict, os.path.join(tmp_dir, WEIGHTS_NAME))
with self.assertRaisesRegex(
ValueError, "The state dictionary of the model you are trying to load is corrupted."
):
_ = ModelWithHead.from_pretrained(tmp_dir)
def test_tied_weights_reload(self):
# Base
model = BaseModelWithTiedWeights(PretrainedConfig())
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
new_model = BaseModelWithTiedWeights.from_pretrained(tmp_dir)
self.assertIs(new_model.linear.weight, new_model.linear_2.weight)
state_dict = model.state_dict()
# Remove tied weight from state_dict -> model should load with no complain of missing keys
del state_dict["linear_2.weight"]
torch.save(state_dict, os.path.join(tmp_dir, WEIGHTS_NAME))
new_model, load_info = BaseModelWithTiedWeights.from_pretrained(tmp_dir, output_loading_info=True)
self.assertListEqual(load_info["missing_keys"], [])
self.assertIs(new_model.linear.weight, new_model.linear_2.weight)
# With head
model.save_pretrained(tmp_dir)
new_model, load_info = ModelWithHeadAndTiedWeights.from_pretrained(tmp_dir, output_loading_info=True)
self.assertIs(new_model.base.linear.weight, new_model.decoder.weight)
# Should only complain about the missing bias
self.assertListEqual(load_info["missing_keys"], ["decoder.bias"])
def test_unexpected_keys_warnings(self):
model = ModelWithHead(PretrainedConfig())
logger = logging.get_logger("transformers.modeling_utils")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
# Loading the model with a new class, we don't get a warning for unexpected weights, just an info
with CaptureLogger(logger) as cl:
_, loading_info = BaseModel.from_pretrained(tmp_dir, output_loading_info=True)
self.assertNotIn("were not used when initializing ModelWithHead", cl.out)
self.assertEqual(
set(loading_info["unexpected_keys"]),
{"linear.weight", "linear.bias", "linear2.weight", "linear2.bias"},
)
# Loading the model with the same class, we do get a warning for unexpected weights
state_dict = model.state_dict()
state_dict["added_key"] = state_dict["linear.weight"]
torch.save(state_dict, os.path.join(tmp_dir, WEIGHTS_NAME))
with CaptureLogger(logger) as cl:
_, loading_info = ModelWithHead.from_pretrained(tmp_dir, output_loading_info=True)
self.assertIn("were not used when initializing ModelWithHead: ['added_key']", cl.out)
self.assertEqual(loading_info["unexpected_keys"], ["added_key"])
def test_warn_if_padding_and_no_attention_mask(self):
logger = logging.get_logger("transformers.modeling_utils")
with self.subTest("Ensure no warnings when pad_token_id is None."):
logger.warning_once.cache_clear()
with CaptureLogger(logger) as cl:
config_no_pad_token = PretrainedConfig()
config_no_pad_token.pad_token_id = None
model = ModelWithHead(config_no_pad_token)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure no warnings when there is an attention_mask."):
logger.warning_once.cache_clear()
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure no warnings when there are no pad_token_ids in the input_ids."):
logger.warning_once.cache_clear()
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[1, 345, 232, 328, 740, 140, 1695, 69, 6078, 2341, 25]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertNotIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure a warning is shown when the input_ids start with a pad_token_id."):
logger.warning_once.cache_clear()
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 432, 5232]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure a warning is shown when the input_ids end with a pad_token_id."):
logger.warning_once.cache_clear()
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[432, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out)
with self.subTest("Ensure that the warning is shown at most once."):
logger.warning_once.cache_clear()
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
model = ModelWithHead(config)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertEqual(cl.out.count("We strongly recommend passing in an `attention_mask`"), 1)
with self.subTest("Ensure a different warning is shown when the pad_token_id is equal to the bos_token_id."):
logger.warning_once.cache_clear()
with CaptureLogger(logger) as cl:
config = PretrainedConfig()
config.pad_token_id = 0
config.bos_token_id = config.pad_token_id
model = ModelWithHead(config)
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 0, 0]])
model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
self.assertIn("You may ignore this warning if your `pad_token_id`", cl.out)
@require_torch_gpu
@slow
def test_pretrained_low_mem_new_config(self):
# Checking for 1 model(the same one which was described in the issue) .
model_ids = ["gpt2"]
for model_id in model_ids:
model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path=model_id)
model_config.n_layer = 48
model_config.n_head = 25
model_config.n_embd = 1600
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=model_id,
config=model_config,
ignore_mismatched_sizes=True,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
)
model_ref = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_id)
self.assertEqual(model.__class__.__name__, model_ref.__class__.__name__)
@require_torch
@is_staging_test
class ModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-model")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = BertModel(config)
model.push_to_hub("test-model", use_auth_token=self._token)
new_model = BertModel.from_pretrained(f"{USER}/test-model")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=self._token, repo_id="test-model")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model", push_to_hub=True, use_auth_token=self._token)
new_model = BertModel.from_pretrained(f"{USER}/test-model")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = BertModel(config)
model.push_to_hub("valid_org/test-model-org", use_auth_token=self._token)
new_model = BertModel.from_pretrained("valid_org/test-model-org")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-org"
)
new_model = BertModel.from_pretrained("valid_org/test-model-org")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_push_to_hub_dynamic_model(self):
CustomConfig.register_for_auto_class()
CustomModel.register_for_auto_class()
config = CustomConfig(hidden_size=32)
model = CustomModel(config)
model.push_to_hub("test-dynamic-model", use_auth_token=self._token)
# checks
self.assertDictEqual(
config.auto_map,
{"AutoConfig": "custom_configuration.CustomConfig", "AutoModel": "custom_modeling.CustomModel"},
)
new_model = AutoModel.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
# Can't make an isinstance check because the new_model is from the CustomModel class of a dynamic module
self.assertEqual(new_model.__class__.__name__, "CustomModel")
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-model", trust_remote_code=True)
new_model = AutoModel.from_config(config, trust_remote_code=True)
self.assertEqual(new_model.__class__.__name__, "CustomModel")
| 51,504 | 44.418871 | 119 | py |
transformers | transformers-main/tests/test_configuration_utils.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPT2Config
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
config_common_kwargs = {
"return_dict": False,
"output_hidden_states": True,
"output_attentions": True,
"torchscript": True,
"torch_dtype": "float16",
"use_bfloat16": True,
"tf_legacy_loss": True,
"pruned_heads": {"a": 1},
"tie_word_embeddings": False,
"is_decoder": True,
"cross_attention_hidden_size": 128,
"add_cross_attention": True,
"tie_encoder_decoder": True,
"max_length": 50,
"min_length": 3,
"do_sample": True,
"early_stopping": True,
"num_beams": 3,
"num_beam_groups": 3,
"diversity_penalty": 0.5,
"temperature": 2.0,
"top_k": 10,
"top_p": 0.7,
"typical_p": 0.2,
"repetition_penalty": 0.8,
"length_penalty": 0.8,
"no_repeat_ngram_size": 5,
"encoder_no_repeat_ngram_size": 5,
"bad_words_ids": [1, 2, 3],
"num_return_sequences": 3,
"chunk_size_feed_forward": 5,
"output_scores": True,
"return_dict_in_generate": True,
"forced_bos_token_id": 2,
"forced_eos_token_id": 3,
"remove_invalid_values": True,
"architectures": ["BertModel"],
"finetuning_task": "translation",
"id2label": {0: "label"},
"label2id": {"label": "0"},
"tokenizer_class": "BertTokenizerFast",
"prefix": "prefix",
"bos_token_id": 6,
"pad_token_id": 7,
"eos_token_id": 8,
"sep_token_id": 9,
"decoder_start_token_id": 10,
"exponential_decay_length_penalty": (5, 1.01),
"suppress_tokens": [0, 1],
"begin_suppress_tokens": 2,
"task_specific_params": {"translation": "some_params"},
"problem_type": "regression",
}
@is_staging_test
class ConfigPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-config")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-config-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-config")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
config.push_to_hub("test-config", use_auth_token=self._token)
new_config = BertConfig.from_pretrained(f"{USER}/test-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-config")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(tmp_dir, repo_id="test-config", push_to_hub=True, use_auth_token=self._token)
new_config = BertConfig.from_pretrained(f"{USER}/test-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
config.push_to_hub("valid_org/test-config-org", use_auth_token=self._token)
new_config = BertConfig.from_pretrained("valid_org/test-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-config-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
tmp_dir, repo_id="valid_org/test-config-org", push_to_hub=True, use_auth_token=self._token
)
new_config = BertConfig.from_pretrained("valid_org/test-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(v, getattr(new_config, k))
def test_push_to_hub_dynamic_config(self):
CustomConfig.register_for_auto_class()
config = CustomConfig(attribute=42)
config.push_to_hub("test-dynamic-config", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map, {"AutoConfig": "custom_configuration.CustomConfig"})
new_config = AutoConfig.from_pretrained(f"{USER}/test-dynamic-config", trust_remote_code=True)
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__, "CustomConfig")
self.assertEqual(new_config.attribute, 42)
class ConfigTestUtils(unittest.TestCase):
def test_config_from_string(self):
c = GPT2Config()
# attempt to modify each of int/float/bool/str config records and verify they were updated
n_embd = c.n_embd + 1 # int
resid_pdrop = c.resid_pdrop + 1.0 # float
scale_attn_weights = not c.scale_attn_weights # bool
summary_type = c.summary_type + "foo" # str
c.update_from_string(
f"n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"
)
self.assertEqual(n_embd, c.n_embd, "mismatch for key: n_embd")
self.assertEqual(resid_pdrop, c.resid_pdrop, "mismatch for key: resid_pdrop")
self.assertEqual(scale_attn_weights, c.scale_attn_weights, "mismatch for key: scale_attn_weights")
self.assertEqual(summary_type, c.summary_type, "mismatch for key: summary_type")
def test_config_common_kwargs_is_complete(self):
base_config = PretrainedConfig()
missing_keys = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
missing_keys, ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]
)
keys_with_defaults = [key for key, value in config_common_kwargs.items() if value == getattr(base_config, key)]
if len(keys_with_defaults) > 0:
raise ValueError(
"The following keys are set with the default values in"
" `test_configuration_common.config_common_kwargs` pick another value for them:"
f" {', '.join(keys_with_defaults)}."
)
def test_from_pretrained_subfolder(self):
with self.assertRaises(OSError):
# config is in subfolder, the following should not work without specifying the subfolder
_ = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder")
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder", subfolder="bert")
self.assertIsNotNone(config)
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = BertConfig.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"
)
def test_local_versioning(self):
configuration = AutoConfig.from_pretrained("bert-base-cased")
configuration.configuration_files = ["config.4.0.0.json"]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(tmp_dir)
configuration.hidden_size = 2
json.dump(configuration.to_dict(), open(os.path.join(tmp_dir, "config.4.0.0.json"), "w"))
# This should pick the new configuration file as the version of Transformers is > 4.0.0
new_configuration = AutoConfig.from_pretrained(tmp_dir)
self.assertEqual(new_configuration.hidden_size, 2)
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
configuration.configuration_files = ["config.42.0.0.json"]
configuration.hidden_size = 768
configuration.save_pretrained(tmp_dir)
shutil.move(os.path.join(tmp_dir, "config.4.0.0.json"), os.path.join(tmp_dir, "config.42.0.0.json"))
new_configuration = AutoConfig.from_pretrained(tmp_dir)
self.assertEqual(new_configuration.hidden_size, 768)
def test_repo_versioning_before(self):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
repo = "hf-internal-testing/test-two-configs"
import transformers as new_transformers
new_transformers.configuration_utils.__version__ = "v4.0.0"
new_configuration, kwargs = new_transformers.models.auto.AutoConfig.from_pretrained(
repo, return_unused_kwargs=True
)
self.assertEqual(new_configuration.hidden_size, 2)
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(kwargs, {})
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
old_transformers.configuration_utils.__version__ = "v3.0.0"
old_configuration = old_transformers.models.auto.AutoConfig.from_pretrained(repo)
self.assertEqual(old_configuration.hidden_size, 768)
| 11,757 | 39.968641 | 124 | py |
transformers | transformers-main/tests/test_modeling_flax_common.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
import json
import random
import tempfile
from typing import List, Tuple
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import CaptureLogger, is_pt_flax_cross_test, require_flax, torch_device
from transformers.utils import CONFIG_NAME, GENERATION_CONFIG_NAME, logging
from transformers.utils.generic import ModelOutput
if is_flax_available():
import os
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
from transformers import (
FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
FLAX_MODEL_MAPPING,
FlaxAutoModel,
FlaxAutoModelForSequenceClassification,
FlaxBertModel,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.modeling_flax_utils import FLAX_WEIGHTS_INDEX_NAME, FLAX_WEIGHTS_NAME
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8
if is_torch_available():
import torch
def ids_tensor(shape, vocab_size, rng=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
output = np.array(values, dtype=jnp.int32).reshape(shape)
return output
def floats_tensor(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.random() * scale)
return np.array(values, dtype=jnp.float32).reshape(shape)
def random_attention_mask(shape, rng=None):
attn_mask = ids_tensor(shape, vocab_size=2, rng=rng)
# make sure that at least one token is attended to for each batch
attn_mask[:, -1] = 1
return attn_mask
def get_params(params, from_head_prefix=None):
"""Function extracts relevant parameters into flatten dict from model params,
appends batch normalization statistics if present"""
# If Both parameters and batch normalization statistics are present
if "batch_stats" in params:
# Extract only parameters for the specified head prefix (if specified) and add batch statistics
if from_head_prefix is not None:
extracted_params = flatten_dict(unfreeze(params["params"][from_head_prefix]))
extracted_params.update(flatten_dict(params["batch_stats"][from_head_prefix]))
else:
extracted_params = flatten_dict(unfreeze(params["params"]))
extracted_params.update(flatten_dict(params["batch_stats"]))
# Only parameters are present
else:
if from_head_prefix is not None:
extracted_params = flatten_dict(unfreeze(params[from_head_prefix]))
else:
extracted_params = flatten_dict(unfreeze(params))
return extracted_params
@require_flax
class FlaxModelTesterMixin:
model_tester = None
all_model_classes = ()
test_mismatched_shapes = True
is_encoder_decoder = False
test_head_masking = False
has_attentions = True
def _prepare_for_class(self, inputs_dict, model_class):
inputs_dict = copy.deepcopy(inputs_dict)
# hack for now until we have AutoModel classes
if "ForMultipleChoice" in model_class.__name__:
inputs_dict = {
k: jnp.broadcast_to(v[:, None], (v.shape[0], self.model_tester.num_choices, v.shape[-1]))
if isinstance(v, (jnp.ndarray, np.ndarray)) and k != "indices_prng_key"
else v
for k, v in inputs_dict.items()
}
return inputs_dict
def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
diff = np.abs((a - b)).max()
self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assert_almost_equals(jnp.nan_to_num(tuple_object), jnp.nan_to_num(dict_object), 1e-5)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
# (Copied from tests.test_modeling_common.ModelTesterMixin.check_pt_flax_outputs)
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
"""
Args:
model_class: The class of the model that is currently testing. For example, ..., etc.
Currently unused, but it could make debugging easier and faster.
names: A string, or a list of strings. These specify what fx_outputs/pt_outputs represent in the model outputs.
Currently unused, but in the future, we could use this information to make the error message clearer
by giving the name(s) of the output tensor(s) with large difference(s) between PT and Flax.
"""
self.assertEqual(type(name), str)
if attributes is not None:
self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`")
# Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`).
if isinstance(fx_outputs, ModelOutput):
self.assertTrue(
isinstance(pt_outputs, ModelOutput),
f"{name}: `pt_outputs` should an instance of `ModelOutput` when `fx_outputs` is",
)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys, f"{name}: Output keys differ between Flax and PyTorch")
# convert to the case of `tuple`
# appending each key to the current (string) `name`
attributes = tuple([f"{name}.{k}" for k in fx_keys])
self.check_pt_flax_outputs(
fx_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes
)
# Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.)
elif type(fx_outputs) in [tuple, list]:
self.assertEqual(
type(fx_outputs), type(pt_outputs), f"{name}: Output types differ between Flax and PyTorch"
)
self.assertEqual(
len(fx_outputs), len(pt_outputs), f"{name}: Output lengths differ between Flax and PyTorch"
)
if attributes is not None:
# case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`)
self.assertEqual(
len(attributes),
len(fx_outputs),
f"{name}: The tuple `attributes` should have the same length as `fx_outputs`",
)
else:
# case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name`
attributes = tuple([f"{name}_{idx}" for idx in range(len(fx_outputs))])
for fx_output, pt_output, attr in zip(fx_outputs, pt_outputs, attributes):
self.check_pt_flax_outputs(fx_output, pt_output, model_class, tol=tol, name=attr)
elif isinstance(fx_outputs, jnp.ndarray):
self.assertTrue(
isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `fx_outputs` is"
)
# Using `np.asarray` gives `ValueError: assignment destination is read-only` at the line `fx_outputs[fx_nans] = 0`.
fx_outputs = np.array(fx_outputs)
pt_outputs = pt_outputs.detach().to("cpu").numpy()
self.assertEqual(
fx_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between Flax and PyTorch"
)
# deal with NumPy's scalars to make replacing nan values by 0 work.
if np.isscalar(fx_outputs):
fx_outputs = np.array([fx_outputs])
pt_outputs = np.array([pt_outputs])
fx_nans = np.isnan(fx_outputs)
pt_nans = np.isnan(pt_outputs)
pt_outputs[fx_nans] = 0
fx_outputs[fx_nans] = 0
pt_outputs[pt_nans] = 0
fx_outputs[pt_nans] = 0
max_diff = np.amax(np.abs(fx_outputs - pt_outputs))
self.assertLessEqual(
max_diff, tol, f"{name}: Difference between PyTorch and Flax is {max_diff} (>= {tol})."
)
else:
raise ValueError(
"`fx_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `jnp.ndarray`. Got"
f" {type(fx_outputs)} instead."
)
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
# It might be better to put this inside the for loop below (because we modify the config there).
# But logically, it is fine.
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# prepare inputs
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
pt_inputs = {k: torch.tensor(v.tolist(), device=torch_device) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
pt_model = pt_model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
fx_model = model_class(config, dtype=jnp.float32)
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**prepared_inputs_dict)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**prepared_inputs_dict)
fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class)
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# prepare inputs
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
pt_inputs = {k: torch.tensor(v.tolist(), device=torch_device) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
pt_model = pt_model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
fx_model = model_class(config, dtype=jnp.float32)
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
# make sure weights are tied in PyTorch
pt_model.tie_weights()
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**prepared_inputs_dict)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True)
# send pytorch model to the correct device
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class)
def test_from_pretrained_save_pretrained(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
model = model_class(config)
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**prepared_inputs_dict).to_tuple()
# verify that normal save_pretrained works as expected
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
# the config file (and the generation config file, if it can generate) should be saved
self.assertTrue(os.path.exists(os.path.join(tmpdirname, CONFIG_NAME)))
self.assertEqual(
model.can_generate(), os.path.exists(os.path.join(tmpdirname, GENERATION_CONFIG_NAME))
)
model_loaded = model_class.from_pretrained(tmpdirname)
outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()
for output_loaded, output in zip(outputs_loaded, outputs):
self.assert_almost_equals(output_loaded, output, 1e-3)
# verify that save_pretrained for distributed training
# with `params=params` works as expected
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, params=model.params)
model_loaded = model_class.from_pretrained(tmpdirname)
outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()
for output_loaded, output in zip(outputs_loaded, outputs):
self.assert_almost_equals(output_loaded, output, 1e-3)
def test_save_load_from_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = base_class(config)
base_params = get_params(model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname)
base_param_from_head = get_params(head_model.params, from_head_prefix=head_model.base_model_prefix)
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def test_save_load_to_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = model_class(config)
base_params_from_head = get_params(model.params, from_head_prefix=model.base_model_prefix)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname)
base_params = get_params(base_model.params)
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
@is_pt_flax_cross_test
def test_save_load_from_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = base_class(config)
base_params = get_params(model.params)
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, base_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
# save pt model
pt_model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname, from_pt=True)
base_param_from_head = get_params(head_model.params, from_head_prefix=head_model.base_model_prefix)
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
@is_pt_flax_cross_test
def test_save_load_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = model_class(config)
base_params_from_head = get_params(model.params, from_head_prefix=model.base_model_prefix)
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = get_params(base_model.params)
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
@is_pt_flax_cross_test
def test_save_load_bf16_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = FLAX_MODEL_MAPPING[config.__class__]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
model = model_class(config)
model.params = model.to_bf16(model.params)
base_params_from_head = get_params(model.params, from_head_prefix=model.base_model_prefix)
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = get_params(base_model.params)
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(input_ids, attention_mask=None, **kwargs):
return model(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
if model.config.is_encoder_decoder:
expected_arg_names = [
"input_ids",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
]
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
else:
expected_arg_names = ["input_ids", "attention_mask"]
self.assertListEqual(arg_names[:2], expected_arg_names)
def test_naming_convention(self):
for model_class in self.all_model_classes:
model_class_name = model_class.__name__
module_class_name = (
model_class_name[:-5] + "Module" if model_class_name[-5:] == "Model" else model_class_name + "Module"
)
bert_modeling_flax_module = __import__(model_class.__module__, fromlist=[module_class_name])
module_cls = getattr(bert_modeling_flax_module, module_class_name)
self.assertIsNotNone(module_cls)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
else:
seq_length = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_attention_outputs(self):
if not self.has_attentions:
self.skipTest(reason="Model does not output attentions")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_length = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_length)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_length)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
if self.is_encoder_decoder:
correct_outlen = 5
# Question Answering model returns start_logits and end_logits
if model_class in get_values(FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
correct_outlen += 1 # start_logits and end_logits instead of only 1 output
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def test_load_with_mismatched_shapes(self):
if not self.test_mismatched_shapes:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if model_class not in get_values(FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
continue
with self.subTest(msg=f"Testing {model_class}"):
with tempfile.TemporaryDirectory() as tmp_dir:
model = model_class(config)
model.save_pretrained(tmp_dir)
# Fails when we don't set ignore_mismatched_sizes=True
with self.assertRaises(ValueError):
new_model = FlaxAutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
with self.assertRaises(ValueError):
new_model_without_prefix = FlaxAutoModel.from_pretrained(tmp_dir, vocab_size=10)
logger = logging.get_logger("transformers.modeling_flax_utils")
with CaptureLogger(logger) as cl:
new_model = FlaxAutoModelForSequenceClassification.from_pretrained(
tmp_dir, num_labels=42, ignore_mismatched_sizes=True
)
self.assertIn("the shapes did not match", cl.out)
logits = new_model(**inputs_dict)["logits"]
self.assertEqual(logits.shape[1], 42)
with CaptureLogger(logger) as cl:
new_model_without_prefix = FlaxAutoModel.from_pretrained(
tmp_dir, vocab_size=10, ignore_mismatched_sizes=True
)
self.assertIn("the shapes did not match", cl.out)
input_ids = ids_tensor((2, 8), 10)
if self.is_encoder_decoder:
new_model_without_prefix(input_ids, decoder_input_ids=input_ids)
else:
new_model_without_prefix(input_ids)
def test_default_params_dtype(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# check if all params are still in float32 when dtype of computation is half-precision
model = model_class(config, dtype=jnp.float16)
types = jax.tree_util.tree_map(lambda x: x.dtype, model.params)
types = flatten_dict(types)
for name, type_ in types.items():
self.assertEquals(type_, jnp.float32, msg=f"param {name} is not initialized in fp32.")
def test_to_bf16(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
# cast all params to bf16
params = model.to_bf16(model.params)
types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
# test if all params are in bf16
for name, type_ in types.items():
self.assertEqual(type_, jnp.bfloat16, msg=f"param {name} is not in bf16.")
# test masking
flat_params = flatten_dict(params)
key = random.choice(list(flat_params.keys())) # choose a random param
mask = {path: path != key for path in flat_params} # don't cast the key
mask = unflatten_dict(mask)
params = model.to_bf16(model.params, mask)
types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
# test if all params are in bf16 except key
for name, type_ in types.items():
if name == key:
self.assertEqual(type_, jnp.float32, msg=f"param {name} should be in fp32.")
else:
self.assertEqual(type_, jnp.bfloat16, msg=f"param {name} is not in bf16.")
def test_to_fp16(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
# cast all params to fp16
params = model.to_fp16(model.params)
types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
# test if all params are in fp16
for name, type_ in types.items():
self.assertEqual(type_, jnp.float16, msg=f"param {name} is not in fp16.")
# test masking
flat_params = flatten_dict(params)
key = random.choice(list(flat_params.keys())) # choose a random param
mask = {path: path != key for path in flat_params} # don't cast the key
mask = unflatten_dict(mask)
params = model.to_fp16(model.params, mask)
types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
# test if all params are in fp16 except key
for name, type_ in types.items():
if name == key:
self.assertEqual(type_, jnp.float32, msg=f"param {name} should be in fp32.")
else:
self.assertEqual(type_, jnp.float16, msg=f"param {name} is not in fp16.")
def test_to_fp32(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
# cast all params to fp16 and back to fp32
params = model.to_fp16(model.params)
params = model.to_fp32(params)
# test if all params are in fp32
types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
for name, type_ in types.items():
self.assertEqual(type_, jnp.float32, msg=f"param {name} is not in fp32.")
# test masking
flat_params = flatten_dict(params)
key = random.choice(list(flat_params.keys())) # choose a random param
mask = {path: path != key for path in flat_params} # don't cast the key
mask = unflatten_dict(mask)
# cast to fp16 and back to fp32 with mask
params = model.to_fp16(model.params)
params = model.to_fp32(params, mask)
# test if all params are in fp32 except key
types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, params))
for name, type_ in types.items():
if name == key:
self.assertEqual(type_, jnp.float16, msg=f"param {name} should be in fp16.")
else:
self.assertEqual(type_, jnp.float32, msg=f"param {name} is not in fp32.")
def test_save_load_in_fp16(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
# convert weights to fp16 and save
params = model.to_fp16(model.params)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, params=params)
# load the weights again and check if they are still in fp16
model = model_class.from_pretrained(tmpdirname)
types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, model.params))
for name, type_ in types.items():
self.assertEqual(type_, jnp.float16, msg=f"param {name} is not in fp16.")
def test_save_load_in_bf16(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
# convert weights to bf16 and save
params = model.to_bf16(model.params)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, params=params)
# load the weights again and check if they are still in fp16
model = model_class.from_pretrained(tmpdirname)
types = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype, model.params))
for name, type_ in types.items():
self.assertEqual(type_, jnp.bfloat16, msg=f"param {name} is not in bf16.")
def test_model_main_input_name(self):
for model_class in self.all_model_classes:
model_signature = inspect.signature(getattr(model_class, "__call__"))
# The main input is the name of the argument after `self`
observed_main_input_name = list(model_signature.parameters.keys())[1]
self.assertEqual(model_class.main_input_name, observed_main_input_name)
def test_headmasking(self):
if not self.test_head_masking:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
def _prepare_layer_head_mask(i, attention_heads, num_hidden_layers):
if i == 0:
return np.concatenate([np.zeros(1, dtype=jnp.int32), np.ones(attention_heads - 1, dtype=jnp.int32)])
if i == num_hidden_layers - 1:
return np.concatenate([np.zeros(attention_heads - 1, dtype=jnp.int32), np.ones(1, dtype=jnp.int32)])
return np.ones(attention_heads, dtype=jnp.int32)
for model_class in self.all_model_classes:
model = model_class(config)
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
inputs = self._prepare_for_class(inputs_dict, model_class).copy()
# Prepare head mask
inputs["head_mask"] = np.stack(
[
_prepare_layer_head_mask(i, config.num_attention_heads, config.num_hidden_layers)
for i in range(config.num_hidden_layers)
]
)
outputs = model(**inputs)
def _check_attentions_validity(attentions):
# Remove NaN
for t in attentions:
# Check we don't have more than 25% nans (arbitrary)
self.assertLess(np.isnan(t).sum(), t.size / 4)
attentions = [np.where(np.isnan(t), 0.0, t) for t in attentions]
self.assertAlmostEqual(attentions[0][..., 0, :, :].sum(), 0.0)
self.assertNotEqual(attentions[0][..., -1, :, :].sum(), 0.0)
if len(attentions) > 2: # encoder-decodere models have only 2 layers in each modules
self.assertNotEqual(attentions[1][..., 0, :, :].sum(), 0.0)
self.assertAlmostEqual(attentions[-1][..., -2, :, :].sum(), 0.0)
self.assertNotEqual(attentions[-1][..., -1, :, :].sum(), 0.0)
if model.config.is_encoder_decoder:
raise NotImplementedError("The test has not been implemented for encoder-decoder models yet.")
else:
_check_attentions_validity(outputs.attentions)
def test_no_automatic_init(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
model = model_class(config, _do_init=False)
# Check that accesing parmas raises an ValueError when _do_init is False
with self.assertRaises(ValueError):
params = model.params
# Check if we params can be properly initialized when calling init_weights
params = model.init_weights(model.key, model.input_shape)
self.assertIsInstance(params, FrozenDict)
# Check if all required parmas are initialized
keys = set(flatten_dict(unfreeze(params)).keys())
self.assertTrue(all(k in keys for k in model.required_params))
# Check if the shapes match
flat_params = flatten_dict(unfreeze(params))
for k, v in flatten_dict(unfreeze(model.params_shape_tree)).items():
self.assertEqual(
v.shape,
flat_params[k].shape,
"Shapes of {} do not match. Expecting {}, got {}.".format(k, v.shape, flat_params[k].shape),
)
# Check that setting params raises an ValueError when _do_init is False
with self.assertRaises(ValueError):
model.params = params
# Check if we can do a forward pass
inputs_dict["output_hidden_states"] = True
inputs = self._prepare_for_class(inputs_dict, model_class).copy()
model(**inputs, params=params)
def test_from_pretrained_with_no_automatic_init(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
def _assert_all_params_initialised(model, params):
# Check if all required parmas are loaded
keys = set(flatten_dict(unfreeze(params)).keys())
self.assertTrue(all(k in keys for k in model.required_params))
# Check if the shapes match
flat_params = flatten_dict(unfreeze(params))
for k, v in flatten_dict(unfreeze(model.params_shape_tree)).items():
self.assertEqual(
v.shape,
flat_params[k].shape,
"Shapes of {} do not match. Expecting {}, got {}.".format(k, v.shape, flat_params[k].shape),
)
for model_class in self.all_model_classes:
# init the model
model = model_class(config)
# save the model in the temporary directory
# load the saved model with _do_init=False
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model, params = model_class.from_pretrained(tmpdirname, _do_init=False)
# Check that accesing parmas raises an ValueError when _do_init is False
with self.assertRaises(ValueError):
params = model.params
# Check if all required parmas are loaded
_assert_all_params_initialised(model, params)
# Check that setting params raises an ValueError when _do_init is False
with self.assertRaises(ValueError):
model.params = params
# Check if init_weights initializes missing keys from from_pretrained
flat_params = flatten_dict(unfreeze(params))
random_key = random.choice(list(flat_params.keys()))
flat_params.pop(random_key)
params = freeze(unflatten_dict(flat_params))
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, params=params)
model, params = model_class.from_pretrained(tmpdirname, _do_init=False)
params = model.init_weights(model.key, model.input_shape, params=params)
# Check if all required parmas are loaded
_assert_all_params_initialised(model, params)
def test_checkpoint_sharding_from_hub(self):
model = FlaxBertModel.from_pretrained("ArthurZ/flax-tiny-random-bert-sharded")
# the model above is the same as the model below, just a sharded version.
ref_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
for p1, p2 in zip(flatten_dict(model.params).values(), flatten_dict(ref_model.params).values()):
assert np.allclose(np.array(p1), np.array(p2))
def test_checkpoint_sharding_local(self):
model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
with tempfile.TemporaryDirectory() as tmp_dir:
# We use the same folder for various sizes to make sure a new save erases the old checkpoint.
for max_size in ["150kB", "150kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
# Get each shard file and its size
shard_to_size = {}
for shard in os.listdir(tmp_dir):
if shard.endswith(".msgpack"):
shard_file = os.path.join(tmp_dir, shard)
shard_to_size[shard_file] = os.path.getsize(shard_file)
index_file = os.path.join(tmp_dir, FLAX_WEIGHTS_INDEX_NAME)
# Check there is an index but no regular weight file
self.assertTrue(os.path.isfile(index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, FLAX_WEIGHTS_NAME)))
# Check a file is bigger than max_size only when it has a single weight
for shard_file, size in shard_to_size.items():
if max_size.endswith("kiB"):
max_size_int = int(max_size[:-3]) * 2**10
else:
max_size_int = int(max_size[:-2]) * 10**3
# Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
# the size asked for (since we count parameters)
if size >= max_size_int + 50000:
with open(shard_file, "rb") as state_f:
state_file = from_bytes(FlaxBertModel, state_f.read())
self.assertEqual(len(state_file), 1)
# Check the index and the shard files found match
with open(index_file, "r", encoding="utf-8") as f:
index = json.loads(f.read())
all_shards = set(index["weight_map"].values())
shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".msgpack")}
self.assertSetEqual(all_shards, shards_found)
# Finally, check the model can be reloaded
new_model = FlaxBertModel.from_pretrained(tmp_dir)
for p1, p2 in zip(flatten_dict(model.params).values(), flatten_dict(new_model.params).values()):
self.assertTrue(np.allclose(np.array(p1), np.array(p2)))
@is_pt_flax_cross_test
def test_from_sharded_pt(self):
model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded", from_pt=True)
ref_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-fx-only")
for key, ref_val in flatten_dict(ref_model.params).items():
val = flatten_dict(model.params)[key]
assert np.allclose(np.array(val), np.array(ref_val))
def test_gradient_checkpointing(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# prepare inputs
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
remat_model = model_class(config)
try:
remat_model.enable_gradient_checkpointing()
except NotImplementedError:
continue
outputs = model(**prepared_inputs_dict)
remat_outputs = remat_model(**prepared_inputs_dict)
# ensure that the dicts of outputs contain the same keys
self.assertEqual(outputs.keys(), remat_outputs.keys())
outputs = outputs.to_tuple()
remat_outputs = remat_outputs.to_tuple()
# ensure that the outputs remain precisely equal
for output, remat_output in zip(outputs, remat_outputs):
self.assertTrue((output == remat_output).all())
| 53,493 | 45.597561 | 127 | py |
transformers | transformers-main/tests/test_configuration_common.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class ConfigTester(object):
def __init__(self, parent, config_class=None, has_text_modality=True, common_properties=None, **kwargs):
self.parent = parent
self.config_class = config_class
self.has_text_modality = has_text_modality
self.inputs_dict = kwargs
self.common_properties = common_properties
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
common_properties = (
["hidden_size", "num_attention_heads", "num_hidden_layers"]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["vocab_size"])
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(config, prop), msg=f"`{prop}` does not exist")
# Test that config has the common properties as setter
for idx, name in enumerate(common_properties):
try:
setattr(config, name, idx)
self.parent.assertEqual(
getattr(config, name), idx, msg=f"`{name} value {idx} expected, but was {getattr(config, name)}"
)
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(common_properties):
try:
config = self.config_class(**{name: idx})
self.parent.assertEqual(
getattr(config, name), idx, msg=f"`{name} value {idx} expected, but was {getattr(config, name)}"
)
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def create_and_test_config_to_json_string(self):
config = self.config_class(**self.inputs_dict)
obj = json.loads(config.to_json_string())
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key], value)
def create_and_test_config_to_json_file(self):
config_first = self.config_class(**self.inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "config.json")
config_first.to_json_file(json_file_path)
config_second = self.config_class.from_json_file(json_file_path)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def create_and_test_config_from_and_save_pretrained(self):
config_first = self.config_class(**self.inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(tmpdirname)
config_second = self.config_class.from_pretrained(tmpdirname)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def create_and_test_config_from_and_save_pretrained_subfolder(self):
config_first = self.config_class(**self.inputs_dict)
subfolder = "test"
with tempfile.TemporaryDirectory() as tmpdirname:
sub_tmpdirname = os.path.join(tmpdirname, subfolder)
config_first.save_pretrained(sub_tmpdirname)
config_second = self.config_class.from_pretrained(tmpdirname, subfolder=subfolder)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def create_and_test_config_with_num_labels(self):
config = self.config_class(**self.inputs_dict, num_labels=5)
self.parent.assertEqual(len(config.id2label), 5)
self.parent.assertEqual(len(config.label2id), 5)
config.num_labels = 3
self.parent.assertEqual(len(config.id2label), 3)
self.parent.assertEqual(len(config.label2id), 3)
def check_config_can_be_init_without_params(self):
if self.config_class.is_composition:
return
config = self.config_class()
self.parent.assertIsNotNone(config)
def check_config_arguments_init(self):
kwargs = copy.deepcopy(config_common_kwargs)
config = self.config_class(**kwargs)
wrong_values = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.float16:
wrong_values.append(("torch_dtype", config.torch_dtype, torch.float16))
elif getattr(config, key) != value:
wrong_values.append((key, getattr(config, key), value))
if len(wrong_values) > 0:
errors = "\n".join([f"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values])
raise ValueError(f"The following keys were not properly set in the config:\n{errors}")
def run_common_tests(self):
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 6,493 | 41.168831 | 116 | py |
transformers | transformers-main/tests/test_pipeline_mixin.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import json
import os
import random
import unittest
from pathlib import Path
from transformers.testing_utils import (
is_pipeline_test,
require_decord,
require_pytesseract,
require_timm,
require_torch,
require_torch_or_tf,
require_vision,
)
from transformers.utils import direct_transformers_import, logging
from .pipelines.test_pipelines_audio_classification import AudioClassificationPipelineTests
from .pipelines.test_pipelines_automatic_speech_recognition import AutomaticSpeechRecognitionPipelineTests
from .pipelines.test_pipelines_conversational import ConversationalPipelineTests
from .pipelines.test_pipelines_depth_estimation import DepthEstimationPipelineTests
from .pipelines.test_pipelines_document_question_answering import DocumentQuestionAnsweringPipelineTests
from .pipelines.test_pipelines_feature_extraction import FeatureExtractionPipelineTests
from .pipelines.test_pipelines_fill_mask import FillMaskPipelineTests
from .pipelines.test_pipelines_image_classification import ImageClassificationPipelineTests
from .pipelines.test_pipelines_image_segmentation import ImageSegmentationPipelineTests
from .pipelines.test_pipelines_image_to_text import ImageToTextPipelineTests
from .pipelines.test_pipelines_mask_generation import MaskGenerationPipelineTests
from .pipelines.test_pipelines_object_detection import ObjectDetectionPipelineTests
from .pipelines.test_pipelines_question_answering import QAPipelineTests
from .pipelines.test_pipelines_summarization import SummarizationPipelineTests
from .pipelines.test_pipelines_table_question_answering import TQAPipelineTests
from .pipelines.test_pipelines_text2text_generation import Text2TextGenerationPipelineTests
from .pipelines.test_pipelines_text_classification import TextClassificationPipelineTests
from .pipelines.test_pipelines_text_generation import TextGenerationPipelineTests
from .pipelines.test_pipelines_token_classification import TokenClassificationPipelineTests
from .pipelines.test_pipelines_translation import TranslationPipelineTests
from .pipelines.test_pipelines_video_classification import VideoClassificationPipelineTests
from .pipelines.test_pipelines_visual_question_answering import VisualQuestionAnsweringPipelineTests
from .pipelines.test_pipelines_zero_shot import ZeroShotClassificationPipelineTests
from .pipelines.test_pipelines_zero_shot_audio_classification import ZeroShotAudioClassificationPipelineTests
from .pipelines.test_pipelines_zero_shot_image_classification import ZeroShotImageClassificationPipelineTests
from .pipelines.test_pipelines_zero_shot_object_detection import ZeroShotObjectDetectionPipelineTests
pipeline_test_mapping = {
"audio-classification": {"test": AudioClassificationPipelineTests},
"automatic-speech-recognition": {"test": AutomaticSpeechRecognitionPipelineTests},
"conversational": {"test": ConversationalPipelineTests},
"depth-estimation": {"test": DepthEstimationPipelineTests},
"document-question-answering": {"test": DocumentQuestionAnsweringPipelineTests},
"feature-extraction": {"test": FeatureExtractionPipelineTests},
"fill-mask": {"test": FillMaskPipelineTests},
"image-classification": {"test": ImageClassificationPipelineTests},
"image-segmentation": {"test": ImageSegmentationPipelineTests},
"image-to-text": {"test": ImageToTextPipelineTests},
"mask-generation": {"test": MaskGenerationPipelineTests},
"object-detection": {"test": ObjectDetectionPipelineTests},
"question-answering": {"test": QAPipelineTests},
"summarization": {"test": SummarizationPipelineTests},
"table-question-answering": {"test": TQAPipelineTests},
"text2text-generation": {"test": Text2TextGenerationPipelineTests},
"text-classification": {"test": TextClassificationPipelineTests},
"text-generation": {"test": TextGenerationPipelineTests},
"token-classification": {"test": TokenClassificationPipelineTests},
"translation": {"test": TranslationPipelineTests},
"video-classification": {"test": VideoClassificationPipelineTests},
"visual-question-answering": {"test": VisualQuestionAnsweringPipelineTests},
"zero-shot": {"test": ZeroShotClassificationPipelineTests},
"zero-shot-audio-classification": {"test": ZeroShotAudioClassificationPipelineTests},
"zero-shot-image-classification": {"test": ZeroShotImageClassificationPipelineTests},
"zero-shot-object-detection": {"test": ZeroShotObjectDetectionPipelineTests},
}
for task, task_info in pipeline_test_mapping.items():
test = task_info["test"]
task_info["mapping"] = {
"pt": getattr(test, "model_mapping", None),
"tf": getattr(test, "tf_model_mapping", None),
}
# The default value `hf-internal-testing` is for running the pipeline testing against the tiny models on the Hub.
# For debugging purpose, we can specify a local path which is the `output_path` argument of a previous run of
# `utils/create_dummy_models.py`.
TRANSFORMERS_TINY_MODEL_PATH = os.environ.get("TRANSFORMERS_TINY_MODEL_PATH", "hf-internal-testing")
if TRANSFORMERS_TINY_MODEL_PATH == "hf-internal-testing":
TINY_MODEL_SUMMARY_FILE_PATH = os.path.join(Path(__file__).parent.parent, "tests/utils/tiny_model_summary.json")
else:
TINY_MODEL_SUMMARY_FILE_PATH = os.path.join(TRANSFORMERS_TINY_MODEL_PATH, "reports", "tiny_model_summary.json")
with open(TINY_MODEL_SUMMARY_FILE_PATH) as fp:
tiny_model_summary = json.load(fp)
PATH_TO_TRANSFORMERS = os.path.join(Path(__file__).parent.parent, "src/transformers")
# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
transformers_module = direct_transformers_import(PATH_TO_TRANSFORMERS)
logger = logging.get_logger(__name__)
class PipelineTesterMixin:
model_tester = None
pipeline_model_mapping = None
supported_frameworks = ["pt", "tf"]
def run_task_tests(self, task):
"""Run pipeline tests for a specific `task`
Args:
task (`str`):
A task name. This should be a key in the mapping `pipeline_test_mapping`.
"""
if task not in self.pipeline_model_mapping:
self.skipTest(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: `{task}` is not in "
f"`self.pipeline_model_mapping` for `{self.__class__.__name__}`."
)
model_architectures = self.pipeline_model_mapping[task]
if not isinstance(model_architectures, tuple):
model_architectures = (model_architectures,)
if not isinstance(model_architectures, tuple):
raise ValueError(f"`model_architectures` must be a tuple. Got {type(model_architectures)} instead.")
for model_architecture in model_architectures:
model_arch_name = model_architecture.__name__
# Get the canonical name
for _prefix in ["Flax", "TF"]:
if model_arch_name.startswith(_prefix):
model_arch_name = model_arch_name[len(_prefix) :]
break
tokenizer_names = []
processor_names = []
commit = None
if model_arch_name in tiny_model_summary:
tokenizer_names = tiny_model_summary[model_arch_name]["tokenizer_classes"]
processor_names = tiny_model_summary[model_arch_name]["processor_classes"]
if "sha" in tiny_model_summary[model_arch_name]:
commit = tiny_model_summary[model_arch_name]["sha"]
# Adding `None` (if empty) so we can generate tests
tokenizer_names = [None] if len(tokenizer_names) == 0 else tokenizer_names
processor_names = [None] if len(processor_names) == 0 else processor_names
repo_name = f"tiny-random-{model_arch_name}"
if TRANSFORMERS_TINY_MODEL_PATH != "hf-internal-testing":
repo_name = model_arch_name
self.run_model_pipeline_tests(
task, repo_name, model_architecture, tokenizer_names, processor_names, commit
)
def run_model_pipeline_tests(self, task, repo_name, model_architecture, tokenizer_names, processor_names, commit):
"""Run pipeline tests for a specific `task` with the give model class and tokenizer/processor class names
Args:
task (`str`):
A task name. This should be a key in the mapping `pipeline_test_mapping`.
repo_name (`str`):
A model repository id on the Hub.
model_architecture (`type`):
A subclass of `PretrainedModel` or `PretrainedModel`.
tokenizer_names (`List[str]`):
A list of names of a subclasses of `PreTrainedTokenizerFast` or `PreTrainedTokenizer`.
processor_names (`List[str]`):
A list of names of subclasses of `BaseImageProcessor` or `FeatureExtractionMixin`.
"""
# Get an instance of the corresponding class `XXXPipelineTests` in order to use `get_test_pipeline` and
# `run_pipeline_test`.
pipeline_test_class_name = pipeline_test_mapping[task]["test"].__name__
for tokenizer_name in tokenizer_names:
for processor_name in processor_names:
if self.is_pipeline_test_to_skip(
pipeline_test_class_name,
model_architecture.config_class,
model_architecture,
tokenizer_name,
processor_name,
):
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: test is "
f"currently known to fail for: model `{model_architecture.__name__}` | tokenizer "
f"`{tokenizer_name}` | processor `{processor_name}`."
)
continue
self.run_pipeline_test(task, repo_name, model_architecture, tokenizer_name, processor_name, commit)
def run_pipeline_test(self, task, repo_name, model_architecture, tokenizer_name, processor_name, commit):
"""Run pipeline tests for a specific `task` with the give model class and tokenizer/processor class name
The model will be loaded from a model repository on the Hub.
Args:
task (`str`):
A task name. This should be a key in the mapping `pipeline_test_mapping`.
repo_name (`str`):
A model repository id on the Hub.
model_architecture (`type`):
A subclass of `PretrainedModel` or `PretrainedModel`.
tokenizer_name (`str`):
The name of a subclass of `PreTrainedTokenizerFast` or `PreTrainedTokenizer`.
processor_name (`str`):
The name of a subclass of `BaseImageProcessor` or `FeatureExtractionMixin`.
"""
repo_id = f"{TRANSFORMERS_TINY_MODEL_PATH}/{repo_name}"
if TRANSFORMERS_TINY_MODEL_PATH != "hf-internal-testing":
model_type = model_architecture.config_class.model_type
repo_id = os.path.join(TRANSFORMERS_TINY_MODEL_PATH, model_type, repo_name)
tokenizer = None
if tokenizer_name is not None:
tokenizer_class = getattr(transformers_module, tokenizer_name)
tokenizer = tokenizer_class.from_pretrained(repo_id, revision=commit)
processor = None
if processor_name is not None:
processor_class = getattr(transformers_module, processor_name)
# If the required packages (like `Pillow` or `torchaudio`) are not installed, this will fail.
try:
processor = processor_class.from_pretrained(repo_id, revision=commit)
except Exception:
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not load the "
f"processor from `{repo_id}` with `{processor_name}`."
)
return
# TODO: Maybe not upload such problematic tiny models to Hub.
if tokenizer is None and processor is None:
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not find or load "
f"any tokenizer / processor from `{repo_id}`."
)
return
# TODO: We should check if a model file is on the Hub repo. instead.
try:
model = model_architecture.from_pretrained(repo_id, revision=commit)
except Exception:
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not find or load "
f"the model from `{repo_id}` with `{model_architecture}`."
)
return
pipeline_test_class_name = pipeline_test_mapping[task]["test"].__name__
if self.is_pipeline_test_to_skip_more(pipeline_test_class_name, model.config, model, tokenizer, processor):
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: test is "
f"currently known to fail for: model `{model_architecture.__name__}` | tokenizer "
f"`{tokenizer_name}` | processor `{processor_name}`."
)
return
# validate
validate_test_components(self, task, model, tokenizer, processor)
if hasattr(model, "eval"):
model = model.eval()
# Get an instance of the corresponding class `XXXPipelineTests` in order to use `get_test_pipeline` and
# `run_pipeline_test`.
task_test = pipeline_test_mapping[task]["test"]()
pipeline, examples = task_test.get_test_pipeline(model, tokenizer, processor)
if pipeline is None:
# The test can disable itself, but it should be very marginal
# Concerns: Wav2Vec2ForCTC without tokenizer test (FastTokenizer don't exist)
logger.warning(
f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not get the "
"pipeline for testing."
)
return
task_test.run_pipeline_test(pipeline, examples)
def run_batch_test(pipeline, examples):
# Need to copy because `Conversation` are stateful
if pipeline.tokenizer is not None and pipeline.tokenizer.pad_token_id is None:
return # No batching for this and it's OK
# 10 examples with batch size 4 means there needs to be a unfinished batch
# which is important for the unbatcher
def data(n):
for _ in range(n):
# Need to copy because Conversation object is mutated
yield copy.deepcopy(random.choice(examples))
out = []
for item in pipeline(data(10), batch_size=4):
out.append(item)
self.assertEqual(len(out), 10)
run_batch_test(pipeline, examples)
@is_pipeline_test
def test_pipeline_audio_classification(self):
self.run_task_tests(task="audio-classification")
@is_pipeline_test
def test_pipeline_automatic_speech_recognition(self):
self.run_task_tests(task="automatic-speech-recognition")
@is_pipeline_test
def test_pipeline_conversational(self):
self.run_task_tests(task="conversational")
@is_pipeline_test
@require_vision
@require_timm
@require_torch
def test_pipeline_depth_estimation(self):
self.run_task_tests(task="depth-estimation")
@is_pipeline_test
@require_pytesseract
@require_torch
@require_vision
def test_pipeline_document_question_answering(self):
self.run_task_tests(task="document-question-answering")
@is_pipeline_test
def test_pipeline_feature_extraction(self):
self.run_task_tests(task="feature-extraction")
@is_pipeline_test
def test_pipeline_fill_mask(self):
self.run_task_tests(task="fill-mask")
@is_pipeline_test
@require_torch_or_tf
@require_vision
def test_pipeline_image_classification(self):
self.run_task_tests(task="image-classification")
@is_pipeline_test
@require_vision
@require_timm
@require_torch
def test_pipeline_image_segmentation(self):
self.run_task_tests(task="image-segmentation")
@is_pipeline_test
@require_vision
def test_pipeline_image_to_text(self):
self.run_task_tests(task="image-to-text")
@unittest.skip(reason="`run_pipeline_test` is currently not implemented.")
@is_pipeline_test
@require_vision
@require_torch
def test_pipeline_mask_generation(self):
self.run_task_tests(task="mask-generation")
@is_pipeline_test
@require_vision
@require_timm
@require_torch
def test_pipeline_object_detection(self):
self.run_task_tests(task="object-detection")
@is_pipeline_test
def test_pipeline_question_answering(self):
self.run_task_tests(task="question-answering")
@is_pipeline_test
def test_pipeline_summarization(self):
self.run_task_tests(task="summarization")
@is_pipeline_test
def test_pipeline_table_question_answering(self):
self.run_task_tests(task="table-question-answering")
@is_pipeline_test
def test_pipeline_text2text_generation(self):
self.run_task_tests(task="text2text-generation")
@is_pipeline_test
def test_pipeline_text_classification(self):
self.run_task_tests(task="text-classification")
@is_pipeline_test
@require_torch_or_tf
def test_pipeline_text_generation(self):
self.run_task_tests(task="text-generation")
@is_pipeline_test
def test_pipeline_token_classification(self):
self.run_task_tests(task="token-classification")
@is_pipeline_test
def test_pipeline_translation(self):
self.run_task_tests(task="translation")
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
def test_pipeline_video_classification(self):
self.run_task_tests(task="video-classification")
@is_pipeline_test
@require_torch
@require_vision
def test_pipeline_visual_question_answering(self):
self.run_task_tests(task="visual-question-answering")
@is_pipeline_test
def test_pipeline_zero_shot(self):
self.run_task_tests(task="zero-shot")
@is_pipeline_test
@require_torch
def test_pipeline_zero_shot_audio_classification(self):
self.run_task_tests(task="zero-shot-audio-classification")
@is_pipeline_test
@require_vision
def test_pipeline_zero_shot_image_classification(self):
self.run_task_tests(task="zero-shot-image-classification")
@is_pipeline_test
@require_vision
@require_torch
def test_pipeline_zero_shot_object_detection(self):
self.run_task_tests(task="zero-shot-object-detection")
# This contains the test cases to be skipped without model architecture being involved.
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
"""Skip some tests based on the classes or their names without the instantiated objects.
This is to avoid calling `from_pretrained` (so reducing the runtime) if we already know the tests will fail.
"""
# No fix is required for this case.
if (
pipeline_test_casse_name == "DocumentQuestionAnsweringPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `DocumentQuestionAnsweringPipelineTests` requires a fast tokenizer.
return True
return False
def is_pipeline_test_to_skip_more(self, pipeline_test_casse_name, config, model, tokenizer, processor): # noqa
"""Skip some more tests based on the information from the instantiated objects."""
# No fix is required for this case.
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer is not None
and getattr(tokenizer, "pad_token", None) is None
and not tokenizer.__class__.__name__.endswith("Fast")
):
# `QAPipelineTests` doesn't work with a slow tokenizer that has no pad token.
return True
return False
def validate_test_components(test_case, task, model, tokenizer, processor):
# TODO: Move this to tiny model creation script
# head-specific (within a model type) necessary changes to the config
# 1. for `BlenderbotForCausalLM`
if model.__class__.__name__ == "BlenderbotForCausalLM":
model.config.encoder_no_repeat_ngram_size = 0
# TODO: Change the tiny model creation script: don't create models with problematic tokenizers
# Avoid `IndexError` in embedding layers
CONFIG_WITHOUT_VOCAB_SIZE = ["CanineConfig"]
if tokenizer is not None:
config_vocab_size = getattr(model.config, "vocab_size", None)
# For CLIP-like models
if config_vocab_size is None and hasattr(model.config, "text_config"):
config_vocab_size = getattr(model.config.text_config, "vocab_size", None)
if config_vocab_size is None and model.config.__class__.__name__ not in CONFIG_WITHOUT_VOCAB_SIZE:
raise ValueError(
"Could not determine `vocab_size` from model configuration while `tokenizer` is not `None`."
)
| 22,373 | 43.569721 | 120 | py |
transformers | transformers-main/tests/test_tokenization_common.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import itertools
import json
import os
import pickle
import re
import shutil
import tempfile
import traceback
import unittest
from collections import OrderedDict
from itertools import takewhile
from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Union
from parameterized import parameterized
from transformers import (
AlbertTokenizer,
AlbertTokenizerFast,
BertTokenizer,
BertTokenizerFast,
PreTrainedTokenizer,
PreTrainedTokenizerBase,
PreTrainedTokenizerFast,
SpecialTokensMixin,
Trainer,
TrainingArguments,
is_flax_available,
is_tf_available,
is_torch_available,
logging,
)
from transformers.testing_utils import (
check_json_file_has_correct_format,
get_tests_dir,
is_pt_tf_cross_test,
require_tf,
require_tokenizers,
require_torch,
run_test_in_subprocess,
slow,
)
from transformers.tokenization_utils import AddedToken
if is_torch_available():
import torch.nn as nn
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel, TFPreTrainedModel
logger = logging.get_logger(__name__)
NON_ENGLISH_TAGS = ["chinese", "dutch", "french", "finnish", "german", "multilingual"]
SMALL_TRAINING_CORPUS = [
["This is the first sentence.", "This is the second one."],
["This sentence (contains #) over symbols and numbers 12 3.", "But not this one."],
]
def filter_non_english(_, pretrained_name: str):
"""Filter all the model for non-english language"""
return not any(lang in pretrained_name for lang in NON_ENGLISH_TAGS)
def filter_roberta_detectors(_, pretrained_name: str):
return "detector" not in pretrained_name
def merge_model_tokenizer_mappings(
model_mapping: Dict["PretrainedConfig", Union["PreTrainedModel", "TFPreTrainedModel"]],
tokenizer_mapping: Dict["PretrainedConfig", Tuple["PreTrainedTokenizer", "PreTrainedTokenizerFast"]],
) -> Dict[
Union["PreTrainedTokenizer", "PreTrainedTokenizerFast"],
Tuple["PretrainedConfig", Union["PreTrainedModel", "TFPreTrainedModel"]],
]:
configurations = list(model_mapping.keys())
model_tokenizer_mapping = OrderedDict([])
for configuration in configurations:
if configuration in model_mapping and configuration in tokenizer_mapping:
model = model_mapping[configuration]
tokenizer = tokenizer_mapping[configuration][0]
tokenizer_fast = tokenizer_mapping[configuration][1]
if tokenizer is not None:
if configuration.__name__.startswith(tokenizer.__name__.replace("Tokenizer", "")):
model_tokenizer_mapping.update({tokenizer: (configuration, model)})
if tokenizer_fast is not None:
if configuration.__name__.startswith(tokenizer_fast.__name__.replace("TokenizerFast", "")):
model_tokenizer_mapping.update({tokenizer_fast: (configuration, model)})
return model_tokenizer_mapping
def _test_subword_regularization_tokenizer(in_queue, out_queue, timeout):
error = None
try:
inputs = in_queue.get(timeout=timeout)
tokenizer = inputs["tokenizer"]
sp_model_kwargs = inputs["sp_model_kwargs"]
test_sentencepiece_ignore_case = inputs["test_sentencepiece_ignore_case"]
unittest.TestCase().assertTrue(hasattr(tokenizer, "sp_model_kwargs"))
unittest.TestCase().assertIsNotNone(tokenizer.sp_model_kwargs)
unittest.TestCase().assertTrue(isinstance(tokenizer.sp_model_kwargs, dict))
unittest.TestCase().assertDictEqual(tokenizer.sp_model_kwargs, sp_model_kwargs)
check_subword_sampling(tokenizer, test_sentencepiece_ignore_case=test_sentencepiece_ignore_case)
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
def check_subword_sampling(
tokenizer: PreTrainedTokenizer,
text: str = None,
test_sentencepiece_ignore_case: bool = True,
) -> None:
"""
Check if the tokenizer generates different results when subword regularization is enabled.
Subword regularization augments training data with subword sampling.
This has a random component.
Args:
tokenizer: The tokenizer to check.
text: The text to use for the checks.
test_sentencepiece_ignore_case: See `TokenizerTesterMixin.test_sentencepiece_ignore_case`.
"""
text = "This is a test for subword regularization." if text is None else text
if test_sentencepiece_ignore_case:
text = text.lower()
tokens_list = []
for _ in range(5):
tokens_list.append(tokenizer.tokenize(text))
# the list of different pairs of tokens_list
combinations = itertools.combinations(tokens_list, 2)
# check of sampling is done
subword_sampling_found = False
for combination in combinations:
if combination[0] != combination[1]:
subword_sampling_found = True
unittest.TestCase().assertTrue(subword_sampling_found)
# check if converting back to original text works
for tokens in tokens_list:
if test_sentencepiece_ignore_case:
unittest.TestCase().assertEqual(text, tokenizer.convert_tokens_to_string(tokens).lower())
else:
unittest.TestCase().assertEqual(text, tokenizer.convert_tokens_to_string(tokens))
class TokenizerTesterMixin:
tokenizer_class = None
rust_tokenizer_class = None
test_slow_tokenizer = True
test_rust_tokenizer = True
space_between_special_tokens = False
from_pretrained_kwargs = None
from_pretrained_filter = None
from_pretrained_vocab_key = "vocab_file"
test_seq2seq = True
# set to True to test a sentencepiece tokenizer
test_sentencepiece = False
# set to True to ignore casing when testing a sentencepiece tokenizer
# test_sentencepiece must also be set to True
test_sentencepiece_ignore_case = False
def setUp(self) -> None:
# Tokenizer.filter makes it possible to filter which Tokenizer to case based on all the
# information available in Tokenizer (name, rust class, python class, vocab key name)
if self.test_rust_tokenizer:
tokenizers_list = [
(
self.rust_tokenizer_class,
pretrained_name,
self.from_pretrained_kwargs if self.from_pretrained_kwargs is not None else {},
)
for pretrained_name in self.rust_tokenizer_class.pretrained_vocab_files_map[
self.from_pretrained_vocab_key
].keys()
if self.from_pretrained_filter is None
or (self.from_pretrained_filter is not None and self.from_pretrained_filter(pretrained_name))
]
self.tokenizers_list = tokenizers_list[:1] # Let's just test the first pretrained vocab for speed
else:
self.tokenizers_list = []
with open(f"{get_tests_dir()}/fixtures/sample_text.txt", encoding="utf-8") as f_data:
self._data = f_data.read().replace("\n\n", "\n").strip()
self.tmpdirname = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def get_input_output_texts(self, tokenizer):
input_txt = self.get_clean_sequence(tokenizer)[0]
return input_txt, input_txt
def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5) -> Tuple[str, list]:
toks = [(i, tokenizer.decode([i], clean_up_tokenization_spaces=False)) for i in range(len(tokenizer))]
toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks))
toks = list(filter(lambda t: [t[0]] == tokenizer.encode(t[1], add_special_tokens=False), toks))
if max_length is not None and len(toks) > max_length:
toks = toks[:max_length]
if min_length is not None and len(toks) < min_length and len(toks) > 0:
while len(toks) < min_length:
toks = toks + toks
# toks_str = [t[1] for t in toks]
toks_ids = [t[0] for t in toks]
# Ensure consistency
output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False)
if " " not in output_txt and len(toks_ids) > 1:
output_txt = (
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
+ " "
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
)
if with_prefix_space:
output_txt = " " + output_txt
output_ids = tokenizer.encode(output_txt, add_special_tokens=False)
return output_txt, output_ids
def get_tokenizers(self, fast=True, **kwargs) -> List[PreTrainedTokenizerBase]:
if fast and self.test_rust_tokenizer and self.test_slow_tokenizer:
return [self.get_tokenizer(**kwargs), self.get_rust_tokenizer(**kwargs)]
elif fast and self.test_rust_tokenizer:
return [self.get_rust_tokenizer(**kwargs)]
elif self.test_slow_tokenizer:
return [self.get_tokenizer(**kwargs)]
else:
raise ValueError("This tokenizer class has no tokenizer to be tested.")
def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
def get_rust_tokenizer(self, **kwargs) -> PreTrainedTokenizerFast:
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
def tokenizer_integration_test_util(
self,
expected_encoding: Dict,
model_name: str,
revision: str = None,
sequences: List[str] = None,
decode_kwargs: Dict[str, Any] = None,
padding: bool = True,
):
"""
Util for integration test.
Text is tokenized and then reverted back to text. Both results are then checked.
Args:
expected_encoding:
The expected result of the tokenizer output.
model_name:
The model name of the tokenizer to load and use.
revision:
The full git revision number of the model. This is to pin the
tokenizer config and to avoid that tests start to fail if the
config gets changed upstream.
sequences:
Can overwrite the texts that are used to check the tokenizer.
This is useful if the tokenizer supports non english languages
like france.
decode_kwargs:
Additional args for the ``decode`` function which reverts the
tokenized text back to a string.
padding:
Activates and controls padding of the tokenizer.
"""
decode_kwargs = {} if decode_kwargs is None else decode_kwargs
if sequences is None:
sequences = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained "
"models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
if self.test_sentencepiece_ignore_case:
sequences = [sequence.lower() for sequence in sequences]
tokenizer_classes = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class)
for tokenizer_class in tokenizer_classes:
tokenizer = tokenizer_class.from_pretrained(
model_name,
revision=revision, # to pin the tokenizer version
)
encoding = tokenizer(sequences, padding=padding)
decoded_sequences = [
tokenizer.decode(seq, skip_special_tokens=True, **decode_kwargs) for seq in encoding["input_ids"]
]
encoding_data = encoding.data
self.assertDictEqual(encoding_data, expected_encoding)
for expected, decoded in zip(sequences, decoded_sequences):
if self.test_sentencepiece_ignore_case:
expected = expected.lower()
self.assertEqual(expected, decoded)
def assert_padded_input_match(self, input_r: list, input_p: list, max_length: int, pad_token_id: int):
# Ensure we match max_length
self.assertEqual(len(input_r), max_length)
self.assertEqual(len(input_p), max_length)
# Ensure the number of padded tokens is the same
padded_tokens_r = list(takewhile(lambda i: i == pad_token_id, reversed(input_r)))
padded_tokens_p = list(takewhile(lambda i: i == pad_token_id, reversed(input_p)))
self.assertSequenceEqual(padded_tokens_r, padded_tokens_p)
def assert_batch_padded_input_match(
self,
input_r: dict,
input_p: dict,
max_length: int,
pad_token_id: int,
model_main_input_name: str = "input_ids",
):
for i_r in input_r.values():
self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
len(i_r[1]), max_length
)
self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
len(i_r[1]), max_length
)
for i_r, i_p in zip(input_r[model_main_input_name], input_p[model_main_input_name]):
self.assert_padded_input_match(i_r, i_p, max_length, pad_token_id)
for i_r, i_p in zip(input_r["attention_mask"], input_p["attention_mask"]):
self.assertSequenceEqual(i_r, i_p)
@staticmethod
def convert_batch_encode_plus_format_to_encode_plus(batch_encode_plus_sequences):
# Switch from batch_encode_plus format: {'input_ids': [[...], [...]], ...}
# to the list of examples/ encode_plus format: [{'input_ids': [...], ...}, {'input_ids': [...], ...}]
return [
{value: batch_encode_plus_sequences[value][i] for value in batch_encode_plus_sequences.keys()}
for i in range(len(batch_encode_plus_sequences["input_ids"]))
]
# TODO: this test can be combined with `test_sentencepiece_tokenize_and_convert_tokens_to_string` after the latter is extended to all tokenizers.
def test_tokenize_special_tokens(self):
"""Test `tokenize` with special tokens."""
tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
SPECIAL_TOKEN_1 = "[SPECIAL_TOKEN_1]"
SPECIAL_TOKEN_2 = "[SPECIAL_TOKEN_2]"
# TODO:
# Can we combine `unique_no_split_tokens` and `all_special_tokens`(and properties related to it)
# with one variable(property) for a better maintainability?
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1], special_tokens=True)
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]})
token_1 = tokenizer.tokenize(SPECIAL_TOKEN_1)
token_2 = tokenizer.tokenize(SPECIAL_TOKEN_2)
self.assertEqual(len(token_1), 1)
self.assertEqual(len(token_2), 1)
self.assertEqual(token_1[0], SPECIAL_TOKEN_1)
self.assertEqual(token_2[0], SPECIAL_TOKEN_2)
# TODO: this test could be extended to all tokenizers - not just the sentencepiece
def test_sentencepiece_tokenize_and_convert_tokens_to_string(self):
"""Test ``_tokenize`` and ``convert_tokens_to_string``."""
if not self.test_sentencepiece:
return
tokenizer = self.get_tokenizer()
text = "This is text to test the tokenizer."
if self.test_sentencepiece_ignore_case:
text = text.lower()
tokens = tokenizer.tokenize(text)
self.assertTrue(len(tokens) > 0)
# check if converting back to original text works
reverse_text = tokenizer.convert_tokens_to_string(tokens)
if self.test_sentencepiece_ignore_case:
reverse_text = reverse_text.lower()
self.assertEqual(reverse_text, text)
special_tokens = tokenizer.all_special_tokens
special_tokens_string = tokenizer.convert_tokens_to_string(special_tokens)
for special_token in special_tokens:
self.assertIn(special_token, special_tokens_string)
if self.test_rust_tokenizer:
rust_tokenizer = self.get_rust_tokenizer()
special_tokens_string_rust = rust_tokenizer.convert_tokens_to_string(special_tokens)
self.assertEqual(special_tokens_string, special_tokens_string_rust)
def test_sentencepiece_tokenize_and_decode(self):
if not self.test_sentencepiece:
return
text = "This is text to test the tokenizer."
if self.test_rust_tokenizer:
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
slow_ids = tokenizer(text).input_ids
fast_ids = rust_tokenizer(text).input_ids
self.assertEqual(slow_ids, fast_ids)
slow_decoded = tokenizer.decode(slow_ids)
fast_decoded = rust_tokenizer.decode(slow_ids)
self.assertEqual(slow_decoded, fast_decoded)
def test_subword_regularization_tokenizer(self) -> None:
if not self.test_sentencepiece:
return
# Subword regularization is only available for the slow tokenizer.
sp_model_kwargs = {"enable_sampling": True, "alpha": 0.1, "nbest_size": -1}
tokenizer = self.get_tokenizer(sp_model_kwargs=sp_model_kwargs)
run_test_in_subprocess(
test_case=self,
target_func=_test_subword_regularization_tokenizer,
inputs={
"tokenizer": tokenizer,
"sp_model_kwargs": sp_model_kwargs,
"test_sentencepiece_ignore_case": self.test_sentencepiece_ignore_case,
},
)
def test_pickle_subword_regularization_tokenizer(self) -> None:
if not self.test_sentencepiece:
return
"""Google pickle __getstate__ __setstate__ if you are struggling with this."""
# Subword regularization is only available for the slow tokenizer.
sp_model_kwargs = {"enable_sampling": True, "alpha": 0.1, "nbest_size": -1}
tokenizer = self.get_tokenizer(sp_model_kwargs=sp_model_kwargs)
tokenizer_bin = pickle.dumps(tokenizer)
del tokenizer
tokenizer_new = pickle.loads(tokenizer_bin)
run_test_in_subprocess(
test_case=self,
target_func=_test_subword_regularization_tokenizer,
inputs={
"tokenizer": tokenizer_new,
"sp_model_kwargs": sp_model_kwargs,
"test_sentencepiece_ignore_case": self.test_sentencepiece_ignore_case,
},
)
def test_save_sentencepiece_tokenizer(self) -> None:
if not self.test_sentencepiece or not self.test_slow_tokenizer:
return
# We want to verify that we will be able to save the tokenizer even if the original files that were used to
# build the tokenizer have been deleted in the meantime.
text = "This is text to test the tokenizer."
tokenizer_slow_1 = self.get_tokenizer()
encoding_tokenizer_slow_1 = tokenizer_slow_1(text)
tmpdirname_1 = tempfile.mkdtemp()
tmpdirname_2 = tempfile.mkdtemp()
tokenizer_slow_1.save_pretrained(tmpdirname_1)
tokenizer_slow_2 = self.tokenizer_class.from_pretrained(tmpdirname_1)
encoding_tokenizer_slow_2 = tokenizer_slow_2(text)
shutil.rmtree(tmpdirname_1)
tokenizer_slow_2.save_pretrained(tmpdirname_2)
tokenizer_slow_3 = self.tokenizer_class.from_pretrained(tmpdirname_2)
encoding_tokenizer_slow_3 = tokenizer_slow_3(text)
shutil.rmtree(tmpdirname_2)
self.assertEqual(encoding_tokenizer_slow_1, encoding_tokenizer_slow_2)
self.assertEqual(encoding_tokenizer_slow_1, encoding_tokenizer_slow_3)
def test_model_input_names_signature(self):
accepted_model_main_input_names = [
"input_ids", # nlp models
"input_values", # speech models
]
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
# first name of model_input_names has to correspond to main model input name
# to make sure `tokenizer.pad(...)` works correctly
self.assertTrue(tokenizer.model_input_names[0] in accepted_model_main_input_names)
def test_rust_tokenizer_signature(self):
if not self.test_rust_tokenizer:
return
signature = inspect.signature(self.rust_tokenizer_class.__init__)
self.assertIn("tokenizer_file", signature.parameters)
self.assertIsNone(signature.parameters["tokenizer_file"].default)
def test_tokenizer_slow_store_full_signature(self):
if not self.test_slow_tokenizer:
return
signature = inspect.signature(self.tokenizer_class.__init__)
tokenizer = self.get_tokenizer()
for parameter_name, parameter in signature.parameters.items():
if parameter.default != inspect.Parameter.empty:
self.assertIn(parameter_name, tokenizer.init_kwargs)
def test_tokenizer_fast_store_full_signature(self):
if not self.test_rust_tokenizer:
return
signature = inspect.signature(self.rust_tokenizer_class.__init__)
tokenizer = self.get_rust_tokenizer()
for parameter_name, parameter in signature.parameters.items():
if parameter.default != inspect.Parameter.empty and parameter_name not in [
"vocab_file",
"merges_file",
"tokenizer_file",
]:
self.assertIn(parameter_name, tokenizer.init_kwargs)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence, _ = self.get_input_output_texts(tokenizer)
# We don't have an exact equivalence on `tokenize()` between Rust and Slow
# Slow tokenizer only split tokens, Rust tokenizers will replace with <unk>
# tokens = tokenizer.tokenize(sequence)
# rust_tokens = rust_tokenizer.tokenize(sequence)
# self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
ids = tokenizer.encode(sequence, add_special_tokens=True)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=True)
self.assertListEqual(ids, rust_ids)
def test_tokenizers_common_properties(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
attributes_list = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
for attr in attributes_list:
self.assertTrue(hasattr(tokenizer, attr))
self.assertTrue(hasattr(tokenizer, attr + "_id"))
self.assertTrue(hasattr(tokenizer, "additional_special_tokens"))
self.assertTrue(hasattr(tokenizer, "additional_special_tokens_ids"))
attributes_list = [
"model_max_length",
"init_inputs",
"init_kwargs",
]
if not isinstance(tokenizer, PreTrainedTokenizerFast):
attributes_list += [
"added_tokens_encoder",
"added_tokens_decoder",
]
for attr in attributes_list:
self.assertTrue(hasattr(tokenizer, attr))
def test_tokenizers_common_ids_setters(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
attributes_list = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
vocab = tokenizer.get_vocab()
token_id_to_test_setters = next(iter(vocab.values()))
token_to_test_setters = tokenizer.convert_ids_to_tokens(
token_id_to_test_setters, skip_special_tokens=False
)
for attr in attributes_list:
setattr(tokenizer, attr + "_id", None)
self.assertEqual(getattr(tokenizer, attr), None)
self.assertEqual(getattr(tokenizer, attr + "_id"), None)
setattr(tokenizer, attr + "_id", token_id_to_test_setters)
self.assertEqual(getattr(tokenizer, attr), token_to_test_setters)
self.assertEqual(getattr(tokenizer, attr + "_id"), token_id_to_test_setters)
setattr(tokenizer, "additional_special_tokens_ids", [])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [])
setattr(tokenizer, "additional_special_tokens_ids", [token_id_to_test_setters])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [token_to_test_setters])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [token_id_to_test_setters])
@parameterized.expand([(True,), (False,)])
def test_tokenizers_special_tokens_properties_unset(self, verbose):
tokenizers = self.get_tokenizers(verbose=verbose)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
attributes_list = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
"additional_special_tokens",
]
for attr in attributes_list:
setattr(tokenizer, attr, None)
self.assertIsNone(getattr(tokenizer, attr))
def test_save_and_load_tokenizer(self):
# safety check on max_len default value so we are sure the test works
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.model_max_length, 42)
# Now let's start the test
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
before_vocab = tokenizer.get_vocab()
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
after_vocab = after_tokenizer.get_vocab()
self.assertListEqual(before_tokens, after_tokens)
self.assertDictEqual(before_vocab, after_vocab)
shutil.rmtree(tmpdirname)
tokenizers = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
tokenizer.add_tokens(["bim", "bambam"])
additional_special_tokens = tokenizer.additional_special_tokens
additional_special_tokens.append("new_additional_special_token")
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
before_vocab = tokenizer.get_vocab()
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
after_vocab = after_tokenizer.get_vocab()
self.assertListEqual(before_tokens, after_tokens)
self.assertDictEqual(before_vocab, after_vocab)
self.assertIn("bim", after_vocab)
self.assertIn("bambam", after_vocab)
self.assertIn("new_additional_special_token", after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length, 42)
tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
self.assertEqual(tokenizer.model_max_length, 43)
shutil.rmtree(tmpdirname)
# Test that we can also use the non-legacy saving format for fast tokenizers
tokenizers = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
if not tokenizer.is_fast:
continue
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
tokenizer.add_tokens(["bim", "bambam"])
additional_special_tokens = tokenizer.additional_special_tokens
additional_special_tokens.append("new_additional_special_token")
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
before_vocab = tokenizer.get_vocab()
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
after_vocab = after_tokenizer.get_vocab()
self.assertListEqual(before_tokens, after_tokens)
self.assertDictEqual(before_vocab, after_vocab)
self.assertIn("bim", after_vocab)
self.assertIn("bambam", after_vocab)
self.assertIn("new_additional_special_token", after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length, 42)
tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
self.assertEqual(tokenizer.model_max_length, 43)
shutil.rmtree(tmpdirname)
def test_pickle_tokenizer(self):
"""Google pickle __getstate__ __setstate__ if you are struggling with this."""
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertIsNotNone(tokenizer)
text = "Munich and Berlin are nice cities"
subwords = tokenizer.tokenize(text)
filename = os.path.join(self.tmpdirname, "tokenizer.bin")
with open(filename, "wb") as handle:
pickle.dump(tokenizer, handle)
with open(filename, "rb") as handle:
tokenizer_new = pickle.load(handle)
subwords_loaded = tokenizer_new.tokenize(text)
self.assertListEqual(subwords, subwords_loaded)
@require_tokenizers
def test_pickle_added_tokens(self):
tok1 = AddedToken("<s>", rstrip=True, lstrip=True, normalized=False, single_word=True)
tok2 = pickle.loads(pickle.dumps(tok1))
self.assertEqual(tok1.__getstate__(), tok2.__getstate__())
def test_added_tokens_do_lower_case(self):
tokenizers = self.get_tokenizers(do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if not hasattr(tokenizer, "do_lower_case") or not tokenizer.do_lower_case:
continue
special_token = tokenizer.all_special_tokens[0]
text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token
text2 = special_token + " AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l " + special_token
toks_before_adding = tokenizer.tokenize(text) # toks before adding new_toks
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", "AAAAA BBBBBB", "CCCCCCCCCDDDDDDDD"]
added = tokenizer.add_tokens([AddedToken(tok, lstrip=True, rstrip=True) for tok in new_toks])
toks_after_adding = tokenizer.tokenize(text)
toks_after_adding2 = tokenizer.tokenize(text2)
# Rust tokenizers dont't lowercase added tokens at the time calling `tokenizer.add_tokens`,
# while python tokenizers do, so new_toks 0 and 2 would be treated as the same, so do new_toks 1 and 3.
self.assertIn(added, [2, 4])
self.assertListEqual(toks_after_adding, toks_after_adding2)
self.assertTrue(
len(toks_before_adding) > len(toks_after_adding), # toks_before_adding should be longer
)
# Check that none of the special tokens are lowercased
sequence_with_special_tokens = "A " + " yEs ".join(tokenizer.all_special_tokens) + " B"
# Convert the tokenized list to str as some special tokens are tokenized like normal tokens
# which have a prefix spacee e.g. the mask token of Albert, and cannot match the original
# special tokens exactly.
tokenized_sequence = "".join(tokenizer.tokenize(sequence_with_special_tokens))
for special_token in tokenizer.all_special_tokens:
self.assertTrue(special_token in tokenized_sequence)
tokenizers = self.get_tokenizers(do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if hasattr(tokenizer, "do_lower_case") and tokenizer.do_lower_case:
continue
special_token = tokenizer.all_special_tokens[0]
text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token
text2 = special_token + " AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l " + special_token
toks_before_adding = tokenizer.tokenize(text) # toks before adding new_toks
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", "AAAAA BBBBBB", "CCCCCCCCCDDDDDDDD"]
added = tokenizer.add_tokens([AddedToken(tok, lstrip=True, rstrip=True) for tok in new_toks])
self.assertIn(added, [2, 4])
toks_after_adding = tokenizer.tokenize(text)
toks_after_adding2 = tokenizer.tokenize(text2)
self.assertEqual(len(toks_after_adding), len(toks_after_adding2)) # Length should still be the same
self.assertNotEqual(
toks_after_adding[1], toks_after_adding2[1]
) # But at least the first non-special tokens should differ
self.assertTrue(
len(toks_before_adding) > len(toks_after_adding), # toks_before_adding should be longer
)
def test_add_tokens_tokenizer(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
vocab_size = tokenizer.vocab_size
all_size = len(tokenizer)
self.assertNotEqual(vocab_size, 0)
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
added_toks = tokenizer.add_tokens(new_toks)
vocab_size_2 = tokenizer.vocab_size
all_size_2 = len(tokenizer)
self.assertNotEqual(vocab_size_2, 0)
self.assertEqual(vocab_size, vocab_size_2)
self.assertEqual(added_toks, len(new_toks))
self.assertEqual(all_size_2, all_size + len(new_toks))
tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
self.assertGreaterEqual(len(tokens), 4)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
vocab_size_3 = tokenizer.vocab_size
all_size_3 = len(tokenizer)
self.assertNotEqual(vocab_size_3, 0)
self.assertEqual(vocab_size, vocab_size_3)
self.assertEqual(added_toks_2, len(new_toks_2))
self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
tokens = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
)
self.assertGreaterEqual(len(tokens), 6)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[0], tokens[1])
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokens[-3])
self.assertEqual(tokens[0], tokenizer.eos_token_id)
self.assertEqual(tokens[-2], tokenizer.pad_token_id)
def test_add_special_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input_text, ids = self.get_clean_sequence(tokenizer)
special_token = "[SPECIAL_TOKEN]"
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(len(encoded_special_token), 1)
text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False)
encoded = tokenizer.encode(text, add_special_tokens=False)
input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(encoded, input_encoded + special_token_id)
decoded = tokenizer.decode(encoded, skip_special_tokens=True)
self.assertTrue(special_token not in decoded)
def test_internal_consistency(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input_text, output_text = self.get_input_output_texts(tokenizer)
tokens = tokenizer.tokenize(input_text)
ids = tokenizer.convert_tokens_to_ids(tokens)
ids_2 = tokenizer.encode(input_text, add_special_tokens=False)
self.assertListEqual(ids, ids_2)
tokens_2 = tokenizer.convert_ids_to_tokens(ids)
self.assertNotEqual(len(tokens_2), 0)
text_2 = tokenizer.decode(ids)
self.assertIsInstance(text_2, str)
self.assertEqual(text_2, output_text)
@require_tokenizers
def test_encode_decode_with_spaces(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
new_toks = [
AddedToken("[ABC]", normalized=False),
AddedToken("[DEF]", normalized=False),
AddedToken("GHI IHG", normalized=False),
]
tokenizer.add_tokens(new_toks)
input = "[ABC][DEF][ABC]GHI IHG[DEF]"
if self.space_between_special_tokens:
output = "[ABC] [DEF] [ABC] GHI IHG [DEF]"
else:
output = input
encoded = tokenizer.encode(input, add_special_tokens=False)
decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
self.assertIn(decoded, [output, output.lower()])
def test_pretrained_model_lists(self):
# We should have at least one default checkpoint for each tokenizer
# We should specify the max input length as well (used in some part to list the pretrained checkpoints)
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1)
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1)
self.assertEqual(
len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]),
len(self.tokenizer_class.max_model_input_sizes),
)
weights_list = list(self.tokenizer_class.max_model_input_sizes.keys())
weights_lists_2 = []
for file_id, map_list in self.tokenizer_class.pretrained_vocab_files_map.items():
weights_lists_2.append(list(map_list.keys()))
for weights_list_2 in weights_lists_2:
self.assertListEqual(weights_list, weights_list_2)
def test_mask_output(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if (
tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
and "token_type_ids" in tokenizer.model_input_names
):
seq_0 = "Test this method."
seq_1 = "With these inputs."
information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True)
sequences, mask = information["input_ids"], information["token_type_ids"]
self.assertEqual(len(sequences), len(mask))
def test_token_type_ids(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0 = "Test this method."
# We want to have sequence 0 and sequence 1 are tagged
# respectively with 0 and 1 token_ids
# (regardless of whether the model use token type ids)
# We use this assumption in the QA pipeline among other place
output = tokenizer(seq_0, return_token_type_ids=True)
self.assertIn(0, output["token_type_ids"])
def test_sequence_ids(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
if not tokenizer.is_fast:
continue
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0 = "Test this method."
seq_1 = "With these inputs."
# We want to have sequence 0 and sequence 1 are tagged
# respectively with 0 and 1 token_ids
# (regardless of whether the model use token type ids)
# We use this assumption in the QA pipeline among other place
output = tokenizer(seq_0)
self.assertIn(0, output.sequence_ids())
output = tokenizer(seq_0, seq_1)
self.assertIn(0, output.sequence_ids())
self.assertIn(1, output.sequence_ids())
if tokenizer.num_special_tokens_to_add(pair=True):
self.assertIn(None, output.sequence_ids())
def test_number_of_added_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0 = "Test this method."
seq_1 = "With these inputs."
sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=False)
attached_sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
# Method is implemented (e.g. not GPT-2)
if len(attached_sequences) != 2:
self.assertEqual(
tokenizer.num_special_tokens_to_add(pair=True), len(attached_sequences) - len(sequences)
)
def test_maximum_encoding_length_single_input(self):
tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
sequence = tokenizer.encode(seq_0, add_special_tokens=False)
total_length = len(sequence)
self.assertGreater(
total_length, 4, "Issue with the testing sequence, please update it, it's too short"
)
# Test with max model input length
model_max_length = tokenizer.model_max_length
self.assertEqual(model_max_length, 100)
seq_1 = seq_0 * model_max_length
sequence1 = tokenizer(seq_1, add_special_tokens=False)
total_length1 = len(sequence1["input_ids"])
self.assertGreater(
total_length1,
model_max_length,
"Issue with the testing sequence, please update it, it's too short",
)
# Simple
padding_strategies = (
[False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
)
for padding_state in padding_strategies:
with self.subTest(f"Padding: {padding_state}"):
for truncation_state in [True, "longest_first", "only_first"]:
with self.subTest(f"Truncation: {truncation_state}"):
output = tokenizer(seq_1, padding=padding_state, truncation=truncation_state)
self.assertEqual(len(output["input_ids"]), model_max_length)
output = tokenizer([seq_1], padding=padding_state, truncation=truncation_state)
self.assertEqual(len(output["input_ids"][0]), model_max_length)
# Simple with no truncation
# Reset warnings
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer(seq_1, padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length"
" for this model"
)
)
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer([seq_1], padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length"
" for this model"
)
)
# Overflowing tokens
stride = 2
information = tokenizer(
seq_0,
max_length=total_length - 2,
add_special_tokens=False,
stride=stride,
truncation="longest_first",
return_overflowing_tokens=True,
# add_prefix_space=False,
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, PreTrainedTokenizerFast):
truncated_sequence = information["input_ids"][0]
overflowing_tokens = information["input_ids"][1]
self.assertEqual(len(information["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), total_length - 2)
self.assertEqual(truncated_sequence, sequence[:-2])
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])
else:
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
self.assertEqual(len(truncated_sequence), total_length - 2)
self.assertEqual(truncated_sequence, sequence[:-2])
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])
def test_maximum_encoding_length_pair_input(self):
tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Build a sequence from our model's vocabulary
stride = 2
seq_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
if len(ids) <= 2 + stride:
seq_0 = (seq_0 + " ") * (2 + stride)
ids = None
seq0_tokens = tokenizer.encode(seq_0, add_special_tokens=False)
self.assertGreater(len(seq0_tokens), 2 + stride)
seq_1 = "This is another sentence to be encoded."
seq1_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
if abs(len(seq0_tokens) - len(seq1_tokens)) <= 2:
seq1_tokens = seq1_tokens + seq1_tokens
seq_1 = tokenizer.decode(seq1_tokens, clean_up_tokenization_spaces=False)
seq1_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
self.assertGreater(len(seq1_tokens), 2 + stride)
smallest = seq1_tokens if len(seq0_tokens) > len(seq1_tokens) else seq0_tokens
# We are not using the special tokens - a bit too hard to test all the tokenizers with this
# TODO try this again later
sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=False) # , add_prefix_space=False)
# Test with max model input length
model_max_length = tokenizer.model_max_length
self.assertEqual(model_max_length, 100)
seq_2 = seq_0 * model_max_length
self.assertGreater(len(seq_2), model_max_length)
sequence1 = tokenizer(seq_1, add_special_tokens=False)
total_length1 = len(sequence1["input_ids"])
sequence2 = tokenizer(seq_2, seq_1, add_special_tokens=False)
total_length2 = len(sequence2["input_ids"])
self.assertLess(
total_length1, model_max_length - 10, "Issue with the testing sequence, please update it."
)
self.assertGreater(
total_length2, model_max_length, "Issue with the testing sequence, please update it."
)
# Simple
padding_strategies = (
[False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
)
for padding_state in padding_strategies:
with self.subTest(f"{tokenizer.__class__.__name__} Padding: {padding_state}"):
for truncation_state in [True, "longest_first", "only_first"]:
with self.subTest(f"{tokenizer.__class__.__name__} Truncation: {truncation_state}"):
output = tokenizer(seq_2, seq_1, padding=padding_state, truncation=truncation_state)
self.assertEqual(len(output["input_ids"]), model_max_length)
output = tokenizer(
[seq_2], [seq_1], padding=padding_state, truncation=truncation_state
)
self.assertEqual(len(output["input_ids"][0]), model_max_length)
# Simple
output = tokenizer(seq_1, seq_2, padding=padding_state, truncation="only_second")
self.assertEqual(len(output["input_ids"]), model_max_length)
output = tokenizer([seq_1], [seq_2], padding=padding_state, truncation="only_second")
self.assertEqual(len(output["input_ids"][0]), model_max_length)
# Simple with no truncation
# Reset warnings
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer(seq_1, seq_2, padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length"
" for this model"
)
)
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer([seq_1], [seq_2], padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length"
" for this model"
)
)
truncated_first_sequence = tokenizer.encode(seq_0, add_special_tokens=False)[:-2] + tokenizer.encode(
seq_1, add_special_tokens=False
)
truncated_second_sequence = (
tokenizer.encode(seq_0, add_special_tokens=False)
+ tokenizer.encode(seq_1, add_special_tokens=False)[:-2]
)
truncated_longest_sequence = (
truncated_first_sequence if len(seq0_tokens) > len(seq1_tokens) else truncated_second_sequence
)
overflow_first_sequence = tokenizer.encode(seq_0, add_special_tokens=False)[
-(2 + stride) :
] + tokenizer.encode(seq_1, add_special_tokens=False)
overflow_second_sequence = (
tokenizer.encode(seq_0, add_special_tokens=False)
+ tokenizer.encode(seq_1, add_special_tokens=False)[-(2 + stride) :]
)
overflow_longest_sequence = (
overflow_first_sequence if len(seq0_tokens) > len(seq1_tokens) else overflow_second_sequence
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, PreTrainedTokenizerFast):
information = tokenizer(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=False,
stride=stride,
truncation="longest_first",
return_overflowing_tokens=True,
# add_prefix_space=False,
)
truncated_sequence = information["input_ids"][0]
overflowing_tokens = information["input_ids"][1]
self.assertEqual(len(information["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_longest_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
self.assertEqual(overflowing_tokens, overflow_longest_sequence)
else:
# No overflowing tokens when using 'longest' in python tokenizers
with self.assertRaises(ValueError) as context:
information = tokenizer(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=False,
stride=stride,
truncation="longest_first",
return_overflowing_tokens=True,
# add_prefix_space=False,
)
self.assertTrue(
context.exception.args[0].startswith(
"Not possible to return overflowing tokens for pair of sequences with the "
"`longest_first`. Please select another truncation strategy than `longest_first`, "
"for instance `only_second` or `only_first`."
)
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, PreTrainedTokenizerFast):
information = tokenizer(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=False,
stride=stride,
truncation=True,
return_overflowing_tokens=True,
# add_prefix_space=False,
)
truncated_sequence = information["input_ids"][0]
overflowing_tokens = information["input_ids"][1]
self.assertEqual(len(information["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_longest_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
self.assertEqual(overflowing_tokens, overflow_longest_sequence)
else:
# No overflowing tokens when using 'longest' in python tokenizers
with self.assertRaises(ValueError) as context:
information = tokenizer(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=False,
stride=stride,
truncation=True,
return_overflowing_tokens=True,
# add_prefix_space=False,
)
self.assertTrue(
context.exception.args[0].startswith(
"Not possible to return overflowing tokens for pair of sequences with the "
"`longest_first`. Please select another truncation strategy than `longest_first`, "
"for instance `only_second` or `only_first`."
)
)
information_first_truncated = tokenizer(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=False,
stride=stride,
truncation="only_first",
return_overflowing_tokens=True,
# add_prefix_space=False,
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, PreTrainedTokenizerFast):
truncated_sequence = information_first_truncated["input_ids"][0]
overflowing_tokens = information_first_truncated["input_ids"][1]
self.assertEqual(len(information_first_truncated["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_first_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq1_tokens))
self.assertEqual(overflowing_tokens, overflow_first_sequence)
else:
truncated_sequence = information_first_truncated["input_ids"]
overflowing_tokens = information_first_truncated["overflowing_tokens"]
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_first_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, seq0_tokens[-(2 + stride) :])
information_second_truncated = tokenizer(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=False,
stride=stride,
truncation="only_second",
return_overflowing_tokens=True,
# add_prefix_space=False,
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, PreTrainedTokenizerFast):
truncated_sequence = information_second_truncated["input_ids"][0]
overflowing_tokens = information_second_truncated["input_ids"][1]
self.assertEqual(len(information_second_truncated["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_second_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq0_tokens))
self.assertEqual(overflowing_tokens, overflow_second_sequence)
else:
truncated_sequence = information_second_truncated["input_ids"]
overflowing_tokens = information_second_truncated["overflowing_tokens"]
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_second_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, seq1_tokens[-(2 + stride) :])
# def test_encode_input_type(self):
# tokenizers = self.get_tokenizers(do_lower_case=False)
# for tokenizer in tokenizers:
# with self.subTest(f"{tokenizer.__class__.__name__}"):
# sequence = "Let's encode this sequence"
# tokens = sequence.split() # tokenizer.tokenize(sequence)
# # input_ids = tokenizer.convert_tokens_to_ids(tokens)
# formatted_input = tokenizer.encode(sequence, add_special_tokens=True, add_prefix_space=False)
# self.assertEqual(
# tokenizer.encode(tokens, is_split_into_words=True, add_special_tokens=True), formatted_input
# )
# # This is not supported with the Rust tokenizers
# # self.assertEqual(tokenizer.encode(input_ids, add_special_tokens=True), formatted_input)
# def test_swap_special_token(self):
# tokenizers = self.get_tokenizers(do_lower_case=False)
# for tokenizer in tokenizers:
# with self.subTest(f"{tokenizer.__class__.__name__}"):
# # Our mask token
# mask = "<mask>"
# # We take a single word in the middle of the vocabulary
# all_tokens = sorted(tokenizer.get_vocab().keys())
# word = tokenizer.decode(tokenizer.encode(all_tokens[len(all_tokens)//2], add_special_tokens=False)[:1])
# sequence_0 = "Encode " + word + " sequence"
# sequence_masked_0 = "Encode " + mask + " sequence"
# sequence_1 = word + " this sequence"
# sequence_masked_1 = mask + " this sequence"
# # Add tokens so that masked token isn't split
# # tokens = [AddedToken(t, lstrip=True, normalized=False) for t in sequence.split()]
# # tokenizer.add_tokens(tokens)
# tokenizer.add_special_tokens(
# {"mask_token": AddedToken(mask, normalized=False)}
# ) # Eat left space on Byte-level BPE tokenizers
# mask_ind = tokenizer.convert_tokens_to_ids(mask)
# # Test first masked sequence
# encoded_0 = tokenizer.encode(sequence_0, add_special_tokens=False)
# encoded_masked = tokenizer.encode(sequence_masked_0, add_special_tokens=False)
# self.assertEqual(len(encoded_masked), len(encoded_0))
# mask_loc = encoded_masked.index(mask_ind)
# encoded_masked[mask_loc] = encoded_0[mask_loc]
# self.assertEqual(encoded_masked, encoded_0)
# # Test second masked sequence
# encoded_1 = tokenizer.encode(sequence_1, add_special_tokens=False)
# encoded_masked = tokenizer.encode(sequence_masked_1, add_special_tokens=False)
# self.assertEqual(len(encoded_masked), len(encoded_1))
# mask_loc = encoded_masked.index(mask_ind)
# encoded_masked[mask_loc] = encoded_1[mask_loc]
# self.assertEqual(encoded_masked, encoded_1)
def test_special_tokens_mask(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence_0 = "Encode this."
# Testing single inputs
encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(
sequence_0, add_special_tokens=True, return_special_tokens_mask=True # , add_prefix_space=False
)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
filtered_sequence = [x for i, x in enumerate(encoded_sequence_w_special) if not special_tokens_mask[i]]
self.assertEqual(encoded_sequence, filtered_sequence)
def test_special_tokens_mask_input_pairs(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence_0 = "Encode this."
sequence_1 = "This one too please."
encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(
sequence_0,
sequence_1,
add_special_tokens=True,
return_special_tokens_mask=True,
# add_prefix_space=False,
)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
filtered_sequence = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
]
filtered_sequence = [x for x in filtered_sequence if x is not None]
self.assertEqual(encoded_sequence, filtered_sequence)
def test_padding_side_in_kwargs(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
if self.test_rust_tokenizer:
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, padding_side="left", **kwargs
)
self.assertEqual(tokenizer_r.padding_side, "left")
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, padding_side="right", **kwargs
)
self.assertEqual(tokenizer_r.padding_side, "right")
self.assertRaises(
ValueError,
self.rust_tokenizer_class.from_pretrained,
pretrained_name,
padding_side="unauthorized",
**kwargs,
)
if self.test_slow_tokenizer:
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, padding_side="left", **kwargs)
self.assertEqual(tokenizer_p.padding_side, "left")
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, padding_side="right", **kwargs)
self.assertEqual(tokenizer_p.padding_side, "right")
self.assertRaises(
ValueError,
self.tokenizer_class.from_pretrained,
pretrained_name,
padding_side="unauthorized",
**kwargs,
)
def test_truncation_side_in_kwargs(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
if self.test_rust_tokenizer:
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, truncation_side="left", **kwargs
)
self.assertEqual(tokenizer_r.truncation_side, "left")
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, truncation_side="right", **kwargs
)
self.assertEqual(tokenizer_r.truncation_side, "right")
self.assertRaises(
ValueError,
self.rust_tokenizer_class.from_pretrained,
pretrained_name,
truncation_side="unauthorized",
**kwargs,
)
if self.test_slow_tokenizer:
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, truncation_side="left", **kwargs
)
self.assertEqual(tokenizer_p.truncation_side, "left")
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, truncation_side="right", **kwargs
)
self.assertEqual(tokenizer_p.truncation_side, "right")
self.assertRaises(
ValueError,
self.tokenizer_class.from_pretrained,
pretrained_name,
truncation_side="unauthorized",
**kwargs,
)
def test_right_and_left_padding(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence = "Sequence"
padding_size = 10
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequence)
padding_idx = tokenizer.pad_token_id
# RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
tokenizer.padding_side = "right"
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
padded_sequence = tokenizer.encode(
sequence, max_length=sequence_length + padding_size, padding="max_length"
)
padded_sequence_length = len(padded_sequence)
self.assertEqual(sequence_length + padding_size, padded_sequence_length)
self.assertEqual(encoded_sequence + [padding_idx] * padding_size, padded_sequence)
# LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
tokenizer.padding_side = "left"
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
padded_sequence = tokenizer.encode(
sequence, max_length=sequence_length + padding_size, padding="max_length"
)
padded_sequence_length = len(padded_sequence)
self.assertEqual(sequence_length + padding_size, padded_sequence_length)
self.assertEqual([padding_idx] * padding_size + encoded_sequence, padded_sequence)
# RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding'
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
tokenizer.padding_side = "right"
padded_sequence_right = tokenizer.encode(sequence, padding=True)
padded_sequence_right_length = len(padded_sequence_right)
self.assertEqual(sequence_length, padded_sequence_right_length)
self.assertEqual(encoded_sequence, padded_sequence_right)
tokenizer.padding_side = "left"
padded_sequence_left = tokenizer.encode(sequence, padding="longest")
padded_sequence_left_length = len(padded_sequence_left)
self.assertEqual(sequence_length, padded_sequence_left_length)
self.assertEqual(encoded_sequence, padded_sequence_left)
tokenizer.padding_side = "right"
padded_sequence_right = tokenizer.encode(sequence)
padded_sequence_right_length = len(padded_sequence_right)
self.assertEqual(sequence_length, padded_sequence_right_length)
self.assertEqual(encoded_sequence, padded_sequence_right)
tokenizer.padding_side = "left"
padded_sequence_left = tokenizer.encode(sequence, padding=False)
padded_sequence_left_length = len(padded_sequence_left)
self.assertEqual(sequence_length, padded_sequence_left_length)
self.assertEqual(encoded_sequence, padded_sequence_left)
def test_right_and_left_truncation(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence = "This is a test sequence"
# RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
truncation_size = 3
tokenizer.truncation_side = "right"
encoded_sequence = tokenizer.encode(sequence, add_special_tokens=False)
sequence_length = len(encoded_sequence)
# Remove EOS/BOS tokens
truncated_sequence = tokenizer.encode(
sequence, max_length=sequence_length - truncation_size, truncation=True, add_special_tokens=False
)
truncated_sequence_length = len(truncated_sequence)
self.assertEqual(sequence_length, truncated_sequence_length + truncation_size)
self.assertEqual(encoded_sequence[:-truncation_size], truncated_sequence)
# LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the truncation flag set to True
tokenizer.truncation_side = "left"
sequence_length = len(encoded_sequence)
truncated_sequence = tokenizer.encode(
sequence, max_length=sequence_length - truncation_size, truncation=True, add_special_tokens=False
)
truncated_sequence_length = len(truncated_sequence)
self.assertEqual(sequence_length, truncated_sequence_length + truncation_size)
self.assertEqual(encoded_sequence[truncation_size:], truncated_sequence)
# RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_truncation'
sequence_length = len(encoded_sequence)
tokenizer.truncation_side = "right"
truncated_sequence_right = tokenizer.encode(sequence, truncation=True, add_special_tokens=False)
truncated_sequence_right_length = len(truncated_sequence_right)
self.assertEqual(sequence_length, truncated_sequence_right_length)
self.assertEqual(encoded_sequence, truncated_sequence_right)
tokenizer.truncation_side = "left"
truncated_sequence_left = tokenizer.encode(
sequence, truncation="longest_first", add_special_tokens=False
)
truncated_sequence_left_length = len(truncated_sequence_left)
self.assertEqual(sequence_length, truncated_sequence_left_length)
self.assertEqual(encoded_sequence, truncated_sequence_left)
tokenizer.truncation_side = "right"
truncated_sequence_right = tokenizer.encode(sequence, add_special_tokens=False)
truncated_sequence_right_length = len(truncated_sequence_right)
self.assertEqual(sequence_length, truncated_sequence_right_length)
self.assertEqual(encoded_sequence, truncated_sequence_right)
tokenizer.truncation_side = "left"
truncated_sequence_left = tokenizer.encode(sequence, truncation=False, add_special_tokens=False)
truncated_sequence_left_length = len(truncated_sequence_left)
self.assertEqual(sequence_length, truncated_sequence_left_length)
self.assertEqual(encoded_sequence, truncated_sequence_left)
def test_padding_to_max_length(self):
"""We keep this test for backward compatibility but it should be remove when `pad_to_max_length` is deprecated."""
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence = "Sequence"
padding_size = 10
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequence)
padding_idx = tokenizer.pad_token_id
# Check that it correctly pads when a maximum length is specified along with the padding flag set to True
tokenizer.padding_side = "right"
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
# FIXME: the next line should be padding(max_length) to avoid warning
padded_sequence = tokenizer.encode(
sequence, max_length=sequence_length + padding_size, pad_to_max_length=True
)
padded_sequence_length = len(padded_sequence)
self.assertEqual(sequence_length + padding_size, padded_sequence_length)
self.assertEqual(encoded_sequence + [padding_idx] * padding_size, padded_sequence)
# Check that nothing is done when a maximum length is not specified
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
tokenizer.padding_side = "right"
padded_sequence_right = tokenizer.encode(sequence, pad_to_max_length=True)
padded_sequence_right_length = len(padded_sequence_right)
self.assertEqual(sequence_length, padded_sequence_right_length)
self.assertEqual(encoded_sequence, padded_sequence_right)
def test_padding_to_multiple_of(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.pad_token is None:
self.skipTest("No padding token.")
else:
empty_tokens = tokenizer("", padding=True, pad_to_multiple_of=8)
normal_tokens = tokenizer("This is a sample input", padding=True, pad_to_multiple_of=8)
for key, value in empty_tokens.items():
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
for key, value in normal_tokens.items():
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
normal_tokens = tokenizer("This", pad_to_multiple_of=8)
for key, value in normal_tokens.items():
self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
# Should also work with truncation
normal_tokens = tokenizer("This", padding=True, truncation=True, pad_to_multiple_of=8)
for key, value in normal_tokens.items():
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
# truncation to something which is not a multiple of pad_to_multiple_of raises an error
self.assertRaises(
ValueError,
tokenizer.__call__,
"This",
padding=True,
truncation=True,
max_length=12,
pad_to_multiple_of=8,
)
def test_padding_with_attention_mask(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.pad_token is None:
self.skipTest("No padding token.")
if "attention_mask" not in tokenizer.model_input_names:
self.skipTest("This model does not use attention mask.")
features = [
{"input_ids": [1, 2, 3, 4, 5, 6], "attention_mask": [1, 1, 1, 1, 1, 0]},
{"input_ids": [1, 2, 3], "attention_mask": [1, 1, 0]},
]
padded_features = tokenizer.pad(features)
if tokenizer.padding_side == "right":
self.assertListEqual(padded_features["attention_mask"], [[1, 1, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0]])
else:
self.assertListEqual(padded_features["attention_mask"], [[1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 0]])
def test_encode_plus_with_padding(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence = "Sequence"
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequence)
padding_size = 10
padding_idx = tokenizer.pad_token_id
token_type_padding_idx = tokenizer.pad_token_type_id
encoded_sequence = tokenizer.encode_plus(sequence, return_special_tokens_mask=True)
input_ids = encoded_sequence["input_ids"]
special_tokens_mask = encoded_sequence["special_tokens_mask"]
sequence_length = len(input_ids)
# Test 'longest' and 'no_padding' don't do anything
tokenizer.padding_side = "right"
not_padded_sequence = tokenizer.encode_plus(
sequence,
padding=True,
return_special_tokens_mask=True,
)
not_padded_input_ids = not_padded_sequence["input_ids"]
not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
not_padded_sequence_length = len(not_padded_input_ids)
self.assertEqual(sequence_length, not_padded_sequence_length)
self.assertEqual(input_ids, not_padded_input_ids)
self.assertEqual(special_tokens_mask, not_padded_special_tokens_mask)
not_padded_sequence = tokenizer.encode_plus(
sequence,
padding=False,
return_special_tokens_mask=True,
)
not_padded_input_ids = not_padded_sequence["input_ids"]
not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
not_padded_sequence_length = len(not_padded_input_ids)
self.assertEqual(sequence_length, not_padded_sequence_length)
self.assertEqual(input_ids, not_padded_input_ids)
self.assertEqual(special_tokens_mask, not_padded_special_tokens_mask)
# Test right padding
tokenizer.padding_side = "right"
right_padded_sequence = tokenizer.encode_plus(
sequence,
max_length=sequence_length + padding_size,
padding="max_length",
return_special_tokens_mask=True,
)
right_padded_input_ids = right_padded_sequence["input_ids"]
right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"]
right_padded_sequence_length = len(right_padded_input_ids)
self.assertEqual(sequence_length + padding_size, right_padded_sequence_length)
self.assertEqual(input_ids + [padding_idx] * padding_size, right_padded_input_ids)
self.assertEqual(special_tokens_mask + [1] * padding_size, right_padded_special_tokens_mask)
# Test left padding
tokenizer.padding_side = "left"
left_padded_sequence = tokenizer.encode_plus(
sequence,
max_length=sequence_length + padding_size,
padding="max_length",
return_special_tokens_mask=True,
)
left_padded_input_ids = left_padded_sequence["input_ids"]
left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
left_padded_sequence_length = len(left_padded_input_ids)
self.assertEqual(sequence_length + padding_size, left_padded_sequence_length)
self.assertEqual([padding_idx] * padding_size + input_ids, left_padded_input_ids)
self.assertEqual([1] * padding_size + special_tokens_mask, left_padded_special_tokens_mask)
if "token_type_ids" in tokenizer.model_input_names:
token_type_ids = encoded_sequence["token_type_ids"]
left_padded_token_type_ids = left_padded_sequence["token_type_ids"]
right_padded_token_type_ids = right_padded_sequence["token_type_ids"]
self.assertEqual(
token_type_ids + [token_type_padding_idx] * padding_size, right_padded_token_type_ids
)
self.assertEqual(
[token_type_padding_idx] * padding_size + token_type_ids, left_padded_token_type_ids
)
if "attention_mask" in tokenizer.model_input_names:
attention_mask = encoded_sequence["attention_mask"]
right_padded_attention_mask = right_padded_sequence["attention_mask"]
left_padded_attention_mask = left_padded_sequence["attention_mask"]
self.assertEqual(attention_mask + [0] * padding_size, right_padded_attention_mask)
self.assertEqual([0] * padding_size + attention_mask, left_padded_attention_mask)
def test_padding_warning_message_fast_tokenizer(self):
if not self.test_rust_tokenizer:
return
sequence = "This is a text"
tokenizer_fast = self.get_rust_tokenizer()
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer_fast, sequence)
encoding_fast = tokenizer_fast(sequence)
with self.assertLogs("transformers", level="WARNING") as cm:
tokenizer_fast.pad(encoding_fast)
self.assertEqual(len(cm.records), 1)
self.assertIn(
"Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to"
" encode the text followed by a call to the `pad` method to get a padded encoding.",
cm.records[0].message,
)
if not self.test_slow_tokenizer:
return
tokenizer_slow = self.get_tokenizer()
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer_slow, sequence)
encoding_slow = tokenizer_slow(sequence)
with self.assertLogs(level="WARNING") as cm:
# We want to assert there are no warnings, but the 'assertLogs' method does not support that.
# Therefore, we are adding a dummy warning, and then we will assert it is the only warning.
logger.warning("Dummy warning")
tokenizer_slow.pad(encoding_slow)
self.assertEqual(len(cm.records), 1)
self.assertIn(
"Dummy warning",
cm.records[0].message,
)
def test_separate_tokenizers(self):
# This tests that tokenizers don't impact others. Unfortunately the case where it fails is when
# we're loading an S3 configuration from a pre-trained identifier, and we have no way of testing those today.
tokenizers = self.get_tokenizers(random_argument=True)
new_tokenizers = self.get_tokenizers(random_argument=False)
for tokenizer, new_tokenizer in zip(tokenizers, new_tokenizers):
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertTrue(tokenizer.init_kwargs["random_argument"])
self.assertTrue(tokenizer.init_kwargs["random_argument"])
self.assertFalse(new_tokenizer.init_kwargs["random_argument"])
def test_get_vocab(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
vocab_dict = tokenizer.get_vocab()
self.assertIsInstance(vocab_dict, dict)
self.assertGreaterEqual(len(tokenizer), len(vocab_dict))
vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
self.assertEqual(len(vocab), len(tokenizer))
tokenizer.add_tokens(["asdfasdfasdfasdf"])
vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
self.assertEqual(len(vocab), len(tokenizer))
def test_conversion_reversible(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
vocab = tokenizer.get_vocab()
for word, ind in vocab.items():
if word == tokenizer.unk_token:
continue
self.assertEqual(tokenizer.convert_tokens_to_ids(word), ind)
self.assertEqual(tokenizer.convert_ids_to_tokens(ind), word)
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
"Testing batch encode plus with different sequence lengths correctly pads",
]
# Test not batched
encoded_sequences_1 = tokenizer.encode_plus(sequences[0])
encoded_sequences_2 = tokenizer(sequences[0])
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
# Test not batched pairs
encoded_sequences_1 = tokenizer.encode_plus(sequences[0], sequences[1])
encoded_sequences_2 = tokenizer(sequences[0], sequences[1])
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
# Test batched
encoded_sequences_1 = tokenizer.batch_encode_plus(sequences)
encoded_sequences_2 = tokenizer(sequences)
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
# Test batched pairs
encoded_sequences_1 = tokenizer.batch_encode_plus(list(zip(sequences, sequences)))
encoded_sequences_2 = tokenizer(sequences, sequences)
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
def test_batch_encode_plus_batch_sequence_length(self):
# Tests that all encoded values have the correct size
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
"Testing batch encode plus with different sequence lengths correctly pads",
]
encoded_sequences = [tokenizer.encode_plus(sequence) for sequence in sequences]
encoded_sequences_batch = tokenizer.batch_encode_plus(sequences, padding=False)
self.assertListEqual(
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
)
maximum_length = len(
max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len)
)
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequences)
encoded_sequences_padded = [
tokenizer.encode_plus(sequence, max_length=maximum_length, padding="max_length")
for sequence in sequences
]
encoded_sequences_batch_padded = tokenizer.batch_encode_plus(sequences, padding=True)
self.assertListEqual(
encoded_sequences_padded,
self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded),
)
# check 'longest' is unsensitive to a max length
encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(sequences, padding=True)
encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
sequences, max_length=maximum_length + 10, padding="longest"
)
for key in encoded_sequences_batch_padded_1.keys():
self.assertListEqual(
encoded_sequences_batch_padded_1[key],
encoded_sequences_batch_padded_2[key],
)
# check 'no_padding' is unsensitive to a max length
encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(sequences, padding=False)
encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
sequences, max_length=maximum_length + 10, padding=False
)
for key in encoded_sequences_batch_padded_1.keys():
self.assertListEqual(
encoded_sequences_batch_padded_1[key],
encoded_sequences_batch_padded_2[key],
)
@require_tokenizers
def test_added_token_are_matched_longest_first(self):
if not self.test_slow_tokenizer:
self.skipTest("This test is only for slow tokenizers")
return
tokenizers = self.get_tokenizers(fast=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
try:
tokenizer.add_tokens([AddedToken("extra_id_1")])
tokenizer.add_tokens([AddedToken("extra_id_100")])
except Exception:
# Canine cannot add tokens which are not codepoints
self.skipTest("Cannot add those Added tokens")
# XXX: This used to split on `extra_id_1` first we're matching
# longest first now.
tokens = tokenizer.tokenize("This is some extra_id_100")
self.assertIn("extra_id_100", tokens)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
tokenizer.add_tokens([AddedToken("extra_id_100")])
tokenizer.add_tokens([AddedToken("extra_id_1")])
tokens = tokenizer.tokenize("This is some extra_id_100")
self.assertIn("extra_id_100", tokens)
@require_tokenizers
def test_added_token_serializable(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
new_token = AddedToken("new_token", lstrip=True)
tokenizer.add_special_tokens({"additional_special_tokens": [new_token]})
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(tmp_dir_name)
tokenizer.from_pretrained(tmp_dir_name)
def test_batch_encode_plus_padding(self):
# Test that padded sequences are equivalent between batch_encode_plus and encode_plus
# Right padding tests
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
"Testing batch encode plus with different sequence lengths correctly pads",
]
max_length = 100
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequences)
encoded_sequences = [
tokenizer.encode_plus(sequence, max_length=max_length, padding="max_length")
for sequence in sequences
]
encoded_sequences_batch = tokenizer.batch_encode_plus(
sequences, max_length=max_length, padding="max_length"
)
self.assertListEqual(
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
)
# Left padding tests
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
tokenizer.padding_side = "left"
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
"Testing batch encode plus with different sequence lengths correctly pads",
]
max_length = 100
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequences)
encoded_sequences = [
tokenizer.encode_plus(sequence, max_length=max_length, padding="max_length")
for sequence in sequences
]
encoded_sequences_batch = tokenizer.batch_encode_plus(
sequences, max_length=max_length, padding="max_length"
)
self.assertListEqual(
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
)
def test_pretokenized_inputs(self):
# Test when inputs are pretokenized
tokenizers = self.get_tokenizers(do_lower_case=False) # , add_prefix_space=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if hasattr(tokenizer, "add_prefix_space") and not tokenizer.add_prefix_space:
continue
# Prepare a sequence from our tokenizer vocabulary
sequence, ids = self.get_clean_sequence(tokenizer, with_prefix_space=True, max_length=20)
# sequence = " " + sequence # To be sure the byte-level tokenizers are feeling good
token_sequence = sequence.split()
# sequence_no_prefix_space = sequence.strip()
# Test encode for pretokenized inputs
output = tokenizer.encode(token_sequence, is_split_into_words=True, add_special_tokens=False)
output_sequence = tokenizer.encode(sequence, add_special_tokens=False)
self.assertEqual(output, output_sequence)
output = tokenizer.encode(token_sequence, is_split_into_words=True, add_special_tokens=True)
output_sequence = tokenizer.encode(sequence, add_special_tokens=True)
self.assertEqual(output, output_sequence)
# Test encode_plus for pretokenized inputs
output = tokenizer.encode_plus(token_sequence, is_split_into_words=True, add_special_tokens=False)
output_sequence = tokenizer.encode_plus(sequence, add_special_tokens=False)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
output = tokenizer.encode_plus(token_sequence, is_split_into_words=True, add_special_tokens=True)
output_sequence = tokenizer.encode_plus(sequence, add_special_tokens=True)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
# Test batch_encode_plus for pretokenized inputs
sequence_batch = [sequence.strip()] * 2 + [sequence.strip() + " " + sequence.strip()]
token_sequence_batch = [s.split() for s in sequence_batch]
sequence_batch_cleaned_up_spaces = [" " + " ".join(s) for s in token_sequence_batch]
output = tokenizer.batch_encode_plus(
token_sequence_batch, is_split_into_words=True, add_special_tokens=False
)
output_sequence = tokenizer.batch_encode_plus(
sequence_batch_cleaned_up_spaces, add_special_tokens=False
)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
output = tokenizer.batch_encode_plus(
token_sequence_batch, is_split_into_words=True, add_special_tokens=True
)
output_sequence = tokenizer.batch_encode_plus(
sequence_batch_cleaned_up_spaces, add_special_tokens=True
)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
# Test encode for pretokenized inputs pairs
output = tokenizer.encode(
token_sequence, token_sequence, is_split_into_words=True, add_special_tokens=False
)
output_sequence = tokenizer.encode(sequence, sequence, add_special_tokens=False)
self.assertEqual(output, output_sequence)
output = tokenizer.encode(
token_sequence, token_sequence, is_split_into_words=True, add_special_tokens=True
)
output_sequence = tokenizer.encode(sequence, sequence, add_special_tokens=True)
self.assertEqual(output, output_sequence)
# Test encode_plus for pretokenized inputs pairs
output = tokenizer.encode_plus(
token_sequence, token_sequence, is_split_into_words=True, add_special_tokens=False
)
output_sequence = tokenizer.encode_plus(sequence, sequence, add_special_tokens=False)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
output = tokenizer.encode_plus(
token_sequence, token_sequence, is_split_into_words=True, add_special_tokens=True
)
output_sequence = tokenizer.encode_plus(sequence, sequence, add_special_tokens=True)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
# Test batch_encode_plus for pretokenized inputs pairs
sequence_pair_batch = [(sequence.strip(), sequence.strip())] * 2 + [
(sequence.strip() + " " + sequence.strip(), sequence.strip())
]
token_sequence_pair_batch = [tuple(s.split() for s in pair) for pair in sequence_pair_batch]
sequence_pair_batch_cleaned_up_spaces = [
tuple(" " + " ".join(s) for s in pair) for pair in token_sequence_pair_batch
]
output = tokenizer.batch_encode_plus(
token_sequence_pair_batch, is_split_into_words=True, add_special_tokens=False
)
output_sequence = tokenizer.batch_encode_plus(
sequence_pair_batch_cleaned_up_spaces, add_special_tokens=False
)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
output = tokenizer.batch_encode_plus(
token_sequence_pair_batch, is_split_into_words=True, add_special_tokens=True
)
output_sequence = tokenizer.batch_encode_plus(
sequence_pair_batch_cleaned_up_spaces, add_special_tokens=True
)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
def test_prepare_for_model(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
string_sequence = "Testing the prepare_for_model method."
ids = tokenizer.encode(string_sequence, add_special_tokens=False)
prepared_input_dict = tokenizer.prepare_for_model(ids, add_special_tokens=True)
input_dict = tokenizer.encode_plus(string_sequence, add_special_tokens=True)
self.assertEqual(input_dict, prepared_input_dict)
def test_batch_encode_plus_overflowing_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
string_sequences = ["Testing the prepare_for_model method.", "Test"]
if tokenizer.pad_token is None:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
tokenizer.batch_encode_plus(
string_sequences, return_overflowing_tokens=True, truncation=True, padding=True, max_length=3
)
@is_pt_tf_cross_test
def test_batch_encode_plus_tensors(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
"Testing batch encode plus with different sequence lengths correctly pads",
]
# A Tensor cannot be build by sequences which are not the same size
self.assertRaises(ValueError, tokenizer.batch_encode_plus, sequences, return_tensors="pt")
self.assertRaises(ValueError, tokenizer.batch_encode_plus, sequences, return_tensors="tf")
if tokenizer.pad_token_id is None:
self.assertRaises(
ValueError,
tokenizer.batch_encode_plus,
sequences,
padding=True,
return_tensors="pt",
)
self.assertRaises(
ValueError,
tokenizer.batch_encode_plus,
sequences,
padding="longest",
return_tensors="tf",
)
else:
pytorch_tensor = tokenizer.batch_encode_plus(sequences, padding=True, return_tensors="pt")
tensorflow_tensor = tokenizer.batch_encode_plus(sequences, padding="longest", return_tensors="tf")
encoded_sequences = tokenizer.batch_encode_plus(sequences, padding=True)
for key in encoded_sequences.keys():
pytorch_value = pytorch_tensor[key].tolist()
tensorflow_value = tensorflow_tensor[key].numpy().tolist()
encoded_value = encoded_sequences[key]
self.assertEqual(pytorch_value, tensorflow_value, encoded_value)
def _check_no_pad_token_padding(self, tokenizer, sequences):
# if tokenizer does not have pad_token_id, an error should be thrown
if tokenizer.pad_token_id is None:
with self.assertRaises(ValueError):
if isinstance(sequences, list):
tokenizer.batch_encode_plus(sequences, padding="longest")
else:
tokenizer.encode_plus(sequences, padding=True)
# add pad_token_id to pass subsequent tests
tokenizer.add_special_tokens({"pad_token": "<PAD>"})
@require_torch
@slow
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import MODEL_MAPPING, TOKENIZER_MAPPING
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
return
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
config = config_class()
if config.is_encoder_decoder or config.pad_token_id is None:
return
model = model_class(config)
# Make sure the model contains at least the full vocabulary size in its embedding matrix
is_using_common_embeddings = hasattr(model.get_input_embeddings(), "weight")
if is_using_common_embeddings:
self.assertGreaterEqual(model.get_input_embeddings().weight.shape[0], len(tokenizer))
# Build sequence
first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="pt")
# Ensure that the BatchEncoding.to() method works.
encoded_sequence.to(model.device)
batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
# This should not fail
with torch.no_grad(): # saves some time
model(**encoded_sequence)
model(**batch_encoded_sequence)
# if self.test_rust_tokenizer:
# fast_tokenizer = self.get_rust_tokenizer()
# encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="pt")
# batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
# # This should not fail
# model(**encoded_sequence_fast)
# model(**batch_encoded_sequence_fast)
@require_tf
@slow
def test_tf_encode_plus_sent_to_model(self):
from transformers import TF_MODEL_MAPPING, TOKENIZER_MAPPING
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(TF_MODEL_MAPPING, TOKENIZER_MAPPING)
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
return
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
config = config_class()
if config.is_encoder_decoder or config.pad_token_id is None:
return
model = model_class(config)
# Make sure the model contains at least the full vocabulary size in its embedding matrix
self.assertGreaterEqual(model.config.vocab_size, len(tokenizer))
# Build sequence
first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="tf")
batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="tf")
# This should not fail
model(encoded_sequence)
model(batch_encoded_sequence)
# TODO: Check if require_torch is the best to test for numpy here ... Maybe move to require_flax when available
@require_torch
@slow
def test_np_encode_plus_sent_to_model(self):
from transformers import MODEL_MAPPING, TOKENIZER_MAPPING
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
return
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
config = config_class()
if config.is_encoder_decoder or config.pad_token_id is None:
return
# Build sequence
first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="np")
batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="np")
# TODO: add forward through JAX/Flax when PR is merged
# This is currently here to make ruff happy !
if encoded_sequence is None:
raise ValueError("Cannot convert list to numpy tensor on encode_plus()")
if batch_encoded_sequence is None:
raise ValueError("Cannot convert list to numpy tensor on batch_encode_plus()")
if self.test_rust_tokenizer:
fast_tokenizer = self.get_rust_tokenizer()
encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="np")
batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus(
[sequence, sequence], return_tensors="np"
)
# TODO: add forward through JAX/Flax when PR is merged
# This is currently here to make ruff happy !
if encoded_sequence_fast is None:
raise ValueError("Cannot convert list to numpy tensor on encode_plus() (fast)")
if batch_encoded_sequence_fast is None:
raise ValueError("Cannot convert list to numpy tensor on batch_encode_plus() (fast)")
@require_torch
def test_prepare_seq2seq_batch(self):
if not self.test_seq2seq:
return
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Longer text that will definitely require truncation.
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
" will only worsen the violence and misery for millions of people.",
]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
try:
batch = tokenizer.prepare_seq2seq_batch(
src_texts=src_text,
tgt_texts=tgt_text,
max_length=3,
max_target_length=10,
return_tensors="pt",
src_lang="en_XX", # this should be ignored (for all but mbart) but not cause an error
)
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.labels.shape[1], 10)
# max_target_length will default to max_length if not specified
batch = tokenizer.prepare_seq2seq_batch(
src_text, tgt_texts=tgt_text, max_length=3, return_tensors="pt"
)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.labels.shape[1], 3)
batch_encoder_only = tokenizer.prepare_seq2seq_batch(
src_texts=src_text, max_length=3, max_target_length=10, return_tensors="pt"
)
self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
self.assertNotIn("decoder_input_ids", batch_encoder_only)
def test_is_fast(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Check is_fast is set correctly
self.assertTrue(tokenizer_r.is_fast)
if self.test_slow_tokenizer:
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
self.assertFalse(tokenizer_p.is_fast)
def test_fast_only_inputs(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Ensure None raise an error
self.assertRaises(TypeError, tokenizer_r.tokenize, None)
self.assertRaises(TypeError, tokenizer_r.encode, None)
self.assertRaises(TypeError, tokenizer_r.encode_plus, None)
self.assertRaises(TypeError, tokenizer_r.batch_encode_plus, None)
def test_alignement_methods(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
words = ["Wonderful", "no", "inspiration", "example", "with", "subtoken"]
text = " ".join(words)
batch_size = 3
encoding = tokenizer_r.encode_plus(text, add_special_tokens=False)
batch_encoding = tokenizer_r.batch_encode_plus([text] * batch_size, add_special_tokens=False)
num_tokens = len(encoding["input_ids"])
last_word_index = len(words) - 1
last_token_index = num_tokens - 1
last_batch_index = batch_size - 1
last_char_index = len(text) - 1
# words, tokens
self.assertEqual(len(encoding.words(0)), num_tokens)
self.assertEqual(max(encoding.words(0)), last_word_index)
self.assertEqual(min(encoding.words(0)), 0)
self.assertEqual(len(batch_encoding.words(last_batch_index)), num_tokens)
self.assertEqual(max(batch_encoding.words(last_batch_index)), last_word_index)
self.assertEqual(min(batch_encoding.words(last_batch_index)), 0)
self.assertEqual(len(encoding.tokens(0)), num_tokens)
# Assert token_to_word
self.assertEqual(encoding.token_to_word(0), 0)
self.assertEqual(encoding.token_to_word(0, 0), 0)
self.assertEqual(encoding.token_to_word(last_token_index), last_word_index)
self.assertEqual(encoding.token_to_word(0, last_token_index), last_word_index)
self.assertEqual(batch_encoding.token_to_word(1, 0), 0)
self.assertEqual(batch_encoding.token_to_word(0, last_token_index), last_word_index)
self.assertEqual(batch_encoding.token_to_word(last_batch_index, last_token_index), last_word_index)
# Assert word_to_tokens
self.assertEqual(encoding.word_to_tokens(0).start, 0)
self.assertEqual(encoding.word_to_tokens(0, 0).start, 0)
self.assertEqual(encoding.word_to_tokens(last_word_index).end, last_token_index + 1)
self.assertEqual(encoding.word_to_tokens(0, last_word_index).end, last_token_index + 1)
self.assertEqual(batch_encoding.word_to_tokens(1, 0).start, 0)
self.assertEqual(batch_encoding.word_to_tokens(0, last_word_index).end, last_token_index + 1)
self.assertEqual(
batch_encoding.word_to_tokens(last_batch_index, last_word_index).end, last_token_index + 1
)
# Assert token_to_chars
self.assertEqual(encoding.token_to_chars(0).start, 0)
self.assertEqual(encoding.token_to_chars(0, 0).start, 0)
self.assertEqual(encoding.token_to_chars(last_token_index).end, last_char_index + 1)
self.assertEqual(encoding.token_to_chars(0, last_token_index).end, last_char_index + 1)
self.assertEqual(batch_encoding.token_to_chars(1, 0).start, 0)
self.assertEqual(batch_encoding.token_to_chars(0, last_token_index).end, last_char_index + 1)
self.assertEqual(
batch_encoding.token_to_chars(last_batch_index, last_token_index).end, last_char_index + 1
)
# Assert char_to_token
self.assertEqual(encoding.char_to_token(0), 0)
self.assertEqual(encoding.char_to_token(0, 0), 0)
self.assertEqual(encoding.char_to_token(last_char_index), last_token_index)
self.assertEqual(encoding.char_to_token(0, last_char_index), last_token_index)
self.assertEqual(batch_encoding.char_to_token(1, 0), 0)
self.assertEqual(batch_encoding.char_to_token(0, last_char_index), last_token_index)
self.assertEqual(batch_encoding.char_to_token(last_batch_index, last_char_index), last_token_index)
# Assert char_to_word
self.assertEqual(encoding.char_to_word(0), 0)
self.assertEqual(encoding.char_to_word(0, 0), 0)
self.assertEqual(encoding.char_to_word(last_char_index), last_word_index)
self.assertEqual(encoding.char_to_word(0, last_char_index), last_word_index)
self.assertEqual(batch_encoding.char_to_word(1, 0), 0)
self.assertEqual(batch_encoding.char_to_word(0, last_char_index), last_word_index)
self.assertEqual(batch_encoding.char_to_word(last_batch_index, last_char_index), last_word_index)
# Assert word_to_chars
self.assertEqual(encoding.word_to_chars(0).start, 0)
self.assertEqual(encoding.word_to_chars(0, 0).start, 0)
self.assertEqual(encoding.word_to_chars(last_word_index).end, last_char_index + 1)
self.assertEqual(encoding.word_to_chars(0, last_word_index).end, last_char_index + 1)
self.assertEqual(batch_encoding.word_to_chars(1, 0).start, 0)
self.assertEqual(batch_encoding.word_to_chars(0, last_word_index).end, last_char_index + 1)
self.assertEqual(
batch_encoding.word_to_chars(last_batch_index, last_word_index).end, last_char_index + 1
)
# Assert token_to_sequence
self.assertEqual(encoding.token_to_sequence(num_tokens // 2), 0)
self.assertEqual(encoding.token_to_sequence(0, num_tokens // 2), 0)
self.assertEqual(batch_encoding.token_to_sequence(1, num_tokens // 2), 0)
self.assertEqual(batch_encoding.token_to_sequence(0, num_tokens // 2), 0)
self.assertEqual(batch_encoding.token_to_sequence(last_batch_index, num_tokens // 2), 0)
# Pair of input sequences
words = ["Wonderful", "no", "inspiration", "example", "with", "subtoken"]
text = " ".join(words)
pair_words = ["Amazing", "example", "full", "of", "inspiration"]
pair_text = " ".join(pair_words)
batch_size = 3
index_word_in_first_seq = words.index("inspiration")
index_word_in_pair_seq = pair_words.index("inspiration")
index_char_in_first_seq = text.find("inspiration")
index_char_in_pair_seq = pair_text.find("inspiration")
pair_encoding = tokenizer_r.encode_plus(text, pair_text, add_special_tokens=False)
pair_batch_encoding = tokenizer_r.batch_encode_plus(
[(text, pair_text)] * batch_size, add_special_tokens=False
)
num_tokens = len(encoding["input_ids"])
last_word_index = len(words) - 1
last_token_index = num_tokens - 1
last_batch_index = batch_size - 1
last_char_index = len(text) - 1
# Assert word_to_tokens
self.assertNotEqual(
pair_encoding.word_to_tokens(index_word_in_first_seq, sequence_index=0).start,
pair_encoding.word_to_tokens(index_word_in_pair_seq, sequence_index=1).start,
)
self.assertEqual(
pair_encoding["input_ids"][
pair_encoding.word_to_tokens(index_word_in_first_seq, sequence_index=0).start
],
pair_encoding["input_ids"][
pair_encoding.word_to_tokens(index_word_in_pair_seq, sequence_index=1).start
],
)
self.assertNotEqual(
pair_batch_encoding.word_to_tokens(1, index_word_in_first_seq, sequence_index=0).start,
pair_batch_encoding.word_to_tokens(1, index_word_in_pair_seq, sequence_index=1).start,
)
self.assertEqual(
pair_batch_encoding["input_ids"][1][
pair_batch_encoding.word_to_tokens(1, index_word_in_first_seq, sequence_index=0).start
],
pair_batch_encoding["input_ids"][1][
pair_batch_encoding.word_to_tokens(1, index_word_in_pair_seq, sequence_index=1).start
],
)
# Assert char_to_token
self.assertNotEqual(
pair_encoding.char_to_token(index_char_in_first_seq, sequence_index=0),
pair_encoding.char_to_token(index_char_in_pair_seq, sequence_index=1),
)
self.assertEqual(
pair_encoding["input_ids"][pair_encoding.char_to_token(index_char_in_first_seq, sequence_index=0)],
pair_encoding["input_ids"][pair_encoding.char_to_token(index_char_in_pair_seq, sequence_index=1)],
)
self.assertNotEqual(
pair_batch_encoding.char_to_token(1, index_char_in_first_seq, sequence_index=0),
pair_batch_encoding.char_to_token(1, index_char_in_pair_seq, sequence_index=1),
)
self.assertEqual(
pair_batch_encoding["input_ids"][1][
pair_batch_encoding.char_to_token(1, index_char_in_first_seq, sequence_index=0)
],
pair_batch_encoding["input_ids"][1][
pair_batch_encoding.char_to_token(1, index_char_in_pair_seq, sequence_index=1)
],
)
# Assert char_to_word
self.assertNotEqual(
pair_encoding.char_to_word(index_char_in_first_seq, sequence_index=0),
pair_encoding.char_to_word(index_char_in_pair_seq, sequence_index=1),
)
self.assertEqual(
words[pair_encoding.char_to_word(index_char_in_first_seq, sequence_index=0)],
pair_words[pair_encoding.char_to_word(index_char_in_pair_seq, sequence_index=1)],
)
self.assertNotEqual(
pair_batch_encoding.char_to_word(1, index_char_in_first_seq, sequence_index=0),
pair_batch_encoding.char_to_word(1, index_char_in_pair_seq, sequence_index=1),
)
self.assertEqual(
words[pair_batch_encoding.char_to_word(1, index_char_in_first_seq, sequence_index=0)],
pair_words[pair_batch_encoding.char_to_word(1, index_char_in_pair_seq, sequence_index=1)],
)
# Assert word_to_chars
self.assertNotEqual(
pair_encoding.word_to_chars(index_word_in_first_seq, sequence_index=0).start,
pair_encoding.word_to_chars(index_word_in_pair_seq, sequence_index=1).start,
)
self.assertEqual(
text[pair_encoding.word_to_chars(index_word_in_first_seq, sequence_index=0).start],
pair_text[pair_encoding.word_to_chars(index_word_in_pair_seq, sequence_index=1).start],
)
self.assertNotEqual(
pair_batch_encoding.word_to_chars(1, index_word_in_first_seq, sequence_index=0).start,
pair_batch_encoding.word_to_chars(1, index_word_in_pair_seq, sequence_index=1).start,
)
self.assertEqual(
text[pair_batch_encoding.word_to_chars(1, index_word_in_first_seq, sequence_index=0).start],
pair_text[pair_batch_encoding.word_to_chars(1, index_word_in_pair_seq, sequence_index=1).start],
)
# Assert token_to_sequence
pair_encoding = tokenizer_r.encode_plus(text, pair_text, add_special_tokens=True)
pair_sequence_ids = [
pair_encoding.token_to_sequence(i) for i in range(len(pair_encoding["input_ids"]))
]
self.assertIn(0, pair_sequence_ids)
self.assertIn(1, pair_sequence_ids)
if tokenizer_r.num_special_tokens_to_add(pair=True):
self.assertIn(None, pair_sequence_ids)
pair_batch_encoding = tokenizer_r.batch_encode_plus(
[(text, pair_text)] * batch_size, add_special_tokens=True
)
pair_batch_sequence_ids = [
pair_batch_encoding.token_to_sequence(1, i)
for i in range(len(pair_batch_encoding["input_ids"][0]))
]
self.assertIn(0, pair_batch_sequence_ids)
self.assertIn(1, pair_batch_sequence_ids)
if tokenizer_r.num_special_tokens_to_add(pair=True):
self.assertIn(None, pair_batch_sequence_ids)
def test_tokenization_python_rust_equals(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Ensure basic input match
input_p = tokenizer_p.encode_plus(self._data)
input_r = tokenizer_r.encode_plus(self._data)
for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
self.assertSequenceEqual(input_p[key], input_r[key])
input_pairs_p = tokenizer_p.encode_plus(self._data, self._data)
input_pairs_r = tokenizer_r.encode_plus(self._data, self._data)
for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
self.assertSequenceEqual(input_pairs_p[key], input_pairs_r[key])
# Ensure truncation match
input_p = tokenizer_p.encode_plus(self._data, max_length=512, truncation=True)
input_r = tokenizer_r.encode_plus(self._data, max_length=512, truncation=True)
for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
self.assertSequenceEqual(input_p[key], input_r[key])
# Ensure truncation with stride match
input_p = tokenizer_p.encode_plus(
self._data, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
)
input_r = tokenizer_r.encode_plus(
self._data, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
)
for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
self.assertSequenceEqual(input_p[key], input_r[key][0])
def test_num_special_tokens_to_add_equal(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Check we have the same number of added_tokens for both pair and non-pair inputs.
self.assertEqual(
tokenizer_r.num_special_tokens_to_add(False), tokenizer_p.num_special_tokens_to_add(False)
)
self.assertEqual(
tokenizer_r.num_special_tokens_to_add(True), tokenizer_p.num_special_tokens_to_add(True)
)
def test_max_length_equal(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Check we have the correct max_length for both pair and non-pair inputs.
self.assertEqual(tokenizer_r.max_len_single_sentence, tokenizer_p.max_len_single_sentence)
self.assertEqual(tokenizer_r.max_len_sentences_pair, tokenizer_p.max_len_sentences_pair)
def test_special_tokens_map_equal(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Assert the set of special tokens match.
self.assertSequenceEqual(
tokenizer_p.special_tokens_map.items(),
tokenizer_r.special_tokens_map.items(),
)
def test_add_tokens(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
vocab_size = len(tokenizer_r)
self.assertEqual(tokenizer_r.add_tokens(""), 0)
self.assertEqual(tokenizer_r.add_tokens("testoken"), 1)
self.assertEqual(tokenizer_r.add_tokens(["testoken1", "testtoken2"]), 2)
self.assertEqual(len(tokenizer_r), vocab_size + 3)
self.assertEqual(tokenizer_r.add_special_tokens({}), 0)
self.assertEqual(tokenizer_r.add_special_tokens({"bos_token": "[BOS]", "eos_token": "[EOS]"}), 2)
self.assertRaises(
AssertionError, tokenizer_r.add_special_tokens, {"additional_special_tokens": "<testtoken1>"}
)
self.assertEqual(tokenizer_r.add_special_tokens({"additional_special_tokens": ["<testtoken2>"]}), 1)
self.assertEqual(
tokenizer_r.add_special_tokens({"additional_special_tokens": ["<testtoken3>", "<testtoken4>"]}), 2
)
self.assertIn("<testtoken3>", tokenizer_r.special_tokens_map["additional_special_tokens"])
self.assertIsInstance(tokenizer_r.special_tokens_map["additional_special_tokens"], list)
self.assertGreaterEqual(len(tokenizer_r.special_tokens_map["additional_special_tokens"]), 2)
self.assertEqual(len(tokenizer_r), vocab_size + 8)
def test_offsets_mapping(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
text = "Wonderful no inspiration example with subtoken"
pair = "Along with an awesome pair"
# No pair
tokens_with_offsets = tokenizer_r.encode_plus(
text, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True
)
added_tokens = tokenizer_r.num_special_tokens_to_add(False)
offsets = tokens_with_offsets["offset_mapping"]
# Assert there is the same number of tokens and offsets
self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))
# Assert there is online added_tokens special_tokens
self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)
# Pairs
tokens_with_offsets = tokenizer_r.encode_plus(
text, pair, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True
)
added_tokens = tokenizer_r.num_special_tokens_to_add(True)
offsets = tokens_with_offsets["offset_mapping"]
# Assert there is the same number of tokens and offsets
self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))
# Assert there is online added_tokens special_tokens
self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)
def test_batch_encode_dynamic_overflowing(self):
"""
When calling batch_encode with multiple sequence it can returns different number of
overflowing encoding for each sequence:
[
Sequence 1: [Encoding 1, Encoding 2],
Sequence 2: [Encoding 1],
Sequence 3: [Encoding 1, Encoding 2, ... Encoding N]
]
This needs to be padded so that it can represented as a tensor
"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"):
if is_torch_available():
returned_tensor = "pt"
elif is_tf_available():
returned_tensor = "tf"
elif is_flax_available():
returned_tensor = "jax"
else:
return
if not tokenizer.pad_token or tokenizer.pad_token_id < 0:
return
tokens = tokenizer.encode_plus(
"HuggingFace is solving NLP one commit at a time",
max_length=6,
padding=True,
truncation=True,
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
self.assertEqual(len(tokens[key].shape), 2)
# Mono sample
tokens = tokenizer.batch_encode_plus(
["HuggingFace is solving NLP one commit at a time"],
max_length=6,
padding=True,
truncation="only_first",
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
self.assertEqual(len(tokens[key].shape), 2)
self.assertEqual(tokens[key].shape[-1], 6)
# Multi sample
tokens = tokenizer.batch_encode_plus(
["HuggingFace is solving NLP one commit at a time", "Very tiny input"],
max_length=6,
padding=True,
truncation="only_first",
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
self.assertEqual(len(tokens[key].shape), 2)
self.assertEqual(tokens[key].shape[-1], 6)
def test_compare_pretokenized_inputs(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
if hasattr(tokenizer_p, "add_prefix_space") and not tokenizer_p.add_prefix_space:
continue # Too hard to test for now
# Input string
pretokenized_input_simple = "This is a sample input".split()
pretokenized_input_pair = "This is a sample pair".split()
# Test encode for pretokenized inputs
output_r = tokenizer_r.encode(
pretokenized_input_simple, is_split_into_words=True, add_special_tokens=False
)
output_p = tokenizer_p.encode(
pretokenized_input_simple, is_split_into_words=True, add_special_tokens=False
)
self.assertEqual(output_p, output_r)
kwargs = {
"is_split_into_words": True,
# "return_token_type_ids": True, # Use the defaults for each tokenizers
# "return_attention_mask": True, # Use the defaults for each tokenizers
"return_overflowing_tokens": False,
"return_special_tokens_mask": True,
"return_offsets_mapping": False, # Not implemented in python tokenizers
# "add_special_tokens": False,
}
batch_kwargs = {
"is_split_into_words": True,
# "return_token_type_ids": True, # Use the defaults for each tokenizers
# "return_attention_mask": True, # Use the defaults for each tokenizers
"return_overflowing_tokens": False,
"return_special_tokens_mask": True,
"return_offsets_mapping": False, # Not implemented in python tokenizers
# "add_special_tokens": False,
}
# Test encode_plus for pretokenized inputs
output_r = tokenizer_r.encode_plus(pretokenized_input_simple, **kwargs)
output_p = tokenizer_p.encode_plus(pretokenized_input_simple, **kwargs)
for key in output_p.keys():
self.assertEqual(output_p[key], output_r[key])
# Test batch_encode_plus for pretokenized inputs
input_batch = ([pretokenized_input_simple] * 2) + [pretokenized_input_simple + pretokenized_input_pair]
output_r = tokenizer_r.batch_encode_plus(input_batch, **batch_kwargs)
output_p = tokenizer_p.batch_encode_plus(input_batch, **batch_kwargs)
for key in output_p.keys():
self.assertEqual(output_p[key], output_r[key])
# Test encode for pretokenized inputs pairs
output_r = tokenizer_r.encode(
pretokenized_input_simple, pretokenized_input_pair, is_split_into_words=True
)
output_p = tokenizer_p.encode(
pretokenized_input_simple, pretokenized_input_pair, is_split_into_words=True
)
self.assertEqual(output_p, output_r)
# Test encode_plus for pretokenized inputs
output_r = tokenizer_r.encode_plus(pretokenized_input_simple, pretokenized_input_pair, **kwargs)
output_p = tokenizer_p.encode_plus(pretokenized_input_simple, pretokenized_input_pair, **kwargs)
for key in output_p.keys():
self.assertEqual(output_p[key], output_r[key])
# Test batch_encode_plus for pretokenized inputs
input_batch_pair = ([pretokenized_input_simple, pretokenized_input_pair] * 2) + [
pretokenized_input_simple + pretokenized_input_pair,
pretokenized_input_pair,
]
output_r = tokenizer_r.batch_encode_plus(input_batch_pair, **batch_kwargs)
output_p = tokenizer_p.batch_encode_plus(input_batch_pair, **batch_kwargs)
for key in output_p.keys():
self.assertEqual(output_p[key], output_r[key])
def test_create_token_type_ids(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
input_simple = [1, 2, 3]
input_pair = [1, 2, 3]
# Generate output
output_r = tokenizer_r.create_token_type_ids_from_sequences(input_simple)
output_p = tokenizer_p.create_token_type_ids_from_sequences(input_simple)
self.assertEqual(output_p, output_r)
# Generate pair output
output_r = tokenizer_r.create_token_type_ids_from_sequences(input_simple, input_pair)
output_p = tokenizer_p.create_token_type_ids_from_sequences(input_simple, input_pair)
self.assertEqual(output_p, output_r)
def test_build_inputs_with_special_tokens(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# # Input string
# input_simple = tokenizer_p.tokenize("This is a sample input", add_special_tokens=False)
# input_pair = tokenizer_p.tokenize("This is a sample pair", add_special_tokens=False)
# # Generate output
# output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
# output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
# self.assertEqual(output_p, output_r)
# # Generate pair output
# output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
# output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
# self.assertEqual(output_p, output_r)
# Input tokens id
input_simple = tokenizer_p.encode("This is a sample input", add_special_tokens=False)
input_pair = tokenizer_p.encode("This is a sample pair", add_special_tokens=False)
# Generate output
output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
self.assertEqual(output_p, output_r)
# Generate pair output
output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
self.assertEqual(output_p, output_r)
def test_padding(self, max_length=50):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
pad_token_id = tokenizer_p.pad_token_id
# Encode - Simple input
input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, padding="max_length")
input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, padding="max_length")
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode("This is a simple input", padding="longest")
input_p = tokenizer_p.encode("This is a simple input", padding=True)
self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
# Encode - Pair input
input_r = tokenizer_r.encode(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
input_p = tokenizer_p.encode(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode("This is a simple input", "This is a pair", padding=True)
input_p = tokenizer_p.encode("This is a simple input", "This is a pair", padding="longest")
self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
# Encode_plus - Simple input
input_r = tokenizer_r.encode_plus(
"This is a simple input", max_length=max_length, pad_to_max_length=True
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus(
"This is a simple input", max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", max_length=max_length, padding="max_length"
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus("This is a simple input", padding="longest")
input_p = tokenizer_p.encode_plus("This is a simple input", padding=True)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
# Encode_plus - Pair input
input_r = tokenizer_r.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus("This is a simple input", "This is a pair", padding="longest")
input_p = tokenizer_p.encode_plus("This is a simple input", "This is a pair", padding=True)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
# Batch_encode_plus - Simple input
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
pad_to_max_length=True,
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
pad_to_max_length=True,
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
padding="max_length",
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
padding="max_length",
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
padding="longest",
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
padding=True,
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], padding="longest"
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], padding=True
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# Batch_encode_plus - Pair input
input_r = tokenizer_r.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
max_length=max_length,
truncation=True,
padding="max_length",
)
input_p = tokenizer_p.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
max_length=max_length,
truncation=True,
padding="max_length",
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
padding=True,
)
input_p = tokenizer_p.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
padding="longest",
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# Using pad on single examples after tokenization
input_r = tokenizer_r.encode_plus("This is a input 1")
input_r = tokenizer_r.pad(input_r)
input_p = tokenizer_p.encode_plus("This is a input 1")
input_p = tokenizer_p.pad(input_p)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
# Using pad on single examples after tokenization
input_r = tokenizer_r.encode_plus("This is a input 1")
input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
input_p = tokenizer_p.encode_plus("This is a input 1")
input_p = tokenizer_p.pad(input_p, max_length=max_length, padding="max_length")
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
# Using pad after tokenization
input_r = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_r = tokenizer_r.pad(input_r)
input_p = tokenizer_p.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_p = tokenizer_p.pad(input_p)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# Using pad after tokenization
input_r = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
input_p = tokenizer_p.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_p = tokenizer_p.pad(input_p, max_length=max_length, padding="max_length")
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
# Test padding nested empty lists (in some use-cases, there is no any token id in the `input_ids` list).
input_r = tokenizer_r.pad({"input_ids": [[], []]}, max_length=max_length, padding="max_length")
input_p = tokenizer_p.pad({"input_ids": [[], []]}, max_length=max_length, padding="max_length")
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
def test_padding_different_model_input_name(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
pad_token_id = tokenizer_p.pad_token_id
input_r = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_p = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
# rename encoded batch to "inputs"
input_r["inputs"] = input_r[tokenizer_r.model_input_names[0]]
del input_r[tokenizer_r.model_input_names[0]]
input_p["inputs"] = input_p[tokenizer_p.model_input_names[0]]
del input_p[tokenizer_p.model_input_names[0]]
# Renaming `input_ids` to `inputs`
tokenizer_r.model_input_names = ["inputs"] + tokenizer_r.model_input_names[1:]
tokenizer_p.model_input_names = ["inputs"] + tokenizer_p.model_input_names[1:]
input_r = tokenizer_r.pad(input_r, padding="longest")
input_p = tokenizer_r.pad(input_p, padding="longest")
max_length = len(input_p["inputs"][0])
self.assert_batch_padded_input_match(
input_r, input_p, max_length, pad_token_id, model_main_input_name="inputs"
)
def test_save_pretrained(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# make sure that all ".json" files are saved in the correct format
for file_path in tokenizer_r_files + tokenizer_p_files:
if os.path.exists(file_path) and file_path.endswith(".json"):
check_json_file_has_correct_format(file_path)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=True
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=False
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
def test_embeded_special_tokens(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
sentence = "A, <mask> AllenNLP sentence."
tokens_r = tokenizer_r.encode_plus(
sentence,
add_special_tokens=True,
)
tokens_p = tokenizer_p.encode_plus(
sentence,
add_special_tokens=True,
)
for key in tokens_p.keys():
self.assertEqual(tokens_r[key], tokens_p[key])
if "token_type_ids" in tokens_r:
self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
tokens_r = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
tokens_p = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
self.assertSequenceEqual(tokens_r, tokens_p)
def test_compare_add_special_tokens(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
simple_num_special_tokens_to_add = tokenizer_r.num_special_tokens_to_add(pair=False)
# pair_num_special_tokens_to_add = tokenizer_r.num_special_tokens_to_add(pair=True)
for text in ["", " "]:
# tokenize()
no_special_tokens = tokenizer_r.tokenize(text, add_special_tokens=False)
with_special_tokens = tokenizer_r.tokenize(text, add_special_tokens=True)
self.assertEqual(
len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add
)
# encode()
no_special_tokens = tokenizer_r.encode(text, add_special_tokens=False)
with_special_tokens = tokenizer_r.encode(text, add_special_tokens=True)
self.assertEqual(
len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add
)
# encode_plus()
no_special_tokens = tokenizer_r.encode_plus(text, add_special_tokens=False)
with_special_tokens = tokenizer_r.encode_plus(text, add_special_tokens=True)
for key in no_special_tokens.keys():
self.assertEqual(
len(no_special_tokens[key]),
len(with_special_tokens[key]) - simple_num_special_tokens_to_add,
)
# # batch_encode_plus
no_special_tokens = tokenizer_r.batch_encode_plus([text, text], add_special_tokens=False)
with_special_tokens = tokenizer_r.batch_encode_plus([text, text], add_special_tokens=True)
for key in no_special_tokens.keys():
for i_no, i_with in zip(no_special_tokens[key], with_special_tokens[key]):
self.assertEqual(len(i_no), len(i_with) - simple_num_special_tokens_to_add)
def test_compare_prepare_for_model(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
string_sequence = "Asserting that both tokenizers are equal"
python_output = tokenizer_p.prepare_for_model(
tokenizer_p.encode(string_sequence, add_special_tokens=False)
)
rust_output = tokenizer_r.prepare_for_model(
tokenizer_r.encode(string_sequence, add_special_tokens=False)
)
for key in python_output:
self.assertEqual(python_output[key], rust_output[key])
def test_special_tokens_initialization(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
added_tokens = [AddedToken("<special>", lstrip=True)]
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
r_output = tokenizer_r.encode("Hey this is a <special> token")
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
self.assertTrue(special_token_id in r_output)
if self.test_slow_tokenizer:
tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
)
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
p_output = tokenizer_p.encode("Hey this is a <special> token")
cr_output = tokenizer_cr.encode("Hey this is a <special> token")
self.assertEqual(p_output, r_output)
self.assertEqual(cr_output, r_output)
self.assertTrue(special_token_id in p_output)
self.assertTrue(special_token_id in cr_output)
def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
tokenizer_list = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(tmp_dir)
with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
special_tokens_map = json.load(json_file)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
tokenizer_config = json.load(json_file)
special_tokens_map["additional_special_tokens"] = ["an_additional_special_token"]
tokenizer_config["additional_special_tokens"] = ["an_additional_special_token"]
with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
json.dump(special_tokens_map, outfile)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
json.dump(tokenizer_config, outfile)
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
tmp_dir,
)
self.assertIn(
"an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
)
self.assertIn("an_additional_special_token", tokenizer_without_change_in_init.get_vocab())
self.assertEqual(
["an_additional_special_token"],
tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
),
)
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
new_added_tokens = [AddedToken("a_new_additional_special_token", lstrip=True)]
tokenizer = tokenizer_class.from_pretrained(
tmp_dir,
additional_special_tokens=new_added_tokens,
)
self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
self.assertEqual(
["a_new_additional_special_token"],
tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
),
)
def test_training_new_tokenizer(self):
# This feature only exists for fast tokenizers
if not self.test_rust_tokenizer:
return
tokenizer = self.get_rust_tokenizer()
new_tokenizer = tokenizer.train_new_from_iterator(SMALL_TRAINING_CORPUS, 100)
# Test we can use the new tokenizer with something not seen during training
inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."])
self.assertEqual(len(inputs["input_ids"]), 2)
decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
expected_result = "This is the first sentence"
if tokenizer.backend_tokenizer.normalizer is not None:
expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result)
self.assertEqual(expected_result, decoded_input)
# We check that the parameters of the tokenizer remained the same
# Check we have the same number of added_tokens for both pair and non-pair inputs.
self.assertEqual(tokenizer.num_special_tokens_to_add(False), new_tokenizer.num_special_tokens_to_add(False))
self.assertEqual(tokenizer.num_special_tokens_to_add(True), new_tokenizer.num_special_tokens_to_add(True))
# Check we have the correct max_length for both pair and non-pair inputs.
self.assertEqual(tokenizer.max_len_single_sentence, new_tokenizer.max_len_single_sentence)
self.assertEqual(tokenizer.max_len_sentences_pair, new_tokenizer.max_len_sentences_pair)
# Assert the set of special tokens match as we didn't ask to change them
self.assertSequenceEqual(
tokenizer.all_special_tokens_extended,
new_tokenizer.all_special_tokens_extended,
)
self.assertDictEqual(tokenizer.special_tokens_map, new_tokenizer.special_tokens_map)
def test_training_new_tokenizer_with_special_tokens_change(self):
# This feature only exists for fast tokenizers
if not self.test_rust_tokenizer:
return
tokenizer = self.get_rust_tokenizer()
# Test with a special tokens map
class_signature = inspect.signature(tokenizer.__class__)
if "cls_token" in class_signature.parameters:
new_tokenizer = tokenizer.train_new_from_iterator(
SMALL_TRAINING_CORPUS, 100, special_tokens_map={tokenizer.cls_token: "<cls>"}
)
cls_id = new_tokenizer.get_vocab()["<cls>"]
self.assertEqual(new_tokenizer.cls_token, "<cls>")
self.assertEqual(new_tokenizer.cls_token_id, cls_id)
# Create a new mapping from the special tokens defined in the original tokenizer
special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
special_tokens_list.remove("additional_special_tokens")
special_tokens_map = {}
for token in special_tokens_list:
# Get the private one to avoid unnecessary warnings.
if getattr(tokenizer, f"_{token}") is not None:
special_token = getattr(tokenizer, token)
special_tokens_map[special_token] = f"{special_token}a"
# Train new tokenizer
new_tokenizer = tokenizer.train_new_from_iterator(
SMALL_TRAINING_CORPUS, 100, special_tokens_map=special_tokens_map
)
# Check the changes
for token in special_tokens_list:
# Get the private one to avoid unnecessary warnings.
if getattr(tokenizer, f"_{token}") is None:
continue
special_token = getattr(tokenizer, token)
if special_token in special_tokens_map:
new_special_token = getattr(new_tokenizer, token)
self.assertEqual(special_tokens_map[special_token], new_special_token)
new_id = new_tokenizer.get_vocab()[new_special_token]
self.assertEqual(getattr(new_tokenizer, f"{token}_id"), new_id)
# Check if the AddedToken / string format has been kept
for special_token in tokenizer.all_special_tokens_extended:
if isinstance(special_token, AddedToken) and special_token.content not in special_tokens_map:
# The special token must appear identically in the list of the new tokenizer.
self.assertTrue(
special_token in new_tokenizer.all_special_tokens_extended,
f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
)
elif isinstance(special_token, AddedToken):
# The special token must appear in the list of the new tokenizer as an object of type AddedToken with
# the same parameters as the old AddedToken except the content that the user has requested to change.
special_token_str = special_token.content
new_special_token_str = special_tokens_map[special_token_str]
find = False
for candidate in new_tokenizer.all_special_tokens_extended:
if (
isinstance(candidate, AddedToken)
and candidate.content == new_special_token_str
and candidate.lstrip == special_token.lstrip
and candidate.rstrip == special_token.rstrip
and candidate.normalized == special_token.normalized
and candidate.single_word == special_token.single_word
):
find = True
break
self.assertTrue(
find,
f"'{new_special_token_str}' doesn't appear in the list "
f"'{new_tokenizer.all_special_tokens_extended}' as an AddedToken with the same parameters as "
f"'{special_token}' in the list {tokenizer.all_special_tokens_extended}",
)
elif special_token not in special_tokens_map:
# The special token must appear identically in the list of the new tokenizer.
self.assertTrue(
special_token in new_tokenizer.all_special_tokens_extended,
f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
)
else:
# The special token must appear in the list of the new tokenizer as an object of type string.
self.assertTrue(special_tokens_map[special_token] in new_tokenizer.all_special_tokens_extended)
# Test we can use the new tokenizer with something not seen during training
inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."])
self.assertEqual(len(inputs["input_ids"]), 2)
decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
expected_result = "This is the first sentence"
if tokenizer.backend_tokenizer.normalizer is not None:
expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result)
self.assertEqual(expected_result, decoded_input)
def test_tokenizer_mismatch_warning(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
with self.assertLogs("transformers", level="WARNING") as cm:
try:
if self.tokenizer_class == BertTokenizer:
AlbertTokenizer.from_pretrained(pretrained_name)
else:
BertTokenizer.from_pretrained(pretrained_name)
except EnvironmentError as e:
# Some tokenizer will raised an error before reaching the logged warning because there are no
# corresponding files to load
error_message = str(e)
except (TypeError, AttributeError):
# Some tokenizers cannot be loaded into the target tokenizer at all and errors are returned,
# here we just check that the warning has been logged before the error is raised
pass
finally:
logged_msg_target = (
"The tokenizer class you load from this checkpoint is not the same type as the class "
"this function is called from."
)
raised_error_msg_target = "Can't load tokenizer for"
self.assertTrue(
cm.records[0].message.startswith(logged_msg_target)
if len(cm.records) > 0
else False or raised_error_msg_target in error_message
)
try:
if self.rust_tokenizer_class == BertTokenizerFast:
AlbertTokenizerFast.from_pretrained(pretrained_name)
else:
BertTokenizerFast.from_pretrained(pretrained_name)
except (TypeError, AttributeError):
# Some tokenizers cannot be loaded into the target tokenizer at all and errors are returned,
# here we just check that the warning has been logged before the error is raised
pass
finally:
self.assertTrue(
cm.records[0].message.startswith(
"The tokenizer class you load from this checkpoint is not the same type as the class"
" this function is called from."
)
)
@require_torch
def test_saving_tokenizer_trainer(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
with tempfile.TemporaryDirectory() as tmp_dir:
# Save the fast tokenizer files in a temporary directory
tokenizer_old = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs, use_fast=True)
tokenizer_old.save_pretrained(tmp_dir, legacy_format=False) # save only fast version
# Initialize toy model for the trainer
model = nn.Module()
# Load tokenizer from a folder without legacy files
tokenizer = self.rust_tokenizer_class.from_pretrained(tmp_dir)
training_args = TrainingArguments(output_dir=tmp_dir, do_train=True, no_cuda=True)
trainer = Trainer(model=model, args=training_args, tokenizer=tokenizer)
# Should not raise an error
trainer.save_model(os.path.join(tmp_dir, "checkpoint"))
self.assertIn("tokenizer.json", os.listdir(os.path.join(tmp_dir, "checkpoint")))
def test_convert_tokens_to_string_format(self):
tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
tokens = ["this", "is", "a", "test"]
string = tokenizer.convert_tokens_to_string(tokens)
self.assertIsInstance(string, str)
def test_save_slow_from_fast_and_reload_fast(self):
if not self.test_slow_tokenizer or not self.test_rust_tokenizer:
# we need both slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
with tempfile.TemporaryDirectory() as tmp_dir_1:
# Here we check that even if we have initialized a fast tokenizer with a tokenizer_file we can
# still save only the slow version and use these saved files to rebuild a tokenizer
tokenizer_fast_old_1 = self.rust_tokenizer_class.from_pretrained(
pretrained_name, **kwargs, use_fast=True
)
tokenizer_file = os.path.join(tmp_dir_1, "tokenizer.json")
tokenizer_fast_old_1.backend_tokenizer.save(tokenizer_file)
tokenizer_fast_old_2 = self.rust_tokenizer_class.from_pretrained(
pretrained_name, **kwargs, use_fast=True, tokenizer_file=tokenizer_file
)
tokenizer_fast_old_2.save_pretrained(tmp_dir_1, legacy_format=True) # save only slow version
tokenizer_slow = self.tokenizer_class.from_pretrained(tmp_dir_1)
with tempfile.TemporaryDirectory() as tmp_dir_2:
tokenizer_slow.save_pretrained(tmp_dir_2)
# Should not raise an error
self.rust_tokenizer_class.from_pretrained(tmp_dir_2)
def test_clean_up_tokenization_spaces(self):
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
assert tokenizer.clean_up_tokenization_spaces is True
tokens = tokenizer.encode("This shouldn't be! He'll go.")
decoded = tokenizer.decode(tokens)
assert decoded == "[CLS] this shouldn't be! he'll go. [SEP]"
tokenizer.clean_up_tokenization_spaces = False
decoded = tokenizer.decode(tokens)
assert decoded == "[CLS] this shouldn ' t be ! he ' ll go . [SEP]"
assert decoded == tokenizer.decode(tokens, clean_up_tokenization_spaces=False)
# Fast from slow
with tempfile.TemporaryDirectory() as tmp_dir_2:
tokenizer.save_pretrained(tmp_dir_2)
tokenizer_fast = BertTokenizerFast.from_pretrained(tmp_dir_2)
del tokenizer
assert tokenizer_fast.clean_up_tokenization_spaces is False
decoded = tokenizer_fast.decode(tokens)
# fast and slow don't have the same output when we don't cleanup
# tokenization space. Here `be!` vs `be !` and `go.` vs `go .`
assert decoded == "[CLS] this shouldn ' t be! he ' ll go. [SEP]"
tokenizer_fast.clean_up_tokenization_spaces = True
assert tokenizer_fast.clean_up_tokenization_spaces is True
decoded = tokenizer_fast.decode(tokens)
assert decoded == "[CLS] this shouldn't be! he'll go. [SEP]"
# Slow from fast
with tempfile.TemporaryDirectory() as tmp_dir_2:
tokenizer_fast.clean_up_tokenization_spaces = False
tokenizer_fast.save_pretrained(tmp_dir_2)
tokenizer = BertTokenizer.from_pretrained(tmp_dir_2)
assert tokenizer.clean_up_tokenization_spaces is False
decoded = tokenizer.decode(tokens)
assert decoded == "[CLS] this shouldn ' t be ! he ' ll go . [SEP]"
tokenizer.clean_up_tokenization_spaces = True
decoded = tokenizer.decode(tokens)
assert decoded == "[CLS] this shouldn't be! he'll go. [SEP]"
| 203,157 | 50.354398 | 149 | py |
transformers | transformers-main/tests/test_image_transforms.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from parameterized import parameterized
from transformers.testing_utils import require_flax, require_tf, require_torch, require_vision
from transformers.utils.import_utils import is_flax_available, is_tf_available, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
if is_flax_available():
import jax
if is_vision_available():
import PIL.Image
from transformers.image_transforms import (
center_crop,
center_to_corners_format,
convert_to_rgb,
corners_to_center_format,
flip_channel_order,
get_resize_output_image_size,
id_to_rgb,
normalize,
pad,
resize,
rgb_to_id,
to_channel_dimension_format,
to_pil_image,
)
def get_random_image(height, width, num_channels=3, channels_first=True):
shape = (num_channels, height, width) if channels_first else (height, width, num_channels)
random_array = np.random.randint(0, 256, shape, dtype=np.uint8)
return random_array
@require_vision
class ImageTransformsTester(unittest.TestCase):
@parameterized.expand(
[
("numpy_float_channels_first", (3, 4, 5), np.float32),
("numpy_float_channels_last", (4, 5, 3), np.float32),
("numpy_float_channels_first", (3, 4, 5), np.float64),
("numpy_float_channels_last", (4, 5, 3), np.float64),
("numpy_int_channels_first", (3, 4, 5), np.int32),
("numpy_uint_channels_first", (3, 4, 5), np.uint8),
]
)
@require_vision
def test_to_pil_image(self, name, image_shape, dtype):
image = np.random.randint(0, 256, image_shape).astype(dtype)
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# make sure image is correctly rescaled
self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0)
@parameterized.expand(
[
("numpy_float_channels_first", (3, 4, 5), np.float32),
("numpy_float_channels_first", (3, 4, 5), np.float64),
("numpy_float_channels_last", (4, 5, 3), np.float32),
("numpy_float_channels_last", (4, 5, 3), np.float64),
]
)
@require_vision
def test_to_pil_image_from_float(self, name, image_shape, dtype):
image = np.random.rand(*image_shape).astype(dtype)
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# make sure image is correctly rescaled
self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0)
# Make sure that an exception is raised if image is not in [0, 1]
image = np.random.randn(*image_shape).astype(dtype)
with self.assertRaises(ValueError):
to_pil_image(image)
@require_vision
def test_to_pil_image_from_mask(self):
# Make sure binary mask remains a binary mask
image = np.random.randint(0, 2, (3, 4, 5)).astype(np.uint8)
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
np_img = np.asarray(pil_image)
self.assertTrue(np_img.min() == 0)
self.assertTrue(np_img.max() == 1)
image = np.random.randint(0, 2, (3, 4, 5)).astype(np.float32)
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
np_img = np.asarray(pil_image)
self.assertTrue(np_img.min() == 0)
self.assertTrue(np_img.max() == 1)
@require_tf
def test_to_pil_image_from_tensorflow(self):
# channels_first
image = tf.random.uniform((3, 4, 5))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# channels_last
image = tf.random.uniform((4, 5, 3))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
@require_torch
def test_to_pil_image_from_torch(self):
# channels first
image = torch.rand((3, 4, 5))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# channels last
image = torch.rand((4, 5, 3))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
@require_flax
def test_to_pil_image_from_jax(self):
key = jax.random.PRNGKey(0)
# channel first
image = jax.random.uniform(key, (3, 4, 5))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# channel last
image = jax.random.uniform(key, (4, 5, 3))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
def test_to_channel_dimension_format(self):
# Test that function doesn't reorder if channel dim matches the input.
image = np.random.rand(3, 4, 5)
image = to_channel_dimension_format(image, "channels_first")
self.assertEqual(image.shape, (3, 4, 5))
image = np.random.rand(4, 5, 3)
image = to_channel_dimension_format(image, "channels_last")
self.assertEqual(image.shape, (4, 5, 3))
# Test that function reorders if channel dim doesn't match the input.
image = np.random.rand(3, 4, 5)
image = to_channel_dimension_format(image, "channels_last")
self.assertEqual(image.shape, (4, 5, 3))
image = np.random.rand(4, 5, 3)
image = to_channel_dimension_format(image, "channels_first")
self.assertEqual(image.shape, (3, 4, 5))
def test_get_resize_output_image_size(self):
image = np.random.randint(0, 256, (3, 224, 224))
# Test the output size defaults to (x, x) if an int is given.
self.assertEqual(get_resize_output_image_size(image, 10), (10, 10))
self.assertEqual(get_resize_output_image_size(image, [10]), (10, 10))
self.assertEqual(get_resize_output_image_size(image, (10,)), (10, 10))
# Test the output size is the same as the input if a two element tuple/list is given.
self.assertEqual(get_resize_output_image_size(image, (10, 20)), (10, 20))
self.assertEqual(get_resize_output_image_size(image, [10, 20]), (10, 20))
self.assertEqual(get_resize_output_image_size(image, (10, 20), default_to_square=True), (10, 20))
# To match pytorch behaviour, max_size is only relevant if size is an int
self.assertEqual(get_resize_output_image_size(image, (10, 20), max_size=5), (10, 20))
# Test output size = (int(size * height / width), size) if size is an int and height > width
image = np.random.randint(0, 256, (3, 50, 40))
self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False), (25, 20))
# Test output size = (size, int(size * width / height)) if size is an int and width <= height
image = np.random.randint(0, 256, (3, 40, 50))
self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False), (20, 25))
# Test size is resized if longer size > max_size
image = np.random.randint(0, 256, (3, 50, 40))
self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False, max_size=22), (22, 17))
# Test correct channel dimension is returned if output size if height == 3
# Defaults to input format - channels first
image = np.random.randint(0, 256, (3, 18, 97))
resized_image = resize(image, (3, 20))
self.assertEqual(resized_image.shape, (3, 3, 20))
# Defaults to input format - channels last
image = np.random.randint(0, 256, (18, 97, 3))
resized_image = resize(image, (3, 20))
self.assertEqual(resized_image.shape, (3, 20, 3))
image = np.random.randint(0, 256, (3, 18, 97))
resized_image = resize(image, (3, 20), data_format="channels_last")
self.assertEqual(resized_image.shape, (3, 20, 3))
image = np.random.randint(0, 256, (18, 97, 3))
resized_image = resize(image, (3, 20), data_format="channels_first")
self.assertEqual(resized_image.shape, (3, 3, 20))
def test_resize(self):
image = np.random.randint(0, 256, (3, 224, 224))
# Check the channel order is the same by default
resized_image = resize(image, (30, 40))
self.assertIsInstance(resized_image, np.ndarray)
self.assertEqual(resized_image.shape, (3, 30, 40))
# Check channel order is changed if specified
resized_image = resize(image, (30, 40), data_format="channels_last")
self.assertIsInstance(resized_image, np.ndarray)
self.assertEqual(resized_image.shape, (30, 40, 3))
# Check PIL.Image.Image is returned if return_numpy=False
resized_image = resize(image, (30, 40), return_numpy=False)
self.assertIsInstance(resized_image, PIL.Image.Image)
# PIL size is in (width, height) order
self.assertEqual(resized_image.size, (40, 30))
# Check an image with float values between 0-1 is returned with values in this range
image = np.random.rand(3, 224, 224)
resized_image = resize(image, (30, 40))
self.assertIsInstance(resized_image, np.ndarray)
self.assertEqual(resized_image.shape, (3, 30, 40))
self.assertTrue(np.all(resized_image >= 0))
self.assertTrue(np.all(resized_image <= 1))
def test_normalize(self):
image = np.random.randint(0, 256, (224, 224, 3)) / 255
# Test that exception is raised if inputs are incorrect
# Not a numpy array image
with self.assertRaises(ValueError):
normalize(5, 5, 5)
# Number of mean values != number of channels
with self.assertRaises(ValueError):
normalize(image, mean=(0.5, 0.6), std=1)
# Number of std values != number of channels
with self.assertRaises(ValueError):
normalize(image, mean=1, std=(0.5, 0.6))
# Test result is correct - output data format is channels_first and normalization
# correctly computed
mean = (0.5, 0.6, 0.7)
std = (0.1, 0.2, 0.3)
expected_image = ((image - mean) / std).transpose((2, 0, 1))
normalized_image = normalize(image, mean=mean, std=std, data_format="channels_first")
self.assertIsInstance(normalized_image, np.ndarray)
self.assertEqual(normalized_image.shape, (3, 224, 224))
self.assertTrue(np.allclose(normalized_image, expected_image))
def test_center_crop(self):
image = np.random.randint(0, 256, (3, 224, 224))
# Test that exception is raised if inputs are incorrect
with self.assertRaises(ValueError):
center_crop(image, 10)
# Test result is correct - output data format is channels_first and center crop
# correctly computed
expected_image = image[:, 52:172, 82:142].transpose(1, 2, 0)
cropped_image = center_crop(image, (120, 60), data_format="channels_last")
self.assertIsInstance(cropped_image, np.ndarray)
self.assertEqual(cropped_image.shape, (120, 60, 3))
self.assertTrue(np.allclose(cropped_image, expected_image))
# Test that image is padded with zeros if crop size is larger than image size
expected_image = np.zeros((300, 260, 3))
expected_image[38:262, 18:242, :] = image.transpose((1, 2, 0))
cropped_image = center_crop(image, (300, 260), data_format="channels_last")
self.assertIsInstance(cropped_image, np.ndarray)
self.assertEqual(cropped_image.shape, (300, 260, 3))
self.assertTrue(np.allclose(cropped_image, expected_image))
def test_center_to_corners_format(self):
bbox_center = np.array([[10, 20, 4, 8], [15, 16, 3, 4]])
expected = np.array([[8, 16, 12, 24], [13.5, 14, 16.5, 18]])
self.assertTrue(np.allclose(center_to_corners_format(bbox_center), expected))
# Check that the function and inverse function are inverse of each other
self.assertTrue(np.allclose(corners_to_center_format(center_to_corners_format(bbox_center)), bbox_center))
def test_corners_to_center_format(self):
bbox_corners = np.array([[8, 16, 12, 24], [13.5, 14, 16.5, 18]])
expected = np.array([[10, 20, 4, 8], [15, 16, 3, 4]])
self.assertTrue(np.allclose(corners_to_center_format(bbox_corners), expected))
# Check that the function and inverse function are inverse of each other
self.assertTrue(np.allclose(center_to_corners_format(corners_to_center_format(bbox_corners)), bbox_corners))
def test_rgb_to_id(self):
# test list input
rgb = [125, 4, 255]
self.assertEqual(rgb_to_id(rgb), 16712829)
# test numpy array input
color = np.array(
[
[
[213, 54, 165],
[88, 207, 39],
[156, 108, 128],
],
[
[183, 194, 46],
[137, 58, 88],
[114, 131, 233],
],
]
)
expected = np.array([[10827477, 2608984, 8416412], [3064503, 5782153, 15303538]])
self.assertTrue(np.allclose(rgb_to_id(color), expected))
def test_id_to_rgb(self):
# test int input
self.assertEqual(id_to_rgb(16712829), [125, 4, 255])
# test array input
id_array = np.array([[10827477, 2608984, 8416412], [3064503, 5782153, 15303538]])
color = np.array(
[
[
[213, 54, 165],
[88, 207, 39],
[156, 108, 128],
],
[
[183, 194, 46],
[137, 58, 88],
[114, 131, 233],
],
]
)
self.assertTrue(np.allclose(id_to_rgb(id_array), color))
def test_pad(self):
# fmt: off
image = np.array([[
[0, 1],
[2, 3],
]])
# fmt: on
# Test that exception is raised if unknown padding mode is specified
with self.assertRaises(ValueError):
pad(image, 10, mode="unknown")
# Test that exception is raised if invalid padding is specified
with self.assertRaises(ValueError):
# Cannot pad on channel dimension
pad(image, (5, 10, 10))
# Test image is padded equally on all sides is padding is an int
# fmt: off
expected_image = np.array([
[[0, 0, 0, 0],
[0, 0, 1, 0],
[0, 2, 3, 0],
[0, 0, 0, 0]],
])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, 1)))
# Test the left and right of each axis is padded (pad_left, pad_right)
# fmt: off
expected_image = np.array(
[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 2, 3, 0],
[0, 0, 0, 0, 0]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, (2, 1))))
# Test only one axis is padded (pad_left, pad_right)
# fmt: off
expected_image = np.array([[
[9, 9],
[9, 9],
[0, 1],
[2, 3],
[9, 9]
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((2, 1), (0, 0)), constant_values=9)))
# Test padding with a constant value
# fmt: off
expected_image = np.array([[
[8, 8, 0, 1, 9],
[8, 8, 2, 3, 9],
[8, 8, 7, 7, 9],
[8, 8, 7, 7, 9]
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), constant_values=((6, 7), (8, 9)))))
# fmt: off
image = np.array([[
[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
]])
# fmt: on
# Test padding with PaddingMode.REFLECT
# fmt: off
expected_image = np.array([[
[2, 1, 0, 1, 2, 1],
[5, 4, 3, 4, 5, 4],
[8, 7, 6, 7, 8, 7],
[5, 4, 3, 4, 5, 4],
[2, 1, 0, 1, 2, 1],
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="reflect")))
# Test padding with PaddingMode.REPLICATE
# fmt: off
expected_image = np.array([[
[0, 0, 0, 1, 2, 2],
[3, 3, 3, 4, 5, 5],
[6, 6, 6, 7, 8, 8],
[6, 6, 6, 7, 8, 8],
[6, 6, 6, 7, 8, 8],
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="replicate")))
# Test padding with PaddingMode.SYMMETRIC
# fmt: off
expected_image = np.array([[
[1, 0, 0, 1, 2, 2],
[4, 3, 3, 4, 5, 5],
[7, 6, 6, 7, 8, 8],
[7, 6, 6, 7, 8, 8],
[4, 3, 3, 4, 5, 5],
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="symmetric")))
# Test we can specify the output data format
# Test padding with PaddingMode.REFLECT
# fmt: off
image = np.array([[
[0, 1],
[2, 3],
]])
expected_image = np.array([
[[0], [1], [0], [1], [0]],
[[2], [3], [2], [3], [2]],
[[0], [1], [0], [1], [0]],
[[2], [3], [2], [3], [2]]
])
# fmt: on
self.assertTrue(
np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="reflect", data_format="channels_last"))
)
@require_vision
def test_convert_to_rgb(self):
# Test that an RGBA image is converted to RGB
image = np.array([[[1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.uint8)
pil_image = PIL.Image.fromarray(image)
self.assertEqual(pil_image.mode, "RGBA")
self.assertEqual(pil_image.size, (2, 1))
# For the moment, numpy images are returned as is
rgb_image = convert_to_rgb(image)
self.assertEqual(rgb_image.shape, (1, 2, 4))
self.assertTrue(np.allclose(rgb_image, image))
# And PIL images are converted
rgb_image = convert_to_rgb(pil_image)
self.assertEqual(rgb_image.mode, "RGB")
self.assertEqual(rgb_image.size, (2, 1))
self.assertTrue(np.allclose(np.array(rgb_image), np.array([[[1, 2, 3], [5, 6, 7]]], dtype=np.uint8)))
# Test that a grayscale image is converted to RGB
image = np.array([[0, 255]], dtype=np.uint8)
pil_image = PIL.Image.fromarray(image)
self.assertEqual(pil_image.mode, "L")
self.assertEqual(pil_image.size, (2, 1))
rgb_image = convert_to_rgb(pil_image)
self.assertEqual(rgb_image.mode, "RGB")
self.assertEqual(rgb_image.size, (2, 1))
self.assertTrue(np.allclose(np.array(rgb_image), np.array([[[0, 0, 0], [255, 255, 255]]], dtype=np.uint8)))
def test_flip_channel_order(self):
# fmt: off
img_channels_first = np.array([
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7]],
[[ 8, 9, 10, 11],
[12, 13, 14, 15]],
[[16, 17, 18, 19],
[20, 21, 22, 23]],
])
# fmt: on
img_channels_last = np.moveaxis(img_channels_first, 0, -1)
# fmt: off
flipped_img_channels_first = np.array([
[[16, 17, 18, 19],
[20, 21, 22, 23]],
[[ 8, 9, 10, 11],
[12, 13, 14, 15]],
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7]],
])
# fmt: on
flipped_img_channels_last = np.moveaxis(flipped_img_channels_first, 0, -1)
self.assertTrue(np.allclose(flip_channel_order(img_channels_first), flipped_img_channels_first))
self.assertTrue(
np.allclose(flip_channel_order(img_channels_first, "channels_last"), flipped_img_channels_last)
)
self.assertTrue(np.allclose(flip_channel_order(img_channels_last), flipped_img_channels_last))
self.assertTrue(
np.allclose(flip_channel_order(img_channels_last, "channels_first"), flipped_img_channels_first)
)
| 21,561 | 37.366548 | 119 | py |
transformers | transformers-main/tests/test_modeling_flax_utils.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class FlaxModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model-flax")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-flax-org")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = FlaxBertModel(config)
model.push_to_hub("test-model-flax", use_auth_token=self._token)
new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# Reset repo
delete_repo(token=self._token, repo_id="test-model-flax")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model-flax", push_to_hub=True, use_auth_token=self._token)
new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = FlaxBertModel(config)
model.push_to_hub("valid_org/test-model-flax-org", use_auth_token=self._token)
new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-flax-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir, repo_id="valid_org/test-model-flax-org", push_to_hub=True, use_auth_token=self._token
)
new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
base_params = flatten_dict(unfreeze(model.params))
new_params = flatten_dict(unfreeze(new_model.params))
for key in base_params.keys():
max_diff = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def check_models_equal(model1, model2):
models_are_equal = True
flat_params_1 = flatten_dict(model1.params)
flat_params_2 = flatten_dict(model2.params)
for key in flat_params_1.keys():
if np.sum(np.abs(flat_params_1[key] - flat_params_2[key])) > 1e-4:
models_are_equal = False
return models_are_equal
@require_flax
class FlaxModelUtilsTest(unittest.TestCase):
def test_model_from_pretrained_subfolder(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
model = FlaxBertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder))
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(tmp_dir)
model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_subfolder_sharded(self):
config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
model = FlaxBertModel(config)
subfolder = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(tmp_dir)
model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)
self.assertTrue(check_models_equal(model, model_loaded))
def test_model_from_pretrained_hub_subfolder(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(model_id)
model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
def test_model_from_pretrained_hub_subfolder_sharded(self):
subfolder = "bert"
model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(OSError):
_ = FlaxBertModel.from_pretrained(model_id)
model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)
self.assertIsNotNone(model)
| 6,855 | 35.663102 | 115 | py |
transformers | transformers-main/tests/test_feature_extraction_utils.py | # coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
class FeatureExtractorUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = Wav2Vec2FeatureExtractor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"
)
@is_staging_test
class FeatureExtractorPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-feature-extractor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-feature-extractor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-feature-extractor")
except HTTPError:
pass
def test_push_to_hub(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="test-feature-extractor", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_in_organization(self):
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("valid_org/test-feature-extractor", use_auth_token=self._token)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-feature-extractor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
tmp_dir, repo_id="valid_org/test-feature-extractor-org", push_to_hub=True, use_auth_token=self._token
)
new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org")
for k, v in feature_extractor.__dict__.items():
self.assertEqual(v, getattr(new_feature_extractor, k))
def test_push_to_hub_dynamic_feature_extractor(self):
CustomFeatureExtractor.register_for_auto_class()
feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
feature_extractor.push_to_hub("test-dynamic-feature-extractor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map,
{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"},
)
new_feature_extractor = AutoFeatureExtractor.from_pretrained(
f"{USER}/test-dynamic-feature-extractor", trust_remote_code=True
)
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__, "CustomFeatureExtractor")
| 6,160 | 41.489655 | 135 | py |
transformers | transformers-main/tests/test_modeling_tf_utils.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import inspect
import json
import os
import random
import tempfile
import unittest
import unittest.mock as mock
from huggingface_hub import HfFolder, Repository, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import is_tf_available, is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import ( # noqa: F401
TOKEN,
USER,
CaptureLogger,
_tf_gpu_memory_limit,
is_pt_tf_cross_test,
is_staging_test,
require_safetensors,
require_tf,
slow,
)
from transformers.utils import SAFE_WEIGHTS_NAME, TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
logger = logging.get_logger(__name__)
if is_tf_available():
import h5py
import numpy as np
import tensorflow as tf
from transformers import (
BertConfig,
PreTrainedModel,
PushToHubCallback,
RagRetriever,
TFBertForMaskedLM,
TFBertForSequenceClassification,
TFBertModel,
TFPreTrainedModel,
TFRagModel,
)
from transformers.modeling_tf_utils import tf_shard_checkpoint, unpack_inputs
from transformers.tf_utils import stable_softmax
tf.config.experimental.enable_tensor_float_32_execution(False)
if _tf_gpu_memory_limit is not None:
gpus = tf.config.list_physical_devices("GPU")
for gpu in gpus:
# Restrict TensorFlow to only allocate x GB of memory on the GPUs
try:
tf.config.set_logical_device_configuration(
gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
)
logical_gpus = tf.config.list_logical_devices("GPU")
print("Logical GPUs", logical_gpus)
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)
if is_torch_available():
from transformers import BertModel
@require_tf
class TFModelUtilsTest(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
def test_load_from_one_file(self):
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", f)
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = TFBertModel.from_pretrained(tmp_file, config=config)
finally:
os.remove(tmp_file)
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert")
_ = TFBertModel.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", config=config
)
# tests whether the unpack_inputs function behaves as expected
def test_unpack_inputs(self):
class DummyModel:
def __init__(self):
config_kwargs = {"output_attentions": False, "output_hidden_states": False, "return_dict": False}
self.config = PretrainedConfig(**config_kwargs)
self.main_input_name = "input_ids"
@unpack_inputs
def call(
self,
input_ids=None,
past_key_values=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
return input_ids, past_key_values, output_attentions, output_hidden_states, return_dict
@unpack_inputs
def foo(self, pixel_values, output_attentions=None, output_hidden_states=None, return_dict=None):
return pixel_values, output_attentions, output_hidden_states, return_dict
dummy_model = DummyModel()
input_ids = tf.constant([0, 1, 2, 3], dtype=tf.int32)
past_key_values = tf.constant([4, 5, 6, 7], dtype=tf.int32)
pixel_values = tf.constant([8, 9, 10, 11], dtype=tf.int32)
# test case 1: Pass inputs as keyword arguments; Booleans are inherited from the config.
output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values)
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertFalse(output[4])
# test case 2: Same as above, but with positional arguments.
output = dummy_model.call(input_ids, past_key_values)
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertFalse(output[4])
# test case 3: We can also pack everything in the first input.
output = dummy_model.call(input_ids={"input_ids": input_ids, "past_key_values": past_key_values})
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertFalse(output[4])
# test case 4: Explicit boolean arguments should override the config.
output = dummy_model.call(
input_ids=input_ids, past_key_values=past_key_values, output_attentions=False, return_dict=True
)
tf.debugging.assert_equal(output[0], input_ids)
tf.debugging.assert_equal(output[1], past_key_values)
self.assertFalse(output[2])
self.assertFalse(output[3])
self.assertTrue(output[4])
# test case 5: Unexpected arguments should raise an exception.
with self.assertRaises(ValueError):
output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values, foo="bar")
# test case 6: the decorator is independent from `main_input_name` -- it treats the first argument of the
# decorated function as its main input.
output = dummy_model.foo(pixel_values=pixel_values)
tf.debugging.assert_equal(output[0], pixel_values)
self.assertFalse(output[1])
self.assertFalse(output[2])
self.assertFalse(output[3])
# Tests whether the stable softmax is stable on CPU, with and without XLA
def test_xla_stable_softmax(self):
large_penalty = -1e9
n_tokens = 10
batch_size = 8
def masked_softmax(x, boolean_mask):
numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty
masked_x = x + numerical_mask
return stable_softmax(masked_x)
xla_masked_softmax = tf.function(masked_softmax, jit_compile=True)
xla_stable_softmax = tf.function(stable_softmax, jit_compile=True)
x = tf.random.normal((batch_size, n_tokens))
# Same outcome regardless of the boolean mask here
masked_tokens = random.randint(0, n_tokens)
boolean_mask = tf.convert_to_tensor([[1] * (n_tokens - masked_tokens) + [0] * masked_tokens], dtype=tf.int32)
# We can randomly mask a random numerical input OUTSIDE XLA
numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty
masked_x = x + numerical_mask
xla_out = xla_stable_softmax(masked_x)
out = stable_softmax(masked_x)
assert tf.experimental.numpy.allclose(xla_out, out)
# The stable softmax has the same output as the original softmax
unstable_out = tf.nn.softmax(masked_x)
assert tf.experimental.numpy.allclose(unstable_out, out)
# We can randomly mask a random numerical input INSIDE XLA
xla_out = xla_masked_softmax(x, boolean_mask)
out = masked_softmax(x, boolean_mask)
assert tf.experimental.numpy.allclose(xla_out, out)
def test_checkpoint_sharding_from_hub(self):
model = TFBertModel.from_pretrained("ArthurZ/tiny-random-bert-sharded")
# the model above is the same as the model below, just a sharded version.
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
def test_sharded_checkpoint_with_prefix(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert", load_weight_prefix="a/b")
sharded_model = TFBertModel.from_pretrained("ArthurZ/tiny-random-bert-sharded", load_weight_prefix="a/b")
for p1, p2 in zip(model.weights, sharded_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
self.assertTrue(p1.name.startswith("a/b/"))
self.assertTrue(p2.name.startswith("a/b/"))
def test_sharded_checkpoint_transfer(self):
# If this doesn't throw an error then the test passes
TFBertForSequenceClassification.from_pretrained("ArthurZ/tiny-random-bert-sharded")
@is_pt_tf_cross_test
def test_checkpoint_sharding_local_from_pt(self):
with tempfile.TemporaryDirectory() as tmp_dir:
_ = Repository(local_dir=tmp_dir, clone_from="hf-internal-testing/tiny-random-bert-sharded")
model = TFBertModel.from_pretrained(tmp_dir, from_pt=True)
# the model above is the same as the model below, just a sharded pytorch version.
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
@is_pt_tf_cross_test
def test_checkpoint_loading_with_prefix_from_pt(self):
model = TFBertModel.from_pretrained(
"hf-internal-testing/tiny-random-bert", from_pt=True, load_weight_prefix="a/b"
)
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert", from_pt=True)
for p1, p2 in zip(model.weights, ref_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
self.assertTrue(p1.name.startswith("a/b/"))
@is_pt_tf_cross_test
def test_checkpoint_sharding_hub_from_pt(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded", from_pt=True)
# the model above is the same as the model below, just a sharded pytorch version.
ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
def test_shard_checkpoint(self):
# This is the model we will use, total size 340,000 bytes.
model = tf.keras.Sequential(
[
tf.keras.layers.Dense(200, use_bias=False), # size 80,000
tf.keras.layers.Dense(200, use_bias=False), # size 160,000
tf.keras.layers.Dense(100, use_bias=False), # size 80,000
tf.keras.layers.Dense(50, use_bias=False), # size 20,000
]
)
inputs = tf.zeros((1, 100), dtype=tf.float32)
model(inputs)
weights = model.weights
weights_dict = {w.name: w for w in weights}
with self.subTest("No shard when max size is bigger than model size"):
shards, index = tf_shard_checkpoint(weights)
self.assertIsNone(index)
self.assertDictEqual(shards, {TF2_WEIGHTS_NAME: weights})
with self.subTest("Test sharding, no weights bigger than max size"):
shards, index = tf_shard_checkpoint(weights, max_shard_size="300kB")
# Split is first two layers then last two.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"dense/kernel:0": "tf_model-00001-of-00002.h5",
"dense_1/kernel:0": "tf_model-00001-of-00002.h5",
"dense_2/kernel:0": "tf_model-00002-of-00002.h5",
"dense_3/kernel:0": "tf_model-00002-of-00002.h5",
},
},
)
shard1 = [weights_dict["dense/kernel:0"], weights_dict["dense_1/kernel:0"]]
shard2 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]]
self.assertDictEqual(shards, {"tf_model-00001-of-00002.h5": shard1, "tf_model-00002-of-00002.h5": shard2})
with self.subTest("Test sharding with weights bigger than max size"):
shards, index = tf_shard_checkpoint(weights, max_shard_size="100kB")
# Split is first layer, second layer then last 2.
self.assertDictEqual(
index,
{
"metadata": {"total_size": 340000},
"weight_map": {
"dense/kernel:0": "tf_model-00001-of-00003.h5",
"dense_1/kernel:0": "tf_model-00002-of-00003.h5",
"dense_2/kernel:0": "tf_model-00003-of-00003.h5",
"dense_3/kernel:0": "tf_model-00003-of-00003.h5",
},
},
)
shard1 = [weights_dict["dense/kernel:0"]]
shard2 = [weights_dict["dense_1/kernel:0"]]
shard3 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]]
self.assertDictEqual(
shards,
{
"tf_model-00001-of-00003.h5": shard1,
"tf_model-00002-of-00003.h5": shard2,
"tf_model-00003-of-00003.h5": shard3,
},
)
@slow
def test_special_layer_name_sharding(self):
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
model = TFRagModel.from_pretrained("facebook/rag-token-nq", retriever=retriever)
with tempfile.TemporaryDirectory() as tmp_dir:
for max_size in ["150kB", "150kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
ref_model = TFRagModel.from_pretrained(tmp_dir, retriever=retriever)
for p1, p2 in zip(model.weights, ref_model.weights):
assert np.allclose(p1.numpy(), p2.numpy())
def test_checkpoint_sharding_local(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
# We use the same folder for various sizes to make sure a new save erases the old checkpoint.
for max_size in ["150kB", "150kiB", "200kB", "200kiB"]:
model.save_pretrained(tmp_dir, max_shard_size=max_size)
# Get each shard file and its size
shard_to_size = {}
for shard in os.listdir(tmp_dir):
if shard.endswith(".h5"):
shard_file = os.path.join(tmp_dir, shard)
shard_to_size[shard_file] = os.path.getsize(shard_file)
index_file = os.path.join(tmp_dir, TF2_WEIGHTS_INDEX_NAME)
# Check there is an index but no regular weight file
self.assertTrue(os.path.isfile(index_file))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME)))
# Check a file is bigger than max_size only when it has a single weight
for shard_file, size in shard_to_size.items():
if max_size.endswith("kiB"):
max_size_int = int(max_size[:-3]) * 2**10
else:
max_size_int = int(max_size[:-2]) * 10**3
# Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
# the size asked for (since we count parameters)
if size >= max_size_int + 50000:
with h5py.File(shard_file, "r") as state_file:
self.assertEqual(len(state_file), 1)
# Check the index and the shard files found match
with open(index_file, "r", encoding="utf-8") as f:
index = json.loads(f.read())
all_shards = set(index["weight_map"].values())
shards_found = {f for f in os.listdir(tmp_dir) if f.endswith(".h5")}
self.assertSetEqual(all_shards, shards_found)
# Finally, check the model can be reloaded
new_model = TFBertModel.from_pretrained(tmp_dir)
model.build()
new_model.build()
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@slow
def test_save_pretrained_signatures(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Short custom TF signature function.
# `input_signature` is specific to BERT.
@tf.function(
input_signature=[
[
tf.TensorSpec([None, None], tf.int32, name="input_ids"),
tf.TensorSpec([None, None], tf.int32, name="token_type_ids"),
tf.TensorSpec([None, None], tf.int32, name="attention_mask"),
]
]
)
def serving_fn(input):
return model(input)
# Using default signature (default behavior) overrides 'serving_default'
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, saved_model=True, signatures=None)
model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
self.assertTrue("serving_default" in list(model_loaded.signatures.keys()))
# Providing custom signature function
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, saved_model=True, signatures={"custom_signature": serving_fn})
model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
self.assertTrue("custom_signature" in list(model_loaded.signatures.keys()))
# Providing multiple custom signature function
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir,
saved_model=True,
signatures={"custom_signature_1": serving_fn, "custom_signature_2": serving_fn},
)
model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
self.assertTrue("custom_signature_1" in list(model_loaded.signatures.keys()))
self.assertTrue("custom_signature_2" in list(model_loaded.signatures.keys()))
@require_safetensors
def test_safetensors_save_and_load(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, safe_serialization=True)
# No tf_model.h5 file, only a model.safetensors
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME)))
new_model = TFBertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@is_pt_tf_cross_test
def test_safetensors_save_and_load_pt_to_tf(self):
model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
pt_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with tempfile.TemporaryDirectory() as tmp_dir:
pt_model.save_pretrained(tmp_dir, safe_serialization=True)
# Check we have a model.safetensors file
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
new_model = TFBertModel.from_pretrained(tmp_dir)
# Check models are equal
for p1, p2 in zip(model.weights, new_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@require_safetensors
def test_safetensors_load_from_hub(self):
tf_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
# Can load from the TF-formatted checkpoint
safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors-tf")
# Check models are equal
for p1, p2 in zip(safetensors_model.weights, tf_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
# Can load from the PyTorch-formatted checkpoint
safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors")
# Check models are equal
for p1, p2 in zip(safetensors_model.weights, tf_model.weights):
self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
@require_tf
@is_staging_test
class TFModelPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-model-tf")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-model-tf-callback")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-model-tf-org")
except HTTPError:
pass
def test_push_to_hub(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = TFBertModel(config)
# Make sure model is properly initialized
model.build()
logging.set_verbosity_info()
logger = logging.get_logger("transformers.utils.hub")
with CaptureLogger(logger) as cl:
model.push_to_hub("test-model-tf", use_auth_token=self._token)
logging.set_verbosity_warning()
# Check the model card was created and uploaded.
self.assertIn("Uploading the following files to __DUMMY_TRANSFORMERS_USER__/test-model-tf", cl.out)
new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
# Reset repo
delete_repo(token=self._token, repo_id="test-model-tf")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir, repo_id="test-model-tf", push_to_hub=True, use_auth_token=self._token)
new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
@is_pt_tf_cross_test
def test_push_to_hub_callback(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = TFBertForMaskedLM(config)
model.compile()
with tempfile.TemporaryDirectory() as tmp_dir:
push_to_hub_callback = PushToHubCallback(
output_dir=tmp_dir,
hub_model_id="test-model-tf-callback",
hub_token=self._token,
)
model.fit(model.dummy_inputs, model.dummy_inputs, epochs=1, callbacks=[push_to_hub_callback])
new_model = TFBertForMaskedLM.from_pretrained(f"{USER}/test-model-tf-callback")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
tf_push_to_hub_params = dict(inspect.signature(TFPreTrainedModel.push_to_hub).parameters)
tf_push_to_hub_params.pop("base_model_card_args")
pt_push_to_hub_params = dict(inspect.signature(PreTrainedModel.push_to_hub).parameters)
pt_push_to_hub_params.pop("deprecated_kwargs")
self.assertDictEaual(tf_push_to_hub_params, pt_push_to_hub_params)
def test_push_to_hub_in_organization(self):
config = BertConfig(
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
)
model = TFBertModel(config)
# Make sure model is properly initialized
model.build()
model.push_to_hub("valid_org/test-model-tf-org", use_auth_token=self._token)
new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-model-tf-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-tf-org"
)
new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org")
models_equal = True
for p1, p2 in zip(model.weights, new_model.weights):
if not tf.math.reduce_all(p1 == p2):
models_equal = False
break
self.assertTrue(models_equal)
| 27,861 | 43.366242 | 118 | py |
transformers | transformers-main/tests/test_backbone_common.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType
@require_torch
class BackboneTesterMixin:
all_model_classes = ()
has_attentions = True
def test_config(self):
config_class = self.config_class
# test default config
config = config_class()
self.assertIsNotNone(config)
expected_stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(config.depths) + 1)]
self.assertEqual(config.stage_names, expected_stage_names)
self.assertTrue(set(config.out_features).issubset(set(config.stage_names)))
# Test out_features and out_indices are correctly set
# out_features and out_indices both None
config = config_class(out_features=None, out_indices=None)
self.assertEqual(config.out_features, [config.stage_names[-1]])
self.assertEqual(config.out_indices, [len(config.stage_names) - 1])
# out_features and out_indices both set
config = config_class(out_features=["stem", "stage1"], out_indices=[0, 1])
self.assertEqual(config.out_features, ["stem", "stage1"])
self.assertEqual(config.out_indices, [0, 1])
# Only out_features set
config = config_class(out_features=["stage1", "stage3"])
self.assertEqual(config.out_features, ["stage1", "stage3"])
self.assertEqual(config.out_indices, [1, 3])
# Only out_indices set
config = config_class(out_indices=[0, 2])
self.assertEqual(config.out_features, [config.stage_names[0], config.stage_names[2]])
self.assertEqual(config.out_indices, [0, 2])
# Error raised when out_indices do not correspond to out_features
with self.assertRaises(ValueError):
config = config_class(out_features=["stage1", "stage2"], out_indices=[0, 2])
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_channels(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertEqual(len(model.channels), len(config.out_features))
num_features = model.num_features
out_indices = [config.stage_names.index(feat) for feat in config.out_features]
out_channels = [num_features[idx] for idx in out_indices]
self.assertListEqual(model.channels, out_channels)
new_config = copy.deepcopy(config)
new_config.out_features = None
model = model_class(new_config)
self.assertEqual(len(model.channels), 1)
self.assertListEqual(model.channels, [num_features[-1]])
new_config = copy.deepcopy(config)
new_config.out_indices = None
model = model_class(new_config)
self.assertEqual(len(model.channels), 1)
self.assertListEqual(model.channels, [num_features[-1]])
def test_create_from_modified_config(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
result = model(**inputs_dict)
self.assertEqual(len(result.feature_maps), len(config.out_features))
self.assertEqual(len(model.channels), len(config.out_features))
self.assertEqual(len(result.feature_maps), len(config.out_indices))
self.assertEqual(len(model.channels), len(config.out_indices))
# Check output of last stage is taken if out_features=None, out_indices=None
modified_config = copy.deepcopy(config)
modified_config.out_features = None
model = model_class(modified_config)
model.to(torch_device)
model.eval()
result = model(**inputs_dict)
self.assertEqual(len(result.feature_maps), 1)
self.assertEqual(len(model.channels), 1)
modified_config = copy.deepcopy(config)
modified_config.out_indices = None
model = model_class(modified_config)
model.to(torch_device)
model.eval()
result = model(**inputs_dict)
self.assertEqual(len(result.feature_maps), 1)
self.assertEqual(len(model.channels), 1)
# Check backbone can be initialized with fresh weights
modified_config = copy.deepcopy(config)
modified_config.use_pretrained_backbone = False
model = model_class(modified_config)
model.to(torch_device)
model.eval()
result = model(**inputs_dict)
def test_backbone_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for backbone_class in self.all_model_classes:
backbone = backbone_class(config)
self.assertTrue(hasattr(backbone, "backbone_type"))
self.assertTrue(hasattr(backbone, "stage_names"))
self.assertTrue(hasattr(backbone, "num_features"))
self.assertTrue(hasattr(backbone, "out_indices"))
self.assertTrue(hasattr(backbone, "out_features"))
self.assertTrue(hasattr(backbone, "out_feature_channels"))
self.assertTrue(hasattr(backbone, "channels"))
self.assertIsInstance(backbone.backbone_type, BackboneType)
# Verify num_features has been initialized in the backbone init
self.assertIsNotNone(backbone.num_features)
self.assertTrue(len(backbone.channels) == len(backbone.out_indices))
self.assertTrue(len(backbone.stage_names) == len(backbone.num_features))
self.assertTrue(len(backbone.channels) <= len(backbone.num_features))
self.assertTrue(len(backbone.out_feature_channels) == len(backbone.stage_names))
def test_backbone_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
batch_size = inputs_dict["pixel_values"].shape[0]
for backbone_class in self.all_model_classes:
backbone = backbone_class(config)
backbone.to(torch_device)
backbone.eval()
outputs = backbone(**inputs_dict)
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps, tuple)
self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels):
self.assertTrue(feature_map.shape[:2], (batch_size, n_channels))
self.assertIsNone(outputs.hidden_states)
self.assertIsNone(outputs.attentions)
# Test output_hidden_states=True
outputs = backbone(**inputs_dict, output_hidden_states=True)
self.assertIsNotNone(outputs.hidden_states)
self.assertTrue(len(outputs.hidden_states), len(backbone.stage_names))
for hidden_state, n_channels in zip(outputs.hidden_states, backbone.channels):
self.assertTrue(hidden_state.shape[:2], (batch_size, n_channels))
# Test output_attentions=True
if self.has_attentions:
outputs = backbone(**inputs_dict, output_attentions=True)
self.assertIsNotNone(outputs.attentions)
| 8,644 | 44.026042 | 101 | py |
transformers | transformers-main/tests/test_tokenization_utils.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPT2TokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class TokenizerUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def test_cached_files_are_used_when_internet_is_down_missing_files(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = GPT2TokenizerFast.from_pretrained("gpt2")
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = GPT2TokenizerFast.from_pretrained("gpt2")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_one_file(self):
# This test is for deprecated behavior and can be removed in v5
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model", f)
_ = AlbertTokenizer.from_pretrained(tmp_file)
finally:
os.remove(tmp_file)
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("tokenizer.json"):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("tokenizer.json", "wb") as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json", f)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size, 1000)
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("tokenizer.json")
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model")
@is_staging_test
class TokenizerPushToHubTester(unittest.TestCase):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-tokenizer")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-tokenizer-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-tokenizer")
except HTTPError:
pass
def test_push_to_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = BertTokenizer(vocab_file)
tokenizer.push_to_hub("test-tokenizer", use_auth_token=self._token)
new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
# Reset repo
delete_repo(token=self._token, repo_id="test-tokenizer")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(tmp_dir, repo_id="test-tokenizer", push_to_hub=True, use_auth_token=self._token)
new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
def test_push_to_hub_in_organization(self):
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = BertTokenizer(vocab_file)
tokenizer.push_to_hub("valid_org/test-tokenizer-org", use_auth_token=self._token)
new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-tokenizer-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
tmp_dir, repo_id="valid_org/test-tokenizer-org", push_to_hub=True, use_auth_token=self._token
)
new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
@require_tokenizers
def test_push_to_hub_dynamic_tokenizer(self):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = CustomTokenizer(vocab_file)
# No fast custom tokenizer
tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token)
tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True)
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer")
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
bert_tokenizer = BertTokenizerFast.from_pretrained(tmp_dir)
bert_tokenizer.save_pretrained(tmp_dir)
tokenizer = CustomTokenizerFast.from_pretrained(tmp_dir)
tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token)
tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True)
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizerFast")
tokenizer = AutoTokenizer.from_pretrained(
f"{USER}/test-dynamic-tokenizer", use_fast=False, trust_remote_code=True
)
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer")
class TrieTest(unittest.TestCase):
def test_trie(self):
trie = Trie()
trie.add("Hello 友達")
self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}})
trie.add("Hello")
trie.data
self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}})
def test_trie_split(self):
trie = Trie()
self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS] This is a extra_id_100"])
trie.add("[CLS]")
trie.add("extra_id_1")
trie.add("extra_id_100")
self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS]", " This is a ", "extra_id_100"])
def test_trie_single(self):
trie = Trie()
trie.add("A")
self.assertEqual(trie.split("ABC"), ["A", "BC"])
self.assertEqual(trie.split("BCA"), ["BC", "A"])
def test_trie_final(self):
trie = Trie()
trie.add("TOKEN]")
trie.add("[SPECIAL_TOKEN]")
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"])
def test_trie_subtokens(self):
trie = Trie()
trie.add("A")
trie.add("P")
trie.add("[SPECIAL_TOKEN]")
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"])
def test_trie_suffix_tokens(self):
trie = Trie()
trie.add("AB")
trie.add("B")
trie.add("C")
self.assertEqual(trie.split("ABC"), ["AB", "C"])
def test_trie_skip(self):
trie = Trie()
trie.add("ABC")
trie.add("B")
trie.add("CD")
self.assertEqual(trie.split("ABCD"), ["ABC", "D"])
def test_cut_text_hardening(self):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
trie = Trie()
parts = trie.cut_text("ABC", [0, 0, 2, 1, 2, 3])
self.assertEqual(parts, ["AB", "C"])
| 11,793 | 40.97153 | 123 | py |
transformers | transformers-main/tests/test_feature_extraction_common.py | # coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class FeatureExtractionSavingTestMixin:
test_cast_dtype = None
def test_feat_extract_to_json_string(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
obj = json.loads(feat_extract.to_json_string())
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key], value)
def test_feat_extract_to_json_file(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "feat_extract.json")
feat_extract_first.to_json_file(json_file_path)
feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
def test_feat_extract_from_and_save_pretrained(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
def test_init_without_params(self):
feat_extract = self.feature_extraction_class()
self.assertIsNotNone(feat_extract)
| 2,230 | 38.839286 | 94 | py |
transformers | transformers-main/tests/test_sequence_feature_extraction_common.py | # coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
# to overwrite at feature extractactor specific tests
feat_extract_tester = None
feature_extraction_class = None
@property
def feat_extract_dict(self):
return self.feat_extract_tester.prepare_feat_extract_dict()
def test_feat_extract_common_properties(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
self.assertTrue(hasattr(feat_extract, "feature_size"))
self.assertTrue(hasattr(feat_extract, "sampling_rate"))
self.assertTrue(hasattr(feat_extract, "padding_value"))
def test_batch_feature(self):
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
input_name = feat_extract.model_input_names[0]
processed_features = BatchFeature({input_name: speech_inputs})
self.assertTrue(all(len(x) == len(y) for x, y in zip(speech_inputs, processed_features[input_name])))
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="np")
batch_features_input = processed_features[input_name]
if len(batch_features_input.shape) < 3:
batch_features_input = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
)
@require_torch
def test_batch_feature_pt(self):
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
input_name = feat_extract.model_input_names[0]
processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="pt")
batch_features_input = processed_features[input_name]
if len(batch_features_input.shape) < 3:
batch_features_input = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
)
@require_tf
def test_batch_feature_tf(self):
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(equal_length=True)
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
input_name = feat_extract.model_input_names[0]
processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="tf")
batch_features_input = processed_features[input_name]
if len(batch_features_input.shape) < 3:
batch_features_input = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size)
)
def _check_padding(self, numpify=False):
def _inputs_have_equal_length(input):
length = len(input[0])
for input_slice in input[1:]:
if len(input_slice) != length:
return False
return True
def _inputs_are_equal(input_1, input_2):
if len(input_1) != len(input_2):
return False
for input_slice_1, input_slice_2 in zip(input_1, input_2):
if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3):
return False
return True
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify)
input_name = feat_extract.model_input_names[0]
processed_features = BatchFeature({input_name: speech_inputs})
pad_diff = self.feat_extract_tester.seq_length_diff
pad_max_length = self.feat_extract_tester.max_seq_length + pad_diff
pad_min_length = self.feat_extract_tester.min_seq_length
batch_size = self.feat_extract_tester.batch_size
feature_size = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
input_1 = feat_extract.pad(processed_features, padding=False)
input_1 = input_1[input_name]
input_2 = feat_extract.pad(processed_features, padding="longest")
input_2 = input_2[input_name]
input_3 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[-1]))
input_3 = input_3[input_name]
input_4 = feat_extract.pad(processed_features, padding="longest", return_tensors="np")
input_4 = input_4[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(ValueError):
feat_extract.pad(processed_features, padding="max_length")[input_name]
input_5 = feat_extract.pad(
processed_features, padding="max_length", max_length=pad_max_length, return_tensors="np"
)
input_5 = input_5[input_name]
self.assertFalse(_inputs_have_equal_length(input_1))
self.assertTrue(_inputs_have_equal_length(input_2))
self.assertTrue(_inputs_have_equal_length(input_3))
self.assertTrue(_inputs_are_equal(input_2, input_3))
self.assertTrue(len(input_1[0]) == pad_min_length)
self.assertTrue(len(input_1[1]) == pad_min_length + pad_diff)
self.assertTrue(input_4.shape[:2] == (batch_size, len(input_3[0])))
self.assertTrue(input_5.shape[:2] == (batch_size, pad_max_length))
if feature_size > 1:
self.assertTrue(input_4.shape[2] == input_5.shape[2] == feature_size)
# test padding for `pad_to_multiple_of` for List[int] + numpy
input_6 = feat_extract.pad(processed_features, pad_to_multiple_of=10)
input_6 = input_6[input_name]
input_7 = feat_extract.pad(processed_features, padding="longest", pad_to_multiple_of=10)
input_7 = input_7[input_name]
input_8 = feat_extract.pad(
processed_features, padding="max_length", pad_to_multiple_of=10, max_length=pad_max_length
)
input_8 = input_8[input_name]
input_9 = feat_extract.pad(
processed_features,
padding="max_length",
pad_to_multiple_of=10,
max_length=pad_max_length,
return_tensors="np",
)
input_9 = input_9[input_name]
self.assertTrue(all(len(x) % 10 == 0 for x in input_6))
self.assertTrue(_inputs_are_equal(input_6, input_7))
expected_mult_pad_length = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(x) == expected_mult_pad_length for x in input_8))
self.assertEqual(input_9.shape[:2], (batch_size, expected_mult_pad_length))
if feature_size > 1:
self.assertTrue(input_9.shape[2] == feature_size)
# Check padding value is correct
padding_vector_sum = (np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_2[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
< 1e-3
)
self.assertTrue(
abs(
np.asarray(input_2[1])[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)
)
< 1e-3
)
self.assertTrue(
abs(
np.asarray(input_2[2])[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)
)
< 1e-3
)
self.assertTrue(
abs(input_5[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3
)
self.assertTrue(
abs(input_9[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
< 1e-3
)
def _check_truncation(self, numpify=False):
def _inputs_have_equal_length(input):
length = len(input[0])
for input_slice in input[1:]:
if len(input_slice) != length:
return False
return True
def _inputs_are_equal(input_1, input_2):
if len(input_1) != len(input_2):
return False
for input_slice_1, input_slice_2 in zip(input_1, input_2):
if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3):
return False
return True
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify)
input_name = feat_extract.model_input_names[0]
processed_features = BatchFeature({input_name: speech_inputs})
# truncate to smallest
input_1 = feat_extract.pad(
processed_features, padding="max_length", max_length=len(speech_inputs[0]), truncation=True
)
input_1 = input_1[input_name]
input_2 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[0]))
input_2 = input_2[input_name]
self.assertTrue(_inputs_have_equal_length(input_1))
self.assertFalse(_inputs_have_equal_length(input_2))
# truncate to smallest with np
input_3 = feat_extract.pad(
processed_features,
padding="max_length",
max_length=len(speech_inputs[0]),
return_tensors="np",
truncation=True,
)
input_3 = input_3[input_name]
input_4 = feat_extract.pad(
processed_features, padding="max_length", max_length=len(speech_inputs[0]), return_tensors="np"
)
input_4 = input_4[input_name]
self.assertTrue(_inputs_have_equal_length(input_3))
self.assertTrue(input_3.shape[1] == len(speech_inputs[0]))
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(input_4))
# truncate to middle
input_5 = feat_extract.pad(
processed_features,
padding="max_length",
max_length=len(speech_inputs[1]),
truncation=True,
return_tensors="np",
)
input_5 = input_5[input_name]
input_6 = feat_extract.pad(
processed_features, padding="max_length", max_length=len(speech_inputs[1]), truncation=True
)
input_6 = input_6[input_name]
input_7 = feat_extract.pad(
processed_features, padding="max_length", max_length=len(speech_inputs[1]), return_tensors="np"
)
input_7 = input_7[input_name]
self.assertTrue(input_5.shape[1] == len(speech_inputs[1]))
self.assertTrue(_inputs_have_equal_length(input_5))
self.assertTrue(_inputs_have_equal_length(input_6))
self.assertTrue(_inputs_are_equal(input_5, input_6))
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(input_7))
self.assertTrue(len(input_7[-1]) == len(speech_inputs[-1]))
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(ValueError):
feat_extract.pad(processed_features, truncation=True)[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(ValueError):
feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(ValueError):
feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(ValueError):
feat_extract.pad(processed_features, padding="max_length", truncation=True)[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
pad_to_multiple_of = 12
input_8 = feat_extract.pad(
processed_features,
padding="max_length",
max_length=len(speech_inputs[0]),
pad_to_multiple_of=pad_to_multiple_of,
truncation=True,
)
input_8 = input_8[input_name]
input_9 = feat_extract.pad(
processed_features,
padding="max_length",
max_length=len(speech_inputs[0]),
pad_to_multiple_of=pad_to_multiple_of,
)
input_9 = input_9[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
expected_length = len(speech_inputs[0])
if expected_length % pad_to_multiple_of != 0:
expected_length = ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_8[0]) == expected_length)
self.assertTrue(_inputs_have_equal_length(input_8))
self.assertFalse(_inputs_have_equal_length(input_9))
def test_padding_from_list(self):
self._check_padding(numpify=False)
def test_padding_from_array(self):
self._check_padding(numpify=True)
def test_truncation_from_list(self):
self._check_truncation(numpify=False)
def test_truncation_from_array(self):
self._check_truncation(numpify=True)
@require_torch
def test_padding_accepts_tensors_pt(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
input_name = feat_extract.model_input_names[0]
processed_features = BatchFeature({input_name: speech_inputs})
input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
input_pt = feat_extract.pad(processed_features, padding="longest", return_tensors="pt")[input_name]
self.assertTrue(abs(input_np.astype(np.float32).sum() - input_pt.numpy().astype(np.float32).sum()) < 1e-2)
@require_tf
def test_padding_accepts_tensors_tf(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
input_name = feat_extract.model_input_names[0]
processed_features = BatchFeature({input_name: speech_inputs})
input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
input_tf = feat_extract.pad(processed_features, padding="longest", return_tensors="tf")[input_name]
self.assertTrue(abs(input_np.astype(np.float32).sum() - input_tf.numpy().astype(np.float32).sum()) < 1e-2)
def test_attention_mask(self):
feat_dict = self.feat_extract_dict
feat_dict["return_attention_mask"] = True
feat_extract = self.feature_extraction_class(**feat_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
input_lenghts = [len(x) for x in speech_inputs]
input_name = feat_extract.model_input_names[0]
processed = BatchFeature({input_name: speech_inputs})
processed = feat_extract.pad(processed, padding="longest", return_tensors="np")
self.assertIn("attention_mask", processed)
self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lenghts)
def test_attention_mask_with_truncation(self):
feat_dict = self.feat_extract_dict
feat_dict["return_attention_mask"] = True
feat_extract = self.feature_extraction_class(**feat_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
input_lenghts = [len(x) for x in speech_inputs]
input_name = feat_extract.model_input_names[0]
processed = BatchFeature({input_name: speech_inputs})
max_length = min(input_lenghts)
processed_pad = feat_extract.pad(
processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np"
)
self.assertIn("attention_mask", processed_pad)
self.assertListEqual(
list(processed_pad.attention_mask.shape), [processed_pad[input_name].shape[0], max_length]
)
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1).tolist(), [max_length for x in speech_inputs]
)
| 18,041 | 41.451765 | 119 | py |
transformers | transformers-main/tests/test_image_processing_common.py | # coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
if is_torch_available():
import numpy as np
import torch
if is_vision_available():
from PIL import Image
def prepare_image_inputs(image_processor_tester, equal_resolution=False, numpify=False, torchify=False):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
One can specify whether the images are of the same resolution or not.
"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
image_inputs = []
for i in range(image_processor_tester.batch_size):
if equal_resolution:
width = height = image_processor_tester.max_resolution
else:
# To avoid getting image width/height 0
min_resolution = image_processor_tester.min_resolution
if getattr(image_processor_tester, "size_divisor", None):
# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
min_resolution = max(image_processor_tester.size_divisor, min_resolution)
width, height = np.random.choice(np.arange(min_resolution, image_processor_tester.max_resolution), 2)
image_inputs.append(
np.random.randint(255, size=(image_processor_tester.num_channels, width, height), dtype=np.uint8)
)
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
image_inputs = [Image.fromarray(np.moveaxis(image, 0, -1)) for image in image_inputs]
if torchify:
image_inputs = [torch.from_numpy(image) for image in image_inputs]
return image_inputs
def prepare_video(image_processor_tester, width=10, height=10, numpify=False, torchify=False):
"""This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
video = []
for i in range(image_processor_tester.num_frames):
video.append(np.random.randint(255, size=(image_processor_tester.num_channels, width, height), dtype=np.uint8))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
video = [Image.fromarray(np.moveaxis(frame, 0, -1)) for frame in video]
if torchify:
video = [torch.from_numpy(frame) for frame in video]
return video
def prepare_video_inputs(image_processor_tester, equal_resolution=False, numpify=False, torchify=False):
"""This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
One can specify whether the videos are of the same resolution or not.
"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
video_inputs = []
for i in range(image_processor_tester.batch_size):
if equal_resolution:
width = height = image_processor_tester.max_resolution
else:
width, height = np.random.choice(
np.arange(image_processor_tester.min_resolution, image_processor_tester.max_resolution), 2
)
video = prepare_video(
image_processor_tester=image_processor_tester,
width=width,
height=height,
numpify=numpify,
torchify=torchify,
)
video_inputs.append(video)
return video_inputs
class ImageProcessingSavingTestMixin:
test_cast_dtype = None
def test_image_processor_to_json_string(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
obj = json.loads(image_processor.to_json_string())
for key, value in self.image_processor_dict.items():
self.assertEqual(obj[key], value)
def test_image_processor_to_json_file(self):
image_processor_first = self.image_processing_class(**self.image_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "image_processor.json")
image_processor_first.to_json_file(json_file_path)
image_processor_second = self.image_processing_class.from_json_file(json_file_path)
self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
def test_image_processor_from_and_save_pretrained(self):
image_processor_first = self.image_processing_class(**self.image_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
image_processor_second = self.image_processing_class.from_pretrained(tmpdirname)
self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
def test_init_without_params(self):
image_processor = self.image_processing_class()
self.assertIsNotNone(image_processor)
@require_torch
@require_vision
def test_cast_dtype_device(self):
if self.test_cast_dtype is not None:
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
encoding = image_processor(image_inputs, return_tensors="pt")
# for layoutLM compatiblity
self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
self.assertEqual(encoding.pixel_values.dtype, torch.float32)
encoding = image_processor(image_inputs, return_tensors="pt").to(torch.float16)
self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
self.assertEqual(encoding.pixel_values.dtype, torch.float16)
encoding = image_processor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
self.assertEqual(encoding.pixel_values.dtype, torch.bfloat16)
with self.assertRaises(TypeError):
_ = image_processor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
# Try with text + image feature
encoding = image_processor(image_inputs, return_tensors="pt")
encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
encoding = encoding.to(torch.float16)
self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
self.assertEqual(encoding.pixel_values.dtype, torch.float16)
self.assertEqual(encoding.input_ids.dtype, torch.long)
| 7,751 | 42.307263 | 119 | py |
transformers | transformers-main/tests/test_modeling_common.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import copy
import gc
import inspect
import os
import os.path
import pickle
import random
import re
import tempfile
import warnings
from collections import defaultdict
from typing import Dict, List, Tuple
import numpy as np
from pytest import mark
import transformers
from transformers import (
AutoModel,
AutoModelForSequenceClassification,
PretrainedConfig,
is_torch_available,
logging,
)
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES,
MODEL_FOR_BACKBONE_MAPPING_NAMES,
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES,
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES,
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
MODEL_MAPPING_NAMES,
)
from transformers.testing_utils import (
CaptureLogger,
is_pt_flax_cross_test,
is_pt_tf_cross_test,
require_accelerate,
require_safetensors,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
torch_device,
)
from transformers.utils import (
CONFIG_NAME,
GENERATION_CONFIG_NAME,
WEIGHTS_NAME,
is_accelerate_available,
is_flax_available,
is_tf_available,
is_torch_fx_available,
)
from transformers.utils.generic import ModelOutput
if is_accelerate_available():
from accelerate.utils import compute_module_sizes
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, AdaptiveEmbedding
from transformers.pytorch_utils import id_tensor_storage
if is_tf_available():
import tensorflow as tf
if is_flax_available():
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_fx_available():
from transformers.utils.fx import symbolic_trace
def _config_zero_init(config):
configs_no_init = copy.deepcopy(config)
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(configs_no_init, key, 1e-10)
if isinstance(getattr(configs_no_init, key, None), PretrainedConfig):
no_init_subconfig = _config_zero_init(getattr(configs_no_init, key))
setattr(configs_no_init, key, no_init_subconfig)
return configs_no_init
def _mock_init_weights(self, module):
for name, param in module.named_parameters(recurse=False):
# Use the first letter of the name to get a value and go from a <> -13 to z <> 12
value = ord(name[0].lower()) - 110
param.data.fill_(value)
def _mock_all_init_weights(self):
# Prune heads if needed
if self.config.pruned_heads:
self.prune_heads(self.config.pruned_heads)
import transformers.modeling_utils
if transformers.modeling_utils._init_weights:
for module in self.modules():
module._is_hf_initialized = False
# Initialize weights
self.apply(self._initialize_weights)
# Tie weights should be skipped when not initializing all weights
# since from_pretrained(...) calls tie weights anyways
self.tie_weights()
@require_torch
class ModelTesterMixin:
model_tester = None
all_model_classes = ()
all_generative_model_classes = ()
fx_compatible = False
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
test_resize_position_embeddings = False
test_head_masking = True
test_mismatched_shapes = True
test_missing_keys = True
test_model_parallel = False
is_encoder_decoder = False
has_attentions = True
model_split_percents = [0.5, 0.7, 0.9]
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if model_class.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES):
inputs_dict = {
k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
if isinstance(v, torch.Tensor) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
elif model_class.__name__ in get_values(MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES):
inputs_dict.pop("attention_mask")
if return_labels:
if model_class.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES):
inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
elif model_class.__name__ in [
*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES),
*get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES),
]:
inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
inputs_dict["end_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class.__name__ in [
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES),
]:
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class.__name__ in [
*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES),
*get_values(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES),
*get_values(MODEL_FOR_MASKED_LM_MAPPING_NAMES),
*get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES),
]:
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
elif model_class.__name__ in get_values(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES):
num_patches = self.model_tester.image_size // self.model_tester.patch_size
inputs_dict["bool_masked_pos"] = torch.zeros(
(self.model_tester.batch_size, num_patches**2), dtype=torch.long, device=torch_device
)
elif model_class.__name__ in get_values(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES):
batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
inputs_dict["labels"] = torch.zeros(
[self.model_tester.batch_size, height, width], device=torch_device
).long()
return inputs_dict
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_save_load(out1, out2):
# make sure we don't have nans
out_2 = out2.cpu().numpy()
out_2[np.isnan(out_2)] = 0
out_1 = out1.cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
# the config file (and the generation config file, if it can generate) should be saved
self.assertTrue(os.path.exists(os.path.join(tmpdirname, CONFIG_NAME)))
self.assertEqual(
model.can_generate(), os.path.exists(os.path.join(tmpdirname, GENERATION_CONFIG_NAME))
)
model = model_class.from_pretrained(tmpdirname)
model.to(torch_device)
with torch.no_grad():
second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
if isinstance(first, tuple) and isinstance(second, tuple):
for tensor1, tensor2 in zip(first, second):
check_save_load(tensor1, tensor2)
else:
check_save_load(first, second)
def test_from_pretrained_no_checkpoint(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
state_dict = model.state_dict()
new_model = model_class.from_pretrained(
pretrained_model_name_or_path=None, config=config, state_dict=state_dict
)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_save_load_keys_to_ignore_on_save(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
_keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None)
if _keys_to_ignore_on_save is None:
continue
# check the keys are in the original state_dict
for k in _keys_to_ignore_on_save:
self.assertIn(k, model.state_dict().keys(), "\n".join(model.state_dict().keys()))
# check that certain keys didn't get saved with the model
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME)
state_dict_saved = torch.load(output_model_file)
for k in _keys_to_ignore_on_save:
self.assertNotIn(k, state_dict_saved.keys(), "\n".join(state_dict_saved.keys()))
# Test we can load the state dict in the model, necessary for the checkpointing API in Trainer.
load_result = model.load_state_dict(state_dict_saved, strict=False)
self.assertTrue(
len(load_result.missing_keys) == 0
or set(load_result.missing_keys) == set(model._keys_to_ignore_on_save)
)
self.assertTrue(len(load_result.unexpected_keys) == 0)
def test_gradient_checkpointing_backward_compatibility(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if not model_class.supports_gradient_checkpointing:
continue
config.gradient_checkpointing = True
model = model_class(config)
self.assertTrue(model.is_gradient_checkpointing)
def test_gradient_checkpointing_enable_disable(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if not model_class.supports_gradient_checkpointing:
continue
# at init model should have gradient checkpointing disabled
model = model_class(config)
self.assertFalse(model.is_gradient_checkpointing)
# check enable works
model.gradient_checkpointing_enable()
self.assertTrue(model.is_gradient_checkpointing)
# check disable works
model.gradient_checkpointing_disable()
self.assertFalse(model.is_gradient_checkpointing)
def test_save_load_fast_init_from_base(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if config.__class__ not in MODEL_MAPPING:
return
base_class = MODEL_MAPPING[config.__class__]
if isinstance(base_class, tuple):
base_class = base_class[0]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
# make a copy of model class to not break future tests
# from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class
class CopyClass(model_class):
pass
model_class_copy = CopyClass
# make sure that all keys are expected for test
model_class_copy._keys_to_ignore_on_load_missing = []
# make init deterministic, but make sure that
# non-initialized weights throw errors nevertheless
model_class_copy._init_weights = _mock_init_weights
model_class_copy.init_weights = _mock_all_init_weights
model = base_class(config)
state_dict = model.state_dict()
# this will often delete a single weight of a multi-weight module
# to test an edge case
random_key_to_del = random.choice(list(state_dict.keys()))
del state_dict[random_key_to_del]
# check that certain keys didn't get saved with the model
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin"))
model_fast_init = model_class_copy.from_pretrained(tmpdirname)
model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False)
# Before we test anything
for key in model_fast_init.state_dict().keys():
if isinstance(model_slow_init.state_dict()[key], torch.BoolTensor):
max_diff = (model_slow_init.state_dict()[key] ^ model_fast_init.state_dict()[key]).sum().item()
else:
max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def test_save_load_fast_init_to_base(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if config.__class__ not in MODEL_MAPPING:
return
base_class = MODEL_MAPPING[config.__class__]
if isinstance(base_class, tuple):
base_class = base_class[0]
for model_class in self.all_model_classes:
if model_class == base_class:
continue
# make a copy of model class to not break future tests
# from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class
class CopyClass(base_class):
pass
base_class_copy = CopyClass
# make sure that all keys are expected for test
base_class_copy._keys_to_ignore_on_load_missing = []
# make init deterministic, but make sure that
# non-initialized weights throw errors nevertheless
base_class_copy._init_weights = _mock_init_weights
base_class_copy.init_weights = _mock_all_init_weights
model = model_class(config)
state_dict = model.state_dict()
# this will often delete a single weight of a multi-weight module
# to test an edge case
random_key_to_del = random.choice(list(state_dict.keys()))
del state_dict[random_key_to_del]
# check that certain keys didn't get saved with the model
with tempfile.TemporaryDirectory() as tmpdirname:
model.config.save_pretrained(tmpdirname)
torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin"))
model_fast_init = base_class_copy.from_pretrained(tmpdirname)
model_slow_init = base_class_copy.from_pretrained(tmpdirname, _fast_init=False)
for key in model_fast_init.state_dict().keys():
if isinstance(model_slow_init.state_dict()[key], torch.BoolTensor):
max_diff = torch.max(
model_slow_init.state_dict()[key] ^ model_fast_init.state_dict()[key]
).item()
else:
max_diff = torch.max(
torch.abs(model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key])
).item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_determinism(first, second):
out_1 = first.cpu().numpy()
out_2 = second.cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
second = model(**self._prepare_for_class(inputs_dict, model_class))[0]
if isinstance(first, tuple) and isinstance(second, tuple):
for tensor1, tensor2 in zip(first, second):
check_determinism(tensor1, tensor2)
else:
check_determinism(first, second)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
if model.config.is_encoder_decoder:
expected_arg_names = [
"input_ids",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
else ["encoder_outputs"]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
else:
expected_arg_names = ["input_ids"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_training(self):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
if model_class.__name__ in [
*get_values(MODEL_MAPPING_NAMES),
*get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES),
]:
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_training_gradient_checkpointing(self):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.use_cache = False
config.return_dict = True
if (
model_class.__name__
in [*get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)]
or not model_class.supports_gradient_checkpointing
):
continue
model = model_class(config)
model.to(torch_device)
model.gradient_checkpointing_enable()
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_attention_outputs(self):
if not self.has_attentions:
self.skipTest(reason="Model does not output attentions")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
if self.is_encoder_decoder:
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Question Answering model returns start_logits and end_logits
if model_class.__name__ in [
*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES),
*get_values(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES),
]:
correct_outlen += 1 # start_logits and end_logits instead of only 1 output
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
if chunk_length is not None:
self.assertListEqual(
list(self_attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
@slow
def test_torchscript_simple(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self._create_and_check_torchscript(config, inputs_dict)
@slow
def test_torchscript_output_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_attentions = True
self._create_and_check_torchscript(config, inputs_dict)
@slow
def test_torchscript_output_hidden_state(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
self._create_and_check_torchscript(config, inputs_dict)
# This is copied from `torch/testing/_internal/jit_utils.py::clear_class_registry`
def clear_torch_jit_class_registry(self):
torch._C._jit_clear_class_registry()
torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
# torch 1.8 has no `_clear_class_state` in `torch.jit._state`
if hasattr(torch.jit._state, "_clear_class_state"):
torch.jit._state._clear_class_state()
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
main_input_name = model_class.main_input_name
try:
if model.config.is_encoder_decoder:
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
main_input = inputs[main_input_name]
attention_mask = inputs["attention_mask"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
model(main_input, attention_mask, decoder_input_ids, decoder_attention_mask)
traced_model = torch.jit.trace(
model, (main_input, attention_mask, decoder_input_ids, decoder_attention_mask)
)
elif "bbox" in inputs and "image" in inputs: # LayoutLMv2 requires additional inputs
input_ids = inputs["input_ids"]
bbox = inputs["bbox"]
image = inputs["image"].tensor
model(input_ids, bbox, image)
traced_model = torch.jit.trace(
model, (input_ids, bbox, image), check_trace=False
) # when traced model is checked, an error is produced due to name mangling
else:
main_input = inputs[main_input_name]
model(main_input)
traced_model = torch.jit.trace(model, main_input)
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
non_persistent_buffers = {}
for key in loaded_model_state_dict.keys():
if key not in model_state_dict.keys():
non_persistent_buffers[key] = loaded_model_state_dict[key]
loaded_model_state_dict = {
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
}
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
models_equal = True
for layer_name, p1 in model_state_dict.items():
if layer_name in loaded_model_state_dict:
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
# (Even with this call, there are still memory leak by ~0.04MB)
self.clear_torch_jit_class_registry()
def test_torch_fx(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self._create_and_check_torch_fx_tracing(config, inputs_dict)
def test_torch_fx_output_loss(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self._create_and_check_torch_fx_tracing(config, inputs_dict, output_loss=True)
def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
if not is_torch_fx_available() or not self.fx_compatible:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.return_dict = False
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)
try:
if model.config.is_encoder_decoder:
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
labels = inputs.get("labels", None)
input_names = [
"attention_mask",
"decoder_attention_mask",
"decoder_input_ids",
"input_features",
"input_ids",
"input_values",
]
if labels is not None:
input_names.append("labels")
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
input_names = list(filtered_inputs.keys())
model_output = model(**filtered_inputs)
traced_model = symbolic_trace(model, input_names)
traced_output = traced_model(**filtered_inputs)
else:
input_names = [
"attention_mask",
"bbox",
"input_features",
"input_ids",
"input_values",
"pixel_values",
"token_type_ids",
"visual_feats",
"visual_pos",
]
labels = inputs.get("labels", None)
start_positions = inputs.get("start_positions", None)
end_positions = inputs.get("end_positions", None)
if labels is not None:
input_names.append("labels")
if start_positions is not None:
input_names.append("start_positions")
if end_positions is not None:
input_names.append("end_positions")
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
input_names = list(filtered_inputs.keys())
if model.__class__.__name__ in set(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values()) and (
not hasattr(model.config, "problem_type") or model.config.problem_type is None
):
model.config.problem_type = "single_label_classification"
traced_model = symbolic_trace(model, input_names)
traced_output = traced_model(**filtered_inputs)
model_output = model(**filtered_inputs)
except Exception as e:
self.fail(f"Couldn't trace module: {e}")
def flatten_output(output):
flatten = []
for x in output:
if isinstance(x, (tuple, list)):
flatten += flatten_output(x)
elif not isinstance(x, torch.Tensor):
continue
else:
flatten.append(x)
return flatten
model_output = flatten_output(model_output)
traced_output = flatten_output(traced_output)
num_outputs = len(model_output)
for i in range(num_outputs):
self.assertTrue(
torch.allclose(model_output[i], traced_output[i]),
f"traced {i}th output doesn't match model {i}th output for {model_class}",
)
# Test that the model can be serialized and restored properly
with tempfile.TemporaryDirectory() as tmp_dir_name:
pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
try:
with open(pkl_file_name, "wb") as f:
pickle.dump(traced_model, f)
with open(pkl_file_name, "rb") as f:
loaded = pickle.load(f)
except Exception as e:
self.fail(f"Couldn't serialize / deserialize the traced model: {e}")
loaded_output = loaded(**filtered_inputs)
loaded_output = flatten_output(loaded_output)
for i in range(num_outputs):
self.assertTrue(
torch.allclose(model_output[i], loaded_output[i]),
f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
)
# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
# (Even with this call, there are still memory leak by ~0.04MB)
self.clear_torch_jit_class_registry()
def test_headmasking(self):
if not self.test_head_masking:
return
global_rng.seed(42)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
global_rng.seed()
inputs_dict["output_attentions"] = True
config.output_hidden_states = True
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
# Prepare head_mask
# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
head_mask = torch.ones(
self.model_tester.num_hidden_layers,
self.model_tester.num_attention_heads,
device=torch_device,
)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
inputs = self._prepare_for_class(inputs_dict, model_class).copy()
inputs["head_mask"] = head_mask
if model.config.is_encoder_decoder:
signature = inspect.signature(model.forward)
arg_names = [*signature.parameters.keys()]
if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model
inputs["decoder_head_mask"] = head_mask
if "cross_attn_head_mask" in arg_names:
inputs["cross_attn_head_mask"] = head_mask
outputs = model(**inputs, return_dict=True)
# Test that we can get a gradient back for importance score computation
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
self.assertIsNotNone(multihead_outputs)
self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
def check_attentions_validity(attentions):
# Remove Nan
for t in attentions:
self.assertLess(
torch.sum(torch.isnan(t)), t.numel() / 4
) # Check we don't have more than 25% nans (arbitrary)
attentions = [
t.masked_fill(torch.isnan(t), 0.0) for t in attentions
] # remove them (the test is less complete)
self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
if len(attentions) > 2: # encoder-decoder models have only 2 layers in each module
self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
if model.config.is_encoder_decoder:
check_attentions_validity(outputs.encoder_attentions)
check_attentions_validity(outputs.decoder_attentions)
check_attentions_validity(outputs.cross_attentions)
else:
check_attentions_validity(outputs.attentions)
def test_head_pruning(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
(
config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
inputs_dict["output_attentions"] = True
config.output_hidden_states = False
model = model_class(config=config)
model.to(torch_device)
model.eval()
heads_to_prune = {
0: list(range(1, self.model_tester.num_attention_heads)),
-1: [0],
}
model.prune_heads(heads_to_prune)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_head_pruning_save_load_from_pretrained(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
(
config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
inputs_dict["output_attentions"] = True
config.output_hidden_states = False
model = model_class(config=config)
model.to(torch_device)
model.eval()
heads_to_prune = {
0: list(range(1, self.model_tester.num_attention_heads)),
-1: [0],
}
model.prune_heads(heads_to_prune)
with tempfile.TemporaryDirectory() as temp_dir_name:
model.save_pretrained(temp_dir_name)
model = model_class.from_pretrained(temp_dir_name)
model.to(torch_device)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_head_pruning_save_load_from_config_init(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
(
config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
inputs_dict["output_attentions"] = True
config.output_hidden_states = False
heads_to_prune = {
0: list(range(1, self.model_tester.num_attention_heads)),
-1: [0],
}
config.pruned_heads = heads_to_prune
model = model_class(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_head_pruning_integration(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
(
config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
inputs_dict["output_attentions"] = True
config.output_hidden_states = False
heads_to_prune = {0: [0], 1: [1, 2]}
config.pruned_heads = heads_to_prune
model = model_class(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
with tempfile.TemporaryDirectory() as temp_dir_name:
model.save_pretrained(temp_dir_name)
model = model_class.from_pretrained(temp_dir_name)
model.to(torch_device)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
heads_to_prune = {0: [0], 2: [1, 2]}
model.prune_heads(heads_to_prune)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
seq_length = seq_length * self.model_tester.chunk_length
else:
seq_length = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
if config.is_encoder_decoder:
# Seq2Seq models
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
decoder_hidden_states = outputs.decoder_hidden_states[0]
decoder_hidden_states.retain_grad()
if self.has_attentions:
encoder_attentions = outputs.encoder_attentions[0]
encoder_attentions.retain_grad()
decoder_attentions = outputs.decoder_attentions[0]
decoder_attentions.retain_grad()
cross_attentions = outputs.cross_attentions[0]
cross_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(decoder_hidden_states.grad)
if self.has_attentions:
self.assertIsNotNone(encoder_attentions.grad)
self.assertIsNotNone(decoder_attentions.grad)
self.assertIsNotNone(cross_attentions.grad)
else:
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
attentions = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
if self.has_attentions:
self.assertIsNotNone(attentions.grad)
def test_feed_forward_chunking(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
torch.manual_seed(0)
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
model.eval()
hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
torch.manual_seed(0)
config.chunk_size_feed_forward = 1
model = model_class(config)
model.to(torch_device)
model.eval()
hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
def test_resize_position_vector_embeddings(self):
if not self.test_resize_position_embeddings:
return
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
max_position_embeddings = config.max_position_embeddings
# Retrieve the embeddings and clone theme
if model.config.is_encoder_decoder:
encoder_model_embed, decoder_model_embed = model.get_position_embeddings()
encoder_cloned_embeddings = encoder_model_embed.weight.clone()
decoder_cloned_embeddings = decoder_model_embed.weight.clone()
else:
model_embed = model.get_position_embeddings()
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the position embeddings with a larger max_position_embeddings increases
# the model's postion embeddings size
model.resize_position_embeddings(max_position_embeddings + 10)
self.assertEqual(model.config.max_position_embeddings, max_position_embeddings + 10)
# Check that it actually resizes the embeddings matrix
if model.config.is_encoder_decoder:
encoder_model_embed, decoder_model_embed = model.get_position_embeddings()
self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] + 10)
self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] + 10)
else:
model_embed = model.get_position_embeddings()
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the position embeddings with a smaller max_position_embeddings decreases
# the model's max_position_embeddings
model.resize_position_embeddings(max_position_embeddings - 5)
self.assertEqual(model.config.max_position_embeddings, max_position_embeddings - 5)
# Check that it actually resizes the embeddings matrix
if model.config.is_encoder_decoder:
encoder_model_embed, decoder_model_embed = model.get_position_embeddings()
self.assertEqual(encoder_model_embed.weight.shape[0], encoder_cloned_embeddings.shape[0] - 5)
self.assertEqual(decoder_model_embed.weight.shape[0], decoder_cloned_embeddings.shape[0] - 5)
else:
model_embed = model.get_position_embeddings()
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 5)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
if model.config.is_encoder_decoder:
for p1, p2 in zip(encoder_cloned_embeddings, encoder_model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
for p1, p2 in zip(decoder_cloned_embeddings, decoder_model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
else:
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_resize_tokens_embeddings(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
# Input ids should be clamped to the maximum size of the vocabulary
inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
# make sure that decoder_input_ids are resized as well
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_resize_embeddings_untied(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
original_config.tie_word_embeddings = False
# if model cannot untied embeddings -> leave test
if original_config.tie_word_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config).to(torch_device)
# if no output embeddings -> leave test
if model.get_output_embeddings() is None:
continue
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_vocab_size = config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
# Input ids should be clamped to the maximum size of the vocabulary
inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding, AdaptiveEmbedding))
model.set_input_embeddings(nn.Embedding(10, 10))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_model_main_input_name(self):
for model_class in self.all_model_classes:
model_signature = inspect.signature(getattr(model_class, "forward"))
# The main input is the name of the argument after `self`
observed_main_input_name = list(model_signature.parameters.keys())[1]
self.assertEqual(model_class.main_input_name, observed_main_input_name)
def test_correct_missing_keys(self):
if not self.test_missing_keys:
return
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
base_model_prefix = model.base_model_prefix
if hasattr(model, base_model_prefix):
extra_params = {k: v for k, v in model.named_parameters() if not k.startswith(base_model_prefix)}
extra_params.update({k: v for k, v in model.named_buffers() if not k.startswith(base_model_prefix)})
# Some models define this as None
if model._keys_to_ignore_on_load_missing:
for key in model._keys_to_ignore_on_load_missing:
extra_params.pop(key, None)
if not extra_params:
# In that case, we *are* on a head model, but every
# single key is not actual parameters and this is
# tested in `test_tied_model_weights_key_ignore` test.
continue
with tempfile.TemporaryDirectory() as temp_dir_name:
model.base_model.save_pretrained(temp_dir_name)
model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True)
self.assertGreater(len(loading_info["missing_keys"]), 0, model.__class__.__name__)
def test_tie_model_weights(self):
if not self.test_torchscript:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_same_values(layer_1, layer_2):
equal = True
for p1, p2 in zip(layer_1.weight, layer_2.weight):
if p1.data.ne(p2.data).sum() > 0:
equal = False
return equal
for model_class in self.all_model_classes:
config.torchscript = True
model_not_tied = model_class(config)
if model_not_tied.get_output_embeddings() is None:
continue
config_tied = copy.deepcopy(config)
config_tied.torchscript = False
model_tied = model_class(config_tied)
params_tied = list(model_tied.parameters())
# Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# embeddings.weight.data.div_(2)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# decoding.weight.data.div_(4)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# Check that after resize they remain tied.
model_tied.resize_token_embeddings(config.vocab_size + 10)
params_tied_2 = list(model_tied.parameters())
self.assertEqual(len(params_tied_2), len(params_tied))
# decoding.weight.data.mul_(20)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
@require_safetensors
def test_can_use_safetensors(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model_tied = model_class(config)
with tempfile.TemporaryDirectory() as d:
try:
model_tied.save_pretrained(d, safe_serialization=True)
except Exception as e:
raise Exception(f"Class {model_class.__name__} cannot be saved using safetensors: {e}")
model_reloaded, infos = model_class.from_pretrained(d, output_loading_info=True)
# Checking the state dicts are correct
reloaded_state = model_reloaded.state_dict()
for k, v in model_tied.state_dict().items():
self.assertIn(k, reloaded_state, f"Key {k} is missing from reloaded")
torch.testing.assert_close(
v, reloaded_state[k], msg=lambda x: f"{model_class.__name__}: Tensor {k}: {x}"
)
# Checking there was no complain of missing weights
self.assertEqual(infos["missing_keys"], [])
# Checking the tensor sharing are correct
ptrs = defaultdict(list)
for k, v in model_tied.state_dict().items():
ptrs[v.data_ptr()].append(k)
shared_ptrs = {k: v for k, v in ptrs.items() if len(v) > 1}
for _, shared_names in shared_ptrs.items():
reloaded_ptrs = {reloaded_state[k].data_ptr() for k in shared_names}
self.assertEqual(
len(reloaded_ptrs),
1,
f"The shared pointers are incorrect, found different pointers for keys {shared_names}",
)
def test_load_save_without_tied_weights(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
config.tie_word_embeddings = False
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as d:
model.save_pretrained(d)
model_reloaded, infos = model_class.from_pretrained(d, output_loading_info=True)
# Checking the state dicts are correct
reloaded_state = model_reloaded.state_dict()
for k, v in model.state_dict().items():
self.assertIn(k, reloaded_state, f"Key {k} is missing from reloaded")
torch.testing.assert_close(
v, reloaded_state[k], msg=lambda x: f"{model_class.__name__}: Tensor {k}: {x}"
)
# Checking there was no complain of missing weights
self.assertEqual(infos["missing_keys"], [])
def test_tied_weights_keys(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
config.tie_word_embeddings = True
for model_class in self.all_model_classes:
model_tied = model_class(config)
ptrs = collections.defaultdict(list)
for name, tensor in model_tied.state_dict().items():
ptrs[id_tensor_storage(tensor)].append(name)
# These are all the pointers of shared tensors.
tied_params = [names for _, names in ptrs.items() if len(names) > 1]
tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else []
# Detect we get a hit for each key
for key in tied_weight_keys:
if not any(re.search(key, p) for group in tied_params for p in group):
raise ValueError(f"{key} is not a tied weight key for {model_class}.")
# Removed tied weights found from tied params -> there should only be one left after
for key in tied_weight_keys:
for i in range(len(tied_params)):
tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None]
tied_params = [group for group in tied_params if len(group) > 1]
self.assertListEqual(
tied_params,
[],
f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.",
)
def test_model_weights_reload_no_missing_tied_weights(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
# We are nuking ALL weights on file, so every parameter should
# yell on load. We're going to detect if we yell too much, or too little.
with open(os.path.join(tmp_dir, "pytorch_model.bin"), "wb") as f:
torch.save({}, f)
model_reloaded, infos = model_class.from_pretrained(tmp_dir, output_loading_info=True)
prefix = f"{model_reloaded.base_model_prefix}."
params = dict(model_reloaded.named_parameters())
params.update(dict(model_reloaded.named_buffers()))
param_names = {k[len(prefix) :] if k.startswith(prefix) else k for k in params.keys()}
missing_keys = set(infos["missing_keys"])
extra_missing = missing_keys - param_names
# Remove tied weights from extra missing: they are normally not warned as missing if their tied
# counterpart is present but here there are no weights at all so we do get the warning.
ptrs = collections.defaultdict(list)
for name, tensor in model_reloaded.state_dict().items():
ptrs[id_tensor_storage(tensor)].append(name)
tied_params = [names for _, names in ptrs.items() if len(names) > 1]
for group in tied_params:
group = {k[len(prefix) :] if k.startswith(prefix) else k for k in group}
# We remove the group from extra_missing if not all weights from group are in it
if len(group - extra_missing) > 0:
extra_missing = extra_missing - set(group)
self.assertEqual(
extra_missing,
set(),
f"This model {model_class.__name__} might be missing some `keys_to_ignore`: {extra_missing}. "
f"For debugging, tied parameters are {tied_params}",
)
missed_missing = param_names - missing_keys
# Remove nonpersistent buffers from missed_missing
buffers = [n for n, _ in model_reloaded.named_buffers()]
nonpersistent_buffers = {n for n in buffers if n not in model_reloaded.state_dict()}
nonpersistent_buffers = {
k[len(prefix) :] if k.startswith(prefix) else k for k in nonpersistent_buffers
}
missed_missing = missed_missing - nonpersistent_buffers
if model_reloaded._keys_to_ignore_on_load_missing is None:
expected_missing = set()
else:
expected_missing = set(model_reloaded._keys_to_ignore_on_load_missing)
self.assertEqual(
missed_missing,
expected_missing,
f"This model {model_class.__name__} ignores keys {missed_missing} but they look like real"
" parameters. If they are non persistent buffers make sure to instantiate them with"
" `persistent=False`",
)
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
if self.has_attentions:
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(
model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
)
# Don't copy this method to model specific test file!
# TODO: remove this method once the issues are all fixed!
def _make_attention_mask_non_null(self, inputs_dict):
"""Make sure no sequence has all zeros as attention mask"""
for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]:
if k in inputs_dict:
attention_mask = inputs_dict[k]
# Make sure no all 0s attention masks - to avoid failure at this moment.
# Put `1` at the beginning of sequences to make it still work when combining causal attention masks.
# TODO: remove this line once a fix regarding large negative values for attention mask is done.
attention_mask = torch.cat(
[torch.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], dim=-1
)
# Here we make the first sequence with all 0s as attention mask.
# Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative
# values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks.
# TODO: enable this block once the large negative values thing is cleaned up.
# (see https://github.com/huggingface/transformers/issues/14859)
# attention_mask = torch.cat(
# [torch.zeros_like(attention_mask[:1], dtype=attention_mask.dtype), attention_mask[1:]],
# dim=0
# )
inputs_dict[k] = attention_mask
# Don't copy this method to model specific test file!
# TODO: remove this method once the issues are all fixed!
def _postprocessing_to_ignore_test_cases(self, tf_outputs, pt_outputs, model_class):
"""For temporarily ignoring some failed test cases (issues to be fixed)"""
tf_keys = {k for k, v in tf_outputs.items() if v is not None}
pt_keys = {k for k, v in pt_outputs.items() if v is not None}
key_differences = tf_keys.symmetric_difference(pt_keys)
if model_class.__name__ in [
"FlaubertWithLMHeadModel",
"FunnelForPreTraining",
"ElectraForPreTraining",
"XLMWithLMHeadModel",
"TransfoXLLMHeadModel",
]:
for k in key_differences:
if k in ["loss", "losses"]:
tf_keys.discard(k)
pt_keys.discard(k)
elif model_class.__name__.startswith("GPT2"):
# `TFGPT2` has `past_key_values` as a tensor while `GPT2` has it as a tuple.
tf_keys.discard("past_key_values")
pt_keys.discard("past_key_values")
# create new outputs from the remaining fields
new_tf_outputs = type(tf_outputs)(**{k: tf_outputs[k] for k in tf_keys})
new_pt_outputs = type(pt_outputs)(**{k: pt_outputs[k] for k in pt_keys})
return new_tf_outputs, new_pt_outputs
# Copied from tests.test_modeling_tf_common.TFModelTesterMixin.check_pt_tf_outputs
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
"""Check the outputs from PyTorch and TensorFlow models are close enough. Checks are done in a recursive way.
Args:
model_class: The class of the model that is currently testing. For example, `TFBertModel`,
TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Mainly used for providing more informative
error messages.
name (`str`): The name of the output. For example, `output.hidden_states`, `output.attentions`, etc.
attributes (`Tuple[str]`): The names of the output's element if the output is a tuple/list with each element
being a named field in the output.
"""
self.assertEqual(type(name), str)
if attributes is not None:
self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`")
# Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`).
if isinstance(tf_outputs, ModelOutput):
self.assertTrue(
isinstance(pt_outputs, ModelOutput),
f"{name}: `pt_outputs` should an instance of `ModelOutput` when `tf_outputs` is",
)
# Don't copy this block to model specific test file!
# TODO: remove this method and this line after issues are fixed
tf_outputs, pt_outputs = self._postprocessing_to_ignore_test_cases(tf_outputs, pt_outputs, model_class)
tf_keys = [k for k, v in tf_outputs.items() if v is not None]
pt_keys = [k for k, v in pt_outputs.items() if v is not None]
self.assertEqual(tf_keys, pt_keys, f"{name}: Output keys differ between TF and PyTorch")
# convert to the case of `tuple`
# appending each key to the current (string) `name`
attributes = tuple([f"{name}.{k}" for k in tf_keys])
self.check_pt_tf_outputs(
tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes
)
# Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.)
elif type(tf_outputs) in [tuple, list]:
self.assertEqual(type(tf_outputs), type(pt_outputs), f"{name}: Output types differ between TF and PyTorch")
self.assertEqual(len(tf_outputs), len(pt_outputs), f"{name}: Output lengths differ between TF and PyTorch")
if attributes is not None:
# case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`)
self.assertEqual(
len(attributes),
len(tf_outputs),
f"{name}: The tuple `attributes` should have the same length as `tf_outputs`",
)
else:
# case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name`
attributes = tuple([f"{name}_{idx}" for idx in range(len(tf_outputs))])
for tf_output, pt_output, attr in zip(tf_outputs, pt_outputs, attributes):
self.check_pt_tf_outputs(tf_output, pt_output, model_class, tol=tol, name=attr)
elif isinstance(tf_outputs, tf.Tensor):
self.assertTrue(
isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `tf_outputs` is"
)
tf_outputs = tf_outputs.numpy()
pt_outputs = pt_outputs.detach().to("cpu").numpy()
self.assertEqual(
tf_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between TF and PyTorch"
)
# deal with NumPy's scalars to make replacing nan values by 0 work.
if np.isscalar(tf_outputs):
tf_outputs = np.array([tf_outputs])
pt_outputs = np.array([pt_outputs])
tf_nans = np.isnan(tf_outputs)
pt_nans = np.isnan(pt_outputs)
pt_outputs[tf_nans] = 0
tf_outputs[tf_nans] = 0
pt_outputs[pt_nans] = 0
tf_outputs[pt_nans] = 0
max_diff = np.amax(np.abs(tf_outputs - pt_outputs))
self.assertLessEqual(max_diff, tol, f"{name}: Difference between PyTorch and TF is {max_diff} (>= {tol}).")
else:
raise ValueError(
"`tf_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `tf.Tensor`. Got"
f" {type(tf_outputs)} instead."
)
def prepare_tf_inputs_from_pt_inputs(self, pt_inputs_dict):
tf_inputs_dict = {}
for key, tensor in pt_inputs_dict.items():
# skip key that does not exist in tf
if type(tensor) == bool:
tf_inputs_dict[key] = tensor
elif key == "input_values":
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
elif key == "pixel_values":
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
elif key == "input_features":
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
# other general float inputs
elif tensor.is_floating_point():
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
else:
tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
return tf_inputs_dict
def check_pt_tf_models(self, tf_model, pt_model, pt_inputs_dict):
tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict)
# send pytorch inputs to the correct device
pt_inputs_dict = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items()
}
# send pytorch model to the correct device
pt_model.to(torch_device)
# Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences
pt_model.eval()
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs_dict)
tf_outputs = tf_model(tf_inputs_dict)
# tf models returned loss is usually a tensor rather than a scalar.
# (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`)
# Change it here to a scalar to match PyTorch models' loss
tf_loss = getattr(tf_outputs, "loss", None)
if tf_loss is not None:
tf_outputs.loss = tf.math.reduce_mean(tf_loss)
self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(pt_model))
@is_pt_tf_cross_test
def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
import transformers
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
tf_model_class_name = "TF" + model_class.__name__ # Add the "TF" at the beginning
if not hasattr(transformers, tf_model_class_name):
# transformers does not have this model in TF version yet
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency
# of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`.
# TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it.
self._make_attention_mask_non_null(inputs_dict)
tf_model_class = getattr(transformers, tf_model_class_name)
pt_model = model_class(config)
tf_model = tf_model_class(config)
pt_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
pt_inputs_dict_with_labels = self._prepare_for_class(
inputs_dict,
model_class,
# Not all models accept "labels" in the forward pass (yet :) )
return_labels=True if "labels" in inspect.signature(model_class.forward).parameters.keys() else False,
)
# make sure only tf inputs are forward that actually exist in function args
tf_input_keys = set(inspect.signature(tf_model.call).parameters.keys())
# remove all head masks
tf_input_keys.discard("head_mask")
tf_input_keys.discard("cross_attn_head_mask")
tf_input_keys.discard("decoder_head_mask")
pt_inputs_dict = {k: v for k, v in pt_inputs_dict.items() if k in tf_input_keys}
pt_inputs_dict_with_labels = {k: v for k, v in pt_inputs_dict_with_labels.items() if k in tf_input_keys}
# For some models (e.g. base models), there is no label returned.
# Set the input dict to `None` to avoid check outputs twice for the same input dicts.
if not set(pt_inputs_dict_with_labels.keys()).symmetric_difference(pt_inputs_dict.keys()):
pt_inputs_dict_with_labels = None
# Check we can load pt model in tf and vice-versa with model => model functions
# Here requires `tf_inputs_dict` to build `tf_model`
tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict)
tf_model = transformers.load_pytorch_model_in_tf2_model(
tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
)
pt_model = transformers.load_tf2_model_in_pytorch_model(
pt_model, tf_model, allow_missing_keys=allow_missing_keys
)
# Original test: check without `labels`
self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict)
# check with `labels`
if pt_inputs_dict_with_labels:
self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with tempfile.TemporaryDirectory() as tmpdirname:
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
)
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
)
# Original test: check without `labels`
self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict)
# check with `labels`
if pt_inputs_dict_with_labels:
self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict_with_labels)
def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
diff = np.abs((a - b)).max()
self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
"""
Args:
model_class: The class of the model that is currently testing. For example, ..., etc.
Currently unused, but it could make debugging easier and faster.
names: A string, or a list of strings. These specify what fx_outputs/pt_outputs represent in the model outputs.
Currently unused, but in the future, we could use this information to make the error message clearer
by giving the name(s) of the output tensor(s) with large difference(s) between PT and Flax.
"""
self.assertEqual(type(name), str)
if attributes is not None:
self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`")
# Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`).
if isinstance(fx_outputs, ModelOutput):
self.assertTrue(
isinstance(pt_outputs, ModelOutput),
f"{name}: `pt_outputs` should an instance of `ModelOutput` when `fx_outputs` is",
)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys, f"{name}: Output keys differ between Flax and PyTorch")
# convert to the case of `tuple`
# appending each key to the current (string) `name`
attributes = tuple([f"{name}.{k}" for k in fx_keys])
self.check_pt_flax_outputs(
fx_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes
)
# Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.)
elif type(fx_outputs) in [tuple, list]:
self.assertEqual(
type(fx_outputs), type(pt_outputs), f"{name}: Output types differ between Flax and PyTorch"
)
self.assertEqual(
len(fx_outputs), len(pt_outputs), f"{name}: Output lengths differ between Flax and PyTorch"
)
if attributes is not None:
# case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`)
self.assertEqual(
len(attributes),
len(fx_outputs),
f"{name}: The tuple `attributes` should have the same length as `fx_outputs`",
)
else:
# case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `name`
attributes = tuple([f"{name}_{idx}" for idx in range(len(fx_outputs))])
for fx_output, pt_output, attr in zip(fx_outputs, pt_outputs, attributes):
self.check_pt_flax_outputs(fx_output, pt_output, model_class, tol=tol, name=attr)
elif isinstance(fx_outputs, jnp.ndarray):
self.assertTrue(
isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `fx_outputs` is"
)
# Using `np.asarray` gives `ValueError: assignment destination is read-only` at the line `fx_outputs[fx_nans] = 0`.
fx_outputs = np.array(fx_outputs)
pt_outputs = pt_outputs.detach().to("cpu").numpy()
self.assertEqual(
fx_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between Flax and PyTorch"
)
# deal with NumPy's scalars to make replacing nan values by 0 work.
if np.isscalar(fx_outputs):
fx_outputs = np.array([fx_outputs])
pt_outputs = np.array([pt_outputs])
fx_nans = np.isnan(fx_outputs)
pt_nans = np.isnan(pt_outputs)
pt_outputs[fx_nans] = 0
fx_outputs[fx_nans] = 0
pt_outputs[pt_nans] = 0
fx_outputs[pt_nans] = 0
max_diff = np.amax(np.abs(fx_outputs - pt_outputs))
self.assertLessEqual(
max_diff, tol, f"{name}: Difference between PyTorch and Flax is {max_diff} (>= {tol})."
)
else:
raise ValueError(
"`fx_outputs` should be an instance of `ModelOutput`, a `tuple`, or an instance of `jnp.ndarray`. Got"
f" {type(fx_outputs)} instead."
)
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class)
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
# make sure weights are tied in PyTorch
pt_model.tie_weights()
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)
# send pytorch model to the correct device
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class)
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
if not self.is_encoder_decoder:
input_ids = inputs["input_ids"]
del inputs["input_ids"]
else:
encoder_input_ids = inputs["input_ids"]
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
del inputs["input_ids"]
inputs.pop("decoder_input_ids", None)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs["inputs_embeds"] = wte(input_ids)
else:
inputs["inputs_embeds"] = wte(encoder_input_ids)
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
model(**inputs)[0]
@require_torch_multi_gpu
def test_multi_gpu_data_parallel_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# some params shouldn't be scattered by nn.DataParallel
# so just remove them if they are present.
blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"]
for k in blacklist_non_batched_params:
inputs_dict.pop(k, None)
# move input tensors to cuda:O
for k, v in inputs_dict.items():
if torch.is_tensor(v):
inputs_dict[k] = v.to(0)
for model_class in self.all_model_classes:
model = model_class(config=config)
model.to(0)
model.eval()
# Wrap model in nn.DataParallel
model = nn.DataParallel(model)
with torch.no_grad():
_ = model(**self._prepare_for_class(inputs_dict, model_class))
@require_torch_multi_gpu
def test_model_parallelization(self):
if not self.test_model_parallel:
return
# a candidate for testing_utils
def get_current_gpu_memory_use():
"""returns a list of cuda memory allocations per GPU in MBs"""
per_device_memory = []
for id in range(torch.cuda.device_count()):
with torch.cuda.device(id):
per_device_memory.append(torch.cuda.memory_allocated() >> 20)
return per_device_memory
# Needs a large model to see the difference.
config = self.model_tester.get_large_model_config()
for model_class in self.all_parallelizable_model_classes:
torch.cuda.empty_cache()
# 1. single gpu memory load + unload + memory measurements
# Retrieve initial memory usage (can easily be ~0.6-1.5GB if cuda-kernels have been preloaded by previous tests)
memory_at_start = get_current_gpu_memory_use()
# Put model on device 0 and take a memory snapshot
model = model_class(config)
model.to("cuda:0")
memory_after_model_load = get_current_gpu_memory_use()
# The memory use on device 0 should be higher than it was initially.
self.assertGreater(memory_after_model_load[0], memory_at_start[0])
del model
gc.collect()
torch.cuda.empty_cache()
# 2. MP test
# it's essential to re-calibrate the usage before the next stage
memory_at_start = get_current_gpu_memory_use()
# Spread model layers over multiple devices
model = model_class(config)
model.parallelize()
memory_after_parallelization = get_current_gpu_memory_use()
# Assert that the memory use on all devices is higher than it was when loaded only on CPU
for n in range(len(model.device_map.keys())):
self.assertGreater(memory_after_parallelization[n], memory_at_start[n])
# Assert that the memory use of device 0 is lower than it was when the entire model was loaded on it
self.assertLess(memory_after_parallelization[0], memory_after_model_load[0])
# Assert that the memory use of device 1 is higher than it was when the entire model was loaded
# on device 0 and device 1 wasn't used at all
self.assertGreater(memory_after_parallelization[1], memory_after_model_load[1])
del model
gc.collect()
torch.cuda.empty_cache()
@require_torch_multi_gpu
def test_model_parallel_equal_results(self):
if not self.test_model_parallel:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_parallelizable_model_classes:
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
def cast_to_device(dictionary, device):
output = {}
for k, v in dictionary.items():
if isinstance(v, torch.Tensor):
output[k] = v.to(device)
else:
output[k] = v
return output
model = model_class(config)
output = model(**cast_to_device(inputs_dict, "cpu"))
model.parallelize()
parallel_output = model(**cast_to_device(inputs_dict, "cuda:0"))
for value, parallel_value in zip(output, parallel_output):
if isinstance(value, torch.Tensor):
self.assertTrue(torch.allclose(value, parallel_value.to("cpu"), atol=1e-7))
elif isinstance(value, (Tuple, List)):
for value_, parallel_value_ in zip(value, parallel_value):
self.assertTrue(torch.allclose(value_, parallel_value_.to("cpu"), atol=1e-7))
@require_torch_multi_gpu
def test_model_parallel_beam_search(self):
if not self.test_model_parallel:
return
all_generative_and_parallelizable_model_classes = tuple(
set(self.all_generative_model_classes).intersection(self.all_parallelizable_model_classes)
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in all_generative_and_parallelizable_model_classes:
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
def cast_to_device(dictionary, device):
output = {}
for k, v in dictionary.items():
if isinstance(v, torch.Tensor):
output[k] = v.to(device)
else:
output[k] = v
return output
model.parallelize()
model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)
def check_device_map_is_respected(self, model, device_map):
for param_name, param in model.named_parameters():
# Find device in device_map
while len(param_name) > 0 and param_name not in device_map:
param_name = ".".join(param_name.split(".")[:-1])
if param_name not in device_map:
raise ValueError("device map is incomplete, it does not contain any device for `param_name`.")
param_device = device_map[param_name]
if param_device in ["cpu", "disk"]:
self.assertEqual(param.device, torch.device("meta"))
else:
self.assertEqual(param.device, torch.device(param_device))
@require_accelerate
@mark.accelerate_tests
@require_torch_gpu
def test_disk_offload(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if model_class._no_split_modules is None:
continue
inputs_dict_class = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config).eval()
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict_class)
model_size = compute_module_sizes(model)[""]
max_size = int(self.model_split_percents[0] * model_size)
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir)
max_memory = {0: max_size, "cpu": max_size}
with self.assertRaises(ValueError):
# This errors out cause it's missing an offload folder
new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
new_model = model_class.from_pretrained(
tmp_dir, device_map="auto", max_memory=max_memory, offload_folder=tmp_dir
)
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
torch.manual_seed(0)
new_output = new_model(**inputs_dict_class)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
@require_accelerate
@mark.accelerate_tests
@require_torch_gpu
def test_cpu_offload(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if model_class._no_split_modules is None:
continue
inputs_dict_class = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config).eval()
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict_class)
model_size = compute_module_sizes(model)[""]
# We test several splits of sizes to make sure it works.
max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents]
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir)
for max_size in max_gpu_sizes:
max_memory = {0: max_size, "cpu": model_size * 2}
new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
# Making sure part of the model will actually end up offloaded
self.assertSetEqual(set(new_model.hf_device_map.values()), {0, "cpu"})
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
torch.manual_seed(0)
new_output = new_model(**inputs_dict_class)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
@require_accelerate
@mark.accelerate_tests
@require_torch_multi_gpu
def test_model_parallelism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if model_class._no_split_modules is None:
continue
inputs_dict_class = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config).eval()
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict_class)
model_size = compute_module_sizes(model)[""]
# We test several splits of sizes to make sure it works.
max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents]
with tempfile.TemporaryDirectory() as tmp_dir:
model.cpu().save_pretrained(tmp_dir)
for max_size in max_gpu_sizes:
max_memory = {0: max_size, 1: model_size * 2, "cpu": model_size * 2}
new_model = model_class.from_pretrained(tmp_dir, device_map="auto", max_memory=max_memory)
# Making sure part of the model will actually end up offloaded
self.assertSetEqual(set(new_model.hf_device_map.values()), {0, 1})
self.check_device_map_is_respected(new_model, new_model.hf_device_map)
torch.manual_seed(0)
new_output = new_model(**inputs_dict_class)
self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
def test_problem_types(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
problem_types = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if model_class.__name__ not in [
*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
]:
continue
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
config.problem_type = problem_type["title"]
config.num_labels = problem_type["num_labels"]
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
if problem_type["num_labels"] > 1:
inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=True) as warning_list:
loss = model(**inputs).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message):
raise ValueError(
f"Something is going wrong in the regression problem: intercepted {w.message}"
)
loss.backward()
def test_load_with_mismatched_shapes(self):
if not self.test_mismatched_shapes:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if model_class.__name__ not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES):
continue
with self.subTest(msg=f"Testing {model_class}"):
with tempfile.TemporaryDirectory() as tmp_dir:
model = model_class(config)
model.save_pretrained(tmp_dir)
# Fails when we don't set ignore_mismatched_sizes=True
with self.assertRaises(RuntimeError):
new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
with self.assertRaises(RuntimeError):
new_model_without_prefix = AutoModel.from_pretrained(tmp_dir, vocab_size=10)
logger = logging.get_logger("transformers.modeling_utils")
with CaptureLogger(logger) as cl:
new_model = AutoModelForSequenceClassification.from_pretrained(
tmp_dir, num_labels=42, ignore_mismatched_sizes=True
)
self.assertIn("the shapes did not match", cl.out)
new_model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
logits = new_model(**inputs).logits
self.assertEqual(logits.shape[1], 42)
with CaptureLogger(logger) as cl:
new_model_without_prefix = AutoModel.from_pretrained(
tmp_dir, vocab_size=10, ignore_mismatched_sizes=True
)
self.assertIn("the shapes did not match", cl.out)
input_ids = ids_tensor((2, 8), 10)
new_model_without_prefix.to(torch_device)
if self.is_encoder_decoder:
new_model_without_prefix(input_ids, decoder_input_ids=input_ids)
else:
new_model_without_prefix(input_ids)
def test_model_is_small(self):
# Just a consistency check to make sure we are not running tests on 80M parameter models.
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
# print(config)
for model_class in self.all_model_classes:
model = model_class(config)
num_params = model.num_parameters()
assert (
num_params < 1000000
), f"{model_class} is too big for the common tests ({num_params})! It should have 200k max."
global_rng = random.Random()
def ids_tensor(shape, vocab_size, rng=None, name=None):
# Creates a random int32 tensor of the shape within the vocab size
if rng is None:
rng = global_rng
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous()
def random_attention_mask(shape, rng=None, name=None):
attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None)
# make sure that at least one token is attended to for each batch
attn_mask[:, -1] = 1
return attn_mask
def floats_tensor(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.random() * scale)
return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous()
| 126,716 | 44.895328 | 147 | py |
transformers | transformers-main/tests/__init__.py | 0 | 0 | 0 | py | |
transformers | transformers-main/tests/test_modeling_tf_common.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import copy
import inspect
import json
import os
import random
import tempfile
import unittest
from importlib import import_module
from math import isnan
from typing import List, Tuple
from datasets import Dataset
from transformers import is_tf_available, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import ( # noqa: F401
CaptureLogger,
_tf_gpu_memory_limit,
is_pt_tf_cross_test,
require_tf,
require_tf2onnx,
slow,
torch_device,
)
from transformers.utils import CONFIG_NAME, GENERATION_CONFIG_NAME, logging
from transformers.utils.generic import ModelOutput
logger = logging.get_logger(__name__)
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TFAutoModel,
TFAutoModelForSequenceClassification,
TFSharedEmbeddings,
)
from transformers.generation import (
TFBeamSampleDecoderOnlyOutput,
TFBeamSampleEncoderDecoderOutput,
TFBeamSearchDecoderOnlyOutput,
TFBeamSearchEncoderDecoderOutput,
TFGreedySearchDecoderOnlyOutput,
TFGreedySearchEncoderDecoderOutput,
TFSampleDecoderOnlyOutput,
TFSampleEncoderDecoderOutput,
)
tf.config.experimental.enable_tensor_float_32_execution(False)
if _tf_gpu_memory_limit is not None:
gpus = tf.config.list_physical_devices("GPU")
for gpu in gpus:
# Restrict TensorFlow to only allocate x GB of memory on the GPUs
try:
tf.config.set_logical_device_configuration(
gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
)
logical_gpus = tf.config.list_logical_devices("GPU")
print("Logical GPUs", logical_gpus)
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)
if is_torch_available():
import torch
def _config_zero_init(config):
configs_no_init = copy.deepcopy(config)
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key:
setattr(configs_no_init, key, 0.0)
return configs_no_init
@require_tf
class TFModelTesterMixin:
model_tester = None
all_model_classes = ()
all_generative_model_classes = ()
test_mismatched_shapes = True
test_resize_embeddings = True
test_head_masking = True
is_encoder_decoder = False
has_attentions = True
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
inputs_dict = copy.deepcopy(inputs_dict)
if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict = {
k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
if isinstance(v, tf.Tensor) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in [
*get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING),
*get_values(TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING),
]:
inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in [
*get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
*get_values(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
]:
inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in get_values(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING):
inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in [
*get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
*get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING),
*get_values(TF_MODEL_FOR_MASKED_LM_MAPPING),
*get_values(TF_MODEL_FOR_PRETRAINING_MAPPING),
*get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
*get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING),
] and "labels" in dict(inspect.signature(model_class.call).parameters):
inputs_dict["labels"] = tf.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32
)
elif model_class in get_values(TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING):
num_patches = self.model_tester.image_size // self.model_tester.patch_size
inputs_dict["bool_masked_pos"] = tf.zeros(
(self.model_tester.batch_size, num_patches**2), dtype=tf.int32
)
elif model_class in get_values(TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING):
batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
inputs_dict["labels"] = tf.zeros((self.model_tester.batch_size, height, width), dtype=tf.int32)
elif model_class.__name__.endswith("ForCTC"):
# When we have enough CTC models for an AutoClass, we should use their mapping instead of name checks
inputs_dict["labels"] = tf.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32
)
return inputs_dict
def test_initialization(self):
pass
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=False)
# the config file (and the generation config file, if it can generate) should be saved
self.assertTrue(os.path.exists(os.path.join(tmpdirname, CONFIG_NAME)))
self.assertEqual(
model.can_generate(), os.path.exists(os.path.join(tmpdirname, GENERATION_CONFIG_NAME))
)
model = model_class.from_pretrained(tmpdirname)
after_outputs = model(self._prepare_for_class(inputs_dict, model_class))
self.assert_outputs_same(after_outputs, outputs)
def test_save_load_config(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
model_config = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(model_config)
new_model = model_class.from_config(model.get_config())
# make sure it also accepts a normal config
_ = model_class.from_config(model.config)
_ = new_model(self._prepare_for_class(inputs_dict, model_class)) # Build model
new_model.set_weights(model.get_weights())
after_outputs = new_model(self._prepare_for_class(inputs_dict, model_class))
self.assert_outputs_same(after_outputs, outputs)
@slow
def test_saved_model_creation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = False
config.output_attentions = False
if hasattr(config, "use_cache"):
config.use_cache = False
model_class = self.all_model_classes[0]
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
model(class_inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=True)
saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
self.assertTrue(os.path.exists(saved_model_dir))
def test_prepare_serving_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = self.has_attentions
for model_class in self.all_model_classes:
model = model_class(config)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(inputs)
serving_outputs = model.serving_output(outputs)
for k, v in serving_outputs.items():
# Check that we have one of three possible outputs: None, tuple of tensors or a tensor
if isinstance(v, tuple):
self.assertTrue(all(isinstance(elem, tf.Tensor) for elem in v))
elif v is not None:
self.assertIsInstance(v, tf.Tensor)
else:
self.assertIsNone(v)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
if model.config.is_encoder_decoder:
expected_arg_names = [
"input_ids",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
]
expected_arg_names.extend(["decoder_position_ids"] if "decoder_position_ids" in arg_names else [])
expected_arg_names.extend(
["head_mask", "decoder_head_mask"] if "head_mask" and "decoder_head_mask" in arg_names else []
)
expected_arg_names.extend(
["cross_attn_head_mask", "encoder_outputs"]
if "cross_attn_head_mask" in arg_names
else ["encoder_outputs"]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
else:
expected_arg_names = ["input_ids"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_onnx_compliancy(self):
if not self.test_onnx:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
INTERNAL_OPS = [
"Assert",
"AssignVariableOp",
"EmptyTensorList",
"ReadVariableOp",
"ResourceGather",
"TruncatedNormal",
"VarHandleOp",
"VarIsInitializedOp",
]
onnx_ops = []
with open(os.path.join(".", "utils", "tf_ops", "onnx.json")) as f:
onnx_opsets = json.load(f)["opsets"]
for i in range(1, self.onnx_min_opset + 1):
onnx_ops.extend(onnx_opsets[str(i)])
for model_class in self.all_model_classes:
model_op_names = set()
with tf.Graph().as_default() as g:
model = model_class(config)
model.build()
for op in g.get_operations():
model_op_names.add(op.node_def.op)
model_op_names = sorted(model_op_names)
incompatible_ops = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(op)
self.assertEqual(len(incompatible_ops), 0, incompatible_ops)
@require_tf2onnx
@slow
def test_onnx_runtime_optimize(self):
if not self.test_onnx:
return
import onnxruntime
import tf2onnx
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:2]:
model = model_class(config)
model.build()
onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset)
onnxruntime.InferenceSession(onnx_model_proto.SerializeToString())
def test_keras_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
tf_main_layer_classes = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__),)
for module_member_name in dir(module)
if module_member_name.endswith("MainLayer")
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
for module_member in (getattr(module, module_member_name),)
if isinstance(module_member, type)
and tf.keras.layers.Layer in module_member.__bases__
and getattr(module_member, "_keras_serializable", False)
}
for main_layer_class in tf_main_layer_classes:
# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
if "T5" in main_layer_class.__name__:
# Take the same values than in TFT5ModelTester for this shared layer
shared = TFSharedEmbeddings(99, 32, name="shared")
config.use_cache = inputs_dict.pop("use_cache", None)
main_layer = main_layer_class(config, embed_tokens=shared)
else:
main_layer = main_layer_class(config)
symbolic_inputs = {
name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
}
model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
outputs = model(inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
filepath = os.path.join(tmpdirname, "keras_model.h5")
model.save(filepath)
if "T5" in main_layer_class.__name__:
model = tf.keras.models.load_model(
filepath,
custom_objects={
main_layer_class.__name__: main_layer_class,
"TFSharedEmbeddings": TFSharedEmbeddings,
},
)
else:
model = tf.keras.models.load_model(
filepath, custom_objects={main_layer_class.__name__: main_layer_class}
)
assert isinstance(model, tf.keras.Model)
after_outputs = model(inputs_dict)
self.assert_outputs_same(after_outputs, outputs)
def assert_outputs_same(self, after_outputs, outputs):
# Make sure we don't have nans
if isinstance(after_outputs, tf.Tensor):
out_1 = after_outputs.numpy()
elif isinstance(after_outputs, dict):
out_1 = after_outputs[list(after_outputs.keys())[0]].numpy()
else:
out_1 = after_outputs[0].numpy()
out_2 = outputs[0].numpy()
self.assertEqual(out_1.shape, out_2.shape)
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
# Don't copy this method to model specific test file!
# TODO: remove this method once the issues are all fixed!
def _make_attention_mask_non_null(self, inputs_dict):
"""Make sure no sequence has all zeros as attention mask"""
for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]:
if k in inputs_dict:
attention_mask = inputs_dict[k]
# Make sure no all 0s attention masks - to avoid failure at this moment.
# Put `1` at the beginning of sequences to make it still work when combining causal attention masks.
# TODO: remove this line once a fix regarding large negative values for attention mask is done.
attention_mask = tf.concat(
[tf.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], axis=-1
)
# Here we make the first sequence with all 0s as attention mask.
# Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative
# values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks.
# TODO: enable this block once the large negative values thing is cleaned up.
# (see https://github.com/huggingface/transformers/issues/14859)
# attention_mask = tf.concat(
# [
# tf.zeros_like(attention_mask[:1], dtype=tf.int32),
# tf.cast(attention_mask[1:], dtype=tf.int32)
# ],
# axis=0
# )
inputs_dict[k] = attention_mask
# Don't copy this method to model specific test file!
# TODO: remove this method once the issues are all fixed!
def _postprocessing_to_ignore_test_cases(self, tf_outputs, pt_outputs, model_class):
"""For temporarily ignoring some failed test cases (issues to be fixed)"""
tf_keys = {k for k, v in tf_outputs.items() if v is not None}
pt_keys = {k for k, v in pt_outputs.items() if v is not None}
key_differences = tf_keys.symmetric_difference(pt_keys)
if model_class.__name__ in [
"TFFlaubertWithLMHeadModel",
"TFFunnelForPreTraining",
"TFElectraForPreTraining",
"TFXLMWithLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
for k in key_differences:
if k in ["loss", "losses"]:
tf_keys.discard(k)
pt_keys.discard(k)
elif model_class.__name__.startswith("TFGPT2"):
# `TFGPT2` has `past_key_values` as a tensor while `GPT2` has it as a tuple.
tf_keys.discard("past_key_values")
pt_keys.discard("past_key_values")
# create new outputs from the remaining fields
new_tf_outputs = type(tf_outputs)(**{k: tf_outputs[k] for k in tf_keys})
new_pt_outputs = type(pt_outputs)(**{k: pt_outputs[k] for k in pt_keys})
return new_tf_outputs, new_pt_outputs
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
"""Check the outputs from PyTorch and TensorFlow models are close enough. Checks are done in a recursive way.
Args:
model_class: The class of the model that is currently testing. For example, `TFBertModel`,
TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Mainly used for providing more informative
error messages.
name (`str`): The name of the output. For example, `output.hidden_states`, `output.attentions`, etc.
attributes (`Tuple[str]`): The names of the output's element if the output is a tuple/list with each element
being a named field in the output.
"""
self.assertEqual(type(name), str)
if attributes is not None:
self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`")
# Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`).
if isinstance(tf_outputs, ModelOutput):
self.assertTrue(
isinstance(pt_outputs, ModelOutput),
f"{name}: `pt_outputs` should an instance of `ModelOutput` when `tf_outputs` is",
)
# Don't copy this block to model specific test file!
# TODO: remove this method and this line after issues are fixed
tf_outputs, pt_outputs = self._postprocessing_to_ignore_test_cases(tf_outputs, pt_outputs, model_class)
tf_keys = [k for k, v in tf_outputs.items() if v is not None]
pt_keys = [k for k, v in pt_outputs.items() if v is not None]
self.assertEqual(tf_keys, pt_keys, f"{name}: Output keys differ between TF and PyTorch")
# convert to the case of `tuple`
# appending each key to the current (string) `names`
attributes = tuple([f"{name}.{k}" for k in tf_keys])
self.check_pt_tf_outputs(
tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes
)
# Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.)
elif type(tf_outputs) in [tuple, list]:
self.assertEqual(type(tf_outputs), type(pt_outputs), f"{name}: Output types differ between TF and PyTorch")
self.assertEqual(len(tf_outputs), len(pt_outputs), f"{name}: Output lengths differ between TF and PyTorch")
if attributes is not None:
# case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`)
self.assertEqual(
len(attributes),
len(tf_outputs),
f"{name}: The tuple `names` should have the same length as `tf_outputs`",
)
else:
# case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `names`
attributes = tuple([f"{name}_{idx}" for idx in range(len(tf_outputs))])
for tf_output, pt_output, attr in zip(tf_outputs, pt_outputs, attributes):
self.check_pt_tf_outputs(tf_output, pt_output, model_class, tol=tol, name=attr)
elif isinstance(tf_outputs, tf.Tensor):
self.assertTrue(
isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `tf_outputs` is"
)
tf_outputs = tf_outputs.numpy()
pt_outputs = pt_outputs.detach().to("cpu").numpy()
self.assertEqual(
tf_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between TF and PyTorch"
)
# deal with NumPy's scalars to make replacing nan values by 0 work.
if np.isscalar(tf_outputs):
tf_outputs = np.array([tf_outputs])
pt_outputs = np.array([pt_outputs])
tf_nans = np.isnan(tf_outputs)
pt_nans = np.isnan(pt_outputs)
pt_outputs[tf_nans] = 0
tf_outputs[tf_nans] = 0
pt_outputs[pt_nans] = 0
tf_outputs[pt_nans] = 0
max_diff = np.amax(np.abs(tf_outputs - pt_outputs))
self.assertLessEqual(max_diff, tol, f"{name}: Difference between torch and tf is {max_diff} (>= {tol}).")
else:
raise ValueError(
"`tf_outputs` should be an instance of `tf.Tensor`, a `tuple`, or an instance of `tf.Tensor`. Got"
f" {type(tf_outputs)} instead."
)
def prepare_pt_inputs_from_tf_inputs(self, tf_inputs_dict):
pt_inputs_dict = {}
for name, key in tf_inputs_dict.items():
if type(key) == bool:
pt_inputs_dict[name] = key
elif name == "input_values":
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
elif name == "pixel_values":
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
elif name == "input_features":
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
# other general float inputs
elif tf_inputs_dict[name].dtype.is_floating:
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
else:
pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
return pt_inputs_dict
def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict):
pt_inputs_dict = self.prepare_pt_inputs_from_tf_inputs(tf_inputs_dict)
# send pytorch inputs to the correct device
pt_inputs_dict = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items()
}
# send pytorch model to the correct device
pt_model.to(torch_device)
# Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences
pt_model.eval()
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs_dict)
tf_outputs = tf_model(tf_inputs_dict)
# tf models returned loss is usually a tensor rather than a scalar.
# (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`)
# Change it here to a scalar to match PyTorch models' loss
tf_loss = getattr(tf_outputs, "loss", None)
if tf_loss is not None:
tf_outputs.loss = tf.math.reduce_mean(tf_loss)
self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(tf_model))
@is_pt_tf_cross_test
def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
import transformers
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency
# of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`.
# TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it.
self._make_attention_mask_non_null(inputs_dict)
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
tf_model = model_class(config)
pt_model = pt_model_class(config)
tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
tf_inputs_dict_with_labels = self._prepare_for_class(
inputs_dict,
model_class,
# Not all models accept "labels" in the forward pass (yet :) )
return_labels=True if "labels" in inspect.signature(model_class.call).parameters.keys() else False,
)
# For some models (e.g. base models), there is no label returned.
# Set the input dict to `None` to avoid check outputs twice for the same input dicts.
if not set(tf_inputs_dict_with_labels.keys()).symmetric_difference(tf_inputs_dict.keys()):
tf_inputs_dict_with_labels = None
# Check we can load pt model in tf and vice-versa with model => model functions
tf_model = transformers.load_pytorch_model_in_tf2_model(
tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
)
pt_model = transformers.load_tf2_model_in_pytorch_model(
pt_model, tf_model, allow_missing_keys=allow_missing_keys
)
# Original test: check without `labels`
self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
# check with `labels`
if tf_inputs_dict_with_labels:
self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict_with_labels)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with tempfile.TemporaryDirectory() as tmpdirname:
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
)
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
)
# Original test: check without `labels`
self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
# check with `labels`
if tf_inputs_dict_with_labels:
self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict_with_labels)
@slow
def test_compile_tf_model(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:2]:
# Prepare our model
model = model_class(config)
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
functional_inputs = {
key: tf.keras.Input(shape=val.shape[1:], dtype=val.dtype, name=key)
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
outputs_dict = model(functional_inputs)
hidden_states = outputs_dict[0]
# Compile extended model
functional_model = tf.keras.Model(inputs=functional_inputs, outputs=hidden_states)
model_out = functional_model.predict(model.dummy_inputs) # Check we can pass inputs with the Keras API
self.assertTrue(model_out is not None)
with tempfile.TemporaryDirectory() as tmpdirname:
functional_model.save(tmpdirname) # Ensure we can save/export the whole functional model
def test_keyword_and_dict_args(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs_dict = model(inputs)
inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
outputs_keywords = model(**inputs_keywords)
output_dict = outputs_dict[0].numpy()
output_keywords = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
def test_attention_outputs(self):
if not self.has_attentions:
self.skipTest(reason="Model does not output attentions")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", self.model_tester.seq_length)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
decoder_key_length = getattr(self.model_tester, "key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
def check_decoder_attentions_output(outputs):
out_len = len(outputs)
self.assertEqual(min(out_len % 2, out_len % 5), 0) # differentiation due to newly added cross_attentions
decoder_attentions = outputs.decoder_attentions
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
def check_encoder_attentions_output(outputs):
attentions = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
config.output_hidden_states = False
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
out_len = len(outputs)
self.assertEqual(config.output_hidden_states, False)
check_encoder_attentions_output(outputs)
if self.is_encoder_decoder:
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(config.output_hidden_states, False)
check_decoder_attentions_output(outputs)
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(config.output_hidden_states, False)
check_encoder_attentions_output(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
config.output_hidden_states = True
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
self.assertEqual(model.config.output_hidden_states, True)
check_encoder_attentions_output(outputs)
def test_headmasking(self):
if not self.test_head_masking:
return
random.Random().seed(42)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
random.Random().seed()
inputs_dict["output_attentions"] = True
config.output_hidden_states = True
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
# Prepare head_mask
def prepare_layer_head_mask(i, attention_heads, num_hidden_layers):
if i == 0:
return tf.concat(
(tf.zeros(1, dtype=tf.float32), tf.ones(attention_heads - 1, dtype=tf.float32)), 0
)
elif i == num_hidden_layers - 1:
return tf.concat(
(tf.zeros(attention_heads - 1, dtype=tf.float32), tf.ones(1, dtype=tf.float32)), 0
)
else:
return tf.ones(attention_heads, dtype=tf.float32)
head_mask = tf.stack(
[
prepare_layer_head_mask(i, config.num_attention_heads, config.num_hidden_layers)
for i in range(config.num_hidden_layers)
],
0,
)
inputs = self._prepare_for_class(inputs_dict, model_class).copy()
inputs["head_mask"] = head_mask
if model.config.is_encoder_decoder:
signature = inspect.signature(model.call)
arg_names = [*signature.parameters.keys()]
if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model
inputs["decoder_head_mask"] = head_mask
if "cross_attn_head_mask" in arg_names:
inputs["cross_attn_head_mask"] = head_mask
outputs = model(**inputs, return_dict=True)
def check_attentions_validity(attentions):
# Remove Nan
for t in attentions:
self.assertLess(
(tf.math.reduce_sum(tf.cast(tf.math.is_nan(t), tf.float32))).numpy(), (tf.size(t) / 4).numpy()
) # Check we don't have more than 25% nans (arbitrary)
attentions = [
tf.where(tf.math.is_nan(t), 0.0, t) for t in attentions
] # remove them (the test is less complete)
self.assertAlmostEqual(tf.math.reduce_sum(attentions[0][..., 0, :, :]).numpy(), 0.0)
self.assertNotEqual(tf.math.reduce_sum(attentions[0][..., -1, :, :]).numpy(), 0.0)
if len(attentions) > 2: # encoder-decodere models have only 2 layers in each modules
self.assertNotEqual(tf.math.reduce_sum(attentions[1][..., 0, :, :]).numpy(), 0.0)
self.assertAlmostEqual(tf.math.reduce_sum(attentions[-1][..., -2, :, :]).numpy(), 0.0)
self.assertNotEqual(tf.math.reduce_sum(attentions[-1][..., -1, :, :]).numpy(), 0.0)
if model.config.is_encoder_decoder:
check_attentions_validity(outputs.encoder_attentions)
check_attentions_validity(outputs.decoder_attentions)
if "cross_attn_head_mask" in arg_names:
check_attentions_validity(outputs.cross_attentions)
else:
check_attentions_validity(outputs.attentions)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_hidden_states_output(config, inputs_dict, model_class):
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
if model.config.is_encoder_decoder:
encoder_hidden_states = outputs.encoder_hidden_states
decoder_hidden_states = outputs.decoder_hidden_states
self.assertEqual(config.output_attentions, False)
self.assertEqual(len(encoder_hidden_states), expected_num_layers)
self.assertListEqual(
list(encoder_hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size],
)
self.assertEqual(len(decoder_hidden_states), expected_num_layers)
self.assertListEqual(
list(decoder_hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size],
)
else:
hidden_states = outputs.hidden_states
self.assertEqual(config.output_attentions, False)
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size],
)
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(config, inputs_dict, model_class)
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(config, inputs_dict, model_class)
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
text_in_text_out_models = (
get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING)
+ get_values(TF_MODEL_FOR_MASKED_LM_MAPPING)
+ get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING)
)
speech_in_text_out_models = get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING)
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), tf.keras.layers.Layer)
legacy_text_in_text_out = model.get_lm_head() is not None
if model_class in text_in_text_out_models or legacy_text_in_text_out:
out_embeddings = model.get_output_embeddings()
self.assertIsInstance(out_embeddings, tf.keras.layers.Layer)
bias = model.get_bias()
if bias is not None:
self.assertIsInstance(bias, dict)
for _, v in bias.items():
self.assertIsInstance(v, tf.Variable)
elif model_class in speech_in_text_out_models:
out_embeddings = model.get_output_embeddings()
self.assertIsInstance(out_embeddings, tf.keras.layers.Layer)
bias = model.get_bias()
self.assertIsNone(bias)
else:
out_embeddings = model.get_output_embeddings()
assert out_embeddings is None
bias = model.get_bias()
self.assertIsNone(bias)
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
first, second = (
model(self._prepare_for_class(inputs_dict, model_class), training=False)[0],
model(self._prepare_for_class(inputs_dict, model_class), training=False)[0],
)
out_1 = first.numpy()
out_2 = second.numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(tuple_object, dict_object)),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
if self.has_attentions:
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
# Not all models accept "labels" in the forward pass (yet :) )
if "labels" in inspect.signature(model.call).parameters.keys():
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
if self.has_attentions:
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(
model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
)
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
inputs = copy.deepcopy(inputs_dict)
if not self.is_encoder_decoder:
input_ids = inputs["input_ids"]
del inputs["input_ids"]
else:
encoder_input_ids = inputs["input_ids"]
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
del inputs["input_ids"]
inputs.pop("decoder_input_ids", None)
if not self.is_encoder_decoder:
inputs["inputs_embeds"] = model.get_input_embeddings()(input_ids)
else:
inputs["inputs_embeds"] = model.get_input_embeddings()(encoder_input_ids)
inputs["decoder_inputs_embeds"] = model.get_input_embeddings()(decoder_input_ids)
inputs = self._prepare_for_class(inputs, model_class)
model(inputs)
def test_numpy_arrays_inputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def prepare_numpy_arrays(inputs_dict):
inputs_np_dict = {}
for k, v in inputs_dict.items():
if tf.is_tensor(v):
inputs_np_dict[k] = v.numpy()
else:
inputs_np_dict[k] = np.array(k)
return inputs_np_dict
for model_class in self.all_model_classes:
model = model_class(config)
inputs = self._prepare_for_class(inputs_dict, model_class)
inputs_np = prepare_numpy_arrays(inputs)
output_for_dict_input = model(inputs_np)
output_for_kw_input = model(**inputs_np)
self.assert_outputs_same(output_for_dict_input, output_for_kw_input)
def test_valid_input_signature_and_dummies(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
call_args = inspect.signature(model.call).parameters
for key in model.input_signature:
self.assertIn(key, call_args)
for key in model.dummy_inputs:
self.assertIn(key, call_args)
def test_resize_token_embeddings(self):
# TODO (joao): after the embeddings refactor is complete, rework this test so as to rely exclusively on
# tf.keras.layers.Embedding
if not self.test_resize_embeddings:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(model, embedding_layer):
if isinstance(embedding_layer, tf.keras.layers.Embedding):
# builds the embeddings layer
model.build()
return embedding_layer.embeddings
else:
return model._get_word_embedding_weight(embedding_layer)
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10, None]:
# build the embeddings
model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config`
old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
old_bias = model.get_bias()
old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
# reshape the embeddings
model.resize_token_embeddings(size)
new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
new_bias = model.get_bias()
new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
# check that the resized embeddings size matches the desired size.
assert_size = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0], assert_size)
# check that weights remain the same after resizing
models_equal = True
for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()):
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
models_equal = False
self.assertTrue(models_equal)
if old_bias is not None and new_bias is not None:
for old_weight, new_weight in zip(old_bias.values(), new_bias.values()):
self.assertEqual(new_weight.shape[-1], assert_size)
models_equal = True
for p1, p2 in zip(tf.squeeze(old_weight), tf.squeeze(new_weight)):
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
models_equal = False
self.assertTrue(models_equal)
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0], assert_size)
self.assertEqual(new_output_embeddings.shape[1], old_output_embeddings.shape[1])
models_equal = True
for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()):
if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
models_equal = False
self.assertTrue(models_equal)
# TODO (Joao): this test is not slow, but it's tagged as such to keep track of failures on the scheduled CI runs,
# while passing push CI. Fix the underlying issues and remove the tag.
@slow
def test_save_load_after_resize_token_embeddings(self):
if not self.test_resize_embeddings:
return
config, original_inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# create a model with resized (expended) embeddings
new_tokens_size = 10
old_total_size = config.vocab_size
new_total_size = old_total_size + new_tokens_size
model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config`
model.build()
model.resize_token_embeddings(new_total_size)
# fetch the output for an input exclusively made of new members of the vocabulary
inputs_dict = copy.deepcopy(original_inputs_dict)
ids_feat_name = None
if "input_ids" in inputs_dict:
ids_feat_name = "input_ids"
elif "decoder_input_ids" in inputs_dict:
ids_feat_name = "decoder_input_ids"
else:
assert False, "No input ids feature found in the inputs dict"
new_vocab_input_ids = ids_tensor(inputs_dict[ids_feat_name].shape, new_tokens_size)
new_vocab_input_ids += old_total_size
inputs_dict[ids_feat_name] = new_vocab_input_ids
if "input_ids" in inputs_dict:
inputs_dict["input_ids"] = new_vocab_input_ids
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"] = new_vocab_input_ids
prepared_inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**prepared_inputs)
# save and load the model
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=False)
model = model_class.from_pretrained(tmpdirname)
restored_model_outputs = model(**prepared_inputs)
# check that the output for the restored model is the same
self.assert_outputs_same(restored_model_outputs, outputs)
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="This test always passes on CPU.",
)
def test_embeddings_out_of_bounds_raise_exception(self):
# TF embeddings layers don't raise an exception when an index is out of bounds on GPU, so we manually raise it.
# This test should only fail on GPU for models where we haven't added the safety check.
if not self.test_resize_embeddings:
return
config, original_inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config=config)
inputs_dict = copy.deepcopy(original_inputs_dict)
if "input_ids" in inputs_dict:
inputs_dict["input_ids"] = inputs_dict["input_ids"] * int(1e9)
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"] = inputs_dict["decoder_input_ids"] * int(1e9)
prepared_inputs = self._prepare_for_class(inputs_dict, model_class)
with self.assertRaises(tf.errors.InvalidArgumentError):
model(**prepared_inputs)
def test_lm_head_model_random_no_beam_search_generate(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict.get("input_ids", None)
# iterate over all generative models
for model_class in self.all_generative_model_classes:
model = model_class(config)
if config.bos_token_id is None:
# if bos token id is not defined model needs input_ids
with self.assertRaises(ValueError):
model.generate(do_sample=True, max_length=5)
# num_return_sequences = 1
self._check_generated_ids(model.generate(input_ids, do_sample=True))
elif model_class.__name__ not in ["TFSpeech2TextForConditionalGeneration"]:
# Models with non-text inputs won't work here; num_return_sequences = 1
self._check_generated_ids(model.generate(do_sample=True, max_length=5))
with self.assertRaises(ValueError):
# generating multiple sequences when no beam search generation
# is not allowed as it would always generate the same sequences
model.generate(input_ids, do_sample=False, num_return_sequences=2)
# num_return_sequences > 1, sample
self._check_generated_ids(model.generate(input_ids, do_sample=True, num_return_sequences=2))
# check bad words tokens language generation
# create list of 1-seq bad token and list of 2-seq of bad tokens
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
output_tokens = model.generate(
input_ids, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2
)
# only count generated tokens
generated_ids = output_tokens[:, input_ids.shape[-1] :]
self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))
def test_lm_head_model_no_beam_search_generate_dict_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict.get("input_ids", None)
if input_ids is None:
input_ids = inputs_dict.get("input_features", None)
# iterate over all generative models
for model_class in self.all_generative_model_classes:
model = model_class(config)
output_greedy = model.generate(
input_ids,
do_sample=False,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
output_sample = model.generate(
input_ids,
do_sample=True,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_greedy, TFGreedySearchEncoderDecoderOutput)
self.assertIsInstance(output_sample, TFSampleEncoderDecoderOutput)
else:
self.assertIsInstance(output_greedy, TFGreedySearchDecoderOnlyOutput)
self.assertIsInstance(output_sample, TFSampleDecoderOnlyOutput)
def test_lm_head_model_random_beam_search_generate(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict.get("input_ids", None)
for model_class in self.all_generative_model_classes:
model = model_class(config)
if config.bos_token_id is None:
# if bos token id is not defined model needs input_ids, num_return_sequences = 1
self._check_generated_ids(model.generate(input_ids, do_sample=True, num_beams=2))
else:
# num_return_sequences = 1
self._check_generated_ids(model.generate(do_sample=True, max_length=5, num_beams=2))
with self.assertRaises(ValueError):
# generating more sequences than having beams leads is not possible
model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)
# num_return_sequences > 1, sample
self._check_generated_ids(
model.generate(
input_ids,
do_sample=True,
num_beams=2,
num_return_sequences=2,
)
)
# num_return_sequences > 1, greedy
self._check_generated_ids(model.generate(input_ids, do_sample=False, num_beams=2, num_return_sequences=2))
# check bad words tokens language generation
# create list of 1-seq bad token and list of 2-seq of bad tokens
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
output_tokens = model.generate(
input_ids, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2
)
# only count generated tokens
generated_ids = output_tokens[:, input_ids.shape[-1] :]
self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))
def test_lm_head_model_beam_search_generate_dict_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict.get("input_ids", None)
if input_ids is None:
input_ids = inputs_dict.get("input_features", None)
# iterate over all generative models
for model_class in self.all_generative_model_classes:
model = model_class(config)
output_beam_search = model.generate(
input_ids,
num_beams=2,
do_sample=False,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
output_beam_sample = model.generate(
input_ids,
num_beams=2,
do_sample=True,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
if model.config.is_encoder_decoder:
self.assertIsInstance(output_beam_search, TFBeamSearchEncoderDecoderOutput)
self.assertIsInstance(output_beam_sample, TFBeamSampleEncoderDecoderOutput)
else:
self.assertIsInstance(output_beam_search, TFBeamSearchDecoderOnlyOutput)
self.assertIsInstance(output_beam_sample, TFBeamSampleDecoderOnlyOutput)
def test_loss_computation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
# The number of elements in the loss should be the same as the number of elements in the label
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
added_label_names = sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)
if not added_label_names:
continue # This test is only for models with easily-separable labels
added_label = prepared_for_class[added_label_names[0]]
expected_loss_size = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
possible_input_names = {"input_ids", "pixel_values", "input_features", "input_values"}
input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
model_input = prepared_for_class.pop(input_name)
outputs = model(model_input, **prepared_for_class)
if not isinstance(outputs, ModelOutput) or not hasattr(outputs, "loss"):
continue
loss = outputs.loss
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
# Test that model correctly compute the loss when we mask some positions
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
possible_input_names = {"input_ids", "pixel_values", "input_features", "input_values"}
input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
model_input = prepared_for_class.pop(input_name)
if "labels" in prepared_for_class:
labels = prepared_for_class["labels"].numpy()
if len(labels.shape) > 1 and labels.shape[1] != 1:
labels[0] = -100
prepared_for_class["labels"] = tf.convert_to_tensor(labels)
loss = model(model_input, **prepared_for_class)[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
self.assertTrue(not np.any(np.isnan(loss.numpy())))
# Test that model correctly compute the loss with a dict
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
loss = model(prepared_for_class)[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
# Test that model correctly compute the loss with a tuple
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
# Get keys that were added with the _prepare_for_class function
label_keys = prepared_for_class.keys() - inputs_dict.keys()
signature = inspect.signature(model.call).parameters
signature_names = list(signature.keys())
# Create a dictionary holding the location of the tensors in the tuple
tuple_index_mapping = {0: input_name}
for label_key in label_keys:
label_key_index = signature_names.index(label_key)
tuple_index_mapping[label_key_index] = label_key
sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
# Initialize a list with their default values, update the values and convert to a tuple
list_input = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default)
for index, value in sorted_tuple_index_mapping:
list_input[index] = prepared_for_class[value]
tuple_input = tuple(list_input)
# Send to model
loss = model(tuple_input[:-1])[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
def check_keras_fit_results(self, val_loss1, val_loss2, atol=1e-2, rtol=1e-3):
self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol))
@slow
def test_keras_fit(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
# Test that model correctly compute the loss with kwargs
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
# We also remove "return_loss" as this is covered by the train_step when using fit()
prepared_for_class = {
key: val
for key, val in prepared_for_class.items()
if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "return_loss")
}
if "labels" in prepared_for_class and "decoder_input_ids" in prepared_for_class:
del prepared_for_class["decoder_input_ids"]
accuracy_classes = [
"ForPreTraining",
"ForCausalLM",
"ForMaskedLM",
"ForQuestionAnswering",
"ForMultipleChoice",
"ForSequenceClassification",
"ForTokenClassification",
"ForNextSentencePrediction",
"LMHeadModel",
]
for accuracy_class in accuracy_classes:
if model.__class__.__name__.endswith(accuracy_class):
metrics = [tf.keras.metrics.SparseCategoricalAccuracy()]
break
else:
metrics = []
if hasattr(self.model_tester, "batch_size"):
sample_weight = tf.convert_to_tensor([0.5] * self.model_tester.batch_size, dtype=tf.float32)
else:
sample_weight = None
# Build the model so we can get some constant weights and check outputs
outputs = model(prepared_for_class)
if getattr(outputs, "loss", None) is None:
continue
model_weights = model.get_weights()
# Run eagerly to save some expensive compilation times
model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True, metrics=metrics)
# Make sure the model fits without crashing regardless of where we pass the labels
history1 = model.fit(
prepared_for_class,
validation_data=prepared_for_class,
sample_weight=sample_weight,
steps_per_epoch=1,
validation_steps=1,
shuffle=False,
)
val_loss1 = history1.history["val_loss"][0]
self.assertTrue(not isnan(val_loss1))
accuracy1 = {key: val[0] for key, val in history1.history.items() if key.endswith("accuracy")}
possible_label_cols = {
"labels",
"label",
"label_ids",
"start_positions",
"start_position",
"end_positions",
"end_position",
"next_sentence_label",
}
label_names = possible_label_cols.intersection(set(prepared_for_class))
if len(label_names) == 0:
# The next tests only make sense for models with separate inputs and labels, and do not make
# sense for models that don't clearly distinguish between the two (e.g. CLIP)
return
labels = {key: val for key, val in prepared_for_class.items() if key in label_names}
inputs_minus_labels = {key: val for key, val in prepared_for_class.items() if key not in label_names}
self.assertGreater(len(inputs_minus_labels), 0)
# We reinitialize the model here even though our learning rate was zero
# because BatchNorm updates weights by means other than gradient descent.
model.set_weights(model_weights)
history2 = model.fit(
inputs_minus_labels,
labels,
validation_data=(inputs_minus_labels, labels),
sample_weight=sample_weight,
steps_per_epoch=1,
validation_steps=1,
shuffle=False,
)
val_loss2 = history2.history["val_loss"][0]
self.assertTrue(not isnan(val_loss2))
accuracy2 = {key: val[0] for key, val in history2.history.items() if key.endswith("accuracy")}
self.check_keras_fit_results(val_loss1, val_loss2)
self.assertEqual(history1.history.keys(), history2.history.keys())
for key in history1.history.keys():
if not key.startswith("val_"):
self.assertTrue("val_" + key in history1.history.keys(), "Outputs differ in train/test step!")
if metrics:
self.assertTrue(len(accuracy1) == len(accuracy2) > 0, "Missing metrics!")
def test_int_support(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
prepared_for_class = self._prepare_for_class(
inputs_dict.copy(),
model_class,
return_labels=True if "labels" in inspect.signature(model_class.call).parameters.keys() else False,
)
if not any(
tensor.dtype.is_integer for tensor in prepared_for_class.values() if isinstance(tensor, tf.Tensor)
):
return # No integer inputs means no need for this test
prepared_for_class = {
key: tf.cast(tensor, tf.int64) if isinstance(tensor, tf.Tensor) and tensor.dtype.is_integer else tensor
for key, tensor in prepared_for_class.items()
}
model = model_class(config)
model(**prepared_for_class) # No assertion, we're just checking this doesn't throw an error
int32_prepared_for_class = {
key: tf.cast(tensor, tf.int32) if isinstance(tensor, tf.Tensor) and tensor.dtype.is_integer else tensor
for key, tensor in prepared_for_class.items()
}
model(**int32_prepared_for_class) # No assertion, we're just checking this doesn't throw an error
# After testing that the model accepts all int inputs, confirm that its dummies are int32
for key, tensor in model.dummy_inputs.items():
self.assertTrue(
isinstance(tensor, tf.Tensor) or tf.keras.backend.is_keras_tensor(tensor),
"Dummy inputs should be tf.Tensor!",
)
if tensor.dtype.is_integer:
self.assertTrue(tensor.dtype == tf.int32, "Integer dummy inputs should be tf.int32!")
# Also confirm that the input_signature uses int32
for key, tensor_spec in model.input_signature.items():
if tensor_spec.dtype.is_integer:
self.assertTrue(tensor_spec.dtype == tf.int32, "Input signatures should use tf.int32 for ints!")
def test_generate_with_headmasking(self):
attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_generative_model_classes:
model = model_class(config)
# We want to test only encoder-decoder models
if not config.is_encoder_decoder:
continue
head_masking = {
"head_mask": tf.zeros((config.encoder_layers, config.encoder_attention_heads)),
"decoder_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)),
"cross_attn_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)),
}
signature = inspect.signature(model.call)
if set(head_masking.keys()) < {*signature.parameters.keys()}:
continue
for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
out = model.generate(
inputs_dict["input_ids"],
num_beams=1,
max_length=inputs_dict["input_ids"] + 5,
output_attentions=True,
return_dict_in_generate=True,
**{name: mask},
)
# We check the state of decoder_attentions and cross_attentions just from the last step
attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([tf.reduce_sum(w).numpy() for w in attn_weights]), 0.0)
def test_load_with_mismatched_shapes(self):
if not self.test_mismatched_shapes:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if model_class not in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
continue
with self.subTest(msg=f"Testing {model_class}"):
with tempfile.TemporaryDirectory() as tmp_dir:
model = model_class(config)
inputs = self._prepare_for_class(inputs_dict, model_class)
_ = model(**inputs)
model.save_pretrained(tmp_dir)
# Fails when we don't set ignore_mismatched_sizes=True
with self.assertRaises(ValueError):
new_model = TFAutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
with self.assertRaises(ValueError):
new_model_without_prefix = TFAutoModel.from_pretrained(tmp_dir, vocab_size=10)
logger = logging.get_logger("transformers.modeling_tf_utils")
with CaptureLogger(logger) as cl:
new_model = TFAutoModelForSequenceClassification.from_pretrained(
tmp_dir, num_labels=42, ignore_mismatched_sizes=True
)
self.assertIn("the shapes did not match", cl.out)
logits = new_model(**inputs).logits
self.assertEqual(logits.shape[1], 42)
with CaptureLogger(logger) as cl:
new_model_without_prefix = TFAutoModel.from_pretrained(
tmp_dir, vocab_size=10, ignore_mismatched_sizes=True
)
self.assertIn("the shapes did not match", cl.out)
# Although Tf models always have a prefix pointing to `MainLayer`,
# we still add this "without prefix" test to keep a consistency between tf and pt tests.
input_ids = ids_tensor((2, 8), 10)
if self.is_encoder_decoder:
new_model_without_prefix(input_ids, decoder_input_ids=input_ids)
else:
new_model_without_prefix(input_ids)
def test_model_main_input_name(self):
for model_class in self.all_model_classes:
model_signature = inspect.signature(getattr(model_class, "call"))
# The main input is the name of the argument after `self`
observed_main_input_name = list(model_signature.parameters.keys())[1]
self.assertEqual(model_class.main_input_name, observed_main_input_name)
def test_dataset_conversion(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=False)
if "labels" in tf_inputs_dict:
return # This is some kinda funky decoder model that needs labels in its forward pass
tf_inputs_dict = {
key: val
for key, val in tf_inputs_dict.items()
if "head_mask" not in key and isinstance(val, tf.Tensor)
}
tf_inputs_dict["extra_unwanted_column"] = list(tf_inputs_dict.values())[0] # Use a random other tensor
input_dataset = Dataset.from_dict(tf_inputs_dict)
tf_dataset = model.prepare_tf_dataset(
input_dataset, batch_size=len(input_dataset), drop_remainder=False, shuffle=False
)
test_batch = next(iter(tf_dataset))
if isinstance(test_batch, tf.Tensor):
self.assertEqual(len(test_batch), len(input_dataset)) # Assert we didn't lose any data
elif isinstance(test_batch, dict):
# Assert we discarded the unwanted extra column but kept everything else
self.assertEqual(len(test_batch), len(input_dataset.features) - 1)
self.assertNotIn("extra_unwanted_column", test_batch)
for tensor in test_batch.values():
self.assertTrue(isinstance(tensor, tf.Tensor))
self.assertEqual(len(tensor), len(input_dataset)) # Assert we didn't lose any data
model(test_batch, training=False)
if "labels" in inspect.signature(model_class.call).parameters.keys():
tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
if "labels" not in tf_inputs_dict:
return # This model isn't giving us labels after all, don't try training with it
tf_inputs_dict = {key: val for key, val in tf_inputs_dict.items() if "head_mask" not in key}
tf_inputs_dict["extra_unwanted_column"] = list(tf_inputs_dict.values())[0] # Use a random other tensor
input_dataset = Dataset.from_dict(tf_inputs_dict)
tf_dataset = model.prepare_tf_dataset(
input_dataset, batch_size=len(input_dataset), drop_remainder=False, shuffle=False
)
test_batch, test_batch_labels = next(iter(tf_dataset))
self.assertGreater(len(test_batch_labels), 0) # Assert the labels are present
feature_columns = 1 if isinstance(test_batch, tf.Tensor) else len(test_batch)
label_columns = 1 if isinstance(test_batch_labels, tf.Tensor) else len(test_batch_labels)
# Assert we discarded the unwanted extra column but kept everything else
self.assertEqual(feature_columns + label_columns, len(input_dataset.features) - 1)
if isinstance(test_batch, dict):
self.assertNotIn("extra_unwanted_column", test_batch)
if isinstance(test_batch_labels, dict):
self.assertNotIn("extra_unwanted_column", test_batch_labels)
model.compile(optimizer="sgd", run_eagerly=True)
model.train_on_batch(test_batch, test_batch_labels)
def _test_xla_generate(self, **generate_kwargs):
def _generate_and_check_results(model, inputs_dict):
if "input_ids" in inputs_dict:
inputs = inputs_dict["input_ids"]
# make sure there are no pad tokens in prompt, which may trigger unwanted behavior
if model.generation_config.pad_token_id is not None:
if config.pad_token_id == 0:
new_pad_token = model.generation_config.pad_token_id + 1
else:
new_pad_token = model.generation_config.pad_token_id - 1
else:
new_pad_token = None
inputs = tf.where(inputs != model.generation_config.pad_token_id, inputs, new_pad_token)
elif "input_features" in inputs_dict:
inputs = inputs_dict["input_features"]
else:
raise ValueError("No valid generate input found in inputs_dict")
generated = model.generate(inputs, **generate_kwargs).numpy()
generate_xla = tf.function(model.generate, jit_compile=True)
generated_xla = generate_xla(inputs, **generate_kwargs).numpy()
# Due to numerical instability, let's fail the test only if there are more than 10% of input sequences give
# different outputs between XLA and non-XLA versions. If there are less than 10 examples, let's be strict
# and not allow any difference.
diff = [[], []]
for _generated, _generated_xla in zip(generated.tolist(), generated_xla.tolist()):
if _generated != _generated_xla:
diff[0].append(_generated)
diff[1].append(_generated_xla)
ratio = len(diff[0]) / len(generated)
if ratio > 0.1 or (len(diff[0]) > 0 and len(generated) < 10):
self.assertListEqual(diff[0], diff[1])
for model_class in self.all_generative_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.eos_token_id = None # Generate until max length
config.do_sample = False
# fix config for models with additional sequence-length limiting settings
for var_name in ["max_position_embeddings", "max_target_positions"]:
attr = getattr(config, var_name, None)
if attr is not None and attr < generate_kwargs["max_new_tokens"]:
try:
setattr(config, var_name, generate_kwargs["max_new_tokens"])
except NotImplementedError:
# xlnet will raise an exception when trying to set
# max_position_embeddings.
pass
model = model_class(config)
if model.supports_xla_generation:
_generate_and_check_results(model, inputs_dict)
else:
with self.assertRaises(ValueError):
_generate_and_check_results(model, inputs_dict)
def test_xla_generate_fast(self):
"""
Basic quick test for generate-compatible classes that confirms that XLA-generated tokens are the same as their
non XLA counterparts.
Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception
"""
self._test_xla_generate(num_beams=1, num_return_sequences=1, max_new_tokens=3)
@slow
def test_xla_generate_contrastive(self):
"""
Slow and challenging version of `test_xla_generate_fast` for contrastive search -- contrastive search directly
manipulates the model cache and other outputs, and this test ensures that they are in a valid format that is
also supported by XLA.
Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception
"""
self._test_xla_generate(num_beams=1, num_return_sequences=1, max_new_tokens=16, penalty_alpha=0.5, top_k=4)
@slow
def test_xla_generate_slow(self):
"""
Slow and challenging version of `test_xla_generate_fast` -- this test asks for several long sequences using
beam search, with and without XLA. The two outputs should match, and a failure in this test indicates that the
model may need further analysis if it is to be used for XLA generation.
Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception
"""
self._test_xla_generate(num_beams=8, num_return_sequences=2, max_new_tokens=128)
def _generate_random_bad_tokens(self, num_bad_tokens, model):
# special tokens cannot be bad tokens
special_tokens = []
if model.config.bos_token_id is not None:
special_tokens.append(model.config.bos_token_id)
if model.config.pad_token_id is not None:
special_tokens.append(model.config.pad_token_id)
if model.config.eos_token_id is not None:
special_tokens.append(model.config.eos_token_id)
# create random bad tokens that are not special tokens
bad_tokens = []
while len(bad_tokens) < num_bad_tokens:
token = tf.squeeze(ids_tensor((1, 1), self.model_tester.vocab_size), 0).numpy()[0]
if token not in special_tokens:
bad_tokens.append(token)
return bad_tokens
def _check_generated_ids(self, output_ids):
for token_id in output_ids[0].numpy().tolist():
self.assertGreaterEqual(token_id, 0)
self.assertLess(token_id, self.model_tester.vocab_size)
def _check_match_tokens(self, generated_ids, bad_words_ids):
# for all bad word tokens
for bad_word_ids in bad_words_ids:
# for all slices in batch
for generated_ids_slice in generated_ids:
# for all word idx
for i in range(len(bad_word_ids), len(generated_ids_slice)):
# if tokens match
if generated_ids_slice[i - len(bad_word_ids) : i] == bad_word_ids:
return True
return False
def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
output = tf.constant(values, shape=shape, dtype=dtype if dtype is not None else tf.int32)
return output
def random_attention_mask(shape, rng=None, name=None, dtype=None):
attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None, dtype=dtype)
# make sure that at least one token is attended to for each batch
attn_mask = tf.concat([attn_mask[:, :-1], tf.ones_like(attn_mask[:, -1:], dtype=dtype)], axis=-1)
return attn_mask
def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.random() * scale)
return tf.reshape(tf.constant(values, dtype=dtype if dtype is not None else tf.float32), shape=shape)
| 91,082 | 47.655449 | 125 | py |
transformers | transformers-main/tests/test_image_processing_utils.py | # coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
SAMPLE_IMAGE_PROCESSING_CONFIG_DIR = get_tests_dir("fixtures")
class ImageProcessorUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):
# A mock response for an HTTP head request to emulate server down
response_mock = mock.Mock()
response_mock.status_code = 500
response_mock.headers = {}
response_mock.raise_for_status.side_effect = HTTPError
response_mock.json.return_value = {}
# Download this model to make sure it's in the cache.
_ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=response_mock) as mock_head:
_ = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def test_legacy_load_from_url(self):
# This test is for deprecated behavior and can be removed in v5
_ = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"
)
def test_image_processor_from_pretrained_subfolder(self):
with self.assertRaises(OSError):
# config is in subfolder, the following should not work without specifying the subfolder
_ = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
config = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor"
)
self.assertIsNotNone(config)
@is_staging_test
class ImageProcessorPushToHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def test_push_to_hub(self):
image_processor = ViTImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
new_image_processor = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
tmp_dir, repo_id="test-image-processor", push_to_hub=True, use_auth_token=self._token
)
new_image_processor = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
def test_push_to_hub_in_organization(self):
image_processor = ViTImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
new_image_processor = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
tmp_dir, repo_id="valid_org/test-image-processor-org", push_to_hub=True, use_auth_token=self._token
)
new_image_processor = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(v, getattr(new_image_processor, k))
def test_push_to_hub_dynamic_image_processor(self):
CustomImageProcessor.register_for_auto_class()
image_processor = CustomImageProcessor.from_pretrained(SAMPLE_IMAGE_PROCESSING_CONFIG_DIR)
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map,
{"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"},
)
new_image_processor = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=True
)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")
| 6,466 | 40.722581 | 131 | py |
transformers | transformers-main/tests/trainer/test_trainer.py | # coding=utf-8
# Copyright 2018 the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import dataclasses
import gc
import json
import math
import os
import random
import re
import subprocess
import sys
import tempfile
import time
import unittest
from itertools import product
from pathlib import Path
from unittest.mock import Mock, patch
import numpy as np
from huggingface_hub import HfFolder, Repository, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import (
AutoTokenizer,
IntervalStrategy,
PretrainedConfig,
TrainingArguments,
is_torch_available,
logging,
)
from transformers.hyperparameter_search import ALL_HYPERPARAMETER_SEARCH_BACKENDS
from transformers.testing_utils import (
ENDPOINT_STAGING,
TOKEN,
USER,
CaptureLogger,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_tests_dir,
is_staging_test,
require_accelerate,
require_intel_extension_for_pytorch,
require_optuna,
require_ray,
require_safetensors,
require_sentencepiece,
require_sigopt,
require_tokenizers,
require_torch,
require_torch_bf16_cpu,
require_torch_bf16_gpu,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
require_torch_tensorrt_fx,
require_torch_tf32,
require_torch_up_to_2_gpus,
require_torchdynamo,
require_wandb,
slow,
)
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, HPSearchBackend
from transformers.training_args import OptimizerNames
from transformers.utils import (
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
is_apex_available,
is_bitsandbytes_available,
is_safetensors_available,
is_torchdistx_available,
)
from transformers.utils.hp_naming import TrialShortNamer
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import IterableDataset
import transformers.optimization
from transformers import (
AutoModelForSequenceClassification,
EarlyStoppingCallback,
GlueDataset,
GlueDataTrainingArguments,
GPT2Config,
GPT2LMHeadModel,
LineByLineTextDataset,
PreTrainedModel,
Trainer,
TrainerState,
)
from transformers.modeling_utils import unwrap_model
if is_safetensors_available():
import safetensors.torch
PATH_SAMPLE_TEXT = f"{get_tests_dir()}/fixtures/sample_text.txt"
class RegressionDataset:
def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
np.random.seed(seed)
self.label_names = ["labels"] if label_names is None else label_names
self.length = length
self.x = np.random.normal(size=(length,)).astype(np.float32)
self.ys = [a * self.x + b + np.random.normal(scale=0.1, size=(length,)) for _ in self.label_names]
self.ys = [y.astype(np.float32) for y in self.ys]
def __len__(self):
return self.length
def __getitem__(self, i):
result = {name: y[i] for name, y in zip(self.label_names, self.ys)}
result["input_x"] = self.x[i]
return result
@dataclasses.dataclass
class RegressionTrainingArguments(TrainingArguments):
a: float = 0.0
b: float = 0.0
def __post_init__(self):
super().__post_init__()
# save resources not dealing with reporting (also avoids the warning when it's not set)
self.report_to = []
class RepeatDataset:
def __init__(self, x, length=64):
self.x = x
self.length = length
def __len__(self):
return self.length
def __getitem__(self, i):
return {"input_ids": self.x, "labels": self.x}
class DynamicShapesDataset:
def __init__(self, length=64, seed=42, batch_size=8):
self.length = length
np.random.seed(seed)
sizes = np.random.randint(1, 20, (length // batch_size,))
# For easy batching, we make every batch_size consecutive samples the same size.
self.xs = [np.random.normal(size=(s,)) for s in sizes.repeat(batch_size)]
self.ys = [np.random.normal(size=(s,)) for s in sizes.repeat(batch_size)]
def __len__(self):
return self.length
def __getitem__(self, i):
return {"input_x": self.xs[i], "labels": self.ys[i]}
class AlmostAccuracy:
def __init__(self, thresh=0.25):
self.thresh = thresh
def __call__(self, eval_pred):
predictions, labels = eval_pred
true = np.abs(predictions - labels) <= self.thresh
return {"accuracy": true.astype(np.float32).mean().item()}
class RegressionModelConfig(PretrainedConfig):
def __init__(self, a=0, b=0, double_output=False, random_torch=True, **kwargs):
super().__init__(**kwargs)
self.a = a
self.b = b
self.double_output = double_output
self.random_torch = random_torch
self.hidden_size = 1
if is_torch_available():
class SampleIterableDataset(IterableDataset):
def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
self.dataset = RegressionDataset(a=a, b=b, length=length, seed=seed, label_names=label_names)
def __iter__(self):
for i in range(len(self.dataset)):
yield self.dataset[i]
class FiniteIterableDataset(SampleIterableDataset):
def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
super().__init__(a, b, length, seed, label_names)
self.current_sample = 0
def __iter__(self):
while self.current_sample < len(self.dataset):
yield self.dataset[self.current_sample]
self.current_sample += 1
class MultiLoader:
def __init__(self, loaders):
self.loaders = loaders
def __len__(self):
return sum(len(loader) for loader in self.loaders)
def __iter__(self):
for loader in self.loaders:
yield from loader
class CustomDataloaderTrainer(Trainer):
def get_train_dataloader(self):
dataloaders = [super().get_train_dataloader(), super().get_train_dataloader()]
return MultiLoader(dataloaders)
def get_eval_dataloader(self, eval_dataset):
dataloaders = [super().get_eval_dataloader(eval_dataset), super().get_eval_dataloader(eval_dataset)]
return MultiLoader(dataloaders)
class RegressionModel(nn.Module):
def __init__(self, a=0, b=0, double_output=False):
super().__init__()
self.a = nn.Parameter(torch.tensor(a).float())
self.b = nn.Parameter(torch.tensor(b).float())
self.double_output = double_output
self.config = None
def forward(self, input_x, labels=None, **kwargs):
y = input_x * self.a + self.b
if labels is None:
return (y, y) if self.double_output else (y,)
loss = nn.functional.mse_loss(y, labels)
return (loss, y, y) if self.double_output else (loss, y)
class RegressionDictModel(nn.Module):
def __init__(self, a=0, b=0):
super().__init__()
self.a = nn.Parameter(torch.tensor(a).float())
self.b = nn.Parameter(torch.tensor(b).float())
self.config = None
def forward(self, input_x, labels=None, **kwargs):
y = input_x * self.a + self.b
result = {"output": y}
if labels is not None:
result["loss"] = nn.functional.mse_loss(y, labels)
return result
class RegressionPreTrainedModel(PreTrainedModel):
config_class = RegressionModelConfig
base_model_prefix = "regression"
def __init__(self, config):
super().__init__(config)
self.a = nn.Parameter(torch.tensor(config.a).float())
self.b = nn.Parameter(torch.tensor(config.b).float())
self.double_output = config.double_output
def forward(self, input_x, labels=None, **kwargs):
y = input_x * self.a + self.b
if labels is None:
return (y, y) if self.double_output else (y,)
loss = nn.functional.mse_loss(y, labels)
return (loss, y, y) if self.double_output else (loss, y)
class RegressionRandomPreTrainedModel(PreTrainedModel):
config_class = RegressionModelConfig
base_model_prefix = "regression"
def __init__(self, config):
super().__init__(config)
self.a = nn.Parameter(torch.tensor(config.a).float())
self.b = nn.Parameter(torch.tensor(config.b).float())
self.random_torch = config.random_torch
def forward(self, input_x, labels=None, **kwargs):
y = input_x * self.a + self.b
if self.random_torch:
torch_rand = torch.randn(1).squeeze()
np_rand = np.random.rand()
rand_rand = random.random()
if self.random_torch:
y += 0.05 * torch_rand
y += 0.05 * torch.tensor(np_rand + rand_rand)
if labels is None:
return (y,)
loss = nn.functional.mse_loss(y, labels)
return (loss, y)
class TstLayer(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.linear1 = nn.Linear(hidden_size, hidden_size)
self.ln1 = nn.LayerNorm(hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.ln2 = nn.LayerNorm(hidden_size)
self.bias = nn.Parameter(torch.zeros(hidden_size))
def forward(self, x):
h = self.ln1(nn.functional.relu(self.linear1(x)))
h = nn.functional.relu(self.linear2(x))
return self.ln2(x + h + self.bias)
def get_regression_trainer(a=0, b=0, double_output=False, train_len=64, eval_len=64, pretrained=True, **kwargs):
label_names = kwargs.get("label_names", None)
train_dataset = RegressionDataset(length=train_len, label_names=label_names)
eval_dataset = RegressionDataset(length=eval_len, label_names=label_names)
model_init = kwargs.pop("model_init", None)
if model_init is not None:
model = None
else:
if pretrained:
config = RegressionModelConfig(a=a, b=b, double_output=double_output)
model = RegressionPreTrainedModel(config)
else:
model = RegressionModel(a=a, b=b, double_output=double_output)
compute_metrics = kwargs.pop("compute_metrics", None)
data_collator = kwargs.pop("data_collator", None)
optimizers = kwargs.pop("optimizers", (None, None))
output_dir = kwargs.pop("output_dir", "./regression")
preprocess_logits_for_metrics = kwargs.pop("preprocess_logits_for_metrics", None)
args = RegressionTrainingArguments(output_dir, a=a, b=b, **kwargs)
return Trainer(
model,
args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
optimizers=optimizers,
model_init=model_init,
preprocess_logits_for_metrics=preprocess_logits_for_metrics,
)
class TrainerIntegrationCommon:
def check_saved_checkpoints(self, output_dir, freq, total, is_pretrained=True, safe_weights=False):
weights_file = WEIGHTS_NAME if not safe_weights else SAFE_WEIGHTS_NAME
file_list = [weights_file, "training_args.bin", "optimizer.pt", "scheduler.pt", "trainer_state.json"]
if is_pretrained:
file_list.append("config.json")
for step in range(freq, total, freq):
checkpoint = os.path.join(output_dir, f"checkpoint-{step}")
self.assertTrue(os.path.isdir(checkpoint))
for filename in file_list:
self.assertTrue(os.path.isfile(os.path.join(checkpoint, filename)))
def check_best_model_has_been_loaded(
self, output_dir, freq, total, trainer, metric, greater_is_better=False, is_pretrained=True, safe_weights=False
):
checkpoint = os.path.join(output_dir, f"checkpoint-{(total // freq) * freq}")
log_history = TrainerState.load_from_json(os.path.join(checkpoint, "trainer_state.json")).log_history
values = [d[metric] for d in log_history]
best_value = max(values) if greater_is_better else min(values)
best_checkpoint = (values.index(best_value) + 1) * freq
checkpoint = os.path.join(output_dir, f"checkpoint-{best_checkpoint}")
if is_pretrained:
best_model = RegressionPreTrainedModel.from_pretrained(checkpoint)
best_model.to(trainer.args.device)
else:
best_model = RegressionModel()
if not safe_weights:
state_dict = torch.load(os.path.join(checkpoint, WEIGHTS_NAME))
else:
state_dict = safetensors.torch.load_file(os.path.join(checkpoint, SAFE_WEIGHTS_NAME))
best_model.load_state_dict(state_dict)
best_model.to(trainer.args.device)
self.assertTrue(torch.allclose(best_model.a, trainer.model.a))
self.assertTrue(torch.allclose(best_model.b, trainer.model.b))
metrics = trainer.evaluate()
self.assertEqual(metrics[metric], best_value)
def check_trainer_state_are_the_same(self, trainer_state, trainer_state1):
# We'll pop things so operate on copies.
state = trainer_state.copy()
state1 = trainer_state1.copy()
# Log history main contain different logs for the time metrics (after resuming a training).
log_history = state.pop("log_history", None)
log_history1 = state1.pop("log_history", None)
self.assertEqual(state, state1)
skip_log_keys = ["train_runtime", "train_samples_per_second", "train_steps_per_second", "train_loss"]
for log, log1 in zip(log_history, log_history1):
for key in skip_log_keys:
_ = log.pop(key, None)
_ = log1.pop(key, None)
self.assertEqual(log, log1)
def convert_to_sharded_checkpoint(self, folder, save_safe=False, load_safe=False):
# Converts a checkpoint of a regression model to a sharded checkpoint.
if load_safe:
loader = safetensors.torch.load_file
weights_file = os.path.join(folder, SAFE_WEIGHTS_NAME)
else:
loader = torch.load
weights_file = os.path.join(folder, WEIGHTS_NAME)
if save_safe:
extension = "safetensors"
saver = safetensors.torch.save_file
index_file = os.path.join(folder, SAFE_WEIGHTS_INDEX_NAME)
shard_name = SAFE_WEIGHTS_NAME
else:
extension = "bin"
saver = torch.save
index_file = os.path.join(folder, WEIGHTS_INDEX_NAME)
shard_name = WEIGHTS_NAME
state_dict = loader(weights_file)
os.remove(weights_file)
keys = list(state_dict.keys())
shard_files = [
shard_name.replace(f".{extension}", f"-{idx+1:05d}-of-{len(keys):05d}.{extension}")
for idx in range(len(keys))
]
index = {"metadata": {}, "weight_map": {key: shard_files[i] for i, key in enumerate(keys)}}
with open(index_file, "w", encoding="utf-8") as f:
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
f.write(content)
for param_name, shard_file in zip(keys, shard_files):
saver({param_name: state_dict[param_name]}, os.path.join(folder, shard_file))
@require_torch
@require_sentencepiece
@require_tokenizers
class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
"""
Only tests that want to tap into the auto-pre-run 2 trainings:
- self.default_trained_model
- self.alternate_trained_model
directly, or via check_trained_model
"""
def setUp(self):
super().setUp()
args = TrainingArguments("..")
self.n_epochs = args.num_train_epochs
self.batch_size = args.train_batch_size
trainer = get_regression_trainer(learning_rate=0.1)
trainer.train()
self.default_trained_model = (trainer.model.a, trainer.model.b)
trainer = get_regression_trainer(learning_rate=0.1, seed=314)
trainer.train()
self.alternate_trained_model = (trainer.model.a, trainer.model.b)
def check_trained_model(self, model, alternate_seed=False):
# Checks a training seeded with learning_rate = 0.1
(a, b) = self.alternate_trained_model if alternate_seed else self.default_trained_model
self.assertTrue(torch.allclose(model.a, a))
self.assertTrue(torch.allclose(model.b, b))
def test_reproducible_training(self):
# Checks that training worked, model trained and seed made a reproducible training.
trainer = get_regression_trainer(learning_rate=0.1)
trainer.train()
self.check_trained_model(trainer.model)
# Checks that a different seed gets different (reproducible) results.
trainer = get_regression_trainer(learning_rate=0.1, seed=314)
trainer.train()
self.check_trained_model(trainer.model, alternate_seed=True)
def test_trainer_with_datasets(self):
import datasets
np.random.seed(42)
x = np.random.normal(size=(64,)).astype(np.float32)
y = 2.0 * x + 3.0 + np.random.normal(scale=0.1, size=(64,))
train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y})
# Base training. Should have the same results as test_reproducible_training
model = RegressionModel()
args = TrainingArguments("./regression", learning_rate=0.1)
trainer = Trainer(model, args, train_dataset=train_dataset)
trainer.train()
self.check_trained_model(trainer.model)
# Can return tensors.
train_dataset.set_format(type="torch", dtype=torch.float32)
model = RegressionModel()
trainer = Trainer(model, args, train_dataset=train_dataset)
trainer.train()
self.check_trained_model(trainer.model)
# Adding one column not used by the model should have no impact
z = np.random.normal(size=(64,)).astype(np.float32)
train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y, "extra": z})
model = RegressionModel()
trainer = Trainer(model, args, train_dataset=train_dataset)
trainer.train()
self.check_trained_model(trainer.model)
def test_model_init(self):
train_dataset = RegressionDataset()
args = TrainingArguments("./regression", learning_rate=0.1)
trainer = Trainer(args=args, train_dataset=train_dataset, model_init=lambda: RegressionModel())
trainer.train()
self.check_trained_model(trainer.model)
# Re-training should restart from scratch, thus lead the same results.
trainer.train()
self.check_trained_model(trainer.model)
# Re-training should restart from scratch, thus lead the same results and new seed should be used.
trainer.args.seed = 314
trainer.train()
self.check_trained_model(trainer.model, alternate_seed=True)
def test_gradient_accumulation(self):
# Training with half the batch size but accumulation steps as 2 should give the same results.
trainer = get_regression_trainer(
gradient_accumulation_steps=2, per_device_train_batch_size=4, learning_rate=0.1
)
trainer.train()
self.check_trained_model(trainer.model)
def test_training_loss(self):
n_gpus = max(1, get_gpu_count())
# With even logs
trainer = get_regression_trainer(logging_steps=64 / (8 * n_gpus))
trainer.train()
log_history = trainer.state.log_history
losses = [log["loss"] for log in log_history if "loss" in log]
train_loss = log_history[-1]["train_loss"]
self.assertAlmostEqual(sum(losses) / len(losses), train_loss, places=4)
# With uneven logs
trainer = get_regression_trainer(logging_steps=5)
trainer.train()
log_history = trainer.state.log_history
# Training loss should be the same as before
new_train_loss = log_history[-1]["train_loss"]
self.assertAlmostEqual(train_loss, new_train_loss, places=4)
def test_custom_optimizer(self):
train_dataset = RegressionDataset()
args = TrainingArguments("./regression")
model = RegressionModel()
optimizer = torch.optim.SGD(model.parameters(), lr=1.0)
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: 1.0)
trainer = Trainer(model, args, train_dataset=train_dataset, optimizers=(optimizer, lr_scheduler))
trainer.train()
(a, b) = self.default_trained_model
self.assertFalse(torch.allclose(trainer.model.a, a))
self.assertFalse(torch.allclose(trainer.model.b, b))
self.assertEqual(trainer.optimizer.state_dict()["param_groups"][0]["lr"], 1.0)
def test_reduce_lr_on_plateau_args(self):
# test passed arguments for a custom ReduceLROnPlateau scheduler
train_dataset = RegressionDataset(length=64)
eval_dataset = RegressionDataset(length=64)
args = TrainingArguments(
"./regression",
evaluation_strategy="epoch",
metric_for_best_model="eval_loss",
)
model = RegressionModel()
optimizer = torch.optim.SGD(model.parameters(), lr=1.0)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.2, patience=5, cooldown=2)
trainer = Trainer(
model, args, train_dataset=train_dataset, eval_dataset=eval_dataset, optimizers=(optimizer, lr_scheduler)
)
trainer.train()
self.assertIsInstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau)
self.assertEqual(trainer.lr_scheduler.factor, 0.2)
self.assertEqual(trainer.lr_scheduler.patience, 5)
self.assertEqual(trainer.lr_scheduler.cooldown, 2)
def test_reduce_lr_on_plateau(self):
# test the ReduceLROnPlateau scheduler
class TrainerWithLRLogs(Trainer):
def log(self, logs):
# the LR is computed after metrics and does not exist for the first epoch
if hasattr(self.lr_scheduler, "_last_lr"):
logs["learning_rate"] = self.lr_scheduler._last_lr
super().log(logs)
train_dataset = RegressionDataset(length=64)
eval_dataset = RegressionDataset(length=64)
args = TrainingArguments(
"./regression",
lr_scheduler_type="reduce_lr_on_plateau",
evaluation_strategy="epoch",
metric_for_best_model="eval_loss",
num_train_epochs=10,
learning_rate=0.2,
)
model = RegressionModel()
trainer = TrainerWithLRLogs(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
trainer.train()
self.assertIsInstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau)
patience = trainer.lr_scheduler.patience
logs = trainer.state.log_history[1:]
best_loss = logs[0]["eval_loss"]
bad_epochs = 0
for i, log in enumerate(logs[:-1]): # Compare learning rate to next epoch's
loss = log["eval_loss"]
just_decreased = False
if loss > best_loss:
bad_epochs += 1
if bad_epochs > patience:
self.assertLess(logs[i + 1]["learning_rate"][0], log["learning_rate"][0])
just_decreased = True
bad_epochs = 0
else:
best_loss = loss
bad_epochs = 0
if not just_decreased:
self.assertEqual(logs[i + 1]["learning_rate"][0], log["learning_rate"][0])
def test_adafactor_lr_none(self):
# test the special case where lr=None, since Trainer can't not have lr_scheduler
from transformers.optimization import Adafactor, AdafactorSchedule
train_dataset = RegressionDataset()
args = TrainingArguments("./regression")
model = RegressionModel()
optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)
lr_scheduler = AdafactorSchedule(optimizer)
trainer = Trainer(model, args, train_dataset=train_dataset, optimizers=(optimizer, lr_scheduler))
trainer.train()
(a, b) = self.default_trained_model
self.assertFalse(torch.allclose(trainer.model.a, a))
self.assertFalse(torch.allclose(trainer.model.b, b))
self.assertGreater(trainer.optimizer.state_dict()["param_groups"][0]["lr"], 0)
@require_torch_gpu
@require_torch_bf16_gpu
def test_mixed_bf16(self):
# very basic test
trainer = get_regression_trainer(learning_rate=0.1, bf16=True)
trainer.train()
self.check_trained_model(trainer.model)
# --bf16 --half_precision_backend apex can't be used together
with self.assertRaises(ValueError):
trainer = get_regression_trainer(learning_rate=0.1, bf16=True, half_precision_backend="apex")
# will add more specific tests once there are some bugs to fix
@require_torch_gpu
@require_torch_tf32
def test_tf32(self):
# very basic test
trainer = get_regression_trainer(learning_rate=0.1, tf32=True)
trainer.train()
self.check_trained_model(trainer.model)
@require_torch
@require_sentencepiece
@require_tokenizers
class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
def setUp(self):
super().setUp()
args = TrainingArguments("..")
self.n_epochs = args.num_train_epochs
self.batch_size = args.train_batch_size
def test_trainer_works_with_dict(self):
# Edge case because Apex with mode O2 will change our models to return dicts. This test checks it doesn't break
# anything.
train_dataset = RegressionDataset()
eval_dataset = RegressionDataset()
model = RegressionDictModel()
args = TrainingArguments("./regression")
trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
trainer.train()
_ = trainer.evaluate()
_ = trainer.predict(eval_dataset)
def test_evaluation_with_keys_to_drop(self):
config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
tiny_gpt2 = GPT2LMHeadModel(config)
x = torch.randint(0, 100, (128,))
eval_dataset = RepeatDataset(x)
args = TrainingArguments("./test")
trainer = Trainer(tiny_gpt2, args, eval_dataset=eval_dataset)
# By default the past_key_values are removed
result = trainer.predict(eval_dataset)
self.assertTrue(isinstance(result.predictions, np.ndarray))
# We can still get them by setting ignore_keys to []
result = trainer.predict(eval_dataset, ignore_keys=[])
self.assertTrue(isinstance(result.predictions, tuple))
self.assertEqual(len(result.predictions), 2)
def test_training_arguments_are_left_untouched(self):
trainer = get_regression_trainer()
trainer.train()
args = TrainingArguments("./regression", report_to=[])
dict1, dict2 = args.to_dict(), trainer.args.to_dict()
for key in dict1.keys():
# Logging dir can be slightly different as they default to something with the time.
if key != "logging_dir":
self.assertEqual(dict1[key], dict2[key])
def test_number_of_steps_in_training(self):
# Regular training has n_epochs * len(train_dl) steps
trainer = get_regression_trainer(learning_rate=0.1)
train_output = trainer.train()
self.assertEqual(train_output.global_step, self.n_epochs * 64 / self.batch_size)
# Check passing num_train_epochs works (and a float version too):
trainer = get_regression_trainer(learning_rate=0.1, num_train_epochs=1.5)
train_output = trainer.train()
self.assertEqual(train_output.global_step, int(1.5 * 64 / self.batch_size))
# If we pass a max_steps, num_train_epochs is ignored
trainer = get_regression_trainer(learning_rate=0.1, max_steps=10)
train_output = trainer.train()
self.assertEqual(train_output.global_step, 10)
@require_torch_bf16_cpu
@require_intel_extension_for_pytorch
def test_number_of_steps_in_training_with_ipex(self):
for mix_bf16 in [True, False]:
# Regular training has n_epochs * len(train_dl) steps
trainer = get_regression_trainer(learning_rate=0.1, use_ipex=True, bf16=mix_bf16, no_cuda=True)
train_output = trainer.train()
self.assertEqual(train_output.global_step, self.n_epochs * 64 / trainer.args.train_batch_size)
# Check passing num_train_epochs works (and a float version too):
trainer = get_regression_trainer(
learning_rate=0.1, num_train_epochs=1.5, use_ipex=True, bf16=mix_bf16, no_cuda=True
)
train_output = trainer.train()
self.assertEqual(train_output.global_step, int(1.5 * 64 / trainer.args.train_batch_size))
# If we pass a max_steps, num_train_epochs is ignored
trainer = get_regression_trainer(
learning_rate=0.1, max_steps=10, use_ipex=True, bf16=mix_bf16, no_cuda=True
)
train_output = trainer.train()
self.assertEqual(train_output.global_step, 10)
def test_logging_inf_nan_filter(self):
config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
tiny_gpt2 = GPT2LMHeadModel(config)
x = torch.randint(0, 100, (128,))
train_dataset = RepeatDataset(x)
# Trainer without inf/nan filter
args = TrainingArguments("./test", learning_rate=1e9, logging_steps=5, logging_nan_inf_filter=False)
trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
trainer.train()
log_history_no_filter = trainer.state.log_history
# Trainer with inf/nan filter
args = TrainingArguments("./test", learning_rate=1e9, logging_steps=5, logging_nan_inf_filter=True)
trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
trainer.train()
log_history_filter = trainer.state.log_history
def is_any_loss_nan_or_inf(log_history):
losses = [l["loss"] for l in log_history[:-1]]
return any(math.isnan(x) for x in losses) or any(math.isinf(x) for x in losses)
self.assertTrue(is_any_loss_nan_or_inf(log_history_no_filter))
self.assertFalse(is_any_loss_nan_or_inf(log_history_filter))
def test_train_and_eval_dataloaders(self):
n_gpu = max(1, torch.cuda.device_count())
trainer = get_regression_trainer(learning_rate=0.1, per_device_train_batch_size=16)
self.assertEqual(trainer.get_train_dataloader().total_batch_size, 16 * n_gpu)
trainer = get_regression_trainer(learning_rate=0.1, per_device_eval_batch_size=16)
self.assertEqual(trainer.get_eval_dataloader().total_batch_size, 16 * n_gpu)
# Check drop_last works
trainer = get_regression_trainer(
train_len=66, eval_len=74, learning_rate=0.1, per_device_train_batch_size=16, per_device_eval_batch_size=32
)
self.assertEqual(len(trainer.get_train_dataloader()), 66 // (16 * n_gpu) + 1)
self.assertEqual(len(trainer.get_eval_dataloader()), 74 // (32 * n_gpu) + 1)
trainer = get_regression_trainer(
train_len=66,
eval_len=74,
learning_rate=0.1,
per_device_train_batch_size=16,
per_device_eval_batch_size=32,
dataloader_drop_last=True,
)
self.assertEqual(len(trainer.get_train_dataloader()), 66 // (16 * n_gpu))
self.assertEqual(len(trainer.get_eval_dataloader()), 74 // (32 * n_gpu))
# Check passing a new dataset for evaluation works
new_eval_dataset = RegressionDataset(length=128)
self.assertEqual(len(trainer.get_eval_dataloader(new_eval_dataset)), 128 // (32 * n_gpu))
# tests that we do not require dataloader to have a .dataset attribute
def test_dataloader_without_dataset(self):
train_dataset = RegressionDataset(length=128)
trainer = CustomDataloaderTrainer(
model=RegressionModel(), train_dataset=train_dataset, eval_dataset=train_dataset
)
trainer.train()
trainer.evaluate()
@require_torch_multi_gpu
def test_data_is_not_parallelized_when_model_is_parallel(self):
model = RegressionModel()
# Make the Trainer believe it's a parallelized model
model.is_parallelizable = True
model.model_parallel = True
args = TrainingArguments("./regression", per_device_train_batch_size=16, per_device_eval_batch_size=16)
trainer = Trainer(model, args, train_dataset=RegressionDataset(), eval_dataset=RegressionDataset())
# Check the Trainer was fooled
self.assertTrue(trainer.is_model_parallel)
self.assertEqual(trainer.args.n_gpu, 1)
# The batch size of the training and evaluation dataloaders should be 16, not 16 * n_gpu
self.assertEqual(trainer.get_train_dataloader().total_batch_size, 16)
self.assertEqual(len(trainer.get_train_dataloader()), 64 // 16)
self.assertEqual(trainer.get_eval_dataloader().total_batch_size, 16)
self.assertEqual(len(trainer.get_eval_dataloader()), 64 // 16)
def test_evaluate(self):
trainer = get_regression_trainer(a=1.5, b=2.5, compute_metrics=AlmostAccuracy())
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
# With a number of elements not a round multiple of the batch size
trainer = get_regression_trainer(a=1.5, b=2.5, eval_len=66, compute_metrics=AlmostAccuracy())
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
# With logits preprocess
trainer = get_regression_trainer(
a=1.5,
b=2.5,
compute_metrics=AlmostAccuracy(),
preprocess_logits_for_metrics=lambda logits, labels: logits + 1,
)
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred + 1, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
def test_evaluate_with_jit(self):
trainer = get_regression_trainer(a=1.5, b=2.5, compute_metrics=AlmostAccuracy(), jit_mode_eval=True)
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
# With a number of elements not a round multiple of the batch size
trainer = get_regression_trainer(
a=1.5, b=2.5, eval_len=66, compute_metrics=AlmostAccuracy(), jit_mode_eval=True
)
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
# With logits preprocess
trainer = get_regression_trainer(
a=1.5,
b=2.5,
compute_metrics=AlmostAccuracy(),
preprocess_logits_for_metrics=lambda logits, labels: logits + 1,
jit_mode_eval=True,
)
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred + 1, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
@require_torch_bf16_cpu
@require_intel_extension_for_pytorch
def test_evaluate_with_ipex(self):
for mix_bf16 in [True, False]:
trainer = get_regression_trainer(
a=1.5, b=2.5, use_ipex=True, compute_metrics=AlmostAccuracy(), bf16=mix_bf16, no_cuda=True
)
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
# With a number of elements not a round multiple of the batch size
trainer = get_regression_trainer(
a=1.5,
b=2.5,
use_ipex=True,
eval_len=66,
compute_metrics=AlmostAccuracy(),
bf16=mix_bf16,
no_cuda=True,
)
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
# With logits preprocess
trainer = get_regression_trainer(
a=1.5,
b=2.5,
use_ipex=True,
compute_metrics=AlmostAccuracy(),
preprocess_logits_for_metrics=lambda logits, labels: logits + 1,
bf16=mix_bf16,
no_cuda=True,
)
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred + 1, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
def test_predict(self):
trainer = get_regression_trainer(a=1.5, b=2.5)
preds = trainer.predict(trainer.eval_dataset).predictions
x = trainer.eval_dataset.x
self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
# With a number of elements not a round multiple of the batch size
trainer = get_regression_trainer(a=1.5, b=2.5, eval_len=66)
preds = trainer.predict(trainer.eval_dataset).predictions
x = trainer.eval_dataset.x
self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
# With more than one output of the model
trainer = get_regression_trainer(a=1.5, b=2.5, double_output=True)
preds = trainer.predict(trainer.eval_dataset).predictions
x = trainer.eval_dataset.x
self.assertEqual(len(preds), 2)
self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
# With more than one output/label of the model
trainer = get_regression_trainer(a=1.5, b=2.5, double_output=True, label_names=["labels", "labels_2"])
outputs = trainer.predict(trainer.eval_dataset)
preds = outputs.predictions
labels = outputs.label_ids
x = trainer.eval_dataset.x
self.assertEqual(len(preds), 2)
self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
self.assertTrue(np.array_equal(labels[0], trainer.eval_dataset.ys[0]))
self.assertTrue(np.array_equal(labels[1], trainer.eval_dataset.ys[1]))
def test_predict_with_jit(self):
trainer = get_regression_trainer(a=1.5, b=2.5, jit_mode_eval=True)
preds = trainer.predict(trainer.eval_dataset).predictions
x = trainer.eval_dataset.x
self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
# With a number of elements not a round multiple of the batch size
trainer = get_regression_trainer(a=1.5, b=2.5, eval_len=66, jit_mode_eval=True)
preds = trainer.predict(trainer.eval_dataset).predictions
x = trainer.eval_dataset.x
self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
# With more than one output of the model
trainer = get_regression_trainer(a=1.5, b=2.5, double_output=True, jit_mode_eval=True)
preds = trainer.predict(trainer.eval_dataset).predictions
x = trainer.eval_dataset.x
self.assertEqual(len(preds), 2)
self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
# With more than one output/label of the model
trainer = get_regression_trainer(
a=1.5, b=2.5, double_output=True, label_names=["labels", "labels_2"], jit_mode_eval=True
)
outputs = trainer.predict(trainer.eval_dataset)
preds = outputs.predictions
labels = outputs.label_ids
x = trainer.eval_dataset.x
self.assertEqual(len(preds), 2)
self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
self.assertTrue(np.array_equal(labels[0], trainer.eval_dataset.ys[0]))
self.assertTrue(np.array_equal(labels[1], trainer.eval_dataset.ys[1]))
@require_torch_bf16_cpu
@require_intel_extension_for_pytorch
def test_predict_with_ipex(self):
for mix_bf16 in [True, False]:
trainer = get_regression_trainer(a=1.5, b=2.5, use_ipex=True, bf16=mix_bf16, no_cuda=True)
preds = trainer.predict(trainer.eval_dataset).predictions
x = trainer.eval_dataset.x
self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
# With a number of elements not a round multiple of the batch size
trainer = get_regression_trainer(a=1.5, b=2.5, eval_len=66, use_ipex=True, bf16=mix_bf16, no_cuda=True)
preds = trainer.predict(trainer.eval_dataset).predictions
x = trainer.eval_dataset.x
self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
# With more than one output of the model
trainer = get_regression_trainer(
a=1.5, b=2.5, double_output=True, use_ipex=True, bf16=mix_bf16, no_cuda=True
)
preds = trainer.predict(trainer.eval_dataset).predictions
x = trainer.eval_dataset.x
self.assertEqual(len(preds), 2)
self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
# With more than one output/label of the model
trainer = get_regression_trainer(
a=1.5,
b=2.5,
double_output=True,
label_names=["labels", "labels_2"],
use_ipex=True,
bf16=mix_bf16,
no_cuda=True,
)
outputs = trainer.predict(trainer.eval_dataset)
preds = outputs.predictions
labels = outputs.label_ids
x = trainer.eval_dataset.x
self.assertEqual(len(preds), 2)
self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
self.assertTrue(np.array_equal(labels[0], trainer.eval_dataset.ys[0]))
self.assertTrue(np.array_equal(labels[1], trainer.eval_dataset.ys[1]))
def test_dynamic_shapes(self):
eval_dataset = DynamicShapesDataset(batch_size=self.batch_size)
model = RegressionModel(a=2, b=1)
args = TrainingArguments("./regression")
trainer = Trainer(model, args, eval_dataset=eval_dataset)
# Check evaluation can run to completion
_ = trainer.evaluate()
# Check predictions
preds = trainer.predict(eval_dataset)
for expected, seen in zip(eval_dataset.ys, preds.label_ids):
self.assertTrue(np.array_equal(expected, seen[: expected.shape[0]]))
self.assertTrue(np.all(seen[expected.shape[0] :] == -100))
for expected, seen in zip(eval_dataset.xs, preds.predictions):
self.assertTrue(np.array_equal(2 * expected + 1, seen[: expected.shape[0]]))
self.assertTrue(np.all(seen[expected.shape[0] :] == -100))
# Same tests with eval accumulation
args = TrainingArguments("./regression", eval_accumulation_steps=2)
trainer = Trainer(model, args, eval_dataset=eval_dataset)
# Check evaluation can run to completion
_ = trainer.evaluate()
# Check predictions
preds = trainer.predict(eval_dataset)
for expected, seen in zip(eval_dataset.ys, preds.label_ids):
self.assertTrue(np.array_equal(expected, seen[: expected.shape[0]]))
self.assertTrue(np.all(seen[expected.shape[0] :] == -100))
for expected, seen in zip(eval_dataset.xs, preds.predictions):
self.assertTrue(np.array_equal(2 * expected + 1, seen[: expected.shape[0]]))
self.assertTrue(np.all(seen[expected.shape[0] :] == -100))
def test_log_level(self):
# testing only --log_level (--log_level_replica requires multiple gpus and DDP and is tested elsewhere)
logger = logging.get_logger()
log_info_string = "Running training"
# test with the default log_level - should be the same as before and thus we test depending on is_info
is_info = logging.get_verbosity() <= 20
with CaptureLogger(logger) as cl:
trainer = get_regression_trainer()
trainer.train()
if is_info:
self.assertIn(log_info_string, cl.out)
else:
self.assertNotIn(log_info_string, cl.out)
# test with low log_level - lower than info
with CaptureLogger(logger) as cl:
trainer = get_regression_trainer(log_level="debug")
trainer.train()
self.assertIn(log_info_string, cl.out)
# test with high log_level - should be quiet
with CaptureLogger(logger) as cl:
trainer = get_regression_trainer(log_level="error")
trainer.train()
self.assertNotIn(log_info_string, cl.out)
def test_save_checkpoints(self):
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5)
trainer.train()
self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size))
# With a regular model that is not a PreTrainedModel
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5, pretrained=False)
trainer.train()
self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False)
@require_safetensors
def test_safe_checkpoints(self):
for save_safetensors in [True, False]:
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5, save_safetensors=save_safetensors)
trainer.train()
self.check_saved_checkpoints(
tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), safe_weights=save_safetensors
)
# With a regular model that is not a PreTrainedModel
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
output_dir=tmpdir, save_steps=5, pretrained=False, save_safetensors=save_safetensors
)
trainer.train()
self.check_saved_checkpoints(
tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False, safe_weights=save_safetensors
)
@require_torch_multi_gpu
def test_run_seq2seq_double_train_wrap_once(self):
# test that we don't wrap the model more than once
# since wrapping primarily happens on multi-gpu setup we want multiple gpus to test for
# example DataParallel(DataParallel(model))
trainer = get_regression_trainer()
trainer.train()
model_wrapped_before = trainer.model_wrapped
trainer.train()
model_wrapped_after = trainer.model_wrapped
self.assertIs(model_wrapped_before, model_wrapped_after, "should be not wrapped twice")
@require_torch_up_to_2_gpus
def test_can_resume_training(self):
# This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
# save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
# won't be the same since the training dataloader is shuffled).
with tempfile.TemporaryDirectory() as tmpdir:
kwargs = {
"output_dir": tmpdir,
"train_len": 128,
"save_steps": 5,
"learning_rate": 0.1,
"logging_steps": 5,
}
trainer = get_regression_trainer(**kwargs)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
state = dataclasses.asdict(trainer.state)
checkpoint = os.path.join(tmpdir, "checkpoint-5")
# Reinitialize trainer
trainer = get_regression_trainer(**kwargs)
trainer.train(resume_from_checkpoint=checkpoint)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
# Now check with a later checkpoint that it also works when we span over one epoch
checkpoint = os.path.join(tmpdir, "checkpoint-15")
# Reinitialize trainer and load model
trainer = get_regression_trainer(**kwargs)
trainer.train(resume_from_checkpoint=checkpoint)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
# With a regular model that is not a PreTrainedModel
with tempfile.TemporaryDirectory() as tmpdir:
kwargs = {
"output_dir": tmpdir,
"train_len": 128,
"save_steps": 5,
"learning_rate": 0.1,
"pretrained": False,
}
trainer = get_regression_trainer(**kwargs)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
state = dataclasses.asdict(trainer.state)
checkpoint = os.path.join(tmpdir, "checkpoint-5")
# Reinitialize trainer and load model
trainer = get_regression_trainer(**kwargs)
trainer.train(resume_from_checkpoint=checkpoint)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
# Now check with a later checkpoint that it also works when we span over one epoch
checkpoint = os.path.join(tmpdir, "checkpoint-15")
# Reinitialize trainer and load model
trainer = get_regression_trainer(**kwargs)
trainer.train(resume_from_checkpoint=checkpoint)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
# Now check failures
# 1. fail to find a bogus checkpoint
trainer = get_regression_trainer()
with self.assertRaises(Exception) as context:
trainer.train(resume_from_checkpoint=f"{checkpoint}-bogus")
self.assertTrue("Can't find a valid checkpoint at" in str(context.exception))
# 2. fail to find any checkpoint - due a fresh output_dir
output_dir2 = self.get_auto_remove_tmp_dir()
trainer = get_regression_trainer(output_dir=output_dir2)
with self.assertRaises(Exception) as context:
trainer.train(resume_from_checkpoint=True)
self.assertTrue("No valid checkpoint found in output directory" in str(context.exception))
def test_resume_training_with_randomness(self):
# For more than 1 GPUs, since the randomness is introduced in the model and with DataParallel (which is used
# in this test for more than 2 GPUs), the calls to the torch RNG will happen in a random order (sometimes
# GPU 0 will call first and sometimes GPU 1).
random_torch = not torch.cuda.is_available() or torch.cuda.device_count() <= 1
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
train_dataset = RegressionDataset(length=128)
eval_dataset = RegressionDataset()
with self.subTest("Test every step"):
config = RegressionModelConfig(a=0, b=2, random_torch=random_torch)
model = RegressionRandomPreTrainedModel(config)
tmp_dir = self.get_auto_remove_tmp_dir()
args = RegressionTrainingArguments(tmp_dir, save_steps=5, learning_rate=0.1)
trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
model = RegressionRandomPreTrainedModel(config)
trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
trainer.train(resume_from_checkpoint=os.path.join(tmp_dir, "checkpoint-15"))
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
self.assertAlmostEqual(a, a1, delta=1e-5)
self.assertAlmostEqual(b, b1, delta=1e-5)
with self.subTest("Test every epoch"):
config = RegressionModelConfig(a=0, b=2, random_torch=random_torch)
model = RegressionRandomPreTrainedModel(config)
tmp_dir = self.get_auto_remove_tmp_dir()
args = RegressionTrainingArguments(tmp_dir, save_strategy="epoch", learning_rate=0.1)
trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
model = RegressionRandomPreTrainedModel(config)
trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
checkpoints = [d for d in os.listdir(tmp_dir) if d.startswith("checkpoint-")]
# There should be one checkpoint per epoch.
self.assertEqual(len(checkpoints), 3)
checkpoint_dir = sorted(checkpoints, key=lambda x: int(x.replace("checkpoint-", "")))[0]
trainer.train(resume_from_checkpoint=os.path.join(tmp_dir, checkpoint_dir))
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
self.assertAlmostEqual(a, a1, delta=1e-5)
self.assertAlmostEqual(b, b1, delta=1e-5)
@slow
@require_accelerate
@require_torch_non_multi_gpu
def test_auto_batch_size_finder(self):
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
SRC_DIR = os.path.abspath(
os.path.join(os.path.dirname(__file__), "..", "..", "examples", "pytorch", "text-classification")
)
sys.path.append(SRC_DIR)
import run_glue
with tempfile.TemporaryDirectory() as tmpdir:
testargs = f"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--task_name mrpc
--do_train
--do_eval
--max_seq_len 128
--per_device_train_batch_size 4096
--learning_rate 2e-5
--num_train_epochs 1
--output_dir {tmpdir}
--auto_find_batch_size 0
""".split()
with self.assertRaises(RuntimeError):
with patch.object(sys, "argv", testargs):
run_glue.main()
testargs[-1] = "1"
with patch.object(sys, "argv", testargs):
run_glue.main()
# regression for this issue: https://github.com/huggingface/transformers/issues/12970
def test_training_with_resume_from_checkpoint_false(self):
train_dataset = RegressionDataset(length=128)
eval_dataset = RegressionDataset()
config = RegressionModelConfig(a=0, b=2)
model = RegressionRandomPreTrainedModel(config)
tmp_dir = self.get_auto_remove_tmp_dir()
args = RegressionTrainingArguments(tmp_dir, save_steps=5, learning_rate=0.1)
trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
trainer.train(resume_from_checkpoint=False)
@require_torch_up_to_2_gpus
def test_resume_training_with_shard_checkpoint(self):
# This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
# save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
# won't be the same since the training dataloader is shuffled).
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
state = dataclasses.asdict(trainer.state)
checkpoint = os.path.join(tmpdir, "checkpoint-5")
self.convert_to_sharded_checkpoint(checkpoint)
# Reinitialize trainer
trainer = get_regression_trainer(output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1)
trainer.train(resume_from_checkpoint=checkpoint)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
@require_safetensors
@require_torch_up_to_2_gpus
def test_resume_training_with_safe_checkpoint(self):
# This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
# save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
# won't be the same since the training dataloader is shuffled).
for initial_safe in [False, True]:
for loaded_safe in [False, True]:
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
output_dir=tmpdir,
train_len=128,
save_steps=5,
learning_rate=0.1,
save_safetensors=initial_safe,
)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
state = dataclasses.asdict(trainer.state)
checkpoint = os.path.join(tmpdir, "checkpoint-5")
self.convert_to_sharded_checkpoint(checkpoint, load_safe=initial_safe, save_safe=loaded_safe)
# Reinitialize trainer
trainer = get_regression_trainer(
output_dir=tmpdir, train_len=128, save_steps=5, learning_rate=0.1, save_safetensors=loaded_safe
)
trainer.train(resume_from_checkpoint=checkpoint)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
@require_torch_up_to_2_gpus
def test_resume_training_with_gradient_accumulation(self):
# This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
# save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
# won't be the same since the training dataloader is shuffled).
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
output_dir=tmpdir,
train_len=128,
gradient_accumulation_steps=2,
per_device_train_batch_size=4,
save_steps=5,
learning_rate=0.1,
)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
state = dataclasses.asdict(trainer.state)
checkpoint = os.path.join(tmpdir, "checkpoint-5")
# Reinitialize trainer
trainer = get_regression_trainer(
output_dir=tmpdir,
train_len=128,
gradient_accumulation_steps=2,
per_device_train_batch_size=4,
save_steps=5,
learning_rate=0.1,
)
trainer.train(resume_from_checkpoint=checkpoint)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
@require_torch_up_to_2_gpus
def test_resume_training_with_frozen_params(self):
# This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
# save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
# won't be the same since the training dataloader is shuffled).
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
output_dir=tmpdir,
train_len=128,
per_device_train_batch_size=4,
save_steps=5,
learning_rate=0.1,
)
trainer.model.a.requires_grad_(False)
trainer.train()
(a, b) = trainer.model.a.item(), trainer.model.b.item()
state = dataclasses.asdict(trainer.state)
checkpoint = os.path.join(tmpdir, "checkpoint-5")
# Reinitialize trainer
trainer = get_regression_trainer(
output_dir=tmpdir,
train_len=128,
per_device_train_batch_size=4,
save_steps=5,
learning_rate=0.1,
)
trainer.model.a.requires_grad_(False)
trainer.train(resume_from_checkpoint=checkpoint)
self.assertFalse(trainer.model.a.requires_grad)
(a1, b1) = trainer.model.a.item(), trainer.model.b.item()
state1 = dataclasses.asdict(trainer.state)
self.assertEqual(a, a1)
self.assertEqual(b, b1)
self.check_trainer_state_are_the_same(state, state1)
def test_load_best_model_at_end(self):
total = int(self.n_epochs * 64 / self.batch_size)
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
a=1.5,
b=2.5,
output_dir=tmpdir,
learning_rate=0.1,
eval_steps=5,
evaluation_strategy="steps",
save_steps=5,
load_best_model_at_end=True,
)
self.assertFalse(trainer.args.greater_is_better)
trainer.train()
self.check_saved_checkpoints(tmpdir, 5, total)
self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_loss")
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
a=1.5,
b=2.5,
output_dir=tmpdir,
learning_rate=0.1,
eval_steps=5,
evaluation_strategy="steps",
save_steps=5,
load_best_model_at_end=True,
metric_for_best_model="accuracy",
compute_metrics=AlmostAccuracy(),
)
self.assertTrue(trainer.args.greater_is_better)
trainer.train()
self.check_saved_checkpoints(tmpdir, 5, total)
self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_accuracy", greater_is_better=True)
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
a=1.5,
b=2.5,
output_dir=tmpdir,
learning_rate=0.1,
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="accuracy",
compute_metrics=AlmostAccuracy(),
)
self.assertTrue(trainer.args.greater_is_better)
trainer.train()
self.check_saved_checkpoints(tmpdir, 64 // self.batch_size, total)
self.check_best_model_has_been_loaded(
tmpdir, 64 // self.batch_size, total, trainer, "eval_accuracy", greater_is_better=True
)
# Test this works with a non PreTrainedModel
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
output_dir=tmpdir,
learning_rate=0.1,
eval_steps=5,
evaluation_strategy="steps",
save_steps=5,
load_best_model_at_end=True,
pretrained=False,
)
self.assertFalse(trainer.args.greater_is_better)
trainer.train()
self.check_saved_checkpoints(tmpdir, 5, total, is_pretrained=False)
self.check_best_model_has_been_loaded(tmpdir, 5, total, trainer, "eval_loss", is_pretrained=False)
@require_safetensors
def test_load_best_model_from_safetensors(self):
total = int(self.n_epochs * 64 / self.batch_size)
for save_safetensors, pretrained in product([False, True], [False, True]):
with tempfile.TemporaryDirectory() as tmpdir:
trainer = get_regression_trainer(
a=1.5,
b=2.5,
output_dir=tmpdir,
learning_rate=0.1,
eval_steps=5,
evaluation_strategy="steps",
save_steps=5,
load_best_model_at_end=True,
save_safetensors=save_safetensors,
pretrained=pretrained,
)
self.assertFalse(trainer.args.greater_is_better)
trainer.train()
self.check_saved_checkpoints(tmpdir, 5, total, is_pretrained=pretrained, safe_weights=save_safetensors)
self.check_best_model_has_been_loaded(
tmpdir, 5, total, trainer, "eval_loss", is_pretrained=pretrained, safe_weights=save_safetensors
)
@slow
def test_trainer_eval_mrpc(self):
MODEL_ID = "bert-base-cased-finetuned-mrpc"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
data_args = GlueDataTrainingArguments(
task_name="mrpc", data_dir=f"{get_tests_dir()}/fixtures/tests_samples/MRPC", overwrite_cache=True
)
eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev")
training_args = TrainingArguments(output_dir="./examples", no_cuda=True)
trainer = Trainer(model=model, args=training_args, eval_dataset=eval_dataset)
result = trainer.evaluate()
self.assertLess(result["eval_loss"], 0.2)
@slow
def test_trainer_eval_lm(self):
MODEL_ID = "distilroberta-base"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
dataset = LineByLineTextDataset(
tokenizer=tokenizer,
file_path=PATH_SAMPLE_TEXT,
block_size=tokenizer.max_len_single_sentence,
)
self.assertEqual(len(dataset), 31)
def test_training_iterable_dataset(self):
config = RegressionModelConfig()
model = RegressionPreTrainedModel(config)
# Adding one column not used by the model should have no impact
train_dataset = SampleIterableDataset(label_names=["labels", "extra"])
args = RegressionTrainingArguments(output_dir="./examples", max_steps=4)
trainer = Trainer(model=model, args=args, train_dataset=train_dataset)
trainer.train()
self.assertEqual(trainer.state.global_step, 4)
loader = trainer.get_train_dataloader()
self.assertIsInstance(loader, torch.utils.data.DataLoader)
self.assertIsInstance(loader.sampler, torch.utils.data.dataloader._InfiniteConstantSampler)
def test_evaluation_iterable_dataset(self):
config = RegressionModelConfig(a=1.5, b=2.5)
model = RegressionPreTrainedModel(config)
# Adding one column not used by the model should have no impact
eval_dataset = SampleIterableDataset(label_names=["labels", "extra"])
args = RegressionTrainingArguments(output_dir="./examples")
trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset, compute_metrics=AlmostAccuracy())
results = trainer.evaluate()
x, y = trainer.eval_dataset.dataset.x, trainer.eval_dataset.dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
# With a number of elements not a round multiple of the batch size
eval_dataset = SampleIterableDataset(length=66)
results = trainer.evaluate(eval_dataset)
x, y = eval_dataset.dataset.x, eval_dataset.dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
expected_acc = AlmostAccuracy()((pred, y))["accuracy"]
self.assertAlmostEqual(results["eval_accuracy"], expected_acc)
def test_predict_iterable_dataset(self):
config = RegressionModelConfig(a=1.5, b=2.5)
model = RegressionPreTrainedModel(config)
eval_dataset = SampleIterableDataset()
args = RegressionTrainingArguments(output_dir="./examples")
trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset, compute_metrics=AlmostAccuracy())
preds = trainer.predict(trainer.eval_dataset).predictions
x = eval_dataset.dataset.x
self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
# With a number of elements not a round multiple of the batch size
# Adding one column not used by the model should have no impact
test_dataset = SampleIterableDataset(length=66, label_names=["labels", "extra"])
preds = trainer.predict(test_dataset).predictions
x = test_dataset.dataset.x
self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
def test_num_train_epochs_in_training(self):
# len(train_dl) < gradient_accumulation_steps shouldn't give ``ZeroDivisionError`` when ``max_steps`` is given.
# It should give 1 update step for each epoch.
trainer = get_regression_trainer(
max_steps=3, train_len=64, per_device_train_batch_size=16, gradient_accumulation_steps=5
)
train_output = trainer.train()
self.assertEqual(train_output.global_step, 3)
# Even ``max_steps`` is not specified, we still expect 1 update step for each epoch if
# len(train_dl) < gradient_accumulation_steps.
trainer = get_regression_trainer(train_len=64, per_device_train_batch_size=16, gradient_accumulation_steps=5)
train_output = trainer.train()
self.assertEqual(train_output.global_step, int(self.n_epochs))
def test_early_stopping_callback(self):
# early stopping stops training before num_training_epochs
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=tmp_dir,
num_train_epochs=20,
gradient_accumulation_steps=1,
per_device_train_batch_size=16,
load_best_model_at_end=True,
evaluation_strategy=IntervalStrategy.EPOCH,
save_strategy=IntervalStrategy.EPOCH,
compute_metrics=AlmostAccuracy(),
metric_for_best_model="accuracy",
)
trainer.add_callback(EarlyStoppingCallback(1, 0.0001))
train_output = trainer.train()
self.assertLess(train_output.global_step, 20 * 64 / 16)
# Invalid inputs to trainer with early stopping callback result in assertion error
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=tmp_dir,
num_train_epochs=20,
gradient_accumulation_steps=1,
per_device_train_batch_size=16,
evaluation_strategy=IntervalStrategy.EPOCH,
compute_metrics=AlmostAccuracy(),
metric_for_best_model="accuracy",
)
trainer.add_callback(EarlyStoppingCallback(1))
self.assertEqual(trainer.state.global_step, 0)
try:
trainer.train()
except AssertionError:
self.assertEqual(trainer.state.global_step, 0)
def test_flos_extraction(self):
trainer = get_regression_trainer(learning_rate=0.1)
def assert_flos_extraction(trainer, wrapped_model_to_check):
self.assertEqual(trainer.model, unwrap_model(wrapped_model_to_check))
self.assertGreaterEqual(getattr(unwrap_model(wrapped_model_to_check).config, "total_flos", 0), 0)
# with plain model
assert_flos_extraction(trainer, trainer.model)
# with enforced DataParallel
assert_flos_extraction(trainer, nn.DataParallel(trainer.model))
trainer.train()
self.assertTrue(isinstance(trainer.state.total_flos, float))
def check_checkpoint_deletion(self, trainer, output_dir, expected):
# Make fake checkpoints
for n in [5, 10, 15, 20, 25]:
os.makedirs(os.path.join(output_dir, f"{PREFIX_CHECKPOINT_DIR}-{n}"), exist_ok=True)
trainer._rotate_checkpoints(output_dir=output_dir)
glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{PREFIX_CHECKPOINT_DIR}-*")]
values = [int(re.match(f".*{PREFIX_CHECKPOINT_DIR}-([0-9]+)", d).groups()[0]) for d in glob_checkpoints]
self.assertSetEqual(set(values), set(expected))
def test_checkpoint_rotation(self):
with tempfile.TemporaryDirectory() as tmp_dir:
# Without best model at end
trainer = get_regression_trainer(output_dir=tmp_dir, save_total_limit=2)
self.check_checkpoint_deletion(trainer, tmp_dir, [20, 25])
# With best model at end
trainer = get_regression_trainer(
output_dir=tmp_dir, evaluation_strategy="steps", load_best_model_at_end=True, save_total_limit=2
)
trainer.state.best_model_checkpoint = os.path.join(tmp_dir, "checkpoint-5")
self.check_checkpoint_deletion(trainer, tmp_dir, [5, 25])
# Edge case: we don't always honor save_total_limit=1 if load_best_model_at_end=True to be able to resume
# from checkpoint
trainer = get_regression_trainer(
output_dir=tmp_dir, evaluation_strategy="steps", load_best_model_at_end=True, save_total_limit=1
)
trainer.state.best_model_checkpoint = os.path.join(tmp_dir, "checkpoint-25")
self.check_checkpoint_deletion(trainer, tmp_dir, [25])
trainer.state.best_model_checkpoint = os.path.join(tmp_dir, "checkpoint-5")
self.check_checkpoint_deletion(trainer, tmp_dir, [5, 25])
def check_mem_metrics(self, trainer, check_func):
metrics = trainer.train().metrics
check_func("init_mem_cpu_alloc_delta", metrics)
check_func("train_mem_cpu_alloc_delta", metrics)
if torch.cuda.device_count() > 0:
check_func("init_mem_gpu_alloc_delta", metrics)
check_func("train_mem_gpu_alloc_delta", metrics)
metrics = trainer.evaluate()
check_func("eval_mem_cpu_alloc_delta", metrics)
if torch.cuda.device_count() > 0:
check_func("eval_mem_gpu_alloc_delta", metrics)
metrics = trainer.predict(RegressionDataset()).metrics
check_func("test_mem_cpu_alloc_delta", metrics)
if torch.cuda.device_count() > 0:
check_func("test_mem_gpu_alloc_delta", metrics)
def test_mem_metrics(self):
# with mem metrics enabled
trainer = get_regression_trainer(skip_memory_metrics=False)
self.check_mem_metrics(trainer, self.assertIn)
# with mem metrics disabled
trainer = get_regression_trainer(skip_memory_metrics=True)
self.check_mem_metrics(trainer, self.assertNotIn)
@require_torch_gpu
def test_fp16_full_eval(self):
# this is a sensitive test so let's keep debugging printouts in place for quick diagnosis.
# it's using pretty large safety margins, but small enough to detect broken functionality.
debug = 0
n_gpus = get_gpu_count()
bs = 8
eval_len = 16 * n_gpus
# make the params somewhat big so that there will be enough RAM consumed to be able to
# measure things. We should get about 64KB for a+b in fp32
a = torch.ones(1000, bs) + 0.001
b = torch.ones(1000, bs) - 0.001
# 1. with fp16_full_eval disabled
trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, skip_memory_metrics=False)
metrics = trainer.evaluate()
del trainer
gc.collect()
fp32_init = metrics["init_mem_gpu_alloc_delta"]
fp32_eval = metrics["eval_mem_gpu_alloc_delta"]
if debug:
print(f"fp32_init {fp32_init}")
print(f"fp32_eval {fp32_eval}")
# here we expect the model to be preloaded in trainer.__init__ and consume around 64K gpu ram.
# perfect world: fp32_init == 64<<10
self.assertGreater(fp32_init, 59_000)
# after eval should be no extra memory allocated - with a small margin (other than the peak
# memory consumption for the forward calculation that gets recovered)
# perfect world: fp32_eval == close to zero
self.assertLess(fp32_eval, 5_000)
# 2. with fp16_full_eval enabled
trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, fp16_full_eval=True, skip_memory_metrics=False)
metrics = trainer.evaluate()
fp16_init = metrics["init_mem_gpu_alloc_delta"]
fp16_eval = metrics["eval_mem_gpu_alloc_delta"]
if debug:
print(f"fp16_init {fp16_init}")
print(f"fp16_eval {fp16_eval}")
# here we expect the model to not be preloaded in trainer.__init__, so with a small margin it should be close to 0
# perfect world: fp16_init == close to zero
self.assertLess(fp16_init, 5_000)
# here we put the model on device in eval and only `half()` of it, i.e. about 32K,(again we ignore the peak margin which gets returned back)
# perfect world: fp32_init == 32<<10
self.assertGreater(fp16_eval, 27_000)
# 3. relative comparison fp32 vs full fp16
# should be about half of fp16_init
# perfect world: fp32_init/2 == fp16_eval
self.assertAlmostEqual(fp16_eval, fp32_init / 2, delta=5_000)
@require_torch_non_multi_gpu
@require_torchdynamo
@require_torch_tensorrt_fx
def test_torchdynamo_full_eval(self):
import torchdynamo
# torchdynamo at the moment doesn't support DP/DDP, therefore require a single gpu
n_gpus = get_gpu_count()
bs = 8
eval_len = 16 * n_gpus
# make the params are somewhat big so that there will be enough RAM consumed to be able to
# measure things. We should get about 64KB for a+b in fp32
a = torch.ones(1000, bs) + 0.001
b = torch.ones(1000, bs) - 0.001
# 1. Default - without TorchDynamo
trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len)
metrics = trainer.evaluate()
original_eval_loss = metrics["eval_loss"]
del trainer
# 2. TorchDynamo eager
trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, torchdynamo="eager")
metrics = trainer.evaluate()
self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss)
del trainer
torchdynamo.reset()
# 3. TorchDynamo nvfuser
trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, torchdynamo="nvfuser")
metrics = trainer.evaluate()
self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss)
torchdynamo.reset()
# 4. TorchDynamo fx2trt
trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, torchdynamo="fx2trt")
metrics = trainer.evaluate()
self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss)
torchdynamo.reset()
@unittest.skip("torch 2.0.0 gives `ModuleNotFoundError: No module named 'torchdynamo'`.")
@require_torch_non_multi_gpu
@require_torchdynamo
def test_torchdynamo_memory(self):
# torchdynamo at the moment doesn't support DP/DDP, therefore require a single gpu
import torchdynamo
class CustomTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
x = inputs["x"]
output = model(x)
if self.args.n_gpu == 1:
return output.mean()
return output
class MyModule(torch.nn.Module):
"""Simple module that does aggressive fusion"""
def __init__(self):
super().__init__()
def forward(self, x):
for _ in range(20):
x = torch.cos(x)
return x
mod = MyModule()
# 1. without TorchDynamo (eager baseline)
a = torch.ones(1024, 1024, device="cuda", requires_grad=True)
a.grad = None
trainer = CustomTrainer(model=mod)
# warmup
for _ in range(10):
orig_loss = trainer.training_step(mod, {"x": a})
# resets
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
orig_loss = trainer.training_step(mod, {"x": a})
orig_peak_mem = torch.cuda.max_memory_allocated()
torchdynamo.reset()
del trainer
# 2. TorchDynamo nvfuser
a = torch.ones(1024, 1024, device="cuda", requires_grad=True)
a.grad = None
args = TrainingArguments(output_dir="None", torchdynamo="nvfuser")
trainer = CustomTrainer(model=mod, args=args)
# warmup
for _ in range(10):
loss = trainer.training_step(mod, {"x": a})
# resets
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
loss = trainer.training_step(mod, {"x": a})
peak_mem = torch.cuda.max_memory_allocated()
torchdynamo.reset()
del trainer
# Functional check
self.assertAlmostEqual(loss, orig_loss)
# AOT Autograd recomputaion and nvfuser recomputation optimization
# aggressively fuses the operations and reduce the memory footprint.
self.assertGreater(orig_peak_mem, peak_mem * 2)
@require_torch_gpu
@require_torch_bf16_gpu
def test_bf16_full_eval(self):
# note: most of the logic is the same as test_fp16_full_eval
# this is a sensitive test so let's keep debugging printouts in place for quick diagnosis.
# it's using pretty large safety margins, but small enough to detect broken functionality.
debug = 0
n_gpus = get_gpu_count()
bs = 8
eval_len = 16 * n_gpus
# make the params somewhat big so that there will be enough RAM consumed to be able to
# measure things. We should get about 64KB for a+b in fp32
a = torch.ones(1000, bs) + 0.001
b = torch.ones(1000, bs) - 0.001
# 1. with bf16_full_eval disabled
trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, skip_memory_metrics=False)
metrics = trainer.evaluate()
del trainer
gc.collect()
fp32_init = metrics["init_mem_gpu_alloc_delta"]
fp32_eval = metrics["eval_mem_gpu_alloc_delta"]
if debug:
print(f"fp32_init {fp32_init}")
print(f"fp32_eval {fp32_eval}")
# here we expect the model to be preloaded in trainer.__init__ and consume around 64K gpu ram.
# perfect world: fp32_init == 64<<10
self.assertGreater(fp32_init, 59_000)
# after eval should be no extra memory allocated - with a small margin (other than the peak
# memory consumption for the forward calculation that gets recovered)
# perfect world: fp32_eval == close to zero
self.assertLess(fp32_eval, 5_000)
# 2. with bf16_full_eval enabled
trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, bf16_full_eval=True, skip_memory_metrics=False)
metrics = trainer.evaluate()
bf16_init = metrics["init_mem_gpu_alloc_delta"]
bf16_eval = metrics["eval_mem_gpu_alloc_delta"]
if debug:
print(f"bf16_init {bf16_init}")
print(f"bf16_eval {bf16_eval}")
# here we expect the model to not be preloaded in trainer.__init__, so with a small margin it should be close to 0
# perfect world: bf16_init == close to zero
self.assertLess(bf16_init, 5_000)
# here we put the model on device in eval and only `half()` of it, i.e. about 32K,(again we ignore the peak margin which gets returned back)
# perfect world: fp32_init == 32<<10
self.assertGreater(bf16_eval, 27_000)
# 3. relative comparison fp32 vs full bf16
# should be about half of bf16_init
# perfect world: fp32_init/2 == bf16_eval
self.assertAlmostEqual(bf16_eval, fp32_init / 2, delta=5_000)
def test_no_wd_param_group(self):
model = nn.Sequential(TstLayer(128), nn.ModuleList([TstLayer(128), TstLayer(128)]))
trainer = Trainer(model=model)
trainer.create_optimizer_and_scheduler(10)
# fmt: off
wd_names = ['0.linear1.weight', '0.linear2.weight', '1.0.linear1.weight', '1.0.linear2.weight', '1.1.linear1.weight', '1.1.linear2.weight']
# fmt: on
wd_params = [p for n, p in model.named_parameters() if n in wd_names]
no_wd_params = [p for n, p in model.named_parameters() if n not in wd_names]
self.assertListEqual(trainer.optimizer.param_groups[0]["params"], wd_params)
self.assertListEqual(trainer.optimizer.param_groups[1]["params"], no_wd_params)
@slow
@require_torch_multi_gpu
def test_end_to_end_example(self):
# Tests that `translation.py` will run without issues
script_path = os.path.abspath(
os.path.join(
os.path.dirname(__file__), "..", "..", "examples", "pytorch", "translation", "run_translation.py"
)
)
with tempfile.TemporaryDirectory() as tmpdir:
command = [
"accelerate",
"launch",
script_path,
"--model_name_or_path",
"t5-small",
"--per_device_train_batch_size",
"1",
"--output_dir",
tmpdir,
"--overwrite_output_dir",
"--do_train",
"--max_train_samples",
"64",
"--num_train_epochs",
"1",
"--dataset_name",
"wmt16",
"--dataset_config",
"ro-en",
"--source_lang",
"en",
"--target_lang",
"ro",
"--do_predict",
"--max_predict_samples",
"64",
"--predict_with_generate",
"--ddp_timeout",
"60",
]
execute_subprocess_async(command)
# successful return here == success - any errors would have caused an error or a timeout in the sub-call
@require_torch
@is_staging_test
class TrainerIntegrationWithHubTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
@classmethod
def tearDownClass(cls):
for model in ["test-trainer", "test-trainer-epoch", "test-trainer-step"]:
try:
delete_repo(token=cls._token, repo_id=model)
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-trainer-org")
except HTTPError:
pass
def test_push_to_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=os.path.join(tmp_dir, "test-trainer"),
push_to_hub=True,
hub_token=self._token,
)
url = trainer.push_to_hub()
# Extract repo_name from the url
re_search = re.search(ENDPOINT_STAGING + r"/([^/]+/[^/]+)/", url)
self.assertTrue(re_search is not None)
repo_name = re_search.groups()[0]
self.assertEqual(repo_name, f"{USER}/test-trainer")
model = RegressionPreTrainedModel.from_pretrained(repo_name)
self.assertEqual(model.a.item(), trainer.model.a.item())
self.assertEqual(model.b.item(), trainer.model.b.item())
def test_push_to_hub_in_organization(self):
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(output_dir=tmp_dir)
trainer.save_model()
trainer = get_regression_trainer(
output_dir=os.path.join(tmp_dir, "test-trainer-org"),
push_to_hub=True,
hub_model_id="valid_org/test-trainer-org",
hub_token=self._token,
)
url = trainer.push_to_hub()
# Extract repo_name from the url
re_search = re.search(ENDPOINT_STAGING + r"/([^/]+/[^/]+)/", url)
self.assertTrue(re_search is not None)
repo_name = re_search.groups()[0]
self.assertEqual(repo_name, "valid_org/test-trainer-org")
model = RegressionPreTrainedModel.from_pretrained("valid_org/test-trainer-org")
self.assertEqual(model.a.item(), trainer.model.a.item())
self.assertEqual(model.b.item(), trainer.model.b.item())
def get_commit_history(self, repo):
commit_logs = subprocess.run(
"git log".split(),
stderr=subprocess.PIPE,
stdout=subprocess.PIPE,
check=True,
encoding="utf-8",
cwd=repo,
).stdout
commits = commit_logs.split("\n\n")[1::2]
return [commit.strip() for commit in commits]
def test_push_to_hub_with_saves_each_epoch(self):
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=os.path.join(tmp_dir, "test-trainer-epoch"),
push_to_hub=True,
hub_token=self._token,
save_strategy="epoch",
)
trainer.train()
# Wait for the async pushes to be finished
while trainer.push_in_progress is not None and not trainer.push_in_progress.is_done:
time.sleep(0.5)
with tempfile.TemporaryDirectory() as tmp_dir:
_ = Repository(tmp_dir, clone_from=f"{USER}/test-trainer-epoch", token=self._token)
commits = self.get_commit_history(tmp_dir)
self.assertIn("initial commit", commits)
# We can't test that epoch 2 and 3 are in the commits without being flaky as those might be skipped if
# the push for epoch 1 wasn't finished at the time.
self.assertIn("Training in progress, epoch 1", commits)
def test_push_to_hub_with_saves_each_n_steps(self):
num_gpus = max(1, get_gpu_count())
if num_gpus > 2:
return
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=os.path.join(tmp_dir, "test-trainer-step"),
push_to_hub=True,
hub_token=self._token,
save_strategy="steps",
save_steps=5,
)
trainer.train()
# Wait for the async pushes to be finished
while trainer.push_in_progress is not None and not trainer.push_in_progress.is_done:
time.sleep(0.5)
with tempfile.TemporaryDirectory() as tmp_dir:
_ = Repository(tmp_dir, clone_from=f"{USER}/test-trainer-step", token=self._token)
commits = self.get_commit_history(tmp_dir)
self.assertIn("initial commit", commits)
# We can't test that epoch 2 and 3 are in the commits without being flaky as those might be skipped if
# the push for epoch 1 wasn't finished at the time.
self.assertIn("Training in progress, step 5", commits)
@require_torch
@require_optuna
class TrainerHyperParameterOptunaIntegrationTest(unittest.TestCase):
def setUp(self):
args = TrainingArguments("..")
self.n_epochs = args.num_train_epochs
self.batch_size = args.train_batch_size
def test_hyperparameter_search(self):
class MyTrialShortNamer(TrialShortNamer):
DEFAULTS = {"a": 0, "b": 0}
def hp_space(trial):
return {}
def model_init(trial):
if trial is not None:
a = trial.suggest_int("a", -4, 4)
b = trial.suggest_int("b", -4, 4)
else:
a = 0
b = 0
config = RegressionModelConfig(a=a, b=b, double_output=False)
return RegressionPreTrainedModel(config)
def hp_name(trial):
return MyTrialShortNamer.shortname(trial.params)
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=tmp_dir,
learning_rate=0.1,
logging_steps=1,
evaluation_strategy=IntervalStrategy.EPOCH,
save_strategy=IntervalStrategy.EPOCH,
num_train_epochs=4,
disable_tqdm=True,
load_best_model_at_end=True,
logging_dir="runs",
run_name="test",
model_init=model_init,
)
trainer.hyperparameter_search(direction="minimize", hp_space=hp_space, hp_name=hp_name, n_trials=4)
@require_torch
@require_ray
class TrainerHyperParameterRayIntegrationTest(unittest.TestCase):
def setUp(self):
args = TrainingArguments("..")
self.n_epochs = args.num_train_epochs
self.batch_size = args.train_batch_size
def ray_hyperparameter_search(self):
class MyTrialShortNamer(TrialShortNamer):
DEFAULTS = {"a": 0, "b": 0}
def hp_space(trial):
from ray import tune
return {
"a": tune.randint(-4, 4),
"b": tune.randint(-4, 4),
}
def model_init(config):
if config is None:
a = 0
b = 0
else:
a = config["a"]
b = config["b"]
model_config = RegressionModelConfig(a=a, b=b, double_output=False)
return RegressionPreTrainedModel(model_config)
def hp_name(params):
return MyTrialShortNamer.shortname(params)
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=tmp_dir,
learning_rate=0.1,
logging_steps=1,
evaluation_strategy=IntervalStrategy.EPOCH,
save_strategy=IntervalStrategy.EPOCH,
num_train_epochs=4,
disable_tqdm=True,
load_best_model_at_end=True,
logging_dir="runs",
run_name="test",
model_init=model_init,
)
trainer.hyperparameter_search(
direction="minimize", hp_space=hp_space, hp_name=hp_name, backend="ray", n_trials=4
)
def test_hyperparameter_search(self):
self.ray_hyperparameter_search()
def test_hyperparameter_search_ray_client(self):
import ray
from ray.util.client.ray_client_helpers import ray_start_client_server
with ray_start_client_server():
assert ray.util.client.ray.is_connected()
self.ray_hyperparameter_search()
@slow
@require_torch
@require_sigopt
class TrainerHyperParameterSigOptIntegrationTest(unittest.TestCase):
def setUp(self):
args = TrainingArguments("..")
self.n_epochs = args.num_train_epochs
self.batch_size = args.train_batch_size
def test_hyperparameter_search(self):
class MyTrialShortNamer(TrialShortNamer):
DEFAULTS = {"a": 0, "b": 0}
def hp_space(trial):
return [
{"bounds": {"min": -4, "max": 4}, "name": "a", "type": "int"},
{"bounds": {"min": -4, "max": 4}, "name": "b", "type": "int"},
]
def model_init(trial):
if trial is not None:
a = trial.assignments["a"]
b = trial.assignments["b"]
else:
a = 0
b = 0
config = RegressionModelConfig(a=a, b=b, double_output=False)
return RegressionPreTrainedModel(config)
def hp_name(trial):
return MyTrialShortNamer.shortname(trial.assignments)
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=tmp_dir,
learning_rate=0.1,
logging_steps=1,
evaluation_strategy=IntervalStrategy.EPOCH,
save_strategy=IntervalStrategy.EPOCH,
num_train_epochs=4,
disable_tqdm=True,
load_best_model_at_end=True,
logging_dir="runs",
run_name="test",
model_init=model_init,
)
trainer.hyperparameter_search(
direction="minimize", hp_space=hp_space, hp_name=hp_name, backend="sigopt", n_trials=4
)
optim_test_params = []
if is_torch_available():
default_adam_kwargs = {
"betas": (TrainingArguments.adam_beta1, TrainingArguments.adam_beta2),
"eps": TrainingArguments.adam_epsilon,
"lr": TrainingArguments.learning_rate,
}
default_lion_kwargs = {
"betas": (TrainingArguments.adam_beta1, TrainingArguments.adam_beta2),
"lr": TrainingArguments.learning_rate,
}
default_anyprecision_kwargs = {
"use_kahan_summation": False,
"momentum_dtype": torch.float32,
"variance_dtype": torch.float32,
"compensation_buffer_dtype": torch.bfloat16,
}
optim_test_params = [
(
TrainingArguments(optim=OptimizerNames.ADAMW_HF, output_dir="None"),
transformers.optimization.AdamW,
default_adam_kwargs,
),
(
TrainingArguments(optim=OptimizerNames.ADAMW_HF.value, output_dir="None"),
transformers.optimization.AdamW,
default_adam_kwargs,
),
(
TrainingArguments(optim=OptimizerNames.ADAMW_TORCH, output_dir="None"),
torch.optim.AdamW,
default_adam_kwargs,
),
(
TrainingArguments(optim=OptimizerNames.ADAFACTOR, output_dir="None"),
transformers.optimization.Adafactor,
{
"scale_parameter": False,
"relative_step": False,
"lr": TrainingArguments.learning_rate,
},
),
]
if is_apex_available():
import apex
optim_test_params.append(
(
TrainingArguments(optim=OptimizerNames.ADAMW_APEX_FUSED, output_dir="None"),
apex.optimizers.FusedAdam,
default_adam_kwargs,
)
)
if is_bitsandbytes_available():
import bitsandbytes as bnb
optim_test_params.append(
(
TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir="None"),
bnb.optim.AdamW,
default_adam_kwargs,
)
)
optim_test_params.append(
(
TrainingArguments(optim=OptimizerNames.ADAMW_8BIT, output_dir="None"),
bnb.optim.AdamW,
default_adam_kwargs,
)
)
optim_test_params.append(
(
TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir="None"),
bnb.optim.AdamW,
default_adam_kwargs,
)
)
optim_test_params.append(
(
TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir="None"),
bnb.optim.AdamW,
default_adam_kwargs,
)
)
optim_test_params.append(
(
TrainingArguments(optim=OptimizerNames.LION, output_dir="None"),
bnb.optim.Lion,
default_lion_kwargs,
)
)
optim_test_params.append(
(
TrainingArguments(optim=OptimizerNames.LION_8BIT, output_dir="None"),
bnb.optim.Lion,
default_lion_kwargs,
)
)
optim_test_params.append(
(
TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir="None"),
bnb.optim.Lion,
default_lion_kwargs,
)
)
if is_torchdistx_available():
import torchdistx
optim_test_params.append(
(
TrainingArguments(optim=OptimizerNames.ADAMW_ANYPRECISION, output_dir="None"),
torchdistx.optimizers.AnyPrecisionAdamW,
dict(default_adam_kwargs, **default_anyprecision_kwargs),
)
)
@require_torch
class TrainerOptimizerChoiceTest(unittest.TestCase):
def check_optim_and_kwargs(self, training_args: TrainingArguments, expected_cls, expected_kwargs):
actual_cls, optim_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)
self.assertEqual(expected_cls, actual_cls)
self.assertIsNotNone(optim_kwargs)
for p, v in expected_kwargs.items():
self.assertTrue(p in optim_kwargs)
actual_v = optim_kwargs[p]
self.assertTrue(actual_v == v, f"Failed check for {p}. Expected {v}, but got {actual_v}.")
@parameterized.expand(optim_test_params, skip_on_empty=True)
def test_optim_supported(self, training_args: TrainingArguments, expected_cls, expected_kwargs):
# exercises all the valid --optim options
self.check_optim_and_kwargs(training_args, expected_cls, expected_kwargs)
trainer = get_regression_trainer(**training_args.to_dict())
trainer.train()
def test_fused_adam(self):
# Pretend that apex is installed and mock apex.optimizers.FusedAdam exists.
# Trainer.get_optimizer_cls_and_kwargs does not use FusedAdam. It only has to return the
# class given, so mocking apex.optimizers.FusedAdam should be fine for testing and allow
# the test to run without requiring an apex installation.
mock = Mock()
modules = {
"apex": mock,
"apex.optimizers": mock.optimizers,
"apex.optimizers.FusedAdam": mock.optimizers.FusedAdam,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.ADAMW_APEX_FUSED, output_dir="None"),
mock.optimizers.FusedAdam,
default_adam_kwargs,
)
def test_fused_adam_no_apex(self):
args = TrainingArguments(optim=OptimizerNames.ADAMW_APEX_FUSED, output_dir="None")
# Pretend that apex does not exist, even if installed. By setting apex to None, importing
# apex will fail even if apex is installed.
with patch.dict("sys.modules", {"apex.optimizers": None}):
with self.assertRaises(ValueError):
Trainer.get_optimizer_cls_and_kwargs(args)
def test_bnb_adam8bit(self):
# Pretend that Bits and Bytes is installed and mock bnb.optim.Adam8bit exists.
# Trainer.get_optimizer_cls_and_kwargs does not use Adam8bit. It only has to return the
# class given, so mocking bnb.optim.Adam8bit should be fine for testing and allow
# the test to run without requiring a bnb installation.
mock = Mock()
modules = {
"bitsandbytes": mock,
"bitsandbytes.optim": mock.optim,
"bitsandbytes.optim.AdamW": mock.optim.AdamW,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir="None"),
mock.optim.AdamW,
default_adam_kwargs,
)
def test_bnb_paged_adam8bit_alias(self):
mock = Mock()
modules = {
"bitsandbytes": mock,
"bitsandbytes.optim": mock.optim,
"bitsandbytes.optim.AdamW": mock.optim.AdamW,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.ADAMW_8BIT, output_dir="None"),
mock.optim.AdamW,
default_adam_kwargs,
)
def test_bnb_paged_adam(self):
mock = Mock()
modules = {
"bitsandbytes": mock,
"bitsandbytes.optim": mock.optim,
"bitsandbytes.optim.AdamW": mock.optim.AdamW,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir="None"),
mock.optim.AdamW,
default_adam_kwargs,
)
def test_bnb_paged_adam8bit(self):
mock = Mock()
modules = {
"bitsandbytes": mock,
"bitsandbytes.optim": mock.optim,
"bitsandbytes.optim.AdamW": mock.optim.AdamW,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir="None"),
mock.optim.AdamW,
default_adam_kwargs,
)
def test_bnb_lion(self):
mock = Mock()
modules = {
"bitsandbytes": mock,
"bitsandbytes.optim": mock.optim,
"bitsandbytes.optim.Lion": mock.optim.Lion,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.LION, output_dir="None"),
mock.optim.Lion,
default_lion_kwargs,
)
def test_bnb_lion8bit(self):
mock = Mock()
modules = {
"bitsandbytes": mock,
"bitsandbytes.optim": mock.optim,
"bitsandbytes.optim.Lion": mock.optim.Lion,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.LION_8BIT, output_dir="None"),
mock.optim.Lion,
default_lion_kwargs,
)
def test_bnb_paged_lion8bit(self):
mock = Mock()
modules = {
"bitsandbytes": mock,
"bitsandbytes.optim": mock.optim,
"bitsandbytes.optim.Lion": mock.optim.Lion,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir="None"),
mock.optim.Lion,
default_lion_kwargs,
)
def test_bnb_paged_lion(self):
mock = Mock()
modules = {
"bitsandbytes": mock,
"bitsandbytes.optim": mock.optim,
"bitsandbytes.optim.Lion": mock.optim.Lion,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.PAGED_LION, output_dir="None"),
mock.optim.Lion,
default_lion_kwargs,
)
def test_bnb_adam8bit_no_bnb(self):
args = TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir="None")
# Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
# bnb will fail even if bnb is installed.
with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
with self.assertRaises(ValueError):
Trainer.get_optimizer_cls_and_kwargs(args)
def test_bnb_paged_adam_no_bnb(self):
args = TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir="None")
# Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
# bnb will fail even if bnb is installed.
with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
with self.assertRaises(ValueError):
Trainer.get_optimizer_cls_and_kwargs(args)
def test_bnb_paged_adam8bit_no_bnb(self):
args = TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir="None")
# Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
# bnb will fail even if bnb is installed.
with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
with self.assertRaises(ValueError):
Trainer.get_optimizer_cls_and_kwargs(args)
def test_bnb_paged_lion_no_bnb(self):
args = TrainingArguments(optim=OptimizerNames.PAGED_LION, output_dir="None")
# Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
# bnb will fail even if bnb is installed.
with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
with self.assertRaises(ValueError):
Trainer.get_optimizer_cls_and_kwargs(args)
def test_bnb_paged_lion8bit_no_bnb(self):
args = TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir="None")
# Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
# bnb will fail even if bnb is installed.
with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
with self.assertRaises(ValueError):
Trainer.get_optimizer_cls_and_kwargs(args)
def test_anyprecision_adamw(self):
# Pretend that torchdistx is installed and mock torchdistx.optimizers.AnyPrecisionAdamW exists.
# Trainer.get_optimizer_cls_and_kwargs does not use AnyPrecisioinAdamW. It only has to return the
# class given, so mocking torchdistx.optimizers.AnyPrecisionAdamW should be fine for testing and allow
# the test to run without requiring a bnb installation.
mock = Mock()
modules = {
"torchdistx": mock,
"torchdistx.optimizers": mock.optimizers,
"torchdistx.optimizers.AnyPrecisionAdamW.": mock.optimizers.AnyPrecisionAdamW,
}
with patch.dict("sys.modules", modules):
self.check_optim_and_kwargs(
TrainingArguments(optim=OptimizerNames.ADAMW_ANYPRECISION, output_dir="None"),
mock.optimizers.AnyPrecisionAdamW,
dict(default_adam_kwargs, **default_anyprecision_kwargs),
)
def test_no_torchdistx_anyprecision_adamw(self):
args = TrainingArguments(optim=OptimizerNames.ADAMW_ANYPRECISION, output_dir="None")
# Pretend that torchdistx does not exist, even if installed. By setting torchdistx to None, importing
# torchdistx.optimizers will fail even if torchdistx is installed.
with patch.dict("sys.modules", {"torchdistx.optimizers": None}):
with self.assertRaises(ValueError):
Trainer.get_optimizer_cls_and_kwargs(args)
@require_torch
@require_wandb
class TrainerHyperParameterWandbIntegrationTest(unittest.TestCase):
def setUp(self):
args = TrainingArguments("..")
self.n_epochs = args.num_train_epochs
self.batch_size = args.train_batch_size
def test_hyperparameter_search(self):
class MyTrialShortNamer(TrialShortNamer):
DEFAULTS = {"a": 0, "b": 0}
def hp_space(trial):
return {
"method": "random",
"metric": {},
"parameters": {
"a": {"distribution": "uniform", "min": 1e-6, "max": 1e-4},
"b": {"distribution": "int_uniform", "min": 1, "max": 6},
},
}
def model_init(config):
if config is None:
a = 0
b = 0
else:
a = config["a"]
b = config["b"]
model_config = RegressionModelConfig(a=a, b=b, double_output=False)
return RegressionPreTrainedModel(model_config)
def hp_name(params):
return MyTrialShortNamer.shortname(params)
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=tmp_dir,
learning_rate=0.1,
logging_steps=1,
evaluation_strategy=IntervalStrategy.EPOCH,
save_strategy=IntervalStrategy.EPOCH,
num_train_epochs=4,
disable_tqdm=True,
load_best_model_at_end=True,
logging_dir="runs",
run_name="test",
model_init=model_init,
)
trainer.hyperparameter_search(
direction="minimize", hp_space=hp_space, hp_name=hp_name, backend="wandb", n_trials=4, anonymous="must"
)
class HyperParameterSearchBackendsTest(unittest.TestCase):
def test_hyperparameter_search_backends(self):
self.assertEqual(
list(ALL_HYPERPARAMETER_SEARCH_BACKENDS.keys()),
list(HPSearchBackend),
)
| 118,879 | 40.551905 | 148 | py |
transformers | transformers-main/tests/trainer/test_trainer_distributed.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
require_torch_npu,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
logger = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class DummyDataset(Dataset):
def __init__(self, length: int = 101):
self.length = length
def __len__(self):
return self.length
def __getitem__(self, i) -> int:
return i
class DummyDataCollator:
def __call__(self, features):
return {"input_ids": torch.tensor(features), "labels": torch.tensor(features)}
class DummyModel(nn.Module):
def __init__(self):
super().__init__()
# Add some (unused) params otherwise DDP will complain.
self.fc = nn.Linear(120, 80)
def forward(self, input_ids, labels=None):
if labels is not None:
return torch.tensor(0.0, device=input_ids.device), input_ids
else:
return input_ids
class TestTrainerDistributedNeuronCore(TestCasePlus):
@require_torch_neuroncore
def test_trainer(self):
distributed_args = f"""--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
""".split()
output_dir = self.get_auto_remove_tmp_dir()
args = f"--output_dir {output_dir}".split()
cmd = ["torchrun"] + distributed_args + args
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
class TestTrainerDistributedNPU(TestCasePlus):
@require_torch_npu
def test_trainer(self):
distributed_args = f"""--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
""".split()
output_dir = self.get_auto_remove_tmp_dir()
args = f"--output_dir {output_dir}".split()
cmd = ["torchrun"] + distributed_args + args
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
class TestTrainerDistributed(TestCasePlus):
@require_torch_multi_gpu
def test_trainer(self):
distributed_args = f"""--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
""".split()
output_dir = self.get_auto_remove_tmp_dir()
args = f"--output_dir {output_dir}".split()
cmd = ["torchrun"] + distributed_args + args
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
parser = HfArgumentParser((TrainingArguments,))
training_args = parser.parse_args_into_dataclasses()[0]
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
f"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
dataset = DummyDataset(dataset_length)
def compute_metrics(p: EvalPrediction) -> Dict:
sequential = list(range(len(dataset)))
success = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
f"{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}"
)
return {"success": success}
trainer = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
metrics = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
p = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
trainer.args.eval_accumulation_steps = 2
metrics = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
p = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
trainer.args.eval_accumulation_steps = None
| 6,342 | 36.093567 | 133 | py |
transformers | transformers-main/tests/trainer/test_trainer_callback.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class MyTestTrainerCallback(TrainerCallback):
"A callback that registers the events that goes through."
def __init__(self):
self.events = []
def on_init_end(self, args, state, control, **kwargs):
self.events.append("on_init_end")
def on_train_begin(self, args, state, control, **kwargs):
self.events.append("on_train_begin")
def on_train_end(self, args, state, control, **kwargs):
self.events.append("on_train_end")
def on_epoch_begin(self, args, state, control, **kwargs):
self.events.append("on_epoch_begin")
def on_epoch_end(self, args, state, control, **kwargs):
self.events.append("on_epoch_end")
def on_step_begin(self, args, state, control, **kwargs):
self.events.append("on_step_begin")
def on_step_end(self, args, state, control, **kwargs):
self.events.append("on_step_end")
def on_evaluate(self, args, state, control, **kwargs):
self.events.append("on_evaluate")
def on_predict(self, args, state, control, **kwargs):
self.events.append("on_predict")
def on_save(self, args, state, control, **kwargs):
self.events.append("on_save")
def on_log(self, args, state, control, **kwargs):
self.events.append("on_log")
def on_prediction_step(self, args, state, control, **kwargs):
self.events.append("on_prediction_step")
@require_torch
class TrainerCallbackTest(unittest.TestCase):
def setUp(self):
self.output_dir = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.output_dir)
def get_trainer(self, a=0, b=0, train_len=64, eval_len=64, callbacks=None, disable_tqdm=False, **kwargs):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
train_dataset = RegressionDataset(length=train_len)
eval_dataset = RegressionDataset(length=eval_len)
config = RegressionModelConfig(a=a, b=b)
model = RegressionPreTrainedModel(config)
args = TrainingArguments(self.output_dir, disable_tqdm=disable_tqdm, report_to=[], **kwargs)
return Trainer(
model,
args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
callbacks=callbacks,
)
def check_callbacks_equality(self, cbs1, cbs2):
self.assertEqual(len(cbs1), len(cbs2))
# Order doesn't matter
cbs1 = sorted(cbs1, key=lambda cb: cb.__name__ if isinstance(cb, type) else cb.__class__.__name__)
cbs2 = sorted(cbs2, key=lambda cb: cb.__name__ if isinstance(cb, type) else cb.__class__.__name__)
for cb1, cb2 in zip(cbs1, cbs2):
if isinstance(cb1, type) and isinstance(cb2, type):
self.assertEqual(cb1, cb2)
elif isinstance(cb1, type) and not isinstance(cb2, type):
self.assertEqual(cb1, cb2.__class__)
elif not isinstance(cb1, type) and isinstance(cb2, type):
self.assertEqual(cb1.__class__, cb2)
else:
self.assertEqual(cb1, cb2)
def get_expected_events(self, trainer):
expected_events = ["on_init_end", "on_train_begin"]
step = 0
train_dl_len = len(trainer.get_eval_dataloader())
evaluation_events = ["on_prediction_step"] * len(trainer.get_eval_dataloader()) + ["on_log", "on_evaluate"]
for _ in range(trainer.state.num_train_epochs):
expected_events.append("on_epoch_begin")
for _ in range(train_dl_len):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log")
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("on_save")
expected_events.append("on_epoch_end")
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def test_init_callback(self):
trainer = self.get_trainer()
expected_callbacks = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
# Callbacks passed at init are added to the default callbacks
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback])
expected_callbacks.append(MyTestTrainerCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
trainer = self.get_trainer(disable_tqdm=True)
expected_callbacks = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
def test_add_remove_callback(self):
expected_callbacks = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
trainer = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(DefaultFlowCallback)
expected_callbacks.remove(DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
trainer = self.get_trainer()
cb = trainer.pop_callback(DefaultFlowCallback)
self.assertEqual(cb.__class__, DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
trainer.add_callback(DefaultFlowCallback)
expected_callbacks.insert(0, DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
# We can also add, pop, or remove by instance
trainer = self.get_trainer()
cb = trainer.callback_handler.callbacks[0]
trainer.remove_callback(cb)
expected_callbacks.remove(DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
trainer = self.get_trainer()
cb1 = trainer.callback_handler.callbacks[0]
cb2 = trainer.pop_callback(cb1)
self.assertEqual(cb1, cb2)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
trainer.add_callback(cb1)
expected_callbacks.insert(0, DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
def test_event_flow(self):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="ignore", category=UserWarning)
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback])
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
# Independent log/save/eval
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], logging_steps=5)
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], save_steps=5)
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], eval_steps=5, evaluation_strategy="steps")
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], evaluation_strategy="epoch")
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
# A bit of everything
trainer = self.get_trainer(
callbacks=[MyTestTrainerCallback],
logging_steps=3,
save_steps=10,
eval_steps=5,
evaluation_strategy="steps",
)
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
# warning should be emitted for duplicated callbacks
with patch("transformers.trainer_callback.logger.warning") as warn_mock:
trainer = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback],
)
assert str(MyTestTrainerCallback) in warn_mock.call_args[0][0]
| 10,273 | 40.764228 | 118 | py |
transformers | transformers-main/tests/trainer/test_trainer_utils.py | # coding=utf-8
# Copyright 2018 the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import unittest
import numpy as np
from transformers.data.data_collator import default_data_collator
from transformers.testing_utils import require_accelerate, require_torch
from transformers.trainer_utils import RemoveColumnsCollator, find_executable_batch_size
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import IterableDataset
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers.tokenization_utils_base import BatchEncoding
from transformers.trainer_pt_utils import (
DistributedLengthGroupedSampler,
DistributedSamplerWithLoop,
DistributedTensorGatherer,
IterableDatasetShard,
LabelSmoother,
LengthGroupedSampler,
SequentialDistributedSampler,
ShardSampler,
get_parameter_names,
numpy_pad_and_concatenate,
torch_pad_and_concatenate,
)
class TstLayer(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.linear1 = nn.Linear(hidden_size, hidden_size)
self.ln1 = nn.LayerNorm(hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.ln2 = nn.LayerNorm(hidden_size)
self.bias = nn.Parameter(torch.zeros(hidden_size))
def forward(self, x):
h = self.ln1(nn.functional.relu(self.linear1(x)))
h = nn.functional.relu(self.linear2(x))
return self.ln2(x + h + self.bias)
class RandomIterableDataset(IterableDataset):
# For testing, an iterable dataset of random length
def __init__(self, p_stop=0.01, max_length=1000):
self.p_stop = p_stop
self.max_length = max_length
self.generator = torch.Generator()
def __iter__(self):
count = 0
stop = False
while not stop and count < self.max_length:
yield count
count += 1
number = torch.rand(1, generator=self.generator).item()
stop = number < self.p_stop
@require_torch
class TrainerUtilsTest(unittest.TestCase):
def test_distributed_tensor_gatherer(self):
# Simulate a result with a dataset of size 21, 4 processes and chunks of lengths 2, 3, 1
world_size = 4
num_samples = 21
input_indices = [
[0, 1, 6, 7, 12, 13, 18, 19],
[2, 3, 4, 8, 9, 10, 14, 15, 16, 20, 0, 1],
[5, 11, 17, 2],
]
predictions = np.random.normal(size=(num_samples, 13))
gatherer = DistributedTensorGatherer(world_size=world_size, num_samples=num_samples)
for indices in input_indices:
gatherer.add_arrays(predictions[indices])
result = gatherer.finalize()
self.assertTrue(np.array_equal(result, predictions))
# With nested tensors
gatherer = DistributedTensorGatherer(world_size=world_size, num_samples=num_samples)
for indices in input_indices:
gatherer.add_arrays([predictions[indices], [predictions[indices], predictions[indices]]])
result = gatherer.finalize()
self.assertTrue(isinstance(result, list))
self.assertEqual(len(result), 2)
self.assertTrue(isinstance(result[1], list))
self.assertEqual(len(result[1]), 2)
self.assertTrue(np.array_equal(result[0], predictions))
self.assertTrue(np.array_equal(result[1][0], predictions))
self.assertTrue(np.array_equal(result[1][1], predictions))
def test_distributed_tensor_gatherer_different_shapes(self):
# Simulate a result with a dataset of size 21, 4 processes and chunks of lengths 2, 3, 1
world_size = 4
num_samples = 21
input_indices = [
[0, 1, 6, 7, 12, 13, 18, 19],
[2, 3, 4, 8, 9, 10, 14, 15, 16, 20, 0, 1],
[5, 11, 17, 2],
]
sequence_lengths = [8, 10, 13]
predictions = np.random.normal(size=(num_samples, 13))
gatherer = DistributedTensorGatherer(world_size=world_size, num_samples=num_samples)
for indices, seq_length in zip(input_indices, sequence_lengths):
gatherer.add_arrays(predictions[indices, :seq_length])
result = gatherer.finalize()
# Remove the extra samples added at the end for a round multiple of num processes.
actual_indices = [input_indices[0], input_indices[1][:-2], input_indices[2][:-1]]
for indices, seq_length in zip(actual_indices, sequence_lengths):
self.assertTrue(np.array_equal(result[indices, :seq_length], predictions[indices, :seq_length]))
# With nested tensors
predictions = np.random.normal(size=(num_samples, 13))
gatherer = DistributedTensorGatherer(world_size=world_size, num_samples=num_samples)
for indices, seq_length in zip(input_indices, sequence_lengths):
gatherer.add_arrays([predictions[indices, :seq_length], predictions[indices]])
result = gatherer.finalize()
for indices, seq_length in zip(actual_indices, sequence_lengths):
self.assertTrue(np.array_equal(result[0][indices, :seq_length], predictions[indices, :seq_length]))
self.assertTrue(np.array_equal(result[1], predictions))
# Check if works if varying seq_length is second
gatherer = DistributedTensorGatherer(world_size=world_size, num_samples=num_samples)
for indices, seq_length in zip(input_indices, sequence_lengths):
gatherer.add_arrays([predictions[indices], predictions[indices, :seq_length]])
result = gatherer.finalize()
self.assertTrue(np.array_equal(result[0], predictions))
for indices, seq_length in zip(actual_indices, sequence_lengths):
self.assertTrue(np.array_equal(result[1][indices, :seq_length], predictions[indices, :seq_length]))
def test_label_smoothing(self):
epsilon = 0.1
num_labels = 12
random_logits = torch.randn(4, 5, num_labels)
random_labels = torch.randint(0, num_labels, (4, 5))
loss = nn.functional.cross_entropy(random_logits.view(-1, num_labels), random_labels.view(-1))
model_output = SequenceClassifierOutput(logits=random_logits)
label_smoothed_loss = LabelSmoother(0.1)(model_output, random_labels)
log_probs = -nn.functional.log_softmax(random_logits, dim=-1)
expected_loss = (1 - epsilon) * loss + epsilon * log_probs.mean()
self.assertTrue(torch.allclose(label_smoothed_loss, expected_loss))
# With a few -100 labels
random_labels[0, 1] = -100
random_labels[2, 1] = -100
random_labels[2, 3] = -100
loss = nn.functional.cross_entropy(random_logits.view(-1, num_labels), random_labels.view(-1))
model_output = SequenceClassifierOutput(logits=random_logits)
label_smoothed_loss = LabelSmoother(0.1)(model_output, random_labels)
log_probs = -nn.functional.log_softmax(random_logits, dim=-1)
# Mask the log probs with the -100 labels
log_probs[0, 1] = 0.0
log_probs[2, 1] = 0.0
log_probs[2, 3] = 0.0
expected_loss = (1 - epsilon) * loss + epsilon * log_probs.sum() / (num_labels * 17)
self.assertTrue(torch.allclose(label_smoothed_loss, expected_loss))
def test_group_by_length(self):
# Get some inputs of random lengths
lengths = torch.randint(0, 25, (100,)).tolist()
# Put one bigger than the others to check it ends up in first position
lengths[32] = 50
indices = list(LengthGroupedSampler(4, lengths=lengths))
# The biggest element should be first
self.assertEqual(lengths[indices[0]], 50)
# The indices should be a permutation of range(100)
self.assertEqual(sorted(indices), list(range(100)))
def test_group_by_length_with_dict(self):
# Get some inputs of random lengths
data = []
for _ in range(6):
input_ids = torch.randint(0, 25, (100,)).tolist()
data.append({"input_ids": input_ids})
# Put one bigger than the others to check it ends up in first position
data[3]["input_ids"] = torch.randint(0, 25, (105,)).tolist()
indices = list(LengthGroupedSampler(4, dataset=data))
# The biggest element should be first
self.assertEqual(len(data[indices[0]]["input_ids"]), 105)
# The indices should be a permutation of range(6)
self.assertEqual(sorted(indices), list(range(6)))
def test_group_by_length_with_batch_encoding(self):
# Get some inputs of random lengths
data = []
for _ in range(6):
input_ids = torch.randint(0, 25, (100,)).tolist()
data.append(BatchEncoding({"input_ids": input_ids}))
# Put one bigger than the others to check it ends up in first position
data[3]["input_ids"] = torch.randint(0, 25, (105,)).tolist()
indices = list(LengthGroupedSampler(4, dataset=data))
# The biggest element should be first
self.assertEqual(len(data[indices[0]]["input_ids"]), 105)
# The indices should be a permutation of range(6)
self.assertEqual(sorted(indices), list(range(6)))
def test_distributed_length_grouped(self):
# Get some inputs of random lengths
lengths = torch.randint(0, 25, (100,)).tolist()
# Put one bigger than the others to check it ends up in first position
lengths[32] = 50
indices_process_0 = list(DistributedLengthGroupedSampler(4, num_replicas=2, rank=0, lengths=lengths))
indices_process_1 = list(DistributedLengthGroupedSampler(4, num_replicas=2, rank=1, lengths=lengths))
# The biggest element should be first
self.assertEqual(lengths[indices_process_0[0]], 50)
# The indices should be a permutation of range(100)
self.assertEqual(sorted(indices_process_0 + indices_process_1), list(range(100)))
def test_get_parameter_names(self):
model = nn.Sequential(TstLayer(128), nn.ModuleList([TstLayer(128), TstLayer(128)]))
# fmt: off
self.assertEqual(
get_parameter_names(model, [nn.LayerNorm]),
['0.linear1.weight', '0.linear1.bias', '0.linear2.weight', '0.linear2.bias', '0.bias', '1.0.linear1.weight', '1.0.linear1.bias', '1.0.linear2.weight', '1.0.linear2.bias', '1.0.bias', '1.1.linear1.weight', '1.1.linear1.bias', '1.1.linear2.weight', '1.1.linear2.bias', '1.1.bias']
)
# fmt: on
def test_distributed_sampler_with_loop(self):
batch_size = 16
for length in [23, 64, 123]:
dataset = list(range(length))
shard1 = DistributedSamplerWithLoop(dataset, batch_size, num_replicas=2, rank=0)
shard2 = DistributedSamplerWithLoop(dataset, batch_size, num_replicas=2, rank=1)
# Set seeds
shard1.set_epoch(0)
shard2.set_epoch(0)
# Sample
samples1 = list(shard1)
samples2 = list(shard2)
self.assertTrue(len(samples1) % batch_size == 0)
self.assertTrue(len(samples2) % batch_size == 0)
total = []
for sample1, sample2 in zip(samples1, samples2):
total += [sample1, sample2]
self.assertEqual(set(total[:length]), set(dataset))
self.assertEqual(set(total[length:]), set(total[: (len(total) - length)]))
def test_sequential_distributed_sampler(self):
batch_size = 16
for length in [23, 64, 123]:
dataset = list(range(length))
shard1 = SequentialDistributedSampler(dataset, num_replicas=2, rank=0)
shard2 = SequentialDistributedSampler(dataset, num_replicas=2, rank=1)
# Sample
samples1 = list(shard1)
samples2 = list(shard2)
total = samples1 + samples2
self.assertListEqual(total[:length], dataset)
self.assertListEqual(total[length:], dataset[: (len(total) - length)])
# With a batch_size passed
shard1 = SequentialDistributedSampler(dataset, num_replicas=2, rank=0, batch_size=batch_size)
shard2 = SequentialDistributedSampler(dataset, num_replicas=2, rank=1, batch_size=batch_size)
# Sample
samples1 = list(shard1)
samples2 = list(shard2)
self.assertTrue(len(samples1) % batch_size == 0)
self.assertTrue(len(samples2) % batch_size == 0)
total = samples1 + samples2
self.assertListEqual(total[:length], dataset)
self.assertListEqual(total[length:], dataset[: (len(total) - length)])
def check_iterable_dataset_shard(self, dataset, batch_size, drop_last, num_processes=2, epoch=0):
# Set the seed for the base dataset to get the proper reference.
dataset.generator.manual_seed(epoch)
reference = list(dataset)
shards = [
IterableDatasetShard(
dataset, batch_size=batch_size, drop_last=drop_last, num_processes=num_processes, process_index=i
)
for i in range(num_processes)
]
for shard in shards:
shard.set_epoch(epoch)
shard_lists = [list(shard) for shard in shards]
for shard in shard_lists:
# All shards have a number of samples that is a round multiple of batch size
self.assertTrue(len(shard) % batch_size == 0)
# All shards have the same number of samples
self.assertEqual(len(shard), len(shard_lists[0]))
for shard in shards:
# All shards know the total number of samples
self.assertEqual(shard.num_examples, len(reference))
observed = []
for idx in range(0, len(shard_lists[0]), batch_size):
for shard in shard_lists:
observed += shard[idx : idx + batch_size]
# If drop_last is False we loop through samples at the beginning to have a size that is a round multiple of
# batch_size
if not drop_last:
while len(reference) < len(observed):
reference += reference
self.assertListEqual(observed, reference[: len(observed)])
# Check equivalence between IterableDataset and ShardSampler
dataset.generator.manual_seed(epoch)
reference = list(dataset)
sampler_shards = [
ShardSampler(
reference, batch_size=batch_size, drop_last=drop_last, num_processes=num_processes, process_index=i
)
for i in range(num_processes)
]
for shard, sampler_shard in zip(shard_lists, sampler_shards):
self.assertListEqual(shard, list(sampler_shard))
def test_iterable_dataset_shard(self):
dataset = RandomIterableDataset()
self.check_iterable_dataset_shard(dataset, 4, drop_last=True, num_processes=2, epoch=0)
self.check_iterable_dataset_shard(dataset, 4, drop_last=False, num_processes=2, epoch=0)
self.check_iterable_dataset_shard(dataset, 4, drop_last=True, num_processes=3, epoch=42)
self.check_iterable_dataset_shard(dataset, 4, drop_last=False, num_processes=3, epoch=42)
def test_iterable_dataset_shard_with_length(self):
sampler_shards = [
IterableDatasetShard(list(range(100)), batch_size=4, drop_last=True, num_processes=2, process_index=i)
for i in range(2)
]
# Build expected shards: each process will have batches of size 4 until there is not enough elements to
# form two full batches (so we stop at 96 = (100 // (4 * 2)) * 4)
expected_shards = [[], []]
current_shard = 0
for i in range(0, 96, 4):
expected_shards[current_shard].extend(list(range(i, i + 4)))
current_shard = 1 - current_shard
self.assertListEqual([list(shard) for shard in sampler_shards], expected_shards)
self.assertListEqual([len(shard) for shard in sampler_shards], [len(shard) for shard in expected_shards])
sampler_shards = [
IterableDatasetShard(list(range(100)), batch_size=4, drop_last=False, num_processes=2, process_index=i)
for i in range(2)
]
# When drop_last=False, we get two last full batches by looping back to the beginning.
expected_shards[0].extend(list(range(96, 100)))
expected_shards[1].extend(list(range(0, 4)))
self.assertListEqual([list(shard) for shard in sampler_shards], expected_shards)
self.assertListEqual([len(shard) for shard in sampler_shards], [len(shard) for shard in expected_shards])
def check_shard_sampler(self, dataset, batch_size, drop_last, num_processes=2):
shards = [
ShardSampler(
dataset, batch_size=batch_size, drop_last=drop_last, num_processes=num_processes, process_index=i
)
for i in range(num_processes)
]
shard_lists = [list(shard) for shard in shards]
for shard in shard_lists:
# All shards have a number of samples that is a round multiple of batch size
self.assertTrue(len(shard) % batch_size == 0)
# All shards have the same number of samples
self.assertEqual(len(shard), len(shard_lists[0]))
observed = []
for idx in range(0, len(shard_lists[0]), batch_size):
for shard in shard_lists:
observed += shard[idx : idx + batch_size]
# If drop_last is False we loop through samples at the beginning to have a size that is a round multiple of
# batch_size
reference = copy.copy(dataset)
if not drop_last:
while len(reference) < len(observed):
reference += reference
self.assertListEqual(observed, reference[: len(observed)])
def test_shard_sampler(self):
for n_elements in [64, 123]:
dataset = list(range(n_elements))
self.check_shard_sampler(dataset, 4, drop_last=True, num_processes=2)
self.check_shard_sampler(dataset, 4, drop_last=False, num_processes=2)
self.check_shard_sampler(dataset, 4, drop_last=True, num_processes=3)
self.check_shard_sampler(dataset, 4, drop_last=False, num_processes=3)
@require_accelerate
def test_executable_batch_size(self):
batch_sizes = []
@find_executable_batch_size(starting_batch_size=64, auto_find_batch_size=True)
def mock_training_loop_function(batch_size):
nonlocal batch_sizes
batch_sizes.append(batch_size)
if batch_size > 16:
raise RuntimeError("CUDA out of memory.")
mock_training_loop_function()
self.assertEqual(batch_sizes, [64, 32, 16])
@require_accelerate
def test_executable_batch_size_no_search(self):
batch_sizes = []
@find_executable_batch_size(starting_batch_size=64, auto_find_batch_size=False)
def mock_training_loop_function(batch_size):
nonlocal batch_sizes
batch_sizes.append(batch_size)
mock_training_loop_function()
self.assertEqual(batch_sizes, [64])
@require_accelerate
def test_executable_batch_size_with_error(self):
@find_executable_batch_size(starting_batch_size=64, auto_find_batch_size=False)
def mock_training_loop_function(batch_size):
raise RuntimeError("CUDA out of memory.")
with self.assertRaises(RuntimeError) as cm:
mock_training_loop_function()
self.assertEqual("CUDA out of memory", cm.args[0])
def test_pad_and_concatenate_with_1d(self):
"""Tests whether pad_and_concatenate works with scalars."""
array1 = 1.0
array2 = 2.0
result = numpy_pad_and_concatenate(array1, array2)
self.assertTrue(np.array_equal(np.array([1.0, 2.0]), result))
tensor1 = torch.tensor(1.0)
tensor2 = torch.tensor(2.0)
result = torch_pad_and_concatenate(tensor1, tensor2)
self.assertTrue(torch.equal(result, torch.Tensor([1.0, 2.0])))
def test_remove_columns_collator(self):
class MockLogger:
def __init__(self) -> None:
self.called = 0
def info(self, msg):
self.called += 1
self.last_msg = msg
data_batch = [
{"col1": 1, "col2": 2, "col3": 3},
{"col1": 1, "col2": 2, "col3": 3},
]
logger = MockLogger()
remove_columns_collator = RemoveColumnsCollator(
default_data_collator, ["col1", "col2"], logger, "model", "training"
)
self.assertNotIn("col3", remove_columns_collator(data_batch))
# check that the logging message is printed out only once
remove_columns_collator(data_batch)
remove_columns_collator(data_batch)
self.assertEqual(logger.called, 1)
self.assertIn("col3", logger.last_msg)
| 21,726 | 42.454 | 290 | py |
transformers | transformers-main/tests/trainer/test_trainer_seq2seq.py | # coding=utf-8
# Copyright 2020 the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from transformers import BertTokenizer, EncoderDecoderModel, Seq2SeqTrainer, Seq2SeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class Seq2seqTrainerTester(TestCasePlus):
@slow
@require_torch
def test_finetune_bert2bert(self):
bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny", "prajjwal1/bert-tiny")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
bert2bert.config.vocab_size = bert2bert.config.encoder.vocab_size
bert2bert.config.eos_token_id = tokenizer.sep_token_id
bert2bert.config.decoder_start_token_id = tokenizer.cls_token_id
bert2bert.config.max_length = 128
train_dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train[:1%]")
val_dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="validation[:1%]")
train_dataset = train_dataset.select(range(32))
val_dataset = val_dataset.select(range(16))
batch_size = 4
def _map_to_encoder_decoder_inputs(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=512)
outputs = tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=128)
batch["input_ids"] = inputs.input_ids
batch["attention_mask"] = inputs.attention_mask
batch["decoder_input_ids"] = outputs.input_ids
batch["labels"] = outputs.input_ids.copy()
batch["labels"] = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
batch["decoder_attention_mask"] = outputs.attention_mask
assert all(len(x) == 512 for x in inputs.input_ids)
assert all(len(x) == 128 for x in outputs.input_ids)
return batch
def _compute_metrics(pred):
labels_ids = pred.label_ids
pred_ids = pred.predictions
# all unnecessary tokens are removed
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
accuracy = sum([int(pred_str[i] == label_str[i]) for i in range(len(pred_str))]) / len(pred_str)
return {"accuracy": accuracy}
# map train dataset
train_dataset = train_dataset.map(
_map_to_encoder_decoder_inputs,
batched=True,
batch_size=batch_size,
remove_columns=["article", "highlights"],
)
train_dataset.set_format(
type="torch",
columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)
# same for validation dataset
val_dataset = val_dataset.map(
_map_to_encoder_decoder_inputs,
batched=True,
batch_size=batch_size,
remove_columns=["article", "highlights"],
)
val_dataset.set_format(
type="torch",
columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)
output_dir = self.get_auto_remove_tmp_dir()
training_args = Seq2SeqTrainingArguments(
output_dir=output_dir,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
predict_with_generate=True,
evaluation_strategy="steps",
do_train=True,
do_eval=True,
warmup_steps=0,
eval_steps=2,
logging_steps=2,
)
# instantiate trainer
trainer = Seq2SeqTrainer(
model=bert2bert,
args=training_args,
compute_metrics=_compute_metrics,
train_dataset=train_dataset,
eval_dataset=val_dataset,
tokenizer=tokenizer,
)
# start training
trainer.train()
| 4,850 | 37.19685 | 118 | py |
transformers | transformers-main/tests/trainer/test_trainer_tpu.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This test is meant to be run in on an instance with TPUs like this:
#
# python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#
import sys
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging
logger = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class DummyDataset(Dataset):
def __init__(self, length: int = 101):
self.length = length
def __len__(self):
return self.length
def __getitem__(self, i) -> int:
return i
class DummyDataCollator:
def __call__(self, features):
return {"input_ids": torch.tensor(features), "labels": torch.tensor(features)}
class DummyModel(nn.Module):
def __init__(self):
super().__init__()
# Add some (unused) params otherwise DDP will complain.
self.fc = nn.Linear(120, 80)
def forward(self, input_ids, labels=None):
if labels is not None:
return torch.tensor(0.0, device=input_ids.device), input_ids
else:
return input_ids
def main():
parser = HfArgumentParser((TrainingArguments,))
sys.argv += ["--output_dir", "./examples"]
training_args = parser.parse_args_into_dataclasses()[0]
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, "
f"tpu_num_cores: {training_args.tpu_num_cores}",
)
# Essentially, what we want to verify in the distributed case is
# that we get all samples back, in the right order.
# (this is crucial for prediction for instance)
for dataset_length in [1001, 256, 15]:
dataset = DummyDataset(dataset_length)
def compute_metrics(p: EvalPrediction) -> Dict:
sequential = list(range(len(dataset)))
success = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
return {"success": success}
trainer = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
metrics = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
p = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
trainer.args.eval_accumulation_steps = 2
metrics = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
p = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
trainer.args.eval_accumulation_steps = None
logger.info("🔥 All distributed tests successful")
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 4,017 | 29.439394 | 97 | py |
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