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a88e206 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 | from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
import torch
from huggingface_hub import login
import re
import sys
from sklearn.metrics import accuracy_score
import argparse
import json
import pathlib
from typing import List, Dict
import numpy as np
import pandas as pd
import logging
from datetime import datetime
import os
MAX_NEW_TOKENS = 5
TEMPERATURE = 0.3
with open("/scratch/jbengoetxea/phd/XNLIvar/scripts/generative/config.json", "r") as f:
config = json.load(f)
def parse_args():
#os.environ['TRANSFORMERS_CACHE'] = '/XXXX-7/users/XXXX-1/metaphor_LLMs/paraphrase_gen/.cache/huggingface/hub'
parser = argparse.ArgumentParser(
description="Finetune a transformers model on a text classification task"
)
parser.add_argument(
"--dataset",
type=str,
default=None,
required=True,
help="Name of the dataset to predict gold_labels",
choices=["xnli-eu-native", "xnli-eu-var", "xnli-es-native", "xnli-es-var", "xnli-en", "xnli-es", "xnli-eu", "xnli-es-var-no-rep", "xnli-eu-var-no-rep", "xnli-eu-var-less-biz", "xnli-eu-var-less-gip", "xnli-eu-biz", "xnli-eu-gip", "xnli-eu-naf", "xnli-eu-nat-biz", "xnli-eu-nat-gip", "xnli-eu-nat-naf"]
)
parser.add_argument(
"--model",
type=str,
default=None,
required=True,
help="Model name in config",
choices=["llama3instruct8", "llama3instruct70", "gemmainstruct9", "gemmainstruct27", "latxainstruct70", "llama3base70"]
)
parser.add_argument(
"--output_dir",
type=str,
default=None,
required=True,
help="Output path to dump predictions"
)
parser.add_argument(
"--task",
type=str,
default=None,
required=True,
help="Type of task formulation",
choices=["binary", "trilabel", "qa-zero", "qa-few"]
)
parser.add_argument(
"--prompt_type",
type=str,
default=None,
required=True,
help="Type of prompt"
)
parser.add_argument(
"--paraphrases",
action="store_true",
required=False,
help="Dataset with paraphrases generated automatically"
)
parser.add_argument(
"--paraphrase_source",
type=str,
default=None,
required=False,
help="Model used to generate paraphrases"
)
args = parser.parse_args()
return args
def load_dataset(data_path: str) -> pd.DataFrame:
df = None
extension = pathlib.Path(data_path).suffix
if extension.endswith("json"):
df = pd.read_json(data_path)
elif extension.endswith("jsonl"):
df = pd.read_json(data_path, lines=True)
elif extension.endswith("tsv"):
df = pd.read_csv(data_path, sep="\t")
else:
df = pd.read_csv(data_path)
return df
def dump_predictions(out_path: str, premises: List, hypotheses: List, gold_labels: List, predictions: List, paraphrased_sents=None):
if paraphrased_sents:
with open(out_path, "w") as o:
o.write("premise\thypothesis\tgold_label\tprediction\tparaphrased_sentence\n")
for p, h, g, pr, paraph in zip(premises, hypotheses, gold_labels, predictions, paraphrased_sents):
o.write(f"{p}\t{h}\t{g}\t{pr}\t{paraph}\n")
else:
with open(out_path, "w") as o:
o.write("premise\thypothesis\tgold_label\tprediction\n")
for p, h, g, pr in zip(premises, hypotheses, gold_labels, predictions):
o.write(f"{p}\t{h}\t{g}\t{pr}\n")
print(f"{len(predictions)} Predictions stored in {out_path}")
def map_labels(predictions: List[str], label_mapping: Dict):
predictions_clean = [pred.strip("<>.,") for pred in predictions.lower().split()]
for pred in predictions_clean:
for label in label_mapping:
label_lower = label.lower()
# Allow partial matching in both directions
if pred in label_lower or label_lower in pred:
return label_mapping[label]
return "unk"
def get_column_values(df, col_id):
return df[col_id].tolist()
def map_labels_to_string(labels: List):
label_strings = []
for label in labels:
if label == 0:
label_strings.append("entailment")
elif label == 1:
label_strings.append("neutral")
else:
label_strings.append("contradiction")
return label_strings
def main():
args = parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir, exist_ok=True)
logger_path = os.path.join(args.output_dir, f"{args.prompt_type}_{args.paraphrase_source+'_' if args.paraphrase_source else ''}{datetime.now().strftime('%d-%m-%Y_%H_%M_%S')}.log")
logger = logging.getLogger(__name__)
logging.basicConfig(filename=os.path.join(logger_path), encoding='utf-8', level=logging.INFO)
# Disable compilation (to avoid recompile_limit errors)
#torch._dynamo.disable()
#torch._dynamo.config.suppress_errors = True
#torch._dynamo.config.recompile_limit = 100
login(token='LOGIN_TOKEN') # Add hf login token
model_id = config.get("models", {}).get(args.model, "")
logger.info(f"Model used: {model_id}")
logger.info(f"Prompt task: {args.task}")
logger.info(f"Dataset with paraphrases: {args.paraphrases}")
logger.info(f"Prompt config: {args.prompt_type}")
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logger.info(f"Device in use: {device}")
# TORCH_LOGS=recompiles
datasets_config = config.get("datasets", {})
prompt_config = config.get("prompts", {}).get(args.task, {})
print(args.task)
print(datasets_config.get(args.dataset, {}).get("prompts", []))
# Ensure trilabel setup only for Meta4XNLI
assert args.task in datasets_config.get(args.dataset, {}).get("prompts", [])
if args.paraphrases:
data_path = datasets_config.get(args.dataset, {}).get("data_path_paraphrase", "")
else:
data_path = datasets_config.get(args.dataset, {}).get("data_path", "")
logger.info(f"Dataset loaded from: {data_path}")
df = load_dataset(data_path)
logger.info(f"Loaded samples: {len(df)}")
premises = get_column_values(df, datasets_config.get(args.dataset, "").get("prem_col", ""))
hypotheses = get_column_values(df, datasets_config.get(args.dataset, "").get("hyp_col", ""))
if args.paraphrases:
gold_labels = get_column_values(df, "gold_label")
else:
gold_labels = [l for l in get_column_values(df, datasets_config.get(args.dataset, "").get("label_col", ""))]
print(gold_labels)
gold_labels = map_labels_to_string(gold_labels)
print(gold_labels)
labels = list(set(gold_labels))
set_seed(5)
tokenizer = AutoTokenizer.from_pretrained(model_id)
print("MODEL ID:", model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
print("here i am")
tokenizer.pad_token_id = tokenizer.eos_token_id
predictions = []
for p, h, l in zip(premises, hypotheses, gold_labels):
preffix_prompt = prompt_config.get(args.prompt_type, {}).get("preffix", "")
print(preffix_prompt)
print(args.prompt_type)
if args.prompt_type == "chain":
prompt = preffix_prompt + f"\n Premise: {p}\n Hypothesis: {h}\n Answer: "
logger.info(f"Prompt: {prompt}")
else:
prompt = preffix_prompt + f" {p} -> {h}: "
logger.info(f"Prompt: {prompt}")
label_mappings = prompt_config.get(args.prompt_type, {}).get("label_mapping")
logger.info(f"Label mappings: {label_mappings}")
inputs = tokenizer([prompt], return_tensors="pt").to(device)
logger.info(f"{p}\t{h}\t{l}")
# #################
# input_text = "Write me a poem about Machine Learning."
# input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
# outputs = model.generate(**input_ids, max_new_tokens=32)
# print(tokenizer.decode(outputs[0]))
# #################
outputs = model.generate(**inputs, max_new_tokens=MAX_NEW_TOKENS, return_dict_in_generate=True, output_scores=True, temperature=TEMPERATURE)
transition_scores = model.compute_transition_scores(
outputs.sequences, outputs.scores, normalize_logits=True
)
logger.info(f"{outputs.sequences}\t{outputs.scores}")
#print(f"transition scores: {transition_scores}", flush=True)
#print(f"transition scores: {transition_scores}", flush=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 | log probability | probability
logger.info(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score}")
#logger.info(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
#o.write(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
answers = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
logger.info(f"Answers: {answers}, split: {answers.split()}")
logger.info(f"Mapped label: {map_labels(answers, label_mappings)}")
predictions.append(map_labels(answers, label_mappings))
logger.info("Label added to predictions.")
logger.debug(gold_labels[:5], predictions[:5], flush=True)
assert len(gold_labels) == len(predictions)
logger.info(f"Gold: {len(gold_labels)}, Pred: {len(predictions)}")
predictions_path = os.path.join(args.output_dir, f"{args.prompt_type}_{args.paraphrase_source+'_' if args.paraphrase_source else ''}{datetime.now().strftime('%d-%m-%Y_%H_%M_%S')}.tsv")
if args.paraphrases:
paraphrased_sents = df.iloc[:, -1].tolist()
logger.info(f"Dumping predictions with paraphrased sentences, met location: {list(df.columns)[-1]}")
dump_predictions(predictions_path, premises, hypotheses, gold_labels, predictions, paraphrased_sents)
else:
dump_predictions(predictions_path, premises, hypotheses, gold_labels, predictions)
logger.info(f"Predictions dumped to {predictions_path}")
accuracy = accuracy_score(gold_labels, predictions, normalize=True)
logger.info(f"Accuracy {len(gold_labels)}, {len(predictions)}: {accuracy}\n")
if __name__ == "__main__":
main() |