id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
22,500 | tensorflow/tensor2tensor | tensor2tensor/models/video/sv2p_params.py | next_frame_sv2p_tiny | def next_frame_sv2p_tiny():
"""Tiny SV2P model."""
hparams = next_frame_sv2p_atari_softmax()
hparams.batch_size = 2
hparams.tiny_mode = True
hparams.num_masks = 1
hparams.video_modality_loss_cutoff = 0.4
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
return hparams | python | def next_frame_sv2p_tiny():
"""Tiny SV2P model."""
hparams = next_frame_sv2p_atari_softmax()
hparams.batch_size = 2
hparams.tiny_mode = True
hparams.num_masks = 1
hparams.video_modality_loss_cutoff = 0.4
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
return hparams | [
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22,501 | tensorflow/tensor2tensor | tensor2tensor/models/video/sv2p_params.py | next_frame_sv2p_cutoff | def next_frame_sv2p_cutoff():
"""SV2P model with additional cutoff in L2 loss for environments like pong."""
hparams = next_frame_sv2p()
hparams.video_modality_loss_cutoff = 0.4
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 1
return hparams | python | def next_frame_sv2p_cutoff():
"""SV2P model with additional cutoff in L2 loss for environments like pong."""
hparams = next_frame_sv2p()
hparams.video_modality_loss_cutoff = 0.4
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 1
return hparams | [
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22,502 | tensorflow/tensor2tensor | tensor2tensor/data_generators/mscoco.py | _get_mscoco | def _get_mscoco(directory):
"""Download and extract MSCOCO datasets to directory unless it is there."""
for url in _MSCOCO_URLS:
filename = os.path.basename(url)
download_url = os.path.join(_MSCOCO_ROOT_URL, url)
path = generator_utils.maybe_download(directory, filename, download_url)
unzip_dir = os... | python | def _get_mscoco(directory):
"""Download and extract MSCOCO datasets to directory unless it is there."""
for url in _MSCOCO_URLS:
filename = os.path.basename(url)
download_url = os.path.join(_MSCOCO_ROOT_URL, url)
path = generator_utils.maybe_download(directory, filename, download_url)
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22,503 | tensorflow/tensor2tensor | tensor2tensor/data_generators/mscoco.py | mscoco_generator | def mscoco_generator(data_dir,
tmp_dir,
training,
how_many,
start_from=0,
eos_list=None,
vocab_filename=None):
"""Image generator for MSCOCO captioning problem with token-wise captions.
Arg... | python | def mscoco_generator(data_dir,
tmp_dir,
training,
how_many,
start_from=0,
eos_list=None,
vocab_filename=None):
"""Image generator for MSCOCO captioning problem with token-wise captions.
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training: a Boolean; if true, we use the train set, otherwise the test set.
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22,504 | tensorflow/tensor2tensor | tensor2tensor/utils/cloud_mlengine.py | flags_as_args | def flags_as_args():
"""Convert FLAGS to list of args suitable for passing on cmd line."""
if hasattr(FLAGS, "flag_values_dict"):
args_dict = FLAGS.flag_values_dict()
else:
args_dict = dict(FLAGS.__dict__["__flags"])
del args_dict["cloud_mlengine"]
# Configured later
del args_dict["t2t_usr_dir"]
a... | python | def flags_as_args():
"""Convert FLAGS to list of args suitable for passing on cmd line."""
if hasattr(FLAGS, "flag_values_dict"):
args_dict = FLAGS.flag_values_dict()
else:
args_dict = dict(FLAGS.__dict__["__flags"])
del args_dict["cloud_mlengine"]
# Configured later
del args_dict["t2t_usr_dir"]
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22,505 | tensorflow/tensor2tensor | tensor2tensor/utils/cloud_mlengine.py | get_default_master_type | def get_default_master_type(num_gpus=1):
"""Returns master_type for trainingInput."""
gpus_to_master_map = {
0: "standard",
1: "standard_p100",
4: "complex_model_m_p100",
8: "complex_model_l_gpu",
}
if num_gpus not in gpus_to_master_map:
raise ValueError("Num gpus must be in %s" %
... | python | def get_default_master_type(num_gpus=1):
"""Returns master_type for trainingInput."""
gpus_to_master_map = {
0: "standard",
1: "standard_p100",
4: "complex_model_m_p100",
8: "complex_model_l_gpu",
}
if num_gpus not in gpus_to_master_map:
raise ValueError("Num gpus must be in %s" %
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22,506 | tensorflow/tensor2tensor | tensor2tensor/utils/cloud_mlengine.py | configure_job | def configure_job():
"""Construct jobSpec for ML Engine job."""
# See documentation:
# https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#traininginput
training_input = {
"pythonModule": "tensor2tensor.bin.t2t_trainer",
"args": flags_as_args(),
"region": text_encoder.native_to_... | python | def configure_job():
"""Construct jobSpec for ML Engine job."""
# See documentation:
# https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#traininginput
training_input = {
"pythonModule": "tensor2tensor.bin.t2t_trainer",
"args": flags_as_args(),
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22,507 | tensorflow/tensor2tensor | tensor2tensor/utils/cloud_mlengine.py | launch_job | def launch_job(job_spec):
"""Launch job on ML Engine."""
project_id = "projects/{}".format(
text_encoder.native_to_unicode(default_project()))
credentials = GoogleCredentials.get_application_default()
cloudml = discovery.build("ml", "v1", credentials=credentials,
cache_discover... | python | def launch_job(job_spec):
"""Launch job on ML Engine."""
project_id = "projects/{}".format(
text_encoder.native_to_unicode(default_project()))
credentials = GoogleCredentials.get_application_default()
cloudml = discovery.build("ml", "v1", credentials=credentials,
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22,508 | tensorflow/tensor2tensor | tensor2tensor/utils/cloud_mlengine.py | _tar_and_copy | def _tar_and_copy(src_dir, target_dir):
"""Tar and gzip src_dir and copy to GCS target_dir."""
src_dir = src_dir.rstrip("/")
target_dir = target_dir.rstrip("/")
tmp_dir = tempfile.gettempdir().rstrip("/")
src_base = os.path.basename(src_dir)
shell_run(
"tar --exclude=.git -zcf {tmp_dir}/{src_base}.tar... | python | def _tar_and_copy(src_dir, target_dir):
"""Tar and gzip src_dir and copy to GCS target_dir."""
src_dir = src_dir.rstrip("/")
target_dir = target_dir.rstrip("/")
tmp_dir = tempfile.gettempdir().rstrip("/")
src_base = os.path.basename(src_dir)
shell_run(
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22,509 | tensorflow/tensor2tensor | tensor2tensor/utils/cloud_mlengine.py | tar_and_copy_t2t | def tar_and_copy_t2t(train_dir):
"""Tar Tensor2Tensor and cp to train_dir."""
tf.logging.info("Tarring and pushing local Tensor2Tensor package.")
output = text_encoder.native_to_unicode(shell_output(
"pip show tensor2tensor")).split("\n")
assert output[1].startswith("Version")
assert output[7].startswi... | python | def tar_and_copy_t2t(train_dir):
"""Tar Tensor2Tensor and cp to train_dir."""
tf.logging.info("Tarring and pushing local Tensor2Tensor package.")
output = text_encoder.native_to_unicode(shell_output(
"pip show tensor2tensor")).split("\n")
assert output[1].startswith("Version")
assert output[7].startswi... | [
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22,510 | tensorflow/tensor2tensor | tensor2tensor/utils/cloud_mlengine.py | tar_and_copy_usr_dir | def tar_and_copy_usr_dir(usr_dir, train_dir):
"""Package, tar, and copy usr_dir to GCS train_dir."""
tf.logging.info("Tarring and pushing t2t_usr_dir.")
usr_dir = os.path.abspath(os.path.expanduser(usr_dir))
# Copy usr dir to a temp location
top_dir = os.path.join(tempfile.gettempdir(), "t2t_usr_container")
... | python | def tar_and_copy_usr_dir(usr_dir, train_dir):
"""Package, tar, and copy usr_dir to GCS train_dir."""
tf.logging.info("Tarring and pushing t2t_usr_dir.")
usr_dir = os.path.abspath(os.path.expanduser(usr_dir))
# Copy usr dir to a temp location
top_dir = os.path.join(tempfile.gettempdir(), "t2t_usr_container")
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22,511 | tensorflow/tensor2tensor | tensor2tensor/utils/cloud_mlengine.py | validate_flags | def validate_flags():
"""Validates flags are set to acceptable values for CloudML Engine runs."""
assert not job_dir()
assert FLAGS.output_dir.startswith("gs://")
assert FLAGS.data_dir.startswith("gs://")
assert FLAGS.worker_replicas <= 1
assert FLAGS.ps_replicas <= 0
if FLAGS.hparams_range:
assert FL... | python | def validate_flags():
"""Validates flags are set to acceptable values for CloudML Engine runs."""
assert not job_dir()
assert FLAGS.output_dir.startswith("gs://")
assert FLAGS.data_dir.startswith("gs://")
assert FLAGS.worker_replicas <= 1
assert FLAGS.ps_replicas <= 0
if FLAGS.hparams_range:
assert FL... | [
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22,512 | tensorflow/tensor2tensor | tensor2tensor/utils/cloud_mlengine.py | launch | def launch():
"""Launch t2t_trainer on Cloud ML Engine."""
validate_flags()
job_spec = configure_job()
job_name = job_spec["jobId"]
tf.logging.info("Launching job %s with ML Engine spec:\n%s", job_name,
pprint.pformat(job_spec))
assert confirm()
train_dir = FLAGS.output_dir
t2t_tar = t... | python | def launch():
"""Launch t2t_trainer on Cloud ML Engine."""
validate_flags()
job_spec = configure_job()
job_name = job_spec["jobId"]
tf.logging.info("Launching job %s with ML Engine spec:\n%s", job_name,
pprint.pformat(job_spec))
assert confirm()
train_dir = FLAGS.output_dir
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22,513 | tensorflow/tensor2tensor | tensor2tensor/layers/bayes.py | add_weight | def add_weight(cls):
"""Decorator for Layers, overriding add_weight for trainable initializers."""
@functools.wraps(cls.add_weight)
def _add_weight(self,
name=None,
shape=None,
dtype=None,
initializer=None,
regularizer=None,... | python | def add_weight(cls):
"""Decorator for Layers, overriding add_weight for trainable initializers."""
@functools.wraps(cls.add_weight)
def _add_weight(self,
name=None,
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22,514 | tensorflow/tensor2tensor | tensor2tensor/models/video/base_vae.py | NextFrameBaseVae.get_beta | def get_beta(self, kl_loss=0.0):
"""Get the KL multiplier, either dynamically or schedule based.
if hparams.latent_loss_multiplier_dynamic is set to true, then beta
is being adjusted to keep KL under hparams.latent_loss_multiplier_epsilon.
In order to do so, the beta is being updated at each iteration
... | python | def get_beta(self, kl_loss=0.0):
"""Get the KL multiplier, either dynamically or schedule based.
if hparams.latent_loss_multiplier_dynamic is set to true, then beta
is being adjusted to keep KL under hparams.latent_loss_multiplier_epsilon.
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22,515 | tensorflow/tensor2tensor | tensor2tensor/models/video/base_vae.py | NextFrameBaseVae.get_kl_loss | def get_kl_loss(self, means, log_vars, means_p=None, log_vars_p=None):
"""Get KL loss for all the predicted Gaussians."""
kl_loss = 0.0
if means_p is None:
means_p = tf.unstack(tf.zeros_like(means))
if log_vars_p is None:
log_vars_p = tf.unstack(tf.zeros_like(log_vars))
enumerated_inputs... | python | def get_kl_loss(self, means, log_vars, means_p=None, log_vars_p=None):
"""Get KL loss for all the predicted Gaussians."""
kl_loss = 0.0
if means_p is None:
means_p = tf.unstack(tf.zeros_like(means))
if log_vars_p is None:
log_vars_p = tf.unstack(tf.zeros_like(log_vars))
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22,516 | tensorflow/tensor2tensor | tensor2tensor/models/video/base_vae.py | NextFrameBaseVae.construct_latent_tower | def construct_latent_tower(self, images, time_axis):
"""Create the latent tower."""
# No latent in the first phase
first_phase = tf.less(
self.get_iteration_num(), self.hparams.num_iterations_1st_stage)
# use all frames by default but this allows more
# predicted frames at inference time
... | python | def construct_latent_tower(self, images, time_axis):
"""Create the latent tower."""
# No latent in the first phase
first_phase = tf.less(
self.get_iteration_num(), self.hparams.num_iterations_1st_stage)
# use all frames by default but this allows more
# predicted frames at inference time
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22,517 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_encode | def transformer_encode(encoder_function, inputs, target_space, hparams,
attention_weights=None, features=None, losses=None,
**kwargs):
"""Encode transformer inputs.
Args:
encoder_function: the encoder function
inputs: Transformer inputs [batch_size, input_lengt... | python | def transformer_encode(encoder_function, inputs, target_space, hparams,
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**kwargs):
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Args:
encoder_function: the encoder function
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22,518 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_decode | def transformer_decode(decoder_function,
decoder_input,
encoder_output,
encoder_decoder_attention_bias,
decoder_self_attention_bias,
hparams,
attention_weights=None,
... | python | def transformer_decode(decoder_function,
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encoder_decoder_attention_bias,
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attention_weights=None,
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22,519 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | _init_transformer_cache | def _init_transformer_cache(cache, hparams, batch_size, attention_init_length,
encoder_output, encoder_decoder_attention_bias,
scope_prefix):
"""Create the initial cache for Transformer fast decoding."""
key_channels = hparams.attention_key_channels or hparams... | python | def _init_transformer_cache(cache, hparams, batch_size, attention_init_length,
encoder_output, encoder_decoder_attention_bias,
scope_prefix):
"""Create the initial cache for Transformer fast decoding."""
key_channels = hparams.attention_key_channels or hparams... | [
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22,520 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_base_vq_ada_32ex_packed | def transformer_base_vq_ada_32ex_packed():
"""Set of hyperparameters for lm1b packed following tpu params."""
hparams = transformer_base_v2()
expert_utils.update_hparams_for_vq_gating(hparams)
hparams.moe_num_experts = 32
hparams.gating_type = "vq"
# this gives us a batch size of 16 because each seq is len ... | python | def transformer_base_vq_ada_32ex_packed():
"""Set of hyperparameters for lm1b packed following tpu params."""
hparams = transformer_base_v2()
expert_utils.update_hparams_for_vq_gating(hparams)
hparams.moe_num_experts = 32
hparams.gating_type = "vq"
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22,521 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_base_v3 | def transformer_base_v3():
"""Base parameters for Transformer model."""
# Update parameters here, then occasionally cut a versioned set, e.g.
# transformer_base_v2.
hparams = transformer_base_v2()
hparams.optimizer_adam_beta2 = 0.997
# New way of specifying learning rate schedule.
# Equivalent to previous... | python | def transformer_base_v3():
"""Base parameters for Transformer model."""
# Update parameters here, then occasionally cut a versioned set, e.g.
# transformer_base_v2.
hparams = transformer_base_v2()
hparams.optimizer_adam_beta2 = 0.997
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22,522 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_big | def transformer_big():
"""HParams for transformer big model on WMT."""
hparams = transformer_base()
hparams.hidden_size = 1024
hparams.filter_size = 4096
# Reduce batch size to 2048 from 4096 to be able to train the model on a GPU
# with 12 GB memory. For example, NVIDIA TITAN V GPU.
hparams.batch_size = ... | python | def transformer_big():
"""HParams for transformer big model on WMT."""
hparams = transformer_base()
hparams.hidden_size = 1024
hparams.filter_size = 4096
# Reduce batch size to 2048 from 4096 to be able to train the model on a GPU
# with 12 GB memory. For example, NVIDIA TITAN V GPU.
hparams.batch_size = ... | [
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22,523 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_tall_finetune_textclass | def transformer_tall_finetune_textclass():
"""Hparams for transformer on LM for finetuning on text class problems."""
hparams = transformer_tall()
hparams.learning_rate_constant = 6.25e-5
hparams.learning_rate_schedule = ("linear_warmup*constant*linear_decay")
hparams.multiproblem_schedule_max_examples = 0
... | python | def transformer_tall_finetune_textclass():
"""Hparams for transformer on LM for finetuning on text class problems."""
hparams = transformer_tall()
hparams.learning_rate_constant = 6.25e-5
hparams.learning_rate_schedule = ("linear_warmup*constant*linear_decay")
hparams.multiproblem_schedule_max_examples = 0
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22,524 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_tall_pretrain_lm_tpu_adafactor_large | def transformer_tall_pretrain_lm_tpu_adafactor_large():
"""Hparams for transformer on LM pretraining on TPU, large model."""
hparams = transformer_tall_pretrain_lm_tpu_adafactor()
hparams.hidden_size = 1024
hparams.num_heads = 16
hparams.filter_size = 32768 # max fitting in 16G memory is 49152, batch 2
hpa... | python | def transformer_tall_pretrain_lm_tpu_adafactor_large():
"""Hparams for transformer on LM pretraining on TPU, large model."""
hparams = transformer_tall_pretrain_lm_tpu_adafactor()
hparams.hidden_size = 1024
hparams.num_heads = 16
hparams.filter_size = 32768 # max fitting in 16G memory is 49152, batch 2
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22,525 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_tall_pretrain_lm_tpu | def transformer_tall_pretrain_lm_tpu():
"""Hparams for transformer on LM pretraining on TPU with AdamW."""
hparams = transformer_tall_pretrain_lm_tpu_adafactor()
# Optimizer gets reset in update_hparams_for_tpu so we set it again here.
hparams.learning_rate_constant = 2e-4
hparams.learning_rate_schedule = ("l... | python | def transformer_tall_pretrain_lm_tpu():
"""Hparams for transformer on LM pretraining on TPU with AdamW."""
hparams = transformer_tall_pretrain_lm_tpu_adafactor()
# Optimizer gets reset in update_hparams_for_tpu so we set it again here.
hparams.learning_rate_constant = 2e-4
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22,526 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_base_single_gpu | def transformer_base_single_gpu():
"""HParams for transformer base model for single GPU."""
hparams = transformer_base()
hparams.batch_size = 1024
hparams.learning_rate_schedule = "constant*linear_warmup*rsqrt_decay"
hparams.learning_rate_constant = 0.1
hparams.learning_rate_warmup_steps = 16000
return hp... | python | def transformer_base_single_gpu():
"""HParams for transformer base model for single GPU."""
hparams = transformer_base()
hparams.batch_size = 1024
hparams.learning_rate_schedule = "constant*linear_warmup*rsqrt_decay"
hparams.learning_rate_constant = 0.1
hparams.learning_rate_warmup_steps = 16000
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22,527 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_parsing_base | def transformer_parsing_base():
"""HParams for parsing on WSJ only."""
hparams = transformer_base()
hparams.attention_dropout = 0.2
hparams.layer_prepostprocess_dropout = 0.2
hparams.max_length = 512
hparams.learning_rate_warmup_steps = 16000
hparams.hidden_size = 1024
hparams.learning_rate = 0.05
hpa... | python | def transformer_parsing_base():
"""HParams for parsing on WSJ only."""
hparams = transformer_base()
hparams.attention_dropout = 0.2
hparams.layer_prepostprocess_dropout = 0.2
hparams.max_length = 512
hparams.learning_rate_warmup_steps = 16000
hparams.hidden_size = 1024
hparams.learning_rate = 0.05
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22,528 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_parsing_big | def transformer_parsing_big():
"""HParams for parsing on WSJ semi-supervised."""
hparams = transformer_big()
hparams.max_length = 512
hparams.shared_source_target_embedding = False
hparams.learning_rate_warmup_steps = 4000
hparams.layer_prepostprocess_dropout = 0.1
hparams.batch_size = 2048
hparams.lear... | python | def transformer_parsing_big():
"""HParams for parsing on WSJ semi-supervised."""
hparams = transformer_big()
hparams.max_length = 512
hparams.shared_source_target_embedding = False
hparams.learning_rate_warmup_steps = 4000
hparams.layer_prepostprocess_dropout = 0.1
hparams.batch_size = 2048
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22,529 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_relative | def transformer_relative():
"""Use relative position embeddings instead of absolute position encodings."""
hparams = transformer_base()
hparams.pos = None
hparams.self_attention_type = "dot_product_relative"
hparams.max_relative_position = 20
return hparams | python | def transformer_relative():
"""Use relative position embeddings instead of absolute position encodings."""
hparams = transformer_base()
hparams.pos = None
hparams.self_attention_type = "dot_product_relative"
hparams.max_relative_position = 20
return hparams | [
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22,530 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_mlperf_tpu | def transformer_mlperf_tpu():
"""HParams for Transformer model on TPU for MLPerf on TPU 2x2."""
hparams = transformer_base_v3()
hparams.mlperf_mode = True
hparams.symbol_modality_num_shards = 1
hparams.max_length = 256 # ignored when using "_packed" problems
hparams.batch_size = 2048 # per-chip batch size... | python | def transformer_mlperf_tpu():
"""HParams for Transformer model on TPU for MLPerf on TPU 2x2."""
hparams = transformer_base_v3()
hparams.mlperf_mode = True
hparams.symbol_modality_num_shards = 1
hparams.max_length = 256 # ignored when using "_packed" problems
hparams.batch_size = 2048 # per-chip batch size... | [
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22,531 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | update_hparams_for_tpu | def update_hparams_for_tpu(hparams):
"""Change hparams to be compatible with TPU training."""
# Adafactor uses less memory than Adam.
# switch to Adafactor with its recommended learning rate scheme.
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"
hparams.learning_rate_warmup_... | python | def update_hparams_for_tpu(hparams):
"""Change hparams to be compatible with TPU training."""
# Adafactor uses less memory than Adam.
# switch to Adafactor with its recommended learning rate scheme.
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"
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22,532 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_clean | def transformer_clean():
"""No dropout, label smoothing, max_length."""
hparams = transformer_base_v2()
hparams.label_smoothing = 0.0
hparams.layer_prepostprocess_dropout = 0.0
hparams.attention_dropout = 0.0
hparams.relu_dropout = 0.0
hparams.max_length = 0
return hparams | python | def transformer_clean():
"""No dropout, label smoothing, max_length."""
hparams = transformer_base_v2()
hparams.label_smoothing = 0.0
hparams.layer_prepostprocess_dropout = 0.0
hparams.attention_dropout = 0.0
hparams.relu_dropout = 0.0
hparams.max_length = 0
return hparams | [
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22,533 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_lm_tpu_0 | def transformer_lm_tpu_0():
"""HParams for training languagemodel_lm1b8k on tpu. 92M Params."""
hparams = transformer_clean_big()
update_hparams_for_tpu(hparams)
hparams.num_heads = 4 # Heads are expensive on TPUs.
hparams.batch_size = 4096
hparams.shared_embedding_and_softmax_weights = False
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"""HParams for training languagemodel_lm1b8k on tpu. 92M Params."""
hparams = transformer_clean_big()
update_hparams_for_tpu(hparams)
hparams.num_heads = 4 # Heads are expensive on TPUs.
hparams.batch_size = 4096
hparams.shared_embedding_and_softmax_weights = False
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22,534 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_librispeech_v1 | def transformer_librispeech_v1():
"""HParams for training ASR model on LibriSpeech V1."""
hparams = transformer_base()
hparams.num_heads = 4
hparams.filter_size = 1024
hparams.hidden_size = 256
hparams.num_encoder_layers = 5
hparams.num_decoder_layers = 3
hparams.learning_rate = 0.15
hparams.batch_si... | python | def transformer_librispeech_v1():
"""HParams for training ASR model on LibriSpeech V1."""
hparams = transformer_base()
hparams.num_heads = 4
hparams.filter_size = 1024
hparams.hidden_size = 256
hparams.num_encoder_layers = 5
hparams.num_decoder_layers = 3
hparams.learning_rate = 0.15
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22,535 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_librispeech_v2 | def transformer_librispeech_v2():
"""HParams for training ASR model on LibriSpeech V2."""
hparams = transformer_base()
hparams.max_length = 1240000
hparams.max_input_seq_length = 1550
hparams.max_target_seq_length = 350
hparams.batch_size = 16
hparams.num_decoder_layers = 4
hparams.num_encoder_layers =... | python | def transformer_librispeech_v2():
"""HParams for training ASR model on LibriSpeech V2."""
hparams = transformer_base()
hparams.max_length = 1240000
hparams.max_input_seq_length = 1550
hparams.max_target_seq_length = 350
hparams.batch_size = 16
hparams.num_decoder_layers = 4
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22,536 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_librispeech_tpu_v1 | def transformer_librispeech_tpu_v1():
"""HParams for training ASR model on Librispeech on TPU v1."""
hparams = transformer_librispeech_v1()
update_hparams_for_tpu(hparams)
hparams.batch_size = 16
librispeech.set_librispeech_length_hparams(hparams)
return hparams | python | def transformer_librispeech_tpu_v1():
"""HParams for training ASR model on Librispeech on TPU v1."""
hparams = transformer_librispeech_v1()
update_hparams_for_tpu(hparams)
hparams.batch_size = 16
librispeech.set_librispeech_length_hparams(hparams)
return hparams | [
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22,537 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_librispeech_tpu_v2 | def transformer_librispeech_tpu_v2():
"""HParams for training ASR model on Librispeech on TPU v2."""
hparams = transformer_librispeech_v2()
update_hparams_for_tpu(hparams)
hparams.batch_size = 16
librispeech.set_librispeech_length_hparams(hparams)
return hparams | python | def transformer_librispeech_tpu_v2():
"""HParams for training ASR model on Librispeech on TPU v2."""
hparams = transformer_librispeech_v2()
update_hparams_for_tpu(hparams)
hparams.batch_size = 16
librispeech.set_librispeech_length_hparams(hparams)
return hparams | [
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22,538 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_tpu_1b | def transformer_tpu_1b():
"""Hparams for machine translation with ~1.1B parameters."""
hparams = transformer_tpu()
hparams.hidden_size = 2048
hparams.filter_size = 8192
hparams.num_hidden_layers = 8
# smaller batch size to avoid OOM
hparams.batch_size = 1024
hparams.activation_dtype = "bfloat16"
hpara... | python | def transformer_tpu_1b():
"""Hparams for machine translation with ~1.1B parameters."""
hparams = transformer_tpu()
hparams.hidden_size = 2048
hparams.filter_size = 8192
hparams.num_hidden_layers = 8
# smaller batch size to avoid OOM
hparams.batch_size = 1024
hparams.activation_dtype = "bfloat16"
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22,539 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_wikitext103_l4k_v0 | def transformer_wikitext103_l4k_v0():
"""HParams for training languagemodel_wikitext103_l4k."""
hparams = transformer_big()
# Adafactor uses less memory than Adam.
# switch to Adafactor with its recommended learning rate scheme.
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"... | python | def transformer_wikitext103_l4k_v0():
"""HParams for training languagemodel_wikitext103_l4k."""
hparams = transformer_big()
# Adafactor uses less memory than Adam.
# switch to Adafactor with its recommended learning rate scheme.
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"... | [
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22,540 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_wikitext103_l4k_memory_v0 | def transformer_wikitext103_l4k_memory_v0():
"""HParams for training languagemodel_wikitext103_l4k with memory."""
hparams = transformer_wikitext103_l4k_v0()
hparams.split_targets_chunk_length = 64
hparams.split_targets_max_chunks = 64
hparams.split_targets_strided_training = True
hparams.add_hparam("memor... | python | def transformer_wikitext103_l4k_memory_v0():
"""HParams for training languagemodel_wikitext103_l4k with memory."""
hparams = transformer_wikitext103_l4k_v0()
hparams.split_targets_chunk_length = 64
hparams.split_targets_max_chunks = 64
hparams.split_targets_strided_training = True
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22,541 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_wikitext103_l16k_memory_v0 | def transformer_wikitext103_l16k_memory_v0():
"""HParams for training languagemodel_wikitext103_l16k with memory."""
hparams = transformer_wikitext103_l4k_memory_v0()
hparams.max_length = 16384
hparams.split_targets_chunk_length = 64
hparams.split_targets_max_chunks = int(
hparams.max_length / hparams.... | python | def transformer_wikitext103_l16k_memory_v0():
"""HParams for training languagemodel_wikitext103_l16k with memory."""
hparams = transformer_wikitext103_l4k_memory_v0()
hparams.max_length = 16384
hparams.split_targets_chunk_length = 64
hparams.split_targets_max_chunks = int(
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22,542 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_cifar10_memory_v0 | def transformer_cifar10_memory_v0():
"""HParams for training image_cifar10_plain_gen_flat_rev with memory."""
hparams = transformer_wikitext103_l4k_memory_v0()
hparams.num_hidden_layers = 6
hparams.max_length = 32 * 32 * 3
hparams.split_targets_chunk_length = 64 * 3
hparams.split_targets_max_chunks = int(... | python | def transformer_cifar10_memory_v0():
"""HParams for training image_cifar10_plain_gen_flat_rev with memory."""
hparams = transformer_wikitext103_l4k_memory_v0()
hparams.num_hidden_layers = 6
hparams.max_length = 32 * 32 * 3
hparams.split_targets_chunk_length = 64 * 3
hparams.split_targets_max_chunks = int(... | [
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22,543 | tensorflow/tensor2tensor | tensor2tensor/models/transformer.py | transformer_imagenet64_memory_v0 | def transformer_imagenet64_memory_v0():
"""HParams for training image_imagenet64_gen_flat_rev with memory."""
hparams = transformer_cifar10_memory_v0()
hparams.max_length = 64 * 64 * 3
hparams.split_targets_chunk_length = 64 * 3
hparams.split_targets_max_chunks = int(
hparams.max_length / hparams.split... | python | def transformer_imagenet64_memory_v0():
"""HParams for training image_imagenet64_gen_flat_rev with memory."""
hparams = transformer_cifar10_memory_v0()
hparams.max_length = 64 * 64 * 3
hparams.split_targets_chunk_length = 64 * 3
hparams.split_targets_max_chunks = int(
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22,544 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | maybe_reshape_4d_to_3d | def maybe_reshape_4d_to_3d(x):
"""Reshape input from 4D to 3D if necessary."""
x_shape = common_layers.shape_list(x)
is_4d = False
if len(x_shape) == 4:
x = tf.reshape(x, [x_shape[0], x_shape[1]*x_shape[2], x_shape[3]])
is_4d = True
return x, x_shape, is_4d | python | def maybe_reshape_4d_to_3d(x):
"""Reshape input from 4D to 3D if necessary."""
x_shape = common_layers.shape_list(x)
is_4d = False
if len(x_shape) == 4:
x = tf.reshape(x, [x_shape[0], x_shape[1]*x_shape[2], x_shape[3]])
is_4d = True
return x, x_shape, is_4d | [
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22,545 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | local_attention_2d | def local_attention_2d(x, hparams, attention_type="local_attention_2d"):
"""Local 2d, self attention layer."""
# self-attention
with tf.variable_scope("local_2d_self_att"):
y = common_attention.multihead_attention_2d(
x,
None,
hparams.attention_key_channels or hparams.hidden_size,
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"""Local 2d, self attention layer."""
# self-attention
with tf.variable_scope("local_2d_self_att"):
y = common_attention.multihead_attention_2d(
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22,546 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | local_within_block_attention | def local_within_block_attention(x,
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hparams,
attention_type="local_within_block_mask_right",
q_padding="VALID",
kv_padding="VALID"):
... | python | def local_within_block_attention(x,
self_attention_bias,
hparams,
attention_type="local_within_block_mask_right",
q_padding="VALID",
kv_padding="VALID"):
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22,547 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | get_dilated_1d_attention_mask | def get_dilated_1d_attention_mask(
num_heads, block_size,
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name="dilated_mask"):
"""Dilated attention with a masking strategy."""
mask = np.ones((num_heads, block_size, 2*block_size), np.bool)
# now going over every row to do the right assignment of
# memory blocks... | python | def get_dilated_1d_attention_mask(
num_heads, block_size,
num_blocks, memory_size, gap_size,
name="dilated_mask"):
"""Dilated attention with a masking strategy."""
mask = np.ones((num_heads, block_size, 2*block_size), np.bool)
# now going over every row to do the right assignment of
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22,548 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | dilated_attention_1d | def dilated_attention_1d(x,
hparams,
attention_type="masked_dilated_1d",
q_padding="VALID",
kv_padding="VALID",
gap_size=2):
"""Dilated 1d self attention."""
# self-attention
x, x_shape, is... | python | def dilated_attention_1d(x,
hparams,
attention_type="masked_dilated_1d",
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kv_padding="VALID",
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22,549 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | local_global_attention | def local_global_attention(x,
self_attention_bias,
hparams,
q_padding="LEFT",
kv_padding="LEFT"):
"""Local and global 1d self attention."""
with tf.variable_scope("self_local_global_att"):
[x_global, x_lo... | python | def local_global_attention(x,
self_attention_bias,
hparams,
q_padding="LEFT",
kv_padding="LEFT"):
"""Local and global 1d self attention."""
with tf.variable_scope("self_local_global_att"):
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22,550 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | full_self_attention | def full_self_attention(x,
self_attention_bias,
hparams,
q_padding="LEFT",
kv_padding="LEFT"):
"""Full self-attention layer."""
x, x_shape, is_4d = maybe_reshape_4d_to_3d(x)
if self_attention_bias is not None:
self... | python | def full_self_attention(x,
self_attention_bias,
hparams,
q_padding="LEFT",
kv_padding="LEFT"):
"""Full self-attention layer."""
x, x_shape, is_4d = maybe_reshape_4d_to_3d(x)
if self_attention_bias is not None:
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22,551 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | transformer_decoder_layers | def transformer_decoder_layers(inputs,
encoder_output,
num_layers,
hparams,
self_attention_bias=None,
encoder_decoder_attention_bias=None,
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hparams,
self_attention_bias=None,
encoder_decoder_attention_bias=None,
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22,552 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | transformer_encoder_layers | def transformer_encoder_layers(inputs,
num_layers,
hparams,
attention_type=AttentionType.GLOBAL,
self_attention_bias=None,
q_padding="VALID",
... | python | def transformer_encoder_layers(inputs,
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hparams,
attention_type=AttentionType.GLOBAL,
self_attention_bias=None,
q_padding="VALID",
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22,553 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | ffn_layer | def ffn_layer(x, hparams, losses=None):
"""ffn layer transformer."""
with tf.variable_scope("ffn"):
if hparams.ffn_layer == "none":
return x
if hparams.ffn_layer == "conv_hidden_relu":
y = common_layers.dense_relu_dense(
x,
hparams.filter_size,
hparams.hidden_size,
... | python | def ffn_layer(x, hparams, losses=None):
"""ffn layer transformer."""
with tf.variable_scope("ffn"):
if hparams.ffn_layer == "none":
return x
if hparams.ffn_layer == "conv_hidden_relu":
y = common_layers.dense_relu_dense(
x,
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22,554 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | get_self_attention_bias | def get_self_attention_bias(x):
"""Creates masked self attention bias.
Args:
x: A tensor of shape [batch, length, depth]
Returns:
self_attention_bias: A tensor of shape [length, length, 1]
"""
x_shape = common_layers.shape_list(x)
self_attention_bias = common_attention.attention_bias_lower_triang... | python | def get_self_attention_bias(x):
"""Creates masked self attention bias.
Args:
x: A tensor of shape [batch, length, depth]
Returns:
self_attention_bias: A tensor of shape [length, length, 1]
"""
x_shape = common_layers.shape_list(x)
self_attention_bias = common_attention.attention_bias_lower_triang... | [
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22,555 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | postprocess_image | def postprocess_image(x, rows, cols, hparams):
"""Postprocessing after decoding.
Args:
x: Tensor of shape [batch, ...], where ... can be any rank such that the
number of elements in x is batch * rows * cols * hparams.hidden_size.
rows: Integer representing number of rows in a 2-D data point.
cols... | python | def postprocess_image(x, rows, cols, hparams):
"""Postprocessing after decoding.
Args:
x: Tensor of shape [batch, ...], where ... can be any rank such that the
number of elements in x is batch * rows * cols * hparams.hidden_size.
rows: Integer representing number of rows in a 2-D data point.
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22,556 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | prepare_encoder | def prepare_encoder(inputs, hparams, attention_type="local_1d"):
"""Prepare encoder for images."""
x = prepare_image(inputs, hparams, name="enc_channels")
# Add position signals.
x = add_pos_signals(x, hparams, "enc_pos")
x_shape = common_layers.shape_list(x)
if attention_type == "local_1d":
x = tf.resh... | python | def prepare_encoder(inputs, hparams, attention_type="local_1d"):
"""Prepare encoder for images."""
x = prepare_image(inputs, hparams, name="enc_channels")
# Add position signals.
x = add_pos_signals(x, hparams, "enc_pos")
x_shape = common_layers.shape_list(x)
if attention_type == "local_1d":
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22,557 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | prepare_decoder | def prepare_decoder(targets, hparams):
"""Prepare decoder for images."""
targets_shape = common_layers.shape_list(targets)
channels = hparams.num_channels
curr_infer_length = None
# during training, images are [batch, IMG_LEN, IMG_LEN, 3].
# At inference, they are [batch, curr_infer_length, 1, 1]
if hpar... | python | def prepare_decoder(targets, hparams):
"""Prepare decoder for images."""
targets_shape = common_layers.shape_list(targets)
channels = hparams.num_channels
curr_infer_length = None
# during training, images are [batch, IMG_LEN, IMG_LEN, 3].
# At inference, they are [batch, curr_infer_length, 1, 1]
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22,558 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | create_output | def create_output(decoder_output, rows, cols, targets, hparams):
"""Creates output from decoder output and vars.
Args:
decoder_output: Tensor of shape [batch, ...], where ... can be any rank such
that the number of elements is batch * rows * cols * hparams.hidden_size.
rows: Integer representing numb... | python | def create_output(decoder_output, rows, cols, targets, hparams):
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22,559 | tensorflow/tensor2tensor | tensor2tensor/layers/common_image_attention.py | get_channel_embeddings | def get_channel_embeddings(io_depth, targets, hidden_size, name="channel"):
"""Get separate embedding for each of the channels."""
targets_split = tf.split(targets, io_depth, axis=3)
rgb_embedding_var = tf.get_variable("rgb_target_emb_%s" % name,
[256 * io_depth, hidden_size]... | python | def get_channel_embeddings(io_depth, targets, hidden_size, name="channel"):
"""Get separate embedding for each of the channels."""
targets_split = tf.split(targets, io_depth, axis=3)
rgb_embedding_var = tf.get_variable("rgb_target_emb_%s" % name,
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22,560 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | include_revision | def include_revision(revision_num, skip_factor=1.1):
"""Decide whether to include a revision.
If the number of revisions is large, we exclude some revisions to avoid
a quadratic blowup in runtime, since the article is likely also large.
We make the ratio between consecutive included revision numbers
appprox... | python | def include_revision(revision_num, skip_factor=1.1):
"""Decide whether to include a revision.
If the number of revisions is large, we exclude some revisions to avoid
a quadratic blowup in runtime, since the article is likely also large.
We make the ratio between consecutive included revision numbers
appprox... | [
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22,561 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | file_page_generator | def file_page_generator(my_file, max_page_size=2**28):
"""Read wikipedia pages from a history dump.
Since some pages can be terabytes in size (with all the revisions),
we limit page size to max_page_size bytes.
Args:
my_file: an open file object.
max_page_size: an integer
Yields:
strings
"""
... | python | def file_page_generator(my_file, max_page_size=2**28):
"""Read wikipedia pages from a history dump.
Since some pages can be terabytes in size (with all the revisions),
we limit page size to max_page_size bytes.
Args:
my_file: an open file object.
max_page_size: an integer
Yields:
strings
"""
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22,562 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | get_id | def get_id(page):
"""Extract the id from a page.
Args:
page: a string
Returns:
an integer
"""
start_pos = page.find("<id>")
end_pos = page.find("</id>")
assert start_pos != -1
assert end_pos != -1
start_pos += len("<id>")
return int(page[start_pos:end_pos]) | python | def get_id(page):
"""Extract the id from a page.
Args:
page: a string
Returns:
an integer
"""
start_pos = page.find("<id>")
end_pos = page.find("</id>")
assert start_pos != -1
assert end_pos != -1
start_pos += len("<id>")
return int(page[start_pos:end_pos]) | [
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22,563 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | get_revisions | def get_revisions(page):
"""Extract the revisions of a page.
Args:
page: a string
Returns:
a list of strings
"""
start_string = " <revision>\n"
end_string = " </revision>\n"
ret = []
current_pos = 0
while True:
start_pos = page.find(start_string, current_pos)
if start_pos == -1:... | python | def get_revisions(page):
"""Extract the revisions of a page.
Args:
page: a string
Returns:
a list of strings
"""
start_string = " <revision>\n"
end_string = " </revision>\n"
ret = []
current_pos = 0
while True:
start_pos = page.find(start_string, current_pos)
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22,564 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | parse_page | def parse_page(raw_page):
"""Create a dictionary with title, id, and list of revisions.
The dictionary contains:
"title": a string
"id": an integer
"revisions": a list of strings
Args:
raw_page: a string
Returns:
a dictionary, or None in the case of an error.
"""
ret = {"title": get_title(r... | python | def parse_page(raw_page):
"""Create a dictionary with title, id, and list of revisions.
The dictionary contains:
"title": a string
"id": an integer
"revisions": a list of strings
Args:
raw_page: a string
Returns:
a dictionary, or None in the case of an error.
"""
ret = {"title": get_title(r... | [
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22,565 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | maybe_copy_file_to_directory | def maybe_copy_file_to_directory(source_filepath, target_directory):
"""Copy a file to a directory if it is not already there.
Returns the target filepath.
Args:
source_filepath: a string
target_directory: a string
Returns:
a string
"""
if not tf.gfile.Exists(target_directory):
tf.logging... | python | def maybe_copy_file_to_directory(source_filepath, target_directory):
"""Copy a file to a directory if it is not already there.
Returns the target filepath.
Args:
source_filepath: a string
target_directory: a string
Returns:
a string
"""
if not tf.gfile.Exists(target_directory):
tf.logging... | [
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22,566 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | corpus_page_generator | def corpus_page_generator(corpus_files, tmp_dir, max_page_size_exp):
"""Generate pages from a list of .7z encoded history dumps.
Args:
corpus_files: a list of strings
tmp_dir: a string
max_page_size_exp: an integer
Yields:
strings
"""
for remote_filepath in corpus_files:
filepath = mayb... | python | def corpus_page_generator(corpus_files, tmp_dir, max_page_size_exp):
"""Generate pages from a list of .7z encoded history dumps.
Args:
corpus_files: a list of strings
tmp_dir: a string
max_page_size_exp: an integer
Yields:
strings
"""
for remote_filepath in corpus_files:
filepath = mayb... | [
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22,567 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | get_text | def get_text(revision, strip=True):
"""Extract the text from a revision.
Args:
revision: a string
strip: a boolean
Returns:
a string
"""
# text start tag looks like "<text ..otherstuff>"
start_pos = revision.find("<text")
assert start_pos != -1
end_tag_pos = revision.find(">", start_pos)
... | python | def get_text(revision, strip=True):
"""Extract the text from a revision.
Args:
revision: a string
strip: a boolean
Returns:
a string
"""
# text start tag looks like "<text ..otherstuff>"
start_pos = revision.find("<text")
assert start_pos != -1
end_tag_pos = revision.find(">", start_pos)
... | [
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22,568 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | _remove_curly_braces | def _remove_curly_braces(text):
"""Remove everything in curly braces.
Curly braces may be nested, so we keep track of depth.
Args:
text: a string
Returns:
a string
"""
current_pos = 0
depth = 0
ret = ""
for match in re.finditer("[{}]", text):
if depth == 0:
ret += text[current_pos:... | python | def _remove_curly_braces(text):
"""Remove everything in curly braces.
Curly braces may be nested, so we keep track of depth.
Args:
text: a string
Returns:
a string
"""
current_pos = 0
depth = 0
ret = ""
for match in re.finditer("[{}]", text):
if depth == 0:
ret += text[current_pos:... | [
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22,569 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | _remove_double_brackets | def _remove_double_brackets(text):
"""Remove double brackets, but leave the viewable text.
Args:
text: a string
Returns:
a string
"""
def replacement_fn(s):
if ":" in s:
# this is probably a category or something like that.
return ""
# keep the part after the bar.
bar_pos = s... | python | def _remove_double_brackets(text):
"""Remove double brackets, but leave the viewable text.
Args:
text: a string
Returns:
a string
"""
def replacement_fn(s):
if ":" in s:
# this is probably a category or something like that.
return ""
# keep the part after the bar.
bar_pos = s... | [
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22,570 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | _remove_boring_lines | def _remove_boring_lines(text):
"""Remove lines that do not start with a letter or a quote.
From inspecting the data, this seems to leave in most prose and remove
most weird stuff.
Args:
text: a string
Returns:
a string
"""
lines = text.split("\n")
filtered = [line for line in lines if re.matc... | python | def _remove_boring_lines(text):
"""Remove lines that do not start with a letter or a quote.
From inspecting the data, this seems to leave in most prose and remove
most weird stuff.
Args:
text: a string
Returns:
a string
"""
lines = text.split("\n")
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22,571 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | get_or_generate_vocabulary | def get_or_generate_vocabulary(data_dir,
tmp_dir,
data_prefix,
max_page_size_exp,
approx_vocab_size=32768,
strip=True):
"""Get or generate the vocabulary.
Args:... | python | def get_or_generate_vocabulary(data_dir,
tmp_dir,
data_prefix,
max_page_size_exp,
approx_vocab_size=32768,
strip=True):
"""Get or generate the vocabulary.
Args:... | [
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22,572 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | get_encoder_from_vocab | def get_encoder_from_vocab(vocab_filepath):
"""Get encoder from vocab file.
If vocab is not found in output dir, it will be copied there by
copy_vocab_to_output_dir to clarify the vocab used to generate the data.
Args:
vocab_filepath: path to vocab, either local or cns
Returns:
A SubwordTextEncoder... | python | def get_encoder_from_vocab(vocab_filepath):
"""Get encoder from vocab file.
If vocab is not found in output dir, it will be copied there by
copy_vocab_to_output_dir to clarify the vocab used to generate the data.
Args:
vocab_filepath: path to vocab, either local or cns
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22,573 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | edit_distance_filter | def edit_distance_filter(source_target_input, max_equal_to_diff_ratio=0):
"""Filter out examples that exceed max_edit_ratio between source and target.
Args:
source_target_input: a list of [source, target] pairs
max_equal_to_diff_ratio: cutoff for ratio of equal chars / diff chars
between source a... | python | def edit_distance_filter(source_target_input, max_equal_to_diff_ratio=0):
"""Filter out examples that exceed max_edit_ratio between source and target.
Args:
source_target_input: a list of [source, target] pairs
max_equal_to_diff_ratio: cutoff for ratio of equal chars / diff chars
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Args:
source_target_input: a list of [source, target] pairs
max_equal_to_diff_ratio: cutoff for ratio of equal chars / diff chars
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Returns:
source_target_output: filtered subset of [source, ... | [
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22,574 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | introduce_errors | def introduce_errors(s,
corruption_rate=3e-3,
infill_marker="|?|",
max_infill_len=8):
"""Artificially add spelling errors and infill markers.
This function should be applied to the inputs of a correction model.
The artificial errors are particularly... | python | def introduce_errors(s,
corruption_rate=3e-3,
infill_marker="|?|",
max_infill_len=8):
"""Artificially add spelling errors and infill markers.
This function should be applied to the inputs of a correction model.
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22,575 | tensorflow/tensor2tensor | tensor2tensor/data_generators/wiki_revision_utils.py | fast_match_sequences | def fast_match_sequences(a,
b,
a_start=0,
a_end=None,
b_start=0,
b_end=None,
min_match_length=3,
max_recursion_depth=128):
"""Compute diffs bet... | python | def fast_match_sequences(a,
b,
a_start=0,
a_end=None,
b_start=0,
b_end=None,
min_match_length=3,
max_recursion_depth=128):
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22,576 | tensorflow/tensor2tensor | tensor2tensor/utils/restore_hook.py | RestoreHook.begin | def begin(self):
"""Load variables from checkpoint.
New model variables have the following name foramt:
new_model_scope/old_model_scope/xxx/xxx:0 To find the map of
name to variable, need to strip the new_model_scope and then
match the old_model_scope and remove the suffix :0.
"""
variable... | python | def begin(self):
"""Load variables from checkpoint.
New model variables have the following name foramt:
new_model_scope/old_model_scope/xxx/xxx:0 To find the map of
name to variable, need to strip the new_model_scope and then
match the old_model_scope and remove the suffix :0.
"""
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22,577 | tensorflow/tensor2tensor | tensor2tensor/envs/time_step.py | TimeStep.create_time_step | def create_time_step(cls,
observation=None,
done=False,
raw_reward=None,
processed_reward=None,
action=None):
"""Creates a TimeStep with both rewards and actions as optional."""
return cls(observa... | python | def create_time_step(cls,
observation=None,
done=False,
raw_reward=None,
processed_reward=None,
action=None):
"""Creates a TimeStep with both rewards and actions as optional."""
return cls(observa... | [
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22,578 | tensorflow/tensor2tensor | tensor2tensor/models/slicenet.py | attention | def attention(targets_shifted, inputs_encoded, norm_fn, hparams, bias=None):
"""Complete attention layer with preprocessing."""
separabilities = [hparams.separability, hparams.separability]
if hparams.separability < 0:
separabilities = [hparams.separability - 1, hparams.separability]
targets_timed = common_... | python | def attention(targets_shifted, inputs_encoded, norm_fn, hparams, bias=None):
"""Complete attention layer with preprocessing."""
separabilities = [hparams.separability, hparams.separability]
if hparams.separability < 0:
separabilities = [hparams.separability - 1, hparams.separability]
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22,579 | tensorflow/tensor2tensor | tensor2tensor/models/slicenet.py | multi_conv_res | def multi_conv_res(x, padding, name, layers, hparams, mask=None, source=None):
"""A stack of separable convolution blocks with residual connections."""
with tf.variable_scope(name):
padding_bias = None
if mask is not None:
padding_bias = (1.0 - mask) * -1e9 # Bias to not attend to padding.
if p... | python | def multi_conv_res(x, padding, name, layers, hparams, mask=None, source=None):
"""A stack of separable convolution blocks with residual connections."""
with tf.variable_scope(name):
padding_bias = None
if mask is not None:
padding_bias = (1.0 - mask) * -1e9 # Bias to not attend to padding.
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22,580 | tensorflow/tensor2tensor | tensor2tensor/models/slicenet.py | rank_loss | def rank_loss(sentence_emb, image_emb, margin=0.2):
"""Experimental rank loss, thanks to kkurach@ for the code."""
with tf.name_scope("rank_loss"):
# Normalize first as this is assumed in cosine similarity later.
sentence_emb = tf.nn.l2_normalize(sentence_emb, 1)
image_emb = tf.nn.l2_normalize(image_emb... | python | def rank_loss(sentence_emb, image_emb, margin=0.2):
"""Experimental rank loss, thanks to kkurach@ for the code."""
with tf.name_scope("rank_loss"):
# Normalize first as this is assumed in cosine similarity later.
sentence_emb = tf.nn.l2_normalize(sentence_emb, 1)
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22,581 | tensorflow/tensor2tensor | tensor2tensor/models/slicenet.py | similarity_cost | def similarity_cost(inputs_encoded, targets_encoded):
"""Loss telling to be more similar to your own targets than to others."""
# This is a first very simple version: handle variable-length by padding
# to same length and putting everything into batch. In need of a better way.
x, y = common_layers.pad_to_same_l... | python | def similarity_cost(inputs_encoded, targets_encoded):
"""Loss telling to be more similar to your own targets than to others."""
# This is a first very simple version: handle variable-length by padding
# to same length and putting everything into batch. In need of a better way.
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22,582 | tensorflow/tensor2tensor | tensor2tensor/models/slicenet.py | slicenet_middle | def slicenet_middle(inputs_encoded, targets, target_space_emb, mask, hparams):
"""Middle part of slicenet, connecting encoder and decoder."""
def norm_fn(x, name):
with tf.variable_scope(name, default_name="norm"):
return common_layers.apply_norm(x, hparams.norm_type, hparams.hidden_size,
... | python | def slicenet_middle(inputs_encoded, targets, target_space_emb, mask, hparams):
"""Middle part of slicenet, connecting encoder and decoder."""
def norm_fn(x, name):
with tf.variable_scope(name, default_name="norm"):
return common_layers.apply_norm(x, hparams.norm_type, hparams.hidden_size,
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22,583 | tensorflow/tensor2tensor | tensor2tensor/models/slicenet.py | embedding_to_padding | def embedding_to_padding(emb):
"""Input embeddings -> is_padding."""
emb_sum = tf.reduce_sum(tf.abs(emb), axis=-1, keep_dims=True)
return tf.to_float(tf.equal(emb_sum, 0.0)) | python | def embedding_to_padding(emb):
"""Input embeddings -> is_padding."""
emb_sum = tf.reduce_sum(tf.abs(emb), axis=-1, keep_dims=True)
return tf.to_float(tf.equal(emb_sum, 0.0)) | [
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22,584 | tensorflow/tensor2tensor | tensor2tensor/models/slicenet.py | slicenet_internal | def slicenet_internal(inputs, targets, target_space, hparams, run_decoder=True):
"""The slicenet model, main step used for training."""
with tf.variable_scope("slicenet"):
# Project to hidden size if necessary
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"""The slicenet model, main step used for training."""
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# Project to hidden size if necessary
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22,585 | tensorflow/tensor2tensor | tensor2tensor/models/slicenet.py | slicenet_params1_noam | def slicenet_params1_noam():
"""Version with Noam's decay scheme."""
hparams = slicenet_params1()
hparams.learning_rate_decay_scheme = "noam"
hparams.learning_rate = 1.0
hparams.learning_rate_warmup_steps = 4000
hparams.initializer = "uniform_unit_scaling"
hparams.optimizer_adam_epsilon = 1e-9
hparams.o... | python | def slicenet_params1_noam():
"""Version with Noam's decay scheme."""
hparams = slicenet_params1()
hparams.learning_rate_decay_scheme = "noam"
hparams.learning_rate = 1.0
hparams.learning_rate_warmup_steps = 4000
hparams.initializer = "uniform_unit_scaling"
hparams.optimizer_adam_epsilon = 1e-9
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22,586 | tensorflow/tensor2tensor | tensor2tensor/models/slicenet.py | slicenet_params1_tiny | def slicenet_params1_tiny():
"""Version for fast local runs."""
hparams = slicenet_params1()
hparams.attention_type = "simple"
hparams.separability = 0
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.batch_size = 512
hparams.learning_rate_warmup_steps = 200
return hparams | python | def slicenet_params1_tiny():
"""Version for fast local runs."""
hparams = slicenet_params1()
hparams.attention_type = "simple"
hparams.separability = 0
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.batch_size = 512
hparams.learning_rate_warmup_steps = 200
return hparams | [
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22,587 | tensorflow/tensor2tensor | tensor2tensor/data_generators/pointer_generator_word.py | TokenTextEncoderOov.decode_list_oov | def decode_list_oov(self, ids, source_oov_id_to_token):
"""decode ids back to tokens, considering OOVs temporary IDs.
Args:
ids: vocab ids. Could possibly include source temporary OOV ID starting
from vocab_size.
source_oov_id_to_token: a list of source OOV tokens, with the order the
sa... | python | def decode_list_oov(self, ids, source_oov_id_to_token):
"""decode ids back to tokens, considering OOVs temporary IDs.
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ids: vocab ids. Could possibly include source temporary OOV ID starting
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22,588 | tensorflow/tensor2tensor | tensor2tensor/data_generators/vqa_utils.py | _distort_color | def _distort_color(image, color_ordering=0, scope=None):
"""Distort the color of a Tensor image.
Each color distortion is non-commutative and thus ordering of the color ops
matters. Ideally we would randomly permute the ordering of the color ops.
Rather then adding that level of complication, we select a disti... | python | def _distort_color(image, color_ordering=0, scope=None):
"""Distort the color of a Tensor image.
Each color distortion is non-commutative and thus ordering of the color ops
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Rather then adding that level of complication, we select a distinct ordering
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... | [
"Distort",
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] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/vqa_utils.py#L98-L140 |
22,589 | tensorflow/tensor2tensor | tensor2tensor/data_generators/vqa_utils.py | vqa_v2_preprocess_image | def vqa_v2_preprocess_image(
image,
height,
width,
mode,
resize_side=512,
distort=True,
image_model_fn="resnet_v1_152",
):
"""vqa v2 preprocess image."""
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
assert resize_side > 0
if resize_side:
image = _aspect_preservi... | python | def vqa_v2_preprocess_image(
image,
height,
width,
mode,
resize_side=512,
distort=True,
image_model_fn="resnet_v1_152",
):
"""vqa v2 preprocess image."""
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
assert resize_side > 0
if resize_side:
image = _aspect_preservi... | [
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... | vqa v2 preprocess image. | [
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22,590 | tensorflow/tensor2tensor | tensor2tensor/layers/transformer_layers.py | transformer_prepare_encoder | def transformer_prepare_encoder(inputs, target_space, hparams, features=None):
"""Prepare one shard of the model for the encoder.
Args:
inputs: a Tensor.
target_space: a Tensor.
hparams: run hyperparameters
features: optionally pass the entire features dictionary as well.
This is needed now f... | python | def transformer_prepare_encoder(inputs, target_space, hparams, features=None):
"""Prepare one shard of the model for the encoder.
Args:
inputs: a Tensor.
target_space: a Tensor.
hparams: run hyperparameters
features: optionally pass the entire features dictionary as well.
This is needed now f... | [
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22,591 | tensorflow/tensor2tensor | tensor2tensor/models/research/lm_experiments.py | lmx_base | def lmx_base():
"""Transformer on languagemodel_lm1b32k_packed. 50M Params."""
hparams = transformer.transformer_tpu()
# sharing is counterproductive when underparameterized
hparams.shared_embedding_and_softmax_weights = False
# we judge by log-ppl, so label smoothing hurts.
hparams.label_smoothing = 0.0
... | python | def lmx_base():
"""Transformer on languagemodel_lm1b32k_packed. 50M Params."""
hparams = transformer.transformer_tpu()
# sharing is counterproductive when underparameterized
hparams.shared_embedding_and_softmax_weights = False
# we judge by log-ppl, so label smoothing hurts.
hparams.label_smoothing = 0.0
... | [
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22,592 | tensorflow/tensor2tensor | tensor2tensor/models/research/lm_experiments.py | lmx_h4k_f16k | def lmx_h4k_f16k():
"""HParams for training languagemodel_lm1b32k_packed. 1470M Params."""
hparams = lmx_base()
hparams.hidden_size = 4096
hparams.filter_size = 16384
hparams.batch_size = 1024
hparams.weight_dtype = "bfloat16"
return hparams | python | def lmx_h4k_f16k():
"""HParams for training languagemodel_lm1b32k_packed. 1470M Params."""
hparams = lmx_base()
hparams.hidden_size = 4096
hparams.filter_size = 16384
hparams.batch_size = 1024
hparams.weight_dtype = "bfloat16"
return hparams | [
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22,593 | tensorflow/tensor2tensor | tensor2tensor/models/research/lm_experiments.py | lmx_relative | def lmx_relative():
"""Language model using relative attention."""
hparams = lmx_base()
hparams.self_attention_type = "dot_product_relative_v2"
hparams.activation_dtype = "float32"
hparams.weight_dtype = "float32"
return hparams | python | def lmx_relative():
"""Language model using relative attention."""
hparams = lmx_base()
hparams.self_attention_type = "dot_product_relative_v2"
hparams.activation_dtype = "float32"
hparams.weight_dtype = "float32"
return hparams | [
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22,594 | tensorflow/tensor2tensor | tensor2tensor/rl/envs/simulated_batch_env.py | compute_uncertainty_reward | def compute_uncertainty_reward(logits, predictions):
"""Uncertainty reward based on logits."""
# TODO(rsepassi): Add support for L1/L2 loss models. Current code only
# works for softmax models.
vocab_size = logits.shape[-1]
assert vocab_size > 1
log_probs = common_layers.log_prob_from_logits(logits)
max_l... | python | def compute_uncertainty_reward(logits, predictions):
"""Uncertainty reward based on logits."""
# TODO(rsepassi): Add support for L1/L2 loss models. Current code only
# works for softmax models.
vocab_size = logits.shape[-1]
assert vocab_size > 1
log_probs = common_layers.log_prob_from_logits(logits)
max_l... | [
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22,595 | tensorflow/tensor2tensor | tensor2tensor/bin/t2t_datagen.py | set_random_seed | def set_random_seed():
"""Set the random seed from flag everywhere."""
tf.set_random_seed(FLAGS.random_seed)
random.seed(FLAGS.random_seed)
np.random.seed(FLAGS.random_seed) | python | def set_random_seed():
"""Set the random seed from flag everywhere."""
tf.set_random_seed(FLAGS.random_seed)
random.seed(FLAGS.random_seed)
np.random.seed(FLAGS.random_seed) | [
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] | 272500b6efe353aeb638d2745ed56e519462ca31 | https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/bin/t2t_datagen.py#L154-L158 |
22,596 | tensorflow/tensor2tensor | tensor2tensor/bin/t2t_datagen.py | generate_data_for_problem | def generate_data_for_problem(problem):
"""Generate data for a problem in _SUPPORTED_PROBLEM_GENERATORS."""
training_gen, dev_gen, test_gen = _SUPPORTED_PROBLEM_GENERATORS[problem]
num_train_shards = FLAGS.num_shards or 10
tf.logging.info("Generating training data for %s.", problem)
train_output_files = gene... | python | def generate_data_for_problem(problem):
"""Generate data for a problem in _SUPPORTED_PROBLEM_GENERATORS."""
training_gen, dev_gen, test_gen = _SUPPORTED_PROBLEM_GENERATORS[problem]
num_train_shards = FLAGS.num_shards or 10
tf.logging.info("Generating training data for %s.", problem)
train_output_files = gene... | [
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22,597 | tensorflow/tensor2tensor | tensor2tensor/bin/t2t_datagen.py | generate_data_for_env_problem | def generate_data_for_env_problem(problem_name):
"""Generate data for `EnvProblem`s."""
assert FLAGS.env_problem_max_env_steps > 0, ("--env_problem_max_env_steps "
"should be greater than zero")
assert FLAGS.env_problem_batch_size > 0, ("--env_problem_batch_size shou... | python | def generate_data_for_env_problem(problem_name):
"""Generate data for `EnvProblem`s."""
assert FLAGS.env_problem_max_env_steps > 0, ("--env_problem_max_env_steps "
"should be greater than zero")
assert FLAGS.env_problem_batch_size > 0, ("--env_problem_batch_size shou... | [
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22,598 | tensorflow/tensor2tensor | tensor2tensor/bin/t2t_datagen.py | generate_data_for_registered_problem | def generate_data_for_registered_problem(problem_name):
"""Generate data for a registered problem."""
tf.logging.info("Generating data for %s.", problem_name)
if FLAGS.num_shards:
raise ValueError("--num_shards should not be set for registered Problem.")
problem = registry.problem(problem_name)
task_id = ... | python | def generate_data_for_registered_problem(problem_name):
"""Generate data for a registered problem."""
tf.logging.info("Generating data for %s.", problem_name)
if FLAGS.num_shards:
raise ValueError("--num_shards should not be set for registered Problem.")
problem = registry.problem(problem_name)
task_id = ... | [
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22,599 | tensorflow/tensor2tensor | tensor2tensor/data_generators/common_voice.py | _file_exists | def _file_exists(path, filename):
"""Checks if the filename exists under the path."""
return os.path.isfile(os.path.join(path, filename)) | python | def _file_exists(path, filename):
"""Checks if the filename exists under the path."""
return os.path.isfile(os.path.join(path, filename)) | [
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