def define_transformer_flags():
"""Add flags and flag validators for running transformer_main."""
# Add common flags (data_dir, model_dir, train_epochs, etc.).
flags_core.define_base()
flags_core.define_performance(num_parallel_calls=True,
inter_op=False,
intra_op=False,
synthetic_data=True,
max_train_steps=False,
dtype=True,
loss_scale=True,
all_reduce_alg=True,
enable_xla=True)
# Additional performance flags
# TODO(b/76028325): Remove when generic layout optimizer is ready.
flags.DEFINE_boolean(
name='enable_grappler_layout_optimizer',
default=True,
help='Enable Grappler layout optimizer. Currently Grappler can '
'de-optimize fp16 graphs by forcing NCHW layout for all '
'convolutions and batch normalizations, and this flag allows to '
'disable it.')
flags_core.define_benchmark()
flags_core.define_device(tpu=True)
flags.DEFINE_integer(
name='train_steps',
short_name='ts',
default=300000,
help=flags_core.help_wrap('The number of steps used to train.'))
flags.DEFINE_integer(
name='steps_between_evals',
short_name='sbe',
default=1000,
help=flags_core.help_wrap(
'The Number of training steps to run between evaluations. This is '
'used if --train_steps is defined.'))
flags.DEFINE_boolean(name='enable_time_history',
default=True,
help='Whether to enable TimeHistory callback.')
flags.DEFINE_boolean(name='enable_tensorboard',
default=False,
help='Whether to enable Tensorboard callback.')
flags.DEFINE_boolean(name='enable_metrics_in_training',
default=False,
help='Whether to enable metrics during training.')
flags.DEFINE_string(
name='profile_steps',
default=None,
help='Save profiling data to model dir at given range of steps. The '
'value must be a comma separated pair of positive integers, specifying '
'the first and last step to profile. For example, "--profile_steps=2,4" '
'triggers the profiler to process 3 steps, starting from the 2nd step. '
'Note that profiler has a non-trivial performance overhead, and the '
'output file can be gigantic if profiling many steps.')
# Set flags from the flags_core module as 'key flags' so they're listed when
# the '-h' flag is used. Without this line, the flags defined above are
# only shown in the full `--helpful` help text.
flags.adopt_module_key_flags(flags_core)
# Add transformer-specific flags
flags.DEFINE_enum(
name='param_set',
short_name='mp',
default='big',
enum_values=PARAMS_MAP.keys(),
help=flags_core.help_wrap(
'Parameter set to use when creating and training the model. The '
'parameters define the input shape (batch size and max length), '
'model configuration (size of embedding, # of hidden layers, etc.), '
'and various other settings. The big parameter set increases the '
'default batch size, embedding/hidden size, and filter size. For a '
'complete list of parameters, please see model/model_params.py.'))
flags.DEFINE_bool(
name='static_batch',
short_name='sb',
default=False,
help=flags_core.help_wrap(
'Whether the batches in the dataset should have static shapes. In '
'general, this setting should be False. Dynamic shapes allow the '
'inputs to be grouped so that the number of padding tokens is '
'minimized, and helps model training. In cases where the input shape '
'must be static (e.g. running on TPU), this setting will be ignored '
'and static batching will always be used.'))
flags.DEFINE_integer(
name='max_length',
short_name='ml',
default=256,
help=flags_core.help_wrap(
'Max sentence length for Transformer. Default is 256. Note: Usually '
'it is more effective to use a smaller max length if static_batch is '
'enabled, e.g. 64.'))
# Flags for training with steps (may be used for debugging)
flags.DEFINE_integer(
name='validation_steps',
short_name='vs',
default=64,
help=flags_core.help_wrap('The number of steps used in validation.'))
# BLEU score computation
flags.DEFINE_string(
name='bleu_source',
short_name='bls',
default=None,
help=flags_core.help_wrap(
'Path to source file containing text translate when calculating the '
'official BLEU score. Both --bleu_source and --bleu_ref must be set. '
'Use the flag --stop_threshold to stop the script based on the '
'uncased BLEU score.'))
flags.DEFINE_string(
name='bleu_ref',
short_name='blr',
default=None,
help=flags_core.help_wrap(
'Path to source file containing text translate when calculating the '
'official BLEU score. Both --bleu_source and --bleu_ref must be set. '
'Use the flag --stop_threshold to stop the script based on the '
'uncased BLEU score.'))
flags.DEFINE_string(
name='vocab_file',
short_name='vf',
default=None,
help=flags_core.help_wrap(
'Path to subtoken vocabulary file. If data_download.py was used to '
'download and encode the training data, look in the data_dir to find '
'the vocab file.'))
flags.DEFINE_string(
name='mode',
default='train',
help=flags_core.help_wrap('mode: train, eval, or predict'))
flags_core.set_defaults(data_dir='/tmp/translate_ende',
model_dir='/tmp/transformer_model',
batch_size=None,
train_epochs=10)
# pylint: disable=unused-variable
@flags.multi_flags_validator(
['mode', 'train_epochs'],
message='--train_epochs must be defined in train mode')
def _check_train_limits(flag_dict):
if flag_dict['mode'] == 'train':
return flag_dict['train_epochs'] is not None
return True
@flags.multi_flags_validator(
['bleu_source', 'bleu_ref'],
message='Both or neither --bleu_source and --bleu_ref must be defined.'
)
def _check_bleu_files(flags_dict):
return (flags_dict['bleu_source'] is None) == (flags_dict['bleu_ref']
is None)
@flags.multi_flags_validator(
['bleu_source', 'bleu_ref', 'vocab_file'],
message='--vocab_file must be defined if --bleu_source and --bleu_ref '
'are defined.')
def _check_bleu_vocab_file(flags_dict):
if flags_dict['bleu_source'] and flags_dict['bleu_ref']:
return flags_dict['vocab_file'] is not None
return True
@flags.multi_flags_validator(
['export_dir', 'vocab_file'],
message='--vocab_file must be defined if --export_dir is set.')
def _check_export_vocab_file(flags_dict):
if flags_dict['export_dir']:
return flags_dict['vocab_file'] is not None
return True
# pylint: enable=unused-variable
flags_core.require_cloud_storage(['data_dir', 'model_dir', 'export_dir'])
def define_transformer_flags():
"""Add flags and flag validators for running transformer_main."""
# Add common flags (data_dir, model_dir, train_epochs, etc.).
flags.DEFINE_integer(name="max_length",
short_name="ml",
default=None,
help=flags_core.help_wrap("Max length."))
flags_core.define_base(clean=True,
train_epochs=True,
epochs_between_evals=True,
stop_threshold=True,
num_gpu=True,
hooks=True,
export_dir=True,
distribution_strategy=True)
flags_core.define_performance(num_parallel_calls=True,
inter_op=False,
intra_op=False,
synthetic_data=True,
max_train_steps=False,
dtype=True,
all_reduce_alg=True)
flags_core.define_benchmark()
flags_core.define_device(tpu=True)
# Set flags from the flags_core module as "key flags" so they're listed when
# the '-h' flag is used. Without this line, the flags defined above are
# only shown in the full `--helpful` help text.
flags.adopt_module_key_flags(flags_core)
# Add transformer-specific flags
flags.DEFINE_enum(
name="param_set",
short_name="mp",
default="big",
enum_values=PARAMS_MAP.keys(),
help=flags_core.help_wrap(
"Parameter set to use when creating and training the model. The "
"parameters define the input shape (batch size and max length), "
"model configuration (size of embedding, # of hidden layers, etc.), "
"and various other settings. The big parameter set increases the "
"default batch size, embedding/hidden size, and filter size. For a "
"complete list of parameters, please see model/model_params.py."))
flags.DEFINE_bool(
name="static_batch",
default=False,
help=flags_core.help_wrap(
"Whether the batches in the dataset should have static shapes. In "
"general, this setting should be False. Dynamic shapes allow the "
"inputs to be grouped so that the number of padding tokens is "
"minimized, and helps model training. In cases where the input shape "
"must be static (e.g. running on TPU), this setting will be ignored "
"and static batching will always be used."))
# Flags for training with steps (may be used for debugging)
flags.DEFINE_integer(
name="train_steps",
short_name="ts",
default=None,
help=flags_core.help_wrap("The number of steps used to train."))
flags.DEFINE_integer(
name="steps_between_evals",
short_name="sbe",
default=1000,
help=flags_core.help_wrap(
"The Number of training steps to run between evaluations. This is "
"used if --train_steps is defined."))
# BLEU score computation
flags.DEFINE_string(
name="bleu_source",
short_name="bls",
default=None,
help=flags_core.help_wrap(
"Path to source file containing text translate when calculating the "
"official BLEU score. Both --bleu_source and --bleu_ref must be set. "
"Use the flag --stop_threshold to stop the script based on the "
"uncased BLEU score."))
flags.DEFINE_string(
name="bleu_ref",
short_name="blr",
default=None,
help=flags_core.help_wrap(
"Path to source file containing text translate when calculating the "
"official BLEU score. Both --bleu_source and --bleu_ref must be set. "
"Use the flag --stop_threshold to stop the script based on the "
"uncased BLEU score."))
flags.DEFINE_string(
name="vocab_file",
short_name="vf",
default=None,
help=flags_core.help_wrap(
"Path to subtoken vocabulary file. If data_download.py was used to "
"download and encode the training data, look in the data_dir to find "
"the vocab file."))
flags.DEFINE_integer(name="save_checkpoints_steps",
short_name="scs",
default=50000,
help=flags_core.help_wrap("the vocab file."))
flags_core.set_defaults(data_dir="/tmp/translate_ende",
model_dir="/tmp/transformer_model",
batch_size=None,
train_epochs=None)
@flags.multi_flags_validator(
["train_epochs", "train_steps"],
message=
"Both --train_steps and --train_epochs were set. Only one may be "
"defined.")
def _check_train_limits(flag_dict):
return flag_dict["train_epochs"] is None or flag_dict[
"train_steps"] is None
@flags.multi_flags_validator(
["bleu_source", "bleu_ref"],
message="Both or neither --bleu_source and --bleu_ref must be defined."
)
def _check_bleu_files(flags_dict):
return (flags_dict["bleu_source"] is None) == (flags_dict["bleu_ref"]
is None)
@flags.multi_flags_validator(
["bleu_source", "bleu_ref", "vocab_file"],
message="--vocab_file must be defined if --bleu_source and --bleu_ref "
"are defined.")
def _check_bleu_vocab_file(flags_dict):
if flags_dict["bleu_source"] and flags_dict["bleu_ref"]:
return flags_dict["vocab_file"] is not None
return True
@flags.multi_flags_validator(
["export_dir", "vocab_file"],
message="--vocab_file must be defined if --export_dir is set.")
def _check_export_vocab_file(flags_dict):
if flags_dict["export_dir"]:
return flags_dict["vocab_file"] is not None
return True
flags_core.require_cloud_storage(["data_dir", "model_dir", "export_dir"])
def define_transformer_flags():
"""Add flags and flag validators for running transformer_main."""
# Add common flags (data_dir, model_dir, train_epochs, etc.).
flags_core.define_base()
flags_core.define_performance(
num_parallel_calls=True,
inter_op=True,
intra_op=True,
synthetic_data=True,
max_train_steps=False,
dtype=False,
all_reduce_alg=False
)
flags_core.define_benchmark()
flags_core.define_device(tpu=True)
# Set flags from the flags_core module as "key flags" so they're listed when
# the '-h' flag is used. Without this line, the flags defined above are
# only shown in the full `--helpful` help text.
flags.adopt_module_key_flags(flags_core)
# Add transformer-specific flags
flags.DEFINE_enum(
name="param_set", short_name="mp", default="big",
enum_values=PARAMS_MAP.keys(),
help=flags_core.help_wrap(
"Parameter set to use when creating and training the model. The "
"parameters define the input shape (batch size and max length), "
"model configuration (size of embedding, # of hidden layers, etc.), "
"and various other settings. The big parameter set increases the "
"default batch size, embedding/hidden size, and filter size. For a "
"complete list of parameters, please see model/model_params.py."))
flags.DEFINE_bool(
name="static_batch", default=False,
help=flags_core.help_wrap(
"Whether the batches in the dataset should have static shapes. In "
"general, this setting should be False. Dynamic shapes allow the "
"inputs to be grouped so that the number of padding tokens is "
"minimized, and helps model training. In cases where the input shape "
"must be static (e.g. running on TPU), this setting will be ignored "
"and static batching will always be used."))
# Flags for training with steps (may be used for debugging)
flags.DEFINE_integer(
name="train_steps", short_name="ts", default=None,
help=flags_core.help_wrap("The number of steps used to train."))
flags.DEFINE_integer(
name="steps_between_evals", short_name="sbe", default=1000,
help=flags_core.help_wrap(
"The Number of training steps to run between evaluations. This is "
"used if --train_steps is defined."))
# add intra_op and inter_op flags as arguments
flags.DEFINE_integer(
name="intra_op", default=None,
help=flags_core.help_wrap("The number of intra_op_parallelism threads"))
flags.DEFINE_integer(
name="inter_op", default=None,
help=flags_core.help_wrap("The number of inter_op_parallelism threads"))
# added flags to override the learning rate, decay, warmup, max_length, info from params file
flags.DEFINE_float(
name="learning_rate",default=2.0,
help=flags_core.help_wrap("Learning rate"))
# learning_rate_decay_rate is not used anywhere. Added just to be in sync with the params file.
flags.DEFINE_float(
name="learning_rate_decay_rate",default=1.0,
help=flags_core.help_wrap("Learning rate decay rate"))
flags.DEFINE_integer(
name="learning_rate_warmup_steps",default=16000,
help=flags_core.help_wrap("Learning rate warmup steps"))
flags.DEFINE_integer(
name="max_length",default=256,
help=flags_core.help_wrap("Maximum number of tokens per example"))
flags.DEFINE_integer(
name="vocab_size",default=33708,
help=flags_core.help_wrap("Number of tokens defined in the vocabulary file"))
flags.DEFINE_integer(
name="save_checkpoints_secs",default=3600,
help=flags_core.help_wrap("Save checkpoints every mentioned seconds"))
flags.DEFINE_integer(
name="log_step_count_steps",default=100,
help=flags_core.help_wrap("Frequency in steps at which loss and global step/sec is logged"))
# added for learning rate decay scheme
flags.DEFINE_integer(
name="lr_scheme",default=1,
help=flags_core.help_wrap("Type of learning rate decay scheme."
"Can be 0,1 or 2."
" 0 - constant learning rate"
" 1 - does noam"
" 2 - does linear lr growth and inverse sqrt decay"))
flags.DEFINE_float(
name="warmup_init_lr",default=1e-07,
help=flags_core.help_wrap("Initial learning rate for the warm up phase"))
flags.DEFINE_float(
name="layer_postprocess_dropout",default=0.1,
help=flags_core.help_wrap("Dropout value"))
# added for optimizers and it's parameters
flags.DEFINE_string(
name="opt_alg", short_name="opt", default="lazyadam",
help=flags_core.help_wrap("Optimizer algorithm to be used"))
flags.DEFINE_float(
name="optimizer_sgd_momentum", short_name="sgdm", default=None,
help=flags_core.help_wrap("Value for SGD's momentum param"))
flags.DEFINE_float(
name="optimizer_rms_decay", short_name="rmsd", default=0.9,
help=flags_core.help_wrap("RMSProp Decay value"))
flags.DEFINE_float(
name="optimizer_rms_momentum", short_name="rmsm", default=0.0,
help=flags_core.help_wrap("RMSProp momentum value"))
flags.DEFINE_float(
name="optimizer_rms_epsilon", short_name="rmse", default=1e-10,
help=flags_core.help_wrap("RMSProp epsilon value"))
# BLEU score computation
flags.DEFINE_string(
name="bleu_source", short_name="bls", default=None,
help=flags_core.help_wrap(
"Path to source file containing text translate when calculating the "
"official BLEU score. Both --bleu_source and --bleu_ref must be set. "
"Use the flag --stop_threshold to stop the script based on the "
"uncased BLEU score."))
flags.DEFINE_string(
name="bleu_ref", short_name="blr", default=None,
help=flags_core.help_wrap(
"Path to source file containing text translate when calculating the "
"official BLEU score. Both --bleu_source and --bleu_ref must be set. "
"Use the flag --stop_threshold to stop the script based on the "
"uncased BLEU score."))
flags.DEFINE_string(
name="vocab_file", short_name="vf", default=None,
help=flags_core.help_wrap(
"Path to subtoken vocabulary file. If data_download.py was used to "
"download and encode the training data, look in the data_dir to find "
"the vocab file."))
flags_core.set_defaults(data_dir="/tmp/translate_ende",
model_dir="/tmp/transformer_model",
batch_size=None,
train_epochs=None)
@flags.multi_flags_validator(
["train_epochs", "train_steps"],
message="Both --train_steps and --train_epochs were set. Only one may be "
"defined.")
def _check_train_limits(flag_dict):
return flag_dict["train_epochs"] is None or flag_dict["train_steps"] is None
@flags.multi_flags_validator(
["bleu_source", "bleu_ref"],
message="Both or neither --bleu_source and --bleu_ref must be defined.")
def _check_bleu_files(flags_dict):
return (flags_dict["bleu_source"] is None) == (
flags_dict["bleu_ref"] is None)
@flags.multi_flags_validator(
["bleu_source", "bleu_ref", "vocab_file"],
message="--vocab_file must be defined if --bleu_source and --bleu_ref "
"are defined.")
def _check_bleu_vocab_file(flags_dict):
if flags_dict["bleu_source"] and flags_dict["bleu_ref"]:
return flags_dict["vocab_file"] is not None
return True
@flags.multi_flags_validator(
["export_dir", "vocab_file"],
message="--vocab_file must be defined if --export_dir is set.")
def _check_export_vocab_file(flags_dict):
if flags_dict["export_dir"]:
return flags_dict["vocab_file"] is not None
return True
flags_core.require_cloud_storage(["data_dir", "model_dir", "export_dir"])
def define_transformer_flags():
"""Add flags and flag validators for running transformer_main."""
# Add common flags (data_dir, model_dir, train_epochs, etc.).
flags_core.define_base()
flags_core.define_performance(
num_parallel_calls=True,
inter_op=False,
intra_op=False,
synthetic_data=True,
max_train_steps=False,
dtype=False,
all_reduce_alg=True
)
flags_core.define_benchmark()
flags_core.define_device(tpu=True)
# Set flags from the flags_core module as "key flags" so they're listed when
# the '-h' flag is used. Without this line, the flags defined above are
# only shown in the full `--helpful` help text.
flags.adopt_module_key_flags(flags_core)
# Add transformer-specific flags
flags.DEFINE_enum(
name="param_set", short_name="mp", default="big",
enum_values=PARAMS_MAP.keys(),
help=flags_core.help_wrap(
"Parameter set to use when creating and training the model. The "
"parameters define the input shape (batch size and max length), "
"model configuration (size of embedding, # of hidden layers, etc.), "
"and various other settings. The big parameter set increases the "
"default batch size, embedding/hidden size, and filter size. For a "
"complete list of parameters, please see model/model_params.py."))
flags.DEFINE_bool(
name="static_batch", default=False,
help=flags_core.help_wrap(
"Whether the batches in the dataset should have static shapes. In "
"general, this setting should be False. Dynamic shapes allow the "
"inputs to be grouped so that the number of padding tokens is "
"minimized, and helps model training. In cases where the input shape "
"must be static (e.g. running on TPU), this setting will be ignored "
"and static batching will always be used."))
# Flags for training with steps (may be used for debugging)
flags.DEFINE_integer(
name="train_steps", short_name="ts", default=None,
help=flags_core.help_wrap("The number of steps used to train."))
flags.DEFINE_integer(
name="steps_between_evals", short_name="sbe", default=1000,
help=flags_core.help_wrap(
"The Number of training steps to run between evaluations. This is "
"used if --train_steps is defined."))
# BLEU score computation
flags.DEFINE_string(
name="bleu_source", short_name="bls", default=None,
help=flags_core.help_wrap(
"Path to source file containing text translate when calculating the "
"official BLEU score. Both --bleu_source and --bleu_ref must be set. "
"Use the flag --stop_threshold to stop the script based on the "
"uncased BLEU score."))
flags.DEFINE_string(
name="bleu_ref", short_name="blr", default=None,
help=flags_core.help_wrap(
"Path to source file containing text translate when calculating the "
"official BLEU score. Both --bleu_source and --bleu_ref must be set. "
"Use the flag --stop_threshold to stop the script based on the "
"uncased BLEU score."))
flags.DEFINE_string(
name="vocab_file", short_name="vf", default=None,
help=flags_core.help_wrap(
"Path to subtoken vocabulary file. If data_download.py was used to "
"download and encode the training data, look in the data_dir to find "
"the vocab file."))
flags_core.set_defaults(data_dir="/tmp/translate_ende",
model_dir="/tmp/transformer_model",
batch_size=None,
train_epochs=None)
@flags.multi_flags_validator(
["train_epochs", "train_steps"],
message="Both --train_steps and --train_epochs were set. Only one may be "
"defined.")
def _check_train_limits(flag_dict):
return flag_dict["train_epochs"] is None or flag_dict["train_steps"] is None
@flags.multi_flags_validator(
["bleu_source", "bleu_ref"],
message="Both or neither --bleu_source and --bleu_ref must be defined.")
def _check_bleu_files(flags_dict):
return (flags_dict["bleu_source"] is None) == (
flags_dict["bleu_ref"] is None)
@flags.multi_flags_validator(
["bleu_source", "bleu_ref", "vocab_file"],
message="--vocab_file must be defined if --bleu_source and --bleu_ref "
"are defined.")
def _check_bleu_vocab_file(flags_dict):
if flags_dict["bleu_source"] and flags_dict["bleu_ref"]:
return flags_dict["vocab_file"] is not None
return True
@flags.multi_flags_validator(
["export_dir", "vocab_file"],
message="--vocab_file must be defined if --export_dir is set.")
def _check_export_vocab_file(flags_dict):
if flags_dict["export_dir"]:
return flags_dict["vocab_file"] is not None
return True
flags_core.require_cloud_storage(["data_dir", "model_dir", "export_dir"])
def define_transformer_flags():
"""Add flags and flag validators for running transformer_main."""
# Add common flags (data_dir, model_dir, train_epochs, etc.).
flags_core.define_base(multi_gpu=False, num_gpu=False, export_dir=False)
flags_core.define_performance(num_parallel_calls=True,
inter_op=False,
intra_op=False,
synthetic_data=False,
max_train_steps=False,
dtype=False)
flags_core.define_benchmark()
flags_core.define_device(tpu=True)
# Set flags from the flags_core module as "key flags" so they're listed when
# the '-h' flag is used. Without this line, the flags defined above are
# only shown in the full `--helpful` help text.
flags.adopt_module_key_flags(flags_core)
# Add transformer-specific flags
flags.DEFINE_enum(
name="param_set",
short_name="mp",
default="big",
enum_values=["base", "big", "tiny"],
help=flags_core.help_wrap(
"Parameter set to use when creating and training the model. The "
"parameters define the input shape (batch size and max length), "
"model configuration (size of embedding, # of hidden layers, etc.), "
"and various other settings. The big parameter set increases the "
"default batch size, embedding/hidden size, and filter size. For a "
"complete list of parameters, please see model/model_params.py."))
flags.DEFINE_bool(
name="static_batch",
default=False,
help=flags_core.help_wrap(
"Whether the batches in the dataset should have static shapes. In "
"general, this setting should be False. Dynamic shapes allow the "
"inputs to be grouped so that the number of padding tokens is "
"minimized, and helps model training. In cases where the input shape "
"must be static (e.g. running on TPU), this setting will be ignored "
"and static batching will always be used."))
# Flags for training with steps (may be used for debugging)
flags.DEFINE_integer(
name="train_steps",
short_name="ts",
default=None,
help=flags_core.help_wrap("The number of steps used to train."))
flags.DEFINE_integer(
name="steps_between_evals",
short_name="sbe",
default=1000,
help=flags_core.help_wrap(
"The Number of training steps to run between evaluations. This is "
"used if --train_steps is defined."))
# BLEU score computation
flags.DEFINE_string(
name="bleu_source",
short_name="bls",
default=None,
help=flags_core.help_wrap(
"Path to source file containing text translate when calculating the "
"official BLEU score. --bleu_source, --bleu_ref, and --vocab_file "
"must be set. Use the flag --stop_threshold to stop the script based "
"on the uncased BLEU score."))
flags.DEFINE_string(
name="bleu_ref",
short_name="blr",
default=None,
help=flags_core.help_wrap(
"Path to source file containing text translate when calculating the "
"official BLEU score. --bleu_source, --bleu_ref, and --vocab_file "
"must be set. Use the flag --stop_threshold to stop the script based "
"on the uncased BLEU score."))
flags.DEFINE_string(
name="vocab_file",
short_name="vf",
default=VOCAB_FILE,
help=flags_core.help_wrap(
"Name of vocabulary file containing subtokens for subtokenizing the "
"bleu_source file. This file is expected to be in the directory "
"defined by --data_dir."))
flags_core.set_defaults(data_dir="/tmp/translate_ende",
model_dir="/tmp/transformer_model",
batch_size=None,
train_epochs=None)
@flags.multi_flags_validator(
["train_epochs", "train_steps"],
message=
"Both --train_steps and --train_epochs were set. Only one may be "
"defined.")
def _check_train_limits(flag_dict):
return flag_dict["train_epochs"] is None or flag_dict[
"train_steps"] is None
@flags.multi_flags_validator(
["data_dir", "bleu_source", "bleu_ref", "vocab_file"],
message="--bleu_source, --bleu_ref, and/or --vocab_file don't exist. "
"Please ensure that the file paths are correct.")
def _check_bleu_files(flags_dict):
"""Validate files when bleu_source and bleu_ref are defined."""
if flags_dict["bleu_source"] is None or flags_dict["bleu_ref"] is None:
return True
# Ensure that bleu_source, bleu_ref, and vocab files exist.
vocab_file_path = os.path.join(flags_dict["data_dir"],
flags_dict["vocab_file"])
return all([
tf.gfile.Exists(flags_dict["bleu_source"]),
tf.gfile.Exists(flags_dict["bleu_ref"]),
tf.gfile.Exists(vocab_file_path)
])
flags_core.require_cloud_storage(["data_dir", "model_dir"])
