def get_parser_with_args():
parser = options.get_parser("Collect Top-K Probs",
default_task="pytorch_translate")
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_dataset_args(parser, gen=True)
generation_group = options.add_generation_args(parser)
generation_group.add_argument(
"--source-binary-file",
default="",
help="Path for the binary file containing source eval examples. "
"(Overrides --source-text-file. Must be used in conjunction with "
"--target-binary-file).",
)
generation_group.add_argument(
"--target-binary-file",
default="",
help="Path for the binary file containing target eval examples. "
"(Overrides --target-text-file. Must be used in conjunction with "
"--source-binary-file).",
)
generation_group.add_argument(
"--k-probs-to-collect",
type=int,
default=8,
help="Number of probabilities to collect for each output step.",
)
generation_group.add_argument(
"--top-k-probs-binary-file",
type=str,
default="",
help="File into which to save top-K probabilities for each token.",
)
return parser
def main():
parser = argparse.ArgumentParser(description="PyTorch Translate - preprocessing")
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_preprocessing_args(parser)
args = parser.parse_args()
pytorch_translate_options.validate_preprocessing_args(args)
pytorch_translate_options.print_args(args)
preprocess_corpora(args)
def get_parser_with_args(default_task="pytorch_translate"):
parser = options.get_parser("Trainer", default_task=default_task)
pytorch_translate_options.add_verbosity_args(parser, train=True)
pytorch_translate_options.add_dataset_args(parser, train=True, gen=True)
options.add_distributed_training_args(parser)
# Adds args related to training (validation and stopping criterions).
optimization_group = options.add_optimization_args(parser)
pytorch_translate_options.expand_optimization_args(optimization_group)
# Adds args related to checkpointing.
checkpointing_group = options.add_checkpoint_args(parser)
pytorch_translate_options.expand_checkpointing_args(checkpointing_group)
# Add model related args
options.add_model_args(parser)
# Adds args for generating intermediate BLEU eval while training.
generation_group = options.add_generation_args(parser)
pytorch_translate_options.expand_generation_args(generation_group, train=True)
# Adds args related to input data files (preprocessing, numberizing, and
# binarizing text files; creating vocab files)
pytorch_translate_options.add_preprocessing_args(parser)
return parser
def get_parser_with_args():
parser = options.get_parser("Generation", default_task="pytorch_translate")
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_dataset_args(parser, gen=True)
generation_group = options.add_generation_args(parser)
pytorch_translate_options.expand_generation_args(generation_group)
# Adds args used by the standalone generate binary.
generation_group.add_argument(
"--source-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--char-source-vocab-file",
default="",
metavar="FILE",
help=(
"Same as --source-vocab-file except using characters. "
"(For use with char_source models only.)"
),
)
generation_group.add_argument(
"--target-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--source-text-file",
default="",
nargs="+",
metavar="FILE",
help="Path to raw text file containing examples in source dialect. "
"This overrides what would be loaded from the data dir. "
"You can specify multiple source files (eg. for use in combination "
"with --source-ensembling). By default this will only translate the "
"first source file",
)
generation_group.add_argument(
"--target-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing examples in target dialect. "
"This overrides what would be loaded from the data dir.",
)
generation_group.add_argument(
"--source-binary-file",
default="",
help="Path for the binary file containing source eval examples. "
"(Overrides --source-text-file. Must be used in conjunction with "
"--target-binary-file).",
)
generation_group.add_argument(
"--target-binary-file",
default="",
help="Path for the binary file containing target eval examples. "
"(Overrides --target-text-file. Must be used in conjunction with "
"--source-binary-file).",
)
generation_group.add_argument(
"--translation-output-file",
default="",
type=str,
metavar="FILE",
help="Path to text file to store the output of the model. ",
)
generation_group.add_argument(
"--translation-probs-file",
default="",
type=str,
metavar="FILE",
help="Path to text file to store the probs of translation output. ",
)
generation_group.add_argument(
"--multiling-source-lang",
action="append",
metavar="SRC",
help=(
"Must be set for decoding with multilingual models. "
"Must match an entry from --multiling-encoder-lang from training."
),
)
generation_group.add_argument(
"--multiling-target-lang",
action="append",
metavar="TARGET",
help=(
"Must be set for decoding with multilingual models. "
"Must match an entry from --multiling-decoder-lang from training."
),
)
generation_group.add_argument(
"--source-ensembling",
action="store_true",
help="If this flag is present, the model will ensemble the predictions "
"conditioned on multiple source sentences (one per source-text-file)",
)
generation_group.add_argument(
"--competing-completed-beam-search",
action="store_true",
help="If this flag is present, use the alternative beam search "
"implementation in research/beam_search. This beam search keeps completed "
"hypos in the beam and let them compete against hypo expansions in the "
"next time step.",
)
return parser
def get_parser_with_args():
parser = options.get_parser("Generation")
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_dataset_args(parser, gen=True)
generation_group = options.add_generation_args(parser)
pytorch_translate_options.expand_generation_args(generation_group)
# Adds args used by the standalone generate binary.
generation_group.add_argument(
"--source-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--char-source-vocab-file",
default="",
metavar="FILE",
help=(
"Same as --source-vocab-file except using characters. "
"(For use with char_source models only.)"
),
)
generation_group.add_argument(
"--target-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--source-text-file",
default="",
nargs="+",
metavar="FILE",
help="Path to raw text file containing examples in source dialect. "
"This overrides what would be loaded from the data dir. "
"You can specify multiple source files (eg. for use in combination "
"with --source-ensembling). By default this will only translate the "
"first source file",
)
generation_group.add_argument(
"--target-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing examples in target dialect. "
"This overrides what would be loaded from the data dir.",
)
generation_group.add_argument(
"--source-binary-file",
default="",
help="Path for the binary file containing source eval examples. "
"(Overrides --source-text-file. Must be used in conjunction with "
"--target-binary-file).",
)
generation_group.add_argument(
"--target-binary-file",
default="",
help="Path for the binary file containing target eval examples. "
"(Overrides --target-text-file. Must be used in conjunction with "
"--source-binary-file).",
)
generation_group.add_argument(
"--translation-output-file",
default="",
type=str,
metavar="FILE",
help="Path to text file to store the output of the model. ",
)
generation_group.add_argument(
"--translation-probs-file",
default="",
type=str,
metavar="FILE",
help="Path to text file to store the probs of translation output. ",
)
generation_group.add_argument(
"--multiling-source-lang-id",
type=int,
default=None,
help=(
"Must be set for decoding with multilingual models. Set to i if "
"the source language is the i-th language in the training parameter "
"--multiling-encoder-lang (0-indexed)"
),
)
generation_group.add_argument(
"--multiling-target-lang-id",
type=int,
default=None,
help=(
"Must be set for decoding with multilingual models. Set to i if "
"the target language is the i-th language in the training parameter "
"--multiling-decoder-lang (0-indexed)"
),
)
generation_group.add_argument(
"--source-ensembling",
action="store_true",
help="If this flag is present, the model will ensemble the predictions "
"conditioned on multiple source sentences (one per source-text-file)",
)
return parser
def get_parser_with_args():
parser = options.get_parser("Generation", default_task="pytorch_translate")
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_dataset_args(parser, gen=True)
generation_group = options.add_generation_args(parser)
pytorch_translate_options.expand_generation_args(generation_group)
generation_group.add_argument(
"--source-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--char-source-vocab-file",
default="",
metavar="FILE",
help=(
"Same as --source-vocab-file except using characters. "
"(For use with char_source models only.)"
),
)
generation_group.add_argument(
"--target-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--multiling-source-lang",
action="append",
metavar="SRC",
help=(
"Must be set for decoding with multilingual models. "
"Must match an entry from --multiling-encoder-lang from training."
),
)
generation_group.add_argument(
"--multiling-target-lang",
action="append",
metavar="TARGET",
help=(
"Must be set for decoding with multilingual models. "
"Must match an entry from --multiling-decoder-lang from training."
),
)
# Add args related to benchmarking.
group = parser.add_argument_group("Benchmarking")
group.add_argument(
"--runs-per-length",
default=10,
type=int,
help="Number of times to run generation on each length.",
)
group.add_argument(
"--examples-per-length",
default=1,
type=int,
help="Sentences of each length to include in each eval (batched if >1).",
)
return parser
