fairseq/options.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import argparse
from pathlib import Path
from typing import Callable, List, Optional, Union

import torch
from fairseq import utils
from fairseq.data.indexed_dataset import get_available_dataset_impl
from fairseq.dataclass.configs import (
    CheckpointConfig,
    CommonConfig,
    CommonEvalConfig,
    DatasetConfig,
    DistributedTrainingConfig,
    EvalLMConfig,
    GenerationConfig,
    InteractiveConfig,
    OptimizationConfig,
)
from fairseq.dataclass.utils import gen_parser_from_dataclass

# this import is for backward compatibility
from fairseq.utils import csv_str_list, eval_bool, eval_str_dict, eval_str_list  # noqa


def get_preprocessing_parser(default_task="translation"):
    parser = get_parser("Preprocessing", default_task)
    add_preprocess_args(parser)
    return parser


def get_training_parser(default_task="translation"):
    parser = get_parser("Trainer", default_task)
    add_dataset_args(parser, train=True)
    add_distributed_training_args(parser)
    add_model_args(parser)
    add_optimization_args(parser)
    add_checkpoint_args(parser)
    return parser


def get_generation_parser(interactive=False, default_task="translation"):
    parser = get_parser("Generation", default_task)
    add_dataset_args(parser, gen=True)
    add_distributed_training_args(parser, default_world_size=1)
    add_generation_args(parser)
    add_checkpoint_args(parser)
    if interactive:
        add_interactive_args(parser)
    return parser


def get_interactive_generation_parser(default_task="translation"):
    return get_generation_parser(interactive=True, default_task=default_task)


def get_eval_lm_parser(default_task="language_modeling"):
    parser = get_parser("Evaluate Language Model", default_task)
    add_dataset_args(parser, gen=True)
    add_distributed_training_args(parser, default_world_size=1)
    add_eval_lm_args(parser)
    return parser


def get_validation_parser(default_task=None):
    parser = get_parser("Validation", default_task)
    add_dataset_args(parser, train=True)
    add_distributed_training_args(parser, default_world_size=1)
    group = parser.add_argument_group("Evaluation")
    gen_parser_from_dataclass(group, CommonEvalConfig())
    return parser


def parse_args_and_arch(
    parser: argparse.ArgumentParser,
    input_args: List[str] = None,
    parse_known: bool = False,
    suppress_defaults: bool = False,
    modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None,
):
    """
    Args:
        parser (ArgumentParser): the parser
        input_args (List[str]): strings to parse, defaults to sys.argv
        parse_known (bool): only parse known arguments, similar to
            `ArgumentParser.parse_known_args`
        suppress_defaults (bool): parse while ignoring all default values
        modify_parser (Optional[Callable[[ArgumentParser], None]]):
            function to modify the parser, e.g., to set default values
    """
    if suppress_defaults:
        # Parse args without any default values. This requires us to parse
        # twice, once to identify all the necessary task/model args, and a second
        # time with all defaults set to None.
        args = parse_args_and_arch(
            parser,
            input_args=input_args,
            parse_known=parse_known,
            suppress_defaults=False,
        )
        suppressed_parser = argparse.ArgumentParser(add_help=False, parents=[parser])
        suppressed_parser.set_defaults(**{k: None for k, v in vars(args).items()})
        args = suppressed_parser.parse_args(input_args)
        return argparse.Namespace(
            **{k: v for k, v in vars(args).items() if v is not None}
        )

    from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY, MODEL_REGISTRY

    # Before creating the true parser, we need to import optional user module
    # in order to eagerly import custom tasks, optimizers, architectures, etc.
    usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
    usr_parser.add_argument("--user-dir", default=None)
    usr_args, _ = usr_parser.parse_known_args(input_args)
    utils.import_user_module(usr_args)

    if modify_parser is not None:
        modify_parser(parser)

    # The parser doesn't know about model/criterion/optimizer-specific args, so
    # we parse twice. First we parse the model/criterion/optimizer, then we
    # parse a second time after adding the *-specific arguments.
    # If input_args is given, we will parse those args instead of sys.argv.
    args, _ = parser.parse_known_args(input_args)

    # Add model-specific args to parser.
    if hasattr(args, "arch"):
        model_specific_group = parser.add_argument_group(
            "Model-specific configuration",
            # Only include attributes which are explicitly given as command-line
            # arguments or which have default values.
            argument_default=argparse.SUPPRESS,
        )
        if args.arch in ARCH_MODEL_REGISTRY:
            ARCH_MODEL_REGISTRY[args.arch].add_args(model_specific_group)
        elif args.arch in MODEL_REGISTRY:
            MODEL_REGISTRY[args.arch].add_args(model_specific_group)
        else:
            raise RuntimeError()

    if hasattr(args, "task"):
        from fairseq.tasks import TASK_REGISTRY

        TASK_REGISTRY[args.task].add_args(parser)
    if getattr(args, "use_bmuf", False):
        # hack to support extra args for block distributed data parallelism
        from fairseq.optim.bmuf import FairseqBMUF

        FairseqBMUF.add_args(parser)

    # Add *-specific args to parser.
    from fairseq.registry import REGISTRIES

    for registry_name, REGISTRY in REGISTRIES.items():
        choice = getattr(args, registry_name, None)
        if choice is not None:
            cls = REGISTRY["registry"][choice]
            if hasattr(cls, "add_args"):
                cls.add_args(parser)
            elif hasattr(cls, "__dataclass"):
                gen_parser_from_dataclass(parser, cls.__dataclass())

    # Modify the parser a second time, since defaults may have been reset
    if modify_parser is not None:
        modify_parser(parser)

    # Parse a second time.
    if parse_known:
        args, extra = parser.parse_known_args(input_args)
    else:
        args = parser.parse_args(input_args)
        extra = None
    # Post-process args.
    if (
        hasattr(args, "batch_size_valid") and args.batch_size_valid is None
    ) or not hasattr(args, "batch_size_valid"):
        args.batch_size_valid = args.batch_size
    if hasattr(args, "max_tokens_valid") and args.max_tokens_valid is None:
        args.max_tokens_valid = args.max_tokens
    if getattr(args, "memory_efficient_fp16", False):
        args.fp16 = True
    if getattr(args, "memory_efficient_bf16", False):
        args.bf16 = True
    args.tpu = getattr(args, "tpu", False)
    args.bf16 = getattr(args, "bf16", False)
    if args.bf16:
        args.tpu = True
    if args.tpu and args.fp16:
        raise ValueError("Cannot combine --fp16 and --tpu, use --bf16 on TPUs")

    if getattr(args, "seed", None) is None:
        args.seed = 1  # default seed for training
        args.no_seed_provided = True
    else:
        args.no_seed_provided = False

    # Apply architecture configuration.
    if hasattr(args, "arch") and args.arch in ARCH_CONFIG_REGISTRY:
        ARCH_CONFIG_REGISTRY[args.arch](args)

    if parse_known:
        return args, extra
    else:
        return args


def get_parser(desc, default_task="translation"):
    # Before creating the true parser, we need to import optional user module
    # in order to eagerly import custom tasks, optimizers, architectures, etc.
    usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
    usr_parser.add_argument("--user-dir", default=None)
    usr_args, _ = usr_parser.parse_known_args()
    utils.import_user_module(usr_args)

    parser = argparse.ArgumentParser(allow_abbrev=False)
    gen_parser_from_dataclass(parser, CommonConfig())

    from fairseq.registry import REGISTRIES

    for registry_name, REGISTRY in REGISTRIES.items():
        parser.add_argument(
            "--" + registry_name.replace("_", "-"),
            default=REGISTRY["default"],
            choices=REGISTRY["registry"].keys(),
        )

    # Task definitions can be found under fairseq/tasks/
    from fairseq.tasks import TASK_REGISTRY

    parser.add_argument(
        "--task",
        metavar="TASK",
        default=default_task,
        choices=TASK_REGISTRY.keys(),
        help="task",
    )
    # fmt: on
    return parser


def add_preprocess_args(parser):
    group = parser.add_argument_group("Preprocessing")
    # fmt: off
    group.add_argument("-s", "--source-lang", default=None, metavar="SRC",
                       help="source language")
    group.add_argument("-t", "--target-lang", default=None, metavar="TARGET",
                       help="target language")
    group.add_argument("--trainpref", metavar="FP", default=None,
                       help="train file prefix (also used to build dictionaries)")
    group.add_argument("--validpref", metavar="FP", default=None,
                       help="comma separated, valid file prefixes "
                            "(words missing from train set are replaced with <unk>)")
    group.add_argument("--testpref", metavar="FP", default=None,
                       help="comma separated, test file prefixes "
                            "(words missing from train set are replaced with <unk>)")
    group.add_argument("--align-suffix", metavar="FP", default=None,
                       help="alignment file suffix")
    group.add_argument("--destdir", metavar="DIR", default="data-bin",
                       help="destination dir")
    group.add_argument("--thresholdtgt", metavar="N", default=0, type=int,
                       help="map words appearing less than threshold times to unknown")
    group.add_argument("--thresholdsrc", metavar="N", default=0, type=int,
                       help="map words appearing less than threshold times to unknown")
    group.add_argument("--tgtdict", metavar="FP",
                       help="reuse given target dictionary")
    group.add_argument("--srcdict", metavar="FP",
                       help="reuse given source dictionary")
    group.add_argument("--nwordstgt", metavar="N", default=-1, type=int,
                       help="number of target words to retain")
    group.add_argument("--nwordssrc", metavar="N", default=-1, type=int,
                       help="number of source words to retain")
    group.add_argument("--alignfile", metavar="ALIGN", default=None,
                       help="an alignment file (optional)")
    parser.add_argument('--dataset-impl', metavar='FORMAT', default='mmap',
                        choices=get_available_dataset_impl(),
                        help='output dataset implementation')
    group.add_argument("--joined-dictionary", action="store_true",
                       help="Generate joined dictionary")
    group.add_argument("--only-source", action="store_true",
                       help="Only process the source language")
    group.add_argument("--padding-factor", metavar="N", default=8, type=int,
                       help="Pad dictionary size to be multiple of N")
    group.add_argument("--workers", metavar="N", default=1, type=int,
                       help="number of parallel workers")
    group.add_argument("--dict-only", action='store_true',
                       help="if true, only builds a dictionary and then exits")
    # fmt: on
    return parser


def add_dataset_args(parser, train=False, gen=False):
    group = parser.add_argument_group("dataset_data_loading")
    gen_parser_from_dataclass(group, DatasetConfig())
    # fmt: on
    return group


def add_distributed_training_args(parser, default_world_size=None):
    group = parser.add_argument_group("distributed_training")
    if default_world_size is None:
        default_world_size = max(1, torch.cuda.device_count())
    gen_parser_from_dataclass(
        group, DistributedTrainingConfig(distributed_world_size=default_world_size)
    )
    return group


def add_optimization_args(parser):
    group = parser.add_argument_group("optimization")
    # fmt: off
    gen_parser_from_dataclass(group, OptimizationConfig())
    # fmt: on
    return group


def add_checkpoint_args(parser):
    group = parser.add_argument_group("checkpoint")
    # fmt: off
    gen_parser_from_dataclass(group, CheckpointConfig())
    # fmt: on
    return group


def add_common_eval_args(group):
    gen_parser_from_dataclass(group, CommonEvalConfig())


def add_eval_lm_args(parser):
    group = parser.add_argument_group("LM Evaluation")
    add_common_eval_args(group)
    gen_parser_from_dataclass(group, EvalLMConfig())


def add_generation_args(parser):
    group = parser.add_argument_group("Generation")
    add_common_eval_args(group)
    gen_parser_from_dataclass(group, GenerationConfig())
    return group


def add_interactive_args(parser):
    group = parser.add_argument_group("Interactive")
    gen_parser_from_dataclass(group, InteractiveConfig())


def add_model_args(parser):
    group = parser.add_argument_group("Model configuration")
    # fmt: off

    # Model definitions can be found under fairseq/models/
    #
    # The model architecture can be specified in several ways.
    # In increasing order of priority:
    # 1) model defaults (lowest priority)
    # 2) --arch argument
    # 3) --encoder/decoder-* arguments (highest priority)
    from fairseq.models import ARCH_MODEL_REGISTRY
    group.add_argument('--arch', '-a', metavar='ARCH',
                       choices=ARCH_MODEL_REGISTRY.keys(),
                       help='model architecture')
    # fmt: on
    return group


def get_args(
    data: Union[str, Path],
    task: str = "translation",
    arch: str = "transformer",
    **overrides
):
    parser = get_training_parser(task)
    args = parse_args_and_arch(parser, [str(data), "--task", task, "--arch", arch])

    for k, v in overrides.items():
        setattr(args, k, v)

    return args