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# 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.
"""isort:skip_file"""
import argparse
import importlib
import os
from contextlib import ExitStack
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils import merge_with_parent
from hydra.core.config_store import ConfigStore
from omegaconf import open_dict, OmegaConf
from .composite_encoder import CompositeEncoder
from .distributed_fairseq_model import DistributedFairseqModel
from .fairseq_decoder import FairseqDecoder
from .fairseq_encoder import FairseqEncoder
from .fairseq_incremental_decoder import FairseqIncrementalDecoder
from .fairseq_model import (
BaseFairseqModel,
FairseqEncoderDecoderModel,
FairseqEncoderModel,
FairseqLanguageModel,
FairseqModel,
FairseqMultiModel,
)
MODEL_REGISTRY = {}
MODEL_DATACLASS_REGISTRY = {}
ARCH_MODEL_REGISTRY = {}
ARCH_MODEL_NAME_REGISTRY = {}
ARCH_MODEL_INV_REGISTRY = {}
ARCH_CONFIG_REGISTRY = {}
__all__ = [
"BaseFairseqModel",
"CompositeEncoder",
"DistributedFairseqModel",
"FairseqDecoder",
"FairseqEncoder",
"FairseqEncoderDecoderModel",
"FairseqEncoderModel",
"FairseqIncrementalDecoder",
"FairseqLanguageModel",
"FairseqModel",
"FairseqMultiModel",
]
def build_model(cfg: FairseqDataclass, task, from_checkpoint=False):
model = None
model_type = getattr(cfg, "_name", None) or getattr(cfg, "arch", None)
if not model_type and len(cfg) == 1:
# this is hit if config object is nested in directory that is named after model type
model_type = next(iter(cfg))
if model_type in MODEL_DATACLASS_REGISTRY:
cfg = cfg[model_type]
else:
raise Exception(
"Could not infer model type from directory. Please add _name field to indicate model type. "
"Available models: "
+ str(MODEL_DATACLASS_REGISTRY.keys())
+ " Requested model type: "
+ model_type
)
if model_type in ARCH_MODEL_REGISTRY:
# case 1: legacy models
model = ARCH_MODEL_REGISTRY[model_type]
elif model_type in MODEL_DATACLASS_REGISTRY:
# case 2: config-driven models
model = MODEL_REGISTRY[model_type]
if model_type in MODEL_DATACLASS_REGISTRY:
# set defaults from dataclass. note that arch name and model name can be the same
dc = MODEL_DATACLASS_REGISTRY[model_type]
if isinstance(cfg, argparse.Namespace):
cfg = dc.from_namespace(cfg)
else:
cfg = merge_with_parent(dc(), cfg, from_checkpoint)
else:
if model_type in ARCH_CONFIG_REGISTRY:
with open_dict(cfg) if OmegaConf.is_config(cfg) else ExitStack():
# this calls the different "arch" functions (like base_architecture()) that you indicate
# if you specify --arch on the command line. this is only applicable to the old argparse based models
# hydra models should expose different architectures via different config files
# it will modify the cfg object and default parameters according to the arch
ARCH_CONFIG_REGISTRY[model_type](cfg)
assert model is not None, (
f"Could not infer model type from {cfg}. "
"Available models: {}".format(MODEL_DATACLASS_REGISTRY.keys())
+ f" Requested model type: {model_type}"
)
return model.build_model(cfg, task)
def register_model(name, dataclass=None):
"""
New model types can be added to fairseq with the :func:`register_model`
function decorator.
For example::
@register_model('lstm')
class LSTM(FairseqEncoderDecoderModel):
(...)
.. note:: All models must implement the :class:`BaseFairseqModel` interface.
Typically you will extend :class:`FairseqEncoderDecoderModel` for
sequence-to-sequence tasks or :class:`FairseqLanguageModel` for
language modeling tasks.
Args:
name (str): the name of the model
"""
def register_model_cls(cls):
if name in MODEL_REGISTRY:
raise ValueError("Cannot register duplicate model ({})".format(name))
if not issubclass(cls, BaseFairseqModel):
raise ValueError(
"Model ({}: {}) must extend BaseFairseqModel".format(name, cls.__name__)
)
MODEL_REGISTRY[name] = cls
if dataclass is not None and not issubclass(dataclass, FairseqDataclass):
raise ValueError(
"Dataclass {} must extend FairseqDataclass".format(dataclass)
)
cls.__dataclass = dataclass
if dataclass is not None:
MODEL_DATACLASS_REGISTRY[name] = dataclass
cs = ConfigStore.instance()
node = dataclass()
node._name = name
cs.store(name=name, group="model", node=node, provider="fairseq")
@register_model_architecture(name, name)
def noop(_):
pass
return cls
return register_model_cls
def register_model_architecture(model_name, arch_name):
"""
New model architectures can be added to fairseq with the
:func:`register_model_architecture` function decorator. After registration,
model architectures can be selected with the ``--arch`` command-line
argument.
For example::
@register_model_architecture('lstm', 'lstm_luong_wmt_en_de')
def lstm_luong_wmt_en_de(cfg):
args.encoder_embed_dim = getattr(cfg.model, 'encoder_embed_dim', 1000)
(...)
The decorated function should take a single argument *cfg*, which is a
:class:`omegaconf.DictConfig`. The decorated function should modify these
arguments in-place to match the desired architecture.
Args:
model_name (str): the name of the Model (Model must already be
registered)
arch_name (str): the name of the model architecture (``--arch``)
"""
def register_model_arch_fn(fn):
if model_name not in MODEL_REGISTRY:
raise ValueError(
"Cannot register model architecture for unknown model type ({})".format(
model_name
)
)
if arch_name in ARCH_MODEL_REGISTRY:
raise ValueError(
"Cannot register duplicate model architecture ({})".format(arch_name)
)
if not callable(fn):
raise ValueError(
"Model architecture must be callable ({})".format(arch_name)
)
ARCH_MODEL_REGISTRY[arch_name] = MODEL_REGISTRY[model_name]
ARCH_MODEL_NAME_REGISTRY[arch_name] = model_name
ARCH_MODEL_INV_REGISTRY.setdefault(model_name, []).append(arch_name)
ARCH_CONFIG_REGISTRY[arch_name] = fn
return fn
return register_model_arch_fn
def import_models(models_dir, namespace):
for file in os.listdir(models_dir):
path = os.path.join(models_dir, file)
if (
not file.startswith("_")
and not file.startswith(".")
and (file.endswith(".py") or os.path.isdir(path))
):
model_name = file[: file.find(".py")] if file.endswith(".py") else file
importlib.import_module(namespace + "." + model_name)
# extra `model_parser` for sphinx
if model_name in MODEL_REGISTRY:
parser = argparse.ArgumentParser(add_help=False)
group_archs = parser.add_argument_group("Named architectures")
group_archs.add_argument(
"--arch", choices=ARCH_MODEL_INV_REGISTRY[model_name]
)
group_args = parser.add_argument_group(
"Additional command-line arguments"
)
MODEL_REGISTRY[model_name].add_args(group_args)
globals()[model_name + "_parser"] = parser
# automatically import any Python files in the models/ directory
models_dir = os.path.dirname(__file__)
import_models(models_dir, "fairseq.models")
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