# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import importlib
import os
from fairseq import registry
build_tokenizer, register_tokenizer, TOKENIZER_REGISTRY = registry.setup_registry(
'--tokenizer',
default=None,
)
build_bpe, register_bpe, BPE_REGISTRY = registry.setup_registry(
'--bpe',
default=None,
)
# automatically import any Python files in the transforms/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.py')]
importlib.import_module('fairseq.data.transforms.' + module)
import importlib
import os
from fairseq import registry
from fairseq.optim.fairseq_optimizer import FairseqOptimizer
from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer
__all__ = [
'FairseqOptimizer',
'FP16Optimizer',
'MemoryEfficientFP16Optimizer',
]
_build_optimizer, register_optimizer, OPTIMIZER_REGISTRY = registry.setup_registry(
'--optimizer',
base_class=FairseqOptimizer,
default='nag',
)
def build_optimizer(args, params, *extra_args, **extra_kwargs):
params = list(filter(lambda p: p.requires_grad, params))
return _build_optimizer(args, params, *extra_args, **extra_kwargs)
# automatically import any Python files in the optim/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.py')]
importlib.import_module('fairseq.optim.' + module)
"""isort:skip_file"""
import importlib
import os
from fairseq import registry
from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import ( # noqa
FairseqLRScheduler, LegacyFairseqLRScheduler,
)
from omegaconf import DictConfig
(
build_lr_scheduler_,
register_lr_scheduler,
LR_SCHEDULER_REGISTRY,
LR_SCHEDULER_DATACLASS_REGISTRY,
) = registry.setup_registry("--lr-scheduler",
base_class=FairseqLRScheduler,
default="fixed")
def build_lr_scheduler(cfg: DictConfig, optimizer):
return build_lr_scheduler_(cfg, optimizer)
# automatically import any Python files in the optim/lr_scheduler/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_"):
file_name = file[:file.find(".py")]
importlib.import_module("fairseq.optim.lr_scheduler." + file_name)
# 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 importlib
import os
from fairseq import registry
from fairseq.criterions.fairseq_criterion import FairseqCriterion
build_criterion, register_criterion, CRITERION_REGISTRY = registry.setup_registry(
'--criterion',
base_class=FairseqCriterion,
default='cross_entropy',
)
# automatically import any Python files in the criterions/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.py')]
importlib.import_module('fairseq.criterions.' + module)
# 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 importlib
import os
from fairseq import registry
build_agent, register_agent, MONOTONIC_AGENT, _ = registry.setup_registry(
"--agent-type")
DEFAULT_EOS = "</s>"
GET = 0
SEND = 1
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_"):
module = file[:file.find(".py")]
importlib.import_module("agents." + module)
import importlib
import os
from fairseq import registry
# ConvNets:
(build_convnet, register_convnet, CONVNET_REGISTRY) = registry.setup_registry('--convnet')
# Aggregators:
(build_aggregator, register_aggregator, AGGREGATOR_REGISTRY) = registry.setup_registry('--aggregator')
from .masked_convolution import MaskedConvolution
from .pa_controller import PAController
# automatically import any Python files in the criterions/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.py')]
importlib.import_module('examples.pervasive.modules.' + module)
# 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 importlib
import os
from fairseq import registry
from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import FairseqLRScheduler, LegacyFairseqLRScheduler # noqa
LR_SCHEDULER_DATACLASS_REGISTRY = {}
build_lr_scheduler, register_lr_scheduler, LR_SCHEDULER_REGISTRY = registry.setup_registry(
'--lr-scheduler',
base_class=FairseqLRScheduler,
default='fixed',
)
# automatically import any Python files in the optim/lr_scheduler/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.py')]
importlib.import_module('fairseq.optim.lr_scheduler.' + module)
# 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 importlib
import os
from fairseq import registry
(build_scorer, register_scorer, SCORER_REGISTRIES,
_) = registry.setup_registry('--scorer-type')
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.py')]
importlib.import_module('scorers.' + module)
# 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 os
import importlib
from fairseq import registry
(
build_monotonic_attention,
register_monotonic_attention,
MONOTONIC_ATTENTION_REGISTRY,
_,
) = registry.setup_registry("--simul-type")
for file in sorted(os.listdir(os.path.dirname(__file__))):
if file.endswith(".py") and not file.startswith("_"):
model_name = file[:file.find(".py")]
importlib.import_module("examples.simultaneous_translation.modules." +
model_name)
from fairseq import registry
from fairseq.optim.fairseq_optimizer import FairseqOptimizer, LegacyFairseqOptimizer # noqa
from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer
from fairseq.optim.bmuf import FairseqBMUF # noqa
from fairseq.optim.shard import shard_
__all__ = [
'FairseqOptimizer',
'FP16Optimizer',
'MemoryEfficientFP16Optimizer',
'shard_',
]
_build_optimizer, register_optimizer, OPTIMIZER_REGISTRY = registry.setup_registry(
'--optimizer',
base_class=FairseqOptimizer,
required=True,
)
def build_optimizer(args, params, *extra_args, **extra_kwargs):
if all(isinstance(p, dict) for p in params):
params = [t for p in params for t in p.values()]
params = list(filter(lambda p: p.requires_grad, params))
return _build_optimizer(args, params, *extra_args, **extra_kwargs)
# automatically import any Python files in the optim/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.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 importlib
import os
from fairseq import registry
from fairseq.optim.fairseq_optimizer import FairseqOptimizer
from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer
from fairseq.optim.bmuf import FairseqBMUF # noqa
__all__ = [
"FairseqOptimizer",
"FP16Optimizer",
"MemoryEfficientFP16Optimizer",
]
build_optimizer, register_optimizer, OPTIMIZER_REGISTRY = registry.setup_registry(
"--optimizer",
base_class=FairseqOptimizer,
default="nag",
)
# automatically import any Python files in the optim/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_"):
module = file[:file.find(".py")]
importlib.import_module("fairseq.optim." + module)
# 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 importlib
import os
from fairseq import registry
(build_monotonic_attention, register_monotonic_attention,
MONOTONIC_ATTENTION_REGISTRY, _) = registry.setup_registry('--simul-type')
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
model_name = file[:file.find('.py')]
importlib.import_module('examples.simultaneous_translation.modules.' +
model_name)
# 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 importlib
import os
from fairseq import registry
_build_scoring, register_scoring, SCORING_REGISTRY = registry.setup_registry(
"--scoring", default="bleu")
def build_scorer(args, tgt_dict):
from fairseq import utils
if args.sacrebleu:
utils.deprecation_warning(
"--sacrebleu is deprecated. Please use --scoring sacrebleu instead."
)
args.scoring = "sacrebleu"
if args.scoring == "bleu":
from fairseq.scoring import bleu
return bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
else:
return _build_scoring(args)
# automatically import any Python files in the current directory
for file in os.listdir(os.path.dirname(__file__)):
# 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 importlib
import os
from fairseq import registry
build_agent, register_agent, MONOTONIC_AGENT = registry.setup_registry(
'--agent-type')
DEFAULT_EOS = '</s>'
GET = 0
SEND = 1
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.py')]
importlib.import_module('agents.' + module)
from omegaconf import DictConfig
__all__ = [
"FairseqOptimizer",
"FP16Optimizer",
"MemoryEfficientFP16Optimizer",
"shard_",
]
(
_build_optimizer,
register_optimizer,
OPTIMIZER_REGISTRY,
OPTIMIZER_DATACLASS_REGISTRY,
) = registry.setup_registry("--optimizer",
base_class=FairseqOptimizer,
required=True)
def build_optimizer(optimizer_cfg: Union[DictConfig, Namespace], params,
*extra_args, **extra_kwargs):
if all(isinstance(p, dict) for p in params):
params = [t for p in params for t in p.values()]
params = list(filter(lambda p: p.requires_grad, params))
return _build_optimizer(optimizer_cfg, params, *extra_args, **extra_kwargs)
# automatically import any Python files in the optim/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_"):
file_name = file[:file.find(".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 importlib
import os
from fairseq import registry
build_tokenizer, register_tokenizer, TOKENIZER_REGISTRY, _ = registry.setup_registry(
"--tokenizer",
default=None,
)
build_bpe, register_bpe, BPE_REGISTRY, _ = registry.setup_registry(
"--bpe",
default=None,
)
# automatically import any Python files in the encoders/ directory
for file in sorted(os.listdir(os.path.dirname(__file__))):
if file.endswith(".py") and not file.startswith("_"):
module = file[: file.find(".py")]
importlib.import_module("fairseq.data.encoders." + module)
# 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 importlib
import os
from fairseq import registry
(build_scorer, register_scorer, SCORER_REGISTRIES,
_) = registry.setup_registry("--scorer-type")
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_"):
module = file[:file.find(".py")]
importlib.import_module("scorers." + module)
"""isort:skip_file"""
import importlib
import os
from fairseq import registry
from fairseq.criterions.fairseq_criterion import ( # noqa
FairseqCriterion, LegacyFairseqCriterion,
)
from omegaconf import DictConfig
(
build_criterion_,
register_criterion,
CRITERION_REGISTRY,
CRITERION_DATACLASS_REGISTRY,
) = registry.setup_registry("--criterion",
base_class=FairseqCriterion,
default="cross_entropy")
def build_criterion(cfg: DictConfig, task):
return build_criterion_(cfg, task)
# automatically import any Python files in the criterions/ directory
for file in sorted(os.listdir(os.path.dirname(__file__))):
if file.endswith(".py") and not file.startswith("_"):
file_name = file[:file.find(".py")]
importlib.import_module("fairseq.criterions." + file_name)
def add_string(self, ref, pred):
self.ref.append(ref)
self.pred.append(pred)
@abstractmethod
def score(self) -> float:
pass
@abstractmethod
def result_string(self) -> str:
pass
_build_scorer, register_scorer, SCORER_REGISTRY, _ = registry.setup_registry(
"--scoring", default="bleu"
)
def build_scorer(choice, tgt_dict):
_choice = choice._name if isinstance(choice, DictConfig) else choice
if _choice == "bleu":
from fairseq.scoring import bleu
return bleu.Scorer(
bleu.BleuConfig(pad=tgt_dict.pad(), eos=tgt_dict.eos(), unk=tgt_dict.unk())
)
return _build_scorer(choice)
"""
Scores references and hypos
"""
from abc import abstractmethod
import logging
from typing import *
from fairseq import registry, metrics
from fairseq.utils import item
import numpy as np
logger = logging.getLogger(__name__)
build_eval_scorer, register_eval_scorer, EVAL_SCORER_REGISTRY = registry.setup_registry(
'--eval-scorer',
default=None,
)
def add_eval_scoring_args(parser):
parser.add_argument('--eval-scorer-args',
type=str,
metavar='JSON',
help='generation args for validation inference, '
'e.g., \'{"beam": 4, "lenpen": 0.6}\'')
parser.add_argument(
'--eval-scorer-detok',
type=str,
default="space",
help='detokenizer before computing any scores (e.g., "moses"); '
'required if using --eval-bleu; use "space" to '