import importlib
import os
from fairseq_stchde import registry
from fairseq_stchde.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 os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_"):
file_name = file[: file.find(".py")]
importlib.import_module("fairseq_stchde.criterions." + 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_stchde 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 os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_"):
module = file[:file.find(".py")]
importlib.import_module("fairseq_stchde.data.encoders." + module)
self.pred = []
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_stchde.scoring import bleu
return bleu.Scorer(
bleu.BleuConfig(pad=tgt_dict.pad(),
eos=tgt_dict.eos(),
unk=tgt_dict.unk()))
return _build_scorer(choice)
import importlib
import os
from fairseq_stchde import registry
from fairseq_stchde.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_stchde.optim.lr_scheduler." +
file_name)
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(cfg: DictConfig, 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(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")]
importlib.import_module("fairseq_stchde.optim." + file_name)