You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
122 lines
4.0 KiB
122 lines
4.0 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from typing import Any
|
|
from typing import Dict
|
|
from typing import Text
|
|
|
|
import paddle
|
|
from paddle.optimizer import Optimizer
|
|
from paddle.regularizer import L2Decay
|
|
|
|
from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
|
|
from deepspeech.utils.dynamic_import import dynamic_import
|
|
from deepspeech.utils.dynamic_import import instance_class
|
|
from deepspeech.utils.log import Log
|
|
|
|
__all__ = ["OptimizerFactory"]
|
|
|
|
logger = Log(__name__).getlog()
|
|
|
|
OPTIMIZER_DICT = {
|
|
"sgd": "paddle.optimizer:SGD",
|
|
"momentum": "paddle.optimizer:Momentum",
|
|
"adadelta": "paddle.optimizer:Adadelta",
|
|
"adam": "paddle.optimizer:Adam",
|
|
"adamw": "paddle.optimizer:AdamW",
|
|
}
|
|
|
|
|
|
def register_optimizer(cls):
|
|
"""Register optimizer."""
|
|
alias = cls.__name__.lower()
|
|
OPTIMIZER_DICT[cls.__name__.lower()] = cls.__module__ + ":" + cls.__name__
|
|
return cls
|
|
|
|
|
|
@register_optimizer
|
|
class Noam(paddle.optimizer.Adam):
|
|
"""Seem to: espnet/nets/pytorch_backend/transformer/optimizer.py """
|
|
|
|
def __init__(self,
|
|
learning_rate=0,
|
|
beta1=0.9,
|
|
beta2=0.98,
|
|
epsilon=1e-9,
|
|
parameters=None,
|
|
weight_decay=None,
|
|
grad_clip=None,
|
|
lazy_mode=False,
|
|
multi_precision=False,
|
|
name=None):
|
|
super().__init__(
|
|
learning_rate=learning_rate,
|
|
beta1=beta1,
|
|
beta2=beta2,
|
|
epsilon=epsilon,
|
|
parameters=parameters,
|
|
weight_decay=weight_decay,
|
|
grad_clip=grad_clip,
|
|
lazy_mode=lazy_mode,
|
|
multi_precision=multi_precision,
|
|
name=name)
|
|
|
|
def __repr__(self):
|
|
echo = f"<{self.__class__.__module__}.{self.__class__.__name__} object at {hex(id(self))}> "
|
|
echo += f"learning_rate: {self._learning_rate}, "
|
|
echo += f"(beta1: {self._beta1} beta2: {self._beta2}), "
|
|
echo += f"epsilon: {self._epsilon}"
|
|
|
|
|
|
def dynamic_import_optimizer(module):
|
|
"""Import Optimizer class dynamically.
|
|
|
|
Args:
|
|
module (str): module_name:class_name or alias in `OPTIMIZER_DICT`
|
|
|
|
Returns:
|
|
type: Optimizer class
|
|
|
|
"""
|
|
module_class = dynamic_import(module, OPTIMIZER_DICT)
|
|
assert issubclass(module_class,
|
|
Optimizer), f"{module} does not implement Optimizer"
|
|
return module_class
|
|
|
|
|
|
class OptimizerFactory():
|
|
@classmethod
|
|
def from_args(cls, name: str, args: Dict[Text, Any]):
|
|
assert "parameters" in args, "parameters not in args."
|
|
assert "learning_rate" in args, "learning_rate not in args."
|
|
|
|
grad_clip = ClipGradByGlobalNormWithLog(
|
|
args['grad_clip']) if "grad_clip" in args else None
|
|
weight_decay = L2Decay(
|
|
args['weight_decay']) if "weight_decay" in args else None
|
|
if weight_decay:
|
|
logger.info(f'<WeightDecay - {weight_decay}>')
|
|
if grad_clip:
|
|
logger.info(f'<GradClip - {grad_clip}>')
|
|
|
|
module_class = dynamic_import_optimizer(name.lower())
|
|
args.update({"grad_clip": grad_clip, "weight_decay": weight_decay})
|
|
opt = instance_class(module_class, args)
|
|
if "__repr__" in vars(opt):
|
|
logger.info(f"{opt}")
|
|
else:
|
|
logger.info(
|
|
f"<Optimizer {module_class.__module__}.{module_class.__name__}> LR: {args['learning_rate']}"
|
|
)
|
|
return opt
|