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123 lines
4.1 KiB
123 lines
4.1 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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from typing import Any
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from typing import Dict
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from typing import Text
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import paddle
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from paddle.optimizer import Optimizer
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from paddle.regularizer import L2Decay
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from paddlespeech.s2t.training.gradclip import ClipGradByGlobalNormWithLog
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.s2t.utils.dynamic_import import instance_class
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from paddlespeech.s2t.utils.log import Log
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__all__ = ["OptimizerFactory"]
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logger = Log(__name__).getlog()
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OPTIMIZER_DICT = {
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"sgd": "paddle.optimizer:SGD",
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"momentum": "paddle.optimizer:Momentum",
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"adadelta": "paddle.optimizer:Adadelta",
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"adam": "paddle.optimizer:Adam",
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"adamw": "paddle.optimizer:AdamW",
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}
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def register_optimizer(cls):
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"""Register optimizer."""
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alias = cls.__name__.lower()
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OPTIMIZER_DICT[cls.__name__.lower()] = cls.__module__ + ":" + cls.__name__
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return cls
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@register_optimizer
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class Noam(paddle.optimizer.Adam):
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"""Seem to: espnet/nets/pytorch_backend/transformer/optimizer.py """
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def __init__(self,
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learning_rate=0,
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beta1=0.9,
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beta2=0.98,
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epsilon=1e-9,
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parameters=None,
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weight_decay=None,
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grad_clip=None,
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lazy_mode=False,
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multi_precision=False,
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name=None):
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super().__init__(
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learning_rate=learning_rate,
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beta1=beta1,
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beta2=beta2,
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epsilon=epsilon,
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parameters=parameters,
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weight_decay=weight_decay,
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grad_clip=grad_clip,
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lazy_mode=lazy_mode,
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multi_precision=multi_precision,
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name=name)
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def __repr__(self):
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echo = f"<{self.__class__.__module__}.{self.__class__.__name__} object at {hex(id(self))}> "
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echo += f"learning_rate: {self._learning_rate}, "
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echo += f"(beta1: {self._beta1} beta2: {self._beta2}), "
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echo += f"epsilon: {self._epsilon}"
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def dynamic_import_optimizer(module):
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"""Import Optimizer class dynamically.
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Args:
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module (str): module_name:class_name or alias in `OPTIMIZER_DICT`
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Returns:
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type: Optimizer class
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"""
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module_class = dynamic_import(module, OPTIMIZER_DICT)
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assert issubclass(module_class,
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Optimizer), f"{module} does not implement Optimizer"
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return module_class
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class OptimizerFactory():
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@classmethod
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def from_args(cls, name: str, args: Dict[Text, Any]):
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assert "parameters" in args, "parameters not in args."
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assert "learning_rate" in args, "learning_rate not in args."
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grad_clip = ClipGradByGlobalNormWithLog(
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args['grad_clip']) if "grad_clip" in args else None
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weight_decay = L2Decay(
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args['weight_decay']) if "weight_decay" in args else None
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if weight_decay:
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logger.info(f'<WeightDecay - {weight_decay}>')
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if grad_clip:
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logger.info(f'<GradClip - {grad_clip}>')
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module_class = dynamic_import_optimizer(name.lower())
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args.update({"grad_clip": grad_clip, "weight_decay": weight_decay})
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opt = instance_class(module_class, args)
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if "__repr__" in vars(opt):
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logger.info(f"{opt}")
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else:
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logger.info(
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f"<Optimizer {module_class.__module__}.{module_class.__name__}> LR: {args['learning_rate']}"
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)
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return opt
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