wehub-resource-sync
111 lines
3.7 KiB
Python
Executable File
111 lines
3.7 KiB
Python
Executable File
# Copyright (c) 2020 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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import logging
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from paddle.incubate.optimizer import LarsMomentumOptimizer
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from paddle.optimizer import Momentum
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from .meta_optimizer_base import MetaOptimizerBase
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__all__ = []
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class LarsOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super().__init__(optimizer)
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self.inner_opt = optimizer
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self.lars_opt = None
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# we do not allow meta optimizer to be inner optimizer currently
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self.meta_optimizers_white_list = []
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self.meta_optimizers_black_list = []
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def _set_basic_info(
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self, loss, role_maker, user_defined_optimizer, user_defined_strategy
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):
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super()._set_basic_info(
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loss, role_maker, user_defined_optimizer, user_defined_strategy
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)
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opt = self.inner_opt
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if not isinstance(opt, Momentum):
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return
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configs = self.user_defined_strategy.lars_configs
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self.lars_opt = LarsMomentumOptimizer(
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learning_rate=opt._learning_rate,
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momentum=opt._momentum,
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lars_coeff=configs['lars_coeff'],
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lars_weight_decay=configs['lars_weight_decay'],
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parameter_list=opt._parameter_list,
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regularization=opt.regularization,
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grad_clip=opt._grad_clip,
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name=opt._name,
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exclude_from_weight_decay=configs['exclude_from_weight_decay'],
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epsilon=configs['epsilon'],
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)
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def _can_apply(self):
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if not self.role_maker._is_collective:
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return False
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if self.user_defined_strategy.lars:
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if not isinstance(self.inner_opt, Momentum):
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logging.warning(
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f"lars need the inner optimizer to be Momentum optimizer but got {self.inner_opt.type}."
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)
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return False
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return True
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return False
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def _disable_strategy(self, dist_strategy):
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dist_strategy.lars = False
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dist_strategy.lars_configs = {}
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def _enable_strategy(self, dist_strategy, context):
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dist_strategy.lars = True
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dist_strategy.lars_configs = {
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"lars_coeff": 0.01,
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"lars_weight_decay": 0.0005,
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}
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def backward(
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self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None,
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callbacks=None,
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):
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return self.lars_opt.backward(
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loss, startup_program, parameter_list, no_grad_set, callbacks
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)
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# the following function will be used by AMP if both LARS and AMP are turn on together.
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def apply_gradients(self, params_grads):
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return self.lars_opt.apply_gradients(params_grads=params_grads)
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def apply_optimize(self, loss, startup_program, params_grads):
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return self.lars_opt._apply_optimize(
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loss, startup_program=startup_program, params_grads=params_grads
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)
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def minimize_impl(
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self, loss, startup_program=None, parameter_list=None, no_grad_set=None
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):
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optimize_ops, params_grads = self.lars_opt.minimize(
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loss, startup_program, parameter_list, no_grad_set
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)
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return optimize_ops, params_grads
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