wehub-resource-sync
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
# Copyright (c) 2019 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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from paddle.incubate.optimizer import GradientMergeOptimizer as GM
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from .meta_optimizer_base import MetaOptimizerBase
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__all__ = []
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class GradientMergeOptimizer(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.wrapped_opt = None
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self.meta_optimizers_white_list = [
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"AMPOptimizer",
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"LarsOptimizer",
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"LambOptimizer",
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"RecomputeOptimizer",
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]
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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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def _init_wrapped_opt(self):
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config = self.user_defined_strategy.gradient_merge_configs
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self.wrapped_opt = GM(self.inner_opt)
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self.wrapped_opt._set_k_steps(
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self.user_defined_strategy.gradient_merge_configs["k_steps"]
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)
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self.wrapped_opt._set_avg(
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self.user_defined_strategy.gradient_merge_configs["avg"]
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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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can_apply = (
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self.user_defined_strategy.gradient_merge
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) and self.user_defined_strategy.gradient_merge_configs["k_steps"] > 1
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return can_apply
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def _disable_strategy(self, dist_strategy):
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dist_strategy.gradient_merge = False
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dist_strategy.gradient_merge_configs = {}
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def _enable_strategy(self, dist_strategy, context):
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# we currently do not support auto-enable GradientMerge
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return
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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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self._init_wrapped_opt()
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optimize_ops, params_grads = self.wrapped_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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