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61 lines
2.4 KiB
61 lines
2.4 KiB
# 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.fluid.optimizer import PipelineOptimizer as PO
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from .meta_optimizer_base import MetaOptimizerBase
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__all__ = ["PipelineOptimizer"]
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class PipelineOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super(PipelineOptimizer, self).__init__(optimizer)
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self.inner_opt = optimizer
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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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def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
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user_defined_strategy):
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super(PipelineOptimizer, self)._set_basic_info(
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loss, role_maker, user_defined_optimizer, user_defined_strategy)
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num_microbatches = user_defined_strategy.pipeline_configs['micro_batch']
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self.wrapped_opt = PO(self.inner_opt, num_microbatches=num_microbatches)
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def _can_apply(self):
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if self.user_defined_strategy.pipeline == True:
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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.pipeline = False
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dist_strategy.pipeline_configs = {"micro_batch": 1}
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def backward(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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return self.wrapped_opt.backward(loss, startup_program, parameter_list,
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no_grad_set, callbacks)
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def minimize_impl(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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optimize_ops, params_grads, prog_list = \
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self.wrapped_opt.minimize(loss, startup_program,
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parameter_list, no_grad_set)
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return optimize_ops, params_grads
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