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61 lines
2.1 KiB
61 lines
2.1 KiB
# 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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# limitations under the License.
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__all__ = ["MetaOptimizerBase"]
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class MetaOptimizerBase(object):
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def __init__(self, optimizer):
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pass
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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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self.loss = loss
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self.role_maker = role_maker
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self.user_defined_optimizer = user_defined_optimizer
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self.user_defined_strategy = user_defined_strategy
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def _update_inner_optimier(self, optimizer):
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self.inner_opt = optimizer
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def _can_apply(self):
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return False
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def _is_graph_out(self):
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return False
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def _can_update(self, optimizer):
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if str(optimizer.__class__.__name__) in self.meta_optimizers_white_list:
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return True
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def _disable_strategy(self, dist_strategy):
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raise NotImplementedError("you should implement disable strategy in {}".
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format(type(self).__name__))
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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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raise NotImplementedError("meta optimizer not implemented")
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def minimize(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 = self.minimize_impl(
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loss, startup_program, parameter_list, no_grad_set)
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return optimize_ops, params_grads
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