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70 lines
2.7 KiB
70 lines
2.7 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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def maximum_path_len_algo(optimizer_list):
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max_idx = 0
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max_len = 0
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candidates = []
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for idx, opt in enumerate(optimizer_list):
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local_buffer = [opt]
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for opt_inner in optimizer_list:
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if opt._can_update(opt_inner):
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local_buffer.append(opt_inner)
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if len(local_buffer) > max_len:
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max_idx = idx
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max_len = len(local_buffer)
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candidates.append(local_buffer)
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if len(candidates) == 0:
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return None
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for idx, opt in enumerate(candidates[max_idx][:-1]):
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opt._update_inner_optimizer(candidates[max_idx][idx + 1])
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return candidates[max_idx][0]
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class StrategyCompilerBase(object):
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def __init__(self):
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pass
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class StrategyCompiler(StrategyCompilerBase):
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"""
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StrategyCompiler is responsible for meta optimizers combination
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Generally, a user can define serveral distributed strategies that
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can generate serveral meta optimizer. The combination of these
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meta optimizers should have the right order to apply the optimizers'
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minimize function.
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This class is responsible for the executable distributed optimizer
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generation.
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"""
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def __init__(self):
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super(StrategyCompiler, self).__init__()
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def generate_optimizer(self, loss, role_maker, optimizer,
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userd_defined_strategy, meta_optimizer_list,
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graph_optimizer_list):
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if len(meta_optimizer_list) == 0 and len(graph_optimizer_list) == 0:
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return optimizer, None
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else:
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# currently, we use heuristic algorithm to select
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# meta optimizers combinations
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meta_optimizer = maximum_path_len_algo(meta_optimizer_list)
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graph_optimizer = maximum_path_len_algo(graph_optimizer_list)
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# should design a distributed strategy update interface
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# when we have finally decided the combination of meta_optimizer
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# and graph_optimizer, the corresponding distributed strategy
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# should be updated.
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return meta_optimizer, graph_optimizer, None
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