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106 lines
4.1 KiB
106 lines
4.1 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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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]
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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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self._meta_optimizer = None
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self._graph_optimizer = None
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self._valid_optimizer_list = None
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self._user_defined_strategy = None
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self._meta_optimizer_candidates = []
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self._graph_optimizer_candidates = []
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def _get_valid_strategy(self, dist_strategy, can_not_apply_optimizer_list):
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import copy
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valid_strategy = copy.copy(dist_strategy)
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invalid_optimizers = []
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for candidate in self._meta_optimizer_candidates:
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is_valid = False
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for valid in self._meta_optimizers:
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if candidate.__class__.__name__ == valid.__class__.__name__:
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is_valid = True
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break
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if not is_valid:
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invalid_optimizers.append(candidate)
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for opt in invalid_optimizers:
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opt._disable_strategy(valid_strategy)
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for opt in can_not_apply_optimizer_list:
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opt._disable_strategy(valid_strategy)
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return valid_strategy
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def generate_optimizer(self, loss, role_maker, optimizer,
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user_defined_strategy, meta_optimizer_list,
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graph_optimizer_list):
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self._user_defined_strategy = user_defined_strategy
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self._meta_optimizer_candidates = meta_optimizer_list
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self._graph_optimizer_candidates = 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_optimizers = maximum_path_len_algo(meta_optimizer_list)
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graph_optimizers = 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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self._meta_optimizers = meta_optimizers
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self._graph_optimizers = graph_optimizers
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return_meta = None if meta_optimizers == None else meta_optimizers[
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0]
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return_graph = None if graph_optimizers == None else graph_optimizers[
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0]
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return return_meta, return_graph
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