strategy_compiler.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


def maximum_path_len_algo(optimizer_list):
    max_idx = 0
    max_len = 0
    candidates = []
    for idx, opt in enumerate(optimizer_list):
        local_buffer = [opt]
        for opt_inner in optimizer_list:
            if opt._can_update(opt_inner):
                local_buffer.append(opt_inner)
        if len(local_buffer) > max_len:
            max_idx = idx
            max_len = len(local_buffer)
        candidates.append(local_buffer)
    if len(candidates) == 0:
        return None
    for idx, opt in enumerate(candidates[max_idx][:-1]):
        opt._update_inner_optimizer(candidates[max_idx][idx + 1])
    return candidates[max_idx][0]


class StrategyCompilerBase(object):
    def __init__(self):
        pass


class StrategyCompiler(StrategyCompilerBase):
    """
    StrategyCompiler is responsible for meta optimizers combination
    Generally, a user can define serveral distributed strategies that
    can generate serveral meta optimizer. The combination of these 
    meta optimizers should have the right order to apply the optimizers'
    minimize function.
    This class is responsible for the executable distributed optimizer
    generation.
    """

    def __init__(self):
        super(StrategyCompiler, self).__init__()

    def generate_optimizer(self, loss, role_maker, optimizer,
                           userd_defined_strategy, meta_optimizer_list,
                           graph_optimizer_list):
        if len(meta_optimizer_list) == 0 and len(graph_optimizer_list) == 0:
            return optimizer, None
        else:
            # currently, we use heuristic algorithm to select
            # meta optimizers combinations
            meta_optimizer = maximum_path_len_algo(meta_optimizer_list)
            graph_optimizer = maximum_path_len_algo(graph_optimizer_list)
            # should design a distributed strategy update interface
            # when we have finally decided the combination of meta_optimizer
            # and graph_optimizer, the corresponding distributed strategy
            # should be updated.
            return meta_optimizer, graph_optimizer, None