# Copyright (c) 2022 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. from .completion import Completer from .dist_context import get_default_distributed_context from .tuner.parallel_tuner import ParallelTuner class Planner: def __init__(self, mode, dist_context): self._mode = mode self._dist_context = dist_context # NOTE: [HighOrderGrad]. There are grad ops in forward phase, and it need # dependency of backward-forward ops in forward completion. default_ctx = get_default_distributed_context() self._dist_context._dist_op_context = default_ctx.dist_op_context if not default_ctx.data_parallel: # Use SSA graph for complex parallism self._dist_context.initialize(with_graph=True) else: # Use program for data parallel parallism self._dist_context.initialize(with_graph=False) self._completer = Completer(self._dist_context) self._strategy = dist_context.strategy # set parallel tuner for auto search if self._strategy.auto_mode == "full": self._parallel_tuner = ParallelTuner(self._dist_context, mode=self._mode) @property def completer(self): return self._completer def plan(self): if self._strategy.auto_mode == "full": self._parallel_tuner.tune() else: self._completer.complete_forward_annotation() # parse forward sub block self._dist_context.block_state.parse_forward_blocks( self._dist_context.serial_main_program)