wehub-resource-sync
67 lines
2.5 KiB
Python
Executable File
67 lines
2.5 KiB
Python
Executable File
# Copyright (c) 2021 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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from ..utils.hybrid_parallel_util import (
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broadcast_dp_parameters,
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broadcast_input_data,
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broadcast_moe_sharding_parameters,
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broadcast_mp_parameters,
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broadcast_sep_parameters,
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broadcast_sharding_parameters,
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)
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from ..utils.log_util import logger
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from .meta_parallel_base import MetaParallelBase
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__all__ = []
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class TensorParallel(MetaParallelBase):
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def __init__(self, layers, hcg, **kwargs):
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super().__init__(layers, hcg, **kwargs)
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def _prepare_for_model(self):
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logger.info("start broadcast mp parameters")
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broadcast_mp_parameters(self._layers, self._hcg)
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if self._hcg.get_sep_parallel_world_size() > 1:
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logger.info("start broadcast sep parameters")
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broadcast_sep_parameters(self._layers, self._hcg, fuse_params=False)
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if self._hcg.get_sharding_parallel_world_size() > 1:
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logger.info("start broadcast sharding parameters")
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broadcast_sharding_parameters(
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self._layers, self._hcg, fuse_params=False
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)
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if self._hcg.get_data_parallel_world_size() > 1:
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logger.info("start broadcast dp parameters")
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broadcast_dp_parameters(self._layers, self._hcg, fuse_params=False)
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if self._hcg.get_moe_sharding_parallel_world_size() > 1:
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logger.info("start broadcast moe sharding parameters")
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broadcast_moe_sharding_parameters(
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self._layers, self._hcg, fuse_params=False
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)
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logger.info("mp's parameters is ready")
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def _pre_forward(self, *inputs, **kwargs):
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need_broadcast_data = True
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if self._strategy is not None:
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mp_configs = self._strategy.hybrid_configs["mp_configs"]
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need_broadcast_data = mp_configs.need_broadcast_data
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if need_broadcast_data:
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logger.debug("mp start broadcast input data")
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return broadcast_input_data(self._hcg, *inputs, **kwargs)
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