wehub-resource-sync
89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
# Copyright (c) 2024 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 paddle.distributed import fleet
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from .parallel_base import ParallelModel, ParallelOptimizer
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class ShardedDataParallel(ParallelModel):
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"""
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ShardedDataParallel converts a single card model to a distributed data parallel model
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Args:
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model (paddle.nn.Layer): A single card model to be distributed.
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optimizer (paddle.optimizer.Optimizer): an optimizer to be distributed.
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level (str): Zero stage, can be the following values:
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0: no sharding (pure dp)
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1: Zero Stage1
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2: Zero Stage2
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3: Zero Stage3
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Default: None, which means optimizer is replicated among all process.
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offload (bool): whether enable cpu offload strategy, not implemented currently.
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exclude_layer (list): Specify which layers do not use the zero stage strategy, not implemented currently.
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"""
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def __init__(
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self,
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model,
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offload=False,
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exclude_layer=None,
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):
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super().__init__(model)
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assert offload is False
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assert exclude_layer is None
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self.sharding_parallelizer = self.sharding_parallelizer_func
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def sharding_parallelizer_func(self, model):
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return model
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def sharded_data_parallel(model, optimizer=None, config=None):
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"""
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sharded_data_parallel converts model and optimizer to distributed and supports set zero stage1/2/3
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Args:
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model (paddle.nn.Layer): A single card model to be distributed
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optimizer (paddle.optimizer.Optimizer): an optimizer to be distributed
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config (dict): {
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"sharding_level": 0,
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"offload": False,
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"exclude_layer": None,
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"sharding_mesh_dim": "dp",
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}
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Returns:
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ShardedDataParallel: a distributed model
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ParallelOptimizer: a distributed optimizer
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"""
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sdp_model = ShardedDataParallel(
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model, bool(config.get('offload')), config.get('exclude_layer')
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)
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if optimizer is not None:
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level = config.get('sharding_level')
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sharding_mesh_dim = config.get('sharding_mesh_dim', "dp")
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optimizer = ParallelOptimizer(optimizer, level, sharding_mesh_dim)
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# check global_mesh
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mesh = fleet.auto.get_mesh()
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assert mesh is not None, (
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"global mesh must not be None, please call fleet.auto.set_mesh(global_mesh) firstly"
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)
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assert "dp" in mesh.dim_names, (
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"dp must in the mesh dim_names when use sharded_data_parallel"
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)
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return sdp_model, optimizer
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