wehub-resource-sync
52 lines
1.7 KiB
Python
52 lines
1.7 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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import paddle
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from paddle.distributed import fleet
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def dist_gather_tensor_with_gradient(tensor):
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if tensor is None:
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return None
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if paddle.distributed.get_world_size() <= 1:
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return tensor
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hcg = fleet.get_hybrid_communicate_group()
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sharding_group = hcg.get_sharding_parallel_group()
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sharding_rank = sharding_group.rank
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data_group = hcg.get_data_parallel_group()
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data_rank = data_group.rank
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if sharding_group.nranks == 1 and data_group.nranks == 1:
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return tensor
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if sharding_group.nranks > 1:
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all_tensors = []
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paddle.distributed.all_gather(all_tensors, tensor.contiguous(), group=sharding_group)
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all_tensors[sharding_rank] = tensor
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all_tensors = paddle.concat(all_tensors, axis=0)
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else:
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all_tensors = tensor
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if data_group.nranks > 1:
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final_tensors = []
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paddle.distributed.all_gather(final_tensors, all_tensors.contiguous(), group=data_group)
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final_tensors[data_rank] = all_tensors
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final_tensors = paddle.concat(final_tensors, axis=0)
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else:
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final_tensors = all_tensors
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return final_tensors
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