wehub-resource-sync
92 lines
2.4 KiB
Python
92 lines
2.4 KiB
Python
# Copyright 2024 MIT Han Lab
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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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#
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# SPDX-License-Identifier: Apache-2.0
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import os
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from typing import Union
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import torch
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import torch.distributed
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from ...models.utils.list import list_mean, list_sum
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__all__ = [
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"dist_init",
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"is_dist_initialized",
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"get_dist_rank",
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"get_dist_size",
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"is_master",
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"dist_barrier",
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"get_dist_local_rank",
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"sync_tensor",
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]
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def dist_init() -> None:
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if is_dist_initialized():
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return
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try:
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torch.distributed.init_process_group(backend="nccl")
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assert torch.distributed.is_initialized()
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except Exception:
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os.environ["RANK"] = "0"
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os.environ["WORLD_SIZE"] = "1"
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os.environ["LOCAL_RANK"] = "0"
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print("warning: dist not init")
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def is_dist_initialized() -> bool:
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return torch.distributed.is_initialized()
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def get_dist_rank() -> int:
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return int(os.environ["RANK"])
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def get_dist_size() -> int:
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return int(os.environ["WORLD_SIZE"])
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def is_master() -> bool:
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return get_dist_rank() == 0
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def dist_barrier() -> None:
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if is_dist_initialized():
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torch.distributed.barrier()
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def get_dist_local_rank() -> int:
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return int(os.environ["LOCAL_RANK"])
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def sync_tensor(tensor: Union[torch.Tensor, float], reduce="mean") -> Union[torch.Tensor, list[torch.Tensor]]:
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if not is_dist_initialized():
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return tensor
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if not isinstance(tensor, torch.Tensor):
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tensor = torch.Tensor(1).fill_(tensor).cuda()
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tensor_list = [torch.empty_like(tensor) for _ in range(get_dist_size())]
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torch.distributed.all_gather(tensor_list, tensor.contiguous(), async_op=False)
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if reduce == "mean":
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return list_mean(tensor_list)
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elif reduce == "sum":
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return list_sum(tensor_list)
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elif reduce == "cat":
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return torch.cat(tensor_list, dim=0)
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elif reduce == "root":
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return tensor_list[0]
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else:
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return tensor_list
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