b64ee7d346
### What this PR does / why we need it?
The rank returned by `torch.distributed.get_rank(device_group)` is the
local rank, but rank (or rank in process group (PG)) is expected.
Thus we change to use `torch.npu.current_device()` to set device
```python
# difference between `local_rank` and `rank_in_group`:
# if we have a group of size 4 across two nodes:
# Process | Node | Rank | Local Rank | Rank in Group
# 0 | 0 | 0 | 0 | 0
# 1 | 0 | 1 | 1 | 1
# 2 | 1 | 2 | 0 | 2
# 3 | 1 | 3 | 1 | 3
```
Tested by @wwfu109 with
`vllm/tests/distributed/test_customops::test_multi_process_tensor_parallel_pipeline_parallel`
Signed-off-by: MengqingCao <cmq0113@163.com>
35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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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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from typing import Optional
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import torch
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from torch.distributed import ProcessGroup
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from vllm.distributed.device_communicators.base_device_communicator import \
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DeviceCommunicatorBase
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class NPUCommunicator(DeviceCommunicatorBase):
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def __init__(self,
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cpu_group: ProcessGroup,
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device: Optional[torch.device] = None,
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device_group: Optional[ProcessGroup] = None,
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unique_name: str = ""):
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super().__init__(cpu_group, device, device_group, unique_name)
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# init device according to rank
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self.device = torch.npu.current_device()
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