b88443b6c6
### What this PR does / why we need it? fix communicator patch so parallel could work. see #52 Signed-off-by: MengqingCao <cmq0113@163.com>
70 lines
2.7 KiB
Python
70 lines
2.7 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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# This file is used to monkey patch communicator in vllm to support ascend.
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# Remove this file when vllm support by
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# https://github.com/vllm-project/vllm/pull/11324.
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import torch
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import vllm
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from vllm.utils import resolve_obj_by_qualname
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class GroupCoordinatorPatch(vllm.distributed.parallel_state.GroupCoordinator):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.device = torch.device(f"npu:{self.local_rank}")
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from vllm.platforms import current_platform
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device_comm_cls = resolve_obj_by_qualname(
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current_platform.get_device_communicator_cls())
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# we have checked and ensure that reusing tpu tag here is fine.
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use_custom_device = kwargs.get("use_tpu_communicator", False)
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if use_custom_device and self.world_size > 1:
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self.communicator = device_comm_cls(group=self.device_group,
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unique_name=self.unique_name)
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def all_reduce(self, input_):
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# Bypass the function if we are using only 1 device.
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if self.world_size == 1:
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return input_
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return self.communicator.all_reduce(input_)
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def gather(self, input_, dst=0, dim=-1):
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# Bypass the function if we are using only 1 device.
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if self.world_size == 1:
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return input_
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assert -input_.dim() <= dim < input_.dim(), (
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f"Invalid dim ({dim}) for input tensor with shape {input_.size()}")
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if dim < 0:
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# Convert negative dim to positive.
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dim += input_.dim()
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return self.communicator.gather(input_, dst, dim)
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def all_gather(self, input_, dim=-1):
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# Bypass the function if we are using only 1 device.
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if self.world_size == 1:
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return input_
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assert -input_.dim() <= dim < input_.dim(), (
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f"Invalid dim ({dim}) for input tensor with shape {input_.size()}")
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return self.communicator.all_gather(input_, dim)
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vllm.distributed.parallel_state.GroupCoordinator = GroupCoordinatorPatch
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