f762ee89cc
Some PR for plugin support is not merged by vllm yet. This PR add monkey patch to vllm-ascend to make vllm-ascend work with vllm directly. This patch code should be removed once the related function is supported by vllm originally. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
79 lines
3.2 KiB
Python
79 lines
3.2 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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import torch
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import torch.distributed as dist
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class NPUCommunicator:
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def __init__(self, group, unique_name=""):
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self.group = group
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self.unique_name = unique_name
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self.rank = dist.get_rank(group)
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self.world_size = dist.get_world_size(self.group)
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self.ranks = dist.get_process_group_ranks(self.group)
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global_rank = dist.get_rank()
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self.rank_in_group = dist.get_group_rank(self.group, global_rank)
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def all_reduce(self, x: torch.Tensor) -> torch.Tensor:
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dist.all_reduce(x, group=self.group)
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return x
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def gather(self, input_: torch.Tensor, dst: int = 0, dim: int = -1):
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# NOTE: We assume that the input tensor is on the same device across
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# all the ranks.
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# NOTE: `dst` is the local rank of the destination rank.
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# Allocate output tensor.
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if self.rank_in_group == dst:
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gather_list = [
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torch.empty_like(input_) for _ in range(self.world_size)
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]
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else:
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gather_list = None
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# Gather.
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dist.gather(input_, gather_list, dst=self.ranks[dst], group=self.group)
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if self.rank_in_group == dst:
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output_tensor = torch.cat(gather_list, dim=dim)
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else:
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output_tensor = None
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return output_tensor
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def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
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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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input_size = input_.size()
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# NOTE: we have to use concat-style all-gather here,
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# stack-style all-gather has compatibility issues with
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# torch.compile . see https://github.com/pytorch/pytorch/issues/138795
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output_size = (input_size[0] * self.world_size, ) + input_size[1:]
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# Allocate output tensor.
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output_tensor = torch.empty(output_size,
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dtype=input_.dtype,
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device=input_.device)
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# All-gather.
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dist.all_gather_into_tensor(output_tensor, input_, group=self.group)
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# Reshape
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output_tensor = output_tensor.reshape((self.world_size, ) + input_size)
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output_tensor = output_tensor.movedim(0, dim)
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output_tensor = output_tensor.reshape(input_size[:dim] +
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(self.world_size *
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input_size[dim], ) +
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input_size[dim + 1:])
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return output_tensor
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