7faa6878a6
### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `vllm_ascend/attention/mla_v1.py` |
| `vllm_ascend/attention/sfa_v1.py` |
| `vllm_ascend/core/recompute_scheduler.py` |
| `vllm_ascend/core/scheduler_dynamic_batch.py` |
| `vllm_ascend/distributed/device_communicators/npu_communicator.py` |
| `vllm_ascend/distributed/device_communicators/pyhccl.py` |
| `vllm_ascend/distributed/device_communicators/pyhccl_wrapper.py` |
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
---------
Signed-off-by: MrZ20 <2609716663@qq.com>
Co-authored-by: Soren <user@SorendeMac-mini.local>
65 lines
2.6 KiB
Python
65 lines
2.6 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. 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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# This file is a part of the vllm-ascend project.
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#
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import torch
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import torch.distributed as dist
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from vllm.distributed.device_communicators.base_device_communicator import DeviceCommunicatorBase
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class NPUCommunicator(DeviceCommunicatorBase):
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def __init__(
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self,
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cpu_group: dist.ProcessGroup,
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device: torch.device | None = None,
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device_group: dist.ProcessGroup | None = None,
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unique_name: str = "",
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):
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super().__init__(cpu_group, device, device_group, unique_name)
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# TODO(hz): Refer to CudaCommunicator's implementation to integrate PyHcclCommunicator
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# init device according to rank
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self.device = torch.npu.current_device()
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def all_to_all(
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self,
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input_: torch.Tensor,
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scatter_dim: int = 0,
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gather_dim: int = -1,
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scatter_sizes: list[int] | None = None,
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gather_sizes: list[int] | None = None,
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) -> torch.Tensor:
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if scatter_dim < 0:
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scatter_dim += input_.dim()
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if gather_dim < 0:
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gather_dim += input_.dim()
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if scatter_sizes is not None and gather_sizes is not None:
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input_list = [t.contiguous() for t in torch.split(input_, scatter_sizes, scatter_dim)]
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output_list = []
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tensor_shape_base = input_list[self.rank].size()
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for i in range(self.world_size):
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tensor_shape = list(tensor_shape_base)
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tensor_shape[gather_dim] = gather_sizes[i]
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output_list.append(torch.empty(tensor_shape, dtype=input_.dtype, device=input_.device))
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else:
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input_list = [t.contiguous() for t in torch.tensor_split(input_, self.world_size, scatter_dim)]
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output_list = [torch.empty_like(input_list[i]) for i in range(self.world_size)]
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dist.all_to_all(output_list, input_list, group=self.device_group)
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output_tensor = torch.cat(output_list, dim=gather_dim).contiguous()
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return output_tensor
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