mirror of
https://github.com/handsomezhuzhu/vllm-npu-plugin.git
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handsomezhuzhu
e75504df72
- NPUPlatform: device management, HCCL process group, config adaptation - AscendAttentionBackend: npu_fusion_attention (prefill) + npu_incre_flash_attention (decode) - NPUCommunicator: HCCL-based distributed communication - NPUWorker: NPU device init, memory profiling - Custom ops: SiluAndMul, RMS norm, rotary embedding - Plugin registered via vllm.platform_plugins entry point Based on vllm-ascend official pattern, targeting Ascend 910B
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
"""
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NPUCommunicator — HCCL-based device communicator for Ascend NPU.
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Extends ``DeviceCommunicatorBase`` with NPU-specific collective
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operations using the HCCL backend.
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"""
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from typing import List, Optional
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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 (
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DeviceCommunicatorBase,
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)
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class NPUCommunicator(DeviceCommunicatorBase):
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"""Device communicator for Ascend NPU using HCCL."""
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def __init__(
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self,
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cpu_group: dist.ProcessGroup,
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device: Optional[torch.device] = None,
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device_group: Optional[dist.ProcessGroup] = 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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import torch_npu # noqa: F401
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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: Optional[List[int]] = None,
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gather_sizes: Optional[List[int]] = None,
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) -> torch.Tensor:
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"""All-to-all communication for NPU tensors."""
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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 = [
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t.contiguous()
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for t in torch.split(input_, scatter_sizes, scatter_dim)
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]
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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(
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torch.empty(
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tensor_shape,
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dtype=input_.dtype,
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device=input_.device,
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)
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)
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else:
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input_list = [
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t.contiguous()
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for t in torch.tensor_split(
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input_, self.world_size, scatter_dim
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)
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]
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output_list = [
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torch.empty_like(input_list[i])
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for i in range(self.world_size)
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]
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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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