1e67089bc9
<!-- Thanks for sending a pull request! BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html --> ### What this PR does / why we need it? 1. This PR introduces native `all_to_all` communication operator to fix `allgather` bugs when dp_size > 1. Besides, it adds a naive implementation of force-load-balance when doing profile runs. 2. The operator `npu_dequant_swiglu_quant` only supports input hidden_states with dtype `torch.int32`. This tensor occupies space of `global_bs * seq_len * topk * hidden_size`, which might be very large as `ep_size` grows. Therefore we need to disable this operator and use original `swiglu` && `quantize`. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? By performing offline inference:  --------- Signed-off-by: angazenn <zengyanjia@huawei.com> Co-authored-by: angazenn <zengyanjia@huawei.com>
76 lines
2.9 KiB
Python
76 lines
2.9 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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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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class NPUCommunicator(DeviceCommunicatorBase):
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def __init__(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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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(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) -> 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 = [
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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(tensor_shape,
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dtype=input_.dtype,
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device=input_.device))
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else:
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input_list = [
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t.contiguous() for t in torch.tensor_split(
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input_, self.world_size, scatter_dim)
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]
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output_list = [
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torch.empty_like(input_list[i]) 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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