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>
49 lines
1.9 KiB
Python
49 lines
1.9 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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from typing import List, Optional
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import torch
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import vllm
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from vllm.distributed.parallel_state import GroupCoordinator
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class GroupCoordinatorPatch(GroupCoordinator):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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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 self.world_size == 1:
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return input_
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assert -input_.dim() <= scatter_dim < input_.dim(), (
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f"Invalid scatter dim ({scatter_dim}) for input tensor with shape {input_.size()}"
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)
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assert -input_.dim() <= gather_dim < input_.dim(), (
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f"Invalid gather dim ({gather_dim}) for input tensor with shape {input_.size()}"
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)
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return self.device_communicator.all_to_all(input_, scatter_dim,
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gather_dim, scatter_sizes,
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gather_sizes)
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vllm.distributed.parallel_state.GroupCoordinator = GroupCoordinatorPatch # Note: check the GroupCoordinator with online serving |