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https://github.com/vllm-project/vllm-ascend.git
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bcc313e8f2
### What this PR does / why we need it? - Adds the `mla_preprocess` custom kernel to provide an optimized pre-processing operator for Multi-head Latent Attention (MLA) on Ascend NPUs. - Wires the new kernel into the C++ extension pipeline so vLLM can invoke it directly, cutting Python-side tensor shuffling and memory copies that previously bottlenecked MLA compilation paths. ### Does this PR introduce any user-facing change? - No. The change only introduces a low-level kernel; public APIs and inference behavior remain unchanged. ### How was this patch tested? - Dedicated Ascend kernels are not covered by our CI yet, so no extra automated tests were added. Future MLA-focused regression runs will cover this path. - vLLM version: v0.11.0 Signed-off-by: Chen Chen <0109chenchen@gmail.com>
83 lines
4.5 KiB
C++
83 lines
4.5 KiB
C++
/* Adapted from
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* https://gitee.com/ascend/ascend-transformer-boost.git
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*
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* Copyright (c) 2024 Huawei Technologies Co., Ltd.
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* This file is a part of the CANN Open Software.
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* Licensed under CANN Open Software License Agreement Version 1.0 (the "License").
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* Please refer to the License for details. You may not use this file except in compliance with the License.
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* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
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* INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
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* See LICENSE in the root of the software repository for the full text of the License.
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*/
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#ifndef INCLUDE_MEM_H
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#define INCLUDE_MEM_H
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#include "hardware.h"
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#include "kernel_event.h"
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#include "kernel_tensor.h"
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enum class BufferType { ASCEND_UB, ASCEND_CB, ASCEND_L0A, ASCEND_L0B, ASCEND_L0C, ASCEND_MAX };
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template <BufferType BufferType_>
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__aicore__ constexpr AscendC::TPosition GetPosition()
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{
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if constexpr (BufferType_ == BufferType::ASCEND_UB) {
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return AscendC::TPosition::VECIN;
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} else if constexpr (BufferType_ == BufferType::ASCEND_CB) {
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return AscendC::TPosition::A1;
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} else if constexpr (BufferType_ == BufferType::ASCEND_L0A) {
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return AscendC::TPosition::A2;
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} else if constexpr (BufferType_ == BufferType::ASCEND_L0B) {
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return AscendC::TPosition::B2;
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} else if constexpr (BufferType_ == BufferType::ASCEND_L0C) {
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return AscendC::TPosition::CO1;
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}
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return AscendC::TPosition::GM;
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}
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template <ArchType ArchTag>
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struct AsdopsBuffer {
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public:
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__aicore__ AsdopsBuffer()
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{
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constexpr uint32_t bufferSize[(uint32_t)BufferType::ASCEND_MAX] = {
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HardwareInfo<ArchTag>::ubSize, HardwareInfo<ArchTag>::l1Size, HardwareInfo<ArchTag>::l0ASize,
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HardwareInfo<ArchTag>::l0BSize, HardwareInfo<ArchTag>::l0CSize};
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#ifdef __DAV_C220_VEC__
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tensor[(uint32_t)BufferType::ASCEND_UB].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_UB]);
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tensor[(uint32_t)BufferType::ASCEND_UB].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::VECIN);
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#elif defined(__DAV_C220_CUBE__)
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tensor[(uint32_t)BufferType::ASCEND_CB].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_CB]);
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tensor[(uint32_t)BufferType::ASCEND_CB].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::A1);
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tensor[(uint32_t)BufferType::ASCEND_L0A].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_L0A]);
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tensor[(uint32_t)BufferType::ASCEND_L0A].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::A2);
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tensor[(uint32_t)BufferType::ASCEND_L0B].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_L0B]);
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tensor[(uint32_t)BufferType::ASCEND_L0B].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::B2);
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tensor[(uint32_t)BufferType::ASCEND_L0C].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_L0C]);
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tensor[(uint32_t)BufferType::ASCEND_L0C].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::CO1);
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#else
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tensor[(uint32_t)BufferType::ASCEND_UB].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_UB]);
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tensor[(uint32_t)BufferType::ASCEND_UB].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::VECIN);
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tensor[(uint32_t)BufferType::ASCEND_CB].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_CB]);
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tensor[(uint32_t)BufferType::ASCEND_CB].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::A1);
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tensor[(uint32_t)BufferType::ASCEND_L0A].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_L0A]);
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tensor[(uint32_t)BufferType::ASCEND_L0A].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::A2);
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tensor[(uint32_t)BufferType::ASCEND_L0B].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_L0B]);
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tensor[(uint32_t)BufferType::ASCEND_L0B].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::B2);
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tensor[(uint32_t)BufferType::ASCEND_L0C].InitBuffer(0, bufferSize[(uint32_t)BufferType::ASCEND_L0C]);
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tensor[(uint32_t)BufferType::ASCEND_L0C].address_.logicPos = static_cast<uint8_t>(AscendC::TPosition::CO1);
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#endif
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};
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template <BufferType BufferType_, typename DstDataType = half>
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__aicore__ AscendC::LocalTensor<DstDataType> GetBuffer(const uint32_t offset) const
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{
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return tensor[(uint32_t)BufferType_][offset].template ReinterpretCast<DstDataType>();
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}
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public:
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AscendC::LocalTensor<uint8_t> tensor[(uint32_t)BufferType::ASCEND_MAX];
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};
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#endif
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