mirror of
https://github.com/vllm-project/vllm-ascend.git
synced 2025-10-20 21:53:54 +08:00
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>
96 lines
2.6 KiB
C++
96 lines
2.6 KiB
C++
// Adapted from
|
|
// https://gitee.com/ascend/ascend-transformer-boost
|
|
//
|
|
// Copyright (c) Huawei Technologies Co., Ltd. 2025. All rights reserved.
|
|
// This file is a part of the CANN Open Software.
|
|
// Licensed under CANN Open Software License Agreement Version 1.0 (the "License").
|
|
// Please refer to the License for details. You may not use this file except in compliance with the License.
|
|
// THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
|
|
// INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
|
|
// See LICENSE in the root of the software repository for the full text of the License.
|
|
//
|
|
|
|
#ifndef MLAPREPROCESS_TILING_H
|
|
#define MLAPREPROCESS_TILING_H
|
|
|
|
#include <cstdint>
|
|
|
|
struct PpMatmulTilingData {
|
|
uint32_t numBatch{0};
|
|
uint32_t m{0};
|
|
uint32_t k{0};
|
|
uint32_t n{0};
|
|
uint32_t m0{0};
|
|
uint32_t k0{0};
|
|
uint32_t n0{0};
|
|
uint32_t mLoop{0};
|
|
uint32_t kLoop{0};
|
|
uint32_t nLoop{0};
|
|
uint32_t coreLoop{0};
|
|
uint32_t swizzleCount{0};
|
|
uint32_t swizzleDirect{0};
|
|
uint32_t enShuffleK{0};
|
|
uint32_t blockDim{0};
|
|
uint32_t enLoadAllAmat{0};
|
|
uint32_t b0matPingPongBufferLen{0};
|
|
};
|
|
|
|
struct MlaTilingData {
|
|
uint32_t tilingKey{0};
|
|
uint64_t userWorkspaceSize{0};
|
|
uint64_t s1Offset{0};
|
|
uint64_t s2Offset{0};
|
|
uint64_t s3Offset{0};
|
|
uint64_t s4Offset{0};
|
|
uint64_t s5Offset{0};
|
|
|
|
uint32_t numCore{0};
|
|
uint32_t n{0};
|
|
uint32_t perTaskNum{0};
|
|
uint32_t resTaskNum{0};
|
|
|
|
PpMatmulTilingData mm1;
|
|
PpMatmulTilingData mm2;
|
|
PpMatmulTilingData mm3;
|
|
// rms1
|
|
uint32_t rmsNumCore1{0};
|
|
uint32_t rmsNumCol1{0};
|
|
uint32_t rmsNumRow1{0};
|
|
uint32_t rmsQuantMin1{0};
|
|
// rms2
|
|
uint32_t rmsNumCore2{0};
|
|
uint32_t rmsNumCol2{0};
|
|
uint32_t rmsNumRow2{0};
|
|
uint32_t rmsQuantMin2{0};
|
|
|
|
uint32_t hiddenSizeQ{0};
|
|
uint32_t headNumQ{0};
|
|
uint32_t headDim{0};
|
|
uint32_t concatSize{0};
|
|
uint32_t rotaryCoeff{0};
|
|
uint32_t ntokens{0};
|
|
uint32_t realCore{0};
|
|
uint32_t nlCoreRun{0};
|
|
uint32_t lCoreRun{0};
|
|
uint32_t maxNPerLoopForUb{0};
|
|
uint32_t preCoreLoopTime{0};
|
|
uint32_t preCoreLoopNLast{0};
|
|
uint32_t lastCoreLoopTime{0};
|
|
uint32_t lastCoreLoopNLast{0};
|
|
|
|
// EinSumQuant
|
|
uint32_t esqFrontCore{0};
|
|
uint32_t esqTailCore{0};
|
|
uint32_t esqFrontCoreBatch{0};
|
|
uint32_t esqTailCoreBatch{0};
|
|
uint32_t esqHeadNum{0};
|
|
uint32_t esqColNum{0};
|
|
uint32_t esqUbHeadLoop{0};
|
|
uint32_t esqHeadPerLoop{0};
|
|
uint32_t esqHeadTail{0};
|
|
uint32_t esqColLoop{0};
|
|
uint32_t esqColTail{0};
|
|
};
|
|
|
|
#endif // MLAPREPROCESS_TILING_H
|