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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>
122 lines
3.5 KiB
C++
122 lines
3.5 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_COMMON_FUNC_H
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#define INCLUDE_COMMON_FUNC_H
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#include <limits>
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#include <type_traits>
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#ifdef __CCE_KT_TEST__
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#include "stub_def.h"
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#include "stub_fun.h"
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#else
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#include "kernel_macros.h"
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#endif
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template <uint32_t ALIGN, typename T = uint32_t>
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inline __aicore__ T RoundUp(const T val)
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{
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static_assert(ALIGN != 0, "align must not be zero");
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static_assert(std::is_arithmetic<T>::value, "T must be an arithmetic type");
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T align = ALIGN;
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if (val + align - 1 < val) {
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return val;
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}
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return (val + align - 1) / align * align;
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}
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template <typename T>
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inline __aicore__ T RoundUp(const T val, const T align)
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{
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static_assert(std::is_arithmetic<T>::value, "T must be an arithmetic type");
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if (align == 0 || val + align - 1 < val) {
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return val;
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}
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return (val + align - 1) / align * align;
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}
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template <uint32_t DIVISOR, typename T = uint32_t>
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inline __aicore__ T CeilDiv(const T dividend)
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{
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static_assert(DIVISOR != 0, "align must not be zero");
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static_assert(std::is_arithmetic<T>::value, "T must be an arithmetic type");
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T divisor = DIVISOR;
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if (dividend + divisor - 1 < dividend) {
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return dividend;
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}
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return (dividend + divisor - 1) / divisor;
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}
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template <typename T>
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constexpr T T_MAX = std::numeric_limits<T>::max();
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template <typename T>
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inline __aicore__ T CeilDiv(const T dividend, const T divisor)
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{
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static_assert(std::is_arithmetic<T>::value, "T must be an arithmetic type");
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if (divisor == 0 || dividend + divisor - 1 < dividend) {
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return T_MAX<T>;
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}
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return (dividend + divisor - 1) / divisor;
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}
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template <typename T>
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__aicore__ inline T Min(const T lhs, const T rhs)
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{
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return lhs < rhs ? lhs : rhs;
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}
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template <typename Dtype>
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__aicore__ __attribute__((always_inline)) inline uint32_t BlockSize()
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{
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return 32 / sizeof(Dtype);
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}
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template <typename Dtype>
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__aicore__ __attribute__((always_inline)) inline uint32_t MatrixSize()
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{
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return 512 / sizeof(Dtype);
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}
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template <typename Dtype>
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__aicore__ __attribute__((always_inline)) inline uint64_t BlockSizeRoundUp(uint64_t num)
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{
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return (num + BlockSize<Dtype>() - 1) / BlockSize<Dtype>() * BlockSize<Dtype>();
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}
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template <typename Dtype>
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__aicore__ __attribute__((always_inline)) inline uint64_t NumBlocksRoundUp(uint64_t num)
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{
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return (num + BlockSize<Dtype>() - 1) / BlockSize<Dtype>();
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}
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template <typename Dtype>
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__aicore__ __attribute__((always_inline)) inline uint64_t MatrixSizeRoundUp(uint64_t num)
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{
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return (num + MatrixSize<Dtype>() - 1) / MatrixSize<Dtype>() * MatrixSize<Dtype>();
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}
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template <typename Dtype>
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__aicore__ __attribute__((always_inline)) inline uint64_t NumMatrixsRoundUp(uint64_t num)
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{
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return (num + MatrixSize<Dtype>() - 1) / MatrixSize<Dtype>();
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}
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template <typename Dtype>
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__aicore__ __attribute__((always_inline)) inline uint64_t L0HalfSize()
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{
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return 32 * 1024 / sizeof(Dtype);
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}
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#endif
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