Files
xc-llm-ascend/csrc/lightning_indexer/op_kernel/lightning_indexer_common.h
Song Mingyang 18b90b501d [kernel] add AscendC op: lightning_indexer and sparse_flash_attention (#4625)
### What this PR does / why we need it?
Provide high-performance AscendC operators lightning_indexer and
sparse_flash_attention to boost the execution performance of the
DeepSeek v3.2 model. Meanwhile, adapt the two AscendC operators to
vllm-ascend framework.

### Does this PR introduce _any_ user-facing change?
No (only underlying operator optimizations, with no user-facing changes)

### How was this patch tested?

- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2

Signed-off-by: MingYang119 <songmingyang@huawei.com>
2025-12-03 09:53:10 +08:00

135 lines
3.9 KiB
C++

/**
* This program is free software, you can redistribute it and/or modify it.
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This file is a part of the CANN Open Software.
* Licensed under CANN Open Software License Agreement Version 2.0 (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.
*/
/*!
* \file lightning_indexer_common.h
* \brief
*/
#ifndef LIGHTNING_INDEXER_COMMON_H
#define LIGHTNING_INDEXER_COMMON_H
namespace LICommon {
enum class LI_LAYOUT {
BSND = 0,
TND = 1,
PA_BSND = 2
};
template <typename Q_T, typename K_T, typename OUT_T, const bool PAGE_ATTENTION = false,
LI_LAYOUT LAYOUT_T = LI_LAYOUT::BSND, LI_LAYOUT K_LAYOUT_T = LI_LAYOUT::PA_BSND, typename... Args>
struct LIType {
using queryType = Q_T;
using keyType = K_T;
using outputType = OUT_T;
static constexpr bool pageAttention = PAGE_ATTENTION;
static constexpr LI_LAYOUT layout = LAYOUT_T;
static constexpr LI_LAYOUT keyLayout = K_LAYOUT_T;
};
struct RunInfo {
uint32_t loop;
uint32_t bN2Idx;
uint32_t bIdx;
uint32_t n2Idx = 0;
uint32_t gS1Idx;
uint32_t s2Idx;
uint32_t actS1Size = 1;
uint32_t actS2Size = 1;
uint32_t actMBaseSize;
uint32_t actualSingleProcessSInnerSize;
uint32_t actualSingleProcessSInnerSizeAlign;
uint64_t tensorQueryOffset;
uint64_t tensorKeyOffset;
uint64_t tensorWeightsOffset;
uint64_t indiceOutOffset;
bool isFirstS2InnerLoop;
bool isLastS2InnerLoop;
bool isAllLoopEnd = false;
};
struct ConstInfo {
static constexpr uint32_t FIA_SYNC_MODE2 = 2;
static constexpr uint32_t BUFFER_SIZE_BYTE_32B = 32;
static constexpr uint32_t BUFFER_SIZE_BYTE_64B = 64;
static constexpr uint32_t BUFFER_SIZE_BYTE_256B = 256;
static constexpr uint32_t BUFFER_SIZE_BYTE_512B = 512;
static constexpr uint32_t BUFFER_SIZE_BYTE_1K = 1024;
static constexpr uint32_t BUFFER_SIZE_BYTE_2K = 2048;
static constexpr uint32_t BUFFER_SIZE_BYTE_4K = 4096;
static constexpr uint32_t BUFFER_SIZE_BYTE_8K = 8192;
static constexpr uint32_t BUFFER_SIZE_BYTE_16K = 16384;
static constexpr uint32_t BUFFER_SIZE_BYTE_32K = 32768;
static constexpr int INVALID_IDX = -1;
uint32_t syncC1V1 = 0U;
uint32_t syncV1C1 = 0U;
uint32_t mBaseSize = 1ULL;
uint32_t s1BaseSize = 1ULL;
uint32_t s2BaseSize = 1ULL;
uint64_t batchSize = 0ULL;
uint64_t gSize = 0ULL;
uint64_t qHeadNum = 0ULL;
uint64_t kHeadNum;
uint64_t headDim;
uint64_t sparseCount;
uint64_t kSeqSize = 0ULL;
uint64_t qSeqSize = 1ULL;
uint32_t kCacheBlockSize = 0;
uint32_t maxBlockNumPerBatch = 0;
LI_LAYOUT outputLayout;
bool attenMaskFlag = false;
uint32_t actualLenQDims = 0U;
uint32_t actualLenDims = 0U;
bool isAccumSeqS1 = false;
bool isAccumSeqS2 = false;
};
struct SplitCoreInfo {
uint32_t s2Start = 0U;
uint32_t s2End = 0U;
uint32_t bN2Start = 0U;
uint32_t bN2End = 0U;
uint32_t gS1Start = 0U;
uint32_t gS1End = 0U;
bool isLD = false;
};
template <typename T>
__aicore__ inline T Align(T num, T rnd)
{
return (((rnd) == 0) ? 0 : (((num) + (rnd)-1) / (rnd) * (rnd)));
}
template <typename T1, typename T2>
__aicore__ inline T1 Min(T1 a, T2 b)
{
return (a > b) ? (b) : (a);
}
template <typename T1, typename T2>
__aicore__ inline T1 Max(T1 a, T2 b)
{
return (a > b) ? (a) : (b);
}
template <typename T>
__aicore__ inline T CeilDiv(T num, T rnd)
{
return (((rnd) == 0) ? 0 : (((num) + (rnd)-1) / (rnd)));
}
} // namespace LICommon
#endif // LIGHTNING_INDEXER_COMMON_H