18b90b501d
### 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>
53 lines
2.9 KiB
C++
53 lines
2.9 KiB
C++
/**
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* This program is free software, you can redistribute it and/or modify it.
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* Copyright (c) 2025 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 2.0 (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, 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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/*!
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* \file sparse_flash_attention.cpp
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* \brief
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*/
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#include "kernel_operator.h"
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#include "sparse_flash_attention_template_tiling_key.h"
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#include "sparse_flash_attention_kernel_mla.h"
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using namespace AscendC;
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#define SFA_OP_IMPL(templateClass, tilingdataClass, ...) \
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do { \
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templateClass<SFAType<__VA_ARGS__>> op; \
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GET_TILING_DATA_WITH_STRUCT(tilingdataClass, tiling_data_in, tiling); \
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const tilingdataClass *__restrict tiling_data = &tiling_data_in; \
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op.Init(query, key, value, sparseIndices, actualSeqLengthsQuery, actualSeqLengthsKV, \
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blocktable, queryRope, keyRope, attentionOut, user, tiling_data, tiling, &tPipe); \
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op.Process(); \
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} while (0)
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template<int FLASH_DECODE, int LAYOUT_T, int KV_LAYOUT_T, int TEMPLATE_MODE>
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__global__ __aicore__ void
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sparse_flash_attention(__gm__ uint8_t *query, __gm__ uint8_t *key, __gm__ uint8_t *value,
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__gm__ uint8_t *sparseIndices, __gm__ uint8_t *blocktable,
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__gm__ uint8_t *actualSeqLengthsQuery, __gm__ uint8_t *actualSeqLengthsKV,
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__gm__ uint8_t* queryRope, __gm__ uint8_t* keyRope,
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__gm__ uint8_t *attentionOut, __gm__ uint8_t *workspace, __gm__ uint8_t *tiling)
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{
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KERNEL_TASK_TYPE_DEFAULT(KERNEL_TYPE_MIX_AIC_1_2);
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TPipe tPipe;
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__gm__ uint8_t *user = GetUserWorkspace(workspace);
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if constexpr (ORIG_DTYPE_QUERY == DT_FLOAT16 && ORIG_DTYPE_KEY == DT_FLOAT16 &&
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ORIG_DTYPE_ATTENTION_OUT == DT_FLOAT16) {
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SFA_OP_IMPL(SparseFlashAttentionMla, SparseFlashAttentionTilingDataMla, half, half, half,
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FLASH_DECODE, static_cast<SFA_LAYOUT>(LAYOUT_T), static_cast<SFA_LAYOUT>(KV_LAYOUT_T), TEMPLATE_MODE);
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} else { // bf16
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SFA_OP_IMPL(SparseFlashAttentionMla, SparseFlashAttentionTilingDataMla, bfloat16_t, bfloat16_t, bfloat16_t,
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FLASH_DECODE, static_cast<SFA_LAYOUT>(LAYOUT_T), static_cast<SFA_LAYOUT>(KV_LAYOUT_T), TEMPLATE_MODE);
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}
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} |