[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>

csrc/sparse_flash_attention/op_kernel/sparse_flash_attention.cpp

@@ -0,0 +1,53 @@
+/**
+ * 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 sparse_flash_attention.cpp
+ * \brief
+ */
+
+#include "kernel_operator.h"
+#include "sparse_flash_attention_template_tiling_key.h"
+#include "sparse_flash_attention_kernel_mla.h"
+
+using namespace AscendC;
+
+#define SFA_OP_IMPL(templateClass, tilingdataClass, ...)                                          \
+    do {                                                                                          \
+        templateClass<SFAType<__VA_ARGS__>> op;                                                   \
+        GET_TILING_DATA_WITH_STRUCT(tilingdataClass, tiling_data_in, tiling);                     \
+        const tilingdataClass *__restrict tiling_data = &tiling_data_in;                          \
+        op.Init(query, key, value, sparseIndices, actualSeqLengthsQuery, actualSeqLengthsKV,      \
+	    blocktable, queryRope, keyRope, attentionOut, user, tiling_data, tiling, &tPipe);         \
+        op.Process();                                                                             \
+    } while (0)
+
+template<int FLASH_DECODE, int LAYOUT_T, int KV_LAYOUT_T, int TEMPLATE_MODE>
+ __global__ __aicore__ void
+sparse_flash_attention(__gm__ uint8_t *query, __gm__ uint8_t *key, __gm__ uint8_t *value,
+                       __gm__ uint8_t *sparseIndices, __gm__ uint8_t *blocktable,
+                       __gm__ uint8_t *actualSeqLengthsQuery, __gm__ uint8_t *actualSeqLengthsKV,
+                       __gm__ uint8_t* queryRope, __gm__ uint8_t* keyRope,
+                       __gm__ uint8_t *attentionOut, __gm__ uint8_t *workspace, __gm__ uint8_t *tiling)
+{
+    KERNEL_TASK_TYPE_DEFAULT(KERNEL_TYPE_MIX_AIC_1_2);
+
+    TPipe tPipe;
+    __gm__ uint8_t *user = GetUserWorkspace(workspace);
+
+    if constexpr (ORIG_DTYPE_QUERY == DT_FLOAT16 && ORIG_DTYPE_KEY == DT_FLOAT16 &&
+                  ORIG_DTYPE_ATTENTION_OUT == DT_FLOAT16) {
+        SFA_OP_IMPL(SparseFlashAttentionMla, SparseFlashAttentionTilingDataMla, half, half, half,
+            FLASH_DECODE, static_cast<SFA_LAYOUT>(LAYOUT_T), static_cast<SFA_LAYOUT>(KV_LAYOUT_T), TEMPLATE_MODE);
+    } else { // bf16
+        SFA_OP_IMPL(SparseFlashAttentionMla, SparseFlashAttentionTilingDataMla, bfloat16_t, bfloat16_t, bfloat16_t,
+            FLASH_DECODE, static_cast<SFA_LAYOUT>(LAYOUT_T), static_cast<SFA_LAYOUT>(KV_LAYOUT_T), TEMPLATE_MODE);
+    }
+}