915 lines
31 KiB
Markdown
915 lines
31 KiB
Markdown
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# aclnnSparseFlashAttention
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[📄 查看源码](https://gitcode.com/cann/ops-transformer/tree/master/attention/sparse_flash_attention)
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## 产品支持情况
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| 产品 | 是否支持 |
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| ------------------------------------------------------------ | :------: |
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|<term>Ascend 950PR/Ascend 950DT</term>| √ |
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|<term>Atlas A2 推理系列产品</term> | √ |
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|<term>Atlas A3 推理系列产品</term> | √ |
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## 功能说明
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- 接口功能：sparse_flash_attention（SFA）是针对大序列长度推理场景的高效注意力计算模块，该模块通过“只计算关键部分”大幅减少计算量，然而会引入大量的离散访存，造成数据搬运时间增加，进而影响整体性能。
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- 计算公式：
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$$
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\text{softmax}(\frac{Q@\tilde{K}^T}{\sqrt{d_k}})@\tilde{V}
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$$
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其中$\tilde{K},\tilde{V}$为基于某种选择算法（如`lightning_indexer`）得到的重要性较高的Key和Value，一般具有稀疏或分块稀疏的特征，$d_k$为$Q,\tilde{K}$每一个头的维度。
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## 函数原型
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每个算子分为[两段式接口](../../../docs/zh/context/两段式接口.md)，必须先调用“aclnnSparseFlashAttentionGetWorkspaceSize”接口获取计算所需workspace大小以及包含了算子计算流程的执行器，再调用“aclnnSparseFlashAttention”接口执行计算。
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```Cpp
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aclnnStatus aclnnSparseFlashAttentionGetWorkspaceSize(
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const aclTensor *query,
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const aclTensor *key,
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const aclTensor *value,
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const aclTensor *sparseIndices,
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const aclTensor *blockTable,
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const aclTensor *actualSeqLengthsQuery,
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const aclTensor *actualSeqLengthsKv,
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const aclTensor *queryRope,
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const aclTensor *keyRope,
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double scaleValue,
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int64_t sparseBlockSize,
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char *layoutQuery,
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char *layoutKv,
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int64_t sparseMode,
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int64_t preTokens,
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int64_t nextTokens,
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int64_t attentionMode,
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bool returnSoftmaxLse,
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const aclTensor *attentionOutOut,
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const aclTensor *softmaxMaxOut,
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const aclTensor *softmaxSumOut,
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uint64_t *workspaceSize,
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aclOpExecutor **executor)
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```
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```Cpp
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aclnnStatus aclnnSparseFlashAttention(
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void *workspace,
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uint64_t workspaceSize,
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aclOpExecutor *executor,
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const aclrtStream stream)
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```
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## aclnnSparseFlashAttentionGetWorkspaceSize
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- **参数说明：**
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> [!NOTE]
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>
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>- query、key、value参数维度含义：B（Batch Size）表示输入样本批量大小、S（Sequence Length）表示输入样本序列长度、H（Head Size）表示hidden层的大小、N（Head Num）表示多头数、D（Head Dim）表示hidden层最小的单元尺寸，且满足D=H/N、T表示所有Batch输入样本序列长度的累加和。
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>- Q\_S和S1表示query shape中的S，KV\_S和S2表示key shape中的S，Q\_N和N1表示num\_query\_heads，KV\_N和N2表示num\_key\_value\_heads，T1表示query shape中的T，T2表示key shape中的输入样本序列长度的累加和。
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<table style="undefined;table-layout: fixed; width: 1494px"><colgroup>
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<col style="width: 146px">
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<col style="width: 110px">
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<col style="width: 301px">
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<col style="width: 500px">
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<col style="width: 328px">
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<col style="width: 101px">
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<col style="width: 400px">
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<col style="width: 146px">
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</colgroup>
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<thead>
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<tr>
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<th>参数名</th>
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<th>输入/输出</th>
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<th>描述</th>
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<th>使用说明</th>
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<th>数据类型</th>
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<th>数据格式</th>
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<th>维度(shape)</th>
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<th>非连续Tensor</th>
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</tr></thead>
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<tbody>
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<tr>
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<td>query（aclTensor）</td>
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<td>输入</td>
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<td>attention结构的Query输入。</td>
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<td>不支持空tensor。</td>
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<td>FLOAT16、BFLOAT16</td>
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<td>ND</td>
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<td>
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<ul>
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<li>layout_query为BSND时，shape为(B,S1,N1,D)。</li>
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<li>layout_query为TND时，shape为(T1,N1,D)。</li>
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</ul>
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</td>
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<td>x</td>
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</tr>
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<tr>
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<td>key（aclTensor）</td>
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<td>输入</td>
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<td>attention结构的Key输入</td>
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<td>
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<ul>
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<li>不支持空tensor。</li>
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<li>block_num为PageAttention时block总数。</li>
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</ul>
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</td>
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<td>FLOAT16、BFLOAT16</td>
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<td>ND</td>
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<td>
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<ul>
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<li>layout_kv为PA_BSND时，shape为(block_num, block_size, KV_N, D)。</li>
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<li>layout_kv为BSND时，shape为(B, S2, KV_N, D)。</li>
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<li>layout_kv为TND时，shape为(T2, KV_N, D)。</li>
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</ul>
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</td>
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<td>x</td>
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</tr>
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<tr>
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<td>value（aclTensor）</td>
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<td>输入</td>
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<td>attention结构的Value输入。</td>
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<td>不支持空tensor。</td>
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<td>FLOAT16、BFLOAT16</td>
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<td>ND</td>
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<td>shape与key的shape一致。</td>
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<td>x</td>
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</tr>
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<tr>
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<td>sparseIndices（aclTensor）</td>
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<td>输入</td>
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<td>离散取kvCache的索引。</td>
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<td>
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<ul>
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<li>不支持空tensor。</li>
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<li>sparse_size为一次离散选取的block数，需要保证每行有效值均在前半部分，无效值均在后半部分，且需要满足sparse_size大于0。</li>
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</ul>
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</td>
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<td>INT32</td>
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<td>ND</td>
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<td>
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<ul>
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<li>layout_query为BSND时，shape为(B, Q_S, KV_N, sparse_size)。</li>
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<li>layout_query为TND时，shape为(Q_T, KV_N, sparse_size)。</li>
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</ul>
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</td>
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<td>x</td>
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</tr>
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<tr>
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<td>blockTable（aclTensor）</td>
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<td>输入</td>
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<td>表示PageAttention中kvCache存储使用的block映射表。</td>
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<td>
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<ul>
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<li>不支持空tensor。</li>
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<li>第二维长度不小于所有batch中最大的S2对应的block数量，即S2_max / block_size向上取整。</li>
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</ul>
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</td>
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<td>INT32</td>
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<td>ND</td>
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<td>shape支持(B,S2/block_size)。</td>
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<td>x</td>
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</tr>
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<tr>
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<td>actualSeqLengthsQuery（aclTensor）</td>
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<td>输入</td>
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<td>表示不同Batch中query的有效token数。</td>
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<td>
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<ul>
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<li>不支持空tensor。</li>
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<li>如果不指定seqlen可传入None，表示和query的shape的S长度相同。</li>
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<li>该入参中每个Batch的有效token数不超过query中的维度S大小且不小于0。支持长度为B的一维tensor。</li>
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<li>layout_query为TND时，该入参必须传入，且以该入参元素的数量作为B值，该参数中每个元素的值表示当前batch与之前所有batch的token数总和。</li>
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</ul>
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</td>
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<td>INT32</td>
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<td>ND</td>
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<td>(B,)</td>
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<td>x</td>
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</tr>
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<tr>
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<td>actualSeqLengthsKv（aclTensor）</td>
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<td>输入</td>
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<td>表示不同Batch中key和value的有效token数。</td>
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<td>
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<ul>
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<li>不支持空tensor。</li>
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<li>如果不指定seqlen可传入None，表示和key的shape的S长度相同。</li>
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<li>该参数中每个Batch的有效token数不超过key/value中的维度S大小且不小于0。支持长度为B的一维tensor。</li>
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<li>当layout_kv为TND或PA_BSND时，该入参必须传入。</li>
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<li>layout_kv为TND，该参数中每个元素的值表示当前batch与之前所有batch的token数总和，即前缀和，因此后一个元素的值必须大于等于前一个元素的值。</li>
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</ul>
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</td>
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<td>INT32</td>
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<td>ND</td>
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<td>(B,)</td>
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<td>x</td>
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</tr>
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<tr>
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<td>queryRope（aclTensor）</td>
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<td>输入</td>
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<td>表示MLA结构中的query的rope信息。</td>
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<td>不支持空tensor。</td>
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<td>FLOAT16、BFLOAT16</td>
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<td>ND</td>
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<td>
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<ul>
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<li>layout_query为TND时，shape为(B,S1,N1,Dr)。</li>
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<li>layout_query为BSND时，shape为(T1,N1,Dr)。</li>
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</ul>
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</td>
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<td>x</td>
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</tr>
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<tr>
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<td>keyRope（aclTensor）</td>
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<td>输入</td>
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<td>表示MLA结构中的key的rope信息。</td>
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<td>不支持空tensor。</td>
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<td>FLOAT16、BFLOAT16</td>
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<td>ND</td>
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<td>
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<ul>
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<li>layout_kv为TND时，shape为(B,S1,N1,Dr)。</li>
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<li>layout_kv为BSND时，shape为(T1,N1,Dr)。</li>
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<li>layout_kv为PA_BSND时，shape为(block_num,block_size,N2,Dr)。</li>
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</ul>
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</td>
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<td>x</td>
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</tr>
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<tr>
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<td>scaleValue（double）</td>
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<td>输入</td>
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<td>代表缩放系数。</td>
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<td>-</td>
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<td>FLOAT16</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td>sparseBlockSize（int64_t）</td>
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<td>输入</td>
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<td>代表sparse阶段的block大小。</td>
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<td>
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<ul>
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<li>sparse_block_size为1时，为Token-wise稀疏化场景，将每个token视为独立单元，在计算重要性分数时，评估每个查询token与每个键值token之间的独立关联程度。</li>
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<li>sparse_block_size为大于1小于等于128时，为Block-wise稀疏化场景，将token序列划分为固定大小的连续块，以块为单位进行重要性评估，块内token共享相同的稀疏化决策。</li>
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</ul>
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</td>
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<td>INT64</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td>layoutQuery（char）</td>
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<td>输入</td>
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<td>标识输入query的数据排布格式。</td>
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<td>
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<ul>
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<li>用户不特意指定时可传入默认值"BSND"。</li>
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<li>支持传入BSND和TND。</li>
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</ul>
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</td>
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<td>STRING</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td>layoutKv（char）</td>
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<td>输入</td>
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<td>标识输入key的数据排布格式。</td>
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<td>
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<ul>
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<li>用户不特意指定时可传入默认值"BSND"。</li>
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<li>支持传入TND、BSND和PA_BSND，其中PA_BSND在使能PageAttention时使用。</li>
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</ul>
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</td>
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<td>STRING</td>
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<td>-</td>
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<td>-</td>
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<td>-</td>
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</tr>
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<tr>
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<td>sparseMode（int64_t）</td>
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<td>输入</td>
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<td>表示sparse的模式。</td>
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<td>
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<ul>
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|
|
<li>sparse_mode为0时，代表全部计算。</li>
|
|||
|
|
<li>sparse_mode为3时，代表rightDownCausal模式的mask，对应以右下顶点往左上为划分线的下三角场景。</li>
|
|||
|
|
</ul>
|
|||
|
|
</td>
|
|||
|
|
<td>INT64</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>preTokens（int64_t）</td>
|
|||
|
|
<td>输入</td>
|
|||
|
|
<td>用于稀疏计算，表示attention需要和前几个Token计算关联。</td>
|
|||
|
|
<td>仅支持默认值2^63-1。</td>
|
|||
|
|
<td>INT64</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>nextTokens（int64_t）</td>
|
|||
|
|
<td>输入</td>
|
|||
|
|
<td>用于稀疏计算，表示attention需要和后几个Token计算关联。</td>
|
|||
|
|
<td>仅支持默认值2^63-1。</td>
|
|||
|
|
<td>INT64</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>attentionMode（int64_t）</td>
|
|||
|
|
<td>输入</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>仅支持传入2，表示MLA-absorb模式。</td>
|
|||
|
|
<td>INT64</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>returnSoftmaxLse（bool）</td>
|
|||
|
|
<td>输入</td>
|
|||
|
|
<td>用于表示是否返回softmax_max和softmax_sum。</td>
|
|||
|
|
<td>
|
|||
|
|
<ul>
|
|||
|
|
<li>True表示返回，False表示不返回；默认值为False。</li>
|
|||
|
|
<li>该参数仅在训练且layout_kv不为PA_BSND场景支持。</li>
|
|||
|
|
</ul>
|
|||
|
|
</td>
|
|||
|
|
<td>BOOL</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>attentionOut（aclTensor）</td>
|
|||
|
|
<td>输出</td>
|
|||
|
|
<td>公式中的输出。</td>
|
|||
|
|
<td>不支持空tensor。</td>
|
|||
|
|
<td>FLOAT16、BFLOAT16</td>
|
|||
|
|
<td>ND</td>
|
|||
|
|
<td>
|
|||
|
|
<ul>
|
|||
|
|
<li>layout_query为BSND时，shape为(B,S1,N1,D)。</li>
|
|||
|
|
<li>layout_query为TND时shape为(T1,N1,D)。</li>
|
|||
|
|
</ul>
|
|||
|
|
</td>
|
|||
|
|
<td>x</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>softmaxMaxOut（aclTensor）</td>
|
|||
|
|
<td>输出</td>
|
|||
|
|
<td>Attention算法对query乘key的结果，取max得到softmax_max。</td>
|
|||
|
|
<td>不支持空tensor。</td>
|
|||
|
|
<td>FLOAT</td>
|
|||
|
|
<td>ND</td>
|
|||
|
|
<td>
|
|||
|
|
<ul>
|
|||
|
|
<li>layout_query为BSND时，shape为(B,N2,S1,N1/N2)。</li>
|
|||
|
|
<li>layout_query为TND时shape为(N2,T1,N1/N2)。</li>
|
|||
|
|
</ul>
|
|||
|
|
</td>
|
|||
|
|
<td>x</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>softmaxSumOut（aclTensor）</td>
|
|||
|
|
<td>输出</td>
|
|||
|
|
<td>Attention算法query乘key的结果减去softmax_max, 再取exp，接着求sum，得到softmax_sum。</td>
|
|||
|
|
<td>不支持空tensor。</td>
|
|||
|
|
<td>FLOAT</td>
|
|||
|
|
<td>ND</td>
|
|||
|
|
<td>
|
|||
|
|
<ul>
|
|||
|
|
<li>layout_query为BSND时，shape为(B,N2,S1,N1/N2)。</li>
|
|||
|
|
<li>layout_query为TND时shape为(N2,T1,N1/N2)。</li>
|
|||
|
|
</ul>
|
|||
|
|
</td>
|
|||
|
|
<td>x</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>workspaceSize（uint64_t*）</td>
|
|||
|
|
<td>输出</td>
|
|||
|
|
<td>返回需要在Device侧申请的workspace大小。</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>executor（aclOpExecutor）</td>
|
|||
|
|
<td>输出</td>
|
|||
|
|
<td>返回op执行器，包含了算子计算流程。</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
<td>-</td>
|
|||
|
|
</tr>
|
|||
|
|
</tbody>
|
|||
|
|
</table>
|
|||
|
|
|
|||
|
|
- **返回值：**
|
|||
|
|
|
|||
|
|
aclnnStatus：返回状态码，具体参见[aclnn返回码](../../../docs/zh/context/aclnn返回码.md)。
|
|||
|
|
|
|||
|
|
第一段接口会完成入参校验，出现以下场景时报错：
|
|||
|
|
|
|||
|
|
<table style="undefined;table-layout: fixed;width: 1155px"><colgroup>
|
|||
|
|
<col style="width: 319px">
|
|||
|
|
<col style="width: 144px">
|
|||
|
|
<col style="width: 671px">
|
|||
|
|
</colgroup>
|
|||
|
|
<thead>
|
|||
|
|
<th>返回值</th>
|
|||
|
|
<th>错误码</th>
|
|||
|
|
<th>描述</th>
|
|||
|
|
</thead>
|
|||
|
|
<tbody>
|
|||
|
|
<tr>
|
|||
|
|
<td>ACLNN_ERR_PARAM_NULLPTR</td>
|
|||
|
|
<td>161001</td>
|
|||
|
|
<td>如果传入参数是必选输入，输出或者必选属性，且是空指针，则返回161001。</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>ACLNN_ERR_PARAM_INVALID</td>
|
|||
|
|
<td>161002</td>
|
|||
|
|
<td>query、key、value、sparseIndices、blockTable、actualSeqLengthsQuery、actualSeqLengthsKv、queryRope、keyRope、scaleValue、sparseBlockSize、layoutQuery、layoutKv、sparseMode、attentionMode、returnSoftmaxLse、attentionOut、softmaxMaxOut、softmaxSumOut的数据类型和数据格式不在支持的范围内。</td>
|
|||
|
|
</tr>
|
|||
|
|
</tbody>
|
|||
|
|
</table>
|
|||
|
|
|
|||
|
|
## aclnnSparseFlashAttention
|
|||
|
|
|
|||
|
|
- **参数说明：**
|
|||
|
|
|
|||
|
|
<table style="undefined;table-layout: fixed; width: 953px"><colgroup>
|
|||
|
|
<col style="width: 173px">
|
|||
|
|
<col style="width: 112px">
|
|||
|
|
<col style="width: 668px">
|
|||
|
|
</colgroup>
|
|||
|
|
<thead>
|
|||
|
|
<tr>
|
|||
|
|
<th>参数名</th>
|
|||
|
|
<th>输入/输出</th>
|
|||
|
|
<th>描述</th>
|
|||
|
|
</tr></thead>
|
|||
|
|
<tbody>
|
|||
|
|
<tr>
|
|||
|
|
<td>workspace</td>
|
|||
|
|
<td>输入</td>
|
|||
|
|
<td>在Device侧申请的workspace内存地址。</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>workspaceSize</td>
|
|||
|
|
<td>输入</td>
|
|||
|
|
<td>在Device侧申请的workspace大小，由第一段接口aclnnSparseFlashAttentionGetWorkspaceSize获取。</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>executor</td>
|
|||
|
|
<td>输入</td>
|
|||
|
|
<td>op执行器，包含了算子计算流程。</td>
|
|||
|
|
</tr>
|
|||
|
|
<tr>
|
|||
|
|
<td>stream</td>
|
|||
|
|
<td>输入</td>
|
|||
|
|
<td>指定执行任务的Stream。</td>
|
|||
|
|
</tr>
|
|||
|
|
</tbody>
|
|||
|
|
</table>
|
|||
|
|
|
|||
|
|
- **返回值：**
|
|||
|
|
|
|||
|
|
aclnnStatus：返回状态码，具体参见[aclnn返回码](../../../docs/zh/context/aclnn返回码.md)。
|
|||
|
|
|
|||
|
|
## 约束说明
|
|||
|
|
|
|||
|
|
- 确定性计算：aclnnSparseFlashAttention默认确定性实现。
|
|||
|
|
- 该接口支持推理场景下使用。
|
|||
|
|
- N1支持1~64和128。
|
|||
|
|
- block_size为一个block的token数，block_size取值为16的倍数，且最大支持1024。
|
|||
|
|
- 参数query中的D和key、value的D值相等为512，参数query_rope中的Dr和key_rope的Dr值相等为64。
|
|||
|
|
- 参数query、key、value的数据类型必须保持一致。
|
|||
|
|
- 支持sparse_block_size整除block_size。
|
|||
|
|
- <term>Ascend 950PR/Ascend 950DT</term>：
|
|||
|
|
- 只支持sparse_block_size为1。
|
|||
|
|
- <term>Atlas A3 训练系列产品/Atlas A3 推理系列产品</term>、<term>Atlas A2 训练系列产品/Atlas A2 推理系列产品</term>：
|
|||
|
|
- 支持[1,128]，且要求是2的幂次方，在PageAttention场景下要求sparse_block_size整除block_size
|
|||
|
|
|
|||
|
|
## 调用示例
|
|||
|
|
|
|||
|
|
示例代码如下，仅供参考，具体编译和执行过程请参考[编译与运行样例](../../../docs/zh/context/编译与运行样例.md)。
|
|||
|
|
|
|||
|
|
```Cpp
|
|||
|
|
/**
|
|||
|
|
* Copyright (c) 2024 Huawei Technologies Co., Ltd.
|
|||
|
|
* 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.
|
|||
|
|
*/
|
|||
|
|
|
|||
|
|
/*!
|
|||
|
|
* \file test_incre_flash_attention_v4.cpp
|
|||
|
|
* \brief
|
|||
|
|
*/
|
|||
|
|
|
|||
|
|
#include <iostream>
|
|||
|
|
#include <vector>
|
|||
|
|
#include <cmath>
|
|||
|
|
#include <cstring>
|
|||
|
|
#include "securec.h"
|
|||
|
|
#include "acl/acl.h"
|
|||
|
|
#include "aclnnop/aclnn_sparse_flash_attention.h"
|
|||
|
|
|
|||
|
|
using namespace std;
|
|||
|
|
|
|||
|
|
namespace {
|
|||
|
|
|
|||
|
|
#define CHECK_RET(cond) ((cond) ? true :(false))
|
|||
|
|
|
|||
|
|
#define LOG_PRINT(message, ...) \
|
|||
|
|
do { \
|
|||
|
|
(void)printf(message, ##__VA_ARGS__); \
|
|||
|
|
} while (0)
|
|||
|
|
|
|||
|
|
int64_t GetShapeSize(const std::vector<int64_t>& shape) {
|
|||
|
|
int64_t shapeSize = 1;
|
|||
|
|
for (auto i : shape) {
|
|||
|
|
shapeSize *= i;
|
|||
|
|
}
|
|||
|
|
return shapeSize;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
int Init(int32_t deviceId, aclrtStream* stream) {
|
|||
|
|
auto ret = aclInit(nullptr);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("aclInit failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
ret = aclrtSetDevice(deviceId);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
ret = aclrtCreateStream(stream);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
return 0;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
template <typename T>
|
|||
|
|
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
|
|||
|
|
aclDataType dataType, aclTensor** tensor) {
|
|||
|
|
auto size = GetShapeSize(shape) * sizeof(T);
|
|||
|
|
auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
std::vector<int64_t> strides(shape.size(), 1);
|
|||
|
|
for (int64_t i = shape.size() - 2; i >= 0; i--) {
|
|||
|
|
strides[i] = shape[i + 1] * strides[i + 1];
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
*tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND,
|
|||
|
|
shape.data(), shape.size(), *deviceAddr);
|
|||
|
|
return 0;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
struct TensorResources {
|
|||
|
|
void* queryDeviceAddr = nullptr;
|
|||
|
|
void* keyDeviceAddr = nullptr;
|
|||
|
|
void* valueDeviceAddr = nullptr;
|
|||
|
|
void* sparseIndicesDeviceAddr = nullptr;
|
|||
|
|
void* attentionOutDeviceAddr = nullptr;
|
|||
|
|
void* softmaxMaxDeviceAddr = nullptr;
|
|||
|
|
void* softmaxSumDeviceAddr = nullptr;
|
|||
|
|
void* queryRopeDeviceAddr = nullptr;
|
|||
|
|
void* keyRopeDeviceAddr = nullptr;
|
|||
|
|
|
|||
|
|
aclTensor* queryTensor = nullptr;
|
|||
|
|
aclTensor* keyTensor = nullptr;
|
|||
|
|
aclTensor* valueTensor = nullptr;
|
|||
|
|
aclTensor* sparseIndicesTensor = nullptr;
|
|||
|
|
aclTensor* attentionOutTensor = nullptr;
|
|||
|
|
aclTensor* softmaxMaxTensor = nullptr;
|
|||
|
|
aclTensor* softmaxSumTensor = nullptr;
|
|||
|
|
aclTensor* queryRopeTensor = nullptr;
|
|||
|
|
aclTensor* keyRopeTensor = nullptr;
|
|||
|
|
};
|
|||
|
|
|
|||
|
|
int InitializeTensors(TensorResources& resources) {
|
|||
|
|
std::vector<int64_t> queryShape = {1, 2, 1, 512};
|
|||
|
|
std::vector<int64_t> keyShape = {1, 2, 1, 512};
|
|||
|
|
std::vector<int64_t> valueShape = {1, 2, 1, 512};
|
|||
|
|
std::vector<int64_t> sparseIndicesShape = {1, 2, 1, 2};
|
|||
|
|
std::vector<int64_t> attentionOutShape = {1, 2, 1, 512};
|
|||
|
|
std::vector<int64_t> softmaxMaxShape = {1, 2, 1, 16};
|
|||
|
|
std::vector<int64_t> softmaxSumShape = {1, 2, 1, 16};
|
|||
|
|
std::vector<int64_t> queryRopeShape = {1, 2, 1, 64};
|
|||
|
|
std::vector<int64_t> keyRopeShape = {1, 2, 1, 64};
|
|||
|
|
|
|||
|
|
int64_t queryShapeSize = GetShapeSize(queryShape);
|
|||
|
|
int64_t keyShapeSize = GetShapeSize(keyShape);
|
|||
|
|
int64_t valueShapeSize = GetShapeSize(valueShape);
|
|||
|
|
int64_t sparseIndicesShapeSize = GetShapeSize(sparseIndicesShape);
|
|||
|
|
int64_t attentionOutShapeSize = GetShapeSize(attentionOutShape);
|
|||
|
|
int64_t softmaxMaxShapeSize = GetShapeSize(softmaxMaxShape);
|
|||
|
|
int64_t softmaxSumShapeSize = GetShapeSize(softmaxSumShape);
|
|||
|
|
int64_t queryRopeShapeSize = GetShapeSize(queryRopeShape);
|
|||
|
|
int64_t keyRopeShapeSize = GetShapeSize(keyRopeShape);
|
|||
|
|
|
|||
|
|
std::vector<float> queryHostData(queryShapeSize, 1);
|
|||
|
|
std::vector<float> keyHostData(keyShapeSize, 1);
|
|||
|
|
std::vector<float> valueHostData(valueShapeSize, 1);
|
|||
|
|
std::vector<int32_t> sparseIndicesHostData(sparseIndicesShapeSize, 1);
|
|||
|
|
std::vector<float> attentionOutHostData(attentionOutShapeSize, 1);
|
|||
|
|
std::vector<float> softmaxMaxHostData(softmaxMaxShapeSize, 1);
|
|||
|
|
std::vector<float> softmaxSumHostData(softmaxSumShapeSize, 1);
|
|||
|
|
std::vector<float> queryRopeHostData(queryRopeShapeSize, 1);
|
|||
|
|
std::vector<float> keyRopeHostData(keyRopeShapeSize, 1);
|
|||
|
|
|
|||
|
|
// Create query aclTensor.
|
|||
|
|
int ret = CreateAclTensor(queryHostData, queryShape, &resources.queryDeviceAddr,
|
|||
|
|
aclDataType::ACL_FLOAT16, &resources.queryTensor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Create key aclTensor.
|
|||
|
|
ret = CreateAclTensor(keyHostData, keyShape, &resources.keyDeviceAddr,
|
|||
|
|
aclDataType::ACL_FLOAT16, &resources.keyTensor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Create value aclTensor.
|
|||
|
|
ret = CreateAclTensor(valueHostData, valueShape, &resources.valueDeviceAddr,
|
|||
|
|
aclDataType::ACL_FLOAT16, &resources.valueTensor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Create sparseIndices aclTensor.
|
|||
|
|
ret = CreateAclTensor(sparseIndicesHostData, sparseIndicesShape, &resources.sparseIndicesDeviceAddr,
|
|||
|
|
aclDataType::ACL_INT32, &resources.sparseIndicesTensor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Create queryRope aclTensor.
|
|||
|
|
ret = CreateAclTensor(queryRopeHostData, queryRopeShape, &resources.queryRopeDeviceAddr,
|
|||
|
|
aclDataType::ACL_FLOAT16, &resources.queryRopeTensor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Create keyRope aclTensor.
|
|||
|
|
ret = CreateAclTensor(keyRopeHostData, keyRopeShape, &resources.keyRopeDeviceAddr,
|
|||
|
|
aclDataType::ACL_FLOAT16, &resources.keyRopeTensor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Create attention_out aclTensor.
|
|||
|
|
ret = CreateAclTensor(attentionOutHostData, attentionOutShape, &resources.attentionOutDeviceAddr,
|
|||
|
|
aclDataType::ACL_FLOAT16, &resources.attentionOutTensor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Create softmax_max aclTensor.
|
|||
|
|
ret = CreateAclTensor(softmaxMaxHostData, softmaxMaxShape, &resources.softmaxMaxDeviceAddr,
|
|||
|
|
aclDataType::ACL_FLOAT, &resources.softmaxMaxTensor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Create softmax_sum aclTensor.
|
|||
|
|
ret = CreateAclTensor(softmaxSumHostData, softmaxSumShape, &resources.softmaxSumDeviceAddr,
|
|||
|
|
aclDataType::ACL_FLOAT, &resources.softmaxSumTensor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
return ACL_SUCCESS;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
int ExecuteSparseFlashAttention(TensorResources& resources, aclrtStream stream,
|
|||
|
|
void** workspaceAddr, uint64_t* workspaceSize) {
|
|||
|
|
int64_t d = 2;
|
|||
|
|
double scaleValue = 1 / sqrt(d);
|
|||
|
|
int64_t sparseBlockSize = 64;
|
|||
|
|
constexpr const char layerOutStr[] = "BSND";
|
|||
|
|
constexpr size_t layerOutLen = sizeof(layerOutStr);
|
|||
|
|
char layoutQuery[layerOutLen];
|
|||
|
|
char layoutKv[layerOutLen];
|
|||
|
|
errno_t memcpyRet = memcpy_s(layoutQuery, sizeof(layoutQuery), layerOutStr, layerOutLen);
|
|||
|
|
if (memcpyRet != 0) {
|
|||
|
|
LOG_PRINT("memcpy_s layoutQuery failed. ERROR: %d\n", memcpyRet);
|
|||
|
|
return -1;
|
|||
|
|
}
|
|||
|
|
memcpyRet = memcpy_s(layoutKv, sizeof(layoutKv), layerOutStr, layerOutLen);
|
|||
|
|
if (memcpyRet != 0) {
|
|||
|
|
LOG_PRINT("memcpy_s layoutKv failed. ERROR: %d\n", memcpyRet);
|
|||
|
|
return -1;
|
|||
|
|
}
|
|||
|
|
int64_t sparseMode = 3;
|
|||
|
|
int64_t preTokens = 9223372036854775807;
|
|||
|
|
int64_t nextTokens = 9223372036854775807;
|
|||
|
|
int64_t attentionMode = 2;
|
|||
|
|
bool returnSoftmaxLse = false;
|
|||
|
|
aclOpExecutor* executor;
|
|||
|
|
|
|||
|
|
int ret = aclnnSparseFlashAttentionGetWorkspaceSize(resources.queryTensor, resources.keyTensor, resources.valueTensor, resources.sparseIndicesTensor, nullptr, nullptr, nullptr, resources.queryRopeTensor, resources.keyRopeTensor,
|
|||
|
|
scaleValue, sparseBlockSize, layoutQuery, layoutKv, sparseMode, preTokens,
|
|||
|
|
nextTokens, attentionMode, returnSoftmaxLse, resources.attentionOutTensor, resources.softmaxMaxTensor, resources.softmaxSumTensor, workspaceSize, &executor);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("aclnnSparseFlashAttentionGetWorkspaceSize failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if (*workspaceSize > 0ULL) {
|
|||
|
|
ret = aclrtMalloc(workspaceAddr, *workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
ret = aclnnSparseFlashAttention(*workspaceAddr, *workspaceSize, executor, stream);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("aclnnSparseFlashAttention failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
return ACL_SUCCESS;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
int PrintOutResult(std::vector<int64_t> &shape, void** deviceAddr) {
|
|||
|
|
auto size = GetShapeSize(shape);
|
|||
|
|
std::vector<aclFloat16> resultData(size, 0);
|
|||
|
|
auto ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]),
|
|||
|
|
*deviceAddr, size * sizeof(resultData[0]), ACL_MEMCPY_DEVICE_TO_HOST);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
for (int64_t i = 0; i < size; i++) {
|
|||
|
|
LOG_PRINT("mean result[%ld] is: %f\n", i, aclFloat16ToFloat(resultData[i]));
|
|||
|
|
}
|
|||
|
|
return ACL_SUCCESS;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
void CleanupResources(TensorResources& resources, void* workspaceAddr,
|
|||
|
|
aclrtStream stream, int32_t deviceId) {
|
|||
|
|
if (resources.queryTensor) {
|
|||
|
|
aclDestroyTensor(resources.queryTensor);
|
|||
|
|
}
|
|||
|
|
if (resources.keyTensor) {
|
|||
|
|
aclDestroyTensor(resources.keyTensor);
|
|||
|
|
}
|
|||
|
|
if (resources.valueTensor) {
|
|||
|
|
aclDestroyTensor(resources.valueTensor);
|
|||
|
|
}
|
|||
|
|
if (resources.sparseIndicesTensor) {
|
|||
|
|
aclDestroyTensor(resources.sparseIndicesTensor);
|
|||
|
|
}
|
|||
|
|
if (resources.attentionOutTensor) {
|
|||
|
|
aclDestroyTensor(resources.attentionOutTensor);
|
|||
|
|
}
|
|||
|
|
if (resources.softmaxMaxTensor) {
|
|||
|
|
aclDestroyTensor(resources.softmaxMaxTensor);
|
|||
|
|
}
|
|||
|
|
if (resources.softmaxSumTensor) {
|
|||
|
|
aclDestroyTensor(resources.softmaxSumTensor);
|
|||
|
|
}
|
|||
|
|
if (resources.queryRopeTensor) {
|
|||
|
|
aclDestroyTensor(resources.queryRopeTensor);
|
|||
|
|
}
|
|||
|
|
if (resources.keyRopeTensor) {
|
|||
|
|
aclDestroyTensor(resources.keyRopeTensor);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if (resources.queryDeviceAddr) {
|
|||
|
|
aclrtFree(resources.queryDeviceAddr);
|
|||
|
|
}
|
|||
|
|
if (resources.keyDeviceAddr) {
|
|||
|
|
aclrtFree(resources.keyDeviceAddr);
|
|||
|
|
}
|
|||
|
|
if (resources.valueDeviceAddr) {
|
|||
|
|
aclrtFree(resources.valueDeviceAddr);
|
|||
|
|
}
|
|||
|
|
if (resources.sparseIndicesDeviceAddr) {
|
|||
|
|
aclrtFree(resources.sparseIndicesDeviceAddr);
|
|||
|
|
}
|
|||
|
|
if (resources.attentionOutDeviceAddr) {
|
|||
|
|
aclrtFree(resources.attentionOutDeviceAddr);
|
|||
|
|
}
|
|||
|
|
if (resources.softmaxMaxDeviceAddr) {
|
|||
|
|
aclrtFree(resources.softmaxMaxDeviceAddr);
|
|||
|
|
}
|
|||
|
|
if (resources.softmaxSumDeviceAddr) {
|
|||
|
|
aclrtFree(resources.softmaxSumDeviceAddr);
|
|||
|
|
}
|
|||
|
|
if (resources.queryRopeDeviceAddr) {
|
|||
|
|
aclrtFree(resources.queryRopeDeviceAddr);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if (resources.keyRopeDeviceAddr) {
|
|||
|
|
aclrtFree(resources.keyRopeDeviceAddr);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if (workspaceAddr) {
|
|||
|
|
aclrtFree(workspaceAddr);
|
|||
|
|
}
|
|||
|
|
if (stream) {
|
|||
|
|
aclrtDestroyStream(stream);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
aclrtResetDevice(deviceId);
|
|||
|
|
aclFinalize();
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
} // namespace
|
|||
|
|
|
|||
|
|
int main() {
|
|||
|
|
|
|||
|
|
int32_t deviceId = 0;
|
|||
|
|
aclrtStream stream = nullptr;
|
|||
|
|
TensorResources resources = {};
|
|||
|
|
void* workspaceAddr = nullptr;
|
|||
|
|
uint64_t workspaceSize = 0;
|
|||
|
|
std::vector<int64_t> attentionOutShape = {1, 2, 1, 16};
|
|||
|
|
std::vector<int64_t> softmaxMaxShape = {1, 2, 1, 16};
|
|||
|
|
std::vector<int64_t> softmaxSumShape = {1, 2, 1, 16};
|
|||
|
|
int ret = ACL_SUCCESS;
|
|||
|
|
|
|||
|
|
// 1. Initialize device and stream
|
|||
|
|
ret = Init(deviceId, &stream);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("Init acl failed. ERROR: %d\n", ret);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
// 2. Initialize tensors
|
|||
|
|
ret = InitializeTensors(resources);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
CleanupResources(resources, workspaceAddr, stream, deviceId);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// 3. Execute the operation
|
|||
|
|
ret = ExecuteSparseFlashAttention(resources, stream, &workspaceAddr, &workspaceSize);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
CleanupResources(resources, workspaceAddr, stream, deviceId);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// 4. Synchronize stream
|
|||
|
|
ret = aclrtSynchronizeStream(stream);
|
|||
|
|
if (!CHECK_RET(ret == ACL_SUCCESS)) {
|
|||
|
|
LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret);
|
|||
|
|
CleanupResources(resources, workspaceAddr, stream, deviceId);
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// 5. Process results
|
|||
|
|
printf("-----------attentionOut输出-----------\n");
|
|||
|
|
PrintOutResult(attentionOutShape, &resources.attentionOutDeviceAddr);
|
|||
|
|
printf("-----------softmaxMax输出-----------\n");
|
|||
|
|
PrintOutResult(softmaxMaxShape, &resources.softmaxMaxDeviceAddr);
|
|||
|
|
printf("-----------softmaxSum输出-----------\n");
|
|||
|
|
PrintOutResult(softmaxSumShape, &resources.softmaxSumDeviceAddr);
|
|||
|
|
// 6. Cleanup resources
|
|||
|
|
CleanupResources(resources, workspaceAddr, stream, deviceId);
|
|||
|
|
return 0;
|
|||
|
|
}
|
|||
|
|
```
|