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Ascend C算子性能优化实用技巧02——内存优化

article 2024/11/20 2:19:13

 Ascend C是CANN针对算子开发场景推出的编程语言，原生支持C和C++标准规范，兼具开发效率和运行性能。使用Ascend C，开发者可以基于昇腾AI硬件，高效的实现自定义的创新算法。

目前已经有越来越多的开发者使用Ascend C，我们将通过几期“Ascend C算子性能优化”专题分享，围绕开发者最为关心的算子性能优化环节，介绍Ascend C算子常用的优化技巧，帮助开发者自主构建出更优性能的算子。专题内容将围绕流水优化、搬运优化、内存优化、API使用优化以及Tiling优化等优化技巧，从方案讲解、优化案例、性能对比等多角度展开介绍。

上期内容分享了《Ascend C算子性能优化实用技巧01——流水优化》，本期您将从内存优化角度，了解到一些实用的内存优化技巧：

  1. 通过Unified Buffer融合实现连续vector计算
  2. 通过L0C Buffer数据暂存实现高效的矩阵乘结果累加
  3. 较小矩阵长驻L1 Buffer，仅分次搬运较大矩阵
  4. 通过BT Buffer实现高效的bias计算
  5. 通过FP Buffer存放量化参数实现高效随路量化

昇腾AI处理器存储单元简介

AI处理器中的计算资源要想发挥强劲算力，必要条件是保证输入数据能够及时准确地出现在计算单元中，需要精心设计存储系统，保证计算单元所需的数据供应。

昇腾AI处理器中的AI Core包含多级内部存储，AI Core需要把外部存储中的数据加载到内部存储中，才能完成相应的计算。AI Core的主要内部存储包括：

  1. L1 Buffer：L1缓冲区，通用内部存储，是AI Core内比较大的一块数据中转区，可暂存AI Core中需要反复使用的一些数据从而减少从总线读写的次数。
  2. L0A Buffer / L0B Buffer：Cube指令的输入。
  3. L0C Buffer：Cube指令的输出，但进行累加计算的时候，也是输入的一部分。
  4. Unified Buffer：统一缓冲区，向量和标量计算的输入和输出。

为了配合AI Core中的数据传输和搬运，AI Core中还包含MTE（Memory Transfer Engine，存储转换引擎）搬运单元，在搬运过程中可执行随路数据格式/类型转换。

图 1AI Core架构图

除L1 Buffer（L1缓冲区），L0 Buffer（L0缓冲区），Unified Buffer（统一缓冲区）这些基本的存储单元外，某些采用AI Core分离架构的昇腾AI处理器还会增加BT Buffer和FP Buffer这两个Buffer。AI Core分离架构将AI Core拆成矩阵计算（AI Cube，AIC）和向量计算（AI Vector，AIV）两个独立的核，每个核都有自己的Scalar单元，能独立加载自己的代码段，从而实现矩阵计算与向量计算的解耦，在系统软件的统一调度下互相配合达到计算效率优化的效果。

  1. BT Buffer：BiasTable Buffer，用于存放Bias。
  2. FP Buffer：Fixpipe Buffer，用于存放量化参数、Relu参数等。

图 2AI Core架构图（分离架构）

通过UB Buffer融合实现连续vector计算

算子实现中涉及多次vector计算，且前一次计算输出是后一次计算输入的情况下，可将前一次计算输出暂存在UB（Unified Buffer）上直接作为下一次计算的输入，不需要将前一次的计算输出从UB搬运到GM后再从GM搬运到UB。这种UB Buffer融合的方式可以减少搬入搬出次数，实现连续vector计算，提升内存使用效率。数据流图对比如下：

图3数据流图对比

举个例子，以下算子的计算逻辑为进行Exp计算后再进行Abs计算。计算过程中先把源操作数从GM搬运到UB进行Exp计算，Exp计算完成后将Exp的结果从UB搬运到GM；再从GM中把Exp的结果搬运到UB上作为Abs计算的输入，Abs计算完成后将目的操作数结果从UB搬运到GM。整个过程从GM搬进搬出共4次。当需要进行的vector计算为n次时，从GM搬进搬出共需要2n次。

class KernelSample {
public:
    __aicore__ inline KernelSample() {}
    __aicore__ inline void Init(__gm__ uint8_t* src0Gm, __gm__ uint8_t* dstGm)
    {
        src0Global.SetGlobalBuffer((__gm__ float*)src0Gm);
        dstGlobal.SetGlobalBuffer((__gm__ float*)dstGm);
        pipe.InitBuffer(inQueueSrc0, 1, 1024 * sizeof(float));
        pipe.InitBuffer(outQueueDst, 1, 1024 * sizeof(float));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        Compute();
        CopyOut();
        CopyIn1();
        Compute1();
        CopyOut1();
    }
 
private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<float> src0Local = inQueueSrc0.AllocTensor<float>();
        DataCopy(src0Local, src0Global, 1024);
        inQueueSrc0.EnQue(src0Local);
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<float> src0Local = inQueueSrc0.DeQue<float>();
        LocalTensor<float> dstLocal = outQueueDst.AllocTensor<float>();
        Exp(dstLocal, src0Local, 1024);
        outQueueDst.EnQue<float>(dstLocal);
        inQueueSrc0.FreeTensor(src0Local);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<float> dstLocal = outQueueDst.DeQue<float>();
        DataCopy(dstGlobal, dstLocal, 1024);
        outQueueDst.FreeTensor(dstLocal);
    }
    __aicore__ inline void CopyIn1()
    {
PipeBarrier<PIPE_ALL>();
        LocalTensor<float> src0Local = inQueueSrc0.AllocTensor<float>();
        DataCopy(src0Local, dstGlobal, 1024);
        inQueueSrc0.EnQue(src0Local);
    }
    __aicore__ inline void Compute1()
    {
        LocalTensor<float> src0Local = inQueueSrc0.DeQue<float>();
        LocalTensor<float> dstLocal = outQueueDst.AllocTensor<float>();
        Abs(dstLocal, src0Local, 1024);
        outQueueDst.EnQue<float>(dstLocal);
        inQueueSrc0.FreeTensor(src0Local);
    }
    __aicore__ inline void CopyOut1()
    {
        LocalTensor<float> dstLocal = outQueueDst.DeQue<float>();
        DataCopy(dstGlobal, dstLocal, 1024);
        outQueueDst.FreeTensor(dstLocal);
    }
 
private:
    TPipe pipe;
    TQue<QuePosition::VECIN, 1> inQueueSrc0;
    TQue<QuePosition::VECOUT, 1> outQueueDst;
    GlobalTensor<float> src0Global, dstGlobal;
};

 使用UB Buffer融合方式后，在UB上进行连续vector计算时，前一次的结果可直接作为后一次计算的输入，继续在UB上进行计算，不需要中间的搬进搬出，只需在开始计算时将源操作数搬运到UB，以及全部计算结束后将最终结果从UB搬运到GM，共2次搬进搬出。

class KernelSample {
public:
    __aicore__ inline KernelSample() {}
    __aicore__ inline void Init(__gm__ uint8_t* src0Gm, __gm__ uint8_t* dstGm)
    {
        src0Global.SetGlobalBuffer((__gm__ float*)src0Gm);
        dstGlobal.SetGlobalBuffer((__gm__ float*)dstGm);
        pipe.InitBuffer(inQueueSrc0, 1, 1024 * sizeof(float));
        pipe.InitBuffer(outQueueDst, 1, 1024 * sizeof(float));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        Compute();
        CopyOut();
    }
 
private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<float> src0Local = inQueueSrc0.AllocTensor<float>();
        DataCopy(src0Local, src0Global, 1024);
        inQueueSrc0.EnQue(src0Local);
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<float> src0Local = inQueueSrc0.DeQue<float>();
        LocalTensor<float> dstLocal = outQueueDst.AllocTensor<float>();
        Exp(dstLocal, src0Local, 1024);
        Abs(dstLocal, dstLocal, 1024);
        outQueueDst.EnQue<float>(dstLocal);
        inQueueSrc0.FreeTensor(src0Local);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<float> dstLocal = outQueueDst.DeQue<float>();
        DataCopy(dstGlobal, dstLocal, 1024);
        outQueueDst.FreeTensor(dstLocal);
    }
 
private:
    TPipe pipe;
    TQue<QuePosition::VECIN, 1> inQueueSrc0;
    TQue<QuePosition::VECOUT, 1> outQueueDst;
    GlobalTensor<float> src0Global, dstGlobal;
};

通过L0C数据暂存实现高效的矩阵乘结果累加

算子实现中对矩阵乘的结果进行累加时（比如矩阵A1 * B1 + A2 * B2...结果的累加），可将前一次矩阵乘的结果暂存在CO1(L0C)上，调用Mmad接口实现矩阵乘结果累加。相比于每次矩阵乘的结果从CO1搬运到GM上，再搬运到UB上进行累加计算，可减少数据搬运的次数，提升内存使用效率。

图4优化前数据流图

图5优化后数据流图

 优化前，算子进行2次矩阵乘结果累加的过程如下：

  1. 将前一次矩阵乘的计算结果从CO1搬运到workspace上，再从workspace搬运到UB上；
  2. 下一次矩阵乘计算重复完成上述步骤将结果搬运到UB上；
  3. 在UB上将2次矩阵乘的结果相加。

 当需要累加n次矩阵乘时，分别增加了n次CO1->workspace、workspace->UB搬运以及n次Add运算。

...
// 该样例仅做示例说明，非完整代码，省略了部分同步控制代码
public:
    __aicore__ inline KernelSample()
    {
        aSize = m * k;
        bSize = k * n;
        cSize = m * n;
    }
    __aicore__ inline void Init(__gm__ uint8_t *a, __gm__ uint8_t *b, __gm__ uint8_t *c)
    {
        aGM.SetGlobalBuffer((__gm__ half *)a);
        bGM.SetGlobalBuffer((__gm__ half *)b);
        cGM.SetGlobalBuffer((__gm__ float *)c);
        pipe.InitBuffer(inQueueA1, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueA2, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueB1, 1, bSize * sizeof(half));
        pipe.InitBuffer(inQueueB2, 2, bSize * sizeof(half));
        pipe.InitBuffer(outQueueCO1, 1, cSize * sizeof(float));
        pipe.InitBuffer(inQueueSrc0, 1, cSize * sizeof(float));
        pipe.InitBuffer(inQueueSrc1, 1, cSize * sizeof(float));
        pipe.InitBuffer(outQueueDst, 1, cSize * sizeof(float));
 
    }
    __aicore__ inline void Process()
    {
        // 第一次矩阵乘计算
        CopyIn();
        SplitA();
        SplitB();
        Compute();
        // 将第一次矩阵乘的结果搬出
        CopyOut();
        // 将第一次矩阵乘的结果搬运到UB
        CopyIn1();
        // 第二次矩阵乘计算
        Compute1();
        // 将第一次矩阵乘的结果搬出
        CopyOut1();
        // 将第二次矩阵乘的结果搬运到UB
        CopyIn1();
        // 将两次矩阵乘的结果累加
        Compute2();
        CopyOut2();
    }
private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<half> a1Local = inQueueA1.AllocTensor<half>();
        LocalTensor<half> b1Local = inQueueB1.AllocTensor<half>();
 
        Nd2NzParams dataCopyA1Params;
        dataCopyA1Params.ndNum = 1;
        dataCopyA1Params.nValue = m;
        dataCopyA1Params.dValue = k;
        dataCopyA1Params.srcNdMatrixStride = 0;
        dataCopyA1Params.srcDValue = k;
        dataCopyA1Params.dstNzC0Stride = m;
        dataCopyA1Params.dstNzNStride = 1;
        dataCopyA1Params.dstNzMatrixStride = 0;
        DataCopy(a1Local, aGM, dataCopyA1Params);
 
        Nd2NzParams dataCopyB1Params;
        dataCopyB1Params.ndNum = 1;
        dataCopyB1Params.nValue = k;
        dataCopyB1Params.dValue = n;
        dataCopyB1Params.srcNdMatrixStride = 0;
        dataCopyB1Params.srcDValue = n;
        dataCopyB1Params.dstNzC0Stride = k;
        dataCopyB1Params.dstNzNStride = 1;
        dataCopyB1Params.dstNzMatrixStride = 0;
        DataCopy(b1Local, bGM, dataCopyB1Params);
 
        inQueueA1.EnQue<half>(a1Local);
        inQueueB1.EnQue<half>(b1Local);
    }
    __aicore__ inline void SplitA()
    {
        ...
    }
    __aicore__ inline void SplitB()
    {
        ...
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<half> a2Local = inQueueA2.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.DeQue<half>();
        LocalTensor<float> c1Local = outQueueCO1.AllocTensor<float>();
        MmadParams mmadParams;
        mmadParams.m = m;
        mmadParams.n = n;
        mmadParams.k = k;
        // 矩阵乘
        Mmad(c1Local, a2Local, b2Local, mmadParams);
        outQueueCO1.EnQue<float>(c1Local);
        inQueueA2.EnQue<half>(a2Local);
        inQueueB2.EnQue<half>(b2Local);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<float> c1Local = outQueueCO1.DeQue<float>();
        GM_ADDR usrWorkspace = AscendC::GetUserWorkspace(workspace);
        xGm.SetGlobalBuffer((__gm__ float *)(usrWorkspace));
        FixpipeParamsV220 fixpipeParams;
        fixpipeParams.nSize = n;
        fixpipeParams.mSize = m;
        fixpipeParams.srcStride = m;
        fixpipeParams.dstStride = n;
        fixpipeParams.ndNum = 1;
        fixpipeParams.srcNdStride = 0;
        fixpipeParams.dstNdStride = 0;
        // 将矩阵乘的计算结果从CO1搬运到workspace
        Fixpipe(xGm, c1Local, fixpipeParams);
        outQueueCO1.EnQue<float>(c1Local);
    }
    __aicore__ inline void CopyIn1()
    {
        PipeBarrier<PIPE_ALL>();
        LocalTensor<float> src0Local = inQueueSrc0.AllocTensor<float>();
        // 将矩阵乘的计算结果从workspace搬运到UB
        DataCopy(src0Local, xGm, cSize);
        inQueueSrc0.EnQue<float>(src0Local);
    }
    __aicore__ inline void Compute1()
    {
        LocalTensor<half> a2Local = inQueueA2.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.DeQue<half>();
        LocalTensor<float> c1Local = outQueueCO1.DeQue<float>();
        MmadParams mmadParams;
        mmadParams.m = m;
        mmadParams.n = n;
        mmadParams.k = k;
        // 矩阵乘
        Mmad(c1Local, a2Local, b2Local, mmadParams);
        outQueueCO1.EnQue<float>(c1Local);
        inQueueA2.FreeTensor(a2Local);
        inQueueB2.FreeTensor(b2Local);
    }
    __aicore__ inline void CopyOut1()
    {
        LocalTensor<float> c1Local = outQueueCO1.DeQue<float>();
        FixpipeParamsV220 fixpipeParams;
        fixpipeParams.nSize = n;
        fixpipeParams.mSize = m;
        fixpipeParams.srcStride = m;
        fixpipeParams.dstStride = n;
        fixpipeParams.ndNum = 1;
        fixpipeParams.srcNdStride = 0;
        fixpipeParams.dstNdStride = 0;
        // 将矩阵乘的计算结果从CO1搬运到workspace
        Fixpipe(xGm, c1Local, fixpipeParams);
        outQueueCO1.FreeTensor(c1Local);
    }
    __aicore__ inline void CopyIn2()
    {
        PipeBarrier<PIPE_ALL>();
        LocalTensor<float> src1Local = inQueueSrc1.AllocTensor<float>();
        // 将矩阵乘的计算结果从workspace搬运到UB
        DataCopy(src1Local, xGm, cSize);
        inQueueSrc1.EnQue<float>(src1Local);
    }
    __aicore__ inline void Compute2()
    {
        LocalTensor<float> src0Local = inQueueSrc0.DeQue<float>();
        LocalTensor<float> src1Local = inQueueSrc1.DeQue<float>();
        LocalTensor<float> dstLocal = outQueueDst.AllocTensor<float>();
        // 两次矩阵乘的结果相加
        Add(dstLocal, src0Local, src1Local, cSize);
        outQueueDst.EnQue<float>(dstLocal);
        inQueueSrc0.FreeTensor(src0Local);
        inQueueSrc1.FreeTensor(src1Local);
    }
    __aicore__ inline void CopyOut2()
    {
        ...
    }
private:
    TPipe pipe;
    TQue<QuePosition::A1, 1> inQueueA1;
    TQue<QuePosition::A2, 1> inQueueA2;
    TQue<QuePosition::B1, 1> inQueueB1;
    TQue<QuePosition::B2, 1> inQueueB2;
    TQue<QuePosition::CO1, 1> outQueueCO1;
    TQue<QuePosition::VECIN, 1> inQueueSrc0;
    TQue<QuePosition::VECIN, 1> inQueueSrc1;
    TQue<QuePosition::VECOUT, 1> outQueueDst;
 
    GlobalTensor<half> aGM;
    GlobalTensor<half> bGM;
    GlobalTensor<dst_T> cGM;
    uint16_t m = 32, k = 32, n = 32;
    uint16_t aSize, bSize, cSize;   
...

 通过优化，该算子对矩阵乘结果累加时，可将前一次矩阵乘的结果暂存在L0C上，通过Mmad接口参数cmatrixInitVal和cmatrixSource配置C矩阵的初始值 ，只调用2次Mmad接口实现2次矩阵乘结果累加。

...
// 该样例仅做示例说明，非完整代码，省略了部分同步控制代码
public:
    __aicore__ inline KernelSample()
    {
        aSize = m * k;
        bSize = k * n;
        cSize = m * n;
    }
    __aicore__ inline void Init(__gm__ uint8_t *a, __gm__ uint8_t *b, __gm__ uint8_t *c)
    {
        aGM.SetGlobalBuffer((__gm__ half *)a);
        bGM.SetGlobalBuffer((__gm__ half *)b);
        cGM.SetGlobalBuffer((__gm__ float *)c);
        pipe.InitBuffer(inQueueA1, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueA2, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueB1, 1, bSize * sizeof(half));
        pipe.InitBuffer(inQueueB2, 2, bSize * sizeof(half));
        pipe.InitBuffer(outQueueCO1, 1, cSize * sizeof(float));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        SplitA();
        SplitB();
        Compute();
        CopyOut();
    }
private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<half> a1Local = inQueueA1.AllocTensor<half>();
        LocalTensor<half> b1Local = inQueueB1.AllocTensor<half>();
 
        Nd2NzParams dataCopyA1Params;
        dataCopyA1Params.ndNum = 1;
        dataCopyA1Params.nValue = m;
        dataCopyA1Params.dValue = k;
        dataCopyA1Params.srcNdMatrixStride = 0;
        dataCopyA1Params.srcDValue = k;
        dataCopyA1Params.dstNzC0Stride = m;
        dataCopyA1Params.dstNzNStride = 1;
        dataCopyA1Params.dstNzMatrixStride = 0;
        DataCopy(a1Local, aGM, dataCopyA1Params);
 
        Nd2NzParams dataCopyB1Params;
        dataCopyB1Params.ndNum = 1;
        dataCopyB1Params.nValue = k;
        dataCopyB1Params.dValue = n;
        dataCopyB1Params.srcNdMatrixStride = 0;
        dataCopyB1Params.srcDValue = n;
        dataCopyB1Params.dstNzC0Stride = k;
        dataCopyB1Params.dstNzNStride = 1;
        dataCopyB1Params.dstNzMatrixStride = 0;
        DataCopy(b1Local, bGM, dataCopyB1Params);
 
        inQueueA1.EnQue(a1Local);
        inQueueB1.EnQue(b1Local);
    }
    __aicore__ inline void SplitA()
    {
        ...
    }
    __aicore__ inline void SplitB()
    {
        ...
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<half> a2Local = inQueueA2.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.DeQue<half>();
        LocalTensor<float> c1Local = outQueueCO1.AllocTensor<float>();
        MmadParams mmadParams;
        mmadParams.m = m;
        mmadParams.n = n;
        mmadParams.k = k;
        // 第一次矩阵乘
        Mmad(c1Local, a2Local, b2Local, mmadParams);
        PipeBarrier<PIPE_M>();
        // 第二次矩阵乘累加第一次矩阵乘的结果
        mmadParams.cmatrixInitVal = false;
        Mmad(c1Local, a2Local, b2Local, c1Local, mmadParams);
        outQueueCO1.EnQue<float>(c1Local);
        inQueueA2.FreeTensor(a2Local);
        inQueueB2.FreeTensor(b2Local);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<float> c1Local = outQueueCO1.DeQue<float>();
        FixpipeParamsV220 fixpipeParams;
        fixpipeParams.nSize = n;
        fixpipeParams.mSize = m;
        fixpipeParams.srcStride = m;
        fixpipeParams.dstStride = n;
 
        fixpipeParams.ndNum = 1;
        fixpipeParams.srcNdStride = 0;
        fixpipeParams.dstNdStride = 0;
        Fixpipe(cGM, c1Local, fixpipeParams);
        outQueueCO1.FreeTensor(c1Local);
    }
private:
    TPipe pipe;
    TQue<QuePosition::A1, 1> inQueueA1;
    TQue<QuePosition::A2, 1> inQueueA2;
    TQue<QuePosition::B1, 1> inQueueB1;
    TQue<QuePosition::B2, 1> inQueueB2;
    TQue<QuePosition::CO1, 1> outQueueCO1;
 
    GlobalTensor<half> aGM;
    GlobalTensor<half> bGM;
    GlobalTensor<dst_T> cGM;
    uint16_t m = 32, k = 32, n = 32;
    uint16_t aSize, bSize, cSize;

较小矩阵长驻L1 Buffer，仅分次搬运较大矩阵

在进行cube计算时，当L1无法全载左右矩阵时，可以让较小的矩阵长驻于L1上，只分次搬运较大的矩阵，减少搬运次数。

假设L1的大小为512K，左矩阵和右矩阵的大小分别为992K、16K，数据类型为half，单次无法将左右矩阵全部载入L1中。开发者规划的切分策略为：不切K轴，将左矩阵平均分成两块A1、A2，shape大小均为[992, 256]；将右矩阵平均分成两块，shape大小均为[256, 16]。计算时的加载顺序如下：先加载A1矩阵至L1，将B1、B2依次加载并计算；然后再加载A2至L1，将B1、B2依次加载并计算。

图6优化前切分策略图示

...
public:
    __aicore__ inline KernelSample()
    {
        aSize = baseM * baseK;
        bSize = baseK * baseN;
        cSize = m * n;
    }
    __aicore__ inline void Init(__gm__ uint8_t *a, __gm__ uint8_t *b, __gm__ uint8_t *c)
    {
        aGM.SetGlobalBuffer((__gm__ half *)a);
        bGM.SetGlobalBuffer((__gm__ half *)b);
        cGM.SetGlobalBuffer((__gm__ float *)c);
        pipe.InitBuffer(inQueueA1, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueA2, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueB1, 1, bSize * sizeof(half));
        pipe.InitBuffer(inQueueB2, 2, bSize * sizeof(half));
        pipe.InitBuffer(outQueueCO1, 1, cSize * sizeof(float));
    }
    __aicore__ inline void Process()
    {
        for (uint32_t i = 0; i < 2; i++) {
            CopyInA1(i);
            SplitA();
            for (uint32_t j = 0; j < 2; j++) {
                CopyInB1(j);
                SplitB();
                Compute(i, j);
            }
        }
        CopyOut();
    }
private:
    __aicore__ inline void CopyInA1(uint32_t i)
    {
        LocalTensor<half> a1Local = inQueueA1.AllocTensor<half>();
        // 左矩阵a1/a2分块载入A1
        Nd2NzParams dataCopyA1Params;
        dataCopyA1Params.ndNum = 1;
        dataCopyA1Params.nValue = baseM;
        dataCopyA1Params.dValue = baseK;
        dataCopyA1Params.srcNdMatrixStride = 0;
        dataCopyA1Params.srcDValue = baseK;
        dataCopyA1Params.dstNzC0Stride = baseM;
        dataCopyA1Params.dstNzNStride = 1;
        dataCopyA1Params.dstNzMatrixStride = 0;
        DataCopy(a1Local, aGM[i * baseM * baseK], dataCopyA1Params);
        inQueueA1.EnQue(a1Local);
    }
    __aicore__ inline void SplitA()
    {
        LocalTensor<half> a1Local = inQueueA1.DeQue<half>();
        LocalTensor<half> a2Local = inQueueA2.AllocTensor<half>();
        // 左矩阵a1/a2分块从A1->A2
        LoadData2dParams loadL0AParams;
        loadL0AParams.repeatTimes = baseM * baseK * sizeof(half) / 512;
        loadL0AParams.srcStride = 1;
        loadL0AParams.dstGap = 0;
        LoadData(a2Local, a1Local, loadL0AParams);
        inQueueA2.EnQue(a2Local);
        inQueueA1.FreeTensor(a1Local);
    }
    __aicore__ inline void CopyInB1(uint32_t j)
    {
        LocalTensor<half> b1Local = inQueueB1.AllocTensor<half>();
        // 右矩阵分块b1/b2载入B1
        Nd2NzParams dataCopyB1Params;
        dataCopyB1Params.ndNum = 1;
        dataCopyB1Params.nValue = baseK;
        dataCopyB1Params.dValue = baseN;
        dataCopyB1Params.srcNdMatrixStride = 0;
        dataCopyB1Params.srcDValue = n;
        dataCopyB1Params.dstNzC0Stride = baseK;
        dataCopyB1Params.dstNzNStride = 1;
        dataCopyB1Params.dstNzMatrixStride = 0;
        DataCopy(b1Local, bGM[j * baseN], dataCopyB1Params);
        inQueueB1.EnQue(b1Local);
    }
    __aicore__ inline void SplitB()
    {
        LocalTensor<half> b1Local = inQueueB1.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.AllocTensor<half>();
        // 右矩阵分块b1/b2从B1->B2
        LoadData2dTransposeParams loadL0BParams;
        loadL0BParams.startIndex = 0;
        loadL0BParams.repeatTimes = baseK / nBlockSize;
        loadL0BParams.srcStride = 1;
        loadL0BParams.dstGap = 1;
        LoadDataWithTranspose(b2Local, b1Local, loadL0BParams);
        inQueueB2.EnQue(b2Local);
        inQueueB1.FreeTensor(b1Local);
    }
    __aicore__ inline void Compute(uint32_t i, uint32_t j)
    {
        LocalTensor<half> a2Local = inQueueA2.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.DeQue<half>();
        LocalTensor<float> c1Local = outQueueCO1.AllocTensor<float>();
        // 矩阵乘
        mmadParams.m = baseM;
        mmadParams.n = baseN;
        mmadParams.k = baseK;
        Mmad(c1Local[i * baseM * baseN + j * m * baseN], a2Local, b2Local, mmadParams);
        outQueueCO1.EnQue<float>(c1Local);
        inQueueA2.FreeTensor(a2Local);
        inQueueB2.FreeTensor(b2Local);
    }
    __aicore__ inline void CopyOut()
    {
        ...
    }
private:
    TPipe pipe;
    TQue<QuePosition::A1, 1> inQueueA1;
    TQue<QuePosition::A2, 1> inQueueA2;
    TQue<QuePosition::B1, 1> inQueueB1;
    TQue<QuePosition::B2, 1> inQueueB2;
    TQue<QuePosition::CO1, 1> outQueueCO1;
 
    GlobalTensor<half> aGM;
    GlobalTensor<half> bGM;
    GlobalTensor<dst_T> cGM;
    uint16_t m = 1984, k = 256, n = 32;
    uint16_t baseM = 992, baseK = 256, baseN = 16;
    uint16_t aSize, bSize, cSize;
    uint16_t nBlockSize = 16;
...

经过优化，将较小的右矩阵一次性搬入L1并长存于L1上，循环内不断搬运A矩阵，当循环次数为2时，共需要3次搬运。

...
public:
    __aicore__ inline KernelSample()
    {
        aSize = baseM * baseK;
        bSize = baseK * n;
        cSize = m * n;
    }
    __aicore__ inline void Init(__gm__ uint8_t *a, __gm__ uint8_t *b, __gm__ uint8_t *c)
    {
        aGM.SetGlobalBuffer((__gm__ half *)a);
        bGM.SetGlobalBuffer((__gm__ half *)b);
        cGM.SetGlobalBuffer((__gm__ float *)c);
        pipe.InitBuffer(inQueueA1, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueA2, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueB1, 1, bSize * sizeof(half));
        pipe.InitBuffer(inQueueB2, 2, bSize * sizeof(half));
        pipe.InitBuffer(outQueueCO1, 1, cSize * sizeof(float));
    }
    __aicore__ inline void Process()
    {
        CopyInB1();
        SplitB();
        for (uint32_t i = 0; i < 2; i++) {
            CopyInA1(i);
            SplitA();
            for (uint32_t j = 0; j < 2; j++) {
                Compute(i, j);
            }
        }
        CopyOut();
    }
private:
    __aicore__ inline void CopyInB1()
    {
        LocalTensor<half> b1Local = inQueueB1.AllocTensor<half>();
        // 右矩阵全载入B1
        Nd2NzParams dataCopyB1Params;
        dataCopyB1Params.ndNum = 1;
        dataCopyB1Params.nValue = baseK;
        dataCopyB1Params.dValue = n;
        dataCopyB1Params.srcNdMatrixStride = 0;
        dataCopyB1Params.srcDValue = n;
        dataCopyB1Params.dstNzC0Stride = baseK;
        dataCopyB1Params.dstNzNStride = 1;
        dataCopyB1Params.dstNzMatrixStride = 0;
        DataCopy(b1Local, bGM, dataCopyB1Params);
        inQueueB1.EnQue(b1Local);
    }
    __aicore__ inline void SplitB()
    {
        LocalTensor<half> b1Local = inQueueB1.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.AllocTensor<half>();
        // 右矩阵全部从B1->B2
        LoadData2dTransposeParams loadL0BParams;
        loadL0BParams.startIndex = 0;
        loadL0BParams.repeatTimes = baseK / nBlockSize;
        loadL0BParams.srcStride = 1;
        loadL0BParams.dstGap = 1;
        for (int blockNum = 0; blockNum < (n / nBlockSize); blockNum++) {
            LoadDataWithTranspose(b2Local[blockNum * 16 * nBlockSize], b1Local[blockNum * baseK * nBlockSize], loadL0BParams);
        }
        inQueueB2.EnQue(b2Local);
        inQueueB1.FreeTensor(b1Local);
    }
    __aicore__ inline void CopyInA1(uint32_t i)
    {
        LocalTensor<half> a1Local = inQueueA1.AllocTensor<half>();
        // 左矩阵a1/a2分块载入A1
        Nd2NzParams dataCopyA1Params;
        dataCopyA1Params.ndNum = 1;
        dataCopyA1Params.nValue = baseM;
        dataCopyA1Params.dValue = baseK;
        dataCopyA1Params.srcNdMatrixStride = 0;
        dataCopyA1Params.srcDValue = baseK;
        dataCopyA1Params.dstNzC0Stride = baseM;
        dataCopyA1Params.dstNzNStride = 1;
        dataCopyA1Params.dstNzMatrixStride = 0;
        DataCopy(a1Local, aGM[i * baseM * baseK], dataCopyA1Params);
        inQueueA1.EnQue(a1Local);
    }
    __aicore__ inline void SplitA()
    {
        LocalTensor<half> a1Local = inQueueA1.DeQue<half>();
        LocalTensor<half> a2Local = inQueueA2.AllocTensor<half>();
        // 左矩阵a1/a2分块从A1->A2
        LoadData2dParams loadL0AParams;
        loadL0AParams.repeatTimes = baseM * baseK * sizeof(half) / 512;
        loadL0AParams.srcStride = 1;
        loadL0AParams.dstGap = 0;
        LoadData(a2Local, a1Local, loadL0AParams);
        inQueueA2.EnQue(a2Local);
        inQueueA1.FreeTensor(a1Local);
    }
    __aicore__ inline void Compute(uint32_t i, uint32_t j)
    {
        LocalTensor<half> a2Local = inQueueA2.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.DeQue<half>();
        LocalTensor<float> c1Local = outQueueCO1.AllocTensor<float>();
        // 矩阵乘
        mmadParams.m = baseM;
        mmadParams.n = baseN;
        mmadParams.k = baseK;
        Mmad(c1Local[i * baseM * baseN + j * m * baseN], a2Local, b2Local, mmadParams);
        outQueueCO1.EnQue<float>(c1Local);
        inQueueA2.FreeTensor(a2Local);
        inQueueB2.FreeTensor(b2Local);
    }
    __aicore__ inline void CopyOut()
    {
        ...
    }
private:
    TPipe pipe;
    TQue<QuePosition::A1, 1> inQueueA1;
    TQue<QuePosition::A2, 1> inQueueA2;
    TQue<QuePosition::B1, 1> inQueueB1;
    TQue<QuePosition::B2, 1> inQueueB2;
    TQue<QuePosition::CO1, 1> outQueueCO1;
 
    GlobalTensor<half> aGM;
    GlobalTensor<half> bGM;
    GlobalTensor<dst_T> cGM;
    uint16_t m = 1984, k = 256, n = 32;
    uint16_t baseM = 992, baseK = 256, baseN = 16;
    uint16_t aSize, bSize, cSize;
    uint16_t nBlockSize = 16;
...

通过BT Buffer实现高效的bias计算

算子中进行带bias的矩阵乘计算时，可将bias数据搬运至C2(Bias Table Buffer)上，调用一次Mmad接口实现矩阵乘加bias的计算。相比于先将矩阵乘的结果从CO1(L0C)搬运到GM上，再搬运到UB上进行加bias的过程，减少了数据搬运的次数，可提升内存使用效率。数据流图对比如下：

​​​​​​​图7优化前数据流图

​​​​​​​图8优化后数据流图

在优化前，算子进行带bias的矩阵乘计算时，过程如下：

  1. 将矩阵乘的计算结果从CO1(L0C)搬运到workspace上；
  2. 从workspace搬运到UB上；
  3. 在UB上进行加bias的运算；
  4. 最后将结果搬运到GM。

当循环n次该计算过程，则分别增加了n次CO1->workspace、workspace->UB的搬运。

// 该样例仅做示例说明，非完整代码，省略了部分同步控制代码
public:
    __aicore__ inline KernelSample()
    {
        aSize = m * k;
        bSize = k * n;
        cSize = m * n;
    }
    __aicore__ inline void Init(__gm__ uint8_t *a, __gm__ uint8_t *b, __gm__ uint8_t *bias, __gm__ uint8_t *c)
    {
        aGM.SetGlobalBuffer((__gm__ half *)a);
        bGM.SetGlobalBuffer((__gm__ half *)b);
        cGM.SetGlobalBuffer((__gm__ float *)c);
        biasGM.SetGlobalBuffer((__gm__ float *)bias);
        pipe.InitBuffer(inQueueA1, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueA2, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueB1, 1, bSize * sizeof(half));
        pipe.InitBuffer(inQueueB2, 2, bSize * sizeof(half));
        pipe.InitBuffer(outQueueCO1, 1, cSize * sizeof(float));
        pipe.InitBuffer(inQueueBias, 1, n * sizeof(float));
        pipe.InitBuffer(inQueueSrc0, 1, cSize * sizeof(float));
        pipe.InitBuffer(outQueueDst, 1, cSize * sizeof(float));
 
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        SplitA();
        SplitB();
        Compute();
        CopyOut();
        CopyIn1();
        Compute1();
        CopyOut1();
    }
private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<half> a1Local = inQueueA1.AllocTensor<half>();
        LocalTensor<half> b1Local = inQueueB1.AllocTensor<half>();
        LocalTensor<float> biasLocal = inQueueBias.AllocTensor<float>();
 
        Nd2NzParams dataCopyA1Params;
        dataCopyA1Params.ndNum = 1;
        dataCopyA1Params.nValue = m;
        dataCopyA1Params.dValue = k;
        dataCopyA1Params.srcNdMatrixStride = 0;
        dataCopyA1Params.srcDValue = k;
        dataCopyA1Params.dstNzC0Stride = m;
        dataCopyA1Params.dstNzNStride = 1;
        dataCopyA1Params.dstNzMatrixStride = 0;
        DataCopy(a1Local, aGM, dataCopyA1Params);
 
        Nd2NzParams dataCopyB1Params;
        dataCopyB1Params.ndNum = 1;
        dataCopyB1Params.nValue = k;
        dataCopyB1Params.dValue = n;
        dataCopyB1Params.srcNdMatrixStride = 0;
        dataCopyB1Params.srcDValue = n;
        dataCopyB1Params.dstNzC0Stride = k;
        dataCopyB1Params.dstNzNStride = 1;
        dataCopyB1Params.dstNzMatrixStride = 0;
        DataCopy(b1Local, bGM, dataCopyB1Params);
        // 将bias搬运到UB
        DataCopy(biasLocal, biasGM, n);
 
        inQueueA1.EnQue(a1Local);
        inQueueB1.EnQue(b1Local);
        inQueueBias.EnQue(biasLocal);
    }
    __aicore__ inline void SplitA()
    {
        ...
    }
    __aicore__ inline void SplitB()
    {
        ...
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<half> a2Local = inQueueA2.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.DeQue<half>();
        LocalTensor<float> c1Local = outQueueCO1.AllocTensor<float>();
        MmadParams mmadParams;
        mmadParams.m = m;
        mmadParams.n = n;
        mmadParams.k = k;
        // 矩阵乘
        Mmad(c1Local, a2Local, b2Local, mmadParams); // m*n
        outQueueCO1.EnQue<float>(c1Local);
        inQueueA2.FreeTensor(a2Local);
        inQueueB2.FreeTensor(b2Local);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<float> c1Local = outQueueCO1.DeQue<float>();
        GM_ADDR usrWorkspace = AscendC::GetUserWorkspace(workspace);
        xGm.SetGlobalBuffer((__gm__ float *)(usrWorkspace));
        FixpipeParamsV220 fixpipeParams;
        fixpipeParams.nSize = n;
        fixpipeParams.mSize = m;
        fixpipeParams.srcStride = m;
        fixpipeParams.dstStride = n;
        fixpipeParams.ndNum = 1;
        fixpipeParams.srcNdStride = 0;
        fixpipeParams.dstNdStride = 0;
        // 将矩阵乘的计算结果从CO1搬运到workspace
        Fixpipe(xGm, c1Local, fixpipeParams);
        outQueueCO1.FreeTensor(c1Local);
    }
    __aicore__ inline void CopyIn1()
    {
        PipeBarrier<PIPE_ALL>();
        // 将矩阵乘的计算结果从workspace搬运到UB
        LocalTensor<float> src0Local = inQueueSrc0.AllocTensor<float>();
        DataCopy(src0Local, xGm, cSize);
        inQueueSrc0.EnQue(src0Local);
    }
    __aicore__ inline void Compute1()
    {
        LocalTensor<float> src0Local = inQueueSrc0.DeQue<float>();
        LocalTensor<float> biasLocal = inQueueBias.DeQue<float>();
        LocalTensor<float> dstLocal = outQueueDst.AllocTensor<float>();
        BinaryRepeatParams addRepeatParams;
        addRepeatParams.dstRepStride = 8;
        addRepeatParams.src0RepStride = 8;
        addRepeatParams.src1RepStride = 0;
        // 加bias的运算
        Add(dstLocal, src0Local, biasLocal, 32, m, addRepeatParams);
        outQueueDst.EnQue<float>(dstLocal);
        inQueueSrc0.FreeTensor(src0Local);
        inQueueBias.FreeTensor(biasLocal);
    }
    __aicore__ inline void CopyOut1()
    {
        ...
    }
private:
    TPipe pipe;
    TQue<QuePosition::A1, 1> inQueueA1;
    TQue<QuePosition::A2, 1> inQueueA2;
    TQue<QuePosition::B1, 1> inQueueB1;
    TQue<QuePosition::B2, 1> inQueueB2;
    TQue<QuePosition::VECIN, 1> inQueueBias;
    TQue<QuePosition::VECIN, 1> inQueueSrc0;
    TQue<QuePosition::VECOUT, 1> outQueueDst;
 
    GlobalTensor<half> aGM;
    GlobalTensor<half> bGM;
    GlobalTensor<dst_T> cGM;
    GlobalTensor<float> biasGM;
    uint16_t m = 32, k = 32, n = 32;
    uint16_t aSize, bSize, cSize;   
...

经过优化，该算子进行带bias的矩阵乘计算时，先将bias搬运到BT上，调用一次Mmad接口实现矩阵乘加bias的计算。

...
// 该样例仅做示例说明，非完整代码，省略了部分同步控制代码
public:
    __aicore__ inline KernelSample()
    {
        aSize = m * k;
        bSize = k * n;
        cSize = m * n;
    }
    __aicore__ inline void Init(__gm__ uint8_t *a, __gm__ uint8_t *b, __gm__ uint8_t *bias, __gm__ uint8_t *c)
    {
        aGM.SetGlobalBuffer((__gm__ half *)a);
        bGM.SetGlobalBuffer((__gm__ half *)b);
        cGM.SetGlobalBuffer((__gm__ float *)c);
        biasGM.SetGlobalBuffer((__gm__ float *)bias);
        pipe.InitBuffer(inQueueA1, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueA2, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueB1, 1, bSize * sizeof(half));
        pipe.InitBuffer(inQueueB2, 2, bSize * sizeof(half));
        pipe.InitBuffer(outQueueCO1, 1, cSize * sizeof(float));
        pipe.InitBuffer(inQueueC1, 1, n * sizeof(float));
        pipe.InitBuffer(outQueueC2, 1, n * sizeof(float));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        SplitA();
        SplitB();
        SplitBias();
        Compute();
        CopyOut();
    }
private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<half> a1Local = inQueueA1.AllocTensor<half>();
        LocalTensor<half> b1Local = inQueueB1.AllocTensor<half>();
        LocalTensor<float> bias1Local = inQueueC1.AllocTensor<float>();
 
        Nd2NzParams dataCopyA1Params;
        dataCopyA1Params.ndNum = 1;
        dataCopyA1Params.nValue = m;
        dataCopyA1Params.dValue = k;
        dataCopyA1Params.srcNdMatrixStride = 0;
        dataCopyA1Params.srcDValue = k;
        dataCopyA1Params.dstNzC0Stride = m;
        dataCopyA1Params.dstNzNStride = 1;
        dataCopyA1Params.dstNzMatrixStride = 0;
        DataCopy(a1Local, aGM, dataCopyA1Params);
 
        Nd2NzParams dataCopyB1Params;
        dataCopyB1Params.ndNum = 1;
        dataCopyB1Params.nValue = k;
        dataCopyB1Params.dValue = n;
        dataCopyB1Params.srcNdMatrixStride = 0;
        dataCopyB1Params.srcDValue = n;
        dataCopyB1Params.dstNzC0Stride = k;
        dataCopyB1Params.dstNzNStride = 1;
        dataCopyB1Params.dstNzMatrixStride = 0;
        DataCopy(b1Local, bGM, dataCopyB1Params);
        // 将bias从GM搬运到L1
        DataCopy(bias1Local, biasGM, n);
 
        inQueueA1.EnQue(a1Local);
        inQueueB1.EnQue(b1Local);
        inQueueC1.EnQue(bias1Local);
    }
    __aicore__ inline void SplitA()
    {
        ...
    }
    __aicore__ inline void SplitB()
    {
        ...
    }
    __aicore__ inline void SplitBias()
    {
        LocalTensor<float> bias1Local = inQueueC1.DeQue<float>();
        LocalTensor<float> bias2Local = outQueueC2.AllocTensor<float>();
        // 将bias从L1搬运到BT
        DataCopy(bias2Local, bias1Local, { 1, (uint16_t)(n * sizeof(float) / 64), 0, 0 });
        outQueueC2.EnQue<float>(bias2Local);
        inQueueC1.FreeTensor(bias1Local);
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<half> a2Local = inQueueA2.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.DeQue<half>();
        LocalTensor<float> bias2Local = outQueueC2.DeQue<float>();
        LocalTensor<float> c1Local = outQueueCO1.AllocTensor<float>();
        MmadParams mmadParams;
        mmadParams.m = m;
        mmadParams.n = n;
        mmadParams.k = k;
        mmadParams.cmatrixInitVal = false;
        // 矩阵乘
        Mmad(c1Local, a2Local, b2Local, bias2Local, mmadParams);
        outQueueCO1.EnQue<float>(c1Local);
        inQueueA2.FreeTensor(a2Local);
        inQueueB2.FreeTensor(b2Local);
        outQueueC2.FreeTensor(bias2Local);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<float> c1Local = outQueueCO1.DeQue<float>();
        FixpipeParamsV220 fixpipeParams;
        fixpipeParams.nSize = n;
        fixpipeParams.mSize = m;
        fixpipeParams.srcStride = m;
        fixpipeParams.dstStride = n;
 
        fixpipeParams.ndNum = 1;
        fixpipeParams.srcNdStride = 0;
        fixpipeParams.dstNdStride = 0;
        Fixpipe(cGM, c1Local, fixpipeParams);
        outQueueCO1.FreeTensor(c1Local);
    }
private:
    TPipe pipe;
    TQue<QuePosition::A1, 1> inQueueA1;
    TQue<QuePosition::A2, 1> inQueueA2;
    TQue<QuePosition::B1, 1> inQueueB1;
    TQue<QuePosition::B2, 1> inQueueB2;
    TQue<QuePosition::CO1, 1> outQueueCO1;
    TQue<QuePosition::C1, 1> inQueueC1;
    TQue<QuePosition::C2, 1> outQueueC2;
 
    GlobalTensor<half> aGM;
    GlobalTensor<half> bGM;
    GlobalTensor<dst_T> cGM;
    GlobalTensor<float> biasGM;
    uint16_t m = 32, k = 32, n = 32;
    uint16_t aSize, bSize, cSize;

通过FP Buffer存放量化参数实现高效随路量化

算子实现中对矩阵乘结果进行量化计算时，可将量化参数搬运到C2PIPE2GM(Fixpipe Buffer)上，调用一次Fixpipe接口实现矩阵乘结果的量化计算。相比于将矩阵乘的结果从CO1（L0C）搬运到GM，再从GM搬运到UB，在UB进行量化计算的过程，数据搬运的次数更少，内存使用效率更高。

​​​​​​​图9优化前数据流图

​​​​​​​图10优化后数据流图

 在优化前，对矩阵乘结果进行量化计算的过程如下：

  1. 将矩阵乘的结果从CO1搬运到workspace上；
  2. 再从workspace搬运到UB上；
  3. 将量化参数搬运到UB上，和矩阵乘的结果一起在UB上进行一系列量化计算；
  4. 将最终量化结果从UB搬运到GM上。

 相比于正确示例多增加了CO1->workspace、workspace->UB的搬运过程和量化的vector计算。

...
// 该样例仅做示例说明，非完整代码，省略了部分同步控制代码
public:
    __aicore__ inline KernelSample()
    {
        aSize = m * k;
        bSize = k * n;
        cSize = m * n;
    }
    __aicore__ inline void Init(__gm__ uint8_t *a, __gm__ uint8_t *b, __gm__ uint8_t *c, __gm__ uint8_t *deqTensor)
    {
        aGM.SetGlobalBuffer((__gm__ half *)a);
        bGM.SetGlobalBuffer((__gm__ half *)b);
        cGM.SetGlobalBuffer((__gm__ float *)c);
        deqGM.SetGlobalBuffer((__gm__ half *)deqTensor);
        pipe.InitBuffer(inQueueA1, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueA2, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueB1, 1, bSize * sizeof(half));
        pipe.InitBuffer(inQueueB2, 2, bSize * sizeof(half));
        pipe.InitBuffer(outQueueCO1, 1, cSize * sizeof(float));
        pipe.InitBuffer(inQueueSrc0, 1, cSize * sizeof(float));
        pipe.InitBuffer(inQueueTmp, 1, cSize * sizeof(half));
        pipe.InitBuffer(inQueueDeq, 1, cSize * sizeof(half));
        pipe.InitBuffer(outQueueDst, 1, cSize * sizeof(int8_t));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        SplitA();
        SplitB();
        Compute();
        CopyOut();
        CopyIn1();
        Compute1();
        CopyOut1();
    }
private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<half> a1Local = inQueueA1.AllocTensor<half>();
        LocalTensor<half> b1Local = inQueueB1.AllocTensor<half>();
        LocalTensor<half> deqLocal = inQueueDeq.AllocTensor<half>();
 
        Nd2NzParams dataCopyA1Params;
        dataCopyA1Params.ndNum = 1;
        dataCopyA1Params.nValue = m;
        dataCopyA1Params.dValue = k;
        dataCopyA1Params.srcNdMatrixStride = 0;
        dataCopyA1Params.srcDValue = k;
        dataCopyA1Params.dstNzC0Stride = m;
        dataCopyA1Params.dstNzNStride = 1;
        dataCopyA1Params.dstNzMatrixStride = 0;
        DataCopy(a1Local, aGM, dataCopyA1Params);
 
        Nd2NzParams dataCopyB1Params;
        dataCopyB1Params.ndNum = 1;
        dataCopyB1Params.nValue = k;
        dataCopyB1Params.dValue = n;
        dataCopyB1Params.srcNdMatrixStride = 0;
        dataCopyB1Params.srcDValue = n;
        dataCopyB1Params.dstNzC0Stride = k;
        dataCopyB1Params.dstNzNStride = 1;
        dataCopyB1Params.dstNzMatrixStride = 0;
        DataCopy(b1Local, bGM, dataCopyB1Params);
        // 将量化参数搬运到UB
        DataCopy(deqLocal, deqGM, cSize);
 
        inQueueA1.EnQue(a1Local);
        inQueueB1.EnQue(b1Local);
        inQueueDeq.EnQue(deqLocal);
    }
    __aicore__ inline void SplitA()
    {
        ...
    }
    __aicore__ inline void SplitB()
    {
        ...
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<half> a2Local = inQueueA2.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.DeQue<half>();
        LocalTensor<float> c1Local = outQueueCO1.AllocTensor<float>();
        MmadParams mmadParams;
        mmadParams.m = m;
        mmadParams.n = n;
        mmadParams.k = k;
        // 矩阵乘
        Mmad(c1Local, a2Local, b2Local, mmadParams); // m*n
        outQueueCO1.EnQue<float>(c1Local);
        inQueueA2.FreeTensor(a2Local);
        inQueueB2.FreeTensor(b2Local);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<float> c1Local = outQueueCO1.DeQue<float>();
        GM_ADDR usrWorkspace = AscendC::GetUserWorkspace(workspace);
        xGm.SetGlobalBuffer((__gm__ float *)(usrWorkspace));
        FixpipeParamsV220 fixpipeParams;
        fixpipeParams.nSize = n;
        fixpipeParams.mSize = m;
        fixpipeParams.srcStride = m;
        fixpipeParams.dstStride = n;
        fixpipeParams.ndNum = 1;
        fixpipeParams.srcNdStride = 0;
        fixpipeParams.dstNdStride = 0;
        // 将矩阵乘的计算结果从CO1搬运到workspace
        Fixpipe(xGm, c1Local, fixpipeParams);
        outQueueCO1.FreeTensor(c1Local);
    }
    __aicore__ inline void CopyIn1()
    {
        PipeBarrier<PIPE_ALL>();
        // 将矩阵乘的计算结果从workspace搬运到UB
        LocalTensor<float> src0Local = inQueueSrc0.AllocTensor<float>();
        DataCopy(src0Local, xGm, cSize);
        inQueueSrc0.EnQue(src0Local);
    }
    __aicore__ inline void Compute1()
    {
        LocalTensor<float> src0Local = inQueueSrc0.DeQue<float>();
        LocalTensor<half> tmpLocal = inQueueTmp.AllocTensor<half>();
        LocalTensor<half> deqLocal = inQueueDeq.DeQue<half>();
        LocalTensor<int8_t> dstLocal = outQueueDst.AllocTensor<int8_t>();
        // 量化计算
        Cast(tmpLocal, src0Local, RoundMode::CAST_NONE, cSize);
        LocalTensor<half> tmpHalfBuffer = src0Local.ReinterpretCast<half>();
        Mul(tmpHalfBuffer, tmpLocal, deqLocal, cSize);
        Cast(dstLocal, tmpHalfBuffer, RoundMode::CAST_NONE, cSize);
        outQueueDst.EnQue<int8_t>(dstLocal);
        inQueueSrc0.FreeTensor(src0Local);
        inQueueTmp.FreeTensor(tmpLocal);
        inQueueDeq.FreeTensor(deqLocal);
    }
    __aicore__ inline void CopyOut1()
    {
        ...
    }
private:
    TPipe pipe;
    TQue<QuePosition::A1, 1> inQueueA1;
    TQue<QuePosition::A2, 1> inQueueA2;
    TQue<QuePosition::B1, 1> inQueueB1;
    TQue<QuePosition::B2, 1> inQueueB2;
    TQue<QuePosition::CO1, 1> outQueueCO1;
    TQue<QuePosition::VECIN, 1> inQueueDeq;
    TQue<QuePosition::VECIN, 1> inQueueSrc0;
    TQue<QuePosition::VECCALC, 1> inQueueTmp;
    TQue<QuePosition::VECOUT, 1> outQueueDst;
 
    GlobalTensor<half> aGM;
    GlobalTensor<half> bGM;
    GlobalTensor<dst_T> cGM;
    GlobalTensor<float> biasGM;
    uint16_t m = 32, k = 32, n = 32;
    uint16_t aSize, bSize, cSize;
    ...

 经过优化，该算子对矩阵乘的结果进行量化计算时，可将量化参数搬运到FB(Fixpipe Buffer)上，调用一次Fixpipe接口实现矩阵乘结果的量化计算。

...
public:
    __aicore__ inline KernelSample()
    {
        aSize = m * k;
        bSize = k * n;
        cSize = m * n;
    }
    __aicore__ inline void Init(__gm__ uint8_t *a, __gm__ uint8_t *b, __gm__ uint8_t *c, __gm__ uint8_t *deqTensor)
    {
        aGM.SetGlobalBuffer((__gm__ half *)a);
        bGM.SetGlobalBuffer((__gm__ half *)b);
        cGM.SetGlobalBuffer((__gm__ float *)c);
        deqGM.SetGlobalBuffer((__gm__ uint64_t *)deqTensor);
        pipe.InitBuffer(inQueueA1, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueA2, 1, aSize * sizeof(half));
        pipe.InitBuffer(inQueueB1, 1, bSize * sizeof(half));
        pipe.InitBuffer(inQueueB2, 2, bSize * sizeof(half));
        pipe.InitBuffer(outQueueCO1, 1, cSize * sizeof(float));
        pipe.InitBuffer(inQueueDeq1, 1, cSize * sizeof(uint64_t));
        pipe.InitBuffer(inQueueDeq, 1, cSize * sizeof(uint64_t));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        SplitA();
        SplitB();
        SplitDeq();
        Compute();
        CopyOut();
    }
private:
    __aicore__ inline void CopyIn()
    {
        LocalTensor<half> a1Local = inQueueA1.AllocTensor<half>();
        LocalTensor<half> b1Local = inQueueB1.AllocTensor<half>();
        LocalTensor<uint64_t> deq1Local = inQueueDeq1.AllocTensor<uint64_t>();
 
        Nd2NzParams dataCopyA1Params;
        dataCopyA1Params.ndNum = 1;
        dataCopyA1Params.nValue = m;
        dataCopyA1Params.dValue = k;
        dataCopyA1Params.srcNdMatrixStride = 0;
        dataCopyA1Params.srcDValue = k;
        dataCopyA1Params.dstNzC0Stride = m;
        dataCopyA1Params.dstNzNStride = 1;
        dataCopyA1Params.dstNzMatrixStride = 0;
        DataCopy(a1Local, aGM, dataCopyA1Params);
 
        Nd2NzParams dataCopyB1Params;
        dataCopyB1Params.ndNum = 1;
        dataCopyB1Params.nValue = k;
        dataCopyB1Params.dValue = n;
        dataCopyB1Params.srcNdMatrixStride = 0;
        dataCopyB1Params.srcDValue = n;
        dataCopyB1Params.dstNzC0Stride = k;
        dataCopyB1Params.dstNzNStride = 1;
        dataCopyB1Params.dstNzMatrixStride = 0;
        DataCopy(b1Local, bGM, dataCopyB1Params);
        // 将量化参数搬运到L1上
        DataCopy(deq1Local, deqGM, cSize);
 
        inQueueA1.EnQue(a1Local);
        inQueueB1.EnQue(b1Local);
        inQueueDeq.EnQue(deq1Local);
    }
    __aicore__ inline void SplitA()
    {
        ...
    }
    __aicore__ inline void SplitB()
    {
        ...
    }
    __aicore__ inline void SplitDeq()
    {
        LocalTensor<uint64_t> deq1Local = inQueueDeq1.DeQue<uint64_t>();
        LocalTensor<uint64_t> deqLocal = inQueueDeq.AllocTensor<uint64_t>();
        // 将量化参数从L1->FB
        DataCopy(deqLocal, deq1Local, { 1, (uint16_t)(cSize * sizeof(uint64_t) / 128), 0, 0 });
        inQueueDeq.EnQue<uint61_t>(deqLocal);
        inQueueDeq1.FreeTensor(deq1Local);
    }
    __aicore__ inline void Compute()
    {
        LocalTensor<half> a2Local = inQueueA2.DeQue<half>();
        LocalTensor<half> b2Local = inQueueB2.DeQue<half>();
        LocalTensor<float> c1Local = outQueueCO1.AllocTensor<float>();
        MmadParams mmadParams;
        mmadParams.m = m;
        mmadParams.n = n;
        mmadParams.k = k;
        // 矩阵乘
        Mmad(c1Local, a2Local, b2Local, mmadParams); // m*n
        outQueueCO1.EnQue<float>(c1Local);
        inQueueA2.FreeTensor(a2Local);
        inQueueB2.FreeTensor(b2Local);
    }
    __aicore__ inline void CopyOut()
    {
        LocalTensor<float> c1Local = outQueueCO1.DeQue<float>();
        LocalTensor<uint64_t> deqLocal = inQueueDeq.DeQue<uint64_t>();
        SetFixpipeNz2ndFlag(1, 0, 0);
        DataCopyCO12DstParams dataCopyParams;
        dataCopyParams.nSize = n;
        dataCopyParams.mSize = m;
        dataCopyParams.srcStride = m;
        dataCopyParams.dstStride = n;
        dataCopyParams.quantPre = QuantMode_t::VQF322B8_PRE;
        dataCopyParams.nz2ndEn = true;
        // 将矩阵乘进行量化后的计算结果搬出
        DataCopy(cGM, c1Local, DataCopyCO12DstParams);
        outQueueCO1.FreeTensor(c1Local);
    }
 
private:
    TPipe pipe;
    TQue<QuePosition::A1, 1> inQueueA1;
    TQue<QuePosition::A2, 1> inQueueA2;
    TQue<QuePosition::B1, 1> inQueueB1;
    TQue<QuePosition::B2, 1> inQueueB2;
    TQue<QuePosition::C1, 1> inQueueDeq1;
    TQue<QuePosition::C2PIPE2GM, 1> inQueueDeq;
    TQue<QuePosition::CO1, 1> outQueueCO1;
    GlobalTensor<half> aGM;
    GlobalTensor<half> bGM;
    GlobalTensor<dst_T> cGM;
    GlobalTensor<uint64_t> deqTensorGM;
    uint16_t m = 32, k = 32, n = 32;
    uint16_t aSize, bSize, cSize;
    ...

更多学习资源

 了解更多Ascend C算子性能优化手段和实践案例，请访问：昇腾Ascend C-入门课程-学习资源-算子文档-昇腾社区

 

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