本次主要讨论下AscendC算子的开发流程，基于Kernel直调工程的算子开发。

1 AscendC算子开发的基本流程

使用Ascend C完成Add算子核函数开发；
使用ICPU_RUN_KF CPU调测宏完成算子核函数CPU侧运行验证；
使用<<<>>>内核调用符完成算子核函数NPU侧运行验证。
在正式的开发之前，还需要先完成环境准备和算子分析工作，开发Ascend C算子的基本流程如下图所示：

2 核函数开发

本次以add_custom.cpp作为参考用例。Gitee也有对应工程和完整代码。
operator/AddCustomSample/KernelLaunch/AddKernelInvocationNeo · Ascend/samples - 码云 - 开源中国 (gitee.com)

2.1 核函数定义

首先要根据核函数定义 核函数-编程模型-Ascend C算子开发-算子开发-开发指南-CANN社区版8.0.RC3.alpha003开发文档-昇腾社区 (hiascend.com) 的规则进行核函数的定义，并在核函数中调用算子类的Init和Process函数。

// 给CPU调用
extern "C" __global__ __aicore__ void add_custom(GM_ADDR x, GM_ADDR y, GM_ADDR z)
{
    KernelAdd op;
    op.Init(x, y, z);
    op.Process();
}


// 给NPU调用
#ifndef ASCENDC_CPU_DEBUG
void add_custom_do(uint32_t blockDim, void *stream, uint8_t *x, uint8_t *y, uint8_t *z)
{
    add_custom<<<blockDim, nullptr, stream>>>(x, y, z);
}
#endif

2.2 算子类定义

根据矢量编程范式实现算子类，本样例中定义KernelAdd算子类，其具体成员如下：

class KernelAdd {
public:
    __aicore__ inline KernelAdd(){}
    // 初始化函数，完成内存初始化相关操作
    __aicore__ inline void Init(GM_ADDR x, GM_ADDR y, GM_ADDR z){}
    // 核心处理函数，实现算子逻辑，调用私有成员函数CopyIn、Compute、CopyOut完成矢量算子的三级流水操作
    __aicore__ inline void Process(){}

private:
    // 搬入函数，完成CopyIn阶段的处理，被核心Process函数调用
    __aicore__ inline void CopyIn(int32_t progress){}
    // 计算函数，完成Compute阶段的处理，被核心Process函数调用
    __aicore__ inline void Compute(int32_t progress){}
    // 搬出函数，完成CopyOut阶段的处理，被核心Process函数调用
    __aicore__ inline void CopyOut(int32_t progress){}

private:
    AscendC::TPipe pipe;  //Pipe内存管理对象
    AscendC::TQue<AscendC::QuePosition::VECIN, BUFFER_NUM> inQueueX, inQueueY;  //输入数据Queue队列管理对象，QuePosition为VECIN
    AscendC::TQue<AscendC::QuePosition::VECOUT, BUFFER_NUM> outQueueZ;  //输出数据Queue队列管理对象，QuePosition为VECOUT
    AscendC::GlobalTensor<half> xGm;  //管理输入输出Global Memory内存地址的对象，其中xGm, yGm为输入，zGm为输出
    AscendC::GlobalTensor<half> yGm;
    AscendC::GlobalTensor<half> zGm;
};

核函数调用关系图

2.3 实现Init，CopyIn，Compute，CopyOut这个4个关键函数

Init函数初始化输入资源

__aicore__ inline void Init(GM_ADDR x, GM_ADDR y, GM_ADDR z)
    {
        xGm.SetGlobalBuffer((__gm__ half *)x + BLOCK_LENGTH * AscendC::GetBlockIdx(), BLOCK_LENGTH);
        yGm.SetGlobalBuffer((__gm__ half *)y + BLOCK_LENGTH * AscendC::GetBlockIdx(), BLOCK_LENGTH);
        zGm.SetGlobalBuffer((__gm__ half *)z + BLOCK_LENGTH * AscendC::GetBlockIdx(), BLOCK_LENGTH);
        pipe.InitBuffer(inQueueX, BUFFER_NUM, TILE_LENGTH * sizeof(half));
        pipe.InitBuffer(inQueueY, BUFFER_NUM, TILE_LENGTH * sizeof(half));
        pipe.InitBuffer(outQueueZ, BUFFER_NUM, TILE_LENGTH * sizeof(half));
    }
Process函数中通过如下方式调用这三个：
    __aicore__ inline void Process()
    {
        // loop count need to be doubled, due to double buffer
        constexpr int32_t loopCount = TILE_NUM * BUFFER_NUM;
        // tiling strategy, pipeline parallel
        for (int32_t i = 0; i < loopCount; i++) {
            CopyIn(i);
            Compute(i);
            CopyOut(i);
        }
    }

CopyIn函数中通过如下方式调用这三个：
1、使用DataCopy接口将GlobalTensor数据拷贝到LocalTensor。
2、使用EnQue将LocalTensor放入VecIn的Queue中。

__aicore__ inline void CopyIn(int32_t progress)
    {
        // alloc tensor from queue memory
        AscendC::LocalTensor<half> xLocal = inQueueX.AllocTensor<half>();
        AscendC::LocalTensor<half> yLocal = inQueueY.AllocTensor<half>();
        // copy progress_th tile from global tensor to local tensor
        AscendC::DataCopy(xLocal, xGm[progress * TILE_LENGTH], TILE_LENGTH);
        AscendC::DataCopy(yLocal, yGm[progress * TILE_LENGTH], TILE_LENGTH);
        // enque input tensors to VECIN queue
        inQueueX.EnQue(xLocal);
        inQueueY.EnQue(yLocal);
    }

Compute函数实现。
1、使用DeQue从VecIn中取出LocalTensor。
2、使用Ascend C接口Add完成矢量计算。
3、使用EnQue将计算结果LocalTensor放入到VecOut的Queue中。
4、使用FreeTensor将释放不再使用的LocalTensor。

__aicore__ inline void Compute(int32_t progress)
{
    // deque input tensors from VECIN queue
    AscendC::LocalTensor<half> xLocal = inQueueX.DeQue<half>();
    AscendC::LocalTensor<half> yLocal = inQueueY.DeQue<half>();
    AscendC::LocalTensor<half> zLocal = outQueueZ.AllocTensor<half>();
    // call Add instr for computation
    AscendC::Add(zLocal, xLocal, yLocal, TILE_LENGTH);
    // enque the output tensor to VECOUT queue
    outQueueZ.EnQue<half>(zLocal);
    // free input tensors for reuse
    inQueueX.FreeTensor(xLocal);
    inQueueY.FreeTensor(yLocal);
}

CopyOut函数实现。
1、使用DeQue接口从VecOut的Queue中取出LocalTensor。
2、使用DataCopy接口将LocalTensor拷贝到GlobalTensor上。
3、使用FreeTensor将不再使用的LocalTensor进行回收。

 __aicore__ inline void CopyOut(int32_t progress)
{
    // deque output tensor from VECOUT queue
    AscendC::LocalTensor<half> zLocal = outQueueZ.DeQue<half>();
    // copy progress_th tile from local tensor to global tensor
    AscendC::DataCopy(zGm[progress * TILE_LENGTH], zLocal, TILE_LENGTH);
    // free output tensor for reuse
    outQueueZ.FreeTensor(zLocal);
}

3 核函数的运行验证

异构计算架构中，NPU（kernel侧）与CPU（host侧）是协同工作的，完成了kernel侧核函数开发后，即可编写host侧的核函数调用程序，实现从host侧的APP程序调用算子，执行计算过程。

3.1 编写CPU侧调用程序

 // 使用GmAlloc分配共享内存，并进行数据初始化
    uint8_t* x = (uint8_t*)AscendC::GmAlloc(inputByteSize);
    uint8_t* y = (uint8_t*)AscendC::GmAlloc(inputByteSize);
    uint8_t* z = (uint8_t*)AscendC::GmAlloc(outputByteSize);

    ReadFile("./input/input_x.bin", inputByteSize, x, inputByteSize);
    ReadFile("./input/input_y.bin", inputByteSize, y, inputByteSize);
    // 调用ICPU_RUN_KF调测宏，完成核函数CPU侧的调用
    AscendC::SetKernelMode(KernelMode::AIV_MODE);
    ICPU_RUN_KF(add_custom, blockDim, x, y, z); // use this macro for cpu debug
    // 输出数据写出
    WriteFile("./output/output_z.bin", z, outputByteSize);
    // 调用GmFree释放申请的资源
    AscendC::GmFree((void *)x);
    AscendC::GmFree((void *)y);
    AscendC::GmFree((void *)z);

3.2 编写NPU侧运行算子的调用程序

  // AscendCL初始化
    CHECK_ACL(aclInit(nullptr));
    // 运行管理资源申请
    int32_t deviceId = 0;
    CHECK_ACL(aclrtSetDevice(deviceId));
    aclrtStream stream = nullptr;
    CHECK_ACL(aclrtCreateStream(&stream));
    // 分配Host内存
    uint8_t *xHost, *yHost, *zHost;
    uint8_t *xDevice, *yDevice, *zDevice;
    CHECK_ACL(aclrtMallocHost((void**)(&xHost), inputByteSize));
    CHECK_ACL(aclrtMallocHost((void**)(&yHost), inputByteSize));
    CHECK_ACL(aclrtMallocHost((void**)(&zHost), outputByteSize));
    // 分配Device内存
    CHECK_ACL(aclrtMalloc((void**)&xDevice, inputByteSize, ACL_MEM_MALLOC_HUGE_FIRST));
    CHECK_ACL(aclrtMalloc((void**)&yDevice, inputByteSize, ACL_MEM_MALLOC_HUGE_FIRST));
    CHECK_ACL(aclrtMalloc((void**)&zDevice, outputByteSize, ACL_MEM_MALLOC_HUGE_FIRST));
    // Host内存初始化
    ReadFile("./input/input_x.bin", inputByteSize, xHost, inputByteSize);
    ReadFile("./input/input_y.bin", inputByteSize, yHost, inputByteSize);
    CHECK_ACL(aclrtMemcpy(xDevice, inputByteSize, xHost, inputByteSize, ACL_MEMCPY_HOST_TO_DEVICE));
    CHECK_ACL(aclrtMemcpy(yDevice, inputByteSize, yHost, inputByteSize, ACL_MEMCPY_HOST_TO_DEVICE));
    // 用内核调用符<<<>>>调用核函数完成指定的运算,add_custom_do中封装了<<<>>>调用
    add_custom_do(blockDim, nullptr, stream, xDevice, yDevice, zDevice);
    CHECK_ACL(aclrtSynchronizeStream(stream));
    // 将Device上的运算结果拷贝回Host
    CHECK_ACL(aclrtMemcpy(zHost, outputByteSize, zDevice, outputByteSize, ACL_MEMCPY_DEVICE_TO_HOST));
    WriteFile("./output/output_z.bin", zHost, outputByteSize);
    // 释放申请的资源
    CHECK_ACL(aclrtFree(xDevice));
    CHECK_ACL(aclrtFree(yDevice));
    CHECK_ACL(aclrtFree(zDevice));
    CHECK_ACL(aclrtFreeHost(xHost));
    CHECK_ACL(aclrtFreeHost(yHost));
    CHECK_ACL(aclrtFreeHost(zHost));
    // AscendCL去初始化
    CHECK_ACL(aclrtDestroyStream(stream));
    CHECK_ACL(aclrtResetDevice(deviceId));
    CHECK_ACL(aclFinalize());

3.3 完整main.cpp

/**
 * @file main.cpp
 *
 * Copyright (C) 2024. Huawei Technologies Co., Ltd. All rights reserved.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 */
#include "data_utils.h"
#ifndef ASCENDC_CPU_DEBUG
#include "acl/acl.h"
extern void add_custom_do(uint32_t blockDim, void *stream, uint8_t *x, uint8_t *y, uint8_t *z);
#else
#include "tikicpulib.h"
extern "C" __global__ __aicore__ void add_custom(GM_ADDR x, GM_ADDR y, GM_ADDR z);
#endif

int32_t main(int32_t argc, char *argv[])
{
    uint32_t blockDim = 8;
    size_t inputByteSize = 8 * 2048 * sizeof(uint16_t);
    size_t outputByteSize = 8 * 2048 * sizeof(uint16_t);

#ifdef ASCENDC_CPU_DEBUG
    uint8_t *x = (uint8_t *)AscendC::GmAlloc(inputByteSize);
    uint8_t *y = (uint8_t *)AscendC::GmAlloc(inputByteSize);
    uint8_t *z = (uint8_t *)AscendC::GmAlloc(outputByteSize);

    ReadFile("./input/input_x.bin", inputByteSize, x, inputByteSize);
    ReadFile("./input/input_y.bin", inputByteSize, y, inputByteSize);

    AscendC::SetKernelMode(KernelMode::AIV_MODE);
    ICPU_RUN_KF(add_custom, blockDim, x, y, z); // use this macro for cpu debug

    WriteFile("./output/output_z.bin", z, outputByteSize);

    AscendC::GmFree((void *)x);
    AscendC::GmFree((void *)y);
    AscendC::GmFree((void *)z);
#else
    CHECK_ACL(aclInit(nullptr));
    int32_t deviceId = 0;
    CHECK_ACL(aclrtSetDevice(deviceId));
    aclrtStream stream = nullptr;
    CHECK_ACL(aclrtCreateStream(&stream));

    uint8_t *xHost, *yHost, *zHost;
    uint8_t *xDevice, *yDevice, *zDevice;

    CHECK_ACL(aclrtMallocHost((void **)(&xHost), inputByteSize));
    CHECK_ACL(aclrtMallocHost((void **)(&yHost), inputByteSize));
    CHECK_ACL(aclrtMallocHost((void **)(&zHost), outputByteSize));
    CHECK_ACL(aclrtMalloc((void **)&xDevice, inputByteSize, ACL_MEM_MALLOC_HUGE_FIRST));
    CHECK_ACL(aclrtMalloc((void **)&yDevice, inputByteSize, ACL_MEM_MALLOC_HUGE_FIRST));
    CHECK_ACL(aclrtMalloc((void **)&zDevice, outputByteSize, ACL_MEM_MALLOC_HUGE_FIRST));

    ReadFile("./input/input_x.bin", inputByteSize, xHost, inputByteSize);
    ReadFile("./input/input_y.bin", inputByteSize, yHost, inputByteSize);

    CHECK_ACL(aclrtMemcpy(xDevice, inputByteSize, xHost, inputByteSize, ACL_MEMCPY_HOST_TO_DEVICE));
    CHECK_ACL(aclrtMemcpy(yDevice, inputByteSize, yHost, inputByteSize, ACL_MEMCPY_HOST_TO_DEVICE));

    add_custom_do(blockDim, stream, xDevice, yDevice, zDevice);
    CHECK_ACL(aclrtSynchronizeStream(stream));

    CHECK_ACL(aclrtMemcpy(zHost, outputByteSize, zDevice, outputByteSize, ACL_MEMCPY_DEVICE_TO_HOST));
    WriteFile("./output/output_z.bin", zHost, outputByteSize);

    CHECK_ACL(aclrtFree(xDevice));
    CHECK_ACL(aclrtFree(yDevice));
    CHECK_ACL(aclrtFree(zDevice));
    CHECK_ACL(aclrtFreeHost(xHost));
    CHECK_ACL(aclrtFreeHost(yHost));
    CHECK_ACL(aclrtFreeHost(zHost));

    CHECK_ACL(aclrtDestroyStream(stream));
    CHECK_ACL(aclrtResetDevice(deviceId));
    CHECK_ACL(aclFinalize());
#endif
    return 0;
}

整体运行起来，请参考operator/AddCustomSample/KernelLaunch/AddKernelInvocationNeo · Ascend/samples - 码云 - 开源中国 (gitee.com)