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Ascend C 自定义算子 Kernel Launch 调用入门

  • 2024-04-09
    广东
  • 本文字数:5096 字

    阅读完需:约 17 分钟

Ascend C 自定义算子 Kernel Launch调用入门

本文分享自华为云社区《Ascend C 自定义算子 Kernel Launch调用入门》,作者: jackwangcumt。

1 Kernel Launch 概述


根据官方说明文档的介绍,Ascend C 对外开放核函数的基础调用(Kernel Launch)方式,是为了简化 Ascend C 自定义算子的开发流程,提供更易用的调试调优功能。当开发者完成算子核函数的开发和 Tiling 实现后,即可通过 AscendCL 运行时接口,完成算子的调用并实现自己的推理应用;同时提供简易的 kernel 开发工程,开发者仅需提供 kernel 侧实现,基于工程框架可以快速实现 Kernel Launch。本文实验前提是完成了《Ascend C 自定义 PRelu 算子》博文的相关算子开发工程。网址为:https://bbs.huaweicloud.com/blogs/425244 。请注意:


  • 8.0.RC1.alpha002 当前版本,Kernel Launch 开放式编程为试用特性,不支持应用于商用产品中。

  • 8.0.RC1.alpha002 当前版本暂不支持获取用户 workspace 特性。

2 Kernel Launch 调用方式


ACLRT_LAUNCH_KERNEL 调用方式对内核调用符方式进行了功能加强,核函数的调用是异步的,调用接口的使用方法如下:


ACLRT_LAUNCH_KERNEL(kernel_name)(blockDim, stream, argument list);
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  • kernel_name:算子核函数的名称。

  • blockDim:规定了核函数将会在几个核上执行。每个执行该核函数的核会被分配一个逻辑 ID,即 block_idx,可以在核函数的实现中调用GetBlockIdx来获取 block_idx。

  • stream,类型为 aclrtStream,stream 用于维护一些异步操作的执行顺序,确保按照应用程序中的代码调用顺序在 Device 上执行。

  • argument list:参数列表,与核函数的参数列表保持一致。


为帮助开发者快速的完成算子的 Kernel Launch 调试,官方提供了简易的算子工程,我们可以基于该算子工程中的样例代码和工程框架进行算子开发。算子工程支持的如下:


  • 该工程支持调试功能,如PRINTF功能、DumpTensor

  • 工程编译生成的应用程序,可通过 msprof 命令行方式采集和解析性能数据。


可以参考工程样例:https://gitee.com/ascend/samples/blob/master/operator/AddCustomSample/KernelLaunch/AddKernelInvocationTilingNeo ,其目录结构如下所示:


AddKernelInvocationNeo|-- cmake                                                 // CMake编译文件|-- scripts|  ├── gen_data.py                                     // 输入数据和真值数据生成脚本文件|  ├── verify_result.py                                // 验证输出数据和真值数据是否一致的验证脚本|-- CMakeLists.txt                                        // CMake编译配置文件|-- add_custom.cpp                                     // 矢量算子kernel实现|-- data_utils.h                                          // 数据读入写出函数|-- main.cpp                                              // 主函数,调用算子的应用程序,含CPU域及NPU域调用|-- run.sh                                                // 编译运行算子的脚本
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基于该算子工程,开发者进行算子开发的步骤如下:


  • 完成算子 kernel 侧实现。

  • 编写算子调用应用程序 main.cpp。

  • 编写 CMake 编译配置文件 CMakeLists.txt。

  • 根据实际需要修改输入数据和真值数据生成脚本文件 gen_data.py 和验证输出数据和真值数据是否一致的验证脚本 verify_result.py。

  • 根据实际需要修改编译运行算子的脚本 run.sh 并执行该脚本,完成算子的编译运行和结果验证。

3 Kernel Launch 实现


在 PReluSample 目录下新建一个目录 KernelLaunch,用于存放 Kernel Launch 调用方式的工程代码,我这里参考官方的https://gitee.com/ascend/samples/tree/master/operator/LeakyReluCustomSample/KernelLaunch/

LeakyReluKernelInvocation 样例工程,并修改了相关参数,p_relu_custom.cpp 代码如下所示:


#include "kernel_operator.h"using namespace AscendC;
constexpr int32_t BUFFER_NUM = 2; constexpr int32_t TOTAL_LENGTH = 8 * 200 * 1024; constexpr int32_t TILE_NUM = 32; constexpr float alpha = 0.002;
class KernelPRelu {public: __aicore__ inline KernelPRelu() {} __aicore__ inline void Init(GM_ADDR x, GM_ADDR y, uint32_t totalLength, uint32_t tileNum, float alpha) { PRINTF("[npu debug] >>> GetBlockNum() %d", GetBlockNum()); ASSERT(GetBlockNum() != 0 && "block dim can not be zero!"); this->blockLength = totalLength / GetBlockNum(); this->tileNum = tileNum; this->alpha = static_cast<float>(alpha); ASSERT(tileNum != 0 && "tile num can not be zero!"); this->tileLength = this->blockLength / tileNum / BUFFER_NUM;
// get start index for current core, core parallel xGm.SetGlobalBuffer((__gm__ float*)x + this->blockLength * GetBlockIdx(), this->blockLength); yGm.SetGlobalBuffer((__gm__ float*)y + this->blockLength * GetBlockIdx(), this->blockLength); // pipe alloc memory to queue, the unit is Bytes pipe.InitBuffer(inQueueX, BUFFER_NUM, this->tileLength * sizeof(float)); pipe.InitBuffer(outQueueY, BUFFER_NUM, this->tileLength * sizeof(float)); pipe.InitBuffer(tmpBuffer1, this->tileLength * sizeof(float)); //pipe.InitBuffer(tmpBuffer2, this->tileLength * sizeof(float)); } __aicore__ inline void Process() { // loop count need to be doubled, due to double buffer int32_t loopCount = this->tileNum * BUFFER_NUM; // tiling strategy, pipeline parallel for (int32_t i = 0; i < loopCount; i++) { CopyIn(i); Compute(i); CopyOut(i); } }
private: __aicore__ inline void CopyIn(int32_t progress) { // alloc tensor from queue memory LocalTensor<float> xLocal = inQueueX.AllocTensor<float>(); // copy progress_th tile from global tensor to local tensor DataCopy(xLocal, xGm[progress * this->tileLength], this->tileLength); // enque input tensors to VECIN queue inQueueX.EnQue(xLocal); } __aicore__ inline void Compute(int32_t progress) { // deque input tensors from VECIN queue LocalTensor<float> xLocal = inQueueX.DeQue<float>(); LocalTensor<float> yLocal = outQueueY.AllocTensor<float>(); LocalTensor<float> tmpTensor1 = tmpBuffer1.Get<float>(); float inputVal = 0.0; Maxs(tmpTensor1, xLocal, inputVal, this->tileLength); // x >= 0 --> x // x < 0 Mins(xLocal, xLocal, inputVal, this->tileLength); Muls(xLocal, xLocal, this->alpha, this->tileLength); Add(yLocal, xLocal, tmpTensor1, this->tileLength); outQueueY.EnQue<float>(yLocal); // free input tensors for reuse inQueueX.FreeTensor(xLocal); } __aicore__ inline void CopyOut(int32_t progress) { // deque output tensor from VECOUT queue LocalTensor<float> yLocal = outQueueY.DeQue<float>(); // copy progress_th tile from local tensor to global tensor DataCopy(yGm[progress * this->tileLength], yLocal, this->tileLength); // free output tensor for reuse outQueueY.FreeTensor(yLocal); }
private: TPipe pipe; TBuf<QuePosition::VECCALC> tmpBuffer1; //TBuf<QuePosition::VECCALC> tmpBuffer1, tmpBuffer2; // create queues for input, in this case depth is equal to buffer num TQue<QuePosition::VECIN, BUFFER_NUM> inQueueX; // create queue for output, in this case depth is equal to buffer num TQue<QuePosition::VECOUT, BUFFER_NUM> outQueueY; GlobalTensor<float> xGm, yGm; uint32_t blockLength; uint32_t tileNum; uint32_t tileLength; float alpha;};extern "C" __global__ __aicore__ void p_relu_custom(GM_ADDR x, GM_ADDR y) { //GET_TILING_DATA(tiling_data, tiling); // TODO: user kernel impl KernelPRelu op; op.Init(x, y, TOTAL_LENGTH, TILE_NUM, alpha); op.Process();}
#ifndef __CCE_KT_TEST__// call of kernel functionvoid p_relu_custom_do(uint32_t blockDim, void* l2ctrl, void* stream, uint8_t* x, uint8_t* y){ p_relu_custom<<<blockDim, l2ctrl, stream>>>(x, y);}#endif
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main.cpp 代码如下所示 :


/* * Copyright (c) Huawei Technologies Co., Ltd. 2022-2023. All rights reserved. * This file constains code of cpu debug and npu code.We read data from bin file * and write result to file. */#include "data_utils.h"#ifndef __CCE_KT_TEST__#include "acl/acl.h"extern void p_relu_custom_do(uint32_t coreDim, void* l2ctrl, void* stream, uint8_t* x, uint8_t* y);#else#include "tikicpulib.h"extern "C" __global__ __aicore__ void p_relu_custom(GM_ADDR x, GM_ADDR y);#endif
int32_t main(int32_t argc, char* argv[]){ uint32_t blockDim = 8; size_t inputByteSize = 8 * 200 * 1024 * sizeof(float); size_t outputByteSize = 8 * 200 * 1024 * sizeof(float);
#ifdef __CCE_KT_TEST__ // CPU uint8_t* x = (uint8_t*)AscendC::GmAlloc(inputByteSize); uint8_t* y = (uint8_t*)AscendC::GmAlloc(outputByteSize); printf("[cpu debug]>>> inputByteSize: %d\n", inputByteSize);
ReadFile("./input/input_x.bin", inputByteSize, x, inputByteSize); AscendC::SetKernelMode(KernelMode::AIV_MODE); ICPU_RUN_KF(p_relu_custom, blockDim, x, y); // use this macro for cpu debug WriteFile("./output/output_y.bin", y, outputByteSize); AscendC::GmFree((void *)x); AscendC::GmFree((void *)y); #else // NPU //CHECK_ACL(aclInit(nullptr)); CHECK_ACL(aclInit("./acl.json")); aclrtContext context; int32_t deviceId = 0; CHECK_ACL(aclrtSetDevice(deviceId)); CHECK_ACL(aclrtCreateContext(&context, deviceId)); aclrtStream stream = nullptr; CHECK_ACL(aclrtCreateStream(&stream));
uint8_t *xHost, *yHost; uint8_t *xDevice, *yDevice; CHECK_ACL(aclrtMallocHost((void**)(&xHost), inputByteSize)); CHECK_ACL(aclrtMallocHost((void**)(&yHost), outputByteSize)); CHECK_ACL(aclrtMalloc((void**)&xDevice, inputByteSize, ACL_MEM_MALLOC_HUGE_FIRST)); CHECK_ACL(aclrtMalloc((void**)&yDevice, outputByteSize, ACL_MEM_MALLOC_HUGE_FIRST));
ReadFile("./input/input_x.bin", inputByteSize, xHost, inputByteSize); CHECK_ACL(aclrtMemcpy(xDevice, inputByteSize, xHost, inputByteSize, ACL_MEMCPY_HOST_TO_DEVICE));
p_relu_custom_do(blockDim, nullptr, stream, xDevice, yDevice); CHECK_ACL(aclrtSynchronizeStream(stream));
CHECK_ACL(aclrtMemcpy(yHost, outputByteSize, yDevice, outputByteSize, ACL_MEMCPY_DEVICE_TO_HOST)); WriteFile("./output/output_y.bin", yHost, outputByteSize);
CHECK_ACL(aclrtFree(xDevice)); CHECK_ACL(aclrtFree(yDevice)); CHECK_ACL(aclrtFreeHost(xHost)); CHECK_ACL(aclrtFreeHost(yHost));
CHECK_ACL(aclrtDestroyStream(stream)); CHECK_ACL(aclrtDestroyContext(context)); CHECK_ACL(aclrtResetDevice(deviceId)); CHECK_ACL(aclFinalize());#endif return 0;}
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执行如下代码进行 NPU 上板调试和 CPU 调试:


#npubash run.sh Ascend310P1 npu_onboard# cpubash run.sh Ascend310P1 cpu
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