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Host侧代码与说明

考虑以下算子计算场景:A矩阵大小为32(M) * 16(K), B矩阵大小为16(K) * 32(N),将A矩阵分在两个核上做矩阵乘运算,每个核上的A的切分大小为16 * 16,用同样的B矩阵运算。

整体流程与CUDA类似,首先在Host侧给参数创建空间并赋初始值,其次在Device侧创建参数指针并分配空间,再将初始值拷贝到Device中就可以拉起Device侧的核函数,Device侧代码与说明包含了函数的具体实现。

Device执行结束后可以将结果从Device侧拷贝到Host侧并与CPU执行结果相对比,最后释放空间与数据流即可。
// host侧文件:main.cce
// 直接通过<<<>>>异构调用语法调用device侧kernel
#include "acl/acl.h"
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#define M 32
#define K 16
#define N 32
#define BLOCKDIM 2
extern "C" __global__ [aicore] void mat_mul_kernel(__gm__ float* __restrict__ tensor_a, __gm__ float* __restrict__ tensor_b, __gm__ float* __restrict__ tensor_c_gm);
int main()
{
    aclrtStream stream;
    uint64_t i, j, k;
    void * input_a = NULL;
    void * input_b = NULL;
    void * output_c  = NULL;
    aclrtSetDevice(0);
    // 创建数据流
    aclrtCreateStream(&stream);
    // 参数初始化
    float a_data[M][K] = {0};
    float b_data[K][N] = {0};
    for (i = 0; i < M; i++) {
        for (k = 0; k < K; k++) {
            a_data[i][k] = 1;
        }
    }
    for (j = 0; j < N; j++) {
        for (k = 0; k < K; k++) {
            b_data[k][j] = 2;
        }
    }
    float c_data[M][N] = {0};
    // 在Device上分配参数空间
    aclrtMalloc((void **)&input_a , M*K*sizeof(float), ACL_MEM_MALLOC_NORMAL_ONLY);
    aclrtMalloc((void **)&input_b , K*N*sizeof(float), ACL_MEM_MALLOC_NORMAL_ONLY);
    aclrtMalloc((void **)&output_c , M*N*sizeof(float), ACL_MEM_MALLOC_NORMAL_ONLY);
    // 将Host侧数据拷贝到Device侧
    aclrtMemcpyAsync((void *)input_a, sizeof(a_data), a_data, sizeof(a_data), ACL_MEMCPY_HOST_TO_DEVICE, stream);
    aclrtMemcpyAsync((void *)input_b, sizeof(b_data), b_data, sizeof(b_data), ACL_MEMCPY_HOST_TO_DEVICE, stream);
    aclrtMemcpyAsync((void *)output_c, sizeof(c_data), c_data, sizeof(c_data), ACL_MEMCPY_HOST_TO_DEVICE, stream);
    // 启动Device侧核函数
    mat_mul_kernel<<<BLOCKDIM, nullptr, stream>>>((float*)input_a, (float*)input_b, (float*)output_c);
    // 获取Device执行结果,并拷贝到Host
    float *hostMemOut;
    aclrtMallocHost((void**)&hostMemOut, M*N);
    aclrtMemcpyAsync(hostMemOut, M*N*sizeof(float), output_c, M*N*sizeof(float), ACL_MEMCPY_DEVICE_TO_HOST, stream);
    aclrtSynchronizeStream(stream);

    //计算golden输出
    float golden[M][N] = {0};
    for (i = 0; i < M; i++) {
        for (j = 0; j < N; j++) {
            for (k = 0; k < K; k++) {
                golden[i][j] += a_data[i][k] * b_data[k][j];
            }
        }
    }
    // 对比结果
    for ( i = 0; i < M; i++) {
        for (j = 0; j < N; j++) {
            printf("i%ld\t Expect: %f\t\t\t\tResult: %f\n", 
                i*N + j,  golden[i][j], *((float *)hostMemOut + i*N + j));
        }
    }
    // 释放数据空间以及数据流
    aclrtFreeHost(hostMemOut);
    aclrtDestroyStream(stream);
    aclrtResetDevice(0);
}