C++中如何使用CUDA自己实现常用的深度学习激活函数？

本文首发于个人博客https://kezunlin.me/post/ee123cac/，欢迎阅读最新内容！

how to implement deep learning activation kernels with cuda in c++

Guide

• Part 1:cpp cuda programming tutorial
• Part 2: cuda activation kernels
• Part 3: cublasSgemm for large matrix multiplication on gpu

cuda utils

cuda.h

#ifndef __CUDA_H_
#define __CUDA_H_
#include "cuda_runtime.h"
#include "curand.h"
#include "cublas_v2.h"

#define BLOCK 512

void check_error(cudaError_t status);

dim3 cuda_gridsize(size_t n);

float* cuda_make_array(float* x,size_t n);

void cuda_free(float* x_gpu);

void cuda_push_array(float *x_gpu,float* x,size_t n);

void cuda_pull_array(float *x_gpu,float* x,size_t n);


#endif

cuda.cpp

#include "cuda.h"
#include "blas.h"

#include 
#include 
#include 
#include 

void error(const char* s)
{
    perror(s);
    assert(0);
    exit(-1);
}

void check_error(cudaError_t status)
{
    //cudaDeviceSynchronize();
    cudaError_t status2 = cudaGetLastError();
    if (status != cudaSuccess)
    {   
        const char *s = cudaGetErrorString(status);
        char buffer[256];
        printf("CUDA Error: %s\n", s);
        assert(0);
        snprintf(buffer, 256, "CUDA Error: %s", s);
        error(buffer);
    } 
    if (status2 != cudaSuccess)
    {   
        const char *s = cudaGetErrorString(status);
        char buffer[256];
        printf("CUDA Error Prev: %s\n", s);
        assert(0);
        snprintf(buffer, 256, "CUDA Error Prev: %s", s);
        error(buffer);
    } 
}

dim3 cuda_gridsize(size_t n){
    size_t k = (n-1) / BLOCK + 1;
    size_t x = k;
    size_t y = 1;
    if(x > 65535){
        x = ceil(sqrt(k));
        y = (n-1)/(x*BLOCK) + 1;
    }
    dim3 d = {x, y, 1};
    //printf("%ld %ld %ld %ld\n", n, x, y, x*y*BLOCK);
    return d;
}

float* cuda_make_array(float* x,size_t n)
{
    float *x_gpu;
    size_t size = sizeof(float)*n;
    cudaError_t status = cudaMalloc((void **)&x_gpu, size);
    check_error(status);
    if(x){
        status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice);
        check_error(status);
    } else {
        fill_gpu(n, 0, x_gpu, 1);
    }
    if(!x_gpu) error("Cuda malloc failed\n");
    return x_gpu;
}

void cuda_free(float* x_gpu)
{
    cudaError_t status = cudaFree(x_gpu);
    check_error(status);
}

void cuda_push_array(float *x_gpu,float* x,size_t n)
{
    size_t size = sizeof(float)*n;
    cudaError_t status = cudaMemcpy(x_gpu,x,size,cudaMemcpyHostToDevice);
    check_error(status);
}

void cuda_pull_array(float *x_gpu,float* x,size_t n)
{
    size_t size = sizeof(float)*n;
    cudaError_t status = cudaMemcpy(x,x_gpu,size,cudaMemcpyDeviceToHost);
    check_error(status);
}

 style="display:block; text-align:center;"
 data-ad-layout="in-article"
 data-ad-format="fluid"
 data-ad-client="ca-pub-5653382914441020"
 data-ad-slot="7925631830">

activation kernels

activations.h

#ifndef __ACTIVATIONS_H_
#define __ACTIVATIONS_H_

typedef enum{
    LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, \
    LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN
} ACTIVATION;

void activate_array_gpu(float* x,int n,ACTIVATION a);

#endif

activation_kernels.cu

#include "activations.h"
#include "cuda.h"
#include "blas.h"

__device__ float lhtan_activate_kernel(float x)
{
    if(x < 0) return .001f*x;
    if(x > 1) return .001f*(x-1.f) + 1.f;
    return x;
}

__device__ float hardtan_activate_kernel(float x)
{
    if (x < -1) return -1;
    if (x > 1) return 1;
    return x;
}

__device__ float linear_activate_kernel(float x){return x;}
__device__ float logistic_activate_kernel(float x){return 1.f/(1.f + expf(-x));}
__device__ float loggy_activate_kernel(float x){return 2.f/(1.f + expf(-x)) - 1;}
__device__ float relu_activate_kernel(float x){return x*(x>0);}
__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);}
__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;}
__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1f*x;}
__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1f*x;}
__device__ float tanh_activate_kernel(float x){return (2.f/(1 + expf(-2*x)) - 1);}
__device__ float plse_activate_kernel(float x)
{
    if(x < -4) return .01f * (x + 4);
    if(x > 4)  return .01f * (x - 4) + 1;
    return .125f*x + .5f;
}
__device__ float stair_activate_kernel(float x)
{
    int n = floorf(x);
    if (n%2 == 0) return floorf(x/2);
    else return (x - n) + floorf(x/2);
}

__device__ float activate_kernel(float x, ACTIVATION a)
{
    switch(a){
        case LINEAR:
            return linear_activate_kernel(x);
        case LOGISTIC:
            return logistic_activate_kernel(x);
        case LOGGY:
            return loggy_activate_kernel(x);
        case RELU:
            return relu_activate_kernel(x);
        case ELU:
            return elu_activate_kernel(x);
        case RELIE:
            return relie_activate_kernel(x);
        case RAMP:
            return ramp_activate_kernel(x);
        case LEAKY:
            return leaky_activate_kernel(x);
        case TANH:
            return tanh_activate_kernel(x);
        case PLSE:
            return plse_activate_kernel(x);
        case STAIR:
            return stair_activate_kernel(x);
        case HARDTAN:
            return hardtan_activate_kernel(x);
        case LHTAN:
            return lhtan_activate_kernel(x);
    }
    return 0;
}

__global__ void activate_array_kernel(float *x, int n, ACTIVATION a)
{
    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
    if(i < n) x[i] = activate_kernel(x[i], a);
}

void activate_array_gpu(float *x, int n, ACTIVATION a)
{
    activate_array_kernel<<>>(x, n, a);
    check_error(cudaPeekAtLastError());
}

Reference

History

• 20191014: created.

Copyright

• Post author: kezunlin
• Post link: https://kezunlin.me/post/ee123cac/
• Copyright Notice: All articles in this blog are licensed under CC BY-NC-SA 3.0 unless stating additionally.