C 에서 어떻게 CUDA 를 사용 하여 자주 사용 하 는 딥 러 닝 활성화 함 수 를 실현 합 니까?

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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
    ", 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
    ", 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, 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
    "); 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); }

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    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.

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  • Post author: kezunlin
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