mirror of https://github.com/pjreddie/darknet.git
1036 lines
33 KiB
Plaintext
1036 lines
33 KiB
Plaintext
#include "cuda_runtime.h"
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#include "curand.h"
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#include "cublas_v2.h"
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#include <assert.h>
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extern "C" {
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#include "blas.h"
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#include "cuda.h"
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#include "utils.h"
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}
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__global__ void scale_bias_kernel(float *output, float *biases, int n, int size)
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{
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int offset = blockIdx.x * blockDim.x + threadIdx.x;
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int filter = blockIdx.y;
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int batch = blockIdx.z;
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if(offset < size) output[(batch*n+filter)*size + offset] *= biases[filter];
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}
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void scale_bias_gpu(float *output, float *biases, int batch, int n, int size)
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{
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dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
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dim3 dimBlock(BLOCK, 1, 1);
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scale_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
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check_error(cudaPeekAtLastError());
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}
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__global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
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{
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__shared__ float part[BLOCK];
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int i,b;
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int filter = blockIdx.x;
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int p = threadIdx.x;
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float sum = 0;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < size; i += BLOCK){
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int index = p + i + size*(filter + n*b);
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sum += (p+i < size) ? delta[index]*x_norm[index] : 0;
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}
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}
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part[p] = sum;
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__syncthreads();
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if (p == 0) {
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for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i];
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}
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}
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void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
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{
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backward_scale_kernel<<<n, BLOCK>>>(x_norm, delta, batch, n, size, scale_updates);
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check_error(cudaPeekAtLastError());
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}
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__global__ void add_bias_kernel(float *output, float *biases, int batch, int n, int size)
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{
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int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (index >= n*size*batch) return;
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int i = index % size;
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index /= size;
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int j = index % n;
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index /= n;
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int k = index;
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output[(k*n+j)*size + i] += biases[j];
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}
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void add_bias_gpu(float *output, float *biases, int batch, int n, int size)
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{
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int num = n*size*batch;
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add_bias_kernel<<<cuda_gridsize(num), BLOCK>>>(output, biases, batch, n, size);
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check_error(cudaPeekAtLastError());
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}
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__global__ void backward_bias_conn_kernel(float *bias_updates, float *delta, int batch, int n)
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{
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int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (index >= n) return;
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int b;
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float sum = 0;
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for(b = 0; b < batch; ++b){
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int i = b*n + index;
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sum += delta[i];
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}
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bias_updates[index] += sum;
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}
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__global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size)
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{
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__shared__ float part[BLOCK];
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int i,b;
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int filter = blockIdx.x;
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int p = threadIdx.x;
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float sum = 0;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < size; i += BLOCK){
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int index = p + i + size*(filter + n*b);
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sum += (p+i < size) ? delta[index] : 0;
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}
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}
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part[p] = sum;
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__syncthreads();
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if (p == 0) {
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for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i];
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}
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}
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void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
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{
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if(size == 1){
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backward_bias_conn_kernel<<<cuda_gridsize(n), BLOCK>>>(bias_updates, delta, batch, n);
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}else{
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backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size);
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}
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check_error(cudaPeekAtLastError());
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}
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/*
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__global__ void dot_kernel(float *output, float scale, int batch, int n, int size, float *delta)
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{
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int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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int f1 = index / n;
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int f2 = index % n;
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if (f2 <= f1) return;
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float sum = 0;
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float norm1 = 0;
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float norm2 = 0;
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int b, i;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < size; ++i){
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int i1 = b * size * n + f1 * size + i;
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int i2 = b * size * n + f2 * size + i;
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sum += output[i1] * output[i2];
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norm1 += output[i1] * output[i1];
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norm2 += output[i2] * output[i2];
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}
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}
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norm1 = sqrt(norm1);
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norm2 = sqrt(norm2);
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float norm = norm1 * norm2;
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sum = sum / norm;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < size; ++i){
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int i1 = b * size * n + f1 * size + i;
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int i2 = b * size * n + f2 * size + i;
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delta[i1] += - scale * sum * output[i2] / norm;
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delta[i2] += - scale * sum * output[i1] / norm;
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}
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}
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}
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void dot_error_gpu(layer l)
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{
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dot_kernel<<<cuda_gridsize(l.n*l.n), BLOCK>>>(l.output_gpu, l.dot, l.batch, l.n, l.out_w * l.out_h, l.delta_gpu);
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check_error(cudaPeekAtLastError());
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}
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*/
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__global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
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{
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int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (index >= N) return;
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float mhat = m[index] / (1.f - powf(B1, t));
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float vhat = v[index] / (1.f - powf(B2, t));
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x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps);
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}
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extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
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{
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adam_kernel<<<cuda_gridsize(n), BLOCK>>>(n, x, m, v, B1, B2, rate, eps, t);
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check_error(cudaPeekAtLastError());
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}
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extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t)
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{
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scal_gpu(n, B1, m, 1);
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scal_gpu(n, B2, v, 1);
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axpy_gpu(n, -decay*batch, w, 1, d, 1);
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axpy_gpu(n, (1-B1), d, 1, m, 1);
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mul_gpu(n, d, 1, d, 1);
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axpy_gpu(n, (1-B2), d, 1, v, 1);
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adam_gpu(n, w, m, v, B1, B2, rate, eps, t);
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fill_gpu(n, 0, d, 1);
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}
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__global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial)
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{
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int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (index >= N) return;
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int f = (index/spatial)%filters;
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x[index] = (x[index] - mean[f])/(sqrtf(variance[f] + .00001f));
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}
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__global__ void normalize_delta_kernel(int N, float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
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{
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int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (index >= N) return;
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int f = (index/spatial)%filters;
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delta[index] = delta[index] * 1.f/(sqrtf(variance[f] + .00001f)) + variance_delta[f] * 2.f * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
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}
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extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
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{
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size_t N = batch*filters*spatial;
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normalize_delta_kernel<<<cuda_gridsize(N), BLOCK>>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta);
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check_error(cudaPeekAtLastError());
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}
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__global__ void variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (i >= filters) return;
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int j,k;
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variance_delta[i] = 0;
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for(j = 0; j < batch; ++j){
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for(k = 0; k < spatial; ++k){
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int index = j*filters*spatial + i*spatial + k;
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variance_delta[i] += delta[index]*(x[index] - mean[i]);
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}
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}
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variance_delta[i] *= -.5f * powf(variance[i] + .00001f, (float)(-3.f/2.f));
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}
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__global__ void accumulate_kernel(float *x, int n, int groups, float *sum)
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{
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int k;
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (i >= groups) return;
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sum[i] = 0;
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for(k = 0; k < n; ++k){
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sum[i] += x[k*groups + i];
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}
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}
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__global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
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{
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const int threads = BLOCK;
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__shared__ float local[threads];
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int id = threadIdx.x;
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local[id] = 0;
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int filter = blockIdx.x;
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int i, j;
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for(j = 0; j < batch; ++j){
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for(i = 0; i < spatial; i += threads){
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int index = j*spatial*filters + filter*spatial + i + id;
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local[id] += (i+id < spatial) ? delta[index] : 0;
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}
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}
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__syncthreads();
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if(id == 0){
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mean_delta[filter] = 0;
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for(i = 0; i < threads; ++i){
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mean_delta[filter] += local[i];
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}
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mean_delta[filter] *= (-1.f/sqrtf(variance[filter] + .00001f));
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}
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}
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__global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
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{
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const int threads = BLOCK;
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__shared__ float local[threads];
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int id = threadIdx.x;
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local[id] = 0;
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int filter = blockIdx.x;
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int i, j;
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for(j = 0; j < batch; ++j){
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for(i = 0; i < spatial; i += threads){
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int index = j*spatial*filters + filter*spatial + i + id;
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local[id] += (i+id < spatial) ? delta[index]*(x[index] - mean[filter]) : 0;
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}
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}
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__syncthreads();
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if(id == 0){
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variance_delta[filter] = 0;
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for(i = 0; i < threads; ++i){
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variance_delta[filter] += local[i];
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}
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variance_delta[filter] *= -.5f * powf(variance[filter] + .00001f, (float)(-3.f/2.f));
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}
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}
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__global__ void mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (i >= filters) return;
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int j,k;
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mean_delta[i] = 0;
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for (j = 0; j < batch; ++j) {
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for (k = 0; k < spatial; ++k) {
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int index = j*filters*spatial + i*spatial + k;
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mean_delta[i] += delta[index];
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}
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}
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mean_delta[i] *= (-1.f/sqrtf(variance[i] + .00001f));
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}
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extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
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{
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mean_delta_kernel<<<cuda_gridsize(filters), BLOCK>>>(delta, variance, batch, filters, spatial, mean_delta);
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check_error(cudaPeekAtLastError());
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}
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extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
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{
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fast_mean_delta_kernel<<<filters, BLOCK>>>(delta, variance, batch, filters, spatial, mean_delta);
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check_error(cudaPeekAtLastError());
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}
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extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
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{
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fast_variance_delta_kernel<<<filters, BLOCK>>>(x, delta, mean, variance, batch, filters, spatial, variance_delta);
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check_error(cudaPeekAtLastError());
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}
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__global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
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{
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float scale = 1.f/(batch * spatial);
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (i >= filters) return;
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int j,k;
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mean[i] = 0;
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for(j = 0; j < batch; ++j){
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for(k = 0; k < spatial; ++k){
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int index = j*filters*spatial + i*spatial + k;
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mean[i] += x[index];
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}
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}
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mean[i] *= scale;
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}
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__global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
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{
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float scale = 1.f/(batch * spatial - 1);
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int j,k;
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if (i >= filters) return;
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variance[i] = 0;
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for(j = 0; j < batch; ++j){
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for(k = 0; k < spatial; ++k){
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int index = j*filters*spatial + i*spatial + k;
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variance[i] += powf((x[index] - mean[i]), 2);
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}
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}
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variance[i] *= scale;
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}
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__global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(i >= N) return;
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int in_index = i;
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int in_w = i%w;
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i = i/w;
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int in_h = i%h;
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i = i/h;
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int in_c = i%c;
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i = i/c;
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int b = i%batch;
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int out_c = c/(stride*stride);
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int c2 = in_c % out_c;
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int offset = in_c / out_c;
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int w2 = in_w*stride + offset % stride;
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int h2 = in_h*stride + offset / stride;
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//printf("%d\n", offset);
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int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b));
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// printf("%d %d %d\n", w2, h2, c2);
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//printf("%d %d\n", in_index, out_index);
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//if(out_index >= N || out_index < 0) printf("bad bad bad \n");
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if(forward) out[out_index] = x[in_index];
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else out[in_index] = x[out_index];
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//if(forward) out[1] = x[1];
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//else out[0] = x[0];
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}
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__global__ void axpy_kernel(int N, float ALPHA, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(i < N) Y[OFFY+i*INCY] += ALPHA*X[OFFX+i*INCX];
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}
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__global__ void pow_kernel(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(i < N) Y[i*INCY] = pow(X[i*INCX], ALPHA);
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}
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__global__ void const_kernel(int N, float ALPHA, float *X, int INCX)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(i < N) X[i*INCX] = ALPHA;
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}
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__global__ void constrain_kernel(int N, float ALPHA, float *X, int INCX)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(i < N) X[i*INCX] = fminf(ALPHA, fmaxf(-ALPHA, X[i*INCX]));
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}
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__global__ void supp_kernel(int N, float ALPHA, float *X, int INCX)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(i < N) {
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if((X[i*INCX] * X[i*INCX]) < (ALPHA * ALPHA)) X[i*INCX] = 0;
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}
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}
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__global__ void add_kernel(int N, float ALPHA, float *X, int INCX)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(i < N) X[i*INCX] += ALPHA;
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}
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|
|
|
__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < N) X[i*INCX] *= ALPHA;
|
|
}
|
|
|
|
__global__ void fill_kernel(int N, float ALPHA, float *X, int INCX)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < N) X[i*INCX] = ALPHA;
|
|
}
|
|
|
|
__global__ void copy_kernel(int N, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < N) Y[i*INCY + OFFY] = X[i*INCX + OFFX];
|
|
}
|
|
|
|
__global__ void mul_kernel(int N, float *X, int INCX, float *Y, int INCY)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < N) Y[i*INCY] *= X[i*INCX];
|
|
}
|
|
|
|
|
|
extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
|
|
{
|
|
size_t N = batch*filters*spatial;
|
|
normalize_kernel<<<cuda_gridsize(N), BLOCK>>>(N, x, mean, variance, batch, filters, spatial);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void l2norm_kernel(int N, float *x, float *dx, int batch, int filters, int spatial)
|
|
{
|
|
int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if (index >= N) return;
|
|
int b = index / spatial;
|
|
int i = index % spatial;
|
|
int f;
|
|
float sum = 0;
|
|
for(f = 0; f < filters; ++f){
|
|
int index = b*filters*spatial + f*spatial + i;
|
|
sum += powf(x[index], 2);
|
|
}
|
|
sum = sqrtf(sum);
|
|
if(sum == 0) sum = 1;
|
|
//printf("%f\n", sum);
|
|
for(f = 0; f < filters; ++f){
|
|
int index = b*filters*spatial + f*spatial + i;
|
|
x[index] /= sum;
|
|
dx[index] = (1 - x[index]) / sum;
|
|
}
|
|
}
|
|
|
|
extern "C" void l2normalize_gpu(float *x, float *dx, int batch, int filters, int spatial)
|
|
{
|
|
size_t N = batch*spatial;
|
|
l2norm_kernel<<<cuda_gridsize(N), BLOCK>>>(N, x, dx, batch, filters, spatial);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
|
|
{
|
|
const int threads = BLOCK;
|
|
__shared__ float local[threads];
|
|
|
|
int id = threadIdx.x;
|
|
local[id] = 0;
|
|
|
|
int filter = blockIdx.x;
|
|
|
|
int i, j;
|
|
for(j = 0; j < batch; ++j){
|
|
for(i = 0; i < spatial; i += threads){
|
|
int index = j*spatial*filters + filter*spatial + i + id;
|
|
local[id] += (i+id < spatial) ? x[index] : 0;
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if(id == 0){
|
|
mean[filter] = 0;
|
|
for(i = 0; i < threads; ++i){
|
|
mean[filter] += local[i];
|
|
}
|
|
mean[filter] /= spatial * batch;
|
|
}
|
|
}
|
|
|
|
__global__ void fast_variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
|
|
{
|
|
const int threads = BLOCK;
|
|
__shared__ float local[threads];
|
|
|
|
int id = threadIdx.x;
|
|
local[id] = 0;
|
|
|
|
int filter = blockIdx.x;
|
|
|
|
int i, j;
|
|
for(j = 0; j < batch; ++j){
|
|
for(i = 0; i < spatial; i += threads){
|
|
int index = j*spatial*filters + filter*spatial + i + id;
|
|
|
|
local[id] += (i+id < spatial) ? powf((x[index] - mean[filter]), 2) : 0;
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if(id == 0){
|
|
variance[filter] = 0;
|
|
for(i = 0; i < threads; ++i){
|
|
variance[filter] += local[i];
|
|
}
|
|
variance[filter] /= (spatial * batch - 1);
|
|
}
|
|
}
|
|
|
|
extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean)
|
|
{
|
|
fast_mean_kernel<<<filters, BLOCK>>>(x, batch, filters, spatial, mean);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
|
|
{
|
|
fast_variance_kernel<<<filters, BLOCK>>>(x, mean, batch, filters, spatial, variance);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
|
|
extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean)
|
|
{
|
|
mean_kernel<<<cuda_gridsize(filters), BLOCK>>>(x, batch, filters, spatial, mean);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
|
|
{
|
|
variance_kernel<<<cuda_gridsize(filters), BLOCK>>>(x, mean, batch, filters, spatial, variance);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY)
|
|
{
|
|
axpy_gpu_offset(N, ALPHA, X, 0, INCX, Y, 0, INCY);
|
|
}
|
|
|
|
extern "C" void pow_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY)
|
|
{
|
|
pow_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX, Y, INCY);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void axpy_gpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY)
|
|
{
|
|
axpy_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void copy_gpu(int N, float * X, int INCX, float * Y, int INCY)
|
|
{
|
|
copy_gpu_offset(N, X, 0, INCX, Y, 0, INCY);
|
|
}
|
|
|
|
extern "C" void mul_gpu(int N, float * X, int INCX, float * Y, int INCY)
|
|
{
|
|
mul_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, INCX, Y, INCY);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void copy_gpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY)
|
|
{
|
|
copy_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, OFFX, INCX, Y, OFFY, INCY);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void flatten_kernel(int N, float *x, int spatial, int layers, int batch, int forward, float *out)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i >= N) return;
|
|
int in_s = i%spatial;
|
|
i = i/spatial;
|
|
int in_c = i%layers;
|
|
i = i/layers;
|
|
int b = i;
|
|
|
|
int i1 = b*layers*spatial + in_c*spatial + in_s;
|
|
int i2 = b*layers*spatial + in_s*layers + in_c;
|
|
|
|
if (forward) out[i2] = x[i1];
|
|
else out[i1] = x[i2];
|
|
}
|
|
|
|
extern "C" void flatten_gpu(float *x, int spatial, int layers, int batch, int forward, float *out)
|
|
{
|
|
int size = spatial*batch*layers;
|
|
flatten_kernel<<<cuda_gridsize(size), BLOCK>>>(size, x, spatial, layers, batch, forward, out);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void reorg_gpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
|
|
{
|
|
int size = w*h*c*batch;
|
|
reorg_kernel<<<cuda_gridsize(size), BLOCK>>>(size, x, w, h, c, batch, stride, forward, out);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void mask_kernel(int n, float *x, float mask_num, float *mask, float val)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n && mask[i] == mask_num) x[i] = val;
|
|
}
|
|
|
|
extern "C" void mask_gpu(int N, float * X, float mask_num, float * mask, float val)
|
|
{
|
|
mask_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, mask_num, mask, val);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void scale_mask_kernel(int n, float *x, float mask_num, float *mask, float scale)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n && mask[i] == mask_num) x[i] *= scale;
|
|
}
|
|
|
|
extern "C" void scale_mask_gpu(int N, float * X, float mask_num, float * mask, float scale)
|
|
{
|
|
scale_mask_kernel<<<cuda_gridsize(N), BLOCK>>>(N, X, mask_num, mask, scale);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void const_gpu(int N, float ALPHA, float * X, int INCX)
|
|
{
|
|
const_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void constrain_gpu(int N, float ALPHA, float * X, int INCX)
|
|
{
|
|
constrain_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
|
|
extern "C" void add_gpu(int N, float ALPHA, float * X, int INCX)
|
|
{
|
|
add_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void scal_gpu(int N, float ALPHA, float * X, int INCX)
|
|
{
|
|
scal_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void supp_gpu(int N, float ALPHA, float * X, int INCX)
|
|
{
|
|
supp_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
extern "C" void fill_gpu(int N, float ALPHA, float * X, int INCX)
|
|
{
|
|
fill_kernel<<<cuda_gridsize(N), BLOCK>>>(N, ALPHA, X, INCX);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out)
|
|
{
|
|
int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if (id >= size) return;
|
|
int i = id % minw;
|
|
id /= minw;
|
|
int j = id % minh;
|
|
id /= minh;
|
|
int k = id % minc;
|
|
id /= minc;
|
|
int b = id % batch;
|
|
|
|
int out_index = i*sample + w2*(j*sample + h2*(k + c2*b));
|
|
int add_index = i*stride + w1*(j*stride + h1*(k + c1*b));
|
|
out[out_index] = s1*out[out_index] + s2*add[add_index];
|
|
//out[out_index] += add[add_index];
|
|
}
|
|
|
|
extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out)
|
|
{
|
|
int minw = (w1 < w2) ? w1 : w2;
|
|
int minh = (h1 < h2) ? h1 : h2;
|
|
int minc = (c1 < c2) ? c1 : c2;
|
|
|
|
int stride = w1/w2;
|
|
int sample = w2/w1;
|
|
assert(stride == h1/h2);
|
|
assert(sample == h2/h1);
|
|
if(stride < 1) stride = 1;
|
|
if(sample < 1) sample = 1;
|
|
|
|
int size = batch * minw * minh * minc;
|
|
shortcut_kernel<<<cuda_gridsize(size), BLOCK>>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, s1, s2, out);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n){
|
|
float diff = truth[i] - pred[i];
|
|
float abs_val = fabsf(diff);
|
|
if(abs_val < 1) {
|
|
error[i] = diff * diff;
|
|
delta[i] = diff;
|
|
}
|
|
else {
|
|
error[i] = 2*abs_val - 1;
|
|
delta[i] = (diff > 0) ? 1 : -1;
|
|
}
|
|
}
|
|
}
|
|
|
|
extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
smooth_l1_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n){
|
|
float t = truth[i];
|
|
float p = pred[i];
|
|
error[i] = (t) ? -log(p) : 0;
|
|
delta[i] = t-p;
|
|
}
|
|
}
|
|
|
|
extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
softmax_x_ent_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void logistic_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n){
|
|
float t = truth[i];
|
|
float p = pred[i];
|
|
error[i] = -t*log(p+.0000001) - (1-t)*log(1-p+.0000001);
|
|
delta[i] = t-p;
|
|
}
|
|
}
|
|
|
|
extern "C" void logistic_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
logistic_x_ent_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n){
|
|
float diff = truth[i] - pred[i];
|
|
error[i] = diff * diff; //I know this is technically wrong, deal with it.
|
|
delta[i] = diff;
|
|
}
|
|
}
|
|
|
|
extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
l2_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void l1_kernel(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n){
|
|
float diff = truth[i] - pred[i];
|
|
error[i] = abs(diff);
|
|
delta[i] = (diff > 0) ? 1 : -1;
|
|
}
|
|
}
|
|
|
|
extern "C" void l1_gpu(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
l1_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
__global__ void wgan_kernel(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n){
|
|
error[i] = truth[i] ? -pred[i] : pred[i];
|
|
delta[i] = (truth[i] > 0) ? 1 : -1;
|
|
}
|
|
}
|
|
|
|
extern "C" void wgan_gpu(int n, float *pred, float *truth, float *delta, float *error)
|
|
{
|
|
wgan_kernel<<<cuda_gridsize(n), BLOCK>>>(n, pred, truth, delta, error);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
|
|
|
|
|
|
__global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float *c)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n){
|
|
c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0);
|
|
}
|
|
}
|
|
|
|
__global__ void deinter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < (NX+NY)*B){
|
|
int b = i / (NX+NY);
|
|
int j = i % (NX+NY);
|
|
if (j < NX){
|
|
if(X) X[b*NX + j] += OUT[i];
|
|
} else {
|
|
if(Y) Y[b*NY + j - NX] += OUT[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
extern "C" void deinter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT)
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{
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deinter_kernel<<<cuda_gridsize((NX+NY)*B), BLOCK>>>(NX, X, NY, Y, B, OUT);
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check_error(cudaPeekAtLastError());
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}
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__global__ void inter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT)
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{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(i < (NX+NY)*B){
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int b = i / (NX+NY);
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int j = i % (NX+NY);
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if (j < NX){
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OUT[i] = X[b*NX + j];
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} else {
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OUT[i] = Y[b*NY + j - NX];
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}
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}
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}
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extern "C" void inter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT)
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|
{
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inter_kernel<<<cuda_gridsize((NX+NY)*B), BLOCK>>>(NX, X, NY, Y, B, OUT);
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check_error(cudaPeekAtLastError());
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}
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extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c)
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|
{
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weighted_sum_kernel<<<cuda_gridsize(num), BLOCK>>>(num, a, b, s, c);
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check_error(cudaPeekAtLastError());
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|
}
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|
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__global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float *da, float *db, float *ds, float *dc)
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|
{
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int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
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if(i < n){
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if(da) da[i] += dc[i] * s[i];
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if(db) db[i] += dc[i] * (1-s[i]);
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ds[i] += dc[i] * (a[i] - b[i]);
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}
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}
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|
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extern "C" void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc)
|
|
{
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|
weighted_delta_kernel<<<cuda_gridsize(num), BLOCK>>>(num, a, b, s, da, db, ds, dc);
|
|
check_error(cudaPeekAtLastError());
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|
}
|
|
|
|
__global__ void mult_add_into_kernel(int n, float *a, float *b, float *c)
|
|
{
|
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i < n){
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|
c[i] += a[i]*b[i];
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|
}
|
|
}
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|
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|
extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c)
|
|
{
|
|
mult_add_into_kernel<<<cuda_gridsize(num), BLOCK>>>(num, a, b, c);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
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|
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|
__device__ void softmax_device(float *input, int n, float temp, int stride, float *output)
|
|
{
|
|
int i;
|
|
float sum = 0;
|
|
float largest = -INFINITY;
|
|
for(i = 0; i < n; ++i){
|
|
int val = input[i*stride];
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|
largest = (val>largest) ? val : largest;
|
|
}
|
|
for(i = 0; i < n; ++i){
|
|
float e = expf(input[i*stride]/temp - largest/temp);
|
|
sum += e;
|
|
output[i*stride] = e;
|
|
}
|
|
for(i = 0; i < n; ++i){
|
|
output[i*stride] /= sum;
|
|
}
|
|
}
|
|
|
|
|
|
__global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset)
|
|
{
|
|
int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if (id >= spatial*batch*groups) return;
|
|
int s = id % spatial;
|
|
id = id / spatial;
|
|
int g = id % groups;
|
|
int b = id / groups;
|
|
int goff = group_offset[g]*spatial;
|
|
int boff = b*stride;
|
|
softmax_device(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s);
|
|
}
|
|
|
|
extern "C" void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier)
|
|
{
|
|
int *tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups);
|
|
int *tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups);
|
|
/*
|
|
static int *tree_groups_size = 0;
|
|
static int *tree_groups_offset = 0;
|
|
if(!tree_groups_size){
|
|
tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups);
|
|
tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups);
|
|
}
|
|
*/
|
|
int num = spatial*batch*hier.groups;
|
|
softmax_tree_kernel<<<cuda_gridsize(num), BLOCK>>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset);
|
|
check_error(cudaPeekAtLastError());
|
|
cuda_free((float *)tree_groups_size);
|
|
cuda_free((float *)tree_groups_offset);
|
|
}
|
|
|
|
__global__ void softmax_kernel(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)
|
|
{
|
|
int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if (id >= batch*groups) return;
|
|
int b = id / groups;
|
|
int g = id % groups;
|
|
softmax_device(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset);
|
|
}
|
|
|
|
extern "C" void softmax_gpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)
|
|
{
|
|
softmax_kernel<<<cuda_gridsize(batch*groups), BLOCK>>>(input, n, batch, batch_offset, groups, group_offset, stride, temp, output);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|
|
|
|
|
|
__global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int batch, int stride, int forward, float scale, float *out)
|
|
{
|
|
size_t i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
|
if(i >= N) return;
|
|
int out_index = i;
|
|
int out_w = i%(w*stride);
|
|
i = i/(w*stride);
|
|
int out_h = i%(h*stride);
|
|
i = i/(h*stride);
|
|
int out_c = i%c;
|
|
i = i/c;
|
|
int b = i%batch;
|
|
|
|
int in_w = out_w / stride;
|
|
int in_h = out_h / stride;
|
|
int in_c = out_c;
|
|
|
|
int in_index = b*w*h*c + in_c*w*h + in_h*w + in_w;
|
|
|
|
|
|
if(forward) out[out_index] += scale * x[in_index];
|
|
else atomicAdd(x+in_index, scale * out[out_index]);
|
|
}
|
|
extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out)
|
|
{
|
|
size_t size = w*h*c*batch*stride*stride;
|
|
upsample_kernel<<<cuda_gridsize(size), BLOCK>>>(size, in, w, h, c, batch, stride, forward, scale, out);
|
|
check_error(cudaPeekAtLastError());
|
|
}
|