#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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#include <float.h>
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#include "blas.h"
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#include "dark_cuda.h"
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#include "utils.h"
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#include "tree.h"
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__inline__ __device__
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float warpAllReduceSum(float val) {
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for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2)
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#if CUDART_VERSION >= 9000
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val += __shfl_xor_sync(0xffffffff, val, mask);
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#else
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val += __shfl_xor(val, mask);
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#endif
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return val;
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}
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__global__ void compare_2_arrays_kernel(float *one, float *two, int size)
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{
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const int index = blockIdx.x*blockDim.x + threadIdx.x;
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if (index >= size) return;
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const float diff = 100 * fabs(one[index] - two[index]) / fabs(one[index]);
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if (diff > 10) printf(" i: %d - one = %f, two = %f, diff = %f %% \n", index, one[index], two[index], diff);
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}
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void compare_2_arrays_gpu(float *one, float *two, int size)
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{
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const int num_blocks = get_number_of_blocks(size, BLOCK);
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compare_2_arrays_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(one, two, size);
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CHECK_CUDA(cudaPeekAtLastError());
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CHECK_CUDA(cudaDeviceSynchronize());
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}
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__global__ void mean_array_kernel(float *src, int size, float alpha, float *avg)
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{
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const int i = blockIdx.x*blockDim.x + threadIdx.x;
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if (i >= size) return;
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avg[i] = avg[i] * (1 - alpha) + src[i] * alpha;
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src[i] = avg[i];
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}
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void mean_array_gpu(float *src, int size, float alpha, float *avg)
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{
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const int num_blocks = get_number_of_blocks(size, BLOCK);
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mean_array_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(src, size, alpha, avg);
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CHECK_CUDA(cudaPeekAtLastError());
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}
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__global__ void scale_bias_kernel(float *output, float *scale, int batch, int filters, int spatial, int current_size)
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{
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const int index = blockIdx.x*blockDim.x + threadIdx.x;
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if (index >= current_size) return;
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int f = (index / spatial) % filters;
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output[index] *= scale[f];
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}
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void scale_bias_gpu(float *output, float *scale, int batch, int filters, int spatial)
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{
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const int current_size = batch * filters * spatial;
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const int num_blocks = get_number_of_blocks(current_size, BLOCK);
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scale_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, scale, batch, filters, spatial, current_size);
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CHECK_CUDA(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, 0, get_cuda_stream() >>>(x_norm, delta, batch, n, size, scale_updates);
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CHECK_CUDA(cudaPeekAtLastError());
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}
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__global__ void add_bias_kernel(float *output, float *biases, int batch, int filters, int spatial, int current_size)
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{
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const int index = blockIdx.x*blockDim.x + threadIdx.x;
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if (index >= current_size) return;
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int f = (index / spatial) % filters;
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output[index] += biases[f];
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}
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void add_bias_gpu(float *output, float *biases, int batch, int filters, int spatial)
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{
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const int current_size = batch * filters * spatial;
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const int num_blocks = get_number_of_blocks(current_size, BLOCK);
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add_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, biases, batch, filters, spatial, current_size);
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CHECK_CUDA(cudaPeekAtLastError());
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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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/*
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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, 0, get_cuda_stream()>>>(l.output_gpu, l.dot, l.batch, l.n, l.out_w * l.out_h, l.delta_gpu);
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CHECK_CUDA(cudaPeekAtLastError());
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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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backward_bias_kernel<<<n, BLOCK, 0, get_cuda_stream() >>>(bias_updates, delta, batch, n, size);
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CHECK_CUDA(cudaPeekAtLastError());
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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, 0, get_cuda_stream() >> >(n, x, m, v, B1, B2, rate, eps, t);
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CHECK_CUDA(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_ongpu(n, B1, m, 1);
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scal_ongpu(n, B2, v, 1);
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axpy_ongpu(n, -decay*batch, w, 1, d, 1);
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axpy_ongpu(n, (1 - B1), d, 1, m, 1);
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mul_ongpu(n, d, 1, d, 1);
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axpy_ongpu(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_ongpu(n, 0, d, 1);
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CHECK_CUDA(cudaPeekAtLastError());
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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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const int index = blockIdx.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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extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
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{
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const int current_size = batch * filters * spatial;
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const int num_blocks = get_number_of_blocks(current_size, BLOCK);
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normalize_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(current_size, x, mean, variance, batch, filters, spatial);
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CHECK_CUDA(cudaPeekAtLastError());
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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]) + .000001f) + variance_delta[f] * 2. * (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, 0, get_cuda_stream() >>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta);
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CHECK_CUDA(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] *= -.5 * powf(variance[i] + .000001f, (float)(-3./2.));
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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] + .000001f));
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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] *= -.5 * powf(variance[filter] + .000001f, (float)(-3./2.));
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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] + .000001f));
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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, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta);
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CHECK_CUDA(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, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta);
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CHECK_CUDA(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, 0, get_cuda_stream() >>>(x, delta, mean, variance, batch, filters, spatial, variance_delta);
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CHECK_CUDA(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 constrain_weight_updates_kernel(int N, float coef, float *weights_gpu, float *weight_updates_gpu)
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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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const float w = weights_gpu[i];
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const float wu = weight_updates_gpu[i];
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const float wu_sign = (wu == 0) ? 0 : (fabs(wu) / wu);
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const float abs_limit = fabs(w * coef);
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if (fabs(wu) > abs_limit) weight_updates_gpu[i] = abs_limit * wu_sign;
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}
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}
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extern "C" void constrain_weight_updates_ongpu(int N, float coef, float *weights_gpu, float *weight_updates_gpu)
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{
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constrain_weight_updates_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, coef, weights_gpu, weight_updates_gpu);
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CHECK_CUDA(cudaPeekAtLastError());
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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] = powf(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 constrain_min_max_kernel(int N, float MIN, float MAX, 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(MAX, fmaxf(MIN, 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 scal_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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__global__ void scal_add_kernel(int N, float ALPHA, float BETA, float *X, int INCX)
|
{
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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] = X[i*INCX] * ALPHA + BETA;
|
}
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__global__ void fill_kernel(int N, float ALPHA, float *X, int INCX)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index >= N) return;
|
X[index*INCX] = ALPHA;
|
}
|
|
__global__ void mask_kernel_new_api(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;
|
}
|
|
__global__ void mask_kernel(int n, float *x, float mask_num, float *mask)
|
{
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
if(i < n && mask[i] == mask_num) x[i] = mask_num;
|
}
|
|
__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 simple_copy_kernel(int size, float *src, float *dst)
|
{
|
int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size)
|
dst[index] = src[index];
|
}
|
|
__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];
|
}
|
|
|
__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){
|
float mean_tmp = 0;
|
for(i = 0; i < threads; ++i){
|
mean_tmp += local[i];
|
}
|
mean_tmp /= spatial * batch;
|
mean[filter] = mean_tmp;
|
}
|
}
|
|
extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean)
|
{
|
fast_mean_kernel << <filters, BLOCK, 0, get_cuda_stream() >> >(x, batch, filters, spatial, mean);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
__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){
|
float variance_tmp = 0;
|
for(i = 0; i < threads; ++i){
|
variance_tmp += local[i];
|
}
|
variance_tmp /= (spatial * batch);// -1);
|
variance[filter] = variance_tmp;
|
}
|
}
|
|
extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
|
{
|
fast_variance_kernel<<<filters, BLOCK, 0, get_cuda_stream() >>>(x, mean, batch, filters, spatial, variance);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
__global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance,
|
const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon)
|
{
|
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], 2) : 0;
|
}
|
}
|
__syncthreads();
|
|
if (id == 0) {
|
float v_tmp = 0;
|
v_tmp = 0;
|
for (i = 0; i < threads; ++i) {
|
v_tmp += local[i];
|
}
|
v_tmp /= (spatial * batch - 1);
|
|
v_tmp = fmax(v_tmp, powf(mean[filter], 2));
|
|
|
const float alpha_cbn = 1.0f / minibatch_index;
|
|
m_avg[filter] = alpha_cbn * mean[filter] + (1 - alpha_cbn) * m_avg[filter];
|
mean[filter] = m_avg[filter];
|
|
v_avg[filter] = alpha_cbn * v_tmp + (1 - alpha_cbn) * v_avg[filter];
|
|
float variance_tmp = fmax(0.0f, v_avg[filter] - powf(m_avg[filter], 2));
|
if (inverse_variance) variance[filter] = 1.0f / sqrtf(variance_tmp + epsilon);
|
else variance[filter] = variance_tmp;
|
|
//if (max_minibatch_index == minibatch_index)
|
{
|
if(rolling_mean_gpu) rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter];
|
|
if(rolling_variance_gpu) rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter];
|
}
|
}
|
}
|
|
extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance,
|
const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon)
|
{
|
fast_v_cbn_kernel << <filters, BLOCK, 0, get_cuda_stream() >> >(x, mean, batch, filters, spatial, minibatch_index, max_minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
__global__ void inverse_variance_kernel(int size, float *src, float *dst, float epsilon)
|
{
|
int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size)
|
dst[index] = 1.0f / sqrtf(src[index] + epsilon);
|
}
|
|
extern "C" void inverse_variance_ongpu(int size, float *src, float *dst, float epsilon)
|
{
|
const int num_blocks = size / BLOCK + 1;
|
inverse_variance_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(size, src, dst, epsilon);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
__global__ void normalize_scale_bias_kernel(int N, float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index >= N) return;
|
int f = (index / spatial) % filters;
|
|
float val = 0;
|
if(inverse_variance) val = (x[index] - mean[f]) * variance[f];
|
else val = (x[index] - mean[f]) / (sqrtf(variance[f] + epsilon));
|
val *= scales[f];
|
val += biases[f];
|
|
if (!isnan(val) && !isinf(val))
|
x[index] = val;
|
}
|
|
extern "C" void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon)
|
{
|
const int current_size = batch * filters * spatial;
|
const int num_blocks = get_number_of_blocks(current_size, BLOCK);
|
|
normalize_scale_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(current_size, x, mean, variance, scales, biases, batch, filters, spatial, inverse_variance, epsilon);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean)
|
{
|
mean_kernel<<<cuda_gridsize(filters), BLOCK, 0, get_cuda_stream() >>>(x, batch, filters, spatial, mean);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
|
{
|
variance_kernel<<<cuda_gridsize(filters), BLOCK, 0, get_cuda_stream() >>>(x, mean, batch, filters, spatial, variance);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY)
|
{
|
axpy_ongpu_offset(N, ALPHA, X, 0, INCX, Y, 0, INCY);
|
}
|
|
extern "C" void pow_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY)
|
{
|
pow_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX, Y, INCY);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY)
|
{
|
axpy_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY)
|
{
|
copy_ongpu_offset(N, X, 0, INCX, Y, 0, INCY);
|
}
|
|
extern "C" void simple_copy_ongpu(int size, float *src, float *dst)
|
{
|
const int num_blocks = size / BLOCK + 1;
|
simple_copy_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(size, src, dst);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void memcpy_ongpu(void *dst, void *src, int size_bytes)
|
{
|
CHECK_CUDA(cudaMemcpyAsync(dst, src, size_bytes, cudaMemcpyDefault, get_cuda_stream()));
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY)
|
{
|
mul_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, INCX, Y, INCY);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY)
|
{
|
copy_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, X, OFFX, INCX, Y, OFFY, INCY);
|
CHECK_CUDA(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_ongpu(float *x, int spatial, int layers, int batch, int forward, float *out)
|
{
|
int size = spatial*batch*layers;
|
flatten_kernel<<<cuda_gridsize(size), BLOCK, 0, get_cuda_stream()>>>(size, x, spatial, layers, batch, forward, out);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void reorg_ongpu(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, 0, get_cuda_stream()>>>(size, x, w, h, c, batch, stride, forward, out);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void mask_gpu_new_api(int N, float * X, float mask_num, float * mask, float val)
|
{
|
mask_kernel_new_api <<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, mask_num, mask, val);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask)
|
{
|
mask_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, X, mask_num, mask);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void const_ongpu(int N, float ALPHA, float * X, int INCX)
|
{
|
const_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void constrain_ongpu(int N, float ALPHA, float * X, int INCX)
|
{
|
constrain_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void constrain_min_max_ongpu(int N, float MIN, float MAX, float * X, int INCX)
|
{
|
constrain_min_max_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, MIN, MAX, X, INCX);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX)
|
{
|
scal_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, INCX);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void scal_add_ongpu(int N, float ALPHA, float BETA, float * X, int INCX)
|
{
|
scal_add_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, ALPHA, BETA, X, INCX);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX)
|
{
|
supp_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, ALPHA, X, INCX);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX)
|
{
|
//fill_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream()>>>(N, ALPHA, X, INCX);
|
//CHECK_CUDA(cudaPeekAtLastError());
|
fill_kernel << <get_number_of_blocks(N, BLOCK), BLOCK, 0, get_cuda_stream() >> >(N, ALPHA, X, INCX);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
__global__ void gradient_centralization_kernel(int filters, int f_size, float *in)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
const int tid = index % WARP_SIZE;
|
const int f = index / WARP_SIZE;
|
|
if (f >= filters) return;
|
|
float mean = 0;
|
for (int i = 0; i < f_size; i += WARP_SIZE) {
|
mean += warpAllReduceSum(in[f*f_size + i + tid]);
|
}
|
mean = mean / f_size;
|
for (int i = 0; i < f_size; i += WARP_SIZE) {
|
in[f*f_size + i + tid] -= mean;
|
}
|
|
}
|
|
extern "C" void gradient_centralization_gpu(int w, int h, int c, int f, float *in)
|
{
|
const int size = f * WARP_SIZE;
|
const int f_size = c * h * w;
|
if (f_size % WARP_SIZE == 0) {
|
|
gradient_centralization_kernel << <get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >> > (f, f_size, in);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
}
|
|
__device__ float relu(float src) {
|
if (src > 0) return src;
|
return 0;
|
}
|
|
__device__ float lrelu(float src) {
|
const float eps = 0.001;
|
if (src > eps) return src;
|
return eps;
|
}
|
|
__device__ float grad_relu(float src) {
|
return (src > 0);
|
}
|
|
__device__ float grad_lrelu(float src) {
|
const float eps = 0.001;
|
return (src > eps);
|
}
|
|
__global__ void shortcut_singlelayer_simple_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization)
|
{
|
const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
if (id >= size) return;
|
|
int src_id = id;
|
const int src_i = src_id % src_outputs;
|
src_id /= src_outputs;
|
int src_b = src_id;
|
|
float out_val = in[id];
|
|
int add_outputs = outputs_of_layers_gpu[0];
|
if (src_i < add_outputs) {
|
int add_index = add_outputs*src_b + src_i;
|
|
float *add = layers_output_gpu[0];
|
out_val += add[add_index];
|
}
|
out[id] = out_val;
|
}
|
|
__global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization)
|
{
|
const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
if (id >= size) return;
|
|
// nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w)
|
const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w)
|
int step = 0;
|
if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1
|
|
int src_id = id;
|
const int src_i = src_id % src_outputs;
|
src_id /= src_outputs;
|
int src_b = src_id;
|
|
float sum = 1, max_val = -FLT_MAX;
|
if (weights_gpu && weights_normalization) {
|
if (weights_normalization == SOFTMAX_NORMALIZATION) {
|
for (int i = 0; i < (n + 1); ++i) {
|
const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)]
|
const float w = weights_gpu[weights_index];
|
if (max_val < w) max_val = w;
|
}
|
}
|
const float eps = 0.0001;
|
sum = eps;
|
for (int i = 0; i < (n + 1); ++i) {
|
const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)]
|
const float w = weights_gpu[weights_index];
|
if (weights_normalization == RELU_NORMALIZATION) sum += lrelu(w);
|
else if (weights_normalization == SOFTMAX_NORMALIZATION) sum += expf(w - max_val);
|
}
|
}
|
|
float out_val = 0;
|
|
if (weights_gpu) {
|
float w = weights_gpu[src_i / step];
|
if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum;
|
else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum;
|
|
out_val = in[id] * w; // [0 or c or (c, h ,w)]
|
}
|
else out_val = in[id];
|
|
// layers
|
for (int i = 0; i < n; ++i) {
|
int add_outputs = outputs_of_layers_gpu[i];
|
if (src_i < add_outputs) {
|
int add_index = add_outputs*src_b + src_i;
|
|
float *add = layers_output_gpu[i];
|
|
if (weights_gpu) {
|
const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)]
|
float w = weights_gpu[weights_index];
|
if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum;
|
else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum;
|
|
out_val += add[add_index] * w; // [0 or c or (c, h ,w)]
|
}
|
else out_val += add[add_index];
|
}
|
}
|
out[id] = out_val;
|
}
|
|
extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization)
|
{
|
//printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n);
|
int size = batch * src_outputs;
|
if (nweights == 0 && n == 1) {
|
shortcut_singlelayer_simple_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization);
|
}
|
else {
|
shortcut_multilayer_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization);
|
}
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
__global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu,
|
float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalization)
|
{
|
const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
if (id >= size) return;
|
|
// nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w)
|
const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w)
|
int step = 0;
|
if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1
|
|
int src_id = id;
|
const int src_i = src_id % src_outputs;
|
src_id /= src_outputs;
|
int src_b = src_id;
|
|
float grad = 1, sum = 1, max_val = -FLT_MAX;
|
int i;
|
if (weights_gpu && weights_normalization) {
|
if (weights_normalization == SOFTMAX_NORMALIZATION) {
|
for (int i = 0; i < (n + 1); ++i) {
|
const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)]
|
float w = weights_gpu[weights_index];
|
if (max_val < w) max_val = w;
|
}
|
}
|
const float eps = 0.0001;
|
sum = eps;
|
for (i = 0; i < (n + 1); ++i) {
|
const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)]
|
const float w = weights_gpu[weights_index];
|
if (weights_normalization == RELU_NORMALIZATION) sum += lrelu(w);
|
else if (weights_normalization == SOFTMAX_NORMALIZATION) sum += expf(w - max_val);
|
}
|
|
}
|
|
if (weights_gpu) {
|
float w = weights_gpu[src_i / step];
|
if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum;
|
else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum;
|
|
if (weights_normalization == RELU_NORMALIZATION) grad = w;
|
else if (weights_normalization == SOFTMAX_NORMALIZATION) grad = w*(1-w);
|
|
delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)]
|
float weights_update_tmp = delta_in[id] * in[id] * grad;// / step;
|
|
if (layer_step == 1 && (size/32) > (id/32 + 1)) {
|
if (isnan(weights_update_tmp) || isinf(weights_update_tmp)) {
|
weights_update_tmp = 0;
|
}
|
float wu = warpAllReduceSum(weights_update_tmp);
|
if (threadIdx.x % 32 == 0) {
|
if (!isnan(wu) && !isinf(wu))
|
atomicAdd(&weight_updates_gpu[src_i / step], wu);
|
}
|
}
|
else {
|
if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp))
|
atomicAdd(&weight_updates_gpu[src_i / step], weights_update_tmp);
|
//weight_updates_gpu[src_i / step] += weights_update_tmp;
|
}
|
}
|
else delta_out[id] += delta_in[id];
|
|
// layers
|
for (int i = 0; i < n; ++i) {
|
int add_outputs = outputs_of_layers_gpu[i];
|
if (src_i < add_outputs) {
|
int add_index = add_outputs*src_b + src_i;
|
int out_index = id;
|
|
float *layer_delta = layers_delta_gpu[i];
|
if (weights_gpu) {
|
float *add = layers_output_gpu[i];
|
|
const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)]
|
float w = weights_gpu[weights_index];
|
if (weights_normalization == RELU_NORMALIZATION) w = lrelu(w) / sum;
|
else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum;
|
|
if (weights_normalization == RELU_NORMALIZATION) grad = w;
|
else if (weights_normalization == SOFTMAX_NORMALIZATION) grad = w*(1 - w);
|
|
layer_delta[add_index] += delta_in[id] * w;
|
float weights_update_tmp = delta_in[id] * add[add_index] * grad;// / step;
|
|
if (layer_step == 1 && (size / 32) > (id / 32 + 1)) {
|
if (isnan(weights_update_tmp) || isinf(weights_update_tmp)) {
|
weights_update_tmp = 0;
|
}
|
float wu = warpAllReduceSum(weights_update_tmp);
|
if (threadIdx.x % 32 == 0) {
|
if (!isnan(wu) && !isinf(wu))
|
atomicAdd(&weight_updates_gpu[weights_index], wu);
|
//if(weights_gpu[weights_index] != 1) printf(" wu = %f, weights_update_tmp = %f, w = %f, weights_gpu[weights_index] = %f, grad = %f, weights_normalization = %d ",
|
// wu, weights_update_tmp, w, weights_gpu[weights_index], grad, weights_normalization);
|
}
|
}
|
else {
|
if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp))
|
atomicAdd(&weight_updates_gpu[weights_index], weights_update_tmp);
|
//weight_updates_gpu[weights_index] += weights_update_tmp;
|
}
|
}
|
else layer_delta[add_index] += delta_in[id];
|
}
|
}
|
}
|
|
extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu,
|
float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalization)
|
{
|
const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w)
|
int step = 0;
|
if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1
|
//printf(" nweights = %d, n = %d, layer_step = %d, step = %d \n", nweights, n, layer_step, step);
|
|
//printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n);
|
int size = batch * src_outputs;
|
backward_shortcut_multilayer_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, src_outputs, batch, n, outputs_of_layers_gpu,
|
layers_delta_gpu, delta_out, delta_in, weights_gpu, weight_updates_gpu, nweights, in, layers_output_gpu, weights_normalization);
|
CHECK_CUDA(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 *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] += 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 *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, 0, get_cuda_stream()>>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
__global__ void simple_input_shortcut_kernel(float *in, int size, float *add, float *out)
|
{
|
int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
if (id >= size) return;
|
|
out[id] = in[id] + add[id];
|
}
|
|
__global__ void input_shortcut_kernel(float *in, 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 *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] = in[out_index] + add[add_index];
|
}
|
|
extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out)
|
{
|
if (w1 == w2 && h1 == h2 && c1 == c2) {
|
int size = batch * w1 * h1 * c1;
|
simple_input_shortcut_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(in, size, add, out);
|
CHECK_CUDA(cudaPeekAtLastError());
|
return;
|
}
|
|
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;
|
//input_shortcut_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(in, size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out);
|
simple_copy_ongpu(w2 * h2 * c2 * batch, in, out);
|
shortcut_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> >(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out);
|
CHECK_CUDA(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 = abs(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, 0, get_cuda_stream() >>>(n, pred, truth, delta, error);
|
CHECK_CUDA(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, 0, get_cuda_stream() >> >(n, pred, truth, delta, error);
|
CHECK_CUDA(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, 0, get_cuda_stream() >>>(n, pred, truth, delta, error);
|
CHECK_CUDA(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);
|
}
|
}
|
|
extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c)
|
{
|
weighted_sum_kernel<<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(num, a, b, s, c);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
__global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float *da, float *db, float *ds, float *dc)
|
{
|
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
if(i < n){
|
if(da) da[i] += dc[i] * s[i];
|
db[i] += dc[i] * (1-s[i]);
|
ds[i] += dc[i] * a[i] + dc[i] * -b[i];
|
}
|
}
|
|
extern "C" void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc)
|
{
|
weighted_delta_kernel<<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(num, a, b, s, da, db, ds, dc);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
__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){
|
c[i] += a[i]*b[i];
|
}
|
}
|
|
extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c)
|
{
|
mult_add_into_kernel<<<cuda_gridsize(num), BLOCK, 0, get_cuda_stream() >>>(num, a, b, c);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
__device__ void softmax_device(int n, float *input, float temp, float *output)
|
{
|
int i;
|
float sum = 0;
|
float largest = -INFINITY;
|
for(i = 0; i < n; ++i){
|
int val = input[i];
|
largest = (val>largest) ? val : largest;
|
}
|
for(i = 0; i < n; ++i){
|
float e = exp(input[i]/temp - largest/temp);
|
sum += e;
|
output[i] = e;
|
}
|
for(i = 0; i < n; ++i){
|
output[i] /= sum;
|
}
|
}
|
|
__global__ void softmax_kernel(int n, int offset, int batch, float *input, float temp, float *output)
|
{
|
int b = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
if(b >= batch) return;
|
softmax_device(n, input + b*offset, temp, output + b*offset);
|
}
|
|
extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output)
|
{
|
int inputs = n;
|
int batch = groups;
|
softmax_kernel<<<cuda_gridsize(batch), BLOCK, 0, get_cuda_stream()>>>(inputs, offset, batch, input, temp, output);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
__device__ void softmax_device_new_api(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];
|
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_kernel_new_api(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_new_api(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset);
|
}
|
|
extern "C" void softmax_gpu_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)
|
{
|
softmax_kernel_new_api << <cuda_gridsize(batch*groups), BLOCK, 0, get_cuda_stream() >> >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output);
|
CHECK_CUDA(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, 0, get_cuda_stream() >> >(size, in, w, h, c, batch, stride, forward, scale, out);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
__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_new_api(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s);
|
}
|
|
extern "C" void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier)
|
{
|
int *tree_groups_size = cuda_make_int_array_new_api(hier.group_size, hier.groups);
|
int *tree_groups_offset = cuda_make_int_array_new_api(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, 0, get_cuda_stream() >>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset);
|
CHECK_CUDA(cudaPeekAtLastError());
|
cuda_free((float *)tree_groups_size);
|
cuda_free((float *)tree_groups_offset);
|
}
|
|
|
__global__ void fix_nan_and_inf_kernel(float *input, size_t size)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size) {
|
float val = input[index];
|
if (isnan(val) || isinf(val)) {
|
input[index] = 1.0f / (fabs((float)index) + 1); // pseudo random value
|
}
|
}
|
}
|
|
extern "C" void fix_nan_and_inf(float *input, size_t size)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
fix_nan_and_inf_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(input, size);
|
CHECK_CUDA(cudaPeekAtLastError());
|
//CHECK_CUDA(cudaDeviceSynchronize());
|
}
|
|
|
__global__ void reset_nan_and_inf_kernel(float *input, size_t size)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size) {
|
float val = input[index];
|
if (isnan(val) || isinf(val)) {
|
input[index] = 0;
|
}
|
}
|
}
|
|
extern "C" void reset_nan_and_inf(float *input, size_t size)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
reset_nan_and_inf_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(input, size);
|
CHECK_CUDA(cudaPeekAtLastError());
|
//CHECK_CUDA(cudaDeviceSynchronize());
|
}
|
|
|
|
__global__ void is_nan_or_inf_kernel(float *input, size_t size, int *pinned_return)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size) {
|
float val = input[index];
|
if (isnan(val) || isinf(val))
|
*pinned_return = 1;
|
}
|
}
|
|
extern "C" int is_nan_or_inf(float *input, size_t size)
|
{
|
int *pinned_return;
|
CHECK_CUDA(cudaHostAlloc(&pinned_return, sizeof(int), cudaHostRegisterMapped));
|
*pinned_return = 0;
|
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
is_nan_or_inf_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(input, size, pinned_return);
|
CHECK_CUDA(cudaDeviceSynchronize());
|
int ret_val = *pinned_return;
|
|
CHECK_CUDA(cudaFreeHost(pinned_return));
|
return ret_val;
|
}
|
|
__global__ void add_3_arrays_activate_kernel(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size) {
|
float val = 0;
|
if (a1) val += a1[index];
|
if (a2) val += a2[index];
|
if (a3) val += a3[index];
|
if (a == LOGISTIC) val = 1.f / (1.f + expf(-val));
|
else if (a == TANH) val = (2 / (1 + expf(-2 * val)) - 1);
|
else if (a == LEAKY) val = (val < 0) ? val*0.1 : val;
|
dst[index] = val;
|
}
|
}
|
|
extern "C" void add_3_arrays_activate(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
if (!(a == LOGISTIC || a == TANH || a == LEAKY || a == LINEAR)) {
|
printf(" add_3_arrays_activate() doesn't support activation %d, it supports only LOGISTIC and TANH \n", a);
|
exit(EXIT_FAILURE);
|
}
|
add_3_arrays_activate_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(a1, a2, a3, size, a, dst);
|
}
|
|
|
__global__ void sum_of_mults_kernel(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size) {
|
dst[index] = a1[index] * a2[index] + b1[index] * b2[index];
|
}
|
}
|
|
extern "C" void sum_of_mults(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
sum_of_mults_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(a1, a2, b1, b2, size, dst);
|
}
|
|
|
__global__ void activate_and_mult_kernel(float *a1, float *a2, size_t size, ACTIVATION a, float *dst)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size) {
|
float val = a1[index];
|
if (a == TANH) val = (2 / (1 + expf(-2 * val)) - 1);
|
else if (a == LEAKY) val = (val < 0) ? val*0.1 : val;
|
dst[index] = val * a2[index];
|
}
|
}
|
|
extern "C" void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION a, float *dst)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
if (!(a == TANH || a == LEAKY || a == LINEAR)) {
|
printf(" activat_and_mult() doesn't support activation %d, it supports only TANH \n", a);
|
exit(EXIT_FAILURE);
|
}
|
activate_and_mult_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(a1, a2, size, a, dst);
|
}
|
|
|
|
__global__ void scale_channels_kernel(float *in_w_h_c, int size, int channel_size, int batch_size, int scale_wh, float *scales_c, float *out)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size) {
|
if (scale_wh) {
|
int osd_index = index % channel_size + (index / batch_size)*channel_size;
|
|
out[index] = in_w_h_c[index] * scales_c[osd_index];
|
}
|
else {
|
out[index] = in_w_h_c[index] * scales_c[index / channel_size];
|
}
|
}
|
}
|
|
extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, int batch_size, int scale_wh, float *scales_c, float *out)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
scale_channels_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(in_w_h_c, size, channel_size, batch_size, scale_wh, scales_c, out);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
|
__global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh,
|
float *in_scales_c, float *out_from_delta,
|
float *in_from_output, float *out_state_delta)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
|
if (index < size) {
|
|
if (scale_wh)
|
{
|
int osd_index = index % channel_size + (index / batch_size)*channel_size;
|
|
//out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?)
|
atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index] / channel_size); // l.delta * from
|
|
out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here
|
|
}
|
else {
|
int osd_index = index / channel_size;
|
//out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?)
|
|
int warp_id = index / 32;
|
int index_warp_start = warp_id * 32;
|
int osd_index_warp_start = index_warp_start / channel_size;
|
int osd_index_warp_end = (index_warp_start + 31) / channel_size;
|
|
if (osd_index_warp_start == osd_index_warp_end) // all thread in warp process the same channel
|
{
|
float sum = warpAllReduceSum(in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from
|
if (threadIdx.x % 32 == 0) {
|
atomicAdd(&out_state_delta[osd_index], sum);
|
//out_state_delta[osd_index] += sum;
|
}
|
}
|
else {
|
atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from
|
}
|
|
out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here
|
}
|
}
|
}
|
|
extern "C" void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh,
|
float *in_scales_c, float *out_from_delta,
|
float *in_from_output, float *out_state_delta)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
backward_scale_channels_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (in_w_h_c_delta, size, channel_size, batch_size, scale_wh,
|
in_scales_c, out_from_delta,
|
in_from_output, out_state_delta);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
__global__ void sam_kernel(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size) {
|
out[index] = in_w_h_c[index] * scales_c[index];
|
}
|
}
|
|
extern "C" void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
sam_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> >(in_w_h_c, size, channel_size, scales_c, out);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
__global__ void backward_sam_kernel(float *in_w_h_c_delta, int size, int channel_size,
|
float *in_scales_c, float *out_from_delta,
|
float *in_from_output, float *out_state_delta)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
if (index < size) {
|
out_state_delta[index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?)
|
out_from_delta[index] += in_scales_c[index] * in_w_h_c_delta[index]; // input * l.delta
|
|
//out_state_delta[index] += in_w_h_c_delta[index];
|
//out_from_delta[index] = in_w_h_c_delta[index];
|
}
|
}
|
|
extern "C" void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_size,
|
float *in_scales_c, float *out_from_delta,
|
float *in_from_output, float *out_state_delta)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
backward_sam_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (in_w_h_c_delta, size, channel_size,
|
in_scales_c, out_from_delta,
|
in_from_output, out_state_delta);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
__global__ void smooth_rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
const int kernel_area = kernel_size * kernel_size;
|
const int i = index * kernel_area;
|
|
const int stage_step = (nweights / kernel_area) / 4; // 4 stages
|
const int stage_id = index / stage_step;
|
|
// nweights = (c / groups) * n * size * size;
|
// kernel_area = size*size
|
|
if (i < nweights)
|
{
|
// rotate left or right
|
if (reverse) angle = -angle;
|
|
const float cos_a = cosf(angle * 3.14159265 / 180);
|
const float sin_a = sinf(angle * 3.14159265 / 180);
|
const int x_c = kernel_size / 2;
|
const int y_c = kernel_size / 2;
|
|
float dropout_sum = 0;
|
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
// Xsource = x*cos(alpha) + y*sin(alpha)
|
// Ysource = -x*sin(alpha) + y*cos(alpha)
|
|
float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a;
|
float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a;
|
|
int x_0 = floorf(x_s); // round down
|
int x_1 = ceilf(x_s); // round up
|
if (x_0 == x_1) x_1 = x_0 + 1;
|
int y_0 = floorf(y_s);
|
int y_1 = ceilf(y_s);
|
if (y_0 == y_1) y_1 = y_0 + 1;
|
|
float c_x_0 = x_1 - x_s;
|
float c_x_1 = x_s - x_0;
|
float c_y_0 = y_1 - y_s;
|
float c_y_1 = y_s - y_0;
|
|
|
float val = 0;
|
if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0;
|
else dropout_sum += c_x_0 * c_y_0;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0;
|
else dropout_sum += c_x_1 * c_y_0;
|
|
if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1;
|
else dropout_sum += c_x_0 * c_y_1;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1;
|
else dropout_sum += c_x_1 * c_y_1;
|
|
weight_deform_gpu[x + y*kernel_size + i] = val;
|
}
|
}
|
|
// compensate for dropped items
|
const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum);
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
weight_deform_gpu[x + y*kernel_size + i] *= coef;
|
}
|
}
|
}
|
}
|
|
|
extern "C" void smooth_rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse)
|
{
|
const int kernel_area = size*size;
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size);
|
smooth_rotate_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
__global__ void stretch_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, float scale, int reverse)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
const int kernel_area = kernel_size * kernel_size;
|
const int i = index * kernel_area;
|
|
const int stage_step = (nweights / kernel_area) / 4; // 4 stages
|
const int stage_id = index / stage_step;
|
|
// nweights = (c / groups) * n * size * size;
|
// kernel_area = size*size
|
|
if (i < nweights)
|
{
|
|
if (stage_id == 0) {
|
// simple copy
|
for (int x = 0; x < kernel_size; ++x) {
|
for (int y = 0; y < kernel_size; ++y) {
|
weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i];
|
}
|
}
|
}
|
else if (stage_id > 0)
|
{
|
if (stage_id == 1) scale = 0.65;
|
else if (stage_id == 2) scale = 0.8;
|
else if (stage_id == 3) scale = 1.3;
|
|
if (reverse) scale = 1 / scale;
|
|
const int x_c = kernel_size / 2;
|
const int y_c = kernel_size / 2;
|
|
float dropout_sum = 0;
|
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
// Xsource = x_c + (x_d - x_c) / scale
|
// Ysource = y_c + (y_d - y_c) / scale
|
|
float x_s = x_c + (x - x_c) / scale;
|
float y_s = y_c + (y - y_c) / scale;
|
|
int x_0 = floorf(x_s); // round down
|
int x_1 = ceilf(x_s); // round up
|
if (x_0 == x_1) x_1 = x_0 + 1;
|
int y_0 = floorf(y_s);
|
int y_1 = ceilf(y_s);
|
if (y_0 == y_1) y_1 = y_0 + 1;
|
|
float c_x_0 = x_1 - x_s;
|
float c_x_1 = x_s - x_0;
|
float c_y_0 = y_1 - y_s;
|
float c_y_1 = y_s - y_0;
|
|
float val = 0;
|
if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0;
|
else dropout_sum += c_x_0 * c_y_0;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0;
|
else dropout_sum += c_x_1 * c_y_0;
|
|
if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1;
|
else dropout_sum += c_x_0 * c_y_1;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1;
|
else dropout_sum += c_x_1 * c_y_1;
|
|
weight_deform_gpu[x + y*kernel_size + i] = val;
|
}
|
}
|
|
// compensate for dropped items
|
//const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum);
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
//if (scale < 1) weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef;
|
weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef;
|
}
|
}
|
}
|
}
|
}
|
|
|
extern "C" void stretch_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, float scale, int reverse)
|
{
|
const int kernel_area = size*size;
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size);
|
stretch_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, scale, reverse);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
__global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
const int kernel_area = kernel_size * kernel_size;
|
const int i = index * kernel_area;
|
|
const int stage_step = (nweights / kernel_area) / 4; // 4 stages
|
const int stage_id = index / stage_step;
|
|
// nweights = (c / groups) * n * size * size;
|
// kernel_area = size*size
|
|
if (i < nweights)
|
{
|
|
if (stage_id == 0) {
|
// simple copy
|
for (int x = 0; x < kernel_size; ++x) {
|
for (int y = 0; y < kernel_size; ++y) {
|
weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i];
|
}
|
}
|
}
|
else if (stage_id == 1 || stage_id == 2)
|
{
|
// rotate left or right
|
if (stage_id == 2) angle = -angle;
|
if (reverse) angle = -angle;
|
|
const float cos_a = cosf(angle * 3.14159265 / 180);
|
const float sin_a = sinf(angle * 3.14159265 / 180);
|
const int x_c = kernel_size / 2;
|
const int y_c = kernel_size / 2;
|
|
float dropout_sum = 0;
|
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
// Xsource = x*cos(alpha) + y*sin(alpha)
|
// Ysource = -x*sin(alpha) + y*cos(alpha)
|
|
float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a;
|
float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a;
|
|
int x_0 = floorf(x_s); // round down
|
int x_1 = ceilf(x_s); // round up
|
if (x_0 == x_1) x_1 = x_0 + 1;
|
int y_0 = floorf(y_s);
|
int y_1 = ceilf(y_s);
|
if (y_0 == y_1) y_1 = y_0 + 1;
|
|
float c_x_0 = x_1 - x_s;
|
float c_x_1 = x_s - x_0;
|
float c_y_0 = y_1 - y_s;
|
float c_y_1 = y_s - y_0;
|
|
float val = 0;
|
if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0;
|
else dropout_sum += c_x_0 * c_y_0;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0;
|
else dropout_sum += c_x_1 * c_y_0;
|
|
if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1;
|
else dropout_sum += c_x_0 * c_y_1;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1;
|
else dropout_sum += c_x_1 * c_y_1;
|
|
weight_deform_gpu[x + y*kernel_size + i] = val;
|
}
|
}
|
|
// compensate for dropped items
|
const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum);
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
weight_deform_gpu[x + y*kernel_size + i] *= coef;
|
}
|
}
|
}
|
else if (stage_id == 3)
|
{
|
// flip
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i];
|
}
|
}
|
}
|
}
|
}
|
|
|
extern "C" void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse)
|
{
|
const int kernel_area = size*size;
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size);
|
sway_and_flip_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
|
|
|
|
__global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int reverse)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
const int kernel_area = kernel_size * kernel_size;
|
const int i = index * kernel_area;
|
|
const int stage_step = (nweights / kernel_area) / 4; // 4 stages
|
const int stage_id = index / stage_step;
|
|
// nweights = (c / groups) * n * size * size;
|
// kernel_area = size*size
|
|
if (i < nweights)
|
{
|
// if(reverse)
|
|
if (stage_id == 0) {
|
// simple copy
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
const int src_i = x + y*kernel_size + i;
|
const int dst_i = x + y*kernel_size + i;
|
if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i];
|
else weight_deform_gpu[dst_i] = src_weight_gpu[src_i];
|
}
|
}
|
}
|
else if (stage_id == 1)
|
{
|
// 90 degree clockwise rotation - 1
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
const int src_i = x + y*kernel_size + i;
|
const int dst_i = (kernel_size - 1 - y) + x*kernel_size + i;
|
if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i];
|
else weight_deform_gpu[dst_i] = src_weight_gpu[src_i];
|
}
|
}
|
}
|
else if (stage_id == 2)
|
{
|
// 180 degree clockwise rotation - 2
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
const int src_i = x + y*kernel_size + i;
|
const int dst_i = (kernel_size - 1 - x) + (kernel_size - 1 - y)*kernel_size + i;
|
if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i];
|
else weight_deform_gpu[dst_i] = src_weight_gpu[src_i];
|
}
|
}
|
}
|
else if (stage_id == 3)
|
{
|
// 270 degree clockwise rotation - 3
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
const int src_i = x + y*kernel_size + i;
|
const int dst_i = y + (kernel_size - 1 - x)*kernel_size + i;
|
if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i];
|
else weight_deform_gpu[dst_i] = src_weight_gpu[src_i];
|
}
|
}
|
}
|
}
|
}
|
|
|
extern "C" void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int reverse)
|
{
|
const int kernel_area = size*size;
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size);
|
rotate_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, reverse);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
__global__ void stretch_sway_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, float angle, int reverse)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
const int kernel_area = kernel_size * kernel_size;
|
const int i = index * kernel_area;
|
|
const int stage_step = (nweights / kernel_area) / 8; // 8 stages
|
const int stage_id = index / stage_step;
|
|
// nweights = (c / groups) * n * size * size;
|
// kernel_area = size*size
|
|
if (i < nweights)
|
{
|
|
if (stage_id == 0) {
|
// simple copy
|
for (int x = 0; x < kernel_size; ++x) {
|
for (int y = 0; y < kernel_size; ++y) {
|
weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i];
|
}
|
}
|
}
|
else if (stage_id == 1 || stage_id == 2 || stage_id == 3 || stage_id == 4)
|
{
|
float scale = 0.5;
|
if (stage_id == 1) scale = 0.65;
|
else if (stage_id == 2) scale = 0.8;
|
else if (stage_id == 3) scale = 1.2;
|
else if (stage_id == 4) scale = 1.4;
|
|
if (reverse) scale = 1 / scale;
|
|
const int x_c = kernel_size / 2;
|
const int y_c = kernel_size / 2;
|
|
float dropout_sum = 0;
|
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
// Xsource = x_c + (x_d - x_c) / scale
|
// Ysource = y_c + (y_d - y_c) / scale
|
|
float x_s = x_c + (x - x_c) / scale;
|
float y_s = y_c + (y - y_c) / scale;
|
|
int x_0 = floorf(x_s); // round down
|
int x_1 = ceilf(x_s); // round up
|
if (x_0 == x_1) x_1 = x_0 + 1;
|
int y_0 = floorf(y_s);
|
int y_1 = ceilf(y_s);
|
if (y_0 == y_1) y_1 = y_0 + 1;
|
|
float c_x_0 = x_1 - x_s;
|
float c_x_1 = x_s - x_0;
|
float c_y_0 = y_1 - y_s;
|
float c_y_1 = y_s - y_0;
|
|
float val = 0;
|
if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0;
|
else dropout_sum += c_x_0 * c_y_0;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0;
|
else dropout_sum += c_x_1 * c_y_0;
|
|
if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1;
|
else dropout_sum += c_x_0 * c_y_1;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1;
|
else dropout_sum += c_x_1 * c_y_1;
|
|
weight_deform_gpu[x + y*kernel_size + i] = val;
|
}
|
}
|
|
// compensate for dropped items
|
//const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum);
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
if(scale > 1)
|
weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef;
|
}
|
}
|
}
|
else if (stage_id == 5 || stage_id == 6)
|
{
|
// rotate left or right
|
if (stage_id == 6) angle = -angle;
|
if (reverse) angle = -angle;
|
|
const float cos_a = cosf(angle * 3.14159265 / 180);
|
const float sin_a = sinf(angle * 3.14159265 / 180);
|
const int x_c = kernel_size / 2;
|
const int y_c = kernel_size / 2;
|
|
float dropout_sum = 0;
|
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
// Xsource = x*cos(alpha) + y*sin(alpha)
|
// Ysource = -x*sin(alpha) + y*cos(alpha)
|
|
float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a;
|
float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a;
|
|
int x_0 = floorf(x_s); // round down
|
int x_1 = ceilf(x_s); // round up
|
if (x_0 == x_1) x_1 = x_0 + 1;
|
int y_0 = floorf(y_s);
|
int y_1 = ceilf(y_s);
|
if (y_0 == y_1) y_1 = y_0 + 1;
|
|
float c_x_0 = x_1 - x_s;
|
float c_x_1 = x_s - x_0;
|
float c_y_0 = y_1 - y_s;
|
float c_y_1 = y_s - y_0;
|
|
float val = 0;
|
if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0;
|
else dropout_sum += c_x_0 * c_y_0;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0;
|
else dropout_sum += c_x_1 * c_y_0;
|
|
if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1;
|
else dropout_sum += c_x_0 * c_y_1;
|
|
if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1;
|
else dropout_sum += c_x_1 * c_y_1;
|
|
weight_deform_gpu[x + y*kernel_size + i] = val;
|
}
|
}
|
|
// compensate for dropped items
|
const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum);
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
weight_deform_gpu[x + y*kernel_size + i] *= coef;
|
}
|
}
|
}
|
else if (stage_id == 7)
|
{
|
// flip
|
for (int y = 0; y < kernel_size; ++y) {
|
for (int x = 0; x < kernel_size; ++x) {
|
weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i];
|
}
|
}
|
}
|
}
|
}
|
|
|
extern "C" void stretch_sway_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse)
|
{
|
const int kernel_area = size*size;
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size);
|
stretch_sway_flip_weights_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
__global__ void reduce_and_expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
|
if (index < current_size) {
|
float val = 0;
|
for (int i = 0; i < groups; ++i) {
|
val += src_gpu[index + i*current_size];
|
}
|
for (int i = 0; i < groups; ++i) {
|
dst_gpu[index + i*current_size] = val / groups;
|
}
|
}
|
}
|
|
extern "C" void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups)
|
{
|
const int current_size = size / groups;
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(current_size, block_size);
|
reduce_and_expand_array_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_gpu, dst_gpu, current_size, groups);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
__global__ void expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
|
if (index < current_size) {
|
for (int i = 0; i < groups; ++i) {
|
dst_gpu[index + i*current_size] = src_gpu[index];
|
}
|
}
|
}
|
|
extern "C" void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups)
|
{
|
const int current_size = size / groups;
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(current_size, block_size);
|
expand_array_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_gpu, dst_gpu, current_size, groups);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
__global__ void mult_inverse_array_kernel(const float *src_gpu, float *dst_gpu, int size, const float eps,
|
float divider, const float clip, const float abs_add)
|
{
|
const int index = blockIdx.x*blockDim.x + threadIdx.x;
|
|
if (index < size) {
|
float val = src_gpu[index];
|
float sign = (val < 0) ? -1 : 1;
|
// eps = 1 by default
|
// eps = 2 - lower delta
|
// eps = 0 - higher delta (linear)
|
// eps = -1 - high delta (inverse number)
|
// = (abs(x)*10+1)^(-1)
|
float unsigned_val = powf(fabs(val)*10 + abs_add, eps);
|
unsigned_val = unsigned_val / divider;
|
if (unsigned_val > clip && clip != 0.0) unsigned_val = clip;
|
if (isnan(unsigned_val) || isinf(unsigned_val)) unsigned_val = 0;
|
dst_gpu[index] = unsigned_val * sign;
|
}
|
}
|
|
extern "C" void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int size, float eps, float divider, float clip, float abs_add)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
mult_inverse_array_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (src_gpu, dst_gpu, size, eps, divider, clip, abs_add);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
__global__ void P_constrastive_f_det_kernel(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size)
|
{
|
const int il = blockIdx.x*blockDim.x + threadIdx.x;
|
|
if (il < contrast_p_size) {
|
const float sim = contrast_p[il].sim;
|
const size_t i = contrast_p[il].i;
|
const size_t j = contrast_p[il].j;
|
|
const float numerator = expf(sim / temperature);
|
|
float denominator = 0;
|
int k;
|
for (k = 0; k < contrast_p_size; ++k) {
|
contrastive_params cp = contrast_p[k];
|
//if (k != i && labels[k] != labels[i]) {
|
//if (k != i) {
|
if (cp.i != i && cp.j == j) {
|
//const float sim_den = cp.sim;
|
////const float sim_den = find_sim(k, l, contrast_p, contrast_p_size); // cosine_similarity(z[k], z[l], feature_size);
|
//denominator += expf(sim_den / temperature);
|
denominator += cp.exp_sim;
|
}
|
}
|
|
float result = 0.9999;
|
if (denominator != 0) result = numerator / denominator;
|
if (result > 1) result = 0.9999;
|
|
contrast_p[il].P = result;
|
}
|
}
|
|
|
extern "C" void P_constrastive_f_det_gpu(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(contrast_p_size, block_size);
|
P_constrastive_f_det_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (labels, feature_size, temperature, contrast_p, contrast_p_size);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
|
__global__ void coord_conv_kernel(float *dst, int w, int h, int chan, int batch, int type)
|
{
|
int i = blockIdx.x*blockDim.x + threadIdx.x;
|
|
const int x = i % w;
|
i = i / w;
|
const int y = i % h;
|
i = i / h;
|
const int c = i % chan;
|
//i = i / chan;
|
//const int b = i % batch;
|
|
if (type == 0) {
|
if (c == 0) {
|
const float x_val = (2.0f * x) / w - 1.0f; // [-1; 1)
|
dst[i] = x_val; // x - coord
|
}
|
else if (c == 1) {
|
const float y_val = (2.0f * y) / h - 1.0f; // [-1; 1)
|
dst[i] = y_val; // y - coord
|
}
|
else if (c == 2) {
|
const float x_val = (2.0f * x) / w - 1.0f; // [-1; 1)
|
const float y_val = (2.0f * y) / h - 1.0f; // [-1; 1)
|
const float rad_val = sqrtf(x_val*x_val + y_val*y_val); // [0; 1.414)
|
dst[i] = rad_val; // rad - coord
|
}
|
}
|
else if (type == 1) {
|
if (c >= 0 && c <= 2) {
|
dst[i] = 0;
|
}
|
}
|
}
|
|
extern "C" void coord_conv_gpu(float *dst, int size, int w, int h, int chan, int b, int type)
|
{
|
const int block_size = BLOCK;
|
const int num_blocks = get_number_of_blocks(size, block_size);
|
coord_conv_kernel << <num_blocks, block_size, 0, get_cuda_stream() >> > (dst, w, h, chan, b, type);
|
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
__global__ void forward_implicit_kernel(int size, int batch, int nweights, float *weight_gpu, float *output_gpu)
|
{
|
const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
if (id >= size) return;
|
|
output_gpu[id] = weight_gpu[id % nweights];
|
}
|
|
extern "C" void forward_implicit_gpu(int batch, int nweights, float *weight_gpu, float *output_gpu)
|
{
|
int size = batch * nweights;
|
forward_implicit_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, batch, nweights, weight_gpu, output_gpu);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
|
|
|
__global__ void backward_implicit_kernel(int size, int batch, int nweights, float *weight_updates_gpu, float *delta_gpu)
|
{
|
const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
|
if (id >= size) return;
|
|
for (int i = 0; i < batch; ++i) {
|
weight_updates_gpu[id] += delta_gpu[id + i * nweights];
|
}
|
}
|
|
extern "C" void backward_implicit_gpu(int batch, int nweights, float *weight_updates_gpu, float *delta_gpu)
|
{
|
int size = nweights;
|
backward_implicit_kernel << <cuda_gridsize(size), BLOCK, 0, get_cuda_stream() >> > (size, batch, nweights, weight_updates_gpu, delta_gpu);
|
CHECK_CUDA(cudaPeekAtLastError());
|
}
|
