From 59efae1b9cb90479b6faaa904463f5539fcc25f9 Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 16:26:28 +0800
Subject: [PATCH] add m
---
lib/detecter_tools/darknet/blas_kernels.cu | 4785 ++++++++++++++++++++++++++++++----------------------------
1 files changed, 2,473 insertions(+), 2,312 deletions(-)
diff --git a/lib/detecter_tools/darknet/blas_kernels.cu b/lib/detecter_tools/darknet/blas_kernels.cu
index a168684..85c55ad 100644
--- a/lib/detecter_tools/darknet/blas_kernels.cu
+++ b/lib/detecter_tools/darknet/blas_kernels.cu
@@ -1,2312 +1,2473 @@
-#include <cuda_runtime.h>
-#include <curand.h>
-#include <cublas_v2.h>
-#include <assert.h>
-#include <float.h>
-
-#include "blas.h"
-#include "dark_cuda.h"
-#include "utils.h"
-#include "tree.h"
-
-__inline__ __device__
-float warpAllReduceSum(float val) {
- for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2)
-#if CUDART_VERSION >= 9000
- val += __shfl_xor_sync(0xffffffff, val, mask);
-#else
- val += __shfl_xor(val, mask);
-#endif
- return val;
-}
-
-__global__ void compare_2_arrays_kernel(float *one, float *two, int size)
-{
- const int index = blockIdx.x*blockDim.x + threadIdx.x;
- if (index >= size) return;
-
- const float diff = 100 * fabs(one[index] - two[index]) / fabs(one[index]);
-
- if (diff > 10) printf(" i: %d - one = %f, two = %f, diff = %f %% \n", index, one[index], two[index], diff);
-}
-
-void compare_2_arrays_gpu(float *one, float *two, int size)
-{
- const int num_blocks = get_number_of_blocks(size, BLOCK);
-
- compare_2_arrays_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(one, two, size);
- CHECK_CUDA(cudaPeekAtLastError());
- CHECK_CUDA(cudaDeviceSynchronize());
-}
-
-__global__ void mean_array_kernel(float *src, int size, float alpha, float *avg)
-{
- const int i = blockIdx.x*blockDim.x + threadIdx.x;
- if (i >= size) return;
-
- avg[i] = avg[i] * (1 - alpha) + src[i] * alpha;
- src[i] = avg[i];
-}
-
-
-void mean_array_gpu(float *src, int size, float alpha, float *avg)
-{
- const int num_blocks = get_number_of_blocks(size, BLOCK);
-
- mean_array_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(src, size, alpha, avg);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-
-__global__ void scale_bias_kernel(float *output, float *scale, int batch, int filters, int spatial, int current_size)
-{
- const int index = blockIdx.x*blockDim.x + threadIdx.x;
- if (index >= current_size) return;
-
- int f = (index / spatial) % filters;
- output[index] *= scale[f];
-}
-
-void scale_bias_gpu(float *output, float *scale, int batch, int filters, int spatial)
-{
- const int current_size = batch * filters * spatial;
- const int num_blocks = get_number_of_blocks(current_size, BLOCK);
-
- scale_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, scale, batch, filters, spatial, current_size);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-
-__global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
-{
- __shared__ float part[BLOCK];
- int i,b;
- int filter = blockIdx.x;
- int p = threadIdx.x;
- float sum = 0;
- for(b = 0; b < batch; ++b){
- for(i = 0; i < size; i += BLOCK){
- int index = p + i + size*(filter + n*b);
- sum += (p+i < size) ? delta[index]*x_norm[index] : 0;
- }
- }
- part[p] = sum;
- __syncthreads();
- if (p == 0) {
- for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i];
- }
-}
-
-void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
-{
- backward_scale_kernel<<<n, BLOCK, 0, get_cuda_stream() >>>(x_norm, delta, batch, n, size, scale_updates);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-__global__ void add_bias_kernel(float *output, float *biases, int batch, int filters, int spatial, int current_size)
-{
- const int index = blockIdx.x*blockDim.x + threadIdx.x;
- if (index >= current_size) return;
-
- int f = (index / spatial) % filters;
- output[index] += biases[f];
-}
-
-void add_bias_gpu(float *output, float *biases, int batch, int filters, int spatial)
-{
- const int current_size = batch * filters * spatial;
- const int num_blocks = get_number_of_blocks(current_size, BLOCK);
-
- add_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, biases, batch, filters, spatial, current_size);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-__global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size)
-{
- __shared__ float part[BLOCK];
- int i,b;
- int filter = blockIdx.x;
- int p = threadIdx.x;
- float sum = 0;
- for(b = 0; b < batch; ++b){
- for(i = 0; i < size; i += BLOCK){
- int index = p + i + size*(filter + n*b);
- sum += (p+i < size) ? delta[index] : 0;
- }
- }
- part[p] = sum;
- __syncthreads();
- if (p == 0) {
- for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i];
- }
-}
-
-/*
-__global__ void dot_kernel(float *output, float scale, int batch, int n, int size, float *delta)
-{
- int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- int f1 = index / n;
- int f2 = index % n;
- if (f2 <= f1) return;
-
- float sum = 0;
- float norm1 = 0;
- float norm2 = 0;
- int b, i;
- for(b = 0; b < batch; ++b){
- for(i = 0; i < size; ++i){
- int i1 = b * size * n + f1 * size + i;
- int i2 = b * size * n + f2 * size + i;
- sum += output[i1] * output[i2];
- norm1 += output[i1] * output[i1];
- norm2 += output[i2] * output[i2];
- }
- }
- norm1 = sqrt(norm1);
- norm2 = sqrt(norm2);
- float norm = norm1 * norm2;
- sum = sum / norm;
- for(b = 0; b < batch; ++b){
- for(i = 0; i < size; ++i){
- int i1 = b * size * n + f1 * size + i;
- int i2 = b * size * n + f2 * size + i;
- delta[i1] += - scale * sum * output[i2] / norm;
- delta[i2] += - scale * sum * output[i1] / norm;
- }
- }
-}
-
-void dot_error_gpu(layer l)
-{
- 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);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-*/
-
-void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
-{
- backward_bias_kernel<<<n, BLOCK, 0, get_cuda_stream() >>>(bias_updates, delta, batch, n, size);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-__global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
-{
- int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (index >= N) return;
-
- float mhat = m[index] / (1.f - powf(B1, t));
- float vhat = v[index] / (1.f - powf(B2, t));
-
- x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps);
-}
-
-extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
-{
- adam_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> >(n, x, m, v, B1, B2, rate, eps, t);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-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)
-{
- scal_ongpu(n, B1, m, 1);
- scal_ongpu(n, B2, v, 1);
- axpy_ongpu(n, -decay*batch, w, 1, d, 1);
-
- axpy_ongpu(n, (1 - B1), d, 1, m, 1);
- mul_ongpu(n, d, 1, d, 1);
- axpy_ongpu(n, (1 - B2), d, 1, v, 1);
-
- adam_gpu(n, w, m, v, B1, B2, rate, eps, t);
- fill_ongpu(n, 0, d, 1);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-__global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial)
-{
- const int index = blockIdx.x*blockDim.x + threadIdx.x;
- if (index >= N) return;
- int f = (index / spatial) % filters;
-
- x[index] = (x[index] - mean[f]) / (sqrtf(variance[f] + .00001f));
-}
-
-extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
-{
- const int current_size = batch * filters * spatial;
- const int num_blocks = get_number_of_blocks(current_size, BLOCK);
-
- normalize_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(current_size, x, mean, variance, batch, filters, spatial);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-__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)
-{
- int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (index >= N) return;
- int f = (index/spatial)%filters;
-
- 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);
-}
-
-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)
-{
- size_t N = batch*filters*spatial;
- normalize_delta_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-__global__ void variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (i >= filters) return;
- int j,k;
- variance_delta[i] = 0;
- for(j = 0; j < batch; ++j){
- for(k = 0; k < spatial; ++k){
- int index = j*filters*spatial + i*spatial + k;
- variance_delta[i] += delta[index]*(x[index] - mean[i]);
- }
- }
- variance_delta[i] *= -.5 * powf(variance[i] + .000001f, (float)(-3./2.));
-}
-
-__global__ void accumulate_kernel(float *x, int n, int groups, float *sum)
-{
- int k;
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (i >= groups) return;
- sum[i] = 0;
- for(k = 0; k < n; ++k){
- sum[i] += x[k*groups + i];
- }
-}
-
-__global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
-{
- 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) ? delta[index] : 0;
- }
- }
- __syncthreads();
-
- if(id == 0){
- mean_delta[filter] = 0;
- for(i = 0; i < threads; ++i){
- mean_delta[filter] += local[i];
- }
- mean_delta[filter] *= (-1.F/sqrtf(variance[filter] + .000001f));
- }
-}
-
-__global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
-{
- 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) ? delta[index]*(x[index] - mean[filter]) : 0;
- }
- }
- __syncthreads();
-
- if(id == 0){
- variance_delta[filter] = 0;
- for(i = 0; i < threads; ++i){
- variance_delta[filter] += local[i];
- }
- variance_delta[filter] *= -.5 * powf(variance[filter] + .000001f, (float)(-3./2.));
- }
-}
-
-
-__global__ void mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (i >= filters) return;
- int j,k;
- mean_delta[i] = 0;
- for (j = 0; j < batch; ++j) {
- for (k = 0; k < spatial; ++k) {
- int index = j*filters*spatial + i*spatial + k;
- mean_delta[i] += delta[index];
- }
- }
- mean_delta[i] *= (-1.F/sqrtf(variance[i] + .000001f));
-}
-
-extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
-{
- mean_delta_kernel<<<cuda_gridsize(filters), BLOCK, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
-{
- fast_mean_delta_kernel<<<filters, BLOCK, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
-{
- fast_variance_delta_kernel<<<filters, BLOCK, 0, get_cuda_stream() >>>(x, delta, mean, variance, batch, filters, spatial, variance_delta);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-__global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
-{
- float scale = 1.F/(batch * spatial);
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (i >= filters) return;
- int j,k;
- mean[i] = 0;
- for(j = 0; j < batch; ++j){
- for(k = 0; k < spatial; ++k){
- int index = j*filters*spatial + i*spatial + k;
- mean[i] += x[index];
- }
- }
- mean[i] *= scale;
-}
-
-__global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
-{
- float scale = 1.F/(batch * spatial - 1);
- int j,k;
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (i >= filters) return;
- variance[i] = 0;
- for(j = 0; j < batch; ++j){
- for(k = 0; k < spatial; ++k){
- int index = j*filters*spatial + i*spatial + k;
- variance[i] += powf((x[index] - mean[i]), 2);
- }
- }
- variance[i] *= scale;
-}
-
-__global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if(i >= N) return;
- int in_index = i;
- int in_w = i%w;
- i = i/w;
- int in_h = i%h;
- i = i/h;
- int in_c = i%c;
- i = i/c;
- int b = i%batch;
-
- int out_c = c/(stride*stride);
-
- int c2 = in_c % out_c;
- int offset = in_c / out_c;
- int w2 = in_w*stride + offset % stride;
- int h2 = in_h*stride + offset / stride;
- //printf("%d\n", offset);
- int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b));
-
- // printf("%d %d %d\n", w2, h2, c2);
- //printf("%d %d\n", in_index, out_index);
- //if(out_index >= N || out_index < 0) printf("bad bad bad \n");
-
- if(forward) out[out_index] = x[in_index];
- else out[in_index] = x[out_index];
- //if(forward) out[1] = x[1];
- //else out[0] = x[0];
-}
-
-__global__ void constrain_weight_updates_kernel(int N, float coef, float *weights_gpu, float *weight_updates_gpu)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (i < N) {
- const float w = weights_gpu[i];
- const float wu = weight_updates_gpu[i];
- const float wu_sign = (wu == 0) ? 0 : (fabs(wu) / wu);
- const float abs_limit = fabs(w * coef);
- if (fabs(wu) > abs_limit) weight_updates_gpu[i] = abs_limit * wu_sign;
- }
-}
-
-extern "C" void constrain_weight_updates_ongpu(int N, float coef, float *weights_gpu, float *weight_updates_gpu)
-{
- constrain_weight_updates_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, coef, weights_gpu, weight_updates_gpu);
- CHECK_CUDA(cudaPeekAtLastError());
-}
-
-__global__ void axpy_kernel(int N, float ALPHA, 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[OFFY+i*INCY] += ALPHA*X[OFFX+i*INCX];
-}
-
-__global__ void pow_kernel(int N, float ALPHA, 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] = powf(X[i*INCX], ALPHA);
-}
-
-__global__ void const_kernel(int N, float ALPHA, float *X, int INCX)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if(i < N) X[i*INCX] = ALPHA;
-}
-
-__global__ void constrain_kernel(int N, float ALPHA, float *X, int INCX)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if(i < N) X[i*INCX] = fminf(ALPHA, fmaxf(-ALPHA, X[i*INCX]));
-}
-__global__ void constrain_min_max_kernel(int N, float MIN, float MAX, float *X, int INCX)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (i < N) X[i*INCX] = fminf(MAX, fmaxf(MIN, X[i*INCX]));
-}
-
-__global__ void supp_kernel(int N, float ALPHA, float *X, int INCX)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if(i < N) {
- if((X[i*INCX] * X[i*INCX]) < (ALPHA * ALPHA)) X[i*INCX] = 0;
- }
-}
-
-__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if(i < N) X[i*INCX] *= ALPHA;
-}
-
-__global__ void scal_add_kernel(int N, float ALPHA, float BETA, float *X, int INCX)
-{
- int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if (i < N) X[i*INCX] = X[i*INCX] * ALPHA + BETA;
-}
-
-__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;
- val += a1[index];
- 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);
- 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) {
- 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);
- 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) {
- 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 = floor(x_s); // round down
- int x_1 = ceil(x_s); // round up
- if (x_0 == x_1) x_1 = x_0 + 1;
- int y_0 = floor(y_s);
- int y_1 = ceil(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 = floor(x_s); // round down
- int x_1 = ceil(x_s); // round up
- if (x_0 == x_1) x_1 = x_0 + 1;
- int y_0 = floor(y_s);
- int y_1 = ceil(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 = floor(x_s); // round down
- int x_1 = ceil(x_s); // round up
- if (x_0 == x_1) x_1 = x_0 + 1;
- int y_0 = floor(y_s);
- int y_1 = ceil(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 = floor(x_s); // round down
- int x_1 = ceil(x_s); // round up
- if (x_0 == x_1) x_1 = x_0 + 1;
- int y_0 = floor(y_s);
- int y_1 = ceil(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 = floor(x_s); // round down
- int x_1 = ceil(x_s); // round up
- if (x_0 == x_1) x_1 = x_0 + 1;
- int y_0 = floor(y_s);
- int y_1 = ceil(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());
-}
\ No newline at end of file
+#include <cuda_runtime.h>
+#include <curand.h>
+#include <cublas_v2.h>
+#include <assert.h>
+#include <float.h>
+
+#include "blas.h"
+#include "dark_cuda.h"
+#include "utils.h"
+#include "tree.h"
+
+__inline__ __device__
+float warpAllReduceSum(float val) {
+ for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2)
+#if CUDART_VERSION >= 9000
+ val += __shfl_xor_sync(0xffffffff, val, mask);
+#else
+ val += __shfl_xor(val, mask);
+#endif
+ return val;
+}
+
+__global__ void compare_2_arrays_kernel(float *one, float *two, int size)
+{
+ const int index = blockIdx.x*blockDim.x + threadIdx.x;
+ if (index >= size) return;
+
+ const float diff = 100 * fabs(one[index] - two[index]) / fabs(one[index]);
+
+ if (diff > 10) printf(" i: %d - one = %f, two = %f, diff = %f %% \n", index, one[index], two[index], diff);
+}
+
+void compare_2_arrays_gpu(float *one, float *two, int size)
+{
+ const int num_blocks = get_number_of_blocks(size, BLOCK);
+
+ compare_2_arrays_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(one, two, size);
+ CHECK_CUDA(cudaPeekAtLastError());
+ CHECK_CUDA(cudaDeviceSynchronize());
+}
+
+__global__ void mean_array_kernel(float *src, int size, float alpha, float *avg)
+{
+ const int i = blockIdx.x*blockDim.x + threadIdx.x;
+ if (i >= size) return;
+
+ avg[i] = avg[i] * (1 - alpha) + src[i] * alpha;
+ src[i] = avg[i];
+}
+
+
+void mean_array_gpu(float *src, int size, float alpha, float *avg)
+{
+ const int num_blocks = get_number_of_blocks(size, BLOCK);
+
+ mean_array_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(src, size, alpha, avg);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+
+__global__ void scale_bias_kernel(float *output, float *scale, int batch, int filters, int spatial, int current_size)
+{
+ const int index = blockIdx.x*blockDim.x + threadIdx.x;
+ if (index >= current_size) return;
+
+ int f = (index / spatial) % filters;
+ output[index] *= scale[f];
+}
+
+void scale_bias_gpu(float *output, float *scale, int batch, int filters, int spatial)
+{
+ const int current_size = batch * filters * spatial;
+ const int num_blocks = get_number_of_blocks(current_size, BLOCK);
+
+ scale_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, scale, batch, filters, spatial, current_size);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+
+__global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
+{
+ __shared__ float part[BLOCK];
+ int i,b;
+ int filter = blockIdx.x;
+ int p = threadIdx.x;
+ float sum = 0;
+ for(b = 0; b < batch; ++b){
+ for(i = 0; i < size; i += BLOCK){
+ int index = p + i + size*(filter + n*b);
+ sum += (p+i < size) ? delta[index]*x_norm[index] : 0;
+ }
+ }
+ part[p] = sum;
+ __syncthreads();
+ if (p == 0) {
+ for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i];
+ }
+}
+
+void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
+{
+ backward_scale_kernel<<<n, BLOCK, 0, get_cuda_stream() >>>(x_norm, delta, batch, n, size, scale_updates);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+__global__ void add_bias_kernel(float *output, float *biases, int batch, int filters, int spatial, int current_size)
+{
+ const int index = blockIdx.x*blockDim.x + threadIdx.x;
+ if (index >= current_size) return;
+
+ int f = (index / spatial) % filters;
+ output[index] += biases[f];
+}
+
+void add_bias_gpu(float *output, float *biases, int batch, int filters, int spatial)
+{
+ const int current_size = batch * filters * spatial;
+ const int num_blocks = get_number_of_blocks(current_size, BLOCK);
+
+ add_bias_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(output, biases, batch, filters, spatial, current_size);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+__global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size)
+{
+ __shared__ float part[BLOCK];
+ int i,b;
+ int filter = blockIdx.x;
+ int p = threadIdx.x;
+ float sum = 0;
+ for(b = 0; b < batch; ++b){
+ for(i = 0; i < size; i += BLOCK){
+ int index = p + i + size*(filter + n*b);
+ sum += (p+i < size) ? delta[index] : 0;
+ }
+ }
+ part[p] = sum;
+ __syncthreads();
+ if (p == 0) {
+ for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i];
+ }
+}
+
+/*
+__global__ void dot_kernel(float *output, float scale, int batch, int n, int size, float *delta)
+{
+ int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ int f1 = index / n;
+ int f2 = index % n;
+ if (f2 <= f1) return;
+
+ float sum = 0;
+ float norm1 = 0;
+ float norm2 = 0;
+ int b, i;
+ for(b = 0; b < batch; ++b){
+ for(i = 0; i < size; ++i){
+ int i1 = b * size * n + f1 * size + i;
+ int i2 = b * size * n + f2 * size + i;
+ sum += output[i1] * output[i2];
+ norm1 += output[i1] * output[i1];
+ norm2 += output[i2] * output[i2];
+ }
+ }
+ norm1 = sqrt(norm1);
+ norm2 = sqrt(norm2);
+ float norm = norm1 * norm2;
+ sum = sum / norm;
+ for(b = 0; b < batch; ++b){
+ for(i = 0; i < size; ++i){
+ int i1 = b * size * n + f1 * size + i;
+ int i2 = b * size * n + f2 * size + i;
+ delta[i1] += - scale * sum * output[i2] / norm;
+ delta[i2] += - scale * sum * output[i1] / norm;
+ }
+ }
+}
+
+void dot_error_gpu(layer l)
+{
+ 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);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+*/
+
+void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
+{
+ backward_bias_kernel<<<n, BLOCK, 0, get_cuda_stream() >>>(bias_updates, delta, batch, n, size);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+__global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
+{
+ int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (index >= N) return;
+
+ float mhat = m[index] / (1.f - powf(B1, t));
+ float vhat = v[index] / (1.f - powf(B2, t));
+
+ x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps);
+}
+
+extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t)
+{
+ adam_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> >(n, x, m, v, B1, B2, rate, eps, t);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+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)
+{
+ scal_ongpu(n, B1, m, 1);
+ scal_ongpu(n, B2, v, 1);
+ axpy_ongpu(n, -decay*batch, w, 1, d, 1);
+
+ axpy_ongpu(n, (1 - B1), d, 1, m, 1);
+ mul_ongpu(n, d, 1, d, 1);
+ axpy_ongpu(n, (1 - B2), d, 1, v, 1);
+
+ adam_gpu(n, w, m, v, B1, B2, rate, eps, t);
+ fill_ongpu(n, 0, d, 1);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+__global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial)
+{
+ const int index = blockIdx.x*blockDim.x + threadIdx.x;
+ if (index >= N) return;
+ int f = (index / spatial) % filters;
+
+ x[index] = (x[index] - mean[f]) / (sqrtf(variance[f] + .00001f));
+}
+
+extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
+{
+ const int current_size = batch * filters * spatial;
+ const int num_blocks = get_number_of_blocks(current_size, BLOCK);
+
+ normalize_kernel << <num_blocks, BLOCK, 0, get_cuda_stream() >> >(current_size, x, mean, variance, batch, filters, spatial);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+__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)
+{
+ int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (index >= N) return;
+ int f = (index/spatial)%filters;
+
+ 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);
+}
+
+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)
+{
+ size_t N = batch*filters*spatial;
+ normalize_delta_kernel<<<cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+__global__ void variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= filters) return;
+ int j,k;
+ variance_delta[i] = 0;
+ for(j = 0; j < batch; ++j){
+ for(k = 0; k < spatial; ++k){
+ int index = j*filters*spatial + i*spatial + k;
+ variance_delta[i] += delta[index]*(x[index] - mean[i]);
+ }
+ }
+ variance_delta[i] *= -.5 * powf(variance[i] + .000001f, (float)(-3./2.));
+}
+
+__global__ void accumulate_kernel(float *x, int n, int groups, float *sum)
+{
+ int k;
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= groups) return;
+ sum[i] = 0;
+ for(k = 0; k < n; ++k){
+ sum[i] += x[k*groups + i];
+ }
+}
+
+__global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+ 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) ? delta[index] : 0;
+ }
+ }
+ __syncthreads();
+
+ if(id == 0){
+ mean_delta[filter] = 0;
+ for(i = 0; i < threads; ++i){
+ mean_delta[filter] += local[i];
+ }
+ mean_delta[filter] *= (-1.F/sqrtf(variance[filter] + .000001f));
+ }
+}
+
+__global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+ 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) ? delta[index]*(x[index] - mean[filter]) : 0;
+ }
+ }
+ __syncthreads();
+
+ if(id == 0){
+ variance_delta[filter] = 0;
+ for(i = 0; i < threads; ++i){
+ variance_delta[filter] += local[i];
+ }
+ variance_delta[filter] *= -.5 * powf(variance[filter] + .000001f, (float)(-3./2.));
+ }
+}
+
+
+__global__ void mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= filters) return;
+ int j,k;
+ mean_delta[i] = 0;
+ for (j = 0; j < batch; ++j) {
+ for (k = 0; k < spatial; ++k) {
+ int index = j*filters*spatial + i*spatial + k;
+ mean_delta[i] += delta[index];
+ }
+ }
+ mean_delta[i] *= (-1.F/sqrtf(variance[i] + .000001f));
+}
+
+extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+ mean_delta_kernel<<<cuda_gridsize(filters), BLOCK, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
+{
+ fast_mean_delta_kernel<<<filters, BLOCK, 0, get_cuda_stream() >>>(delta, variance, batch, filters, spatial, mean_delta);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
+{
+ fast_variance_delta_kernel<<<filters, BLOCK, 0, get_cuda_stream() >>>(x, delta, mean, variance, batch, filters, spatial, variance_delta);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+__global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean)
+{
+ float scale = 1.F/(batch * spatial);
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= filters) return;
+ int j,k;
+ mean[i] = 0;
+ for(j = 0; j < batch; ++j){
+ for(k = 0; k < spatial; ++k){
+ int index = j*filters*spatial + i*spatial + k;
+ mean[i] += x[index];
+ }
+ }
+ mean[i] *= scale;
+}
+
+__global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance)
+{
+ float scale = 1.F/(batch * spatial - 1);
+ int j,k;
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i >= filters) return;
+ variance[i] = 0;
+ for(j = 0; j < batch; ++j){
+ for(k = 0; k < spatial; ++k){
+ int index = j*filters*spatial + i*spatial + k;
+ variance[i] += powf((x[index] - mean[i]), 2);
+ }
+ }
+ variance[i] *= scale;
+}
+
+__global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i >= N) return;
+ int in_index = i;
+ int in_w = i%w;
+ i = i/w;
+ int in_h = i%h;
+ i = i/h;
+ int in_c = i%c;
+ i = i/c;
+ int b = i%batch;
+
+ int out_c = c/(stride*stride);
+
+ int c2 = in_c % out_c;
+ int offset = in_c / out_c;
+ int w2 = in_w*stride + offset % stride;
+ int h2 = in_h*stride + offset / stride;
+ //printf("%d\n", offset);
+ int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b));
+
+ // printf("%d %d %d\n", w2, h2, c2);
+ //printf("%d %d\n", in_index, out_index);
+ //if(out_index >= N || out_index < 0) printf("bad bad bad \n");
+
+ if(forward) out[out_index] = x[in_index];
+ else out[in_index] = x[out_index];
+ //if(forward) out[1] = x[1];
+ //else out[0] = x[0];
+}
+
+__global__ void constrain_weight_updates_kernel(int N, float coef, float *weights_gpu, float *weight_updates_gpu)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i < N) {
+ const float w = weights_gpu[i];
+ const float wu = weight_updates_gpu[i];
+ const float wu_sign = (wu == 0) ? 0 : (fabs(wu) / wu);
+ const float abs_limit = fabs(w * coef);
+ if (fabs(wu) > abs_limit) weight_updates_gpu[i] = abs_limit * wu_sign;
+ }
+}
+
+extern "C" void constrain_weight_updates_ongpu(int N, float coef, float *weights_gpu, float *weight_updates_gpu)
+{
+ constrain_weight_updates_kernel << <cuda_gridsize(N), BLOCK, 0, get_cuda_stream() >> >(N, coef, weights_gpu, weight_updates_gpu);
+ CHECK_CUDA(cudaPeekAtLastError());
+}
+
+__global__ void axpy_kernel(int N, float ALPHA, 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[OFFY+i*INCY] += ALPHA*X[OFFX+i*INCX];
+}
+
+__global__ void pow_kernel(int N, float ALPHA, 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] = powf(X[i*INCX], ALPHA);
+}
+
+__global__ void const_kernel(int N, float ALPHA, float *X, int INCX)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < N) X[i*INCX] = ALPHA;
+}
+
+__global__ void constrain_kernel(int N, float ALPHA, float *X, int INCX)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < N) X[i*INCX] = fminf(ALPHA, fmaxf(-ALPHA, X[i*INCX]));
+}
+__global__ void constrain_min_max_kernel(int N, float MIN, float MAX, float *X, int INCX)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i < N) X[i*INCX] = fminf(MAX, fmaxf(MIN, X[i*INCX]));
+}
+
+__global__ void supp_kernel(int N, float ALPHA, float *X, int INCX)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < N) {
+ if((X[i*INCX] * X[i*INCX]) < (ALPHA * ALPHA)) X[i*INCX] = 0;
+ }
+}
+
+__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if(i < N) X[i*INCX] *= ALPHA;
+}
+
+__global__ void scal_add_kernel(int N, float ALPHA, float BETA, float *X, int INCX)
+{
+ int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+ if (i < N) X[i*INCX] = X[i*INCX] * ALPHA + BETA;
+}
+
+__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());
+}
--
Gitblit v1.8.0