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CUDA so fast

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Joseph Redmon
2015-01-22 16:38:24 -08:00
parent 4ac78c8926
commit 809f924db2
57 changed files with 1116 additions and 2181 deletions
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src/convolutional_kernels.cu Normal file
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extern "C" {
#include "convolutional_layer.h"
#include "gemm.h"
#include "blas.h"
#include "im2col.h"
#include "col2im.h"
#include "utils.h"
#include "cuda.h"
}
__global__ void bias(int n, int size, float *biases, float *output)
{
int offset = blockIdx.x * blockDim.x + threadIdx.x;
int filter = blockIdx.y;
int batch = blockIdx.z;
if(offset < size) output[(batch*n+filter)*size + offset] = biases[filter];
}
extern "C" void bias_output_gpu(const convolutional_layer layer)
{
int size = convolutional_out_height(layer)*convolutional_out_width(layer);
dim3 dimBlock(BLOCK, 1, 1);
dim3 dimGrid((size-1)/BLOCK + 1, layer.n, layer.batch);
bias<<<dimGrid, dimBlock>>>(layer.n, size, layer.biases_gpu, layer.output_gpu);
check_error(cudaPeekAtLastError());
}
__global__ void learn_bias(int batch, int n, int size, float *delta, float *bias_updates)
{
__shared__ float part[BLOCK];
int i,b;
int filter = (blockIdx.x + blockIdx.y*gridDim.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];
}
}
extern "C" void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
{
int size = convolutional_out_height(layer)*convolutional_out_width(layer);
learn_bias<<<cuda_gridsize(layer.n), BLOCK>>>(layer.batch, layer.n, size, layer.delta_gpu, layer.bias_updates_gpu);
check_error(cudaPeekAtLastError());
}
extern "C" void test_learn_bias(convolutional_layer l)
{
int i;
int size = convolutional_out_height(l) * convolutional_out_width(l);
for(i = 0; i < size*l.batch*l.n; ++i){
l.delta[i] = rand_uniform();
}
for(i = 0; i < l.n; ++i){
l.bias_updates[i] = rand_uniform();
}
cuda_push_array(l.delta_gpu, l.delta, size*l.batch*l.n);
cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n);
float *gpu = (float *) calloc(l.n, sizeof(float));
cuda_pull_array(l.bias_updates_gpu, gpu, l.n);
for(i = 0; i < l.n; ++i) printf("%.9g %.9g\n", l.bias_updates[i], gpu[i]);
learn_bias_convolutional_layer_ongpu(l);
learn_bias_convolutional_layer(l);
cuda_pull_array(l.bias_updates_gpu, gpu, l.n);
for(i = 0; i < l.n; ++i) printf("%.9g %.9g\n", l.bias_updates[i], gpu[i]);
}
extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, float *in)
{
int i;
int m = layer.n;
int k = layer.size*layer.size*layer.c;
int n = convolutional_out_height(layer)*
convolutional_out_width(layer);
bias_output_gpu(layer);
for(i = 0; i < layer.batch; ++i){
im2col_ongpu(in, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
float * a = layer.filters_gpu;
float * b = layer.col_image_gpu;
float * c = layer.output_gpu;
gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
}
activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation);
cuda_pull_array(layer.output_gpu, layer.output, m*n*layer.batch);
//for(i = 0; i < m*n*layer.batch; ++i) printf("%f, ", layer.output[i]);
//printf("\n");
}
extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, float *in, float *delta_gpu)
{
int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
int k = convolutional_out_height(layer)*
convolutional_out_width(layer);
gradient_array_ongpu(layer.output_gpu, m*k*layer.batch, layer.activation, layer.delta_gpu);
learn_bias_convolutional_layer_ongpu(layer);
if(delta_gpu) scal_ongpu(layer.batch*layer.h*layer.w*layer.c, 0, delta_gpu, 1);
for(i = 0; i < layer.batch; ++i){
float * a = layer.delta_gpu;
float * b = layer.col_image_gpu;
float * c = layer.filter_updates_gpu;
im2col_ongpu(in, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
if(delta_gpu){
float * a = layer.filters_gpu;
float * b = layer.delta_gpu;
float * c = layer.col_image_gpu;
gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k);
col2im_ongpu(layer.col_image_gpu, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta_gpu);
}
}
}
extern "C" void pull_convolutional_layer(convolutional_layer layer)
{
cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
}
extern "C" void push_convolutional_layer(convolutional_layer layer)
{
cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
cuda_push_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
}
extern "C" void update_convolutional_layer_gpu(convolutional_layer layer)
{
int size = layer.size*layer.size*layer.c*layer.n;
axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
scal_ongpu(layer.n,layer.momentum, layer.bias_updates_gpu, 1);
axpy_ongpu(size, -layer.decay, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
axpy_ongpu(size, layer.learning_rate, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
scal_ongpu(size, layer.momentum, layer.filter_updates_gpu, 1);
//pull_convolutional_layer(layer);
}