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softmax on gpu

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This commit is contained in:
Joseph Redmon
2014-10-21 14:49:18 -07:00
parent 9b3c7136f3
commit 158bb1bee9
17 changed files with 440 additions and 97 deletions
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103
src/softmax_layer.c
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@ -1,5 +1,6 @@
#include "softmax_layer.h"
#include "mini_blas.h"
#include <float.h>
#include <math.h>
#include <stdlib.h>
#include <stdio.h>
@ -13,36 +14,25 @@ softmax_layer *make_softmax_layer(int batch, int inputs)
layer->output = calloc(inputs*batch, sizeof(float));
layer->delta = calloc(inputs*batch, sizeof(float));
layer->jacobian = calloc(inputs*inputs*batch, sizeof(float));
#ifdef GPU
layer->output_cl = cl_make_array(layer->output, inputs*batch);
layer->delta_cl = cl_make_array(layer->delta, inputs*batch);
#endif
return layer;
}
/* UNSTABLE!
void forward_softmax_layer(const softmax_layer layer, float *input)
{
int i;
float sum = 0;
for(i = 0; i < layer.inputs; ++i){
sum += exp(input[i]);
}
for(i = 0; i < layer.inputs; ++i){
layer.output[i] = exp(input[i])/sum;
}
}
*/
void forward_softmax_layer(const softmax_layer layer, float *input)
{
int i,b;
for(b = 0; b < layer.batch; ++b){
float sum = 0;
float largest = 0;
float largest = -FLT_MAX;
for(i = 0; i < layer.inputs; ++i){
if(input[i+b*layer.inputs] > largest) largest = input[i+b*layer.inputs];
}
for(i = 0; i < layer.inputs; ++i){
sum += exp(input[i+b*layer.inputs]-largest);
//printf("%f, ", input[i]);
}
//printf("\n");
if(sum) sum = largest+log(sum);
else sum = largest-100;
for(i = 0; i < layer.inputs; ++i){
@ -51,33 +41,68 @@ void forward_softmax_layer(const softmax_layer layer, float *input)
}
}
void backward_softmax_layer(const softmax_layer layer, float *input, float *delta)
void backward_softmax_layer(const softmax_layer layer, float *delta)
{
/*
int i,j,b;
for(b = 0; b < layer.batch; ++b){
for(i = 0; i < layer.inputs; ++i){
for(j = 0; j < layer.inputs; ++j){
int d = (i==j);
layer.jacobian[b*layer.inputs*layer.inputs + i*layer.inputs + j] =
layer.output[b*layer.inputs + i] * (d - layer.output[b*layer.inputs + j]);
}
}
}
for(b = 0; b < layer.batch; ++b){
int M = layer.inputs;
int N = 1;
int K = layer.inputs;
float *A = layer.jacobian + b*layer.inputs*layer.inputs;
float *B = layer.delta + b*layer.inputs;
float *C = delta + b*layer.inputs;
gemm(0,0,M,N,K,1,A,K,B,N,0,C,N);
}
*/
int i;
for(i = 0; i < layer.inputs*layer.batch; ++i){
delta[i] = layer.delta[i];
}
}
#ifdef GPU
cl_kernel get_softmax_forward_kernel()
{
static int init = 0;
static cl_kernel kernel;
if(!init){
kernel = get_kernel("src/softmax_layer.cl", "forward", 0);
init = 1;
}
return kernel;
}
void forward_softmax_layer_gpu(const softmax_layer layer, cl_mem input)
{
cl_setup();
cl_kernel kernel = get_softmax_forward_kernel();
cl_command_queue queue = cl.queue;
cl_uint i = 0;
cl.error = clSetKernelArg(kernel, i++, sizeof(layer.inputs), (void*) &layer.inputs);
cl.error = clSetKernelArg(kernel, i++, sizeof(input), (void*) &input);
cl.error = clSetKernelArg(kernel, i++, sizeof(layer.output_cl), (void*) &layer.output_cl);
check_error(cl);
const size_t global_size[] = {layer.batch};
clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
check_error(cl);
}
void backward_softmax_layer_gpu(const softmax_layer layer, cl_mem delta)
{
copy_ongpu(layer.batch*layer.inputs, layer.delta_cl, 1, delta, 1);
}
#endif
/* This is if you want softmax w/o log-loss classification. You probably don't.
int i,j,b;
for(b = 0; b < layer.batch; ++b){
for(i = 0; i < layer.inputs; ++i){
for(j = 0; j < layer.inputs; ++j){
int d = (i==j);
layer.jacobian[b*layer.inputs*layer.inputs + i*layer.inputs + j] =
layer.output[b*layer.inputs + i] * (d - layer.output[b*layer.inputs + j]);
}
}
}
for(b = 0; b < layer.batch; ++b){
int M = layer.inputs;
int N = 1;
int K = layer.inputs;
float *A = layer.jacobian + b*layer.inputs*layer.inputs;
float *B = layer.delta + b*layer.inputs;
float *C = delta + b*layer.inputs;
gemm(0,0,M,N,K,1,A,K,B,N,0,C,N);
}
*/