#include "softmax_layer.h"
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#include "blas.h"
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#include "cuda.h"
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#include <float.h>
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#include <math.h>
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#include <stdlib.h>
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#include <stdio.h>
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#include <assert.h>
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softmax_layer make_softmax_layer(int batch, int inputs, int groups)
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{
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assert(inputs%groups == 0);
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fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
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softmax_layer l = {0};
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l.type = SOFTMAX;
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l.batch = batch;
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l.groups = groups;
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l.inputs = inputs;
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l.outputs = inputs;
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l.output = calloc(inputs*batch, sizeof(float));
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l.delta = calloc(inputs*batch, sizeof(float));
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l.forward = forward_softmax_layer;
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l.backward = backward_softmax_layer;
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#ifdef GPU
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l.forward_gpu = forward_softmax_layer_gpu;
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l.backward_gpu = backward_softmax_layer_gpu;
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l.output_gpu = cuda_make_array(l.output, inputs*batch);
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l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
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#endif
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return l;
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}
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void forward_softmax_layer(const softmax_layer l, network_state state)
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{
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int b;
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int inputs = l.inputs / l.groups;
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int batch = l.batch * l.groups;
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if(l.softmax_tree){
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for(b = 0; b < batch; ++b){
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int i;
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int count = 0;
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for(i = 0; i < l.softmax_tree->groups; ++i){
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int group_size = l.softmax_tree->group_size[i];
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softmax(state.input+b*inputs + count, group_size, l.temperature, l.output+b*inputs + count);
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count += group_size;
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}
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}
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} else {
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for(b = 0; b < batch; ++b){
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softmax(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs);
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}
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}
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}
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void backward_softmax_layer(const softmax_layer l, network_state state)
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{
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int i;
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for(i = 0; i < l.inputs*l.batch; ++i){
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state.delta[i] += l.delta[i];
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}
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}
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#ifdef GPU
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void pull_softmax_layer_output(const softmax_layer layer)
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{
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cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch);
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}
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void forward_softmax_layer_gpu(const softmax_layer l, network_state state)
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{
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int inputs = l.inputs / l.groups;
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int batch = l.batch * l.groups;
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int b;
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if(l.softmax_tree){
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if(0){
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float *buff = calloc(inputs * batch, sizeof(float));
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cuda_pull_array(state.input, buff, batch * inputs);
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state.input = buff;
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forward_softmax_layer(l, state);
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cuda_push_array(l.output_gpu, l.output, batch*inputs);
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free(buff);
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} else {
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int i;
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const int nstreams = 32;
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cudaStream_t streams[nstreams];
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for (i = 0; i < nstreams; ++i) {
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cudaStreamCreate(&streams[i]);
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}
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for (b = 0; b < batch; ++b) {
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int i;
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int count = 0;
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for (i = 0; i < l.softmax_tree->groups; ++i) {
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int group_size = l.softmax_tree->group_size[i];
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softmax_gpu(state.input+b*inputs + count, group_size, 1, l.temperature, l.output_gpu+b*inputs + count, streams[(b*l.softmax_tree->groups + i) % nstreams]);
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count += group_size;
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}
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}
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for(i = 0; i < nstreams; ++i){
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cudaStreamDestroy(streams[i]);
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}
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}
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} else {
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softmax_gpu(state.input, inputs, batch, l.temperature, l.output_gpu, 0);
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}
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}
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void backward_softmax_layer_gpu(const softmax_layer layer, network_state state)
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{
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axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1);
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}
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#endif
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