From a723e1c62a27aeb39aaf7fcdeb3beb4e89fba32d Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Wed, 15 Aug 2018 20:52:09 +0000
Subject: [PATCH] Merge pull request #766 from HotChick91/AlexeyAB-mask
---
src/softmax_layer.c | 150 +++++++++++++++++++++----------------------------
1 files changed, 65 insertions(+), 85 deletions(-)
diff --git a/src/softmax_layer.c b/src/softmax_layer.c
index abd9abf..27f73fd 100644
--- a/src/softmax_layer.c
+++ b/src/softmax_layer.c
@@ -1,51 +1,70 @@
#include "softmax_layer.h"
-#include "mini_blas.h"
+#include "blas.h"
+#include "cuda.h"
#include <float.h>
#include <math.h>
#include <stdlib.h>
#include <stdio.h>
+#include <assert.h>
-softmax_layer *make_softmax_layer(int batch, int inputs)
+softmax_layer make_softmax_layer(int batch, int inputs, int groups)
{
- fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
- softmax_layer *layer = calloc(1, sizeof(softmax_layer));
- layer->batch = batch;
- layer->inputs = inputs;
- layer->output = calloc(inputs*batch, sizeof(float));
- layer->delta = calloc(inputs*batch, sizeof(float));
- layer->jacobian = calloc(inputs*inputs*batch, sizeof(float));
+ assert(inputs%groups == 0);
+ fprintf(stderr, "softmax %4d\n", inputs);
+ softmax_layer l = {0};
+ l.type = SOFTMAX;
+ l.batch = batch;
+ l.groups = groups;
+ l.inputs = inputs;
+ l.outputs = inputs;
+ l.output = calloc(inputs*batch, sizeof(float));
+ l.delta = calloc(inputs*batch, sizeof(float));
+
+ l.forward = forward_softmax_layer;
+ l.backward = backward_softmax_layer;
#ifdef GPU
- layer->output_cl = cl_make_array(layer->output, inputs*batch);
- layer->delta_cl = cl_make_array(layer->delta, inputs*batch);
+ l.forward_gpu = forward_softmax_layer_gpu;
+ l.backward_gpu = backward_softmax_layer_gpu;
+
+ l.output_gpu = cuda_make_array(l.output, inputs*batch);
+ l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
#endif
- return layer;
+ return l;
}
-void forward_softmax_layer(const softmax_layer layer, float *input)
+void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output)
{
- int i,b;
- for(b = 0; b < layer.batch; ++b){
- float sum = 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);
- }
- if(sum) sum = largest+log(sum);
- else sum = largest-100;
- for(i = 0; i < layer.inputs; ++i){
- layer.output[i+b*layer.inputs] = exp(input[i+b*layer.inputs]-sum);
+ int b;
+ for(b = 0; b < batch; ++b){
+ int i;
+ int count = 0;
+ for(i = 0; i < hierarchy->groups; ++i){
+ int group_size = hierarchy->group_size[i];
+ softmax(input+b*inputs + count, group_size, temp, output+b*inputs + count, 1);
+ count += group_size;
}
}
}
-void backward_softmax_layer(const softmax_layer layer, float *delta)
+void forward_softmax_layer(const softmax_layer l, network_state state)
+{
+ int b;
+ int inputs = l.inputs / l.groups;
+ int batch = l.batch * l.groups;
+ if(l.softmax_tree){
+ softmax_tree(state.input, batch, inputs, l.temperature, l.softmax_tree, l.output);
+ } else {
+ for(b = 0; b < batch; ++b){
+ softmax(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs, 1);
+ }
+ }
+}
+
+void backward_softmax_layer(const softmax_layer l, network_state state)
{
int i;
- for(i = 0; i < layer.inputs*layer.batch; ++i){
- delta[i] = layer.delta[i];
+ for(i = 0; i < l.inputs*l.batch; ++i){
+ state.delta[i] += l.delta[i];
}
}
@@ -53,68 +72,29 @@
void pull_softmax_layer_output(const softmax_layer layer)
{
- cl_read_array(layer.output_cl, layer.output, layer.inputs*layer.batch);
+ cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch);
}
-cl_kernel get_softmax_forward_kernel()
+void forward_softmax_layer_gpu(const softmax_layer l, network_state state)
{
- static int init = 0;
- static cl_kernel kernel;
- if(!init){
- kernel = get_kernel("src/softmax_layer.cl", "forward", 0);
- init = 1;
+ int inputs = l.inputs / l.groups;
+ int batch = l.batch * l.groups;
+ if(l.softmax_tree){
+ int i;
+ int count = 0;
+ for (i = 0; i < l.softmax_tree->groups; ++i) {
+ int group_size = l.softmax_tree->group_size[i];
+ softmax_gpu(state.input+count, group_size, inputs, batch, l.temperature, l.output_gpu + count);
+ count += group_size;
+ }
+ } else {
+ softmax_gpu(state.input, inputs, inputs, batch, l.temperature, l.output_gpu);
}
- return kernel;
}
-void forward_softmax_layer_gpu(const softmax_layer layer, cl_mem input)
+void backward_softmax_layer_gpu(const softmax_layer layer, network_state state)
{
- 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};
-
- cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
- check_error(cl);
-
- /*
- cl_read_array(layer.output_cl, layer.output, layer.inputs*layer.batch);
- int z;
- for(z = 0; z < layer.inputs*layer.batch; ++z) printf("%f,",layer.output[z]);
- */
+ axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1);
}
-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);
- }
- */
--
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