From 564877ad6a3f53d3d866b0015237d07f4af2eaa2 Mon Sep 17 00:00:00 2001
From: vinjn <vinjn.z@gmail.com>
Date: Sat, 07 Jul 2018 04:30:45 +0000
Subject: [PATCH] cuda.h - converts tab to space

---
 src/softmax_layer.c |   70 ++++++++++++++++++++++++++--------
 1 files changed, 53 insertions(+), 17 deletions(-)

diff --git a/src/softmax_layer.c b/src/softmax_layer.c
index e189701..27f73fd 100644
--- a/src/softmax_layer.c
+++ b/src/softmax_layer.c
@@ -10,7 +10,7 @@
 softmax_layer make_softmax_layer(int batch, int inputs, int groups)
 {
     assert(inputs%groups == 0);
-    fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
+    fprintf(stderr, "softmax                                        %4d\n",  inputs);
     softmax_layer l = {0};
     l.type = SOFTMAX;
     l.batch = batch;
@@ -19,28 +19,30 @@
     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
+    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 l;
 }
 
-void softmax_array(float *input, int n, float temp, float *output)
+void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output)
 {
-    int i;
-    float sum = 0;
-    float largest = -FLT_MAX;
-    for(i = 0; i < n; ++i){
-        if(input[i] > largest) largest = input[i];
-    }
-    for(i = 0; i < n; ++i){
-        sum += exp(input[i]/temp-largest/temp);
-    }
-    if(sum) sum = largest/temp+log(sum);
-    else sum = largest-100;
-    for(i = 0; i < n; ++i){
-        output[i] = exp(input[i]/temp-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;
+        }
     }
 }
 
@@ -49,8 +51,12 @@
     int b;
     int inputs = l.inputs / l.groups;
     int batch = l.batch * l.groups;
-    for(b = 0; b < batch; ++b){
-        softmax_array(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs);
+    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);
+        }
     }
 }
 
@@ -62,3 +68,33 @@
     }
 }
 
+#ifdef GPU
+
+void pull_softmax_layer_output(const softmax_layer layer)
+{
+    cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch);
+}
+
+void forward_softmax_layer_gpu(const softmax_layer l, network_state state)
+{
+    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);
+    }
+}
+
+void backward_softmax_layer_gpu(const softmax_layer layer, network_state state)
+{
+    axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1);
+}
+
+#endif

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