temp fix, don't use it

src/convolutional_kernels.cu

@@ -3,7 +3,7 @@
 #include "cublas_v2.h"
 
 #ifdef CUDNN
-#pragma comment(lib, "cudnn.lib")  
+#pragma comment(lib, "cudnn.lib")
 #endif
 
 extern "C" {
@@ -117,18 +117,160 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
     }
 
     if(l.xnor){
-        binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
+        if (!l.align_bit_weights_gpu || state.train) {
+            binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu);
+        }
         swap_binary(&l);
         binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu);
         state.input = l.binary_input_gpu;
+
+        if (l.align_bit_weights_gpu && !state.train)
+        {
+            cudaError_t status;
+            status = cudaMemcpy(l.align_bit_weights, l.align_bit_weights_gpu, l.align_bit_weights_size, cudaMemcpyDeviceToHost);
+            check_error(status);
+
+            float *input = (float *)calloc(l.c*l.h*l.w*l.batch, sizeof(float));
+            float *workspace = (float *)calloc(l.bit_align*l.size*l.size*l.c, sizeof(float));
+            float *output = (float *)calloc(l.batch*l.out_c*l.out_h*l.out_w, sizeof(float));
+
+            status = cudaMemcpy(input, state.input, l.c*l.h*l.w*l.batch*sizeof(float), cudaMemcpyDeviceToHost);
+            check_error(status);
+
+            int m = l.n;
+            int k = l.size*l.size*l.c;
+            int n = l.out_w*l.out_h;
+            float * a = l.weights_gpu;
+            //float * b = state.workspace;
+            float *b = workspace;
+            //float * c = l.output_gpu;
+            float *c = output;
+
+            int ldb_align = l.lda_align;
+            size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
+            size_t t_intput_size = new_ldb * n;
+            size_t t_bit_input_size = t_intput_size / 8;// +1;
+
+            char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char));
+            int src_size = k * l.bit_align;
+
+            //im2col_cpu_custom_bin(input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align);
+
+            float *align_workspace = NULL;
+            int align_workspace_size = l.bit_align * k; // aligned: n*k
+            status = cudaMalloc((void **)&align_workspace, align_workspace_size*sizeof(float));
+            check_error(status);
+
+            int i = 0;
+            im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, align_workspace, l.bit_align);
+
+            float_to_bit_gpu(align_workspace, (unsigned char *)state.workspace, align_workspace_size);
+
+            if(1)
+            {
+                {
+                    /*
+                    status = cudaMemcpy(t_bit_input, state.workspace, t_bit_input_size, cudaMemcpyDeviceToHost);
+                    check_error(status);
+                    for (int y = 0; y < 8; ++y) {
+                        for (int x = 0; x < 8; ++x) {
+                            int index = x + y*l.bit_align;
+                            if (get_bit((unsigned char *)t_bit_input, index)) printf("1, ");
+                            else printf("0, ");
+                        }
+                        printf("\n");
+                    }
+                    printf("\n");
+                    */
+                }
+
+                fill_int8_gpu((unsigned char *)align_workspace, 0, t_bit_input_size);
+
+                transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)align_workspace, k, n, l.bit_align, new_ldb, 8);
+                //cudaDeviceSynchronize();
+
+                //int size_transposed_array = l.bit_align * new_ldb;
+                status = cudaMemcpy(t_bit_input, align_workspace, t_bit_input_size, cudaMemcpyDeviceToHost);
+                check_error(status);
+
+                /*
+                for (int y = 0; y < 8; ++y) {
+                    for (int x = 0; x < 8; ++x) {
+                        int index = x + y*new_ldb;
+                        if (get_bit((unsigned char *)t_bit_input, index)) printf("1, ");
+                        else printf("0, ");
+                    }
+                    printf("\n");
+                }
+                printf("-----------\n");
+                //getchar();
+                */
+            }
+
+            if (0) {
+                status = cudaMemcpy(b, state.workspace, align_workspace_size / 8, cudaMemcpyDeviceToHost);
+                check_error(status);
+
+                for (int y = 0; y < 8; ++y) {
+                    for (int x = 0; x < 8; ++x) {
+                        int index = x + y*l.bit_align;
+                        if (get_bit((unsigned char *)b, index)) printf("1, ");
+                        else printf("0, ");
+                    }
+                    printf("\n");
+                }
+                printf("\n");
+
+                //float *im2 = (float *)calloc(align_workspace_size, sizeof(float));
+                //status = cudaMemcpy(im2, align_workspace, align_workspace_size * sizeof(float), cudaMemcpyDeviceToHost);
+                //check_error(status);
+                //float_to_bit(im2, (unsigned char *)b, align_workspace_size);
+
+                memset(t_bit_input, 0, t_bit_input_size);
+                // b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size
+                // t_input - [bit_align, k] - [n', k]
+                // t_bit_input - [new_ldb, n] - [k', n]
+                transpose_bin((char *)b, t_bit_input, k, n, l.bit_align, new_ldb, 8);
+
+                for (int y = 0; y < 8; ++y) {
+                    for (int x = 0; x < 8; ++x) {
+                        int index = x + y*new_ldb;
+                        if (get_bit((unsigned char *)t_bit_input, index)) printf("1, ");
+                        else printf("0, ");
+                    }
+                    printf("\n");
+                }
+                printf("-----------\n");
+                //getchar();
+
+                //free(im2);
+            }
+
+            // 5x times faster than gemm()-float32
+            gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char *)l.align_bit_weights, new_ldb, (unsigned char *)t_bit_input, new_ldb, c, n, l.mean_arr);
+
+            status = cudaMemcpy(l.output_gpu, output, l.batch*l.out_c*l.out_h*l.out_w * sizeof(float), cudaMemcpyHostToDevice);
+            check_error(status);
+
+            free(t_bit_input);
+            free(input);
+            free(workspace);
+            free(output);
+            cudaFree(align_workspace);
+
+            add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
+            activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
+            if (l.binary || l.xnor) swap_binary(&l);
+            return;
+        }
     }
 
 #ifdef CUDNN
     float one = 1;    // alpha[0], beta[0] is float for HALF and FLOAT
-    float alpha = 1, beta = 0; 
+    float alpha = 1, beta = 0;
 
 #ifdef CUDNN_HALF
-    // Note: For improved performance it is advised to use beta[0] = 0.0. 
+    // Note: For improved performance it is advised to use beta[0] = 0.0.
     // For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH;
     // 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF
     // 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED
@@ -168,10 +310,10 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
         &beta,
         l.dstTensorDesc,
         output16);
-    
 
-    if (l.batch_normalize) 
-    {        
+
+    if (l.batch_normalize)
+    {
         if (state.train) // Training
         {
             copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1);
@@ -213,7 +355,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
     {
         cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu);
         add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
-    }    
+    }
 
 #else
 
@@ -283,11 +425,11 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
     float alpha = 1, beta = 0;
 
 #ifdef CUDNN_HALF
-        
+
     const size_t input16_size = l.batch*l.c*l.w*l.h;
     const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h;
-    
-    if (*state.net.max_input16_size < input16_size) {        
+
+    if (*state.net.max_input16_size < input16_size) {
         *state.net.max_input16_size = input16_size;
         if(*state.net.input16_gpu) cuda_free(*state.net.input16_gpu);
         *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size);
@@ -368,7 +510,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
         // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData
         // calculate delta for the next layer
         // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16
-        // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16)    
+        // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16)
         cudnnConvolutionBackwardData(cudnn_handle(),
             &alpha,
             l.weightDesc,
@@ -524,11 +666,11 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float
     }else{
         // update weights:
         // weights_gpu = weights_gpu*(1 - decay*lr) + weight_updates_gpu*lr / (batch*subdivision) =
-        //  weights_gpu*(1 - 0.0005*0.001) + weight_updates_gpu*0.001/(64*8) = 
+        //  weights_gpu*(1 - 0.0005*0.001) + weight_updates_gpu*0.001/(64*8) =
         //  weights_gpu * 0.999 999 5 + weight_updates_gpu * 0.000 001 953125
-        // 
-        // weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum = 
-        //  (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 = 
+        //
+        // weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum =
+        //  (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 =
         //  weight_updates_gpu*0.9 - weights_gpu*0.2304
         axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
         axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);