Optimize convolution opencl kernel remove unused select.

mace/kernels/opencl/cl/conv_2d.cl

@@ -53,22 +53,28 @@ __kernel void conv_2d(KERNEL_ERROR_PARAMS
   int in_width1 = in_width0 + in_width_stride;
   int in_width2 = in_width1 + in_width_stride;
   int in_width3 = in_width2 + in_width_stride;
-  const int height_idx = mad24((out_hb % out_height), stride, -padding_top);
+  const int height_start = mad24((out_hb % out_height), stride, -padding_top);
+  int in_height_gap = select(
+      0,
+      (-height_start + dilation_h - 1) / dilation_h,
+      height_start < 0);
+  int in_height_start = mad24(in_height_gap, dilation_h, height_start);
+  int in_height_end = min(mad24(filter_height, dilation_h, height_start),
+                          in_height);
 
   const int batch_idx = mul24((out_hb / out_height), in_height);
   const int filter_hw = mul24(filter_width, filter_height);
+  const int filter_y_idx_start = mul24(out_ch_blk, filter_hw)
+      + mul24(in_height_gap, filter_width);
 
   DATA_TYPE4 in0, in1, in2, in3;
   DATA_TYPE4 weights0, weights1, weights2, weights3;
   for (short in_ch_blk = 0; in_ch_blk < in_ch_blks; ++in_ch_blk) {
     const int in_idx = mul24(in_ch_blk, in_width);
     int filter_x_idx = in_ch_blk << 2;
-    int filter_y_idx = mul24(out_ch_blk, filter_hw);
-    for (short hb_idx = 0; hb_idx < filter_height; ++hb_idx) {
-      int in_hb_value = height_idx + mul24(hb_idx, dilation_h);
-      in_hb_value = select(in_hb_value + batch_idx,
-                           -1,
-                           (in_hb_value < 0 || in_hb_value >= in_height));
+    int filter_y_idx = filter_y_idx_start;
+    for (int hb_idx = in_height_start; hb_idx < in_height_end; hb_idx += dilation_h) {
+      int in_hb_value = hb_idx + batch_idx;
 
 #pragma unroll
       for (short width_idx = 0; width_idx < filter_width; ++width_idx) {

mace/kernels/opencl/cl/conv_2d_3x3.cl

@@ -54,21 +54,26 @@ __kernel void conv_2d_3x3(KERNEL_ERROR_PARAMS
   int in_width2 = in_width1 + in_width_stride;
   int in_width3 = in_width2 + in_width_stride;
   int in_width4 = in_width3 + in_width_stride;
-  const int height_idx = mad24((out_hb % out_height), stride, -padding_top);
+  const int height_start = mad24((out_hb % out_height), stride, -padding_top);
+  int in_height_gap = select(
+      0,
+      (-height_start + dilation_h - 1) / dilation_h,
+      height_start < 0);
+  int in_height_start = mad24(in_height_gap, dilation_h, height_start);
+  int in_height_end = min(mad24(3, dilation_h, height_start),
+                          in_height);
 
   const int batch_idx = mul24((out_hb / out_height), in_height);
+  const int filter_y_idx_start = mul24(out_ch_blk, 9) + mul24(in_height_gap, 3);
 
   DATA_TYPE4 in0, in1, in2, in3, in4;
   DATA_TYPE4 weights0, weights1, weights2, weights3;
   for (short in_ch_blk = 0; in_ch_blk < in_ch_blks; ++in_ch_blk) {
     const int in_idx = mul24(in_ch_blk, in_width);
     int filter_x_idx = in_ch_blk << 2;
-    int filter_y_idx = mul24(out_ch_blk, 9);
-    int in_hb_idx = height_idx;
-    for (short hb_idx = 0; hb_idx < 3; ++hb_idx) {
-      int in_hb_value = select(in_hb_idx + batch_idx,
-                               -1,
-                               (in_hb_idx < 0 || in_hb_idx >= in_height));
+    int filter_y_idx = filter_y_idx_start;
+    for (int hb_idx = in_height_start; hb_idx < in_height_end; hb_idx += dilation_h) {
+      int in_hb_value = hb_idx + batch_idx;
       int in_width_idx = 0;
       for (short width_idx = 0; width_idx < 3; ++width_idx) {
         int in_width_value;
@@ -122,7 +127,6 @@ __kernel void conv_2d_3x3(KERNEL_ERROR_PARAMS
         in_width_idx += dilation_w;
         filter_y_idx += 1;
       }
-      in_hb_idx += dilation_h;
     }
   }