First version of direct reading from image tensor for convgemm: only for edge cases now

src/kernels/level3/xconvgemm.opencl

@@ -19,15 +19,52 @@ R"(
 // =================================================================================================
 #if defined(ROUTINE_CONVGEMM)
 
+// Loads global off-chip memory into thread-private register files. This function is specific for
+// loading the image input tensor. This includes a bounds check.
+INLINE_FUNC real GlobalToPrivateCheckedImage(const __global real* restrict imagegm, const int image_offset_batch,
+                                             const int h_id, const int w_id, const int kwg,
+                                             const int input_h, const int input_w, const int channels,
+                                             const int kernel_h, const int kernel_w,
+                                             const int pad_h, const int pad_w,
+                                             const int stride_h, const int stride_w,
+                                             const int dilation_h, const int dilation_w) {
+  real result;
+
+  const int kernel_2d_index = kwg % (kernel_h * kernel_w);
+  const int kw_id = kernel_2d_index % kernel_w;
+  const int kh_id = kernel_2d_index / kernel_w;
+  const int c_id = kwg / (kernel_h * kernel_w);
+
+  const int h_index = -pad_h + kh_id * dilation_h + stride_h * h_id;
+  const int w_index = -pad_w + kw_id * dilation_w + stride_w * w_id;
+  if (h_index >= 0 && h_index < input_h &&
+      w_index >= 0 && w_index < input_w) {
+    const int image_index = w_index + input_w * (h_index + input_h * c_id);
+    result = imagegm[image_index + image_offset_batch];
+  }
+  else {
+    SetToZero(result);
+  }
+  return result;
+}
+
 // ConvGEMM kernel
 __kernel __attribute__((reqd_work_group_size(MDIMCD, NDIMCD, 1)))
 void Xconvgemm(const int num_patches, const int num_kernels, const int patch_size,
                const __global realMD* restrict colgm, const int col_offset, const int col_stride,
                const __global realND* restrict kernelgm, const int kernel_offset,
-               __global real* resultgm, const int result_offset, const int result_stride) {
+               __global real* resultgm, const int result_offset, const int result_stride,
+               const int input_h, const int input_w, const int channels,
+               const int kernel_h, const int kernel_w,
+               const int pad_h, const int pad_w,
+               const int stride_h, const int stride_w,
+               const int dilation_h, const int dilation_w,
+               const __global realMD* restrict imagegm, const int image_offset,
+               const int output_h, const int output_w) {
 
   // Batch offsets
   const int batch = get_group_id(2);
+  const int image_offset_batch = image_offset + channels * input_h * input_w * batch;
   const int col_offset_batch = col_offset + col_stride * batch;
   const int result_offset_batch = result_offset + result_stride * batch;
 
@@ -59,6 +96,8 @@ void Xconvgemm(const int num_patches, const int num_kernels, const int patch_siz
   // processes only the main parts: output blocks of WGD by WGD.
   const int idm = get_local_id(0) * MWID + GetGroupID0() * WGD;
   const int idn = get_local_id(1) * NWID + GetGroupID1() * WGD;
+  const int w_id = idm % output_w;
+  const int h_id = idm / output_w;
   if ((idm < (num_patches/WGD)*WGD) && (idn < (num_kernels/WGD)*WGD)) {
 
     // Loops over all complete workgroup tiles (K-dimension)
@@ -190,7 +229,10 @@ void Xconvgemm(const int num_patches, const int num_kernels, const int patch_siz
       // Loads data: off-chip --> private (matrix A and B)
       #pragma unroll
       for (int _mi = 0; _mi < MWID; _mi += 1) {
-        apd[_mi] = GlobalToPrivateCheckedA(colgms, _mi, num_patches, col_offset_batch, idm, kwg, false, false, num_patches);
+        apd[_mi] = GlobalToPrivateCheckedImage(imagegm, image_offset_batch, h_id, w_id, kwg,
+                                               input_h, input_w, channels, kernel_h, kernel_w,
+                                               pad_h, pad_w, stride_h, stride_w,
+                                               dilation_h, dilation_w);
       }
       #pragma unroll
       for (int _ni = 0; _ni < NWID; _ni += 1) {

src/routines/levelx/xconvgemm.cpp

@@ -117,6 +117,21 @@ void Xconvgemm<T>::DoConvgemm(const size_t channels, const size_t height, const
   kernel.SetArgument(8, result_buffer());
   kernel.SetArgument(9, static_cast<int>(result_offset));
   kernel.SetArgument(10, static_cast<int>(result_stride));
+  kernel.SetArgument(11, static_cast<int>(height));
+  kernel.SetArgument(12, static_cast<int>(width));
+  kernel.SetArgument(13, static_cast<int>(channels));
+  kernel.SetArgument(14, static_cast<int>(kernel_h));
+  kernel.SetArgument(15, static_cast<int>(kernel_w));
+  kernel.SetArgument(16, static_cast<int>(pad_h));
+  kernel.SetArgument(17, static_cast<int>(pad_w));
+  kernel.SetArgument(18, static_cast<int>(stride_h));
+  kernel.SetArgument(19, static_cast<int>(stride_w));
+  kernel.SetArgument(20, static_cast<int>(dilation_h));
+  kernel.SetArgument(21, static_cast<int>(dilation_w));
+  kernel.SetArgument(22, im_buffer());
+  kernel.SetArgument(23, static_cast<int>(im_offset));
+  kernel.SetArgument(24, static_cast<int>(output_h));
+  kernel.SetArgument(25, static_cast<int>(output_w));
 
   // Computes the global and local thread sizes
   const auto m_ceiled = Ceil(num_patches, db_["WGD"]);