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authorGian Marco Iodice <gianmarco.iodice@arm.com>2018-07-06 12:59:28 +0100
committerAnthony Barbier <anthony.barbier@arm.com>2018-11-02 16:54:10 +0000
commitd28b751cf2ba9fcf4ccf294b31bf9d2ec5dfd8bb (patch)
tree111a96797e6b1cd20a2db7088e5fc4cd1903ff02
parent98f085bf87d55bff3866963e8220cfcb4872709f (diff)
downloadComputeLibrary-d28b751cf2ba9fcf4ccf294b31bf9d2ec5dfd8bb.tar.gz
COMPMID-1340 - Implementing Winograd Convolution Layer 1x5/5x1 on OpenCL NHWC
Change-Id: Id5e0795238f77c049df9c109dafc5ef878c1897d Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/139234 Tested-by: Jenkins <bsgcomp@arm.com> Reviewed-by: Giorgio Arena <giorgio.arena@arm.com> Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat
-rw-r--r--arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h10
-rw-r--r--arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h10
-rw-r--r--arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h10
-rw-r--r--arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h6
-rw-r--r--arm_compute/runtime/CL/functions/CLWinogradInputTransform.h10
-rw-r--r--src/core/CL/CLHelpers.cpp4
-rw-r--r--src/core/CL/CLKernelLibrary.cpp6
-rw-r--r--src/core/CL/cl_kernels/winograd_filter_transform.cl550
-rw-r--r--src/core/CL/cl_kernels/winograd_input_transform.cl822
-rw-r--r--src/core/CL/cl_kernels/winograd_output_transform.cl413
-rw-r--r--tests/datasets/WinogradOutputTransformDataset.h28
-rw-r--r--tests/validation/CL/Winograd.cpp18
12 files changed, 1183 insertions, 704 deletions
diff --git a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
index 9d0833d695..62f55fa91e 100644
--- a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
@@ -54,8 +54,9 @@ public:
* F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
*
* @note Winograd filter transform supports the following configurations for NHWC data layout
- * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3)
- * F(4x4, 5x5)
+ * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
+ *
* Strides: only unit strides
*
* @param[in] input Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout) or [IFM, kernel_x, kernel_y, OFM] (NHWC data layout). Data types supported: F32.
@@ -71,8 +72,9 @@ public:
* F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
*
* @note Winograd filter transform supports the following configurations for NHWC data layout
- * F(output tile, kernel size):F(4x4, 3x3),
- * F(4x4, 5x5)
+ * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
+ *
* Strides: only unit strides
*
* @param[in] input Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout) or [IFM, kernel_x, kernel_y, OFM] (NHWC data layout). Data types supported: F32.
diff --git a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
index 410e8ba765..517b348ffb 100644
--- a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
@@ -52,8 +52,9 @@ public:
* F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
*
* @note Winograd input transform supports the following configurations for NHWC data layout
- * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3)
- * F(4x4, 5x5)
+ * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
+ *
* Strides: only unit strides
*
* @param[in] input The input tensor to transform. Data types supported: F32
@@ -69,8 +70,9 @@ public:
* F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
*
* @note Winograd input transform supports the following configurations for NHWC data layout
- * F(output tile, kernel size):F(4x4, 3x3),
- * F(4x4, 5x5)
+ * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
+ *
* Strides: only unit strides
*
* @param[in] input The input tensor to transform. Data types supported: F32
diff --git a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
index 0798172ba7..bab93de4b0 100644
--- a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
@@ -54,8 +54,9 @@ public:
* F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
*
* @note Winograd output transform supports the following configurations for NHWC data layout
- * F(output tile, kernel size):F(4x4, 3x3),
- * F(4x4, 5x5)
+ * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
+ *
* Strides: only unit strides
*
* @param[in] input Source tensor with shape [C, N, K, batches]. Data types supported: F32.
@@ -72,8 +73,9 @@ public:
* F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
*
* @note Winograd output transform supports the following configurations for NHWC data layout
- * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3)
- * F(4x4, 5x5)
+ * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
+ *
* Strides: only unit strides
*
* @param[in] input Source tensor with shape [C, N, K, batches]. Data types supported: F32.
diff --git a/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
index 683aa79788..a24ae31d41 100644
--- a/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
@@ -59,8 +59,7 @@ public:
CLWinogradConvolutionLayer &operator=(CLWinogradConvolutionLayer &&) = default;
/** Set the input and output tensors.
*
- * @note: This function only works with 3x3,3x1,1x3,5x5,5x1 and 1x5 kernels along with unit strides for NCHW data layout
- * @note: This function only works with 3x3, 3x1, 1x3 and 5x5 kernels along with unit strides for NHWC data layout
+ * @note: This function only works with 3x3,3x1,1x3,5x5,5x1 and 1x5 kernels along with unit strides for both NCHW and NHWC data layout
* @note Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
@@ -79,8 +78,7 @@ public:
const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
/** Static function to check if given info will lead to a valid configuration of @ref CLWinogradConvolutionLayer
*
- * @note: This function only works with 3x3,3x1,1x3,5x5,5x1 and 1x5 kernels along with unit strides for NCHW data layout
- * @note: This function only works with 3x3 and 5x5 kernels along with unit strides for NHWC data layout
+ * @note: This function only works with 3x3,3x1,1x3,5x5,5x1 and 1x5 kernels along with unit strides for both NCHW and NHWC data layout
* @note Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true
*
* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
diff --git a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h
index 1f89455aee..8ea25a116a 100644
--- a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h
+++ b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h
@@ -45,8 +45,9 @@ public:
* F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
*
* @note Winograd input transform supports the following configurations for NHWC data layout
- * F(output tile, kernel size):F(4x4, 3x3),
- * F(4x4, 5x5)
+ * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
+ *
* Strides: only unit strides
*
* @param[in] input The input tensor to transform. Data types supported: F32
@@ -62,8 +63,9 @@ public:
* F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
*
* @note Winograd input transform supports the following configurations for NHWC data layout
- * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3)
- * F(4x4, 5x5)
+ * F(output tile, kernel size):F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * F(4x4, 5x5), F(4x1, 5x1), F(1x4, 1x5)
+ *
* Strides: only unit strides
*
* @param[in] input The input tensor to transform. Data types supported: F32
diff --git a/src/core/CL/CLHelpers.cpp b/src/core/CL/CLHelpers.cpp
index 3965be76fd..0e83ff20c8 100644
--- a/src/core/CL/CLHelpers.cpp
+++ b/src/core/CL/CLHelpers.cpp
@@ -172,7 +172,9 @@ bool cl_winograd_convolution_layer_supported(const Size2D &output_tile, const Si
WinogradConfiguration(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3)),
WinogradConfiguration(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1)),
WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3)),
- WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
+ WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)),
+ WinogradConfiguration(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1)),
+ WinogradConfiguration(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5))
};
auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 03731ee93c..9a3ebc0021 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -380,6 +380,8 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
{ "winograd_filter_transform_1x4_1x3_nhwc", "winograd_filter_transform.cl" },
{ "winograd_filter_transform_4x4_3x3_nhwc", "winograd_filter_transform.cl" },
{ "winograd_filter_transform_4x4_5x5_nhwc", "winograd_filter_transform.cl" },
+ { "winograd_filter_transform_4x1_5x1_nhwc", "winograd_filter_transform.cl" },
+ { "winograd_filter_transform_1x4_1x5_nhwc", "winograd_filter_transform.cl" },
{ "winograd_input_transform_2x2_3x3_stepz1_nchw", "winograd_input_transform.cl" },
{ "winograd_input_transform_2x2_3x3_stepz2_nchw", "winograd_input_transform.cl" },
{ "winograd_input_transform_2x1_3x1_stepz1_nchw", "winograd_input_transform.cl" },
@@ -396,6 +398,8 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
{ "winograd_input_transform_1x4_1x3_stepz1_nhwc", "winograd_input_transform.cl" },
{ "winograd_input_transform_4x4_3x3_stepz1_nhwc", "winograd_input_transform.cl" },
{ "winograd_input_transform_4x4_5x5_stepz1_nhwc", "winograd_input_transform.cl" },
+ { "winograd_input_transform_4x1_5x1_stepz1_nhwc", "winograd_input_transform.cl" },
+ { "winograd_input_transform_1x4_1x5_stepz1_nhwc", "winograd_input_transform.cl" },
{ "winograd_output_transform_2x2_3x3_nchw", "winograd_output_transform.cl" },
{ "winograd_output_transform_2x1_3x1_nchw", "winograd_output_transform.cl" },
{ "winograd_output_transform_1x2_1x3_nchw", "winograd_output_transform.cl" },
@@ -409,6 +413,8 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
{ "winograd_output_transform_1x4_1x3_nhwc", "winograd_output_transform.cl" },
{ "winograd_output_transform_4x4_3x3_nhwc", "winograd_output_transform.cl" },
{ "winograd_output_transform_4x4_5x5_nhwc", "winograd_output_transform.cl" },
+ { "winograd_output_transform_4x1_5x1_nhwc", "winograd_output_transform.cl" },
+ { "winograd_output_transform_1x4_1x5_nhwc", "winograd_output_transform.cl" },
{ "YUYV422_to_IYUV_bt709", "color_convert.cl" },
{ "YUYV422_to_NV12_bt709", "color_convert.cl" },
{ "YUYV422_to_RGB888_bt709", "color_convert.cl" },
diff --git a/src/core/CL/cl_kernels/winograd_filter_transform.cl b/src/core/CL/cl_kernels/winograd_filter_transform.cl
index e53da9b278..73da005996 100644
--- a/src/core/CL/cl_kernels/winograd_filter_transform.cl
+++ b/src/core/CL/cl_kernels/winograd_filter_transform.cl
@@ -442,105 +442,6 @@ __kernel void winograd_filter_transform_4x4_3x3_nhwc(
#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
}
-#if defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
-/** This OpenCL kernel performs Winograd filter transform 3x1 when the data layout is NHWC and the output tile is 4x1
- *
- * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
- * @note -DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL has to be passed at compile time to perform Winograd Filter Transform
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)
- * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void winograd_filter_transform_4x1_3x1_nhwc(
- TENSOR4D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst))
-{
- winograd_filter_transform_4x4_3x3_nhwc(src_ptr,
- src_stride_x,
- src_step_x,
- src_stride_y,
- src_step_y,
- src_stride_z,
- src_step_z,
- src_stride_w,
- src_step_w,
- src_offset_first_element_in_bytes,
- dst_ptr,
- dst_stride_x,
- dst_step_x,
- dst_stride_y,
- dst_step_y,
- dst_stride_z,
- dst_step_z,
- dst_offset_first_element_in_bytes);
-}
-#endif // defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
-
-#if defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
-/** This OpenCL kernel performs Winograd filter transform 1x3 when the data layout is NHWC and the output tile is 1x4
- *
- * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
- * @note -DWINOGRAD_FILTER_TRANSFORM_VERTICAL has to be passed at compile time to perform Winograd Filter Transform
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)
- * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void winograd_filter_transform_1x4_1x3_nhwc(
- TENSOR4D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst))
-{
- winograd_filter_transform_4x4_3x3_nhwc(src_ptr,
- src_stride_x,
- src_step_x,
- src_stride_y,
- src_step_y,
- src_stride_z,
- src_step_z,
- src_stride_w,
- src_step_w,
- src_offset_first_element_in_bytes,
- dst_ptr,
- dst_stride_x,
- dst_step_x,
- dst_stride_y,
- dst_step_y,
- dst_stride_z,
- dst_step_z,
- dst_offset_first_element_in_bytes);
-}
-#endif // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
/** This OpenCL kernel performs Winograd filter transform 5x5/5x1 or 1x5 when the data layout is NCHW and the output tile is 4x4/4x1 or 1x4
*
* @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
@@ -827,9 +728,11 @@ __kernel void winograd_filter_transform_4x4_5x5_nchw(
#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
}
-/** This OpenCL kernel performs Winograd filter transform 5x5 when the data layout is NHWC and the output tile is 4x4
+/** This OpenCL kernel performs Winograd filter transform 5x5/5x1 or 1x5 when the data layout is NHWC and the output tile is 4x4/4x1 or 1x4
*
* @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note If this kernel is used to perform Winograd filter transform 5x1, -DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd filter transform 1x5, -DWINOGRAD_FILTER_TRANSFORM_VERTICAL has to be passed at compile time
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
@@ -858,12 +761,23 @@ __kernel void winograd_filter_transform_4x4_5x5_nhwc(
const __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + get_global_id(0) * sizeof(float) + get_global_id(1) * src_step_y + get_global_id(2) * src_step_w;
+#if defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
// Load the values from the input tensor
- float w00 = *((__global float *)(src_addr + 0 * src_stride_z + 0 * src_stride_y));
- float w01 = *((__global float *)(src_addr + 0 * src_stride_z + 1 * src_stride_y));
- float w02 = *((__global float *)(src_addr + 0 * src_stride_z + 2 * src_stride_y));
- float w03 = *((__global float *)(src_addr + 0 * src_stride_z + 3 * src_stride_y));
- float w04 = *((__global float *)(src_addr + 0 * src_stride_z + 4 * src_stride_y));
+ float w00 = *((__global float *)(src_addr + 0 * src_stride_z));
+ float w01 = *((__global float *)(src_addr + 1 * src_stride_z));
+ float w02 = *((__global float *)(src_addr + 2 * src_stride_z));
+ float w03 = *((__global float *)(src_addr + 3 * src_stride_z));
+ float w04 = *((__global float *)(src_addr + 4 * src_stride_z));
+#else // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
+ // Load the values from the input tensor
+ float w00 = *((__global float *)(src_addr + 0 * src_stride_y));
+ float w01 = *((__global float *)(src_addr + 1 * src_stride_y));
+ float w02 = *((__global float *)(src_addr + 2 * src_stride_y));
+ float w03 = *((__global float *)(src_addr + 3 * src_stride_y));
+ float w04 = *((__global float *)(src_addr + 4 * src_stride_y));
+#endif // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
+
+#if !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
float w10 = *((__global float *)(src_addr + 1 * src_stride_z + 0 * src_stride_y));
float w11 = *((__global float *)(src_addr + 1 * src_stride_z + 1 * src_stride_y));
float w12 = *((__global float *)(src_addr + 1 * src_stride_z + 2 * src_stride_y));
@@ -884,128 +798,129 @@ __kernel void winograd_filter_transform_4x4_5x5_nhwc(
float w42 = *((__global float *)(src_addr + 4 * src_stride_z + 2 * src_stride_y));
float w43 = *((__global float *)(src_addr + 4 * src_stride_z + 3 * src_stride_y));
float w44 = *((__global float *)(src_addr + 4 * src_stride_z + 4 * src_stride_y));
-
- // Transform the 3x3 tile in a 8x8 tile
- float8 out0 = 0.0f;
- float8 out1 = 0.0f;
- float8 out2 = 0.0f;
- float8 out3 = 0.0f;
- float8 out4 = 0.0f;
- float8 out5 = 0.0f;
- float8 out6 = 0.0f;
- float8 out7 = 0.0f;
+#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
// Row 0
- out0.s0 = w00;
- out0.s1 = -2.f * (w00 + w01 + w02 + w03 + w04) / 9.f;
- out0.s2 = -2.f * (w00 - w01 + w02 - w03 + w04) / 9.f;
- out0.s3 = (w00 + 2.f * w01 + 4.f * w02 + 8.f * w03 + 16.f * w04) / 90.f;
- out0.s4 = (w00 - 2.f * w01 + 4.f * w02 - 8.f * w03 + 16.f * w04) / 90.f;
- out0.s5 = (16.f * w00 + 8.f * w01 + 4.f * w02 + 2.f * w03 + w04) / 180.f;
- out0.s6 = (16.f * w00 - 8.f * w01 + 4.f * w02 - 2.f * w03 + w04) / 180.f;
- out0.s7 = w04;
+ float8 out0 = 0.0f;
+ out0.s0 = w00;
+ out0.s1 = -2.f * (w00 + w01 + w02 + w03 + w04) / 9.f;
+ out0.s2 = -2.f * (w00 - w01 + w02 - w03 + w04) / 9.f;
+ out0.s3 = (w00 + 2.f * w01 + 4.f * w02 + 8.f * w03 + 16.f * w04) / 90.f;
+ out0.s4 = (w00 - 2.f * w01 + 4.f * w02 - 8.f * w03 + 16.f * w04) / 90.f;
+ out0.s5 = (16.f * w00 + 8.f * w01 + 4.f * w02 + 2.f * w03 + w04) / 180.f;
+ out0.s6 = (16.f * w00 - 8.f * w01 + 4.f * w02 - 2.f * w03 + w04) / 180.f;
+ out0.s7 = w04;
+#if !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
// Row 1
- out1.s0 = -2.f * (w00 + w10 + w20 + w30 + w40) / 9.f;
- out1.s1 = 4.f * ((w00 + w10 + w20 + w30 + w40) + (w01 + w11 + w21 + w31 + w41) + (w02 + w12 + w22 + w32 + w42) + (w03 + w13 + w23 + w33 + w43) + (w04 + w14 + w24 + w34 + w44)) / 81.f;
- out1.s2 = 4.f * ((w00 + w10 + w20 + w30 + w40) - (w01 + w11 + w21 + w31 + w41) + (w02 + w12 + w22 + w32 + w42) - (w03 + w13 + w23 + w33 + w43) + (w04 + w14 + w24 + w34 + w44)) / 81.f;
- out1.s3 = -((w00 + w10 + w20 + w30 + w40) + 2.f * (w01 + w11 + w21 + w31 + w41) + 4.f * (w02 + w12 + w22 + w32 + w42) + 8.f * (w03 + w13 + w23 + w33 + w43) + 16.f *
- (w04 + w14 + w24 + w34 + w44)) / 405.f;
- out1.s4 = -((w00 + w10 + w20 + w30 + w40) - 2.f * (w01 + w11 + w21 + w31 + w41) + 4.f * (w02 + w12 + w22 + w32 + w42) - 8.f * (w03 + w13 + w23 + w33 + w43) + 16.f *
- (w04 + w14 + w24 + w34 + w44)) / 405.f;
- out1.s5 = -(16.f * (w00 + w10 + w20 + w30 + w40) + 8.f * (w01 + w11 + w21 + w31 + w41) + 4.f * (w02 + w12 + w22 + w32 + w42) + 2.f * (w03 + w13 + w23 + w33 + w43) +
- (w04 + w14 + w24 + w34 + w44)) / 810.f;
- out1.s6 = -(16.f * (w00 + w10 + w20 + w30 + w40) - 8.f * (w01 + w11 + w21 + w31 + w41) + 4.f * (w02 + w12 + w22 + w32 + w42) - 2.f * (w03 + w13 + w23 + w33 + w43) +
- (w04 + w14 + w24 + w34 + w44)) / 810.f;
- out1.s7 = -2.f * (w04 + w14 + w24 + w34 + w44) / 9.f;
+ float8 out1 = 0.0f;
+ out1.s0 = -2.f * (w00 + w10 + w20 + w30 + w40) / 9.f;
+ out1.s1 = 4.f * ((w00 + w10 + w20 + w30 + w40) + (w01 + w11 + w21 + w31 + w41) + (w02 + w12 + w22 + w32 + w42) + (w03 + w13 + w23 + w33 + w43) + (w04 + w14 + w24 + w34 + w44)) / 81.f;
+ out1.s2 = 4.f * ((w00 + w10 + w20 + w30 + w40) - (w01 + w11 + w21 + w31 + w41) + (w02 + w12 + w22 + w32 + w42) - (w03 + w13 + w23 + w33 + w43) + (w04 + w14 + w24 + w34 + w44)) / 81.f;
+ out1.s3 = -((w00 + w10 + w20 + w30 + w40) + 2.f * (w01 + w11 + w21 + w31 + w41) + 4.f * (w02 + w12 + w22 + w32 + w42) + 8.f * (w03 + w13 + w23 + w33 + w43) + 16.f *
+ (w04 + w14 + w24 + w34 + w44)) / 405.f;
+ out1.s4 = -((w00 + w10 + w20 + w30 + w40) - 2.f * (w01 + w11 + w21 + w31 + w41) + 4.f * (w02 + w12 + w22 + w32 + w42) - 8.f * (w03 + w13 + w23 + w33 + w43) + 16.f *
+ (w04 + w14 + w24 + w34 + w44)) / 405.f;
+ out1.s5 = -(16.f * (w00 + w10 + w20 + w30 + w40) + 8.f * (w01 + w11 + w21 + w31 + w41) + 4.f * (w02 + w12 + w22 + w32 + w42) + 2.f * (w03 + w13 + w23 + w33 + w43) +
+ (w04 + w14 + w24 + w34 + w44)) / 810.f;
+ out1.s6 = -(16.f * (w00 + w10 + w20 + w30 + w40) - 8.f * (w01 + w11 + w21 + w31 + w41) + 4.f * (w02 + w12 + w22 + w32 + w42) - 2.f * (w03 + w13 + w23 + w33 + w43) +
+ (w04 + w14 + w24 + w34 + w44)) / 810.f;
+ out1.s7 = -2.f * (w04 + w14 + w24 + w34 + w44) / 9.f;
// Row 2
- out2.s0 = -2.f * (w00 - w10 + w20 - w30 + w40) / 9.f;
- out2.s1 = 4.f * ((w00 - w10 + w20 - w30 + w40) + (w01 - w11 + w21 - w31 + w41) + (w02 - w12 + w22 - w32 + w42) + (w03 - w13 + w23 - w33 + w43) + (w04 - w14 + w24 - w34 + w44)) / 81.f;
- out2.s2 = 4.f * ((w00 - w10 + w20 - w30 + w40) - (w01 - w11 + w21 - w31 + w41) + (w02 - w12 + w22 - w32 + w42) - (w03 - w13 + w23 - w33 + w43) + (w04 - w14 + w24 - w34 + w44)) / 81.f;
- out2.s3 = -((w00 - w10 + w20 - w30 + w40) + 2.f * (w01 - w11 + w21 - w31 + w41) + 4.f * (w02 - w12 + w22 - w32 + w42) + 8.f * (w03 - w13 + w23 - w33 + w43) + 16.f *
- (w04 - w14 + w24 - w34 + w44)) / 405.f;
- out2.s4 = -((w00 - w10 + w20 - w30 + w40) - 2.f * (w01 - w11 + w21 - w31 + w41) + 4.f * (w02 - w12 + w22 - w32 + w42) - 8.f * (w03 - w13 + w23 - w33 + w43) + 16.f *
- (w04 - w14 + w24 - w34 + w44)) / 405.f;
- out2.s5 = -(16.f * (w00 - w10 + w20 - w30 + w40) + 8.f * (w01 - w11 + w21 - w31 + w41) + 4.f * (w02 - w12 + w22 - w32 + w42) + 2.f * (w03 - w13 + w23 - w33 + w43) +
- (w04 - w14 + w24 - w34 + w44)) / 810.f;
- out2.s6 = -(16.f * (w00 - w10 + w20 - w30 + w40) - 8.f * (w01 - w11 + w21 - w31 + w41) + 4.f * (w02 - w12 + w22 - w32 + w42) - 2.f * (w03 - w13 + w23 - w33 + w43) +
- (w04 - w14 + w24 - w34 + w44)) / 810.f;
- out2.s7 = -2.f * (w04 - w14 + w24 - w34 + w44) / 9.f;
+ float8 out2 = 0.0f;
+ out2.s0 = -2.f * (w00 - w10 + w20 - w30 + w40) / 9.f;
+ out2.s1 = 4.f * ((w00 - w10 + w20 - w30 + w40) + (w01 - w11 + w21 - w31 + w41) + (w02 - w12 + w22 - w32 + w42) + (w03 - w13 + w23 - w33 + w43) + (w04 - w14 + w24 - w34 + w44)) / 81.f;
+ out2.s2 = 4.f * ((w00 - w10 + w20 - w30 + w40) - (w01 - w11 + w21 - w31 + w41) + (w02 - w12 + w22 - w32 + w42) - (w03 - w13 + w23 - w33 + w43) + (w04 - w14 + w24 - w34 + w44)) / 81.f;
+ out2.s3 = -((w00 - w10 + w20 - w30 + w40) + 2.f * (w01 - w11 + w21 - w31 + w41) + 4.f * (w02 - w12 + w22 - w32 + w42) + 8.f * (w03 - w13 + w23 - w33 + w43) + 16.f *
+ (w04 - w14 + w24 - w34 + w44)) / 405.f;
+ out2.s4 = -((w00 - w10 + w20 - w30 + w40) - 2.f * (w01 - w11 + w21 - w31 + w41) + 4.f * (w02 - w12 + w22 - w32 + w42) - 8.f * (w03 - w13 + w23 - w33 + w43) + 16.f *
+ (w04 - w14 + w24 - w34 + w44)) / 405.f;
+ out2.s5 = -(16.f * (w00 - w10 + w20 - w30 + w40) + 8.f * (w01 - w11 + w21 - w31 + w41) + 4.f * (w02 - w12 + w22 - w32 + w42) + 2.f * (w03 - w13 + w23 - w33 + w43) +
+ (w04 - w14 + w24 - w34 + w44)) / 810.f;
+ out2.s6 = -(16.f * (w00 - w10 + w20 - w30 + w40) - 8.f * (w01 - w11 + w21 - w31 + w41) + 4.f * (w02 - w12 + w22 - w32 + w42) - 2.f * (w03 - w13 + w23 - w33 + w43) +
+ (w04 - w14 + w24 - w34 + w44)) / 810.f;
+ out2.s7 = -2.f * (w04 - w14 + w24 - w34 + w44) / 9.f;
// Row 3
- out3.s0 = (w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) / 90.f;
- out3.s1 = -((w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) + (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) +
- (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 405.f;
- out3.s2 = -((w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) - (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) -
- (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 405.f;
- out3.s3 = ((w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) + 2.f * (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + 4.f * (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) + 8.f
- * (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + 16.f * (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 8100.f;
- out3.s4 = ((w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) - 2.f * (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + 4.f * (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) - 8.f
- * (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + 16.f * (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 8100.f;
- out3.s5 = (16.f * (w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) + 8.f * (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + 4.f *
- (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) + 2.f * (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 16200.f;
- out3.s6 = (16.f * (w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) - 8.f * (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + 4.f *
- (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) - 2.f * (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 16200.f;
- out3.s7 = (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44) / 90.f;
+ float8 out3 = 0.0f;
+ out3.s0 = (w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) / 90.f;
+ out3.s1 = -((w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) + (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) +
+ (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 405.f;
+ out3.s2 = -((w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) - (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) -
+ (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 405.f;
+ out3.s3 = ((w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) + 2.f * (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + 4.f *
+ (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) + 8.f * (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + 16.f * (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 8100.f;
+ out3.s4 = ((w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) - 2.f * (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + 4.f *
+ (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) - 8.f * (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + 16.f * (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 8100.f;
+ out3.s5 = (16.f * (w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) + 8.f * (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + 4.f *
+ (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) + 2.f * (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 16200.f;
+ out3.s6 = (16.f * (w00 + 2.f * w10 + 4.f * w20 + 8.f * w30 + 16.f * w40) - 8.f * (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) + 4.f *
+ (w02 + 2.f * w12 + 4.f * w22 + 8.f * w32 + 16.f * w42) - 2.f * (w03 + 2.f * w13 + 4.f * w23 + 8.f * w33 + 16.f * w43) + (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44)) / 16200.f;
+ out3.s7 = (w04 + 2.f * w14 + 4.f * w24 + 8.f * w34 + 16.f * w44) / 90.f;
// Row 4
- out4.s0 = (w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) / 90.f;
- out4.s1 = -((w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) + (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) +
- (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 405.f;
- out4.s2 = -((w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) - (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) -
- (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 405.f;
- out4.s3 = ((w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) + 2.f * (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + 4.f * (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) + 8.f
- * (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + 16.f * (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 8100.f;
- out4.s4 = ((w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) - 2.f * (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + 4.f * (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) - 8.f
- * (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + 16.f * (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 8100.f;
- out4.s5 = (16.f * (w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) + 8.f * (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + 4.f *
- (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) + 2.f * (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 16200.f;
- out4.s6 = (16.f * (w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) - 8.f * (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + 4.f *
- (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) - 2.f * (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 16200.f;
- out4.s7 = (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44) / 90.f;
+ float8 out4 = 0.0f;
+ out4.s0 = (w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) / 90.f;
+ out4.s1 = -((w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) + (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) +
+ (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 405.f;
+ out4.s2 = -((w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) - (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) -
+ (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 405.f;
+ out4.s3 = ((w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) + 2.f * (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + 4.f *
+ (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) + 8.f * (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + 16.f * (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 8100.f;
+ out4.s4 = ((w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) - 2.f * (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + 4.f *
+ (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) - 8.f * (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + 16.f * (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 8100.f;
+ out4.s5 = (16.f * (w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) + 8.f * (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + 4.f *
+ (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) + 2.f * (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 16200.f;
+ out4.s6 = (16.f * (w00 - 2.f * w10 + 4.f * w20 - 8.f * w30 + 16.f * w40) - 8.f * (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) + 4.f *
+ (w02 - 2.f * w12 + 4.f * w22 - 8.f * w32 + 16.f * w42) - 2.f * (w03 - 2.f * w13 + 4.f * w23 - 8.f * w33 + 16.f * w43) + (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44)) / 16200.f;
+ out4.s7 = (w04 - 2.f * w14 + 4.f * w24 - 8.f * w34 + 16.f * w44) / 90.f;
// Row 5
- out5.s0 = (16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) / 180.f;
- out5.s1 = -((16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) + (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) +
- (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 810.f;
- out5.s2 = -((16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) - (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) -
- (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 810.f;
- out5.s3 = ((16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) + 2.f * (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + 4.f * (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) + 8.f
- * (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + 16.f * (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 16200.f;
- out5.s4 = ((16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) - 2.f * (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + 4.f * (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) - 8.f
- * (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + 16.f * (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 16200.f;
- out5.s5 = (16.f * (16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) + 8.f * (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + 4.f *
- (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) + 2.f * (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 32400.f;
- out5.s6 = (16.f * (16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) - 8.f * (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + 4.f *
- (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) - 2.f * (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 32400.f;
- out5.s7 = (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44) / 180.f;
+ float8 out5 = 0.0f;
+ out5.s0 = (16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) / 180.f;
+ out5.s1 = -((16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) + (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) +
+ (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 810.f;
+ out5.s2 = -((16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) - (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) -
+ (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 810.f;
+ out5.s3 = ((16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) + 2.f * (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + 4.f *
+ (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) + 8.f * (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + 16.f * (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 16200.f;
+ out5.s4 = ((16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) - 2.f * (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + 4.f *
+ (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) - 8.f * (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + 16.f * (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 16200.f;
+ out5.s5 = (16.f * (16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) + 8.f * (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + 4.f *
+ (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) + 2.f * (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 32400.f;
+ out5.s6 = (16.f * (16.f * w00 + 8.f * w10 + 4.f * w20 + 2.f * w30 + w40) - 8.f * (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) + 4.f *
+ (16.f * w02 + 8.f * w12 + 4.f * w22 + 2.f * w32 + w42) - 2.f * (16.f * w03 + 8.f * w13 + 4.f * w23 + 2.f * w33 + w43) + (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44)) / 32400.f;
+ out5.s7 = (16.f * w04 + 8.f * w14 + 4.f * w24 + 2.f * w34 + w44) / 180.f;
// Row 6
- out6.s0 = (16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) / 180.f;
- out6.s1 = -((16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) + (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) +
- (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 810.f;
- out6.s2 = -((16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) - (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) -
- (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 810.f;
- out6.s3 = ((16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) + 2.f * (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + 4.f * (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) + 8.f
- * (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + 16.f * (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 16200.f;
- out6.s4 = ((16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) - 2.f * (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + 4.f * (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) - 8.f
- * (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + 16.f * (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 16200.f;
- out6.s5 = (16.f * (16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) + 8.f * (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + 4.f *
- (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) + 2.f * (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 32400.f;
- out6.s6 = (16.f * (16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) - 8.f * (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + 4.f *
- (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) - 2.f * (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 32400.f;
- out6.s7 = (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44) / 180.f;
+ float8 out6 = 0.0f;
+ out6.s0 = (16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) / 180.f;
+ out6.s1 = -((16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) + (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) +
+ (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 810.f;
+ out6.s2 = -((16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) - (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) -
+ (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 810.f;
+ out6.s3 = ((16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) + 2.f * (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + 4.f *
+ (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) + 8.f * (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + 16.f * (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 16200.f;
+ out6.s4 = ((16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) - 2.f * (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + 4.f *
+ (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) - 8.f * (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + 16.f * (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 16200.f;
+ out6.s5 = (16.f * (16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) + 8.f * (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + 4.f *
+ (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) + 2.f * (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 32400.f;
+ out6.s6 = (16.f * (16.f * w00 - 8.f * w10 + 4.f * w20 - 2.f * w30 + w40) - 8.f * (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) + 4.f *
+ (16.f * w02 - 8.f * w12 + 4.f * w22 - 2.f * w32 + w42) - 2.f * (16.f * w03 - 8.f * w13 + 4.f * w23 - 2.f * w33 + w43) + (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44)) / 32400.f;
+ out6.s7 = (16.f * w04 - 8.f * w14 + 4.f * w24 - 2.f * w34 + w44) / 180.f;
// Row 7
- out7.s0 = w40;
- out7.s1 = -2.f * (w40 + w41 + w42 + w43 + w44) / 9.f;
- out7.s2 = -2.f * (w40 - w41 + w42 - w43 + w44) / 9.f;
- out7.s3 = (w40 + 2.f * w41 + 4.f * w42 + 8.f * w43 + 16.f * w44) / 90.f;
- out7.s4 = (w40 - 2.f * w41 + 4.f * w42 - 8.f * w43 + 16.f * w44) / 90.f;
- out7.s5 = (16.f * w40 + 8.f * w41 + 4.f * w42 + 2.f * w43 + w44) / 180.f;
- out7.s6 = (16.f * w40 - 8.f * w41 + 4.f * w42 - 2.f * w43 + w44) / 180.f;
- out7.s7 = w44;
+ float8 out7 = 0.0f;
+ out7.s0 = w40;
+ out7.s1 = -2.f * (w40 + w41 + w42 + w43 + w44) / 9.f;
+ out7.s2 = -2.f * (w40 - w41 + w42 - w43 + w44) / 9.f;
+ out7.s3 = (w40 + 2.f * w41 + 4.f * w42 + 8.f * w43 + 16.f * w44) / 90.f;
+ out7.s4 = (w40 - 2.f * w41 + 4.f * w42 - 8.f * w43 + 16.f * w44) / 90.f;
+ out7.s5 = (16.f * w40 + 8.f * w41 + 4.f * w42 + 2.f * w43 + w44) / 180.f;
+ out7.s6 = (16.f * w40 - 8.f * w41 + 4.f * w42 - 2.f * w43 + w44) / 180.f;
+ out7.s7 = w44;
+#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
int x0 = get_global_id(2); // idx filter
int y0 = get_global_id(0); // idx channel
@@ -1013,15 +928,17 @@ __kernel void winograd_filter_transform_4x4_5x5_nhwc(
// Get output address
__global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x0 * sizeof(float) + y0 * dst_stride_y;
- // Store the 64 values across the 64 channels
- *(__global float *)(dst_addr + 0 * dst_stride_z) = out0.s0;
- *(__global float *)(dst_addr + 1 * dst_stride_z) = out0.s1;
- *(__global float *)(dst_addr + 2 * dst_stride_z) = out0.s2;
- *(__global float *)(dst_addr + 3 * dst_stride_z) = out0.s3;
- *(__global float *)(dst_addr + 4 * dst_stride_z) = out0.s4;
- *(__global float *)(dst_addr + 5 * dst_stride_z) = out0.s5;
- *(__global float *)(dst_addr + 6 * dst_stride_z) = out0.s6;
- *(__global float *)(dst_addr + 7 * dst_stride_z) = out0.s7;
+ // Store the values across the channels
+ *(__global float *)(dst_addr + 0 * dst_stride_z) = out0.s0;
+ *(__global float *)(dst_addr + 1 * dst_stride_z) = out0.s1;
+ *(__global float *)(dst_addr + 2 * dst_stride_z) = out0.s2;
+ *(__global float *)(dst_addr + 3 * dst_stride_z) = out0.s3;
+ *(__global float *)(dst_addr + 4 * dst_stride_z) = out0.s4;
+ *(__global float *)(dst_addr + 5 * dst_stride_z) = out0.s5;
+ *(__global float *)(dst_addr + 6 * dst_stride_z) = out0.s6;
+ *(__global float *)(dst_addr + 7 * dst_stride_z) = out0.s7;
+
+#if !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
*(__global float *)(dst_addr + 8 * dst_stride_z) = out1.s0;
*(__global float *)(dst_addr + 9 * dst_stride_z) = out1.s1;
*(__global float *)(dst_addr + 10 * dst_stride_z) = out1.s2;
@@ -1078,6 +995,7 @@ __kernel void winograd_filter_transform_4x4_5x5_nhwc(
*(__global float *)(dst_addr + 61 * dst_stride_z) = out7.s5;
*(__global float *)(dst_addr + 62 * dst_stride_z) = out7.s6;
*(__global float *)(dst_addr + 63 * dst_stride_z) = out7.s7;
+#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
}
#endif // defined(SRC_DIM_Z)
@@ -1225,6 +1143,102 @@ __kernel void winograd_filter_transform_4x1_5x1_nchw(
dst_step_z,
dst_offset_first_element_in_bytes);
}
+
+/** This OpenCL kernel performs Winograd filter transform 3x1 when the data layout is NHWC and the output tile is 4x1
+ *
+ * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note -DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL has to be passed at compile time to perform Winograd Filter Transform
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_filter_transform_4x1_3x1_nhwc(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_4x4_3x3_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_stride_w,
+ src_step_w,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes);
+}
+
+/** This OpenCL kernel performs Winograd filter transform 5x1 when the data layout is NHWC and the output tile is 4x1
+ *
+ * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note -DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL has to be passed at compile time to perform Winograd Filter Transform
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_filter_transform_4x1_5x1_nhwc(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_4x4_5x5_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_stride_w,
+ src_step_w,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes);
+}
#endif // defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
#if defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
@@ -1371,4 +1385,100 @@ __kernel void winograd_filter_transform_1x4_1x5_nchw(
dst_step_z,
dst_offset_first_element_in_bytes);
}
+
+/** This OpenCL kernel performs Winograd filter transform 1x3 when the data layout is NHWC and the output tile is 1x4
+ *
+ * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note -DWINOGRAD_FILTER_TRANSFORM_VERTICAL has to be passed at compile time to perform Winograd Filter Transform
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_filter_transform_1x4_1x3_nhwc(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_4x4_3x3_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_stride_w,
+ src_step_w,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes);
+}
+
+/** This OpenCL kernel performs Winograd filter transform 1x5 when the data layout is NHWC and the output tile is 1x4
+ *
+ * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note -DWINOGRAD_FILTER_TRANSFORM_VERTICAL has to be passed at compile time to perform Winograd Filter Transform
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_filter_transform_1x4_1x5_nhwc(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_4x4_5x5_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_stride_w,
+ src_step_w,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes);
+}
#endif // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
diff --git a/src/core/CL/cl_kernels/winograd_input_transform.cl b/src/core/CL/cl_kernels/winograd_input_transform.cl
index 01cbc84ff3..fcd1b3b9ce 100644
--- a/src/core/CL/cl_kernels/winograd_input_transform.cl
+++ b/src/core/CL/cl_kernels/winograd_input_transform.cl
@@ -23,6 +23,26 @@
*/
#include "helpers.h"
+#define OUTPUT_ROW_4x4_5x5(out, tmp, comm_fact) \
+ ({ \
+ comm_fact.s0 = tmp.s2 - 4.25f * tmp.s4 + tmp.s6; \
+ comm_fact.s1 = tmp.s1 - 4.25f * tmp.s3 + tmp.s5; \
+ comm_fact.s2 = 2.5f * tmp.s3; \
+ comm_fact.s3 = 0.5f * tmp.s1 + 2.f * tmp.s5 - comm_fact.s2; \
+ comm_fact.s4 = 0.25f * tmp.s2 - 1.25f * tmp.s4 + tmp.s6; \
+ comm_fact.s5 = 4.f * tmp.s2 + tmp.s6 - 5.f * tmp.s4; \
+ comm_fact.s6 = 2.f * tmp.s1 + 0.5f * tmp.s5 - comm_fact.s2; \
+ \
+ out.s0 = tmp.s0 - tmp.s6 + 5.25f * tmp.s4 - 5.25f * tmp.s2; \
+ out.s1 = comm_fact.s0 + comm_fact.s1; \
+ out.s2 = comm_fact.s0 - comm_fact.s1; \
+ out.s3 = comm_fact.s3 + comm_fact.s4; \
+ out.s4 = comm_fact.s4 - comm_fact.s3; \
+ out.s5 = comm_fact.s5 + comm_fact.s6; \
+ out.s6 = comm_fact.s5 - comm_fact.s6; \
+ out.s7 = tmp.s7 - tmp.s1 + 5.25f * tmp.s3 - 5.25f * tmp.s5; \
+ })
+
#if defined(NUM_TILES_X) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
/** This OpenCL kernel computes the input transform when the kernel size is 3x3/3x1 or 1x3 and the output tile is 2x2/2x1 or 1x2
*
@@ -936,63 +956,16 @@ __kernel void winograd_input_transform_4x4_3x3_stepz1_nhwc(
#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
}
-#if defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
-/** This OpenCL kernel computes the input transform when the kernel size is 3x1 and the output tile is 4x1 for data layout NHWC
+/** This OpenCL kernel computes the input transform when the kernel size is 5x5/5x1 or 1x5 and the output tile is 4x4/4x1 or 1x4 when the data layout is NHWC
*
* @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
* @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note Dimension one of the input tensor (width for NHWC data layout) must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM_1=112)
+ * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)
* @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
- * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
- * @note -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
- *
- * @param[in] src_ptr Pointer to the source image. Supported data types: F32
- * @param[in] src_stride_x Stride of the source image in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void winograd_input_transform_4x1_3x1_stepz1_nhwc(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst))
-{
- winograd_input_transform_4x4_3x3_stepz1_nhwc(src_ptr,
- src_stride_x,
- src_step_x,
- src_stride_y,
- src_step_y,
- src_stride_z,
- src_step_z,
- src_offset_first_element_in_bytes,
- dst_ptr,
- dst_stride_x,
- dst_step_x,
- dst_stride_y,
- dst_step_y,
- dst_stride_z,
- dst_step_z,
- dst_offset_first_element_in_bytes);
-}
-#endif // defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
-
-#if defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
-/** This OpenCL kernel computes the input transform when the kernel size is 1x3 and the output tile is 1x4 for data layout NHWC
- *
- * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
- * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
- * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
* @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
- * @note -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd input transform 5x1, -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd input transform 1x5, -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
*
* @param[in] src_ptr Pointer to the source image. Supported data types: F32
* @param[in] src_stride_x Stride of the source image in X dimension (in bytes)
@@ -1011,50 +984,308 @@ __kernel void winograd_input_transform_4x1_3x1_stepz1_nhwc(
* @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
*/
-__kernel void winograd_input_transform_1x4_1x3_stepz1_nhwc(
+__kernel void winograd_input_transform_4x4_5x5_stepz1_nhwc(
TENSOR3D_DECLARATION(src),
TENSOR3D_DECLARATION(dst))
{
- winograd_input_transform_4x4_3x3_stepz1_nhwc(src_ptr,
- src_stride_x,
- src_step_x,
- src_stride_y,
- src_step_y,
- src_stride_z,
- src_step_z,
- src_offset_first_element_in_bytes,
- dst_ptr,
- dst_stride_x,
- dst_step_x,
- dst_stride_y,
- dst_step_y,
- dst_stride_z,
- dst_step_z,
- dst_offset_first_element_in_bytes);
-}
-#endif // defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+ int x = get_global_id(0);
+ int y = get_global_id(1);
+ int z = get_global_id(2);
-#endif // defined(SRC_DIM_1) && defined(SRC_DIM_2)
+ // Compute input address
+ __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(float);
-#define OUTPUT_ROW_4x4_5x5(out, tmp, comm_fact) \
- ({ \
- comm_fact.s0 = tmp.s2 - 4.25f * tmp.s4 + tmp.s6; \
- comm_fact.s1 = tmp.s1 - 4.25f * tmp.s3 + tmp.s5; \
- comm_fact.s2 = 2.5f * tmp.s3; \
- comm_fact.s3 = 0.5f * tmp.s1 + 2.f * tmp.s5 - comm_fact.s2; \
- comm_fact.s4 = 0.25f * tmp.s2 - 1.25f * tmp.s4 + tmp.s6; \
- comm_fact.s5 = 4.f * tmp.s2 + tmp.s6 - 5.f * tmp.s4; \
- comm_fact.s6 = 2.f * tmp.s1 + 0.5f * tmp.s5 - comm_fact.s2; \
- \
- out.s0 = tmp.s0 - tmp.s6 + 5.25f * tmp.s4 - 5.25f * tmp.s2; \
- out.s1 = comm_fact.s0 + comm_fact.s1; \
- out.s2 = comm_fact.s0 - comm_fact.s1; \
- out.s3 = comm_fact.s3 + comm_fact.s4; \
- out.s4 = comm_fact.s4 - comm_fact.s3; \
- out.s5 = comm_fact.s5 + comm_fact.s6; \
- out.s6 = comm_fact.s5 - comm_fact.s6; \
- out.s7 = tmp.s7 - tmp.s1 + 5.25f * tmp.s3 - 5.25f * tmp.s5; \
- })
+#if defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
+ // Clamp coordinates. This clamp is valid for all rows
+ int8 y_coord = (int8)(y * OUTPUT_TILE_W) + (int8)(0, 1, 2, 3, 4, 5, 6, 7) - (int8)PAD_LEFT;
+ y_coord = clamp(y_coord, -1, SRC_DIM_1);
+
+ // Row0
+ // We can skip the border clamping along the z dimension as we cannot read out-of-bound in case of 5x1 kernels
+ int z_coord = z * OUTPUT_TILE_H;
+
+ // Load the input tile
+ float8 in_row0;
+ in_row0.s0 = *(__global float *)(src_addr + y_coord.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s1 = *(__global float *)(src_addr + y_coord.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s2 = *(__global float *)(src_addr + y_coord.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s3 = *(__global float *)(src_addr + y_coord.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s4 = *(__global float *)(src_addr + y_coord.s4 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s5 = *(__global float *)(src_addr + y_coord.s5 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s6 = *(__global float *)(src_addr + y_coord.s6 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s7 = *(__global float *)(src_addr + y_coord.s7 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Calculate common factors for intermediate tensor
+ float8 comm_fact0 = 0.0f;
+ float8 tmp0 = in_row0;
+
+ float8 out0 = (float8)0.0f;
+
+ OUTPUT_ROW_4x4_5x5(out0, tmp0, comm_fact0);
+
+#elif defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL) // defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
+ // We can skip the border clamping along the y dimension as we cannot read out-of-bound in case of 1x5 kernels
+ int y_coord = y * OUTPUT_TILE_W;
+
+ // Row0
+ // We can skip the border clamping along the z dimension as we cannot read out-of-bound in case of 5x1 kernels
+ int8 z_coord = (int8)(z * OUTPUT_TILE_H) + (int8)(0, 1, 2, 3, 4, 5, 6, 7) - (int8)PAD_TOP;
+ int8 valid_y = select((int8)y_coord, (int8) - 1, z_coord < (int8)0); // If z < 0, set y to -1
+ valid_y = select(valid_y, SRC_DIM_1, z_coord >= (int8)SRC_DIM_2); // If z >= SRC_DIM_2, set y to SRC_DIM_2
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1); // Clamp z coordinate
+
+ // Load the input tile
+ float8 in_row0;
+ in_row0.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord.s0 * src_stride_z);
+ in_row0.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord.s1 * src_stride_z);
+ in_row0.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord.s2 * src_stride_z);
+ in_row0.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord.s3 * src_stride_z);
+ in_row0.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord.s4 * src_stride_z);
+ in_row0.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord.s5 * src_stride_z);
+ in_row0.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord.s6 * src_stride_z);
+ in_row0.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord.s7 * src_stride_z);
+
+ // Calculate common factors for intermediate tensor
+ float8 comm_fact0 = 0.0f;
+ float8 tmp0 = in_row0;
+
+ float8 out0 = (float8)0.0f;
+
+ OUTPUT_ROW_4x4_5x5(out0, tmp0, comm_fact0);
+#else // defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
+ float8 in_row0, in_row1, in_row2, in_row3, in_row4, in_row5, in_row6, in_row7;
+
+ // Clamp coordinates. This clamp is valid for all rows
+ int8 y_coord = (int8)(y * OUTPUT_TILE_W) + (int8)(0, 1, 2, 3, 4, 5, 6, 7) - (int8)PAD_LEFT;
+ y_coord = clamp(y_coord, -1, SRC_DIM_1);
+
+ // Row0
+ int z_coord = (z * OUTPUT_TILE_H) - PAD_TOP + 0;
+ int8 valid_y = select(y_coord, -1, (int8)z_coord < 0); // If z < 0, set y to -1
+ valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2); // If z >= SRC_DIM_2, set y to SRC_DIM_2
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1); // Clamp z coordinate
+
+ // Load the input tile
+ in_row0.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row0.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Row1
+ z_coord = (z * OUTPUT_TILE_H) - PAD_TOP + 1;
+ valid_y = select(y_coord, -1, (int8)z_coord < 0);
+ valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ in_row1.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row1.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row1.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row1.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row1.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row1.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row1.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row1.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Row2
+ z_coord = (z * OUTPUT_TILE_H) - PAD_TOP + 2;
+ valid_y = select(y_coord, -1, (int8)z_coord < 0);
+ valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ in_row2.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row2.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row2.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row2.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row2.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row2.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row2.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row2.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Row3
+ z_coord = (z * OUTPUT_TILE_H) - PAD_TOP + 3;
+ valid_y = select(y_coord, -1, (int8)z_coord < 0);
+ valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ in_row3.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row3.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row3.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row3.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row3.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row3.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row3.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row3.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Row4
+ z_coord = (z * OUTPUT_TILE_H) - PAD_TOP + 4;
+ valid_y = select(y_coord, -1, (int8)z_coord < 0);
+ valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ in_row4.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row4.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row4.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row4.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row4.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row4.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row4.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row4.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Row5
+ z_coord = (z * OUTPUT_TILE_H) - PAD_TOP + 5;
+ valid_y = select(y_coord, -1, (int8)z_coord < 0);
+ valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ in_row5.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row5.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row5.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row5.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row5.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row5.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row5.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row5.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Row6
+ z_coord = (z * OUTPUT_TILE_H) - PAD_TOP + 6;
+ valid_y = select(y_coord, -1, (int8)z_coord < 0);
+ valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ in_row6.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row6.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row6.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row6.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row6.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row6.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row6.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row6.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
+
+ // Row7
+ z_coord = (z * OUTPUT_TILE_H) - PAD_TOP + 7;
+ valid_y = select(y_coord, -1, (int8)z_coord < 0);
+ valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
+ z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
+
+ in_row7.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row7.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row7.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row7.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row7.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row7.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row7.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
+ in_row7.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
+
+ float8 comm_fact0 = in_row2 + in_row6 - 4.25f * in_row4;
+ float8 comm_fact1 = in_row1 + in_row5 - 4.25f * in_row3;
+ float8 comm_fact2 = 0.25f * in_row2 - 1.25f * in_row4 + in_row6;
+
+ // Calculate intermediate tensor and reuse common factor vectors
+ const float8 tmp0 = in_row0 - in_row6 + 5.25f * in_row4 - 5.25f * in_row2;
+ const float8 tmp1 = comm_fact0 + comm_fact1;
+ const float8 tmp2 = comm_fact0 - comm_fact1;
+
+ comm_fact0 = 2.5f * in_row3;
+ comm_fact1 = 0.5f * in_row1 - comm_fact0 + 2.f * in_row5;
+
+ const float8 tmp3 = comm_fact1 + comm_fact2;
+ const float8 tmp4 = comm_fact2 - comm_fact1;
+
+ comm_fact1 = 2.f * in_row1 - comm_fact0 + 0.5f * in_row5;
+ comm_fact2 = 4.f * in_row2 - 5.f * in_row4 + in_row6;
+
+ const float8 tmp5 = comm_fact1 + comm_fact2;
+ const float8 tmp6 = comm_fact2 - comm_fact1;
+ const float8 tmp7 = in_row7 - in_row1 + 5.25f * in_row3 - 5.25f * in_row5;
+
+ // Calculate output rows (reuse comm_fact0 vector)
+ float8 out0, out1, out2, out3, out4, out5, out6, out7;
+ OUTPUT_ROW_4x4_5x5(out0, tmp0, comm_fact0);
+ OUTPUT_ROW_4x4_5x5(out1, tmp1, comm_fact0);
+ OUTPUT_ROW_4x4_5x5(out2, tmp2, comm_fact0);
+ OUTPUT_ROW_4x4_5x5(out3, tmp3, comm_fact0);
+ OUTPUT_ROW_4x4_5x5(out4, tmp4, comm_fact0);
+ OUTPUT_ROW_4x4_5x5(out5, tmp5, comm_fact0);
+ OUTPUT_ROW_4x4_5x5(out6, tmp6, comm_fact0);
+ OUTPUT_ROW_4x4_5x5(out7, tmp7, comm_fact0);
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+
+ // Store values across the channels
+ __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * sizeof(float) + (y + z * (int)NUM_TILES_X) * dst_stride_y;
+
+ *((__global float *)(dst_addr + 0 * dst_stride_z)) = out0.s0;
+ *((__global float *)(dst_addr + 1 * dst_stride_z)) = out0.s1;
+ *((__global float *)(dst_addr + 2 * dst_stride_z)) = out0.s2;
+ *((__global float *)(dst_addr + 3 * dst_stride_z)) = out0.s3;
+ *((__global float *)(dst_addr + 4 * dst_stride_z)) = out0.s4;
+ *((__global float *)(dst_addr + 5 * dst_stride_z)) = out0.s5;
+ *((__global float *)(dst_addr + 6 * dst_stride_z)) = out0.s6;
+ *((__global float *)(dst_addr + 7 * dst_stride_z)) = out0.s7;
+
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+ *((__global float *)(dst_addr + 8 * dst_stride_z)) = out1.s0;
+ *((__global float *)(dst_addr + 9 * dst_stride_z)) = out1.s1;
+ *((__global float *)(dst_addr + 10 * dst_stride_z)) = out1.s2;
+ *((__global float *)(dst_addr + 11 * dst_stride_z)) = out1.s3;
+ *((__global float *)(dst_addr + 12 * dst_stride_z)) = out1.s4;
+ *((__global float *)(dst_addr + 13 * dst_stride_z)) = out1.s5;
+ *((__global float *)(dst_addr + 14 * dst_stride_z)) = out1.s6;
+ *((__global float *)(dst_addr + 15 * dst_stride_z)) = out1.s7;
+ *((__global float *)(dst_addr + 16 * dst_stride_z)) = out2.s0;
+ *((__global float *)(dst_addr + 17 * dst_stride_z)) = out2.s1;
+ *((__global float *)(dst_addr + 18 * dst_stride_z)) = out2.s2;
+ *((__global float *)(dst_addr + 19 * dst_stride_z)) = out2.s3;
+ *((__global float *)(dst_addr + 20 * dst_stride_z)) = out2.s4;
+ *((__global float *)(dst_addr + 21 * dst_stride_z)) = out2.s5;
+ *((__global float *)(dst_addr + 22 * dst_stride_z)) = out2.s6;
+ *((__global float *)(dst_addr + 23 * dst_stride_z)) = out2.s7;
+ *((__global float *)(dst_addr + 24 * dst_stride_z)) = out3.s0;
+ *((__global float *)(dst_addr + 25 * dst_stride_z)) = out3.s1;
+ *((__global float *)(dst_addr + 26 * dst_stride_z)) = out3.s2;
+ *((__global float *)(dst_addr + 27 * dst_stride_z)) = out3.s3;
+ *((__global float *)(dst_addr + 28 * dst_stride_z)) = out3.s4;
+ *((__global float *)(dst_addr + 29 * dst_stride_z)) = out3.s5;
+ *((__global float *)(dst_addr + 30 * dst_stride_z)) = out3.s6;
+ *((__global float *)(dst_addr + 31 * dst_stride_z)) = out3.s7;
+ *((__global float *)(dst_addr + 32 * dst_stride_z)) = out4.s0;
+ *((__global float *)(dst_addr + 33 * dst_stride_z)) = out4.s1;
+ *((__global float *)(dst_addr + 34 * dst_stride_z)) = out4.s2;
+ *((__global float *)(dst_addr + 35 * dst_stride_z)) = out4.s3;
+ *((__global float *)(dst_addr + 36 * dst_stride_z)) = out4.s4;
+ *((__global float *)(dst_addr + 37 * dst_stride_z)) = out4.s5;
+ *((__global float *)(dst_addr + 38 * dst_stride_z)) = out4.s6;
+ *((__global float *)(dst_addr + 39 * dst_stride_z)) = out4.s7;
+ *((__global float *)(dst_addr + 40 * dst_stride_z)) = out5.s0;
+ *((__global float *)(dst_addr + 41 * dst_stride_z)) = out5.s1;
+ *((__global float *)(dst_addr + 42 * dst_stride_z)) = out5.s2;
+ *((__global float *)(dst_addr + 43 * dst_stride_z)) = out5.s3;
+ *((__global float *)(dst_addr + 44 * dst_stride_z)) = out5.s4;
+ *((__global float *)(dst_addr + 45 * dst_stride_z)) = out5.s5;
+ *((__global float *)(dst_addr + 46 * dst_stride_z)) = out5.s6;
+ *((__global float *)(dst_addr + 47 * dst_stride_z)) = out5.s7;
+ *((__global float *)(dst_addr + 48 * dst_stride_z)) = out6.s0;
+ *((__global float *)(dst_addr + 49 * dst_stride_z)) = out6.s1;
+ *((__global float *)(dst_addr + 50 * dst_stride_z)) = out6.s2;
+ *((__global float *)(dst_addr + 51 * dst_stride_z)) = out6.s3;
+ *((__global float *)(dst_addr + 52 * dst_stride_z)) = out6.s4;
+ *((__global float *)(dst_addr + 53 * dst_stride_z)) = out6.s5;
+ *((__global float *)(dst_addr + 54 * dst_stride_z)) = out6.s6;
+ *((__global float *)(dst_addr + 55 * dst_stride_z)) = out6.s7;
+ *((__global float *)(dst_addr + 56 * dst_stride_z)) = out7.s0;
+ *((__global float *)(dst_addr + 57 * dst_stride_z)) = out7.s1;
+ *((__global float *)(dst_addr + 58 * dst_stride_z)) = out7.s2;
+ *((__global float *)(dst_addr + 59 * dst_stride_z)) = out7.s3;
+ *((__global float *)(dst_addr + 60 * dst_stride_z)) = out7.s4;
+ *((__global float *)(dst_addr + 61 * dst_stride_z)) = out7.s5;
+ *((__global float *)(dst_addr + 62 * dst_stride_z)) = out7.s6;
+ *((__global float *)(dst_addr + 63 * dst_stride_z)) = out7.s7;
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+}
+#endif // defined(SRC_DIM_1) && defined(SRC_DIM_2)
/** This OpenCL kernel computes the input transform when the kernel size is 5x5/5x1 or 1x5 and the output tile is 4x4/4x1 or 1x4 when the data layout is NCHW
*
@@ -1424,6 +1655,105 @@ __kernel void winograd_input_transform_4x1_5x1_stepz1_nchw(
dst_offset_first_element_in_bytes);
}
+#if defined(SRC_DIM_1) && defined(SRC_DIM_2)
+/** This OpenCL kernel computes the input transform when the kernel size is 3x1 and the output tile is 4x1 for data layout NHWC
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note Dimension one of the input tensor (width for NHWC data layout) must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM_1=112)
+ * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
+ * @note -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ *
+ * @param[in] src_ptr Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_4x1_3x1_stepz1_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_input_transform_4x4_3x3_stepz1_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes);
+}
+
+/** This OpenCL kernel computes the input transform when the kernel size is 5x1 and the output tile is 4x1 for data layout NHWC
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note Dimension one of the input tensor (width for NHWC data layout) must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM_1=112)
+ * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
+ * @note -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ *
+ * @param[in] src_ptr Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_4x1_5x1_stepz1_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_input_transform_4x4_5x5_stepz1_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes);
+}
+#endif // defined(SRC_DIM_1) && defined(SRC_DIM_2)
#endif // defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
#if defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
@@ -1614,15 +1944,17 @@ __kernel void winograd_input_transform_1x4_1x5_stepz1_nchw(
dst_step_z,
dst_offset_first_element_in_bytes);
}
-#endif // defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
#if defined(SRC_DIM_1) && defined(SRC_DIM_2)
-/** This OpenCL kernel computes the input transform when the kernel size is 5x5 and the output tile is 4x4 when the data layout is NHWC
+/** This OpenCL kernel computes the input transform when the kernel size is 1x3 and the output tile is 1x4 for data layout NHWC
*
* @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note Dimension one of the input tensor (width for NHWC data layout) must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM_1=112)
+ * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)
* @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
- * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
* @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
+ * @note -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
*
* @param[in] src_ptr Pointer to the source image. Supported data types: F32
* @param[in] src_stride_x Stride of the source image in X dimension (in bytes)
@@ -1641,246 +1973,76 @@ __kernel void winograd_input_transform_1x4_1x5_stepz1_nchw(
* @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
*/
-__kernel void winograd_input_transform_4x4_5x5_stepz1_nhwc(
+__kernel void winograd_input_transform_1x4_1x3_stepz1_nhwc(
TENSOR3D_DECLARATION(src),
TENSOR3D_DECLARATION(dst))
{
- int x = get_global_id(0);
- int y = get_global_id(1);
- int z = get_global_id(2);
-
- // Compute input address
- __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(float);
-
- // Clamp coordinates. This clamp is valid for all rows
- int8 y_coord = (int8)(y * 4) + (int8)(0, 1, 2, 3, 4, 5, 6, 7) - (int8)PAD_LEFT;
- y_coord = clamp(y_coord, -1, SRC_DIM_1);
-
- // Load 8x8 input tile
- float8 in_row0, in_row1, in_row2, in_row3, in_row4, in_row5, in_row6, in_row7;
-
- // Row0
- int z_coord = (z * 4) - PAD_TOP + 0;
- int8 valid_y = select(y_coord, -1, (int8)z_coord < 0); // If z < 0, set y to -1
- valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2); // If z >= SRC_DIM_2, set y to SRC_DIM_2
- z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1); // Clamp z coordinate
-
- in_row0.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
- in_row0.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
- in_row0.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
- in_row0.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
- in_row0.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
- in_row0.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
- in_row0.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
- in_row0.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
-
- // Row1
- z_coord = (z * 4) - PAD_TOP + 1;
- valid_y = select(y_coord, -1, (int8)z_coord < 0);
- valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
- z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
-
- in_row1.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
- in_row1.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
- in_row1.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
- in_row1.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
- in_row1.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
- in_row1.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
- in_row1.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
- in_row1.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
-
- // Row2
- z_coord = (z * 4) - PAD_TOP + 2;
- valid_y = select(y_coord, -1, (int8)z_coord < 0);
- valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
- z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
-
- in_row2.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
- in_row2.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
- in_row2.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
- in_row2.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
- in_row2.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
- in_row2.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
- in_row2.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
- in_row2.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
-
- // Row3
- z_coord = (z * 4) - PAD_TOP + 3;
- valid_y = select(y_coord, -1, (int8)z_coord < 0);
- valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
- z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
-
- in_row3.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
- in_row3.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
- in_row3.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
- in_row3.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
- in_row3.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
- in_row3.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
- in_row3.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
- in_row3.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
-
- // Row4
- z_coord = (z * 4) - PAD_TOP + 4;
- valid_y = select(y_coord, -1, (int8)z_coord < 0);
- valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
- z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
-
- in_row4.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
- in_row4.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
- in_row4.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
- in_row4.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
- in_row4.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
- in_row4.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
- in_row4.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
- in_row4.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
-
- // Row5
- z_coord = (z * 4) - PAD_TOP + 5;
- valid_y = select(y_coord, -1, (int8)z_coord < 0);
- valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
- z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
-
- in_row5.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
- in_row5.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
- in_row5.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
- in_row5.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
- in_row5.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
- in_row5.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
- in_row5.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
- in_row5.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
-
- // Row6
- z_coord = (z * 4) - PAD_TOP + 6;
- valid_y = select(y_coord, -1, (int8)z_coord < 0);
- valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
- z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
-
- in_row6.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
- in_row6.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
- in_row6.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
- in_row6.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
- in_row6.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
- in_row6.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
- in_row6.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
- in_row6.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
-
- // Row7
- z_coord = (z * 4) - PAD_TOP + 7;
- valid_y = select(y_coord, -1, (int8)z_coord < 0);
- valid_y = select(valid_y, SRC_DIM_1, (int8)z_coord >= SRC_DIM_2);
- z_coord = clamp(z_coord, 0, SRC_DIM_2 - 1);
-
- in_row7.s0 = *(__global float *)(src_addr + valid_y.s0 * (int)src_stride_y + z_coord * src_stride_z);
- in_row7.s1 = *(__global float *)(src_addr + valid_y.s1 * (int)src_stride_y + z_coord * src_stride_z);
- in_row7.s2 = *(__global float *)(src_addr + valid_y.s2 * (int)src_stride_y + z_coord * src_stride_z);
- in_row7.s3 = *(__global float *)(src_addr + valid_y.s3 * (int)src_stride_y + z_coord * src_stride_z);
- in_row7.s4 = *(__global float *)(src_addr + valid_y.s4 * (int)src_stride_y + z_coord * src_stride_z);
- in_row7.s5 = *(__global float *)(src_addr + valid_y.s5 * (int)src_stride_y + z_coord * src_stride_z);
- in_row7.s6 = *(__global float *)(src_addr + valid_y.s6 * (int)src_stride_y + z_coord * src_stride_z);
- in_row7.s7 = *(__global float *)(src_addr + valid_y.s7 * (int)src_stride_y + z_coord * src_stride_z);
-
- // Calculate common factors for intermediate tensor
- float8 comm_fact0 = in_row2 + in_row6 - 4.25f * in_row4;
- float8 comm_fact1 = in_row1 + in_row5 - 4.25f * in_row3;
- float8 comm_fact2 = 0.25f * in_row2 - 1.25f * in_row4 + in_row6;
-
- // Calculate intermediate tensor and reuse common factor vectors
- const float8 tmp0 = in_row0 - in_row6 + 5.25f * in_row4 - 5.25f * in_row2;
- const float8 tmp1 = comm_fact0 + comm_fact1;
- const float8 tmp2 = comm_fact0 - comm_fact1;
-
- comm_fact0 = 2.5f * in_row3;
- comm_fact1 = 0.5f * in_row1 - comm_fact0 + 2.f * in_row5;
-
- const float8 tmp3 = comm_fact1 + comm_fact2;
- const float8 tmp4 = comm_fact2 - comm_fact1;
-
- comm_fact1 = 2.f * in_row1 - comm_fact0 + 0.5f * in_row5;
- comm_fact2 = 4.f * in_row2 - 5.f * in_row4 + in_row6;
-
- const float8 tmp5 = comm_fact1 + comm_fact2;
- const float8 tmp6 = comm_fact2 - comm_fact1;
- const float8 tmp7 = in_row7 - in_row1 + 5.25f * in_row3 - 5.25f * in_row5;
-
- // Calculate output rows (reuse comm_fact0 vector)
- float8 out0, out1, out2, out3, out4, out5, out6, out7;
-
- OUTPUT_ROW_4x4_5x5(out0, tmp0, comm_fact0);
- OUTPUT_ROW_4x4_5x5(out1, tmp1, comm_fact0);
- OUTPUT_ROW_4x4_5x5(out2, tmp2, comm_fact0);
- OUTPUT_ROW_4x4_5x5(out3, tmp3, comm_fact0);
- OUTPUT_ROW_4x4_5x5(out4, tmp4, comm_fact0);
- OUTPUT_ROW_4x4_5x5(out5, tmp5, comm_fact0);
- OUTPUT_ROW_4x4_5x5(out6, tmp6, comm_fact0);
- OUTPUT_ROW_4x4_5x5(out7, tmp7, comm_fact0);
-
- // Store values across the 64 channels
- __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * sizeof(float) + (y + z * (int)NUM_TILES_X) * dst_stride_y;
+ winograd_input_transform_4x4_3x3_stepz1_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes);
+}
- *((__global float *)(dst_addr + 0 * dst_stride_z)) = out0.s0;
- *((__global float *)(dst_addr + 1 * dst_stride_z)) = out0.s1;
- *((__global float *)(dst_addr + 2 * dst_stride_z)) = out0.s2;
- *((__global float *)(dst_addr + 3 * dst_stride_z)) = out0.s3;
- *((__global float *)(dst_addr + 4 * dst_stride_z)) = out0.s4;
- *((__global float *)(dst_addr + 5 * dst_stride_z)) = out0.s5;
- *((__global float *)(dst_addr + 6 * dst_stride_z)) = out0.s6;
- *((__global float *)(dst_addr + 7 * dst_stride_z)) = out0.s7;
- *((__global float *)(dst_addr + 8 * dst_stride_z)) = out1.s0;
- *((__global float *)(dst_addr + 9 * dst_stride_z)) = out1.s1;
- *((__global float *)(dst_addr + 10 * dst_stride_z)) = out1.s2;
- *((__global float *)(dst_addr + 11 * dst_stride_z)) = out1.s3;
- *((__global float *)(dst_addr + 12 * dst_stride_z)) = out1.s4;
- *((__global float *)(dst_addr + 13 * dst_stride_z)) = out1.s5;
- *((__global float *)(dst_addr + 14 * dst_stride_z)) = out1.s6;
- *((__global float *)(dst_addr + 15 * dst_stride_z)) = out1.s7;
- *((__global float *)(dst_addr + 16 * dst_stride_z)) = out2.s0;
- *((__global float *)(dst_addr + 17 * dst_stride_z)) = out2.s1;
- *((__global float *)(dst_addr + 18 * dst_stride_z)) = out2.s2;
- *((__global float *)(dst_addr + 19 * dst_stride_z)) = out2.s3;
- *((__global float *)(dst_addr + 20 * dst_stride_z)) = out2.s4;
- *((__global float *)(dst_addr + 21 * dst_stride_z)) = out2.s5;
- *((__global float *)(dst_addr + 22 * dst_stride_z)) = out2.s6;
- *((__global float *)(dst_addr + 23 * dst_stride_z)) = out2.s7;
- *((__global float *)(dst_addr + 24 * dst_stride_z)) = out3.s0;
- *((__global float *)(dst_addr + 25 * dst_stride_z)) = out3.s1;
- *((__global float *)(dst_addr + 26 * dst_stride_z)) = out3.s2;
- *((__global float *)(dst_addr + 27 * dst_stride_z)) = out3.s3;
- *((__global float *)(dst_addr + 28 * dst_stride_z)) = out3.s4;
- *((__global float *)(dst_addr + 29 * dst_stride_z)) = out3.s5;
- *((__global float *)(dst_addr + 30 * dst_stride_z)) = out3.s6;
- *((__global float *)(dst_addr + 31 * dst_stride_z)) = out3.s7;
- *((__global float *)(dst_addr + 32 * dst_stride_z)) = out4.s0;
- *((__global float *)(dst_addr + 33 * dst_stride_z)) = out4.s1;
- *((__global float *)(dst_addr + 34 * dst_stride_z)) = out4.s2;
- *((__global float *)(dst_addr + 35 * dst_stride_z)) = out4.s3;
- *((__global float *)(dst_addr + 36 * dst_stride_z)) = out4.s4;
- *((__global float *)(dst_addr + 37 * dst_stride_z)) = out4.s5;
- *((__global float *)(dst_addr + 38 * dst_stride_z)) = out4.s6;
- *((__global float *)(dst_addr + 39 * dst_stride_z)) = out4.s7;
- *((__global float *)(dst_addr + 40 * dst_stride_z)) = out5.s0;
- *((__global float *)(dst_addr + 41 * dst_stride_z)) = out5.s1;
- *((__global float *)(dst_addr + 42 * dst_stride_z)) = out5.s2;
- *((__global float *)(dst_addr + 43 * dst_stride_z)) = out5.s3;
- *((__global float *)(dst_addr + 44 * dst_stride_z)) = out5.s4;
- *((__global float *)(dst_addr + 45 * dst_stride_z)) = out5.s5;
- *((__global float *)(dst_addr + 46 * dst_stride_z)) = out5.s6;
- *((__global float *)(dst_addr + 47 * dst_stride_z)) = out5.s7;
- *((__global float *)(dst_addr + 48 * dst_stride_z)) = out6.s0;
- *((__global float *)(dst_addr + 49 * dst_stride_z)) = out6.s1;
- *((__global float *)(dst_addr + 50 * dst_stride_z)) = out6.s2;
- *((__global float *)(dst_addr + 51 * dst_stride_z)) = out6.s3;
- *((__global float *)(dst_addr + 52 * dst_stride_z)) = out6.s4;
- *((__global float *)(dst_addr + 53 * dst_stride_z)) = out6.s5;
- *((__global float *)(dst_addr + 54 * dst_stride_z)) = out6.s6;
- *((__global float *)(dst_addr + 55 * dst_stride_z)) = out6.s7;
- *((__global float *)(dst_addr + 56 * dst_stride_z)) = out7.s0;
- *((__global float *)(dst_addr + 57 * dst_stride_z)) = out7.s1;
- *((__global float *)(dst_addr + 58 * dst_stride_z)) = out7.s2;
- *((__global float *)(dst_addr + 59 * dst_stride_z)) = out7.s3;
- *((__global float *)(dst_addr + 60 * dst_stride_z)) = out7.s4;
- *((__global float *)(dst_addr + 61 * dst_stride_z)) = out7.s5;
- *((__global float *)(dst_addr + 62 * dst_stride_z)) = out7.s6;
- *((__global float *)(dst_addr + 63 * dst_stride_z)) = out7.s7;
+/** This OpenCL kernel computes the input transform when the kernel size is 1x5 and the output tile is 1x4 for data layout NHWC
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note Dimension one of the input tensor (width for NHWC data layout) must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM_1=112)
+ * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
+ * @note -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
+ *
+ * @param[in] src_ptr Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_1x4_1x5_stepz1_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_input_transform_4x4_5x5_stepz1_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes);
}
#endif // defined(SRC_DIM_1) && defined(SRC_DIM_2)
+#endif // defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
#endif // defined(NUM_TILES_X) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H) \ No newline at end of file
diff --git a/src/core/CL/cl_kernels/winograd_output_transform.cl b/src/core/CL/cl_kernels/winograd_output_transform.cl
index 61f0f61db7..bb159a7f4c 100644
--- a/src/core/CL/cl_kernels/winograd_output_transform.cl
+++ b/src/core/CL/cl_kernels/winograd_output_transform.cl
@@ -577,124 +577,6 @@ __kernel void winograd_output_transform_4x4_3x3_nhwc(
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
}
-#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
-/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 3x1 and the data layout is NHWC
- *
- * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
- * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
- * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
- * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void winograd_output_transform_4x1_3x1_nhwc(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
-#if defined(HAS_BIAS)
- VECTOR_DECLARATION(bias),
-#endif // defined(HAS_BIAS)
- int dst_size)
-{
- winograd_output_transform_4x4_3x3_nhwc(src_ptr,
- src_stride_x,
- src_step_x,
- src_stride_y,
- src_step_y,
- src_stride_z,
- src_step_z,
- src_offset_first_element_in_bytes,
- dst_ptr,
- dst_stride_x,
- dst_step_x,
- dst_stride_y,
- dst_step_y,
- dst_stride_z,
- dst_step_z,
- dst_offset_first_element_in_bytes,
-#if defined(HAS_BIAS)
- bias_ptr,
- bias_stride_x,
- bias_step_x,
- bias_offset_first_element_in_bytes,
-#endif // defined(HAS_BIAS)
- dst_size);
-}
-#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
-
-#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x3 and the data layout is NHWC
- *
- * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
- * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
- * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
- * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
- * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void winograd_output_transform_1x4_1x3_nhwc(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
-#if defined(HAS_BIAS)
- VECTOR_DECLARATION(bias),
-#endif // defined(HAS_BIAS)
- int dst_size)
-{
- winograd_output_transform_4x4_3x3_nhwc(src_ptr,
- src_stride_x,
- src_step_x,
- src_stride_y,
- src_step_y,
- src_stride_z,
- src_step_z,
- src_offset_first_element_in_bytes,
- dst_ptr,
- dst_stride_x,
- dst_step_x,
- dst_stride_y,
- dst_step_y,
- dst_stride_z,
- dst_step_z,
- dst_offset_first_element_in_bytes,
-#if defined(HAS_BIAS)
- bias_ptr,
- bias_stride_x,
- bias_step_x,
- bias_offset_first_element_in_bytes,
-#endif // defined(HAS_BIAS)
- dst_size);
-}
-#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-
#define COMPUTE_TMP_COL(col, d0, d1, d2, d3, d4, d5, d6, d7, comm_fact) \
({ \
comm_fact.s0 = d1 + d2; \
@@ -910,9 +792,13 @@ __kernel void winograd_output_transform_4x4_5x5_nchw(
#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
}
-/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 5x5 and the data layout is NHWC
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4/4x1 or 1x4, the filter size 5x5/5x1 or 1x5 and the data layout is NHWC
*
* @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
+ * @note If this kernel is used to perform Winograd output transform 5x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd output transform 1x5, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
@@ -939,12 +825,17 @@ __kernel void winograd_output_transform_4x4_5x5_nhwc(
#endif // defined(HAS_BIAS)
int dst_size)
{
- // Each thread stores a 4x4 tile
+ // Each thread stores a 4x4/4x1 or 1x4 tile
Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
const __global uchar *src_addr = tensor3D_offset(&src, 0, 0, 0);
- // Load the values across the 64 channels to compose the 8x8 input tile
+ int y_in = get_global_id(1);
+ int x_out = get_global_id(0);
+ int y_out = (y_in % NUM_TILES_X) * OUTPUT_TILE_W;
+ int z_out = (y_in / NUM_TILES_X) * OUTPUT_TILE_H;
+
+ // Load the values across the channels to compose the input tile
float d00 = *((__global float *)(src_addr + 0 * src_stride_z));
float d01 = *((__global float *)(src_addr + 1 * src_stride_z));
float d02 = *((__global float *)(src_addr + 2 * src_stride_z));
@@ -954,6 +845,47 @@ __kernel void winograd_output_transform_4x4_5x5_nhwc(
float d06 = *((__global float *)(src_addr + 6 * src_stride_z));
float d07 = *((__global float *)(src_addr + 7 * src_stride_z));
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+ // Compute out00, out01, out02 and out03
+ float out00 = d00 + d01 + d02 + d03 + d04 + 8.0f * d05 + 8.0f * d06;
+ float out01 = d01 - d02 + 2.0f * d03 - 2.0f * d04 + 4.0f * d05 - 4.0f * d06;
+ float out02 = d01 + d02 + 4.0f * d03 + 4.0f * d04 + 2.0f * d05 + 2.0f * d06;
+ float out03 = d01 - d02 + 8.0f * d03 - 8.0f * d04 + d05 - d06 + d07;
+
+#if defined(HAS_BIAS)
+ // Add bias
+ Vector bias = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bias);
+
+ float b = (float) * ((__global float *)(vector_offset(&bias, z_out)));
+
+ out00 += (float)b;
+ out01 += (float)b;
+ out02 += (float)b;
+ out03 += (float)b;
+#endif // defined(HAS_BIAS)
+
+ // Store the output tile
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+ // Get output address
+ int4 offset = (int4)(dst_offset_first_element_in_bytes + x_out * sizeof(float) + y_out * dst_stride_y + z_out * dst_stride_z);
+ offset = min(offset + (int4)(0, 1, 2, 3) * (int4)dst_stride_z, dst_size); // If address is beyond the last plane, clamp it to dst_size (which points to the last padding).
+
+ *(__global float *)(dst_ptr + offset.s0) = out00;
+ *(__global float *)(dst_ptr + offset.s1) = out01;
+ *(__global float *)(dst_ptr + offset.s2) = out02;
+ *(__global float *)(dst_ptr + offset.s3) = out03;
+#else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+ // Get output address
+ int offset = dst_offset_first_element_in_bytes + x_out * sizeof(float) + y_out * dst_stride_y + z_out * dst_stride_z;
+
+ *(__global float *)(dst_ptr + 0 * dst_stride_y + offset) = out00;
+ *(__global float *)(dst_ptr + 1 * dst_stride_y + offset) = out01;
+ *(__global float *)(dst_ptr + 2 * dst_stride_y + offset) = out02;
+ *(__global float *)(dst_ptr + 3 * dst_stride_y + offset) = out03;
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
+#else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
float d10 = *((__global float *)(src_addr + 8 * src_stride_z));
float d11 = *((__global float *)(src_addr + 9 * src_stride_z));
float d12 = *((__global float *)(src_addr + 10 * src_stride_z));
@@ -1030,7 +962,7 @@ __kernel void winograd_output_transform_4x4_5x5_nhwc(
COMPUTE_TMP_COL(tmp_col6, d06, d16, d26, d36, d46, d56, d66, d76, comm_fact0);
COMPUTE_TMP_COL(tmp_col7, d07, d17, d27, d37, d47, d57, d67, d77, comm_fact0);
- // Compute the 4x4 output tile
+ // Compute the output tile
comm_fact0 = tmp_col1 + tmp_col2;
comm_fact1 = tmp_col3 + tmp_col4;
comm_fact2 = tmp_col5 + tmp_col6;
@@ -1045,11 +977,6 @@ __kernel void winograd_output_transform_4x4_5x5_nhwc(
float4 out_col1 = comm_fact0 + 2.f * comm_fact1 + 4.f * comm_fact2;
float4 out_col3 = comm_fact0 + 8.f * comm_fact1 + comm_fact2 + tmp_col7;
- int y_in = get_global_id(1);
- int x_out = get_global_id(0);
- int y_out = (y_in % NUM_TILES_X) * 4;
- int z_out = (y_in / NUM_TILES_X) * 4;
-
#if defined(HAS_BIAS)
// Add bias
Vector bias = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bias);
@@ -1061,13 +988,12 @@ __kernel void winograd_output_transform_4x4_5x5_nhwc(
out_col2 += (float4)b;
out_col3 += (float4)b;
#endif // defined(HAS_BIAS)
-
// Get output address
int4 offset = (int4)(dst_offset_first_element_in_bytes + x_out * sizeof(float) + y_out * dst_stride_y + z_out * dst_stride_z);
offset = min(offset + (int4)(0, 1, 2, 3) * (int4)dst_stride_z, dst_size); // If address is beyond the last plane, clamp it to dst_size (which points to the last padding).
int4 mult_y = min(dst_size - offset, 1); // If out of bound, we don't want to increase dst_stride_y, so we set the multiplier to 0. It will be 1 otherwise.
- // Store the 4x4 output tile
+ // Store the output tile
*(__global float *)(dst_ptr + mult_y.s0 * 0 * dst_stride_y + offset.s0) = out_col0.s0;
*(__global float *)(dst_ptr + mult_y.s0 * 1 * dst_stride_y + offset.s0) = out_col1.s0;
*(__global float *)(dst_ptr + mult_y.s0 * 2 * dst_stride_y + offset.s0) = out_col2.s0;
@@ -1084,6 +1010,7 @@ __kernel void winograd_output_transform_4x4_5x5_nhwc(
*(__global float *)(dst_ptr + mult_y.s0 * 1 * dst_stride_y + offset.s3) = out_col1.s3;
*(__global float *)(dst_ptr + mult_y.s0 * 2 * dst_stride_y + offset.s3) = out_col2.s3;
*(__global float *)(dst_ptr + mult_y.s0 * 3 * dst_stride_y + offset.s3) = out_col3.s3;
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
}
#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
@@ -1263,6 +1190,120 @@ __kernel void winograd_output_transform_4x1_5x1_nchw(
#endif // defined(HAS_BIAS)
);
}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 3x1 and the data layout is NHWC
+ *
+ * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
+ * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_output_transform_4x1_3x1_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+#if defined(HAS_BIAS)
+ VECTOR_DECLARATION(bias),
+#endif // defined(HAS_BIAS)
+ int dst_size)
+{
+ winograd_output_transform_4x4_3x3_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes,
+#if defined(HAS_BIAS)
+ bias_ptr,
+ bias_stride_x,
+ bias_step_x,
+ bias_offset_first_element_in_bytes,
+#endif // defined(HAS_BIAS)
+ dst_size);
+}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 5x1 and the data layout is NHWC
+ *
+ * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
+ * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_output_transform_4x1_5x1_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+#if defined(HAS_BIAS)
+ VECTOR_DECLARATION(bias),
+#endif // defined(HAS_BIAS)
+ int dst_size)
+{
+ winograd_output_transform_4x4_5x5_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes,
+#if defined(HAS_BIAS)
+ bias_ptr,
+ bias_stride_x,
+ bias_step_x,
+ bias_offset_first_element_in_bytes,
+#endif // defined(HAS_BIAS)
+ dst_size);
+}
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
@@ -1442,5 +1483,119 @@ __kernel void winograd_output_transform_1x4_1x5_nchw(
#endif // defined(HAS_BIAS)
);
}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x3 and the data layout is NHWC
+ *
+ * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
+ * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_output_transform_1x4_1x3_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+#if defined(HAS_BIAS)
+ VECTOR_DECLARATION(bias),
+#endif // defined(HAS_BIAS)
+ int dst_size)
+{
+ winograd_output_transform_4x4_3x3_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes,
+#if defined(HAS_BIAS)
+ bias_ptr,
+ bias_stride_x,
+ bias_step_x,
+ bias_offset_first_element_in_bytes,
+#endif // defined(HAS_BIAS)
+ dst_size);
+}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x5 and the data layout is NHWC
+ *
+ * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
+ * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_output_transform_1x4_1x5_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+#if defined(HAS_BIAS)
+ VECTOR_DECLARATION(bias),
+#endif // defined(HAS_BIAS)
+ int dst_size)
+{
+ winograd_output_transform_4x4_5x5_nhwc(src_ptr,
+ src_stride_x,
+ src_step_x,
+ src_stride_y,
+ src_step_y,
+ src_stride_z,
+ src_step_z,
+ src_offset_first_element_in_bytes,
+ dst_ptr,
+ dst_stride_x,
+ dst_step_x,
+ dst_stride_y,
+ dst_step_y,
+ dst_stride_z,
+ dst_step_z,
+ dst_offset_first_element_in_bytes,
+#if defined(HAS_BIAS)
+ bias_ptr,
+ bias_stride_x,
+ bias_step_x,
+ bias_offset_first_element_in_bytes,
+#endif // defined(HAS_BIAS)
+ dst_size);
+}
#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
#endif // defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
diff --git a/tests/datasets/WinogradOutputTransformDataset.h b/tests/datasets/WinogradOutputTransformDataset.h
index 4085e91854..617a67de07 100644
--- a/tests/datasets/WinogradOutputTransformDataset.h
+++ b/tests/datasets/WinogradOutputTransformDataset.h
@@ -206,6 +206,22 @@ public:
add_config(TensorShape(7U, 2U, 64U, 3U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
add_config(TensorShape(24U, 9U, 64U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NHWC));
add_config(TensorShape(7U, 2U, 64U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
+
+ // (4x1, 5x1)
+ add_config(TensorShape(13U, 6U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
+ add_config(TensorShape(7U, 22U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
+ add_config(TensorShape(5U, 462U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(53U, 33U), PadStrideInfo(1, 1, 2, 0), DataLayout::NHWC));
+ add_config(TensorShape(7U, 10U, 8U, 3U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
+ add_config(TensorShape(24U, 42U, 8U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 0), DataLayout::NHWC));
+ add_config(TensorShape(7U, 20U, 8U, 5U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(8U, 10U), PadStrideInfo(1, 1, 2, 0), DataLayout::NHWC));
+
+ // (1x4, 1x5)
+ add_config(TensorShape(13U, 7U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
+ add_config(TensorShape(7U, 20U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
+ add_config(TensorShape(5U, 477U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(53U, 33U), PadStrideInfo(1, 1, 0, 2), DataLayout::NHWC));
+ add_config(TensorShape(7U, 16U, 8U, 3U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
+ add_config(TensorShape(24U, 42U, 8U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(14U, 14U), PadStrideInfo(1, 1, 0, 1), DataLayout::NHWC));
+ add_config(TensorShape(7U, 24U, 8U, 5U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 2), DataLayout::NHWC));
}
};
@@ -316,6 +332,18 @@ public:
add_config(TensorShape(13U, 182U, 64U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NHWC));
add_config(TensorShape(32U, 756U, 64U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NHWC));
add_config(TensorShape(13U, 182U, 64U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NHWC));
+
+ // (4x1, 5x1)
+ add_config(TensorShape(32U, 3136U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(112U, 112U), PadStrideInfo(1, 1, 2, 0), DataLayout::NHWC));
+ add_config(TensorShape(13U, 784U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(56U, 56U), PadStrideInfo(1, 1, 1, 0), DataLayout::NHWC));
+ add_config(TensorShape(32U, 3024U, 8U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(112U, 112U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
+ add_config(TensorShape(13U, 784U, 8U, 5U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(56U, 56U), PadStrideInfo(1, 1, 1, 0), DataLayout::NHWC));
+
+ // (1x4, 1x5)
+ add_config(TensorShape(32U, 3136U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(112U, 112U), PadStrideInfo(1, 1, 0, 2), DataLayout::NHWC));
+ add_config(TensorShape(13U, 784U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NHWC));
+ add_config(TensorShape(32U, 3024U, 8U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(112U, 112U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
+ add_config(TensorShape(13U, 784U, 8U, 5U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NHWC));
}
};
} // namespace datasets
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index c39cb4e790..004d7c98a0 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -72,7 +72,9 @@ const auto SmallWinogradInputTransformDatasetNCHW =
const auto SmallWinogradInputTransformDatasetNHWC = framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(),
framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x1_3x1(),
framework::dataset::concat(datasets::SmallWinogradInputTransformDataset1x4_1x3(),
- datasets::SmallWinogradInputTransformDataset4x4_5x5())));
+ framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_5x5(),
+ framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x1_5x1(),
+ datasets::SmallWinogradInputTransformDataset1x4_1x5())))));
const auto LargeWinogradInputTransformDatasetNCHW =
framework::dataset::concat(datasets::LargeWinogradInputTransformDataset2x2_3x3(),
@@ -87,7 +89,9 @@ const auto LargeWinogradInputTransformDatasetNCHW =
const auto LargeWinogradInputTransformDatasetNHWC =
framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(),
- datasets::LargeWinogradInputTransformDataset4x4_5x5());
+ framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_5x5(),
+ framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x1_5x1(),
+ datasets::LargeWinogradInputTransformDataset1x4_1x5())));
// Filter transform
const auto SmallWinogradFilterTransformDatasetNCHW =
@@ -102,7 +106,9 @@ const auto SmallWinogradFilterTransformDatasetNHWC =
framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })),
framework::dataset::concat(combine(datasets::Small3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })),
framework::dataset::concat(combine(datasets::Small1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) })),
- combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })))));
+ framework::dataset::concat(combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })),
+ framework::dataset::concat(combine(datasets::Small5x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })),
+ combine(datasets::Small1x5Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) })))))));
const auto LargeWinogradFilterTransformDatasetNCHW =
framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })),
@@ -114,7 +120,11 @@ const auto LargeWinogradFilterTransformDatasetNCHW =
const auto LargeWinogradFilterTransformDatasetNHWC =
framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })),
- combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })));
+ framework::dataset::concat(combine(datasets::Large3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })),
+ framework::dataset::concat(combine(datasets::Large1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) })),
+ framework::dataset::concat(combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })),
+ framework::dataset::concat(combine(datasets::Large5x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })),
+ combine(datasets::Large1x5Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) })))))));
// Output transform
const auto SmallWinogradOutputTransformDatasetNCHW = datasets::SmallWinogradOutputTransformDatasetNCHW();
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-09 08:47:05 +0000