COMPMID-1339 - Implementing Winograd Convolution Layer 1x5 and 5x1 kernels on OpenCL NCHW
Change-Id: Ia293cd89651146a0e27e5f7c74ca9c924807e83c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/138707
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
index 5e3d815..9d0833d 100644
--- a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
@@ -48,10 +48,15 @@
~CLWinogradFilterTransformKernel() = default;
/** Set the input and output tensor.
*
- * @note Winograd filter transform supports the following configurations:
- * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
+ * @note Winograd filter transform supports the following configurations for NCWH data layout
+ * F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
+ * F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * 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)
* Strides: only unit strides
- * Data Layout: NCHW for all configurations, NHWC for F(4x4, 3x3) and F(4x4, 5x5)
*
* @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.
* @param[out] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_filter_transform_shape. Data types supported: Same as @p input
@@ -60,10 +65,15 @@
void configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLWinogradFilterTransformKernel
*
- * @note Winograd filter transform supports the following configurations:
- * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
+ * @note Winograd filter transform supports the following configurations for NCWH data layout
+ * F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
+ * F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * 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)
* Strides: only unit strides
- * Data Layout: NCHW for all configurations, NHWC for F(4x4, 3x3) and F(4x4, 5x5)
*
* @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.
* @param[out] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_filter_transform_shape. Data types supported: Same as @p input
diff --git a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
index ddf0720..410e8ba 100644
--- a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
@@ -46,10 +46,15 @@
CLWinogradInputTransformKernel &operator=(CLWinogradInputTransformKernel &&) = default;
/** Set the input and output of the kernel.
*
- * @note Winograd input transform supports the following configurations:
- * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
+ * @note Winograd input transform supports the following configurations for NCWH data layout
+ * F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
+ * F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * 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)
* Strides: only unit strides
- * Data Layout: NCHW for all configurations, NHWC for F(4x4, 3x3), F(4x4, 5x5)
*
* @param[in] input The input tensor to transform. Data types supported: F32
* @param[in] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_input_transform_shape. Data types supported: Same as @p input
@@ -58,10 +63,15 @@
void configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLWinogradInputTransformKernel
*
- * @note Winograd input transform supports the following configurations:
- * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
+ * @note Winograd input transform supports the following configurations for NCWH data layout
+ * F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
+ * F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * 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)
* Strides: only unit strides
- * Data Layout: NCHW for all configurations, NHWC for F(4x4, 3x3), F(4x4, 5x5)
*
* @param[in] input The input tensor to transform. Data types supported: F32
* @param[in] output The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_input_transform_shape. Data types supported: Same as @p input
diff --git a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
index cd46e98..0798172 100644
--- a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
@@ -48,10 +48,15 @@
~CLWinogradOutputTransformKernel() = default;
/** Set the input and output tensor.
*
- * @note Winograd output transform supports the following configurations:
- * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
+ * @note Winograd output transform supports the following configurations for NCWH data layout
+ * F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
+ * F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * 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)
* Strides: only unit strides
- * Data Layout: NCHW for all configurations, NHWC for F(4x4, 3x3) and F(4x4, 5x5)
*
* @param[in] input Source tensor with shape [C, N, K, batches]. Data types supported: F32.
* @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
@@ -61,10 +66,15 @@
void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLWinogradOutputTransformKernel
*
- * @note Winograd output transform supports the following configurations:
- * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
+ * @note Winograd output transform supports the following configurations for NCWH data layout
+ * F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
+ * F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * 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)
* Strides: only unit strides
- * Data Layout: NCHW for all configurations, NHWC for F(4x4, 3x3) and F(4x4, 5x5)
*
* @param[in] input Source tensor with shape [C, N, K, batches]. Data types supported: F32.
* @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
diff --git a/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
index 594d602..683aa79 100644
--- a/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
@@ -59,8 +59,9 @@
CLWinogradConvolutionLayer &operator=(CLWinogradConvolutionLayer &&) = default;
/** Set the input and output tensors.
*
- * @note: This function only works with 3x3 and 5x5 kernels along with unit strides
- * @note Some Winograd configurations (i.e. F(4x4, 3x3) and F(4x4, 5x5)) are supported only with enable_fast_math = true
+ * @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 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],
* while every optional dimension from 4 and above represent a batch of inputs.
@@ -78,8 +79,9 @@
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 and 5x5 kernels along with unit strides
- * @note Some Winograd configurations (i.e. F(4x4, 3x3) and F(4x4, 5x5)) are supported only with enable_fast_math = true
+ * @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 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],
* while every optional dimension from 4 and above represent a batch of inputs.
diff --git a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h
index 64d3e80..1f89455 100644
--- a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h
+++ b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h
@@ -39,8 +39,14 @@
public:
/** Set the input and output tensors.
*
- * @note Winograd input transform supports the following configurations:
- * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
+ * @note Winograd input transform supports the following configurations for NCWH data layout
+ * F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
+ * F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * 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)
* Strides: only unit strides
*
* @param[in] input The input tensor to transform. Data types supported: F32
@@ -50,8 +56,14 @@
void configure(ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLWinogradInputTransform.
*
- * @note Winograd input transform supports the following configurations:
- * F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
+ * @note Winograd input transform supports the following configurations for NCWH data layout
+ * F(output tile, kernel size):F(2x2, 3x3), F(2x1, 3x1), F(1x2, 1x3),
+ * F(4x4, 3x3), F(4x1, 3x1), F(1x4, 1x3),
+ * 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)
* 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 55da527..cd60c6e 100644
--- a/src/core/CL/CLHelpers.cpp
+++ b/src/core/CL/CLHelpers.cpp
@@ -161,7 +161,9 @@
WinogradConfiguration(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1)),
WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3)),
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))
};
std::vector<WinogradConfiguration> winograd_filter_transform_nhwc =
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 42cf213..7f26b04 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -374,6 +374,8 @@
{ "winograd_filter_transform_4x1_3x1_nchw", "winograd_filter_transform.cl" },
{ "winograd_filter_transform_1x4_1x3_nchw", "winograd_filter_transform.cl" },
{ "winograd_filter_transform_4x4_5x5_nchw", "winograd_filter_transform.cl" },
+ { "winograd_filter_transform_4x1_5x1_nchw", "winograd_filter_transform.cl" },
+ { "winograd_filter_transform_1x4_1x5_nchw", "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_input_transform_2x2_3x3_stepz1_nchw", "winograd_input_transform.cl" },
@@ -386,6 +388,8 @@
{ "winograd_input_transform_4x1_3x1_stepz1_nchw", "winograd_input_transform.cl" },
{ "winograd_input_transform_1x4_1x3_stepz1_nchw", "winograd_input_transform.cl" },
{ "winograd_input_transform_4x4_5x5_stepz1_nchw", "winograd_input_transform.cl" },
+ { "winograd_input_transform_4x1_5x1_stepz1_nchw", "winograd_input_transform.cl" },
+ { "winograd_input_transform_1x4_1x5_stepz1_nchw", "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_output_transform_2x2_3x3_nchw", "winograd_output_transform.cl" },
@@ -395,6 +399,8 @@
{ "winograd_output_transform_4x1_3x1_nchw", "winograd_output_transform.cl" },
{ "winograd_output_transform_1x4_1x3_nchw", "winograd_output_transform.cl" },
{ "winograd_output_transform_4x4_5x5_nchw", "winograd_output_transform.cl" },
+ { "winograd_output_transform_4x1_5x1_nchw", "winograd_output_transform.cl" },
+ { "winograd_output_transform_1x4_1x5_nchw", "winograd_output_transform.cl" },
{ "winograd_output_transform_4x4_3x3_nhwc", "winograd_output_transform.cl" },
{ "winograd_output_transform_4x4_5x5_nhwc", "winograd_output_transform.cl" },
{ "YUYV422_to_IYUV_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 1ee6981..5f528d4 100644
--- a/src/core/CL/cl_kernels/winograd_filter_transform.cl
+++ b/src/core/CL/cl_kernels/winograd_filter_transform.cl
@@ -66,9 +66,9 @@
*((__global float *)(src_addr + 1 * src_stride_y)),
*((__global float *)(src_addr + 2 * src_stride_y)));
#else // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
- float3 w0 = vload3(0, (__global float *)(src_addr + 0 * src_stride_y));
- float3 w1 = vload3(0, (__global float *)(src_addr + 1 * src_stride_y));
- float3 w2 = vload3(0, (__global float *)(src_addr + 2 * src_stride_y));
+ float3 w0 = vload3(0, (__global float *)(src_addr + 0 * src_stride_y));
+ float3 w1 = vload3(0, (__global float *)(src_addr + 1 * src_stride_y));
+ float3 w2 = vload3(0, (__global float *)(src_addr + 2 * src_stride_y));
#endif // defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
// Row 0
@@ -173,9 +173,9 @@
*((__global float *)(src_addr + 1 * src_stride_y)),
*((__global float *)(src_addr + 2 * src_stride_y)));
#else // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
- float3 w0 = vload3(0, (__global float *)(src_addr + 0 * src_stride_y));
- float3 w1 = vload3(0, (__global float *)(src_addr + 1 * src_stride_y));
- float3 w2 = vload3(0, (__global float *)(src_addr + 2 * src_stride_y));
+ float3 w0 = vload3(0, (__global float *)(src_addr + 0 * src_stride_y));
+ float3 w1 = vload3(0, (__global float *)(src_addr + 1 * src_stride_y));
+ float3 w2 = vload3(0, (__global float *)(src_addr + 2 * src_stride_y));
#endif // defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
// Row 0
@@ -285,202 +285,6 @@
#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 NCHW and the output tile is 2x1
- *
- * @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_2x1_3x1_nchw(
- TENSOR4D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst))
-{
- winograd_filter_transform_2x2_3x3_nchw(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 3x1 when the data layout is NCHW 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_nchw(
- TENSOR4D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst))
-{
- winograd_filter_transform_4x4_3x3_nchw(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 NCHW and the output tile is 1x2
- *
- * @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_1x2_1x3_nchw(
- TENSOR4D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst))
-{
- winograd_filter_transform_2x2_3x3_nchw(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 1x3 when the data layout is NCHW 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_nchw(
- TENSOR4D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst))
-{
- winograd_filter_transform_4x4_3x3_nchw(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 3x3 when the data layout is NHWC and the output tile is 4x4
*
* @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
@@ -630,10 +434,13 @@
*(__global float *)(dst_addr + 34 * dst_stride_z) = out54;
*(__global float *)(dst_addr + 35 * dst_stride_z) = out55;
}
-/** This OpenCL kernel performs Winograd filter transform 5x5 when the data layout is NCHW and the output tile is 4x4
+/** 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
*
+ * @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)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
@@ -662,186 +469,198 @@
const __global uchar *src_addr = tensor4D_offset(&src, 0, 0, 0, 0);
// Load the values from the input tensor
- const char stride_x = 4 * sizeof(float); // Used for accessing the last value in each row
- const uchar8 stride_y = (uchar8)(0, 1, 2, 3, 4, 0, 0, 0) * (uchar8)src_stride_y;
+#if defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+ float4 w00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
+ float w01 = *((__global float *)(src_addr + 0 * src_stride_y) + 4);
+#elif defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
+ float4 w00 = (float4)(*((__global float *)(src_addr + 0 * src_stride_y)),
+ *((__global float *)(src_addr + 1 * src_stride_y)),
+ *((__global float *)(src_addr + 2 * src_stride_y)),
+ *((__global float *)(src_addr + 3 * src_stride_y)));
+ float w01 = *((__global float *)(src_addr + 4 * src_stride_y));
+#else // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
+ float4 w00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
+ float w01 = *((__global float *)(src_addr + 0 * src_stride_y) + 4);
+ float4 w10 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
+ float w11 = *((__global float *)(src_addr + 1 * src_stride_y) + 4);
+ float4 w20 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
+ float w21 = *((__global float *)(src_addr + 2 * src_stride_y) + 4);
+ float4 w30 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
+ float w31 = *((__global float *)(src_addr + 3 * src_stride_y) + 4);
+ float4 w40 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y));
+ float w41 = *((__global float *)(src_addr + 4 * src_stride_y) + 4);
+#endif // defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
- float4 w00 = vload4(0, (__global float *)(src_addr + stride_y.s0));
- float w01 = *((__global float *)(src_addr + stride_y.s0 + stride_x));
- float4 w10 = vload4(0, (__global float *)(src_addr + stride_y.s1));
- float w11 = *((__global float *)(src_addr + stride_y.s1 + stride_x));
- float4 w20 = vload4(0, (__global float *)(src_addr + stride_y.s2));
- float w21 = *((__global float *)(src_addr + stride_y.s2 + stride_x));
- float4 w30 = vload4(0, (__global float *)(src_addr + stride_y.s3));
- float w31 = *((__global float *)(src_addr + stride_y.s3 + stride_x));
- float4 w40 = vload4(0, (__global float *)(src_addr + stride_y.s4));
- float w41 = *((__global float *)(src_addr + stride_y.s4 + stride_x));
-
- // 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;
+ // Transform the input tile
// Row 0
- out0.s0 = w00.s0;
- out0.s1 = -2.f * (w00.s0 + w00.s1 + w00.s2 + w00.s3 + w01) / 9.f;
- out0.s2 = -2.f * (w00.s0 - w00.s1 + w00.s2 - w00.s3 + w01) / 9.f;
- out0.s3 = (w00.s0 + 2.f * w00.s1 + 4.f * w00.s2 + 8.f * w00.s3 + 16.f * w01) / 90.f;
- out0.s4 = (w00.s0 - 2.f * w00.s1 + 4.f * w00.s2 - 8.f * w00.s3 + 16.f * w01) / 90.f;
- out0.s5 = (16.f * w00.s0 + 8.f * w00.s1 + 4.f * w00.s2 + 2.f * w00.s3 + w01) / 180.f;
- out0.s6 = (16.f * w00.s0 - 8.f * w00.s1 + 4.f * w00.s2 - 2.f * w00.s3 + w01) / 180.f;
- out0.s7 = w01;
+ float8 out0 = 0.0f;
+ out0.s0 = w00.s0;
+ out0.s1 = -2.f * (w00.s0 + w00.s1 + w00.s2 + w00.s3 + w01) / 9.f;
+ out0.s2 = -2.f * (w00.s0 - w00.s1 + w00.s2 - w00.s3 + w01) / 9.f;
+ out0.s3 = (w00.s0 + 2.f * w00.s1 + 4.f * w00.s2 + 8.f * w00.s3 + 16.f * w01) / 90.f;
+ out0.s4 = (w00.s0 - 2.f * w00.s1 + 4.f * w00.s2 - 8.f * w00.s3 + 16.f * w01) / 90.f;
+ out0.s5 = (16.f * w00.s0 + 8.f * w00.s1 + 4.f * w00.s2 + 2.f * w00.s3 + w01) / 180.f;
+ out0.s6 = (16.f * w00.s0 - 8.f * w00.s1 + 4.f * w00.s2 - 2.f * w00.s3 + w01) / 180.f;
+ out0.s7 = w01;
+#if !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
// Row 1
- out1.s0 = -2.f * (w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) / 9.f;
- out1.s1 = 4.f * ((w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) + (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) +
- (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + (w01 + w11 + w21 + w31 + w41)) / 81.f;
- out1.s2 = 4.f * ((w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) - (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) -
- (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + (w01 + w11 + w21 + w31 + w41)) / 81.f;
- out1.s3 = -((w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) + 2.f * (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + 4.f * (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) + 8.f *
- (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + 16.f * (w01 + w11 + w21 + w31 + w41)) / 405.f;
- out1.s4 = -((w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) - 2.f * (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + 4.f * (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) - 8.f *
- (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + 16.f * (w01 + w11 + w21 + w31 + w41)) / 405.f;
- out1.s5 = -(16.f * (w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) + 8.f * (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + 4.f * (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) + 2.f *
- (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + (w01 + w11 + w21 + w31 + w41)) / 810.f;
- out1.s6 = -(16.f * (w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) - 8.f * (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + 4.f * (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) - 2.f *
- (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + (w01 + w11 + w21 + w31 + w41)) / 810.f;
- out1.s7 = -2.f * (w01 + w11 + w21 + w31 + w41) / 9.f;
+ float8 out1 = 0.0f;
+ out1.s0 = -2.f * (w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) / 9.f;
+ out1.s1 = 4.f * ((w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) + (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) +
+ (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + (w01 + w11 + w21 + w31 + w41)) / 81.f;
+ out1.s2 = 4.f * ((w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) - (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) -
+ (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + (w01 + w11 + w21 + w31 + w41)) / 81.f;
+ out1.s3 = -((w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) + 2.f * (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + 4.f * (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) + 8.f *
+ (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + 16.f * (w01 + w11 + w21 + w31 + w41)) / 405.f;
+ out1.s4 = -((w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) - 2.f * (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + 4.f * (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) - 8.f *
+ (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + 16.f * (w01 + w11 + w21 + w31 + w41)) / 405.f;
+ out1.s5 = -(16.f * (w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) + 8.f * (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + 4.f * (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) + 2.f *
+ (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + (w01 + w11 + w21 + w31 + w41)) / 810.f;
+ out1.s6 = -(16.f * (w00.s0 + w10.s0 + w20.s0 + w30.s0 + w40.s0) - 8.f * (w00.s1 + w10.s1 + w20.s1 + w30.s1 + w40.s1) + 4.f * (w00.s2 + w10.s2 + w20.s2 + w30.s2 + w40.s2) - 2.f *
+ (w00.s3 + w10.s3 + w20.s3 + w30.s3 + w40.s3) + (w01 + w11 + w21 + w31 + w41)) / 810.f;
+ out1.s7 = -2.f * (w01 + w11 + w21 + w31 + w41) / 9.f;
// Row 2
- out2.s0 = -2.f * (w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) / 9.f;
- out2.s1 = 4.f * ((w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) + (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) +
- (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + (w01 - w11 + w21 - w31 + w41)) / 81.f;
- out2.s2 = 4.f * ((w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) - (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) -
- (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + (w01 - w11 + w21 - w31 + w41)) / 81.f;
- out2.s3 = -((w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) + 2.f * (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + 4.f * (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) + 8.f *
- (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + 16.f * (w01 - w11 + w21 - w31 + w41)) / 405.f;
- out2.s4 = -((w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) - 2.f * (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + 4.f * (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) - 8.f *
- (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + 16.f * (w01 - w11 + w21 - w31 + w41)) / 405.f;
- out2.s5 = -(16.f * (w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) + 8.f * (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + 4.f * (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) + 2.f *
- (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + (w01 - w11 + w21 - w31 + w41)) / 810.f;
- out2.s6 = -(16.f * (w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) - 8.f * (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + 4.f * (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) - 2.f *
- (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + (w01 - w11 + w21 - w31 + w41)) / 810.f;
- out2.s7 = -2.f * (w01 - w11 + w21 - w31 + w41) / 9.f;
+ float8 out2 = 0.0f;
+ out2.s0 = -2.f * (w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) / 9.f;
+ out2.s1 = 4.f * ((w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) + (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) +
+ (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + (w01 - w11 + w21 - w31 + w41)) / 81.f;
+ out2.s2 = 4.f * ((w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) - (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) -
+ (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + (w01 - w11 + w21 - w31 + w41)) / 81.f;
+ out2.s3 = -((w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) + 2.f * (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + 4.f * (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) + 8.f *
+ (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + 16.f * (w01 - w11 + w21 - w31 + w41)) / 405.f;
+ out2.s4 = -((w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) - 2.f * (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + 4.f * (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) - 8.f *
+ (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + 16.f * (w01 - w11 + w21 - w31 + w41)) / 405.f;
+ out2.s5 = -(16.f * (w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) + 8.f * (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + 4.f * (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) + 2.f *
+ (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + (w01 - w11 + w21 - w31 + w41)) / 810.f;
+ out2.s6 = -(16.f * (w00.s0 - w10.s0 + w20.s0 - w30.s0 + w40.s0) - 8.f * (w00.s1 - w10.s1 + w20.s1 - w30.s1 + w40.s1) + 4.f * (w00.s2 - w10.s2 + w20.s2 - w30.s2 + w40.s2) - 2.f *
+ (w00.s3 - w10.s3 + w20.s3 - w30.s3 + w40.s3) + (w01 - w11 + w21 - w31 + w41)) / 810.f;
+ out2.s7 = -2.f * (w01 - w11 + w21 - w31 + w41) / 9.f;
// Row 3
- out3.s0 = (w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) / 90.f;
- out3.s1 = -((w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) + (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) +
- (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) + (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) +
- (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 405.f;
- out3.s2 = -((w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) - (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) +
- (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) - (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) +
- (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 405.f;
- out3.s3 = ((w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) + 2.f * (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
- (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) + 8.f * (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) + 16.f *
- (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 8100.f;
- out3.s4 = ((w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) - 2.f * (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
- (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) - 8.f * (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) + 16.f *
- (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 8100.f;
- out3.s5 = (16.f * (w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) + 8.f * (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
- (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) + 2.f * (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) +
- (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 16200.f;
- out3.s6 = (16.f * (w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) - 8.f * (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
- (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) - 2.f * (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) +
- (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 16200.f;
- out3.s7 = (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) / 90.f;
+ float8 out3 = 0.0f;
+ out3.s0 = (w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) / 90.f;
+ out3.s1 = -((w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) + (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) +
+ (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) + (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) +
+ (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 405.f;
+ out3.s2 = -((w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) - (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) +
+ (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) - (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) +
+ (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 405.f;
+ out3.s3 = ((w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) + 2.f * (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
+ (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) + 8.f * (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) + 16.f *
+ (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 8100.f;
+ out3.s4 = ((w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) - 2.f * (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
+ (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) - 8.f * (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) + 16.f *
+ (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 8100.f;
+ out3.s5 = (16.f * (w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) + 8.f * (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
+ (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) + 2.f * (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) +
+ (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 16200.f;
+ out3.s6 = (16.f * (w00.s0 + 2.f * w10.s0 + 4.f * w20.s0 + 8.f * w30.s0 + 16.f * w40.s0) - 8.f * (w00.s1 + 2.f * w10.s1 + 4.f * w20.s1 + 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
+ (w00.s2 + 2.f * w10.s2 + 4.f * w20.s2 + 8.f * w30.s2 + 16.f * w40.s2) - 2.f * (w00.s3 + 2.f * w10.s3 + 4.f * w20.s3 + 8.f * w30.s3 + 16.f * w40.s3) +
+ (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41)) / 16200.f;
+ out3.s7 = (w01 + 2.f * w11 + 4.f * w21 + 8.f * w31 + 16.f * w41) / 90.f;
// Row 4
- out4.s0 = (w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) / 90.f;
- out4.s1 = -((w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) + (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) +
- (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) + (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) +
- (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 405.f;
- out4.s2 = -((w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) - (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) +
- (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) - (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) +
- (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 405.f;
- out4.s3 = ((w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) + 2.f * (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
- (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) + 8.f * (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) + 16.f *
- (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 8100.f;
- out4.s4 = ((w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) - 2.f * (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
- (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) - 8.f * (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) + 16.f *
- (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 8100.f;
- out4.s5 = (16.f * (w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) + 8.f * (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
- (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) + 2.f * (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) +
- (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 16200.f;
- out4.s6 = (16.f * (w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) - 8.f * (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
- (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) - 2.f * (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) +
- (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 16200.f;
- out4.s7 = (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) / 90.f;
+ float8 out4 = 0.0f;
+ out4.s0 = (w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) / 90.f;
+ out4.s1 = -((w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) + (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) +
+ (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) + (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) +
+ (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 405.f;
+ out4.s2 = -((w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) - (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) +
+ (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) - (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) +
+ (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 405.f;
+ out4.s3 = ((w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) + 2.f * (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
+ (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) + 8.f * (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) + 16.f *
+ (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 8100.f;
+ out4.s4 = ((w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) - 2.f * (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
+ (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) - 8.f * (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) + 16.f *
+ (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 8100.f;
+ out4.s5 = (16.f * (w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) + 8.f * (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
+ (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) + 2.f * (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) +
+ (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 16200.f;
+ out4.s6 = (16.f * (w00.s0 - 2.f * w10.s0 + 4.f * w20.s0 - 8.f * w30.s0 + 16.f * w40.s0) - 8.f * (w00.s1 - 2.f * w10.s1 + 4.f * w20.s1 - 8.f * w30.s1 + 16.f * w40.s1) + 4.f *
+ (w00.s2 - 2.f * w10.s2 + 4.f * w20.s2 - 8.f * w30.s2 + 16.f * w40.s2) - 2.f * (w00.s3 - 2.f * w10.s3 + 4.f * w20.s3 - 8.f * w30.s3 + 16.f * w40.s3) +
+ (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41)) / 16200.f;
+ out4.s7 = (w01 - 2.f * w11 + 4.f * w21 - 8.f * w31 + 16.f * w41) / 90.f;
// Row 5
- out5.s0 = (16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) / 180.f;
- out5.s1 = -((16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) + (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) +
- (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) + (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) +
- (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 810.f;
- out5.s2 = -((16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) - (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) +
- (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) - (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) +
- (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 810.f;
- out5.s3 = ((16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) + 2.f * (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) + 4.f *
- (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) + 8.f * (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) + 16.f *
- (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 16200.f;
- out5.s4 = ((16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) - 2.f * (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) + 4.f *
- (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) - 8.f * (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) + 16.f *
- (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 16200.f;
- out5.s5 = (16.f * (16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) + 8.f * (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) + 4.f *
- (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) + 2.f * (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) +
- (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 32400.f;
- out5.s6 = (16.f * (16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) - 8.f * (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) + 4.f *
- (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) - 2.f * (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) +
- (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 32400.f;
- out5.s7 = (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) / 180.f;
+ float8 out5 = 0.0f;
+ out5.s0 = (16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) / 180.f;
+ out5.s1 = -((16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) + (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) +
+ (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) + (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) +
+ (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 810.f;
+ out5.s2 = -((16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) - (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) +
+ (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) - (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) +
+ (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 810.f;
+ out5.s3 = ((16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) + 2.f * (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) + 4.f *
+ (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) + 8.f * (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) + 16.f *
+ (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 16200.f;
+ out5.s4 = ((16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) - 2.f * (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) + 4.f *
+ (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) - 8.f * (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) + 16.f *
+ (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 16200.f;
+ out5.s5 = (16.f * (16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) + 8.f * (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) + 4.f *
+ (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) + 2.f * (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) +
+ (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 32400.f;
+ out5.s6 = (16.f * (16.f * w00.s0 + 8.f * w10.s0 + 4.f * w20.s0 + 2.f * w30.s0 + w40.s0) - 8.f * (16.f * w00.s1 + 8.f * w10.s1 + 4.f * w20.s1 + 2.f * w30.s1 + w40.s1) + 4.f *
+ (16.f * w00.s2 + 8.f * w10.s2 + 4.f * w20.s2 + 2.f * w30.s2 + w40.s2) - 2.f * (16.f * w00.s3 + 8.f * w10.s3 + 4.f * w20.s3 + 2.f * w30.s3 + w40.s3) +
+ (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41)) / 32400.f;
+ out5.s7 = (16.f * w01 + 8.f * w11 + 4.f * w21 + 2.f * w31 + w41) / 180.f;
// Row 6
- out6.s0 = (16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) / 180.f;
- out6.s1 = -((16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) + (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) +
- (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) + (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) +
- (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 810.f;
- out6.s2 = -((16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) - (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) +
- (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) - (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) +
- (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 810.f;
- out6.s3 = ((16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) + 2.f * (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) + 4.f *
- (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) + 8.f * (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) + 16.f *
- (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 16200.f;
- out6.s4 = ((16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) - 2.f * (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) + 4.f *
- (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) - 8.f * (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) + 16.f *
- (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 16200.f;
- out6.s5 = (16.f * (16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) + 8.f * (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) + 4.f *
- (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) + 2.f * (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) +
- (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 32400.f;
- out6.s6 = (16.f * (16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) - 8.f * (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) + 4.f *
- (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) - 2.f * (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) +
- (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 32400.f;
- out6.s7 = (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) / 180.f;
+ float8 out6 = 0.0f;
+ out6.s0 = (16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) / 180.f;
+ out6.s1 = -((16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) + (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) +
+ (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) + (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) +
+ (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 810.f;
+ out6.s2 = -((16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) - (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) +
+ (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) - (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) +
+ (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 810.f;
+ out6.s3 = ((16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) + 2.f * (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) + 4.f *
+ (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) + 8.f * (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) + 16.f *
+ (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 16200.f;
+ out6.s4 = ((16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) - 2.f * (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) + 4.f *
+ (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) - 8.f * (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) + 16.f *
+ (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 16200.f;
+ out6.s5 = (16.f * (16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) + 8.f * (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) + 4.f *
+ (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) + 2.f * (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) +
+ (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 32400.f;
+ out6.s6 = (16.f * (16.f * w00.s0 - 8.f * w10.s0 + 4.f * w20.s0 - 2.f * w30.s0 + w40.s0) - 8.f * (16.f * w00.s1 - 8.f * w10.s1 + 4.f * w20.s1 - 2.f * w30.s1 + w40.s1) + 4.f *
+ (16.f * w00.s2 - 8.f * w10.s2 + 4.f * w20.s2 - 2.f * w30.s2 + w40.s2) - 2.f * (16.f * w00.s3 - 8.f * w10.s3 + 4.f * w20.s3 - 2.f * w30.s3 + w40.s3) +
+ (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41)) / 32400.f;
+ out6.s7 = (16.f * w01 - 8.f * w11 + 4.f * w21 - 2.f * w31 + w41) / 180.f;
// Row 7
- out7.s0 = w40.s0;
- out7.s1 = -2.f * (w40.s0 + w40.s1 + w40.s2 + w40.s3 + w41) / 9.f;
- out7.s2 = -2.f * (w40.s0 - w40.s1 + w40.s2 - w40.s3 + w41) / 9.f;
- out7.s3 = (w40.s0 + 2.f * w40.s1 + 4.f * w40.s2 + 8.f * w40.s3 + 16.f * w41) / 90.f;
- out7.s4 = (w40.s0 - 2.f * w40.s1 + 4.f * w40.s2 - 8.f * w40.s3 + 16.f * w41) / 90.f;
- out7.s5 = (16.f * w40.s0 + 8.f * w40.s1 + 4.f * w40.s2 + 2.f * w40.s3 + w41) / 180.f;
- out7.s6 = (16.f * w40.s0 - 8.f * w40.s1 + 4.f * w40.s2 - 2.f * w40.s3 + w41) / 180.f;
- out7.s7 = w41;
+ float8 out7 = 0.0f;
+ out7.s0 = w40.s0;
+ out7.s1 = -2.f * (w40.s0 + w40.s1 + w40.s2 + w40.s3 + w41) / 9.f;
+ out7.s2 = -2.f * (w40.s0 - w40.s1 + w40.s2 - w40.s3 + w41) / 9.f;
+ out7.s3 = (w40.s0 + 2.f * w40.s1 + 4.f * w40.s2 + 8.f * w40.s3 + 16.f * w41) / 90.f;
+ out7.s4 = (w40.s0 - 2.f * w40.s1 + 4.f * w40.s2 - 8.f * w40.s3 + 16.f * w41) / 90.f;
+ out7.s5 = (16.f * w40.s0 + 8.f * w40.s1 + 4.f * w40.s2 + 2.f * w40.s3 + w41) / 180.f;
+ out7.s6 = (16.f * w40.s0 - 8.f * w40.s1 + 4.f * w40.s2 - 2.f * w40.s3 + w41) / 180.f;
+ out7.s7 = w41;
+#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
int z = get_global_id(2);
int x0 = z / SRC_DIM_Z; // idx filter
int y0 = z % SRC_DIM_Z; // idx channel
// Get output address
- __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x0 * dst_stride_x + y0 * dst_stride_y;
+ __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;
@@ -898,6 +717,7 @@
*(__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)
}
/** This OpenCL kernel performs Winograd filter transform 5x5 when the data layout is NHWC and the output tile is 4x4
@@ -1152,4 +972,296 @@
*(__global float *)(dst_addr + 62 * dst_stride_z) = out7.s6;
*(__global float *)(dst_addr + 63 * dst_stride_z) = out7.s7;
}
-#endif // defined(SRC_DIM_Z)
\ No newline at end of file
+#endif // defined(SRC_DIM_Z)
+
+#if defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+/** This OpenCL kernel performs Winograd filter transform 3x1 when the data layout is NCHW and the output tile is 2x1
+ *
+ * @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_2x1_3x1_nchw(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_2x2_3x3_nchw(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 3x1 when the data layout is NCHW 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_nchw(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_4x4_3x3_nchw(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 NCHW 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_nchw(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_4x4_5x5_nchw(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 NCHW and the output tile is 1x2
+ *
+ * @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_1x2_1x3_nchw(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_2x2_3x3_nchw(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 1x3 when the data layout is NCHW 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_nchw(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_4x4_3x3_nchw(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 NCHW 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_nchw(
+ TENSOR4D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_filter_transform_4x4_5x5_nchw(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)
\ No newline at end of file
diff --git a/src/core/CL/cl_kernels/winograd_input_transform.cl b/src/core/CL/cl_kernels/winograd_input_transform.cl
index 4662426..fe1c0b3 100644
--- a/src/core/CL/cl_kernels/winograd_input_transform.cl
+++ b/src/core/CL/cl_kernels/winograd_input_transform.cl
@@ -71,10 +71,10 @@
*((__global float *)(src_addr + 2 * src_stride_y)),
*((__global float *)(src_addr + 3 * src_stride_y)));
#else // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
- float4 in_row0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
- float4 in_row1 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
- float4 in_row2 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
- float4 in_row3 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
+ float4 in_row0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
+ float4 in_row1 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
+ float4 in_row2 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
+ float4 in_row3 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
float4 tmp0 = in_row0;
@@ -179,10 +179,10 @@
*((__global float *)(src_addr + 2 * src_stride_y)),
*((__global float *)(src_addr + 3 * src_stride_y)));
#else // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
- float4 in_row0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
- float4 in_row1 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
- float4 in_row2 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
- float4 in_row3 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
+ float4 in_row0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
+ float4 in_row1 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
+ float4 in_row2 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
+ float4 in_row3 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
src_addr += src_stride_z;
@@ -194,10 +194,10 @@
*((__global float *)(src_addr + 2 * src_stride_y)),
*((__global float *)(src_addr + 3 * src_stride_y)));
#else // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
- float4 in_row4 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
- float4 in_row5 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
- float4 in_row6 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
- float4 in_row7 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
+ float4 in_row4 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
+ float4 in_row5 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
+ float4 in_row6 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
+ float4 in_row7 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
float4 tmp0 = in_row0;
@@ -261,7 +261,7 @@
#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
}
-/** This OpenCL kernel computes the input transform when the output tile is 4x4, the filter size 3x3 and the data layout is NCHW
+/** This OpenCL kernel computes the input transform when the output tile is 4x4/4x1 or 1x4, the filter size 3x3/3x1 or 1x3 and the data layout is NCHW
*
* @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).
@@ -310,8 +310,8 @@
*((__global float *)(src_addr + 5 * src_stride_y)));
#else // defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
// Row0
- float4 d00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
- float2 d01 = vload2(2, (__global float *)(src_addr + 0 * src_stride_y));
+ float4 d00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
+ float2 d01 = vload2(2, (__global float *)(src_addr + 0 * src_stride_y));
#endif // defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
float out0 = 0.0f;
@@ -918,10 +918,14 @@
out.s7 = tmp.s7 - tmp.s1 + 5.25f * tmp.s3 - 5.25f * tmp.s5; \
})
-/** This OpenCL kernel computes the input transform when the kernel size is 5x5 and the output tile is 4x4 when the data layout is NCHW
+/** 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
*
* @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=2
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2
+ * @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)
@@ -949,11 +953,23 @@
int z = get_global_id(2);
// Compute input address
- __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * 4 * src_stride_x + y * 4 * src_stride_y + z * src_stride_z;
+ __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * OUTPUT_TILE_W * sizeof(float) + y * OUTPUT_TILE_H * src_stride_y + z * src_stride_z;
- src_addr = src_addr - ((int)PAD_LEFT * src_stride_x) - ((int)PAD_TOP * src_stride_y);
+ src_addr = src_addr - ((int)PAD_LEFT * sizeof(float)) - ((int)PAD_TOP * src_stride_y);
- // Load 8x8 input tile
+ // Load input tile
+#if defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
+ const float8 in_row0 = vload8(0, (__global float *)(src_addr));
+#elif defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL) // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+ const float8 in_row0 = (float8)(*((__global float *)(src_addr + 0 * src_stride_y)),
+ *((__global float *)(src_addr + 1 * src_stride_y)),
+ *((__global float *)(src_addr + 2 * src_stride_y)),
+ *((__global float *)(src_addr + 3 * src_stride_y)),
+ *((__global float *)(src_addr + 4 * src_stride_y)),
+ *((__global float *)(src_addr + 5 * src_stride_y)),
+ *((__global float *)(src_addr + 6 * src_stride_y)),
+ *((__global float *)(src_addr + 7 * src_stride_y)));
+#else // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
const float8 in_row0 = vload8(0, (__global float *)(src_addr + 0 * src_stride_y));
const float8 in_row1 = vload8(0, (__global float *)(src_addr + 1 * src_stride_y));
const float8 in_row2 = vload8(0, (__global float *)(src_addr + 2 * src_stride_y));
@@ -962,14 +978,19 @@
const float8 in_row5 = vload8(0, (__global float *)(src_addr + 5 * src_stride_y));
const float8 in_row6 = vload8(0, (__global float *)(src_addr + 6 * src_stride_y));
const float8 in_row7 = vload8(0, (__global float *)(src_addr + 7 * src_stride_y));
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
// Calculate common factors for intermediate tensor
- float8 comm_fact0 = in_row2 + in_row6 - 4.25f * in_row4;
+ float8 tmp0 = in_row0;
+ float8 comm_fact0 = 0.0f;
+
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+ comm_fact0 += in_row2 + in_row6 - 4.25f * in_row4;
+ tmp0 += -in_row6 + 5.25f * in_row4 - 5.25f * in_row2;
+
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;
@@ -985,11 +1006,16 @@
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;
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
// Calculate output rows (reuse comm_fact0 vector)
- float8 out0, out1, out2, out3, out4, out5, out6, out7;
+ float8 out0;
OUTPUT_ROW_4x4_5x5(out0, tmp0, comm_fact0);
+
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+ float8 out1, out2, out3, out4, out5, out6, out7;
+
OUTPUT_ROW_4x4_5x5(out1, tmp1, comm_fact0);
OUTPUT_ROW_4x4_5x5(out2, tmp2, comm_fact0);
OUTPUT_ROW_4x4_5x5(out3, tmp3, comm_fact0);
@@ -997,18 +1023,21 @@
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 64 channels
- __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + z * dst_stride_x + (x + y * (int)NUM_TILES_X) * dst_stride_y;
+ // Store values across the channels
+ __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + z * sizeof(float) + (x + y * (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;
+ *((__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;
@@ -1065,6 +1094,7 @@
*((__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)
}
#if defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
@@ -1208,6 +1238,54 @@
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 when the data layout is NCHW
+ *
+ * @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=2
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2
+ * @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_nchw(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_input_transform_4x4_5x5_stepz1_nchw(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)
@@ -1351,6 +1429,53 @@
dst_step_z,
dst_offset_first_element_in_bytes);
}
+
+/** This OpenCL kernel computes the input transform when the kernel size is 1x5 and the output tile is 1x4
+ *
+ * @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
+ *
+ * @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_nchw(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst))
+{
+ winograd_input_transform_4x4_5x5_stepz1_nchw(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)
#if defined(SRC_DIM_1) && defined(SRC_DIM_2)
diff --git a/src/core/CL/cl_kernels/winograd_output_transform.cl b/src/core/CL/cl_kernels/winograd_output_transform.cl
index d195c14..c63b206 100644
--- a/src/core/CL/cl_kernels/winograd_output_transform.cl
+++ b/src/core/CL/cl_kernels/winograd_output_transform.cl
@@ -351,246 +351,6 @@
#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
}
-#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
-/** This OpenCL kernel performs Winograd output transform when the output tile is 2x1, the filter size 3x1 and the data layout is NCHW
- *
- * @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=2
- * @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_2x1_3x1_nchw(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst)
-#if defined(HAS_BIAS)
- ,
- VECTOR_DECLARATION(bias)
-#endif // defined(HAS_BIAS)
-)
-{
- winograd_output_transform_2x2_3x3_nchw(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)
- );
-}
-
-/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 3x1 and the data layout is NCHW
- *
- * @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_nchw(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst)
-#if defined(HAS_BIAS)
- ,
- VECTOR_DECLARATION(bias)
-#endif // defined(HAS_BIAS)
-)
-{
- winograd_output_transform_4x4_3x3_nchw(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)
- );
-}
-#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
-
-#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-/** This OpenCL kernel performs Winograd output transform when the output tile is 1x2, the filter size 1x3 and the data layout is NCHW
- *
- * @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=2
- * @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_1x2_1x3_nchw(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst)
-#if defined(HAS_BIAS)
- ,
- VECTOR_DECLARATION(bias)
-#endif // defined(HAS_BIAS)
-)
-{
- winograd_output_transform_2x2_3x3_nchw(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)
- );
-}
-
-/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x3 and the data layout is NCHW
- *
- * @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_nchw(
- TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst)
-#if defined(HAS_BIAS)
- ,
- VECTOR_DECLARATION(bias)
-#endif // defined(HAS_BIAS)
-)
-{
- winograd_output_transform_4x4_3x3_nchw(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)
- );
-}
-#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-
/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 3x3 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
@@ -799,9 +559,13 @@
col.s3 = comm_fact.s0 + 8.f * comm_fact.s1 + comm_fact.s2 + d7; \
})
-/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 5x5 and the data layout is NCHW
+/** 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 NCHW
*
* @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 3x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd output transform 1x3, -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)
@@ -829,12 +593,20 @@
#endif // defined(HAS_BIAS)
)
{
- // 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
+ // Compute output address
+ int y_in = get_global_id(1);
+ int x_out = (y_in % NUM_TILES_X) * OUTPUT_TILE_W;
+ int y_out = (y_in / NUM_TILES_X) * OUTPUT_TILE_H;
+ int z_out = get_global_id(0);
+
+ __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(float) + y_out * dst_stride_y + z_out * dst_stride_z;
+
+ // 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));
@@ -844,6 +616,36 @@
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)
+ *((__global float *)(dst_addr + 0 * dst_stride_y)) = out00;
+ *((__global float *)(dst_addr + 1 * dst_stride_y)) = out01;
+ *((__global float *)(dst_addr + 2 * dst_stride_y)) = out02;
+ *((__global float *)(dst_addr + 3 * dst_stride_y)) = out03;
+#else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+ vstore4((float4)(out00, out01, out02, out03), 0, (__global float *)(dst_addr));
+#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));
@@ -935,11 +737,6 @@
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 = (y_in % NUM_TILES_X) * 4;
- int y_out = (y_in / NUM_TILES_X) * 4;
- int z_out = get_global_id(0);
-
#if defined(HAS_BIAS)
// Add bias
Vector bias = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bias);
@@ -952,26 +749,12 @@
out_col3 += (float4)b;
#endif // defined(HAS_BIAS)
- // Get output address
- __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * dst_stride_x + y_out * dst_stride_y + z_out * dst_stride_z;
-
- // Store the 4x4 output tile
- *(__global float *)(dst_addr + 0 * dst_stride_x + 0 * dst_stride_y) = out_col0.s0;
- *(__global float *)(dst_addr + 1 * dst_stride_x + 0 * dst_stride_y) = out_col1.s0;
- *(__global float *)(dst_addr + 2 * dst_stride_x + 0 * dst_stride_y) = out_col2.s0;
- *(__global float *)(dst_addr + 3 * dst_stride_x + 0 * dst_stride_y) = out_col3.s0;
- *(__global float *)(dst_addr + 0 * dst_stride_x + 1 * dst_stride_y) = out_col0.s1;
- *(__global float *)(dst_addr + 1 * dst_stride_x + 1 * dst_stride_y) = out_col1.s1;
- *(__global float *)(dst_addr + 2 * dst_stride_x + 1 * dst_stride_y) = out_col2.s1;
- *(__global float *)(dst_addr + 3 * dst_stride_x + 1 * dst_stride_y) = out_col3.s1;
- *(__global float *)(dst_addr + 0 * dst_stride_x + 2 * dst_stride_y) = out_col0.s2;
- *(__global float *)(dst_addr + 1 * dst_stride_x + 2 * dst_stride_y) = out_col1.s2;
- *(__global float *)(dst_addr + 2 * dst_stride_x + 2 * dst_stride_y) = out_col2.s2;
- *(__global float *)(dst_addr + 3 * dst_stride_x + 2 * dst_stride_y) = out_col3.s2;
- *(__global float *)(dst_addr + 0 * dst_stride_x + 3 * dst_stride_y) = out_col0.s3;
- *(__global float *)(dst_addr + 1 * dst_stride_x + 3 * dst_stride_y) = out_col1.s3;
- *(__global float *)(dst_addr + 2 * dst_stride_x + 3 * dst_stride_y) = out_col2.s3;
- *(__global float *)(dst_addr + 3 * dst_stride_x + 3 * dst_stride_y) = out_col3.s3;
+ // Store the output tile
+ vstore4((float4)(out_col0.s0, out_col1.s0, out_col2.s0, out_col3.s0), 0, (__global float *)(dst_addr + 0 * dst_stride_y));
+ vstore4((float4)(out_col0.s1, out_col1.s1, out_col2.s1, out_col3.s1), 0, (__global float *)(dst_addr + 1 * dst_stride_y));
+ vstore4((float4)(out_col0.s2, out_col1.s2, out_col2.s2, out_col3.s2), 0, (__global float *)(dst_addr + 2 * dst_stride_y));
+ vstore4((float4)(out_col0.s3, out_col1.s3, out_col2.s3, out_col3.s3), 0, (__global float *)(dst_addr + 3 * dst_stride_y));
+#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
@@ -1149,4 +932,362 @@
*(__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;
}
+
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
+/** This OpenCL kernel performs Winograd output transform when the output tile is 2x1, the filter size 3x1 and the data layout is NCHW
+ *
+ * @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=2
+ * @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_2x1_3x1_nchw(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+ ,
+ VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+ winograd_output_transform_2x2_3x3_nchw(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)
+ );
+}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 3x1 and the data layout is NCHW
+ *
+ * @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_nchw(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+ ,
+ VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+ winograd_output_transform_4x4_3x3_nchw(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)
+ );
+}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 5x1 and the data layout is NCHW
+ *
+ * @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_nchw(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+ ,
+ VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+ winograd_output_transform_4x4_5x5_nchw(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)
+ );
+}
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
+
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+/** This OpenCL kernel performs Winograd output transform when the output tile is 1x2, the filter size 1x3 and the data layout is NCHW
+ *
+ * @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=2
+ * @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_1x2_1x3_nchw(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+ ,
+ VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+ winograd_output_transform_2x2_3x3_nchw(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)
+ );
+}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x3 and the data layout is NCHW
+ *
+ * @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_nchw(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+ ,
+ VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+ winograd_output_transform_4x4_3x3_nchw(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)
+ );
+}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x5 and the data layout is NCHW
+ *
+ * @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_nchw(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+ ,
+ VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+ winograd_output_transform_4x4_5x5_nchw(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)
+ );
+}
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
#endif // defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
\ No newline at end of file
diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
index 11714fa..f9ea91d 100644
--- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
@@ -59,7 +59,8 @@
}
else if(kernel_max_dim == 5U)
{
- output_tile = Size2D(4U, 4U);
+ output_tile = Size2D(kernel_dims.width == 1 ? 1U : 4U,
+ kernel_dims.height == 1 ? 1U : 4U);
}
return output_tile;
diff --git a/tests/datasets/LargeConvolutionLayerDataset.h b/tests/datasets/LargeConvolutionLayerDataset.h
index ae25c8c..3eb98db 100644
--- a/tests/datasets/LargeConvolutionLayerDataset.h
+++ b/tests/datasets/LargeConvolutionLayerDataset.h
@@ -122,6 +122,42 @@
}
};
+class LargeWinogradConvolutionLayer5x1Dataset final : public ConvolutionLayerDataset
+{
+public:
+ LargeWinogradConvolutionLayer5x1Dataset()
+ {
+ // Batch size 1
+ add_config(TensorShape(224U, 224U, 3U), TensorShape(5U, 1U, 3U, 64U), TensorShape(64U), TensorShape(222U, 224U, 64U), PadStrideInfo(1, 1, 1, 0));
+ add_config(TensorShape(123U, 134U, 16U), TensorShape(5U, 1U, 16U, 7U), TensorShape(7U), TensorShape(121U, 134U, 7U), PadStrideInfo(1, 1, 1, 0));
+ add_config(TensorShape(181U, 152U, 42U), TensorShape(5U, 1U, 42U, 100U), TensorShape(100U), TensorShape(177U, 152U, 100U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(200U, 201U, 24U), TensorShape(5U, 1U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 2, 0));
+
+ // Batch size 2, 3 and 4
+ add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(5U, 1U, 3U, 64U), TensorShape(64U), TensorShape(222U, 224U, 64U, 2U), PadStrideInfo(1, 1, 1, 0));
+ add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(5U, 1U, 16U, 7U), TensorShape(7U), TensorShape(121U, 134U, 7U, 3U), PadStrideInfo(1, 1, 1, 0));
+ add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(5U, 1U, 42U, 100U), TensorShape(100U), TensorShape(177U, 152U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
+ }
+};
+
+class LargeWinogradConvolutionLayer1x5Dataset final : public ConvolutionLayerDataset
+{
+public:
+ LargeWinogradConvolutionLayer1x5Dataset()
+ {
+ // Batch size 1
+ add_config(TensorShape(224U, 224U, 3U), TensorShape(1U, 5U, 3U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U), PadStrideInfo(1, 1, 0, 1));
+ add_config(TensorShape(123U, 134U, 16U), TensorShape(1U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 130U, 7U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(181U, 152U, 42U), TensorShape(1U, 5U, 42U, 100U), TensorShape(100U), TensorShape(181U, 148U, 100U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(200U, 201U, 24U), TensorShape(1U, 5U, 24U, 61), TensorShape(61U), TensorShape(200U, 201U, 61), PadStrideInfo(1, 1, 0, 2));
+
+ // Batch size 2, 3 and 4
+ add_config(TensorShape(224U, 224U, 3U, 2U), TensorShape(1U, 5U, 3U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U, 2U), PadStrideInfo(1, 1, 0, 1));
+ add_config(TensorShape(123U, 134U, 16U, 3U), TensorShape(1U, 5U, 16U, 7U), TensorShape(7U), TensorShape(123U, 130U, 7U, 3U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(181U, 152U, 42U, 4U), TensorShape(1U, 5U, 42U, 100U), TensorShape(100U), TensorShape(181U, 148U, 100U, 4U), PadStrideInfo(1, 1, 0, 0));
+ }
+};
+
class LargeConvolutionLayerDataset final : public ConvolutionLayerDataset
{
public:
diff --git a/tests/datasets/ShapeDatasets.h b/tests/datasets/ShapeDatasets.h
index 766530b..bc98b1e 100644
--- a/tests/datasets/ShapeDatasets.h
+++ b/tests/datasets/ShapeDatasets.h
@@ -500,6 +500,70 @@
}
};
+/** Data set containing small 5x1 tensor shapes. */
+class Small5x1Shapes final : public ShapeDataset
+{
+public:
+ Small5x1Shapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 5U, 1U, 7U, 4U },
+ TensorShape{ 5U, 1U, 4U, 13U },
+ TensorShape{ 5U, 1U, 9U, 2U },
+ TensorShape{ 5U, 1U, 3U, 5U },
+ })
+ {
+ }
+};
+
+/** Data set containing large 5x1 tensor shapes. */
+class Large5x1Shapes final : public ShapeDataset
+{
+public:
+ Large5x1Shapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 5U, 1U, 32U, 64U },
+ TensorShape{ 5U, 1U, 51U, 13U },
+ TensorShape{ 5U, 1U, 53U, 47U },
+ TensorShape{ 5U, 1U, 128U, 384U },
+ })
+ {
+ }
+};
+
+/** Data set containing small 1x5 tensor shapes. */
+class Small1x5Shapes final : public ShapeDataset
+{
+public:
+ Small1x5Shapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 1U, 5U, 7U, 4U },
+ TensorShape{ 1U, 5U, 4U, 13U },
+ TensorShape{ 1U, 5U, 9U, 2U },
+ TensorShape{ 1U, 5U, 3U, 5U },
+ })
+ {
+ }
+};
+
+/** Data set containing large 1x5 tensor shapes. */
+class Large1x5Shapes final : public ShapeDataset
+{
+public:
+ Large1x5Shapes()
+ : ShapeDataset("Shape",
+ {
+ TensorShape{ 1U, 5U, 32U, 64U },
+ TensorShape{ 1U, 5U, 51U, 13U },
+ TensorShape{ 1U, 5U, 53U, 47U },
+ TensorShape{ 1U, 5U, 128U, 384U },
+ })
+ {
+ }
+};
+
/** Data set containing small tensor shapes for deconvolution. */
class SmallDeconvolutionShapes final : public ShapeDataset
{
diff --git a/tests/datasets/SmallConvolutionLayerDataset.h b/tests/datasets/SmallConvolutionLayerDataset.h
index f05cc15..ae12dd4 100644
--- a/tests/datasets/SmallConvolutionLayerDataset.h
+++ b/tests/datasets/SmallConvolutionLayerDataset.h
@@ -92,6 +92,26 @@
}
};
+class SmallWinogradConvolutionLayer5x1Dataset final : public ConvolutionLayerDataset
+{
+public:
+ SmallWinogradConvolutionLayer5x1Dataset()
+ {
+ add_config(TensorShape(8U, 8U, 2U), TensorShape(5U, 1U, 2U, 1U), TensorShape(1U), TensorShape(4U, 8U, 1U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(8U, 8U, 2U), TensorShape(5U, 1U, 2U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 2, 0));
+ }
+};
+
+class SmallWinogradConvolutionLayer1x5Dataset final : public ConvolutionLayerDataset
+{
+public:
+ SmallWinogradConvolutionLayer1x5Dataset()
+ {
+ add_config(TensorShape(8U, 8U, 2U), TensorShape(1U, 5U, 2U, 1U), TensorShape(1U), TensorShape(8U, 4U, 1U), PadStrideInfo(1, 1, 0, 0));
+ add_config(TensorShape(8U, 8U, 2U), TensorShape(1U, 5U, 2U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 0, 2));
+ }
+};
+
class SmallConvolutionLayerDataset final : public ConvolutionLayerDataset
{
public:
diff --git a/tests/datasets/WinogradInputTransformDataset.h b/tests/datasets/WinogradInputTransformDataset.h
index ca23984..bd910fb 100644
--- a/tests/datasets/WinogradInputTransformDataset.h
+++ b/tests/datasets/WinogradInputTransformDataset.h
@@ -202,6 +202,36 @@
}
};
+class SmallWinogradInputTransformDataset4x1_5x1 final : public WinogradInputTransformDataset
+{
+public:
+ SmallWinogradInputTransformDataset4x1_5x1()
+ {
+ add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(9U, 9U), PadStrideInfo(1, 1, 2, 0), DataLayout::NCHW));
+ add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+ add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+ add_config(TensorShape(9U, 9U, 3U, 4U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(9U, 9U), PadStrideInfo(1, 1, 2, 0), DataLayout::NCHW));
+ add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+ add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(14U, 14U), PadStrideInfo(1, 1, 2, 0), DataLayout::NCHW));
+ }
+};
+
+class SmallWinogradInputTransformDataset1x4_1x5 final : public WinogradInputTransformDataset
+{
+public:
+ SmallWinogradInputTransformDataset1x4_1x5()
+ {
+ add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 2), DataLayout::NCHW));
+ add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+ add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+ add_config(TensorShape(9U, 9U, 3U, 4U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 2), DataLayout::NCHW));
+ add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+ add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(14U, 14U), PadStrideInfo(1, 1, 0, 2), DataLayout::NCHW));
+ }
+};
+
class LargeWinogradInputTransformDataset2x2_3x3 final : public WinogradInputTransformDataset
{
public:
@@ -285,6 +315,30 @@
add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
}
};
+
+class LargeWinogradInputTransformDataset4x1_5x1 final : public WinogradInputTransformDataset
+{
+public:
+ LargeWinogradInputTransformDataset4x1_5x1()
+ {
+ add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(42U, 37U), PadStrideInfo(1, 1, 2, 0), DataLayout::NCHW));
+ add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+ add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(83U, 72U), PadStrideInfo(1, 1, 2, 0), DataLayout::NCHW));
+ }
+};
+
+class LargeWinogradInputTransformDataset1x4_1x5 final : public WinogradInputTransformDataset
+{
+public:
+ LargeWinogradInputTransformDataset1x4_1x5()
+ {
+ add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(42U, 37U), PadStrideInfo(1, 1, 0, 2), DataLayout::NCHW));
+ add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(57U, 60U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+ add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 2), DataLayout::NCHW));
+ }
+};
} // namespace datasets
} // namespace test
} // namespace arm_compute
diff --git a/tests/datasets/WinogradOutputTransformDataset.h b/tests/datasets/WinogradOutputTransformDataset.h
index a4689c6..fc23e65 100644
--- a/tests/datasets/WinogradOutputTransformDataset.h
+++ b/tests/datasets/WinogradOutputTransformDataset.h
@@ -154,6 +154,22 @@
add_config(TensorShape(7U, 2U, 64U, 3U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
add_config(TensorShape(24U, 9U, 64U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
add_config(TensorShape(7U, 2U, 64U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+
+ // (4x1, 5x1)
+ add_config(TensorShape(13U, 6U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(7U, 22U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(5U, 462U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(53U, 33U), PadStrideInfo(1, 1, 2, 0), DataLayout::NCHW));
+ add_config(TensorShape(7U, 10U, 8U, 3U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(24U, 42U, 8U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+ add_config(TensorShape(7U, 20U, 8U, 5U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(8U, 10U), PadStrideInfo(1, 1, 2, 0), DataLayout::NCHW));
+
+ // (1x4, 1x5)
+ add_config(TensorShape(13U, 7U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(7U, 20U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(5U, 477U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(53U, 33U), PadStrideInfo(1, 1, 0, 2), DataLayout::NCHW));
+ add_config(TensorShape(7U, 16U, 8U, 3U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(24U, 42U, 8U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(14U, 14U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+ add_config(TensorShape(7U, 24U, 8U, 5U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 2), DataLayout::NCHW));
}
};
@@ -238,6 +254,18 @@
add_config(TensorShape(13U, 182U, 64U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
add_config(TensorShape(32U, 756U, 64U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
add_config(TensorShape(13U, 182U, 64U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+
+ // (4x1, 5x1)
+ add_config(TensorShape(32U, 3136U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(112U, 112U), PadStrideInfo(1, 1, 2, 0), DataLayout::NCHW));
+ add_config(TensorShape(13U, 784U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(56U, 56U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+ add_config(TensorShape(32U, 3024U, 8U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(112U, 112U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(13U, 784U, 8U, 5U), WinogradInfo(Size2D(4U, 1U), Size2D(5U, 1U), Size2D(56U, 56U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+
+ // (1x4, 1x5)
+ add_config(TensorShape(32U, 3136U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(112U, 112U), PadStrideInfo(1, 1, 0, 2), DataLayout::NCHW));
+ add_config(TensorShape(13U, 784U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+ add_config(TensorShape(32U, 3024U, 8U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(112U, 112U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+ add_config(TensorShape(13U, 784U, 8U, 5U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
}
};
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index 501afac..849d0c1 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -65,7 +65,9 @@
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 SmallWinogradInputTransformDatasetNHWC = framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(),
datasets::SmallWinogradInputTransformDataset4x4_5x5());
@@ -77,7 +79,9 @@
framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(),
framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x1_3x1(),
framework::dataset::concat(datasets::LargeWinogradInputTransformDataset1x4_1x3(),
- datasets::LargeWinogradInputTransformDataset4x4_5x5()))))));
+ framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_5x5(),
+ framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x1_5x1(),
+ datasets::LargeWinogradInputTransformDataset1x4_1x5()))))))));
const auto LargeWinogradInputTransformDatasetNHWC =
framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(),
@@ -88,7 +92,9 @@
framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })),
framework::dataset::concat(combine(datasets::Small3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 1U), Size2D(4U, 1U) })),
framework::dataset::concat(combine(datasets::Small1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 2U), 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 SmallWinogradFilterTransformDatasetNHWC =
framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })),
@@ -98,7 +104,9 @@
framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })),
framework::dataset::concat(combine(datasets::Large3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 1U), Size2D(4U, 1U) })),
framework::dataset::concat(combine(datasets::Large1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 2U), Size2D(1U, 4U) })),
- combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 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) })))))));
const auto LargeWinogradFilterTransformDatasetNHWC =
framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })),
@@ -643,6 +651,54 @@
}
TEST_SUITE_END() // Conv5x5
+TEST_SUITE(Conv5x1)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(),
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(),
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
+}
+TEST_SUITE_END() // Conv5x1
+
+TEST_SUITE(Conv1x5)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(),
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(),
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
+}
+TEST_SUITE_END() // Conv1x5
+
TEST_SUITE_END() // ConvolutionLayer
TEST_SUITE_END() // Winograd
TEST_SUITE_END() // CL
diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp
index 5be4fe2..026b300 100644
--- a/tests/validation/reference/Winograd.cpp
+++ b/tests/validation/reference/Winograd.cpp
@@ -148,6 +148,8 @@
{ WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::INPUT), imatrix2x2_3x3 },
{ WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::INPUT), imatrix4x4_3x3 },
{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::INPUT), imatrix4x4_5x5 },
+ { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1), WinogradTransformType::INPUT), imatrix4x4_5x5 },
+ { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5), WinogradTransformType::INPUT), imatrix4x4_5x5 },
{ WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3 },
{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3 },
{ WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::FILTER), fmatrix2x2_3x3 },
@@ -155,6 +157,8 @@
{ WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3 },
{ WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3 },
{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::FILTER), fmatrix4x4_5x5 },
+ { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1), WinogradTransformType::FILTER), fmatrix4x4_5x5 },
+ { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5), WinogradTransformType::FILTER), fmatrix4x4_5x5 },
{ WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3 },
{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3 },
{ WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::OUTPUT), omatrix2x2_3x3 },
@@ -162,6 +166,8 @@
{ WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3 },
{ WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3 },
{ WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::OUTPUT), omatrix4x4_5x5 },
+ { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(5, 1), WinogradTransformType::OUTPUT), omatrix4x4_5x5 },
+ { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 5), WinogradTransformType::OUTPUT), omatrix4x4_5x5 },
};
// Find transformation matrix