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| author | Georgios Pinitas <georgios.pinitas@arm.com> | 2019-03-26 17:23:28 +0000 |
|---|---|---|
| committer | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2019-04-11 09:34:26 +0000 |
| commit | 8be9148814b88e5b0cabd5a4d2b1f4ff470a8c1c (patch) | |
| tree | 760658b8c7b8917379467bd3fc119a5502faa850 /src | |
| parent | a50e702289af66944e860eafc7f3b32f6c5f30be (diff) | |
| download | ComputeLibrary-8be9148814b88e5b0cabd5a4d2b1f4ff470a8c1c.tar.gz | |
COMPMID-1959: Implements 2D FFT on OpenCL
Change-Id: I73cf3984a5463acc854c8a59dc2bd9a5234cd99c
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/936
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Diffstat (limited to 'src')
| -rw-r--r-- | src/core/CL/CLKernelLibrary.cpp | 25 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/fft.cl | 1077 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/fft_digit_reverse.cl | 148 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/fft_scale.cl | 78 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/pixelwise_mul_float.cl | 52 | ||||
| -rw-r--r-- | src/core/CL/cl_kernels/reduction_operation.cl | 17 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLFFTDigitReverseKernel.cpp | 36 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLFFTRadixStageKernel.cpp | 19 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLFFTScaleKernel.cpp | 143 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp | 142 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLReductionOperationKernel.cpp | 12 | ||||
| -rw-r--r-- | src/runtime/CL/functions/CLFFT1D.cpp | 49 | ||||
| -rw-r--r-- | src/runtime/CL/functions/CLFFT2D.cpp | 95 | ||||
| -rw-r--r-- | src/runtime/CL/functions/CLFFTConvolutionLayer.cpp | 380 | ||||
| -rw-r--r-- | src/runtime/CL/functions/CLPixelWiseMultiplication.cpp | 29 |
15 files changed, 2098 insertions, 204 deletions
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 4fa8ac4142..322ff517d9 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -219,7 +219,6 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "depthwise_convolution_3x3_f16", "depthwise_convolution.cl" }, { "depthwise_convolution_3x3_nhwc", "depthwise_convolution.cl" }, { "depthwise_convolution_3x3_nhwc_stride1", "depthwise_convolution.cl" }, - { "digit_reverse", "fft.cl" }, { "dwc_3x3_native_qasymm8_nchw", "depthwise_convolution_quantized.cl" }, { "dwc_3x3_native_qasymm8_dot8_nchw", "depthwise_convolution_quantized.cl" }, { "dwc_3x3_reshaped_qasymm8_nhwc", "depthwise_convolution_quantized.cl" }, @@ -261,18 +260,33 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "elementwise_unary", "elementwise_unary.cl" }, { "erode", "erode.cl" }, { "fast_corners", "fast_corners.cl" }, + { "fft_digit_reverse_axis_0", "fft_digit_reverse.cl" }, + { "fft_digit_reverse_axis_1", "fft_digit_reverse.cl" }, { "fft_radix_2_first_stage_axis_0", "fft.cl" }, + { "fft_radix_2_first_stage_axis_1", "fft.cl" }, { "fft_radix_2_axis_0", "fft.cl" }, + { "fft_radix_2_axis_1", "fft.cl" }, { "fft_radix_3_first_stage_axis_0", "fft.cl" }, + { "fft_radix_3_first_stage_axis_1", "fft.cl" }, { "fft_radix_3_axis_0", "fft.cl" }, + { "fft_radix_3_axis_1", "fft.cl" }, { "fft_radix_4_first_stage_axis_0", "fft.cl" }, + { "fft_radix_4_first_stage_axis_1", "fft.cl" }, { "fft_radix_4_axis_0", "fft.cl" }, + { "fft_radix_4_axis_1", "fft.cl" }, { "fft_radix_5_first_stage_axis_0", "fft.cl" }, + { "fft_radix_5_first_stage_axis_1", "fft.cl" }, { "fft_radix_5_axis_0", "fft.cl" }, + { "fft_radix_5_axis_1", "fft.cl" }, { "fft_radix_7_first_stage_axis_0", "fft.cl" }, + { "fft_radix_7_first_stage_axis_1", "fft.cl" }, { "fft_radix_7_axis_0", "fft.cl" }, + { "fft_radix_7_axis_1", "fft.cl" }, { "fft_radix_8_first_stage_axis_0", "fft.cl" }, + { "fft_radix_8_first_stage_axis_1", "fft.cl" }, { "fft_radix_8_axis_0", "fft.cl" }, + { "fft_radix_8_axis_1", "fft.cl" }, + { "fft_scale_conj", "fft_scale.cl" }, { "fill_image_borders_constant", "fill_border.cl" }, { "fill_image_borders_replicate", "fill_border.cl" }, { "finalize", "optical_flow_pyramid_lk.cl" }, @@ -391,6 +405,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "NV21_to_YUV444_bt709", "color_convert.cl" }, { "output_stage_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl" }, { "permute", "permute.cl" }, + { "pixelwise_mul_complex", "pixelwise_mul_float.cl" }, { "pixelwise_mul_float", "pixelwise_mul_float.cl" }, { "pixelwise_mul_int", "pixelwise_mul_int.cl" }, { "pixelwise_mul_quantized", "pixelwise_mul_int.cl" }, @@ -710,6 +725,14 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map = #include "./cl_kernels/fft.clembed" }, { + "fft_digit_reverse.cl", +#include "./cl_kernels/fft_digit_reverse.clembed" + }, + { + "fft_scale.cl", +#include "./cl_kernels/fft_scale.clembed" + }, + { "fill_border.cl", #include "./cl_kernels/fill_border.clembed" }, diff --git a/src/core/CL/cl_kernels/fft.cl b/src/core/CL/cl_kernels/fft.cl index 5f1ef2483b..0027fd5b66 100644 --- a/src/core/CL/cl_kernels/fft.cl +++ b/src/core/CL/cl_kernels/fft.cl @@ -23,48 +23,6 @@ */ #include "helpers.h" -/** Computes the digit reverse stage - * - * @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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_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 - * @param[in] idx_ptr Pointer to the index tensor. Supported data types: U32 - * @param[in] idx_stride_x Stride of the index tensor in X dimension (in bytes) - * @param[in] idx_step_x idx_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] idx_offset_first_element_in_bytes The offset of the first element in the index tensor - */ -__kernel void digit_reverse( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - VECTOR_DECLARATION(idx)) -{ - // Get tensor pointers - Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(src); - Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); - Vector idx = CONVERT_TO_VECTOR_STRUCT(idx); - - const unsigned int iidx = *((__global uint *)(idx.ptr)); - - // Load data - float2 data = vload2(0, (__global float *)tensor3D_offset(&src, iidx, get_global_id(1), get_global_id(2))); - - // Store result - vstore2(data, 0, (__global float *)dst.ptr); -} - /** Calculates and applies the twiddle factor to a given input. * * @param[in] phi The angle. @@ -252,7 +210,7 @@ __kernel void digit_reverse( c7 = s4 + t1; \ } -/** Computes the first stage of a radix-2 DFT. +/** Computes the first stage of a radix-2 DFT on axis 0. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -264,14 +222,14 @@ __kernel void digit_reverse( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image */ kernel void fft_radix_2_first_stage_axis_0( TENSOR3D_DECLARATION(input) @@ -289,17 +247,17 @@ kernel void fft_radix_2_first_stage_axis_0( Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); #endif /* IN_PLACE */ - // Load eight complex input values + // Load two complex input values float4 data = vload4(0, (__global float *)input.ptr); // Compute DFT N = 2 DFT_2(data.s01, data.s23); - // Store eight complex output values + // Store two complex output values vstore4(data, 0, (__global float *)output.ptr); } -/** Computes the first stage of a radix-3 DFT. +/** Computes the first stage of a radix-2 DFT on axis 1. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -311,14 +269,63 @@ kernel void fft_radix_2_first_stage_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + */ +kernel void fft_radix_2_first_stage_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ +) +{ + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); +#endif /* IN_PLACE */ + + // Load two complex input values + float2 data1 = vload2(0, (__global float *)input.ptr); + float2 data2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 1, 0)); + + // Compute DFT N = 2 + DFT_2(data1, data2); + + // Store two complex output values + vstore2(data1, 0, (__global float *)output.ptr); + vstore2(data2, 0, (__global float *)tensor3D_offset(&output, 0, 1, 0)); +} + +/** Computes the first stage of a radix-3 DFT on axis 0. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image */ kernel void fft_radix_3_first_stage_axis_0( TENSOR3D_DECLARATION(input) @@ -336,19 +343,19 @@ kernel void fft_radix_3_first_stage_axis_0( Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); #endif /* IN_PLACE */ - // Load eight complex input values + // Load three complex input values float4 data0 = vload4(0, (__global float *)input.ptr); float2 data1 = vload2(0, (__global float *)tensor3D_offset(&input, 2, 0, 0)); // Compute DFT N = 3 DFT_3(data0.s01, data0.s23, data1.s01); - // Store eight complex output values + // Store three complex output values vstore4(data0, 0, (__global float *)output.ptr); vstore2(data1, 0, (__global float *)tensor3D_offset(&output, 2, 0, 0)); } -/** Computes the first stage of a radix-4 DFT. +/** Computes the first stage of a radix-3 DFT on axis 1. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -360,14 +367,65 @@ kernel void fft_radix_3_first_stage_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + */ +kernel void fft_radix_3_first_stage_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ +) +{ + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); +#endif /* IN_PLACE */ + + // Load three complex input values + float2 data0 = vload2(0, (__global float *)input.ptr); + float2 data1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 1, 0)); + float2 data2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2, 0)); + + // Compute DFT N = 3 + DFT_3(data0, data1, data2); + + // Store three complex output values + vstore2(data0, 0, (__global float *)output.ptr); + vstore2(data1, 0, (__global float *)tensor3D_offset(&output, 0, 1, 0)); + vstore2(data2, 0, (__global float *)tensor3D_offset(&output, 0, 2, 0)); +} + +/** Computes the first stage of a radix-4 DFT on axis 0. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image */ kernel void fft_radix_4_first_stage_axis_0( TENSOR3D_DECLARATION(input) @@ -385,17 +443,70 @@ kernel void fft_radix_4_first_stage_axis_0( Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); #endif /* IN_PLACE */ - // Load eight complex input values + // Load four complex input values float8 data = vload8(0, (__global float *)input.ptr); // Compute DFT N = 4 DFT_4(data.s01, data.s23, data.s45, data.s67); - // Store eight complex output values + // Store four complex output values vstore8(data, 0, (__global float *)output.ptr); } -/** Computes the first stage of a radix-5 DFT. +/** Computes the first stage of a radix-4 DFT on axis 1. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + */ +kernel void fft_radix_4_first_stage_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ +) +{ + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); +#endif /* IN_PLACE */ + + // Load four complex input values + float2 data0 = vload2(0, (__global float *)input.ptr); + float2 data1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 1, 0)); + float2 data2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2, 0)); + float2 data3 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 3, 0)); + + // Compute DFT N = 4 + DFT_4(data0, data1, data2, data3); + + // Store four complex output values + vstore2(data0, 0, (__global float *)output.ptr); + vstore2(data1, 0, (__global float *)tensor3D_offset(&output, 0, 1, 0)); + vstore2(data2, 0, (__global float *)tensor3D_offset(&output, 0, 2, 0)); + vstore2(data3, 0, (__global float *)tensor3D_offset(&output, 0, 3, 0)); +} + +/** Computes the first stage of a radix-5 DFT on axis 0. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -407,14 +518,14 @@ kernel void fft_radix_4_first_stage_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image */ kernel void fft_radix_5_first_stage_axis_0( TENSOR3D_DECLARATION(input) @@ -432,19 +543,19 @@ kernel void fft_radix_5_first_stage_axis_0( Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); #endif /* IN_PLACE */ - // Load eight complex input values + // Load five complex input values float8 data0 = vload8(0, (__global float *)input.ptr); float2 data1 = vload2(0, (__global float *)tensor3D_offset(&input, 4, 0, 0)); // Compute DFT N = 5 DFT_5(data0.s01, data0.s23, data0.s45, data0.s67, data1.s01); - // Store eight complex output values + // Store five complex output values vstore8(data0, 0, (__global float *)output.ptr); vstore2(data1, 0, (__global float *)tensor3D_offset(&output, 4, 0, 0)); } -/** Computes the first stage of a radix-7 DFT. +/** Computes the first stage of a radix-5 DFT on axis 1. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -456,14 +567,69 @@ kernel void fft_radix_5_first_stage_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + */ +kernel void fft_radix_5_first_stage_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ +) +{ + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); +#endif /* IN_PLACE */ + + // Load five complex input values + float2 data0 = vload2(0, (__global float *)input.ptr); + float2 data1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 1, 0)); + float2 data2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2, 0)); + float2 data3 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 3, 0)); + float2 data4 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 4, 0)); + + // Compute DFT N = 5 + DFT_5(data0, data1, data2, data3, data4); + + // Store five complex output values + vstore2(data0, 0, (__global float *)output.ptr); + vstore2(data1, 0, (__global float *)tensor3D_offset(&output, 0, 1, 0)); + vstore2(data2, 0, (__global float *)tensor3D_offset(&output, 0, 2, 0)); + vstore2(data3, 0, (__global float *)tensor3D_offset(&output, 0, 3, 0)); + vstore2(data4, 0, (__global float *)tensor3D_offset(&output, 0, 4, 0)); +} + +/** Computes the first stage of a radix-7 DFT on axis 0. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image */ kernel void fft_radix_7_first_stage_axis_0( TENSOR3D_DECLARATION(input) @@ -481,7 +647,7 @@ kernel void fft_radix_7_first_stage_axis_0( Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); #endif /* IN_PLACE */ - // Load eight complex input values + // Load seven complex input values float8 data0 = vload8(0, (__global float *)input.ptr); float4 data1 = vload4(0, (__global float *)tensor3D_offset(&input, 4, 0, 0)); float2 data2 = vload2(0, (__global float *)tensor3D_offset(&input, 6, 0, 0)); @@ -489,13 +655,72 @@ kernel void fft_radix_7_first_stage_axis_0( // Compute DFT N = 7 DFT_7(data0.s01, data0.s23, data0.s45, data0.s67, data1.s01, data1.s23, data2.s01); - // Store eight complex output values + // Store seven complex output values vstore8(data0, 0, (__global float *)output.ptr); vstore4(data1, 0, (__global float *)tensor3D_offset(&output, 4, 0, 0)); vstore2(data2, 0, (__global float *)tensor3D_offset(&output, 6, 0, 0)); } -/** Computes the first stage of a radix-8 DFT. +/** Computes the first stage of a radix-7 DFT on axis 1. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + */ +kernel void fft_radix_7_first_stage_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ +) +{ + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); +#endif /* IN_PLACE */ + + // Load seven complex input values + float2 data0 = vload2(0, (__global float *)input.ptr); + float2 data1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 1, 0)); + float2 data2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2, 0)); + float2 data3 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 3, 0)); + float2 data4 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 4, 0)); + float2 data5 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 5, 0)); + float2 data6 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 6, 0)); + + // Compute DFT N = 7 + DFT_7(data0, data1, data2, data3, data4, data5, data6); + + // Store seven complex output values + vstore2(data0, 0, (__global float *)output.ptr); + vstore2(data1, 0, (__global float *)tensor3D_offset(&output, 0, 1, 0)); + vstore2(data2, 0, (__global float *)tensor3D_offset(&output, 0, 2, 0)); + vstore2(data3, 0, (__global float *)tensor3D_offset(&output, 0, 3, 0)); + vstore2(data4, 0, (__global float *)tensor3D_offset(&output, 0, 4, 0)); + vstore2(data5, 0, (__global float *)tensor3D_offset(&output, 0, 5, 0)); + vstore2(data6, 0, (__global float *)tensor3D_offset(&output, 0, 6, 0)); +} + +/** Computes the first stage of a radix-8 DFT on axis 0. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -507,14 +732,14 @@ kernel void fft_radix_7_first_stage_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image */ kernel void fft_radix_8_first_stage_axis_0( TENSOR3D_DECLARATION(input) @@ -542,7 +767,68 @@ kernel void fft_radix_8_first_stage_axis_0( vstore16(data, 0, (__global float *)output.ptr); } -/** Computes a stage of a radix-2 FFT. +/** Computes the first stage of a radix-8 DFT on axis 1. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + */ +kernel void fft_radix_8_first_stage_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ +) +{ + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); +#endif /* IN_PLACE */ + + // Load eight complex input values + float2 data0 = vload2(0, (__global float *)input.ptr); + float2 data1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 1, 0)); + float2 data2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2, 0)); + float2 data3 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 3, 0)); + float2 data4 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 4, 0)); + float2 data5 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 5, 0)); + float2 data6 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 6, 0)); + float2 data7 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 7, 0)); + + // Compute DFT N = 8 + DFT_8(data0, data1, data2, data3, data4, data5, data6, data7); + + // Store eight complex output values + vstore2(data0, 0, (__global float *)output.ptr); + vstore2(data1, 0, (__global float *)tensor3D_offset(&output, 0, 1, 0)); + vstore2(data2, 0, (__global float *)tensor3D_offset(&output, 0, 2, 0)); + vstore2(data3, 0, (__global float *)tensor3D_offset(&output, 0, 3, 0)); + vstore2(data4, 0, (__global float *)tensor3D_offset(&output, 0, 4, 0)); + vstore2(data5, 0, (__global float *)tensor3D_offset(&output, 0, 5, 0)); + vstore2(data6, 0, (__global float *)tensor3D_offset(&output, 0, 6, 0)); + vstore2(data7, 0, (__global float *)tensor3D_offset(&output, 0, 7, 0)); +} + +/** Computes a stage of a radix-2 FFT on axis 0. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -554,14 +840,14 @@ kernel void fft_radix_8_first_stage_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage * @param[in] Ni Nx * Ny. * @param[in] exp_const Exponent constant @@ -612,7 +898,77 @@ kernel void fft_radix_2_axis_0( vstore2(c1, 0, (__global float *)tensor3D_offset(&output, Nx, 0, 0)); } -/** Computes a stage of a radix-3 FFT. +/** Computes a stage of a radix-2 FFT on axis 1. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage + * @param[in] Ni Nx * Ny. + * @param[in] exp_const Exponent constant + */ +kernel void fft_radix_2_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ + , + uint Nx, uint Ni, float exp_const) +{ + // Each work-item computes a single radix-2 + uint kx = get_global_id(1); + + // Compute nx + uint nx = kx % Nx; + + // Compute n index + uint n = nx + (kx / Nx) * Ni; + + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input); + input.ptr += get_global_id(0) * input.stride_x + n * input.stride_y + get_global_id(2) * input.stride_z; +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); + output.ptr += get_global_id(0) * output.stride_x + n * output.stride_y + get_global_id(2) * output.stride_z; +#endif /* IN_PLACE */ + + // Load two complex input values + float2 c0 = vload2(0, (__global float *)input.ptr); + float2 c1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, Nx, 0)); + + // Compute phi + float phi = (float)nx * exp_const; + + // Multiply by twiddle factor + TWIDDLE_FACTOR_MULTIPLICATION(phi, c1); + + // Compute DFT N = 2 + DFT_2(c0, c1); + + // Store two complex output values + vstore2(c0, 0, (__global float *)output.ptr); + vstore2(c1, 0, (__global float *)tensor3D_offset(&output, 0, Nx, 0)); +} + +/** Computes a stage of a radix-3 FFT on axis 0. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -624,14 +980,14 @@ kernel void fft_radix_2_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage * @param[in] Ni Nx * Ny. * @param[in] exp_const Exponent constant @@ -685,7 +1041,80 @@ kernel void fft_radix_3_axis_0( vstore2(c2, 0, (__global float *)tensor3D_offset(&output, 2 * Nx, 0, 0)); } -/** Computes a stage of a radix-4 FFT. +/** Computes a stage of a radix-3 FFT on axis 1. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage + * @param[in] Ni Nx * Ny. + * @param[in] exp_const Exponent constant + */ +kernel void fft_radix_3_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ + , + uint Nx, uint Ni, float exp_const) +{ + // Each work-item computes a single radix-3 + uint kx = get_global_id(1); + + // Compute nx + uint nx = kx % Nx; + + // Compute n index + uint n = nx + (kx / Nx) * Ni; + + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input); + input.ptr += get_global_id(0) * input.stride_x + n * input.stride_y + get_global_id(2) * input.stride_z; +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); + output.ptr += get_global_id(0) * output.stride_x + n * output.stride_y + get_global_id(2) * output.stride_z; +#endif /* IN_PLACE */ + + // Load three complex input values + float2 c0 = vload2(0, (__global float *)input.ptr); + float2 c1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, Nx, 0)); + float2 c2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2 * Nx, 0)); + + // Compute phi + float phi = (float)nx * exp_const; + + // Multiply by twiddle factor + TWIDDLE_FACTOR_MULTIPLICATION(phi, c1); + TWIDDLE_FACTOR_MULTIPLICATION(2 * phi, c2); + + // Compute DFT N = 3 + DFT_3(c0, c1, c2); + + // Store three complex output values + vstore2(c0, 0, (__global float *)output.ptr); + vstore2(c1, 0, (__global float *)tensor3D_offset(&output, 0, Nx, 0)); + vstore2(c2, 0, (__global float *)tensor3D_offset(&output, 0, 2 * Nx, 0)); +} + +/** Computes a stage of a radix-4 FFT on axis 0. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -697,14 +1126,14 @@ kernel void fft_radix_3_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage * @param[in] Ni Nx * Ny. * @param[in] exp_const Exponent constant @@ -761,7 +1190,7 @@ kernel void fft_radix_4_axis_0( vstore2(c3, 0, (__global float *)tensor3D_offset(&output, 3 * Nx, 0, 0)); } -/** Computes a stage of a radix-5 FFT. +/** Computes a stage of a radix-4 FFT on axis 1. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -773,14 +1202,90 @@ kernel void fft_radix_4_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage + * @param[in] Ni Nx * Ny. + * @param[in] exp_const Exponent constant + */ +kernel void fft_radix_4_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ + , + uint Nx, uint Ni, float exp_const) +{ + // Each work-item computes a single radix-4 + uint kx = get_global_id(1); + + // Compute nx + uint nx = kx % Nx; + + // Compute n index + uint n = nx + (kx / Nx) * Ni; + + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input); + input.ptr += get_global_id(0) * input.stride_x + n * input.stride_y + get_global_id(2) * input.stride_z; +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); + output.ptr += get_global_id(0) * output.stride_x + n * output.stride_y + get_global_id(2) * output.stride_z; +#endif /* IN_PLACE */ + + // Load four complex input values + float2 c0 = vload2(0, (__global float *)input.ptr); + float2 c1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, Nx, 0)); + float2 c2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2 * Nx, 0)); + float2 c3 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 3 * Nx, 0)); + + // Compute phi + float phi = (float)nx * exp_const; + + // Multiply by twiddle factor + TWIDDLE_FACTOR_MULTIPLICATION(phi, c1); + TWIDDLE_FACTOR_MULTIPLICATION(2 * phi, c2); + TWIDDLE_FACTOR_MULTIPLICATION(3 * phi, c3); + + // Compute DFT N = 4 + DFT_4(c0, c1, c2, c3); + + // Store four complex output values + vstore2(c0, 0, (__global float *)output.ptr); + vstore2(c1, 0, (__global float *)tensor3D_offset(&output, 0, Nx, 0)); + vstore2(c2, 0, (__global float *)tensor3D_offset(&output, 0, 2 * Nx, 0)); + vstore2(c3, 0, (__global float *)tensor3D_offset(&output, 0, 3 * Nx, 0)); +} + +/** Computes a stage of a radix-5 FFT on axis 0. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage * @param[in] Ni Nx * Ny. * @param[in] exp_const Exponent constant @@ -840,7 +1345,7 @@ kernel void fft_radix_5_axis_0( vstore2(c4, 0, (__global float *)tensor3D_offset(&output, 4 * Nx, 0, 0)); } -/** Computes a stage of a radix-7 FFT. +/** Computes a stage of a radix-5 FFT on axis 1. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -852,14 +1357,93 @@ kernel void fft_radix_5_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage + * @param[in] Ni Nx * Ny. + * @param[in] exp_const Exponent constant + */ +kernel void fft_radix_5_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ + , + uint Nx, uint Ni, float exp_const) +{ + // Each work-item computes a single radix-5 + uint kx = get_global_id(1); + + // Compute nx + uint nx = kx % Nx; + + // Compute n index + uint n = nx + (kx / Nx) * Ni; + + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input); + input.ptr += get_global_id(0) * input.stride_x + n * input.stride_y + get_global_id(2) * input.stride_z; +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); + output.ptr += get_global_id(0) * output.stride_x + n * output.stride_y + get_global_id(2) * output.stride_z; +#endif /* IN_PLACE */ + + // Load five complex input values + float2 c0 = vload2(0, (__global float *)input.ptr); + float2 c1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, Nx, 0)); + float2 c2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2 * Nx, 0)); + float2 c3 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 3 * Nx, 0)); + float2 c4 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 4 * Nx, 0)); + + // Compute phi + float phi = (float)nx * exp_const; + + // Multiply by twiddle factor + TWIDDLE_FACTOR_MULTIPLICATION(phi, c1); + TWIDDLE_FACTOR_MULTIPLICATION(2 * phi, c2); + TWIDDLE_FACTOR_MULTIPLICATION(3 * phi, c3); + TWIDDLE_FACTOR_MULTIPLICATION(4 * phi, c4); + + // Compute DFT N = 5 + DFT_5(c0, c1, c2, c3, c4); + + // Store five complex output values + vstore2(c0, 0, (__global float *)output.ptr); + vstore2(c1, 0, (__global float *)tensor3D_offset(&output, 0, Nx, 0)); + vstore2(c2, 0, (__global float *)tensor3D_offset(&output, 0, 2 * Nx, 0)); + vstore2(c3, 0, (__global float *)tensor3D_offset(&output, 0, 3 * Nx, 0)); + vstore2(c4, 0, (__global float *)tensor3D_offset(&output, 0, 4 * Nx, 0)); +} + +/** Computes a stage of a radix-7 FFT on axis 0. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage * @param[in] Ni Nx * Ny. * @param[in] exp_const Exponent constant @@ -925,7 +1509,92 @@ kernel void fft_radix_7_axis_0( vstore2(c6, 0, (__global float *)tensor3D_offset(&output, 6 * Nx, 0, 0)); } -/** Computes a stage of a radix-8 FFT. +/** Computes a stage of a radix-7 FFT on axis 1. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage + * @param[in] Ni Nx * Ny. + * @param[in] exp_const Exponent constant + */ +kernel void fft_radix_7_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ + , + uint Nx, uint Ni, float exp_const) +{ + // Each work-item computes a single radix-7 + uint kx = get_global_id(1); + + // Compute nx + uint nx = kx % Nx; + + // Compute n index + uint n = nx + (kx / Nx) * Ni; + + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input); + input.ptr += get_global_id(0) * input.stride_x + n * input.stride_y + get_global_id(2) * input.stride_z; +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); + output.ptr += get_global_id(0) * output.stride_x + n * output.stride_y + get_global_id(2) * output.stride_z; +#endif /* IN_PLACE */ + + // Load seven complex input values + float2 c0 = vload2(0, (__global float *)input.ptr); + float2 c1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, Nx, 0)); + float2 c2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2 * Nx, 0)); + float2 c3 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 3 * Nx, 0)); + float2 c4 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 4 * Nx, 0)); + float2 c5 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 5 * Nx, 0)); + float2 c6 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 6 * Nx, 0)); + + // Compute phi + float phi = (float)nx * exp_const; + + // Multiply by twiddle factor + TWIDDLE_FACTOR_MULTIPLICATION(phi, c1); + TWIDDLE_FACTOR_MULTIPLICATION(2 * phi, c2); + TWIDDLE_FACTOR_MULTIPLICATION(3 * phi, c3); + TWIDDLE_FACTOR_MULTIPLICATION(4 * phi, c4); + TWIDDLE_FACTOR_MULTIPLICATION(5 * phi, c5); + TWIDDLE_FACTOR_MULTIPLICATION(6 * phi, c6); + + // Compute DFT N = 7 + DFT_7(c0, c1, c2, c3, c4, c5, c6); + + // Store seven complex output values + vstore2(c0, 0, (__global float *)output.ptr); + vstore2(c1, 0, (__global float *)tensor3D_offset(&output, 0, Nx, 0)); + vstore2(c2, 0, (__global float *)tensor3D_offset(&output, 0, 2 * Nx, 0)); + vstore2(c3, 0, (__global float *)tensor3D_offset(&output, 0, 3 * Nx, 0)); + vstore2(c4, 0, (__global float *)tensor3D_offset(&output, 0, 4 * Nx, 0)); + vstore2(c5, 0, (__global float *)tensor3D_offset(&output, 0, 5 * Nx, 0)); + vstore2(c6, 0, (__global float *)tensor3D_offset(&output, 0, 6 * Nx, 0)); +} + +/** Computes a stage of a radix-8 FFT on axis 0. * * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time * @@ -937,14 +1606,14 @@ kernel void fft_radix_7_axis_0( * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr - * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage * @param[in] Ni Nx * Ny. * @param[in] exp_const Exponent constant @@ -1011,4 +1680,92 @@ kernel void fft_radix_8_axis_0( vstore2(c5, 0, (__global float *)tensor3D_offset(&output, 5 * Nx, 0, 0)); vstore2(c6, 0, (__global float *)tensor3D_offset(&output, 6 * Nx, 0, 0)); vstore2(c7, 0, (__global float *)tensor3D_offset(&output, 7 * Nx, 0, 0)); +} + +/** Computes a stage of a radix-8 FFT on axis 1. + * + * @note In order to perform the FFT function "in-place", the pre-processor -DIN_PLACE must be passed at compile time + * + * @param[in,out] input_ptr Pointer to the source tensor. Supported data types: F32 + * @param[in,out] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in,out] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in,out] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in,out] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in,out] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in,out] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in,out] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] output_ptr (Optional) Pointer to the destination image. Supported data types: same as @p input_ptr + * @param[in] output_stride_x (Optional) Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x (Optional) output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y (Optional) Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y (Optional) output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z (Optional) output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes (Optional) The offset of the first element in the destination image + * @param[in] Nx The butterfly span. Products of radix order of previous radix's stage + * @param[in] Ni Nx * Ny. + * @param[in] exp_const Exponent constant + */ +kernel void fft_radix_8_axis_1( + TENSOR3D_DECLARATION(input) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(output) +#endif /* not IN_PLACE */ + , + uint Nx, uint Ni, float exp_const) +{ + // Each work-item computes a single radix-8 + uint kx = get_global_id(1); + + // Compute nx + uint nx = kx % Nx; + + // Compute n index + uint n = nx + (kx / Nx) * Ni; + + // Get tensor pointers + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input); + input.ptr += get_global_id(0) * input.stride_x + n * input.stride_y + get_global_id(2) * input.stride_z; +#ifdef IN_PLACE + Tensor3D output = input; +#else /* IN_PLACE */ + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output); + output.ptr += get_global_id(0) * output.stride_x + n * output.stride_y + get_global_id(2) * output.stride_z; +#endif /* IN_PLACE */ + + // Load eight complex input values + float2 c0 = vload2(0, (__global float *)input.ptr); + float2 c1 = vload2(0, (__global float *)tensor3D_offset(&input, 0, Nx, 0)); + float2 c2 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 2 * Nx, 0)); + float2 c3 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 3 * Nx, 0)); + float2 c4 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 4 * Nx, 0)); + float2 c5 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 5 * Nx, 0)); + float2 c6 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 6 * Nx, 0)); + float2 c7 = vload2(0, (__global float *)tensor3D_offset(&input, 0, 7 * Nx, 0)); + + // Compute phi + float phi = (float)nx * exp_const; + + // Multiply by twiddle factor + TWIDDLE_FACTOR_MULTIPLICATION(phi, c1); + TWIDDLE_FACTOR_MULTIPLICATION(2 * phi, c2); + TWIDDLE_FACTOR_MULTIPLICATION(3 * phi, c3); + TWIDDLE_FACTOR_MULTIPLICATION(4 * phi, c4); + TWIDDLE_FACTOR_MULTIPLICATION(5 * phi, c5); + TWIDDLE_FACTOR_MULTIPLICATION(6 * phi, c6); + TWIDDLE_FACTOR_MULTIPLICATION(7 * phi, c7); + + // Compute DFT N = 8 + DFT_8(c0, c1, c2, c3, c4, c5, c6, c7); + + // Store eight complex output values + vstore2(c0, 0, (__global float *)output.ptr); + vstore2(c1, 0, (__global float *)tensor3D_offset(&output, 0, Nx, 0)); + vstore2(c2, 0, (__global float *)tensor3D_offset(&output, 0, 2 * Nx, 0)); + vstore2(c3, 0, (__global float *)tensor3D_offset(&output, 0, 3 * Nx, 0)); + vstore2(c4, 0, (__global float *)tensor3D_offset(&output, 0, 4 * Nx, 0)); + vstore2(c5, 0, (__global float *)tensor3D_offset(&output, 0, 5 * Nx, 0)); + vstore2(c6, 0, (__global float *)tensor3D_offset(&output, 0, 6 * Nx, 0)); + vstore2(c7, 0, (__global float *)tensor3D_offset(&output, 0, 7 * Nx, 0)); }
\ No newline at end of file diff --git a/src/core/CL/cl_kernels/fft_digit_reverse.cl b/src/core/CL/cl_kernels/fft_digit_reverse.cl new file mode 100644 index 0000000000..040c2846bd --- /dev/null +++ b/src/core/CL/cl_kernels/fft_digit_reverse.cl @@ -0,0 +1,148 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "helpers.h" + +#if defined(VEC_SIZE) +/** Computes the digit reverse stage on axis X + * + * @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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_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 + * @param[in] idx_ptr Pointer to the index tensor. Supported data types: U32 + * @param[in] idx_stride_x Stride of the index tensor in X dimension (in bytes) + * @param[in] idx_step_x idx_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] idx_offset_first_element_in_bytes The offset of the first element in the index tensor + */ +__kernel void fft_digit_reverse_axis_0( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst), + VECTOR_DECLARATION(idx)) +{ + // Get tensor pointers + Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(src); + Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); + Vector idx = CONVERT_TO_VECTOR_STRUCT(idx); + + const unsigned int iidx = *((__global uint *)(idx.ptr)); + + // Load data +#if VEC_SIZE == 1 + float data = *((__global float *)tensor3D_offset(&src, iidx, get_global_id(1), get_global_id(2))); +#elif VEC_SIZE == 2 + float2 data = vload2(0, (__global float *)tensor3D_offset(&src, iidx, get_global_id(1), get_global_id(2))); +#else // VEC_SIZE == 1 +#error "vec_size of 1 and 2 are supported" +#endif // VEC_SIZE == 1 + + // Create result +#if VEC_SIZE == 1 + float2 res = { data, 0 }; +#elif VEC_SIZE == 2 + float2 res = data; +#else // VEC_SIZE == 1 +#error "vec_size of 1 and 2 are supported" +#endif // VEC_SIZE == 1 + + // Store result +#if defined(CONJ) + vstore2((float2)(res.s0, -res.s1), 0, (__global float *)dst.ptr); +#else // defined(CONJ) + vstore2(res, 0, (__global float *)dst.ptr); +#endif // defined(CONJ) +} + +/** Computes the digit reverse stage on axis Y + * + * @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] dst_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z dst_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 + * @param[in] idx_ptr Pointer to the index tensor. Supported data types: U32 + * @param[in] idx_stride_x Stride of the index tensor in X dimension (in bytes) + * @param[in] idx_step_x idx_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] idx_offset_first_element_in_bytes The offset of the first element in the index tensor + */ +__kernel void fft_digit_reverse_axis_1( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst), + VECTOR_DECLARATION(idx)) +{ + // Get tensor pointers + Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(src); + Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); + Vector idx = CONVERT_TO_VECTOR_STRUCT_NO_STEP(idx); + + const unsigned int iidx = *((__global uint *)vector_offset(&idx, (int)(get_global_id(1)))); + + // Load data +#if VEC_SIZE == 1 + float data = *((__global float *)tensor3D_offset(&src, get_global_id(0), iidx, get_global_id(2))); +#elif VEC_SIZE == 2 + float2 data = vload2(0, (__global float *)tensor3D_offset(&src, get_global_id(0), iidx, get_global_id(2))); +#else // VEC_SIZE == 1 +#error "vec_size of 1 and 2 are supported" +#endif // VEC_SIZE == 1 + + // Create result +#if VEC_SIZE == 1 + float2 res = { data, 0 }; +#elif VEC_SIZE == 2 + float2 res = data; +#else // VEC_SIZE == 1 +#error "vec_size of 1 and 2 are supported" +#endif // VEC_SIZE == 1 + + // Store result +#if defined(CONJ) + vstore2((float2)(res.s0, -res.s1), 0, (__global float *)dst.ptr); +#else // defined(CONJ) + vstore2(res, 0, (__global float *)dst.ptr); +#endif // defined(CONJ) +} +#endif // defined(VEC_SIZE)
\ No newline at end of file diff --git a/src/core/CL/cl_kernels/fft_scale.cl b/src/core/CL/cl_kernels/fft_scale.cl new file mode 100644 index 0000000000..bf78a26eb8 --- /dev/null +++ b/src/core/CL/cl_kernels/fft_scale.cl @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "helpers.h" + +/** Computes the fft scale stage + * + * @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 (Optional) Pointer to the destination tensor. Supported data types: same as @p src_ptr + * @param[in] dst_stride_x (Optional) Stride of the destination tensor in X dimension (in bytes) + * @param[in] dst_step_x (Optional) dst_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] dst_stride_y (Optional) Stride of the destination tensor in Y dimension (in bytes) + * @param[in] dst_step_y (Optional) dst_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] dst_stride_z (Optional) Stride of the source tensor in Z dimension (in bytes) + * @param[in] dst_step_z (Optional) dst_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] dst_offset_first_element_in_bytes (Optional) The offset of the first element in the destination tensor + * @param[in] scale Scale to apply to the complex value + */ +__kernel void fft_scale_conj( + TENSOR3D_DECLARATION(src) +#ifndef IN_PLACE + , + TENSOR3D_DECLARATION(dst) +#endif /* not IN_PLACE */ + , + float scale) +{ + // Get tensor pointers + Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); +#if defined(IN_PLACE) + Tensor3D dst = src; +#else /* IN_PLACE */ + Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); +#endif /* IN_PLACE */ + + // Store result +#if VEC_SIZE == 1 + *((__global float *)dst.ptr) = (*(__global float *)src.ptr) / scale; +#elif VEC_SIZE == 2 + // Load data + float2 data = vload2(0, (__global float *)src.ptr); + data /= scale; +#if defined(CONJ) + vstore2((float2)(data.s0, -data.s1), 0, (__global float *)dst.ptr); +#else // defined(CONJ) + vstore2(data, 0, (__global float *)dst.ptr); +#endif // defined(CONJ) +#else // VEC_SIZE == 1 +#error "vec_size of 1 and 2 are supported" +#endif // VEC_SIZE == 1 +}
\ No newline at end of file diff --git a/src/core/CL/cl_kernels/pixelwise_mul_float.cl b/src/core/CL/cl_kernels/pixelwise_mul_float.cl index 9fa540e946..d0e04b2ffe 100644 --- a/src/core/CL/cl_kernels/pixelwise_mul_float.cl +++ b/src/core/CL/cl_kernels/pixelwise_mul_float.cl @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2018 ARM Limited. + * Copyright (c) 2016-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -94,4 +94,52 @@ __kernel void pixelwise_mul_float( // Store result vstore16(res, 0, (__global DATA_TYPE_OUT *)out.ptr); } -#endif /* defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_RES) && defined(DATA_TYPE_OUT) */
\ No newline at end of file +#endif /* defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_RES) && defined(DATA_TYPE_OUT) */ + +/** Performs a pixelwise multiplication of complex float values + * + * @param[in] in1_ptr Pointer to the source image. Supported data types: F32 + * @param[in] in1_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in1_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in1_stride_z Stride of the source image in Y dimension (in bytes) + * @param[in] in1_step_z in1_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source image + * @param[in] in2_ptr Pointer to the source image. Supported data types: same as @p in1_ptr + * @param[in] in2_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in2_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in2_stride_z Stride of the source image in Y dimension (in bytes) + * @param[in] in2_step_z in2_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source image + * @param[out] out_ptr Pointer to the destination image. Supported data types: same as @p in1_ptr + * @param[in] out_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] out_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] out_stride_z Stride of the destination image in Y dimension (in bytes) + * @param[in] out_step_z out_stride_z * number of elements along Y processed per workitem(in bytes) + * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination image + */ +__kernel void pixelwise_mul_complex( + TENSOR3D_DECLARATION(in1), + TENSOR3D_DECLARATION(in2), + TENSOR3D_DECLARATION(out)) +{ + // Get pixels pointer + Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); + Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); + Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); + + // Load data + float2 vin1 = vload2(0, (__global float *)in1.ptr); + float2 vin2 = vload2(0, (__global float *)in2.ptr); + + // Perform complex multiplication + float2 res = { vin1.x *vin2.x - vin1.y * vin2.y, vin1.x *vin2.y + vin2.x * vin1.y }; + + // Store result + vstore2(res, 0, (__global float *)out.ptr); +} diff --git a/src/core/CL/cl_kernels/reduction_operation.cl b/src/core/CL/cl_kernels/reduction_operation.cl index b4ede25296..2651123cf5 100644 --- a/src/core/CL/cl_kernels/reduction_operation.cl +++ b/src/core/CL/cl_kernels/reduction_operation.cl @@ -307,6 +307,10 @@ __kernel void reduction_operation_z( VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) res = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); +#if defined(COMPLEX) + VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) + res1 = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 8, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); +#endif // defined(COMPLEX) #if defined(SUM_SQUARE) res *= res; #endif // defined(SUM_SQUARE) @@ -320,6 +324,11 @@ __kernel void reduction_operation_z( VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) in = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, z)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); +#if defined(COMPLEX) + VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) + in1 = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 8, 0, z)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); +#endif // defined(COMPLEX) + #if defined(ARG_MAX) uint16 cond_conv = CONVERT(isgreater(in, res), uint16); indx = select(indx, z, cond_conv); @@ -334,8 +343,11 @@ __kernel void reduction_operation_z( #endif // defined(SUM_SQUARE) #if defined(PROD) res *= in; -#else //!defined(PROD) +#else //!defined(PROD) res += in; +#if defined(COMPLEX) + res1 += in1; +#endif // defined(COMPLEX) #endif //defined(PROD) #endif // defined(ARG_MAX) || defined(ARG_MIN) } @@ -348,6 +360,9 @@ __kernel void reduction_operation_z( res /= DEPTH; #endif // defined(MEAN) vstore16(CONVERT(res, VEC_DATA_TYPE(DATA_TYPE, 16)), 0, (__global DATA_TYPE *)output.ptr); +#if defined(COMPLEX) + vstore16(CONVERT(res1, VEC_DATA_TYPE(DATA_TYPE, 16)), 0, (__global DATA_TYPE *)tensor3D_offset(&output, 8, 0, 0)); +#endif // defined(COMPLEX) #endif // defined(ARG_MAX) || defined(ARG_MIN) } #endif /* defined(DEPTH) */ diff --git a/src/core/CL/kernels/CLFFTDigitReverseKernel.cpp b/src/core/CL/kernels/CLFFTDigitReverseKernel.cpp index d72647c3c9..b04293db5b 100644 --- a/src/core/CL/kernels/CLFFTDigitReverseKernel.cpp +++ b/src/core/CL/kernels/CLFFTDigitReverseKernel.cpp @@ -34,16 +34,19 @@ namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *idx, unsigned int axis) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *idx, const FFTDigitReverseKernelInfo &config) { ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() != DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(input->num_channels() != 1 && input->num_channels() != 2); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(idx, 1, DataType::U32); - ARM_COMPUTE_RETURN_ERROR_ON(axis != 0); + ARM_COMPUTE_RETURN_ERROR_ON(std::set<unsigned int>({ 0, 1 }).count(config.axis) == 0); + ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[config.axis] != idx->tensor_shape().x()); // Checks performed when output is configured if((output != nullptr) && (output->total_size() != 0)) { + ARM_COMPUTE_RETURN_ERROR_ON(output->num_channels() != 2); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); } @@ -51,11 +54,11 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *idx, unsigned int axis) +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *idx, const FFTDigitReverseKernelInfo &config) { - ARM_COMPUTE_UNUSED(idx, axis); + ARM_COMPUTE_UNUSED(idx, config); - auto_init_if_empty(*output, *input); + auto_init_if_empty(*output, input->clone()->set_num_channels(2)); Window win = calculate_max_window(*output, Steps()); output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); @@ -69,25 +72,30 @@ CLFFTDigitReverseKernel::CLFFTDigitReverseKernel() { } -void CLFFTDigitReverseKernel::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *idx, unsigned int axis) +void CLFFTDigitReverseKernel::configure(const ICLTensor *input, ICLTensor *output, const ICLTensor *idx, const FFTDigitReverseKernelInfo &config) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, idx); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), idx->info(), axis)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), idx->info(), config)); _input = input; _output = output; _idx = idx; // Create kernel - _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("digit_reverse")); + CLBuildOptions build_opts; + build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(input->info()->num_channels())); + build_opts.add_option_if(config.conjugate, "-DCONJ"); + std::string kernel_name = "fft_digit_reverse_axis_" + support::cpp11::to_string(config.axis); + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), output->info(), idx->info(), axis); + auto win_config = validate_and_configure_window(input->info(), output->info(), idx->info(), config); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); // Set config_id for enabling LWS tuning - _config_id = "digit_reverse_"; + _config_id = kernel_name; + _config_id += "_"; _config_id += lower_string(string_from_data_type(input->info()->data_type())); _config_id += "_"; _config_id += support::cpp11::to_string(input->info()->dimension(0)); @@ -95,10 +103,10 @@ void CLFFTDigitReverseKernel::configure(const ICLTensor *input, ICLTensor *outpu _config_id += support::cpp11::to_string(input->info()->dimension(1)); } -Status CLFFTDigitReverseKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *idx, unsigned int axis) +Status CLFFTDigitReverseKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *idx, const FFTDigitReverseKernelInfo &config) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, idx, axis)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), idx->clone().get(), axis).first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, idx, config)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), idx->clone().get(), config).first); return Status{}; } diff --git a/src/core/CL/kernels/CLFFTRadixStageKernel.cpp b/src/core/CL/kernels/CLFFTRadixStageKernel.cpp index 87a12b9da9..83d55b7092 100644 --- a/src/core/CL/kernels/CLFFTRadixStageKernel.cpp +++ b/src/core/CL/kernels/CLFFTRadixStageKernel.cpp @@ -38,12 +38,13 @@ namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const FFTRadixStageKernelDescriptor &config) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const FFTRadixStageKernelInfo &config) { ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(config.axis != 0); ARM_COMPUTE_RETURN_ERROR_ON(CLFFTRadixStageKernel::supported_radix().count(config.radix) == 0); + ARM_COMPUTE_RETURN_ERROR_ON(std::set<unsigned int>({ 0, 1 }).count(config.axis) == 0); + ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[config.axis] % config.radix); // Checks performed when output is configured if((output != nullptr) && (output->total_size() != 0)) @@ -55,14 +56,18 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const FFTRadixStageKernelDescriptor &config) +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const FFTRadixStageKernelInfo &config) { if(output != nullptr) { auto_init_if_empty(*output, *input); } - Window win = calculate_max_window(*input, Steps(config.radix)); + // Setup window steps + Steps steps; + steps.set(config.axis, config.radix); + + Window win = calculate_max_window(*input, steps); if(output != nullptr) { output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); @@ -77,7 +82,7 @@ CLFFTRadixStageKernel::CLFFTRadixStageKernel() { } -void CLFFTRadixStageKernel::configure(ICLTensor *input, ICLTensor *output, const FFTRadixStageKernelDescriptor &config) +void CLFFTRadixStageKernel::configure(ICLTensor *input, ICLTensor *output, const FFTRadixStageKernelInfo &config) { ARM_COMPUTE_ERROR_ON_NULLPTR(input); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr, config)); @@ -105,7 +110,7 @@ void CLFFTRadixStageKernel::configure(ICLTensor *input, ICLTensor *output, const unsigned int idx = (1 + (_run_in_place ? 0 : 1)) * num_arguments_per_3D_tensor(); // Skip the input and output parameters _kernel.setArg<cl_uint>(idx++, config.Nx); _kernel.setArg<cl_uint>(idx++, Ni); - _kernel.setArg<cl_float>(idx++, exp_const); + _kernel.setArg<cl_float>(idx, exp_const); } // Configure kernel window @@ -123,7 +128,7 @@ void CLFFTRadixStageKernel::configure(ICLTensor *input, ICLTensor *output, const _config_id += support::cpp11::to_string(input->info()->dimension(1)); } -Status CLFFTRadixStageKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const FFTRadixStageKernelDescriptor &config) +Status CLFFTRadixStageKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const FFTRadixStageKernelInfo &config) { const bool run_in_place = (output == nullptr) || (output == input); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, config)); diff --git a/src/core/CL/kernels/CLFFTScaleKernel.cpp b/src/core/CL/kernels/CLFFTScaleKernel.cpp new file mode 100644 index 0000000000..59f1fd7502 --- /dev/null +++ b/src/core/CL/kernels/CLFFTScaleKernel.cpp @@ -0,0 +1,143 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/CL/kernels/CLFFTScaleKernel.h" + +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/CLValidate.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Window.h" + +namespace arm_compute +{ +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) +{ + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F32); + + // Checks performed when output is configured + if((output != nullptr) && (output->total_size() != 0)) + { + ARM_COMPUTE_RETURN_ERROR_ON(output->num_channels() != 1 && output->num_channels() != 2); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) +{ + // Configure kernel window + Window win = calculate_max_window(*input, Steps()); + + if(output != nullptr) + { + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, *input->clone()); + + // CLFFTScaleKernel doesn't need padding so update_window_and_padding() can be skipped + Coordinates coord; + coord.set_num_dimensions(output->num_dimensions()); + output->set_valid_region(ValidRegion(coord, output->tensor_shape())); + } + + return std::make_pair(Status{}, win); +} +} // namespace + +CLFFTScaleKernel::CLFFTScaleKernel() + : _input(nullptr), _output(nullptr), _run_in_place(false) +{ +} + +void CLFFTScaleKernel::configure(ICLTensor *input, ICLTensor *output, const FFTScaleKernelInfo &config) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr)); + + _input = input; + _output = output; + _run_in_place = (output == nullptr) || (output == input); + + // Create kernel + CLBuildOptions build_opts; + build_opts.add_option_if(_run_in_place, "-DIN_PLACE"); + build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(output != nullptr ? output->info()->num_channels() : input->info()->num_channels())); + build_opts.add_option_if(config.conjugate, "-DCONJ"); + std::string kernel_name = "fft_scale_conj"; + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); + + // Set static arguments + unsigned int idx = (1 + (_run_in_place ? 0 : 1)) * num_arguments_per_3D_tensor(); // Skip the input and output parameters + _kernel.setArg<cl_float>(idx, config.scale); + + // Configure kernel window + auto win_config = validate_and_configure_window(input->info(), _run_in_place ? nullptr : output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure_internal(win_config.second); + + // Set config_id for enabling LWS tuning + _config_id = kernel_name; + _config_id += "_"; + _config_id += lower_string(string_from_data_type(input->info()->data_type())); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(1)); +} + +Status CLFFTScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const FFTScaleKernelInfo &config) +{ + ARM_COMPUTE_UNUSED(config); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first); + + return Status{}; +} + +void CLFFTScaleKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); + Window slice = collapsed.first_slice_window_3D(); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, slice); + if(!_run_in_place) + { + add_3D_tensor_argument(idx, _output, slice); + } + enqueue(queue, *this, slice, lws_hint()); + } + while(collapsed.slide_window_slice_3D(slice)); +} +} // namespace arm_compute diff --git a/src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp b/src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp index 286b94ebdc..9fa92bde75 100644 --- a/src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp +++ b/src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2018 ARM Limited. + * Copyright (c) 2016-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -38,8 +38,8 @@ #include <set> #include <string> -using namespace arm_compute; - +namespace arm_compute +{ namespace { constexpr unsigned int num_elems_processed_per_iteration = 16; @@ -276,3 +276,139 @@ BorderSize CLPixelWiseMultiplicationKernel::border_size() const const unsigned int border = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize); return BorderSize(0, border, 0, 0); } + +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration_complex = 1; + +Status validate_arguments_complex(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 2, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 2, DataType::F32); + + const TensorShape &out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); + + // Validate in case of configured output + if(output->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 2, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), "Wrong shape for output"); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window_complex(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output) +{ + const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2); + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; + + // Auto initialize output if not initialized + const TensorInfo out_info(out_shape, input1->num_channels(), input1->data_type()); + auto_init_if_empty(*output, out_info); + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration_complex)); + Window win_input1 = win.broadcast_if_dimension_le_one(*input1); + Window win_input2 = win.broadcast_if_dimension_le_one(*input2); + + AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration_complex); + AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration_complex); + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_complex); + + bool window_changed = update_window_and_padding(win_input1, input1_access) + || update_window_and_padding(win_input2, input2_access) + || update_window_and_padding(win, output_access); + + output_access.set_valid_region(win, valid_region); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLComplexPixelWiseMultiplicationKernel::CLComplexPixelWiseMultiplicationKernel() + : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLComplexPixelWiseMultiplicationKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(input1->info(), input2->info(), output->info())); + + // Configure kernel window + auto win_config = validate_and_configure_window_complex(input1->info(), input2->info(), output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + + _input1 = input1; + _input2 = input2; + _output = output; + + // Create kernel + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("pixelwise_mul_complex")); + + ICLKernel::configure_internal(win_config.second); +} + +Status CLComplexPixelWiseMultiplicationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_complex(input1, input2, output)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_complex(input1->clone().get(), input2->clone().get(), output->clone().get()).first); + + return Status{}; +} + +void CLComplexPixelWiseMultiplicationKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &in_shape1 = _input1->info()->tensor_shape(); + const TensorShape &in_shape2 = _input2->info()->tensor_shape(); + const TensorShape &out_shape = _output->info()->tensor_shape(); + + bool can_collapse = true; + if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) + { + can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); + for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d) + { + can_collapse = (in_shape1[d] == in_shape2[d]); + } + } + + bool has_collapsed = false; + Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window; + + const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; + const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; + + Window slice = collapsed.first_slice_window_3D(); + Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); + Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input1, slice_input1); + add_3D_tensor_argument(idx, _input2, slice_input2); + add_3D_tensor_argument(idx, _output, slice); + enqueue(queue, *this, slice); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); + } + while(collapsed.slide_window_slice_3D(slice)); +} + +BorderSize CLComplexPixelWiseMultiplicationKernel::border_size() const +{ + const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); + const unsigned int border = std::min<unsigned int>(num_elems_processed_per_iteration_complex - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} +} // namespace arm_compute
\ No newline at end of file diff --git a/src/core/CL/kernels/CLReductionOperationKernel.cpp b/src/core/CL/kernels/CLReductionOperationKernel.cpp index 9f498b8273..db4850f14e 100644 --- a/src/core/CL/kernels/CLReductionOperationKernel.cpp +++ b/src/core/CL/kernels/CLReductionOperationKernel.cpp @@ -47,7 +47,14 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, u { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); + if(input->num_channels() == 1) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F32); + } ARM_COMPUTE_RETURN_ERROR_ON_MSG(op == ReductionOperation::SUM_SQUARE && input->data_type() == DataType::QASYMM8, "Not supported reduction operation for QASYMM8"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); @@ -77,7 +84,7 @@ std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITe output_shape.set(axis, 1); const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX); DataType output_data_type = is_arg_min_max ? DataType::U32 : input->data_type(); - auto_init_if_empty(*output, output_shape, 1, output_data_type, input->quantization_info()); + auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); const unsigned int num_elems_processed_per_iteration = (is_data_type_quantized(input->data_type()) && (axis == 0)) ? 1 : 16; Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); @@ -160,6 +167,7 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MAX, "-DARG_MAX"); build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MIN, "-DARG_MIN"); build_opts.add_option_if(op == ReductionOperation::PROD, "-DPROD"); + build_opts.add_option_if(input->info()->num_channels() == 2, "-DCOMPLEX"); switch(op) { diff --git a/src/runtime/CL/functions/CLFFT1D.cpp b/src/runtime/CL/functions/CLFFT1D.cpp index d893cd3d1b..67111e7e5c 100644 --- a/src/runtime/CL/functions/CLFFT1D.cpp +++ b/src/runtime/CL/functions/CLFFT1D.cpp @@ -31,7 +31,7 @@ namespace arm_compute { CLFFT1D::CLFFT1D(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), _digit_reversed_input(), _digit_reverse_indices(), _digit_reverse_kernel(), _fft_kernels(), _num_ffts(0) + : _memory_group(std::move(memory_manager)), _digit_reverse_kernel(), _fft_kernels(), _scale_kernel(), _digit_reversed_input(), _digit_reverse_indices(), _num_ffts(0), _run_scale(false) { } @@ -46,11 +46,18 @@ void CLFFT1D::configure(const ICLTensor *input, ICLTensor *output, const FFT1DIn const auto decomposed_vector = arm_compute::helpers::fft::decompose_stages(N, supported_radix); ARM_COMPUTE_ERROR_ON(decomposed_vector.empty()); + // Flags + _run_scale = config.direction == FFTDirection::Inverse; + const bool is_c2r = input->info()->num_channels() == 2 && output->info()->num_channels() == 1; + // Configure digit reverse + FFTDigitReverseKernelInfo digit_reverse_config; + digit_reverse_config.axis = config.axis; + digit_reverse_config.conjugate = config.direction == FFTDirection::Inverse; TensorInfo digit_reverse_indices_info(TensorShape(input->info()->tensor_shape()[config.axis]), 1, DataType::U32); _digit_reverse_indices.allocator()->init(digit_reverse_indices_info); _memory_group.manage(&_digit_reversed_input); - _digit_reverse_kernel.configure(input, &_digit_reversed_input, &_digit_reverse_indices, config.axis); + _digit_reverse_kernel.configure(input, &_digit_reversed_input, &_digit_reverse_indices, digit_reverse_config); // Create and configure FFT kernels unsigned int Nx = 1; @@ -60,16 +67,25 @@ void CLFFT1D::configure(const ICLTensor *input, ICLTensor *output, const FFT1DIn { const unsigned int radix_for_stage = decomposed_vector.at(i); - FFTRadixStageKernelDescriptor fft_kernel_desc; - fft_kernel_desc.axis = config.axis; - fft_kernel_desc.radix = radix_for_stage; - fft_kernel_desc.Nx = Nx; - fft_kernel_desc.is_first_stage = (i == 0); - _fft_kernels[i].configure(&_digit_reversed_input, i == (_num_ffts - 1) ? output : nullptr, fft_kernel_desc); + FFTRadixStageKernelInfo fft_kernel_info; + fft_kernel_info.axis = config.axis; + fft_kernel_info.radix = radix_for_stage; + fft_kernel_info.Nx = Nx; + fft_kernel_info.is_first_stage = (i == 0); + _fft_kernels[i].configure(&_digit_reversed_input, ((i == (_num_ffts - 1)) && !is_c2r) ? output : nullptr, fft_kernel_info); Nx *= radix_for_stage; } + // Configure scale kernel + if(_run_scale) + { + FFTScaleKernelInfo scale_config; + scale_config.scale = static_cast<float>(N); + scale_config.conjugate = config.direction == FFTDirection::Inverse; + is_c2r ? _scale_kernel.configure(&_digit_reversed_input, output, scale_config) : _scale_kernel.configure(output, nullptr, scale_config); + } + // Allocate tensors _digit_reversed_input.allocator()->allocate(); _digit_reverse_indices.allocator()->allocate(); @@ -84,8 +100,9 @@ void CLFFT1D::configure(const ICLTensor *input, ICLTensor *output, const FFT1DIn Status CLFFT1D::validate(const ITensorInfo *input, const ITensorInfo *output, const FFT1DInfo &config) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(config.axis != 0); + ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() != DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(input->num_channels() != 1 && input->num_channels() != 2); + ARM_COMPUTE_RETURN_ERROR_ON(std::set<unsigned int>({ 0, 1 }).count(config.axis) == 0); // Check if FFT is decomposable const auto supported_radix = CLFFTRadixStageKernel::supported_radix(); @@ -96,6 +113,8 @@ Status CLFFT1D::validate(const ITensorInfo *input, const ITensorInfo *output, co // Checks performed when output is configured if((output != nullptr) && (output->total_size() != 0)) { + ARM_COMPUTE_RETURN_ERROR_ON(output->num_channels() == 1 && input->num_channels() == 1); + ARM_COMPUTE_RETURN_ERROR_ON(output->num_channels() != 1 && output->num_channels() != 2); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); } @@ -107,11 +126,19 @@ void CLFFT1D::run() { MemoryGroupResourceScope scope_mg(_memory_group); + // Run digit reverse CLScheduler::get().enqueue(_digit_reverse_kernel, false); + // Run radix kernels for(unsigned int i = 0; i < _num_ffts; ++i) { - CLScheduler::get().enqueue(_fft_kernels[i], i == (_num_ffts - 1)); + CLScheduler::get().enqueue(_fft_kernels[i], i == (_num_ffts - 1) && !_run_scale); + } + + // Run output scaling + if(_run_scale) + { + CLScheduler::get().enqueue(_scale_kernel, true); } } } // namespace arm_compute diff --git a/src/runtime/CL/functions/CLFFT2D.cpp b/src/runtime/CL/functions/CLFFT2D.cpp new file mode 100644 index 0000000000..4300fb4e32 --- /dev/null +++ b/src/runtime/CL/functions/CLFFT2D.cpp @@ -0,0 +1,95 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/runtime/CL/functions/CLFFT2D.h" + +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/runtime/CL/CLScheduler.h" + +namespace arm_compute +{ +CLFFT2D::CLFFT2D(std::shared_ptr<IMemoryManager> memory_manager) + : _memory_group(memory_manager), _first_pass_func(memory_manager), _second_pass_func(memory_manager), _first_pass_tensor() +{ +} + +void CLFFT2D::configure(const ICLTensor *input, ICLTensor *output, const FFT2DInfo &config) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON(CLFFT2D::validate(input->info(), output->info(), config)); + + // Setup first pass + FFT1DInfo first_pass_config; + first_pass_config.axis = config.axes.first; + first_pass_config.direction = config.direction; + _memory_group.manage(&_first_pass_tensor); + _first_pass_func.configure(input, &_first_pass_tensor, first_pass_config); + + // Setup second pass + FFT1DInfo second_pass_config; + second_pass_config.axis = config.axes.second; + second_pass_config.direction = config.direction; + _second_pass_func.configure(&_first_pass_tensor, output, second_pass_config); + _first_pass_tensor.allocator()->allocate(); +} + +Status CLFFT2D::validate(const ITensorInfo *input, const ITensorInfo *output, const FFT2DInfo &config) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); + + // Create intermediate tensor info + TensorInfo first_pass_tensor(input->clone()->set_is_resizable(true).reset_padding().set_num_channels(2)); + + // Validate first pass + FFT1DInfo first_pass_config; + first_pass_config.axis = config.axes.first; + first_pass_config.direction = config.direction; + ARM_COMPUTE_RETURN_ON_ERROR(CLFFT1D::validate(input, &first_pass_tensor, first_pass_config)); + + // Validate second pass + FFT1DInfo second_pass_config; + second_pass_config.axis = config.axes.second; + second_pass_config.direction = config.direction; + ARM_COMPUTE_RETURN_ON_ERROR(CLFFT1D::validate(&first_pass_tensor, output, second_pass_config)); + + // Checks performed when output is configured + if((output != nullptr) && (output->total_size() != 0)) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + + return Status{}; +} + +void CLFFT2D::run() +{ + _memory_group.acquire(); + + _first_pass_func.run(); + _second_pass_func.run(); + + _memory_group.release(); +} +} // namespace arm_compute diff --git a/src/runtime/CL/functions/CLFFTConvolutionLayer.cpp b/src/runtime/CL/functions/CLFFTConvolutionLayer.cpp new file mode 100644 index 0000000000..441c1c7214 --- /dev/null +++ b/src/runtime/CL/functions/CLFFTConvolutionLayer.cpp @@ -0,0 +1,380 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/runtime/CL/functions/CLFFTConvolutionLayer.h" + +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/helpers/fft.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/CL/CLScheduler.h" +#include "arm_compute/runtime/CPP/CPPScheduler.h" + +namespace arm_compute +{ +namespace +{ +int pad_decomposable(int N) +{ + const auto supported_radix = CLFFTRadixStageKernel::supported_radix(); + + int pad = 0; + bool is_decomposed = false; + while(!is_decomposed) + { + const auto decomposed_vector = arm_compute::helpers::fft::decompose_stages(N++, supported_radix); + is_decomposed = !decomposed_vector.empty(); + if(!is_decomposed) + { + ++pad; + } + } + return pad; +} +} // namespace +CLFFTConvolutionLayer::CLFFTConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) + : _memory_group(memory_manager), + _flip_weights_func(), + _permute_input_func(), + _permute_output_func(), + _permute_weights_func(), + _permute_bias_func(), + _pad_input_func(), + _pad_weights_func(), + _transform_input_func(memory_manager), + _transform_weights_func(memory_manager), + _itransform_output_func(memory_manager), + _prod_func(), + _reduce_func(), + _extract_output_func(), + _bias_add_func(), + _activation_layer_func(), + _permuted_input(), + _permuted_weights(), + _permuted_bias(), + _permuted_output(), + _padded_input(), + _padded_weights(), + _flip_axis(), + _flipped_weights(), + _transformed_input(), + _transformed_weights(), + _input_weights_product(), + _output_product(), + _output_reduced(), + _itransformed_output(), + _reshaped_output(), + _bias_output(), + _original_weights(nullptr), + _original_bias(nullptr), + _is_activationlayer_enabled(false), + _needs_permute(false), + _has_bias(false), + _is_prepared(false) +{ +} + +void CLFFTConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info) +{ + _original_weights = weights; + _original_bias = biases; + + // Flat if bias addition is required + _has_bias = biases != nullptr; + + // Get indices for the width and height + const size_t idx_width = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); + + // Input shape, kernel size and output tile + const Size2D input_dims = Size2D(input->info()->tensor_shape()[idx_width], input->info()->tensor_shape()[idx_height]); + const Size2D kernel_size = Size2D(weights->info()->tensor_shape()[idx_width], weights->info()->tensor_shape()[idx_height]); + const Size2D pad_valid = Size2D(pad_decomposable(input_dims.x() + kernel_size.x() - 1), + pad_decomposable(input_dims.y() + kernel_size.y() - 1)); + // Tensors to use + ICLTensor *input_to_use = input; + const ICLTensor *weights_to_use = weights; + ICLTensor *output_to_use = _has_bias ? &_bias_output : output; + + // Permute bias + _permute_bias_func.configure(biases, &_permuted_bias, PermutationVector(1U, 2U, 0U)); + _permuted_bias.info()->set_data_layout(DataLayout::NCHW); + + // Permute input if needed + _needs_permute = input->info()->data_layout() == DataLayout::NHWC; + if(_needs_permute) + { + _memory_group.manage(&_permuted_input); + // Configure the function to transform the input tensor from NHWC -> NCHW + _permute_input_func.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); + _permuted_input.info()->set_data_layout(DataLayout::NCHW); + + // Configure the function to transform the weights tensor from HWI -> IHW + _permute_weights_func.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); + _permuted_weights.info()->set_data_layout(DataLayout::NCHW); + + input_to_use = &_permuted_input; + weights_to_use = &_permuted_weights; + } + + // Flip weights + _flipped_weights.allocator()->init(weights_to_use->info()->clone()->set_is_resizable(true).reset_padding()); + _flip_axis.allocator()->init(TensorInfo(TensorShape(2U), 1, DataType::U32)); + _flip_weights_func.configure(weights_to_use, &_flipped_weights, &_flip_axis); + + // Pad weights + const PaddingList padding_w = { { 0, input_dims.x() + pad_valid.x() - 1 }, { 0, input_dims.y() + pad_valid.y() - 1 } }; + _pad_weights_func.configure(&_flipped_weights, &_padded_weights, padding_w); + + // Transform weights + _transform_weights_func.configure(&_padded_weights, &_transformed_weights, FFT2DInfo()); + + // Pad input + const PaddingList padding_in = { { 0, kernel_size.x() + pad_valid.x() - 1 }, { 0, kernel_size.y() + pad_valid.y() - 1 } }; + _memory_group.manage(&_padded_input); + _pad_input_func.configure(input_to_use, &_padded_input, padding_in); + if(_needs_permute) + { + _permuted_input.allocator()->allocate(); + } + + // Transform input + _memory_group.manage(&_transformed_input); + _transform_input_func.configure(&_padded_input, &_transformed_input, FFT2DInfo()); + _padded_input.allocator()->allocate(); + + // Perform product + _memory_group.manage(&_output_product); + _prod_func.configure(&_transformed_input, &_transformed_weights, &_output_product); + _transformed_input.allocator()->allocate(); + + // Perform reduction + _memory_group.manage(&_output_reduced); + _reduce_func.configure(&_output_product, &_output_reduced, 2, ReductionOperation::SUM); + _output_product.allocator()->allocate(); + + // Transform output + _memory_group.manage(&_itransformed_output); + FFT2DInfo itranform_info; + itranform_info.direction = FFTDirection::Inverse; + _itransformed_output.allocator()->init(_output_reduced.info()->clone()->set_is_resizable(true).set_num_channels(1).reset_padding()); + _itransform_output_func.configure(&_output_reduced, &_itransformed_output, itranform_info); + _output_reduced.allocator()->allocate(); + + // Reshape output + TensorShape reshaped_shape = _itransformed_output.info()->tensor_shape(); + reshaped_shape.remove_dimension(2); + _reshaped_output.allocator()->init(_itransformed_output.info()->clone()->set_tensor_shape(reshaped_shape)); + + // Extract correct region + const int start_left = kernel_size.x() - conv_info.pad_left() - 1; + const int start_top = kernel_size.y() - conv_info.pad_top() - 1; + const int end_right = _reshaped_output.info()->tensor_shape().x() - (kernel_size.x() - conv_info.pad_right() - 1) - pad_valid.x(); + const int end_botton = _reshaped_output.info()->tensor_shape().y() - (kernel_size.y() - conv_info.pad_bottom() - 1) - pad_valid.y(); + if(_has_bias) + { + _memory_group.manage(&_bias_output); + } + else if(_needs_permute) + { + output_to_use = &_permuted_output; + _memory_group.manage(&_permuted_output); + } + _extract_output_func.configure(&_reshaped_output, output_to_use, Coordinates(start_left, start_top), Coordinates(end_right, end_botton)); + _itransformed_output.allocator()->allocate(); + + // Add bias + if(biases != nullptr) + { + output_to_use = output; + if(_needs_permute) + { + output_to_use = &_permuted_output; + _memory_group.manage(&_permuted_output); + } + auto_init_if_empty(*output_to_use->info(), *_bias_output.info()); + _bias_add_func.configure(&_bias_output, &_permuted_bias, output_to_use, ConvertPolicy::WRAP); + _bias_output.allocator()->allocate(); + } + + // Permute output + if(_needs_permute) + { + // Configure the function to transform the convoluted output to ACL's native ordering format NCHW + _permuted_output.info()->set_data_layout(DataLayout::NCHW); + _permute_output_func.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); + + // Allocate tensors + _permuted_output.allocator()->allocate(); + } + + // Configure Activation Layer + _is_activationlayer_enabled = act_info.enabled(); + if(_is_activationlayer_enabled) + { + _activation_layer_func.configure(output, nullptr, act_info); + } + + // Setup flip axis data + _flip_axis.allocator()->allocate(); + _flip_axis.map(true); + auto axis_data = reinterpret_cast<uint32_t *>(_flip_axis.buffer()); + axis_data[0] = 0; + axis_data[1] = 1; + _flip_axis.unmap(); +} + +Status CLFFTConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + + // Get indices for the width and height + const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + + // Input shape, kernel size and output tile + const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]); + + // Strides + const auto strides = conv_info.stride(); + ARM_COMPUTE_RETURN_ERROR_ON(strides.first != strides.second && strides.first != 1); + ARM_COMPUTE_RETURN_ERROR_ON(kernel_size.x() != kernel_size.y()); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_left() != (kernel_size.x() / 2) || conv_info.pad_right() != (kernel_size.x() / 2)); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_top() != (kernel_size.y() / 2) || conv_info.pad_bottom() != (kernel_size.y() / 2)); + + // Validate biases + if(biases != nullptr) + { + const size_t idx_channels = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[idx_channels] != biases->tensor_shape().x()); + } + + // Checks performed when output is configured + if((output != nullptr) && (output->total_size() != 0)) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + + // Validate Activation Layer + if(act_info.enabled()) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info)); + } + } + + return Status{}; +} + +void CLFFTConvolutionLayer::run() +{ + prepare(); + + _memory_group.acquire(); + + // Transform input + if(_needs_permute) + { + _permute_input_func.run(); + } + _pad_input_func.run(); + _transform_input_func.run(); + + // Perform operations to frequency domain + _prod_func.run(); + _reduce_func.run(); + + // Transform output + _itransform_output_func.run(); + _reshaped_output.allocator()->import_memory(_itransformed_output.cl_buffer()); + _extract_output_func.run(); + // Add bias + if(_has_bias) + { + _bias_add_func.run(); + } + if(_needs_permute) + { + _permute_output_func.run(); + } + + // Run activation layer + if(_is_activationlayer_enabled) + { + _activation_layer_func.run(); + } + + _memory_group.release(); +} + +void CLFFTConvolutionLayer::prepare() +{ + if(!_is_prepared) + { + // Permute bias to NCHW + if(_original_bias != nullptr) + { + _permuted_bias.allocator()->allocate(); + _permute_bias_func.run(); + _original_bias->mark_as_unused(); + } + + const ICLTensor *cur_weights = _original_weights; + // Permute weights + if(_needs_permute) + { + ARM_COMPUTE_ERROR_ON(!cur_weights->is_used()); + + _permuted_weights.allocator()->allocate(); + _permute_weights_func.run(); + cur_weights->mark_as_unused(); + cur_weights = &_permuted_weights; + } + + // Flip weights + _flipped_weights.allocator()->allocate(); + _flip_weights_func.run(); + cur_weights->mark_as_unused(); + + // Pad weights + _padded_weights.allocator()->allocate(); + _pad_weights_func.run(); + _flipped_weights.mark_as_unused(); + CLScheduler::get().queue().finish(); + _flipped_weights.allocator()->free(); + + // Transform weights to frequence domain + _transformed_weights.allocator()->allocate(); + _transform_weights_func.run(); + _padded_weights.mark_as_unused(); + CLScheduler::get().queue().finish(); + _padded_weights.allocator()->free(); + + _is_prepared = true; + } +} +} // namespace arm_compute diff --git a/src/runtime/CL/functions/CLPixelWiseMultiplication.cpp b/src/runtime/CL/functions/CLPixelWiseMultiplication.cpp index b4c20db3da..959464ce14 100644 --- a/src/runtime/CL/functions/CLPixelWiseMultiplication.cpp +++ b/src/runtime/CL/functions/CLPixelWiseMultiplication.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2018 ARM Limited. + * Copyright (c) 2016-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -29,8 +29,8 @@ #include <utility> -using namespace arm_compute; - +namespace arm_compute +{ void CLPixelWiseMultiplication::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy) { @@ -54,3 +54,26 @@ Status CLPixelWiseMultiplication::validate(const ITensorInfo *input1, const ITen { return CLPixelWiseMultiplicationKernel::validate(input1, input2, output, scale, overflow_policy, rounding_policy); } + +void CLComplexPixelWiseMultiplication::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique<CLComplexPixelWiseMultiplicationKernel>(); + k->configure(input1, input2, output); + _kernel = std::move(k); + + if(output->info()->dimension(0) > 1) + { + ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2; + + if(broadcasted_info->info()->dimension(0) == 1) + { + _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE); + } + } +} + +Status CLComplexPixelWiseMultiplication::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + return CLComplexPixelWiseMultiplicationKernel::validate(input1, input2, output); +} +} // namespace arm_compute
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