From 9fb1159e2501f276a27d32264bece54b3d42d258 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Thu, 26 Apr 2018 20:34:58 +0100 Subject: COMPMID-1074: Rename WinograLayer.cpp to WinogradConvolutionLayer.cpp Change-Id: Iccac7cd6cb458469568d0cd6fb36b262353f4188 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129261 Tested-by: Jenkins Reviewed-by: Pablo Tello --- arm_compute/core/NEON/NEKernels.h | 2 +- .../kernels/NEWinogradConvolutionLayerKernel.h | 553 ++++++++++++++++++++ .../core/NEON/kernels/NEWinogradLayerKernel.h | 553 -------------------- arm_compute/runtime/NEON/NEFunctions.h | 2 +- .../runtime/NEON/functions/NEConvolutionLayer.h | 8 +- .../NEON/functions/NEWinogradConvolutionLayer.h | 122 +++++ .../runtime/NEON/functions/NEWinogradLayer.h | 122 ----- docs/00_introduction.dox | 9 +- .../kernels/NEWinogradConvolutionLayerKernel.cpp | 561 +++++++++++++++++++++ src/core/NEON/kernels/NEWinogradLayerKernel.cpp | 561 --------------------- src/graph/backends/NEON/NEFunctionFactory.cpp | 4 +- src/graph/backends/NEON/NENodeValidator.cpp | 2 +- src/runtime/NEON/functions/NEConvolutionLayer.cpp | 4 +- .../NEON/functions/NEWinogradConvolutionLayer.cpp | 390 ++++++++++++++ src/runtime/NEON/functions/NEWinogradLayer.cpp | 390 -------------- tests/benchmark/CL/ConvolutionLayer.cpp | 4 +- tests/benchmark/NEON/ConvolutionLayer.cpp | 22 +- .../fixtures/WinogradConvolutionLayerFixture.h | 101 ++++ tests/benchmark/fixtures/WinogradLayerFixture.h | 101 ---- tests/validation/CL/Winograd.cpp | 2 +- tests/validation/NEON/ConvolutionLayer.cpp | 8 +- .../fixtures/WinogradConvolutionLayerFixture.h | 389 ++++++++++++++ tests/validation/fixtures/WinogradLayerFixture.h | 389 -------------- 23 files changed, 2150 insertions(+), 2149 deletions(-) create mode 100644 arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h delete mode 100644 arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h create mode 100644 arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h delete mode 100644 arm_compute/runtime/NEON/functions/NEWinogradLayer.h create mode 100644 src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp delete mode 100644 src/core/NEON/kernels/NEWinogradLayerKernel.cpp create mode 100644 src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp delete mode 100644 src/runtime/NEON/functions/NEWinogradLayer.cpp create mode 100644 tests/benchmark/fixtures/WinogradConvolutionLayerFixture.h delete mode 100644 tests/benchmark/fixtures/WinogradLayerFixture.h create mode 100644 tests/validation/fixtures/WinogradConvolutionLayerFixture.h delete mode 100644 tests/validation/fixtures/WinogradLayerFixture.h diff --git a/arm_compute/core/NEON/NEKernels.h b/arm_compute/core/NEON/NEKernels.h index 31f4881ef5..0e271efa49 100644 --- a/arm_compute/core/NEON/NEKernels.h +++ b/arm_compute/core/NEON/NEKernels.h @@ -111,6 +111,6 @@ #include "arm_compute/core/NEON/kernels/NETransposeKernel.h" #include "arm_compute/core/NEON/kernels/NEWarpKernel.h" #include "arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h" -#include "arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h" +#include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h" #endif /* __ARM_COMPUTE_NEKERNELS_H__ */ diff --git a/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h b/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h new file mode 100644 index 0000000000..9912076cd5 --- /dev/null +++ b/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h @@ -0,0 +1,553 @@ +/* + * Copyright (c) 2017-2018 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. + */ +#ifndef __ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H__ +#define __ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H__ + +#include "arm_compute/core/NEON/INEKernel.h" +#include "arm_compute/core/NEON/kernels/convolution/common/convolution.hpp" +#include "arm_compute/core/NEON/kernels/convolution/common/tensor.hpp" +#include "arm_compute/core/NEON/kernels/convolution/winograd/batched_blocked_gemm.hpp" +#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" + +namespace arm_compute +{ +class ITensor; + +/** Interface for the NEON kernel to perform Winograd input transform. */ +template +class INEWinogradLayerTransformInputKernel : public INEKernel +{ +public: + /** Determine how much memory (in units of TIn) to allocate for the + * transformed input. + * + * @param[in] n_batches Number of batches in the input tensor. + * @param[in] n_channels Number of feature maps in the input tensor. + * @param[in] n_rows Number of rows in each feature map. + * @param[in] n_cols Number of columns in each feature map. + * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". + * + * @return Storage size (in units of TIn) required. + */ + virtual unsigned int get_input_storage_size(int n_batches, int n_channels, int n_rows, int n_cols, bool same_padding) const = 0; + + /** Gets the stride between matrices in the input worspace + * + * @param[in] kernel_shape The shape of the weights tensor. + * @param[in] input_shape The shape of the input tensor. + * @param[in] padding_type The type of padding to be used. + * + * @return Stride expressed in bytes. + */ + virtual int get_matrix_stride(const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const = 0; + + /** Configure the output transform kernel. + * + * @param[in] input Input tensor data + * @param[in] n_batches Number of batches in input tensor. + * @param[in] n_rows Number of rows in input tensor. + * @param[in] n_cols Number of columns in input tensor. + * @param[in] n_channels Number of channels in input tensor. + * @param[in] padding Padding type. + * @param[out] output Base of output matrices. + * @param[in] matrix_stride Stride between output matrices. + */ + virtual void configure(const T *const input, const int n_batches, const int n_rows, const int n_cols, const int n_channels, const PaddingType padding, T *const output, const int matrix_stride) = 0; + + /** Destructor */ + virtual ~INEWinogradLayerTransformInputKernel() + { + } +}; + +/** NEON kernel to perform Winograd input transform. */ +template +class NEWinogradLayerTransformInputKernel : public INEWinogradLayerTransformInputKernel +{ +public: + /** Determine how much memory (in units of TIn) to allocate for the + * transformed input. + * + * @param[in] n_batches Number of batches in the input tensor. + * @param[in] n_channels Number of feature maps in the input tensor. + * @param[in] n_rows Number of rows in each feature map. + * @param[in] n_cols Number of columns in each feature map. + * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". + * + * @return Storage size (in units of TIn) required. + */ + unsigned int get_input_storage_size( + int n_batches, + int n_channels, + int n_rows, + int n_cols, + bool same_padding) const override; + + /** Gets the stride between matrices in the input worspace + * + * @param[in] kernel_shape The shape of the weights tensor. + * @param[in] input_shape The shape of the input tensor. + * @param[in] padding_type The type of padding to be used. + * + * @return Stride expressed in bytes. + */ + int get_matrix_stride(const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const override; + + /** Default constructor */ + NEWinogradLayerTransformInputKernel(); + + const char *name() const override + { + return "NEWinogradLayerTransformInputKernel"; + } + + /** Configure the output transform kernel. + * + * @param[in] input Input tensor data. Data types supported: F32. + * @param[in] n_batches Number of batches in input tensor. + * @param[in] n_rows Number of rows in input tensor. + * @param[in] n_cols Number of columns in input tensor. + * @param[in] n_channels Number of channels in input tensor. + * @param[in] padding Padding type. + * @param[out] output Base of output matrices. + * @param[in] matrix_stride Stride between output matrices. + */ + void configure( + const T *const input, + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels, + const PaddingType padding, + T *const output, + const int matrix_stride) override; + + // Inherited methods overridden: + void run(const Window &window, const ThreadInfo &info) override; + bool is_parallelisable() const override; + + /** Winograd base kernel */ + using WinogradBase = winograd::WinogradGEMM; + /** Winograd convolution kernel */ + using WinogradConv = typename WinogradBase::template Convolution; + + /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformInputKernel + * + * @param[in] input First tensor input info. Data types supported: F32. + * @param[in] output Output tensor info. Data types supported: same as @p input. + * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info); + +private: + using InputTransform = typename WinogradBase::template InputTransform; + std::unique_ptr _transform; +}; + +/** Interface for the NEON kernel to perform Winograd output transform. */ +template +class INEWinogradLayerTransformOutputKernel : public INEKernel +{ +public: + /** Determine how much memory (in units of TOut) to allocate for the + * (Winograd domain) output. + * + * @param[in] n_batches Number of batches in the output tensor. + * @param[in] n_rows Number of rows in each feature map of the input tensor. + * @param[in] n_cols Number of columns in each feature map of the input tensor. + * @param[in] n_output_channels Number of feature maps in the output tensor. + * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". + * + * @return Storage size (in units of TOut) required. + */ + virtual unsigned int get_output_storage_size(int n_batches, int n_rows, int n_cols, int n_output_channels, bool same_padding) const = 0; + + /** Gets the stride between matrices in the output worspace + * + * @param[in] kernel_shape The shape of the weights tensor. + * @param[in] input_shape The shape of the input tensor. + * @param[in] padding_type The type of padding to be used. + * + * @return Stride expressed in bytes. + */ + virtual int get_matrix_stride(const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const = 0; + + /** Get the output shape of a convolution. + * + * @param[in] kernel_shape The shape of the weights tensor. + * @param[in] in_shape The shape of the input tensor. + * @param[in] padding The type of padding to be used. + * + * @return Stride expressed in bytes. + */ + virtual Tensor4DShape get_output_shape(const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding) const = 0; + + /** Configure the output transform kernel. + * + * @param[in] biases Pointer to the biases tensor. + * @param[in] output_workingspace Pointer to working space for the output tensor in the Winograd domain. + * @param[in] matrix_stride Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution::get_output_matrix_stride() + * @param[out] output Pointer to NHWC ordered output tensor, in the spatial domain. + * @param[in] n_batches Number of batches in the input tensor. + * @param[in] n_rows Number of rows in output tensor. + * @param[in] n_cols Number of columns in output tensor. + * @param[in] n_channels Number of feature maps in the output tensor. + */ + virtual void configure( + const ITensor *biases, + const T *const output_workingspace, + const int matrix_stride, + T *const output, + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels) = 0; + + virtual ~INEWinogradLayerTransformOutputKernel() + { + } +}; + +/** NEON kernel to perform Winograd output transform. */ +template +class NEWinogradLayerTransformOutputKernel : public INEWinogradLayerTransformOutputKernel +{ +public: + const char *name() const override + { + return "NEWinogradLayerTransformOutputKernel"; + } + /** Constructor */ + NEWinogradLayerTransformOutputKernel(); + + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEWinogradLayerTransformOutputKernel(const NEWinogradLayerTransformOutputKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEWinogradLayerTransformOutputKernel &operator=(const NEWinogradLayerTransformOutputKernel &) = delete; + /** Allow instances of this class to be moved */ + NEWinogradLayerTransformOutputKernel(NEWinogradLayerTransformOutputKernel &&) = default; + /** Allow instances of this class to be moved */ + NEWinogradLayerTransformOutputKernel &operator=(NEWinogradLayerTransformOutputKernel &&) = default; + /** Default destructor */ + ~NEWinogradLayerTransformOutputKernel() = default; + + // Inherited methods overridden: + /** Determine how much memory (in units of TOut) to allocate for the + * (Winograd domain) output. + * + * @param[in] n_batches Number of batches in the output tensor. + * @param[in] n_rows Number of rows in each feature map of the input tensor. + * @param[in] n_cols Number of columns in each feature map of the input tensor. + * @param[in] n_output_channels Number of feature maps in the output tensor. + * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". + * + * @return Storage size (in units of TOut) required. + */ + unsigned int get_output_storage_size(int n_batches, int n_rows, int n_cols, int n_output_channels, bool same_padding) const override; + + /** Gets the stride between matrices in the output worspace + * + * @param[in] kernel_shape The shape of the weights tensor. + * @param[in] input_shape The shape of the input tensor. + * @param[in] padding_type The type of padding to be used. + * + * @return Stride expressed in bytes. + */ + int get_matrix_stride(const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const override; + /** Get the output shape of a convolution. + * + * @param[in] kernel_shape The shape of the weights tensor. + * @param[in] in_shape The shape of the input tensor. + * @param[in] padding The type of padding to be used. + * + * @return Stride expressed in bytes. + */ + Tensor4DShape get_output_shape(const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding) const override; + + /** Configure the output transform kernel. + * + * @param[in] biases Pointer to the biases tensor. + * @param[in] output_workingspace Pointer to working space for the output tensor in the Winograd domain. + * @param[in] matrix_stride Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution::get_output_matrix_stride() + * @param[out] output Pointer to NHWC ordered output tensor, in the spatial domain. + * @param[in] n_batches Number of batches in the input tensor. + * @param[in] n_rows Number of rows in output tensor. + * @param[in] n_cols Number of columns in output tensor. + * @param[in] n_channels Number of feature maps in the output tensor. + */ + void configure( + const ITensor *biases, + const T *const output_workingspace, + const int matrix_stride, + T *const output, + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels) override; + + void run(const Window &window, const ThreadInfo &info) override; + bool is_parallelisable() const override; + + /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformOutputKernel + * + * @param[in] input Source tensor with shape [C, N, 16, batches] or [C, N, 36, batches]. Data types supported: F32. + * @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input + * @param[out] output Destination tensor with shape [output_convolved_dims.width, output_convolved_dims.height, C, batches]. Data type supported: same as @p input + * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info); + +private: + using WinogradBase = winograd::WinogradGEMM; + using WinogradConv = typename WinogradBase::template Convolution; + using OutputTransform = typename WinogradBase::template OutputTransform; + + const ITensor *_biases; + const T *_output_workspace; + int _matrix_stride; + int _matrix_row_stride; + T *_output; + int _n_batches; + int _n_rows; + int _n_cols; + int _n_channels; +}; + +/** Interface for the NEON kernel to perform Winograd weights transform. */ +template +class INEWinogradLayerTransformWeightsKernel : public INEKernel +{ +public: + /** Determine how much memory (in units of T) to allocate for the + * transformed weights. + * + * @param[in] n_output_channels Number of output feature maps. + * @param[in] n_input_channels Number of input feature maps. + * + * @return Storage size (in units of T) required. + */ + virtual unsigned int get_weight_storage_size(int n_output_channels, int n_input_channels) const = 0; + /** Gets the stride between matrices in the kernel worspace + * + * @param[in] kernel_shape The shape of the weights tensor. + * + * @return Stride expressed in bytes. + */ + virtual int get_matrix_stride(const KernelShape &kernel_shape) const = 0; + + /** Configure the weights transform kernel. + * + * @param[in] weights_hwio Pointer to the weights tensor + * @param[in] output Pointer to working space for the output tensor in the Winograd domain. + * @param[in] matrix_stride Stride across matrices in the output workspace. + * @param[in] n_output_channels Number of filters. + * @param[in] n_input_channels Number of channels in each filter. + */ + virtual void configure(const ITensor *weights_hwio, T *const output, const int matrix_stride, const int n_output_channels, const int n_input_channels) = 0; + + virtual ~INEWinogradLayerTransformWeightsKernel() + { + } +}; + +/** NEON kernel to perform Winograd weights transform. */ +template +class NEWinogradLayerTransformWeightsKernel final : public INEWinogradLayerTransformWeightsKernel +{ +public: + /** Default constructor. */ + NEWinogradLayerTransformWeightsKernel(); + const char *name() const override + { + return "NEWinogradLayerTransformWeightsKernel"; + } + + /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel + * + * @param[in] input Source tensor info. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout). + * kernel_x must be 3 and equal to kernel_y. Data types supported: F32. + * @param[in] output Destination tensor info. The output is a 3D tensor with dimensions [OFM, IFM, 16] or [OFM, IFM, 36]. Data type supported: same as @p input + * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info); + + // Inherited methods overridden: + void configure(const ITensor *weights_hwio, T *const output, const int matrix_stride, const int n_output_channels, const int n_input_channels) override; + unsigned int get_weight_storage_size(int n_output_channels, int n_input_channels) const override; + int get_matrix_stride(const KernelShape &kernel_shape) const override; + void run(const Window &window, const ThreadInfo &info) override; + bool is_parallelisable() const override; + +private: + using WinogradBase = winograd::WinogradGEMM; + using WinogradConv = typename WinogradBase::template Convolution; + using WeightsTransform = typename WinogradBase::template WeightsTransform; + std::unique_ptr _transform; +}; + +/** Interface for the NEON kernel to perform Winograd. */ +template +class INEWinogradLayerBatchedGEMMKernel : public INEKernel +{ +public: + /** Get the number of GEMMs to compute + */ + virtual unsigned int get_number_gemms() const = 0; + /** Initialise the kernel + * + * @param[in] n_gemms Number of GEMMs to compute. + * @param[in] M in_shape.n_batches * tile_rows * tile_cols. + * @param[in] K Number of channels in the input tensor. + * @param[in] N Number of channels in the output tensor. + * @param[in] a_matrix_stride Stride between input matrices. + * @param[in] a_row_stride Row stride inside input matrix. + * @param[in] b_matrix_stride Stride between weights matrices. + * @param[in] b_row_stride Row stride inside the weights matrix. + * @param[in] c_matrix_stride Stride between output matrices. + * @param[in] c_row_stride Row stride inside the output matrix. + * @param[out] a_ptr Input workspace. + * @param[out] b_ptr Kernel workspace. + * @param[out] c_ptr Output workspace. + */ + virtual void configure( + const unsigned int n_gemms, + const int M, const int K, const int N, + const int a_matrix_stride, + const int a_row_stride, + const int b_matrix_stride, + const int b_row_stride, + const int c_matrix_stride, + const int c_row_stride, + const TIn *const a_ptr, + const TIn *const b_ptr, + TOut *const c_ptr) = 0; + + /** Get the number of tiles per row + */ + virtual int get_output_tile_rows() const = 0; + /** Get the number of tiles per columns + */ + virtual int get_output_tile_cols() const = 0; + /** Get the number of blocks + */ + virtual int get_number_blocks() const = 0; +}; + +/** NEON kernel to perform Winograd. */ +template +class NEWinogradLayerBatchedGEMMKernel : public INEWinogradLayerBatchedGEMMKernel +{ +public: + /** Winograd base kernel */ + using WinogradBase = winograd::WinogradGEMM; + /** Winograd convolution kernel */ + using WinogradConv = typename WinogradBase::template Convolution; + /** Winograd batched blocked GEMM operator */ + using MultiGEMM = winograd::BatchedBlockedGemm; + + const char *name() const override + { + return "NEWinogradLayerBatchedGEMMKernel"; + } + /** Constructor */ + NEWinogradLayerBatchedGEMMKernel(); + + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEWinogradLayerBatchedGEMMKernel(const NEWinogradLayerBatchedGEMMKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEWinogradLayerBatchedGEMMKernel &operator=(const NEWinogradLayerBatchedGEMMKernel &) = delete; + /** Allow instances of this class to be moved */ + NEWinogradLayerBatchedGEMMKernel(NEWinogradLayerBatchedGEMMKernel &&) = default; + /** Allow instances of this class to be moved */ + NEWinogradLayerBatchedGEMMKernel &operator=(NEWinogradLayerBatchedGEMMKernel &&) = default; + /** Default destructor. */ + ~NEWinogradLayerBatchedGEMMKernel() = default; + + // Inherited methods overridden: + + unsigned int get_number_gemms() const override; + int get_output_tile_rows() const override; + int get_output_tile_cols() const override; + int get_number_blocks() const override; + + /** Initialise the kernel + * + * @param[in] n_gemms Number of GEMMs to compute. + * @param[in] M in_shape.n_batches * tile_rows * tile_cols. + * @param[in] K Number of channels in the input tensor. + * @param[in] N Number of channels in the output tensor. + * @param[in] a_matrix_stride Stride between input matrices. + * @param[in] a_row_stride Row stride inside input matrix. + * @param[in] b_matrix_stride Stride between weights matrices. + * @param[in] b_row_stride Row stride inside the weights matrix. + * @param[in] c_matrix_stride Stride between output matrices. + * @param[in] c_row_stride Row stride inside the output matrix. + * @param[out] a_ptr Input workspace. + * @param[out] b_ptr Kernel workspace. + * @param[out] c_ptr Output workspace. + */ + void configure( + const unsigned int n_gemms, + const int M, const int K, const int N, + const int a_matrix_stride, + const int a_row_stride, + const int b_matrix_stride, + const int b_row_stride, + const int c_matrix_stride, + const int c_row_stride, + const TIn *const a_ptr, + const TIn *const b_ptr, + TOut *const c_ptr) override; + + void run(const Window &window, const ThreadInfo &info) override; + + /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerBatchedGEMMKernel. + * + * @param[in] a First input tensor (Matrix or Vector A). Data types supported: F32 + * @param[in] b Second input tensor (Matrix B). Data type supported: same as @p a. + * @param[in] c Third input tensor (Matrix C). It can be a nullptr if just the multiplication between @p a and @p b is needed. Data type supported: same as @p a. + * @param[out] output Output tensor. Data type supported: same as @p a + * @param[in] alpha Weight of the matrix product + * @param[in] beta Weight of matrix C + * @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and + * if the reshape of matrix B should happen only for the first run + * + * @return a status + */ + static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo()); + +private: + static const int _output_tile_rows = OutputTileRows; + static const int _output_tile_cols = OutputTileCols; + std::unique_ptr _gemms; +}; + +} // namespace arm_compute +#endif /*__ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H__*/ diff --git a/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h b/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h deleted file mode 100644 index 7284f9fdc4..0000000000 --- a/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h +++ /dev/null @@ -1,553 +0,0 @@ -/* - * Copyright (c) 2017-2018 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. - */ -#ifndef __ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__ -#define __ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__ - -#include "arm_compute/core/NEON/INEKernel.h" -#include "arm_compute/core/NEON/kernels/convolution/common/convolution.hpp" -#include "arm_compute/core/NEON/kernels/convolution/common/tensor.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/batched_blocked_gemm.hpp" -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" - -namespace arm_compute -{ -class ITensor; - -/** Interface for the NEON kernel to perform Winograd input transform. */ -template -class INEWinogradLayerTransformInputKernel : public INEKernel -{ -public: - /** Determine how much memory (in units of TIn) to allocate for the - * transformed input. - * - * @param[in] n_batches Number of batches in the input tensor. - * @param[in] n_channels Number of feature maps in the input tensor. - * @param[in] n_rows Number of rows in each feature map. - * @param[in] n_cols Number of columns in each feature map. - * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". - * - * @return Storage size (in units of TIn) required. - */ - virtual unsigned int get_input_storage_size(int n_batches, int n_channels, int n_rows, int n_cols, bool same_padding) const = 0; - - /** Gets the stride between matrices in the input worspace - * - * @param[in] kernel_shape The shape of the weights tensor. - * @param[in] input_shape The shape of the input tensor. - * @param[in] padding_type The type of padding to be used. - * - * @return Stride expressed in bytes. - */ - virtual int get_matrix_stride(const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const = 0; - - /** Configure the output transform kernel. - * - * @param[in] input Input tensor data - * @param[in] n_batches Number of batches in input tensor. - * @param[in] n_rows Number of rows in input tensor. - * @param[in] n_cols Number of columns in input tensor. - * @param[in] n_channels Number of channels in input tensor. - * @param[in] padding Padding type. - * @param[out] output Base of output matrices. - * @param[in] matrix_stride Stride between output matrices. - */ - virtual void configure(const T *const input, const int n_batches, const int n_rows, const int n_cols, const int n_channels, const PaddingType padding, T *const output, const int matrix_stride) = 0; - - /** Destructor */ - virtual ~INEWinogradLayerTransformInputKernel() - { - } -}; - -/** NEON kernel to perform Winograd input transform. */ -template -class NEWinogradLayerTransformInputKernel : public INEWinogradLayerTransformInputKernel -{ -public: - /** Determine how much memory (in units of TIn) to allocate for the - * transformed input. - * - * @param[in] n_batches Number of batches in the input tensor. - * @param[in] n_channels Number of feature maps in the input tensor. - * @param[in] n_rows Number of rows in each feature map. - * @param[in] n_cols Number of columns in each feature map. - * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". - * - * @return Storage size (in units of TIn) required. - */ - unsigned int get_input_storage_size( - int n_batches, - int n_channels, - int n_rows, - int n_cols, - bool same_padding) const override; - - /** Gets the stride between matrices in the input worspace - * - * @param[in] kernel_shape The shape of the weights tensor. - * @param[in] input_shape The shape of the input tensor. - * @param[in] padding_type The type of padding to be used. - * - * @return Stride expressed in bytes. - */ - int get_matrix_stride(const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const override; - - /** Default constructor */ - NEWinogradLayerTransformInputKernel(); - - const char *name() const override - { - return "NEWinogradLayerTransformInputKernel"; - } - - /** Configure the output transform kernel. - * - * @param[in] input Input tensor data. Data types supported: F32. - * @param[in] n_batches Number of batches in input tensor. - * @param[in] n_rows Number of rows in input tensor. - * @param[in] n_cols Number of columns in input tensor. - * @param[in] n_channels Number of channels in input tensor. - * @param[in] padding Padding type. - * @param[out] output Base of output matrices. - * @param[in] matrix_stride Stride between output matrices. - */ - void configure( - const T *const input, - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels, - const PaddingType padding, - T *const output, - const int matrix_stride) override; - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - bool is_parallelisable() const override; - - /** Winograd base kernel */ - using WinogradBase = winograd::WinogradGEMM; - /** Winograd convolution kernel */ - using WinogradConv = typename WinogradBase::template Convolution; - - /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformInputKernel - * - * @param[in] input First tensor input info. Data types supported: F32. - * @param[in] output Output tensor info. Data types supported: same as @p input. - * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info); - -private: - using InputTransform = typename WinogradBase::template InputTransform; - std::unique_ptr _transform; -}; - -/** Interface for the NEON kernel to perform Winograd output transform. */ -template -class INEWinogradLayerTransformOutputKernel : public INEKernel -{ -public: - /** Determine how much memory (in units of TOut) to allocate for the - * (Winograd domain) output. - * - * @param[in] n_batches Number of batches in the output tensor. - * @param[in] n_rows Number of rows in each feature map of the input tensor. - * @param[in] n_cols Number of columns in each feature map of the input tensor. - * @param[in] n_output_channels Number of feature maps in the output tensor. - * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". - * - * @return Storage size (in units of TOut) required. - */ - virtual unsigned int get_output_storage_size(int n_batches, int n_rows, int n_cols, int n_output_channels, bool same_padding) const = 0; - - /** Gets the stride between matrices in the output worspace - * - * @param[in] kernel_shape The shape of the weights tensor. - * @param[in] input_shape The shape of the input tensor. - * @param[in] padding_type The type of padding to be used. - * - * @return Stride expressed in bytes. - */ - virtual int get_matrix_stride(const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const = 0; - - /** Get the output shape of a convolution. - * - * @param[in] kernel_shape The shape of the weights tensor. - * @param[in] in_shape The shape of the input tensor. - * @param[in] padding The type of padding to be used. - * - * @return Stride expressed in bytes. - */ - virtual Tensor4DShape get_output_shape(const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding) const = 0; - - /** Configure the output transform kernel. - * - * @param[in] biases Pointer to the biases tensor. - * @param[in] output_workingspace Pointer to working space for the output tensor in the Winograd domain. - * @param[in] matrix_stride Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution::get_output_matrix_stride() - * @param[out] output Pointer to NHWC ordered output tensor, in the spatial domain. - * @param[in] n_batches Number of batches in the input tensor. - * @param[in] n_rows Number of rows in output tensor. - * @param[in] n_cols Number of columns in output tensor. - * @param[in] n_channels Number of feature maps in the output tensor. - */ - virtual void configure( - const ITensor *biases, - const T *const output_workingspace, - const int matrix_stride, - T *const output, - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels) = 0; - - virtual ~INEWinogradLayerTransformOutputKernel() - { - } -}; - -/** NEON kernel to perform Winograd output transform. */ -template -class NEWinogradLayerTransformOutputKernel : public INEWinogradLayerTransformOutputKernel -{ -public: - const char *name() const override - { - return "NEWinogradLayerTransformOutputKernel"; - } - /** Constructor */ - NEWinogradLayerTransformOutputKernel(); - - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerTransformOutputKernel(const NEWinogradLayerTransformOutputKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerTransformOutputKernel &operator=(const NEWinogradLayerTransformOutputKernel &) = delete; - /** Allow instances of this class to be moved */ - NEWinogradLayerTransformOutputKernel(NEWinogradLayerTransformOutputKernel &&) = default; - /** Allow instances of this class to be moved */ - NEWinogradLayerTransformOutputKernel &operator=(NEWinogradLayerTransformOutputKernel &&) = default; - /** Default destructor */ - ~NEWinogradLayerTransformOutputKernel() = default; - - // Inherited methods overridden: - /** Determine how much memory (in units of TOut) to allocate for the - * (Winograd domain) output. - * - * @param[in] n_batches Number of batches in the output tensor. - * @param[in] n_rows Number of rows in each feature map of the input tensor. - * @param[in] n_cols Number of columns in each feature map of the input tensor. - * @param[in] n_output_channels Number of feature maps in the output tensor. - * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". - * - * @return Storage size (in units of TOut) required. - */ - unsigned int get_output_storage_size(int n_batches, int n_rows, int n_cols, int n_output_channels, bool same_padding) const override; - - /** Gets the stride between matrices in the output worspace - * - * @param[in] kernel_shape The shape of the weights tensor. - * @param[in] input_shape The shape of the input tensor. - * @param[in] padding_type The type of padding to be used. - * - * @return Stride expressed in bytes. - */ - int get_matrix_stride(const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const override; - /** Get the output shape of a convolution. - * - * @param[in] kernel_shape The shape of the weights tensor. - * @param[in] in_shape The shape of the input tensor. - * @param[in] padding The type of padding to be used. - * - * @return Stride expressed in bytes. - */ - Tensor4DShape get_output_shape(const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding) const override; - - /** Configure the output transform kernel. - * - * @param[in] biases Pointer to the biases tensor. - * @param[in] output_workingspace Pointer to working space for the output tensor in the Winograd domain. - * @param[in] matrix_stride Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution::get_output_matrix_stride() - * @param[out] output Pointer to NHWC ordered output tensor, in the spatial domain. - * @param[in] n_batches Number of batches in the input tensor. - * @param[in] n_rows Number of rows in output tensor. - * @param[in] n_cols Number of columns in output tensor. - * @param[in] n_channels Number of feature maps in the output tensor. - */ - void configure( - const ITensor *biases, - const T *const output_workingspace, - const int matrix_stride, - T *const output, - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels) override; - - void run(const Window &window, const ThreadInfo &info) override; - bool is_parallelisable() const override; - - /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformOutputKernel - * - * @param[in] input Source tensor with shape [C, N, 16, batches] or [C, N, 36, batches]. Data types supported: F32. - * @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input - * @param[out] output Destination tensor with shape [output_convolved_dims.width, output_convolved_dims.height, C, batches]. Data type supported: same as @p input - * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info); - -private: - using WinogradBase = winograd::WinogradGEMM; - using WinogradConv = typename WinogradBase::template Convolution; - using OutputTransform = typename WinogradBase::template OutputTransform; - - const ITensor *_biases; - const T *_output_workspace; - int _matrix_stride; - int _matrix_row_stride; - T *_output; - int _n_batches; - int _n_rows; - int _n_cols; - int _n_channels; -}; - -/** Interface for the NEON kernel to perform Winograd weights transform. */ -template -class INEWinogradLayerTransformWeightsKernel : public INEKernel -{ -public: - /** Determine how much memory (in units of T) to allocate for the - * transformed weights. - * - * @param[in] n_output_channels Number of output feature maps. - * @param[in] n_input_channels Number of input feature maps. - * - * @return Storage size (in units of T) required. - */ - virtual unsigned int get_weight_storage_size(int n_output_channels, int n_input_channels) const = 0; - /** Gets the stride between matrices in the kernel worspace - * - * @param[in] kernel_shape The shape of the weights tensor. - * - * @return Stride expressed in bytes. - */ - virtual int get_matrix_stride(const KernelShape &kernel_shape) const = 0; - - /** Configure the weights transform kernel. - * - * @param[in] weights_hwio Pointer to the weights tensor - * @param[in] output Pointer to working space for the output tensor in the Winograd domain. - * @param[in] matrix_stride Stride across matrices in the output workspace. - * @param[in] n_output_channels Number of filters. - * @param[in] n_input_channels Number of channels in each filter. - */ - virtual void configure(const ITensor *weights_hwio, T *const output, const int matrix_stride, const int n_output_channels, const int n_input_channels) = 0; - - virtual ~INEWinogradLayerTransformWeightsKernel() - { - } -}; - -/** NEON kernel to perform Winograd weights transform. */ -template -class NEWinogradLayerTransformWeightsKernel final : public INEWinogradLayerTransformWeightsKernel -{ -public: - /** Default constructor. */ - NEWinogradLayerTransformWeightsKernel(); - const char *name() const override - { - return "NEWinogradLayerTransformWeightsKernel"; - } - - /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel - * - * @param[in] input Source tensor info. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout). - * kernel_x must be 3 and equal to kernel_y. Data types supported: F32. - * @param[in] output Destination tensor info. The output is a 3D tensor with dimensions [OFM, IFM, 16] or [OFM, IFM, 36]. Data type supported: same as @p input - * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info); - - // Inherited methods overridden: - void configure(const ITensor *weights_hwio, T *const output, const int matrix_stride, const int n_output_channels, const int n_input_channels) override; - unsigned int get_weight_storage_size(int n_output_channels, int n_input_channels) const override; - int get_matrix_stride(const KernelShape &kernel_shape) const override; - void run(const Window &window, const ThreadInfo &info) override; - bool is_parallelisable() const override; - -private: - using WinogradBase = winograd::WinogradGEMM; - using WinogradConv = typename WinogradBase::template Convolution; - using WeightsTransform = typename WinogradBase::template WeightsTransform; - std::unique_ptr _transform; -}; - -/** Interface for the NEON kernel to perform Winograd. */ -template -class INEWinogradLayerBatchedGEMMKernel : public INEKernel -{ -public: - /** Get the number of GEMMs to compute - */ - virtual unsigned int get_number_gemms() const = 0; - /** Initialise the kernel - * - * @param[in] n_gemms Number of GEMMs to compute. - * @param[in] M in_shape.n_batches * tile_rows * tile_cols. - * @param[in] K Number of channels in the input tensor. - * @param[in] N Number of channels in the output tensor. - * @param[in] a_matrix_stride Stride between input matrices. - * @param[in] a_row_stride Row stride inside input matrix. - * @param[in] b_matrix_stride Stride between weights matrices. - * @param[in] b_row_stride Row stride inside the weights matrix. - * @param[in] c_matrix_stride Stride between output matrices. - * @param[in] c_row_stride Row stride inside the output matrix. - * @param[out] a_ptr Input workspace. - * @param[out] b_ptr Kernel workspace. - * @param[out] c_ptr Output workspace. - */ - virtual void configure( - const unsigned int n_gemms, - const int M, const int K, const int N, - const int a_matrix_stride, - const int a_row_stride, - const int b_matrix_stride, - const int b_row_stride, - const int c_matrix_stride, - const int c_row_stride, - const TIn *const a_ptr, - const TIn *const b_ptr, - TOut *const c_ptr) = 0; - - /** Get the number of tiles per row - */ - virtual int get_output_tile_rows() const = 0; - /** Get the number of tiles per columns - */ - virtual int get_output_tile_cols() const = 0; - /** Get the number of blocks - */ - virtual int get_number_blocks() const = 0; -}; - -/** NEON kernel to perform Winograd. */ -template -class NEWinogradLayerBatchedGEMMKernel : public INEWinogradLayerBatchedGEMMKernel -{ -public: - /** Winograd base kernel */ - using WinogradBase = winograd::WinogradGEMM; - /** Winograd convolution kernel */ - using WinogradConv = typename WinogradBase::template Convolution; - /** Winograd batched blocked GEMM operator */ - using MultiGEMM = winograd::BatchedBlockedGemm; - - const char *name() const override - { - return "NEWinogradLayerBatchedGEMMKernel"; - } - /** Constructor */ - NEWinogradLayerBatchedGEMMKernel(); - - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerBatchedGEMMKernel(const NEWinogradLayerBatchedGEMMKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayerBatchedGEMMKernel &operator=(const NEWinogradLayerBatchedGEMMKernel &) = delete; - /** Allow instances of this class to be moved */ - NEWinogradLayerBatchedGEMMKernel(NEWinogradLayerBatchedGEMMKernel &&) = default; - /** Allow instances of this class to be moved */ - NEWinogradLayerBatchedGEMMKernel &operator=(NEWinogradLayerBatchedGEMMKernel &&) = default; - /** Default destructor. */ - ~NEWinogradLayerBatchedGEMMKernel() = default; - - // Inherited methods overridden: - - unsigned int get_number_gemms() const override; - int get_output_tile_rows() const override; - int get_output_tile_cols() const override; - int get_number_blocks() const override; - - /** Initialise the kernel - * - * @param[in] n_gemms Number of GEMMs to compute. - * @param[in] M in_shape.n_batches * tile_rows * tile_cols. - * @param[in] K Number of channels in the input tensor. - * @param[in] N Number of channels in the output tensor. - * @param[in] a_matrix_stride Stride between input matrices. - * @param[in] a_row_stride Row stride inside input matrix. - * @param[in] b_matrix_stride Stride between weights matrices. - * @param[in] b_row_stride Row stride inside the weights matrix. - * @param[in] c_matrix_stride Stride between output matrices. - * @param[in] c_row_stride Row stride inside the output matrix. - * @param[out] a_ptr Input workspace. - * @param[out] b_ptr Kernel workspace. - * @param[out] c_ptr Output workspace. - */ - void configure( - const unsigned int n_gemms, - const int M, const int K, const int N, - const int a_matrix_stride, - const int a_row_stride, - const int b_matrix_stride, - const int b_row_stride, - const int c_matrix_stride, - const int c_row_stride, - const TIn *const a_ptr, - const TIn *const b_ptr, - TOut *const c_ptr) override; - - void run(const Window &window, const ThreadInfo &info) override; - - /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerBatchedGEMMKernel. - * - * @param[in] a First input tensor (Matrix or Vector A). Data types supported: F32 - * @param[in] b Second input tensor (Matrix B). Data type supported: same as @p a. - * @param[in] c Third input tensor (Matrix C). It can be a nullptr if just the multiplication between @p a and @p b is needed. Data type supported: same as @p a. - * @param[out] output Output tensor. Data type supported: same as @p a - * @param[in] alpha Weight of the matrix product - * @param[in] beta Weight of matrix C - * @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and - * if the reshape of matrix B should happen only for the first run - * - * @return a status - */ - static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo()); - -private: - static const int _output_tile_rows = OutputTileRows; - static const int _output_tile_cols = OutputTileCols; - std::unique_ptr _gemms; -}; - -} // namespace arm_compute -#endif /*__ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__*/ diff --git a/arm_compute/runtime/NEON/NEFunctions.h b/arm_compute/runtime/NEON/NEFunctions.h index 1531377e2e..8091acd258 100644 --- a/arm_compute/runtime/NEON/NEFunctions.h +++ b/arm_compute/runtime/NEON/NEFunctions.h @@ -109,6 +109,6 @@ #include "arm_compute/runtime/NEON/functions/NETranspose.h" #include "arm_compute/runtime/NEON/functions/NEWarpAffine.h" #include "arm_compute/runtime/NEON/functions/NEWarpPerspective.h" -#include "arm_compute/runtime/NEON/functions/NEWinogradLayer.h" +#include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h" #endif /* __ARM_COMPUTE_NEFUNCTIONS_H__ */ diff --git a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h index 220d1cb249..ce9a3ed4f2 100644 --- a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h +++ b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h @@ -30,7 +30,7 @@ #include "arm_compute/runtime/MemoryGroup.h" #include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h" #include "arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h" -#include "arm_compute/runtime/NEON/functions/NEWinogradLayer.h" +#include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h" #include namespace arm_compute @@ -38,9 +38,9 @@ namespace arm_compute class ITensor; /** Basic function to simulate a convolution layer. This function calls one of the following NEON functions: - * -# @ref NEGEMMConvolutionLayer (executed only in case GEMM is required for the operation) - * -# @ref NEWinogradLayer (executed only in case Winograd is required for the operation) - * -# @ref NEDirectConvolutionLayer (executed only in case Direct Convolution is required for the operation) + * -# @ref NEGEMMConvolutionLayer (executed only in case GEMM is required for the operation) + * -# @ref NEWinogradConvolutionLayer (executed only in case Winograd is required for the operation) + * -# @ref NEDirectConvolutionLayer (executed only in case Direct Convolution is required for the operation) */ class NEConvolutionLayer : public IFunction { diff --git a/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h new file mode 100644 index 0000000000..037c74c1a8 --- /dev/null +++ b/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h @@ -0,0 +1,122 @@ +/* + * Copyright (c) 2017-2018 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. + */ +#ifndef __ARM_COMPUTE_NEWINOGRADCONVOLUTIONLAYER_H__ +#define __ARM_COMPUTE_NEWINOGRADCONVOLUTIONLAYER_H__ + +#include "arm_compute/runtime/IFunction.h" + +#include "arm_compute/core/NEON/INEKernel.h" +#include "arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/CPP/functions/CPPPermute.h" +#include "arm_compute/runtime/MemoryGroup.h" +#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h" +#include "arm_compute/runtime/Tensor.h" + +#include + +namespace arm_compute +{ +class ITensor; +/** Basic function to simulate a convolution layer. This function calls the following NEON kernels: + * -# @ref NEWinogradLayerTransformWeightsKernel (executed only once in the first call to the run() method ) + * -# @ref NEWinogradLayerTransformInputKernel + * -# @ref NEWinogradLayerTransformOutputKernel + * -# @ref NEWinogradLayerBatchedGEMMKernel + * -# @ref CPPPermute (three times: weights, input and output) + */ +class NEWinogradConvolutionLayer : public IFunction +{ +public: + /** Constructor */ + NEWinogradConvolutionLayer(std::shared_ptr memory_manager = nullptr); + + /** Set the input and output tensors. + * + * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], + * while every optional dimension from 4 and above represent a batch of inputs. + * Data types supported: F32. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input. + * Currently only 3x3 and 5x5 kernels are supported. + * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. + * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. + * Data types supported: Same as @p input. + * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported. + * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + */ + void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info = ActivationLayerInfo()); + + // Inherited methods overridden: + void run() override; + + /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer + * + * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], + * while every optional dimension from 4 and above represent a batch of inputs. + * Data types supported: F32. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input. + * Currently only 3x3 and 5x5 kernels are supported. + * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. + * @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. + * Data types supported: Same as @p input. + * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported. + * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info = ActivationLayerInfo()); + + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEWinogradConvolutionLayer(const NEWinogradConvolutionLayer &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEWinogradConvolutionLayer &operator=(const NEWinogradConvolutionLayer &) = delete; + +private: + MemoryGroup _memory_group; + std::unique_ptr> _arm_gemm; + std::unique_ptr _gemm_kernel; + std::unique_ptr _transform_input_kernel; + std::unique_ptr _transform_output_kernel; + std::unique_ptr _transform_weights_kernel; + NEActivationLayer _activationlayer_function; + + CPPPermute _permute_input; + CPPPermute _permute_weights; + CPPPermute _permute_output; + Tensor _input_workspace; + Tensor _output_workspace; + Tensor _kernel_storage; + Tensor _input_nhwc; + Tensor _output_nhwc; + Tensor _weights_hwio; + Tensor _workspace; + const ITensor *_input; + const ITensor *_weights; + ITensor *_output; + bool _reshaped_kernel; + bool _is_activationlayer_enabled; +}; +} +#endif /* __ARM_COMPUTE_NEWINOGRADCONVOLUTIONLAYER_H__ */ diff --git a/arm_compute/runtime/NEON/functions/NEWinogradLayer.h b/arm_compute/runtime/NEON/functions/NEWinogradLayer.h deleted file mode 100644 index 8010810253..0000000000 --- a/arm_compute/runtime/NEON/functions/NEWinogradLayer.h +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Copyright (c) 2017-2018 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. - */ -#ifndef __ARM_COMPUTE_NEWINOGRADLAYER_H__ -#define __ARM_COMPUTE_NEWINOGRADLAYER_H__ - -#include "arm_compute/runtime/IFunction.h" - -#include "arm_compute/core/NEON/INEKernel.h" -#include "arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp" -#include "arm_compute/core/Types.h" -#include "arm_compute/runtime/CPP/functions/CPPPermute.h" -#include "arm_compute/runtime/MemoryGroup.h" -#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h" -#include "arm_compute/runtime/Tensor.h" - -#include - -namespace arm_compute -{ -class ITensor; -/** Basic function to simulate a convolution layer. This function calls the following NEON kernels: - * -# @ref NEWinogradLayerTransformWeightsKernel (executed only once in the first call to the run() method ) - * -# @ref NEWinogradLayerTransformInputKernel - * -# @ref NEWinogradLayerTransformOutputKernel - * -# @ref NEWinogradLayerBatchedGEMMKernel - * -# @ref CPPPermute (three times: weights, input and output) - */ -class NEWinogradLayer : public IFunction -{ -public: - /** Constructor */ - NEWinogradLayer(std::shared_ptr memory_manager = nullptr); - - /** Set the input and output tensors. - * - * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], - * while every optional dimension from 4 and above represent a batch of inputs. - * Data types supported: F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input. - * Currently only 3x3 and 5x5 kernels are supported. - * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. - * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. - * Data types supported: Same as @p input. - * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. - */ - void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info = ActivationLayerInfo()); - - // Inherited methods overridden: - void run() override; - - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer - * - * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], - * while every optional dimension from 4 and above represent a batch of inputs. - * Data types supported: F32. - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input. - * Currently only 3x3 and 5x5 kernels are supported. - * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. - * @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. - * Data types supported: Same as @p input. - * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const ActivationLayerInfo &act_info = ActivationLayerInfo()); - - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayer(const NEWinogradLayer &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEWinogradLayer &operator=(const NEWinogradLayer &) = delete; - -private: - MemoryGroup _memory_group; - std::unique_ptr> _arm_gemm; - std::unique_ptr _gemm_kernel; - std::unique_ptr _transform_input_kernel; - std::unique_ptr _transform_output_kernel; - std::unique_ptr _transform_weights_kernel; - NEActivationLayer _activationlayer_function; - - CPPPermute _permute_input; - CPPPermute _permute_weights; - CPPPermute _permute_output; - Tensor _input_workspace; - Tensor _output_workspace; - Tensor _kernel_storage; - Tensor _input_nhwc; - Tensor _output_nhwc; - Tensor _weights_hwio; - Tensor _workspace; - const ITensor *_input; - const ITensor *_weights; - ITensor *_output; - bool _reshaped_kernel; - bool _is_activationlayer_enabled; -}; -} -#endif /* __ARM_COMPUTE_NEWINOGRADLAYER_H__ */ diff --git a/docs/00_introduction.dox b/docs/00_introduction.dox index 39a7ee1a79..1d309cb80f 100644 --- a/docs/00_introduction.dox +++ b/docs/00_introduction.dox @@ -208,6 +208,7 @@ v18.03 Public maintenance release - Updated recommended NDK version to r16b (And fixed warnings). - Fixed bug in validation code. - Added Inception v4 graph example. + - Renamed NEWinogradLayer.cpp to @ref NEWinogradConvolutionLayer v18.02 Public major release - Various NEON / OpenCL / GLES optimisations. @@ -239,9 +240,9 @@ v18.02 Public major release - Added name() method to all kernels. - Added support for Winograd 5x5. - @ref NEPermuteKernel / @ref NEPermute - - @ref NEWinogradLayerTransformInputKernel / @ref NEWinogradLayer - - @ref NEWinogradLayerTransformOutputKernel / @ref NEWinogradLayer - - @ref NEWinogradLayerTransformWeightsKernel / @ref NEWinogradLayer + - @ref NEWinogradLayerTransformInputKernel / NEWinogradLayer + - @ref NEWinogradLayerTransformOutputKernel / NEWinogradLayer + - @ref NEWinogradLayerTransformWeightsKernel / NEWinogradLayer - Renamed NEWinogradLayerKernel into @ref NEWinogradLayerBatchedGEMMKernel - New GLES kernels / functions: - @ref GCTensorShiftKernel / @ref GCTensorShift @@ -313,7 +314,7 @@ v17.12 Public major release - @ref NEGEMMLowpOffsetContributionKernel / @ref NEGEMMLowpMatrixAReductionKernel / @ref NEGEMMLowpMatrixBReductionKernel / @ref NEGEMMLowpMatrixMultiplyCore - @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel / @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint - @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel / @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale - - @ref NEWinogradLayer / NEWinogradLayerKernel + - NEWinogradLayer / NEWinogradLayerKernel - New OpenCL kernels / functions - @ref CLGEMMLowpOffsetContributionKernel / @ref CLGEMMLowpMatrixAReductionKernel / @ref CLGEMMLowpMatrixBReductionKernel / @ref CLGEMMLowpMatrixMultiplyCore diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp new file mode 100644 index 0000000000..fa76194529 --- /dev/null +++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp @@ -0,0 +1,561 @@ +/* + * Copyright (c) 2017-2018 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/NEON/kernels/NEWinogradConvolutionLayerKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/IAccessWindow.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +//Batched Gemms + +namespace +{ +Status validate_arguments_winograd_gemm(const ITensorInfo *a, const ITensorInfo *b, const ITensor *c, const ITensorInfo *output, const float alpha, const float beta, + const GEMMInfo &gemm_info = GEMMInfo()) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(a); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(b); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); + + if(c != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, c->info()); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != c->info()->dimension(1), "The matrix C must have the same number of rows as the matrix A"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != c->info()->dimension(0), "The matrix C must have the same number of columns as the matrix B"); + } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, output); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != output->dimension(0), "The output matrix must have the same number of columns as the matrix B"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != output->dimension(1), "The output matrix must have the same number of rows as the matrix A"); + ARM_COMPUTE_RETURN_ERROR_ON(output->num_dimensions() != a->num_dimensions()); + } + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); + ARM_COMPUTE_UNUSED(alpha, beta); + return Status{}; +} + +Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + + 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); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != 3 && input->dimension(idx_width) != 5); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != input->dimension(idx_height)); + ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4); + const Size2D &output_tile = winograd_info.output_tile_size; + ARM_COMPUTE_RETURN_ERROR_ON(output_tile != Size2D(2U, 2U) && output_tile != Size2D(4U, 4U)); + + // Checks performed when output is configured + if(output->total_size() != 0) + { + const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info)); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + + return Status{}; +} + +std::pair validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) +{ + const Size2D kernel_dims = winograd_info.kernel_size; + // Output tensor auto inizialitation if not yet initialized + auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info))); + + unsigned int num_elems_processed_per_iteration_x = kernel_dims.width; + unsigned int num_elems_processed_per_iteration_y = kernel_dims.height; + + Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + bool window_changed = false; + + AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); + AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->dimension(1)); + window_changed = update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape())); + + Window win_collapsed = win.collapse(win, Window::DimZ); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + + return std::make_pair(err, win_collapsed); +} + +Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) +{ + const Size2D &kernel_dims = winograd_info.kernel_size; + const PadStrideInfo &conv_info = winograd_info.convolution_info; + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != 3U && kernel_dims.width != 5U), "Winograd input transform only supports 3x3 and 5x5 kernels"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != kernel_dims.height), "Winograd input transform only supports 3x3 and 5x5 kernels"); + + // Validate configured output + if(output->total_size() != 0) + { + const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + + return Status{}; +} + +std::pair validate_and_configure_window_winograd_input_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) +{ + const PadStrideInfo conv_info = winograd_info.convolution_info; + const Size2D output_tile_size = winograd_info.output_tile_size; + const Size2D kernel_dims = winograd_info.kernel_size; + const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); + + unsigned int num_elems_read_per_iteration_x = (output_tile_size.width + kernel_dims.width - 1); + unsigned int num_elems_read_per_iteration_y = (output_tile_size.height + kernel_dims.height - 1); + + Window win = calculate_max_window(*input, Steps(1, 1)); + + AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y); + + bool window_changed = update_window_and_padding(win, input_access); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} + +Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info) +{ + const PadStrideInfo &conv_info = winograd_info.convolution_info; + const Size2D kernel_dims = winograd_info.kernel_size; + + // Number of tiles along the X and Y direction + const unsigned int num_tiles_x = std::ceil((winograd_info.input_dimensions.x() - (kernel_dims.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); + const unsigned int num_tiles_y = std::ceil((winograd_info.input_dimensions.y() - (kernel_dims.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); + const Size2D num_tiles = Size2D(num_tiles_x, num_tiles_y); + + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(winograd_info.output_data_layout != DataLayout::NCHW); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area()); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != 3U && kernel_dims.width != 5U), "Winograd output transform only supports 3x3 and 5x5 kernels"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != kernel_dims.height), "Winograd output transform only supports 3x3 and 5x5 kernels"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(((input->dimension(2) != size_t(16U)) && (input->dimension(2) != size_t(36U))), "Only 2x2 and 4x4 output tile is supported"); + ARM_COMPUTE_UNUSED(kernel_dims); + if(bias != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != size_t(1)); + } + + // Checks performed when output is configured + if(output->total_size() != 0) + { + const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + return Status{}; +} + +std::pair validate_and_configure_window_winograd_output_trans(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output, const WinogradInfo &winograd_info) +{ + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info))); + + constexpr unsigned int num_elems_processed_per_iteration = 1; + + Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); + bool window_changed = false; + + AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration, num_elems_processed_per_iteration); + AccessWindowStatic output_access(output, 0, 0, ceil_to_multiple(output->dimension(0), 2), ceil_to_multiple(output->dimension(1), 2)); + + if(bias != nullptr) + { + AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1)); + window_changed = update_window_and_padding(win, input_access, bias_access, output_access); + } + else + { + window_changed = update_window_and_padding(win, input_access, output_access); + } + output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} +} // namespace +template +NEWinogradLayerBatchedGEMMKernel::NEWinogradLayerBatchedGEMMKernel() + : _gemms() +{ +} + +template +void NEWinogradLayerBatchedGEMMKernel::configure( + const unsigned int n_gemms, + const int M, const int K, const int N, + const int a_matrix_stride, + const int a_row_stride, + const int b_matrix_stride, + const int b_row_stride, + const int c_matrix_stride, + const int c_row_stride, + const TIn *const a_ptr, + const TIn *const b_ptr, + TOut *const c_ptr) +{ + _gemms = support::cpp14::make_unique(n_gemms, M, K, N, a_matrix_stride, a_row_stride, b_matrix_stride, b_row_stride, c_matrix_stride, c_row_stride, a_ptr, b_ptr, c_ptr); + Window win; + auto win_last = _gemms->get_window(); + win.set(Window::DimX, Window::Dimension(0, win_last, 1)); + INEKernel::configure(win); +} + +template +void NEWinogradLayerBatchedGEMMKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + const size_t first_gemm = window.x().start(); + const size_t last_gemm = window.x().end(); + _gemms->run(first_gemm, last_gemm); +} + +template +unsigned int NEWinogradLayerBatchedGEMMKernel::get_number_gemms() const +{ + return WinogradBase::N_GEMMS; +} + +template +int NEWinogradLayerBatchedGEMMKernel::get_output_tile_rows() const +{ + return _output_tile_rows; +} + +template +int NEWinogradLayerBatchedGEMMKernel::get_output_tile_cols() const +{ + return _output_tile_cols; +} + +template +int NEWinogradLayerBatchedGEMMKernel::get_number_blocks() const +{ + return WinogradConv::N_BLOCK; +} + +template +Status NEWinogradLayerBatchedGEMMKernel::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensor *c, + const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_gemm(a, b, c, output, alpha, beta, gemm_info)); + return Status{}; +} + +template class NEWinogradLayerBatchedGEMMKernel; +template class NEWinogradLayerBatchedGEMMKernel; + +// Weights transform + +template +unsigned int NEWinogradLayerTransformWeightsKernel::get_weight_storage_size(int n_output_channels, int n_input_channels) const +{ + const KernelShape shape(n_output_channels, KernelRows, KernelCols, n_input_channels); + return static_cast( + // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T + WinogradConv::get_kernel_storage_size(shape) / sizeof(T)); +} + +template +NEWinogradLayerTransformWeightsKernel::NEWinogradLayerTransformWeightsKernel() + : _transform() +{ +} + +template +int NEWinogradLayerTransformWeightsKernel::get_matrix_stride(const KernelShape &kernel_shape) const +{ + return WinogradConv::get_kernel_matrix_stride(kernel_shape); +} + +template +void NEWinogradLayerTransformWeightsKernel::configure( + const ITensor *weights_hwio, + T *const output, + const int matrix_stride, /** Stride across matrices in the output. */ + const int n_output_channels, /** Number of filters. */ + const int n_input_channels) /** Number of channels in each filter. */ +{ + const int matrix_row_stride = roundup(n_output_channels, WinogradConv::N_BLOCK); + _transform = support::cpp14::make_unique(reinterpret_cast(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels, + n_input_channels); + Window win; + auto win_last = _transform->get_window(); + win.set(Window::DimX, Window::Dimension(0, win_last, 1)); + INEKernel::configure(win); +} + +template +void NEWinogradLayerTransformWeightsKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + const size_t fst = window.x().start(); + const size_t lst = window.x().end(); + _transform->run(fst, lst); +} + +template +bool NEWinogradLayerTransformWeightsKernel::is_parallelisable() const +{ + return false; +} + +template +Status NEWinogradLayerTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *output, + const WinogradInfo &winograd_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, winograd_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), winograd_info).first); + return Status{}; +} + +template class NEWinogradLayerTransformWeightsKernel; +template class NEWinogradLayerTransformWeightsKernel; + +// Input transform + +template +unsigned int NEWinogradLayerTransformInputKernel::get_input_storage_size( + int n_batches, /** Number of batches in the input tensor. */ + int n_channels, /** Number of feature maps in the input tensor. */ + int n_rows, /** Number of rows in each feature map. */ + int n_cols, /** Number of columns in each feature map. */ + bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ +) const +{ + // Construct shapes for the input and kernel tensors. + const Tensor4DShape input_shape(n_batches, n_rows, n_cols, n_channels); + const KernelShape kern_shape(1, KernelRows, KernelCols, n_channels); + const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; + // Return the size, converted into units of TIn + return static_cast(WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / sizeof(T)); +} + +template +int NEWinogradLayerTransformInputKernel::get_matrix_stride( + const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const +{ + return WinogradConv::get_input_matrix_stride(kernel_shape, input_shape, padding_type); +} + +template +NEWinogradLayerTransformInputKernel::NEWinogradLayerTransformInputKernel() + : _transform() +{ +} + +template +void NEWinogradLayerTransformInputKernel::configure( + const T *const input, /** Input tensor data */ + const int n_batches, /** Number of batches in input tensor. */ + const int n_rows, /** Number of rows in input tensor. */ + const int n_cols, /** Number of columns in input tensor. */ + const int n_channels, /** Number of channels in input tensor. */ + const PaddingType padding, /** Padding type. */ + T *const output, /** Base of output matrices. */ + const int matrix_stride) /** Stride between output matrices. */ +{ + // _input_matrix_row_stride(n_input_channels), + _transform = support::cpp14::make_unique(input, n_batches, n_rows, n_cols, n_channels, padding, output, matrix_stride, n_channels); + Window win; + auto win_last = _transform->get_window(); + win.set(Window::DimX, Window::Dimension(0, win_last, 1)); + INEKernel::configure(win); +} + +template +void NEWinogradLayerTransformInputKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + const size_t fst = window.x().start(); + const size_t lst = window.x().end(); + _transform->run(fst, lst); +} + +template +bool NEWinogradLayerTransformInputKernel::is_parallelisable() const +{ + return false; +} + +template +Status NEWinogradLayerTransformInputKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, winograd_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), winograd_info).first); + + return Status{}; +} + +template class NEWinogradLayerTransformInputKernel; +template class NEWinogradLayerTransformInputKernel; + +// Output transform + +template +unsigned int NEWinogradLayerTransformOutputKernel::get_output_storage_size( + int n_batches, /** Number of batches in the output tensor. */ + int n_rows, /** Number of rows in each feature map of the input tensor. */ + int n_cols, /** Number of columns in each feature map of the input tensor. */ + int n_output_channels, /** Number of feature maps in the output tensor. */ + bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ +) const +{ + // Construct shapes for the input and kernel tensors. + const Tensor4DShape input_shape(n_batches, n_rows, n_cols, 1); + const KernelShape kern_shape(n_output_channels, KernelRows, KernelCols, 1); + const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; + + // Return the size, converted into units of TOut + return static_cast( + WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(T)); +} + +template +NEWinogradLayerTransformOutputKernel::NEWinogradLayerTransformOutputKernel() + : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0) +{ +} + +template +int NEWinogradLayerTransformOutputKernel::get_matrix_stride( + const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const +{ + return WinogradConv::get_output_matrix_stride(kernel_shape, input_shape, padding_type); +} +template +Tensor4DShape NEWinogradLayerTransformOutputKernel::get_output_shape( + const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding) const +{ + return WinogradConv::get_output_shape(kernel_shape, in_shape, padding); +} + +template +void NEWinogradLayerTransformOutputKernel::configure( + const ITensor *biases, + const T *const output_workingspace, + const int matrix_stride, + T *const output, + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels) +{ + _biases = biases; + _output_workspace = output_workingspace; + _matrix_stride = matrix_stride; + _matrix_row_stride = roundup(n_channels, WinogradConv::N_BLOCK); + _output = output; + _n_batches = n_batches; + _n_rows = n_rows; + _n_cols = n_cols; + _n_channels = n_channels; + + // We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window + OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, _output, _n_batches, _n_rows, _n_cols, _n_channels); + Window win; + auto win_last = output_transform.get_window(); + win.set(Window::DimX, Window::Dimension(0, win_last, 1)); + INEKernel::configure(win); +} + +template +void NEWinogradLayerTransformOutputKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace); + ARM_COMPUTE_ERROR_ON_NULLPTR(_output); + + OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, + (_biases ? reinterpret_cast(_biases->buffer()) : nullptr), _output, + _n_batches, _n_rows, _n_cols, _n_channels); + + // The code below cannot be moved to configure because biases hasn't been allocated at that point + const size_t fst = window.x().start(); + const size_t lst = window.x().end(); + output_transform.run(fst, lst); +} + +template +bool NEWinogradLayerTransformOutputKernel::is_parallelisable() const +{ + return false; +} + +template +Status NEWinogradLayerTransformOutputKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, + const WinogradInfo &winograd_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_output_trans(input->clone().get(), (bias != nullptr ? bias->clone().get() : nullptr), output->clone().get(), + winograd_info) + .first); + + return Status{}; +} + +template class NEWinogradLayerTransformOutputKernel; +template class NEWinogradLayerTransformOutputKernel; + +} // namespace arm_compute diff --git a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp deleted file mode 100644 index 3cfe2af470..0000000000 --- a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp +++ /dev/null @@ -1,561 +0,0 @@ -/* - * Copyright (c) 2017-2018 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/NEON/kernels/NEWinogradLayerKernel.h" - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/IAccessWindow.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "support/ToolchainSupport.h" - -namespace arm_compute -{ -//Batched Gemms - -namespace -{ -Status validate_arguments_winograd_gemm(const ITensorInfo *a, const ITensorInfo *b, const ITensor *c, const ITensorInfo *output, const float alpha, const float beta, - const GEMMInfo &gemm_info = GEMMInfo()) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(a); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(b); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); - - if(c != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, c->info()); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != c->info()->dimension(1), "The matrix C must have the same number of rows as the matrix A"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != c->info()->dimension(0), "The matrix C must have the same number of columns as the matrix B"); - } - - if(output->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, output); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != output->dimension(0), "The output matrix must have the same number of columns as the matrix B"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != output->dimension(1), "The output matrix must have the same number of rows as the matrix A"); - ARM_COMPUTE_RETURN_ERROR_ON(output->num_dimensions() != a->num_dimensions()); - } - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); - ARM_COMPUTE_UNUSED(alpha, beta); - return Status{}; -} - -Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); - - 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); - ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != 3 && input->dimension(idx_width) != 5); - ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != input->dimension(idx_height)); - ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4); - const Size2D &output_tile = winograd_info.output_tile_size; - ARM_COMPUTE_RETURN_ERROR_ON(output_tile != Size2D(2U, 2U) && output_tile != Size2D(4U, 4U)); - - // Checks performed when output is configured - if(output->total_size() != 0) - { - const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info)); - - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - } - - return Status{}; -} - -std::pair validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) -{ - const Size2D kernel_dims = winograd_info.kernel_size; - // Output tensor auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info))); - - unsigned int num_elems_processed_per_iteration_x = kernel_dims.width; - unsigned int num_elems_processed_per_iteration_y = kernel_dims.height; - - Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - bool window_changed = false; - - AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); - AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->dimension(1)); - window_changed = update_window_and_padding(win, input_access, output_access); - output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape())); - - Window win_collapsed = win.collapse(win, Window::DimZ); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - - return std::make_pair(err, win_collapsed); -} - -Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) -{ - const Size2D &kernel_dims = winograd_info.kernel_size; - const PadStrideInfo &conv_info = winograd_info.convolution_info; - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != 3U && kernel_dims.width != 5U), "Winograd input transform only supports 3x3 and 5x5 kernels"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != kernel_dims.height), "Winograd input transform only supports 3x3 and 5x5 kernels"); - - // Validate configured output - if(output->total_size() != 0) - { - const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); - - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - } - - return Status{}; -} - -std::pair validate_and_configure_window_winograd_input_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) -{ - const PadStrideInfo conv_info = winograd_info.convolution_info; - const Size2D output_tile_size = winograd_info.output_tile_size; - const Size2D kernel_dims = winograd_info.kernel_size; - const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); - - unsigned int num_elems_read_per_iteration_x = (output_tile_size.width + kernel_dims.width - 1); - unsigned int num_elems_read_per_iteration_y = (output_tile_size.height + kernel_dims.height - 1); - - Window win = calculate_max_window(*input, Steps(1, 1)); - - AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y); - - bool window_changed = update_window_and_padding(win, input_access); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} - -Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info) -{ - const PadStrideInfo &conv_info = winograd_info.convolution_info; - const Size2D kernel_dims = winograd_info.kernel_size; - - // Number of tiles along the X and Y direction - const unsigned int num_tiles_x = std::ceil((winograd_info.input_dimensions.x() - (kernel_dims.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); - const unsigned int num_tiles_y = std::ceil((winograd_info.input_dimensions.y() - (kernel_dims.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); - const Size2D num_tiles = Size2D(num_tiles_x, num_tiles_y); - - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(winograd_info.output_data_layout != DataLayout::NCHW); - ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area()); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != 3U && kernel_dims.width != 5U), "Winograd output transform only supports 3x3 and 5x5 kernels"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != kernel_dims.height), "Winograd output transform only supports 3x3 and 5x5 kernels"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(((input->dimension(2) != size_t(16U)) && (input->dimension(2) != size_t(36U))), "Only 2x2 and 4x4 output tile is supported"); - ARM_COMPUTE_UNUSED(kernel_dims); - if(bias != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); - ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); - ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != size_t(1)); - } - - // Checks performed when output is configured - if(output->total_size() != 0) - { - const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - } - return Status{}; -} - -std::pair validate_and_configure_window_winograd_output_trans(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output, const WinogradInfo &winograd_info) -{ - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info))); - - constexpr unsigned int num_elems_processed_per_iteration = 1; - - Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); - bool window_changed = false; - - AccessWindowRectangle input_access(input, 0, 0, num_elems_processed_per_iteration, num_elems_processed_per_iteration); - AccessWindowStatic output_access(output, 0, 0, ceil_to_multiple(output->dimension(0), 2), ceil_to_multiple(output->dimension(1), 2)); - - if(bias != nullptr) - { - AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1)); - window_changed = update_window_and_padding(win, input_access, bias_access, output_access); - } - else - { - window_changed = update_window_and_padding(win, input_access, output_access); - } - output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape())); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} -} // namespace -template -NEWinogradLayerBatchedGEMMKernel::NEWinogradLayerBatchedGEMMKernel() - : _gemms() -{ -} - -template -void NEWinogradLayerBatchedGEMMKernel::configure( - const unsigned int n_gemms, - const int M, const int K, const int N, - const int a_matrix_stride, - const int a_row_stride, - const int b_matrix_stride, - const int b_row_stride, - const int c_matrix_stride, - const int c_row_stride, - const TIn *const a_ptr, - const TIn *const b_ptr, - TOut *const c_ptr) -{ - _gemms = support::cpp14::make_unique(n_gemms, M, K, N, a_matrix_stride, a_row_stride, b_matrix_stride, b_row_stride, c_matrix_stride, c_row_stride, a_ptr, b_ptr, c_ptr); - Window win; - auto win_last = _gemms->get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); - INEKernel::configure(win); -} - -template -void NEWinogradLayerBatchedGEMMKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - const size_t first_gemm = window.x().start(); - const size_t last_gemm = window.x().end(); - _gemms->run(first_gemm, last_gemm); -} - -template -unsigned int NEWinogradLayerBatchedGEMMKernel::get_number_gemms() const -{ - return WinogradBase::N_GEMMS; -} - -template -int NEWinogradLayerBatchedGEMMKernel::get_output_tile_rows() const -{ - return _output_tile_rows; -} - -template -int NEWinogradLayerBatchedGEMMKernel::get_output_tile_cols() const -{ - return _output_tile_cols; -} - -template -int NEWinogradLayerBatchedGEMMKernel::get_number_blocks() const -{ - return WinogradConv::N_BLOCK; -} - -template -Status NEWinogradLayerBatchedGEMMKernel::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensor *c, - const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_gemm(a, b, c, output, alpha, beta, gemm_info)); - return Status{}; -} - -template class NEWinogradLayerBatchedGEMMKernel; -template class NEWinogradLayerBatchedGEMMKernel; - -// Weights transform - -template -unsigned int NEWinogradLayerTransformWeightsKernel::get_weight_storage_size(int n_output_channels, int n_input_channels) const -{ - const KernelShape shape(n_output_channels, KernelRows, KernelCols, n_input_channels); - return static_cast( - // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T - WinogradConv::get_kernel_storage_size(shape) / sizeof(T)); -} - -template -NEWinogradLayerTransformWeightsKernel::NEWinogradLayerTransformWeightsKernel() - : _transform() -{ -} - -template -int NEWinogradLayerTransformWeightsKernel::get_matrix_stride(const KernelShape &kernel_shape) const -{ - return WinogradConv::get_kernel_matrix_stride(kernel_shape); -} - -template -void NEWinogradLayerTransformWeightsKernel::configure( - const ITensor *weights_hwio, - T *const output, - const int matrix_stride, /** Stride across matrices in the output. */ - const int n_output_channels, /** Number of filters. */ - const int n_input_channels) /** Number of channels in each filter. */ -{ - const int matrix_row_stride = roundup(n_output_channels, WinogradConv::N_BLOCK); - _transform = support::cpp14::make_unique(reinterpret_cast(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels, - n_input_channels); - Window win; - auto win_last = _transform->get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); - INEKernel::configure(win); -} - -template -void NEWinogradLayerTransformWeightsKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - const size_t fst = window.x().start(); - const size_t lst = window.x().end(); - _transform->run(fst, lst); -} - -template -bool NEWinogradLayerTransformWeightsKernel::is_parallelisable() const -{ - return false; -} - -template -Status NEWinogradLayerTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *output, - const WinogradInfo &winograd_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, winograd_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), winograd_info).first); - return Status{}; -} - -template class NEWinogradLayerTransformWeightsKernel; -template class NEWinogradLayerTransformWeightsKernel; - -// Input transform - -template -unsigned int NEWinogradLayerTransformInputKernel::get_input_storage_size( - int n_batches, /** Number of batches in the input tensor. */ - int n_channels, /** Number of feature maps in the input tensor. */ - int n_rows, /** Number of rows in each feature map. */ - int n_cols, /** Number of columns in each feature map. */ - bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ -) const -{ - // Construct shapes for the input and kernel tensors. - const Tensor4DShape input_shape(n_batches, n_rows, n_cols, n_channels); - const KernelShape kern_shape(1, KernelRows, KernelCols, n_channels); - const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; - // Return the size, converted into units of TIn - return static_cast(WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / sizeof(T)); -} - -template -int NEWinogradLayerTransformInputKernel::get_matrix_stride( - const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const -{ - return WinogradConv::get_input_matrix_stride(kernel_shape, input_shape, padding_type); -} - -template -NEWinogradLayerTransformInputKernel::NEWinogradLayerTransformInputKernel() - : _transform() -{ -} - -template -void NEWinogradLayerTransformInputKernel::configure( - const T *const input, /** Input tensor data */ - const int n_batches, /** Number of batches in input tensor. */ - const int n_rows, /** Number of rows in input tensor. */ - const int n_cols, /** Number of columns in input tensor. */ - const int n_channels, /** Number of channels in input tensor. */ - const PaddingType padding, /** Padding type. */ - T *const output, /** Base of output matrices. */ - const int matrix_stride) /** Stride between output matrices. */ -{ - // _input_matrix_row_stride(n_input_channels), - _transform = support::cpp14::make_unique(input, n_batches, n_rows, n_cols, n_channels, padding, output, matrix_stride, n_channels); - Window win; - auto win_last = _transform->get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); - INEKernel::configure(win); -} - -template -void NEWinogradLayerTransformInputKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - const size_t fst = window.x().start(); - const size_t lst = window.x().end(); - _transform->run(fst, lst); -} - -template -bool NEWinogradLayerTransformInputKernel::is_parallelisable() const -{ - return false; -} - -template -Status NEWinogradLayerTransformInputKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, winograd_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), winograd_info).first); - - return Status{}; -} - -template class NEWinogradLayerTransformInputKernel; -template class NEWinogradLayerTransformInputKernel; - -// Output transform - -template -unsigned int NEWinogradLayerTransformOutputKernel::get_output_storage_size( - int n_batches, /** Number of batches in the output tensor. */ - int n_rows, /** Number of rows in each feature map of the input tensor. */ - int n_cols, /** Number of columns in each feature map of the input tensor. */ - int n_output_channels, /** Number of feature maps in the output tensor. */ - bool same_padding /** Use "SAME" padding, otherwise use "VALID". */ -) const -{ - // Construct shapes for the input and kernel tensors. - const Tensor4DShape input_shape(n_batches, n_rows, n_cols, 1); - const KernelShape kern_shape(n_output_channels, KernelRows, KernelCols, 1); - const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID; - - // Return the size, converted into units of TOut - return static_cast( - WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(T)); -} - -template -NEWinogradLayerTransformOutputKernel::NEWinogradLayerTransformOutputKernel() - : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0) -{ -} - -template -int NEWinogradLayerTransformOutputKernel::get_matrix_stride( - const KernelShape &kernel_shape, const Tensor4DShape &input_shape, const PaddingType padding_type) const -{ - return WinogradConv::get_output_matrix_stride(kernel_shape, input_shape, padding_type); -} -template -Tensor4DShape NEWinogradLayerTransformOutputKernel::get_output_shape( - const KernelShape &kernel_shape, const Tensor4DShape &in_shape, const PaddingType padding) const -{ - return WinogradConv::get_output_shape(kernel_shape, in_shape, padding); -} - -template -void NEWinogradLayerTransformOutputKernel::configure( - const ITensor *biases, - const T *const output_workingspace, - const int matrix_stride, - T *const output, - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels) -{ - _biases = biases; - _output_workspace = output_workingspace; - _matrix_stride = matrix_stride; - _matrix_row_stride = roundup(n_channels, WinogradConv::N_BLOCK); - _output = output; - _n_batches = n_batches; - _n_rows = n_rows; - _n_cols = n_cols; - _n_channels = n_channels; - - // We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window - OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, _output, _n_batches, _n_rows, _n_cols, _n_channels); - Window win; - auto win_last = output_transform.get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); - INEKernel::configure(win); -} - -template -void NEWinogradLayerTransformOutputKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace); - ARM_COMPUTE_ERROR_ON_NULLPTR(_output); - - OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, - (_biases ? reinterpret_cast(_biases->buffer()) : nullptr), _output, - _n_batches, _n_rows, _n_cols, _n_channels); - - // The code below cannot be moved to configure because biases hasn't been allocated at that point - const size_t fst = window.x().start(); - const size_t lst = window.x().end(); - output_transform.run(fst, lst); -} - -template -bool NEWinogradLayerTransformOutputKernel::is_parallelisable() const -{ - return false; -} - -template -Status NEWinogradLayerTransformOutputKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, - const WinogradInfo &winograd_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_output_trans(input->clone().get(), (bias != nullptr ? bias->clone().get() : nullptr), output->clone().get(), - winograd_info) - .first); - - return Status{}; -} - -template class NEWinogradLayerTransformOutputKernel; -template class NEWinogradLayerTransformOutputKernel; - -} // namespace arm_compute diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp index 906378c565..7a37dfa39d 100644 --- a/src/graph/backends/NEON/NEFunctionFactory.cpp +++ b/src/graph/backends/NEON/NEFunctionFactory.cpp @@ -169,8 +169,8 @@ std::unique_ptr create_convolution_layer(ConvolutionLayerNode &node, } else if(conv_algorithm == ConvolutionMethod::WINOGRAD) { - std::tie(func, func_name) = create_named_memory_managed_function(std::string("NEWinogradLayer"), mm, - input, weights, biases, output, conv_info); + std::tie(func, func_name) = create_named_memory_managed_function(std::string("NEWinogradConvolutionLayer"), mm, + input, weights, biases, output, conv_info); } else { diff --git a/src/graph/backends/NEON/NENodeValidator.cpp b/src/graph/backends/NEON/NENodeValidator.cpp index 074f03580f..e438e79c76 100644 --- a/src/graph/backends/NEON/NENodeValidator.cpp +++ b/src/graph/backends/NEON/NENodeValidator.cpp @@ -51,7 +51,7 @@ Status NENodeValidator::validate(INode *node) return detail::validate_convolution_layer(*polymorphic_downcast(node)); + NEWinogradConvolutionLayer>(*polymorphic_downcast(node)); case NodeType::DepthwiseConvolutionLayer: return detail::validate_depthwise_convolution_layer(*polymorphic_downcast(node)); diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp index 61ea2db15b..0ad4babedc 100644 --- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp @@ -51,7 +51,7 @@ void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const { case ConvolutionMethod::WINOGRAD: { - auto f = arm_compute::support::cpp14::make_unique(_memory_manager); + auto f = arm_compute::support::cpp14::make_unique(_memory_manager); f->configure(input, weights, biases, output, conv_info, act_info); _function = std::move(f); break; @@ -83,7 +83,7 @@ Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo { case ConvolutionMethod::WINOGRAD: //Validate Winograd - NEWinogradLayer::validate(input, weights, biases, output, conv_info, act_info); + NEWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info); break; case ConvolutionMethod::GEMM: //Validate Gemm-based Convolution diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp new file mode 100644 index 0000000000..a1256ac8cb --- /dev/null +++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp @@ -0,0 +1,390 @@ +/* + * Copyright (c) 2017-2018 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/NEON/functions/NEWinogradConvolutionLayer.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/NEON/AssemblyHelper.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" +#include "support/ToolchainSupport.h" + +#include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h" + +#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" + +namespace +{ +inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input) +{ + const int in_width = input->info()->dimension(0); + const int in_height = input->info()->dimension(1); + const int in_batches = input->info()->dimension(3); + const int in_channels = input->info()->dimension(2); + return Tensor4DShape({ in_batches, in_height, in_width, in_channels }); +} +} /* namespace */ + +namespace arm_compute +{ +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != 3 && weights->dimension(0) != 5, "Only 3 and 5 kernels are supported"); + ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); + + if(biases != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); + } + + // Get parameters from conv_info + unsigned int stride_x = 0; + unsigned int stride_y = 0; + std::tie(stride_x, stride_y) = conv_info.stride(); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides."); + + ARM_COMPUTE_UNUSED(output); + return Status{}; +} +} //namespace + +NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(std::shared_ptr memory_manager) + : _memory_group(std::move(memory_manager)), _arm_gemm(nullptr), _gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), + _activationlayer_function(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), + _workspace(), _input(), _weights(), _output(), _reshaped_kernel(false), _is_activationlayer_enabled(false) +{ +} /* arm_compute */ + +void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_UNUSED(conv_info); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info)); + + _weights = weights; + _input = input; + _output = output; + + std::unique_ptr> transform_input_kernel; + std::unique_ptr> transform_weights_kernel; + std::unique_ptr> transform_output_kernel; + + const int weights_width = weights->info()->dimension(0); + const int weights_height = weights->info()->dimension(1); + + int output_tile_rows = 0; + int output_tile_cols = 0; + int n_gemms = 0; + int N_BLOCK = 0; // Size of block used by GEMM. + + switch(weights_width) + { + case 3: + { + transform_input_kernel = support::cpp14::make_unique>(); + transform_weights_kernel = support::cpp14::make_unique>(); + transform_output_kernel = support::cpp14::make_unique>(); + output_tile_rows = 2; + output_tile_cols = 2; + n_gemms = NEWinogradLayerBatchedGEMMKernel::WinogradBase::N_GEMMS; + N_BLOCK = NEWinogradLayerBatchedGEMMKernel::WinogradConv::N_BLOCK; + break; + } + case 5: + { + transform_input_kernel = support::cpp14::make_unique>(); + transform_weights_kernel = support::cpp14::make_unique>(); + transform_output_kernel = support::cpp14::make_unique>(); + output_tile_rows = 2; + output_tile_cols = 2; + n_gemms = NEWinogradLayerBatchedGEMMKernel::WinogradBase::N_GEMMS; + N_BLOCK = NEWinogradLayerBatchedGEMMKernel::WinogradConv::N_BLOCK; + break; + } + default: + { + ARM_COMPUTE_ERROR("Not supported."); + break; + } + } + + const PaddingType use_padding_type = (conv_info.pad_left() != 0u) ? PADDING_SAME : PADDING_VALID; + const bool use_same_padding = use_padding_type == PADDING_SAME; + + // Get parameters from conv_info + unsigned int stride_x = 0; + unsigned int stride_y = 0; + std::tie(stride_x, stride_y) = conv_info.stride(); + ARM_COMPUTE_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides."); + + // Get convolved dimensions + const int in_channels = input->info()->dimension(2); + const int out_channels = output->info()->dimension(2); + + const Tensor4DShape in_shape(internal_get_input_shape(input)); + const size_t data_type_size = input->info()->element_size(); + // Get the memory required to instantiate a new Winograd operator. + constexpr size_t storage_alignment = 64; + const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size; + _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8)); + _kernel_storage.allocator()->allocate(); + // Input storage + const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size; + _input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8)); + _input_workspace.allocator()->allocate(); + + // Output storage + const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, use_same_padding) * data_type_size; + _output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8)); + _output_workspace.allocator()->allocate(); + + // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output() + TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0), + _output->info()->dimension(1), _output->info()->dimension(3)), + 1, _output->info()->data_type()); + _output_nhwc.allocator()->init(info); + _output_nhwc.allocator()->allocate(); + + // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map] + _permute_weights.configure(weights, &_weights_hwio, PermutationVector(3U, 2U, 0U, 1U)); + _weights_hwio.allocator()->allocate(); + + // configure the kernel to transform the input tensor from NCHW -> NHWC + _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U)); + _input_nhwc.allocator()->allocate(); + + const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels }); + + // Configure the InputTransform + const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); + transform_input_kernel->configure(reinterpret_cast(_input_nhwc.buffer()), in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type, + reinterpret_cast(_input_workspace.buffer()), input_matrix_stride); + + // Configure WeightsTransform + const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(kernel_shape); + transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels); + + // Configure OutputTransform + //The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method + const int output_matrix_stride = transform_output_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); + const auto output_shape(transform_output_kernel->get_output_shape(kernel_shape, in_shape, use_padding_type)); + + transform_output_kernel->configure(biases, reinterpret_cast(_output_workspace.buffer()), + output_matrix_stride, reinterpret_cast(_output_nhwc.buffer()), + in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels); + + // Configure GEMM + const int tile_rows = iceildiv(output_shape.n_rows, output_tile_rows); + const int tile_cols = iceildiv(output_shape.n_cols, output_tile_cols); + const int m = in_shape.n_batches * tile_rows * tile_cols; + const int k = in_shape.n_channels; + const int n = out_channels; + const int input_matrix_row_stride = in_shape.n_channels; + const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK); + const int output_matrix_row_stride = kernel_matrix_row_stride; + unsigned int num_threads = NEScheduler::get().num_threads(); + + _arm_gemm = arm_gemm::gemm(NEScheduler::get().cpu_info(), m, n, k, 1, n_gemms, false, false, 1.f, 0.f, num_threads, false); + _arm_gemm->set_arrays(reinterpret_cast(_input_workspace.buffer()), input_matrix_row_stride, 0, input_matrix_stride, reinterpret_cast(_kernel_storage.buffer()), + kernel_matrix_row_stride, kernel_matrix_stride, reinterpret_cast(_output_workspace.buffer()), output_matrix_row_stride, 0, output_matrix_stride); + + auto acl_gemm_wrapper = support::cpp14::make_unique>>(); + acl_gemm_wrapper->configure(_arm_gemm.get()); + const size_t workspace_size = _arm_gemm->get_working_size(); + + // Allocate workspace + if(workspace_size > 0) + { + const unsigned int alignment = 4096; + allocate_workspace(workspace_size, _workspace, _memory_group, alignment, 1); + _arm_gemm->set_working_space(reinterpret_cast(_workspace.buffer())); + } + + const unsigned int window_size = _arm_gemm->get_window_size(); + if(window_size < num_threads) + { + num_threads = window_size; + _arm_gemm->set_nthreads(num_threads); + } + + _gemm_kernel = std::move(acl_gemm_wrapper); + + // Reorder the convoluted output to ACL's ordering NCHW + _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U)); + + _transform_input_kernel = std::move(transform_input_kernel); + _transform_weights_kernel = std::move(transform_weights_kernel); + _transform_output_kernel = std::move(transform_output_kernel); + + //Configure Activation Layer + _is_activationlayer_enabled = act_info.enabled(); + if(_is_activationlayer_enabled) + { + _activationlayer_function.configure(output, nullptr, act_info); + } +} + +void NEWinogradConvolutionLayer::run() +{ + _memory_group.acquire(); + if(!_reshaped_kernel) + { + _reshaped_kernel = true; + _permute_weights.run(); + NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX); + } + //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC + _permute_input.run(); + + // Transform input tensor to the winograd domain + NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX); + + //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs + NEScheduler::get().schedule(_gemm_kernel.get(), Window::DimX); + + // Transform output tensor to the spatial domain + NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX); + + // Reorder the convoluted output to ACL's ordering NCHW + _permute_output.run(); + + if(_is_activationlayer_enabled) + { + _activationlayer_function.run(); + } + _memory_group.release(); +} + +Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info)); + + // 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 + const TensorShape input_shape = input->tensor_shape(); + + // Kernel size + const unsigned int kernel_w = weights->tensor_shape()[idx_width]; + const unsigned int kernel_h = weights->tensor_shape()[idx_height]; + + const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2), + Size2D(kernel_w, kernel_h), + Size2D(input_shape[idx_width], input_shape[idx_height]), + conv_info, + input->data_layout()); + + // Validate input transform + const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); + const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape); + switch(weights->dimension(0)) + { + case 3: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, winograd_info))); + break; + } + case 5: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, winograd_info))); + break; + } + default: + { + ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); + break; + } + } + // Validate filter transform + const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); + const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); + + switch(weights->dimension(0)) + { + case 3: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, winograd_info))); + break; + } + case 5: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, winograd_info))); + break; + } + default: + { + ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); + break; + } + } + // Validate batched matrix multiply + TensorShape batched_mm_output_shape = input0.tensor_shape(); + batched_mm_output_shape[0] = input1.tensor_shape()[0]; + const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); + switch(weights->dimension(0)) + { + case 3: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, + true /* Reshape weights only for the first run*/)))); + // Validate output transform + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, winograd_info))); + break; + } + case 5: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, + true /* Reshape weights only for the first run*/)))); + // Validate output transform + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, winograd_info))); + break; + } + default: + { + ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); + break; + } + } + + // Validate Activation Layer + if(act_info.enabled()) + { + NEActivationLayer::validate(output, nullptr, act_info); + } + return Status{}; +} + +} // namespace arm_compute diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp deleted file mode 100644 index 7d93bcff07..0000000000 --- a/src/runtime/NEON/functions/NEWinogradLayer.cpp +++ /dev/null @@ -1,390 +0,0 @@ -/* - * Copyright (c) 2017-2018 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/NEON/functions/NEWinogradLayer.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/runtime/NEON/AssemblyHelper.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" -#include "support/ToolchainSupport.h" - -#include "arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h" - -#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" - -namespace -{ -inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input) -{ - const int in_width = input->info()->dimension(0); - const int in_height = input->info()->dimension(1); - const int in_batches = input->info()->dimension(3); - const int in_channels = input->info()->dimension(2); - return Tensor4DShape({ in_batches, in_height, in_width, in_channels }); -} -} /* namespace */ - -namespace arm_compute -{ -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != 3 && weights->dimension(0) != 5, "Only 3 and 5 kernels are supported"); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); - - if(biases != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - // Get parameters from conv_info - unsigned int stride_x = 0; - unsigned int stride_y = 0; - std::tie(stride_x, stride_y) = conv_info.stride(); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides."); - - ARM_COMPUTE_UNUSED(output); - return Status{}; -} -} //namespace - -NEWinogradLayer::NEWinogradLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _arm_gemm(nullptr), _gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), - _activationlayer_function(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), - _workspace(), _input(), _weights(), _output(), _reshaped_kernel(false), _is_activationlayer_enabled(false) -{ -} /* arm_compute */ - -void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_UNUSED(conv_info); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info)); - - _weights = weights; - _input = input; - _output = output; - - std::unique_ptr> transform_input_kernel; - std::unique_ptr> transform_weights_kernel; - std::unique_ptr> transform_output_kernel; - - const int weights_width = weights->info()->dimension(0); - const int weights_height = weights->info()->dimension(1); - - int output_tile_rows = 0; - int output_tile_cols = 0; - int n_gemms = 0; - int N_BLOCK = 0; // Size of block used by GEMM. - - switch(weights_width) - { - case 3: - { - transform_input_kernel = support::cpp14::make_unique>(); - transform_weights_kernel = support::cpp14::make_unique>(); - transform_output_kernel = support::cpp14::make_unique>(); - output_tile_rows = 2; - output_tile_cols = 2; - n_gemms = NEWinogradLayerBatchedGEMMKernel::WinogradBase::N_GEMMS; - N_BLOCK = NEWinogradLayerBatchedGEMMKernel::WinogradConv::N_BLOCK; - break; - } - case 5: - { - transform_input_kernel = support::cpp14::make_unique>(); - transform_weights_kernel = support::cpp14::make_unique>(); - transform_output_kernel = support::cpp14::make_unique>(); - output_tile_rows = 2; - output_tile_cols = 2; - n_gemms = NEWinogradLayerBatchedGEMMKernel::WinogradBase::N_GEMMS; - N_BLOCK = NEWinogradLayerBatchedGEMMKernel::WinogradConv::N_BLOCK; - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported."); - break; - } - } - - const PaddingType use_padding_type = (conv_info.pad_left() != 0u) ? PADDING_SAME : PADDING_VALID; - const bool use_same_padding = use_padding_type == PADDING_SAME; - - // Get parameters from conv_info - unsigned int stride_x = 0; - unsigned int stride_y = 0; - std::tie(stride_x, stride_y) = conv_info.stride(); - ARM_COMPUTE_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides."); - - // Get convolved dimensions - const int in_channels = input->info()->dimension(2); - const int out_channels = output->info()->dimension(2); - - const Tensor4DShape in_shape(internal_get_input_shape(input)); - const size_t data_type_size = input->info()->element_size(); - // Get the memory required to instantiate a new Winograd operator. - constexpr size_t storage_alignment = 64; - const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size; - _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8)); - _kernel_storage.allocator()->allocate(); - // Input storage - const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size; - _input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8)); - _input_workspace.allocator()->allocate(); - - // Output storage - const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, use_same_padding) * data_type_size; - _output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8)); - _output_workspace.allocator()->allocate(); - - // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output() - TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0), - _output->info()->dimension(1), _output->info()->dimension(3)), - 1, _output->info()->data_type()); - _output_nhwc.allocator()->init(info); - _output_nhwc.allocator()->allocate(); - - // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map] - _permute_weights.configure(weights, &_weights_hwio, PermutationVector(3U, 2U, 0U, 1U)); - _weights_hwio.allocator()->allocate(); - - // configure the kernel to transform the input tensor from NCHW -> NHWC - _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U)); - _input_nhwc.allocator()->allocate(); - - const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels }); - - // Configure the InputTransform - const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); - transform_input_kernel->configure(reinterpret_cast(_input_nhwc.buffer()), in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type, - reinterpret_cast(_input_workspace.buffer()), input_matrix_stride); - - // Configure WeightsTransform - const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(kernel_shape); - transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels); - - // Configure OutputTransform - //The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method - const int output_matrix_stride = transform_output_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); - const auto output_shape(transform_output_kernel->get_output_shape(kernel_shape, in_shape, use_padding_type)); - - transform_output_kernel->configure(biases, reinterpret_cast(_output_workspace.buffer()), - output_matrix_stride, reinterpret_cast(_output_nhwc.buffer()), - in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels); - - // Configure GEMM - const int tile_rows = iceildiv(output_shape.n_rows, output_tile_rows); - const int tile_cols = iceildiv(output_shape.n_cols, output_tile_cols); - const int m = in_shape.n_batches * tile_rows * tile_cols; - const int k = in_shape.n_channels; - const int n = out_channels; - const int input_matrix_row_stride = in_shape.n_channels; - const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK); - const int output_matrix_row_stride = kernel_matrix_row_stride; - unsigned int num_threads = NEScheduler::get().num_threads(); - - _arm_gemm = arm_gemm::gemm(NEScheduler::get().cpu_info(), m, n, k, 1, n_gemms, false, false, 1.f, 0.f, num_threads, false); - _arm_gemm->set_arrays(reinterpret_cast(_input_workspace.buffer()), input_matrix_row_stride, 0, input_matrix_stride, reinterpret_cast(_kernel_storage.buffer()), - kernel_matrix_row_stride, kernel_matrix_stride, reinterpret_cast(_output_workspace.buffer()), output_matrix_row_stride, 0, output_matrix_stride); - - auto acl_gemm_wrapper = support::cpp14::make_unique>>(); - acl_gemm_wrapper->configure(_arm_gemm.get()); - const size_t workspace_size = _arm_gemm->get_working_size(); - - // Allocate workspace - if(workspace_size > 0) - { - const unsigned int alignment = 4096; - allocate_workspace(workspace_size, _workspace, _memory_group, alignment, 1); - _arm_gemm->set_working_space(reinterpret_cast(_workspace.buffer())); - } - - const unsigned int window_size = _arm_gemm->get_window_size(); - if(window_size < num_threads) - { - num_threads = window_size; - _arm_gemm->set_nthreads(num_threads); - } - - _gemm_kernel = std::move(acl_gemm_wrapper); - - // Reorder the convoluted output to ACL's ordering NCHW - _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U)); - - _transform_input_kernel = std::move(transform_input_kernel); - _transform_weights_kernel = std::move(transform_weights_kernel); - _transform_output_kernel = std::move(transform_output_kernel); - - //Configure Activation Layer - _is_activationlayer_enabled = act_info.enabled(); - if(_is_activationlayer_enabled) - { - _activationlayer_function.configure(output, nullptr, act_info); - } -} - -void NEWinogradLayer::run() -{ - _memory_group.acquire(); - if(!_reshaped_kernel) - { - _reshaped_kernel = true; - _permute_weights.run(); - NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX); - } - //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC - _permute_input.run(); - - // Transform input tensor to the winograd domain - NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX); - - //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs - NEScheduler::get().schedule(_gemm_kernel.get(), Window::DimX); - - // Transform output tensor to the spatial domain - NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX); - - // Reorder the convoluted output to ACL's ordering NCHW - _permute_output.run(); - - if(_is_activationlayer_enabled) - { - _activationlayer_function.run(); - } - _memory_group.release(); -} - -Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info)); - - // 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 - const TensorShape input_shape = input->tensor_shape(); - - // Kernel size - const unsigned int kernel_w = weights->tensor_shape()[idx_width]; - const unsigned int kernel_h = weights->tensor_shape()[idx_height]; - - const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2), - Size2D(kernel_w, kernel_h), - Size2D(input_shape[idx_width], input_shape[idx_height]), - conv_info, - input->data_layout()); - - // Validate input transform - const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); - const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape); - switch(weights->dimension(0)) - { - case 3: - { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, winograd_info))); - break; - } - case 5: - { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, winograd_info))); - break; - } - default: - { - ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); - break; - } - } - // Validate filter transform - const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); - const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); - - switch(weights->dimension(0)) - { - case 3: - { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, winograd_info))); - break; - } - case 5: - { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, winograd_info))); - break; - } - default: - { - ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); - break; - } - } - // Validate batched matrix multiply - TensorShape batched_mm_output_shape = input0.tensor_shape(); - batched_mm_output_shape[0] = input1.tensor_shape()[0]; - const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); - switch(weights->dimension(0)) - { - case 3: - { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, - true /* Reshape weights only for the first run*/)))); - // Validate output transform - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, winograd_info))); - break; - } - case 5: - { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, - true /* Reshape weights only for the first run*/)))); - // Validate output transform - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, winograd_info))); - break; - } - default: - { - ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); - break; - } - } - - // Validate Activation Layer - if(act_info.enabled()) - { - NEActivationLayer::validate(output, nullptr, act_info); - } - return Status{}; -} - -} // namespace arm_compute diff --git a/tests/benchmark/CL/ConvolutionLayer.cpp b/tests/benchmark/CL/ConvolutionLayer.cpp index 3cf04295e4..e1cf99b573 100644 --- a/tests/benchmark/CL/ConvolutionLayer.cpp +++ b/tests/benchmark/CL/ConvolutionLayer.cpp @@ -29,7 +29,7 @@ #include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h" #include "tests/CL/CLAccessor.h" #include "tests/benchmark/fixtures/ConvolutionLayerFixture.h" -#include "tests/benchmark/fixtures/WinogradLayerFixture.h" +#include "tests/benchmark/fixtures/WinogradConvolutionLayerFixture.h" #include "tests/datasets/system_tests/alexnet/AlexNetConvolutionLayerDataset.h" #include "tests/datasets/system_tests/googlenet/inceptionv1/GoogLeNetInceptionV1ConvolutionLayerDataset.h" #include "tests/datasets/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4ConvolutionLayerDataset.h" @@ -57,7 +57,7 @@ using CLGEMMConvolutionLayerFixture = ConvolutionLayerFixture; +using CLWinogradLayerFixture = WinogradConvolutionLayerFixture; REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetWinogradLayer, CLWinogradLayerFixture, framework::DatasetMode::ALL, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetWinogradLayerDataset(), diff --git a/tests/benchmark/NEON/ConvolutionLayer.cpp b/tests/benchmark/NEON/ConvolutionLayer.cpp index e7f2788020..ac27e7ad31 100644 --- a/tests/benchmark/NEON/ConvolutionLayer.cpp +++ b/tests/benchmark/NEON/ConvolutionLayer.cpp @@ -24,12 +24,12 @@ #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" -#include "arm_compute/runtime/NEON/functions/NEWinogradLayer.h" +#include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/runtime/TensorAllocator.h" #include "tests/NEON/Accessor.h" #include "tests/benchmark/fixtures/ConvolutionLayerFixture.h" -#include "tests/benchmark/fixtures/WinogradLayerFixture.h" +#include "tests/benchmark/fixtures/WinogradConvolutionLayerFixture.h" #include "tests/datasets/system_tests/alexnet/AlexNetConvolutionLayerDataset.h" #include "tests/datasets/system_tests/googlenet/inceptionv1/GoogLeNetInceptionV1ConvolutionLayerDataset.h" #include "tests/datasets/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4ConvolutionLayerDataset.h" @@ -62,27 +62,27 @@ using NEGEMMConvolutionLayerFixture = ConvolutionLayerFixture; +using NEWinogradConvolutionLayerFixture = WinogradConvolutionLayerFixture; -REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetWinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::ALL, +REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetWinogradLayer, NEWinogradConvolutionLayerFixture, framework::DatasetMode::ALL, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetWinogradLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Batches", 1))); -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1WinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::ALL, +REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1WinogradLayer, NEWinogradConvolutionLayerFixture, framework::DatasetMode::ALL, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1WinogradLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Batches", 1))); -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4WinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::ALL, +REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4WinogradLayer, NEWinogradConvolutionLayerFixture, framework::DatasetMode::ALL, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4WinogradLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Batches", 1))); -REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetWinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::ALL, +REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetWinogradLayer, NEWinogradConvolutionLayerFixture, framework::DatasetMode::ALL, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetWinogradLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), framework::dataset::make("DataType", DataType::F32)), @@ -170,25 +170,25 @@ REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2ConvolutionLayer, NEGEMMConvolutionLayerFi framework::dataset::make("Batches", { 1, 4, 8 }))); #if defined(__aarch64__) -REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetWinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::NIGHTLY, +REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetWinogradLayer, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetWinogradLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Batches", { 4, 8 }))); -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1WinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::NIGHTLY, +REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1WinogradLayer, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1WinogradLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Batches", { 4, 8 }))); -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4WinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::NIGHTLY, +REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4WinogradLayer, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4WinogradLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Batches", { 4, 8 }))); -REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetWinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::NIGHTLY, +REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetWinogradLayer, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetWinogradLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), framework::dataset::make("DataType", DataType::F32)), diff --git a/tests/benchmark/fixtures/WinogradConvolutionLayerFixture.h b/tests/benchmark/fixtures/WinogradConvolutionLayerFixture.h new file mode 100644 index 0000000000..8ed75af664 --- /dev/null +++ b/tests/benchmark/fixtures/WinogradConvolutionLayerFixture.h @@ -0,0 +1,101 @@ +/* + * Copyright (c) 2018 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. + */ +#ifndef ARM_COMPUTE_TEST_WINOGRAD_CONVOLUTION_LAYER_FIXTURE +#define ARM_COMPUTE_TEST_WINOGRAD_CONVOLUTION_LAYER_FIXTURE + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "tests/Globals.h" +#include "tests/Utils.h" +#include "tests/framework/Fixture.h" + +namespace arm_compute +{ +namespace test +{ +namespace benchmark +{ +/** Fixture that can be used for NEON and CL */ +template +class WinogradConvolutionLayerFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, ActivationLayerInfo act_info, DataType data_type, + int batches) + { + ARM_COMPUTE_UNUSED(dilation); + + // Set batched in source and destination shapes + const unsigned int fixed_point_position = 4; + src_shape.set(3 /* batch */, batches); + dst_shape.set(3 /* batch */, batches); + DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; + + // Create tensors + src = create_tensor(src_shape, data_type, 1, fixed_point_position); + weights = create_tensor(weights_shape, data_type, 1, fixed_point_position); + biases = create_tensor(biases_shape, bias_data_type, 1, fixed_point_position); + dst = create_tensor(dst_shape, data_type, 1, fixed_point_position); + + // Create and configure function + conv_layer.configure(&src, &weights, &biases, &dst, info, act_info); + + // Allocate tensors + src.allocator()->allocate(); + weights.allocator()->allocate(); + biases.allocator()->allocate(); + dst.allocator()->allocate(); + } + + void run() + { + conv_layer.run(); + } + + void sync() + { + sync_if_necessary(); + sync_tensor_if_necessary(dst); + } + + void teardown() + { + src.allocator()->free(); + weights.allocator()->free(); + biases.allocator()->free(); + dst.allocator()->free(); + } + +private: + TensorType src{}; + TensorType weights{}; + TensorType biases{}; + TensorType dst{}; + Function conv_layer{}; +}; +} // namespace benchmark +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_WINOGRAD_CONVOLUTION_LAYER_FIXTURE */ diff --git a/tests/benchmark/fixtures/WinogradLayerFixture.h b/tests/benchmark/fixtures/WinogradLayerFixture.h deleted file mode 100644 index 0be535f4cc..0000000000 --- a/tests/benchmark/fixtures/WinogradLayerFixture.h +++ /dev/null @@ -1,101 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ -#ifndef ARM_COMPUTE_TEST_WINOGRADLAYERFIXTURE -#define ARM_COMPUTE_TEST_WINOGRADLAYERFIXTURE - -#include "arm_compute/core/TensorShape.h" -#include "arm_compute/core/Types.h" -#include "tests/Globals.h" -#include "tests/Utils.h" -#include "tests/framework/Fixture.h" - -namespace arm_compute -{ -namespace test -{ -namespace benchmark -{ -/** Fixture that can be used for NEON and CL */ -template -class WinogradLayerFixture : public framework::Fixture -{ -public: - template - void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, ActivationLayerInfo act_info, DataType data_type, - int batches) - { - ARM_COMPUTE_UNUSED(dilation); - - // Set batched in source and destination shapes - const unsigned int fixed_point_position = 4; - src_shape.set(3 /* batch */, batches); - dst_shape.set(3 /* batch */, batches); - DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; - - // Create tensors - src = create_tensor(src_shape, data_type, 1, fixed_point_position); - weights = create_tensor(weights_shape, data_type, 1, fixed_point_position); - biases = create_tensor(biases_shape, bias_data_type, 1, fixed_point_position); - dst = create_tensor(dst_shape, data_type, 1, fixed_point_position); - - // Create and configure function - conv_layer.configure(&src, &weights, &biases, &dst, info, act_info); - - // Allocate tensors - src.allocator()->allocate(); - weights.allocator()->allocate(); - biases.allocator()->allocate(); - dst.allocator()->allocate(); - } - - void run() - { - conv_layer.run(); - } - - void sync() - { - sync_if_necessary(); - sync_tensor_if_necessary(dst); - } - - void teardown() - { - src.allocator()->free(); - weights.allocator()->free(); - biases.allocator()->free(); - dst.allocator()->free(); - } - -private: - TensorType src{}; - TensorType weights{}; - TensorType biases{}; - TensorType dst{}; - Function conv_layer{}; -}; -} // namespace benchmark -} // namespace test -} // namespace arm_compute -#endif /* ARM_COMPUTE_TEST_WINOGRADLAYERFIXTURE */ diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp index 6e673a5f96..30d8d751af 100644 --- a/tests/validation/CL/Winograd.cpp +++ b/tests/validation/CL/Winograd.cpp @@ -41,7 +41,7 @@ #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Validation.h" -#include "tests/validation/fixtures/WinogradLayerFixture.h" +#include "tests/validation/fixtures/WinogradConvolutionLayerFixture.h" namespace arm_compute { diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp index 3a365253cb..8b2e21e796 100644 --- a/tests/validation/NEON/ConvolutionLayer.cpp +++ b/tests/validation/NEON/ConvolutionLayer.cpp @@ -24,7 +24,7 @@ #include "arm_compute/core/Types.h" #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" #include "arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h" -#include "arm_compute/runtime/NEON/functions/NEWinogradLayer.h" +#include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/runtime/TensorAllocator.h" #include "tests/NEON/Accessor.h" @@ -37,7 +37,7 @@ #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Validation.h" #include "tests/validation/fixtures/ConvolutionLayerFixture.h" -#include "tests/validation/fixtures/WinogradLayerFixture.h" +#include "tests/validation/fixtures/WinogradConvolutionLayerFixture.h" namespace arm_compute { @@ -109,10 +109,10 @@ TEST_SUITE_END() TEST_SUITE(WinogradLayer) template -using NEWinogradConvolutionLayerFixture = WinogradConvolutionLayerValidationFixture; +using NEWinogradConvolutionLayerFixture = WinogradConvolutionLayerValidationFixture; template -using NEWinogradConvolutionLayerNoBiasFixture = WinogradConvolutionLayerValidationFixture; +using NEWinogradConvolutionLayerNoBiasFixture = WinogradConvolutionLayerValidationFixture; TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, diff --git a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h new file mode 100644 index 0000000000..249f9d5649 --- /dev/null +++ b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h @@ -0,0 +1,389 @@ +/* + * Copyright (c) 2018 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. + */ +#ifndef ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE +#define ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "tests/AssetsLibrary.h" +#include "tests/Globals.h" +#include "tests/IAccessor.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Fixture.h" +#include "tests/validation/Helpers.h" +#include "tests/validation/reference/ActivationLayer.h" +#include "tests/validation/reference/ConvolutionLayer.h" +#include "tests/validation/reference/Utils.h" +#include "tests/validation/reference/Winograd.h" + +#include + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +using namespace arm_compute::misc::shape_calculator; + +template +class WinogradConvolutionLayerValidationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, DataType data_type, ActivationLayerInfo act_info) + { + ARM_COMPUTE_UNUSED(dilation); + + _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info); + _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info); + } + +protected: + template + void fill(U &&tensor, int i, float min, float max) + { + switch(tensor.data_type()) + { + case DataType::F32: + { + std::uniform_real_distribution<> distribution(min, max); + library->fill(tensor, distribution, i); + break; + } + default: + { + ARM_COMPUTE_ERROR("Not supported"); + library->fill_tensor_uniform(tensor, i); + break; + } + } + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, + DataType data_type, ActivationLayerInfo act_info) + { + // Create tensors + TensorType src = create_tensor(input_shape, data_type, 1); + TensorType weights = create_tensor(weights_shape, data_type, 1); + TensorType bias = create_tensor(bias_shape, data_type, 1); + TensorType dst = create_tensor(output_shape, data_type, 1); + + // Create and configure function + FunctionType conv; + ARM_COMPUTE_EXPECT(static_cast(conv.validate(src.info(), weights.info(), (use_bias) ? bias.info() : nullptr, dst.info(), info, act_info)), framework::LogLevel::ERRORS); + conv.configure(&src, &weights, (use_bias) ? &bias : nullptr, &dst, info, act_info); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + weights.allocator()->allocate(); + dst.allocator()->allocate(); + bias.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src), 0, -1.f, 1.f); + fill(AccessorType(weights), 1, -1.f, 1.f); + fill(AccessorType(bias), 2, -1.f, 1.f); + + // Compute Winograd Convolution function + conv.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, + DataType data_type, ActivationLayerInfo act_info) + { + // Create reference + SimpleTensor src{ input_shape, data_type, 1 }; + SimpleTensor weights{ weights_shape, data_type, 1 }; + SimpleTensor bias{ bias_shape, data_type, 1 }; + + // Fill reference + fill(src, 0, -1.f, 1.f); + fill(weights, 1, -1.f, 1.f); + if(use_bias) + { + fill(bias, 2, -1.f, 1.f); + } + else + { + fill(bias, 2, 0.f, 0.f); + } + + SimpleTensor conv_out = reference::convolution_layer(src, weights, bias, output_shape, info); + + return (act_info.enabled()) ? reference::activation_layer(conv_out, act_info) : conv_out; + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + +template +class WinogradInputTransformValidationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape input_shape, WinogradInfo winograd_info, DataLayout data_layout, DataType data_type) + { + TensorShape output_shape = compute_winograd_input_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info); + + _target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type); + _reference = compute_reference(input_shape, output_shape, winograd_info, data_layout, data_type); + } + +protected: + template + void fill(U &&tensor, int i, float min, float max) + { + switch(tensor.data_type()) + { + case DataType::F32: + { + std::uniform_real_distribution<> distribution(min, max); + library->fill(tensor, distribution, i); + break; + } + default: + { + ARM_COMPUTE_ERROR("Not supported"); + library->fill_tensor_uniform(tensor, i); + break; + } + } + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type) + { + TensorType src = create_tensor(input_shape, data_type, 1, 0, QuantizationInfo(), data_layout); + TensorType dst = create_tensor(output_shape, data_type, 1, 0, QuantizationInfo(), data_layout); + + // Create and configure function + FunctionType transf; + transf.configure(&src, &dst, winograd_info); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src), 0, -1.f, 1.f); + + // Compute Winograd input transform function + transf.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type) + { + // Create reference + SimpleTensor src{ input_shape, data_type, 1, 0, QuantizationInfo(), data_layout }; + + // Fill reference + fill(src, 0, -1.f, 1.f); + + return reference::winograd_input_transform(src, output_shape, winograd_info); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + +template +class WinogradFilterTransformValidationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape input_shape, Size2D output_tile, DataLayout data_layout, DataType data_type) + { + WinogradInfo winograd_info(output_tile, Size2D(input_shape[0], input_shape[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */); + TensorShape output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info); + + _target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type); + _reference = compute_reference(input_shape, output_shape, winograd_info, data_layout, data_type); + } + +protected: + template + void fill(U &&tensor, int i, float min, float max) + { + switch(tensor.data_type()) + { + case DataType::F32: + { + std::uniform_real_distribution<> distribution(min, max); + library->fill(tensor, distribution, i); + break; + } + default: + { + ARM_COMPUTE_ERROR("Not supported"); + library->fill_tensor_uniform(tensor, i); + break; + } + } + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type) + { + // Create tensors + TensorType src = create_tensor(input_shape, data_type, 1, 0, QuantizationInfo(), data_layout); + TensorType dst = create_tensor(output_shape, data_type, 1, 0, QuantizationInfo(), data_layout); + + // Create and configure function + FunctionType filter_transform; + filter_transform.configure(&src, &dst, winograd_info); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src), 0, -1.f, 1.f); + + filter_transform.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type) + { + // Create reference + SimpleTensor src{ input_shape, data_type, 1, 0, QuantizationInfo(), data_layout }; + + // Fill reference + fill(src, 0, -1.f, 1.f); + + return reference::winograd_filter_transform(src, output_shape, winograd_info); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + +template +class WinogradOutputTransformValidationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape input_shape, WinogradInfo winograd_info, DataType data_type) + { + TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info); + + _target = compute_target(input_shape, output_shape, winograd_info, data_type); + _reference = compute_reference(input_shape, output_shape, winograd_info, data_type); + } + +protected: + template + void fill(U &&tensor, int i, float min, float max) + { + switch(tensor.data_type()) + { + case DataType::F32: + { + std::uniform_real_distribution<> distribution(min, max); + library->fill(tensor, distribution, i); + break; + } + default: + { + ARM_COMPUTE_ERROR("Not supported"); + library->fill_tensor_uniform(tensor, i); + break; + } + } + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataType data_type) + { + // Create tensors + TensorType src = create_tensor(input_shape, data_type); + TensorType dst = create_tensor(output_shape, data_type, 1, 0, QuantizationInfo(), winograd_info.output_data_layout); + + // Create and configure function + FunctionType output_transform; + output_transform.configure(&src, nullptr, &dst, winograd_info); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src), 0, -1.f, 1.f); + + output_transform.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataType data_type) + { + // Create reference + SimpleTensor src{ input_shape, data_type }; + + // Fill reference + fill(src, 0, -1.f, 1.f); + + return reference::winograd_output_transform(src, output_shape, winograd_info); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE */ diff --git a/tests/validation/fixtures/WinogradLayerFixture.h b/tests/validation/fixtures/WinogradLayerFixture.h deleted file mode 100644 index 173444ccd8..0000000000 --- a/tests/validation/fixtures/WinogradLayerFixture.h +++ /dev/null @@ -1,389 +0,0 @@ -/* - * Copyright (c) 2017-2018 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. - */ -#ifndef ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE -#define ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE - -#include "arm_compute/core/TensorShape.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "tests/AssetsLibrary.h" -#include "tests/Globals.h" -#include "tests/IAccessor.h" -#include "tests/framework/Asserts.h" -#include "tests/framework/Fixture.h" -#include "tests/validation/Helpers.h" -#include "tests/validation/reference/ActivationLayer.h" -#include "tests/validation/reference/ConvolutionLayer.h" -#include "tests/validation/reference/Utils.h" -#include "tests/validation/reference/Winograd.h" - -#include - -namespace arm_compute -{ -namespace test -{ -namespace validation -{ -using namespace arm_compute::misc::shape_calculator; - -template -class WinogradConvolutionLayerValidationFixture : public framework::Fixture -{ -public: - template - void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, DataType data_type, ActivationLayerInfo act_info) - { - ARM_COMPUTE_UNUSED(dilation); - - _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info); - _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info); - } - -protected: - template - void fill(U &&tensor, int i, float min, float max) - { - switch(tensor.data_type()) - { - case DataType::F32: - { - std::uniform_real_distribution<> distribution(min, max); - library->fill(tensor, distribution, i); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - library->fill_tensor_uniform(tensor, i); - break; - } - } - } - - TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, - DataType data_type, ActivationLayerInfo act_info) - { - // Create tensors - TensorType src = create_tensor(input_shape, data_type, 1); - TensorType weights = create_tensor(weights_shape, data_type, 1); - TensorType bias = create_tensor(bias_shape, data_type, 1); - TensorType dst = create_tensor(output_shape, data_type, 1); - - // Create and configure function - FunctionType conv; - ARM_COMPUTE_EXPECT(static_cast(conv.validate(src.info(), weights.info(), (use_bias) ? bias.info() : nullptr, dst.info(), info, act_info)), framework::LogLevel::ERRORS); - conv.configure(&src, &weights, (use_bias) ? &bias : nullptr, &dst, info, act_info); - - ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); - - // Allocate tensors - src.allocator()->allocate(); - weights.allocator()->allocate(); - dst.allocator()->allocate(); - bias.allocator()->allocate(); - - ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); - - // Fill tensors - fill(AccessorType(src), 0, -1.f, 1.f); - fill(AccessorType(weights), 1, -1.f, 1.f); - fill(AccessorType(bias), 2, -1.f, 1.f); - - // Compute Winograd Convolution function - conv.run(); - - return dst; - } - - SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, - DataType data_type, ActivationLayerInfo act_info) - { - // Create reference - SimpleTensor src{ input_shape, data_type, 1 }; - SimpleTensor weights{ weights_shape, data_type, 1 }; - SimpleTensor bias{ bias_shape, data_type, 1 }; - - // Fill reference - fill(src, 0, -1.f, 1.f); - fill(weights, 1, -1.f, 1.f); - if(use_bias) - { - fill(bias, 2, -1.f, 1.f); - } - else - { - fill(bias, 2, 0.f, 0.f); - } - - SimpleTensor conv_out = reference::convolution_layer(src, weights, bias, output_shape, info); - - return (act_info.enabled()) ? reference::activation_layer(conv_out, act_info) : conv_out; - } - - TensorType _target{}; - SimpleTensor _reference{}; -}; - -template -class WinogradInputTransformValidationFixture : public framework::Fixture -{ -public: - template - void setup(TensorShape input_shape, WinogradInfo winograd_info, DataLayout data_layout, DataType data_type) - { - TensorShape output_shape = compute_winograd_input_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info); - - _target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type); - _reference = compute_reference(input_shape, output_shape, winograd_info, data_layout, data_type); - } - -protected: - template - void fill(U &&tensor, int i, float min, float max) - { - switch(tensor.data_type()) - { - case DataType::F32: - { - std::uniform_real_distribution<> distribution(min, max); - library->fill(tensor, distribution, i); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - library->fill_tensor_uniform(tensor, i); - break; - } - } - } - - TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type) - { - TensorType src = create_tensor(input_shape, data_type, 1, 0, QuantizationInfo(), data_layout); - TensorType dst = create_tensor(output_shape, data_type, 1, 0, QuantizationInfo(), data_layout); - - // Create and configure function - FunctionType transf; - transf.configure(&src, &dst, winograd_info); - - ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); - - // Allocate tensors - src.allocator()->allocate(); - dst.allocator()->allocate(); - - ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); - - // Fill tensors - fill(AccessorType(src), 0, -1.f, 1.f); - - // Compute Winograd input transform function - transf.run(); - - return dst; - } - - SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type) - { - // Create reference - SimpleTensor src{ input_shape, data_type, 1, 0, QuantizationInfo(), data_layout }; - - // Fill reference - fill(src, 0, -1.f, 1.f); - - return reference::winograd_input_transform(src, output_shape, winograd_info); - } - - TensorType _target{}; - SimpleTensor _reference{}; -}; - -template -class WinogradFilterTransformValidationFixture : public framework::Fixture -{ -public: - template - void setup(TensorShape input_shape, Size2D output_tile, DataLayout data_layout, DataType data_type) - { - WinogradInfo winograd_info(output_tile, Size2D(input_shape[0], input_shape[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */); - TensorShape output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info); - - _target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type); - _reference = compute_reference(input_shape, output_shape, winograd_info, data_layout, data_type); - } - -protected: - template - void fill(U &&tensor, int i, float min, float max) - { - switch(tensor.data_type()) - { - case DataType::F32: - { - std::uniform_real_distribution<> distribution(min, max); - library->fill(tensor, distribution, i); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - library->fill_tensor_uniform(tensor, i); - break; - } - } - } - - TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type) - { - // Create tensors - TensorType src = create_tensor(input_shape, data_type, 1, 0, QuantizationInfo(), data_layout); - TensorType dst = create_tensor(output_shape, data_type, 1, 0, QuantizationInfo(), data_layout); - - // Create and configure function - FunctionType filter_transform; - filter_transform.configure(&src, &dst, winograd_info); - - ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); - - // Allocate tensors - src.allocator()->allocate(); - dst.allocator()->allocate(); - - ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); - - // Fill tensors - fill(AccessorType(src), 0, -1.f, 1.f); - - filter_transform.run(); - - return dst; - } - - SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type) - { - // Create reference - SimpleTensor src{ input_shape, data_type, 1, 0, QuantizationInfo(), data_layout }; - - // Fill reference - fill(src, 0, -1.f, 1.f); - - return reference::winograd_filter_transform(src, output_shape, winograd_info); - } - - TensorType _target{}; - SimpleTensor _reference{}; -}; - -template -class WinogradOutputTransformValidationFixture : public framework::Fixture -{ -public: - template - void setup(TensorShape input_shape, WinogradInfo winograd_info, DataType data_type) - { - TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info); - - _target = compute_target(input_shape, output_shape, winograd_info, data_type); - _reference = compute_reference(input_shape, output_shape, winograd_info, data_type); - } - -protected: - template - void fill(U &&tensor, int i, float min, float max) - { - switch(tensor.data_type()) - { - case DataType::F32: - { - std::uniform_real_distribution<> distribution(min, max); - library->fill(tensor, distribution, i); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - library->fill_tensor_uniform(tensor, i); - break; - } - } - } - - TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataType data_type) - { - // Create tensors - TensorType src = create_tensor(input_shape, data_type); - TensorType dst = create_tensor(output_shape, data_type, 1, 0, QuantizationInfo(), winograd_info.output_data_layout); - - // Create and configure function - FunctionType output_transform; - output_transform.configure(&src, nullptr, &dst, winograd_info); - - ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); - - // Allocate tensors - src.allocator()->allocate(); - dst.allocator()->allocate(); - - ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); - - // Fill tensors - fill(AccessorType(src), 0, -1.f, 1.f); - - output_transform.run(); - - return dst; - } - - SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataType data_type) - { - // Create reference - SimpleTensor src{ input_shape, data_type }; - - // Fill reference - fill(src, 0, -1.f, 1.f); - - return reference::winograd_output_transform(src, output_shape, winograd_info); - } - - TensorType _target{}; - SimpleTensor _reference{}; -}; -} // namespace validation -} // namespace test -} // namespace arm_compute -#endif /* ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE */ -- cgit v1.2.1