COMPMID-3520: Move ndrange.hpp header from arm_gemm to assembly
Change-Id: I6352a520ce38230cdfbad346b176cb659ab242a7
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3327
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/NEON/NEKernels.h b/arm_compute/core/NEON/NEKernels.h
index dfe0cca..1c87b11 100644
--- a/arm_compute/core/NEON/NEKernels.h
+++ b/arm_compute/core/NEON/NEKernels.h
@@ -150,7 +150,6 @@
#include "arm_compute/core/NEON/kernels/NEWarpKernel.h"
#include "arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h"
#include "arm_compute/core/NEON/kernels/NEWidthConcatenateLayerKernel.h"
-#include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h"
#include "arm_compute/core/NEON/kernels/NEYOLOLayerKernel.h"
#endif /* ARM_COMPUTE_NEKERNELS_H */
diff --git a/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h b/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
deleted file mode 100644
index 1740df0..0000000
--- a/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
+++ /dev/null
@@ -1,596 +0,0 @@
-/*
- * Copyright (c) 2017-2020 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/winograd_layer.hpp"
-
-namespace arm_compute
-{
-// Forward declarations
-class ITensor;
-
-/** Interface for the NEON kernel to perform Winograd input transform. */
-class INEWinogradLayerTransformInputKernel : public INEKernel
-{
-public:
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param num_threads The greatest number of threads that will be used to execute the transform.
- * @return Size of working space required in bytes.
- */
- virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
-
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed input.
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_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 num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
-
- /** Gets the stride between matrices in the input worspace
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Stride expressed in bytes.
- */
- virtual int get_matrix_stride(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
-
- /** Configure the output transform kernel.
- *
- * @param[in] input_nhwc Input tensor in NHWC data layout format.
- * @param[in] num_batches Number of batches in input tensor.
- * @param[in] num_rows Number of rows in input tensor.
- * @param[in] num_cols Number of columns in input tensor.
- * @param[in] num_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.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- */
- virtual void configure(const ITensor *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels,
- const PaddingType padding, ITensor *output, const int matrix_stride, ITensor *workspace) = 0;
-
- /** Destructor */
- virtual ~INEWinogradLayerTransformInputKernel()
- {
- }
-};
-
-/** NEON kernel to perform Winograd input transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class NEWinogradLayerTransformInputKernel : public INEWinogradLayerTransformInputKernel
-{
-public:
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformInputKernel(const NEWinogradLayerTransformInputKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformInputKernel &operator=(const NEWinogradLayerTransformInputKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEWinogradLayerTransformInputKernel(NEWinogradLayerTransformInputKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEWinogradLayerTransformInputKernel &operator=(NEWinogradLayerTransformInputKernel &&) = default;
- /** Default destructor */
- ~NEWinogradLayerTransformInputKernel() = default;
-
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed input.
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_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 num_batches,
- int num_channels,
- int num_rows,
- int num_cols,
- bool same_padding) const override;
-
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
- *
- * @return Size of working space required in bytes.
- */
- unsigned int get_working_space_size(unsigned int num_threads) const override;
-
- /** Gets the stride between matrices in the input worspace
- *
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_channels Number of feature maps in the input tensor.
- * @param[in] num_rows Number of rows in each feature map.
- * @param[in] num_cols Number of columns in each feature map.
- * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
- *
- * @return Stride expressed in bytes.
- */
- int get_matrix_stride(
- int num_batches,
- int num_channels,
- int num_rows,
- int num_cols,
- bool same_padding) const override;
-
- /** Default constructor */
- NEWinogradLayerTransformInputKernel();
-
- const char *name() const override
- {
- return "NEWinogradLayerTransformInputKernel";
- }
-
- /** Configure the output transform kernel.
- *
- * @param[in] input_nhwc Input tensor. Data types supported: F16/F32. Layout supported NHWC.
- * @param[in] num_batches Number of batches in input tensor.
- * @param[in] num_rows Number of rows in input tensor.
- * @param[in] num_cols Number of columns in input tensor.
- * @param[in] num_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.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- */
- void configure(
- const ITensor *input_nhwc,
- const int num_batches,
- const int num_rows,
- const int num_cols,
- const int num_channels,
- const PaddingType padding,
- ITensor *output,
- const int matrix_stride,
- ITensor *workspace) override;
-
- // Inherited methods overridden:
- void run(const Window &window, const ThreadInfo &info) override;
-
- /** Winograd base kernel */
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- /** Winograd convolution kernel */
- using WinogradConv = typename WinogradBase::template Convolution<T, T>;
-
- /** 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: F16/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<T, T>;
-
- std::unique_ptr<InputTransform> _transform{ nullptr };
- const ITensor *_input_nhwc;
- int _num_batches; /**< Number of batches in input tensor. */
- int _num_rows; /**< Number of rows in input tensor. */
- int _num_cols; /**< Number of columns in input tensor. */
- int _num_channels; /**< Number of channels in input tensor. */
- PaddingType _padding; /**< Padding type. */
- ITensor *_output; /**< Base of output matrices. */
- int _matrix_stride; /**< Stride between output matrices. */
- int _padding_top; /**< Padding to apply to the top of the image. */
- int _padding_left; /**< Padding to apply to the left of the image. */
- int _padding_right; /**< Padding to apply to the right of the image. */
- int _padding_bottom; /**< Padding to apply to the bottom of the image. */
- ITensor *_workspace;
-};
-
-/** Interface for the NEON kernel to perform Winograd output transform. */
-class INEWinogradLayerTransformOutputKernel : public INEKernel
-{
-public:
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
- *
- * @return Size of working space required in bytes.
- */
- virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
-
- /** Determine how much memory (in units of TOut) to allocate for the
- * (Winograd domain) output.
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Storage size (in units of TOut) required.
- */
- virtual unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
-
- /** Gets the stride between matrices in the output worspace
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Stride expressed in bytes.
- */
- virtual int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
-
- /** Get the output shape of a convolution.
- *
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] padding_same True if padding is SAME, false otherwise
- *
- * @return Shape of the output tensor
- */
- virtual std::pair<unsigned int, unsigned int> get_output_shape(
- int num_rows, /* Number of rows in each feature map of the input tensor. */
- int num_cols, /* Number of columns in each feature map of the input tensor. */
- bool padding_same /* True if padding is SAME, false otherwise */
- ) const = 0;
-
- /** Configure the output transform kernel.
- *
- * @param[in] biases Pointer to the biases tensor.
- * @param[in] transformed_output 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<float, float>::get_output_matrix_stride()
- * @param[out] output_nhwc Pointer to a tensor in NHWC data layout ordered output tensor, in the spatial domain.
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_rows Number of rows in output tensor.
- * @param[in] num_cols Number of columns in output tensor.
- * @param[in] num_channels Number of feature maps in the output tensor.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- * @param[in] activation Activation to be used
- */
- virtual void configure(
- const ITensor *biases,
- const ITensor *transformed_output,
- const int matrix_stride,
- ITensor *output_nhwc,
- const int num_batches,
- const int num_rows,
- const int num_cols,
- const int num_channels,
- ITensor *workspace,
- const arm_gemm::Activation &activation) = 0;
-
- virtual ~INEWinogradLayerTransformOutputKernel()
- {
- }
-};
-
-/** NEON kernel to perform Winograd output transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-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] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Storage size (in units of TOut) required.
- */
- unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
-
- /** Gets the stride between matrices in the output worspace
- *
- * @param[in] num_batches Number of batches in the output tensor.
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] num_output_channels Number of feature maps in the output tensor.
- *
- * @return Stride expressed in bytes.
- */
- int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
- /** Get the output shape of a convolution.
- *
- * @param[in] num_rows Number of rows in each feature map of the input tensor.
- * @param[in] num_cols Number of columns in each feature map of the input tensor.
- * @param[in] padding_same True if padding is SAME, false otherwise
- *
- * @return Shape of the output tensor
- */
- std::pair<unsigned int, unsigned int> get_output_shape(
- int num_rows, /* Number of rows in each feature map of the input tensor. */
- int num_cols, /* Number of columns in each feature map of the input tensor. */
- bool padding_same) const override;
-
- /** Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
- *
- * @return Size of working space required in bytes.
- */
- unsigned int get_working_space_size(unsigned int num_threads) const override;
-
- /** Configure the output transform kernel.
- *
- * @param[in] biases Pointer to the biases tensor.
- * @param[in] transformed_output 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<float, float>::get_output_matrix_stride()
- * @param[out] output_nhwc Pointer to a tensor with NHWC data layout, in the spatial domain.
- * @param[in] num_batches Number of batches in the input tensor.
- * @param[in] num_rows Number of rows in output tensor.
- * @param[in] num_cols Number of columns in output tensor.
- * @param[in] num_channels Number of feature maps in the output tensor.
- * @param[in] workspace Tensor to be used as the working space during the computation.
- * @param[in] activation Activation to be used
- */
- void configure(
- const ITensor *biases,
- const ITensor *transformed_output,
- const int matrix_stride,
- ITensor *output_nhwc,
- const int num_batches,
- const int num_rows,
- const int num_cols,
- const int num_channels,
- ITensor *workspace,
- const arm_gemm::Activation &activation) 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 NEWinogradLayerTransformOutputKernel
- *
- * @param[in] input Source tensor info with shape [C, N, 16, batches] or [C, N, 36, batches]. Data types supported: F16/F32.
- * @param[in] bias Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
- * @param[in] output Destination tensor info 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<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- using WinogradConv = typename WinogradBase::template Convolution<T, T>;
- using OutputTransform = typename WinogradBase::template OutputTransform<T, T>;
-
- std::unique_ptr<OutputTransform> _transform{ nullptr };
- const ITensor *_biases;
- const ITensor *_transformed_output;
- ITensor *_workspace;
- int _matrix_stride;
- int _matrix_row_stride;
- ITensor *_output_nhwc;
- int _num_batches;
- int _num_rows;
- int _num_cols;
- int _num_channels;
-};
-
-/** Interface for the NEON kernel to perform Winograd weights transform. */
-class INEWinogradLayerTransformWeightsKernel : public INEKernel
-{
-public:
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- INEWinogradLayerTransformWeightsKernel(const INEWinogradLayerTransformWeightsKernel &) = default;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- INEWinogradLayerTransformWeightsKernel &operator=(const INEWinogradLayerTransformWeightsKernel &) = default;
- /** Allow instances of this class to be moved */
- INEWinogradLayerTransformWeightsKernel(INEWinogradLayerTransformWeightsKernel &&) = default;
- /** Allow instances of this class to be moved */
- INEWinogradLayerTransformWeightsKernel &operator=(INEWinogradLayerTransformWeightsKernel &&) = default;
-
- INEWinogradLayerTransformWeightsKernel()
- {
- }
- virtual ~INEWinogradLayerTransformWeightsKernel()
- {
- }
- /** Determine how much memory (in units of T) to allocate for the
- * transformed weights.
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Storage size (in units of T) required.
- */
- virtual unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const = 0;
- /** Gets the stride between matrices in the kernel worspace
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Stride expressed in bytes.
- */
- virtual int get_matrix_stride(int num_output_channels, int num_input_channels) const = 0;
-
- /** Configure the weights transform kernel.
- *
- * @param[in] weights_hwio Pointer to the weights tensor
- * @param[out] 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] num_output_channels Number of filters.
- * @param[in] num_input_channels Number of channels in each filter.
- */
-
- virtual void configure(const ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0;
-
- /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel
- *
- * @param[in] input First tensor input info. Data types supported: F16/F32.
- * @param[in] weights Weights tensor info. Data types supported: same as @p input.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input, const ITensorInfo *weights);
-};
-
-/** NEON kernel to perform Winograd weights transform. */
-template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class NEWinogradLayerTransformWeightsKernel final : public INEWinogradLayerTransformWeightsKernel
-{
-public:
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformWeightsKernel(const NEWinogradLayerTransformWeightsKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEWinogradLayerTransformWeightsKernel &operator=(const NEWinogradLayerTransformWeightsKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEWinogradLayerTransformWeightsKernel(NEWinogradLayerTransformWeightsKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEWinogradLayerTransformWeightsKernel &operator=(NEWinogradLayerTransformWeightsKernel &&) = default;
- /** Default destructor */
- ~NEWinogradLayerTransformWeightsKernel() = default;
-
- /** 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: F16/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:
-
-#ifndef DOXYGEN_SKIP_THIS
- /** Configure the weights transform kernel.
- *
- * @param[in] weights_hwio Pointer to the weights tensor
- * @param[out] 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] num_output_channels Number of filters.
- * @param[in] num_input_channels Number of channels in each filter.
- */
- void configure(const ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) override;
-#endif /* DOXYGEN_SKIP_THIS */
-
- /** Determine how much memory (in units of T) to allocate for the
- * transformed weights.
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Storage size (in units of T) required.
- */
- unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const override;
-
- /** Gets the stride between matrices in the input worspace
- *
- * @param[in] num_output_channels Number of output feature maps.
- * @param[in] num_input_channels Number of input feature maps.
- *
- * @return Stride expressed in bytes.
- */
- int get_matrix_stride(int num_output_channels, int num_input_channels) const override;
- void run(const Window &window, const ThreadInfo &info) override;
- bool is_parallelisable() const override;
-
-private:
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- using WinogradConv = typename WinogradBase::template Convolution<T, T>;
- using WeightsTransform = typename WinogradBase::template WeightsTransform<T, T>;
-
- std::unique_ptr<WeightsTransform> _transform{ nullptr };
- const ITensor *_weights_hwio;
- ITensor *_output;
- int _matrix_stride;
- int _num_output_channels;
- int _num_input_channels;
-};
-
-/** NEON kernel to perform Winograd. */
-template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
-class NEWinogradLayerConfiguration
-{
-public:
- /** Winograd base kernel */
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
- /** Winograd convolution kernel */
-
- using WinogradConv = typename WinogradBase::template Convolution<TIn, TOut>;
-
- using TransformInputKernel = NEWinogradLayerTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
- using TransformWeightsKernel = NEWinogradLayerTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
- using TransformOutputKernel = NEWinogradLayerTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
-};
-
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H*/
diff --git a/arm_compute/core/NEON/kernels/arm_gemm/ndrange.hpp b/arm_compute/core/NEON/kernels/arm_gemm/ndrange.hpp
deleted file mode 100644
index 4ff83fb..0000000
--- a/arm_compute/core/NEON/kernels/arm_gemm/ndrange.hpp
+++ /dev/null
@@ -1,185 +0,0 @@
-/*
- * Copyright (c) 2019-2020 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.
- */
-#pragma once
-
-#include <array>
-#include <algorithm>
-#include <initializer_list>
-
-#include <cassert>
-
-namespace arm_gemm {
-
-template<unsigned int D>
-class NDRange {
-private:
- std::array<unsigned int, D> m_sizes {};
- std::array<unsigned int, D> m_totalsizes {};
-
- class NDRangeIterator {
- private:
- const NDRange &m_parent;
- unsigned int m_pos = 0;
- unsigned int m_end = 0;
-
- public:
- NDRangeIterator(const NDRange &p, unsigned int s, unsigned int e) : m_parent(p), m_pos(s), m_end(e) { }
-
- bool done() const {
- return (m_pos >= m_end);
- }
-
- unsigned int dim(unsigned int d) const {
- unsigned int r = m_pos;
-
- if (d < (D - 1)) {
- r %= m_parent.m_totalsizes[d];
- }
-
- if (d > 0) {
- r /= m_parent.m_totalsizes[d-1];
- }
-
- return r;
- }
-
- bool next_dim0() {
- m_pos++;
-
- return !done();
- }
-
- bool next_dim1() {
- m_pos += m_parent.m_sizes[0] - dim(0);
-
- return !done();
- }
-
- unsigned int dim0_max() const {
- unsigned int offset = std::min(m_end - m_pos, m_parent.m_sizes[0] - dim(0));
-
- return dim(0) + offset;
- }
- };
-
-public:
- NDRange& operator=(const NDRange& rhs)=default;
- NDRange(const NDRange& rhs) =default;
-
- template <typename... T>
- NDRange(T... ts)
- : m_sizes{ts...}
- {
- unsigned int t=1;
-
- for (unsigned int i=0; i<D; i++) {
- t *= m_sizes[i];
-
- m_totalsizes[i] = t;
- }
- }
-
- NDRange(const std::array<unsigned int, D>& n)
- : m_sizes(n)
- {
- unsigned int t=1;
-
- for (unsigned int i=0; i<D; i++) {
- t *= m_sizes[i];
-
- m_totalsizes[i] = t;
- }
- }
-
- NDRangeIterator iterator(unsigned int start, unsigned int end) const {
- return NDRangeIterator(*this, start, end);
- }
-
- unsigned int total_size() const {
- return m_totalsizes[D - 1];
- }
-
- unsigned int get_size(unsigned int v) const {
- return m_sizes[v];
- }
-};
-
-/** NDCoordinate builds upon a range, but specifies a starting position
- * in addition to a size which it inherits from NDRange
- */
-template<unsigned int N>
-class NDCoordinate : public NDRange<N> {
- using int_t =unsigned int;
- using ndrange_t = NDRange<N>;
-
- std::array<int_t, N> m_positions {};
-public:
- NDCoordinate& operator=(const NDCoordinate& rhs)=default;
- NDCoordinate(const NDCoordinate& rhs) =default;
- NDCoordinate(const std::initializer_list<std::pair<int_t, int_t>>& list)
- {
- std::array<int_t, N> sizes{};
-
- std::size_t i = 0;
- for(auto& p : list) {
- m_positions[i]= p.first;
- sizes[i++] = p.second;
- }
-
- //update the parents sizes
- static_cast<ndrange_t&>(*this) = ndrange_t(sizes);
- }
-
- int_t get_position(int_t d) const {
- assert(d < m_positions.size());
- return m_positions[d];
- }
-
- void set_position(int_t d, int_t v) {
- assert(d < size(m_positions));
- assert(v < ndrange_t::get_size(d));
-
- m_positions[d] = v;
- }
-
- int_t get_position_end(int_t d) const {
- return get_position(d) + NDRange<N>::get_size(d);
- }
-}; //class NDCoordinate
-
-/** @returns the number of dimensions in the NDRange which have none-1 values
- * IE there is actual work in these dimensions that can be broken up
- */
-template<unsigned int N>
-std::size_t ndrange_popcount(const NDRange<N>& ndr) {
- std::size_t count = 0;
-
- for(unsigned int d = 0; d != N; ++d) {
- if(ndr.get_size(d) != 1)
- ++count;
- }
- return count;
-}
-
-} // namespace arm_gemm
diff --git a/arm_compute/core/NEON/kernels/assembly/Helpers.h b/arm_compute/core/NEON/kernels/assembly/Helpers.h
deleted file mode 100644
index 9372e05..0000000
--- a/arm_compute/core/NEON/kernels/assembly/Helpers.h
+++ /dev/null
@@ -1,122 +0,0 @@
-/*
- * Copyright (c) 2018-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_ASSEMBLY_HELPERS_H
-#define ARM_COMPUTE_ASSEMBLY_HELPERS_H
-
-#include "arm_compute/core/CPP/CPPTypes.h"
-#include "arm_compute/core/Utils.h"
-
-#include "arm_compute/core/NEON/kernels/assembly/INEGEMMWrapperKernel.h"
-#include "arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp"
-
-namespace arm_compute
-{
-/** Block sizes to use to break the M, N, K dimension */
-struct BlockSizes
-{
- unsigned int k_block{ 0 }; /**< Block size alon the K dimension */
- unsigned int x_block{ 0 }; /**< Block size along the N (x) dimension */
- unsigned int m_round{ 0 }; /**< Block size along the M dimension (Must be a multiple of strategy_out_height) */
- unsigned int strategy_out_height{ 0 }; /**< Number of rows (M) processed by the selected strategy */
-};
-
-/** Extracts the kernel description of the selected kernel by the GEMM backend heuristics
- *
- * @param[in] input_type Data type of the input tensor.
- * @param[in] ci CPU information.
- * @param[in] num_threads Maximum number of threads that might be used for the calculations.
- * @param[in] p M, N, K sizes.
- * @param[in] activation Activation struct
- * @param[in] pretranspose_hint Is B also pretransposed ?
- *
- * @return Kernel description that the assembly heuristics picked for the given configuration
- */
-arm_gemm::KernelDescription get_gemm_info(DataType input_type,
- const CPUInfo &ci,
- const unsigned int num_threads,
- const INEGEMMWrapperKernel::Params &p,
- arm_gemm::Activation activation,
- bool pretranspose_hint);
-
-/** Calculate the recommended block sizes to use based on the CPU cache sizes and the strategy which will be used
- *
- * @param[in] ci CPU information.
- * @param[in] M M dimension.
- * @param[in] N N dimension.
- * @param[in] K K dimension.
- *
- * @return Recommeded block sizes to use for the given M, N, K dimensions.
- */
-template <typename strategy>
-BlockSizes calculate_block_sizes(const CPUInfo &ci, unsigned int M, unsigned int N, unsigned int K)
-{
- BlockSizes bs;
-
- using Toi = typename strategy::operand_type;
-
- const unsigned int L1_size = ci.get_L1_cache_size();
- const unsigned int L2_size = ci.get_L2_cache_size();
-
- // Work out blocking parameters
-
- // k_block: Find out how much of the larger array can be loaded into half the cache.
- // This should account for associative caches.
- bs.k_block = (L1_size / 2) / (sizeof(Toi) * (std::max(strategy::out_width(), strategy::out_height())));
-
- // Needs to be (at least a single) multiple of the K unroll level.
- bs.k_block /= strategy::k_unroll();
- bs.k_block = std::max(bs.k_block, 1U) * strategy::k_unroll();
-
- // Now tune to presented problem size; this is how many blocks we need.
- int num_k_blocks = DIV_CEIL(K, bs.k_block);
-
- // So divide the space equally into that many blocks.
- bs.k_block = DIV_CEIL(K, num_k_blocks);
-
- // And round UP to the K unroll level required.
- bs.k_block = ceil_to_multiple(bs.k_block, strategy::k_unroll());
-
- // x_block: Work out how many rows (of length k_block) will fit in the L2
- // Don't allocate more than 90% of the L2 to allow for overheads, and subtract off the L1 contents.
- bs.x_block = (((L2_size * 9) / 10) - (bs.k_block * sizeof(Toi) * (strategy::out_width() + strategy::out_height()))) / (sizeof(Toi) * bs.k_block);
-
- // Needs to be (at least a single) multiple of the kernel output width.
- bs.x_block /= strategy::out_width();
- bs.x_block = std::max(bs.x_block, 1U) * strategy::out_width();
-
- // And tune to the presented problem size.
- int num_x_blocks = DIV_CEIL(N, bs.x_block);
- bs.x_block = DIV_CEIL(N, num_x_blocks);
-
- bs.x_block = ceil_to_multiple(bs.x_block, strategy::out_width());
-
- // Work out the rounded size of M - needed for some buffers.
- bs.m_round = ceil_to_multiple(M, strategy::out_height());
- bs.strategy_out_height = strategy::out_height();
-
- return bs;
-}
-
-} // namespace arm_compute
-#endif /* ARM_COMPUTE_ASSEMBLY_HELPERS_H */
diff --git a/arm_compute/core/NEON/kernels/assembly/NEGEMMAssemblyWrapperKernel.h b/arm_compute/core/NEON/kernels/assembly/NEGEMMAssemblyWrapperKernel.h
deleted file mode 100644
index 0e3dd74..0000000
--- a/arm_compute/core/NEON/kernels/assembly/NEGEMMAssemblyWrapperKernel.h
+++ /dev/null
@@ -1,121 +0,0 @@
-/*
- * Copyright (c) 2018-2020 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_ASSEMBLY_GEMM_KERNEL_WRAPPER_KERNEL_H
-#define ARM_COMPUTE_ASSEMBLY_GEMM_KERNEL_WRAPPER_KERNEL_H
-
-#include "arm_compute/core/NEON/kernels/assembly/arm_gemm_compute_iface.hpp"
-#include "arm_compute/core/NEON/INEKernel.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
-
-#include "gemm_common.hpp"
-
-namespace arm_compute
-{
-class ITensor;
-
-/** This class is a wrapper for the assembly kernels.
- *
- * Some kernels were written in assembly and highly optimised for specific CPUs like A53 or A55.
- * This class works as a wrapper for these assembly kernels. The arm compute library creates an instance
- * of NEGEMMAssemblyWrapperKernel and other auxiliary data structures to execute a single assembly kernel
- * in the context of an NEFunctions.
- *
- * The type T is the type of the actual kernel implemented in assembly which is of type
- * template<typename To, typename Tr> class GemmCommon
- *
- *
- */
-template <typename TypeInput, typename TypeOutput>
-class NEGEMMAssemblyWrapperKernel final : public INEKernel
-{
-public:
- /** Constructor
- */
- NEGEMMAssemblyWrapperKernel()
- : _kernel(nullptr), _name("NEGEMMAssemblyWrapperKernel")
- {
- }
-
- NEGEMMAssemblyWrapperKernel(NEGEMMAssemblyWrapperKernel &) = delete;
- NEGEMMAssemblyWrapperKernel(NEGEMMAssemblyWrapperKernel &&) = default;
- NEGEMMAssemblyWrapperKernel &operator=(NEGEMMAssemblyWrapperKernel &) = delete;
-
- const char *name() const override
- {
- return _name.c_str();
- }
-
-
- void run(const Window &window, const ThreadInfo &info) override
- {
- ARM_COMPUTE_ERROR_ON_NULLPTR((reinterpret_cast<void *>(_kernel)));
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-
- auto win=arm_gemm::to_ndcoord(window);
-
- arm_gemm::ndcoord_t thread_locator { };
-
- _kernel->execute(win, thread_locator, info.thread_id);
- }
-
- // Inherited methods overridden:
- void run_nd(const Window &window, const ThreadInfo &info, const Window &thread_locator) override
- {
- ARM_COMPUTE_ERROR_ON_NULLPTR((reinterpret_cast<void *>(_kernel)));
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-
- //convert between arm_compute and arm_gemm types
- auto ndc_win = arm_gemm::to_ndcoord(window);
- auto ndc_tlc = arm_gemm::to_ndcoord(thread_locator);
-
- _kernel->execute(ndc_win, ndc_tlc, info.thread_id);
- }
-
- /** Initialise the kernel's input and output.
- *
- * @param[in] kernel Pointer to an assembly kernel implementation.
- * @param[in] num_threads Number of concurrent threads which will execute the kernel.
- */
- void configure(arm_gemm::GemmCommon<TypeInput, TypeOutput> *kernel, std::string kernel_name_tag)
- {
- ARM_COMPUTE_ERROR_ON_NULLPTR((reinterpret_cast<void *>(kernel)));
- _kernel = kernel;
-
- Window win = to_window(kernel->get_window_size());
-
- INEKernel::configure(win);
-
- if(!kernel_name_tag.empty())
- {
- _name += "/" + kernel_name_tag;
- }
- }
-
-private:
- arm_gemm::GemmCommon<TypeInput, TypeOutput> *_kernel;
- std::string _name;
-};
-} // namespace arm_compute
-#endif /* ARM_COMPUTE_ASSEMBLY_GEMM_KERNEL_WRAPPER_KERNEL_H */
diff --git a/arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp b/arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp
deleted file mode 100644
index 7723224..0000000
--- a/arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp
+++ /dev/null
@@ -1,176 +0,0 @@
-/*
- * Copyright (c) 2018-2020 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.
- */
-#pragma once
-
-#include <memory>
-#include <cstring>
-
-#include "arm_gemm_local.hpp"
-#include "gemm_common.hpp"
-
-namespace arm_gemm {
-
-enum class GemmMethod
-{
- DEFAULT,
- GEMV_BATCHED,
- GEMV_PRETRANSPOSED,
- GEMV_NATIVE_TRANSPOSED,
- GEMM_NATIVE,
- GEMM_HYBRID,
- GEMM_INTERLEAVED,
- GEMM_INTERLEAVED_2D,
- QUANTIZE_WRAPPER,
- GEMM_HYBRID_QUANTIZED
-};
-
-struct KernelDescription
-{
- GemmMethod method = GemmMethod::DEFAULT;
- std::string name = "";
- bool is_default = false;
-
- KernelDescription(GemmMethod m, std::string n, bool d=false) : method(m), name(n), is_default(d) { }
- KernelDescription() noexcept { }
-};
-
-struct GemmConfig
-{
- GemmMethod method = GemmMethod::DEFAULT;
- std::string filter = "";
- unsigned int inner_block_size = 0;
- unsigned int outer_block_size = 0;
-
- GemmConfig(GemmMethod method) : method(method) { }
- GemmConfig() { }
-};
-
-struct Activation
-{
- enum class Type {
- None,
- ReLU,
- BoundedReLU
- };
-
- Type type;
- float param1;
- float param2;
-
- Activation(Type type=Type::None, float p1=0.0f, float p2=0.0f) : type(type), param1(p1), param2(p2) { }
-};
-
-struct GemmArgs
-{
-public:
- const CPUInfo *_ci;
- unsigned int _Msize;
- unsigned int _Nsize;
- unsigned int _Ksize;
- unsigned int _nbatches;
- unsigned int _nmulti;
- bool _trA;
- bool _trB;
- Activation _act;
- int _maxthreads;
- bool _pretransposed_hint;
- const GemmConfig *_cfg;
-
- GemmArgs(const CPUInfo *ci, const unsigned int M, const unsigned int N,
- const unsigned int K, const unsigned int nbatches,
- const unsigned int nmulti, const bool trA, const bool trB,
- Activation act, const int maxthreads,
- const bool pretransposed_hint, const GemmConfig *cfg=nullptr ) :
- _ci(ci), _Msize(M), _Nsize(N), _Ksize(K), _nbatches(nbatches), _nmulti(nmulti),
- _trA(trA), _trB(trB), _act(act), _maxthreads(maxthreads),
- _pretransposed_hint(pretransposed_hint), _cfg(cfg)
- {
- }
-};
-
-struct Requantize32
-{
-public:
- const int32_t *bias = nullptr;
- size_t bias_multi_stride = 0;
- int32_t a_offset = 0;
- int32_t b_offset = 0;
- int32_t c_offset = 0;
- bool per_channel_requant = false;
- int32_t per_layer_shift = 0;
- int32_t per_layer_mul = 0;
- const int32_t *per_channel_shifts = nullptr;
- const int32_t *per_channel_muls = nullptr;
- int32_t minval = 0;
- int32_t maxval = 0;
-
- Requantize32() = default;
-
- // Constructor for per-tensor quantization
- Requantize32(const int32_t *bias, size_t bias_multi_stride,
- int32_t a_offset, int32_t b_offset, int32_t c_offset,
- int32_t requant_shift, int32_t requant_mul,
- int32_t minv, int32_t maxv) :
- bias(bias), bias_multi_stride(bias_multi_stride),
- a_offset(a_offset), b_offset(b_offset), c_offset(c_offset),
- per_channel_requant(false), per_layer_shift(requant_shift), per_layer_mul(requant_mul),
- minval(minv), maxval(maxv)
- {
- }
-
- // Constructor for per-channel quantization
- Requantize32(const int32_t *bias, size_t bias_multi_stride,
- int32_t a_offset, int32_t b_offset, int32_t c_offset,
- const int32_t *requant_shifts, const int32_t *requant_muls,
- int32_t minv, int32_t maxv) :
- bias(bias), bias_multi_stride(bias_multi_stride),
- a_offset(a_offset), b_offset(b_offset), c_offset(c_offset),
- per_channel_requant(true), per_channel_shifts(requant_shifts), per_channel_muls(requant_muls),
- minval(minv), maxval(maxv)
- {
- }
-};
-
-struct Nothing
-{
-};
-
-template<typename Top, typename Tret>
-using UniqueGemmCommon = std::unique_ptr<GemmCommon<Top, Tret> >;
-
-/* Low level API calls.
- * These are implemented as 'GemmArgs' versions, or with the arguments explicitly listed. */
-
-/* get_gemm_method(): Given the templated types and provided parameters,
- * which is the preferred method to implement this GEMM? */
-template<typename Top, typename Tret, class OutputStage = Nothing>
-KernelDescription get_gemm_method(const GemmArgs &args, const OutputStage & ={});
-
-template<typename Top, typename Tret, class OutputStage = Nothing>
-UniqueGemmCommon<Top, Tret> gemm(const GemmArgs &args, const OutputStage & ={});
-
-template<typename Top, typename Tret, class OutputStage = Nothing>
-std::vector<KernelDescription> get_compatible_kernels(const GemmArgs &args, const OutputStage & ={});
-
-} // namespace arm_gemm
diff --git a/arm_compute/core/NEON/kernels/assembly/arm_gemm_compute_iface.hpp b/arm_compute/core/NEON/kernels/assembly/arm_gemm_compute_iface.hpp
deleted file mode 100644
index 6f345c1..0000000
--- a/arm_compute/core/NEON/kernels/assembly/arm_gemm_compute_iface.hpp
+++ /dev/null
@@ -1,121 +0,0 @@
-/*
- * Copyright (c) 2020 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.
- */
-#pragma once
-
-#include "arm_compute/core/Window.h"
-#include "arm_compute/core/Dimensions.h"
-#include "arm_compute/core/NEON/kernels/arm_gemm/ndrange.hpp"
-
-#include <cassert>
-
-/* This file contains mapping between integral types used in arm_compute and arm_gemm
- * These two codebases both require a degree of separation for the sake of modularity
- * so maintain their own types which represent similar information.
- */
-
-namespace arm_gemm {
-
-//we want to unify the maximum number of dimensions used beween arm_gemm and arm compute library
-constexpr std::size_t ndrange_max =
- arm_compute::Dimensions<unsigned int>::num_max_dimensions;
-
-using ndrange_t=NDRange<ndrange_max>;
-using ndcoord_t=NDCoordinate<ndrange_max>;
-
-/* Converts an `arm_gemm::ndrange_t` to a `arm_compute::Window`
- *
- * As `NDRange<T>` does not not encode start positions, we specify
- * the start to be zero in the produced `arm_compute::Window`
- *
- * @param [ndr] the `arm_gemm::ndrange_t` we wish to convert into a `arm_compute::Window`
- * @returns an `arm_compute::Window` representing the same dimensional ranges as `ndr`
- */
-inline arm_compute::Window to_window(const ndrange_t& ndr) {
- arm_compute::Window win;
-
- for(unsigned int i = 0; i!=ndrange_max; ++i) {
- //populate the window with the dimensions of the NDRange
- win.set(i, arm_compute::Window::Dimension(0, ndr.get_size(i)));
- }
-
- return win;
-}
-
-/*
- * Converts an `arm_gemm::ndcoord_t` to a `arm_compute::Window`
- *
- * @param [ndc] the `arm_gemm::ndcoord_t` we wish to convert into a `arm_compute::Window`
- * @returns an `arm_compute::Window` representing the same dimensional ranges as `ndc`
- */
-inline arm_compute::Window to_window(const ndcoord_t& ndc) {
- arm_compute::Window win;
-
- for(unsigned int i = 0; i!=ndrange_max; ++i) {
- const auto start = ndc.get_position(i);
- const auto size = ndc.get_size(i);
- const auto stop = start + size;
-
- //populate the window with the dimensions of the NDRange
- win.set(i, arm_compute::Window::Dimension(start, stop));
- }
-
- return win;
-}
-
-/** Convert an `arm_compute::Window` to an `arm_gemm::NDRange` of the same max dimensions
- *
- * It should be noted that `arm_compute::Window` specifies a `start()` and an `end()`
- * where as `arm_gemm::ndrange_t` only has a size, as a result we store the delta between the range
- *
- * @param [win] the `arm_compute::Window` we want to convert to `arm_gemm::ndrange_t`
- * @return the resultant ndrange_t
- */
-inline ndrange_t to_ndrange(const arm_compute::Window& win) {
- return {
- static_cast<unsigned int>(win[0].end() - win[0].start()),
- static_cast<unsigned int>(win[1].end() - win[1].start()),
- static_cast<unsigned int>(win[2].end() - win[2].start()),
- static_cast<unsigned int>(win[3].end() - win[3].start()),
- static_cast<unsigned int>(win[4].end() - win[4].start()),
- static_cast<unsigned int>(win[5].end() - win[5].start())
- };
-}
-
-/** Convert an `arm_compute::Window` to an `arm_gemm::NDCoord` of the same max dimensions
- *
- * @param [win] the `arm_compute::Window` we want to convert to `arm_gemm::ndcoord_t`
- * @return the resultant ndcoord_t
- */
-inline ndcoord_t to_ndcoord(const arm_compute::Window& win) {
- return {
- { static_cast<unsigned int>(win[0].start()), static_cast<unsigned int>(win[0].end() - win[0].start()) },
- { static_cast<unsigned int>(win[1].start()), static_cast<unsigned int>(win[1].end() - win[1].start()) },
- { static_cast<unsigned int>(win[2].start()), static_cast<unsigned int>(win[2].end() - win[2].start()) },
- { static_cast<unsigned int>(win[3].start()), static_cast<unsigned int>(win[3].end() - win[3].start()) },
- { static_cast<unsigned int>(win[4].start()), static_cast<unsigned int>(win[4].end() - win[4].start()) },
- { static_cast<unsigned int>(win[5].start()), static_cast<unsigned int>(win[5].end() - win[5].start()) }
- };
-}
-
-} //namespace arm_gemm
diff --git a/arm_compute/core/NEON/kernels/assembly/gemm_common.hpp b/arm_compute/core/NEON/kernels/assembly/gemm_common.hpp
deleted file mode 100644
index ea9b524..0000000
--- a/arm_compute/core/NEON/kernels/assembly/gemm_common.hpp
+++ /dev/null
@@ -1,201 +0,0 @@
-/*
- * Copyright (c) 2017-2020 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.
- */
-#pragma once
-
-#include "arm_compute/core/NEON/kernels/assembly/arm_gemm_compute_iface.hpp"
-
-#include <cstddef>
-#include <cassert>
-
-#define UNUSED(x) (void)(x)
-
-namespace arm_gemm {
-
-// Abstract class for the GEMM/GEMV functions.
-//
-// GEMM implementations may be "native" (never require any input
-// permutation), "pretransposed" (require permutation up-front) or require
-// working space (permute as they go along). This interface should support
-// all of them.
-
-// The real GemmCommon class is templated based on the operand and return
-// type. This is an interface class which is independent of those types.
-class IGemmCommon {
-public:
- /* Pass in the pointers to the arrays to be operated on and their
- * strides. This "generic" version uses void *s, the preferred version
- * is the one provided by templated GemmCommon (below) which takes
- * appropriately typed pointers. If B is pretransposed (see below) then
- * the settings for B here are ignored.
- */
- virtual void set_arrays_generic(const void *A, const int lda, const int A_batch_stride, const int A_multi_stride,
- const void *B, const int ldb, /* batches share B */ const int B_multi_stride,
- void *C, const int ldc, const int C_batch_stride, const int C_multi_stride,
- const void *bias, /* no row or batch stride needed */ const int bias_multi_stride) = 0;
-
- /** @returns an ndrange containing ranges of the compute space which can be
- * broken up and parallelised over
- */
- virtual ndrange_t get_window_size() const = 0;
-
- /* The maximum thread count is specified when the GEMM is created. Some
- * implementations need to know how many threads will actually run in
- * order to work properly.
- *
- * In some cases, after creating the GEMM the number of threads needs to
- * be reduced (e.g. not enough work to split across threads). This
- * method allows the number of actual threads to be run to be set (must
- * be equal or lower).
- *
- * This has an empty default implementation, as GEMMs which don't care
- * about thread count can safely ignore this.
- */
- virtual void set_nthreads(int) { };
-
- /* Whether this GEMM can be dynamically scheduled or not. */
- virtual bool supports_dynamic_scheduling() const { return false; }
-
- /** Main execute member fucntion
- * @param [in] work_range specifies the range of work we want to be computed, total range defined by get_window_size()
- * @param [in] thread_locator where are we inside of the thread space
- * @naram [in] threadid a unique threadid
- */
- virtual void execute(const ndcoord_t& work_range, const ndcoord_t& thread_locator, int threadid) = 0;
-
- /*** Working space interface (optional) ***/
- /* Total number of bytes of temporary working space needed. If zero, it's not necessary to call set_working_space(). */
- virtual size_t get_working_size() const { return 0; }
- /* Provide working space buffer - the void * passed in must remain allocated for the duration of any execute calls. */
- virtual void set_working_space(void *) { };
-
- /*** "Pretransposed" interface (optional) ***/
- /* Is this object set up for pretranspose? If so, pretranspose_array() needs to be called before execute(); */
- virtual bool B_is_pretransposed() const { return false; }
- /* Does pretranspose still need to be done? */
- virtual bool B_pretranspose_required() const { return false; }
- /* Total number of bytes of space needed for pretransposed arrays. */
- virtual size_t get_B_pretransposed_array_size() const { return 0; }
- /* Perform pretranspose - arguments are output, input, input row stride and input multi stride. */
- /* The "real" version of this depends on the templated operand type (see below). */
- virtual void pretranspose_B_array_generic(void *, const void *, const int, const int) = 0;
- /* Set pretransposed data - the void * passed in must previously have been passed to pretranspose_B_array() for the same or a similar GEMM. */
- virtual void set_pretransposed_B_data(void *) { }
-
- /*** "Quantized bias" interface (optional) ***/
- /* Set the bias vector for quantized GEMMs */
- virtual void set_quantized_bias(const int32_t *bias, size_t bias_multi_stride)
- {
- UNUSED(bias);
- UNUSED(bias_multi_stride);
- }
-
- // Destructor
- virtual ~IGemmCommon() { }
-};
-
-/* "Real" GemmCommon class which is templated on the operand and return types.
- *
- * In addition to correctly typed versions of the functions that operate on
- * operand and return data, this class provides a default implementation of
- * 'set_arrays' to capture the provided arguments in protected class
- * members, as essentially any implementation will need these.
- */
-template<typename To, typename Tr>
-class GemmCommon : public IGemmCommon {
-protected:
- const To *_Aptr=nullptr;
- int _lda=0;
- int _A_batch_stride=0;
- int _A_multi_stride=0;
- const To *_Bptr=nullptr;
- int _ldb=0;
- int _B_multi_stride=0;
- Tr *_Cptr=nullptr;
- int _ldc=0;
- int _C_batch_stride=0;
- int _C_multi_stride=0;
- const Tr *_bias=nullptr;
- int _bias_multi_stride=0;
-
-public:
- /* Pass in the pointers to the arrays to be operated on and their
- * strides (templated version with appropriate types). */
- virtual void set_arrays(const To *A, const int lda, const int A_batch_stride, const int A_multi_stride,
- const To *B, const int ldb, /* batches share B */ const int B_multi_stride,
- Tr *C, const int ldc, const int C_batch_stride, const int C_multi_stride,
- const Tr *bias, /* no row or batch stride needed */ const int bias_multi_stride) {
- _Aptr = A;
- _lda = lda;
- _A_batch_stride = A_batch_stride;
- _A_multi_stride = A_multi_stride;
- _Bptr = B;
- _ldb = ldb;
- _B_multi_stride = B_multi_stride;
- _Cptr = C;
- _ldc = ldc;
- _C_batch_stride = C_batch_stride;
- _C_multi_stride = C_multi_stride;
- _bias = bias;
- _bias_multi_stride = bias_multi_stride;
- }
-
- /* Implementation of the void * overload which casts its arguments to the appropriate type. */
- void set_arrays_generic(const void *A, const int lda, const int A_batch_stride, const int A_multi_stride,
- const void *B, const int ldb, /* batches share B */ const int B_multi_stride,
- void *C, const int ldc, const int C_batch_stride, const int C_multi_stride,
- const void *bias, /* no row or batch stride needed */ const int bias_multi_stride) override {
- set_arrays(static_cast<const To *>(A), lda, A_batch_stride, A_multi_stride,
- static_cast<const To *>(B), ldb, B_multi_stride,
- static_cast<Tr *>(C), ldc, C_batch_stride, C_multi_stride,
- static_cast<const Tr *>(bias), bias_multi_stride);
- }
-
- /*** "Pretransposed" interface ***/
-
- /* Perform pretranspose - the void * passed in must remain allocated for the duration of any execute calls. */
- /* Arguments are: output buffer pointer, source pointer, source row stride, source multi stride */
- virtual void pretranspose_B_array(void *, const To *, const int, const int) { };
-
- /* Implementation of the void * overload which casts its arguments to the appropriate type. */
- void pretranspose_B_array_generic(void *out, const void *in, const int row_stride, const int multi_stride) override {
- pretranspose_B_array(out, static_cast<const To *>(in), row_stride, multi_stride);
- }
-};
-
-template<typename GemmKernel>
-inline
-int unsigned get_total_window_size(const GemmKernel& kernel)
-{
- auto window=kernel.get_window_size();
-
- unsigned int total = 1;
- for(unsigned i = 0; i != arm_gemm::ndrange_max; ++i)
- {
- total *= window.get_size(i);
- }
-
- return total;
-}
-
-} // namespace arm_gemm
diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/winograd.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/winograd.hpp
deleted file mode 100644
index bc0d9d4..0000000
--- a/arm_compute/core/NEON/kernels/convolution/winograd/winograd.hpp
+++ /dev/null
@@ -1,621 +0,0 @@
-/*
- * Copyright (c) 2017-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#pragma once
-
-#include "arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp"
-
-#include <cstddef>
-#include <utility>
-
-namespace winograd
-{
-
-class ITransform
-{
- public:
- virtual ~ITransform() = default;
-
- /**
- * Get the working space required to perform the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param nthreads The greatest number of threads that will be used to execute the transform.
- * @return Size of working space required in bytes.
- */
- virtual size_t get_working_space_size(unsigned int nthreads=1) const = 0;
-
- /**
- * Set the working space to be used by the transformation.
- *
- * Note, the working space is only required when performing the
- * transformation - hence it can be reused whenever the transformation is
- * not running.
- *
- * @param Pointer to the working space.
- */
- virtual void set_working_space(void *buffer) = 0;
-
- /**
- * Get the window of work a given operator can perform.
- */
- virtual unsigned int get_window() const = 0;
-
- /**
- * Perform work upon a window of the transform.
- */
- virtual void run(unsigned int start, unsigned int stop, unsigned int threadid=0) = 0;
-};
-
-class IInputTransform : public ITransform
-{
- public:
- virtual ~IInputTransform() = default;
-
- /**
- * Set the pointer to the (NHWC-ordered) tensor to be transformed.
- */
- virtual void set_input_tensor(const void *input) = 0;
-
- /**
- * Set the pointer to the (NHWC-ordered) tensor to be transformed.
- * @param col_stride Stride between columns of the tensor, measured in elements (not bytes).
- */
- virtual void set_input_tensor(const void *input, int col_stride) = 0;
-
- /**
- * Set the pointer to the (NHWC-ordered) tensor to be transformed.
- * @param row_stride Stride between rows of the tensor, measured in elements (not bytes).
- * @param col_stride Stride between columns of the tensor, measured in elements (not bytes).
- */
- virtual void set_input_tensor(const void *input, int row_stride, int col_stride) = 0;
-
- /**
- * Set the pointer to the (NHWC-ordered) tensor to be transformed.
- * @param batch_stride Stride between batches of the tensor, measured in elements (not bytes).
- * @param row_stride Stride between rows of the tensor, measured in elements (not bytes).
- * @param col_stride Stride between columns of the tensor, measured in elements (not bytes).
- */
- virtual void set_input_tensor(const void *input, int batch_stride, int row_stride, int col_stride) = 0;
-
- /**
- * Set pointers to the matrices written by the transform.
- * @param matrices Pointer to the start of the first matrix representing the transformed input.
- * @param inter_matrix_stride Stride (in elements) between matrices.
- * @param matrix_row_stride Stride (in elements) between the rows within a single matrix.
- */
- virtual void set_output_matrices(void *matrices, int inter_matrix_stride, int matrix_row_stride) = 0;
-};
-
-class IOutputTransform : public ITransform
-{
- public:
- virtual ~IOutputTransform() = default;
-
- /**
- * Set pointers to the matrices written by the transform.
- * @param matrices Pointer to the start of the first matrix representing the input to the transform.
- * @param inter_matrix_stride Stride (in elements) between matrices.
- * @param matrix_row_stride Stride (in elements) between the rows within a single matrix.
- */
- virtual void set_input_matrices(const void *matrices, int inter_matrix_stride, int matrix_row_stride) = 0;
-
- /**
- * Set pointer to the bias tensor (can be ignored or called with nullptr for no bias.
- */
- virtual void set_bias(const void *bias=nullptr) = 0;
-
- /**
- * Set pointer to the output tensor produced by the transform.
- */
- virtual void set_output_tensor(void *output) = 0;
-
- /**
- * Set pointer to the output tensor produced by the transform.
- * @param col_stride Stride between columns of the tensor, measured in elements (not bytes).
- */
- virtual void set_output_tensor(void *output, int col_stride) = 0;
-
- /**
- * Set pointer to the output tensor produced by the transform.
- * @param row_stride Stride between rows of the tensor, measured in elements (not bytes).
- * @param col_stride Stride between columns of the tensor, measured in elements (not bytes).
- */
- virtual void set_output_tensor(void *output, int row_stride, int col_stride) = 0;
-
- /**
- * Set pointer to the output tensor produced by the transform.
- * @param batch_stride Stride between batches of the tensor, measured in elements (not bytes).
- * @param row_stride Stride between rows of the tensor, measured in elements (not bytes).
- * @param col_stride Stride between columns of the tensor, measured in elements (not bytes).
- */
- virtual void set_output_tensor(void *output, int batch_stride, int row_stride, int col_stride) = 0;
-};
-
-class IWeightTransform : public ITransform
-{
- public:
- virtual ~IWeightTransform() = default;
-
- /** Set pointer to the weight tensor read by the transform. */
- virtual void set_weight_tensor(const void *weights) = 0;
-
- /**
- * Set pointers to the matrices written by the transform.
- * @param matrices Pointer to the start of the first matrix representing the transformed input.
- * @param inter_matrix_stride Stride (in elements) between matrices.
- * @param matrix_row_stride Stride (in elements) between the rows within a single matrix.
- */
- virtual void set_output_matrices(void *matrices, int inter_matrix_stride, int matrix_row_stride) = 0;
-};
-
-enum class WinogradRoots
-{
- Integers,
-};
-
-template <int InnerTileRows, int InnerTileCols, typename TIn, typename TOut, WinogradRoots Roots>
-class InputTransform : public IInputTransform
-{
- public:
- /** Create an InputTransform operator fixed on a given problem and set of
- * pointers.
- */
- InputTransform(
- int kernel_rows, /**< Number of rows in the kernel */
- int kernel_cols, /**< Number of columns in the kernel */
- int n_batches, /**< Number of batches in input tensor. */
- int n_rows, /**< Number of rows in input tensor. */
- int n_cols, /**< Number of columns in input tensor. */
- int n_channels, /**< Number of channels in input tensor. */
- int padding_top, /**< Padding to apply to the top of the image. */
- int padding_left, /**< Padding to apply to the left of the image. */
- int padding_bottom, /**< Padding to apply to the bottom of the image. */
- int padding_right /**< Padding to apply to the right of the image. */
- );
-
- InputTransform(InputTransform&) = delete;
- InputTransform operator=(InputTransform&) = delete;
-
- /** Set pointers to the input tensor read by the transform. */
- void set_input_tensor(const void *input) override;
- void set_input_tensor(const void *input, int col_stride) override;
- void set_input_tensor(const void *input, int row_stride, int col_stride) override;
- void set_input_tensor(const void *input, int batch_stride, int row_stride, int col_stride) override;
-
- /** Set pointers to the matrices written by the transform. */
- void set_output_matrices(void *matrices, int iter_matrix_stride, int matrix_row_stride) override;
-
- /** Get the working space required to perform the transformation. */
- size_t get_working_space_size(unsigned int nthreads=1) const override;
- void set_working_space(void *buffer) override;
-
- /** Get the window of work a given operator can perform. */
- unsigned int get_window() const override;
- static constexpr unsigned int WINDOW_BLOCK = 16; // Base size of window
-
- /** Perform work upon a window of the input. */
- void run(unsigned int start, unsigned int stop, unsigned int threadid=0) override;
-
- protected:
- const int _n_batches, _n_rows, _n_cols, _n_channels;
-
- private:
- void transform_unpadded_tile(
- unsigned int threadid,
- int n_channels,
- TOut *outptr,
- const TIn *inptr
- );
-
- void transform_padded_tile(
- unsigned int threadid,
- int n_channels,
- TOut *outptr,
- const TIn *inptr,
- int padding_top,
- int padding_left,
- int padding_bottom,
- int padding_right
- );
-
- /* Tile implementation */
- static void transform_tile(
- int n_channels, /** @param[in] Number of channels in the tensor. */
- const TIn* inptr_base, /** @param[in] Pointer to the base of the input tile. */
- int input_row_stride, /** @param[in] Stride between rows of the input tensor. */
- int input_col_stride, /** @param[in] Stride between columns of the input tensor. */
- TOut* mptr_base, /** @param[out] Base pointer to transformed input matrices. */
- int matrix_stride /** @param[in] Stride between matrices in the input space. */
- );
-
- /** Get the working space for a thread. */
- void * get_working_space(unsigned int threadid) const;
-
- const TIn* _inptr;
- TOut* _outptr;
-
- const int _overlap_rows, _overlap_cols;
- const int _padding_top, _padding_left, _padding_bottom, _padding_right;
- const int _tiles_M, _tiles_N;
- int _matrix_stride, _matrix_row_stride, _matrix_batch_stride;
- int _in_col_stride, _in_row_stride, _in_batch_stride;
-
- const int _working_space_col_stride, _working_space_row_stride;
- TIn *_working_space;
-};
-
-template <int InnerTileRows, typename TIn, typename TOut, WinogradRoots Roots>
-class InputTransform<InnerTileRows, 1, TIn, TOut, Roots> :
- public InputTransform<1, InnerTileRows, TIn, TOut, Roots>
-{
- using Base = InputTransform<1, InnerTileRows, TIn, TOut, Roots>;
-
- public:
- InputTransform(
- int kernel_rows, /**< Number of rows in the kernel. */
- int kernel_cols, /**< Number of columns in the kernel. */
- int n_batches, /**< Number of batches in input tensor. */
- int n_rows, /**< Number of rows in input tensor. */
- int n_cols, /**< Number of columns in input tensor. */
- int n_channels, /**< Number of channels in input tensor. */
- int padding_top, /**< Padding to apply to the top of the image. */
- int padding_left, /**< Padding to apply to the left of the image. */
- int padding_bottom, /**< Padding to apply to the bottom of the image. */
- int padding_right /**< Padding to apply to the right of the image. */
- );
-
- /** Set pointers to the input tensor read by the transform. */
- void set_input_tensor(const void *input) override;
- void set_input_tensor(const void *input, int col_stride) override;
- void set_input_tensor(const void *input, int row_stride, int col_stride) override;
- void set_input_tensor(const void *input, int batch_stride, int row_stride, int col_stride) override;
-};
-
-template <
- int KernelRows, int KernelCols,
- int InnerTileRows, int InnerTileCols,
- typename TIn, typename TOut,
- WinogradRoots Roots
->
-class OutputTransform : public IOutputTransform
-{
- public:
- OutputTransform(
- int n_batches, /**< Number of batches in output tensor. */
- int n_rows, /**< Number of rows in output tensor. */
- int n_cols, /**< Number of columns in output tensor. */
- int n_channels, /**< Number of channels in output tensor. */
- const arm_gemm::Activation &activation
- );
-
- OutputTransform(OutputTransform&) = delete;
- OutputTransform operator=(OutputTransform&) = delete;
-
- /** Set pointers to the matrices read by the transform. */
- void set_input_matrices(const void *matrices, int iter_matrix_stride, int matrix_row_stride) override;
-
- /** Set pointer to the bias tensor (can be ignored or called with nullptr for no bias */
- void set_bias(const void *bias=nullptr) override;
-
- /** Set pointers to the output tensor written by the transform. */
- void set_output_tensor(void *output) override;
- void set_output_tensor(void *output, int col_stride) override;
- void set_output_tensor(void *output, int row_stride, int col_stride) override;
- void set_output_tensor(void *output, int batch_stride, int row_stride, int col_stride) override;
-
- /** Get the working space required to perform the transformation. */
- size_t get_working_space_size(unsigned int nthreads=1) const override;
- void set_working_space(void *buffer) override;
-
- /** Get the window of work a given operator can perform. */
- unsigned int get_window() const override;
- static constexpr unsigned int WINDOW_BLOCK = 16; // Base size of window
-
- /** Perform work upon a window of the input. */
- void run(unsigned int start, unsigned int stop, unsigned int threadid=0) override;
-
- protected:
- static constexpr int inner_tile_rows = InnerTileRows;
- static constexpr int inner_tile_cols = InnerTileCols;
- static constexpr int output_tile_rows = InnerTileRows - KernelRows + 1;
- static constexpr int output_tile_cols = InnerTileCols - KernelCols + 1;
-
- const int _n_batches, _n_rows, _n_cols, _n_channels;
- const TOut _output_min, _output_max;
-
- private:
- void transform_uncropped_tile(
- unsigned int threadid,
- int n_channels,
- TOut *outptr,
- const TIn *inptr,
- const TOut *biases
- );
-
- void transform_cropped_tile(
- unsigned int threadid,
- int n_channels,
- TOut *outptr,
- const TIn *inptr,
- const TOut *biases,
- int pad_bottom,
- int pad_right
- );
-
- /** Implementation of the tile transformation method. */
- static void transform_tile(
- int n_channels,
- const TIn* matrix_base,
- int matrix_stride,
- const TOut* biases,
- TOut* output,
- int output_row_stride,
- int output_col_stride,
- TOut output_min,
- TOut output_max
- );
-
- /** Get the working space for a thread. */
- void * get_working_space(unsigned int threadid) const;
-
- const TIn* _matrix_base;
- const TOut* _biases;
- int _matrix_stride, _matrix_row_stride, _matrix_batch_stride;
- TOut* _outptr;
- const int _tiles_M, _tiles_N;
- int _out_col_stride, _out_row_stride, _out_batch_stride;
-
- const int _working_space_col_stride, _working_space_row_stride;
- TOut *_working_space;
-};
-
-template <
- int KernelRows,
- int InnerTileRows,
- typename TIn, typename TOut,
- WinogradRoots Roots
->
-class OutputTransform<KernelRows, 1, InnerTileRows, 1, TIn, TOut, Roots> :
- public OutputTransform<1, KernelRows, 1, InnerTileRows, TIn, TOut, Roots>
-{
- using Base = OutputTransform<1, KernelRows, 1, InnerTileRows, TIn, TOut, Roots>;
-
- public:
- OutputTransform(
- int n_batches, /**< Number of batches in output tensor. */
- int n_rows, /**< Number of rows in output tensor. */
- int n_cols, /**< Number of columns in output tensor. */
- int n_channels, /**< Number of channels in output tensor. */
- const arm_gemm::Activation &activation
- );
-
- /** Set pointers to the output tensor written by the transform. */
- void set_output_tensor(void *output) override;
- void set_output_tensor(void *output, int col_stride) override;
- void set_output_tensor(void *output, int row_stride, int col_stride) override;
- void set_output_tensor(void *output, int batch_stride, int row_stride, int col_stride) override;
-};
-
-template <
- int KernelRows, int KernelCols,
- int InnerTileRows, int InnerTileCols,
- typename TIn, typename TOut,
- WinogradRoots Roots
->
-class WeightTransform : public IWeightTransform
-{
- public:
- WeightTransform(
- int n_output_channels, /**< Number of output channels in the kernel. */
- int n_input_channels /**< Number of input channels in the kernel. */
- );
-
- WeightTransform(WeightTransform&) = delete;
- WeightTransform operator=(WeightTransform&) = delete;
-
- /** Set pointer to the weight tensor read by the transform. */
- void set_weight_tensor(const void *weights) override;
-
- /** Set pointer to the matrices written by the transform. */
- void set_output_matrices(void *matrices, int inter_matrix_stride, int matrix_row_stride) override;
-
- /** Get the working space required to perform the transformation. */
- size_t get_working_space_size(unsigned int nthreads=1) const override;
- void set_working_space(void *buffer) override;
-
- /** Get the window of work a given operator can perform. */
- unsigned int get_window() const override;
- static constexpr unsigned int WINDOW_BLOCK = 16; // Base size of window
-
- /** Perform work upon a window of the input. */
- void run(unsigned int start, unsigned int stop, unsigned int threadid=0) override;
-
- protected:
- static const int kernel_rows = KernelRows;
- static const int kernel_cols = KernelCols;
- static const int inner_tile_rows = InnerTileRows;
- static const int inner_tile_cols = InnerTileCols;
-
- private:
- /** Apply the transform to a tensor. */
- static void execute(
- int n_output_channels,
- int n_input_channels,
- const TIn* input,
- TOut* output,
- int matrix_stride,
- int matrix_row_stride
- );
-
- const int _n_output_channels, _n_input_channels;
- TOut *_matrices;
- int _matrix_stride, _matrix_row_stride;
- const TIn *_weights;
-};
-
-template <int KernelRows, int InnerTileRows, typename TIn, typename TOut, WinogradRoots Roots>
-class WeightTransform<KernelRows, 1, InnerTileRows, 1, TIn, TOut, Roots> :
- public WeightTransform<1, KernelRows, 1, InnerTileRows, TIn, TOut, Roots>
-{
- public:
- using WeightTransform<1, KernelRows, 1, InnerTileRows, TIn, TOut, Roots>::WeightTransform;
-};
-
-template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols, WinogradRoots Roots>
-class WinogradGEMM
-{
- public:
- // Information about the specific Winograd instance
- static constexpr int output_tile_rows = OutputTileRows;
- static constexpr int output_tile_cols = OutputTileCols;
- static constexpr int kernel_rows = KernelRows;
- static constexpr int kernel_cols = KernelCols;
- static constexpr int inner_tile_rows = output_tile_rows + kernel_rows - 1;
- static constexpr int inner_tile_cols = output_tile_cols + kernel_cols - 1;
- static constexpr int N_GEMMS = inner_tile_rows * inner_tile_cols;
-
- /** Transform weights from the spatial to the Winograd domain. */
- template <typename TIn, typename TOut>
- using WeightsTransform = WeightTransform<
- KernelRows, KernelCols, inner_tile_rows, inner_tile_cols,
- TIn, TOut, Roots
- >;
-
- /** Transform input feature maps from the spatial to the Winograd domain.
- */
- template <typename TIn, typename TOut>
- using InputTransform = InputTransform<
- inner_tile_rows, inner_tile_cols, TIn, TOut, Roots
- >;
-
- /** Transform output feature maps from the Winograd to the spatial domain.
- */
- template <typename TIn, typename TOut>
- using OutputTransform = OutputTransform<
- KernelRows, KernelCols, inner_tile_rows, inner_tile_cols,
- TIn, TOut, Roots
- >;
-
- /** Perform a convolution.
- */
- template <typename TOut, typename TIn, typename TInGEMM=TIn, typename TOutGEMM=TOut>
- class Convolution
- {
- public:
- // Information about the typed Winograd instance
- typedef TOut OutputType;
- typedef TOutGEMM GemmOutputType;
- typedef TInGEMM GemmInputType;
- typedef TIn InputType;
-
- /** Get the output shape of a convolution. */
- static std::pair<unsigned int, unsigned int> get_output_shape(
- const std::pair<unsigned int, unsigned int> input_shape,
- bool padding_same);
-
- /** Get the memory required to store the kernel transformed into the
- * Winograd domain.
- */
- static size_t get_kernel_storage_size(unsigned int n_input_channels,
- unsigned int n_output_channels);
-
- /** Get the memory required to store the input tensor transformed into
- * the Winograd domain.
- */
- static size_t get_input_storage_size(
- unsigned int n_batches, // Number of batches
- unsigned int n_rows, // Number of input rows
- unsigned int n_cols, // Number of input columns
- unsigned int n_channels, // Number of input channels
- bool padding_same);
-
- /** Get the memory required to store the output tensor in the Winograd
- * domain.
- */
- static size_t get_output_storage_size(
- unsigned int n_batches, // Number of batches
- unsigned int n_rows, // Number of output rows
- unsigned int n_cols, // Number of output columns
- unsigned int n_channels // Number of output channels
- );
-
- /** Get the memory required to apply a Winograd operator to some input.
- */
- static size_t get_working_space_size(
- unsigned int n_batches,
- unsigned int n_rows, // Number of input rows
- unsigned int n_cols, // Number of input columns
- unsigned int n_input_channels, // Number of input channels
- unsigned int n_output_channels, // Number of output channels
- bool padding_same);
-
- /* Get the memory required by a single "input" matrix.
- */
- static size_t get_input_matrix_size(
- unsigned int n_batches, // Number of batches
- unsigned int n_rows, // Number of input rows
- unsigned int n_cols, // Number of input columns
- unsigned int n_channels, // Number of input channels
- bool padding_same);
-
- static int get_input_matrix_stride(
- unsigned int n_batches, // Number of batches
- unsigned int n_rows, // Number of input rows
- unsigned int n_cols, // Number of input columns
- unsigned int n_channels, // Number of input channels
- bool padding_same);
-
- /* Get the memory required by a single "output" matrix.
- */
- static size_t get_output_matrix_size(
- unsigned int n_batches, // Number of batches
- unsigned int n_rows, // Number of output rows
- unsigned int n_cols, // Number of output columns
- unsigned int n_channels // Number of output channels
- );
-
- static int get_output_matrix_stride(
- unsigned int n_batches, // Number of batches
- unsigned int n_rows, // Number of output rows
- unsigned int n_cols, // Number of output columns
- unsigned int n_channels // Number of output channels
- );
-
- /* Get the memory required by a single "kernel" matrix.
- */
- static size_t get_kernel_matrix_size(unsigned int n_input_channels,
- unsigned int n_output_channels);
- static int get_kernel_matrix_stride(unsigned int n_input_channels,
- unsigned int n_output_channels);
-
- static constexpr int M_BLOCK = 4; /** Size of block used by GEMM. */
- static constexpr int N_BLOCK = 16; /** Size of block used by GEMM. */
- };
-};
-
-} // namespace winograd
diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/winograd_layer.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_layer.hpp
deleted file mode 100644
index ed8fede..0000000
--- a/arm_compute/core/NEON/kernels/convolution/winograd/winograd_layer.hpp
+++ /dev/null
@@ -1,207 +0,0 @@
-/*
- * Copyright (c) 2017-2019 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-#pragma once
-#include "arm_gemm_local.hpp"
-#include "arm_gemm.hpp"
-#include "winograd.hpp"
-
-namespace winograd
-{
-
-
-class IWinogradConvolutionLayer
-{
- public:
- virtual ~IWinogradConvolutionLayer() = default;
-
- virtual unsigned int weight_transform_get_window(void) const = 0;
- virtual void weight_transform_run(unsigned int start, unsigned int stop) = 0;
-
- virtual IInputTransform& input_transform(void) = 0; // Expose the input transform
- virtual IOutputTransform& output_transform(void) = 0; // Expose the output transform
- virtual arm_gemm::IGemmCommon *gemm(void) = 0; // Expose the underlying GEMM
-};
-
-/** Example of how to construct an ACL-like interface.
- *
- * Use `get_weight_storage_size`, `get_input_storage_size` and
- * `get_output_storage_size` to allocate memory for the convolution engine.
- * Then create a `WinogradConvolutionLayer`.
- *
- * Initialise the weights using `weights_transform.run(...)`.
- *
- * For each inference:
- * 1. Transform the inputs to the Winograd domain using `input_transform.run(...)`
- * 2. Perform a number of GEMMs using `gemms.run(...)`
- * 3. Transform the output to the spatial domain using `output_transform.run(...)`
- */
-template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols,
- typename TIn, typename TInGEMM, typename TOutGEMM, typename TOut,
- WinogradRoots Roots>
-class WinogradConvolutionLayer : public IWinogradConvolutionLayer
-{
- public:
- using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, Roots>;
- using WeightsTransform = typename WinogradBase::template WeightsTransform<TIn, TInGEMM>;
- using InputTransform = typename WinogradBase::template InputTransform<TIn, TInGEMM>;
- using WinogradConv = typename WinogradBase::template Convolution<TOut, TIn, TInGEMM, TOutGEMM>;
- using OutputTransform = typename WinogradBase::template OutputTransform<TOutGEMM, TOut>;
-
- private:
- static constexpr int InnerTileRows = OutputTileRows + KernelRows - 1;
- static constexpr int InnerTileCols = OutputTileCols + KernelCols - 1;
- static constexpr int N_GEMMS = InnerTileRows * InnerTileCols;
-
- const int _n_output_rows, _n_output_cols;
- const int _kernel_matrix_stride, _kernel_matrix_row_stride;
- const int _input_matrix_stride, _input_matrix_row_stride;
- const int _output_matrix_stride, _output_matrix_row_stride;
- const int _tile_rows, _tile_cols;
- const int _m, _k, _n;
-
- WeightsTransform weights_transform; /** Operator to transform weights to Winograd domain. */
- InputTransform _input_transform; /** Operator to transform input to Winograd domain. */
- const arm_gemm::GemmArgs gemm_args;
- arm_gemm::UniqueGemmCommon<TInGEMM, TOutGEMM> gemms; /** Operator to perform multiple GEMMs. */
- OutputTransform _output_transform; /** Operator to transform output from Winograd domain. */
-
- public:
-
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed weights.
- */
- static unsigned int get_weight_storage_size(
- const int n_output_channels, /** Number of output feature maps. */
- const int n_input_channels /** Number of input feature maps. */
- );
-
- static unsigned int get_weight_stride(
- const int n_output_channels, /** Number of output feature maps. */
- const int n_input_channels /** Number of input feature maps. */
- );
-
- static unsigned int get_weight_multi_stride(
- const int n_output_channels, /** Number of output feature maps. */
- const int n_input_channels /** Number of input feature maps. */
- );
-
- /** Determine how much memory (in units of TIn) to allocate for the
- * transformed input.
- */
- static unsigned int get_input_storage_size(
- const int n_batches, /** Number of batches in the input tensor. */
- const int n_channels, /** Number of feature maps in the input tensor. */
- const int n_rows, /** Number of rows in each feature map. */
- const int n_cols, /** Number of columns in each feature map. */
- const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
- );
-
- /** Get the row stride for the A matrix in the Winograd domain. */
- static unsigned int get_input_stride(
- const int n_batches, /** Number of batches in the input tensor. */
- const int n_channels, /** Number of feature maps in the input tensor. */
- const int n_rows, /** Number of rows in each feature map. */
- const int n_cols, /** Number of columns in each feature map. */
- const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
- );
-
- /** Get the stride between A matrices in the Winograd domain. */
- static unsigned int get_input_multi_stride(
- const int n_batches, /** Number of batches in the input tensor. */
- const int n_channels, /** Number of feature maps in the input tensor. */
- const int n_rows, /** Number of rows in each feature map. */
- const int n_cols, /** Number of columns in each feature map. */
- const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
- );
-
- /** Determine how much memory (in units of TOut) to allocate for the
- * (Winograd domain) output.
- */
- static unsigned int get_output_storage_size(
- const int n_batches, /** Number of batches in the output tensor. */
- const int n_rows, /** Number of rows in each feature map of the input tensor. */
- const int n_cols, /** Number of columns in each feature map of the input tensor. */
- const int n_output_channels, /** Number of feature maps in the output tensor. */
- const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
- );
-
- static unsigned int get_output_stride(
- const int n_batches, /** Number of batches in the output tensor. */
- const int n_rows, /** Number of rows in each feature map of the input tensor. */
- const int n_cols, /** Number of columns in each feature map of the input tensor. */
- const int n_output_channels, /** Number of feature maps in the output tensor. */
- const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
- );
-
- static unsigned int get_output_multi_stride(
- const int n_batches, /** Number of batches in the output tensor. */
- const int n_rows, /** Number of rows in each feature map of the input tensor. */
- const int n_cols, /** Number of columns in each feature map of the input tensor. */
- const int n_output_channels, /** Number of feature maps in the output tensor. */
- const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
- );
-
- /** Get the shape (rows, cols) of a feature map of the output tensor. */
- static std::pair<int, int> get_output_feature_map_shape(
- const int n_input_rows, /** Number of rows in the input feature map. */
- const int n_input_cols, /** Number of columns in the input feature map. */
- const bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
- );
-
- /** Create a new Winograd convolution layer.
- */
- WinogradConvolutionLayer(
- const arm_gemm::CPUInfo &cpuinfo, /** Describes CPU properties. */
- const int n_threads, /** Maximum number of threads used to execute the convolution. */
- const int n_batches, /** Number of batches in the input and output tensors. */
- const int n_input_channels, /** Number of feature maps in a batch of the input tensor. */
- const int n_input_rows, /** Number of rows in a feature map of the input tensor. */
- const int n_input_cols, /** Number of columns in a feature map of the input tensor. */
- const int n_output_channels, /** Number of feature maps in the output tensor. */
- const bool same_padding, /** Use "SAME" padding, otherwise use "VALID". */
- const arm_gemm::Activation &activation,
- const TIn* const weights, /** Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. */
- TInGEMM* const weights_storage, /** Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size`. */
- const TIn* const input, /** Pointer to NHWC ordered input tensor, in the spatial domain. */
- TInGEMM* const winograd_input, /** Pointer to working space for the input tensor in the Winograd domain. Must be at least the size returned by `get_input_storage_size`. */
- const TOut* const biases, /** Pointer to biases vector. Pass nullptr if no bias is provided. */
- TOut* const output, /** Pointer to NHWC ordered output tensor, in the spatial domain. */
- TOutGEMM* const winograd_output, /** Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`. */
- const bool pretranspose_B=true, /** Hint that the B matrix can be pretransposed. */
- arm_gemm::GemmConfig *gemm_cfg=nullptr /** Pointer to GEMM configuration. */
- );
-
- /* Utility methods for interacting with the layer. */
- unsigned int weight_transform_get_window(void) const;
- void weight_transform_run(const unsigned int start, const unsigned int stop);
-
- IInputTransform& input_transform(void);
- IOutputTransform& output_transform(void);
-
- /* Get a pointer to the GEMM underlying the Winograd transform. */
- arm_gemm::IGemmCommon *gemm(void);
-};
-
-}
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h b/arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h
index ae0ae44..f469eae 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,15 +24,12 @@
#ifndef ARM_COMPUTE_NEGEMMASSEMBLYDISPATCH_H
#define ARM_COMPUTE_NEGEMMASSEMBLYDISPATCH_H
-#include "arm_compute/core/NEON/kernels/assembly/NEGEMMAssemblyWrapperKernel.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"
#include "arm_compute/runtime/IWeightsManager.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/Tensor.h"
-#include "arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp"
-
namespace arm_compute
{
/** Assembly kernel glue */