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/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
new file mode 100644
index 0000000..bd141ef
--- /dev/null
+++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
@@ -0,0 +1,597 @@
+/*
+ * 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 "src/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*/