diff options
Diffstat (limited to 'arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h')
| -rw-r--r-- | arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h | 259 |
1 files changed, 136 insertions, 123 deletions
diff --git a/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h b/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h index ea6c8d813d..97532f3574 100644 --- a/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h +++ b/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h @@ -25,104 +25,93 @@ #define __ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__ #include "arm_compute/core/NEON/INEKernel.h" +#include "arm_compute/core/NEON/kernels/winograd/batched_blocked_gemm.hpp" #include "arm_compute/core/NEON/kernels/winograd/convolution.hpp" #include "arm_compute/core/NEON/kernels/winograd/tensor.hpp" +#include "arm_compute/core/NEON/kernels/winograd/winograd_gemm.hpp" namespace arm_compute { class ITensor; -class NEWinogradLayerKernel; -class NEWinogradLayerTransformInputKernel; -class NEWinogradLayerTransformWeightsKernel; -class Winograd3x3F32 final +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +class NEWinogradLayerTransformInputKernel : public INEKernel { public: - /** Create a new Winograd convolution layer. + /** Determine how much memory (in units of TIn) to allocate for the + * transformed input. * - * @param[in] n_batches Number of batches in the input and output tensors. - * @param[in] n_input_channels Number of feature maps in a batch of the input tensor. - * @param[in] n_input_rows Number of rows in a feature map of the input tensor. - * @param[in] n_input_cols Number of columns in a feature map of the input tensor. - * @param[in] n_output_channels Number of feature maps in the output tensor. - * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". - * @param[in] weights Pointer to weight tensor in spatial domain. Must be ordered as "Height x Rows x Input Feature Maps x Output Feature Maps. - * @param[out] weights_storage Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size - * @param[in] input Pointer to NHWC ordered input tensor, in the spatial domain. - * @param[out] 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`. - * @param[in] biases Pointer to the biases vector. - * @param[out] output Pointer to NHWC ordered output tensor, in the spatial domain. - * @param[out] 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`. + * @param[in] n_batches Number of batches in the input tensor. + * @param[in] n_channels Number of feature maps in the input tensor. + * @param[in] n_rows Number of rows in each feature map. + * @param[in] n_cols Number of columns in each feature map. + * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". */ - friend class NEWinogradLayerKernel; - friend class NEWinogradLayerTransformInputKernel; - friend class NEWinogradLayerTransformOutputKernel; - friend class NEWinogradLayerTransformWeightsKernel; + static unsigned int get_input_storage_size( + int n_batches, + int n_channels, + int n_rows, + int n_cols, + bool same_padding); - Winograd3x3F32( - const int n_batches, - const int n_input_channels, - const int n_input_rows, - const int n_input_cols, - const int n_output_channels, - const bool same_padding, - const float *const weights, - float *const weights_storage, + NEWinogradLayerTransformInputKernel(); + const char *name() const override + { + return "NEWinogradLayerTransformInputKernel"; + } + + /** Configure the output transform kernel. + * + * @param[in] input Input tensor data + * @param[in] n_batches Number of batches in input tensor. + * @param[in] n_rows Number of rows in input tensor. + * @param[in] n_cols Number of columns in input tensor. + * @param[in] n_channels Number of channels in input tensor. + * @param[in] padding Padding type. + * @param[out] output Base of output matrices. + * @param[in] matrix_stride Stride between output matrices. + */ + void configure( const float *const input, - float *const winograd_input, + const int n_batches, + const int n_rows, + const int n_cols, + const int n_channels, + const PaddingType padding, float *const output, - float *const winograd_output); + const int matrix_stride); - ~Winograd3x3F32(); + // Inherited methods overridden: + void run(const Window &window, const ThreadInfo &info) override; + bool is_parallelisable() const override; private: - class Private; - std::unique_ptr<Private> _pimpl; + using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelCols, KernelCols>; + using WinogradConv = typename WinogradBase::template Convolution<float, float>; + using InputTransform = typename WinogradBase::template InputTransform<float>; + std::unique_ptr<InputTransform> _transform; }; -class INEWinogradLayerTransformKernel : public INEKernel +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +class NEWinogradLayerTransformOutputKernel : public INEKernel { public: - /** Constructor */ - INEWinogradLayerTransformKernel(); - - /** Prevent instances of this class from being copied (As this class contains pointers) */ - INEWinogradLayerTransformKernel(const INEWinogradLayerTransformKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - INEWinogradLayerTransformKernel &operator=(const INEWinogradLayerTransformKernel &) = delete; - /** Allow instances of this class to be moved */ - INEWinogradLayerTransformKernel(INEWinogradLayerTransformKernel &&) = default; - /** Allow instances of this class to be moved */ - INEWinogradLayerTransformKernel &operator=(INEWinogradLayerTransformKernel &&) = default; - - virtual ~INEWinogradLayerTransformKernel() = default; - - /** Initialise the kernel + /** Determine how much memory (in units of TOut) to allocate for the + * (Winograd domain) output. * - * @param[in] convolver A pointer to the winograd convolver, this object must have been configured and is ready to execute 16 GEMMS . + * @param[in] n_batches Number of batches in the output tensor. + * @param[in] n_rows Number of rows in each feature map of the input tensor. + * @param[in] n_cols Number of columns in each feature map of the input tensor. + * @param[in] n_output_channels Number of feature maps in the output tensor. + * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". */ - virtual void configure(Winograd3x3F32 *convolver); - -protected: - Winograd3x3F32 *_convolver; -}; - -class NEWinogradLayerTransformInputKernel final : public INEWinogradLayerTransformKernel -{ -public: - const char *name() const override - { - return "NEWinogradLayerTransformInputKernel"; - } - // Inherited methods overridden: - void configure(Winograd3x3F32 *convolver) override; - void run(const Window &window, const ThreadInfo &info) override; - bool is_parallelisable() const override; -}; + static unsigned int get_output_storage_size( + int n_batches, + int n_rows, + int n_cols, + int n_output_channels, + bool same_padding); -class NEWinogradLayerTransformOutputKernel final : public INEKernel -{ -public: const char *name() const override { return "NEWinogradLayerTransformOutputKernel"; @@ -167,6 +156,10 @@ public: bool is_parallelisable() const override; private: + using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols>; + using WinogradConv = typename WinogradBase::template Convolution<float, float>; + using OutputTransform = typename WinogradBase::template OutputTransform<float>; + const ITensor *_biases; const float *_output_workspace; int _matrix_stride; @@ -178,22 +171,61 @@ private: int _n_channels; }; -class NEWinogradLayerTransformWeightsKernel final : public INEWinogradLayerTransformKernel +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +class NEWinogradLayerTransformWeightsKernel final : public INEKernel { public: + /** Determine how much memory (in units of TIn) to allocate for the + * transformed weights. + * + * @param[in] n_output_channels Number of output feature maps. + * @param[in] n_input_channels Number of input feature maps. + */ + static unsigned int get_weight_storage_size(int n_output_channels, int n_input_channels); + + NEWinogradLayerTransformWeightsKernel(); const char *name() const override { return "NEWinogradLayerTransformWeightsKernel"; } + /** Configure the output transform kernel. + * + * @param[in] weights_hwio Pointer to the weights tensor + * @param[in] output Pointer to working space for the output tensor in the Winograd domain. + * @param[in] matrix_stride Stride across matrices in the output workspace. + * @param[in] n_output_channels Number of filters. + * @param[in] n_input_channels Number of channels in each filter. + */ + void configure( + const ITensor *weights_hwio, + float *const output, + const int matrix_stride, + const int n_output_channels, + const int n_input_channels); + // Inherited methods overridden: - void configure(Winograd3x3F32 *convolver) override; + void run(const Window &window, const ThreadInfo &info) override; bool is_parallelisable() const override; + +private: + using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols>; + using WinogradConv = typename WinogradBase::template Convolution<float, float>; + using WeightsTransform = typename WinogradBase::template WeightsTransform<float>; + std::unique_ptr<WeightsTransform> _transform; }; -class NEWinogradLayerKernel final : public INEKernel +template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols> +class NEWinogradLayerKernel : public INEKernel { public: + using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols>; + using WinogradConv = typename WinogradBase::template Convolution<float, float>; + using MultiGEMM = winograd::BatchedBlockedGemm<WinogradConv::M_BLOCK, WinogradConv::N_BLOCK, float, float>; + + static const int _output_tile_rows = OutputTileRows; + static const int _output_tile_cols = OutputTileCols; + const char *name() const override { return "NEWinogradLayerKernel"; @@ -214,57 +246,38 @@ public: /** Initialise the kernel * - * @param[in] convolver A pointer to the winograd convolver, this object must have been configured and is ready to execute 16 GEMMS . + * @param[in] n_gemms Number of GEMMs to compute. + * @param[in] M in_shape.n_batches * tile_rows * tile_cols. + * @param[in] K Number of channels in the input tensor. + * @param[in] N Number of channels in the output tensor. + * @param[in] a_matrix_stride Stride between input matrices. + * @param[in] a_row_stride Row stride inside input matrix. + * @param[in] b_matrix_stride Stride between weights matrices. + * @param[in] b_row_stride Row stride inside the weights matrix. + * @param[in] c_matrix_stride Stride between output matrices. + * @param[in] c_row_stride Row stride inside the output matrix. + * @param[out] a_ptr Input workspace. + * @param[out] b_ptr Kernel workspace. + * @param[out] c_ptr Output workspace. */ - void configure(Winograd3x3F32 *convolver); + void configure( + const unsigned int n_gemms, + const int M, const int K, const int N, + const int a_matrix_stride, + const int a_row_stride, + const int b_matrix_stride, + const int b_row_stride, + const int c_matrix_stride, + const int c_row_stride, + const float *const a_ptr, + const float *const b_ptr, + float *const c_ptr); // Inherited methods overridden: void run(const Window &window, const ThreadInfo &info) override; - /** Determine how much memory (in units of TIn) to allocate for the - * transformed weights. - * - * @param[in] n_output_channels Number of output feature maps. - * @param[in] n_input_channels Number of input feature maps. - */ - static unsigned int get_weight_storage_size( - const int n_output_channels, - const int n_input_channels); - - /** Determine how much memory (in units of TIn) to allocate for the - * transformed input. - * - * @param[in] n_batches Number of batches in the input tensor. - * @param[in] n_channels Number of feature maps in the input tensor. - * @param[in] n_rows Number of rows in each feature map. - * @param[in] n_cols Number of columns in each feature map. - * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". - */ - static unsigned int get_input_storage_size( - const int n_batches, - const int n_channels, - const int n_rows, - const int n_cols, - const bool same_padding); - - /** Determine how much memory (in units of TOut) to allocate for the - * (Winograd domain) output. - * - * @param[in] n_batches Number of batches in the output tensor. - * @param[in] n_rows Number of rows in each feature map of the input tensor. - * @param[in] n_cols Number of columns in each feature map of the input tensor. - * @param[in] n_output_channels Number of feature maps in the output tensor. - * @param[in] same_padding Use "SAME" padding, otherwise use "VALID". - */ - static unsigned int get_output_storage_size( - const int n_batches, - const int n_rows, - const int n_cols, - const int n_output_channels, - const bool same_padding); - -protected: - Winograd3x3F32 *_convolver; +private: + std::unique_ptr<MultiGEMM> _gemms; }; } // namespace arm_compute |