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/*
* Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef __ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__
#define __ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__
#include "arm_compute/core/NEON/INEKernel.h"
#include "arm_compute/core/NEON/kernels/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;
template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
class NEWinogradLayerTransformInputKernel : public INEKernel
{
public:
/** Determine how much memory (in units of TIn) to allocate for the
* transformed input.
*
* @param[in] n_batches Number of batches in the input tensor.
* @param[in] n_channels Number of feature maps in the input tensor.
* @param[in] n_rows Number of rows in each feature map.
* @param[in] n_cols Number of columns in each feature map.
* @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
*/
static unsigned int get_input_storage_size(
int n_batches,
int n_channels,
int n_rows,
int n_cols,
bool same_padding);
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,
const int n_batches,
const int n_rows,
const int n_cols,
const int n_channels,
const PaddingType padding,
float *const output,
const int matrix_stride);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
bool is_parallelisable() const override;
private:
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;
};
template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
class NEWinogradLayerTransformOutputKernel : public INEKernel
{
public:
/** Determine how much memory (in units of TOut) to allocate for the
* (Winograd domain) output.
*
* @param[in] n_batches Number of batches in the output tensor.
* @param[in] n_rows Number of rows in each feature map of the input tensor.
* @param[in] n_cols Number of columns in each feature map of the input tensor.
* @param[in] n_output_channels Number of feature maps in the output tensor.
* @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
*/
static unsigned int get_output_storage_size(
int n_batches,
int n_rows,
int n_cols,
int n_output_channels,
bool same_padding);
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;
~NEWinogradLayerTransformOutputKernel() = default;
/** Configure the output transform kernel.
*
* @param[in] biases Pointer to the biases tensor.
* @param[in] output_workingspace Pointer to working space for the output tensor in the Winograd domain.
* @param[in] matrix_stride Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
* @param[out] output Pointer to NHWC ordered output tensor, in the spatial domain.
* @param[in] n_batches Number of batches in the input tensor.
* @param[in] n_rows Number of rows in output tensor.
* @param[in] n_cols Number of columns in output tensor.
* @param[in] n_channels Number of feature maps in the output tensor.
*/
void configure(
const ITensor *biases,
const float *const output_workingspace,
const int matrix_stride,
float *const output,
const int n_batches,
const int n_rows,
const int n_cols,
const int n_channels);
// Inherited methods overridden:
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 OutputTransform = typename WinogradBase::template OutputTransform<float>;
const ITensor *_biases;
const float *_output_workspace;
int _matrix_stride;
int _matrix_row_stride;
float *_output;
int _n_batches;
int _n_rows;
int _n_cols;
int _n_channels;
};
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 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;
};
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";
}
/** Constructor */
NEWinogradLayerKernel();
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEWinogradLayerKernel(const NEWinogradLayerKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEWinogradLayerKernel &operator=(const NEWinogradLayerKernel &) = delete;
/** Allow instances of this class to be moved */
NEWinogradLayerKernel(NEWinogradLayerKernel &&) = default;
/** Allow instances of this class to be moved */
NEWinogradLayerKernel &operator=(NEWinogradLayerKernel &&) = default;
~NEWinogradLayerKernel() = default;
/** Initialise the kernel
*
* @param[in] n_gemms Number of GEMMs to compute.
* @param[in] M in_shape.n_batches * tile_rows * tile_cols.
* @param[in] K Number of channels in the input tensor.
* @param[in] N Number of channels in the output tensor.
* @param[in] a_matrix_stride Stride between input matrices.
* @param[in] a_row_stride Row stride inside input matrix.
* @param[in] b_matrix_stride Stride between weights matrices.
* @param[in] b_row_stride Row stride inside the weights matrix.
* @param[in] c_matrix_stride Stride between output matrices.
* @param[in] c_row_stride Row stride inside the output matrix.
* @param[out] a_ptr Input workspace.
* @param[out] b_ptr Kernel workspace.
* @param[out] c_ptr Output workspace.
*/
void configure(
const unsigned int n_gemms,
const int M, const int K, const int N,
const int a_matrix_stride,
const int a_row_stride,
const int b_matrix_stride,
const int b_row_stride,
const int c_matrix_stride,
const int c_row_stride,
const 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;
private:
std::unique_ptr<MultiGEMM> _gemms;
};
} // namespace arm_compute
#endif /*__ARM_COMPUTE_NEGEMMWINOGRADLAYERKERNEL_H__*/
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