arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h - ml/ComputeLibrary - Gitiles

 /*
  * 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_NEGEMMCONVOLUTIONLAYER_H__
 #define __ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H__

 #include "arm_compute/runtime/IFunction.h"

 #include "arm_compute/core/NEON/kernels/NECol2ImKernel.h"
 #include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMAssemblyBaseKernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
 #include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h"
 #include "arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/MemoryGroup.h"
 #include "arm_compute/runtime/NEON/AssemblyHelper.h"
 #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
 #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
 #include "arm_compute/runtime/Tensor.h"

 #include <memory>

 namespace arm_compute
 {
 class ITensor;

 /** Function to reshape and perform 1xW transposition on the weights. This function calls the following kernels:
  * -# @ref NEWeightsReshapeKernel
  * -# @ref NEGEMMTranspose1xWKernel (executed in case GEMM is required for the operation)
  */
 class NEConvolutionLayerReshapeWeights : public IFunction
 {
 public:
     /** Constructor */
     NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
     /** Set the input and output tensors.
      *
      * @param[in]  weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/F32.
      * @param[in]  biases       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
      * @param[out] output       Destination tensor. Data types supported: Same as @p weights.
      * @param[in]  transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
      *                          Data types supported: Same as @p weights.
      */
     void configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW);
     /** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
      *
      * @param[in] weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/F16/F32.
      * @param[in] biases       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
      * @param[in] output       Destination tensor. Data types supported: Same as @p weights.
      * @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
      *                         Data types supported: Same as @p weights.
      *
      * @return an error status
      */
     static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW);

     // Inherited methods overridden:
     void run() override;

 private:
     MemoryGroup              _memory_group;
     NEWeightsReshapeKernel   _weights_reshape_kernel;
     NEGEMMTranspose1xWKernel _weights_transposed_kernel;
     Tensor                   _weights_reshaped;
     bool                     _transpose1xW;
 };

 /** Basic function to simulate a convolution layer. This function calls the following NEON kernels:
  * -# @ref NEWeightsReshapeKernel   (executed only once for each configuration)
  * -# @ref NEIm2ColKernel
  * -# @ref NEGEMMInterleave4x4Kernel (executed only in case GEMM is required for the operation)
  * -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
  * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale (if quantized asymmetric)
  * -# @ref NECol2ImKernel
  */
 class NEGEMMConvolutionLayer : public IFunction
 {
 public:
     /** Constructor */
     NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr);

     /** Set the input and output tensors.
      *
      * @param[in]  input        Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
      *                          while every optional dimension from 4 and above represent a batch of inputs.
      *                          Data types supported: QS8/QASYMM8/QS16/F32.
      * @param[in]  weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
      * @param[in]  biases       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
      *                          Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
      * @param[out] output       Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
      *                          Data types supported: Same as @p input.
      * @param[in]  conv_info    Contains padding and stride information described in @ref PadStrideInfo.
      * @param[in]  weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
      *                          tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
      * @param[in]  dilation     (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
      */
     void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
                    const Size2D &dilation = Size2D(1U, 1U));
     /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer
      *
      * @param[in] input        Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
      *                         while every optional dimension from 4 and above represent a batch of inputs.
      *                         Data types supported: QS8/QASYMM8/QS16/F16/F32.
      * @param[in] weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
      * @param[in] biases       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
      *                         Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
      * @param[in] output       Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
      *                         Data types supported: Same as @p input.
      * @param[in] conv_info    Contains padding and stride information described in @ref PadStrideInfo.
      * @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
      *                         tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
      * @param[in] dilation     (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
      *
      * @return a status
      */
     static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                            const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U));

     // Inherited methods overridden:
     void run() override;

 private:
     /** Configures the appropriate matrix multiply routine
      *
      * @param[in]  input          Input tensor. Data types supported: QS8/QASYMM8/QS16/F16/F32.
      * @param[in]  weights        Weights tensor. Data type supported: Same as @p input.
      * @param[out] output         Output tensor. Data types supported: Same as @p input,
      *                            except for input of QASYMM8 type where output should be of S32 type.
      * @param[in]  is_interleaved (Optional) True if input0 and input1 have been reshaped respectively using @ref CLGEMMInterleave4x4Kernel and @ref CLGEMMTranspose1xWKernel
      * @param[in]  reshape_info   (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
      */
     void configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, bool is_interleaved, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo());

 private:
     AssemblyKernelGlueF32                               _asm_glue;
     MemoryGroup                                         _memory_group;
     NEIm2ColKernel                                      _input_im2col_kernel;
     NEGEMMInterleave4x4Kernel                           _input_interleave_kernel;
     NEConvolutionLayerReshapeWeights                    _reshape_weights;
     NEGEMMMatrixMultiplyKernel                          _mm_kernel;
     NEGEMMLowpMatrixMultiplyCore                        _mm_gemmlowp;
     NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
     NECol2ImKernel                                      _output_col2im_kernel;

     Tensor _input_im2col_reshaped;
     Tensor _input_interleaved_reshaped;
     Tensor _weights_reshaped;
     Tensor _gemm_output;
     Tensor _tmp_output;
     Tensor _workspace;

     bool _append_bias;
     bool _is_fully_connected_convolution;
     bool _are_weights_reshaped;
     bool _is_quantized;
     bool _is_interleaved;
 };
 }
 #endif /* __ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H__ */
	/*
	* 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_NEGEMMCONVOLUTIONLAYER_H__
	#define __ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H__

	#include "arm_compute/runtime/IFunction.h"

	#include "arm_compute/core/NEON/kernels/NECol2ImKernel.h"
	#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
	#include "arm_compute/core/NEON/kernels/NEGEMMAssemblyBaseKernel.h"
	#include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
	#include "arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
	#include "arm_compute/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
	#include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h"
	#include "arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/runtime/MemoryGroup.h"
	#include "arm_compute/runtime/NEON/AssemblyHelper.h"
	#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
	#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
	#include "arm_compute/runtime/Tensor.h"

	#include <memory>

	namespace arm_compute
	{
	class ITensor;

	/** Function to reshape and perform 1xW transposition on the weights. This function calls the following kernels:
	* -# @ref NEWeightsReshapeKernel
	* -# @ref NEGEMMTranspose1xWKernel (executed in case GEMM is required for the operation)
	*/
	class NEConvolutionLayerReshapeWeights : public IFunction
	{
	public:
	/** Constructor */
	NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
	/** Set the input and output tensors.
	*
	* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/F32.
	* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
	* @param[out] output Destination tensor. Data types supported: Same as @p weights.
	* @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
	* Data types supported: Same as @p weights.
	*/
	void configure(const ITensor weights, const ITensor biases, ITensor *output, bool transpose1xW);
	/** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
	*
	* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/F16/F32.
	* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
	* @param[in] output Destination tensor. Data types supported: Same as @p weights.
	* @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
	* Data types supported: Same as @p weights.
	*
	* @return an error status
	*/
	static Status validate(const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo *output, bool transpose1xW);

	// Inherited methods overridden:
	void run() override;

	private:
	MemoryGroup _memory_group;
	NEWeightsReshapeKernel _weights_reshape_kernel;
	NEGEMMTranspose1xWKernel _weights_transposed_kernel;
	Tensor _weights_reshaped;
	bool _transpose1xW;
	};

	/** Basic function to simulate a convolution layer. This function calls the following NEON kernels:
	* -# @ref NEWeightsReshapeKernel (executed only once for each configuration)
	* -# @ref NEIm2ColKernel
	* -# @ref NEGEMMInterleave4x4Kernel (executed only in case GEMM is required for the operation)
	* -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
	* -# @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale (if quantized asymmetric)
	* -# @ref NECol2ImKernel
	*/
	class NEGEMMConvolutionLayer : public IFunction
	{
	public:
	/** Constructor */
	NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr);

	/** Set the input and output tensors.
	*
	* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
	* while every optional dimension from 4 and above represent a batch of inputs.
	* Data types supported: QS8/QASYMM8/QS16/F32.
	* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
	* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
	* Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
	* @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
	* Data types supported: Same as @p input.
	* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
	* @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
	* tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
	* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
	*/
	void configure(const ITensor input, const ITensor weights, const ITensor biases, ITensor output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
	const Size2D &dilation = Size2D(1U, 1U));
	/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer
	*
	* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
	* while every optional dimension from 4 and above represent a batch of inputs.
	* Data types supported: QS8/QASYMM8/QS16/F16/F32.
	* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
	* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
	* Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
	* @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
	* Data types supported: Same as @p input.
	* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
	* @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
	* tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
	* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
	*
	* @return a status
	*/
	static Status validate(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo output, const PadStrideInfo &conv_info,
	const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U));

	// Inherited methods overridden:
	void run() override;

	private:
	/** Configures the appropriate matrix multiply routine
	*
	* @param[in] input Input tensor. Data types supported: QS8/QASYMM8/QS16/F16/F32.
	* @param[in] weights Weights tensor. Data type supported: Same as @p input.
	* @param[out] output Output tensor. Data types supported: Same as @p input,
	* except for input of QASYMM8 type where output should be of S32 type.
	* @param[in] is_interleaved (Optional) True if input0 and input1 have been reshaped respectively using @ref CLGEMMInterleave4x4Kernel and @ref CLGEMMTranspose1xWKernel
	* @param[in] reshape_info (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
	*/
	void configure_mm(const ITensor input, const ITensor weights, ITensor *output, bool is_interleaved, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo());

	private:
	AssemblyKernelGlueF32 _asm_glue;
	MemoryGroup _memory_group;
	NEIm2ColKernel _input_im2col_kernel;
	NEGEMMInterleave4x4Kernel _input_interleave_kernel;
	NEConvolutionLayerReshapeWeights _reshape_weights;
	NEGEMMMatrixMultiplyKernel _mm_kernel;
	NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
	NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
	NECol2ImKernel _output_col2im_kernel;

	Tensor _input_im2col_reshaped;
	Tensor _input_interleaved_reshaped;
	Tensor _weights_reshaped;
	Tensor _gemm_output;
	Tensor _tmp_output;
	Tensor _workspace;

	bool _append_bias;
	bool _is_fully_connected_convolution;
	bool _are_weights_reshaped;
	bool _is_quantized;
	bool _is_interleaved;
	};
	}
	#endif /* __ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H__ */