arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h - ml/ComputeLibrary - Gitiles

 /*
  * 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.
  */
 #ifndef __ARM_COMPUTE_CLGEMMCONVOLUTIONLAYER_H__
 #define __ARM_COMPUTE_CLGEMMCONVOLUTIONLAYER_H__

 #include "arm_compute/runtime/IFunction.h"

 #include "arm_compute/core/CL/kernels/CLCol2ImKernel.h"
 #include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
 #include "arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h"
 #include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
 #include "arm_compute/core/CL/kernels/CLIm2ColKernel.h"
 #include "arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/CL/CLMemoryGroup.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
 #include "arm_compute/runtime/CL/functions/CLGEMM.h"
 #include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
 #include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
 #include "arm_compute/runtime/CL/functions/CLReshapeLayer.h"
 #include "arm_compute/runtime/IMemoryManager.h"

 #include <memory>

 namespace arm_compute
 {
 class ICLTensor;

 /** Function to reshape and transpose the weights. This function calls the following kernels:
  * -# @ref CLWeightsReshapeKernel
  */
 class CLConvolutionLayerReshapeWeights : public IFunction
 {
 public:
     /** Constructor */
     CLConvolutionLayerReshapeWeights();
     /** 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: QASYMM8/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[out] output     Destination tensor. Data types supported: Same as @p weights.
      * @param[in]  num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
      */
     void configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, unsigned int num_groups = 1);
     /** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayerReshapeWeights
      *
      * @param[in] weights    Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
      *                       Data type supported: QASYMM8/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] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
      *
      * @return a status
      */
     static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, unsigned int num_groups = 1);
     // Inherited methods overridden:
     void run() override;

 private:
     CLWeightsReshapeKernel _weights_reshape_kernel;
 };

 /** Basic function to compute the convolution layer. This function calls the following OpenCL kernels/functions:
  *
  * -# @ref CLIm2ColKernel
  * -# @ref CLGEMM (if the data type is FP32 or FP16)
  * -# @ref CLGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8)
  * -# @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8)
  * -# @ref CLElementwiseOperationKernel for addition (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)
  * -# @ref CLCol2ImKernel (if NCHW data layout)
  */
 class CLGEMMConvolutionLayer : public IFunction
 {
 public:
     /** Default constructor
      *
      * @param[in] memory_manager (Optional) Memory manager.
      */
     CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     CLGEMMConvolutionLayer(const CLGEMMConvolutionLayer &) = delete;
     /** Default move constructor */
     CLGEMMConvolutionLayer(CLGEMMConvolutionLayer &&) = default;
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     CLGEMMConvolutionLayer &operator=(const CLGEMMConvolutionLayer &) = delete;
     /** Default move assignment operator */
     CLGEMMConvolutionLayer &operator=(CLGEMMConvolutionLayer &&) = default;
     /** 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: QASYMM8/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[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 CLWeightsReshapeKernel. If this is not part of the fully connected layer the weights
      *                          tensor has also been transposed with CLGEMMTranspose1xWKernel. Data type supported: Same as @p input.
      * @param[in]  dilation     (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
      * @param[in]  act_info     (Optional) Activation layer information in case of a fused activation.
      * @param[in]  num_groups   (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
      */
     void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
                    const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1);
     /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMConvolutionLayer.
      *
      * @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: QASYMM8/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[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 CLWeightsReshapeKernel. If this is not part of the fully connected layer the weights
      *                          tensor has also been transposed with CLGEMMTranspose1xWKernel. Data type supported: Same as @p input.
      * @param[in]  dilation     (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
      * @param[in]  act_info     (Optional) Activation layer information in case of a fused activation.
      * @param[in]  num_groups   (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
      *
      * @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), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1);

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

 private:
     /** Configures the appropriate matrix multiply routine
      *
      * @param[in]      input                 Input tensor. Data types supported: QASYMM8/F16/F32.
      * @param[in]      weights               Weights tensor. 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, 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]      gemmlowp_output_stage GEMMLowp output stage info
      * @param[in]      gemm_3d_depth         Depth of GEMM 3D
      */
     void configure_mm(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth = 1);
     /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMConvolutionLayer matrix multiply routines
      *
      * @param[in] input                 Input tensor. Data types supported: QASYMM8/F16/F32.
      * @param[in] weights               Weights tensor. Data type supported: Same as @p input.
      * @param[in] 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] 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] gemmlowp_output_stage GEMMLowp output stage info
      * @param[in] gemm_3d_depth         Depth of GEMM 3D
      * @param[in] skip_im2col           Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout.
      * @param[in] run_addition          Flag which specifies if @ref CLGEMMMatrixMatrixMultiplyAddition to be run.
      *
      * @return a status
      */
     static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
                               int gemm_3d_depth, bool skip_im2col, bool run_addition);

 private:
     CLMemoryGroup                        _memory_group;
     CLConvolutionLayerReshapeWeights     _reshape_weights;
     CLIm2ColKernel                       _im2col_kernel;
     CLGEMM                               _mm_gemm;
     CLGEMMLowpMatrixMultiplyCore         _mm_gemmlowp;
     CLCol2ImKernel                       _col2im_kernel;
     CLActivationLayer                    _activationlayer_function;
     CLSaturatedArithmeticOperationKernel _add_bias_kernel;

     const ICLTensor *_original_weights;

     CLTensor _im2col_output;
     CLTensor _weights_reshaped;
     CLTensor _gemm_output;

     DataLayout _data_layout;

     bool _append_bias;
     bool _skip_im2col;
     bool _skip_col2im;
     bool _is_quantized;
     bool _is_activationlayer_enabled;
     bool _is_prepared;
     bool _run_addition;
 };
 } // namespace arm_compute
 #endif /* __ARM_COMPUTE_CLGEMMCONVOLUTIONLAYER_H__ */
	/*
	* 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.
	*/
	#ifndef __ARM_COMPUTE_CLGEMMCONVOLUTIONLAYER_H__
	#define __ARM_COMPUTE_CLGEMMCONVOLUTIONLAYER_H__

	#include "arm_compute/runtime/IFunction.h"

	#include "arm_compute/core/CL/kernels/CLCol2ImKernel.h"
	#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
	#include "arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h"
	#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
	#include "arm_compute/core/CL/kernels/CLIm2ColKernel.h"
	#include "arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/runtime/CL/CLMemoryGroup.h"
	#include "arm_compute/runtime/CL/CLTensor.h"
	#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
	#include "arm_compute/runtime/CL/functions/CLGEMM.h"
	#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
	#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
	#include "arm_compute/runtime/CL/functions/CLReshapeLayer.h"
	#include "arm_compute/runtime/IMemoryManager.h"

	#include <memory>

	namespace arm_compute
	{
	class ICLTensor;

	/** Function to reshape and transpose the weights. This function calls the following kernels:
	* -# @ref CLWeightsReshapeKernel
	*/
	class CLConvolutionLayerReshapeWeights : public IFunction
	{
	public:
	/** Constructor */
	CLConvolutionLayerReshapeWeights();
	/** 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: QASYMM8/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[out] output Destination tensor. Data types supported: Same as @p weights.
	* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
	*/
	void configure(const ICLTensor weights, const ICLTensor biases, ICLTensor *output, unsigned int num_groups = 1);
	/** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayerReshapeWeights
	*
	* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
	* Data type supported: QASYMM8/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] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
	*
	* @return a status
	*/
	static Status validate(const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo *output, unsigned int num_groups = 1);
	// Inherited methods overridden:
	void run() override;

	private:
	CLWeightsReshapeKernel _weights_reshape_kernel;
	};

	/** Basic function to compute the convolution layer. This function calls the following OpenCL kernels/functions:
	*
	* -# @ref CLIm2ColKernel
	* -# @ref CLGEMM (if the data type is FP32 or FP16)
	* -# @ref CLGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8)
	* -# @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8)
	* -# @ref CLElementwiseOperationKernel for addition (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)
	* -# @ref CLCol2ImKernel (if NCHW data layout)
	*/
	class CLGEMMConvolutionLayer : public IFunction
	{
	public:
	/** Default constructor
	*
	* @param[in] memory_manager (Optional) Memory manager.
	*/
	CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	CLGEMMConvolutionLayer(const CLGEMMConvolutionLayer &) = delete;
	/** Default move constructor */
	CLGEMMConvolutionLayer(CLGEMMConvolutionLayer &&) = default;
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	CLGEMMConvolutionLayer &operator=(const CLGEMMConvolutionLayer &) = delete;
	/** Default move assignment operator */
	CLGEMMConvolutionLayer &operator=(CLGEMMConvolutionLayer &&) = default;
	/** 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: QASYMM8/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[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 CLWeightsReshapeKernel. If this is not part of the fully connected layer the weights
	* tensor has also been transposed with CLGEMMTranspose1xWKernel. Data type supported: Same as @p input.
	* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
	* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
	* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
	*/
	void configure(const ICLTensor input, const ICLTensor weights, const ICLTensor biases, ICLTensor output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
	const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1);
	/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMConvolutionLayer.
	*
	* @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: QASYMM8/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[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 CLWeightsReshapeKernel. If this is not part of the fully connected layer the weights
	* tensor has also been transposed with CLGEMMTranspose1xWKernel. Data type supported: Same as @p input.
	* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
	* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
	* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
	*
	* @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), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1);

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

	private:
	/** Configures the appropriate matrix multiply routine
	*
	* @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
	* @param[in] weights Weights tensor. 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, 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] gemmlowp_output_stage GEMMLowp output stage info
	* @param[in] gemm_3d_depth Depth of GEMM 3D
	*/
	void configure_mm(const ICLTensor input, const ICLTensor weights, const ICLTensor biases, ICLTensor output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth = 1);
	/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMConvolutionLayer matrix multiply routines
	*
	* @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
	* @param[in] weights Weights tensor. Data type supported: Same as @p input.
	* @param[in] 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] 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] gemmlowp_output_stage GEMMLowp output stage info
	* @param[in] gemm_3d_depth Depth of GEMM 3D
	* @param[in] skip_im2col Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout.
	* @param[in] run_addition Flag which specifies if @ref CLGEMMMatrixMatrixMultiplyAddition to be run.
	*
	* @return a status
	*/
	static Status validate_mm(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
	int gemm_3d_depth, bool skip_im2col, bool run_addition);

	private:
	CLMemoryGroup _memory_group;
	CLConvolutionLayerReshapeWeights _reshape_weights;
	CLIm2ColKernel _im2col_kernel;
	CLGEMM _mm_gemm;
	CLGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
	CLCol2ImKernel _col2im_kernel;
	CLActivationLayer _activationlayer_function;
	CLSaturatedArithmeticOperationKernel _add_bias_kernel;

	const ICLTensor *_original_weights;

	CLTensor _im2col_output;
	CLTensor _weights_reshaped;
	CLTensor _gemm_output;

	DataLayout _data_layout;

	bool _append_bias;
	bool _skip_im2col;
	bool _skip_col2im;
	bool _is_quantized;
	bool _is_activationlayer_enabled;
	bool _is_prepared;
	bool _run_addition;
	};
	} // namespace arm_compute
	#endif /* __ARM_COMPUTE_CLGEMMCONVOLUTIONLAYER_H__ */