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

 /*
  * 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_NEGEMMCONVOLUTIONLAYER_H
 #define ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H

 #include "arm_compute/runtime/IFunction.h"

 #include "arm_compute/core/Types.h"
 #include "arm_compute/runtime/IWeightsManager.h"
 #include "arm_compute/runtime/MemoryGroup.h"
 #include "arm_compute/runtime/NEON/functions/NEGEMM.h"
 #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
 #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
 #include "arm_compute/runtime/NEON/functions/NEReshapeLayer.h"
 #include "arm_compute/runtime/Tensor.h"

 #include <memory>

 namespace arm_compute
 {
 class ITensor;
 class NECol2ImKernel;
 class NEIm2ColKernel;
 class NEWeightsReshapeKernel;

 /** Function to reshape the weights. This function calls the following kernel:
  * -# @ref NEWeightsReshapeKernel
  */
 class NEConvolutionLayerReshapeWeights : public IFunction
 {
 public:
     /** Constructor */
     NEConvolutionLayerReshapeWeights();
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     NEConvolutionLayerReshapeWeights(const NEConvolutionLayerReshapeWeights &) = delete;
     /** Default move constructor */
     NEConvolutionLayerReshapeWeights(NEConvolutionLayerReshapeWeights &&) = default;
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     NEConvolutionLayerReshapeWeights &operator=(const NEConvolutionLayerReshapeWeights &) = delete;
     /** Default move assignment operator */
     NEConvolutionLayerReshapeWeights &operator=(NEConvolutionLayerReshapeWeights &&) = default;
     /** Default destructor */
     ~NEConvolutionLayerReshapeWeights();
     /** 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: All.
      * @param[in]  biases  Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
      *                     Data type supported: same as @p weights.
      *                     @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
      * @param[out] output  Destination tensor. Data types supported: same as @p weights.
      */
     void configure(const ITensor *weights, const ITensor *biases, ITensor *output);
     /** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
      *
      * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
      *                    Data type supported: All.
      * @param[in] biases  Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
      *                    Data type supported: same as @p weights.
      *                    @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
      * @param[in] output  Destination tensor. Data types supported: same as @p weights.
      *
      * @return an error status
      */
     static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output);

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

 private:
     std::unique_ptr<NEWeightsReshapeKernel> _weights_reshape_kernel;
 };

 namespace weights_transformations
 {
 /** Basic function to manage the reshape weights generated from @ref NEConvolutionLayerReshapeWeights */
 class NEConvolutionLayerReshapeWeightsTransform : public ITransformWeights
 {
 public:
     /** Constructor */
     NEConvolutionLayerReshapeWeightsTransform() = default;
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     NEConvolutionLayerReshapeWeightsTransform(const NEConvolutionLayerReshapeWeightsTransform &) = delete;
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     NEConvolutionLayerReshapeWeightsTransform &operator=(const NEConvolutionLayerReshapeWeightsTransform &) = delete;
     /** Prevent instances of this class from being moved (As this class contains non movable objects) */
     NEConvolutionLayerReshapeWeightsTransform(NEConvolutionLayerReshapeWeightsTransform &&) = delete;
     /** Prevent instances of this class from being moved (As this class contains non movable objects) */
     NEConvolutionLayerReshapeWeightsTransform &operator=(NEConvolutionLayerReshapeWeightsTransform &&) = delete;
     /** Default destructor */
     ~NEConvolutionLayerReshapeWeightsTransform() = default;
     void configure(const ITensor *input, const ITensor *biases)
     {
         _bias_bit = (biases != nullptr) ? 1 : 0;
         _func.configure(input, biases, &_output);
     }

     void run() override
     {
         _output.allocator()->allocate();
         _func.run();
         _reshape_run = true;
     }

     ITensor *get_weights() override
     {
         return &_output;
     }

     void release() override
     {
         _output.allocator()->free();
     }

     uint32_t uid() override
     {
         return ((0x8) | (_bias_bit << 7));
     }

     bool is_reshape_run()
     {
         return _reshape_run;
     }

 private:
     Tensor                           _output{};
     NEConvolutionLayerReshapeWeights _func{};
     int32_t                          _bias_bit{ 0 };
 };
 } // namespace weights_transformations

 /** Basic function to compute the convolution layer. This function calls the following NEON kernels/functions:
  *
  * -# @ref NEIm2ColKernel
  * -# @ref NEGEMM (if the data type is BFLOAT16/FP16/FP32)
  * -# @ref NEGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8/QASYMM8_SIGNED)
  * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8/QASYMM8_SIGNED)
  * -# @ref NEArithmeticAdditionKernel (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)
  * -# @ref NECol2ImKernel (if NCHW data layout)
  *
  */
 class NEGEMMConvolutionLayer : public IFunction
 {
 public:
     /** Constructor */
     NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr, IWeightsManager *weights_manager = nullptr);
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     NEGEMMConvolutionLayer(const NEGEMMConvolutionLayer &) = delete;
     /** Default move constructor */
     NEGEMMConvolutionLayer(NEGEMMConvolutionLayer &&) = default;
     /** Prevent instances of this class from being copied (As this class contains pointers) */
     NEGEMMConvolutionLayer &operator=(const NEGEMMConvolutionLayer &) = delete;
     /** Default move assignment operator */
     NEGEMMConvolutionLayer &operator=(NEGEMMConvolutionLayer &&) = default;
     /** Default destructor */
     ~NEGEMMConvolutionLayer();
     /** 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/QASYMM8_SIGNED/BFLOAT16/F16/F32.
      * @param[in]  weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
      *                          Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
      * @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/QASYMM8_SIGNED 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).
      * @param[in]  act_info     (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
      * @param[in]  num_groups   (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
      */
     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), 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 NEGEMMConvolutionLayer
      *
      * @param[in] input        Source tensor info. 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/QASYMM8_SIGNED/BFLOAT16/F16/F32.
      * @param[in] weights      Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
      *                         Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
      * @param[in] biases       Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
      *                         Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
      * @param[in] output       Destination tensor info. 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).
      * @param[in] act_info     (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
      * @param[in] num_groups   (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
      *
      * @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/QASYMM8_SIGNED/BFLOAT16/F16/F32.
      * @param[in]  weights       Weights tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
      * @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/QASYMM8_SIGNED type where biases should be of S32 type.
      * @param[out] output        Output tensor. Data types supported: Same as @p input,
      *                           except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type.
      * @param[in]  act_info      (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
      * @param[in]  gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
      */
     void configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(), int gemm_3d_depth = 1);
     /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer matrix multiply routines
      *
      * @param[in] input         Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
      * @param[in] weights       Weights tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
      * @param[in] biases        Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
      *                          Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
      * @param[in] output        Output tensor info. Data types supported: Same as @p input,
      *                          except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type.
      * @param[in] act_info      (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
      * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
      * @param[in] skip_im2col   (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false)
      *
      * @return a status
      */
     static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(),
                               int gemm_3d_depth = 1, bool skip_im2col = false);
     /** Static function to check if GEMM3D is supported in @ref NEGEMM or in @ref NEGEMMLowpMatrixMultiplyCore
      *
      * @param[in] input_info    Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
      * @param[in] weights_info  Weights tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
      * @param[in] act_info      Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
      * @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
      *
      * @return a status
      */
     static Status validate_gemm3d(const ITensorInfo *input_info, const ITensorInfo *weights_info, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col);

 private:
     MemoryGroup                                                        _memory_group;
     IWeightsManager                                                   *_weights_manager;
     NEConvolutionLayerReshapeWeights                                   _reshape_weights;
     weights_transformations::NEConvolutionLayerReshapeWeightsTransform _reshape_weights_managed;
     std::unique_ptr<NEIm2ColKernel>                                    _im2col_kernel;
     NEGEMM                                                             _mm_gemm;
     NEGEMMLowpMatrixMultiplyCore                                       _mm_gemmlowp;
     std::unique_ptr<NECol2ImKernel>                                    _col2im_kernel;
     NEReshapeLayer                                                     _reshape_layer;

     const ITensor *_original_weights;

     Tensor _im2col_output;
     Tensor _weights_reshaped;
     Tensor _gemm_output;
     Tensor _tmp_output;

     DataLayout _data_layout;

     bool _skip_im2col;
     bool _skip_col2im;
     bool _is_quantized;
     bool _is_prepared;
 };
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H */
	/*
	* 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_NEGEMMCONVOLUTIONLAYER_H
	#define ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H

	#include "arm_compute/runtime/IFunction.h"

	#include "arm_compute/core/Types.h"
	#include "arm_compute/runtime/IWeightsManager.h"
	#include "arm_compute/runtime/MemoryGroup.h"
	#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
	#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
	#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
	#include "arm_compute/runtime/NEON/functions/NEReshapeLayer.h"
	#include "arm_compute/runtime/Tensor.h"

	#include <memory>

	namespace arm_compute
	{
	class ITensor;
	class NECol2ImKernel;
	class NEIm2ColKernel;
	class NEWeightsReshapeKernel;

	/** Function to reshape the weights. This function calls the following kernel:
	* -# @ref NEWeightsReshapeKernel
	*/
	class NEConvolutionLayerReshapeWeights : public IFunction
	{
	public:
	/** Constructor */
	NEConvolutionLayerReshapeWeights();
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	NEConvolutionLayerReshapeWeights(const NEConvolutionLayerReshapeWeights &) = delete;
	/** Default move constructor */
	NEConvolutionLayerReshapeWeights(NEConvolutionLayerReshapeWeights &&) = default;
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	NEConvolutionLayerReshapeWeights &operator=(const NEConvolutionLayerReshapeWeights &) = delete;
	/** Default move assignment operator */
	NEConvolutionLayerReshapeWeights &operator=(NEConvolutionLayerReshapeWeights &&) = default;
	/** Default destructor */
	~NEConvolutionLayerReshapeWeights();
	/** 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: All.
	* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
	* Data type supported: same as @p weights.
	* @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
	* @param[out] output Destination tensor. Data types supported: same as @p weights.
	*/
	void configure(const ITensor weights, const ITensor biases, ITensor *output);
	/** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
	*
	* @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
	* Data type supported: All.
	* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
	* Data type supported: same as @p weights.
	* @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
	* @param[in] output Destination tensor. Data types supported: same as @p weights.
	*
	* @return an error status
	*/
	static Status validate(const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo *output);

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

	private:
	std::unique_ptr<NEWeightsReshapeKernel> _weights_reshape_kernel;
	};

	namespace weights_transformations
	{
	/** Basic function to manage the reshape weights generated from @ref NEConvolutionLayerReshapeWeights */
	class NEConvolutionLayerReshapeWeightsTransform : public ITransformWeights
	{
	public:
	/** Constructor */
	NEConvolutionLayerReshapeWeightsTransform() = default;
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	NEConvolutionLayerReshapeWeightsTransform(const NEConvolutionLayerReshapeWeightsTransform &) = delete;
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	NEConvolutionLayerReshapeWeightsTransform &operator=(const NEConvolutionLayerReshapeWeightsTransform &) = delete;
	/** Prevent instances of this class from being moved (As this class contains non movable objects) */
	NEConvolutionLayerReshapeWeightsTransform(NEConvolutionLayerReshapeWeightsTransform &&) = delete;
	/** Prevent instances of this class from being moved (As this class contains non movable objects) */
	NEConvolutionLayerReshapeWeightsTransform &operator=(NEConvolutionLayerReshapeWeightsTransform &&) = delete;
	/** Default destructor */
	~NEConvolutionLayerReshapeWeightsTransform() = default;
	void configure(const ITensor input, const ITensor biases)
	{
	_bias_bit = (biases != nullptr) ? 1 : 0;
	_func.configure(input, biases, &_output);
	}

	void run() override
	{
	_output.allocator()->allocate();
	_func.run();
	_reshape_run = true;
	}

	ITensor *get_weights() override
	{
	return &_output;
	}

	void release() override
	{
	_output.allocator()->free();
	}

	uint32_t uid() override
	{
	return ((0x8) \| (_bias_bit << 7));
	}

	bool is_reshape_run()
	{
	return _reshape_run;
	}

	private:
	Tensor _output{};
	NEConvolutionLayerReshapeWeights _func{};
	int32_t _bias_bit{ 0 };
	};
	} // namespace weights_transformations

	/** Basic function to compute the convolution layer. This function calls the following NEON kernels/functions:
	*
	* -# @ref NEIm2ColKernel
	* -# @ref NEGEMM (if the data type is BFLOAT16/FP16/FP32)
	* -# @ref NEGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8/QASYMM8_SIGNED)
	* -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8/QASYMM8_SIGNED)
	* -# @ref NEArithmeticAdditionKernel (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)
	* -# @ref NECol2ImKernel (if NCHW data layout)
	*
	*/
	class NEGEMMConvolutionLayer : public IFunction
	{
	public:
	/** Constructor */
	NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr, IWeightsManager *weights_manager = nullptr);
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	NEGEMMConvolutionLayer(const NEGEMMConvolutionLayer &) = delete;
	/** Default move constructor */
	NEGEMMConvolutionLayer(NEGEMMConvolutionLayer &&) = default;
	/** Prevent instances of this class from being copied (As this class contains pointers) */
	NEGEMMConvolutionLayer &operator=(const NEGEMMConvolutionLayer &) = delete;
	/** Default move assignment operator */
	NEGEMMConvolutionLayer &operator=(NEGEMMConvolutionLayer &&) = default;
	/** Default destructor */
	~NEGEMMConvolutionLayer();
	/** 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/QASYMM8_SIGNED/BFLOAT16/F16/F32.
	* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
	* Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
	* @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/QASYMM8_SIGNED 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).
	* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
	* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
	*/
	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), 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 NEGEMMConvolutionLayer
	*
	* @param[in] input Source tensor info. 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/QASYMM8_SIGNED/BFLOAT16/F16/F32.
	* @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
	* Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
	* @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
	* Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
	* @param[in] output Destination tensor info. 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).
	* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
	* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
	*
	* @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/QASYMM8_SIGNED/BFLOAT16/F16/F32.
	* @param[in] weights Weights tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
	* @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/QASYMM8_SIGNED type where biases should be of S32 type.
	* @param[out] output Output tensor. Data types supported: Same as @p input,
	* except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type.
	* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
	* @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
	*/
	void configure_mm(const ITensor input, const ITensor weights, const ITensor biases, ITensor output, const ActivationLayerInfo &act_info = ActivationLayerInfo(), int gemm_3d_depth = 1);
	/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer matrix multiply routines
	*
	* @param[in] input Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
	* @param[in] weights Weights tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
	* @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
	* Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
	* @param[in] output Output tensor info. Data types supported: Same as @p input,
	* except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type.
	* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
	* @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
	* @param[in] skip_im2col (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false)
	*
	* @return a status
	*/
	static Status validate_mm(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo output, const ActivationLayerInfo &act_info = ActivationLayerInfo(),
	int gemm_3d_depth = 1, bool skip_im2col = false);
	/** Static function to check if GEMM3D is supported in @ref NEGEMM or in @ref NEGEMMLowpMatrixMultiplyCore
	*
	* @param[in] input_info Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
	* @param[in] weights_info Weights tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
	* @param[in] act_info Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
	* @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
	*
	* @return a status
	*/
	static Status validate_gemm3d(const ITensorInfo input_info, const ITensorInfo weights_info, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col);

	private:
	MemoryGroup _memory_group;
	IWeightsManager *_weights_manager;
	NEConvolutionLayerReshapeWeights _reshape_weights;
	weights_transformations::NEConvolutionLayerReshapeWeightsTransform _reshape_weights_managed;
	std::unique_ptr<NEIm2ColKernel> _im2col_kernel;
	NEGEMM _mm_gemm;
	NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
	std::unique_ptr<NECol2ImKernel> _col2im_kernel;
	NEReshapeLayer _reshape_layer;

	const ITensor *_original_weights;

	Tensor _im2col_output;
	Tensor _weights_reshaped;
	Tensor _gemm_output;
	Tensor _tmp_output;

	DataLayout _data_layout;

	bool _skip_im2col;
	bool _skip_col2im;
	bool _is_quantized;
	bool _is_prepared;
	};
	} // namespace arm_compute
	#endif /* ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H */