src/runtime/NEON/functions/NEConvolutionLayer.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 2017 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.
  */
 #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"

 #include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h"
 #include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
 #include "arm_compute/core/PixelValue.h"
 #include "arm_compute/core/Size2D.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "support/ToolchainSupport.h"

 namespace arm_compute
 {
 #include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
 #include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp"
 #include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp"
 } // namespace arm_compute

 #include <cmath>
 #include <tuple>

 namespace arm_compute
 {
 NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
 {
 }

 void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);

     if(biases != nullptr)
     {
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
         ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
         ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
         ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
     }

     // Check if bias are present, if yes they will be embedded to the weights matrix
     const bool _has_bias = (biases != nullptr);

     _transpose1xW = transpose1xW;

     if(transpose1xW)
     {
         // Create tensor to store the reshaped weights
         const unsigned int mat_weights_cols = weights->info()->dimension(3);
         const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);
         TensorShape        shape_wr(mat_weights_cols, mat_weights_rows);
         TensorInfo         info_wr(shape_wr, 1, weights->info()->data_type(), weights->info()->fixed_point_position());

         _weights_reshaped.allocator()->init(info_wr);
         _memory_group.manage(&_weights_reshaped);

         _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
         _weights_transposed_kernel.configure(&_weights_reshaped, output);

         _weights_reshaped.allocator()->allocate();
     }
     else
     {
         _weights_reshape_kernel.configure(weights, biases, output);
     }
 }

 void NEConvolutionLayerReshapeWeights::run()
 {
     _memory_group.acquire();

     NEScheduler::get().schedule(&_weights_reshape_kernel, 3);

     if(_transpose1xW)
     {
         NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY);
     }

     _memory_group.release();
 }

 NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _output_col2im_kernel(),
       _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _workspace(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
 {
 }

 void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
     ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);

     if(biases != nullptr)
     {
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
         ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
         ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
         ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
     }

     const DataType dt                   = input->info()->data_type();
     const int      fixed_point_position = input->info()->fixed_point_position();

     _has_bias             = (biases != nullptr);
     _are_weights_reshaped = weights_info.are_reshaped();

     // Get parameters from conv_info
     unsigned int stride_x = 0;
     unsigned int stride_y = 0;
     unsigned int pad_x    = 0;
     unsigned int pad_y    = 0;
     std::tie(stride_x, stride_y) = conv_info.stride();
     std::tie(pad_x, pad_y)       = conv_info.pad();

     // Get convolved dimensions
     unsigned int conv_w = 0;
     unsigned int conv_h = 0;

     const unsigned int kernel_width  = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
     const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
     std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
                                                  conv_info);

     // Check if its a "fully connected" convolution, i.e. the output size is 1x1xnum_kernels
     _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));

 #if defined(__arm__)
     if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
     {
         _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>();
     }
 #elif defined(__aarch64__)
     if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
     {
         _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
     }
 #endif /* defined(__arm__) || defined(__aarch64__) */

     unsigned int mat_weights_cols = weights->info()->dimension(3);
     unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);

     // Reshape weights if needed
     if(_mm_optimised_kernel != nullptr)
     {
         if(_are_weights_reshaped)
         {
             mat_weights_cols = weights_info.num_kernels();
             mat_weights_rows = weights->info()->dimension(1);
         }
         else
         {
             TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };

             // Create tensor to store the reshaped weights
             _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
             _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
             weights = &_weights_reshaped;
         }
     }
     else
     {
         if(_are_weights_reshaped)
         {
             const unsigned int transpose_width = 16 / input->info()->element_size();
             mat_weights_cols                   = weights_info.num_kernels();
             mat_weights_rows                   = weights->info()->dimension(0) / transpose_width + (_has_bias ? 1 : 0);
         }
         else
         {
             TensorShape reshaped_weights_shape;

             if(_is_fully_connected_convolution)
             {
                 reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
             }
             else
             {
                 // Create tensor to store transposed weights
                 const float transpose_width = 16.0f / input->info()->element_size();
                 reshaped_weights_shape      = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
                                                            static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
             }

             // Create tensor to store the reshaped weights
             _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
             _reshape_weights.configure(weights, biases, &_weights_reshaped, !_is_fully_connected_convolution /* 1xW transpose */);
             weights = &_weights_reshaped;
         }
     }

     // Create tensor to store im2col reshaped inputs
     const unsigned int mat_input_cols = mat_weights_rows;
     const unsigned int mat_input_rows = conv_w * conv_h;

     TensorShape shape_im2col(input->info()->tensor_shape());
     shape_im2col.set(0, mat_input_cols);
     shape_im2col.set(1, mat_input_rows);
     shape_im2col.set(2, 1);
     _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));
     _memory_group.manage(&_input_im2col_reshaped);

     // Create tensor (interleave) to prepare input tensor for GEMM
     if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr)
     {
         TensorShape shape_interleaved(shape_im2col);
         shape_interleaved.set(0, shape_interleaved.x() * 4);
         shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
         _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));
         _memory_group.manage(&_input_interleaved_reshaped);
     }

     // Create GEMM output tensor
     TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
     shape_gemm.set(0, mat_weights_cols);
     shape_gemm.set(1, mat_input_rows);
     _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, dt, fixed_point_position));
     _memory_group.manage(&_gemm_output);

     // Configure kernels
     _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);

 #if defined(__arm__) || defined(__aarch64__)
     if(_mm_optimised_kernel != nullptr)
     {
         struct CPUInfo ci = NEScheduler::get().cpu_info();

         const int M = _gemm_output.info()->tensor_shape().y();
         const int N = _gemm_output.info()->tensor_shape().x();
         const int K = _input_im2col_reshaped.info()->tensor_shape().x();

 #if defined(__arm__)
         GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false);
 #elif defined(__aarch64__)
         GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
 #endif /* defined(__arm__) || defined(__aarch64__) */

         constexpr size_t alignment = 4096;
         _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
         _memory_group.manage(&_workspace);

         // Configure matrix multiplication kernel
         if(_is_fully_connected_convolution)
         {
             _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace, 1.f, 0.f, false, false);
         }
         else
         {
             _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace);
         }

         _workspace.allocator()->allocate();
     }
     else
 #endif /* defined(__arm__) || defined(__aarch64__) */
     {
         if(_is_fully_connected_convolution)
         {
             _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f);
         }
         else
         {
             _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
             _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
             _input_interleaved_reshaped.allocator()->allocate();
         }
     }

     _input_im2col_reshaped.allocator()->allocate();
     _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
     _gemm_output.allocator()->allocate();

     ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");

     // Allocate intermediate tensor
     if(!_are_weights_reshaped)
     {
         _weights_reshaped.allocator()->allocate();
     }
 }

 void NEConvolutionLayer::run()
 {
     // Run weights reshaping (Runs once for every configure)
     if(!_are_weights_reshaped)
     {
         _are_weights_reshaped = true;
         _reshape_weights.run();
     }

     _memory_group.acquire();

     // Run input reshaping
     NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);

     // Runs matrix multiply on reshaped matrices
     if(_mm_optimised_kernel != nullptr)
     {
         NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
     }
     else
     {
         if(!_is_fully_connected_convolution)
         {
             // Run interleave
             NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
         }

         NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
     }

     // Reshape output matrix
     NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);

     _memory_group.release();
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2017 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.
	*/
	#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"

	#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h"
	#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
	#include "arm_compute/core/PixelValue.h"
	#include "arm_compute/core/Size2D.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/runtime/NEON/NEScheduler.h"
	#include "support/ToolchainSupport.h"

	namespace arm_compute
	{
	#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
	#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp"
	#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp"
	} // namespace arm_compute

	#include <cmath>
	#include <tuple>

	namespace arm_compute
	{
	NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
	{
	}

	void NEConvolutionLayerReshapeWeights::configure(const ITensor weights, const ITensor biases, ITensor *output, bool transpose1xW)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
	ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);

	if(biases != nullptr)
	{
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
	ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
	ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
	}

	// Check if bias are present, if yes they will be embedded to the weights matrix
	const bool _has_bias = (biases != nullptr);

	_transpose1xW = transpose1xW;

	if(transpose1xW)
	{
	// Create tensor to store the reshaped weights
	const unsigned int mat_weights_cols = weights->info()->dimension(3);
	const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);
	TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
	TensorInfo info_wr(shape_wr, 1, weights->info()->data_type(), weights->info()->fixed_point_position());

	_weights_reshaped.allocator()->init(info_wr);
	_memory_group.manage(&_weights_reshaped);

	_weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
	_weights_transposed_kernel.configure(&_weights_reshaped, output);

	_weights_reshaped.allocator()->allocate();
	}
	else
	{
	_weights_reshape_kernel.configure(weights, biases, output);
	}
	}

	void NEConvolutionLayerReshapeWeights::run()
	{
	_memory_group.acquire();

	NEScheduler::get().schedule(&_weights_reshape_kernel, 3);

	if(_transpose1xW)
	{
	NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY);
	}

	_memory_group.release();
	}

	NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _output_col2im_kernel(),
	_input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _workspace(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
	{
	}

	void NEConvolutionLayer::configure(const ITensor input, const ITensor weights, const ITensor biases, ITensor output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
	ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
	ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);

	if(biases != nullptr)
	{
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
	ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
	ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
	}

	const DataType dt = input->info()->data_type();
	const int fixed_point_position = input->info()->fixed_point_position();

	_has_bias = (biases != nullptr);
	_are_weights_reshaped = weights_info.are_reshaped();

	// Get parameters from conv_info
	unsigned int stride_x = 0;
	unsigned int stride_y = 0;
	unsigned int pad_x = 0;
	unsigned int pad_y = 0;
	std::tie(stride_x, stride_y) = conv_info.stride();
	std::tie(pad_x, pad_y) = conv_info.pad();

	// Get convolved dimensions
	unsigned int conv_w = 0;
	unsigned int conv_h = 0;

	const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
	const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
	std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
	conv_info);

	// Check if its a "fully connected" convolution, i.e. the output size is 1x1xnum_kernels
	_is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));

	#if defined(__arm__)
	if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
	{
	_mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>();
	}
	#elif defined(__aarch64__)
	if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
	{
	_mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
	}
	#endif /* defined(__arm__) \|\| defined(__aarch64__) */

	unsigned int mat_weights_cols = weights->info()->dimension(3);
	unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);

	// Reshape weights if needed
	if(_mm_optimised_kernel != nullptr)
	{
	if(_are_weights_reshaped)
	{
	mat_weights_cols = weights_info.num_kernels();
	mat_weights_rows = weights->info()->dimension(1);
	}
	else
	{
	TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };

	// Create tensor to store the reshaped weights
	_weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
	_reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
	weights = &_weights_reshaped;
	}
	}
	else
	{
	if(_are_weights_reshaped)
	{
	const unsigned int transpose_width = 16 / input->info()->element_size();
	mat_weights_cols = weights_info.num_kernels();
	mat_weights_rows = weights->info()->dimension(0) / transpose_width + (_has_bias ? 1 : 0);
	}
	else
	{
	TensorShape reshaped_weights_shape;

	if(_is_fully_connected_convolution)
	{
	reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
	}
	else
	{
	// Create tensor to store transposed weights
	const float transpose_width = 16.0f / input->info()->element_size();
	reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
	static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
	}

	// Create tensor to store the reshaped weights
	_weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
	_reshape_weights.configure(weights, biases, &_weights_reshaped, !_is_fully_connected_convolution /* 1xW transpose */);
	weights = &_weights_reshaped;
	}
	}

	// Create tensor to store im2col reshaped inputs
	const unsigned int mat_input_cols = mat_weights_rows;
	const unsigned int mat_input_rows = conv_w * conv_h;

	TensorShape shape_im2col(input->info()->tensor_shape());
	shape_im2col.set(0, mat_input_cols);
	shape_im2col.set(1, mat_input_rows);
	shape_im2col.set(2, 1);
	_input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));
	_memory_group.manage(&_input_im2col_reshaped);

	// Create tensor (interleave) to prepare input tensor for GEMM
	if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr)
	{
	TensorShape shape_interleaved(shape_im2col);
	shape_interleaved.set(0, shape_interleaved.x() * 4);
	shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
	_input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));
	_memory_group.manage(&_input_interleaved_reshaped);
	}

	// Create GEMM output tensor
	TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
	shape_gemm.set(0, mat_weights_cols);
	shape_gemm.set(1, mat_input_rows);
	_gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, dt, fixed_point_position));
	_memory_group.manage(&_gemm_output);

	// Configure kernels
	_input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);

	#if defined(__arm__) \|\| defined(__aarch64__)
	if(_mm_optimised_kernel != nullptr)
	{
	struct CPUInfo ci = NEScheduler::get().cpu_info();

	const int M = _gemm_output.info()->tensor_shape().y();
	const int N = _gemm_output.info()->tensor_shape().x();
	const int K = _input_im2col_reshaped.info()->tensor_shape().x();

	#if defined(__arm__)
	GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false);
	#elif defined(__aarch64__)
	GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
	#endif /* defined(__arm__) \|\| defined(__aarch64__) */

	constexpr size_t alignment = 4096;
	_workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
	_memory_group.manage(&_workspace);

	// Configure matrix multiplication kernel
	if(_is_fully_connected_convolution)
	{
	_mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace, 1.f, 0.f, false, false);
	}
	else
	{
	_mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace);
	}

	_workspace.allocator()->allocate();
	}
	else
	#endif /* defined(__arm__) \|\| defined(__aarch64__) */
	{
	if(_is_fully_connected_convolution)
	{
	_mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f);
	}
	else
	{
	_input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
	_mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
	_input_interleaved_reshaped.allocator()->allocate();
	}
	}

	_input_im2col_reshaped.allocator()->allocate();
	_output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
	_gemm_output.allocator()->allocate();

	ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) \|\| (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");

	// Allocate intermediate tensor
	if(!_are_weights_reshaped)
	{
	_weights_reshaped.allocator()->allocate();
	}
	}

	void NEConvolutionLayer::run()
	{
	// Run weights reshaping (Runs once for every configure)
	if(!_are_weights_reshaped)
	{
	_are_weights_reshaped = true;
	_reshape_weights.run();
	}

	_memory_group.acquire();

	// Run input reshaping
	NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);

	// Runs matrix multiply on reshaped matrices
	if(_mm_optimised_kernel != nullptr)
	{
	NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
	}
	else
	{
	if(!_is_fully_connected_convolution)
	{
	// Run interleave
	NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
	}

	NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
	}

	// Reshape output matrix
	NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);

	_memory_group.release();
	}
	} // namespace arm_compute