src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp - 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.
  */
 #include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.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/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"

 #include <cmath>
 #include <memory>
 #include <tuple>

 using namespace arm_compute;
 using namespace arm_compute::misc::shape_calculator;

 CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights()
     : _weights_reshape_kernel()
 {
 }

 void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output)
 {
     // Perform validation step
     ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
     ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayerReshapeWeights::validate(weights->info(),
                                                                           (biases != nullptr) ? biases->info() : nullptr,
                                                                           output->info()));

     const bool       append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
     const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr;

     _weights_reshape_kernel.configure(weights, biases_to_use, output);

     output->info()->set_quantization_info(weights->info()->quantization_info());
 }

 Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);

     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }

     if((output != nullptr) && (output->total_size() != 0))
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);

         CLWeightsReshapeKernel::validate(weights, biases, output);
     }

     return Status{};
 }

 void CLConvolutionLayerReshapeWeights::run()
 {
     CLScheduler::get().enqueue(_weights_reshape_kernel);
 }

 CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _im2col_output(),
       _weights_reshaped(), _gemm_output(), _tmp_output(), _is_quantized(false), _is_first_run(true)
 {
 }

 void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
     ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info()));

     if(_is_quantized)
     {
         // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
         // Extract and negate input and weights offset
         const QuantizationInfo input_quantization_info   = input->info()->quantization_info();
         const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();

         input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
         weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));

         _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));

         // Revert back QuantizatioInfo as input and weights could be used in other convolution layers
         input->info()->set_quantization_info(input_quantization_info);
         weights->info()->set_quantization_info(weights_quantization_info);
     }
     else
     {
         // Configure matrix multiply function
         _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
     }
 }

 Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output)
 {
     const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());

     const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */);
     if(is_quantized)
     {
         // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
         // Extract and negate input and weights offset
         const QuantizationInfo input_quantization_info   = input->quantization_info();
         const QuantizationInfo weights_quantization_info = weights->quantization_info();

         std::unique_ptr<ITensorInfo> input_qa   = input->clone();
         std::unique_ptr<ITensorInfo> weights_qa = weights->clone();
         input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
         weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));

         // Perform validation step on GEMMLowp
         CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), output, gemm_info);
     }
     else
     {
         // Perform validation step on Matrix multiply function
         CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
     }
     return Status{};
 }

 void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);

     ARM_COMPUTE_ERROR_THROW_ON(CLGEMMConvolutionLayer::validate(input->info(),
                                                                 weights->info(),
                                                                 biases != nullptr ? biases->info() : nullptr,
                                                                 output->info(),
                                                                 conv_info,
                                                                 weights_info));

     _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());

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

     // Set the GPU target for im2col and col2im
     _im2col_kernel.set_target(CLScheduler::get().target());
     _col2im_kernel.set_target(CLScheduler::get().target());

     const bool append_bias = (biases != nullptr) && (!_is_quantized);

     const unsigned   bias_element  = (append_bias) ? 1 : 0;
     const ICLTensor *biases_to_use = (append_bias) ? biases : nullptr;

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

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

     const unsigned int kernel_width  = weights->info()->dimension(0);
     const unsigned int kernel_height = 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);

     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) + bias_element;

     // _weights_reshaped will be auto configured in the kernel.
     // Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM
     _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped);

     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);
     // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
     TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
     im2col_reshaped_info.set_quantization_info(input->info()->quantization_info());
     _im2col_output.allocator()->init(im2col_reshaped_info);
     _memory_group.manage(&_im2col_output);

     // Create GEMM output tensor
     TensorShape shape_gemm = _im2col_output.info()->tensor_shape();
     shape_gemm.set(0, mat_weights_cols);
     shape_gemm.set(1, mat_input_rows);
     const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
     // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
     // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
     TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
     info_gemm.set_quantization_info(output->info()->quantization_info());
     _gemm_output.allocator()->init(info_gemm);
     _memory_group.manage(&_gemm_output);

     // Configure im2col
     _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias);

     // Configure GEMM
     configure_mm(&_im2col_output, weights, &_gemm_output);

     _im2col_output.allocator()->allocate();

     // Configure output stage for quantized case
     if(_is_quantized)
     {
         const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();

         float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
         int   output_multiplier, output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
         _memory_group.manage(&_tmp_output);
         _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
     }

     // Configure Col2Im
     _col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
     if(_is_quantized)
     {
         _tmp_output.allocator()->allocate();
     }
     _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
     _weights_reshaped.allocator()->allocate();

     ARM_COMPUTE_UNUSED(weights_info);
 }

 Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                                         const WeightsInfo &weights_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
     ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2));
     ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);

     const bool     is_quantized = is_data_type_quantized_asymmetric(input->data_type());
     const bool     append_bias  = (biases != nullptr) && (!is_quantized);
     const unsigned bias_element = (append_bias) ? 1 : 0;
     const DataType dt           = input->data_type();

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

     const unsigned int kernel_width  = weights->dimension(0);
     const unsigned int kernel_height = weights->dimension(1);

     std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, conv_info);

     unsigned int mat_weights_cols = weights->dimension(3);
     unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + bias_element;

     CLConvolutionLayerReshapeWeights::validate(weights, biases, nullptr);

     // Create tensor info for 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->tensor_shape();
     shape_im2col.set(0, mat_input_cols);
     shape_im2col.set(1, mat_input_rows);
     shape_im2col.set(2, 1);
     TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->fixed_point_position());
     im2col_reshaped_info.set_quantization_info(input->quantization_info());
     CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias);

     // Create GEMM output tensor
     TensorShape shape_gemm = im2col_reshaped_info.tensor_shape();
     shape_gemm.set(0, mat_weights_cols);
     shape_gemm.set(1, mat_input_rows);
     const DataType gemm_data_type = is_quantized ? DataType::S32 : dt;
     // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
     TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->fixed_point_position());
     info_gemm.set_quantization_info(output->quantization_info());

     validate_mm(&im2col_reshaped_info, weights, &info_gemm);

     TensorInfo tmp_info(input->tensor_shape(), 1, DataType::QASYMM8, input->fixed_point_position());
     if(is_quantized)
     {
         float multiplier = input->quantization_info().scale * weights->quantization_info().scale / output->quantization_info().scale;
         int   output_multiplier, output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
         // Validate output stage for quantized case
         CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&info_gemm, biases, &tmp_info, output->quantization_info().offset);
     }

     // Validate Col2Im
     CLCol2ImKernel::validate(is_quantized ? &tmp_info : &info_gemm, output, std::make_pair(conv_w, conv_h));

     if(biases != nullptr)
     {
         if(is_quantized)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
         }
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }

     return Status{};
 }

 void CLGEMMConvolutionLayer::run()
 {
     // Run weights reshaping (Runs once for every configure)
     if(_is_first_run)
     {
         _reshape_weights.run();

         _is_first_run = false;
     }

     _memory_group.acquire();

     // Run im2col
     CLScheduler::get().enqueue(_im2col_kernel);

     // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions
     if(_is_quantized)
     {
         // Run gemmlowp
         _mm_gemmlowp.run();

         // Run output stage
         _gemmlowp_output_stage.run();
     }
     else
     {
         // Run gemm
         _mm_gemm.run();
     }

     // Reshape output matrix
     CLScheduler::get().enqueue(_col2im_kernel, false);

     _memory_group.release();
 }
	/*
	* 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.
	*/
	#include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.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/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
	#include "arm_compute/runtime/CL/CLScheduler.h"

	#include <cmath>
	#include <memory>
	#include <tuple>

	using namespace arm_compute;
	using namespace arm_compute::misc::shape_calculator;

	CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights()
	: _weights_reshape_kernel()
	{
	}

	void CLConvolutionLayerReshapeWeights::configure(const ICLTensor weights, const ICLTensor biases, ICLTensor *output)
	{
	// Perform validation step
	ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
	ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayerReshapeWeights::validate(weights->info(),
	(biases != nullptr) ? biases->info() : nullptr,
	output->info()));

	const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
	const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr;

	_weights_reshape_kernel.configure(weights, biases_to_use, output);

	output->info()->set_quantization_info(weights->info()->quantization_info());
	}

	Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo *output)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);

	if(biases != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
	ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
	ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
	}

	if((output != nullptr) && (output->total_size() != 0))
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);

	CLWeightsReshapeKernel::validate(weights, biases, output);
	}

	return Status{};
	}

	void CLConvolutionLayerReshapeWeights::run()
	{
	CLScheduler::get().enqueue(_weights_reshape_kernel);
	}

	CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _im2col_output(),
	_weights_reshaped(), _gemm_output(), _tmp_output(), _is_quantized(false), _is_first_run(true)
	{
	}

	void CLGEMMConvolutionLayer::configure_mm(const ICLTensor input, const ICLTensor weights, ICLTensor *output)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
	ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info()));

	if(_is_quantized)
	{
	// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
	// Extract and negate input and weights offset
	const QuantizationInfo input_quantization_info = input->info()->quantization_info();
	const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();

	input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
	weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));

	_mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));

	// Revert back QuantizatioInfo as input and weights could be used in other convolution layers
	input->info()->set_quantization_info(input_quantization_info);
	weights->info()->set_quantization_info(weights_quantization_info);
	}
	else
	{
	// Configure matrix multiply function
	_mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
	}
	}

	Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo *output)
	{
	const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());

	const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */);
	if(is_quantized)
	{
	// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
	// Extract and negate input and weights offset
	const QuantizationInfo input_quantization_info = input->quantization_info();
	const QuantizationInfo weights_quantization_info = weights->quantization_info();

	std::unique_ptr<ITensorInfo> input_qa = input->clone();
	std::unique_ptr<ITensorInfo> weights_qa = weights->clone();
	input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
	weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));

	// Perform validation step on GEMMLowp
	CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), output, gemm_info);
	}
	else
	{
	// Perform validation step on Matrix multiply function
	CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
	}
	return Status{};
	}

	void CLGEMMConvolutionLayer::configure(const ICLTensor input, const ICLTensor weights, const ICLTensor biases, ICLTensor output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);

	ARM_COMPUTE_ERROR_THROW_ON(CLGEMMConvolutionLayer::validate(input->info(),
	weights->info(),
	biases != nullptr ? biases->info() : nullptr,
	output->info(),
	conv_info,
	weights_info));

	_is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());

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

	// Set the GPU target for im2col and col2im
	_im2col_kernel.set_target(CLScheduler::get().target());
	_col2im_kernel.set_target(CLScheduler::get().target());

	const bool append_bias = (biases != nullptr) && (!_is_quantized);

	const unsigned bias_element = (append_bias) ? 1 : 0;
	const ICLTensor *biases_to_use = (append_bias) ? biases : nullptr;

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

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

	const unsigned int kernel_width = weights->info()->dimension(0);
	const unsigned int kernel_height = 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);

	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) + bias_element;

	// _weights_reshaped will be auto configured in the kernel.
	// Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM
	_reshape_weights.configure(weights, biases_to_use, &_weights_reshaped);

	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);
	// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
	TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
	im2col_reshaped_info.set_quantization_info(input->info()->quantization_info());
	_im2col_output.allocator()->init(im2col_reshaped_info);
	_memory_group.manage(&_im2col_output);

	// Create GEMM output tensor
	TensorShape shape_gemm = _im2col_output.info()->tensor_shape();
	shape_gemm.set(0, mat_weights_cols);
	shape_gemm.set(1, mat_input_rows);
	const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
	// GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
	// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
	TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
	info_gemm.set_quantization_info(output->info()->quantization_info());
	_gemm_output.allocator()->init(info_gemm);
	_memory_group.manage(&_gemm_output);

	// Configure im2col
	_im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias);

	// Configure GEMM
	configure_mm(&_im2col_output, weights, &_gemm_output);

	_im2col_output.allocator()->allocate();

	// Configure output stage for quantized case
	if(_is_quantized)
	{
	const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();

	float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
	int output_multiplier, output_shift;
	quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
	_memory_group.manage(&_tmp_output);
	_gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
	}

	// Configure Col2Im
	_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
	if(_is_quantized)
	{
	_tmp_output.allocator()->allocate();
	}
	_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
	_weights_reshaped.allocator()->allocate();

	ARM_COMPUTE_UNUSED(weights_info);
	}

	Status CLGEMMConvolutionLayer::validate(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo output, const PadStrideInfo &conv_info,
	const WeightsInfo &weights_info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
	ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2));
	ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);

	const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
	const bool append_bias = (biases != nullptr) && (!is_quantized);
	const unsigned bias_element = (append_bias) ? 1 : 0;
	const DataType dt = input->data_type();

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

	const unsigned int kernel_width = weights->dimension(0);
	const unsigned int kernel_height = weights->dimension(1);

	std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, conv_info);

	unsigned int mat_weights_cols = weights->dimension(3);
	unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + bias_element;

	CLConvolutionLayerReshapeWeights::validate(weights, biases, nullptr);

	// Create tensor info for 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->tensor_shape();
	shape_im2col.set(0, mat_input_cols);
	shape_im2col.set(1, mat_input_rows);
	shape_im2col.set(2, 1);
	TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->fixed_point_position());
	im2col_reshaped_info.set_quantization_info(input->quantization_info());
	CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias);

	// Create GEMM output tensor
	TensorShape shape_gemm = im2col_reshaped_info.tensor_shape();
	shape_gemm.set(0, mat_weights_cols);
	shape_gemm.set(1, mat_input_rows);
	const DataType gemm_data_type = is_quantized ? DataType::S32 : dt;
	// GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
	TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->fixed_point_position());
	info_gemm.set_quantization_info(output->quantization_info());

	validate_mm(&im2col_reshaped_info, weights, &info_gemm);

	TensorInfo tmp_info(input->tensor_shape(), 1, DataType::QASYMM8, input->fixed_point_position());
	if(is_quantized)
	{
	float multiplier = input->quantization_info().scale * weights->quantization_info().scale / output->quantization_info().scale;
	int output_multiplier, output_shift;
	quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
	// Validate output stage for quantized case
	CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&info_gemm, biases, &tmp_info, output->quantization_info().offset);
	}

	// Validate Col2Im
	CLCol2ImKernel::validate(is_quantized ? &tmp_info : &info_gemm, output, std::make_pair(conv_w, conv_h));

	if(biases != nullptr)
	{
	if(is_quantized)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
	}
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
	ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
	ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
	}

	return Status{};
	}

	void CLGEMMConvolutionLayer::run()
	{
	// Run weights reshaping (Runs once for every configure)
	if(_is_first_run)
	{
	_reshape_weights.run();

	_is_first_run = false;
	}

	_memory_group.acquire();

	// Run im2col
	CLScheduler::get().enqueue(_im2col_kernel);

	// Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions
	if(_is_quantized)
	{
	// Run gemmlowp
	_mm_gemmlowp.run();

	// Run output stage
	_gemmlowp_output_stage.run();
	}
	else
	{
	// Run gemm
	_mm_gemm.run();
	}

	// Reshape output matrix
	CLScheduler::get().enqueue(_col2im_kernel, false);

	_memory_group.release();
	}