src/runtime/NEON/functions/NEFullyConnectedLayer.cpp - 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.
  */
 #include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"

 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Size2D.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/NEON/NEScheduler.h"
 #include "src/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.h"
 #include "src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h"
 #include "src/core/NEON/kernels/NEFlattenLayerKernel.h"
 #include "src/core/NEON/kernels/NEFlattenLayerKernel.h"
 #include "src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
 #include "src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h"
 #include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h"
 #include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h"
 #include "src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.h"
 #include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
 #include "src/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
 #include "src/core/NEON/kernels/NETransposeKernel.h"

 #include "support/MemorySupport.h"

 #include <algorithm>
 #include <cmath>

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

 namespace
 {
 // Get min, max bound of a quantized assymetric output tensor, with the effect of fused activation
 std::pair<PixelValue, PixelValue> get_quantized_asymmetric_output_min_max(const QuantizationInfo &q_info, const ActivationLayerInfo &act_info, DataType data_type)
 {
     PixelValue type_min{};
     PixelValue type_max{};
     std::tie(type_min, type_max) = get_min_max(data_type);
     const UniformQuantizationInfo q_unif = q_info.uniform();

     if(act_info.enabled())
     {
         switch(act_info.activation())
         {
             case ActivationLayerInfo::ActivationFunction::RELU:
                 type_min = PixelValue(q_unif.offset);
                 break;
             case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
                 type_min = PixelValue(q_unif.offset);
                 type_max = PixelValue(act_info.a(), data_type, q_info);
                 break;
             case ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU:
                 type_min = PixelValue(act_info.b(), data_type, q_info);
                 type_max = PixelValue(act_info.a(), data_type, q_info);
                 break;
             default:
                 ARM_COMPUTE_ERROR("Activation function not supported.");
                 break;
         }
     }

     return std::make_pair(type_min, type_max);
 }

 Status get_gemmlowp_output_stage_info(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const ActivationLayerInfo &act,
                                       GEMMLowpOutputStageInfo &gemmlowp_output_stage_info)
 {
     const auto                    data_type = input->data_type();
     const QuantizationInfo        oq_info   = output->quantization_info();
     const UniformQuantizationInfo iq_unif   = input->quantization_info().uniform();
     const UniformQuantizationInfo wq_unif   = weights->quantization_info().uniform();
     const UniformQuantizationInfo oq_unif   = oq_info.uniform();

     float   multiplier = (iq_unif.scale * wq_unif.scale) / oq_unif.scale;
     int32_t output_multiplier;
     int32_t output_shift;

     ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));

     PixelValue type_min{};
     PixelValue type_max{};
     std::tie(type_min, type_max) = get_quantized_asymmetric_output_min_max(oq_info, act, data_type);

     gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier;
     gemmlowp_output_stage_info.gemmlowp_shift      = output_shift;
     gemmlowp_output_stage_info.gemmlowp_offset     = oq_unif.offset;
     gemmlowp_output_stage_info.type                = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
     gemmlowp_output_stage_info.gemmlowp_min_bound  = type_min.get<int32_t>();
     gemmlowp_output_stage_info.gemmlowp_max_bound  = type_max.get<int32_t>();

     return Status{};
 }

 Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const ActivationLayerInfo &act)
 {
     if(is_data_type_quantized_asymmetric(input->data_type()))
     {
         // 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().uniform().scale, -input->quantization_info().uniform().offset);
         const QuantizationInfo weights_quantization_info(weights->quantization_info().uniform().scale, -weights->quantization_info().uniform().offset);

         GEMMLowpOutputStageInfo gemmlowp_output_stage_info;
         ARM_COMPUTE_RETURN_ON_ERROR(get_gemmlowp_output_stage_info(input, weights, output, act, gemmlowp_output_stage_info));

         GEMMInfo gemm_info;
         gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info);

         // Validate gemmlowp function
         ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_quantization_info(input_quantization_info),
                                                                            &weights->clone()->set_quantization_info(weights_quantization_info),
                                                                            biases,
                                                                            output,
                                                                            gemm_info));
     }
     else
     {
         ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::validate(input, weights, biases, output, 1.f, 1.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)));
     }

     return Status{};
 }
 } // namespace

 void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITensor *output)
 {
     auto k = arm_compute::support::cpp14::make_unique<NETransposeKernel>();
     k->configure(input, output);
     _kernel = std::move(k);
 }

 Status NEFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, const ITensorInfo *output)
 {
     return NETransposeKernel::validate(input, output);
 }

 NEFullyConnectedLayer::~NEFullyConnectedLayer() = default;

 NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
     : _memory_group(std::move(memory_manager)), _weights_manager(weights_manager), _flatten_kernel(), _convert_weights(), _convert_weights_managed(), _reshape_weights_function(),
       _reshape_weights_managed_function(), _mm_gemm(nullptr, weights_manager), _mm_gemmlowp(nullptr, weights_manager), _flatten_output(), _converted_weights_output(), _reshape_weights_output(),
       _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false), _is_fc_after_conv(false), _is_quantized_asymmetric(false), _is_prepared(false)
 {
 }

 void NEFullyConnectedLayer::configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act)
 {
     if(_is_quantized_asymmetric)
     {
         // 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.uniform().scale, -input_quantization_info.uniform().offset));
         weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));

         // Configure gemmlowp function and output stage for asymmetric quantized types
         GEMMLowpOutputStageInfo gemmlowp_output_stage_info;
         const Status            status = get_gemmlowp_output_stage_info(input->info(), weights->info(), output->info(), act, gemmlowp_output_stage_info);
         ARM_COMPUTE_ERROR_ON(status.error_code() != ErrorCode::OK);

         GEMMInfo gemm_info;
         gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info);
         gemm_info.set_activation_info(act);
         _mm_gemmlowp.configure(input, weights, biases, output, gemm_info);

         // Revert back QuantizatioInfo as input and weights could be used in other fully connected layers
         input->info()->set_quantization_info(input_quantization_info);
         weights->info()->set_quantization_info(weights_quantization_info);
     }
     else
     {
         // Configure matrix multiply kernel
         GEMMInfo gemm_info(false, false, true /* Reshape weights only for the first run */);
         gemm_info.set_activation_info(act);
         _mm_gemm.configure(input, weights, biases, output, 1.f, 1.0f, gemm_info);
     }
 }

 void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act)
 {
     ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));

     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized

     // Initialize output tensor for flatten
     TensorShape shape_flatten = compute_flatten_shape(input->info());
     _flatten_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));

     // Configure flatten kernel
     _memory_group.manage(&_flatten_output);

     _flatten_kernel = arm_compute::support::cpp14::make_unique<NEFlattenLayerKernel>();
     _flatten_kernel->configure(input, &_flatten_output);

     // Configure matrix multiply kernel
     configure_mm(&_flatten_output, weights, biases, output, act);

     // Allocate the output tensor for flatten once all the configure methods have been called
     _flatten_output.allocator()->allocate();
 }

 void NEFullyConnectedLayer::configure_fc_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act)
 {
     ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));

     // Configure matrix multiply kernel
     configure_mm(input, weights, biases, output, act);
 }

 void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output,
                                       FullyConnectedLayerInfo fc_info)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
     ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedLayer::validate(input->info(),
                                                                weights->info(),
                                                                biases != nullptr ? biases->info() : nullptr,
                                                                output->info(),
                                                                fc_info));

     _are_weights_converted   = true;
     _are_weights_reshaped    = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
     _is_fc_after_conv        = true;
     _is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->info()->data_type());
     _original_weights        = weights;

     if(_weights_manager)
     {
         _weights_manager->manage(weights);
     }

     // With the Fully Connected layer we can have 4 different cases:
     //  1) Convolution layer -> Fully Connected layer without batches
     //  2) Fully Connected layer -> Fully Connected layer without batches
     //  3) Convolution layer -> Fully Connected layer with batches
     //  4) Fully Connected layer -> Fully Connected layer with batches

     const ITensor *weights_to_use = weights;

     // Check if we have a fully connected layer with batches
     const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
     if(is_batched_fc_layer)
     {
         _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
                                                                                   input->info()->tensor_shape().cend(),
                                                                                   output->info()->tensor_shape().cbegin() + 1));
     }
     else
     {
         _is_fc_after_conv = input->info()->num_dimensions() > 1;
     }

     // Reshape weights if needed
     if(!_are_weights_reshaped)
     {
         if(_weights_manager && _weights_manager->are_weights_managed(weights))
         {
             _reshape_weights_managed_function.configure(weights);
             weights_to_use = _weights_manager->acquire(weights, &_reshape_weights_managed_function);
         }
         else
         {
             // Reshape the weights
             _reshape_weights_function.configure(weights, &_reshape_weights_output);
             weights_to_use = &_reshape_weights_output;
         }
     }

     // Convert weights if needed
     if(_is_fc_after_conv && (input->info()->data_layout() != fc_info.weights_trained_layout))
     {
         if(_weights_manager && _weights_manager->are_weights_managed(weights_to_use))
         {
             _convert_weights_managed.configure(weights_to_use,
                                                input->info()->tensor_shape(),
                                                fc_info.weights_trained_layout);
             weights_to_use = _weights_manager->acquire(weights, &_convert_weights_managed);
         }
         else
         {
             // Convert weights
             _convert_weights.configure(weights_to_use,
                                        &_converted_weights_output,
                                        input->info()->tensor_shape(),
                                        fc_info.weights_trained_layout);

             weights_to_use = &_converted_weights_output;
         }
         _are_weights_converted = false;
     }

     if(_is_fc_after_conv)
     {
         // Fully Connected layer after a Convolution Layer without batches
         configure_conv_fc(input, weights_to_use, biases, output, fc_info.activation_info);
     }
     else
     {
         // Fully Connected layer after a Fully Connected Layer without batches
         configure_fc_fc(input, weights_to_use, biases, output, fc_info.activation_info);
     }

     _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;
 }

 Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
                                        FullyConnectedLayerInfo fc_info)
 {
     ARM_COMPUTE_UNUSED(fc_info.retain_internal_weights);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
     ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
     ARM_COMPUTE_RETURN_ERROR_ON(biases != nullptr && biases->num_dimensions() > 1);

     bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
     bool is_fc_after_conv = true;

     const ITensorInfo &flatten_input     = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)));
     const ITensorInfo &reshaped_weights  = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
     const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone());

     // With the Fully Connected layer we can have 4 different cases:
     //  1) Convolution layer -> Fully Connected layer without batches
     //  2) Fully Connected layer -> Fully Connected layer without batches
     //  3) Convolution layer -> Fully Connected layer with batches
     //  4) Fully Connected layer -> Fully Connected layer with batches

     const ITensorInfo *input_to_use   = input;
     const ITensorInfo *weights_to_use = weights;

     // Check if we have a fully connected layer with batches
     const bool is_batched_fc_layer = output->dimension(1) > 1;

     if(is_batched_fc_layer)
     {
         is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->tensor_shape().cbegin() + 3,
                                                                                  input->tensor_shape().cend(),
                                                                                  output->tensor_shape().cbegin() + 1));
     }
     else
     {
         is_fc_after_conv = input->num_dimensions() > 1;
     }

     if(!weights_reshaped)
     {
         // Validate reshape weights kernel
         ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayerReshapeWeights::validate(weights, &reshaped_weights));
         weights_to_use = &reshaped_weights;
     }

     if(is_fc_after_conv && (input->data_layout() != fc_info.weights_trained_layout))
     {
         // Validate convert weights kernel
         ARM_COMPUTE_RETURN_ON_ERROR(NEConvertFullyConnectedWeights::validate(weights_to_use,
                                                                              &converted_weights,
                                                                              input->tensor_shape(),
                                                                              fc_info.weights_trained_layout));
         weights_to_use = &converted_weights;
     }

     if(is_fc_after_conv)
     {
         // Fully Connected layer after a Convolution Layer without batches
         ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (input->dimension(0) * input->dimension(1) * input->dimension(2))));

         // Validate flatten kernel
         ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayerKernel::validate(input, &flatten_input));
         input_to_use = &flatten_input;
     }
     else
     {
         // Fully Connected layer after a Fully Connected Layer without batches
         ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1));
     }
     // Validate matrix multiply kernel
     ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(input_to_use, weights_to_use, biases, output, fc_info.activation_info));

     return Status{};
 }

 void NEFullyConnectedLayer::run()
 {
     prepare();

     MemoryGroupResourceScope scope_mg(_memory_group);

     // Linearize input if it comes from a convolutional layer
     if(_is_fc_after_conv)
     {
         NEScheduler::get().schedule(_flatten_kernel.get(), Window::DimY);
     }

     // Run matrix multiply
     if(_is_quantized_asymmetric)
     {
         _mm_gemmlowp.run();
     }
     else
     {
         _mm_gemm.run();
     }
 }

 void NEFullyConnectedLayer::prepare()
 {
     if(!_is_prepared)
     {
         if(!_weights_manager)
         {
             ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
         }

         auto release_unused = [](Tensor * w)
         {
             if(!w->is_used())
             {
                 w->allocator()->free();
             }
         };

         // Pointer to current weights
         const ITensor *cur_weights = _original_weights;

         // Reshape of the weights (happens only once)
         if(!_are_weights_reshaped)
         {
             if(_weights_manager && _weights_manager->are_weights_managed(_original_weights))
             {
                 cur_weights = _weights_manager->run(cur_weights, &_reshape_weights_managed_function);
             }
             else
             {
                 // Reshape of the weights (happens only once)
                 if(!_are_weights_reshaped)
                 {
                     // Run reshape weights kernel and mark weights as unused
                     _reshape_weights_output.allocator()->allocate();
                     _reshape_weights_function.run();
                 }
                 cur_weights->mark_as_unused();
                 cur_weights = &_reshape_weights_output;
             }
             _are_weights_reshaped = true;
         }

         // Convert weights if needed (happens only once)
         if(!_are_weights_converted)
         {
             if(_weights_manager && _weights_manager->are_weights_managed(cur_weights))
             {
                 _weights_manager->run(cur_weights, &_convert_weights_managed);
             }
             else
             {
                 _converted_weights_output.allocator()->allocate();
                 _convert_weights.run();
                 cur_weights->mark_as_unused();
             }

             _are_weights_converted = true;
         }

         // Release reshaped weights if unused
         release_unused(&_reshape_weights_output);

         // Prepare GEMM prepare and release unused weights
         if(!_is_quantized_asymmetric)
         {
             _mm_gemm.prepare();
         }

         // Release converted weights if unused
         release_unused(&_reshape_weights_output);
         release_unused(&_converted_weights_output);

         _is_prepared = true;
     }
 }
 } // namespace arm_compute
	/*
	* 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.
	*/
	#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"

	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/Size2D.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/NEON/NEScheduler.h"
	#include "src/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.h"
	#include "src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h"
	#include "src/core/NEON/kernels/NEFlattenLayerKernel.h"
	#include "src/core/NEON/kernels/NEFlattenLayerKernel.h"
	#include "src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
	#include "src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h"
	#include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h"
	#include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h"
	#include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h"
	#include "src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.h"
	#include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
	#include "src/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
	#include "src/core/NEON/kernels/NETransposeKernel.h"

	#include "support/MemorySupport.h"

	#include <algorithm>
	#include <cmath>

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

	namespace
	{
	// Get min, max bound of a quantized assymetric output tensor, with the effect of fused activation
	std::pair<PixelValue, PixelValue> get_quantized_asymmetric_output_min_max(const QuantizationInfo &q_info, const ActivationLayerInfo &act_info, DataType data_type)
	{
	PixelValue type_min{};
	PixelValue type_max{};
	std::tie(type_min, type_max) = get_min_max(data_type);
	const UniformQuantizationInfo q_unif = q_info.uniform();

	if(act_info.enabled())
	{
	switch(act_info.activation())
	{
	case ActivationLayerInfo::ActivationFunction::RELU:
	type_min = PixelValue(q_unif.offset);
	break;
	case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
	type_min = PixelValue(q_unif.offset);
	type_max = PixelValue(act_info.a(), data_type, q_info);
	break;
	case ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU:
	type_min = PixelValue(act_info.b(), data_type, q_info);
	type_max = PixelValue(act_info.a(), data_type, q_info);
	break;
	default:
	ARM_COMPUTE_ERROR("Activation function not supported.");
	break;
	}
	}

	return std::make_pair(type_min, type_max);
	}

	Status get_gemmlowp_output_stage_info(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo *output, const ActivationLayerInfo &act,
	GEMMLowpOutputStageInfo &gemmlowp_output_stage_info)
	{
	const auto data_type = input->data_type();
	const QuantizationInfo oq_info = output->quantization_info();
	const UniformQuantizationInfo iq_unif = input->quantization_info().uniform();
	const UniformQuantizationInfo wq_unif = weights->quantization_info().uniform();
	const UniformQuantizationInfo oq_unif = oq_info.uniform();

	float multiplier = (iq_unif.scale * wq_unif.scale) / oq_unif.scale;
	int32_t output_multiplier;
	int32_t output_shift;

	ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));

	PixelValue type_min{};
	PixelValue type_max{};
	std::tie(type_min, type_max) = get_quantized_asymmetric_output_min_max(oq_info, act, data_type);

	gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier;
	gemmlowp_output_stage_info.gemmlowp_shift = output_shift;
	gemmlowp_output_stage_info.gemmlowp_offset = oq_unif.offset;
	gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
	gemmlowp_output_stage_info.gemmlowp_min_bound = type_min.get<int32_t>();
	gemmlowp_output_stage_info.gemmlowp_max_bound = type_max.get<int32_t>();

	return Status{};
	}

	Status validate_mm(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo output, const ActivationLayerInfo &act)
	{
	if(is_data_type_quantized_asymmetric(input->data_type()))
	{
	// 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().uniform().scale, -input->quantization_info().uniform().offset);
	const QuantizationInfo weights_quantization_info(weights->quantization_info().uniform().scale, -weights->quantization_info().uniform().offset);

	GEMMLowpOutputStageInfo gemmlowp_output_stage_info;
	ARM_COMPUTE_RETURN_ON_ERROR(get_gemmlowp_output_stage_info(input, weights, output, act, gemmlowp_output_stage_info));

	GEMMInfo gemm_info;
	gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info);

	// Validate gemmlowp function
	ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_quantization_info(input_quantization_info),
	&weights->clone()->set_quantization_info(weights_quantization_info),
	biases,
	output,
	gemm_info));
	}
	else
	{
	ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::validate(input, weights, biases, output, 1.f, 1.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)));
	}

	return Status{};
	}
	} // namespace

	void NEFullyConnectedLayerReshapeWeights::configure(const ITensor input, ITensor output)
	{
	auto k = arm_compute::support::cpp14::make_unique<NETransposeKernel>();
	k->configure(input, output);
	_kernel = std::move(k);
	}

	Status NEFullyConnectedLayerReshapeWeights::validate(const ITensorInfo input, const ITensorInfo output)
	{
	return NETransposeKernel::validate(input, output);
	}

	NEFullyConnectedLayer::~NEFullyConnectedLayer() = default;

	NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
	: _memory_group(std::move(memory_manager)), _weights_manager(weights_manager), _flatten_kernel(), _convert_weights(), _convert_weights_managed(), _reshape_weights_function(),
	_reshape_weights_managed_function(), _mm_gemm(nullptr, weights_manager), _mm_gemmlowp(nullptr, weights_manager), _flatten_output(), _converted_weights_output(), _reshape_weights_output(),
	_original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false), _is_fc_after_conv(false), _is_quantized_asymmetric(false), _is_prepared(false)
	{
	}

	void NEFullyConnectedLayer::configure_mm(const ITensor input, const ITensor weights, const ITensor biases, ITensor output, const ActivationLayerInfo &act)
	{
	if(_is_quantized_asymmetric)
	{
	// 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.uniform().scale, -input_quantization_info.uniform().offset));
	weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset));

	// Configure gemmlowp function and output stage for asymmetric quantized types
	GEMMLowpOutputStageInfo gemmlowp_output_stage_info;
	const Status status = get_gemmlowp_output_stage_info(input->info(), weights->info(), output->info(), act, gemmlowp_output_stage_info);
	ARM_COMPUTE_ERROR_ON(status.error_code() != ErrorCode::OK);

	GEMMInfo gemm_info;
	gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info);
	gemm_info.set_activation_info(act);
	_mm_gemmlowp.configure(input, weights, biases, output, gemm_info);

	// Revert back QuantizatioInfo as input and weights could be used in other fully connected layers
	input->info()->set_quantization_info(input_quantization_info);
	weights->info()->set_quantization_info(weights_quantization_info);
	}
	else
	{
	// Configure matrix multiply kernel
	GEMMInfo gemm_info(false, false, true /* Reshape weights only for the first run */);
	gemm_info.set_activation_info(act);
	_mm_gemm.configure(input, weights, biases, output, 1.f, 1.0f, gemm_info);
	}
	}

	void NEFullyConnectedLayer::configure_conv_fc(const ITensor input, const ITensor weights, const ITensor biases, ITensor output, const ActivationLayerInfo &act)
	{
	ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));

	// If the fully connected layer is called after a convolution layer, the input tensor must be linearized

	// Initialize output tensor for flatten
	TensorShape shape_flatten = compute_flatten_shape(input->info());
	_flatten_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));

	// Configure flatten kernel
	_memory_group.manage(&_flatten_output);

	_flatten_kernel = arm_compute::support::cpp14::make_unique<NEFlattenLayerKernel>();
	_flatten_kernel->configure(input, &_flatten_output);

	// Configure matrix multiply kernel
	configure_mm(&_flatten_output, weights, biases, output, act);

	// Allocate the output tensor for flatten once all the configure methods have been called
	_flatten_output.allocator()->allocate();
	}

	void NEFullyConnectedLayer::configure_fc_fc(const ITensor input, const ITensor weights, const ITensor biases, ITensor output, const ActivationLayerInfo &act)
	{
	ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));

	// Configure matrix multiply kernel
	configure_mm(input, weights, biases, output, act);
	}

	void NEFullyConnectedLayer::configure(const ITensor input, const ITensor weights, const ITensor biases, ITensor output,
	FullyConnectedLayerInfo fc_info)
	{
	// Perform validate step
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
	ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedLayer::validate(input->info(),
	weights->info(),
	biases != nullptr ? biases->info() : nullptr,
	output->info(),
	fc_info));

	_are_weights_converted = true;
	_are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
	_is_fc_after_conv = true;
	_is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->info()->data_type());
	_original_weights = weights;

	if(_weights_manager)
	{
	_weights_manager->manage(weights);
	}

	// With the Fully Connected layer we can have 4 different cases:
	// 1) Convolution layer -> Fully Connected layer without batches
	// 2) Fully Connected layer -> Fully Connected layer without batches
	// 3) Convolution layer -> Fully Connected layer with batches
	// 4) Fully Connected layer -> Fully Connected layer with batches

	const ITensor *weights_to_use = weights;

	// Check if we have a fully connected layer with batches
	const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
	if(is_batched_fc_layer)
	{
	_is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
	input->info()->tensor_shape().cend(),
	output->info()->tensor_shape().cbegin() + 1));
	}
	else
	{
	_is_fc_after_conv = input->info()->num_dimensions() > 1;
	}

	// Reshape weights if needed
	if(!_are_weights_reshaped)
	{
	if(_weights_manager && _weights_manager->are_weights_managed(weights))
	{
	_reshape_weights_managed_function.configure(weights);
	weights_to_use = _weights_manager->acquire(weights, &_reshape_weights_managed_function);
	}
	else
	{
	// Reshape the weights
	_reshape_weights_function.configure(weights, &_reshape_weights_output);
	weights_to_use = &_reshape_weights_output;
	}
	}

	// Convert weights if needed
	if(_is_fc_after_conv && (input->info()->data_layout() != fc_info.weights_trained_layout))
	{
	if(_weights_manager && _weights_manager->are_weights_managed(weights_to_use))
	{
	_convert_weights_managed.configure(weights_to_use,
	input->info()->tensor_shape(),
	fc_info.weights_trained_layout);
	weights_to_use = _weights_manager->acquire(weights, &_convert_weights_managed);
	}
	else
	{
	// Convert weights
	_convert_weights.configure(weights_to_use,
	&_converted_weights_output,
	input->info()->tensor_shape(),
	fc_info.weights_trained_layout);

	weights_to_use = &_converted_weights_output;
	}
	_are_weights_converted = false;
	}

	if(_is_fc_after_conv)
	{
	// Fully Connected layer after a Convolution Layer without batches
	configure_conv_fc(input, weights_to_use, biases, output, fc_info.activation_info);
	}
	else
	{
	// Fully Connected layer after a Fully Connected Layer without batches
	configure_fc_fc(input, weights_to_use, biases, output, fc_info.activation_info);
	}

	_are_weights_reshaped = _are_weights_reshaped \|\| fc_info.retain_internal_weights;
	}

	Status NEFullyConnectedLayer::validate(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo output,
	FullyConnectedLayerInfo fc_info)
	{
	ARM_COMPUTE_UNUSED(fc_info.retain_internal_weights);
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
	ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
	ARM_COMPUTE_RETURN_ERROR_ON(biases != nullptr && biases->num_dimensions() > 1);

	bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
	bool is_fc_after_conv = true;

	const ITensorInfo &flatten_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)));
	const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
	const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone());

	// With the Fully Connected layer we can have 4 different cases:
	// 1) Convolution layer -> Fully Connected layer without batches
	// 2) Fully Connected layer -> Fully Connected layer without batches
	// 3) Convolution layer -> Fully Connected layer with batches
	// 4) Fully Connected layer -> Fully Connected layer with batches

	const ITensorInfo *input_to_use = input;
	const ITensorInfo *weights_to_use = weights;

	// Check if we have a fully connected layer with batches
	const bool is_batched_fc_layer = output->dimension(1) > 1;

	if(is_batched_fc_layer)
	{
	is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->tensor_shape().cbegin() + 3,
	input->tensor_shape().cend(),
	output->tensor_shape().cbegin() + 1));
	}
	else
	{
	is_fc_after_conv = input->num_dimensions() > 1;
	}

	if(!weights_reshaped)
	{
	// Validate reshape weights kernel
	ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayerReshapeWeights::validate(weights, &reshaped_weights));
	weights_to_use = &reshaped_weights;
	}

	if(is_fc_after_conv && (input->data_layout() != fc_info.weights_trained_layout))
	{
	// Validate convert weights kernel
	ARM_COMPUTE_RETURN_ON_ERROR(NEConvertFullyConnectedWeights::validate(weights_to_use,
	&converted_weights,
	input->tensor_shape(),
	fc_info.weights_trained_layout));
	weights_to_use = &converted_weights;
	}

	if(is_fc_after_conv)
	{
	// Fully Connected layer after a Convolution Layer without batches
	ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (input->dimension(0) * input->dimension(1) * input->dimension(2))));

	// Validate flatten kernel
	ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayerKernel::validate(input, &flatten_input));
	input_to_use = &flatten_input;
	}
	else
	{
	// Fully Connected layer after a Fully Connected Layer without batches
	ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1));
	}
	// Validate matrix multiply kernel
	ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(input_to_use, weights_to_use, biases, output, fc_info.activation_info));

	return Status{};
	}

	void NEFullyConnectedLayer::run()
	{
	prepare();

	MemoryGroupResourceScope scope_mg(_memory_group);

	// Linearize input if it comes from a convolutional layer
	if(_is_fc_after_conv)
	{
	NEScheduler::get().schedule(_flatten_kernel.get(), Window::DimY);
	}

	// Run matrix multiply
	if(_is_quantized_asymmetric)
	{
	_mm_gemmlowp.run();
	}
	else
	{
	_mm_gemm.run();
	}
	}

	void NEFullyConnectedLayer::prepare()
	{
	if(!_is_prepared)
	{
	if(!_weights_manager)
	{
	ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
	}

	auto release_unused = [](Tensor * w)
	{
	if(!w->is_used())
	{
	w->allocator()->free();
	}
	};

	// Pointer to current weights
	const ITensor *cur_weights = _original_weights;

	// Reshape of the weights (happens only once)
	if(!_are_weights_reshaped)
	{
	if(_weights_manager && _weights_manager->are_weights_managed(_original_weights))
	{
	cur_weights = _weights_manager->run(cur_weights, &_reshape_weights_managed_function);
	}
	else
	{
	// Reshape of the weights (happens only once)
	if(!_are_weights_reshaped)
	{
	// Run reshape weights kernel and mark weights as unused
	_reshape_weights_output.allocator()->allocate();
	_reshape_weights_function.run();
	}
	cur_weights->mark_as_unused();
	cur_weights = &_reshape_weights_output;
	}
	_are_weights_reshaped = true;
	}

	// Convert weights if needed (happens only once)
	if(!_are_weights_converted)
	{
	if(_weights_manager && _weights_manager->are_weights_managed(cur_weights))
	{
	_weights_manager->run(cur_weights, &_convert_weights_managed);
	}
	else
	{
	_converted_weights_output.allocator()->allocate();
	_convert_weights.run();
	cur_weights->mark_as_unused();
	}

	_are_weights_converted = true;
	}

	// Release reshaped weights if unused
	release_unused(&_reshape_weights_output);

	// Prepare GEMM prepare and release unused weights
	if(!_is_quantized_asymmetric)
	{
	_mm_gemm.prepare();
	}

	// Release converted weights if unused
	release_unused(&_reshape_weights_output);
	release_unused(&_converted_weights_output);

	_is_prepared = true;
	}
	}
	} // namespace arm_compute