src/runtime/CL/functions/CLFullyConnectedLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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/CL/functions/CLFullyConnectedLayer.h"

 #include "arm_compute/core/Size2D.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "support/ToolchainSupport.h"

 #include <algorithm>

 using namespace arm_compute;

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

 CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(memory_manager), _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _accumulate_biases_kernel(), _im2col_output(),
       _gemmlowp_output(), _reshape_weights_output(), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false)
 {
 }

 void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed)
 {
     if(_is_quantized)
     {
         // Extract and negate input and weights offset
         QuantizationInfo input_quantization_info   = input->info()->quantization_info();
         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));
         // Configure gemmlowp function
         _mm_gemmlowp.configure(input, weights, output);
     }
     else
     {
         // Configure matrix multiply kernel
         _mm_kernel.set_target(CLScheduler::get().target());
         _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
     }
 }

 void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
 {
     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 im2col
     TensorShape shape_im2col = input->info()->tensor_shape();
     shape_im2col.collapse(3);
     _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));

     // Configure im2col kernel
     _memory_group.manage(&_im2col_output);
     _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);

     // Configure matrix multiply kernel
     configure_mm(&_im2col_output, weights, output, false);

     // Allocate the output tensor for im2col once all the configure methods have been called
     _im2col_output.allocator()->allocate();
 }

 void CLFullyConnectedLayer::configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
 {
     ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));

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

 void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose_weights, bool are_weights_reshaped)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);

     _are_weights_reshaped = transpose_weights ? are_weights_reshaped : true;
     _is_fc_after_conv     = true;
     _accumulate_biases    = false;
     _is_quantized         = is_data_type_quantized_asymmetric(input->info()->data_type());

     // Configure gemmlowp output
     if(_is_quantized)
     {
         _gemmlowp_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
     }

     // Configure accumulate biases kernel for non quantized asymmetric types
     if(biases != nullptr && !_is_quantized)
     {
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);

         _accumulate_biases = true;

         // Configure accumulate biases kernel
         _accumulate_biases_kernel.set_target(CLScheduler::get().target());
         _accumulate_biases_kernel.configure(output, biases);
     }

     // 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 ICLTensor *weights_to_use = weights;

     if(!_are_weights_reshaped)
     {
         weights_to_use = &_reshape_weights_output;

         // Reshape the weights
         _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
     }

     // 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;
     }

     ICLTensor *tmp_output = (_is_quantized) ? &_gemmlowp_output : output;
     if(_is_fc_after_conv)
     {
         // Fully Connected layer after a Convolution Layer without batches
         configure_conv_fc(input, weights_to_use, tmp_output);
     }
     else
     {
         // Fully Connected layer after a Fully Connected Layer without batches
         configure_fc_fc(input, weights_to_use, tmp_output);
     }

     // Configure output stage for asymmetric quantized types
     if(_is_quantized)
     {
         float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale;
         int   output_multiplier, output_shift;
         quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
         _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier, output_shift, output->info()->quantization_info().offset);
         _gemmlowp_output.allocator()->allocate();
     }

     // Allocate the transpose tensor if the are_weights_reshaped flag is false and once all the configure methods have been called
     if(!_are_weights_reshaped)
     {
         // Allocate the tensor for the weights reshaped
         _reshape_weights_output.allocator()->allocate();
     }
 }

 void CLFullyConnectedLayer::run()
 {
     // Reshape of the weights (happens only once)
     if(!_are_weights_reshaped)
     {
         _are_weights_reshaped = true;
         _reshape_weights_kernel.run();
     }

     _memory_group.acquire();

     // Linearize input if it comes from a convolutional layer
     if(_is_fc_after_conv)
     {
         CLScheduler::get().enqueue(_im2col_kernel, false);
     }

     // Run matrix multiply
     if(_is_quantized)
     {
         _mm_gemmlowp.run();
     }
     else
     {
         CLScheduler::get().enqueue(_mm_kernel, !_accumulate_biases);
     }

     // Accumulate biases if provided
     if(_is_quantized)
     {
         _gemmlowp_output_stage.run();
     }
     else
     {
         if(_accumulate_biases)
         {
             CLScheduler::get().enqueue(_accumulate_biases_kernel);
         }
     }

     _memory_group.release();
 }
	/*
	* 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/CL/functions/CLFullyConnectedLayer.h"

	#include "arm_compute/core/Size2D.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
	#include "arm_compute/runtime/CL/CLScheduler.h"
	#include "support/ToolchainSupport.h"

	#include <algorithm>

	using namespace arm_compute;

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

	CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(memory_manager), _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _accumulate_biases_kernel(), _im2col_output(),
	_gemmlowp_output(), _reshape_weights_output(), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false)
	{
	}

	void CLFullyConnectedLayer::configure_mm(const ICLTensor input, const ICLTensor weights, ICLTensor *output, bool is_interleaved_transposed)
	{
	if(_is_quantized)
	{
	// Extract and negate input and weights offset
	QuantizationInfo input_quantization_info = input->info()->quantization_info();
	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));
	// Configure gemmlowp function
	_mm_gemmlowp.configure(input, weights, output);
	}
	else
	{
	// Configure matrix multiply kernel
	_mm_kernel.set_target(CLScheduler::get().target());
	_mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
	}
	}

	void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor input, const ICLTensor weights, ICLTensor *output)
	{
	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 im2col
	TensorShape shape_im2col = input->info()->tensor_shape();
	shape_im2col.collapse(3);
	_im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));

	// Configure im2col kernel
	_memory_group.manage(&_im2col_output);
	_im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);

	// Configure matrix multiply kernel
	configure_mm(&_im2col_output, weights, output, false);

	// Allocate the output tensor for im2col once all the configure methods have been called
	_im2col_output.allocator()->allocate();
	}

	void CLFullyConnectedLayer::configure_fc_fc(const ICLTensor input, const ICLTensor weights, ICLTensor *output)
	{
	ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));

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

	void CLFullyConnectedLayer::configure(const ICLTensor input, const ICLTensor weights, const ICLTensor biases, ICLTensor output, bool transpose_weights, bool are_weights_reshaped)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
	ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);

	_are_weights_reshaped = transpose_weights ? are_weights_reshaped : true;
	_is_fc_after_conv = true;
	_accumulate_biases = false;
	_is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());

	// Configure gemmlowp output
	if(_is_quantized)
	{
	_gemmlowp_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
	}

	// Configure accumulate biases kernel for non quantized asymmetric types
	if(biases != nullptr && !_is_quantized)
	{
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);

	_accumulate_biases = true;

	// Configure accumulate biases kernel
	_accumulate_biases_kernel.set_target(CLScheduler::get().target());
	_accumulate_biases_kernel.configure(output, biases);
	}

	// 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 ICLTensor *weights_to_use = weights;

	if(!_are_weights_reshaped)
	{
	weights_to_use = &_reshape_weights_output;

	// Reshape the weights
	_reshape_weights_kernel.configure(weights, &_reshape_weights_output);
	}

	// 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;
	}

	ICLTensor *tmp_output = (_is_quantized) ? &_gemmlowp_output : output;
	if(_is_fc_after_conv)
	{
	// Fully Connected layer after a Convolution Layer without batches
	configure_conv_fc(input, weights_to_use, tmp_output);
	}
	else
	{
	// Fully Connected layer after a Fully Connected Layer without batches
	configure_fc_fc(input, weights_to_use, tmp_output);
	}

	// Configure output stage for asymmetric quantized types
	if(_is_quantized)
	{
	float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale;
	int output_multiplier, output_shift;
	quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
	_gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier, output_shift, output->info()->quantization_info().offset);
	_gemmlowp_output.allocator()->allocate();
	}

	// Allocate the transpose tensor if the are_weights_reshaped flag is false and once all the configure methods have been called
	if(!_are_weights_reshaped)
	{
	// Allocate the tensor for the weights reshaped
	_reshape_weights_output.allocator()->allocate();
	}
	}

	void CLFullyConnectedLayer::run()
	{
	// Reshape of the weights (happens only once)
	if(!_are_weights_reshaped)
	{
	_are_weights_reshaped = true;
	_reshape_weights_kernel.run();
	}

	_memory_group.acquire();

	// Linearize input if it comes from a convolutional layer
	if(_is_fc_after_conv)
	{
	CLScheduler::get().enqueue(_im2col_kernel, false);
	}

	// Run matrix multiply
	if(_is_quantized)
	{
	_mm_gemmlowp.run();
	}
	else
	{
	CLScheduler::get().enqueue(_mm_kernel, !_accumulate_biases);
	}

	// Accumulate biases if provided
	if(_is_quantized)
	{
	_gemmlowp_output_stage.run();
	}
	else
	{
	if(_accumulate_biases)
	{
	CLScheduler::get().enqueue(_accumulate_biases_kernel);
	}
	}

	_memory_group.release();
	}