src/runtime/NEON/functions/NEFullyConnectedLayer.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/NEON/functions/NEFullyConnectedLayer.h"

 #include "arm_compute/core/Size2D.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"

 #include <algorithm>
 #include <cmath>

 namespace arm_compute
 {
 NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
 {
 }

 void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITensor *output, bool transpose_weights, bool is_batched_fc_layer)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() > 2);
     ARM_COMPUTE_ERROR_ON(output == nullptr);
     ARM_COMPUTE_ERROR_ON(!transpose_weights && !is_batched_fc_layer);

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

     _transpose_weights   = transpose_weights;
     _is_batched_fc_layer = is_batched_fc_layer;

     // Check if we need to transpose the weights
     if(_transpose_weights)
     {
         if(_is_batched_fc_layer)
         {
             // Initialize the output tensor for transpose
             TensorShape shape_transposed(input->info()->dimension(1), input->info()->dimension(0));
             _transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, data_type, fixed_point_position));
             _memory_group.manage(&_transpose_output);
             _transpose_kernel.configure(input, &_transpose_output);

             // Configure transpose 1xW kernel
             _transpose1xW_kernel.configure(&_transpose_output, output);

             // Allocate temporary tensor used for transposing the weights
             _transpose_output.allocator()->allocate();
         }
         else
         {
             _transpose_kernel.configure(input, output);
         }
     }
     else
     {
         if(_is_batched_fc_layer)
         {
             // Configure transpose 1xW kernel
             _transpose1xW_kernel.configure(input, output);
         }
         else
         {
             ARM_COMPUTE_ERROR("Configuration transpose_weights=false & is_batched_fc_layer=false not supported");
         }
     }
 }

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

     if(_transpose_weights)
     {
         NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
     }

     if(_is_batched_fc_layer)
     {
         NEScheduler::get().schedule(&_transpose1xW_kernel, Window::DimY);
     }

     _memory_group.release();
 }

 NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(),
       _reshape_weights_output(), _are_weights_reshaped(false), _is_batched_fc_layer(false), _linearize_input(false), _accumulate_biases(false)
 {
 }

 void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose_weights, bool are_weights_reshaped)
 {
     // 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

     // Expected shape before transpose and reshaping
     // Input: In x B (In and B can be multi-dimensional)
     // Weights: flat(In) x Out
     // Biases: Out
     // Output: Out x B (B can be multi-dimensional)

     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, weights, output);

     const DataType data_type            = input->info()->data_type();
     const int      fixed_point_position = input->info()->fixed_point_position();
     const int      num_batch_dimensions = std::max(0, static_cast<int>(output->info()->tensor_shape().num_dimensions()) - 1);
     const int      num_input_dimensions = input->info()->tensor_shape().num_dimensions() - num_batch_dimensions;
     const size_t   linear_input_size    = input->info()->tensor_shape().total_size_lower(num_input_dimensions);

     _linearize_input      = input->info()->tensor_shape().x() != linear_input_size;
     _are_weights_reshaped = are_weights_reshaped;
     _accumulate_biases    = biases != nullptr;
     _is_batched_fc_layer  = num_batch_dimensions > 0;

     // Check if number of batches match
     ARM_COMPUTE_ERROR_ON(input->info()->tensor_shape().total_size_upper(num_input_dimensions) != output->info()->tensor_shape().total_size_upper(1));
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);

     const size_t   interleave_width = 16 / input->info()->element_size();
     const ITensor *weights_to_use   = weights;

     if(!are_weights_reshaped && (transpose_weights || _is_batched_fc_layer))
     {
         weights_to_use = &_reshape_weights_output;

         TensorShape reshaped_weights_shape(weights->info()->tensor_shape());

         // Transpose weights if the user hasn't done it
         if(transpose_weights)
         {
             const size_t shape_x = reshaped_weights_shape.x();
             reshaped_weights_shape.set(0, reshaped_weights_shape.y());
             reshaped_weights_shape.set(1, shape_x);
         }

         // If the we run multiple batches we need 1xW transpose, too.
         if(_is_batched_fc_layer)
         {
             const float shape_x = reshaped_weights_shape.x();
             reshaped_weights_shape.set(0, reshaped_weights_shape.y() * interleave_width);
             reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(shape_x / interleave_width)));
         }

         _reshape_weights_output.allocator()->init(TensorInfo(reshaped_weights_shape, 1, data_type, fixed_point_position));

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

     // Check correct shape of weights
     if(_is_batched_fc_layer)
     {
         // Transpose + Transpose1xW
         ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != linear_input_size * interleave_width);
         ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != static_cast<unsigned int>(std::ceil(static_cast<float>(output->info()->tensor_shape().x()) / interleave_width)));
     }
     else
     {
         // Transpose
         ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != output->info()->tensor_shape().x());
         ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != linear_input_size);
     }

     const ITensor *multiply_input = input;

     if(_linearize_input)
     {
         TensorShape shape_im2col(input->info()->tensor_shape());
         shape_im2col.collapse(num_input_dimensions);
         _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, data_type, fixed_point_position));

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

         multiply_input = &_im2col_output;
     }

     if(_is_batched_fc_layer)
     {
         TensorShape shape_interleaved(multiply_input->info()->tensor_shape());
         shape_interleaved.set(0, shape_interleaved.x() * 4);
         shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
         _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, data_type, fixed_point_position));

         // Configure interleave4x4 kernel
         _memory_group.manage(&_interleave4x4_output);
         _interleave4x4_kernel.configure(multiply_input, &_interleave4x4_output);

         multiply_input = &_interleave4x4_output;
     }

     // Configure matrix multiply kernel
     _mm_kernel.configure(multiply_input, weights_to_use, output, 1.0f);

     if(_accumulate_biases)
     {
         ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
         ARM_COMPUTE_ERROR_ON(biases->info()->tensor_shape().x() != output->info()->tensor_shape().x());

         // Configure accumulate biases kernel
         _accumulate_biases_kernel.configure(output, biases);
     }

     // 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 && (transpose_weights || _is_batched_fc_layer))
     {
         // Allocate the tensor for the weights reshaped
         _reshape_weights_output.allocator()->allocate();
     }

     if(_linearize_input)
     {
         _im2col_output.allocator()->allocate();
     }

     if(_is_batched_fc_layer)
     {
         _interleave4x4_output.allocator()->allocate();
     }
 }

 void NEFullyConnectedLayer::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(_linearize_input)
     {
         NEScheduler::get().schedule(&_im2col_kernel, Window::DimY);
     }

     // Interleave input
     if(_is_batched_fc_layer)
     {
         NEScheduler::get().schedule(&_interleave4x4_kernel, Window::DimY);
     }

     // Run matrix multiply
     NEScheduler::get().schedule(&_mm_kernel, _is_batched_fc_layer ? Window::DimY : Window::DimX);

     // Accumulate biases if provided
     if(_accumulate_biases)
     {
         NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY);
     }

     _memory_group.release();
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2017 ARM Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"

	#include "arm_compute/core/Size2D.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/runtime/NEON/NEScheduler.h"

	#include <algorithm>
	#include <cmath>

	namespace arm_compute
	{
	NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)), _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
	{
	}

	void NEFullyConnectedLayerReshapeWeights::configure(const ITensor input, ITensor output, bool transpose_weights, bool is_batched_fc_layer)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() > 2);
	ARM_COMPUTE_ERROR_ON(output == nullptr);
	ARM_COMPUTE_ERROR_ON(!transpose_weights && !is_batched_fc_layer);

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

	_transpose_weights = transpose_weights;
	_is_batched_fc_layer = is_batched_fc_layer;

	// Check if we need to transpose the weights
	if(_transpose_weights)
	{
	if(_is_batched_fc_layer)
	{
	// Initialize the output tensor for transpose
	TensorShape shape_transposed(input->info()->dimension(1), input->info()->dimension(0));
	_transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, data_type, fixed_point_position));
	_memory_group.manage(&_transpose_output);
	_transpose_kernel.configure(input, &_transpose_output);

	// Configure transpose 1xW kernel
	_transpose1xW_kernel.configure(&_transpose_output, output);

	// Allocate temporary tensor used for transposing the weights
	_transpose_output.allocator()->allocate();
	}
	else
	{
	_transpose_kernel.configure(input, output);
	}
	}
	else
	{
	if(_is_batched_fc_layer)
	{
	// Configure transpose 1xW kernel
	_transpose1xW_kernel.configure(input, output);
	}
	else
	{
	ARM_COMPUTE_ERROR("Configuration transpose_weights=false & is_batched_fc_layer=false not supported");
	}
	}
	}

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

	if(_transpose_weights)
	{
	NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
	}

	if(_is_batched_fc_layer)
	{
	NEScheduler::get().schedule(&_transpose1xW_kernel, Window::DimY);
	}

	_memory_group.release();
	}

	NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)), _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(),
	_reshape_weights_output(), _are_weights_reshaped(false), _is_batched_fc_layer(false), _linearize_input(false), _accumulate_biases(false)
	{
	}

	void NEFullyConnectedLayer::configure(const ITensor input, const ITensor weights, const ITensor biases, ITensor output, bool transpose_weights, bool are_weights_reshaped)
	{
	// 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

	// Expected shape before transpose and reshaping
	// Input: In x B (In and B can be multi-dimensional)
	// Weights: flat(In) x Out
	// Biases: Out
	// Output: Out x B (B can be multi-dimensional)

	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, weights, output);

	const DataType data_type = input->info()->data_type();
	const int fixed_point_position = input->info()->fixed_point_position();
	const int num_batch_dimensions = std::max(0, static_cast<int>(output->info()->tensor_shape().num_dimensions()) - 1);
	const int num_input_dimensions = input->info()->tensor_shape().num_dimensions() - num_batch_dimensions;
	const size_t linear_input_size = input->info()->tensor_shape().total_size_lower(num_input_dimensions);

	_linearize_input = input->info()->tensor_shape().x() != linear_input_size;
	_are_weights_reshaped = are_weights_reshaped;
	_accumulate_biases = biases != nullptr;
	_is_batched_fc_layer = num_batch_dimensions > 0;

	// Check if number of batches match
	ARM_COMPUTE_ERROR_ON(input->info()->tensor_shape().total_size_upper(num_input_dimensions) != output->info()->tensor_shape().total_size_upper(1));
	ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);

	const size_t interleave_width = 16 / input->info()->element_size();
	const ITensor *weights_to_use = weights;

	if(!are_weights_reshaped && (transpose_weights \|\| _is_batched_fc_layer))
	{
	weights_to_use = &_reshape_weights_output;

	TensorShape reshaped_weights_shape(weights->info()->tensor_shape());

	// Transpose weights if the user hasn't done it
	if(transpose_weights)
	{
	const size_t shape_x = reshaped_weights_shape.x();
	reshaped_weights_shape.set(0, reshaped_weights_shape.y());
	reshaped_weights_shape.set(1, shape_x);
	}

	// If the we run multiple batches we need 1xW transpose, too.
	if(_is_batched_fc_layer)
	{
	const float shape_x = reshaped_weights_shape.x();
	reshaped_weights_shape.set(0, reshaped_weights_shape.y() * interleave_width);
	reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(shape_x / interleave_width)));
	}

	_reshape_weights_output.allocator()->init(TensorInfo(reshaped_weights_shape, 1, data_type, fixed_point_position));

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

	// Check correct shape of weights
	if(_is_batched_fc_layer)
	{
	// Transpose + Transpose1xW
	ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != linear_input_size * interleave_width);
	ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != static_cast<unsigned int>(std::ceil(static_cast<float>(output->info()->tensor_shape().x()) / interleave_width)));
	}
	else
	{
	// Transpose
	ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != output->info()->tensor_shape().x());
	ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != linear_input_size);
	}

	const ITensor *multiply_input = input;

	if(_linearize_input)
	{
	TensorShape shape_im2col(input->info()->tensor_shape());
	shape_im2col.collapse(num_input_dimensions);
	_im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, data_type, fixed_point_position));

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

	multiply_input = &_im2col_output;
	}

	if(_is_batched_fc_layer)
	{
	TensorShape shape_interleaved(multiply_input->info()->tensor_shape());
	shape_interleaved.set(0, shape_interleaved.x() * 4);
	shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
	_interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, data_type, fixed_point_position));

	// Configure interleave4x4 kernel
	_memory_group.manage(&_interleave4x4_output);
	_interleave4x4_kernel.configure(multiply_input, &_interleave4x4_output);

	multiply_input = &_interleave4x4_output;
	}

	// Configure matrix multiply kernel
	_mm_kernel.configure(multiply_input, weights_to_use, output, 1.0f);

	if(_accumulate_biases)
	{
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
	ARM_COMPUTE_ERROR_ON(biases->info()->tensor_shape().x() != output->info()->tensor_shape().x());

	// Configure accumulate biases kernel
	_accumulate_biases_kernel.configure(output, biases);
	}

	// 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 && (transpose_weights \|\| _is_batched_fc_layer))
	{
	// Allocate the tensor for the weights reshaped
	_reshape_weights_output.allocator()->allocate();
	}

	if(_linearize_input)
	{
	_im2col_output.allocator()->allocate();
	}

	if(_is_batched_fc_layer)
	{
	_interleave4x4_output.allocator()->allocate();
	}
	}

	void NEFullyConnectedLayer::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(_linearize_input)
	{
	NEScheduler::get().schedule(&_im2col_kernel, Window::DimY);
	}

	// Interleave input
	if(_is_batched_fc_layer)
	{
	NEScheduler::get().schedule(&_interleave4x4_kernel, Window::DimY);
	}

	// Run matrix multiply
	NEScheduler::get().schedule(&_mm_kernel, _is_batched_fc_layer ? Window::DimY : Window::DimX);

	// Accumulate biases if provided
	if(_accumulate_biases)
	{
	NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY);
	}

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