| /* |
| * 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/Validate.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| #include <algorithm> |
| #include <cmath> |
| |
| using namespace arm_compute; |
| |
| NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights() |
| : _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::F32); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() != 2); |
| ARM_COMPUTE_ERROR_ON((transpose_weights == false) && (is_batched_fc_layer == false)); |
| |
| const DataType dt = 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, dt, fixed_point_position)); |
| _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() |
| { |
| if(_transpose_weights) |
| { |
| NEScheduler::get().schedule(&_transpose_kernel, Window::DimY); |
| } |
| if(_is_batched_fc_layer) |
| { |
| NEScheduler::get().schedule(&_transpose1xW_kernel, Window::DimY); |
| } |
| } |
| |
| NEFullyConnectedLayer::NEFullyConnectedLayer() |
| : _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(), _reshape_weights_output(), |
| _are_weights_reshaped(false), _is_fc_after_conv(false), _is_batched_fc_layer(false), _accumulate_biases(false) |
| { |
| } |
| |
| void NEFullyConnectedLayer::configure_conv_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2) * (16 / weights->info()->element_size()))); |
| |
| const DataType dt = input->info()->data_type(); |
| const int fixed_point_position = input->info()->fixed_point_position(); |
| |
| // 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; |
| shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)); |
| shape_im2col.set(1, input->info()->dimension(3)); |
| shape_im2col.set(2, input->info()->dimension(4)); |
| shape_im2col.set(3, input->info()->dimension(5)); |
| _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position)); |
| |
| // Initialize output tensor for interleave 4x4 |
| TensorShape shape_interleaved = _im2col_output.info()->tensor_shape(); |
| shape_interleaved.set(0, shape_interleaved.x() * 4); |
| shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4)); |
| _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position)); |
| |
| // Configure im2col kernel |
| _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false); |
| |
| // Configure interleave4x4 kernel |
| _interleave4x4_kernel.configure(&_im2col_output, &_interleave4x4_output); |
| |
| // Configure matrix multiply kernel |
| _mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f); |
| |
| // Allocate the tensors once all the configure methods have been called |
| _im2col_output.allocator()->allocate(); |
| _interleave4x4_output.allocator()->allocate(); |
| } |
| |
| void NEFullyConnectedLayer::configure_fc_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output) |
| { |
| const DataType dt = input->info()->data_type(); |
| const int fixed_point_position = input->info()->fixed_point_position(); |
| |
| // Initialize output tensor for interleave 4x4 |
| TensorShape shape_interleaved = input->info()->tensor_shape(); |
| shape_interleaved.set(0, shape_interleaved.x() * 4); |
| shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4)); |
| _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position)); |
| |
| // Configure interleave4x4 kernel |
| _interleave4x4_kernel.configure(input, &_interleave4x4_output); |
| |
| // Configure matrix multiply kernel |
| _mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f); |
| |
| // Allocate the tensors once all the configure methods have been called |
| _interleave4x4_output.allocator()->allocate(); |
| } |
| |
| void NEFullyConnectedLayer::configure_conv_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)))); |
| |
| const DataType dt = input->info()->data_type(); |
| const int fixed_point_position = input->info()->fixed_point_position(); |
| |
| // 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; |
| shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)); |
| shape_im2col.set(1, 1); |
| _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position)); |
| |
| // Configure im2col kernel |
| _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false); |
| |
| // Configure matrix multiply kernel |
| _mm_kernel.configure(&_im2col_output, weights, output, 1.0f); |
| |
| // Allocate the output tensor for im2col once all the configure methods have been called |
| _im2col_output.allocator()->allocate(); |
| } |
| |
| void NEFullyConnectedLayer::configure_fc_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1)); |
| |
| // Configure matrix multiply kernel |
| _mm_kernel.configure(input, weights, output, 1.0f); |
| } |
| |
| void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose_weights, bool are_weights_reshaped) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); |
| ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() != 2); |
| |
| const DataType dt = input->info()->data_type(); |
| const int fixed_point_position = input->info()->fixed_point_position(); |
| |
| _are_weights_reshaped = are_weights_reshaped; |
| _is_fc_after_conv = true; |
| _is_batched_fc_layer = false; |
| _accumulate_biases = false; |
| |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| |
| _accumulate_biases = true; |
| |
| // Configure accumulate biases kernel |
| _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 |
| |
| // Check if we have a fully connected layer with batches |
| _is_batched_fc_layer = (output->info()->dimension(1) > 1); |
| |
| const ITensor *weights_to_use = weights; |
| |
| if(!are_weights_reshaped) |
| { |
| if((transpose_weights || _is_batched_fc_layer)) |
| { |
| weights_to_use = &_reshape_weights_output; |
| |
| if(transpose_weights) |
| { |
| if(_is_batched_fc_layer) |
| { |
| const float transpose_width = 16.0f / input->info()->element_size(); |
| TensorShape shape_wt(weights->info()->dimension(0) * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(weights->info()->dimension(1) / transpose_width))); |
| TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); |
| _reshape_weights_output.allocator()->init(info_wt); |
| } |
| else |
| { |
| TensorShape shape_wt(weights->info()->dimension(1), weights->info()->dimension(0)); |
| TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); |
| _reshape_weights_output.allocator()->init(info_wt); |
| } |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR_ON(!_is_batched_fc_layer); |
| |
| const float transpose_width = 16.0f / input->info()->element_size(); |
| TensorShape shape_wt(weights->info()->dimension(1) * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(weights->info()->dimension(0) / transpose_width))); |
| TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); |
| _reshape_weights_output.allocator()->init(info_wt); |
| } |
| |
| // Reshape the weights |
| _reshape_weights_kernel.configure(weights, &_reshape_weights_output, transpose_weights, _is_batched_fc_layer); |
| } |
| } |
| |
| 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)); |
| |
| if(_is_fc_after_conv) |
| { |
| // Fully Connected layer after a Convolution Layer with batches |
| configure_conv_fc_wb(input, weights_to_use, output); |
| } |
| else |
| { |
| // Fully Connected layer after a Fully Connected Layer with batches |
| configure_fc_fc_wb(input, weights_to_use, output); |
| } |
| } |
| else |
| { |
| // In case of not batched fully connected layer, the weights will not be reshaped using transposed1xW |
| _is_fc_after_conv = ((weights_to_use->info()->dimension(1)) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))); |
| |
| if(_is_fc_after_conv) |
| { |
| // Fully Connected layer after a Convolution Layer without batches |
| configure_conv_fc_nb(input, weights_to_use, output); |
| } |
| else |
| { |
| // Fully Connected layer after a Fully Connected Layer without batches |
| configure_fc_fc_nb(input, weights_to_use, output); |
| } |
| } |
| |
| // 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) |
| { |
| if(transpose_weights || _is_batched_fc_layer) |
| { |
| // Allocate the tensor for the weights reshaped |
| _reshape_weights_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(); |
| } |
| |
| // Linearize input if comes from a convolutional layer |
| if(_is_fc_after_conv) |
| { |
| 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); |
| } |
| } |