| /* |
| * 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) || (num_input_dimensions > 1 && linear_input_size == 1); |
| _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 |