COMPMID-344 Updated doxygen
Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae
diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
new file mode 100644
index 0000000..abb41e9
--- /dev/null
+++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
@@ -0,0 +1,344 @@
+/*
+ * 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);
+ }
+}