COMPMID-477 - Optimized batched case in CLConvolutionLayer
Change-Id: I4ef18f49f1da0cb816aaa0762466b940792c15ed
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/84162
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 66a858d..f7cea55 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -26,217 +26,127 @@
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "support/ToolchainSupport.h"
#include <algorithm>
-#include <cmath>
-namespace arm_compute
+using namespace arm_compute;
+
+void CLFullyConnectedLayerReshapeWeights::configure(const ICLTensor *input, ICLTensor *output)
{
-CLFullyConnectedLayerReshapeWeights::CLFullyConnectedLayerReshapeWeights()
- : _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
-{
-}
-
-void CLFullyConnectedLayerReshapeWeights::configure(const ICLTensor *input, ICLTensor *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));
- _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 CLFullyConnectedLayerReshapeWeights::run()
-{
- if(_transpose_weights)
- {
- CLScheduler::get().enqueue(_transpose_kernel, _is_batched_fc_layer);
- }
-
- if(_is_batched_fc_layer)
- {
- CLScheduler::get().enqueue(_transpose1xW_kernel);
- }
+ auto k = arm_compute::support::cpp14::make_unique<CLTransposeKernel>();
+ k->configure(input, output);
+ _kernel = std::move(k);
}
CLFullyConnectedLayer::CLFullyConnectedLayer()
- : _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)
+ : _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _reshape_weights_output(), _are_weights_reshaped(true), _is_fc_after_conv(true),
+ _accumulate_biases(false)
{
}
+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))));
+
+ 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));
+
+ // Configure im2col kernel
+ _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
+
+ // Configure matrix multiply kernel
+ _mm_kernel.configure(&_im2col_output, weights, output, 1.0f, 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
+ _mm_kernel.configure(input, weights, output, 1.0f, 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::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;
+
+ 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
- // 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)
+ const ICLTensor *weights_to_use = weights;
- 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 ICLTensor *weights_to_use = weights;
-
- if(!are_weights_reshaped && (transpose_weights || _is_batched_fc_layer))
+ if(!_are_weights_reshaped)
{
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);
+ _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
}
- // Check correct shape of weights
- if(_is_batched_fc_layer)
+ // 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)
{
- // 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)));
+ _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
{
- // 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);
+ _is_fc_after_conv = input->info()->num_dimensions() > 1;
}
- const ICLTensor *multiply_input = input;
-
- if(_linearize_input)
+ if(_is_fc_after_conv)
{
- 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
- _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
-
- multiply_input = &_im2col_output;
+ // Fully Connected layer after a Convolution Layer without batches
+ configure_conv_fc(input, weights_to_use, output);
}
-
- if(_is_batched_fc_layer)
+ else
{
- 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
- _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);
+ // Fully Connected layer after a Fully Connected Layer without batches
+ configure_fc_fc(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 && (transpose_weights || _is_batched_fc_layer))
+ if(!_are_weights_reshaped)
{
// 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 CLFullyConnectedLayer::run()
@@ -249,17 +159,11 @@
}
// Linearize input if it comes from a convolutional layer
- if(_linearize_input)
+ if(_is_fc_after_conv)
{
CLScheduler::get().enqueue(_im2col_kernel, false);
}
- // Interleave input
- if(_is_batched_fc_layer)
- {
- CLScheduler::get().enqueue(_interleave4x4_kernel, false);
- }
-
// Run matrix multiply
CLScheduler::get().enqueue(_mm_kernel, !_accumulate_biases);
@@ -269,4 +173,3 @@
CLScheduler::get().enqueue(_accumulate_biases_kernel);
}
}
-} // namespace arm_compute