arm_compute v17.09
Change-Id: I4bf8f4e6e5f84ce0d5b6f5ba570d276879f42a81
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 57d57d5..ee1558f 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -23,88 +23,31 @@
*/
#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
+#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>
using namespace arm_compute;
-CLFullyConnectedLayerReshapeWeights::CLFullyConnectedLayerReshapeWeights()
- : _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
+void CLFullyConnectedLayerReshapeWeights::configure(const ICLTensor *input, ICLTensor *output)
+{
+ auto k = arm_compute::support::cpp14::make_unique<CLTransposeKernel>();
+ k->configure(input, output);
+ _kernel = std::move(k);
+}
+
+CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _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 CLFullyConnectedLayerReshapeWeights::configure(const ICLTensor *input, ICLTensor *output, bool transpose_weights, bool is_batched_fc_layer)
+void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
{
- 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 CLFullyConnectedLayerReshapeWeights::run()
-{
- if(_transpose_weights)
- {
- CLScheduler::get().enqueue(_transpose_kernel, _is_batched_fc_layer);
- }
- if(_is_batched_fc_layer)
- {
- CLScheduler::get().enqueue(_transpose1xW_kernel);
- }
-}
-
-CLFullyConnectedLayer::CLFullyConnectedLayer()
- : _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(), _reshape_weights_output(),
- _are_weights_reshaped(true), _is_fc_after_conv(true), _is_batched_fc_layer(false), _accumulate_biases(false)
-{
-}
-
-void CLFullyConnectedLayer::configure_conv_fc_wb(const ICLTensor *input, const ICLTensor *weights, ICLTensor *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())));
+ 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();
@@ -119,93 +62,33 @@
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);
+ _memory_group.manage(&_im2col_output);
+ _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
// 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 CLFullyConnectedLayer::configure_fc_fc_wb(const ICLTensor *input, const ICLTensor *weights, ICLTensor *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 CLFullyConnectedLayer::configure_conv_fc_nb(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, 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);
+ _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_nb(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
+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);
+ _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::F32);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
+ 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);
- const DataType dt = input->info()->data_type();
- const int fixed_point_position = input->info()->fixed_point_position();
-
- _are_weights_reshaped = are_weights_reshaped;
+ _are_weights_reshaped = transpose_weights ? are_weights_reshaped : true;
_is_fc_after_conv = true;
- _is_batched_fc_layer = false;
_accumulate_biases = false;
if(biases != nullptr)
@@ -224,90 +107,46 @@
// 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 ICLTensor *weights_to_use = weights;
- if(!are_weights_reshaped)
+ if(!_are_weights_reshaped)
{
- if((transpose_weights || _is_batched_fc_layer))
- {
- weights_to_use = &_reshape_weights_output;
+ 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);
- }
+ // Reshape the weights
+ _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
}
- 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)
{
_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)));
+ _is_fc_after_conv = input->info()->num_dimensions() > 1;
+ }
- 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);
- }
+ if(_is_fc_after_conv)
+ {
+ // Fully Connected layer after a Convolution Layer without batches
+ configure_conv_fc(input, weights_to_use, output);
+ }
+ else
+ {
+ // 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)
+ if(!_are_weights_reshaped)
{
- if(transpose_weights || _is_batched_fc_layer)
- {
- // Allocate the tensor for the weights reshaped
- _reshape_weights_output.allocator()->allocate();
- }
+ // Allocate the tensor for the weights reshaped
+ _reshape_weights_output.allocator()->allocate();
}
}
@@ -320,18 +159,14 @@
_reshape_weights_kernel.run();
}
+ _memory_group.acquire();
+
// Linearize input if it comes from a convolutional layer
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);
@@ -340,4 +175,6 @@
{
CLScheduler::get().enqueue(_accumulate_biases_kernel);
}
+
+ _memory_group.release();
}