Refactor: Don't include all ComputeLibrary function definitions everywhere.
Just include the function definition that is specifically needed for each workload.
Also, tighten up the scope where Compute Library functions are available.
Knocks about 30seconds off a 4m30s single-threaded compile of the Neon workloads.
Change-Id: Idac438f3bc77ff978295fbc9505cb42447def145
diff --git a/src/backends/neon/workloads/NeonFullyConnectedWorkload.cpp b/src/backends/neon/workloads/NeonFullyConnectedWorkload.cpp
index e432a6b..7395270 100644
--- a/src/backends/neon/workloads/NeonFullyConnectedWorkload.cpp
+++ b/src/backends/neon/workloads/NeonFullyConnectedWorkload.cpp
@@ -5,10 +5,13 @@
#include "NeonFullyConnectedWorkload.hpp"
+#include "NeonWorkloadUtils.hpp"
#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <aclCommon/ArmComputeUtils.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
+#include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h>
+
namespace armnn
{
using namespace armcomputetensorutils;
@@ -45,7 +48,6 @@
NeonFullyConnectedWorkload::NeonFullyConnectedWorkload(const FullyConnectedQueueDescriptor& descriptor,
const WorkloadInfo& info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
: BaseWorkload<FullyConnectedQueueDescriptor>(descriptor, info)
- , m_FullyConnectedLayer(memoryManager)
{
m_Data.ValidateInputsOutputs("NeonFullyConnectedWorkload", 1, 1);
@@ -64,7 +66,10 @@
// Construct
arm_compute::FullyConnectedLayerInfo fc_info;
fc_info.transpose_weights = m_Data.m_Parameters.m_TransposeWeightMatrix;
- m_FullyConnectedLayer.configure(&input, m_WeightsTensor.get(), m_BiasesTensor.get(), &output, fc_info);
+
+ auto layer = std::make_unique<arm_compute::NEFullyConnectedLayer>(memoryManager);
+ layer->configure(&input, m_WeightsTensor.get(), m_BiasesTensor.get(), &output, fc_info);
+ m_FullyConnectedLayer.reset(layer.release());
// Allocate
if (m_Data.m_Weight->GetTensorInfo().GetDataType() == DataType::QuantisedAsymm8)
@@ -90,14 +95,14 @@
// Force Compute Library to perform the necessary copying and reshaping, after which
// delete all the input tensors that will no longer be needed
- m_FullyConnectedLayer.prepare();
+ m_FullyConnectedLayer->prepare();
FreeUnusedTensors();
}
void NeonFullyConnectedWorkload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonFullyConnectedWorkload_Execute");
- m_FullyConnectedLayer.run();
+ m_FullyConnectedLayer->run();
}
void NeonFullyConnectedWorkload::FreeUnusedTensors()