IVGCVSW-2861 Refactor the Reference Elementwise workloads

 * Refactored Elementwise Workload into the single workload.
 * Execute() function will react based on the DataType.

Change-Id: I6d4d6a74cec150ed8cb252e70b629ed968e7093d
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>

1 files changed, 105 insertions, 56 deletions

diff --git a/src/backends/reference/workloads/RefElementwiseWorkload.cpp b/src/backends/reference/workloads/RefElementwiseWorkload.cpp
index c9b93c8524..356d7a0c16 100644
--- a/src/backends/reference/workloads/RefElementwiseWorkload.cpp
+++ b/src/backends/reference/workloads/RefElementwiseWorkload.cpp
@@ -7,69 +7,118 @@
 #include "ElementwiseFunction.hpp"
 #include "RefWorkloadUtils.hpp"
 #include "Profiling.hpp"
+#include "StringMapping.hpp"
+#include "TypeUtils.hpp"
 #include <vector>
 
 namespace armnn
 {
 
-template <typename ParentDescriptor, typename Functor>
-void BaseFloat32ElementwiseWorkload<ParentDescriptor, Functor>::ExecuteImpl(const char * debugString) const
+template <typename Functor,
+          typename armnn::DataType DataType,
+          typename ParentDescriptor,
+          typename armnn::StringMapping::Id DebugString>
+void RefElementwiseWorkload<Functor, DataType, ParentDescriptor, DebugString>::Execute() const
 {
-    ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, debugString);
-
-    auto data = Float32Workload<ParentDescriptor>::GetData();
-    const TensorShape& inShape0 = GetTensorInfo(data.m_Inputs[0]).GetShape();
-    const TensorShape& inShape1 = GetTensorInfo(data.m_Inputs[1]).GetShape();
-    const TensorShape& outShape = GetTensorInfo(data.m_Outputs[0]).GetShape();
-
-    const float* inData0 = GetInputTensorDataFloat(0, data);
-    const float* inData1 = GetInputTensorDataFloat(1, data);
-    float* outData = GetOutputTensorDataFloat(0, data);
-
-    ElementwiseFunction<Functor, float, float>(inShape0, inShape1, outShape, inData0, inData1, outData);
-}
-
-template <typename ParentDescriptor, typename Functor>
-void BaseUint8ElementwiseWorkload<ParentDescriptor, Functor>::ExecuteImpl(const char * debugString) const
-{
-    ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, debugString);
-
-    auto data = Uint8Workload<ParentDescriptor>::GetData();
-    const TensorInfo& inputInfo0 = GetTensorInfo(data.m_Inputs[0]);
-    const TensorInfo& inputInfo1 = GetTensorInfo(data.m_Inputs[1]);
-    const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[0]);
-
-    auto dequant0 = Dequantize(GetInputTensorDataU8(0, data), inputInfo0);
-    auto dequant1 = Dequantize(GetInputTensorDataU8(1, data), inputInfo1);
-
-    std::vector<float> results(outputInfo.GetNumElements());
-
-    ElementwiseFunction<Functor, float, float>(inputInfo0.GetShape(),
-                                               inputInfo1.GetShape(),
-                                               outputInfo.GetShape(),
-                                               dequant0.data(),
-                                               dequant1.data(),
-                                               results.data());
-
-    Quantize(GetOutputTensorDataU8(0, data), results.data(), outputInfo);
+    ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, StringMapping::Instance().Get(DebugString));
+
+    const TensorInfo& inputInfo0 = GetTensorInfo(m_Data.m_Inputs[0]);
+    const TensorInfo& inputInfo1 = GetTensorInfo(m_Data.m_Inputs[1]);
+    const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]);
+
+    const TensorShape& inShape0 = inputInfo0.GetShape();
+    const TensorShape& inShape1 = inputInfo1.GetShape();
+    const TensorShape& outShape = outputInfo.GetShape();
+
+    switch(DataType)
+    {
+        case armnn::DataType::QuantisedAsymm8:
+        {
+            std::vector<float> results(outputInfo.GetNumElements());
+            ElementwiseFunction<Functor, float, float>(inShape0,
+                                                       inShape1,
+                                                       outShape,
+                                                       Dequantize(GetInputTensorDataU8(0, m_Data), inputInfo0).data(),
+                                                       Dequantize(GetInputTensorDataU8(1, m_Data), inputInfo1).data(),
+                                                       results.data());
+            Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), outputInfo);
+            break;
+        }
+        case armnn::DataType::Float32:
+        {
+            ElementwiseFunction<Functor, float, float>(inShape0,
+                                                       inShape1,
+                                                       outShape,
+                                                       GetInputTensorDataFloat(0, m_Data),
+                                                       GetInputTensorDataFloat(1, m_Data),
+                                                       GetOutputTensorDataFloat(0, m_Data));
+            break;
+        }
+        default:
+            BOOST_ASSERT_MSG(false, "Unknown Data Type!");
+            break;
+    }
 }
 
 }
 
-template class armnn::BaseFloat32ElementwiseWorkload<armnn::AdditionQueueDescriptor, std::plus<float>>;
-template class armnn::BaseUint8ElementwiseWorkload<armnn::AdditionQueueDescriptor, std::plus<float>>;
-
-template class armnn::BaseFloat32ElementwiseWorkload<armnn::SubtractionQueueDescriptor, std::minus<float>>;
-template class armnn::BaseUint8ElementwiseWorkload<armnn::SubtractionQueueDescriptor, std::minus<float>>;
-
-template class armnn::BaseFloat32ElementwiseWorkload<armnn::MultiplicationQueueDescriptor, std::multiplies<float>>;
-template class armnn::BaseUint8ElementwiseWorkload<armnn::MultiplicationQueueDescriptor, std::multiplies<float>>;
-
-template class armnn::BaseFloat32ElementwiseWorkload<armnn::DivisionQueueDescriptor, std::divides<float>>;
-template class armnn::BaseUint8ElementwiseWorkload<armnn::DivisionQueueDescriptor, std::divides<float>>;
-
-template class armnn::BaseFloat32ElementwiseWorkload<armnn::MaximumQueueDescriptor, armnn::maximum<float>>;
-template class armnn::BaseUint8ElementwiseWorkload<armnn::MaximumQueueDescriptor, armnn::maximum<float>>;
-
-template class armnn::BaseFloat32ElementwiseWorkload<armnn::MinimumQueueDescriptor, armnn::minimum<float>>;
-template class armnn::BaseUint8ElementwiseWorkload<armnn::MinimumQueueDescriptor, armnn::minimum<float>>;
+template class armnn::RefElementwiseWorkload<std::plus<float>,
+    armnn::DataType::Float32,
+    armnn::AdditionQueueDescriptor,
+    armnn::StringMapping::RefAdditionWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<std::plus<float>,
+    armnn::DataType::QuantisedAsymm8,
+    armnn::AdditionQueueDescriptor,
+    armnn::StringMapping::RefAdditionWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<std::minus<float>,
+    armnn::DataType::Float32,
+    armnn::SubtractionQueueDescriptor,
+    armnn::StringMapping::RefSubtractionWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<std::minus<float>,
+    armnn::DataType::QuantisedAsymm8,
+    armnn::SubtractionQueueDescriptor,
+    armnn::StringMapping::RefSubtractionWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<std::multiplies<float>,
+    armnn::DataType::Float32,
+    armnn::MultiplicationQueueDescriptor,
+    armnn::StringMapping::RefMultiplicationWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<std::multiplies<float>,
+    armnn::DataType::QuantisedAsymm8,
+    armnn::MultiplicationQueueDescriptor,
+    armnn::StringMapping::RefMultiplicationWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<std::divides<float>,
+    armnn::DataType::Float32,
+    armnn::DivisionQueueDescriptor,
+    armnn::StringMapping::RefDivisionWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<std::divides<float>,
+    armnn::DataType::QuantisedAsymm8,
+    armnn::DivisionQueueDescriptor,
+    armnn::StringMapping::RefDivisionWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<armnn::maximum<float>,
+    armnn::DataType::Float32,
+    armnn::MaximumQueueDescriptor,
+    armnn::StringMapping::RefMaximumWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<armnn::maximum<float>,
+    armnn::DataType::QuantisedAsymm8,
+    armnn::MaximumQueueDescriptor,
+    armnn::StringMapping::RefMaximumWorkload_Execute>;
+
+
+template class armnn::RefElementwiseWorkload<armnn::minimum<float>,
+    armnn::DataType::Float32,
+    armnn::MinimumQueueDescriptor,
+    armnn::StringMapping::RefMinimumWorkload_Execute>;
+
+template class armnn::RefElementwiseWorkload<armnn::minimum<float>,
+    armnn::DataType::QuantisedAsymm8,
+    armnn::MinimumQueueDescriptor,
+    armnn::StringMapping::RefMinimumWorkload_Execute>;