From 3ec3077b4eaedcc0c20ab5774bdbe365da541445 Mon Sep 17 00:00:00 2001
From: Mike Kelly <mike.kelly@arm.com>
Date: Wed, 8 Mar 2023 13:47:17 +0000
Subject: IVGCVSW-3808 Add ElementwiseBinaryLayer

!android-nn-driver:9329

 * Added ElementwiseBinaryLayer that can represent all ElementwiseBinary
   operations including Add, Div, Sub, Maximum, Mul and Minimum.
 * Updated Delegate to use ElementwiseBinaryLayer instead of the Add,
   Div, Sub, Maximum, Mul and Minimum layers.
 * Updated Deserializer to use ElementwiseBinaryLayer instead of the Add,
   Div, Sub, Maximum, Mul and Minimum layers.
 * Updated OnnxParser to use ElementwiseBinaryLayer instead of the Add
   layer.
 * Updated TfLiteParser to use ElementwiseBinaryLayer instead of the Add,
   Div, Sub, Maximum, Mul and Minimum layers.
 * Updated CL and Neon tests to use ElementwiseBinaryLayer.
 * Updated CL and Neon Backend Specific Optimizations to accept
   ElementBinaryLayers as well as Add, Div, Mul, Sub, Maximum and Minimum
   layers.

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I7cbb96b60eb01f0e2b57b0541016d48a08b86c75
---
 .../workloads/RefElementwiseBinaryWorkload.cpp     | 120 +++++++++++++++++++++
 1 file changed, 120 insertions(+)
 create mode 100644 src/backends/reference/workloads/RefElementwiseBinaryWorkload.cpp

(limited to 'src/backends/reference/workloads/RefElementwiseBinaryWorkload.cpp')

diff --git a/src/backends/reference/workloads/RefElementwiseBinaryWorkload.cpp b/src/backends/reference/workloads/RefElementwiseBinaryWorkload.cpp
new file mode 100644
index 0000000000..5dc77f8496
--- /dev/null
+++ b/src/backends/reference/workloads/RefElementwiseBinaryWorkload.cpp
@@ -0,0 +1,120 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "RefElementwiseBinaryWorkload.hpp"
+
+#include "Decoders.hpp"
+#include "ElementwiseFunction.hpp"
+#include "Encoders.hpp"
+#include "RefWorkloadUtils.hpp"
+#include "Maximum.hpp"
+#include "Minimum.hpp"
+
+#include <Profiling.hpp>
+
+#include <armnn/TypesUtils.hpp>
+
+#include <functional>
+
+namespace armnn
+{
+
+template<typename DataType>
+void ExecuteFunction(std::vector<ITensorHandle*> inputs,
+                     std::vector<ITensorHandle*> outputs,
+                     BinaryOperation operation)
+{
+    const TensorInfo& inputInfo0 = GetTensorInfo(inputs[0]);
+    const TensorInfo& inputInfo1 = GetTensorInfo(inputs[1]);
+    const TensorInfo& outputInfo = GetTensorInfo(outputs[0]);
+
+    const TensorShape& inShape0 = inputInfo0.GetShape();
+    const TensorShape& inShape1 = inputInfo1.GetShape();
+    const TensorShape& outShape = outputInfo.GetShape();
+
+    std::unique_ptr<Decoder<DataType>> input0 = MakeDecoder<DataType>(inputInfo0, inputs[0]->Map());
+    std::unique_ptr<Decoder<DataType>> input1 = MakeDecoder<DataType>(inputInfo1, inputs[1]->Map());
+    std::unique_ptr<Encoder<DataType>> output = MakeEncoder<DataType>(outputInfo, outputs[0]->Map());
+
+    using AddFunction     = ElementwiseBinaryFunction<std::plus<DataType>>;
+    using DivFunction     = ElementwiseBinaryFunction<std::divides<DataType>>;
+    using MaximumFunction = ElementwiseBinaryFunction<armnn::maximum<DataType>>;
+    using MinimumFunction = ElementwiseBinaryFunction<armnn::minimum<DataType>>;
+    using MulFunction     = ElementwiseBinaryFunction<std::multiplies<DataType>>;
+    using SubFunction     = ElementwiseBinaryFunction<std::minus<DataType>>;
+
+    switch (operation)
+    {
+        case BinaryOperation::Add:
+        {
+            AddFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+            break;
+        }
+        case BinaryOperation::Div:
+        {
+            DivFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+            break;
+        }
+        case BinaryOperation::Maximum:
+        {
+            MaximumFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+            break;
+        }
+        case BinaryOperation::Minimum:
+        {
+            MinimumFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+            break;
+        }
+        case BinaryOperation::Mul:
+        {
+            MulFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+            break;
+        }
+        case BinaryOperation::Sub:
+        {
+            SubFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
+            break;
+        }
+        default:
+        {
+            throw InvalidArgumentException(std::string("Unsupported binary operation ") +
+                                           GetBinaryOperationAsCString(operation), CHECK_LOCATION());
+        }
+    }
+}
+
+RefElementwiseBinaryWorkload::RefElementwiseBinaryWorkload(const ElementwiseBinaryQueueDescriptor& desc,
+                                                         const WorkloadInfo& info)
+    : RefBaseWorkload<ElementwiseBinaryQueueDescriptor>(desc, info)
+{}
+
+void RefElementwiseBinaryWorkload::Execute() const
+{
+    Execute(m_Data.m_Inputs, m_Data.m_Outputs);
+}
+
+void RefElementwiseBinaryWorkload::ExecuteAsync(ExecutionData& executionData)
+{
+
+    WorkingMemDescriptor* workingMemDescriptor = static_cast<WorkingMemDescriptor*>(executionData.m_Data);
+    Execute(workingMemDescriptor->m_Inputs, workingMemDescriptor->m_Outputs);
+}
+
+void RefElementwiseBinaryWorkload::Execute(std::vector<ITensorHandle*> inputs,
+                                           std::vector<ITensorHandle*> outputs) const
+{
+    ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefElementwiseBinaryWorkload_Execute");
+
+    if (GetTensorInfo(inputs[0]).GetDataType() == DataType::Signed32)
+    {
+        ExecuteFunction<int32_t>(inputs, outputs, m_Data.m_Parameters.m_Operation);
+    }
+    else
+    {
+        ExecuteFunction<float>(inputs, outputs, m_Data.m_Parameters.m_Operation);
+    }
+}
+
+} // namespace armnn
-- 
cgit v1.2.1