IVGCVSW-7505 Create FusedLayer and NeonFusedWorkload for AddMulAdd Neon kernel

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ic778d35b001474b44fb1e433a6fe276e4ec9f565

1 files changed, 182 insertions, 0 deletions

diff --git a/src/backends/backendsCommon/test/layerTests/AddMulAddTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/AddMulAddTestImpl.hpp
new file mode 100644
index 0000000000..9dece9be3b
--- /dev/null
+++ b/src/backends/backendsCommon/test/layerTests/AddMulAddTestImpl.hpp
@@ -0,0 +1,182 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnnTestUtils/LayerTestResult.hpp>
+
+#include <armnnUtils/QuantizeHelper.hpp>
+#include <ResolveType.hpp>
+
+#include <armnn/backends/IBackendInternal.hpp>
+#include <armnn/backends/WorkloadFactory.hpp>
+
+#include <armnnTestUtils/TensorCopyUtils.hpp>
+#include <backendsCommon/test/WorkloadFactoryHelper.hpp>
+#include <armnnTestUtils/WorkloadTestUtils.hpp>
+
+#include <armnnTestUtils/TensorHelpers.hpp>
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+std::vector<LayerTestResult<T,4>> AddMulAddTest(armnn::IWorkloadFactory& workloadFactory,
+                                                const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+                                                const armnn::ITensorHandleFactory& tensorHandleFactory,
+                                                bool addOutput)
+{
+    using namespace armnn;
+    IgnoreUnused(memoryManager);
+
+    TensorInfo input0TensorInfo({ 1, 2, 2, 3 }, ArmnnType);
+    TensorInfo input1TensorInfo({ 1, 2, 2, 3 }, ArmnnType);
+    TensorInfo mulInput1TensorInfo({ 3 }, ArmnnType);
+    TensorInfo addInput1TensorInfo({ 3 }, ArmnnType);
+
+    TensorInfo output0TensorInfo({ 1, 2, 2, 3 }, ArmnnType);
+    TensorInfo output1TensorInfo({ 1, 2, 2, 3 }, ArmnnType);
+
+    if (IsQuantizedType<T>())
+    {
+        input0TensorInfo.SetQuantizationScale(0.25f);
+        input0TensorInfo.SetQuantizationOffset(128);
+        input1TensorInfo.SetQuantizationScale(0.25f);
+        input1TensorInfo.SetQuantizationOffset(128);
+        mulInput1TensorInfo.SetQuantizationScale(0.25f);
+        mulInput1TensorInfo.SetQuantizationOffset(128);
+        addInput1TensorInfo.SetQuantizationScale(0.25f);
+        addInput1TensorInfo.SetQuantizationOffset(128);
+
+        output0TensorInfo.SetQuantizationScale(0.5f);
+        output0TensorInfo.SetQuantizationOffset(120);
+        output1TensorInfo.SetQuantizationScale(0.5f);
+        output1TensorInfo.SetQuantizationOffset(120);
+    }
+
+    std::vector<float> input0Data
+    {
+         0.0f,  0.0f,  0.0f,
+         1.0f,  1.0f,  1.0f,
+        -1.0f, -1.0f, -1.0f,
+        -2.0f, -2.0f, -2.0f
+    };
+    std::vector<float> input1Data
+    {
+         0.0f,  0.0f,  0.0f,
+         1.0f,  1.0f,  1.0f,
+        -1.0f, -1.0f, -1.0f,
+        -2.0f, -2.0f, -2.0f
+    };
+    std::vector<float> mulInput1Data
+    {
+         2.0f, 1.0f, 1.0f
+    };
+    std::vector<float> addInput1Data
+    {
+         3.0f, 0.0f, 0.0f
+    };
+    std::vector<float> output0ExpectedData =
+    {
+         0.0f,  0.0f,  0.0f,
+         2.0f,  2.0f,  2.0f,
+        -2.0f, -2.0f, -2.0f,
+        -4.0f, -4.0f, -4.0f
+    };
+
+    std::vector<float> output1ExpectedData =
+    {
+         3.0f,  0.0f,  0.0f,
+         7.0f,  2.0f,  2.0f,
+        -1.0f, -2.0f, -2.0f,
+        -5.0f, -4.0f, -4.0f
+    };
+
+    std::vector<T> input0 = armnnUtils::QuantizedVector<T>(input0Data,
+                                                           input0TensorInfo.GetQuantizationScale(),
+                                                           input0TensorInfo.GetQuantizationOffset());
+
+    std::vector<T> input1 = armnnUtils::QuantizedVector<T>(input1Data,
+                                                           input1TensorInfo.GetQuantizationScale(),
+                                                           input1TensorInfo.GetQuantizationOffset());
+
+    std::vector<T> mulInput1 = armnnUtils::QuantizedVector<T>(mulInput1Data,
+                                                              mulInput1TensorInfo.GetQuantizationScale(),
+                                                              mulInput1TensorInfo.GetQuantizationOffset());
+
+    std::vector<T> addInput1 = armnnUtils::QuantizedVector<T>(addInput1Data,
+                                                              addInput1TensorInfo.GetQuantizationScale(),
+                                                              addInput1TensorInfo.GetQuantizationOffset());
+
+    std::vector<T> output0Expected = armnnUtils::QuantizedVector<T>(output0ExpectedData,
+                                                                    output0TensorInfo.GetQuantizationScale(),
+                                                                    output0TensorInfo.GetQuantizationOffset());
+
+    std::vector<T> output1Expected = armnnUtils::QuantizedVector<T>(output1ExpectedData,
+                                                                    output1TensorInfo.GetQuantizationScale(),
+                                                                    output1TensorInfo.GetQuantizationOffset());
+
+    std::vector<T> output0Actual(output0TensorInfo.GetNumElements());
+    std::vector<T> output1Actual(output1TensorInfo.GetNumElements());
+
+    std::unique_ptr<ITensorHandle> input0Handle = tensorHandleFactory.CreateTensorHandle(input0TensorInfo);
+    std::unique_ptr<ITensorHandle> input1Handle = tensorHandleFactory.CreateTensorHandle(input1TensorInfo);
+    std::unique_ptr<ITensorHandle> mulInput1Handle = tensorHandleFactory.CreateTensorHandle(mulInput1TensorInfo);
+    std::unique_ptr<ITensorHandle> addInput1Handle = tensorHandleFactory.CreateTensorHandle(addInput1TensorInfo);
+    std::unique_ptr<ITensorHandle> output0Handle = tensorHandleFactory.CreateTensorHandle(output0TensorInfo);
+    std::unique_ptr<ITensorHandle> output1Handle = tensorHandleFactory.CreateTensorHandle(output1TensorInfo);
+
+    uint32_t numOutputs = addOutput ? 2 : 1;
+    FusedDescriptor descriptor(4, numOutputs, FusedKernelType::AddMulAdd);
+    FusedQueueDescriptor fusedQueueDescriptor;
+    fusedQueueDescriptor.m_Parameters = descriptor;
+    WorkloadInfo info;
+    AddInputToWorkload (fusedQueueDescriptor, info, input0TensorInfo, input0Handle.get());
+    AddInputToWorkload (fusedQueueDescriptor, info, input1TensorInfo, input1Handle.get());
+    AddInputToWorkload (fusedQueueDescriptor, info, mulInput1TensorInfo, mulInput1Handle.get());
+    AddInputToWorkload (fusedQueueDescriptor, info, addInput1TensorInfo, addInput1Handle.get());
+    if (addOutput)
+    {
+        AddOutputToWorkload(fusedQueueDescriptor, info, output0TensorInfo, output0Handle.get());
+    }
+    AddOutputToWorkload(fusedQueueDescriptor, info, output1TensorInfo, output1Handle.get());
+
+    std::unique_ptr<IWorkload> workload = workloadFactory.CreateWorkload(LayerType::Fused,
+                                                                         fusedQueueDescriptor,
+                                                                         info);
+
+    input0Handle->Allocate();
+    input1Handle->Allocate();
+    mulInput1Handle->Allocate();
+    addInput1Handle->Allocate();
+    if (addOutput)
+    {
+        output0Handle->Allocate();
+    }
+    output1Handle->Allocate();
+
+    CopyDataToITensorHandle(input0Handle.get(), input0.data());
+    CopyDataToITensorHandle(input1Handle.get(), input1.data());
+    CopyDataToITensorHandle(mulInput1Handle.get(), mulInput1.data());
+    CopyDataToITensorHandle(addInput1Handle.get(), addInput1.data());
+
+    workload->Execute();
+
+    CopyDataFromITensorHandle(output1Actual.data(), output1Handle.get());
+    LayerTestResult<T,4> ret1(output1Actual,
+                              output1Expected,
+                              output1Handle->GetShape(),
+                              output1TensorInfo.GetShape());
+
+    std::vector<LayerTestResult<T,4>> ret = {ret1};
+
+    if (addOutput)
+    {
+        CopyDataFromITensorHandle(output0Actual.data(), output0Handle.get());
+        LayerTestResult<T,4> ret0(output0Actual,
+                                  output0Expected,
+                                  output0Handle->GetShape(),
+                                  output0TensorInfo.GetShape());
+        ret = {ret0, ret1};
+    }
+    return ret;
+}