IVGCVSW-3381 Break up LayerTests.hpp into more manageable files

Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Icf39434f09fd340ad664cb3b97b8bee6d9da4838

1 files changed, 682 insertions, 0 deletions

diff --git a/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp
new file mode 100644
index 0000000000..49184edde9
--- /dev/null
+++ b/src/backends/backendsCommon/test/layerTests/SoftmaxTestImpl.cpp
@@ -0,0 +1,682 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "SoftmaxTestImpl.hpp"
+
+#include <ResolveType.hpp>
+
+#include <armnn/ArmNN.hpp>
+
+#include <backendsCommon/CpuTensorHandle.hpp>
+
+#include <backendsCommon/test/QuantizeHelper.hpp>
+#include <backendsCommon/test/TensorCopyUtils.hpp>
+#include <backendsCommon/test/WorkloadTestUtils.hpp>
+
+#include <test/TensorHelpers.hpp>
+
+#include <algorithm>
+
+namespace
+{
+
+struct Simple3dSoftmaxOutputData
+{
+    const std::vector<float> outputData =
+    {
+        0.0964599f, 0.26220518f, 0.0964599f, 0.0964599f,
+        0.15903549f, 0.0964599f, 0.0964599f, 0.0964599f
+    };
+
+    const armnn::TensorShape inputShape{ 1, 8, 1 };
+
+    const std::vector<float> inputData =
+    {
+        0.0f, 1.0f, 0.0f, 0.0f,
+        0.5f, 0.0f, 0.0f, 0.0f,
+    };
+};
+
+struct Simple4dSoftmaxData
+{
+    const armnn::TensorShape inputShape{ 1, 8, 1, 1 };
+
+    const std::vector<float> outputData =
+    {
+        0.0964599f, 0.26220518f, 0.0964599f, 0.0964599f,
+        0.15903549f, 0.0964599f, 0.0964599f, 0.0964599f
+    };
+
+    const std::vector<float> inputData =
+    {
+         0.0f, 1.0f, 0.0f, 0.0f,
+         0.5f, 0.0f, 0.0f, 0.0f
+    };
+};
+
+template<armnn::DataType ArmnnType, std::size_t n, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, n> SimpleSoftmaxBaseTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float beta,
+    const armnn::TensorShape& inputShape,
+    const std::vector<float>& outputData,
+    const std::vector<float>& inputData,
+    int axis = 1)
+{
+    using std::exp;
+
+    const float qScale = 1.f / 256.f;
+    const int qOffset = 0;
+
+    armnn::TensorInfo inputTensorInfo;
+    armnn::TensorInfo outputTensorInfo;
+
+    inputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType);
+    inputTensorInfo.SetQuantizationScale(qScale);
+    inputTensorInfo.SetQuantizationOffset(qOffset);
+
+    outputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType);
+    outputTensorInfo.SetQuantizationScale(qScale);
+    outputTensorInfo.SetQuantizationOffset(qOffset);
+
+    LayerTestResult<T, n> ret(outputTensorInfo);
+
+    // Each row is independently softmax'd.
+    auto input = MakeTensor<T, n>(inputTensorInfo, std::vector<T>(
+        QuantizedVector<T>(qScale, qOffset, inputData)));
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    armnn::SoftmaxQueueDescriptor data;
+    data.m_Parameters.m_Beta = beta;
+    data.m_Parameters.m_Axis = axis;
+
+    armnn::WorkloadInfo info;
+    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSoftmax(data, info);
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+    CopyDataToITensorHandle(inputHandle.get(), input.origin());
+
+    BOOST_ASSERT(workload);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get());
+
+    std::vector<T> expectedOutput = std::vector<T>(
+            QuantizedVector<T>(qScale, qOffset, outputData));
+    ret.outputExpected = MakeTensor<T, n>(outputTensorInfo, expectedOutput);
+
+    return ret;
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 2> SimpleSoftmaxTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float beta)
+{
+    using std::exp;
+    const armnn::TensorShape inputShape{ 2, 4 };
+
+    float x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta),
+                    exp((0.0f - 1.0f) * beta), exp((0.0f - 1.0f) * beta) };
+    float sum0 = x0[0] + x0[1] + x0[2] + x0[3];
+    float x1[4] = { exp((0.5f - 0.5f) * beta), exp((0.0f - 0.5f) * beta),
+                    exp((0.0f - 0.5f) * beta), exp((0.0f - 0.5f) * beta) };
+    float sum1 = x1[0] + x1[1] + x1[2] + x1[3];
+
+    const std::vector<float> outputData = { x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0,
+                                            x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1 };
+
+    const std::vector<float> inputData =
+            {
+                0.f, 1.f, 0.f, 0.f,
+                .5f, 0.f, 0.f, 0.f,
+            };
+
+    return SimpleSoftmaxBaseTestImpl<ArmnnType, 2>(workloadFactory, memoryManager, beta,
+                                                   inputShape, outputData, inputData);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 2> SimpleSoftmaxTestImpl(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta,
+        int axis)
+{
+    armnn::TensorShape inputShape;
+    std::vector<float> inputData;
+    std::vector<float> outputData;
+    switch (axis)
+    {
+    case -2:
+    case 0:
+        {
+        inputShape = {5, 2};
+
+        inputData =
+                {
+                        17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f
+                };
+
+        outputData =
+                {
+                        0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,
+                        0.087144312427294f,
+                        0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,
+                        7.246299848982885e-08f
+                };
+        break;
+        }
+    case -1:
+    case 1:
+        {
+        inputShape = {2, 5};
+
+        inputData =
+                {
+                        17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f
+                };
+
+        outputData =
+                {
+                        0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                        7.246299848982885e-08f,
+                        0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                        7.246299848982885e-08f
+                };
+        break;
+        }
+    }
+    return SimpleSoftmaxBaseTestImpl<ArmnnType, 2>(workloadFactory, memoryManager, beta,
+                                                   inputShape, outputData, inputData, axis);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 3> Simple3dSoftmaxTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float beta,
+    const armnn::TensorShape& inputShape,
+    const std::vector<float>& outputData,
+    const std::vector<float>& inputData,
+    int axis = 1)
+{
+    return SimpleSoftmaxBaseTestImpl<ArmnnType, 3>(workloadFactory, memoryManager, beta,
+                                                   inputShape, outputData, inputData, axis);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> Simple4dSoftmaxTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float beta,
+    const armnn::TensorShape& inputShape,
+    const std::vector<float>& outputData,
+    const std::vector<float>& inputData,
+    int axis = 1)
+{
+
+    return SimpleSoftmaxBaseTestImpl<ArmnnType, 4>(workloadFactory, memoryManager, beta,
+                                                   inputShape, outputData, inputData, axis);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 2> CompareSoftmaxTestImpl(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        armnn::IWorkloadFactory& refWorkloadFactory,
+        float beta)
+{
+
+    const int batchSize = 20;
+    const int channels = 30;
+
+    armnn::TensorInfo inputTensorInfo;
+    armnn::TensorInfo outputTensorInfo;
+
+    unsigned int inputShape[] = { batchSize, channels };
+
+    inputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType);
+    outputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType);
+    float qScale = 1.f / 256.f;
+    int qOffset = 0;
+    inputTensorInfo.SetQuantizationScale(qScale);
+    inputTensorInfo.SetQuantizationOffset(qOffset);
+    outputTensorInfo.SetQuantizationScale(qScale);
+    outputTensorInfo.SetQuantizationOffset(qOffset);
+
+
+    LayerTestResult<T, 2> ret(outputTensorInfo);
+    auto input = MakeRandomTensor<T, 2>(inputTensorInfo, 0xF00D, 0.0f, 1.0f);
+
+    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
+
+    armnn::SoftmaxQueueDescriptor data;
+    data.m_Parameters.m_Beta = beta;
+
+    armnn::WorkloadInfo info;
+    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
+    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
+
+    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
+    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
+
+
+    armnn::SoftmaxQueueDescriptor refData = data;
+    armnn::WorkloadInfo refInfo = info;
+    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
+    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSoftmax(data, info);
+    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateSoftmax(refData, refInfo);
+
+    outputHandleRef->Allocate();
+    inputHandleRef->Allocate();
+
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0]);
+    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0]);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    workloadRef->Execute();
+
+    CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+    CopyDataFromITensorHandle(&ret.outputExpected[0][0], outputHandleRef.get());
+
+    return ret;
+}
+
+} // anonymous namespace
+
+LayerTestResult<float,2> SimpleSoftmaxTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float beta)
+{
+    return SimpleSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta);
+}
+
+LayerTestResult<float,2> SimpleAxisSoftmaxTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta,
+        int axis)
+{
+    return SimpleSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta, axis);
+}
+
+LayerTestResult<float,3> Simple3dSoftmaxTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta)
+{
+    Simple3dSoftmaxOutputData data;
+    return Simple3dSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta,
+                                                             data.inputShape, data.outputData, data.inputData);
+}
+
+LayerTestResult<float,3> Simple3dAxisSoftmaxTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta,
+        int axis)
+{
+    armnn::TensorShape inputShape;
+    std::vector<float> inputData;
+    std::vector<float> outputData;
+    switch (axis)
+    {
+    case -3:
+    case 0:
+        {
+            inputShape = {5, 2, 2};
+
+            inputData =
+                    {
+                            17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f,
+
+                            15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f
+                    };
+
+            outputData =
+                    {
+                            0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f,
+                            0.236882800924671f,
+                            0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.087144312427294f,
+                            0.087144312427294f,
+
+                            0.087144312427294f, 0.087144312427294f, 0.032058600957022f, 0.032058600957022f,
+                            0.032058600957022f,
+                            0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f,
+                            7.246299848982885e-08f
+                    };
+            break;
+        }
+    case -2:
+    case 1:
+        {
+            inputShape = {2, 5, 2};
+
+            inputData =
+                    {
+                            17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f,
+
+                            17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f
+                    };
+
+            outputData =
+                    {
+                            0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,
+                            0.087144312427294f,
+                            0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,
+                            7.246299848982885e-08f,
+
+                            0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,
+                            0.087144312427294f,
+                            0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,
+                            7.246299848982885e-08f
+                    };
+        break;
+        }
+    case -1:
+    case 2:
+        {
+            inputShape = {2, 2, 5};
+
+            inputData =
+                    {
+                            17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f,
+                            17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f
+                    };
+
+            outputData =
+                    {
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f
+                    };
+            break;
+        }
+    }
+
+    return Simple3dSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta,
+                                                             inputShape, outputData, inputData, axis);
+}
+
+LayerTestResult<float,4> Simple4dSoftmaxTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta)
+{
+    Simple4dSoftmaxData data;
+    return Simple4dSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta, data.inputShape,
+                                                             data.outputData, data.inputData);
+}
+
+LayerTestResult<float,4> Simple4dAxisSoftmaxTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta,
+        int axis)
+{
+    armnn::TensorShape inputShape;
+    std::vector<float> inputData;
+    std::vector<float> outputData;
+    switch (axis)
+    {
+    case -4:
+    case 0:
+        {
+            inputShape = {5, 2, 2, 2};
+
+            inputData =
+                    {
+                            17.0f, -1.0f, 17.0f, -1.0f, 17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f,
+                            16.0f, -2.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f, 15.0f, -3.0f,
+                            15.0f, -3.0f, 15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 14.0f, -4.0f,
+                            14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f, 1.0f, -17.0f, 1.0f, -17.0f
+                    };
+
+            outputData =
+                    {
+                            0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f,
+                            0.643914213228014f,
+                            0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.236882800924671f,
+                            0.236882800924671f,
+                            0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.236882800924671f,
+                            0.236882800924671f,
+                            0.236882800924671f, 0.087144312427294f, 0.087144312427294f, 0.087144312427294f,
+                            0.087144312427294f,
+
+                            0.087144312427294f, 0.087144312427294f, 0.087144312427294f, 0.087144312427294f,
+                            0.032058600957022f,
+                            0.032058600957022f, 0.032058600957022f, 0.032058600957022f, 0.032058600957022f,
+                            0.032058600957022f,
+                            0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f,
+                            7.246299848982885e-08f,
+                            7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f,
+                            7.246299848982885e-08f, 7.246299848982885e-08f
+                    };
+            break;
+        }
+    case -3:
+    case 1:
+        {
+            inputShape = {2, 5, 2, 2};
+
+            inputData =
+                    {
+                            17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f,
+                            15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f,
+                            17.0f, -1.0f, 17.0f, -1.0f, 16.0f, -2.0f, 16.0f, -2.0f, 15.0f, -3.0f,
+                            15.0f, -3.0f, 14.0f, -4.0f, 14.0f, -4.0f, 1.0f, -17.0f, 1.0f, -17.0f
+                    };
+
+            outputData =
+                    {
+                            0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f,
+                            0.236882800924671f,
+                            0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.087144312427294f,
+                            0.087144312427294f,
+                            0.087144312427294f, 0.087144312427294f, 0.032058600957022f, 0.032058600957022f,
+                            0.032058600957022f,
+                            0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f,
+                            7.246299848982885e-08f,
+
+
+                            0.643914213228014f, 0.643914213228014f, 0.643914213228014f, 0.643914213228014f,
+                            0.236882800924671f,
+                            0.236882800924671f, 0.236882800924671f, 0.236882800924671f, 0.087144312427294f,
+                            0.087144312427294f,
+                            0.087144312427294f, 0.087144312427294f, 0.032058600957022f, 0.032058600957022f,
+                            0.032058600957022f,
+                            0.032058600957022f, 7.246299848982885e-08f, 7.246299848982885e-08f, 7.246299848982885e-08f,
+                            7.246299848982885e-08f
+                    };
+            break;
+        }
+    case -2:
+    case 2:
+        {
+        inputShape = {2, 2, 5, 2};
+
+        inputData =
+                {
+                        17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f,
+                        17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f,
+                        17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f,
+                        17.0f, -1.0f, 16.0f, -2.0f, 15.0f, -3.0f, 14.0f, -4.0f, 1.0f, -17.0f
+                };
+
+        outputData =
+                {
+                        0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,
+                        0.087144312427294f,
+                        0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,
+                        7.246299848982885e-08f,
+                        0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,
+                        0.087144312427294f,
+                        0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,
+                        7.246299848982885e-08f,
+
+                        0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,
+                        0.087144312427294f,
+                        0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,
+                        7.246299848982885e-08f,
+                        0.643914213228014f, 0.643914213228014f, 0.236882800924671f, 0.236882800924671f,
+                        0.087144312427294f,
+                        0.087144312427294f, 0.032058600957022f, 0.032058600957022f, 7.246299848982885e-08f,
+                        7.246299848982885e-08f
+                };
+        break;
+        }
+    case -1:
+    case 3:
+        {
+            inputShape = {2, 2, 2, 5};
+
+            inputData =
+                    {
+                            17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f,
+                            17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f,
+                            17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f,
+                            17.0f, 16.0f, 15.0f, 14.0f, 1.0f, -1.0f, -2.0f, -3.0f, -4.0f, -17.0f
+                    };
+
+            outputData =
+                    {
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f,
+                            0.643914213228014f, 0.236882800924671f, 0.087144312427294f, 0.032058600957022f,
+                            7.246299848982885e-08f
+                    };
+            break;
+        }
+    }
+
+    return Simple4dSoftmaxTestImpl<armnn::DataType::Float32>(
+        workloadFactory,
+        memoryManager,
+        beta,
+        inputShape,
+        outputData,
+        inputData,
+        axis);
+}
+
+LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float beta)
+{
+    return SimpleSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, beta);
+}
+
+LayerTestResult<uint8_t,3> Simple3dSoftmaxUint8Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta)
+{
+    Simple3dSoftmaxOutputData data;
+    return Simple3dSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(
+        workloadFactory,
+        memoryManager,
+        beta,
+        data.inputShape,
+        data.outputData,
+        data.inputData);
+}
+
+LayerTestResult<uint8_t,4> Simple4dSoftmaxUint8Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta)
+{
+    Simple4dSoftmaxData data;
+
+    return Simple4dSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, beta,
+                                                                     data.inputShape, data.outputData, data.inputData);
+}
+
+LayerTestResult<int16_t,2> SimpleSoftmaxUint16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta)
+{
+    return SimpleSoftmaxTestImpl<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, beta);
+}
+
+LayerTestResult<int16_t,3> Simple3dSoftmaxUint16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta)
+{
+    Simple3dSoftmaxOutputData data;
+    return Simple3dSoftmaxTestImpl<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, beta,
+                                                                     data.inputShape, data.outputData, data.inputData);
+}
+
+LayerTestResult<int16_t,4> Simple4dSoftmaxUint16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float beta)
+{
+    Simple4dSoftmaxData data;
+
+    return Simple4dSoftmaxTestImpl<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, beta,
+                                                                     data.inputShape, data.outputData, data.inputData);
+}
+
+LayerTestResult<float,2> CompareSoftmaxTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory,
+    float beta)
+{
+    return CompareSoftmaxTestImpl<armnn::DataType::Float32>(
+        workloadFactory, memoryManager, refWorkloadFactory, beta);
+}
+
+LayerTestResult<uint8_t,2> CompareSoftmaxUint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory,
+    float beta)
+{
+    return CompareSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(
+        workloadFactory, memoryManager, refWorkloadFactory, beta);
+}