IVGCVSW-5379 'TfLiteDelegate: Implement the ElementWiseBinary operators'
* Implemented ADD operator
* Implemented FP32 unit tests for ADD operator
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: Id7238749308855bd2b2118f4b6e60e765815c38f
diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index aa48435..acce828 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -89,6 +89,8 @@
set(armnnDelegate_unittest_sources)
list(APPEND armnnDelegate_unittest_sources
src/test/ArmnnDelegateTest.cpp
+ src/test/ElementwiseBinaryTest.cpp
+ src/test/ElementwiseBinaryTestHelper.hpp
src/test/ElementwiseUnaryTest.cpp
src/test/ElementwiseUnaryTestHelper.hpp)
diff --git a/delegate/src/DelegateUtils.hpp b/delegate/src/DelegateUtils.hpp
index 16dc8a8..fca6a6c 100644
--- a/delegate/src/DelegateUtils.hpp
+++ b/delegate/src/DelegateUtils.hpp
@@ -8,6 +8,7 @@
#include <armnn/ArmNN.hpp>
#include <armnn/BackendHelper.hpp>
#include <armnn/utility/Assert.hpp>
+#include <armnn/utility/NumericCast.hpp>
#include <tensorflow/lite/builtin_ops.h>
#include <tensorflow/lite/c/builtin_op_data.h>
@@ -103,6 +104,198 @@
return false;
}
+TfLiteStatus Connect(armnn::IConnectableLayer* layer,
+ TfLiteNode* tfLiteNode,
+ armnnDelegate::DelegateData& data)
+{
+ ARMNN_ASSERT(tfLiteNode->inputs->size == layer->GetNumInputSlots());
+ ARMNN_ASSERT(tfLiteNode->outputs->size == layer->GetNumOutputSlots());
+
+ // Connect the input slots
+ for (unsigned int inputIndex = 0; inputIndex < layer->GetNumInputSlots(); ++inputIndex)
+ {
+ data.m_OutputSlotForNode[tfLiteNode->inputs->data[inputIndex]]->Connect(layer->GetInputSlot(inputIndex));
+ }
+
+ // Prepare output slots
+ for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex)
+ {
+ armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex);
+ data.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]] = &outputSlot;
+ }
+ return kTfLiteOk;
+}
+
+armnn::IConnectableLayer* BroadcastTensor(const armnn::TensorInfo& inputInfo0,
+ const armnn::TensorInfo& inputInfo1,
+ armnn::IConnectableLayer* startLayer,
+ TfLiteContext* tfLiteContext,
+ TfLiteNode* tfLiteNode,
+ armnnDelegate::DelegateData& delegateData)
+{
+ unsigned int inputDimensions0 = inputInfo0.GetNumDimensions();
+ unsigned int inputDimensions1 = inputInfo1.GetNumDimensions();
+
+ if (inputDimensions0 == inputDimensions1)
+ {
+ auto status = Connect(startLayer, tfLiteNode, delegateData);
+ if(status == kTfLiteOk)
+ {
+ return startLayer;
+ }
+ else
+ {
+ return nullptr;
+ }
+ }
+
+ unsigned int biggerInputDimensions = std::max(inputDimensions0, inputDimensions1);
+ unsigned int dimDifference =
+ std::abs(armnn::numeric_cast<int>(inputDimensions0) - armnn::numeric_cast<int>(inputDimensions1));
+
+ bool input0IsSmaller = inputDimensions0 < inputDimensions1;
+ const armnn::TensorInfo& smallInfo = input0IsSmaller ? inputInfo0 : inputInfo1;
+ const armnn::TensorShape& smallShape = smallInfo.GetShape();
+
+ std::vector<unsigned int> reshapedDimensions(biggerInputDimensions, 1);
+ for (unsigned int i = dimDifference; i < biggerInputDimensions; ++i)
+ {
+ reshapedDimensions[i] = smallShape[i - dimDifference];
+ }
+
+ armnn::TensorInfo reshapedInfo = smallInfo;
+ reshapedInfo.SetShape(armnn::TensorShape{ armnn::numeric_cast<unsigned int>(reshapedDimensions.size()),
+ reshapedDimensions.data() });
+
+ armnn::ReshapeDescriptor reshapeDescriptor;
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ tfLiteContext,
+ IsReshapeSupported,
+ delegateData.m_Backends,
+ isSupported,
+ smallInfo,
+ reshapedInfo,
+ reshapeDescriptor);
+ if (!isSupported)
+ {
+ return nullptr;
+ }
+
+ ARMNN_ASSERT(delegateData.m_Network != nullptr);
+ // Add Reshape layer
+ reshapeDescriptor.m_TargetShape = reshapedInfo.GetShape();
+
+ armnn::IConnectableLayer* reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor);
+ ARMNN_ASSERT(reshapeLayer != nullptr);
+ reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo);
+
+ if (input0IsSmaller)
+ {
+ delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(reshapeLayer->GetInputSlot(0));
+ reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
+ delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[1]]->Connect(startLayer->GetInputSlot(1));
+ }
+ else
+ {
+ delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[1]]->Connect(reshapeLayer->GetInputSlot(0));
+ reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(1));
+ delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(startLayer->GetInputSlot(0));
+ }
+
+ // Prepare output slots
+ for (unsigned int outputIndex = 0; outputIndex < startLayer->GetNumOutputSlots(); ++outputIndex)
+ {
+ armnn::IOutputSlot& outputSlot = startLayer->GetOutputSlot(outputIndex);
+ delegateData.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]] = &outputSlot;
+ }
+
+ return reshapeLayer;
+}
+
+TfLiteStatus FusedActivation(TfLiteContext* tfLiteContext,
+ TfLiteNode* tfLiteNode,
+ TfLiteFusedActivation activationType,
+ armnn::IConnectableLayer* prevLayer,
+ unsigned int outputSlotIndex,
+ armnnDelegate::DelegateData& data)
+{
+
+ armnn::IOutputSlot& outputSlot = prevLayer->GetOutputSlot(outputSlotIndex);
+ const armnn::TensorInfo& activationOutputInfo = outputSlot.GetTensorInfo();
+
+ armnn::ActivationDescriptor activationDesc;
+
+ switch (activationType)
+ {
+ case kTfLiteActNone:
+ {
+ // No Activation
+ return kTfLiteOk;
+ }
+ case kTfLiteActRelu:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::ReLu;
+ break;
+ }
+ case kTfLiteActRelu1:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu;
+ activationDesc.m_A = 1.0f;
+ activationDesc.m_B = -1.0f;
+ break;
+ }
+ case kTfLiteActRelu6:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu;
+ activationDesc.m_A = 6.0f;
+ activationDesc.m_B = 0.0f;
+ break;
+ }
+ case kTfLiteActSigmoid:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::Sigmoid;
+ break;
+ }
+ case kTfLiteActTanh:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::TanH;
+ activationDesc.m_A = 1.0f;
+ activationDesc.m_B = 1.0f;
+ break;
+ }
+ default:
+ return kTfLiteError;
+ }
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ tfLiteContext,
+ IsActivationSupported,
+ data.m_Backends,
+ isSupported,
+ prevLayer->GetOutputSlot(0).GetTensorInfo(),
+ activationOutputInfo,
+ activationDesc);
+ if (!isSupported)
+ {
+ return kTfLiteError;
+ }
+ armnn::IConnectableLayer* activationLayer = data.m_Network->AddActivationLayer(activationDesc);
+
+ ARMNN_ASSERT(activationLayer != nullptr);
+ activationLayer->GetOutputSlot(0).SetTensorInfo(activationOutputInfo);
+
+ // Connect and prepare output slots
+ for (unsigned int outputIndex = 0; outputIndex < activationLayer->GetNumOutputSlots(); ++outputIndex)
+ {
+ data.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]]->Connect(activationLayer->GetInputSlot(0));
+ armnn::IOutputSlot& outputSlot = activationLayer->GetOutputSlot(outputIndex);
+ data.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]] = &outputSlot;
+ }
+ return kTfLiteOk;
+}
+
armnn::TensorInfo GetTensorInfoForTfLiteTensor(const TfLiteTensor& tfLiteTensor)
{
armnn::DataType type;
@@ -162,13 +355,21 @@
// get per-channel quantization parameters
const auto* affineQuantization =
reinterpret_cast<TfLiteAffineQuantization*>(tfLiteTensor.quantization.params);
- std::vector<float> quantizationScales;
- for (unsigned int i = 1; i < affineQuantization->scale->size; ++i)
+ if (affineQuantization->scale->size > 1)
{
- quantizationScales.push_back(affineQuantization->scale->data[i]);
+ std::vector<float> quantizationScales;
+ for (unsigned int i = 1; i < affineQuantization->scale->size; ++i)
+ {
+ quantizationScales.push_back(affineQuantization->scale->data[i]);
+ }
+ ret.SetQuantizationScales(quantizationScales);
+ ret.SetQuantizationDim(armnn::MakeOptional<unsigned int>(affineQuantization->quantized_dimension));
}
- ret.SetQuantizationScales(quantizationScales);
- ret.SetQuantizationDim(armnn::MakeOptional<unsigned int>(affineQuantization->quantized_dimension));
+ else
+ {
+ ret.SetQuantizationScale(affineQuantization->scale->data[0]);
+ ret.SetQuantizationOffset(affineQuantization->zero_point->data[0]);
+ }
}
else
{
@@ -180,26 +381,4 @@
return ret;
}
-TfLiteStatus Connect(armnn::IConnectableLayer& layer,
- TfLiteNode* tfLiteNode,
- armnnDelegate::DelegateData& data)
-{
- ARMNN_ASSERT(tfLiteNode->inputs->size == layer.GetNumInputSlots());
- ARMNN_ASSERT(tfLiteNode->outputs->size == layer.GetNumOutputSlots());
-
- // connect the input slots
- for (unsigned int inputIndex = 0; inputIndex < layer.GetNumInputSlots(); ++inputIndex)
- {
- data.m_OutputSlotForNode[tfLiteNode->inputs->data[inputIndex]]->Connect(layer.GetInputSlot(inputIndex));
- }
-
- // prepare output slots
- for (unsigned int outputIndex = 0; outputIndex < layer.GetNumOutputSlots(); ++outputIndex)
- {
- armnn::IOutputSlot& outputSlot = layer.GetOutputSlot(outputIndex);
- data.m_OutputSlotForNode[tfLiteNode->outputs->data[outputIndex]] = &outputSlot;
- }
- return kTfLiteOk;
-}
-
} // namespace anonymous
diff --git a/delegate/src/ElementwiseBinary.hpp b/delegate/src/ElementwiseBinary.hpp
index ff24012..a22d9f5 100644
--- a/delegate/src/ElementwiseBinary.hpp
+++ b/delegate/src/ElementwiseBinary.hpp
@@ -5,6 +5,8 @@
#pragma once
+#include "DelegateUtils.hpp"
+
#include <tensorflow/lite/builtin_ops.h>
#include <tensorflow/lite/c/builtin_op_data.h>
#include <tensorflow/lite/c/common.h>
@@ -13,13 +15,134 @@
namespace armnnDelegate
{
+TfLiteStatus ValidateAddOperator(DelegateData& delegateData,
+ TfLiteContext* tfLiteContext,
+ const armnn::TensorInfo& inputInfo1,
+ const armnn::TensorInfo& inputInfo2,
+ const armnn::TensorInfo& outputInfo)
+{
+ bool isSupported = false;
+ auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
+ {
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ tfLiteContext,
+ IsAdditionSupported,
+ delegateData.m_Backends,
+ isSupported,
+ inputInfo1,
+ inputInfo2,
+ outputTensorInfo);
+ };
+
+ validateFunc(outputInfo, isSupported);
+ return isSupported ? kTfLiteOk : kTfLiteError;
+}
+
+armnn::IConnectableLayer* AddAdditionLayer(DelegateData& delegateData)
+{
+
+ if (!delegateData.m_Network)
+ {
+ return nullptr;
+ }
+
+ return delegateData.m_Network->AddAdditionLayer();
+}
+
TfLiteStatus VisitElementwiseBinaryOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t elementwiseBinaryOperatorCode)
{
- return kTfLiteError;
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+ TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+ const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+ const TfLiteTensor& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]];
+ if (IsDynamicTensor(tfLiteInputTensor0))
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+ elementwiseBinaryOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ const TfLiteTensor& tfLiteInputTensor1 = tfLiteTensors[tfLiteNode->inputs->data[1]];
+ if (IsDynamicTensor(tfLiteInputTensor1))
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+ elementwiseBinaryOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+ if (IsDynamicTensor(tfLiteOutputTensor))
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
+ elementwiseBinaryOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0);
+ const armnn::TensorInfo& inputTensorInfo1 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor1);
+ const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+ if (!delegateData.m_Network)
+ {
+ switch(elementwiseBinaryOperatorCode)
+ {
+ case kTfLiteBuiltinAdd:
+ return ValidateAddOperator(delegateData,
+ tfLiteContext,
+ inputTensorInfo0,
+ inputTensorInfo1,
+ outputTensorInfo);
+ default:
+ return kTfLiteError;
+ }
+ }
+
+ armnn::IConnectableLayer* elementwiseBinaryLayer = nullptr;
+
+ switch(elementwiseBinaryOperatorCode)
+ {
+ case kTfLiteBuiltinAdd:
+ elementwiseBinaryLayer = AddAdditionLayer(delegateData);
+ break;
+ default:
+ return kTfLiteError;
+ }
+ ARMNN_ASSERT(elementwiseBinaryLayer != nullptr);
+
+ armnn::IOutputSlot& outputSlot = elementwiseBinaryLayer->GetOutputSlot(0);
+ outputSlot.SetTensorInfo(outputTensorInfo);
+
+ auto reshapeLayer = BroadcastTensor(inputTensorInfo0,
+ inputTensorInfo1,
+ elementwiseBinaryLayer,
+ tfLiteContext,
+ tfLiteNode,
+ delegateData);
+ if (!reshapeLayer)
+ {
+ return kTfLiteError;
+ }
+
+ auto* tfLiteNodeParameters = reinterpret_cast<TfLiteAddParams*>(tfLiteNode->builtin_data);
+ if (!tfLiteNodeParameters)
+ {
+ // No Activation
+ return kTfLiteOk;
+ }
+ // Check activation
+ TfLiteFusedActivation activationType = tfLiteNodeParameters->activation;
+ return FusedActivation(tfLiteContext, tfLiteNode, activationType, reshapeLayer, 0, delegateData);
}
} // namespace armnnDelegate
diff --git a/delegate/src/ElementwiseUnary.hpp b/delegate/src/ElementwiseUnary.hpp
index 7527fa1..f2f5301 100644
--- a/delegate/src/ElementwiseUnary.hpp
+++ b/delegate/src/ElementwiseUnary.hpp
@@ -77,7 +77,7 @@
outputSlot.SetTensorInfo(outputTensorInfo);
// Connect
- return Connect(*layer, tfLiteNode, delegateData);
+ return Connect(layer, tfLiteNode, delegateData);
}
} // namespace armnnDelegate
diff --git a/delegate/src/test/ArmnnDelegateTest.cpp b/delegate/src/test/ArmnnDelegateTest.cpp
index fdf786f..7cec70b 100644
--- a/delegate/src/test/ArmnnDelegateTest.cpp
+++ b/delegate/src/test/ArmnnDelegateTest.cpp
@@ -7,6 +7,7 @@
#include <doctest/doctest.h>
#include <armnn_delegate.hpp>
+#include "ElementwiseUnaryTestHelper.hpp"
#include "tensorflow/lite/kernels/builtin_op_kernels.h"
#include <tensorflow/lite/interpreter.h>
@@ -19,30 +20,31 @@
TEST_CASE ("ArmnnDelegate Registered")
{
- std::unique_ptr<tflite::impl::Interpreter> tfLiteInterpreter;
- tfLiteInterpreter.reset(new tflite::impl::Interpreter);
+ using namespace tflite;
+ auto tfLiteInterpreter = std::make_unique<Interpreter>();
- // Create the network
tfLiteInterpreter->AddTensors(3);
- tfLiteInterpreter->SetInputs({0});
+ tfLiteInterpreter->SetInputs({0, 1});
tfLiteInterpreter->SetOutputs({2});
- TfLiteQuantizationParams quantizationParams;
- tfLiteInterpreter->SetTensorParametersReadWrite(0, kTfLiteFloat32, "", {3}, quantizationParams);
- tfLiteInterpreter->SetTensorParametersReadWrite(1, kTfLiteFloat32, "", {3}, quantizationParams);
- tfLiteInterpreter->SetTensorParametersReadWrite(2, kTfLiteFloat32, "", {3}, quantizationParams);
- TfLiteRegistration* nodeRegistration = tflite::ops::builtin::Register_ABS();
- void* data = malloc(sizeof(int));
+ tfLiteInterpreter->SetTensorParametersReadWrite(0, kTfLiteFloat32, "input1", {1,2,2,1}, TfLiteQuantization());
+ tfLiteInterpreter->SetTensorParametersReadWrite(1, kTfLiteFloat32, "input2", {1,2,2,1}, TfLiteQuantization());
+ tfLiteInterpreter->SetTensorParametersReadWrite(2, kTfLiteFloat32, "output", {1,2,2,1}, TfLiteQuantization());
- tfLiteInterpreter->AddNodeWithParameters({0}, {2}, nullptr, 0, data, nodeRegistration);
+ tflite::ops::builtin::BuiltinOpResolver opResolver;
+ const TfLiteRegistration* opRegister = opResolver.FindOp(BuiltinOperator_ADD, 1);
+ tfLiteInterpreter->AddNodeWithParameters({0, 1}, {2}, "", 0, nullptr, opRegister);
// create the Armnn Delegate
- auto delegateOptions = TfLiteArmnnDelegateOptionsDefault();
- auto delegate = TfLiteArmnnDelegateCreate(delegateOptions);
- auto status = tfLiteInterpreter->ModifyGraphWithDelegate(std::move(delegate));
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ armnnDelegate::DelegateOptions delegateOptions(backends);
+ std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
+ theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+ armnnDelegate::TfLiteArmnnDelegateDelete);
+
+ auto status = tfLiteInterpreter->ModifyGraphWithDelegate(std::move(theArmnnDelegate));
CHECK(status == kTfLiteOk);
CHECK(tfLiteInterpreter != nullptr);
-
}
}
diff --git a/delegate/src/test/ElementwiseBinaryTest.cpp b/delegate/src/test/ElementwiseBinaryTest.cpp
new file mode 100644
index 0000000..bd4019a
--- /dev/null
+++ b/delegate/src/test/ElementwiseBinaryTest.cpp
@@ -0,0 +1,169 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ElementwiseBinaryTestHelper.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+
+TEST_SUITE("ElementwiseBinaryTest")
+{
+
+TEST_CASE ("Add_Float32_GpuAcc_Test")
+{
+ // Create the ArmNN Delegate
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc,
+ armnn::Compute::CpuRef };
+ // Set input data
+ std::vector<int32_t> input0Shape { 2, 2, 2, 3 };
+ std::vector<int32_t> input1Shape { 2, 2, 2, 3 };
+ std::vector<int32_t> outputShape { 2, 2, 2, 3 };
+
+ std::vector<float> input0Values =
+ {
+ 0.0f, 2.0f, 1.0f,
+ 0.2f, 1.0f, 2.0f,
+
+ 1.0f, 2.0f, 1.0f,
+ 0.2f, 1.0f, 2.0f,
+
+ 0.0f, 2.0f, 1.0f,
+ 4.2f, 1.0f, 2.0f,
+
+ 0.0f, 0.0f, 1.0f,
+ 0.2f, 1.0f, 2.0f,
+
+ };
+
+ std::vector<float> input1Values =
+ {
+ 1.0f, 2.0f, 1.0f,
+ 0.0f, 1.0f, 2.0f,
+
+ 1.0f, 2.0f, -2.0f,
+ 0.2f, 1.0f, 2.0f,
+
+ 0.0f, 2.0f, 1.0f,
+ 4.2f, 0.0f, -3.0f,
+
+ 0.0f, 0.0f, 1.0f,
+ 0.7f, 1.0f, 5.0f,
+ };
+
+ std::vector<float> expectedOutputValues =
+ {
+ 1.0f, 4.0f, 2.0f,
+ 0.2f, 2.0f, 4.0f,
+
+ 2.0f, 4.0f, -1.0f,
+ 0.4f, 2.0f, 4.0f,
+
+ 0.0f, 4.0f, 2.0f,
+ 8.4f, 1.0f, -1.0f,
+
+ 0.0f, 0.0f, 2.0f,
+ 0.9f, 2.0f, 7.0f,
+ };
+
+
+ ElementwiseBinaryFP32Test(tflite::BuiltinOperator_ADD,
+ tflite::ActivationFunctionType_NONE,
+ backends,
+ input0Shape,
+ input1Shape,
+ outputShape,
+ input0Values,
+ input1Values,
+ expectedOutputValues);
+}
+
+TEST_CASE ("Add_Broadcast_Float32_GpuAcc_Test")
+{
+ // Create the ArmNN Delegate
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc,
+ armnn::Compute::CpuRef };
+ // Set input data
+ std::vector<int32_t> input0Shape { 1, 3, 2, 1 };
+ std::vector<int32_t> input1Shape { 1, 1, 2, 3 };
+ std::vector<int32_t> outputShape { 1, 3, 2, 3 };
+
+ std::vector<float> input0Values
+ {
+ 0.0f,
+ 1.0f,
+
+ 2.0f,
+ 3.0f,
+
+ 4.0f,
+ 5.0f,
+ };
+ std::vector<float> input1Values
+ {
+ 0.5f, 1.5f, 2.5f,
+ 3.5f, 4.5f, 5.5f,
+ };
+ // Set output data
+ std::vector<float> expectedOutputValues
+ {
+ 0.5f, 1.5f, 2.5f,
+ 4.5f, 5.5f, 6.5f,
+
+ 2.5f, 3.5f, 4.5f,
+ 6.5f, 7.5f, 8.5f,
+
+ 4.5f, 5.5f, 6.5f,
+ 8.5f, 9.5f, 10.5f,
+ };
+ ElementwiseBinaryFP32Test(tflite::BuiltinOperator_ADD,
+ tflite::ActivationFunctionType_NONE,
+ backends,
+ input0Shape,
+ input1Shape,
+ outputShape,
+ input0Values,
+ input1Values,
+ expectedOutputValues);
+}
+
+TEST_CASE ("Add_ActivationRELU_Float32_GpuAcc_Test")
+{
+ // Create the ArmNN Delegate
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc,
+ armnn::Compute::CpuRef };
+ // Set input data
+ std::vector<int32_t> input0Shape { 1, 2, 2, 1 };
+ std::vector<int32_t> input1Shape { 1, 2, 2, 1 };
+ std::vector<int32_t> outputShape { 1, 2, 2, 1 };
+
+ std::vector<float> input0Values { 4.0f, 0.8f, 0.7f, -0.8f };
+ std::vector<float> input1Values { 0.7f, -1.2f, 0.8f, 0.5f };
+ // Set output data
+ std::vector<float> expectedOutputValues { 4.7f, 0.0f, 1.5f, 0.0f };
+ ElementwiseBinaryFP32Test(tflite::BuiltinOperator_ADD,
+ tflite::ActivationFunctionType_RELU,
+ backends,
+ input0Shape,
+ input1Shape,
+ outputShape,
+ input0Values,
+ input1Values,
+ expectedOutputValues);
+}
+
+}
+
+} // namespace armnnDelegate
\ No newline at end of file
diff --git a/delegate/src/test/ElementwiseBinaryTestHelper.hpp b/delegate/src/test/ElementwiseBinaryTestHelper.hpp
new file mode 100644
index 0000000..72f9f85
--- /dev/null
+++ b/delegate/src/test/ElementwiseBinaryTestHelper.hpp
@@ -0,0 +1,211 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+
+std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator binaryOperatorCode,
+ tflite::ActivationFunctionType activationType,
+ tflite::TensorType tensorType,
+ const std::vector <int32_t>& input0TensorShape,
+ const std::vector <int32_t>& input1TensorShape,
+ const std::vector <int32_t>& outputTensorShape)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+
+ std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+ tensors[0] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(),
+ input0TensorShape.size()),
+ tensorType, 0);
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
+ input1TensorShape.size()),
+ tensorType, 0);
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ tensorType);
+
+ // create operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
+ flatbuffers::Offset<void> operatorBuiltinOptions = 0;
+ switch (binaryOperatorCode)
+ {
+ case BuiltinOperator_ADD:
+ {
+ operatorBuiltinOptionsType = BuiltinOptions_AddOptions;
+ operatorBuiltinOptions = CreateAddOptions(flatBufferBuilder, activationType).Union();
+ break;
+ }
+ case BuiltinOperator_DIV:
+ {
+ operatorBuiltinOptionsType = BuiltinOptions_DivOptions;
+ operatorBuiltinOptions = CreateDivOptions(flatBufferBuilder, activationType).Union();
+ break;
+ }
+ case BuiltinOperator_MUL:
+ {
+ operatorBuiltinOptionsType = BuiltinOptions_MulOptions;
+ operatorBuiltinOptions = CreateMulOptions(flatBufferBuilder, activationType).Union();
+ break;
+ }
+ case BuiltinOperator_SUB:
+ {
+ operatorBuiltinOptionsType = BuiltinOptions_SubOptions;
+ operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union();
+ break;
+ }
+ default:
+ break;
+ }
+ const std::vector<int32_t> operatorInputs{ {0, 1} };
+ const std::vector<int32_t> operatorOutputs{{2}};
+ flatbuffers::Offset <Operator> elementwiseBinaryOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOptions);
+
+ const std::vector<int> subgraphInputs{ {0, 1} };
+ const std::vector<int> subgraphOutputs{{2}};
+ flatbuffers::Offset <SubGraph> subgraph =
+ CreateSubGraph(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
+ flatBufferBuilder.CreateVector(&elementwiseBinaryOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Binary Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, binaryOperatorCode);
+
+ flatbuffers::Offset <Model> flatbufferModel =
+ CreateModel(flatBufferBuilder,
+ TFLITE_SCHEMA_VERSION,
+ flatBufferBuilder.CreateVector(&operatorCode, 1),
+ flatBufferBuilder.CreateVector(&subgraph, 1),
+ modelDescription,
+ flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+ flatBufferBuilder.Finish(flatbufferModel);
+
+ return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+ flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+}
+
+void ElementwiseBinaryFP32Test(tflite::BuiltinOperator binaryOperatorCode,
+ tflite::ActivationFunctionType activationType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& input0Shape,
+ std::vector<int32_t>& input1Shape,
+ std::vector<int32_t>& outputShape,
+ std::vector<float>& input0Values,
+ std::vector<float>& input1Values,
+ std::vector<float>& expectedOutputValues)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel(binaryOperatorCode,
+ activationType,
+ ::tflite::TensorType_FLOAT32,
+ input0Shape,
+ input1Shape,
+ outputShape);
+
+ const Model* tfLiteModel = GetModel(modelBuffer.data());
+ // Create TfLite Interpreters
+ std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&armnnDelegateInterpreter) == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter != nullptr);
+ CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+ std::unique_ptr<Interpreter> tfLiteInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&tfLiteInterpreter) == kTfLiteOk);
+ CHECK(tfLiteInterpreter != nullptr);
+ CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
+
+ // Create the ArmNN Delegate
+ armnnDelegate::DelegateOptions delegateOptions(backends);
+ std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
+ theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+ armnnDelegate::TfLiteArmnnDelegateDelete);
+ CHECK(theArmnnDelegate != nullptr);
+ // Modify armnnDelegateInterpreter to use armnnDelegate
+ CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+ // Set input data
+ auto tfLiteDelegateInput0Id = tfLiteInterpreter->inputs()[0];
+ auto tfLiteDelageInput0Data = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInput0Id);
+ for (unsigned int i = 0; i < input0Values.size(); ++i)
+ {
+ tfLiteDelageInput0Data[i] = input0Values[i];
+ }
+
+ auto tfLiteDelegateInput1Id = tfLiteInterpreter->inputs()[1];
+ auto tfLiteDelageInput1Data = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInput1Id);
+ for (unsigned int i = 0; i < input1Values.size(); ++i)
+ {
+ tfLiteDelageInput1Data[i] = input1Values[i];
+ }
+
+ auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0];
+ auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInput0Id);
+ for (unsigned int i = 0; i < input0Values.size(); ++i)
+ {
+ armnnDelegateInput0Data[i] = input0Values[i];
+ }
+
+ auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1];
+ auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInput1Id);
+ for (unsigned int i = 0; i < input1Values.size(); ++i)
+ {
+ armnnDelegateInput1Data[i] = input1Values[i];
+ }
+
+ // Run EnqueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+ auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
+ auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+ auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
+ for (size_t i = 0; i < expectedOutputValues.size(); i++)
+ {
+ CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]);
+ CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]);
+ CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]);
+ }
+
+ armnnDelegateInterpreter.reset(nullptr);
+}
+
+} // anonymous namespace
+
+
+
+
diff --git a/delegate/src/test/ElementwiseUnaryTestHelper.hpp b/delegate/src/test/ElementwiseUnaryTestHelper.hpp
index 4d45f4e..b4a55cb 100644
--- a/delegate/src/test/ElementwiseUnaryTestHelper.hpp
+++ b/delegate/src/test/ElementwiseUnaryTestHelper.hpp
@@ -97,12 +97,15 @@
(&tfLiteInterpreter) == kTfLiteOk);
CHECK(tfLiteInterpreter != nullptr);
CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
+
// Create the ArmNN Delegate
armnnDelegate::DelegateOptions delegateOptions(backends);
- auto armnnDelegate = TfLiteArmnnDelegateCreate(delegateOptions);
- CHECK(armnnDelegate != nullptr);
+ std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
+ theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+ armnnDelegate::TfLiteArmnnDelegateDelete);
+ CHECK(theArmnnDelegate != nullptr);
// Modify armnnDelegateInterpreter to use armnnDelegate
- CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(armnnDelegate) == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
// Set input data
auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0];