IVGCVSW-7555 Restructure Delegate
* New folders created:
* common is for common code where TfLite API is not used
* classic is for existing delegate implementations
* opaque is for new opaque delegate implementation,
* tests is for shared between existing Delegate and Opaque Delegate which have test utils to work which delegate to use.
* Existing delegate is built to libarmnnDelegate.so and opaque delegate is built as libarmnnOpaqueDelegate.so
* Opaque structure is introduced but no API is added yet.
* CmakeList.txt and delegate/CMakeList.txt have been modified and 2 new CmakeList.txt added
* Rename BUILD_ARMNN_TFLITE_DELEGATE as BUILD_CLASSIC_DELEGATE
* Rename BUILD_ARMNN_TFLITE_OPAQUE_DELEGATE as BUILD_OPAQUE_DELEGATE
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ib682b9ad0ac8d8acdc4ec6d9099bb0008a9fe8ed
diff --git a/delegate/test/ElementwiseBinaryTestHelper.hpp b/delegate/test/ElementwiseBinaryTestHelper.hpp
new file mode 100644
index 0000000..47ee7c2
--- /dev/null
+++ b/delegate/test/ElementwiseBinaryTestHelper.hpp
@@ -0,0 +1,243 @@
+//
+// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.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 <schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+
+template <typename T>
+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,
+ std::vector<T>& input1Values,
+ bool constantInput = false,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ if (constantInput)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(input1Values.data()),
+ sizeof(T) * input1Values.size())));
+ }
+ else
+ {
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ }
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+
+ auto quantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ quantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+
+ std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+ tensors[0] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(),
+ input0TensorShape.size()),
+ tensorType,
+ 1,
+ flatBufferBuilder.CreateString("input_0"),
+ quantizationParameters);
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
+ input1TensorShape.size()),
+ tensorType,
+ 2,
+ flatBufferBuilder.CreateString("input_1"),
+ quantizationParameters);
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ tensorType,
+ 3,
+ flatBufferBuilder.CreateString("output"),
+ quantizationParameters);
+
+ // 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_MAXIMUM:
+ {
+ operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions;
+ operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union();
+ break;
+ }
+ case BuiltinOperator_MINIMUM:
+ {
+ operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions;
+ operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).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;
+ }
+ case BuiltinOperator_FLOOR_DIV:
+ {
+ operatorBuiltinOptionsType = tflite::BuiltinOptions_FloorDivOptions;
+ 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());
+}
+
+template <typename T>
+void ElementwiseBinaryTest(tflite::BuiltinOperator binaryOperatorCode,
+ tflite::ActivationFunctionType activationType,
+ tflite::TensorType tensorType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& input0Shape,
+ std::vector<int32_t>& input1Shape,
+ std::vector<int32_t>& outputShape,
+ std::vector<T>& input0Values,
+ std::vector<T>& input1Values,
+ std::vector<T>& expectedOutputValues,
+ float quantScale = 1.0f,
+ int quantOffset = 0,
+ bool constantInput = false)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel<T>(binaryOperatorCode,
+ activationType,
+ tensorType,
+ input0Shape,
+ input1Shape,
+ outputShape,
+ input1Values,
+ constantInput,
+ quantScale,
+ quantOffset);
+
+ 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
+ armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, input0Values);
+ armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values);
+ if (!constantInput)
+ {
+ armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, input1Values);
+ armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, input1Values);
+ }
+ // Run EnqueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
+ armnnDelegateInterpreter,
+ outputShape,
+ expectedOutputValues);
+ armnnDelegateInterpreter.reset(nullptr);
+}
+
+} // anonymous namespace
\ No newline at end of file