| // |
| // Copyright © 2022 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 <tensorflow/lite/schema/schema_generated.h> |
| #include <tensorflow/lite/version.h> |
| |
| #include <doctest/doctest.h> |
| |
| namespace |
| { |
| |
| std::vector<char> CreateBatchMatMulTfLiteModel( |
| tflite::BuiltinOperator bmmOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector <int32_t>& LHSInputTensorShape, |
| const std::vector <int32_t>& RHSInputTensorShape, |
| const std::vector <int32_t>& outputTensorShape, |
| bool adjX = false, |
| bool adjY = 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, flatBufferBuilder.CreateVector({}))); |
| |
| 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>(LHSInputTensorShape.data(), |
| LHSInputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("LHSInput"), |
| quantizationParameters); |
| |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(RHSInputTensorShape.data(), |
| RHSInputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("RHSInput"), |
| quantizationParameters); |
| |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| // create operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_BatchMatMulOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = CreateBatchMatMulOptions(flatBufferBuilder, |
| adjX, |
| adjY).Union(); |
| |
| const std::vector<int32_t> operatorInputs{{0, 1}}; |
| const std::vector<int32_t> operatorOutputs{2}; |
| flatbuffers::Offset <Operator> bmmOperator = |
| 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(&bmmOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: BatchMatMul Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, bmmOperatorCode); |
| |
| 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 BatchMatMulTest(tflite::BuiltinOperator bmmOperatorCode, |
| tflite::TensorType tensorType, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<int32_t>& LHSInputShape, |
| std::vector<int32_t>& RHSInputShape, |
| std::vector<int32_t>& outputShape, |
| std::vector<T>& LHSInputValues, |
| std::vector<T>& RHSInputValues, |
| std::vector<T>& expectedOutputValues, |
| bool adjX = false, |
| bool adjY = false, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| std::vector<char> modelBuffer = CreateBatchMatMulTfLiteModel(bmmOperatorCode, |
| tensorType, |
| LHSInputShape, |
| RHSInputShape, |
| outputShape, |
| adjX, |
| adjY, |
| quantScale, |
| quantOffset); |
| |
| const Model* tfLiteModel = GetModel(modelBuffer.data()); |
| CHECK(tfLiteModel != nullptr); |
| // 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 tfLiteDelegateLHSInputId = tfLiteInterpreter->inputs()[0]; |
| auto tfLiteDelegateLHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateLHSInputId); |
| auto tfLiteDelegateRHSInputId = tfLiteInterpreter->inputs()[1]; |
| auto tfLiteDelegateRHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateRHSInputId); |
| for (unsigned int i = 0; i < LHSInputValues.size(); ++i) |
| { |
| tfLiteDelegateLHSInputData[i] = LHSInputValues[i]; |
| } |
| for (unsigned int i = 0; i < RHSInputValues.size(); ++i) |
| { |
| tfLiteDelegateRHSInputData[i] = RHSInputValues[i]; |
| } |
| |
| auto armnnDelegateLHSInputId = armnnDelegateInterpreter->inputs()[0]; |
| auto armnnDelegateLHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateLHSInputId); |
| auto armnnDelegateRHSInputId = armnnDelegateInterpreter->inputs()[1]; |
| auto armnnDelegateRHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateRHSInputId); |
| for (unsigned int i = 0; i < LHSInputValues.size(); ++i) |
| { |
| armnnDelegateLHSInputData[i] = LHSInputValues[i]; |
| } |
| for (unsigned int i = 0; i < RHSInputValues.size(); ++i) |
| { |
| armnnDelegateRHSInputData[i] = RHSInputValues[i]; |
| } |
| // Run EnqueueWorkload |
| CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); |
| CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); |
| |
| armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, |
| outputShape, expectedOutputValues); |
| } |
| |
| } // anonymous namespace |
| |
| |
| |
| |