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Diffstat (limited to 'delegate/src/test/BatchMatMulTestHelper.hpp')
-rw-r--r-- | delegate/src/test/BatchMatMulTestHelper.hpp | 208 |
1 files changed, 0 insertions, 208 deletions
diff --git a/delegate/src/test/BatchMatMulTestHelper.hpp b/delegate/src/test/BatchMatMulTestHelper.hpp deleted file mode 100644 index 7437064a42..0000000000 --- a/delegate/src/test/BatchMatMulTestHelper.hpp +++ /dev/null @@ -1,208 +0,0 @@ -// -// Copyright © 2022-2023 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 <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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - 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>(LHSInputTensorShape.data(), - LHSInputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("LHSInput"), - quantizationParameters); - - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(RHSInputTensorShape.data(), - RHSInputTensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("RHSInput"), - quantizationParameters); - - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 3, - 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 - - - - |