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-rw-r--r--delegate/src/test/BatchMatMulTestHelper.hpp350
1 files changed, 176 insertions, 174 deletions
diff --git a/delegate/src/test/BatchMatMulTestHelper.hpp b/delegate/src/test/BatchMatMulTestHelper.hpp
index 42c1ed6a1e..7437064a42 100644
--- a/delegate/src/test/BatchMatMulTestHelper.hpp
+++ b/delegate/src/test/BatchMatMulTestHelper.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -20,184 +20,186 @@
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)
+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)
{
- 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());
+ tfLiteDelegateRHSInputData[i] = RHSInputValues[i];
}
- 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)
+ 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)
{
- 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);
+ armnnDelegateRHSInputData[i] = RHSInputValues[i];
}
+ // Run EnqueueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter,
+ outputShape, expectedOutputValues);
+}
} // anonymous namespace
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-06 04:55:54 +0000