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/LstmTestHelper.hpp b/delegate/test/LstmTestHelper.hpp
new file mode 100644
index 0000000..14776ca
--- /dev/null
+++ b/delegate/test/LstmTestHelper.hpp
@@ -0,0 +1,691 @@
+//
+// Copyright © 2021, 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 <tensorflow/lite/c/common.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+
+template <typename T>
+std::vector<char> CreateLstmTfLiteModel(tflite::TensorType tensorType,
+ int32_t batchSize,
+ int32_t inputSize,
+ int32_t outputSize,
+ int32_t numUnits,
+ bool hasInputToInputWeights,
+ const std::vector<T>& inputToInputWeights,
+ const std::vector<T>& inputToForgetWeights,
+ const std::vector<T>& inputToCellWeights,
+ const std::vector<T>& inputToOutputWeights,
+ bool hasRecurrentToInputWeights,
+ const std::vector<T>& recurrentToInputWeights,
+ const std::vector<T>& recurrentToForgetWeights,
+ const std::vector<T>& recurrentToCellWeights,
+ const std::vector<T>& recurrentToOutputWeights,
+ bool hasCellToInputWeights,
+ const std::vector<T>& cellToInputWeights,
+ bool hasCellToForgetWeights,
+ const std::vector<T>& cellToForgetWeights,
+ bool hasCellToOutputWeights,
+ const std::vector<T>& cellToOutputWeights,
+ bool hasInputGateBias,
+ const std::vector<T>& inputGateBias,
+ const std::vector<T>& forgetGateBias,
+ const std::vector<T>& cellBias,
+ const std::vector<T>& outputGateBias,
+ bool hasProjectionWeights,
+ const std::vector<T>& projectionWeights,
+ bool hasProjectionBias,
+ const std::vector<T>& projectionBias,
+ bool hasInputLayerNormWeights,
+ const std::vector<T>& inputLayerNormWeights,
+ bool hasForgetLayerNormWeights,
+ const std::vector<T>& forgetLayerNormWeights,
+ bool hasCellLayerNormWeights,
+ const std::vector<T>& cellLayerNormWeights,
+ bool hasOutputLayerNormWeights,
+ const std::vector<T>& outputLayerNormWeights,
+ tflite::ActivationFunctionType activationFunction,
+ float clippingThresCell,
+ float clippingThresProj,
+ float quantScale = 1.0f,
+ int quantOffset = 0,
+ float outputQuantScale = 2.0f,
+ int outputQuantOffset = 0)
+{
+
+ std::vector <int32_t> tensorInfo0 {};
+ std::vector <int32_t> tensorInfo4 {numUnits};
+ std::vector <int32_t> tensorInfo8 {numUnits, static_cast<int32_t>(2)};
+ std::vector <int32_t> tensorInfo16 {numUnits, static_cast<int32_t>(4)};
+
+ std::vector<int32_t> inputShape {batchSize , inputSize};
+ std::vector<int32_t> outputShape {batchSize , outputSize};
+
+ std::vector<int32_t> outputStateInDimensions{batchSize, outputSize};
+ std::vector<int32_t> cellStateInDimensions{batchSize, numUnits};
+
+ std::vector<int> operatorInputs;
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+ std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+ std::vector<flatbuffers::Offset<Tensor>> tensors;
+
+ auto quantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ quantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+ auto outputQuantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ outputQuantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset }));
+
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputShape.data(),
+ inputShape.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("input_0"),
+ quantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ if (hasInputToInputWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToInputWeights.data()),
+ sizeof(T) * inputToInputWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo8.data(),
+ tensorInfo8.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputToInputWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToForgetWeights.data()),
+ sizeof(T) * inputToForgetWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo8.data(),
+ tensorInfo8.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputToForgetWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToCellWeights.data()),
+ sizeof(T) * inputToCellWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo8.data(),
+ tensorInfo8.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputToCellWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToOutputWeights.data()),
+ sizeof(T) * inputToOutputWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo8.data(),
+ tensorInfo8.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputToOutputWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ if (hasRecurrentToInputWeights)
+ {
+ buffers.push_back(CreateBuffer(
+ flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(recurrentToInputWeights.data()),
+ sizeof(T) * recurrentToInputWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo16.data(),
+ tensorInfo16.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("recurrentToInputWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToForgetWeights.data()),
+ sizeof(T) * recurrentToForgetWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo16.data(),
+ tensorInfo16.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("recurrentToForgetWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToCellWeights.data()),
+ sizeof(T) * recurrentToCellWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo16.data(),
+ tensorInfo16.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("recurrentToCellWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToOutputWeights.data()),
+ sizeof(T) * recurrentToOutputWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo16.data(),
+ tensorInfo16.size()),
+ tensorType,
+ buffers.size() - 1 ,
+ flatBufferBuilder.CreateString("recurrentToOutputWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ if (hasCellToInputWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToInputWeights.data()),
+ sizeof(T) * cellToInputWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellToInputWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ if (hasCellToForgetWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToForgetWeights.data()),
+ sizeof(T) * cellToForgetWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellToForgetWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ if (hasCellToOutputWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToOutputWeights.data()),
+ sizeof(T) * cellToOutputWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellToOutputWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ if (hasInputGateBias)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputGateBias.data()),
+ sizeof(T) * inputGateBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputGateBias"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(forgetGateBias.data()),
+ sizeof(T) * forgetGateBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("forgetGateBias"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellBias.data()),
+ sizeof(T) * cellBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellBias"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(outputGateBias.data()),
+ sizeof(T) * outputGateBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("outputGateBias"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ if (hasProjectionWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(projectionWeights.data()),
+ sizeof(T) * projectionWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("outputGateBias"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ if (hasProjectionBias)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(projectionBias.data()),
+ sizeof(T) * projectionBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("projectionBias"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputStateInDimensions.data(),
+ outputStateInDimensions.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("outputStateInInfo"),
+ outputQuantizationParameters,
+ true));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(cellStateInDimensions.data(),
+ cellStateInDimensions.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellStateInInfo"),
+ outputQuantizationParameters,
+ true));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ if (hasInputLayerNormWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(
+ reinterpret_cast<const uint8_t *>(inputLayerNormWeights.data()),
+ sizeof(T) * inputLayerNormWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputLayerNormWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ if (hasForgetLayerNormWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(
+ reinterpret_cast<const uint8_t *>(forgetLayerNormWeights.data()),
+ sizeof(T) * forgetLayerNormWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("forgetLayerNormWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ if (hasCellLayerNormWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellLayerNormWeights.data()),
+ sizeof(T) * cellLayerNormWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellLayerNormWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ if (hasOutputLayerNormWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(
+ reinterpret_cast<const uint8_t *>(outputLayerNormWeights.data()),
+ sizeof(T) * outputLayerNormWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(),
+ tensorInfo4.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("outputLayerNormWeights"),
+ outputQuantizationParameters));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+ int outputBufferId = buffers.size();
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputShape.data(),
+ outputShape.size()),
+ tensorType,
+ outputBufferId,
+ flatBufferBuilder.CreateString("output"),
+ outputQuantizationParameters));
+ std::vector<int> operatorOutputs;
+ operatorOutputs.push_back(buffers.size() - 1);
+
+ // create operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_LSTMOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions =
+ CreateLSTMOptions(flatBufferBuilder,
+ activationFunction,
+ clippingThresCell,
+ clippingThresProj).Union();
+
+ flatbuffers::Offset <Operator> lstmOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType, operatorBuiltinOptions);
+
+ flatbuffers::Offset <SubGraph> subgraph =
+ CreateSubGraph(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ flatBufferBuilder.CreateVector(&lstmOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: LSTM Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
+ tflite::BuiltinOperator_LSTM);
+
+ 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 LstmTestImpl(std::vector<armnn::BackendId>& backends,
+ tflite::TensorType tensorType,
+ int32_t batchSize,
+ int32_t inputSize,
+ int32_t outputSize,
+ int32_t numUnits,
+ bool hasInputToInputWeights,
+ const std::vector<T>& inputToInputWeights,
+ const std::vector<T>& inputToForgetWeights,
+ const std::vector<T>& inputToCellWeights,
+ const std::vector<T>& inputToOutputWeights,
+ bool hasRecurrentToInputWeights,
+ const std::vector<T>& recurrentToInputWeights,
+ const std::vector<T>& recurrentToForgetWeights,
+ const std::vector<T>& recurrentToCellWeights,
+ const std::vector<T>& recurrentToOutputWeights,
+ bool hasCellToInputWeights,
+ const std::vector<T>& cellToInputWeights,
+ bool hasCellToForgetWeights,
+ const std::vector<T>& cellToForgetWeights,
+ bool hasCellToOutputWeights,
+ const std::vector<T>& cellToOutputWeights,
+ bool hasInputGateBias,
+ const std::vector<T>& inputGateBias,
+ const std::vector<T>& forgetGateBias,
+ const std::vector<T>& cellBias,
+ const std::vector<T>& outputGateBias,
+ bool hasProjectionWeights,
+ const std::vector<T>& projectionWeights,
+ bool hasProjectionBias,
+ const std::vector<T>& projectionBias,
+ bool hasInputLayerNormWeights,
+ const std::vector<T>& inputLayerNormWeights,
+ bool hasForgetLayerNormWeights,
+ const std::vector<T>& forgetLayerNormWeights,
+ bool hasCellLayerNormWeights,
+ const std::vector<T>& cellLayerNormWeights,
+ bool hasOutputLayerNormWeights,
+ const std::vector<T>& outputLayerNormWeights,
+ std::vector<T>& inputValues,
+ std::vector<T>& expectedOutputValues,
+ tflite::ActivationFunctionType activationFunction,
+ float clippingThresCell,
+ float clippingThresProj)
+{
+ using namespace tflite;
+
+ std::vector<char> modelBuffer = CreateLstmTfLiteModel(tensorType,
+ batchSize,
+ inputSize,
+ outputSize,
+ numUnits,
+ hasInputToInputWeights,
+ inputToInputWeights,
+ inputToForgetWeights,
+ inputToCellWeights,
+ inputToOutputWeights,
+ hasRecurrentToInputWeights,
+ recurrentToInputWeights,
+ recurrentToForgetWeights,
+ recurrentToCellWeights,
+ recurrentToOutputWeights,
+ hasCellToInputWeights,
+ cellToInputWeights,
+ hasCellToForgetWeights,
+ cellToForgetWeights,
+ hasCellToOutputWeights,
+ cellToOutputWeights,
+ hasInputGateBias,
+ inputGateBias,
+ forgetGateBias,
+ cellBias,
+ outputGateBias,
+ hasProjectionWeights,
+ projectionWeights,
+ hasProjectionBias,
+ projectionBias,
+ hasInputLayerNormWeights,
+ inputLayerNormWeights,
+ hasForgetLayerNormWeights,
+ forgetLayerNormWeights,
+ hasCellLayerNormWeights,
+ cellLayerNormWeights,
+ hasOutputLayerNormWeights,
+ outputLayerNormWeights,
+ activationFunction,
+ clippingThresCell,
+ clippingThresProj);
+
+ 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 tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0];
+ auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ tfLiteDelageInputData[i] = inputValues[i];
+ }
+
+ auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0];
+ auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ armnnDelegateInputData[i] = inputValues[i];
+ }
+
+ // Run EnqueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+ auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId);
+ auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+ auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId);
+
+ armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, expectedOutputValues.size());
+ armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, expectedOutputValues.size());
+ armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, expectedOutputValues.size());
+}
+
+} // anonymous namespace
\ No newline at end of file