MLCE-530 Add support for UnidirectionalSequenceLstm to armnn delegate
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: Ib04f8d6b9e60a4204c56eba4c2ecd2b316509dcc
diff --git a/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp b/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp
new file mode 100644
index 0000000..9d6ef87
--- /dev/null
+++ b/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp
@@ -0,0 +1,722 @@
+//
+// Copyright © 2021 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 <tensorflow/lite/c/common.h>
+
+#include <doctest/doctest.h>
+
+
+#include <armnn/utility/IgnoreUnused.hpp>
+#include <armnn/utility/NumericCast.hpp>
+#include <armnn/TypesUtils.hpp>
+
+#include <armnn/Types.hpp>
+
+#include <initializer_list>
+#include <iterator>
+#include <vector>
+
+namespace
+{
+
+template <typename T>
+std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType tensorType,
+ int32_t batchSize,
+ int32_t timeSize,
+ 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<float>& inputGateBias,
+ const std::vector<float>& forgetGateBias,
+ const std::vector<float>& cellBias,
+ const std::vector<float>& outputGateBias,
+ bool hasProjectionWeights,
+ const std::vector<T>& projectionWeights,
+ bool hasProjectionBias,
+ const std::vector<float>& projectionBias,
+ bool hasInputLayerNormWeights,
+ const std::vector<float>& inputLayerNormWeights,
+ bool hasForgetLayerNormWeights,
+ const std::vector<float>& forgetLayerNormWeights,
+ bool hasCellLayerNormWeights,
+ const std::vector<float>& cellLayerNormWeights,
+ bool hasOutputLayerNormWeights,
+ const std::vector<float>& outputLayerNormWeights,
+ tflite::ActivationFunctionType activationFunction,
+ float clippingThresCell,
+ float clippingThresProj,
+ bool isTimeMajor,
+ float quantScale,
+ int quantOffset = 0)
+{
+
+ std::vector<int32_t> tensorInfo0{};
+ std::vector<int32_t> tensorInfoNumUnits{numUnits};
+ std::vector<int32_t> tensorInfoInputSize{numUnits, inputSize};
+ std::vector<int32_t> tensorInfoOutputSize{numUnits, outputSize};
+
+ std::vector<int32_t> inputShape;
+ std::vector<int32_t> outputShape;
+ if (isTimeMajor)
+ {
+ inputShape = {timeSize, batchSize, inputSize};
+ outputShape = {timeSize, batchSize, outputSize};
+ }
+ else
+ {
+ inputShape = {batchSize, timeSize, inputSize};
+ outputShape = {batchSize, timeSize, outputSize};
+ }
+ std::vector<int32_t> outputStateInDimensions{batchSize, outputSize};
+ std::vector<int32_t> cellStateInDimensions{batchSize, numUnits};
+ std::vector<int32_t> projectionWeightDimensions{outputSize, numUnits};
+ std::vector<int32_t> projectionBiasDimensions{outputSize};
+
+ 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>({ 1.0f }),
+ flatBufferBuilder.CreateVector<int64_t>({ 0 }));
+
+ auto weightQuantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ quantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputShape.data(),
+ inputShape.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("input_0")));
+ 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>(tensorInfoInputSize.data(),
+ tensorInfoInputSize.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputToInputWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoInputSize.data(),
+ tensorInfoInputSize.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputToForgetWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoInputSize.data(),
+ tensorInfoInputSize.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputToCellWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoInputSize.data(),
+ tensorInfoInputSize.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputToOutputWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoOutputSize.data(),
+ tensorInfoOutputSize.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("recurrentToInputWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoOutputSize.data(),
+ tensorInfoOutputSize.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("recurrentToForgetWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoOutputSize.data(),
+ tensorInfoOutputSize.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("recurrentToCellWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoOutputSize.data(),
+ tensorInfoOutputSize.size()),
+ tensorType,
+ buffers.size() - 1 ,
+ flatBufferBuilder.CreateString("recurrentToOutputWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellToInputWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellToForgetWeights"),
+ weightQuantizationParameters));
+ 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>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellToOutputWeights"),
+ weightQuantizationParameters));
+ 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(float) * inputGateBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputGateBias")));
+ 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(float) * forgetGateBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("forgetGateBias")));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellBias.data()),
+ sizeof(float) * cellBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellBias")));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(outputGateBias.data()),
+ sizeof(float) * outputGateBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("outputGateBias")));
+ 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>(projectionWeightDimensions.data(),
+ projectionWeightDimensions.size()),
+ tensorType,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("projectionWeights"),
+ weightQuantizationParameters));
+ 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(float) * projectionBias.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(projectionBiasDimensions.data(),
+ projectionBiasDimensions.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("projectionBias")));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputStateInDimensions.data(),
+ outputStateInDimensions.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("outputStateInInfo"),
+ quantizationParameters,
+ true));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(cellStateInDimensions.data(),
+ cellStateInDimensions.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellStateInInfo"),
+ quantizationParameters,
+ true));
+ operatorInputs.push_back(buffers.size() - 1);
+
+ if (hasInputLayerNormWeights)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(
+ reinterpret_cast<const uint8_t *>(inputLayerNormWeights.data()),
+ sizeof(float) * inputLayerNormWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("inputLayerNormWeights")));
+ 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(float) * forgetLayerNormWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("forgetLayerNormWeights")));
+ 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(float) * cellLayerNormWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("cellLayerNormWeights")));
+ 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(float) * outputLayerNormWeights.size())));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(),
+ tensorInfoNumUnits.size()),
+ ::tflite::TensorType_FLOAT32,
+ buffers.size() - 1,
+ flatBufferBuilder.CreateString("outputLayerNormWeights")));
+ operatorInputs.push_back(buffers.size() - 1);
+ }
+ else
+ {
+ operatorInputs.push_back(kTfLiteOptionalTensor);
+ }
+ int outputBufferId = buffers.size();
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+ tensors.push_back(CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputShape.data(),
+ outputShape.size()),
+ ::tflite::TensorType_FLOAT32,
+ outputBufferId,
+ flatBufferBuilder.CreateString("output")));
+ std::vector<int> operatorOutputs;
+ operatorOutputs.push_back(buffers.size() - 1);
+
+ // create operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_UnidirectionalSequenceLSTMOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions =
+ CreateUnidirectionalSequenceLSTMOptions(flatBufferBuilder,
+ activationFunction,
+ clippingThresCell,
+ clippingThresProj,
+ isTimeMajor).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: UnidirectionalSequenceLSTM Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode =
+ CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_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 UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends,
+ tflite::TensorType tensorType,
+ int32_t batchSize,
+ int32_t timeSize,
+ 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<float>& inputGateBias,
+ const std::vector<float>& forgetGateBias,
+ const std::vector<float>& cellBias,
+ const std::vector<float>& outputGateBias,
+ bool hasProjectionWeights,
+ const std::vector<T>& projectionWeights,
+ bool hasProjectionBias,
+ const std::vector<float>& projectionBias,
+ bool hasInputLayerNormWeights,
+ const std::vector<float>& inputLayerNormWeights,
+ bool hasForgetLayerNormWeights,
+ const std::vector<float>& forgetLayerNormWeights,
+ bool hasCellLayerNormWeights,
+ const std::vector<float>& cellLayerNormWeights,
+ bool hasOutputLayerNormWeights,
+ const std::vector<float>& outputLayerNormWeights,
+ std::vector<float>& inputValues,
+ std::vector<float>& expectedOutputValues,
+ tflite::ActivationFunctionType activationFunction,
+ float clippingThresCell,
+ float clippingThresProj,
+ bool isTimeMajor,
+ float quantScale = 0.1f)
+{
+ using namespace tflite;
+
+ std::vector<char> modelBuffer = CreateUnidirectionalSequenceLstmTfLiteModel(tensorType,
+ batchSize,
+ timeSize,
+ 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,
+ isTimeMajor,
+ quantScale);
+
+ 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<float>(tfLiteDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ tfLiteDelageInputData[i] = inputValues[i];
+ }
+
+ auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0];
+ auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ armnnDelegateInputData[i] = inputValues[i];
+ }
+
+ // Run EnqueueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+ auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
+ auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+ auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
+
+ if (tensorType == ::tflite::TensorType_INT8)
+ {
+ // Allow 2% tolerance for Quantized weights
+ armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData,
+ expectedOutputValues.size(), 2);
+ armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData,
+ expectedOutputValues.size(), 2);
+ armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData,
+ expectedOutputValues.size(), 2);
+ }
+ else
+ {
+ 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