| // |
| // Copyright © 2020 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "QLstmEndToEndTestImpl.hpp" |
| |
| #include "CommonTestUtils.hpp" |
| #include "EndToEndTestImpl.hpp" |
| |
| #include <armnn/INetwork.hpp> |
| #include <armnn/LstmParams.hpp> |
| |
| #include <boost/test/unit_test.hpp> |
| |
| namespace |
| { |
| |
| // Checks if two values of an arithmetic type are close enough to each other |
| // with regard to a given tolerance value. |
| template<typename T> |
| typename std::enable_if<std::is_arithmetic<T>::value, bool>::type |
| IsCloseEnough(T value1, T value2, T tolerance) |
| { |
| if (tolerance < 0) |
| { |
| throw armnn::InvalidArgumentException("Tolerance cannot be < 0"); |
| } |
| |
| T diff = value1 >= value2 ? static_cast<T>(value1 - value2) : static_cast<T>(value2 - value1); |
| return diff <= tolerance; |
| } |
| |
| } // anonymous namespace |
| |
| void QLstmEndToEnd(const std::vector<armnn::BackendId>& backends) |
| { |
| const unsigned int numBatches = 2; |
| const unsigned int inputSize = 5; |
| const unsigned int outputSize = 4; |
| const unsigned int numUnits = 4; |
| |
| bool cifgEnabled = true; |
| bool peepholeEnabled = false; |
| bool projectionEnabled = false; |
| bool layerNormEnabled = true; |
| |
| // Scale/Offset quantization info |
| const float inputScale = 0.0078125f; |
| const int32_t inputOffset = 0; |
| |
| const int32_t hiddenStateZeroPoint = 0; |
| const float hiddenStateScale = 0.007f; |
| |
| // if (!projectionEnabled) outputScale == hiddenStateScale |
| const float outputScale = hiddenStateScale; |
| const int32_t outputOffset = hiddenStateZeroPoint; |
| |
| const float cellStateScale = 3.05176e-05f; |
| const int32_t cellStateOffset = 0; |
| |
| const float weightsScale = 0.00784314f; |
| const int32_t weightsOffset = 0; |
| |
| const float layerNormScale = 3.05182e-05f; |
| const int32_t layerNormOffset = 0; |
| |
| const float biasScale = layerNormScale / 1024; |
| const int32_t biasOffset = 0; |
| |
| const float inputIntermediateScale = 0.007059f; |
| const float forgetIntermediateScale = 0.007812f; |
| const float cellIntermediateScale = inputIntermediateScale; |
| const float outputIntermediateScale = forgetIntermediateScale; |
| |
| const float cellClip = 0.0f; |
| const float projectionClip = 0.0f; |
| |
| // Weights and bias tensor info |
| const armnn::TensorInfo inputWeightsInfo({outputSize, inputSize}, |
| armnn::DataType::QSymmS8, |
| weightsScale, |
| weightsOffset); |
| |
| const armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize}, |
| armnn::DataType::QSymmS8, |
| weightsScale, |
| weightsOffset); |
| |
| const armnn::TensorInfo biasInfo({outputSize}, |
| armnn::DataType::Signed32, |
| biasScale, |
| biasOffset); |
| |
| const armnn::TensorInfo layerNormWeightsInfo({numUnits}, |
| armnn::DataType::QSymmS16, |
| layerNormScale, |
| layerNormOffset); |
| |
| // Mandatory params |
| const std::vector<int8_t> inputToForgetWeightsVector = |
| {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64}; |
| const std::vector<int8_t> inputToCellWeightsTensorVector = |
| {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77}; |
| const std::vector<int8_t> inputToOutputWeightsTensorVector = |
| {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51}; |
| |
| armnn::ConstTensor inputToForgetWeightsTensor(inputWeightsInfo, inputToForgetWeightsVector.data()); |
| armnn::ConstTensor inputToCellWeightsTensor(inputWeightsInfo, inputToCellWeightsTensorVector.data()); |
| armnn::ConstTensor inputToOutputWeightsTensor(inputWeightsInfo, inputToOutputWeightsTensorVector.data()); |
| |
| const std::vector<int8_t> recurrentToForgetWeightsTensorVector = |
| {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25, 25, 38, -13, 51}; |
| const std::vector<int8_t> recurrentToCellWeightsTensorVector = |
| {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25, 38, -13, 25, 64}; |
| const std::vector<int8_t> recurrentToOutputWeightsTensorVector = |
| {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25, 13, 64, 25, -38}; |
| |
| armnn::ConstTensor recurrentToForgetWeightsTensor(recurrentWeightsInfo, |
| recurrentToForgetWeightsTensorVector.data()); |
| armnn::ConstTensor recurrentToCellWeightsTensor(recurrentWeightsInfo, |
| recurrentToCellWeightsTensorVector.data()); |
| armnn::ConstTensor recurrentToOutputWeightsTensor(recurrentWeightsInfo, |
| recurrentToOutputWeightsTensorVector.data()); |
| |
| const std::vector<int32_t> forgetGateBiasTensorVector = {2147484, -6442451, -4294968, 2147484}; |
| const std::vector<int32_t> cellBiasTensorVector = {-1073742, 15461883, 5368709, 1717987}; |
| const std::vector<int32_t> outputGateBiasTensorVector = {1073742, -214748, 4294968, 2147484}; |
| |
| armnn::ConstTensor forgetGateBiasTensor(biasInfo, forgetGateBiasTensorVector.data()); |
| armnn::ConstTensor cellBiasTensor(biasInfo, cellBiasTensorVector.data()); |
| armnn::ConstTensor outputGateBiasTensor(biasInfo, outputGateBiasTensorVector.data()); |
| |
| // Layer Norm |
| const std::vector<int16_t> forgetLayerNormWeightsVector = {6553, 6553, 13107, 9830}; |
| const std::vector<int16_t> cellLayerNormWeightsVector = {22937, 6553, 9830, 26214}; |
| const std::vector<int16_t> outputLayerNormWeightsVector = {19660, 6553, 6553, 16384}; |
| |
| armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsVector.data()); |
| armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsVector.data()); |
| armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsVector.data()); |
| |
| // Set up params |
| armnn::LstmInputParams params; |
| params.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| params.m_InputToCellWeights = &inputToCellWeightsTensor; |
| params.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| |
| params.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| params.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| params.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| |
| params.m_ForgetGateBias = &forgetGateBiasTensor; |
| params.m_CellBias = &cellBiasTensor; |
| params.m_OutputGateBias = &outputGateBiasTensor; |
| |
| params.m_ForgetLayerNormWeights = &forgetLayerNormWeights; |
| params.m_CellLayerNormWeights = &cellLayerNormWeights; |
| params.m_OutputLayerNormWeights = &outputLayerNormWeights; |
| |
| QLstmDescriptor descriptor; |
| descriptor.m_CifgEnabled = cifgEnabled; |
| descriptor.m_PeepholeEnabled = peepholeEnabled; |
| descriptor.m_ProjectionEnabled = projectionEnabled; |
| descriptor.m_LayerNormEnabled = layerNormEnabled; |
| |
| descriptor.m_CellClip = cellClip; |
| descriptor.m_ProjectionClip = projectionClip; |
| |
| descriptor.m_HiddenStateZeroPoint = hiddenStateZeroPoint; |
| descriptor.m_HiddenStateScale = hiddenStateScale; |
| |
| descriptor.m_InputIntermediateScale = inputIntermediateScale; |
| descriptor.m_ForgetIntermediateScale = forgetIntermediateScale; |
| descriptor.m_CellIntermediateScale = cellIntermediateScale; |
| descriptor.m_OutputIntermediateScale = outputIntermediateScale; |
| |
| // Input/Output tensor info |
| const armnn::TensorInfo inputInfo({numBatches , inputSize}, |
| armnn::DataType::QAsymmS8, |
| inputScale, |
| inputOffset); |
| |
| const armnn::TensorInfo cellStateInfo({numBatches , numUnits}, |
| armnn::DataType::QSymmS16, |
| cellStateScale, |
| cellStateOffset); |
| |
| const armnn::TensorInfo outputStateInfo({numBatches , outputSize}, |
| armnn::DataType::QAsymmS8, |
| outputScale, |
| outputOffset); |
| |
| // Input tensor data |
| const std::vector<int8_t> inputVector = {90, 102, 13, 26, 38, 102, 13, 26, 51, 64}; |
| const std::vector<int8_t> outputStateInVector = {0, 0, 0, 0, 0, 0, 0, 0}; |
| const std::vector<int16_t> cellStateInVector = {0, 0, 0, 0, 0, 0, 0, 0}; |
| |
| // Expected output tensor data |
| const std::vector<int8_t> outputStateOutVector = {-15, 21, 14, 20, -15, 15, 5, 27}; |
| const std::vector<int16_t> cellStateOutVector = {-11692, 9960, 5491, 8861, -9422, 7726, 2056, 13149}; |
| const std::vector<int8_t> outputVector = {-15, 21, 14, 20, -15, 15, 5, 27}; |
| |
| // Build network |
| armnn::INetworkPtr net(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* const input = net->AddInputLayer(0); |
| armnn::IConnectableLayer* const outputStateIn = net->AddInputLayer(1); |
| armnn::IConnectableLayer* const cellStateIn = net->AddInputLayer(2); |
| |
| armnn::IConnectableLayer* const qLstmLayer = net->AddQLstmLayer(descriptor, params, "qLstm"); |
| |
| armnn::IConnectableLayer* const outputStateOut = net->AddOutputLayer(0); |
| armnn::IConnectableLayer* const cellStateOut = net->AddOutputLayer(1); |
| armnn::IConnectableLayer* const output = net->AddOutputLayer(2); |
| |
| // Connect input/output slots |
| Connect(input, qLstmLayer, inputInfo, 0, 0); |
| Connect(outputStateIn, qLstmLayer, outputStateInfo, 0, 1); |
| Connect(cellStateIn, qLstmLayer, cellStateInfo, 0, 2); |
| |
| Connect(qLstmLayer, outputStateOut, outputStateInfo, 0, 0); |
| Connect(qLstmLayer, cellStateOut, cellStateInfo, 1, 0); |
| Connect(qLstmLayer, output, outputStateInfo, 2, 0); |
| |
| // Create runtime |
| IRuntime::CreationOptions options; |
| IRuntimePtr runtime(IRuntime::Create(options)); |
| |
| // Optimize the network |
| IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); |
| |
| // Loads network into runtime |
| NetworkId netId; |
| runtime->LoadNetwork(netId, std::move(optNet)); |
| |
| // Push back input tensors |
| InputTensors inputTensors; |
| inputTensors.reserve(3); |
| |
| inputTensors.push_back({0, ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputVector.data())}); |
| inputTensors.push_back({1, ConstTensor(runtime->GetInputTensorInfo(netId, 1), outputStateInVector.data())}); |
| inputTensors.push_back({2, ConstTensor(runtime->GetInputTensorInfo(netId, 2), cellStateInVector.data())}); |
| |
| // Push back output tensors |
| OutputTensors outputTensors; |
| outputTensors.reserve(3); |
| |
| std::vector<int8_t> outputStateOutResult(outputStateOutVector.size()); |
| std::vector<int16_t> cellStateOutResult(cellStateOutVector.size()); |
| std::vector<int8_t> outputResult(outputStateOutVector.size()); |
| |
| outputTensors.push_back({0, Tensor(runtime->GetOutputTensorInfo(netId, 0), outputStateOutResult.data())}); |
| outputTensors.push_back({1, Tensor(runtime->GetOutputTensorInfo(netId, 1), cellStateOutResult.data())}); |
| outputTensors.push_back({2, Tensor(runtime->GetOutputTensorInfo(netId, 2), outputResult.data())}); |
| |
| // Execute inference |
| runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| |
| constexpr int8_t toleranceInt8 = 1; |
| for (unsigned int i = 0u; i < outputStateOutResult.size(); ++i) |
| { |
| BOOST_TEST(IsCloseEnough(outputStateOutVector[i], outputStateOutResult[i], toleranceInt8)); |
| } |
| |
| for (unsigned int i = 0u; i < outputResult.size(); ++i) |
| { |
| BOOST_TEST(IsCloseEnough(outputVector[i], outputResult[i], toleranceInt8)); |
| } |
| } |