| // |
| // Copyright © 2019 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "QuantizedLstmEndToEndTestImpl.hpp" |
| |
| #include <CommonTestUtils.hpp> |
| #include "EndToEndTestImpl.hpp" |
| |
| #include <ResolveType.hpp> |
| |
| #include <armnn/INetwork.hpp> |
| #include <armnn/QuantizedLstmParams.hpp> |
| |
| #include <armnn/utility/NumericCast.hpp> |
| |
| #include <TensorHelpers.hpp> |
| |
| #include <doctest/doctest.h> |
| |
| #include <type_traits> |
| |
| namespace |
| { |
| |
| armnn::INetworkPtr CreateQuantizedLstmNetwork(armnn::TensorShape& inputShape, |
| armnn::TensorShape& outputExpectedShape) |
| { |
| auto batchSize = armnn::numeric_cast<unsigned int>(inputShape[0]); |
| auto inputSize = armnn::numeric_cast<unsigned int>(inputShape[1]); |
| auto outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[1]); |
| |
| float inputOutputScale = 0.0078125f; |
| int32_t inputOutputOffset = 128; |
| |
| float weightsScale = 0.00408021f; |
| int32_t weightsOffset = 100; |
| |
| float biasScale = 3.1876640625e-05f; |
| int32_t biasOffset = 0; |
| |
| float cellStateScale = 0.00048828125f; |
| int32_t cellStateOffset = 0; |
| |
| armnn::TensorInfo inputWeightsInfo({outputSize, inputSize}, |
| armnn::DataType::QAsymmU8, |
| weightsScale, |
| weightsOffset, |
| true); |
| |
| armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize}, |
| armnn::DataType::QAsymmU8, |
| weightsScale, |
| weightsOffset, |
| true); |
| |
| armnn::TensorInfo biasInfo({outputSize}, armnn::DataType::Signed32, biasScale, biasOffset, true); |
| |
| armnn::QuantizedLstmInputParams data; |
| |
| const std::vector<uint8_t> inputToInputWeightsVector = {146, 250, 235, 171, 10, 218, 171, 108}; |
| armnn::ConstTensor inputToInputWeightsTensor(inputWeightsInfo, inputToInputWeightsVector.data()); |
| |
| const std::vector<uint8_t> inputToForgetWeightsVector = {24, 50, 132, 179, 158, 110, 3, 169}; |
| armnn::ConstTensor inputToForgetWeightsTensor(inputWeightsInfo, inputToForgetWeightsVector.data()); |
| |
| const std::vector<uint8_t> inputToCellWeightsTensorVector = {133, 34, 29, 49, 206, 109, 54, 183}; |
| armnn::ConstTensor inputToCellWeightsTensor(inputWeightsInfo, inputToCellWeightsTensorVector.data()); |
| |
| const std::vector<uint8_t> inputToOutputWeightsTensorVector = {195, 187, 11, 99, 109, 10, 218, 48}; |
| armnn::ConstTensor inputToOutputWeightsTensor(inputWeightsInfo, inputToOutputWeightsTensorVector.data()); |
| |
| const std::vector<uint8_t> recurrentToInputWeightsTensorVector = |
| {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26}; |
| armnn::ConstTensor recurrentToInputWeightsTensor(recurrentWeightsInfo, recurrentToInputWeightsTensorVector.data()); |
| |
| const std::vector<uint8_t> recurrentToForgetWeightsTensorVector = |
| {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253}; |
| armnn::ConstTensor recurrentToForgetWeightsTensor(recurrentWeightsInfo, |
| recurrentToForgetWeightsTensorVector.data()); |
| |
| const std::vector<uint8_t> recurrentToCellWeightsTensorVector = |
| {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216}; |
| armnn::ConstTensor recurrentToCellWeightsTensor(recurrentWeightsInfo, recurrentToCellWeightsTensorVector.data()); |
| |
| const std::vector<uint8_t> recurrentToOutputWeightsTensorVector = |
| {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98}; |
| armnn::ConstTensor recurrentToOutputWeightsTensor(recurrentWeightsInfo, |
| recurrentToOutputWeightsTensorVector.data()); |
| |
| const std::vector<int32_t> inputGateBiasTensorVector = {-7876, 13488, -726, 32839}; |
| armnn::ConstTensor inputGateBiasTensor(biasInfo, inputGateBiasTensorVector.data()); |
| |
| const std::vector<int32_t> forgetGateBiasTensorVector = {9206, -46884, -11693, -38724}; |
| armnn::ConstTensor forgetGateBiasTensor(biasInfo, forgetGateBiasTensorVector.data()); |
| |
| const std::vector<int32_t> cellBiasTensorVector = {39481, 48624, 48976, -21419}; |
| armnn::ConstTensor cellBiasTensor(biasInfo, cellBiasTensorVector.data()); |
| |
| const std::vector<int32_t> outputGateBiasTensorVector = {-58999, -17050, -41852, -40538}; |
| armnn::ConstTensor outputGateBiasTensor(biasInfo, outputGateBiasTensorVector.data()); |
| |
| data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| data.m_InputGateBias = &inputGateBiasTensor; |
| data.m_ForgetGateBias = &forgetGateBiasTensor; |
| data.m_CellBias = &cellBiasTensor; |
| data.m_OutputGateBias = &outputGateBiasTensor; |
| |
| armnn::INetworkPtr net(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* const inputLayer = net->AddInputLayer(0); |
| armnn::IConnectableLayer* const cellStateIn = net->AddInputLayer(1); |
| armnn::IConnectableLayer* const outputStateIn = net->AddInputLayer(2); |
| armnn::IConnectableLayer* const quantizedLstmLayer = net->AddQuantizedLstmLayer(data, "quantizedLstm"); |
| armnn::IConnectableLayer* const cellStateOut = net->AddOutputLayer(0); |
| armnn::IConnectableLayer* const outputStateOut = net->AddOutputLayer(1); |
| |
| armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, |
| armnn::DataType::QAsymmU8, |
| inputOutputScale, |
| inputOutputOffset); |
| |
| armnn::TensorInfo cellStateInTensorInfo({batchSize , outputSize}, |
| armnn::DataType::QSymmS16, |
| cellStateScale, |
| cellStateOffset); |
| |
| armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, |
| armnn::DataType::QAsymmU8, |
| inputOutputScale, |
| inputOutputOffset); |
| |
| armnn::TensorInfo cellStateOutTensorInfo({batchSize, outputSize}, |
| armnn::DataType::QSymmS16, |
| cellStateScale, |
| cellStateOffset); |
| |
| armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, |
| armnn::DataType::QAsymmU8, |
| inputOutputScale, |
| inputOutputOffset); |
| |
| // connect up |
| // inputs |
| Connect(inputLayer, quantizedLstmLayer, inputTensorInfo, 0, 0); |
| Connect(cellStateIn, quantizedLstmLayer, cellStateInTensorInfo, 0, 1); |
| Connect(outputStateIn, quantizedLstmLayer, outputStateInTensorInfo, 0, 2); |
| |
| // outputs |
| Connect(quantizedLstmLayer, cellStateOut, cellStateOutTensorInfo, 0, 0); |
| Connect(quantizedLstmLayer, outputStateOut, outputTensorInfo, 1, 0); |
| |
| return net; |
| } |
| |
| // 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 QuantizedLstmEndToEnd(const std::vector<armnn::BackendId>& backends) |
| { |
| std::vector<uint8_t> inputVector = {166, 179, 50, 150}; |
| armnn::TensorInfo inputDesc({2, 2}, armnn::DataType::QAsymmU8); |
| |
| std::vector<int16_t> cellStateInVector = {876, 1034, 955, -909, 761, 1029, 796, -1036}; |
| armnn::TensorInfo cellStateInDesc({2, 4}, armnn::DataType::QSymmS16); |
| |
| std::vector<uint8_t> outputStateInVector = {136, 150, 140, 115, 135, 152, 138, 112}; |
| armnn::TensorInfo outputStateInDesc({2, 4}, armnn::DataType::QAsymmU8); |
| |
| std::vector<int16_t> cellStateOutVector = {1485, 1177, 1373, -1023, 1019, 1355, 1097, -1235}; |
| armnn::TensorInfo cellStateOutVectorDesc({2, 4}, armnn::DataType::QSymmS16); |
| |
| std::vector<uint8_t> outputStateOutVector = {140, 151, 146, 112, 136, 156, 142, 112}; |
| armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::QAsymmU8); |
| |
| // Builds up the structure of the network |
| armnn::INetworkPtr net = CreateQuantizedLstmNetwork(inputDesc.GetShape(), outputDesc.GetShape()); |
| |
| IRuntime::CreationOptions options; |
| IRuntimePtr runtime(IRuntime::Create(options)); |
| |
| // optimize the network |
| IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec()); |
| |
| // Loads it into the runtime. |
| NetworkId netId; |
| runtime->LoadNetwork(netId, std::move(optNet)); |
| |
| InputTensors inputTensors; |
| inputTensors.reserve(3); |
| |
| // input |
| TensorInfo inputTensorInfo0 = runtime->GetInputTensorInfo(netId, 0); |
| TensorInfo inputTensorInfo1 = runtime->GetInputTensorInfo(netId, 1); |
| TensorInfo inputTensorInfo2 = runtime->GetInputTensorInfo(netId, 2); |
| inputTensorInfo0.SetConstant(true); |
| inputTensorInfo1.SetConstant(true); |
| inputTensorInfo2.SetConstant(true); |
| |
| inputTensors.push_back({0, ConstTensor(inputTensorInfo0, inputVector.data())}); |
| inputTensors.push_back({1, ConstTensor(inputTensorInfo1, cellStateInVector.data())}); |
| inputTensors.push_back({2, ConstTensor(inputTensorInfo2, outputStateInVector.data())}); |
| |
| OutputTensors outputTensors; |
| outputTensors.reserve(2); |
| |
| //output |
| std::vector<int16_t> cellStateOutResult(cellStateOutVector.size()); |
| std::vector<uint8_t> outputStateOutResult(outputStateOutVector.size()); |
| outputTensors.push_back({0, Tensor(runtime->GetOutputTensorInfo(netId, 0), cellStateOutResult.data())}); |
| outputTensors.push_back({1, Tensor(runtime->GetOutputTensorInfo(netId, 1), outputStateOutResult.data())}); |
| |
| // Does the inference. |
| runtime->EnqueueWorkload(netId, inputTensors, outputTensors); |
| |
| // Checks the results |
| constexpr int16_t toleranceInt16 = 2; |
| for (unsigned int i = 0u; i < cellStateOutResult.size(); ++i) |
| { |
| CHECK(IsCloseEnough(cellStateOutVector[i], cellStateOutResult[i], toleranceInt16)); |
| } |
| |
| constexpr uint8_t toleranceUint8 = 1; |
| for (unsigned int i = 0u; i < outputStateOutResult.size(); ++i) |
| { |
| CHECK(IsCloseEnough(outputStateOutVector[i], outputStateOutResult[i], toleranceUint8)); |
| } |
| } |