| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "DriverTestHelpers.hpp" |
| |
| #include <armnn/utility/IgnoreUnused.hpp> |
| |
| #include <array> |
| |
| using ArmnnDriver = armnn_driver::ArmnnDriver; |
| using DriverOptions = armnn_driver::DriverOptions; |
| using RequestArgument = V1_0::RequestArgument; |
| |
| #ifdef ARMNN_ANDROID_S |
| #include <nnapi/Types.h> |
| #endif |
| |
| using namespace driverTestHelpers; |
| using namespace android::hardware; |
| |
| namespace |
| { |
| |
| template<typename T> |
| RequestArgument CreateRequestArgument(const std::vector<T>& value, unsigned int poolIndex) |
| { |
| V1_0::DataLocation inputInloc = {}; |
| inputInloc.poolIndex = poolIndex; |
| inputInloc.offset = 0; |
| inputInloc.length = value.size() * sizeof(T); |
| RequestArgument inputRequestArgument = {}; |
| inputRequestArgument.location = inputInloc; |
| inputRequestArgument.dimensions = hidl_vec<uint32_t>{}; |
| return inputRequestArgument; |
| } |
| |
| // Helper function to create an OperandLifeTime::NO_VALUE for testing. |
| // To be used on optional input operands that have no values - these are valid and should be tested. |
| V1_0::OperandLifeTime CreateNoValueLifeTime(const hidl_vec<uint32_t>& dimensions) |
| { |
| // Only create a NO_VALUE for optional operands that have no elements |
| if (dimensions.size() == 0 || dimensions[0] == 0) |
| { |
| return V1_0::OperandLifeTime::NO_VALUE; |
| } |
| return V1_0::OperandLifeTime::CONSTANT_COPY; |
| } |
| |
| template<typename HalModel> |
| void ExecuteModel(const HalModel& model, armnn_driver::ArmnnDriver& driver, const V1_0::Request& request) |
| { |
| android::sp<V1_0::IPreparedModel> preparedModel = PrepareModel(model, driver); |
| if (preparedModel.get() != nullptr) |
| { |
| Execute(preparedModel, request); |
| } |
| } |
| |
| #if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3) |
| |
| template<> |
| void ExecuteModel<armnn_driver::hal_1_2::HalPolicy::Model>(const armnn_driver::hal_1_2::HalPolicy::Model& model, |
| armnn_driver::ArmnnDriver& driver, |
| const V1_0::Request& request) |
| { |
| android::sp<V1_2::IPreparedModel> preparedModel = PrepareModel_1_2(model, driver); |
| if (preparedModel.get() != nullptr) |
| { |
| Execute(preparedModel, request); |
| } |
| } |
| |
| #endif |
| |
| } // anonymous namespace |
| |
| // Add our own tests here since we fail the unidirectional sequence lstm |
| // tests which Google supplies (because of non-const weights) |
| template <typename HalPolicy> |
| void UnidirectionalSequenceLstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, |
| const std::vector<float>& inputValue, |
| const hidl_vec<uint32_t>& inputToInputWeightsDimensions, |
| const std::vector<float>& inputToInputWeightsValue, |
| const hidl_vec<uint32_t>& inputToForgetWeightsDimensions, |
| const std::vector<float>& inputToForgetWeightsValue, |
| const hidl_vec<uint32_t>& inputToCellWeightsDimensions, |
| const std::vector<float>& inputToCellWeightsValue, |
| const hidl_vec<uint32_t>& inputToOutputWeightsDimensions, |
| const std::vector<float>& inputToOutputWeightsValue, |
| const hidl_vec<uint32_t>& recurrentToInputWeightsDimensions, |
| const std::vector<float>& recurrentToInputWeightsValue, |
| const hidl_vec<uint32_t>& recurrentToForgetWeightsDimensions, |
| const std::vector<float>& recurrentToForgetWeightsValue, |
| const hidl_vec<uint32_t>& recurrentToCellWeightsDimensions, |
| const std::vector<float>& recurrentToCellWeightsValue, |
| const hidl_vec<uint32_t>& recurrentToOutputWeightsDimensions, |
| const std::vector<float>& recurrentToOutputWeightsValue, |
| const hidl_vec<uint32_t>& cellToInputWeightsDimensions, |
| const std::vector<float>& cellToInputWeightsValue, |
| const hidl_vec<uint32_t>& cellToForgetWeightsDimensions, |
| const std::vector<float>& cellToForgetWeightsValue, |
| const hidl_vec<uint32_t>& cellToOutputWeightsDimensions, |
| const std::vector<float>& cellToOutputWeightsValue, |
| const hidl_vec<uint32_t>& inputGateBiasDimensions, |
| const std::vector<float>& inputGateBiasValue, |
| const hidl_vec<uint32_t>& forgetGateBiasDimensions, |
| const std::vector<float>& forgetGateBiasValue, |
| const hidl_vec<uint32_t>& cellBiasDimensions, |
| const std::vector<float>& cellBiasValue, |
| const hidl_vec<uint32_t>& outputGateBiasDimensions, |
| const std::vector<float>& outputGateBiasValue, |
| const hidl_vec<uint32_t>& projectionWeightsDimensions, |
| const std::vector<float>& projectionWeightsValue, |
| const hidl_vec<uint32_t>& projectionBiasDimensions, |
| const std::vector<float>& projectionBiasValue, |
| const hidl_vec<uint32_t>& outputStateInDimensions, |
| const std::vector<float>& outputStateInValue, |
| const hidl_vec<uint32_t>& cellStateInDimensions, |
| const std::vector<float>& cellStateInValue, |
| const hidl_vec<uint32_t>& activationFunctionDimensions, |
| const std::vector<int32_t>& activationFunctionValue, |
| const hidl_vec<uint32_t>& cellClippingThresholdDimensions, |
| const std::vector<float>& cellClippingThresholdValue, |
| const hidl_vec<uint32_t>& projectionClippingThresholdDimensions, |
| const std::vector<float>& projectionClippingThresholdValue, |
| const bool& timeMajorValue, |
| const hidl_vec<uint32_t>& inputLayerNormWeightsDimensions, |
| const std::vector<float>& inputLayerNormWeightsValue, |
| const hidl_vec<uint32_t>& forgetLayerNormWeightsDimensions, |
| const std::vector<float>& forgetLayerNormWeightsValue, |
| const hidl_vec<uint32_t>& cellLayerNormWeightsDimensions, |
| const std::vector<float>& cellLayerNormWeightsValue, |
| const hidl_vec<uint32_t>& outputLayerNormWeightsDimensions, |
| const std::vector<float>& outputLayerNormWeightsValue, |
| const hidl_vec<uint32_t>& outputDimensions, |
| const std::vector<float>& outputValue, |
| const hidl_vec<uint32_t>&, // outputStateOutDimensions, |
| const std::vector<float>&, // outputStateOutValue, |
| const hidl_vec<uint32_t>&, // cellStateOutDimensions, |
| const std::vector<float>&, // cellStateOutValue, |
| armnn::Compute compute, |
| float epsilonValue = 0) |
| { |
| auto driver = std::make_unique<ArmnnDriver>(DriverOptions(compute)); |
| using Model = typename HalPolicy::Model; |
| Model model = {}; |
| |
| // Inputs: |
| // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where |
| // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| AddInputOperand<HalPolicy>(model, inputDimensions); |
| |
| // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| AddTensorOperand<HalPolicy>(model, |
| inputToInputWeightsDimensions, |
| inputToInputWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(inputToInputWeightsDimensions)); |
| // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| AddTensorOperand<HalPolicy>(model, inputToForgetWeightsDimensions, inputToForgetWeightsValue); |
| // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| AddTensorOperand<HalPolicy>(model, inputToCellWeightsDimensions, inputToCellWeightsValue); |
| // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| AddTensorOperand<HalPolicy>(model, inputToOutputWeightsDimensions, inputToOutputWeightsValue); |
| // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| AddTensorOperand<HalPolicy>(model, |
| recurrentToInputWeightsDimensions, |
| recurrentToInputWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(recurrentToInputWeightsDimensions)); |
| // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| AddTensorOperand<HalPolicy>(model, recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue); |
| // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| AddTensorOperand<HalPolicy>(model, recurrentToCellWeightsDimensions, recurrentToCellWeightsValue); |
| // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| AddTensorOperand<HalPolicy>(model, recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue); |
| // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| AddTensorOperand<HalPolicy>(model, |
| cellToInputWeightsDimensions, |
| cellToInputWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(cellToInputWeightsDimensions)); |
| // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| AddTensorOperand<HalPolicy>(model, |
| cellToForgetWeightsDimensions, |
| cellToForgetWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(cellToForgetWeightsDimensions)); |
| // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| AddTensorOperand<HalPolicy>(model, |
| cellToOutputWeightsDimensions, |
| cellToOutputWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(cellToOutputWeightsDimensions)); |
| // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| AddTensorOperand<HalPolicy>(model, |
| inputGateBiasDimensions, |
| inputGateBiasValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(inputGateBiasDimensions)); |
| // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| AddTensorOperand<HalPolicy>(model, forgetGateBiasDimensions, forgetGateBiasValue); |
| // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| AddTensorOperand<HalPolicy>(model, cellBiasDimensions, cellBiasValue); |
| // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| AddTensorOperand<HalPolicy>(model, outputGateBiasDimensions, outputGateBiasValue); |
| // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [output_size, num_units]. |
| AddTensorOperand<HalPolicy>(model, |
| projectionWeightsDimensions, |
| projectionWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(projectionWeightsDimensions)); |
| // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| AddTensorOperand<HalPolicy>(model, |
| projectionBiasDimensions, |
| projectionBiasValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(projectionBiasDimensions)); |
| |
| // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| AddInputOperand<HalPolicy>(model, outputStateInDimensions); |
| // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| AddInputOperand<HalPolicy>(model, cellStateInDimensions); |
| |
| // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| // 20: The activation function: A value indicating the activation function: |
| // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| AddTensorOperand<HalPolicy>(model, |
| activationFunctionDimensions, |
| activationFunctionValue, |
| HalPolicy::OperandType::INT32); |
| // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| // If set to 0.0 then clipping is disabled. |
| AddTensorOperand<HalPolicy>(model, |
| cellClippingThresholdDimensions, |
| cellClippingThresholdValue, |
| HalPolicy::OperandType::FLOAT32); |
| // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| AddTensorOperand<HalPolicy>(model, |
| projectionClippingThresholdDimensions, |
| projectionClippingThresholdValue, |
| HalPolicy::OperandType::FLOAT32); |
| |
| // 23: Time-major if true, batch-major if false. |
| AddBoolOperand<HalPolicy>(model, timeMajorValue); |
| |
| // Normalization: |
| // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at input gate. |
| AddTensorOperand<HalPolicy>(model, |
| inputLayerNormWeightsDimensions, |
| inputLayerNormWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(inputLayerNormWeightsDimensions)); |
| // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at forget gate. |
| AddTensorOperand<HalPolicy>(model, |
| forgetLayerNormWeightsDimensions, |
| forgetLayerNormWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(forgetLayerNormWeightsDimensions)); |
| // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at cell gate. |
| AddTensorOperand<HalPolicy>(model, |
| cellLayerNormWeightsDimensions, |
| cellLayerNormWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(cellLayerNormWeightsDimensions)); |
| // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at output gate. |
| AddTensorOperand<HalPolicy>(model, |
| outputLayerNormWeightsDimensions, |
| outputLayerNormWeightsValue, |
| HalPolicy::OperandType::TENSOR_FLOAT32, |
| CreateNoValueLifeTime(outputLayerNormWeightsDimensions)); |
| |
| // Outputs: |
| // 00: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| AddOutputOperand<HalPolicy>(model, outputDimensions); |
| // 01: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| // is present then output #2 must be present as well. |
| //AddOutputOperand<HalPolicy>(model, hiddenStateOutDimensions); |
| // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, num_units]. This output is optional and can be omitted. |
| //AddOutputOperand<HalPolicy>(model, cellStateOutDimensions); |
| |
| // make the lstm operation |
| model.operations.resize(1); |
| model.operations[0].type = HalPolicy::OperationType::UNIDIRECTIONAL_SEQUENCE_LSTM; |
| |
| model.operations[0].inputs = hidl_vec<uint32_t> {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, |
| 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27}; |
| model.operations[0].outputs = hidl_vec<uint32_t> {28}; |
| |
| // define the input values |
| hidl_vec<RequestArgument> inputArguments; |
| inputArguments.resize(3); |
| |
| inputArguments[0] = CreateRequestArgument<float>(inputValue, 0); |
| inputArguments[1] = CreateRequestArgument<float>(outputStateInValue, 1); |
| inputArguments[2] = CreateRequestArgument<float>(cellStateInValue, 2); |
| |
| // define the expected output values |
| hidl_vec<RequestArgument> outputArguments; |
| outputArguments.resize(1); |
| |
| outputArguments[0] = CreateRequestArgument<float>(outputValue, 3); |
| |
| V1_0::Request request = {}; |
| request.inputs = inputArguments; |
| request.outputs = outputArguments; |
| |
| // set the input data |
| AddPoolAndSetData(inputValue.size(), request, inputValue.data()); |
| AddPoolAndSetData(outputStateInValue.size(), request, outputStateInValue.data()); |
| AddPoolAndSetData(cellStateInValue.size(), request, cellStateInValue.data()); |
| |
| // add memory for the outputs |
| android::sp<IMemory> outputMemory = AddPoolAndGetData<float>(outputValue.size(), request); |
| float* outputData = static_cast<float*>(static_cast<void*>(outputMemory->getPointer())); |
| |
| // make the prepared model and run the execution |
| ExecuteModel(model, *driver, request); |
| |
| // check the results |
| if (epsilonValue != 0) |
| { |
| for (size_t i = 0; i < outputValue.size(); ++i) |
| { |
| DOCTEST_CHECK_MESSAGE(outputValue[i] == doctest::Approx(outputData[i]).epsilon(epsilonValue), |
| "outputValue[" << i << "]: " << outputValue[i] << " != " << outputData[i]); |
| } |
| } |
| else |
| { |
| for (size_t i = 0; i < outputValue.size(); ++i) |
| { |
| DOCTEST_CHECK_MESSAGE(outputValue[i] == doctest::Approx(outputData[i]), |
| "outputValue[" << i << "]: " << outputValue[i] << " != " << outputData[i]); |
| } |
| } |
| } |
| |
| template<typename HalPolicy> |
| void UnidirectionalSequenceLstmLayerFloat32TestImpl(armnn::Compute compute) |
| { |
| uint32_t batchSize = 3; |
| uint32_t timeSize = 2; |
| uint32_t inputSize = 3; |
| uint32_t outputSize = 4; |
| uint32_t numUnits = outputSize; |
| |
| // Inputs: |
| // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| hidl_vec<uint32_t> inputDimensions{batchSize, timeSize, inputSize}; |
| std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2.}; |
| |
| // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| hidl_vec<uint32_t> inputToInputWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToInputWeightsValue{-0.49536117f, -0.0556083915f, -0.102400711f, |
| -0.117484632f, 0.3298470976f, -0.1179017122f, |
| 0.214305695f, 0.42135173085f, 0.003878414626f, |
| -0.348303917f, -0.1881275477f, 0.0343011027f}; |
| // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToForgetWeightsValue{0.2415594226f, 0.15400093799f, 0.4566498398f, |
| -0.3810434485f, 0.268383264f, -0.009807467424f, |
| -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 0.13512269116f, -0.4932442977f, -0.10039821991f}; |
| // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToCellWeightsValue{-0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 0.386399507f, -0.259465157985f, -0.16545993089f, |
| -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| -0.2277662414f, -0.55275535589f, 0.34184026718f}; |
| // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToOutputWeightsValue{0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| -0.51717478049f, 0.1889653282f, -0.367323637f}; |
| // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| hidl_vec<uint32_t> recurrentToInputWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToInputWeightsValue{-0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, |
| -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, |
| 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, |
| 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f}; |
| // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToForgetWeightsValue{-0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f}; |
| // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToCellWeightsValue{-0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f}; |
| // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToOutputWeightsValue{-0.32921677827f, 0.32624614238f, -0.1388191282f, |
| -0.17879831790f, -0.15185534954f, -0.16918526583f, |
| -0.10087361183f, -0.5436913968f, 0.016758225858f, |
| 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| -0.12584099173f, -0.12319286912f, 0.2407919466f, |
| -0.08879069983f}; |
| // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToInputWeightsDimensions{0}; |
| std::vector<float> cellToInputWeightsValue; |
| // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToForgetWeightsDimensions{0}; |
| std::vector<float> cellToForgetWeightsValue; |
| // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToOutputWeightsDimensions{0}; |
| std::vector<float> cellToOutputWeightsValue; |
| // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> inputGateBiasDimensions{numUnits}; |
| std::vector<float> inputGateBiasValue(numUnits, 0.0f); |
| // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| std::vector<float> forgetGateBiasValue(numUnits, 1.0f); |
| // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| std::vector<float> cellBiasValue(numUnits, 0.0f); |
| // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| std::vector<float> outputGateBiasValue(numUnits, 0.0f); |
| // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [output_size, num_units]. |
| hidl_vec<uint32_t> projectionWeightsDimensions{0}; |
| std::vector<float> projectionWeightsValue; |
| // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| hidl_vec<uint32_t> projectionBiasDimensions{0}; |
| std::vector<float> projectionBiasValue; |
| |
| // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| std::vector<float> outputStateInValue(batchSize * outputSize, 0.0f); |
| // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateInValue(batchSize * numUnits, 0.0f); |
| |
| // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| // 20: The activation function: A value indicating the activation function: |
| // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| hidl_vec<uint32_t> activationFunctionDimensions{}; |
| std::vector<int32_t> activationFunctionValue{4}; |
| // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| // If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| std::vector<float> cellClippingThresholdValue{10.0f}; |
| // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| std::vector<float> projectionClippingThresholdValue{0.f}; |
| |
| // 23: Time-major if true, batch-major if false. |
| bool timeMajorValue = false; |
| |
| // Normalization: |
| // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at input gate. |
| hidl_vec<uint32_t> inputLayerNormWeightsDimensions{0}; |
| std::vector<float> inputLayerNormWeightsValue; |
| // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at forget gate. |
| hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{0}; |
| std::vector<float> forgetLayerNormWeightsValue; |
| // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at cell gate. |
| hidl_vec<uint32_t> cellLayerNormWeightsDimensions{0}; |
| std::vector<float> cellLayerNormWeightsValue; |
| // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at output gate. |
| hidl_vec<uint32_t> outputLayerNormWeightsDimensions{0}; |
| std::vector<float> outputLayerNormWeightsValue; |
| |
| // Outputs: |
| // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| hidl_vec<uint32_t> outputDimensions{batchSize, timeSize, outputSize}; |
| std::vector<float> outputValue{-0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f, |
| -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f, |
| -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f, |
| -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f, |
| -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f, |
| -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f}; |
| |
| // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| // is present then output #2 must be present as well. |
| hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
| // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, num_units]. This output is optional and can be omitted. |
| hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
| |
| UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| inputToInputWeightsDimensions, inputToInputWeightsValue, |
| inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| inputToCellWeightsDimensions, inputToCellWeightsValue, |
| inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| cellToInputWeightsDimensions, cellToInputWeightsValue, |
| cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| inputGateBiasDimensions, inputGateBiasValue, |
| forgetGateBiasDimensions, forgetGateBiasValue, |
| cellBiasDimensions, cellBiasValue, |
| outputGateBiasDimensions, outputGateBiasValue, |
| projectionWeightsDimensions, projectionWeightsValue, |
| projectionBiasDimensions, projectionBiasValue, |
| outputStateInDimensions, outputStateInValue, |
| cellStateInDimensions, cellStateInValue, |
| activationFunctionDimensions, activationFunctionValue, |
| cellClippingThresholdDimensions, cellClippingThresholdValue, |
| projectionClippingThresholdDimensions, |
| projectionClippingThresholdValue, |
| timeMajorValue, |
| inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| outputDimensions, outputValue, |
| hiddenStateOutDimensions, hiddenStateOutValue, |
| cellStateOutDimensions, cellStateOutValue, |
| compute); |
| } |
| |
| template<typename HalPolicy> |
| void UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl(armnn::Compute compute) |
| { |
| uint32_t batchSize = 3; |
| uint32_t timeSize = 2; |
| uint32_t inputSize = 3; |
| uint32_t outputSize = 4; |
| uint32_t numUnits = outputSize; |
| |
| // Inputs: |
| // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| hidl_vec<uint32_t> inputDimensions{timeSize, batchSize, inputSize}; |
| std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2.}; |
| |
| // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| hidl_vec<uint32_t> inputToInputWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToInputWeightsValue{0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f, |
| 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f, |
| 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f, |
| -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f}; |
| // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToForgetWeightsValue{-0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f, |
| -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f, |
| -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f, |
| -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f}; |
| // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToCellWeightsValue{-0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f, |
| 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f, |
| 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f, |
| -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f}; |
| // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToOutputWeightsValue{-0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f, |
| -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f, |
| 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f, |
| -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f}; |
| // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| hidl_vec<uint32_t> recurrentToInputWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToInputWeightsValue{0.23788475990f, -0.24948765337f, 0.50044941902f, |
| 0.14431896805f, -0.115940228137f, -0.717082679f, |
| -0.17208620906f, 0.17850610617f, -0.16702319684f, |
| -0.11384502053f, -0.309785276245f, -0.3316611672f, |
| 0.52380162477f, -0.06839632987f, -0.391478359627f, |
| -0.10756178963f}; |
| // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToForgetWeightsValue{0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f, |
| 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f, |
| -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f, |
| 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f}; |
| // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToCellWeightsValue{0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f, |
| -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f, |
| -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f, |
| -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f}; |
| // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToOutputWeightsValue{-0.079031050201f, 0.041414566286f, -0.583727357285f, |
| 0.1025384515f, -0.172372072937f, 0.09214124082f, |
| 0.178184121827f, -0.2439443916f, 0.104485116899f, |
| 0.2600405514f, 0.064414866268f, 0.24141204357f, |
| 0.281875759363f, -0.14234502664f, 0.15126448862f, |
| -0.24421440064f}; |
| // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToInputWeightsDimensions{0}; |
| std::vector<float> cellToInputWeightsValue; |
| // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToForgetWeightsDimensions{0}; |
| std::vector<float> cellToForgetWeightsValue; |
| // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToOutputWeightsDimensions{0}; |
| std::vector<float> cellToOutputWeightsValue; |
| // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> inputGateBiasDimensions{numUnits}; |
| std::vector<float> inputGateBiasValue(numUnits, 0.0f); |
| // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| std::vector<float> forgetGateBiasValue(numUnits, 1.0f); |
| // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| std::vector<float> cellBiasValue(numUnits, 0.0f); |
| // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| std::vector<float> outputGateBiasValue(numUnits, 0.0f); |
| // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [output_size, num_units]. |
| hidl_vec<uint32_t> projectionWeightsDimensions{0}; |
| std::vector<float> projectionWeightsValue; |
| // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| hidl_vec<uint32_t> projectionBiasDimensions{0}; |
| std::vector<float> projectionBiasValue; |
| |
| // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| std::vector<float> outputStateInValue(batchSize * outputSize, 0.0f); |
| // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateInValue(batchSize * numUnits, 0.0f); |
| |
| // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| // 20: The activation function: A value indicating the activation function: |
| // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| hidl_vec<uint32_t> activationFunctionDimensions{}; |
| std::vector<int32_t> activationFunctionValue{4}; |
| // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| // If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| std::vector<float> cellClippingThresholdValue{10.0f}; |
| // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| std::vector<float> projectionClippingThresholdValue{0.f}; |
| |
| // 23: Time-major if true, batch-major if false. |
| bool timeMajorValue = true; |
| |
| // Normalization: |
| // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at input gate. |
| hidl_vec<uint32_t> inputLayerNormWeightsDimensions{0}; |
| std::vector<float> inputLayerNormWeightsValue; |
| // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at forget gate. |
| hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{0}; |
| std::vector<float> forgetLayerNormWeightsValue; |
| // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at cell gate. |
| hidl_vec<uint32_t> cellLayerNormWeightsDimensions{0}; |
| std::vector<float> cellLayerNormWeightsValue; |
| // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at output gate. |
| hidl_vec<uint32_t> outputLayerNormWeightsDimensions{0}; |
| std::vector<float> outputLayerNormWeightsValue; |
| |
| // Outputs: |
| // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| hidl_vec<uint32_t> outputDimensions{timeSize, batchSize, outputSize}; |
| std::vector<float> outputValue{0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f, |
| 0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f, |
| -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f, |
| 0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f, |
| 0.11171641f, 0.043119f , 0.0762981f , -0.01228541f, |
| 0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f}; |
| |
| // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| // is present then output #2 must be present as well. |
| hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
| // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, num_units]. This output is optional and can be omitted. |
| hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
| |
| UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| inputToInputWeightsDimensions, inputToInputWeightsValue, |
| inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| inputToCellWeightsDimensions, inputToCellWeightsValue, |
| inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| cellToInputWeightsDimensions, cellToInputWeightsValue, |
| cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| inputGateBiasDimensions, inputGateBiasValue, |
| forgetGateBiasDimensions, forgetGateBiasValue, |
| cellBiasDimensions, cellBiasValue, |
| outputGateBiasDimensions, outputGateBiasValue, |
| projectionWeightsDimensions, projectionWeightsValue, |
| projectionBiasDimensions, projectionBiasValue, |
| outputStateInDimensions, outputStateInValue, |
| cellStateInDimensions, cellStateInValue, |
| activationFunctionDimensions, activationFunctionValue, |
| cellClippingThresholdDimensions, cellClippingThresholdValue, |
| projectionClippingThresholdDimensions, |
| projectionClippingThresholdValue, |
| timeMajorValue, |
| inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| outputDimensions, outputValue, |
| hiddenStateOutDimensions, hiddenStateOutValue, |
| cellStateOutDimensions, cellStateOutValue, |
| compute); |
| } |
| |
| template<typename HalPolicy> |
| void UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTestImpl(armnn::Compute compute) |
| { |
| uint32_t batchSize = 2; |
| uint32_t timeSize = 3; |
| uint32_t inputSize = 4; |
| uint32_t outputSize = 5; |
| uint32_t numUnits = 6; |
| |
| // Inputs: |
| // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| hidl_vec<uint32_t> inputDimensions{batchSize, timeSize, inputSize}; |
| std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2., |
| 1., 2., 3., 4., 5., 4.}; |
| |
| // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| hidl_vec<uint32_t> inputToInputWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToInputWeightsValue{0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, |
| -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, |
| -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, |
| -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, |
| -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, |
| -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f}; |
| // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToForgetWeightsValue{-0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, |
| 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, |
| 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, |
| -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, |
| -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, |
| 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f}; |
| // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToCellWeightsValue{-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, |
| -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, |
| -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, |
| -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, |
| -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, |
| 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f}; |
| // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToOutputWeightsValue{-0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f, |
| -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f, |
| -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f, |
| 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f, |
| 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f, |
| -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f}; |
| // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| hidl_vec<uint32_t> recurrentToInputWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToInputWeightsValue{-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, |
| -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, |
| -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, |
| -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, |
| 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, |
| 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, |
| -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, |
| 0.14283475f, -0.07390571f}; |
| // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToForgetWeightsValue{-0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, |
| 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, |
| -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, |
| 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, |
| 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, |
| -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, |
| -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, |
| 0.061878487f, -0.04729229f}; |
| // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToCellWeightsValue{-0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, |
| 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, |
| 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, |
| -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, |
| 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, |
| 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, |
| -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, |
| -0.019443132f, -0.030755889f}; |
| // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToOutputWeightsValue{0.025825322f, -0.05813119f, 0.09495884f, |
| -0.045984812f,-0.01255415f, -0.0026479573f, |
| -0.08196161f, -0.054914974f, -0.0046604523f, |
| -0.029587349f, -0.044576716f, -0.07480124f, |
| -0.082868785f, 0.023254942f, 0.027502948f, |
| -0.0039728214f, -0.08683098f, -0.08116779f, |
| -0.014675607f, -0.037924774f, -0.023314456f, |
| -0.007401714f, -0.09255757f, 0.029460307f, |
| -0.08829125f, -0.005139627f, -0.08989442f, |
| -0.0555066f, 0.13596267f, 0.025062224f}; |
| // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToInputWeightsDimensions{numUnits}; |
| std::vector<float> cellToInputWeightsValue{0.040369894f, 0.030746894f, 0.24704495f, |
| 0.018586371f, -0.037586458f, -0.15312155f}; |
| // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToForgetWeightsDimensions{numUnits}; |
| std::vector<float> cellToForgetWeightsValue{-0.01998659f, -0.15568835f, -0.24248174f, |
| -0.012770197f, 0.041331276f, -0.072311886f}; |
| // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToOutputWeightsDimensions{numUnits}; |
| std::vector<float> cellToOutputWeightsValue{0.08286371f, -0.08261836f, -0.51210177f, |
| 0.002913762f, 0.17764764f, -0.5495371f}; |
| // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> inputGateBiasDimensions{numUnits}; |
| std::vector<float> inputGateBiasValue{0.02234832f, 0.14757581f, 0.18176508f, |
| 0.10380666f, 0.053110216f, -0.06928846f}; |
| // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| std::vector<float> forgetGateBiasValue{0.035185695f, -0.042891346f, -0.03032477f, |
| 0.23027696f, 0.11098921f, 0.08989442f}; |
| // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| std::vector<float> cellBiasValue{-0.024379363f, 0.0055531194f, 0.23377132f, |
| 0.033463873f, -0.1483596f, 0.029460307f}; |
| // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| std::vector<float> outputGateBiasValue{0.046159424f, -0.0012809046f, 0.03563469f, |
| 0.12648113f, 0.027195795f, 0.35373217f}; |
| // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [output_size, num_units]. |
| hidl_vec<uint32_t> projectionWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> projectionWeightsValue{-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, |
| 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, |
| -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, |
| -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, |
| 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, |
| 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f}; |
| // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| hidl_vec<uint32_t> projectionBiasDimensions{outputSize}; |
| std::vector<float> projectionBiasValue(outputSize, 0.f); |
| |
| // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| std::vector<float> outputStateInValue(batchSize * outputSize, 0.f); |
| // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateInValue(batchSize * numUnits, 0.f); |
| |
| // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| // 20: The activation function: A value indicating the activation function: |
| // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| hidl_vec<uint32_t> activationFunctionDimensions{}; |
| std::vector<int32_t> activationFunctionValue{4}; |
| // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| // If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| std::vector<float> cellClippingThresholdValue{10.0f}; |
| // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| std::vector<float> projectionClippingThresholdValue{0.f}; |
| |
| // 23: Time-major if true, batch-major if false. |
| bool timeMajorValue = false; |
| |
| // Normalization: |
| // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at input gate. |
| hidl_vec<uint32_t> inputLayerNormWeightsDimensions{0}; |
| std::vector<float> inputLayerNormWeightsValue; |
| // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at forget gate. |
| hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{0}; |
| std::vector<float> forgetLayerNormWeightsValue; |
| // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at cell gate. |
| hidl_vec<uint32_t> cellLayerNormWeightsDimensions{0}; |
| std::vector<float> cellLayerNormWeightsValue; |
| // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at output gate. |
| hidl_vec<uint32_t> outputLayerNormWeightsDimensions{0}; |
| std::vector<float> outputLayerNormWeightsValue; |
| |
| // Outputs: |
| // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| hidl_vec<uint32_t> outputDimensions{batchSize, timeSize, outputSize}; |
| std::vector<float> outputValue{-0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f, |
| -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f, |
| -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f, |
| 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f, |
| -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f, |
| -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f}; |
| |
| // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| // is present then output #2 must be present as well. |
| hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
| // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, num_units]. This output is optional and can be omitted. |
| hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
| |
| UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| inputToInputWeightsDimensions, inputToInputWeightsValue, |
| inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| inputToCellWeightsDimensions, inputToCellWeightsValue, |
| inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| cellToInputWeightsDimensions, cellToInputWeightsValue, |
| cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| inputGateBiasDimensions, inputGateBiasValue, |
| forgetGateBiasDimensions, forgetGateBiasValue, |
| cellBiasDimensions, cellBiasValue, |
| outputGateBiasDimensions, outputGateBiasValue, |
| projectionWeightsDimensions, projectionWeightsValue, |
| projectionBiasDimensions, projectionBiasValue, |
| outputStateInDimensions, outputStateInValue, |
| cellStateInDimensions, cellStateInValue, |
| activationFunctionDimensions, activationFunctionValue, |
| cellClippingThresholdDimensions, cellClippingThresholdValue, |
| projectionClippingThresholdDimensions, |
| projectionClippingThresholdValue, |
| timeMajorValue, |
| inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| outputDimensions, outputValue, |
| hiddenStateOutDimensions, hiddenStateOutValue, |
| cellStateOutDimensions, cellStateOutValue, |
| compute, 0.0031454); |
| } |
| |
| template<typename HalPolicy> |
| void UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTestImpl(armnn::Compute compute) |
| { |
| uint32_t batchSize = 3; |
| uint32_t timeSize = 2; |
| uint32_t inputSize = 3; |
| uint32_t outputSize = 4; |
| uint32_t numUnits = 5; |
| |
| // Inputs: |
| // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| hidl_vec<uint32_t> inputDimensions{batchSize, timeSize, inputSize}; |
| std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2.}; |
| |
| // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| hidl_vec<uint32_t> inputToInputWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToInputWeightsValue{-0.49536117f, -0.0556083915f, -0.102400711f, |
| -0.117484632f, 0.3298470976f, -0.1179017122f, |
| 0.214305695f, 0.42135173085f, 0.003878414626f, |
| -0.348303917f, -0.1881275477f, 0.0343011027f, |
| -0.38837709614f, -0.05636804124f, 0.4259087456f}; |
| // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToForgetWeightsValue{0.2415594226f, 0.15400093799f, 0.4566498398f, |
| -0.3810434485f, 0.268383264f, -0.009807467424f, |
| -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 0.13512269116f, -0.4932442977f, -0.10039821991f, |
| 0.2726137042f, 0.09216640889f, -0.06551410215f}; |
| // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToCellWeightsValue{-0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 0.386399507f, -0.259465157985f, -0.16545993089f, |
| -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| -0.2277662414f, -0.55275535589f, 0.34184026718f, |
| 0.3954237699f, -0.19407111404f, 0.30412107706f}; |
| // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToOutputWeightsValue{0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| -0.51717478049f, 0.1889653282f, -0.367323637f, |
| -0.12584099173f, -0.12319286912f, 0.2407919466f}; |
| // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| hidl_vec<uint32_t> recurrentToInputWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToInputWeightsValue{-0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, |
| -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, |
| 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, |
| 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f, |
| 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f}; |
| // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToForgetWeightsValue{-0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f, |
| 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f}; |
| // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToCellWeightsValue{-0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f, |
| 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f}; |
| // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToOutputWeightsValue{-0.32921677827f, 0.32624614238f, -0.1388191282f, |
| -0.17879831790f,-0.15185534954f, -0.16918526583f, |
| -0.10087361183f, -0.5436913968f, 0.016758225858f, |
| 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| -0.12584099173f, -0.12319286912f, 0.2407919466f, |
| -0.08879069983f, 0.11178309f, 0.09481031f, |
| -0.26424935f, 0.46261835f}; |
| // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToInputWeightsDimensions{numUnits}; |
| std::vector<float> cellToInputWeightsValue{0.05f, 0.1f, 0.25f, 0.15f, -0.02f}; |
| // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToForgetWeightsDimensions{numUnits}; |
| std::vector<float> cellToForgetWeightsValue{-0.02f, -0.15f, -0.25f, -0.03f, 0.15f}; |
| // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToOutputWeightsDimensions{numUnits}; |
| std::vector<float> cellToOutputWeightsValue{0.1f, -0.1f, -0.5f, 0.05f, 0.01f}; |
| // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> inputGateBiasDimensions{numUnits}; |
| std::vector<float> inputGateBiasValue{0.03f, 0.15f, 0.22f, 0.38f, 0.05f}; |
| // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| std::vector<float> forgetGateBiasValue{0.1f, -0.3f, -0.2f, 0.1f, 0.4f}; |
| // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| std::vector<float> cellBiasValue{-0.05f, 0.72f, 0.25f, 0.08f, 0.1f}; |
| // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| std::vector<float> outputGateBiasValue{0.05f, -0.01f, 0.2f, 0.1f, -0.2f}; |
| // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [output_size, num_units]. |
| hidl_vec<uint32_t> projectionWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> projectionWeightsValue{-0.1f, 0.2f, 0.01f, -0.2f, |
| 0.1f, 0.5f, 0.3f, 0.08f, |
| 0.07f, 0.2f, -0.4f, 0.2f, |
| 0.5f, -0.4f, 0.3f, -0.2f, |
| 0.3f, 0.08f, -0.07f, 0.2f}; |
| // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| hidl_vec<uint32_t> projectionBiasDimensions{outputSize}; |
| std::vector<float> projectionBiasValue(outputSize, 0.f); |
| |
| // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| std::vector<float> outputStateInValue(batchSize * outputSize, 0.f); |
| // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateInValue(batchSize * numUnits, 0.f); |
| |
| // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| // 20: The activation function: A value indicating the activation function: |
| // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| hidl_vec<uint32_t> activationFunctionDimensions{}; |
| std::vector<int32_t> activationFunctionValue{4}; |
| // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| // If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| std::vector<float> cellClippingThresholdValue{10.0f}; |
| // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| std::vector<float> projectionClippingThresholdValue{0.f}; |
| |
| // 23: Time-major if true, batch-major if false. |
| bool timeMajorValue = false; |
| |
| // Normalization: |
| // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at input gate. |
| hidl_vec<uint32_t> inputLayerNormWeightsDimensions{numUnits}; |
| std::vector<float> inputLayerNormWeightsValue{0.1f, 0.2f, 0.3f, 0.5f, 0.8f}; |
| // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at forget gate. |
| hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{numUnits}; |
| std::vector<float> forgetLayerNormWeightsValue{0.1f, 0.2f, 0.3f, 0.5f, 0.2f}; |
| // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at cell gate. |
| hidl_vec<uint32_t> cellLayerNormWeightsDimensions{numUnits}; |
| std::vector<float> cellLayerNormWeightsValue{0.7f, 0.2f, 0.3f, 0.8f, 0.5f}; |
| // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at output gate. |
| hidl_vec<uint32_t> outputLayerNormWeightsDimensions{numUnits}; |
| std::vector<float> outputLayerNormWeightsValue{0.6f, 0.2f, 0.2f, 0.5f, 0.1f}; |
| |
| // Outputs: |
| // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| hidl_vec<uint32_t> outputDimensions{batchSize, timeSize, outputSize}; |
| std::vector<float> outputValue{0.0642256f, 0.0343966f, 0.184122f, 0.114717f, |
| 0.11458f, 0.0407109f, 0.300327f, 0.174301f, |
| 0.0864761f, 0.0362912f, 0.178635f, 0.115689f, |
| 0.108008f, 0.0386623f, 0.273471f, 0.167115f, |
| 0.0859545f, 0.0331481f, 0.186051f, 0.11888f, |
| 0.106649f, 0.0276847f, 0.229863f, 0.166958f}; |
| |
| // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| // is present then output #2 must be present as well. |
| hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
| // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, num_units]. This output is optional and can be omitted. |
| hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
| |
| UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| inputToInputWeightsDimensions, inputToInputWeightsValue, |
| inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| inputToCellWeightsDimensions, inputToCellWeightsValue, |
| inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| cellToInputWeightsDimensions, cellToInputWeightsValue, |
| cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| inputGateBiasDimensions, inputGateBiasValue, |
| forgetGateBiasDimensions, forgetGateBiasValue, |
| cellBiasDimensions, cellBiasValue, |
| outputGateBiasDimensions, outputGateBiasValue, |
| projectionWeightsDimensions, projectionWeightsValue, |
| projectionBiasDimensions, projectionBiasValue, |
| outputStateInDimensions, outputStateInValue, |
| cellStateInDimensions, cellStateInValue, |
| activationFunctionDimensions, activationFunctionValue, |
| cellClippingThresholdDimensions, cellClippingThresholdValue, |
| projectionClippingThresholdDimensions, |
| projectionClippingThresholdValue, |
| timeMajorValue, |
| inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| outputDimensions, outputValue, |
| hiddenStateOutDimensions, hiddenStateOutValue, |
| cellStateOutDimensions, cellStateOutValue, |
| compute); |
| } |
| |
| template<typename HalPolicy> |
| void UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTestImpl(armnn::Compute compute) |
| { |
| uint32_t batchSize = 3; |
| uint32_t timeSize = 2; |
| uint32_t inputSize = 3; |
| uint32_t outputSize = 4; |
| uint32_t numUnits = outputSize; |
| |
| // Inputs: |
| // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| hidl_vec<uint32_t> inputDimensions{batchSize, timeSize, inputSize}; |
| std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2.}; |
| |
| // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| hidl_vec<uint32_t> inputToInputWeightsDimensions{0}; |
| std::vector<float> inputToInputWeightsValue; |
| // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToForgetWeightsValue{0.2415594226f, 0.15400093799f, 0.4566498398f, |
| -0.3810434485f, 0.268383264f, -0.009807467424f, |
| -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 0.13512269116f, -0.4932442977f, -0.10039821991f}; |
| // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToCellWeightsValue{-0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 0.386399507f, -0.259465157985f, -0.16545993089f, |
| -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| -0.2277662414f, -0.55275535589f, 0.34184026718f}; |
| // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, input_size]. |
| hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| std::vector<float> inputToOutputWeightsValue{0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| -0.51717478049f, 0.1889653282f, -0.367323637f}; |
| // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| hidl_vec<uint32_t> recurrentToInputWeightsDimensions{0}; |
| std::vector<float> recurrentToInputWeightsValue; |
| // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToForgetWeightsValue{-0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f}; |
| // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToCellWeightsValue{-0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f}; |
| // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [num_units, output_size]. |
| hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| std::vector<float> recurrentToOutputWeightsValue{-0.32921677827f, 0.32624614238f, -0.1388191282f, |
| -0.17879831790f, -0.15185534954f, -0.16918526583f, |
| -0.10087361183f, -0.5436913968f, 0.016758225858f, |
| 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| -0.12584099173f, -0.12319286912f, 0.2407919466f, |
| -0.08879069983f}; |
| // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToInputWeightsDimensions{0}; |
| std::vector<float> cellToInputWeightsValue; |
| // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToForgetWeightsDimensions{numUnits}; |
| std::vector<float> cellToForgetWeightsValue{0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; |
| // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellToOutputWeightsDimensions{numUnits}; |
| std::vector<float> cellToOutputWeightsValue{-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; |
| // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> inputGateBiasDimensions{0}; |
| std::vector<float> inputGateBiasValue; |
| // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| std::vector<float> forgetGateBiasValue{1., 1., 1., 1.}; |
| // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| std::vector<float> cellBiasValue{0., 0., 0., 0.}; |
| // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| std::vector<float> outputGateBiasValue{0., 0., 0., 0.}; |
| // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| // [output_size, num_units]. |
| hidl_vec<uint32_t> projectionWeightsDimensions{0}; |
| std::vector<float> projectionWeightsValue; |
| // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| hidl_vec<uint32_t> projectionBiasDimensions{0}; |
| std::vector<float> projectionBiasValue; |
| |
| // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| std::vector<float> outputStateInValue(batchSize * outputSize, 0.f); |
| // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateInValue(batchSize * numUnits, 0.f); |
| |
| // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| // 20: The activation function: A value indicating the activation function: |
| // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| hidl_vec<uint32_t> activationFunctionDimensions{}; |
| std::vector<int32_t> activationFunctionValue{4}; |
| // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| // If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| std::vector<float> cellClippingThresholdValue{10.0f}; |
| // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| std::vector<float> projectionClippingThresholdValue{0.f}; |
| |
| // 23: Time-major if true, batch-major if false. |
| bool timeMajorValue = false; |
| |
| // Normalization: |
| // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at input gate. |
| hidl_vec<uint32_t> inputLayerNormWeightsDimensions{0}; |
| std::vector<float> inputLayerNormWeightsValue; |
| // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at forget gate. |
| hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{0}; |
| std::vector<float> forgetLayerNormWeightsValue; |
| // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at cell gate. |
| hidl_vec<uint32_t> cellLayerNormWeightsDimensions{0}; |
| std::vector<float> cellLayerNormWeightsValue; |
| // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| // Used to rescale normalized inputs to activation at output gate. |
| hidl_vec<uint32_t> outputLayerNormWeightsDimensions{0}; |
| std::vector<float> outputLayerNormWeightsValue; |
| |
| // Outputs: |
| // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| hidl_vec<uint32_t> outputDimensions{batchSize, timeSize, outputSize}; |
| std::vector<float> outputValue{-0.0129257f, -0.070531f, -0.153508f, -0.0392391f, |
| -0.0300169f, -0.195717f, -0.528679f, -0.0818106f, |
| -0.0332748f, 0.155429f, -0.353966f, -0.0801505f, |
| -0.032312f, -0.0407911f, -0.435053f, -0.0932317f, |
| -0.0108233f, 0.165584f, -0.640424f, -0.0447535f, |
| -0.031675f, 0.125987f, -0.526695f, -0.110093f}; |
| |
| // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| // is present then output #2 must be present as well. |
| hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
| // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| // [batch_size, num_units]. This output is optional and can be omitted. |
| hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
| |
| UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| inputToInputWeightsDimensions, inputToInputWeightsValue, |
| inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| inputToCellWeightsDimensions, inputToCellWeightsValue, |
| inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| cellToInputWeightsDimensions, cellToInputWeightsValue, |
| cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| inputGateBiasDimensions, inputGateBiasValue, |
| forgetGateBiasDimensions, forgetGateBiasValue, |
| cellBiasDimensions, cellBiasValue, |
| outputGateBiasDimensions, outputGateBiasValue, |
| projectionWeightsDimensions, projectionWeightsValue, |
| projectionBiasDimensions, projectionBiasValue, |
| outputStateInDimensions, outputStateInValue, |
| cellStateInDimensions, cellStateInValue, |
| activationFunctionDimensions, activationFunctionValue, |
| cellClippingThresholdDimensions, cellClippingThresholdValue, |
| projectionClippingThresholdDimensions, |
| projectionClippingThresholdValue, |
| timeMajorValue, |
| inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| outputDimensions, outputValue, |
| hiddenStateOutDimensions, hiddenStateOutValue, |
| cellStateOutDimensions, cellStateOutValue, |
| compute); |
| } |