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rk35xx-android14/external/android-nn-driver/test/UnidirectionalSequenceLstm.hpp

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//
// 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);
}
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