Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include "DriverTestHelpers.hpp" |
| 9 | |
| 10 | #include <armnn/utility/IgnoreUnused.hpp> |
| 11 | |
| 12 | #include <array> |
| 13 | |
| 14 | using ArmnnDriver = armnn_driver::ArmnnDriver; |
| 15 | using DriverOptions = armnn_driver::DriverOptions; |
| 16 | using RequestArgument = V1_0::RequestArgument; |
| 17 | |
| 18 | #ifdef ARMNN_ANDROID_S |
| 19 | #include <nnapi/Types.h> |
| 20 | #endif |
| 21 | |
| 22 | using namespace driverTestHelpers; |
| 23 | using namespace android::hardware; |
| 24 | |
| 25 | namespace |
| 26 | { |
| 27 | |
| 28 | template<typename T> |
| 29 | RequestArgument CreateRequestArgument(const std::vector<T>& value, unsigned int poolIndex) |
| 30 | { |
| 31 | V1_0::DataLocation inputInloc = {}; |
| 32 | inputInloc.poolIndex = poolIndex; |
| 33 | inputInloc.offset = 0; |
| 34 | inputInloc.length = value.size() * sizeof(T); |
| 35 | RequestArgument inputRequestArgument = {}; |
| 36 | inputRequestArgument.location = inputInloc; |
| 37 | inputRequestArgument.dimensions = hidl_vec<uint32_t>{}; |
| 38 | return inputRequestArgument; |
| 39 | } |
| 40 | |
| 41 | // Helper function to create an OperandLifeTime::NO_VALUE for testing. |
| 42 | // To be used on optional input operands that have no values - these are valid and should be tested. |
| 43 | V1_0::OperandLifeTime CreateNoValueLifeTime(const hidl_vec<uint32_t>& dimensions) |
| 44 | { |
| 45 | // Only create a NO_VALUE for optional operands that have no elements |
| 46 | if (dimensions.size() == 0 || dimensions[0] == 0) |
| 47 | { |
| 48 | return V1_0::OperandLifeTime::NO_VALUE; |
| 49 | } |
| 50 | return V1_0::OperandLifeTime::CONSTANT_COPY; |
| 51 | } |
| 52 | |
| 53 | template<typename HalModel> |
| 54 | void ExecuteModel(const HalModel& model, armnn_driver::ArmnnDriver& driver, const V1_0::Request& request) |
| 55 | { |
| 56 | android::sp<V1_0::IPreparedModel> preparedModel = PrepareModel(model, driver); |
| 57 | if (preparedModel.get() != nullptr) |
| 58 | { |
| 59 | Execute(preparedModel, request); |
| 60 | } |
| 61 | } |
| 62 | |
| 63 | #if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3) |
| 64 | |
| 65 | template<> |
| 66 | void ExecuteModel<armnn_driver::hal_1_2::HalPolicy::Model>(const armnn_driver::hal_1_2::HalPolicy::Model& model, |
| 67 | armnn_driver::ArmnnDriver& driver, |
| 68 | const V1_0::Request& request) |
| 69 | { |
| 70 | android::sp<V1_2::IPreparedModel> preparedModel = PrepareModel_1_2(model, driver); |
| 71 | if (preparedModel.get() != nullptr) |
| 72 | { |
| 73 | Execute(preparedModel, request); |
| 74 | } |
| 75 | } |
| 76 | |
| 77 | #endif |
| 78 | |
| 79 | } // anonymous namespace |
| 80 | |
| 81 | // Add our own tests here since we fail the unidirectional sequence lstm |
| 82 | // tests which Google supplies (because of non-const weights) |
| 83 | template <typename HalPolicy> |
| 84 | void UnidirectionalSequenceLstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, |
| 85 | const std::vector<float>& inputValue, |
| 86 | const hidl_vec<uint32_t>& inputToInputWeightsDimensions, |
| 87 | const std::vector<float>& inputToInputWeightsValue, |
| 88 | const hidl_vec<uint32_t>& inputToForgetWeightsDimensions, |
| 89 | const std::vector<float>& inputToForgetWeightsValue, |
| 90 | const hidl_vec<uint32_t>& inputToCellWeightsDimensions, |
| 91 | const std::vector<float>& inputToCellWeightsValue, |
| 92 | const hidl_vec<uint32_t>& inputToOutputWeightsDimensions, |
| 93 | const std::vector<float>& inputToOutputWeightsValue, |
| 94 | const hidl_vec<uint32_t>& recurrentToInputWeightsDimensions, |
| 95 | const std::vector<float>& recurrentToInputWeightsValue, |
| 96 | const hidl_vec<uint32_t>& recurrentToForgetWeightsDimensions, |
| 97 | const std::vector<float>& recurrentToForgetWeightsValue, |
| 98 | const hidl_vec<uint32_t>& recurrentToCellWeightsDimensions, |
| 99 | const std::vector<float>& recurrentToCellWeightsValue, |
| 100 | const hidl_vec<uint32_t>& recurrentToOutputWeightsDimensions, |
| 101 | const std::vector<float>& recurrentToOutputWeightsValue, |
| 102 | const hidl_vec<uint32_t>& cellToInputWeightsDimensions, |
| 103 | const std::vector<float>& cellToInputWeightsValue, |
| 104 | const hidl_vec<uint32_t>& cellToForgetWeightsDimensions, |
| 105 | const std::vector<float>& cellToForgetWeightsValue, |
| 106 | const hidl_vec<uint32_t>& cellToOutputWeightsDimensions, |
| 107 | const std::vector<float>& cellToOutputWeightsValue, |
| 108 | const hidl_vec<uint32_t>& inputGateBiasDimensions, |
| 109 | const std::vector<float>& inputGateBiasValue, |
| 110 | const hidl_vec<uint32_t>& forgetGateBiasDimensions, |
| 111 | const std::vector<float>& forgetGateBiasValue, |
| 112 | const hidl_vec<uint32_t>& cellBiasDimensions, |
| 113 | const std::vector<float>& cellBiasValue, |
| 114 | const hidl_vec<uint32_t>& outputGateBiasDimensions, |
| 115 | const std::vector<float>& outputGateBiasValue, |
| 116 | const hidl_vec<uint32_t>& projectionWeightsDimensions, |
| 117 | const std::vector<float>& projectionWeightsValue, |
| 118 | const hidl_vec<uint32_t>& projectionBiasDimensions, |
| 119 | const std::vector<float>& projectionBiasValue, |
| 120 | const hidl_vec<uint32_t>& outputStateInDimensions, |
| 121 | const std::vector<float>& outputStateInValue, |
| 122 | const hidl_vec<uint32_t>& cellStateInDimensions, |
| 123 | const std::vector<float>& cellStateInValue, |
| 124 | const hidl_vec<uint32_t>& activationFunctionDimensions, |
| 125 | const std::vector<int32_t>& activationFunctionValue, |
| 126 | const hidl_vec<uint32_t>& cellClippingThresholdDimensions, |
| 127 | const std::vector<float>& cellClippingThresholdValue, |
| 128 | const hidl_vec<uint32_t>& projectionClippingThresholdDimensions, |
| 129 | const std::vector<float>& projectionClippingThresholdValue, |
| 130 | const bool& timeMajorValue, |
| 131 | const hidl_vec<uint32_t>& inputLayerNormWeightsDimensions, |
| 132 | const std::vector<float>& inputLayerNormWeightsValue, |
| 133 | const hidl_vec<uint32_t>& forgetLayerNormWeightsDimensions, |
| 134 | const std::vector<float>& forgetLayerNormWeightsValue, |
| 135 | const hidl_vec<uint32_t>& cellLayerNormWeightsDimensions, |
| 136 | const std::vector<float>& cellLayerNormWeightsValue, |
| 137 | const hidl_vec<uint32_t>& outputLayerNormWeightsDimensions, |
| 138 | const std::vector<float>& outputLayerNormWeightsValue, |
| 139 | const hidl_vec<uint32_t>& outputDimensions, |
| 140 | const std::vector<float>& outputValue, |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 141 | const hidl_vec<uint32_t>&, // outputStateOutDimensions, |
| 142 | const std::vector<float>&, // outputStateOutValue, |
| 143 | const hidl_vec<uint32_t>&, // cellStateOutDimensions, |
| 144 | const std::vector<float>&, // cellStateOutValue, |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 145 | armnn::Compute compute, |
| 146 | float epsilonValue = 0) |
| 147 | { |
| 148 | auto driver = std::make_unique<ArmnnDriver>(DriverOptions(compute)); |
| 149 | using Model = typename HalPolicy::Model; |
| 150 | Model model = {}; |
| 151 | |
| 152 | // Inputs: |
| 153 | // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where |
| 154 | // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| 155 | AddInputOperand<HalPolicy>(model, inputDimensions); |
| 156 | |
| 157 | // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 158 | // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| 159 | AddTensorOperand<HalPolicy>(model, |
| 160 | inputToInputWeightsDimensions, |
| 161 | inputToInputWeightsValue, |
| 162 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 163 | CreateNoValueLifeTime(inputToInputWeightsDimensions)); |
| 164 | // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 165 | // [num_units, input_size]. |
| 166 | AddTensorOperand<HalPolicy>(model, inputToForgetWeightsDimensions, inputToForgetWeightsValue); |
| 167 | // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 168 | // [num_units, input_size]. |
| 169 | AddTensorOperand<HalPolicy>(model, inputToCellWeightsDimensions, inputToCellWeightsValue); |
| 170 | // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 171 | // [num_units, input_size]. |
| 172 | AddTensorOperand<HalPolicy>(model, inputToOutputWeightsDimensions, inputToOutputWeightsValue); |
| 173 | // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 174 | // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| 175 | // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| 176 | AddTensorOperand<HalPolicy>(model, |
| 177 | recurrentToInputWeightsDimensions, |
| 178 | recurrentToInputWeightsValue, |
| 179 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 180 | CreateNoValueLifeTime(recurrentToInputWeightsDimensions)); |
| 181 | // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 182 | // [num_units, output_size]. |
| 183 | AddTensorOperand<HalPolicy>(model, recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue); |
| 184 | // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 185 | // [num_units, output_size]. |
| 186 | AddTensorOperand<HalPolicy>(model, recurrentToCellWeightsDimensions, recurrentToCellWeightsValue); |
| 187 | // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 188 | // [num_units, output_size]. |
| 189 | AddTensorOperand<HalPolicy>(model, recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue); |
| 190 | // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 191 | AddTensorOperand<HalPolicy>(model, |
| 192 | cellToInputWeightsDimensions, |
| 193 | cellToInputWeightsValue, |
| 194 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 195 | CreateNoValueLifeTime(cellToInputWeightsDimensions)); |
| 196 | // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 197 | AddTensorOperand<HalPolicy>(model, |
| 198 | cellToForgetWeightsDimensions, |
| 199 | cellToForgetWeightsValue, |
| 200 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 201 | CreateNoValueLifeTime(cellToForgetWeightsDimensions)); |
| 202 | // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 203 | AddTensorOperand<HalPolicy>(model, |
| 204 | cellToOutputWeightsDimensions, |
| 205 | cellToOutputWeightsValue, |
| 206 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 207 | CreateNoValueLifeTime(cellToOutputWeightsDimensions)); |
| 208 | // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 209 | AddTensorOperand<HalPolicy>(model, |
| 210 | inputGateBiasDimensions, |
| 211 | inputGateBiasValue, |
| 212 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 213 | CreateNoValueLifeTime(inputGateBiasDimensions)); |
| 214 | // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 215 | AddTensorOperand<HalPolicy>(model, forgetGateBiasDimensions, forgetGateBiasValue); |
| 216 | // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 217 | AddTensorOperand<HalPolicy>(model, cellBiasDimensions, cellBiasValue); |
| 218 | // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 219 | AddTensorOperand<HalPolicy>(model, outputGateBiasDimensions, outputGateBiasValue); |
| 220 | // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 221 | // [output_size, num_units]. |
| 222 | AddTensorOperand<HalPolicy>(model, |
| 223 | projectionWeightsDimensions, |
| 224 | projectionWeightsValue, |
| 225 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 226 | CreateNoValueLifeTime(projectionWeightsDimensions)); |
| 227 | // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| 228 | AddTensorOperand<HalPolicy>(model, |
| 229 | projectionBiasDimensions, |
| 230 | projectionBiasValue, |
| 231 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 232 | CreateNoValueLifeTime(projectionBiasDimensions)); |
| 233 | |
| 234 | // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| 235 | AddInputOperand<HalPolicy>(model, outputStateInDimensions); |
| 236 | // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| 237 | AddInputOperand<HalPolicy>(model, cellStateInDimensions); |
| 238 | |
| 239 | // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| 240 | // 20: The activation function: A value indicating the activation function: |
| 241 | // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| 242 | AddTensorOperand<HalPolicy>(model, |
| 243 | activationFunctionDimensions, |
| 244 | activationFunctionValue, |
| 245 | HalPolicy::OperandType::INT32); |
| 246 | // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| 247 | // If set to 0.0 then clipping is disabled. |
| 248 | AddTensorOperand<HalPolicy>(model, |
| 249 | cellClippingThresholdDimensions, |
| 250 | cellClippingThresholdValue, |
| 251 | HalPolicy::OperandType::FLOAT32); |
| 252 | // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| 253 | // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| 254 | AddTensorOperand<HalPolicy>(model, |
| 255 | projectionClippingThresholdDimensions, |
| 256 | projectionClippingThresholdValue, |
| 257 | HalPolicy::OperandType::FLOAT32); |
| 258 | |
| 259 | // 23: Time-major if true, batch-major if false. |
| 260 | AddBoolOperand<HalPolicy>(model, timeMajorValue); |
| 261 | |
| 262 | // Normalization: |
| 263 | // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| 264 | // Used to rescale normalized inputs to activation at input gate. |
| 265 | AddTensorOperand<HalPolicy>(model, |
| 266 | inputLayerNormWeightsDimensions, |
| 267 | inputLayerNormWeightsValue, |
| 268 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 269 | CreateNoValueLifeTime(inputLayerNormWeightsDimensions)); |
| 270 | // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| 271 | // Used to rescale normalized inputs to activation at forget gate. |
| 272 | AddTensorOperand<HalPolicy>(model, |
| 273 | forgetLayerNormWeightsDimensions, |
| 274 | forgetLayerNormWeightsValue, |
| 275 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 276 | CreateNoValueLifeTime(forgetLayerNormWeightsDimensions)); |
| 277 | // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| 278 | // Used to rescale normalized inputs to activation at cell gate. |
| 279 | AddTensorOperand<HalPolicy>(model, |
| 280 | cellLayerNormWeightsDimensions, |
| 281 | cellLayerNormWeightsValue, |
| 282 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 283 | CreateNoValueLifeTime(cellLayerNormWeightsDimensions)); |
| 284 | // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| 285 | // Used to rescale normalized inputs to activation at output gate. |
| 286 | AddTensorOperand<HalPolicy>(model, |
| 287 | outputLayerNormWeightsDimensions, |
| 288 | outputLayerNormWeightsValue, |
| 289 | HalPolicy::OperandType::TENSOR_FLOAT32, |
| 290 | CreateNoValueLifeTime(outputLayerNormWeightsDimensions)); |
| 291 | |
| 292 | // Outputs: |
| 293 | // 00: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| 294 | // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| 295 | AddOutputOperand<HalPolicy>(model, outputDimensions); |
| 296 | // 01: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 297 | // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| 298 | // is present then output #2 must be present as well. |
| 299 | //AddOutputOperand<HalPolicy>(model, hiddenStateOutDimensions); |
| 300 | // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 301 | // [batch_size, num_units]. This output is optional and can be omitted. |
| 302 | //AddOutputOperand<HalPolicy>(model, cellStateOutDimensions); |
| 303 | |
| 304 | // make the lstm operation |
| 305 | model.operations.resize(1); |
| 306 | model.operations[0].type = HalPolicy::OperationType::UNIDIRECTIONAL_SEQUENCE_LSTM; |
| 307 | |
| 308 | model.operations[0].inputs = hidl_vec<uint32_t> {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, |
| 309 | 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27}; |
| 310 | model.operations[0].outputs = hidl_vec<uint32_t> {28}; |
| 311 | |
| 312 | // define the input values |
| 313 | hidl_vec<RequestArgument> inputArguments; |
| 314 | inputArguments.resize(3); |
| 315 | |
| 316 | inputArguments[0] = CreateRequestArgument<float>(inputValue, 0); |
| 317 | inputArguments[1] = CreateRequestArgument<float>(outputStateInValue, 1); |
| 318 | inputArguments[2] = CreateRequestArgument<float>(cellStateInValue, 2); |
| 319 | |
| 320 | // define the expected output values |
| 321 | hidl_vec<RequestArgument> outputArguments; |
| 322 | outputArguments.resize(1); |
| 323 | |
| 324 | outputArguments[0] = CreateRequestArgument<float>(outputValue, 3); |
| 325 | |
| 326 | V1_0::Request request = {}; |
| 327 | request.inputs = inputArguments; |
| 328 | request.outputs = outputArguments; |
| 329 | |
| 330 | // set the input data |
| 331 | AddPoolAndSetData(inputValue.size(), request, inputValue.data()); |
| 332 | AddPoolAndSetData(outputStateInValue.size(), request, outputStateInValue.data()); |
| 333 | AddPoolAndSetData(cellStateInValue.size(), request, cellStateInValue.data()); |
| 334 | |
| 335 | // add memory for the outputs |
| 336 | android::sp<IMemory> outputMemory = AddPoolAndGetData<float>(outputValue.size(), request); |
| 337 | float* outputData = static_cast<float*>(static_cast<void*>(outputMemory->getPointer())); |
| 338 | |
| 339 | // make the prepared model and run the execution |
| 340 | ExecuteModel(model, *driver, request); |
| 341 | |
| 342 | // check the results |
| 343 | if (epsilonValue != 0) |
| 344 | { |
| 345 | for (size_t i = 0; i < outputValue.size(); ++i) |
| 346 | { |
| 347 | DOCTEST_CHECK_MESSAGE(outputValue[i] == doctest::Approx(outputData[i]).epsilon(epsilonValue), |
| 348 | "outputValue[" << i << "]: " << outputValue[i] << " != " << outputData[i]); |
| 349 | } |
| 350 | } |
| 351 | else |
| 352 | { |
| 353 | for (size_t i = 0; i < outputValue.size(); ++i) |
| 354 | { |
| 355 | DOCTEST_CHECK_MESSAGE(outputValue[i] == doctest::Approx(outputData[i]), |
| 356 | "outputValue[" << i << "]: " << outputValue[i] << " != " << outputData[i]); |
| 357 | } |
| 358 | } |
| 359 | } |
| 360 | |
| 361 | template<typename HalPolicy> |
| 362 | void UnidirectionalSequenceLstmLayerFloat32TestImpl(armnn::Compute compute) |
| 363 | { |
| 364 | uint32_t batchSize = 3; |
| 365 | uint32_t timeSize = 2; |
| 366 | uint32_t inputSize = 3; |
| 367 | uint32_t outputSize = 4; |
| 368 | uint32_t numUnits = outputSize; |
| 369 | |
| 370 | // Inputs: |
| 371 | // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| 372 | // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| 373 | // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| 374 | hidl_vec<uint32_t> inputDimensions{batchSize, timeSize, inputSize}; |
| 375 | std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 376 | 3., 2., 1., 2., 3., 4., |
| 377 | 5., 4., 3., 2., 1., 2.}; |
| 378 | |
| 379 | // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 380 | // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| 381 | hidl_vec<uint32_t> inputToInputWeightsDimensions{numUnits, inputSize}; |
| 382 | std::vector<float> inputToInputWeightsValue{-0.49536117f, -0.0556083915f, -0.102400711f, |
| 383 | -0.117484632f, 0.3298470976f, -0.1179017122f, |
| 384 | 0.214305695f, 0.42135173085f, 0.003878414626f, |
| 385 | -0.348303917f, -0.1881275477f, 0.0343011027f}; |
| 386 | // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 387 | // [num_units, input_size]. |
| 388 | hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| 389 | std::vector<float> inputToForgetWeightsValue{0.2415594226f, 0.15400093799f, 0.4566498398f, |
| 390 | -0.3810434485f, 0.268383264f, -0.009807467424f, |
| 391 | -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 392 | 0.13512269116f, -0.4932442977f, -0.10039821991f}; |
| 393 | // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| 394 | hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| 395 | std::vector<float> inputToCellWeightsValue{-0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 396 | 0.386399507f, -0.259465157985f, -0.16545993089f, |
| 397 | -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| 398 | -0.2277662414f, -0.55275535589f, 0.34184026718f}; |
| 399 | // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 400 | // [num_units, input_size]. |
| 401 | hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| 402 | std::vector<float> inputToOutputWeightsValue{0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 403 | 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 404 | 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| 405 | -0.51717478049f, 0.1889653282f, -0.367323637f}; |
| 406 | // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 407 | // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| 408 | // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| 409 | hidl_vec<uint32_t> recurrentToInputWeightsDimensions{numUnits, outputSize}; |
| 410 | std::vector<float> recurrentToInputWeightsValue{-0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, |
| 411 | -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, |
| 412 | 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, |
| 413 | 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f}; |
| 414 | // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 415 | // [num_units, output_size]. |
| 416 | hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| 417 | std::vector<float> recurrentToForgetWeightsValue{-0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| 418 | -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| 419 | -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| 420 | -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f}; |
| 421 | // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 422 | // [num_units, output_size]. |
| 423 | hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| 424 | std::vector<float> recurrentToCellWeightsValue{-0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| 425 | -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 426 | 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 427 | 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f}; |
| 428 | // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 429 | // [num_units, output_size]. |
| 430 | hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| 431 | std::vector<float> recurrentToOutputWeightsValue{-0.32921677827f, 0.32624614238f, -0.1388191282f, |
| 432 | -0.17879831790f, -0.15185534954f, -0.16918526583f, |
| 433 | -0.10087361183f, -0.5436913968f, 0.016758225858f, |
| 434 | 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| 435 | -0.12584099173f, -0.12319286912f, 0.2407919466f, |
| 436 | -0.08879069983f}; |
| 437 | // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 438 | hidl_vec<uint32_t> cellToInputWeightsDimensions{0}; |
| 439 | std::vector<float> cellToInputWeightsValue; |
| 440 | // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 441 | hidl_vec<uint32_t> cellToForgetWeightsDimensions{0}; |
| 442 | std::vector<float> cellToForgetWeightsValue; |
| 443 | // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 444 | hidl_vec<uint32_t> cellToOutputWeightsDimensions{0}; |
| 445 | std::vector<float> cellToOutputWeightsValue; |
| 446 | // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 447 | hidl_vec<uint32_t> inputGateBiasDimensions{numUnits}; |
| 448 | std::vector<float> inputGateBiasValue(numUnits, 0.0f); |
| 449 | // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 450 | hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| 451 | std::vector<float> forgetGateBiasValue(numUnits, 1.0f); |
| 452 | // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 453 | hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| 454 | std::vector<float> cellBiasValue(numUnits, 0.0f); |
| 455 | // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 456 | hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| 457 | std::vector<float> outputGateBiasValue(numUnits, 0.0f); |
| 458 | // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 459 | // [output_size, num_units]. |
| 460 | hidl_vec<uint32_t> projectionWeightsDimensions{0}; |
| 461 | std::vector<float> projectionWeightsValue; |
| 462 | // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| 463 | hidl_vec<uint32_t> projectionBiasDimensions{0}; |
| 464 | std::vector<float> projectionBiasValue; |
| 465 | |
| 466 | // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| 467 | hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| 468 | std::vector<float> outputStateInValue(batchSize * outputSize, 0.0f); |
| 469 | // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| 470 | hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| 471 | std::vector<float> cellStateInValue(batchSize * numUnits, 0.0f); |
| 472 | |
| 473 | // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| 474 | // 20: The activation function: A value indicating the activation function: |
| 475 | // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| 476 | hidl_vec<uint32_t> activationFunctionDimensions{}; |
| 477 | std::vector<int32_t> activationFunctionValue{4}; |
| 478 | // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| 479 | // If set to 0.0 then clipping is disabled. |
| 480 | hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| 481 | std::vector<float> cellClippingThresholdValue{10.0f}; |
| 482 | // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| 483 | // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| 484 | hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| 485 | std::vector<float> projectionClippingThresholdValue{0.f}; |
| 486 | |
| 487 | // 23: Time-major if true, batch-major if false. |
| 488 | bool timeMajorValue = false; |
| 489 | |
| 490 | // Normalization: |
| 491 | // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| 492 | // Used to rescale normalized inputs to activation at input gate. |
| 493 | hidl_vec<uint32_t> inputLayerNormWeightsDimensions{0}; |
| 494 | std::vector<float> inputLayerNormWeightsValue; |
| 495 | // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| 496 | // Used to rescale normalized inputs to activation at forget gate. |
| 497 | hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{0}; |
| 498 | std::vector<float> forgetLayerNormWeightsValue; |
| 499 | // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| 500 | // Used to rescale normalized inputs to activation at cell gate. |
| 501 | hidl_vec<uint32_t> cellLayerNormWeightsDimensions{0}; |
| 502 | std::vector<float> cellLayerNormWeightsValue; |
| 503 | // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| 504 | // Used to rescale normalized inputs to activation at output gate. |
| 505 | hidl_vec<uint32_t> outputLayerNormWeightsDimensions{0}; |
| 506 | std::vector<float> outputLayerNormWeightsValue; |
| 507 | |
| 508 | // Outputs: |
| 509 | // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| 510 | // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| 511 | hidl_vec<uint32_t> outputDimensions{batchSize, timeSize, outputSize}; |
| 512 | std::vector<float> outputValue{-0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f, |
| 513 | -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f, |
| 514 | -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f, |
| 515 | -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f, |
| 516 | -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f, |
| 517 | -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f}; |
| 518 | |
| 519 | // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 520 | // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| 521 | // is present then output #2 must be present as well. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 522 | hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| 523 | std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 524 | // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 525 | // [batch_size, num_units]. This output is optional and can be omitted. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 526 | hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| 527 | std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 528 | |
| 529 | UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| 530 | inputToInputWeightsDimensions, inputToInputWeightsValue, |
| 531 | inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| 532 | inputToCellWeightsDimensions, inputToCellWeightsValue, |
| 533 | inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| 534 | recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| 535 | recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| 536 | recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| 537 | recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| 538 | cellToInputWeightsDimensions, cellToInputWeightsValue, |
| 539 | cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| 540 | cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| 541 | inputGateBiasDimensions, inputGateBiasValue, |
| 542 | forgetGateBiasDimensions, forgetGateBiasValue, |
| 543 | cellBiasDimensions, cellBiasValue, |
| 544 | outputGateBiasDimensions, outputGateBiasValue, |
| 545 | projectionWeightsDimensions, projectionWeightsValue, |
| 546 | projectionBiasDimensions, projectionBiasValue, |
| 547 | outputStateInDimensions, outputStateInValue, |
| 548 | cellStateInDimensions, cellStateInValue, |
| 549 | activationFunctionDimensions, activationFunctionValue, |
| 550 | cellClippingThresholdDimensions, cellClippingThresholdValue, |
| 551 | projectionClippingThresholdDimensions, |
| 552 | projectionClippingThresholdValue, |
| 553 | timeMajorValue, |
| 554 | inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| 555 | forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| 556 | cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| 557 | outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| 558 | outputDimensions, outputValue, |
| 559 | hiddenStateOutDimensions, hiddenStateOutValue, |
| 560 | cellStateOutDimensions, cellStateOutValue, |
| 561 | compute); |
| 562 | } |
| 563 | |
| 564 | template<typename HalPolicy> |
| 565 | void UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl(armnn::Compute compute) |
| 566 | { |
| 567 | uint32_t batchSize = 3; |
| 568 | uint32_t timeSize = 2; |
| 569 | uint32_t inputSize = 3; |
| 570 | uint32_t outputSize = 4; |
| 571 | uint32_t numUnits = outputSize; |
| 572 | |
| 573 | // Inputs: |
| 574 | // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| 575 | // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| 576 | // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| 577 | hidl_vec<uint32_t> inputDimensions{timeSize, batchSize, inputSize}; |
| 578 | std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 579 | 3., 2., 1., 2., 3., 4., |
| 580 | 5., 4., 3., 2., 1., 2.}; |
| 581 | |
| 582 | // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 583 | // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| 584 | hidl_vec<uint32_t> inputToInputWeightsDimensions{numUnits, inputSize}; |
| 585 | std::vector<float> inputToInputWeightsValue{0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f, |
| 586 | 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f, |
| 587 | 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f, |
| 588 | -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f}; |
| 589 | // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 590 | // [num_units, input_size]. |
| 591 | hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| 592 | std::vector<float> inputToForgetWeightsValue{-0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f, |
| 593 | -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f, |
| 594 | -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f, |
| 595 | -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f}; |
| 596 | // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| 597 | hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| 598 | std::vector<float> inputToCellWeightsValue{-0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f, |
| 599 | 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f, |
| 600 | 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f, |
| 601 | -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f}; |
| 602 | // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 603 | // [num_units, input_size]. |
| 604 | hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| 605 | std::vector<float> inputToOutputWeightsValue{-0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f, |
| 606 | -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f, |
| 607 | 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f, |
| 608 | -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f}; |
| 609 | // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 610 | // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| 611 | // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| 612 | hidl_vec<uint32_t> recurrentToInputWeightsDimensions{numUnits, outputSize}; |
| 613 | std::vector<float> recurrentToInputWeightsValue{0.23788475990f, -0.24948765337f, 0.50044941902f, |
| 614 | 0.14431896805f, -0.115940228137f, -0.717082679f, |
| 615 | -0.17208620906f, 0.17850610617f, -0.16702319684f, |
| 616 | -0.11384502053f, -0.309785276245f, -0.3316611672f, |
| 617 | 0.52380162477f, -0.06839632987f, -0.391478359627f, |
| 618 | -0.10756178963f}; |
| 619 | // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 620 | // [num_units, output_size]. |
| 621 | hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| 622 | std::vector<float> recurrentToForgetWeightsValue{0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f, |
| 623 | 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f, |
| 624 | -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f, |
| 625 | 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f}; |
| 626 | // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 627 | // [num_units, output_size]. |
| 628 | hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| 629 | std::vector<float> recurrentToCellWeightsValue{0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f, |
| 630 | -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f, |
| 631 | -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f, |
| 632 | -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f}; |
| 633 | // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 634 | // [num_units, output_size]. |
| 635 | hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| 636 | std::vector<float> recurrentToOutputWeightsValue{-0.079031050201f, 0.041414566286f, -0.583727357285f, |
| 637 | 0.1025384515f, -0.172372072937f, 0.09214124082f, |
| 638 | 0.178184121827f, -0.2439443916f, 0.104485116899f, |
| 639 | 0.2600405514f, 0.064414866268f, 0.24141204357f, |
| 640 | 0.281875759363f, -0.14234502664f, 0.15126448862f, |
| 641 | -0.24421440064f}; |
| 642 | // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 643 | hidl_vec<uint32_t> cellToInputWeightsDimensions{0}; |
| 644 | std::vector<float> cellToInputWeightsValue; |
| 645 | // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 646 | hidl_vec<uint32_t> cellToForgetWeightsDimensions{0}; |
| 647 | std::vector<float> cellToForgetWeightsValue; |
| 648 | // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 649 | hidl_vec<uint32_t> cellToOutputWeightsDimensions{0}; |
| 650 | std::vector<float> cellToOutputWeightsValue; |
| 651 | // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 652 | hidl_vec<uint32_t> inputGateBiasDimensions{numUnits}; |
| 653 | std::vector<float> inputGateBiasValue(numUnits, 0.0f); |
| 654 | // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 655 | hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| 656 | std::vector<float> forgetGateBiasValue(numUnits, 1.0f); |
| 657 | // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 658 | hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| 659 | std::vector<float> cellBiasValue(numUnits, 0.0f); |
| 660 | // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 661 | hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| 662 | std::vector<float> outputGateBiasValue(numUnits, 0.0f); |
| 663 | // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 664 | // [output_size, num_units]. |
| 665 | hidl_vec<uint32_t> projectionWeightsDimensions{0}; |
| 666 | std::vector<float> projectionWeightsValue; |
| 667 | // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| 668 | hidl_vec<uint32_t> projectionBiasDimensions{0}; |
| 669 | std::vector<float> projectionBiasValue; |
| 670 | |
| 671 | // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| 672 | hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| 673 | std::vector<float> outputStateInValue(batchSize * outputSize, 0.0f); |
| 674 | // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| 675 | hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| 676 | std::vector<float> cellStateInValue(batchSize * numUnits, 0.0f); |
| 677 | |
| 678 | // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| 679 | // 20: The activation function: A value indicating the activation function: |
| 680 | // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| 681 | hidl_vec<uint32_t> activationFunctionDimensions{}; |
| 682 | std::vector<int32_t> activationFunctionValue{4}; |
| 683 | // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| 684 | // If set to 0.0 then clipping is disabled. |
| 685 | hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| 686 | std::vector<float> cellClippingThresholdValue{10.0f}; |
| 687 | // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| 688 | // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| 689 | hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| 690 | std::vector<float> projectionClippingThresholdValue{0.f}; |
| 691 | |
| 692 | // 23: Time-major if true, batch-major if false. |
| 693 | bool timeMajorValue = true; |
| 694 | |
| 695 | // Normalization: |
| 696 | // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| 697 | // Used to rescale normalized inputs to activation at input gate. |
| 698 | hidl_vec<uint32_t> inputLayerNormWeightsDimensions{0}; |
| 699 | std::vector<float> inputLayerNormWeightsValue; |
| 700 | // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| 701 | // Used to rescale normalized inputs to activation at forget gate. |
| 702 | hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{0}; |
| 703 | std::vector<float> forgetLayerNormWeightsValue; |
| 704 | // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| 705 | // Used to rescale normalized inputs to activation at cell gate. |
| 706 | hidl_vec<uint32_t> cellLayerNormWeightsDimensions{0}; |
| 707 | std::vector<float> cellLayerNormWeightsValue; |
| 708 | // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| 709 | // Used to rescale normalized inputs to activation at output gate. |
| 710 | hidl_vec<uint32_t> outputLayerNormWeightsDimensions{0}; |
| 711 | std::vector<float> outputLayerNormWeightsValue; |
| 712 | |
| 713 | // Outputs: |
| 714 | // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| 715 | // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| 716 | hidl_vec<uint32_t> outputDimensions{timeSize, batchSize, outputSize}; |
| 717 | std::vector<float> outputValue{0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f, |
| 718 | 0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f, |
| 719 | -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f, |
| 720 | 0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f, |
| 721 | 0.11171641f, 0.043119f , 0.0762981f , -0.01228541f, |
| 722 | 0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f}; |
| 723 | |
| 724 | // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 725 | // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| 726 | // is present then output #2 must be present as well. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 727 | hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| 728 | std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 729 | // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 730 | // [batch_size, num_units]. This output is optional and can be omitted. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 731 | hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| 732 | std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 733 | |
| 734 | UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| 735 | inputToInputWeightsDimensions, inputToInputWeightsValue, |
| 736 | inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| 737 | inputToCellWeightsDimensions, inputToCellWeightsValue, |
| 738 | inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| 739 | recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| 740 | recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| 741 | recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| 742 | recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| 743 | cellToInputWeightsDimensions, cellToInputWeightsValue, |
| 744 | cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| 745 | cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| 746 | inputGateBiasDimensions, inputGateBiasValue, |
| 747 | forgetGateBiasDimensions, forgetGateBiasValue, |
| 748 | cellBiasDimensions, cellBiasValue, |
| 749 | outputGateBiasDimensions, outputGateBiasValue, |
| 750 | projectionWeightsDimensions, projectionWeightsValue, |
| 751 | projectionBiasDimensions, projectionBiasValue, |
| 752 | outputStateInDimensions, outputStateInValue, |
| 753 | cellStateInDimensions, cellStateInValue, |
| 754 | activationFunctionDimensions, activationFunctionValue, |
| 755 | cellClippingThresholdDimensions, cellClippingThresholdValue, |
| 756 | projectionClippingThresholdDimensions, |
| 757 | projectionClippingThresholdValue, |
| 758 | timeMajorValue, |
| 759 | inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| 760 | forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| 761 | cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| 762 | outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| 763 | outputDimensions, outputValue, |
| 764 | hiddenStateOutDimensions, hiddenStateOutValue, |
| 765 | cellStateOutDimensions, cellStateOutValue, |
| 766 | compute); |
| 767 | } |
| 768 | |
| 769 | template<typename HalPolicy> |
| 770 | void UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTestImpl(armnn::Compute compute) |
| 771 | { |
| 772 | uint32_t batchSize = 2; |
| 773 | uint32_t timeSize = 3; |
| 774 | uint32_t inputSize = 4; |
| 775 | uint32_t outputSize = 5; |
| 776 | uint32_t numUnits = 6; |
| 777 | |
| 778 | // Inputs: |
| 779 | // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| 780 | // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| 781 | // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| 782 | hidl_vec<uint32_t> inputDimensions{batchSize, timeSize, inputSize}; |
| 783 | std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 784 | 3., 2., 1., 2., 3., 4., |
| 785 | 5., 4., 3., 2., 1., 2., |
| 786 | 1., 2., 3., 4., 5., 4.}; |
| 787 | |
| 788 | // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 789 | // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| 790 | hidl_vec<uint32_t> inputToInputWeightsDimensions{numUnits, inputSize}; |
| 791 | std::vector<float> inputToInputWeightsValue{0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, |
| 792 | -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, |
| 793 | -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, |
| 794 | -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, |
| 795 | -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, |
| 796 | -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f}; |
| 797 | // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 798 | // [num_units, input_size]. |
| 799 | hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| 800 | std::vector<float> inputToForgetWeightsValue{-0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, |
| 801 | 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, |
| 802 | 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, |
| 803 | -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, |
| 804 | -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, |
| 805 | 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f}; |
| 806 | // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| 807 | hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| 808 | std::vector<float> inputToCellWeightsValue{-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, |
| 809 | -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, |
| 810 | -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, |
| 811 | -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, |
| 812 | -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, |
| 813 | 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f}; |
| 814 | // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 815 | // [num_units, input_size]. |
| 816 | hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| 817 | std::vector<float> inputToOutputWeightsValue{-0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f, |
| 818 | -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f, |
| 819 | -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f, |
| 820 | 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f, |
| 821 | 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f, |
| 822 | -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f}; |
| 823 | // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 824 | // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| 825 | // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| 826 | hidl_vec<uint32_t> recurrentToInputWeightsDimensions{numUnits, outputSize}; |
| 827 | std::vector<float> recurrentToInputWeightsValue{-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, |
| 828 | -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, |
| 829 | -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, |
| 830 | -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, |
| 831 | 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, |
| 832 | 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, |
| 833 | -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, |
| 834 | 0.14283475f, -0.07390571f}; |
| 835 | // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 836 | // [num_units, output_size]. |
| 837 | hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| 838 | std::vector<float> recurrentToForgetWeightsValue{-0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, |
| 839 | 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, |
| 840 | -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, |
| 841 | 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, |
| 842 | 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, |
| 843 | -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, |
| 844 | -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, |
| 845 | 0.061878487f, -0.04729229f}; |
| 846 | // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 847 | // [num_units, output_size]. |
| 848 | hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| 849 | std::vector<float> recurrentToCellWeightsValue{-0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, |
| 850 | 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, |
| 851 | 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, |
| 852 | -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, |
| 853 | 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, |
| 854 | 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, |
| 855 | -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, |
| 856 | -0.019443132f, -0.030755889f}; |
| 857 | // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 858 | // [num_units, output_size]. |
| 859 | hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| 860 | std::vector<float> recurrentToOutputWeightsValue{0.025825322f, -0.05813119f, 0.09495884f, |
| 861 | -0.045984812f,-0.01255415f, -0.0026479573f, |
| 862 | -0.08196161f, -0.054914974f, -0.0046604523f, |
| 863 | -0.029587349f, -0.044576716f, -0.07480124f, |
| 864 | -0.082868785f, 0.023254942f, 0.027502948f, |
| 865 | -0.0039728214f, -0.08683098f, -0.08116779f, |
| 866 | -0.014675607f, -0.037924774f, -0.023314456f, |
| 867 | -0.007401714f, -0.09255757f, 0.029460307f, |
| 868 | -0.08829125f, -0.005139627f, -0.08989442f, |
| 869 | -0.0555066f, 0.13596267f, 0.025062224f}; |
| 870 | // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 871 | hidl_vec<uint32_t> cellToInputWeightsDimensions{numUnits}; |
| 872 | std::vector<float> cellToInputWeightsValue{0.040369894f, 0.030746894f, 0.24704495f, |
| 873 | 0.018586371f, -0.037586458f, -0.15312155f}; |
| 874 | // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 875 | hidl_vec<uint32_t> cellToForgetWeightsDimensions{numUnits}; |
| 876 | std::vector<float> cellToForgetWeightsValue{-0.01998659f, -0.15568835f, -0.24248174f, |
| 877 | -0.012770197f, 0.041331276f, -0.072311886f}; |
| 878 | // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 879 | hidl_vec<uint32_t> cellToOutputWeightsDimensions{numUnits}; |
| 880 | std::vector<float> cellToOutputWeightsValue{0.08286371f, -0.08261836f, -0.51210177f, |
| 881 | 0.002913762f, 0.17764764f, -0.5495371f}; |
| 882 | // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 883 | hidl_vec<uint32_t> inputGateBiasDimensions{numUnits}; |
| 884 | std::vector<float> inputGateBiasValue{0.02234832f, 0.14757581f, 0.18176508f, |
| 885 | 0.10380666f, 0.053110216f, -0.06928846f}; |
| 886 | // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 887 | hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| 888 | std::vector<float> forgetGateBiasValue{0.035185695f, -0.042891346f, -0.03032477f, |
| 889 | 0.23027696f, 0.11098921f, 0.08989442f}; |
| 890 | // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 891 | hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| 892 | std::vector<float> cellBiasValue{-0.024379363f, 0.0055531194f, 0.23377132f, |
| 893 | 0.033463873f, -0.1483596f, 0.029460307f}; |
| 894 | // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 895 | hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| 896 | std::vector<float> outputGateBiasValue{0.046159424f, -0.0012809046f, 0.03563469f, |
| 897 | 0.12648113f, 0.027195795f, 0.35373217f}; |
| 898 | // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 899 | // [output_size, num_units]. |
| 900 | hidl_vec<uint32_t> projectionWeightsDimensions{numUnits, outputSize}; |
| 901 | std::vector<float> projectionWeightsValue{-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, |
| 902 | 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, |
| 903 | -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, |
| 904 | -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, |
| 905 | 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, |
| 906 | 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f}; |
| 907 | // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| 908 | hidl_vec<uint32_t> projectionBiasDimensions{outputSize}; |
| 909 | std::vector<float> projectionBiasValue(outputSize, 0.f); |
| 910 | |
| 911 | // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| 912 | hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| 913 | std::vector<float> outputStateInValue(batchSize * outputSize, 0.f); |
| 914 | // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| 915 | hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| 916 | std::vector<float> cellStateInValue(batchSize * numUnits, 0.f); |
| 917 | |
| 918 | // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| 919 | // 20: The activation function: A value indicating the activation function: |
| 920 | // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| 921 | hidl_vec<uint32_t> activationFunctionDimensions{}; |
| 922 | std::vector<int32_t> activationFunctionValue{4}; |
| 923 | // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| 924 | // If set to 0.0 then clipping is disabled. |
| 925 | hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| 926 | std::vector<float> cellClippingThresholdValue{10.0f}; |
| 927 | // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| 928 | // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| 929 | hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| 930 | std::vector<float> projectionClippingThresholdValue{0.f}; |
| 931 | |
| 932 | // 23: Time-major if true, batch-major if false. |
| 933 | bool timeMajorValue = false; |
| 934 | |
| 935 | // Normalization: |
| 936 | // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| 937 | // Used to rescale normalized inputs to activation at input gate. |
| 938 | hidl_vec<uint32_t> inputLayerNormWeightsDimensions{0}; |
| 939 | std::vector<float> inputLayerNormWeightsValue; |
| 940 | // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| 941 | // Used to rescale normalized inputs to activation at forget gate. |
| 942 | hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{0}; |
| 943 | std::vector<float> forgetLayerNormWeightsValue; |
| 944 | // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| 945 | // Used to rescale normalized inputs to activation at cell gate. |
| 946 | hidl_vec<uint32_t> cellLayerNormWeightsDimensions{0}; |
| 947 | std::vector<float> cellLayerNormWeightsValue; |
| 948 | // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| 949 | // Used to rescale normalized inputs to activation at output gate. |
| 950 | hidl_vec<uint32_t> outputLayerNormWeightsDimensions{0}; |
| 951 | std::vector<float> outputLayerNormWeightsValue; |
| 952 | |
| 953 | // Outputs: |
| 954 | // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| 955 | // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| 956 | hidl_vec<uint32_t> outputDimensions{batchSize, timeSize, outputSize}; |
| 957 | std::vector<float> outputValue{-0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f, |
| 958 | -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f, |
| 959 | -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f, |
| 960 | 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f, |
| 961 | -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f, |
| 962 | -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f}; |
| 963 | |
| 964 | // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 965 | // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| 966 | // is present then output #2 must be present as well. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 967 | hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| 968 | std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 969 | // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 970 | // [batch_size, num_units]. This output is optional and can be omitted. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 971 | hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| 972 | std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 973 | |
| 974 | UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| 975 | inputToInputWeightsDimensions, inputToInputWeightsValue, |
| 976 | inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| 977 | inputToCellWeightsDimensions, inputToCellWeightsValue, |
| 978 | inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| 979 | recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| 980 | recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| 981 | recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| 982 | recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| 983 | cellToInputWeightsDimensions, cellToInputWeightsValue, |
| 984 | cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| 985 | cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| 986 | inputGateBiasDimensions, inputGateBiasValue, |
| 987 | forgetGateBiasDimensions, forgetGateBiasValue, |
| 988 | cellBiasDimensions, cellBiasValue, |
| 989 | outputGateBiasDimensions, outputGateBiasValue, |
| 990 | projectionWeightsDimensions, projectionWeightsValue, |
| 991 | projectionBiasDimensions, projectionBiasValue, |
| 992 | outputStateInDimensions, outputStateInValue, |
| 993 | cellStateInDimensions, cellStateInValue, |
| 994 | activationFunctionDimensions, activationFunctionValue, |
| 995 | cellClippingThresholdDimensions, cellClippingThresholdValue, |
| 996 | projectionClippingThresholdDimensions, |
| 997 | projectionClippingThresholdValue, |
| 998 | timeMajorValue, |
| 999 | inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| 1000 | forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| 1001 | cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| 1002 | outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| 1003 | outputDimensions, outputValue, |
| 1004 | hiddenStateOutDimensions, hiddenStateOutValue, |
| 1005 | cellStateOutDimensions, cellStateOutValue, |
| 1006 | compute, 0.0031454); |
| 1007 | } |
| 1008 | |
| 1009 | template<typename HalPolicy> |
| 1010 | void UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTestImpl(armnn::Compute compute) |
| 1011 | { |
| 1012 | uint32_t batchSize = 3; |
| 1013 | uint32_t timeSize = 2; |
| 1014 | uint32_t inputSize = 3; |
| 1015 | uint32_t outputSize = 4; |
| 1016 | uint32_t numUnits = 5; |
| 1017 | |
| 1018 | // Inputs: |
| 1019 | // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| 1020 | // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| 1021 | // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| 1022 | hidl_vec<uint32_t> inputDimensions{batchSize, timeSize, inputSize}; |
| 1023 | std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 1024 | 3., 2., 1., 2., 3., 4., |
| 1025 | 5., 4., 3., 2., 1., 2.}; |
| 1026 | |
| 1027 | // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1028 | // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| 1029 | hidl_vec<uint32_t> inputToInputWeightsDimensions{numUnits, inputSize}; |
| 1030 | std::vector<float> inputToInputWeightsValue{-0.49536117f, -0.0556083915f, -0.102400711f, |
| 1031 | -0.117484632f, 0.3298470976f, -0.1179017122f, |
| 1032 | 0.214305695f, 0.42135173085f, 0.003878414626f, |
| 1033 | -0.348303917f, -0.1881275477f, 0.0343011027f, |
| 1034 | -0.38837709614f, -0.05636804124f, 0.4259087456f}; |
| 1035 | // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1036 | // [num_units, input_size]. |
| 1037 | hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| 1038 | std::vector<float> inputToForgetWeightsValue{0.2415594226f, 0.15400093799f, 0.4566498398f, |
| 1039 | -0.3810434485f, 0.268383264f, -0.009807467424f, |
| 1040 | -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 1041 | 0.13512269116f, -0.4932442977f, -0.10039821991f, |
| 1042 | 0.2726137042f, 0.09216640889f, -0.06551410215f}; |
| 1043 | // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| 1044 | hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| 1045 | std::vector<float> inputToCellWeightsValue{-0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 1046 | 0.386399507f, -0.259465157985f, -0.16545993089f, |
| 1047 | -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| 1048 | -0.2277662414f, -0.55275535589f, 0.34184026718f, |
| 1049 | 0.3954237699f, -0.19407111404f, 0.30412107706f}; |
| 1050 | // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1051 | // [num_units, input_size]. |
| 1052 | hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| 1053 | std::vector<float> inputToOutputWeightsValue{0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 1054 | 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 1055 | 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| 1056 | -0.51717478049f, 0.1889653282f, -0.367323637f, |
| 1057 | -0.12584099173f, -0.12319286912f, 0.2407919466f}; |
| 1058 | // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1059 | // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| 1060 | // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| 1061 | hidl_vec<uint32_t> recurrentToInputWeightsDimensions{numUnits, outputSize}; |
| 1062 | std::vector<float> recurrentToInputWeightsValue{-0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, |
| 1063 | -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, |
| 1064 | 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, |
| 1065 | 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f, |
| 1066 | 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f}; |
| 1067 | // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1068 | // [num_units, output_size]. |
| 1069 | hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| 1070 | std::vector<float> recurrentToForgetWeightsValue{-0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| 1071 | -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| 1072 | -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| 1073 | -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f, |
| 1074 | 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f}; |
| 1075 | // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1076 | // [num_units, output_size]. |
| 1077 | hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| 1078 | std::vector<float> recurrentToCellWeightsValue{-0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| 1079 | -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 1080 | 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 1081 | 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f, |
| 1082 | 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f}; |
| 1083 | // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1084 | // [num_units, output_size]. |
| 1085 | hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| 1086 | std::vector<float> recurrentToOutputWeightsValue{-0.32921677827f, 0.32624614238f, -0.1388191282f, |
| 1087 | -0.17879831790f,-0.15185534954f, -0.16918526583f, |
| 1088 | -0.10087361183f, -0.5436913968f, 0.016758225858f, |
| 1089 | 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| 1090 | -0.12584099173f, -0.12319286912f, 0.2407919466f, |
| 1091 | -0.08879069983f, 0.11178309f, 0.09481031f, |
| 1092 | -0.26424935f, 0.46261835f}; |
| 1093 | // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1094 | hidl_vec<uint32_t> cellToInputWeightsDimensions{numUnits}; |
| 1095 | std::vector<float> cellToInputWeightsValue{0.05f, 0.1f, 0.25f, 0.15f, -0.02f}; |
| 1096 | // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1097 | hidl_vec<uint32_t> cellToForgetWeightsDimensions{numUnits}; |
| 1098 | std::vector<float> cellToForgetWeightsValue{-0.02f, -0.15f, -0.25f, -0.03f, 0.15f}; |
| 1099 | // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1100 | hidl_vec<uint32_t> cellToOutputWeightsDimensions{numUnits}; |
| 1101 | std::vector<float> cellToOutputWeightsValue{0.1f, -0.1f, -0.5f, 0.05f, 0.01f}; |
| 1102 | // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1103 | hidl_vec<uint32_t> inputGateBiasDimensions{numUnits}; |
| 1104 | std::vector<float> inputGateBiasValue{0.03f, 0.15f, 0.22f, 0.38f, 0.05f}; |
| 1105 | // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1106 | hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| 1107 | std::vector<float> forgetGateBiasValue{0.1f, -0.3f, -0.2f, 0.1f, 0.4f}; |
| 1108 | // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1109 | hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| 1110 | std::vector<float> cellBiasValue{-0.05f, 0.72f, 0.25f, 0.08f, 0.1f}; |
| 1111 | // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1112 | hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| 1113 | std::vector<float> outputGateBiasValue{0.05f, -0.01f, 0.2f, 0.1f, -0.2f}; |
| 1114 | // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1115 | // [output_size, num_units]. |
| 1116 | hidl_vec<uint32_t> projectionWeightsDimensions{numUnits, outputSize}; |
| 1117 | std::vector<float> projectionWeightsValue{-0.1f, 0.2f, 0.01f, -0.2f, |
| 1118 | 0.1f, 0.5f, 0.3f, 0.08f, |
| 1119 | 0.07f, 0.2f, -0.4f, 0.2f, |
| 1120 | 0.5f, -0.4f, 0.3f, -0.2f, |
| 1121 | 0.3f, 0.08f, -0.07f, 0.2f}; |
| 1122 | // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| 1123 | hidl_vec<uint32_t> projectionBiasDimensions{outputSize}; |
| 1124 | std::vector<float> projectionBiasValue(outputSize, 0.f); |
| 1125 | |
| 1126 | // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| 1127 | hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| 1128 | std::vector<float> outputStateInValue(batchSize * outputSize, 0.f); |
| 1129 | // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| 1130 | hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| 1131 | std::vector<float> cellStateInValue(batchSize * numUnits, 0.f); |
| 1132 | |
| 1133 | // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| 1134 | // 20: The activation function: A value indicating the activation function: |
| 1135 | // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| 1136 | hidl_vec<uint32_t> activationFunctionDimensions{}; |
| 1137 | std::vector<int32_t> activationFunctionValue{4}; |
| 1138 | // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| 1139 | // If set to 0.0 then clipping is disabled. |
| 1140 | hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| 1141 | std::vector<float> cellClippingThresholdValue{10.0f}; |
| 1142 | // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| 1143 | // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| 1144 | hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| 1145 | std::vector<float> projectionClippingThresholdValue{0.f}; |
| 1146 | |
| 1147 | // 23: Time-major if true, batch-major if false. |
| 1148 | bool timeMajorValue = false; |
| 1149 | |
| 1150 | // Normalization: |
| 1151 | // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| 1152 | // Used to rescale normalized inputs to activation at input gate. |
| 1153 | hidl_vec<uint32_t> inputLayerNormWeightsDimensions{numUnits}; |
| 1154 | std::vector<float> inputLayerNormWeightsValue{0.1f, 0.2f, 0.3f, 0.5f, 0.8f}; |
| 1155 | // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| 1156 | // Used to rescale normalized inputs to activation at forget gate. |
| 1157 | hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{numUnits}; |
| 1158 | std::vector<float> forgetLayerNormWeightsValue{0.1f, 0.2f, 0.3f, 0.5f, 0.2f}; |
| 1159 | // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| 1160 | // Used to rescale normalized inputs to activation at cell gate. |
| 1161 | hidl_vec<uint32_t> cellLayerNormWeightsDimensions{numUnits}; |
| 1162 | std::vector<float> cellLayerNormWeightsValue{0.7f, 0.2f, 0.3f, 0.8f, 0.5f}; |
| 1163 | // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| 1164 | // Used to rescale normalized inputs to activation at output gate. |
| 1165 | hidl_vec<uint32_t> outputLayerNormWeightsDimensions{numUnits}; |
| 1166 | std::vector<float> outputLayerNormWeightsValue{0.6f, 0.2f, 0.2f, 0.5f, 0.1f}; |
| 1167 | |
| 1168 | // Outputs: |
| 1169 | // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| 1170 | // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| 1171 | hidl_vec<uint32_t> outputDimensions{batchSize, timeSize, outputSize}; |
| 1172 | std::vector<float> outputValue{0.0642256f, 0.0343966f, 0.184122f, 0.114717f, |
| 1173 | 0.11458f, 0.0407109f, 0.300327f, 0.174301f, |
| 1174 | 0.0864761f, 0.0362912f, 0.178635f, 0.115689f, |
| 1175 | 0.108008f, 0.0386623f, 0.273471f, 0.167115f, |
| 1176 | 0.0859545f, 0.0331481f, 0.186051f, 0.11888f, |
| 1177 | 0.106649f, 0.0276847f, 0.229863f, 0.166958f}; |
| 1178 | |
| 1179 | // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 1180 | // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| 1181 | // is present then output #2 must be present as well. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 1182 | hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| 1183 | std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 1184 | // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 1185 | // [batch_size, num_units]. This output is optional and can be omitted. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 1186 | hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| 1187 | std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 1188 | |
| 1189 | UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| 1190 | inputToInputWeightsDimensions, inputToInputWeightsValue, |
| 1191 | inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| 1192 | inputToCellWeightsDimensions, inputToCellWeightsValue, |
| 1193 | inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| 1194 | recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| 1195 | recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| 1196 | recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| 1197 | recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| 1198 | cellToInputWeightsDimensions, cellToInputWeightsValue, |
| 1199 | cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| 1200 | cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| 1201 | inputGateBiasDimensions, inputGateBiasValue, |
| 1202 | forgetGateBiasDimensions, forgetGateBiasValue, |
| 1203 | cellBiasDimensions, cellBiasValue, |
| 1204 | outputGateBiasDimensions, outputGateBiasValue, |
| 1205 | projectionWeightsDimensions, projectionWeightsValue, |
| 1206 | projectionBiasDimensions, projectionBiasValue, |
| 1207 | outputStateInDimensions, outputStateInValue, |
| 1208 | cellStateInDimensions, cellStateInValue, |
| 1209 | activationFunctionDimensions, activationFunctionValue, |
| 1210 | cellClippingThresholdDimensions, cellClippingThresholdValue, |
| 1211 | projectionClippingThresholdDimensions, |
| 1212 | projectionClippingThresholdValue, |
| 1213 | timeMajorValue, |
| 1214 | inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| 1215 | forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| 1216 | cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| 1217 | outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| 1218 | outputDimensions, outputValue, |
| 1219 | hiddenStateOutDimensions, hiddenStateOutValue, |
| 1220 | cellStateOutDimensions, cellStateOutValue, |
| 1221 | compute); |
| 1222 | } |
| 1223 | |
| 1224 | template<typename HalPolicy> |
| 1225 | void UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTestImpl(armnn::Compute compute) |
| 1226 | { |
| 1227 | uint32_t batchSize = 3; |
| 1228 | uint32_t timeSize = 2; |
| 1229 | uint32_t inputSize = 3; |
| 1230 | uint32_t outputSize = 4; |
| 1231 | uint32_t numUnits = outputSize; |
| 1232 | |
| 1233 | // Inputs: |
| 1234 | // 00: The input: A 3-D tensor of shape: If time-major: [max_time, batch_size, input_size] If batch-major: |
| 1235 | // [batch_size, max_time, input_size] where “max_time” is the number of timesteps (sequence length), |
| 1236 | // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. |
| 1237 | hidl_vec<uint32_t> inputDimensions{batchSize, timeSize, inputSize}; |
| 1238 | std::vector<float> inputValue{1., 2., 3., 4., 5., 4., |
| 1239 | 3., 2., 1., 2., 3., 4., |
| 1240 | 5., 4., 3., 2., 1., 2.}; |
| 1241 | |
| 1242 | // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1243 | // [num_units, input_size], where “num_units” corresponds to the number of cell units. |
| 1244 | hidl_vec<uint32_t> inputToInputWeightsDimensions{0}; |
| 1245 | std::vector<float> inputToInputWeightsValue; |
| 1246 | // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1247 | // [num_units, input_size]. |
| 1248 | hidl_vec<uint32_t> inputToForgetWeightsDimensions{numUnits, inputSize}; |
| 1249 | std::vector<float> inputToForgetWeightsValue{0.2415594226f, 0.15400093799f, 0.4566498398f, |
| 1250 | -0.3810434485f, 0.268383264f, -0.009807467424f, |
| 1251 | -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 1252 | 0.13512269116f, -0.4932442977f, -0.10039821991f}; |
| 1253 | // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. |
| 1254 | hidl_vec<uint32_t> inputToCellWeightsDimensions{numUnits, inputSize}; |
| 1255 | std::vector<float> inputToCellWeightsValue{-0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 1256 | 0.386399507f, -0.259465157985f, -0.16545993089f, |
| 1257 | -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| 1258 | -0.2277662414f, -0.55275535589f, 0.34184026718f}; |
| 1259 | // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1260 | // [num_units, input_size]. |
| 1261 | hidl_vec<uint32_t> inputToOutputWeightsDimensions{numUnits, inputSize}; |
| 1262 | std::vector<float> inputToOutputWeightsValue{0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 1263 | 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 1264 | 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| 1265 | -0.51717478049f, 0.1889653282f, -0.367323637f}; |
| 1266 | // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1267 | // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., |
| 1268 | // “num_units”), or the second dimension of the “projection_weights”, if defined. |
| 1269 | hidl_vec<uint32_t> recurrentToInputWeightsDimensions{0}; |
| 1270 | std::vector<float> recurrentToInputWeightsValue; |
| 1271 | // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1272 | // [num_units, output_size]. |
| 1273 | hidl_vec<uint32_t> recurrentToForgetWeightsDimensions{numUnits, outputSize}; |
| 1274 | std::vector<float> recurrentToForgetWeightsValue{-0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| 1275 | -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| 1276 | -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| 1277 | -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f}; |
| 1278 | // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1279 | // [num_units, output_size]. |
| 1280 | hidl_vec<uint32_t> recurrentToCellWeightsDimensions{numUnits, outputSize}; |
| 1281 | std::vector<float> recurrentToCellWeightsValue{-0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| 1282 | -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 1283 | 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 1284 | 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f}; |
| 1285 | // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1286 | // [num_units, output_size]. |
| 1287 | hidl_vec<uint32_t> recurrentToOutputWeightsDimensions{numUnits, outputSize}; |
| 1288 | std::vector<float> recurrentToOutputWeightsValue{-0.32921677827f, 0.32624614238f, -0.1388191282f, |
| 1289 | -0.17879831790f, -0.15185534954f, -0.16918526583f, |
| 1290 | -0.10087361183f, -0.5436913968f, 0.016758225858f, |
| 1291 | 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| 1292 | -0.12584099173f, -0.12319286912f, 0.2407919466f, |
| 1293 | -0.08879069983f}; |
| 1294 | // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1295 | hidl_vec<uint32_t> cellToInputWeightsDimensions{0}; |
| 1296 | std::vector<float> cellToInputWeightsValue; |
| 1297 | // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1298 | hidl_vec<uint32_t> cellToForgetWeightsDimensions{numUnits}; |
| 1299 | std::vector<float> cellToForgetWeightsValue{0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; |
| 1300 | // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1301 | hidl_vec<uint32_t> cellToOutputWeightsDimensions{numUnits}; |
| 1302 | std::vector<float> cellToOutputWeightsValue{-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; |
| 1303 | // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1304 | hidl_vec<uint32_t> inputGateBiasDimensions{0}; |
| 1305 | std::vector<float> inputGateBiasValue; |
| 1306 | // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1307 | hidl_vec<uint32_t> forgetGateBiasDimensions{numUnits}; |
| 1308 | std::vector<float> forgetGateBiasValue{1., 1., 1., 1.}; |
| 1309 | // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1310 | hidl_vec<uint32_t> cellBiasDimensions{numUnits}; |
| 1311 | std::vector<float> cellBiasValue{0., 0., 0., 0.}; |
| 1312 | // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. |
| 1313 | hidl_vec<uint32_t> outputGateBiasDimensions{numUnits}; |
| 1314 | std::vector<float> outputGateBiasValue{0., 0., 0., 0.}; |
| 1315 | // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape |
| 1316 | // [output_size, num_units]. |
| 1317 | hidl_vec<uint32_t> projectionWeightsDimensions{0}; |
| 1318 | std::vector<float> projectionWeightsValue; |
| 1319 | // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. |
| 1320 | hidl_vec<uint32_t> projectionBiasDimensions{0}; |
| 1321 | std::vector<float> projectionBiasValue; |
| 1322 | |
| 1323 | // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. |
| 1324 | hidl_vec<uint32_t> outputStateInDimensions{batchSize, outputSize}; |
| 1325 | std::vector<float> outputStateInValue(batchSize * outputSize, 0.f); |
| 1326 | // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. |
| 1327 | hidl_vec<uint32_t> cellStateInDimensions{batchSize, numUnits}; |
| 1328 | std::vector<float> cellStateInValue(batchSize * numUnits, 0.f); |
| 1329 | |
| 1330 | // Constant scalar values (the VTS test adds these as tensors of dim {}) |
| 1331 | // 20: The activation function: A value indicating the activation function: |
| 1332 | // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. |
| 1333 | hidl_vec<uint32_t> activationFunctionDimensions{}; |
| 1334 | std::vector<int32_t> activationFunctionValue{4}; |
| 1335 | // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. |
| 1336 | // If set to 0.0 then clipping is disabled. |
| 1337 | hidl_vec<uint32_t> cellClippingThresholdDimensions{}; |
| 1338 | std::vector<float> cellClippingThresholdValue{10.0f}; |
| 1339 | // 22: The clipping threshold: for the output from the projection layer, such that values are bound within |
| 1340 | // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| 1341 | hidl_vec<uint32_t> projectionClippingThresholdDimensions{}; |
| 1342 | std::vector<float> projectionClippingThresholdValue{0.f}; |
| 1343 | |
| 1344 | // 23: Time-major if true, batch-major if false. |
| 1345 | bool timeMajorValue = false; |
| 1346 | |
| 1347 | // Normalization: |
| 1348 | // 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. |
| 1349 | // Used to rescale normalized inputs to activation at input gate. |
| 1350 | hidl_vec<uint32_t> inputLayerNormWeightsDimensions{0}; |
| 1351 | std::vector<float> inputLayerNormWeightsValue; |
| 1352 | // 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. |
| 1353 | // Used to rescale normalized inputs to activation at forget gate. |
| 1354 | hidl_vec<uint32_t> forgetLayerNormWeightsDimensions{0}; |
| 1355 | std::vector<float> forgetLayerNormWeightsValue; |
| 1356 | // 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. |
| 1357 | // Used to rescale normalized inputs to activation at cell gate. |
| 1358 | hidl_vec<uint32_t> cellLayerNormWeightsDimensions{0}; |
| 1359 | std::vector<float> cellLayerNormWeightsValue; |
| 1360 | // 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. |
| 1361 | // Used to rescale normalized inputs to activation at output gate. |
| 1362 | hidl_vec<uint32_t> outputLayerNormWeightsDimensions{0}; |
| 1363 | std::vector<float> outputLayerNormWeightsValue; |
| 1364 | |
| 1365 | // Outputs: |
| 1366 | // 0: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16. Shape: if time-major: |
| 1367 | // [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] |
| 1368 | hidl_vec<uint32_t> outputDimensions{batchSize, timeSize, outputSize}; |
| 1369 | std::vector<float> outputValue{-0.0129257f, -0.070531f, -0.153508f, -0.0392391f, |
| 1370 | -0.0300169f, -0.195717f, -0.528679f, -0.0818106f, |
| 1371 | -0.0332748f, 0.155429f, -0.353966f, -0.0801505f, |
| 1372 | -0.032312f, -0.0407911f, -0.435053f, -0.0932317f, |
| 1373 | -0.0108233f, 0.165584f, -0.640424f, -0.0447535f, |
| 1374 | -0.031675f, 0.125987f, -0.526695f, -0.110093f}; |
| 1375 | |
| 1376 | // 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 1377 | // [batch_size, output_size]. This output is optional and can be omitted. If this output |
| 1378 | // is present then output #2 must be present as well. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 1379 | hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize}; |
| 1380 | std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 1381 | // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape |
| 1382 | // [batch_size, num_units]. This output is optional and can be omitted. |
Mike Kelly | 0ae102a | 2022-04-25 16:18:57 +0100 | [diff] [blame] | 1383 | hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits}; |
| 1384 | std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f); |
Cathal Corbett | 0fa5e6d | 2022-01-21 16:55:13 +0000 | [diff] [blame] | 1385 | |
| 1386 | UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue, |
| 1387 | inputToInputWeightsDimensions, inputToInputWeightsValue, |
| 1388 | inputToForgetWeightsDimensions, inputToForgetWeightsValue, |
| 1389 | inputToCellWeightsDimensions, inputToCellWeightsValue, |
| 1390 | inputToOutputWeightsDimensions, inputToOutputWeightsValue, |
| 1391 | recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, |
| 1392 | recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, |
| 1393 | recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, |
| 1394 | recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, |
| 1395 | cellToInputWeightsDimensions, cellToInputWeightsValue, |
| 1396 | cellToForgetWeightsDimensions, cellToForgetWeightsValue, |
| 1397 | cellToOutputWeightsDimensions, cellToOutputWeightsValue, |
| 1398 | inputGateBiasDimensions, inputGateBiasValue, |
| 1399 | forgetGateBiasDimensions, forgetGateBiasValue, |
| 1400 | cellBiasDimensions, cellBiasValue, |
| 1401 | outputGateBiasDimensions, outputGateBiasValue, |
| 1402 | projectionWeightsDimensions, projectionWeightsValue, |
| 1403 | projectionBiasDimensions, projectionBiasValue, |
| 1404 | outputStateInDimensions, outputStateInValue, |
| 1405 | cellStateInDimensions, cellStateInValue, |
| 1406 | activationFunctionDimensions, activationFunctionValue, |
| 1407 | cellClippingThresholdDimensions, cellClippingThresholdValue, |
| 1408 | projectionClippingThresholdDimensions, |
| 1409 | projectionClippingThresholdValue, |
| 1410 | timeMajorValue, |
| 1411 | inputLayerNormWeightsDimensions, inputLayerNormWeightsValue, |
| 1412 | forgetLayerNormWeightsDimensions, forgetLayerNormWeightsValue, |
| 1413 | cellLayerNormWeightsDimensions, cellLayerNormWeightsValue, |
| 1414 | outputLayerNormWeightsDimensions, outputLayerNormWeightsValue, |
| 1415 | outputDimensions, outputValue, |
| 1416 | hiddenStateOutDimensions, hiddenStateOutValue, |
| 1417 | cellStateOutDimensions, cellStateOutValue, |
| 1418 | compute); |
| 1419 | } |