Cathal Corbett | 4952a3e | 2022-03-03 15:14:18 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "ClUnidirectionalSequenceLstmFloatWorkload.hpp" |
| 7 | #include "ClWorkloadUtils.hpp" |
| 8 | |
| 9 | #include <aclCommon/ArmComputeUtils.hpp> |
| 10 | #include <aclCommon/ArmComputeTensorUtils.hpp> |
| 11 | |
| 12 | #include <armnn/utility/NumericCast.hpp> |
| 13 | #include <armnnUtils/Permute.hpp> |
| 14 | #include <cl/test/ClWorkloadFactoryHelper.hpp> |
| 15 | #include <backendsCommon/WorkloadUtils.hpp> |
| 16 | |
| 17 | #include "cl/ClTensorHandle.hpp" |
| 18 | |
| 19 | namespace |
| 20 | { |
| 21 | unsigned int CalcAclAxis(unsigned int numDimensions, unsigned int axis) |
| 22 | { |
| 23 | return (numDimensions - axis) - 1; |
| 24 | } |
| 25 | } //namespace |
| 26 | |
| 27 | namespace armnn |
| 28 | { |
| 29 | using namespace armcomputetensorutils; |
| 30 | |
| 31 | ClUnidirectionalSequenceLstmFloatWorkload::ClUnidirectionalSequenceLstmFloatWorkload |
| 32 | (const UnidirectionalSequenceLstmQueueDescriptor& descriptor, |
| 33 | const WorkloadInfo& info, |
| 34 | const arm_compute::CLCompileContext& clCompileContext) |
| 35 | : FloatWorkload<UnidirectionalSequenceLstmQueueDescriptor>(descriptor, info) |
| 36 | { |
| 37 | // Report Profiling Details |
| 38 | ARMNN_REPORT_PROFILING_WORKLOAD_DESC("ClUnidirectionalSequenceLstmFloatWorkload_Construct", |
| 39 | descriptor.m_Parameters, |
| 40 | info, |
| 41 | GetGuid()); |
| 42 | |
| 43 | const arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| 44 | arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| 45 | |
| 46 | TensorInfo inputInfo = info.m_InputTensorInfos[0]; |
| 47 | TensorInfo outputInfo = info.m_OutputTensorInfos[0]; |
| 48 | |
| 49 | arm_compute::DataType armComputeDataType = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetDataType(); |
| 50 | armnn::DataType armnnDataType = GetArmNNDataType(armComputeDataType); |
| 51 | |
| 52 | TensorShape inputLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetShape(); |
| 53 | TensorShape cellStateLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetShape(); |
| 54 | TensorShape outputLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetShape(); |
| 55 | |
| 56 | unsigned int maxTime = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1]; |
| 57 | unsigned int batchSize = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0]; |
| 58 | unsigned int inputSize = inputLayerShape[2]; |
| 59 | unsigned int outputSize = outputLayerShape[2]; |
| 60 | unsigned int numUnits = cellStateLayerShape[1]; |
| 61 | |
| 62 | const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize}); |
| 63 | const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize}); |
| 64 | |
| 65 | // |
| 66 | // Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format. |
| 67 | // |
| 68 | if (!m_Data.m_Parameters.m_TimeMajor) |
| 69 | { |
| 70 | std::unique_ptr<arm_compute::CLPermute> layer(new arm_compute::CLPermute()); |
| 71 | |
| 72 | TensorInfo permuteOutInfo = inputInfo; |
| 73 | permuteOutInfo.SetShape(timeMajorShapeInput); |
| 74 | BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo); |
| 75 | armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut); |
| 76 | |
| 77 | // Permute to time major format. |
| 78 | layer->configure(clCompileContext, &input, &m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U)); |
| 79 | m_Permute1.reset(layer.release()); |
| 80 | } |
| 81 | |
| 82 | // |
| 83 | // Split and Concat Tensors |
| 84 | // |
| 85 | for (unsigned int i = 0; i < maxTime; ++i) |
| 86 | { |
| 87 | arm_compute::CLTensor splitter_out; |
| 88 | arm_compute::CLTensor concat_in; |
| 89 | |
| 90 | auto splitterTensorInfo = inputInfo; |
| 91 | auto concatTensorInfo = outputInfo; |
| 92 | splitterTensorInfo.SetShape({batchSize, inputSize}); |
| 93 | concatTensorInfo.SetShape({batchSize, outputSize}); |
| 94 | BuildArmComputeTensor(splitter_out, splitterTensorInfo); |
| 95 | BuildArmComputeTensor(concat_in, concatTensorInfo); |
| 96 | |
| 97 | armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out); |
| 98 | armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in); |
| 99 | |
| 100 | // append to std::vector<arm_compute::CLTensor> |
| 101 | m_SplitterOutputsTensors.push_back(std::move(splitter_out)); |
| 102 | m_ConcatInputsTensors.push_back(std::move(concat_in)); |
| 103 | } |
| 104 | |
| 105 | for (unsigned int i = 0; i < maxTime; ++i) |
| 106 | { |
| 107 | // append to std::vector<arm_compute::ICLTensor*> |
| 108 | m_SplitterOutputs.push_back(&m_SplitterOutputsTensors[i]); |
| 109 | m_ConcatInputs.push_back(&m_ConcatInputsTensors[i]); |
| 110 | } |
| 111 | |
| 112 | // |
| 113 | // Split |
| 114 | // |
| 115 | unsigned int numberDimensions = 3; |
| 116 | unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension) |
| 117 | |
| 118 | if (maxTime != 1) // ACL split does not work with only one element to split. |
| 119 | { |
| 120 | ViewsDescriptor splitterDesc(maxTime, numberDimensions); |
| 121 | unsigned int splitterDimSizes[3] = {1, batchSize, inputSize}; |
| 122 | for (unsigned int outputIdx = 0u; outputIdx < maxTime; ++outputIdx) |
| 123 | { |
| 124 | splitterDesc.SetViewOriginCoord(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx); |
| 125 | for (unsigned int dimIdx = 0u; dimIdx < numberDimensions; ++dimIdx) |
| 126 | { |
| 127 | splitterDesc.SetViewSize(outputIdx, dimIdx, splitterDimSizes[dimIdx]); |
| 128 | } |
| 129 | } |
| 130 | |
| 131 | std::set<unsigned int> splitAxis = ComputeSplitAxis(splitterDesc, timeMajorShapeInput); |
| 132 | |
| 133 | std::unique_ptr<arm_compute::CLSplit> split_layer(new arm_compute::CLSplit()); |
| 134 | unsigned int aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(), *splitAxis.begin()); |
| 135 | if (!m_Data.m_Parameters.m_TimeMajor) |
| 136 | { |
| 137 | split_layer->configure(&m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit); |
| 138 | } |
| 139 | else |
| 140 | { |
| 141 | split_layer->configure(&input, m_SplitterOutputs, aclAxisSplit); |
| 142 | } |
| 143 | |
| 144 | split_layer->prepare(); |
| 145 | m_Splitter.reset(split_layer.release()); |
| 146 | } |
| 147 | |
| 148 | // |
| 149 | // Lstm |
| 150 | // |
| 151 | arm_compute::LSTMParams<arm_compute::ICLTensor> lstm_param; |
| 152 | |
| 153 | m_InputToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 154 | BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo()); |
| 155 | |
| 156 | m_InputToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 157 | BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo()); |
| 158 | |
| 159 | m_InputToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 160 | BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo()); |
| 161 | |
| 162 | m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 163 | BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo()); |
| 164 | |
| 165 | m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 166 | BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo()); |
| 167 | |
| 168 | m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 169 | BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo()); |
| 170 | |
| 171 | m_ForgetGateBiasTensor = std::make_unique<arm_compute::CLTensor>(); |
| 172 | BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo()); |
| 173 | |
| 174 | m_CellBiasTensor = std::make_unique<arm_compute::CLTensor>(); |
| 175 | BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo()); |
| 176 | |
| 177 | m_OutputGateBiasTensor = std::make_unique<arm_compute::CLTensor>(); |
| 178 | BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo()); |
| 179 | |
| 180 | // for future reference: check the AndroidNN API for the logic here |
| 181 | if (!m_Data.m_Parameters.m_CifgEnabled) |
| 182 | { |
| 183 | m_InputToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 184 | BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo()); |
| 185 | |
| 186 | m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 187 | BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo()); |
| 188 | |
| 189 | m_CellToInputWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 190 | if (m_Data.m_CellToInputWeights != nullptr) |
| 191 | { |
| 192 | BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo()); |
| 193 | } |
| 194 | |
| 195 | m_InputGateBiasTensor = std::make_unique<arm_compute::CLTensor>(); |
| 196 | BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo()); |
| 197 | |
| 198 | lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(), |
| 199 | m_RecurrentToInputWeightsTensor.get(), |
| 200 | m_Data.m_CellToInputWeights ? m_CellToInputWeightsTensor.get() : nullptr, |
| 201 | m_InputGateBiasTensor.get()); |
| 202 | } |
| 203 | |
| 204 | if (m_Data.m_Parameters.m_ProjectionEnabled) |
| 205 | { |
| 206 | m_ProjectionWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 207 | BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo()); |
| 208 | |
| 209 | m_ProjectionBiasTensor = std::make_unique<arm_compute::CLTensor>(); |
| 210 | if (m_Data.m_ProjectionBias != nullptr) |
| 211 | { |
| 212 | BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo()); |
| 213 | } |
| 214 | |
| 215 | lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(), |
| 216 | m_Data.m_ProjectionBias ? m_ProjectionBiasTensor.get() : nullptr); |
| 217 | } |
| 218 | |
| 219 | if (m_Data.m_Parameters.m_PeepholeEnabled) |
| 220 | { |
| 221 | m_CellToForgetWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 222 | BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo()); |
| 223 | |
| 224 | m_CellToOutputWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 225 | BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo()); |
| 226 | |
| 227 | lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get()); |
| 228 | } |
| 229 | |
| 230 | if (m_Data.m_Parameters.m_LayerNormEnabled) |
| 231 | { |
| 232 | m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 233 | if (!m_Data.m_Parameters.m_CifgEnabled) |
| 234 | { |
| 235 | BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo()); |
| 236 | } |
| 237 | |
| 238 | m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 239 | BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo()); |
| 240 | |
| 241 | m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 242 | BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo()); |
| 243 | |
| 244 | m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| 245 | BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo()); |
| 246 | |
| 247 | auto inputNormWeightTensor = m_Data.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get(); |
| 248 | lstm_param.set_layer_normalization_params(inputNormWeightTensor, |
| 249 | m_ForgetLayerNormWeightsTensor.get(), |
| 250 | m_CellLayerNormWeightsTensor.get(), |
| 251 | m_OutputLayerNormWeightsTensor.get()); |
| 252 | } |
| 253 | |
| 254 | arm_compute::ICLTensor& output_state_in = static_cast<IClTensorHandle*>(m_Data.m_Inputs[1])->GetTensor(); |
| 255 | arm_compute::ICLTensor& cell_state_in = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetTensor(); |
| 256 | |
| 257 | arm_compute::ICLTensor& output_state_out = static_cast<IClTensorHandle*>(m_Data.m_Inputs[1])->GetTensor(); |
| 258 | arm_compute::ICLTensor& cell_state_out = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetTensor(); |
| 259 | |
| 260 | m_ScratchBuffer = std::make_unique<arm_compute::CLTensor>(); |
| 261 | if (m_Data.m_Parameters.m_CifgEnabled) |
| 262 | { |
| 263 | // scratch_buffer [num_units * 3, batch_size] with CIFG |
| 264 | BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 3}, armnnDataType)); |
| 265 | } |
| 266 | else |
| 267 | { |
| 268 | // scratch_buffer [num_units * 4, batch_size] without CIFG |
| 269 | BuildArmComputeTensor(*m_ScratchBuffer, TensorInfo({batchSize, numUnits * 4}, armnnDataType)); |
| 270 | } |
| 271 | |
| 272 | // Need to be set at negative threshold to be compatible for ACL |
| 273 | float cell_threshold = m_Data.m_Parameters.m_ClippingThresCell; |
| 274 | float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj; |
| 275 | |
| 276 | // For preparing the object for the class ActivationLayerInfo, consider 5 situations |
| 277 | arm_compute::ActivationLayerInfo activationLayerInfo = |
| 278 | ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc); |
| 279 | |
| 280 | for (unsigned int i = 0; i != maxTime; ++i) |
| 281 | { |
| 282 | // Set LSTM input and output ITensors depending on: |
| 283 | // input format (timeMajor) & number of LSTM batches (maxTime). |
| 284 | arm_compute::ICLTensor* outputLSTM; |
| 285 | arm_compute::ICLTensor* inputLSTM; |
| 286 | // If there is only one LSTM time major batch, we will not concat OR permute. |
| 287 | // Set input of LSTM to be first input ITensor. |
| 288 | // Set output of LSTM to be final output ITensor. |
| 289 | // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo. |
| 290 | if (maxTime == 1 && m_Data.m_Parameters.m_TimeMajor) |
| 291 | { |
| 292 | TensorShape inputShape = GetTensorShape((&input)->info()->tensor_shape(), 1U); |
| 293 | TensorShape outputShape = GetTensorShape((&output)->info()->tensor_shape(), 1U); |
| 294 | TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); |
| 295 | TensorShape outputShapeShrink({outputShape[1], outputShape[2]}); |
| 296 | auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); |
| 297 | auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink); |
| 298 | (&input)->info()->set_tensor_shape(acl_input_shape_shrink); |
| 299 | inputLSTM = const_cast<arm_compute::ICLTensor*>(&input); |
| 300 | (&output)->info()->set_tensor_shape(acl_output_shape_shrink); |
| 301 | outputLSTM = &output; |
| 302 | } |
| 303 | // If there is only one LSTM batch major batch, we will not concat, only permute. |
| 304 | // Set input of LSTM to be output of initial permute. |
| 305 | // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute. |
| 306 | // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo. |
| 307 | else if (maxTime == 1 && !m_Data.m_Parameters.m_TimeMajor) |
| 308 | { |
| 309 | TensorShape inputShape = GetTensorShape(m_PermuteFirstOut.info()->tensor_shape(), 1U); |
| 310 | TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); |
| 311 | auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); |
| 312 | m_PermuteFirstOut.info()->set_tensor_shape(acl_input_shape_shrink); |
| 313 | inputLSTM = &m_PermuteFirstOut; |
| 314 | outputLSTM = const_cast<arm_compute::ICLTensor*>(m_ConcatInputs[i]); |
| 315 | } |
| 316 | // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on. |
| 317 | else |
| 318 | { |
| 319 | inputLSTM = m_SplitterOutputs[i]; |
| 320 | outputLSTM = const_cast<arm_compute::ICLTensor*>(m_ConcatInputs[i]); |
| 321 | } |
| 322 | |
| 323 | std::unique_ptr<arm_compute::CLLSTMLayer> lstm_layer(new arm_compute::CLLSTMLayer()); |
| 324 | lstm_layer->configure(clCompileContext, |
| 325 | inputLSTM, |
| 326 | m_InputToForgetWeightsTensor.get(), |
| 327 | m_InputToCellWeightsTensor.get(), |
| 328 | m_InputToOutputWeightsTensor.get(), |
| 329 | m_RecurrentToForgetWeightsTensor.get(), |
| 330 | m_RecurrentToCellWeightsTensor.get(), |
| 331 | m_RecurrentToOutputWeightsTensor.get(), |
| 332 | m_ForgetGateBiasTensor.get(), |
| 333 | m_CellBiasTensor.get(), |
| 334 | m_OutputGateBiasTensor.get(), |
| 335 | &output_state_in, |
| 336 | &cell_state_in, |
| 337 | m_ScratchBuffer.get(), |
| 338 | &output_state_out, |
| 339 | &cell_state_out, |
| 340 | outputLSTM, |
| 341 | lstm_param, |
| 342 | activationLayerInfo, |
| 343 | cell_threshold, |
| 344 | projection_threshold); |
| 345 | |
| 346 | m_Layers.emplace_back(std::move(lstm_layer)); |
| 347 | } |
| 348 | |
| 349 | armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer); |
| 350 | |
| 351 | InitializeArmComputeClTensorData(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights); |
| 352 | InitializeArmComputeClTensorData(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights); |
| 353 | InitializeArmComputeClTensorData(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights); |
| 354 | InitializeArmComputeClTensorData(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights); |
| 355 | InitializeArmComputeClTensorData(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights); |
| 356 | InitializeArmComputeClTensorData(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights); |
| 357 | InitializeArmComputeClTensorData(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias); |
| 358 | InitializeArmComputeClTensorData(*m_CellBiasTensor, m_Data.m_CellBias); |
| 359 | InitializeArmComputeClTensorData(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias); |
| 360 | |
| 361 | if (!m_Data.m_Parameters.m_CifgEnabled) |
| 362 | { |
| 363 | InitializeArmComputeClTensorData(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights); |
| 364 | InitializeArmComputeClTensorData(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights); |
| 365 | if (m_Data.m_CellToInputWeights != nullptr) |
| 366 | { |
| 367 | InitializeArmComputeClTensorData(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights); |
| 368 | } |
| 369 | InitializeArmComputeClTensorData(*m_InputGateBiasTensor, m_Data.m_InputGateBias); |
| 370 | } |
| 371 | |
| 372 | if (m_Data.m_Parameters.m_ProjectionEnabled) |
| 373 | { |
| 374 | InitializeArmComputeClTensorData(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights); |
| 375 | if (m_Data.m_ProjectionBias != nullptr) |
| 376 | { |
| 377 | InitializeArmComputeClTensorData(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias); |
| 378 | } |
| 379 | } |
| 380 | |
| 381 | if (m_Data.m_Parameters.m_PeepholeEnabled) |
| 382 | { |
| 383 | InitializeArmComputeClTensorData(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights); |
| 384 | InitializeArmComputeClTensorData(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights); |
| 385 | } |
| 386 | |
| 387 | if (m_Data.m_Parameters.m_LayerNormEnabled) |
| 388 | { |
| 389 | if (!m_Data.m_Parameters.m_CifgEnabled) |
| 390 | { |
| 391 | InitializeArmComputeClTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights); |
| 392 | } |
| 393 | InitializeArmComputeClTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights); |
| 394 | InitializeArmComputeClTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights); |
| 395 | InitializeArmComputeClTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights); |
| 396 | } |
| 397 | |
| 398 | // Force Compute Library to perform the necessary copying and reshaping. |
| 399 | // After which delete all the input tensors that will no longer be needed. |
| 400 | for (uint32_t i = 0; i < m_Layers.size(); ++i) |
| 401 | { |
| 402 | m_Layers[i]->prepare(); |
| 403 | } |
| 404 | |
| 405 | // |
| 406 | // Concat |
| 407 | // |
| 408 | |
| 409 | // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs. |
| 410 | TensorShape shape = GetTensorShape(m_ConcatInputs[0]->info()->tensor_shape(), 1U); |
| 411 | TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]}); |
| 412 | TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]}); |
| 413 | |
| 414 | if (maxTime != 1) // ACL concat does not work with only one element to concatenate. |
| 415 | { |
| 416 | for (unsigned int i = 0; i < maxTime; ++i) |
| 417 | { |
| 418 | m_ConcatInputs[i]->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor)); |
| 419 | } |
| 420 | |
| 421 | ConcatDescriptor concatDescriptor(maxTime, numberDimensions); // maxTime = num inputs (aka. number of views). |
| 422 | for (unsigned int inputIdx = 0u; inputIdx < maxTime; ++inputIdx) |
| 423 | { |
| 424 | concatDescriptor.SetViewOriginCoord(inputIdx, dimension, inputIdx); |
| 425 | concatDescriptor.SetConcatAxis(dimension); |
| 426 | } |
| 427 | |
| 428 | m_Concat.reset(new arm_compute::CLConcatenateLayer()); |
| 429 | unsigned int aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(), |
| 430 | concatDescriptor.GetConcatAxis()); |
| 431 | if (!m_Data.m_Parameters.m_TimeMajor) |
| 432 | { |
| 433 | TensorInfo concatOuputTensorInfo = outputInfo; |
| 434 | concatOuputTensorInfo.SetShape(timeMajorShapeOutput); |
| 435 | BuildArmComputeTensor(concat_out, concatOuputTensorInfo); |
| 436 | armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out); |
| 437 | |
| 438 | m_Concat->configure(m_ConcatInputs, &concat_out, aclAxisConcat); |
| 439 | } |
| 440 | else |
| 441 | { |
| 442 | m_Concat->configure(m_ConcatInputs, &output, aclAxisConcat); |
| 443 | } |
| 444 | |
| 445 | m_Concat->prepare(); |
| 446 | } |
| 447 | // If only one LSTM batch, we do not concat and/or permute. |
| 448 | // Must ensure final output info is expanded to correct batch major dimensions. |
| 449 | else |
| 450 | { |
| 451 | if (!m_Data.m_Parameters.m_TimeMajor) |
| 452 | { |
| 453 | (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor)); |
| 454 | } |
| 455 | else |
| 456 | { |
| 457 | (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor)); |
| 458 | } |
| 459 | } |
| 460 | |
| 461 | // |
| 462 | // Permute: only done if input/output are in batch major format. |
| 463 | // |
| 464 | if (!m_Data.m_Parameters.m_TimeMajor) |
| 465 | { |
| 466 | // Output now time major. Permute output back to batch major. |
| 467 | std::unique_ptr<arm_compute::CLPermute> layer(new arm_compute::CLPermute()); |
| 468 | if (maxTime != 1) |
| 469 | { |
| 470 | layer->configure(clCompileContext, &concat_out, &output, arm_compute::PermutationVector(0U, 2U, 1U)); |
| 471 | } |
| 472 | else |
| 473 | { |
| 474 | layer->configure(clCompileContext, m_ConcatInputs[0], &output, arm_compute::PermutationVector(0U, 2U, 1U)); |
| 475 | } |
| 476 | m_Permute2.reset(layer.release()); |
| 477 | } |
| 478 | |
| 479 | FreeUnusedTensors(); |
| 480 | } |
| 481 | |
| 482 | void ClUnidirectionalSequenceLstmFloatWorkload::Execute() const |
| 483 | { |
| 484 | ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClUnidirectionalSequenceLstmFloatWorkload_Execute", GetGuid()); |
| 485 | if (m_Permute1) |
| 486 | { |
| 487 | m_Permute1->run(); |
| 488 | } |
| 489 | if (m_Splitter) |
| 490 | { |
| 491 | m_Splitter->run(); |
| 492 | } |
| 493 | for (uint32_t i = 0; i < m_Layers.size(); ++i) |
| 494 | { |
| 495 | m_Layers[i]->run(); |
| 496 | } |
| 497 | if (m_Concat) |
| 498 | { |
| 499 | m_Concat->run(); |
| 500 | } |
| 501 | if (m_Permute2) |
| 502 | { |
| 503 | m_Permute2->run(); |
| 504 | } |
| 505 | } |
| 506 | |
| 507 | arm_compute::Status |
| 508 | ClUnidirectionalSequenceLstmFloatWorkloadValidate(const TensorInfo& input, |
| 509 | const TensorInfo& outputStateIn, |
| 510 | const TensorInfo& cellStateIn, |
| 511 | const TensorInfo& output, |
| 512 | const Optional<TensorInfo>& hiddenStateOutput, |
| 513 | const Optional<TensorInfo>& cellStateOutput, |
| 514 | const UnidirectionalSequenceLstmDescriptor& descriptor, |
| 515 | const LstmInputParamsInfo& paramsInfo) |
| 516 | { |
| 517 | IgnoreUnused(hiddenStateOutput, cellStateOutput); |
| 518 | |
| 519 | TensorShape inputLayerShape = input.GetShape(); |
| 520 | TensorShape outputLayerShape = outputStateIn.GetShape(); |
| 521 | |
| 522 | unsigned int maxTime = descriptor.m_TimeMajor?inputLayerShape[0]:inputLayerShape[1]; |
| 523 | unsigned int batchSize = descriptor.m_TimeMajor?inputLayerShape[1]:inputLayerShape[0]; |
| 524 | unsigned int inputSize = inputLayerShape[2]; |
| 525 | unsigned int outputSize = outputLayerShape[2]; |
| 526 | |
| 527 | const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize}); |
| 528 | const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize}); |
| 529 | |
| 530 | arm_compute::Status statusPermute1 = arm_compute::Status(arm_compute::ErrorCode::OK, |
| 531 | "Permute1 status"); |
| 532 | arm_compute::Status statusSplit = arm_compute::Status(arm_compute::ErrorCode::OK, |
| 533 | "Split status"); |
| 534 | arm_compute::Status statusLSTM = arm_compute::Status(arm_compute::ErrorCode::OK, |
| 535 | "LSTM status"); |
| 536 | arm_compute::Status statusConcat = arm_compute::Status(arm_compute::ErrorCode::OK, |
| 537 | "Concat status"); |
| 538 | arm_compute::Status statusPermute2 = arm_compute::Status(arm_compute::ErrorCode::OK, |
| 539 | "Permute2 status"); |
| 540 | |
| 541 | const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input); |
| 542 | const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output); |
| 543 | |
| 544 | // |
| 545 | // Permute validate |
| 546 | // |
| 547 | TensorInfo permuteOutInfo = TensorInfo(input); |
| 548 | arm_compute::TensorInfo aclPermuteOutInfo = armcomputetensorutils::BuildArmComputeTensorInfo(permuteOutInfo); |
| 549 | if (!descriptor.m_TimeMajor) |
| 550 | { |
| 551 | statusPermute1 = arm_compute::CLPermute::validate(&aclInputInfo, |
| 552 | &aclPermuteOutInfo, |
| 553 | arm_compute::PermutationVector(0U, 2U, 1U)); |
| 554 | } |
| 555 | |
| 556 | // |
| 557 | // Split and Concat Tensors validate |
| 558 | // |
| 559 | std::vector<arm_compute::TensorInfo> splitterOutputsTensorInfos; |
| 560 | std::vector<arm_compute::TensorInfo> concatInputsTensorInfos; |
| 561 | std::vector<arm_compute::ITensorInfo*> splitterOutputsTensorInfosPtr; |
| 562 | std::vector<const arm_compute::ITensorInfo*> concatInputsTensorInfosPtr; |
| 563 | splitterOutputsTensorInfos.reserve(maxTime); |
| 564 | concatInputsTensorInfos.reserve(maxTime); |
| 565 | for (unsigned int i = 0; i < maxTime; ++i) |
| 566 | { |
| 567 | arm_compute::TensorInfo splitter_out; |
| 568 | arm_compute::TensorInfo concat_in; |
| 569 | |
| 570 | auto splitterTensorInfo = TensorInfo(input); |
| 571 | auto concatTensorInfo = TensorInfo(output); |
| 572 | splitterTensorInfo.SetShape({batchSize, inputSize}); |
| 573 | concatTensorInfo.SetShape({batchSize, outputSize}); |
| 574 | |
| 575 | arm_compute::TensorInfo aclSplitterTensorInfo |
| 576 | = armcomputetensorutils::BuildArmComputeTensorInfo(splitterTensorInfo); |
| 577 | arm_compute::TensorInfo aclConcatTensorInfo |
| 578 | = armcomputetensorutils::BuildArmComputeTensorInfo(concatTensorInfo); |
| 579 | |
| 580 | splitterOutputsTensorInfos.emplace_back(aclSplitterTensorInfo); |
| 581 | concatInputsTensorInfos.emplace_back(aclConcatTensorInfo); |
| 582 | splitterOutputsTensorInfosPtr.emplace_back(&splitterOutputsTensorInfos[i]); |
| 583 | concatInputsTensorInfosPtr.emplace_back(&concatInputsTensorInfos[i]); |
| 584 | } |
| 585 | |
| 586 | // |
| 587 | // Split validate |
| 588 | // |
| 589 | unsigned int numberDimensions = 3; |
| 590 | unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension) |
| 591 | unsigned int aclAxisSplit = CalcAclAxis(numberDimensions, dimension); |
| 592 | |
| 593 | if (maxTime != 1) // ACL split does not work with only one element to split. |
| 594 | { |
| 595 | if (!descriptor.m_TimeMajor) |
| 596 | { |
| 597 | statusSplit = arm_compute::CLSplit::validate(&aclPermuteOutInfo, |
| 598 | splitterOutputsTensorInfosPtr, |
| 599 | aclAxisSplit); |
| 600 | } |
| 601 | else |
| 602 | { |
| 603 | statusSplit = arm_compute::CLSplit::validate(&aclInputInfo, splitterOutputsTensorInfosPtr, aclAxisSplit); |
| 604 | } |
| 605 | } |
| 606 | |
| 607 | // |
| 608 | // LSTM validate |
| 609 | // |
| 610 | |
| 611 | arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info; |
| 612 | |
| 613 | const TensorInfo& scratchBuffer = TensorInfo(cellStateIn.GetShape(), input.GetDataType()); |
| 614 | const TensorInfo& outputStateOut = TensorInfo(outputStateIn.GetShape(), input.GetDataType()); |
| 615 | const TensorInfo& cellStateOut = TensorInfo(cellStateIn.GetShape(), input.GetDataType()); |
| 616 | |
| 617 | // The inputs and outputs |
| 618 | const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn); |
| 619 | const arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn); |
| 620 | const arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer); |
| 621 | const arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut); |
| 622 | const arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut); |
| 623 | |
| 624 | // Basic parameters |
| 625 | const arm_compute::TensorInfo aclInputToForgetWeightsInfo |
| 626 | = BuildArmComputeTensorInfo(paramsInfo.GetInputToForgetWeights()); |
| 627 | const arm_compute::TensorInfo aclInputToCellWeightsInfo |
| 628 | = BuildArmComputeTensorInfo(paramsInfo.GetInputToCellWeights()); |
| 629 | const arm_compute::TensorInfo aclInputToOutputWeightsInfo |
| 630 | = BuildArmComputeTensorInfo(paramsInfo.GetInputToOutputWeights()); |
| 631 | const arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo |
| 632 | = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToForgetWeights()); |
| 633 | const arm_compute::TensorInfo aclRecurrentToCellWeightsInfo |
| 634 | = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToCellWeights()); |
| 635 | const arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo |
| 636 | = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToOutputWeights()); |
| 637 | const arm_compute::TensorInfo aclForgetGateBiasInfo |
| 638 | = BuildArmComputeTensorInfo(paramsInfo.GetForgetGateBias()); |
| 639 | const arm_compute::TensorInfo aclCellBiasInfo |
| 640 | = BuildArmComputeTensorInfo(paramsInfo.GetCellBias()); |
| 641 | const arm_compute::TensorInfo aclOutputGateBiasInfo |
| 642 | = BuildArmComputeTensorInfo(paramsInfo.GetOutputGateBias()); |
| 643 | |
| 644 | arm_compute::TensorInfo aclInputToInputWeightsInfo; |
| 645 | arm_compute::TensorInfo aclRecurrentToInputWeightsInfo; |
| 646 | arm_compute::TensorInfo aclCellToInputWeightsInfo; |
| 647 | arm_compute::TensorInfo aclInputGateBiasInfo; |
| 648 | arm_compute::TensorInfo aclProjectionWeightsInfo; |
| 649 | arm_compute::TensorInfo aclProjectionBiasInfo; |
| 650 | arm_compute::TensorInfo aclCellToForgetWeightsInfo; |
| 651 | arm_compute::TensorInfo aclCellToOutputWeightsInfo; |
| 652 | |
| 653 | arm_compute::TensorInfo aclInputLayerNormWeightsInfo; |
| 654 | arm_compute::TensorInfo aclForgetLayerNormWeightsInfo; |
| 655 | arm_compute::TensorInfo aclCellLayerNormWeightsInfo; |
| 656 | arm_compute::TensorInfo aclOutputLayerNormWeightsInfo; |
| 657 | |
| 658 | |
| 659 | if (!descriptor.m_CifgEnabled) |
| 660 | { |
| 661 | if (descriptor.m_PeepholeEnabled) |
| 662 | { |
| 663 | aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToInputWeights()); |
| 664 | } |
| 665 | aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToInputWeights()); |
| 666 | aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToInputWeights()); |
| 667 | aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputGateBias()); |
| 668 | |
| 669 | lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo, |
| 670 | &aclRecurrentToInputWeightsInfo, |
| 671 | descriptor.m_PeepholeEnabled ? &aclCellToInputWeightsInfo : nullptr, |
| 672 | &aclInputGateBiasInfo); |
| 673 | } |
| 674 | |
| 675 | if (descriptor.m_ProjectionEnabled) |
| 676 | { |
| 677 | if (paramsInfo.m_ProjectionBias != nullptr) |
| 678 | { |
| 679 | aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionBias()); |
| 680 | } |
| 681 | aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionWeights()); |
| 682 | |
| 683 | lstm_params_info.set_projection_params(&aclProjectionWeightsInfo, |
| 684 | paramsInfo.m_ProjectionBias ? &aclProjectionBiasInfo : nullptr); |
| 685 | } |
| 686 | |
| 687 | if (descriptor.m_PeepholeEnabled) |
| 688 | { |
| 689 | aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToForgetWeights()); |
| 690 | aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToOutputWeights()); |
| 691 | |
| 692 | lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo); |
| 693 | } |
| 694 | |
| 695 | if (descriptor.m_LayerNormEnabled) |
| 696 | { |
| 697 | if (!descriptor.m_CifgEnabled) |
| 698 | { |
| 699 | aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputLayerNormWeights()); |
| 700 | } |
| 701 | aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetForgetLayerNormWeights()); |
| 702 | aclCellLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellLayerNormWeights()); |
| 703 | aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputLayerNormWeights()); |
| 704 | |
| 705 | lstm_params_info.set_layer_normalization_params(descriptor.m_CifgEnabled ? nullptr : |
| 706 | &aclInputLayerNormWeightsInfo, |
| 707 | &aclForgetLayerNormWeightsInfo, |
| 708 | &aclCellLayerNormWeightsInfo, |
| 709 | &aclOutputLayerNormWeightsInfo); |
| 710 | } |
| 711 | |
| 712 | // Need to be set at negative threshold to be compatible for ACL |
| 713 | float cell_threshold = descriptor.m_ClippingThresCell; |
| 714 | float projection_threshold = descriptor.m_ClippingThresProj; |
| 715 | |
| 716 | arm_compute::ActivationLayerInfo activationLayerInfo = |
| 717 | ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc); |
| 718 | |
| 719 | for (unsigned int i = 0; i != maxTime; ++i) |
| 720 | { |
| 721 | |
| 722 | // Set LSTM input and output ITensors depending on: |
| 723 | // input format (timeMajor) & number of LSTM batches (maxTime). |
| 724 | arm_compute::ITensorInfo* outputLSTM; |
| 725 | arm_compute::ITensorInfo* inputLSTM; |
| 726 | // If there is only one LSTM time major batch, we will not concat OR permute. |
| 727 | // Set input of LSTM to be first input ITensor. |
| 728 | // Set output of LSTM to be final output ITensor. |
| 729 | // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo. |
| 730 | if (maxTime == 1 && !descriptor.m_TimeMajor) |
| 731 | { |
| 732 | TensorShape inputShape = GetTensorShape(aclInputInfo.tensor_shape(), 1U); |
| 733 | TensorShape outputShape = GetTensorShape(aclOutputInfo.tensor_shape(), 1U); |
| 734 | TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); |
| 735 | TensorShape outputShapeShrink({outputShape[1], outputShape[2]}); |
| 736 | auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); |
| 737 | auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink); |
| 738 | const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(acl_input_shape_shrink); |
| 739 | inputLSTM = const_cast<arm_compute::TensorInfo*>(&aclInputInfo); |
| 740 | const_cast<arm_compute::TensorInfo*>(&aclOutputInfo)->set_tensor_shape(acl_output_shape_shrink); |
| 741 | outputLSTM = const_cast<arm_compute::TensorInfo*>(&aclOutputInfo); |
| 742 | } |
| 743 | // If there is only one LSTM batch major batch, we will not concat, only permute. |
| 744 | // Set input of LSTM to be output of initial permute. |
| 745 | // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute. |
| 746 | // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo. |
| 747 | else if (maxTime == 1 && !descriptor.m_TimeMajor) |
| 748 | { |
| 749 | TensorShape inputShape = GetTensorShape(aclPermuteOutInfo.tensor_shape(), 1U); |
| 750 | TensorShape inputShapeShrink({inputShape[1], inputShape[2]}); |
| 751 | auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink); |
| 752 | aclPermuteOutInfo.set_tensor_shape(acl_input_shape_shrink); |
| 753 | inputLSTM = &aclPermuteOutInfo; |
| 754 | outputLSTM = const_cast<arm_compute::ITensorInfo*>(concatInputsTensorInfosPtr[i]); |
| 755 | } |
| 756 | // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on. |
| 757 | else |
| 758 | { |
| 759 | inputLSTM = splitterOutputsTensorInfosPtr[i]; |
| 760 | outputLSTM = const_cast<arm_compute::ITensorInfo*>(concatInputsTensorInfosPtr[i]); |
| 761 | } |
| 762 | |
| 763 | statusLSTM = arm_compute::CLLSTMLayer::validate(inputLSTM, |
| 764 | &aclInputToForgetWeightsInfo, |
| 765 | &aclInputToCellWeightsInfo, |
| 766 | &aclInputToOutputWeightsInfo, |
| 767 | &aclRecurrentToForgetWeightsInfo, |
| 768 | &aclRecurrentToCellWeightsInfo, |
| 769 | &aclRecurrentToOutputWeightsInfo, |
| 770 | &aclForgetGateBiasInfo, |
| 771 | &aclCellBiasInfo, |
| 772 | &aclOutputGateBiasInfo, |
| 773 | &aclOutputStateInInfo, |
| 774 | &aclCellStateInInfo, |
| 775 | &aclScratchBufferInfo, |
| 776 | &aclOutputStateOutInfo, |
| 777 | &aclCellStateOutInfo, |
| 778 | outputLSTM, |
| 779 | lstm_params_info, |
| 780 | activationLayerInfo, |
| 781 | cell_threshold, |
| 782 | projection_threshold); |
| 783 | |
| 784 | if (statusLSTM.error_code() != arm_compute::ErrorCode::OK) |
| 785 | { |
| 786 | break; |
| 787 | } |
| 788 | } |
| 789 | |
| 790 | // |
| 791 | // Concat validate |
| 792 | // |
| 793 | |
| 794 | // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs. |
| 795 | TensorShape shape = GetTensorShape(concatInputsTensorInfosPtr[0]->tensor_shape(), 1U); |
| 796 | TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]}); |
| 797 | TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]}); |
| 798 | |
| 799 | TensorInfo concatOuputTensorInfo = TensorInfo(output); |
| 800 | concatOuputTensorInfo.SetShape(timeMajorShapeOutput); |
| 801 | arm_compute::TensorInfo aclConcatOuputTensorInfo= BuildArmComputeTensorInfo(concatOuputTensorInfo); |
| 802 | |
| 803 | if (maxTime != 1) // ACL concat does not work with only one element to concatenate. |
| 804 | { |
| 805 | for (unsigned int i = 0; i < maxTime; ++i) |
| 806 | { |
| 807 | auto acl_shape_expand = BuildArmComputeTensorShape(shapeExpandTimeMajor); |
| 808 | concatInputsTensorInfos[i].set_tensor_shape(acl_shape_expand); |
| 809 | } |
| 810 | |
| 811 | unsigned int aclAxisConcat = CalcAclAxis(numberDimensions, dimension); |
| 812 | if (!descriptor.m_TimeMajor) |
| 813 | { |
| 814 | statusConcat = arm_compute::CLConcatenateLayer::validate(concatInputsTensorInfosPtr, |
| 815 | &aclConcatOuputTensorInfo, |
| 816 | aclAxisConcat); |
| 817 | } |
| 818 | else |
| 819 | { |
| 820 | statusConcat = arm_compute::CLConcatenateLayer::validate(concatInputsTensorInfosPtr, |
| 821 | &aclOutputInfo, |
| 822 | aclAxisConcat); |
| 823 | } |
| 824 | } |
| 825 | // If only one LSTM batch, we do not concat and/or permute. |
| 826 | // Must ensure final output info is expanded to correct batch major dimensions. |
| 827 | else |
| 828 | { |
| 829 | if (!descriptor.m_TimeMajor) |
| 830 | { |
| 831 | const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape( |
| 832 | BuildArmComputeTensorShape(shapeExpandBatchMajor)); |
| 833 | } |
| 834 | else |
| 835 | { |
| 836 | const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape( |
| 837 | BuildArmComputeTensorShape(shapeExpandTimeMajor)); |
| 838 | } |
| 839 | } |
| 840 | // |
| 841 | // Permute validate |
| 842 | // |
| 843 | if (!descriptor.m_TimeMajor) |
| 844 | { |
| 845 | // Output now time major. Permute output back to batch major. |
| 846 | if (maxTime != 1) |
| 847 | { |
| 848 | statusPermute2 = arm_compute::CLPermute::validate(&aclConcatOuputTensorInfo, |
| 849 | &aclOutputInfo, |
| 850 | arm_compute::PermutationVector(0U, 2U, 1U)); |
| 851 | } |
| 852 | else |
| 853 | { |
| 854 | statusPermute2 = arm_compute::CLPermute::validate(concatInputsTensorInfosPtr[0], |
| 855 | &aclOutputInfo, |
| 856 | arm_compute::PermutationVector(0U, 2U, 1U)); |
| 857 | } |
| 858 | } |
| 859 | |
| 860 | auto okCode = arm_compute::ErrorCode::OK; |
| 861 | if (statusPermute1.error_code() == okCode && |
| 862 | statusSplit.error_code() == okCode && |
| 863 | statusLSTM .error_code() == okCode && |
| 864 | statusConcat.error_code() == okCode && |
| 865 | statusPermute2.error_code() == okCode) |
| 866 | { |
| 867 | return arm_compute::Status(arm_compute::ErrorCode::OK, |
| 868 | "All Unidirectional Sequence LSTM layer validate status OK."); |
| 869 | } |
| 870 | else |
| 871 | { |
| 872 | return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR, |
| 873 | "Unidirectional Sequence LSTM layer validate status failed."); |
| 874 | } |
| 875 | } |
| 876 | |
| 877 | void ClUnidirectionalSequenceLstmFloatWorkload::FreeUnusedTensors() |
| 878 | { |
| 879 | FreeTensorIfUnused(m_InputToInputWeightsTensor); |
| 880 | FreeTensorIfUnused(m_InputToForgetWeightsTensor); |
| 881 | FreeTensorIfUnused(m_InputToCellWeightsTensor); |
| 882 | FreeTensorIfUnused(m_InputToOutputWeightsTensor); |
| 883 | FreeTensorIfUnused(m_RecurrentToInputWeightsTensor); |
| 884 | FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor); |
| 885 | FreeTensorIfUnused(m_RecurrentToCellWeightsTensor); |
| 886 | FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor); |
| 887 | FreeTensorIfUnused(m_CellToInputWeightsTensor); |
| 888 | FreeTensorIfUnused(m_CellToForgetWeightsTensor); |
| 889 | FreeTensorIfUnused(m_CellToOutputWeightsTensor); |
| 890 | FreeTensorIfUnused(m_InputGateBiasTensor); |
| 891 | FreeTensorIfUnused(m_ForgetGateBiasTensor); |
| 892 | FreeTensorIfUnused(m_CellBiasTensor); |
| 893 | FreeTensorIfUnused(m_OutputGateBiasTensor); |
| 894 | FreeTensorIfUnused(m_ProjectionWeightsTensor); |
| 895 | FreeTensorIfUnused(m_ProjectionBiasTensor); |
| 896 | FreeTensorIfUnused(m_InputLayerNormWeightsTensor); |
| 897 | FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor); |
| 898 | FreeTensorIfUnused(m_CellLayerNormWeightsTensor); |
| 899 | FreeTensorIfUnused(m_OutputLayerNormWeightsTensor); |
| 900 | FreeTensorIfUnused(m_ScratchBuffer); |
| 901 | } |
| 902 | |
| 903 | } //namespace armnn |