diff options
Diffstat (limited to 'src/armnnTfLiteParser/TfLiteParser.cpp')
| -rw-r--r-- | src/armnnTfLiteParser/TfLiteParser.cpp | 476 |
1 files changed, 448 insertions, 28 deletions
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp index fddd93adc8..44dcacc3db 100644 --- a/src/armnnTfLiteParser/TfLiteParser.cpp +++ b/src/armnnTfLiteParser/TfLiteParser.cpp @@ -6,6 +6,7 @@ #include "TfLiteParser.hpp" #include "armnnTfLiteParser/Version.hpp" +#include "armnn/LstmParams.hpp" #include <armnn/BackendOptions.hpp> #include <armnn/Descriptors.hpp> @@ -33,11 +34,9 @@ #include <fmt/format.h> #include <algorithm> -#include <fstream> #include <iostream> #include <limits> #include <numeric> -#include <sstream> #define ARMNN_THROW_PARSE_EXCEPTION(msg) \ { \ @@ -375,6 +374,11 @@ std::vector<unsigned int> AsUnsignedVector(const std::vector<int32_t>& in) return result; } +bool IsOptionalOperandPresent(int input) +{ + return (input >= 0); +} + void CalcPadding(uint32_t inputSize, uint32_t filterSize, uint32_t stride, @@ -738,6 +742,8 @@ TfLiteParserImpl::TfLiteParserImpl(const Optional<ITfLiteParser::TfLiteParserOpt m_ParserFunctions[tflite::BuiltinOperator_TANH] = &TfLiteParserImpl::ParseTanH; m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE] = &TfLiteParserImpl::ParseTranspose; m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE_CONV] = &TfLiteParserImpl::ParseTransposeConv; + m_ParserFunctions[tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM] + = &TfLiteParserImpl::ParseUnidirectionalSequenceLSTM; m_ParserFunctions[tflite::BuiltinOperator_UNPACK] = &TfLiteParserImpl::ParseUnpack; // register supported custom operators @@ -749,6 +755,9 @@ void TfLiteParserImpl::ResetParser() m_Network = armnn::INetworkPtr(nullptr, nullptr); m_Model = nullptr; m_SubgraphConnections.clear(); + m_OverridenOutputShapes.clear(); + m_ConstantsToDequantize.clear(); + m_ConstantsToBeCreated.clear(); } INetworkPtr TfLiteParserImpl::CreateNetworkFromBinaryFile(const char* graphFile) @@ -869,10 +878,47 @@ INetworkPtr TfLiteParserImpl::CreateNetworkFromModel() } } } - return std::move(m_Network); } +std::unique_ptr<float[]> AsFloatArray(TfLiteParserImpl::BufferRawPtr bufferPtr, + const TensorInfo& tensorInfo) +{ + if (tensorInfo.GetDataType() == DataType::QAsymmS8 || tensorInfo.GetDataType() == DataType::QSymmS8 || + tensorInfo.GetDataType() == DataType::QAsymmU8) + { + std::unique_ptr<float[]> buffer(new float[tensorInfo.GetNumElements()]); + + if (tensorInfo.HasPerAxisQuantization()) + { + unsigned int axis = tensorInfo.GetQuantizationDim().value(); + auto axisDimensionality = tensorInfo.GetShape()[axis]; + auto axisFactor = armnnUtils::GetNumElementsAfter(tensorInfo.GetShape(), axis); + + for (unsigned int i = 0; i < tensorInfo.GetNumDimensions(); ++i) + { + unsigned int axisIndex = (i / axisFactor) % axisDimensionality; + buffer[i] = Dequantize<int8_t>(bufferPtr->data[i], tensorInfo.GetQuantizationScales()[axisIndex], + tensorInfo.GetQuantizationOffset()); + } + } + else + { + for (unsigned int i = 0; i < tensorInfo.GetNumElements(); ++i) + { + buffer[i] = Dequantize<int8_t>(bufferPtr->data[i], tensorInfo.GetQuantizationScale(), + tensorInfo.GetQuantizationOffset()); + } + } + return buffer; + } + throw ParseException( + fmt::format("Unsupported input/weights combination: Input {} not supported with Weights {}", + GetDataTypeName(DataType::Float32), + GetDataTypeName(tensorInfo.GetDataType()), + CHECK_LOCATION().AsString())); +} + void TfLiteParserImpl::RegisterProducerOfTensor(size_t subgraphIndex, size_t tensorIndex, armnn::IOutputSlot* slot) @@ -1050,7 +1096,7 @@ void TfLiteParserImpl::ParseConv2D(size_t subgraphIndex, size_t operatorIndex) CalcPadding(inputWidth, filterWidth, desc.m_StrideX, desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, options->padding); - auto filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo); + auto filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo, inputTensorInfo.GetDataType()); armnn::IConnectableLayer* layer = nullptr; auto layerName = fmt::format("Conv2D:{}:{}", subgraphIndex, operatorIndex); @@ -1059,16 +1105,16 @@ void TfLiteParserImpl::ParseConv2D(size_t subgraphIndex, size_t operatorIndex) { desc.m_BiasEnabled = true; armnn::TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); - auto biasTensorAndData = CreateConstTensorNonPermuted(inputs[2], biasTensorInfo); + auto biasTensorAndData = CreateConstTensorNonPermuted(inputs[2], biasTensorInfo, inputTensorInfo.GetDataType()); layer = m_Network->AddConvolution2dLayer(desc, - filterTensorAndData, - Optional<ConstTensor>(biasTensorAndData), + filterTensorAndData.first, + Optional<ConstTensor>(biasTensorAndData.first), layerName.c_str()); } else { layer = m_Network->AddConvolution2dLayer(desc, - filterTensorAndData, + filterTensorAndData.first, EmptyOptional(), layerName.c_str()); } @@ -1136,7 +1182,7 @@ void TfLiteParserImpl::ParseConv3D(size_t subgraphIndex, size_t operatorIndex) CalcPadding(inputWidth, filterWidth, desc.m_StrideX, desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, options->padding); - auto filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo); + auto filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo, inputTensorInfo.GetDataType()); auto layerName = fmt::format("Conv3D:{}:{}", subgraphIndex, operatorIndex); @@ -1209,7 +1255,7 @@ void TfLiteParserImpl::ParseDepthwiseConv2D(size_t subgraphIndex, size_t operato desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, options->padding); // ArmNN uses the same filter tensor layout at TfLite [1, H, W, O] no need for any permutation - auto filterTensor = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo); + auto filterTensor = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo, inputTensorInfo.GetDataType()); armnn::IConnectableLayer* layer = nullptr; auto layerName = fmt::format("DepthwiseConv2D:{}:{}", subgraphIndex, operatorIndex); @@ -1217,16 +1263,16 @@ void TfLiteParserImpl::ParseDepthwiseConv2D(size_t subgraphIndex, size_t operato { desc.m_BiasEnabled = true; TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); - auto biasTensorAndData = CreateConstTensorNonPermuted(inputs[2], biasTensorInfo); + auto biasTensorAndData = CreateConstTensorNonPermuted(inputs[2], biasTensorInfo, inputTensorInfo.GetDataType()); layer = m_Network->AddDepthwiseConvolution2dLayer(desc, - filterTensor, - Optional<ConstTensor>(biasTensorAndData), + filterTensor.first, + Optional<ConstTensor>(biasTensorAndData.first), layerName.c_str()); } else { layer = m_Network->AddDepthwiseConvolution2dLayer(desc, - filterTensor, + filterTensor.first, EmptyOptional(), layerName.c_str()); } @@ -1453,7 +1499,7 @@ void TfLiteParserImpl::ParseTransposeConv(size_t subgraphIndex, size_t operatorI desc.m_PadRight, options->padding); - auto filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo); + auto filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo, inputTensorInfo.GetDataType()); armnn::IConnectableLayer* layer = nullptr; auto layerName = fmt::format("TransposeConv:{}:{}", subgraphIndex, operatorIndex); @@ -1461,16 +1507,16 @@ void TfLiteParserImpl::ParseTransposeConv(size_t subgraphIndex, size_t operatorI if (desc.m_BiasEnabled) { auto biasTensorInfo = ToTensorInfo(inputs[3]); - auto biasConstTensor = CreateConstTensorNonPermuted(inputs[3], biasTensorInfo); + auto biasConstTensor = CreateConstTensorNonPermuted(inputs[3], biasTensorInfo, inputTensorInfo.GetDataType()); layer = m_Network->AddTransposeConvolution2dLayer(desc, - filterTensorAndData, - biasConstTensor, + filterTensorAndData.first, + biasConstTensor.first, layerName.c_str()); } else { layer = m_Network->AddTransposeConvolution2dLayer(desc, - filterTensorAndData, + filterTensorAndData.first, EmptyOptional(), layerName.c_str()); } @@ -2436,10 +2482,11 @@ void TfLiteParserImpl::ParsePrelu(size_t subgraphIndex, size_t operatorIndex) armnn::IInputSlot* slot = &(layer->GetInputSlot(0)); RegisterConsumerOfTensor(subgraphIndex, inputTensorIndexes[0], slot); - auto alphaTensorAndData = CreateConstTensorNonPermuted(inputs[1], alphaTensorInfo); + auto alphaTensorAndData = CreateConstTensorNonPermuted(inputs[1], alphaTensorInfo, + inputTensorInfo.GetDataType()); std::string constLayerName = fmt::format("Constant:{}", inputs[1]->name); IConnectableLayer* constLayer = - m_Network->AddConstantLayer(alphaTensorAndData, constLayerName.c_str()); + m_Network->AddConstantLayer(alphaTensorAndData.first, constLayerName.c_str()); ARMNN_ASSERT(constLayer != nullptr); constLayer->GetOutputSlot(0).SetTensorInfo(alphaTensorInfo); @@ -2933,25 +2980,40 @@ void TfLiteParserImpl::ParseFullyConnected(size_t subgraphIndex, size_t operator // Add the first input tensor to the registration list std::vector<unsigned int> tensorIndexesToRegister = {inputTensorIndexes[0]}; std::vector<unsigned int> ignoreInputWhenRegister = {}; + armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); desc.m_ConstantWeights = IsConstTensor(inputs[1]); // Add the weights input to the registration list, constant layers will be added by SetupConstantLayers if constant. tensorIndexesToRegister.emplace_back(inputTensorIndexes[1]); + if (desc.m_ConstantWeights && inputTensorInfo.GetDataType() == DataType::Float32 && + (filterTensorInfo.GetDataType() == DataType::QAsymmU8 || + filterTensorInfo.GetDataType() == DataType::QAsymmS8)) + { + m_ConstantsToDequantize.emplace_back(inputs[1]->buffer); + } + if (inputs.size() == 3) { desc.m_BiasEnabled = true; + armnn::TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]); // Add the biases input to the registration list, constant layer will be added by SetupConstantLayers. tensorIndexesToRegister.emplace_back(inputTensorIndexes[2]); + + if (desc.m_ConstantWeights && inputTensorInfo.GetDataType() == DataType::Float32 && + (biasTensorInfo.GetDataType() == DataType::QAsymmU8 || + biasTensorInfo.GetDataType() == DataType::QAsymmS8)) + { + m_ConstantsToDequantize.emplace_back(inputs[2]->buffer); + } } // Filters and biases are always passed to fully connected as inputs layer = m_Network->AddFullyConnectedLayer(desc, layerName.c_str()); ARMNN_ASSERT(layer != nullptr); - armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]); unsigned int startingSlotIndex = 0; if (inputTensorInfo.GetNumDimensions() > 2) @@ -3120,6 +3182,278 @@ void TfLiteParserImpl::ParsePack(size_t subgraphIndex, size_t operatorIndex) RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]}); } +void TfLiteParserImpl::ParseUnidirectionalSequenceLSTM(size_t subgraphIndex, size_t operatorIndex) +{ + CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); + + auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex); + auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex); + + if (inputs.size() < 2) + { + throw ParseException("UnidirectionalSequenceLSTM must have at least 2 input."); + } + + const auto& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex]; + const auto& subgraphPtr = m_Model->subgraphs[subgraphIndex]; + const auto nodeParams = operatorPtr->builtin_options.AsUnidirectionalSequenceLSTMOptions(); + CHECK_SUPPORTED_FUSED_ACTIVATION(nodeParams, subgraphIndex, operatorIndex); + auto inputTensorInfo = ToTensorInfo(inputs[0]); + auto outputTensorInfo = ToTensorInfo(outputs[0]); + + // Set the params structure for the AddUnidirectionalSequenceLstmLayer call + // Please refer to each operand at + // https://www.tensorflow.org/mlir/tfl_ops#tflunidirectional_sequence_lstm_tflunidirectionalsequencelstmop + armnn::LstmInputParams params; + + if (IsOptionalOperandPresent(operatorPtr->inputs[1])) + { + params.m_InputToInputWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[1]].get(), + inputTensorInfo).first; + } + + params.m_InputToForgetWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[2]].get(), + inputTensorInfo).first; + params.m_InputToCellWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[3]].get(), + inputTensorInfo).first; + params.m_InputToOutputWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[4]].get(), + inputTensorInfo).first; + + // Recurrent weight tensors of size {n_cell, n_output} + if (IsOptionalOperandPresent(operatorPtr->inputs[5])) + { + params.m_RecurrentToInputWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[5]].get(), + inputTensorInfo).first; + } + + params.m_RecurrentToForgetWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[6]].get(), + inputTensorInfo).first; + params.m_RecurrentToCellWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[7]].get(), + inputTensorInfo).first; + params.m_RecurrentToOutputWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[8]].get(), + inputTensorInfo).first; + + // Peephole weights tensors of size {n_cell}, representing a diagonal matrix. + if (IsOptionalOperandPresent(operatorPtr->inputs[9])) + { + params.m_CellToInputWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[9]].get(), + inputTensorInfo).first; + } + + if (IsOptionalOperandPresent(operatorPtr->inputs[10])) + { + params.m_CellToForgetWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[10]].get(), + inputTensorInfo).first; + } + + if (IsOptionalOperandPresent(operatorPtr->inputs[11])) + { + params.m_CellToOutputWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[11]].get(), + inputTensorInfo).first; + } + + // Gates bias tensors of size {n_cell} + if (IsOptionalOperandPresent(operatorPtr->inputs[12])) + { + params.m_InputGateBias = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[12]].get(), + inputTensorInfo).first; + } + + params.m_ForgetGateBias = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[13]].get(), + inputTensorInfo).first; + params.m_CellBias = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[14]].get(), + inputTensorInfo).first; + params.m_OutputGateBias = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[15]].get(), + inputTensorInfo).first; + + // Projection weight tensor of size {n_output, n_cell} + if (IsOptionalOperandPresent(operatorPtr->inputs[16])) + { + params.m_ProjectionWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[16]].get(), + inputTensorInfo).first; + } + // Projection bias tensor of size {n_output} + if (IsOptionalOperandPresent(operatorPtr->inputs[17])) + { + params.m_ProjectionBias = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[17]].get(), + inputTensorInfo).first; + } + + // These state tensors are defined as variable tensors, and will be modified by this op. + armnn::TensorInfo outputStateInInfo = ToTensorInfo(subgraphPtr->tensors[operatorPtr->inputs[18]].get()); + m_ConstantsToBeCreated.push_back(operatorPtr->inputs[18]); + armnn::TensorInfo cellStateInInfo = ToTensorInfo(subgraphPtr->tensors[operatorPtr->inputs[19]].get()); + m_ConstantsToBeCreated.push_back(operatorPtr->inputs[19]); + + // Layer norm coefficient tensors of size {n_cell}, representing a diagonal matrix. + if (inputs.size() >= 21 && IsOptionalOperandPresent(operatorPtr->inputs[20])) + { + params.m_InputLayerNormWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[20]].get(), + inputTensorInfo).first; + } + + if (inputs.size() >= 22 && IsOptionalOperandPresent(operatorPtr->inputs[21])) + { + params.m_ForgetLayerNormWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[21]].get(), + inputTensorInfo).first; + } + + if (inputs.size() >= 23 && IsOptionalOperandPresent(operatorPtr->inputs[22])) + { + params.m_CellLayerNormWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[22]].get(), + inputTensorInfo).first; + } + + if (inputs.size() >= 24 && IsOptionalOperandPresent(operatorPtr->inputs[23])) + { + params.m_OutputLayerNormWeights = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->inputs[23]].get(), + inputTensorInfo).first; + } + + // set the layer descriptor + armnn::UnidirectionalSequenceLstmDescriptor desc; + desc.m_ActivationFunc = nodeParams->fused_activation_function; + desc.m_ClippingThresCell = nodeParams->cell_clip; + desc.m_ClippingThresProj = nodeParams->proj_clip; + desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr + || params.m_RecurrentToInputWeights == nullptr + || params.m_InputGateBias == nullptr); + desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr || params.m_CellToOutputWeights != nullptr); + desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr); + desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr + || params.m_ForgetLayerNormWeights != nullptr + || params.m_CellLayerNormWeights != nullptr + || params.m_OutputLayerNormWeights != nullptr); + desc.m_TimeMajor = nodeParams->time_major; + + if (desc.m_LayerNormEnabled) + { + auto inputIntermediate = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[0]].get(), + inputTensorInfo).first; + auto inputIntermediateTensorInfo = inputIntermediate->GetInfo(); + desc.m_InputIntermediateScale = inputIntermediateTensorInfo.GetQuantizationScale(); + + auto forgetIntermediate = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[1]].get(), + inputTensorInfo).first; + auto forgetIntermediateTensorInfo = forgetIntermediate->GetInfo(); + desc.m_ForgetIntermediateScale = forgetIntermediateTensorInfo.GetQuantizationScale(); + + auto cellIntermediate = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[2]].get(), + inputTensorInfo).first; + auto cellIntermediateTensorInfo = cellIntermediate->GetInfo(); + desc.m_CellIntermediateScale = cellIntermediateTensorInfo.GetQuantizationScale(); + + auto outputIntermediate = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[3]].get(), + inputTensorInfo).first; + auto outputIntermediateTensorInfo = outputIntermediate->GetInfo(); + desc.m_OutputIntermediateScale = outputIntermediateTensorInfo.GetQuantizationScale(); + } + else + { + float defaultIntermediate = std::pow(2, -12); + desc.m_InputIntermediateScale = defaultIntermediate; + desc.m_ForgetIntermediateScale = defaultIntermediate; + desc.m_CellIntermediateScale = defaultIntermediate; + desc.m_OutputIntermediateScale = defaultIntermediate; + } + + auto hiddentensor = CreateConstTensorPtr(subgraphPtr->tensors[operatorPtr->intermediates[4]].get(), + inputTensorInfo).first; + + desc.m_HiddenStateScale = hiddentensor->GetInfo().GetQuantizationScale(); + desc.m_HiddenStateZeroPoint = hiddentensor->GetInfo().GetQuantizationOffset(); + + unsigned int batchSize = inputTensorInfo.GetShape()[0]; + unsigned int outputSize = outputTensorInfo.GetShape()[2]; + unsigned int numUnits = cellStateInInfo.GetShape()[1]; + + armnn::DataType dataType = inputTensorInfo.GetDataType(); + float qScale = inputTensorInfo.GetQuantizationScale(); + float qOffset = inputTensorInfo.GetQuantizationOffset(); + + armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, dataType, qScale, qOffset); + if (!desc.m_CifgEnabled) + { + scratchBufferTensorInfo = armnn::TensorInfo({batchSize, numUnits * 4}, dataType, qScale, qOffset); + } + armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, + cellStateInInfo.GetDataType(), + cellStateInInfo.GetQuantizationScale(), + cellStateInInfo.GetQuantizationOffset()); + armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset); + + armnn::LstmInputParamsInfo paramsInfo; + paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); + paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); + paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); + paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); + paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); + paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); + paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); + paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); + paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); + + if (!desc.m_CifgEnabled) + { + paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); + paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); + if (params.m_CellToInputWeights != nullptr) + { + paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo()); + } + paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); + } + + if (desc.m_ProjectionEnabled) + { + paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo()); + if (params.m_ProjectionBias != nullptr) + { + paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo()); + } + } + + if (desc.m_PeepholeEnabled) + { + paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo()); + paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo()); + } + + if (desc.m_LayerNormEnabled) + { + if(!desc.m_CifgEnabled) + { + paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo()); + } + paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo()); + paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo()); + paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo()); + } + + auto layerName = fmt::format("UnidirectionalSequenceLSTM:{}:{}", subgraphIndex, operatorIndex); + armnn::IConnectableLayer* layer = m_Network->AddUnidirectionalSequenceLstmLayer(desc, params); + ARMNN_ASSERT(layer != nullptr); + + // register the input connection slots for the layer, connections are made after all layers have been created + // only the tensors for the inputs are relevant, exclude the const tensors + auto inputTensorIndexes = AsUnsignedVector({operatorPtr->inputs[0], + operatorPtr->inputs[18], + operatorPtr->inputs[19]}); + RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], + inputTensorIndexes[1], + inputTensorIndexes[2]}); + + auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex)); + + layer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo); + layer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo); + layer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo); + + unsigned int tensorIndex = outputTensorIndexes[0]; + armnn::IOutputSlot* slot = &(layer->GetOutputSlot(2)); + RegisterProducerOfTensor(subgraphIndex, tensorIndex, slot); +} + void TfLiteParserImpl::ParseUnpack(size_t subgraphIndex, size_t operatorIndex) { CHECK_MODEL(m_Model, subgraphIndex, operatorIndex); @@ -4222,11 +4556,11 @@ void TfLiteParserImpl::SetupOutputLayers(size_t subgraphIndex) } } -void TfLiteParserImpl::SetupConstantLayers(size_t subgraphIndex) +void TfLiteParserImpl::SetupConstantLayers(size_t subgraph) { - CHECK_SUBGRAPH(m_Model, subgraphIndex); + CHECK_SUBGRAPH(m_Model, subgraph); - const auto& subgraphPtr = m_Model->subgraphs[subgraphIndex]; + const auto & subgraphPtr = m_Model->subgraphs[subgraph]; for (unsigned int subgraphIndex = 0; subgraphIndex < m_SubgraphConnections.size(); ++subgraphIndex) { for (unsigned int tensorIndex = 0; tensorIndex < m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex) @@ -4236,10 +4570,42 @@ void TfLiteParserImpl::SetupConstantLayers(size_t subgraphIndex) { TensorRawPtr tensorPtr = subgraphPtr->tensors[tensorIndex].get(); - if(IsConstTensor(tensorPtr)) + if (IsConstTensor(tensorPtr)) { armnn::TensorInfo tensorInfo = ToTensorInfo(tensorPtr); - auto tensorAndData = CreateConstTensorNonPermuted(tensorPtr, tensorInfo); + armnn::DataType dataType = tensorInfo.GetDataType(); + + if (std::find(m_ConstantsToDequantize.begin(), m_ConstantsToDequantize.end(), tensorPtr->buffer) + != m_ConstantsToDequantize.end()) + { + dataType = DataType::Float32; + } + auto tensorAndData = CreateConstTensorNonPermuted(tensorPtr, tensorInfo, dataType); + + std::string layerName = fmt::format("Constant:{}", tensorPtr->name); + IConnectableLayer *layer = m_Network->AddConstantLayer(tensorAndData.first, layerName.c_str()); + + layer->GetOutputSlot(0).SetTensorInfo(tensorAndData.first.GetInfo()); + RegisterOutputSlots(subgraphIndex, + VIRTUAL_OPERATOR_ID, + layer, + { tensorIndex }); + } + else if (ShouldConstantTensorBeCreated(tensorIndex)) + { + armnn::TensorInfo tensorInfo = ToTensorInfo(tensorPtr); + armnn::DataType dataType = tensorInfo.GetDataType(); + + if (std::find(m_ConstantsToDequantize.begin(), m_ConstantsToDequantize.end(), tensorPtr->buffer) + != m_ConstantsToDequantize.end()) + { + dataType = DataType::Float32; + } + // Make sure isConstant flag is set. + tensorInfo.SetConstant(); + tensorInfo.SetDataType(dataType); + + auto tensorAndData = ConstTensor(tensorInfo, std::vector<uint8_t>(tensorInfo.GetNumBytes())); std::string layerName = fmt::format("Constant:{}", tensorPtr->name); IConnectableLayer* layer = m_Network->AddConstantLayer(tensorAndData, layerName.c_str()); @@ -4248,7 +4614,7 @@ void TfLiteParserImpl::SetupConstantLayers(size_t subgraphIndex) RegisterOutputSlots(subgraphIndex, VIRTUAL_OPERATOR_ID, layer, - { tensorIndex }); + {tensorIndex}); } else { @@ -4286,6 +4652,13 @@ TfLiteParserImpl::CreateConstTensorAndStoreData(TfLiteParserImpl::BufferRawPtr b return std::make_pair(constData.first, std::move(storage)); } +bool TfLiteParserImpl::ShouldConstantTensorBeCreated(unsigned int tensorIndex) +{ + // If the TensorIndex appears in the list of ConstantsToBeCreated then return true + return (std::find(m_ConstantsToBeCreated.begin(), m_ConstantsToBeCreated.end(), tensorIndex) + != m_ConstantsToBeCreated.end()); +} + bool TfLiteParserImpl::IsConstTensor(TensorRawPtr tensorPtr) { CHECK_TENSOR_PTR(tensorPtr); @@ -4364,6 +4737,53 @@ armnn::ConstTensor TfLiteParserImpl::CreateConstTensorNonPermuted(TensorRawPtr t return ConstTensor(tensorInfo, bufferPtr->data.data()); } +std::pair<armnn::ConstTensor, std::unique_ptr<float[]>> +TfLiteParserImpl::CreateConstTensorNonPermuted(TensorRawPtr tensorPtr, + armnn::TensorInfo& tensorInfo, + armnn::DataType inputDataType) +{ + CHECK_TENSOR_PTR(tensorPtr); + auto bufferPtr = GetBuffer(m_Model, tensorPtr->buffer); + CHECK_BUFFER_SIZE(bufferPtr, tensorInfo, tensorPtr->buffer); + + // Make sure isConstant flag is set. + tensorInfo.SetConstant(); + + if (inputDataType == DataType::Float32 && tensorInfo.GetDataType() != DataType::Float32) + { + TensorInfo constTensorInfo(tensorInfo.GetShape(), DataType::Float32, 0.0f, 0, true); + std::unique_ptr<float[]> data = AsFloatArray(bufferPtr, tensorInfo); + return std::make_pair(ConstTensor(constTensorInfo, data.get()), std::move(data)); + } + else + { + return std::make_pair(ConstTensor(tensorInfo, bufferPtr->data.data()), std::unique_ptr<float[]>()); + } +} + +std::pair<armnn::ConstTensor*, std::unique_ptr<float[]>> +TfLiteParserImpl::CreateConstTensorPtr(TensorRawPtr tensorPtr, armnn::TensorInfo& inputTensorInfo) +{ + CHECK_TENSOR_PTR(tensorPtr); + armnn::TensorInfo tensorInfo = ToTensorInfo(tensorPtr); + auto bufferPtr = GetBuffer(m_Model, tensorPtr->buffer); + CHECK_BUFFER_SIZE(bufferPtr, tensorInfo, tensorPtr->buffer); + + // Make sure isConstant flag is set. + tensorInfo.SetConstant(); + + if (inputTensorInfo.GetDataType() == DataType::Float32 && tensorInfo.GetDataType() != DataType::Float32) + { + TensorInfo constTensorInfo(tensorInfo.GetShape(), DataType::Float32, 0.0f, 0, true); + std::unique_ptr<float[]> data = AsFloatArray(bufferPtr, tensorInfo); + return std::make_pair(new ConstTensor(constTensorInfo, data.get()), std::move(data)); + } + else + { + return std::make_pair(new ConstTensor(tensorInfo, bufferPtr->data.data()), std::unique_ptr<float[]>()); + } +} + BindingPointInfo TfLiteParserImpl::GetNetworkInputBindingInfo(size_t subgraphId, const std::string& name) const { |