| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "DelegateUtils.hpp" |
| |
| #include <armnn/LstmParams.hpp> |
| #include <armnn/Tensor.hpp> |
| #include <armnn/utility/IgnoreUnused.hpp> |
| |
| #include <tensorflow/lite/builtin_ops.h> |
| #include <tensorflow/lite/c/builtin_op_data.h> |
| #include <tensorflow/lite/c/common.h> |
| #include <tensorflow/lite/minimal_logging.h> |
| |
| namespace armnnDelegate |
| { |
| |
| TfLiteStatus VisitUnidirectionalSequenceLstmOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t operatorCode) |
| { |
| auto numInputs = tfLiteNode->inputs->size; |
| if (numInputs < 2) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", |
| 2, numInputs, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| const auto nodeParams = reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams *>(tfLiteNode->builtin_data); |
| const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| |
| const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; |
| if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; |
| if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| // 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(tfLiteNode, 1)) |
| { |
| params.m_InputToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 1); |
| } |
| |
| params.m_InputToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 2); |
| params.m_InputToCellWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 3); |
| params.m_InputToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 4); |
| |
| // Recurrent weight tensors of size {n_cell, n_output} |
| if (IsOptionalOperandPresent(tfLiteNode, 5)) |
| { |
| params.m_RecurrentToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 5); |
| } |
| |
| params.m_RecurrentToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 6); |
| params.m_RecurrentToCellWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 7); |
| params.m_RecurrentToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 8); |
| |
| // Peephole weights tensors of size {n_cell}, representing a diagonal matrix. |
| if (IsOptionalOperandPresent(tfLiteNode, 9)) |
| { |
| params.m_CellToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 9); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 10)) |
| { |
| params.m_CellToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 10); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 11)) |
| { |
| params.m_CellToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 11); |
| } |
| |
| // Gates bias tensors of size {n_cell} |
| if (IsOptionalOperandPresent(tfLiteNode, 12)) |
| { |
| params.m_InputGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 12); |
| } |
| |
| params.m_ForgetGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 13); |
| params.m_CellBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 14); |
| params.m_OutputGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 15); |
| |
| // Projection weight tensor of size {n_output, n_cell} |
| if (IsOptionalOperandPresent(tfLiteNode, 16)) |
| { |
| params.m_ProjectionWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 16); |
| } |
| // Projection bias tensor of size {n_output} |
| if (IsOptionalOperandPresent(tfLiteNode, 17)) |
| { |
| params.m_ProjectionBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 17); |
| } |
| |
| // These state tensors are defined as variable tensors, and will be modified by this op. |
| armnn::TensorInfo outputStateInInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->inputs->data[18]]); |
| armnn::TensorInfo cellStateInInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->inputs->data[19]]); |
| |
| // Layer norm coefficient tensors of size {n_cell}, representing a diagonal matrix. |
| if (IsOptionalOperandPresent(tfLiteNode, 20)) |
| { |
| params.m_InputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 20); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 21)) |
| { |
| params.m_ForgetLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 21); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 22)) |
| { |
| params.m_CellLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 22); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 23)) |
| { |
| params.m_OutputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 23); |
| } |
| |
| // set the layer descriptor |
| armnn::UnidirectionalSequenceLstmDescriptor desc; |
| desc.m_ActivationFunc = NonNegative(nodeParams->activation, nodeIndex); |
| 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 (tfLiteNode->intermediates->size > 3 && desc.m_LayerNormEnabled) |
| { |
| auto inputIntermediateTensorInfo = GetTensorInfoForTfLiteTensor( |
| tfLiteTensors[tfLiteNode->intermediates->data[0]]); |
| auto forgetIntermediateTensorInfo = GetTensorInfoForTfLiteTensor( |
| tfLiteTensors[tfLiteNode->intermediates->data[1]]); |
| auto cellIntermediateTensorInfo = GetTensorInfoForTfLiteTensor( |
| tfLiteTensors[tfLiteNode->intermediates->data[2]]); |
| auto outputIntermediateTensorInfo = GetTensorInfoForTfLiteTensor( |
| tfLiteTensors[tfLiteNode->intermediates->data[3]]); |
| |
| desc.m_InputIntermediateScale = inputIntermediateTensorInfo.GetQuantizationScale(); |
| desc.m_ForgetIntermediateScale = forgetIntermediateTensorInfo.GetQuantizationScale(); |
| desc.m_CellIntermediateScale = cellIntermediateTensorInfo.GetQuantizationScale(); |
| 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; |
| } |
| if (tfLiteNode->intermediates->size > 4) |
| { |
| auto hiddentensorInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->intermediates->data[4]]); |
| desc.m_HiddenStateScale = hiddentensorInfo.GetQuantizationScale(); |
| desc.m_HiddenStateZeroPoint = hiddentensorInfo.GetQuantizationOffset(); |
| } |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); |
| |
| 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()); |
| } |
| |
| bool isSupported = false; |
| auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| { |
| FORWARD_LAYER_SUPPORT_FUNC("UNIDIRECTIONAL_SEQUENCE_LSTM", |
| tfLiteContext, |
| IsUnidirectionalSequenceLstmSupported, |
| delegateData.m_Backends, |
| isSupported, |
| inputTensorInfo, |
| outputStateInInfo, |
| cellStateInInfo, |
| outputStateOutTensorInfo, |
| cellStateOutTensorInfo, |
| outputInfo, |
| desc, |
| paramsInfo); |
| }; |
| |
| if (!delegateData.m_Network) |
| { |
| validateFunc(outputTensorInfo, isSupported); |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| armnn::IConnectableLayer* layer = delegateData.m_Network->AddUnidirectionalSequenceLstmLayer(desc, params); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo); |
| layer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo); |
| layer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo); |
| |
| // Connect the inputs |
| // input_layer |
| delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(layer->GetInputSlot(0)); |
| // cellStateIn |
| delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[18]]->Connect(layer->GetInputSlot(1)); |
| //outputStateIn |
| delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[19]]->Connect(layer->GetInputSlot(2)); |
| |
| armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(2); |
| delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->outputs->data[0])] = &outputSlot; |
| return kTfLiteOk; |
| } |
| |
| } // namespace armnnDelegate |