| // |
| // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <OpaqueDelegateUtils.hpp> |
| |
| namespace armnnOpaqueDelegate |
| { |
| |
| TfLiteStatus VisitLstmOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| TfLiteOpaqueNode* tfLiteNode, |
| int nodeIndex, |
| int32_t operatorCode) |
| { |
| auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); |
| if (numInputs < 2) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Minimum number of inputs (%d != %d) in node #%d", |
| 2, numInputs, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // Gather input indices and use to get input tensor. |
| const int* inputTensors; |
| if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", |
| nodeIndex); |
| return kTfLiteError; |
| } |
| |
| const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| // Gather output indices and use to get output tensors. |
| int numOutputs = 0; |
| const int* outputTensors; |
| if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", |
| nodeIndex); |
| return kTfLiteError; |
| } |
| |
| const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| // Set the params structure for the AddLstmLayer call |
| armnn::LstmInputParams params; |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 1)) |
| { |
| params.m_InputToInputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 1); |
| } |
| |
| params.m_InputToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 2); |
| params.m_InputToCellWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 3); |
| params.m_InputToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 4); |
| |
| // Recurrent weight tensors of size {n_cell, n_output} |
| if (IsOptionalOperandPresent(tfLiteNode, 5)) |
| { |
| params.m_RecurrentToInputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 5); |
| } |
| |
| params.m_RecurrentToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 6); |
| params.m_RecurrentToCellWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 7); |
| params.m_RecurrentToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 8); |
| |
| // Peephole weights tensors of size {n_cell}, representing a diagonal matrix. |
| if (IsOptionalOperandPresent(tfLiteNode, 9)) |
| { |
| params.m_CellToInputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 9); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 10)) |
| { |
| params.m_CellToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 10); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 11)) |
| { |
| params.m_CellToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 11); |
| } |
| |
| // Gates bias tensors of size {n_cell} |
| if (IsOptionalOperandPresent(tfLiteNode, 12)) |
| { |
| params.m_InputGateBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 12); |
| } |
| |
| params.m_ForgetGateBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 13); |
| params.m_CellBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 14); |
| params.m_OutputGateBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 15); |
| |
| // Projection weight tensor of size {n_output, n_cell} |
| if (IsOptionalOperandPresent(tfLiteNode, 16)) |
| { |
| params.m_ProjectionWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 16); |
| } |
| // Projection bias tensor of size {n_output} |
| if (IsOptionalOperandPresent(tfLiteNode, 17)) |
| { |
| params.m_ProjectionBias = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 17); |
| } |
| |
| // These state tensors are defined as variable tensors, and will be modified by this op. |
| const TfLiteOpaqueTensor* tfLiteOutputStateIn = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[18]); |
| if (!IsValid(tfLiteContext, tfLiteOutputStateIn, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| const TfLiteOpaqueTensor* cellStateIn = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[19]); |
| if (!IsValid(tfLiteContext, cellStateIn, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| armnn::TensorInfo outputStateInInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputStateIn); |
| armnn::TensorInfo cellStateInInfo = GetTensorInfoForTfLiteOpaqueTensor(cellStateIn); |
| |
| // Layer norm coefficient tensors of size {n_cell}, representing a diagonal matrix. |
| if (IsOptionalOperandPresent(tfLiteNode, 20)) |
| { |
| params.m_InputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 20); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 21)) |
| { |
| params.m_ForgetLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 21); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 22)) |
| { |
| params.m_CellLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 22); |
| } |
| |
| if (IsOptionalOperandPresent(tfLiteNode, 23)) |
| { |
| params.m_OutputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteContext, tfLiteNode, 23); |
| } |
| |
| const auto nodeParams = reinterpret_cast<TfLiteLSTMParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| |
| // set the layer descriptor |
| armnn::LstmDescriptor 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); |
| |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| |
| unsigned int batchSize = inputTensorInfo.GetShape()[0]; |
| unsigned int outputSize = outputTensorInfo.GetShape()[1]; |
| 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}, dataType, qScale, qOffset); |
| 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; |
| armnn::BackendId setBackend; |
| auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) |
| { |
| FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("LSTM", |
| tfLiteContext, |
| IsLstmSupported, |
| delegateData.m_Backends, |
| isSupported, |
| setBackend, |
| inputTensorInfo, |
| outputStateInInfo, |
| cellStateInInfo, |
| scratchBufferTensorInfo, |
| outputStateOutTensorInfo, |
| cellStateOutTensorInfo, |
| outputInfo, |
| desc, |
| paramsInfo); |
| }; |
| |
| if (!delegateData.m_Network) |
| { |
| validateFunc(outputTensorInfo, isSupported); |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| armnn::IConnectableLayer* layer = delegateData.m_Network->AddLstmLayer(desc, params); |
| layer->SetBackendId(setBackend); |
| ARMNN_ASSERT(layer != nullptr); |
| |
| layer->GetOutputSlot(0).SetTensorInfo(scratchBufferTensorInfo); |
| layer->GetOutputSlot(1).SetTensorInfo(outputStateOutTensorInfo); |
| layer->GetOutputSlot(2).SetTensorInfo(cellStateOutTensorInfo); |
| layer->GetOutputSlot(3).SetTensorInfo(outputTensorInfo); |
| |
| // Connect the inputs |
| // input_layer |
| delegateData.m_OutputSlotForNode[inputTensors[0]]->Connect(layer->GetInputSlot(0)); |
| // cellStateIn |
| delegateData.m_OutputSlotForNode[inputTensors[18]]->Connect(layer->GetInputSlot(1)); |
| //outputStateIn |
| delegateData.m_OutputSlotForNode[inputTensors[19]]->Connect(layer->GetInputSlot(2)); |
| |
| // In the test_model there is only 1 Output |
| armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(1); |
| delegateData.m_OutputSlotForNode[static_cast<unsigned long>(outputTensors[0])] = &outputSlot; |
| |
| return kTfLiteOk; |
| } |
| |
| } // namespace armnnOpaqueDelegate |