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Diffstat (limited to 'delegate/opaque/src/Lstm.hpp')
| -rw-r--r-- | delegate/opaque/src/Lstm.hpp | 289 |
1 files changed, 289 insertions, 0 deletions
diff --git a/delegate/opaque/src/Lstm.hpp b/delegate/opaque/src/Lstm.hpp index e16969768e..b896b462d3 100644 --- a/delegate/opaque/src/Lstm.hpp +++ b/delegate/opaque/src/Lstm.hpp @@ -2,3 +2,292 @@ // 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 |