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Diffstat (limited to 'delegate/classic/src/ScatterNd.hpp')
| -rw-r--r-- | delegate/classic/src/ScatterNd.hpp | 161 |
1 files changed, 161 insertions, 0 deletions
diff --git a/delegate/classic/src/ScatterNd.hpp b/delegate/classic/src/ScatterNd.hpp new file mode 100644 index 0000000000..c73e231c46 --- /dev/null +++ b/delegate/classic/src/ScatterNd.hpp @@ -0,0 +1,161 @@ +// +// Copyright © 2024 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#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> +#include <tensorflow/lite/kernels/internal/tensor_ctypes.h> +#include <tensorflow/lite/schema/schema_generated.h> + +namespace armnnDelegate +{ +TfLiteStatus ValidateScatterNdOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + const armnn::TensorInfo& indicesInfo, + const armnn::TensorInfo& updatesInfo, + const armnn::TensorInfo& shapeInfo, + const armnn::TensorInfo& outputInfo, + const armnn::ScatterNdDescriptor& descriptor) +{ + bool isSupported = false; + FORWARD_LAYER_SUPPORT_FUNC("SCATTER_ND", + tfLiteContext, + IsScatterNdSupported, + delegateData.m_Backends, + isSupported, + armnn::BackendId(), + shapeInfo, + indicesInfo, + updatesInfo, + outputInfo, + descriptor); + return isSupported ? kTfLiteOk : kTfLiteError; +} + +TfLiteStatus VisitScatterNdOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t scatterNdOperatorCode) +{ + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + + // The indices tensor are the positions the data is updated/scattered into + const TfLiteTensor& tfLiteIndicesTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (IsDynamicTensor(tfLiteIndicesTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + scatterNdOperatorCode, nodeIndex); + return kTfLiteError; + } + + // The updates tensor provides the data which will be updated/scattered into the relevant indices + const TfLiteTensor& tfLiteUpdatesTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if (IsDynamicTensor(tfLiteUpdatesTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + scatterNdOperatorCode, nodeIndex); + return kTfLiteError; + } + + // For tflite scatternd there is no input tensor + // The shape tensor is a 1D tensor which represents the shape of an input tensor to be filled with zeros + const TfLiteTensor& tfLiteShapeTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if (IsDynamicTensor(tfLiteUpdatesTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + scatterNdOperatorCode, nodeIndex); + return kTfLiteError; + } + + // The output tensor + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + scatterNdOperatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& indicesTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteIndicesTensor); + const armnn::TensorInfo& updatesTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteUpdatesTensor); + const armnn::TensorInfo& shapeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteShapeTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + + armnn::ScatterNdDescriptor scatterNdDescriptor; + scatterNdDescriptor.m_Function = armnn::ScatterNdFunction::Update; + scatterNdDescriptor.m_InputEnabled = false; + scatterNdDescriptor.m_Axis = 0; + scatterNdDescriptor.m_AxisEnabled = false; + + // Check output dimensions + if (shapeTensorInfo.GetShape().GetNumElements() != outputTensorInfo.GetNumDimensions()) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Shape tensor number of elements and output tensor dimension differ", + "Operator: #%d node #%d: ", + scatterNdOperatorCode, nodeIndex); + return kTfLiteError; + } + + // No network pointer indicates that only support for this operator should be checked + if (!delegateData.m_Network) + { + return ValidateScatterNdOperator(delegateData, + tfLiteContext, + indicesTensorInfo, + updatesTensorInfo, + shapeTensorInfo, + outputTensorInfo, + scatterNdDescriptor); + } + + auto layerName = GetLayerName(armnn::LayerType::ScatterNd, nodeIndex); + armnn::IConnectableLayer* layer = delegateData.m_Network->AddScatterNdLayer(scatterNdDescriptor, layerName.c_str()); + + if (layer == nullptr) + { + return kTfLiteError; + } + + layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk) + { + return kTfLiteError; + } + + if (static_cast<unsigned int>(tfLiteNode->outputs->size) != layer->GetNumOutputSlots()) + { + return kTfLiteError; + } + + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[2]]->Connect(layer->GetInputSlot(0)); + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(layer->GetInputSlot(1)); + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[1]]->Connect(layer->GetInputSlot(2)); + + // Prepare output slots + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->outputs->data[0])] = &outputSlot; + + return kTfLiteOk; +} + +} // namespace armnnDelegate
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