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//
// Copyright © 2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <OpaqueDelegateUtils.hpp>
namespace armnnOpaqueDelegate
{
TfLiteStatus ValidateScatterNdOperator(DelegateData& delegateData,
TfLiteOpaqueContext *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_OPAQUE_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,
TfLiteOpaqueContext* tfLiteContext,
TfLiteOpaqueNode* tfLiteNode,
int nodeIndex,
int32_t scatterNdOperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
// Gather input indices and use to get input tensor.
auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);
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;
}
// Gather input indices and use to get output tensor.
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;
}
// The indices tensor are the positions the data is updated/scattered into
const TfLiteOpaqueTensor* tfLiteIndicesTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
if (IsDynamicTensor(tfLiteIndicesTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: 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 TfLiteOpaqueTensor* tfLiteUpdatesTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
if (IsDynamicTensor(tfLiteUpdatesTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: 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 TfLiteOpaqueTensor* tfLiteShapeTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[2]);
if (IsDynamicTensor(tfLiteShapeTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
scatterNdOperatorCode, nodeIndex);
return kTfLiteError;
}
// The output tensor
const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
if (IsDynamicTensor(tfLiteOutputTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
scatterNdOperatorCode, nodeIndex);
return kTfLiteError;
}
const armnn::TensorInfo& shapeTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteShapeTensor);
const armnn::TensorInfo& indicesTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteIndicesTensor);
const armnn::TensorInfo& updatesTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteUpdatesTensor);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
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_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Input tensor dimension 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 = GetName(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;
}
delegateData.m_OutputSlotForNode[inputTensors[2]]->Connect(layer->GetInputSlot(0));
delegateData.m_OutputSlotForNode[inputTensors[0]]->Connect(layer->GetInputSlot(1));
delegateData.m_OutputSlotForNode[inputTensors[1]]->Connect(layer->GetInputSlot(2));
// Prepare output slots
armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
delegateData.m_OutputSlotForNode[static_cast<unsigned long>(outputTensors[0])] = &outputSlot;
return kTfLiteOk;
}
} // namespace armnnOpaqueDelegate
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