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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <OpaqueDelegateUtils.hpp>
namespace armnnOpaqueDelegate
{
TfLiteStatus ValidateTileOperator(DelegateData& delegateData,
TfLiteOpaqueContext *tfLiteContext,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& outputInfo,
const armnn::TileDescriptor& descriptor)
{
bool isSupported = false;
FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("TILE",
tfLiteContext,
IsTileSupported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo,
outputInfo,
descriptor);
return isSupported ? kTfLiteOk : kTfLiteError;
}
TfLiteStatus VisitTileOperator(DelegateData& delegateData,
TfLiteOpaqueContext* tfLiteContext,
TfLiteOpaqueNode* tfLiteNode,
int nodeIndex,
int32_t tileOperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
// Gather input tensors
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 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;
}
// The input contains the data that should be tiled
const TfLiteOpaqueTensor* tfLiteInputTensor =
TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
if (IsDynamicTensor(tfLiteInputTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
tileOperatorCode, nodeIndex);
return kTfLiteError;
}
// The multiples tensor contains the number of copies for each axis
const TfLiteOpaqueTensor* tfLiteMultiplesTensor =
TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);;
if (IsDynamicTensor(tfLiteMultiplesTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
tileOperatorCode, 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: ",
tileOperatorCode, nodeIndex);
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
const armnn::TensorInfo& multiplesTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteMultiplesTensor);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
// Multiples length must be the same as the number of dimension in input tensor
if (multiplesTensorInfo.GetNumElements() != inputTensorInfo.GetNumDimensions())
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate:",
"The Multiples length must be the same as the number of dimension in input tensor",
"Operator: #%d node #%d: ",
tileOperatorCode, nodeIndex);
return kTfLiteError;
}
// Get the Multiples data: In armnn, the values of the multiples input tensor is saved in the operator descriptor
// We have to read it from the input tensor and write it the descriptor
auto* multiplesTensorDataPtr = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteMultiplesTensor));
auto multiplesTensorNum = TfLiteOpaqueTensorDim(tfLiteMultiplesTensor, 0);
std::vector<int32_t> multiplesIntData(multiplesTensorDataPtr, multiplesTensorDataPtr + multiplesTensorNum);
// The multiples must be positive
for (auto multiple : multiplesIntData)
{
if (multiple < 0)
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: The Multiples must be positive values",
"Operator: #%d node #%d: ",
tileOperatorCode, nodeIndex);
return kTfLiteError;
}
}
// The original input from TFLite is int32, and we have to make it as uint32 for our descriptor
std::vector<uint32_t> multiplesUintData;
std::transform(multiplesIntData.begin(),
multiplesIntData.end(),
std::back_inserter(multiplesUintData),
[] (const int value)
{
return static_cast<uint32_t>(value);
});
armnn::TileDescriptor tileDescriptor;
tileDescriptor.m_Multiples = multiplesUintData;
// Check output dimensions
if (inputTensorInfo.GetNumDimensions() != outputTensorInfo.GetNumDimensions())
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Input tensor dimension and output tensor dimension differ",
"Operator: #%d node #%d: ",
tileOperatorCode, nodeIndex);
return kTfLiteError;
}
// No network pointer indicates that only support for this operator should be checked
if (!delegateData.m_Network)
{
return ValidateTileOperator(delegateData,
tfLiteContext,
inputTensorInfo,
outputTensorInfo,
tileDescriptor);
}
std::string layerName("Tile");
armnn::IConnectableLayer* layer = delegateData.m_Network->AddTileLayer(tileDescriptor, layerName.c_str());
if (layer == nullptr)
{
return kTfLiteError;
}
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk)
{
return kTfLiteError;
}
return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
}
} // namespace armnnOpaqueDelegate
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