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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <OpaqueDelegateUtils.hpp>
#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>
namespace armnnOpaqueDelegate
{
TfLiteStatus VisitArgMinMaxOperator(DelegateData& delegateData,
TfLiteOpaqueContext* tfLiteContext,
TfLiteOpaqueNode* tfLiteNode,
int nodeIndex,
int32_t argMinMaxOperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, 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;
}
const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
if (!IsValid(tfLiteContext, tfLiteInputTensor, argMinMaxOperatorCode, nodeIndex))
{
return kTfLiteError;
}
// Use input indices to get filter tensor.
const TfLiteOpaqueTensor* tfLiteAxisTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
if(!IsValid(tfLiteAxisTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Invalid filter tensor in operator #%d node #%d: ",
argMinMaxOperatorCode, 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, argMinMaxOperatorCode, nodeIndex))
{
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
// Get const axis value from model and set it to descriptor.
if (!IsValid(tfLiteContext, tfLiteAxisTensor, argMinMaxOperatorCode, nodeIndex))
{
return kTfLiteError;
}
armnn::ArgMinMaxDescriptor desc;
auto* axisData = static_cast<int*>(TfLiteOpaqueTensorData(tfLiteAxisTensor));
// Get the axis value from the input tensor
switch (TfLiteOpaqueTensorType(tfLiteAxisTensor))
{
case kTfLiteInt32:
case kTfLiteInt64:
desc.m_Axis = axisData[0];
break;
default:
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Axis value data type is not supported in operator #%d node #%d: ",
argMinMaxOperatorCode, nodeIndex);
return kTfLiteError;
}
// If output_type is int32 then set Signed32 else Signed64. Default type is Signed64.
if (argMinMaxOperatorCode == kTfLiteBuiltinArgMax)
{
desc.m_Function = armnn::ArgMinMaxFunction::Max;
auto* argMaxParameters = reinterpret_cast<TfLiteArgMaxParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
if (argMaxParameters->output_type != kTfLiteInt32 && argMaxParameters->output_type != kTfLiteInt64)
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: output_type data type is not supported in operator #%d node #%d: ",
argMinMaxOperatorCode, nodeIndex);
return kTfLiteError;
}
}
else
{
desc.m_Function = armnn::ArgMinMaxFunction::Min;
auto* argMinParameters = reinterpret_cast<TfLiteArgMinParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
if (argMinParameters->output_type != kTfLiteInt32 && argMinParameters->output_type != kTfLiteInt64)
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: output_type data type is not supported in operator #%d node #%d: ",
argMinMaxOperatorCode, nodeIndex);
return kTfLiteError;
}
}
bool isSupported = false;
armnn::BackendId setBackend;
auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
{
FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("ARGMINMAX",
tfLiteContext,
IsArgMinMaxSupported,
delegateData.m_Backends,
isSupported,
setBackend,
inputTensorInfo,
outInfo,
desc);
};
if (!delegateData.m_Network)
{
validateFunc(outputTensorInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
// Add an ArgMinMax layer
auto layerName = GetName(desc.m_Function, nodeIndex);
armnn::IConnectableLayer* layer = delegateData.m_Network->AddArgMinMaxLayer(desc, layerName.c_str());
layer->SetBackendId(setBackend);
ARMNN_ASSERT(layer != nullptr);
armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
// try to connect the Constant Inputs if there are any
if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
{
return kTfLiteError;
}
// Connect
return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
}
}
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