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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "DelegateUtils.hpp"
#include "MultiLayerFacade.hpp"
#include "SharedFunctions.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>
#include "tensorflow/lite/delegates/utils.h"
namespace armnnDelegate
{
TfLiteStatus ValidateAddOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& inputInfo2,
const armnn::TensorInfo& outputInfo)
{
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
tfLiteContext,
IsAdditionSupported,
delegateData.m_Backends,
isSupported,
inputInfo1,
inputInfo2,
outputTensorInfo);
};
validateFunc(outputInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
TfLiteStatus ValidateDivOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& inputInfo2,
const armnn::TensorInfo& outputInfo)
{
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
tfLiteContext,
IsDivisionSupported,
delegateData.m_Backends,
isSupported,
inputInfo1,
inputInfo2,
outputTensorInfo);
};
validateFunc(outputInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
TfLiteStatus ValidateFloorDivOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& inputInfo2,
const armnn::TensorInfo& outputInfo)
{
// need first to validate that the div operator is supported
// then that the floor operator is supported
TfLiteStatus status = ValidateDivOperator(delegateData, tfLiteContext, inputInfo1, inputInfo2, outputInfo);
if (status != kTfLiteOk)
{
return status;
}
// if the inputs and output of the div are all Signed32 we don't need to add the floor operator afterward.
if (AreAllSigned32(inputInfo1, inputInfo2, outputInfo))
{
return status;
}
// in case broadcasting is being done from one of the inputs to the div
// choose the full sized input tensor to pass to the floor validation routine
armnn::TensorInfo floorInputInfo = inputInfo1;
if (inputInfo1.GetNumDimensions() < inputInfo2.GetNumDimensions())
{
floorInputInfo = inputInfo2;
}
status = ValidateFloorOperator(delegateData, tfLiteContext, floorInputInfo, outputInfo);
return status;
}
TfLiteStatus ValidateMaximumOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& inputInfo2,
const armnn::TensorInfo& outputInfo)
{
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
tfLiteContext,
IsMaximumSupported,
delegateData.m_Backends,
isSupported,
inputInfo1,
inputInfo2,
outputTensorInfo);
};
validateFunc(outputInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
TfLiteStatus ValidateMinimumOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& inputInfo2,
const armnn::TensorInfo& outputInfo)
{
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
tfLiteContext,
IsMinimumSupported,
delegateData.m_Backends,
isSupported,
inputInfo1,
inputInfo2,
outputTensorInfo);
};
validateFunc(outputInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
TfLiteStatus ValidateMulOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& inputInfo2,
const armnn::TensorInfo& outputInfo)
{
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
tfLiteContext,
IsMultiplicationSupported,
delegateData.m_Backends,
isSupported,
inputInfo1,
inputInfo2,
outputTensorInfo);
};
validateFunc(outputInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
TfLiteStatus ValidateSubOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& inputInfo2,
const armnn::TensorInfo& outputInfo)
{
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
tfLiteContext,
IsSubtractionSupported,
delegateData.m_Backends,
isSupported,
inputInfo1,
inputInfo2,
outputTensorInfo);
};
validateFunc(outputInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
std::pair<armnn::IConnectableLayer*, armnn::IConnectableLayer*> AddFloorDivLayer(
DelegateData& delegateData,
const armnn::TensorInfo& outputTensorInfo)
{
armnn::IConnectableLayer* divisionLayer = delegateData.m_Network->AddDivisionLayer();
// if the output of the div is Signed32 the Floor layer is not required
if (armnn::DataType::Signed32 == outputTensorInfo.GetDataType())
{
return std::make_pair(divisionLayer, divisionLayer);
}
armnn::IOutputSlot& outputSlot = divisionLayer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
armnn::IConnectableLayer* floorLayer = delegateData.m_Network->AddFloorLayer();
outputSlot.Connect(floorLayer->GetInputSlot(0));
return std::make_pair(divisionLayer, floorLayer);
}
TfLiteStatus VisitElementwiseBinaryOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t elementwiseBinaryOperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
const TfLiteTensor& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]];
if (IsDynamicTensor(tfLiteInputTensor0))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
elementwiseBinaryOperatorCode, nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteInputTensor1 = tfLiteTensors[tfLiteNode->inputs->data[1]];
if (IsDynamicTensor(tfLiteInputTensor1))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
elementwiseBinaryOperatorCode, nodeIndex);
return kTfLiteError;
}
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: ",
elementwiseBinaryOperatorCode, nodeIndex);
return kTfLiteError;
}
armnn::TensorInfo inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0);
armnn::TensorInfo inputTensorInfo1 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor1);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
if (!delegateData.m_Network)
{
switch(elementwiseBinaryOperatorCode)
{
case kTfLiteBuiltinAdd:
return ValidateAddOperator(delegateData,
tfLiteContext,
inputTensorInfo0,
inputTensorInfo1,
outputTensorInfo);
case kTfLiteBuiltinDiv:
return ValidateDivOperator(delegateData,
tfLiteContext,
inputTensorInfo0,
inputTensorInfo1,
outputTensorInfo);
case kTfLiteBuiltinFloorDiv:
return ValidateFloorDivOperator(delegateData,
tfLiteContext,
inputTensorInfo0,
inputTensorInfo1,
outputTensorInfo);
case kTfLiteBuiltinMaximum:
return ValidateMaximumOperator(delegateData,
tfLiteContext,
inputTensorInfo0,
inputTensorInfo1,
outputTensorInfo);
case kTfLiteBuiltinMinimum:
return ValidateMinimumOperator(delegateData,
tfLiteContext,
inputTensorInfo0,
inputTensorInfo1,
outputTensorInfo);
case kTfLiteBuiltinMul:
return ValidateMulOperator(delegateData,
tfLiteContext,
inputTensorInfo0,
inputTensorInfo1,
outputTensorInfo);
case kTfLiteBuiltinSub:
return ValidateSubOperator(delegateData,
tfLiteContext,
inputTensorInfo0,
inputTensorInfo1,
outputTensorInfo);
default:
return kTfLiteError;
}
}
armnn::IConnectableLayer* elementwiseBinaryLayer = nullptr;
MultiLayerFacade multiLayer;
switch(elementwiseBinaryOperatorCode)
{
case kTfLiteBuiltinAdd:
elementwiseBinaryLayer = delegateData.m_Network->AddAdditionLayer();
break;
case kTfLiteBuiltinDiv:
elementwiseBinaryLayer = delegateData.m_Network->AddDivisionLayer();
break;
case kTfLiteBuiltinFloorDiv:
{
auto layers = AddFloorDivLayer(delegateData, outputTensorInfo);
multiLayer.AssignValues(layers.first, layers.second);
elementwiseBinaryLayer = &multiLayer;
}
break;
case kTfLiteBuiltinMaximum:
elementwiseBinaryLayer = delegateData.m_Network->AddMaximumLayer();
break;
case kTfLiteBuiltinMinimum:
elementwiseBinaryLayer = delegateData.m_Network->AddMinimumLayer();
break;
case kTfLiteBuiltinMul:
elementwiseBinaryLayer = delegateData.m_Network->AddMultiplicationLayer();
break;
case kTfLiteBuiltinSub:
elementwiseBinaryLayer = delegateData.m_Network->AddSubtractionLayer();
break;
default:
return kTfLiteError;
}
ARMNN_ASSERT(elementwiseBinaryLayer != nullptr);
armnn::IOutputSlot& outputSlot = elementwiseBinaryLayer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
auto inputsTensorsProcess = ProcessInputs(elementwiseBinaryLayer,
delegateData,
tfLiteContext,
tfLiteNode);
if (inputsTensorsProcess == kTfLiteError)
{
return inputsTensorsProcess;
}
auto reshapeLayer = BroadcastTensor(inputTensorInfo0,
inputTensorInfo1,
elementwiseBinaryLayer,
tfLiteContext,
tfLiteNode,
delegateData);
if (!reshapeLayer)
{
return kTfLiteError;
}
auto* tfLiteNodeParameters = reinterpret_cast<TfLiteAddParams*>(tfLiteNode->builtin_data);
if (!tfLiteNodeParameters)
{
// No Activation
return kTfLiteOk;
}
// Check activation
TfLiteFusedActivation activationType = tfLiteNodeParameters->activation;
return FusedActivation(tfLiteContext, tfLiteNode, activationType, elementwiseBinaryLayer, 0, delegateData);
}
} // namespace armnnDelegate
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