| // |
| // Copyright © 2022 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("ADD", |
| 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("DIV", |
| 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("MAXIMUM", |
| 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("MINIMUM", |
| 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("MUL", |
| 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("SUB", |
| 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, true); |
| |
| 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 |