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Diffstat (limited to 'delegate/opaque/src/ElementwiseBinary.hpp')
| -rw-r--r-- | delegate/opaque/src/ElementwiseBinary.hpp | 411 |
1 files changed, 411 insertions, 0 deletions
diff --git a/delegate/opaque/src/ElementwiseBinary.hpp b/delegate/opaque/src/ElementwiseBinary.hpp index e16969768e..d6a0947b96 100644 --- a/delegate/opaque/src/ElementwiseBinary.hpp +++ b/delegate/opaque/src/ElementwiseBinary.hpp @@ -2,3 +2,414 @@ // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // +#pragma once + +#include <OpaqueDelegateUtils.hpp> +#include <MultiLayerFacade.hpp> + + +namespace armnnOpaqueDelegate +{ + +TfLiteStatus ValidateAddOperator(DelegateData& delegateData, + TfLiteOpaqueContext* tfLiteContext, + const armnn::TensorInfo& inputInfo1, + const armnn::TensorInfo& inputInfo2, + const armnn::TensorInfo& outputInfo) +{ + bool isSupported = false; + auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) + { + std::vector<armnn::TensorInfo> infos { inputInfo1, inputInfo2, outputInfo }; + FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("ADD", + tfLiteContext, + IsElementwiseBinarySupported, + delegateData.m_Backends, + isSupported, + armnn::BackendId(), + inputInfo1, + inputInfo2, + outputInfo, + armnn::BinaryOperation::Add); + }; + + validateFunc(outputInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; +} + + +TfLiteStatus ValidateDivOperator(DelegateData& delegateData, + TfLiteOpaqueContext* 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_OPAQUE_SUPPORT_FUNC("DIV", + tfLiteContext, + IsElementwiseBinarySupported, + delegateData.m_Backends, + isSupported, + armnn::BackendId(), + inputInfo1, + inputInfo2, + outputTensorInfo, + armnn::BinaryOperation::Div); + }; + + validateFunc(outputInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; +} + +TfLiteStatus ValidateFloorDivOperator(DelegateData& delegateData, + TfLiteOpaqueContext* 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, + TfLiteOpaqueContext* 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_OPAQUE_SUPPORT_FUNC("MAXIMUM", + tfLiteContext, + IsElementwiseBinarySupported, + delegateData.m_Backends, + isSupported, + armnn::BackendId(), + inputInfo1, + inputInfo2, + outputTensorInfo, + armnn::BinaryOperation::Maximum); + }; + + validateFunc(outputInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; +} + +TfLiteStatus ValidateMinimumOperator(DelegateData& delegateData, + TfLiteOpaqueContext* 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_OPAQUE_SUPPORT_FUNC("MINIMUM", + tfLiteContext, + IsElementwiseBinarySupported, + delegateData.m_Backends, + isSupported, + armnn::BackendId(), + inputInfo1, + inputInfo2, + outputTensorInfo, + armnn::BinaryOperation::Minimum); + }; + + validateFunc(outputInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; +} + +TfLiteStatus ValidateMulOperator(DelegateData& delegateData, + TfLiteOpaqueContext* 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_OPAQUE_SUPPORT_FUNC("MUL", + tfLiteContext, + IsElementwiseBinarySupported, + delegateData.m_Backends, + isSupported, + armnn::BackendId(), + inputInfo1, + inputInfo2, + outputTensorInfo, + armnn::BinaryOperation::Mul); + }; + + validateFunc(outputInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; +} + +TfLiteStatus ValidateSubOperator(DelegateData& delegateData, + TfLiteOpaqueContext* 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_OPAQUE_SUPPORT_FUNC("SUB", + tfLiteContext, + IsElementwiseBinarySupported, + delegateData.m_Backends, + isSupported, + armnn::BackendId(), + inputInfo1, + inputInfo2, + outputTensorInfo, + armnn::BinaryOperation::Sub); + }; + + 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->AddElementwiseBinaryLayer( + armnn::BinaryOperation::Div); + // 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, + TfLiteOpaqueContext* tfLiteContext, + TfLiteOpaqueNode* tfLiteNode, + int nodeIndex, + int32_t elementwiseBinaryOperatorCode) +{ + 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 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; + } + const TfLiteOpaqueTensor* tfLiteInputTensor0 = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); + if (!IsValid(tfLiteContext, tfLiteInputTensor0, elementwiseBinaryOperatorCode, nodeIndex)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Invalid input tensor in operator #%d node #%d: ", + elementwiseBinaryOperatorCode, nodeIndex); + return kTfLiteError; + } + // Use input indices to get filter tensor. + const TfLiteOpaqueTensor* tfLiteInputTensor1 = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); + if(!IsValid(tfLiteInputTensor1)) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Invalid input tensor in operator #%d node #%d: ", + elementwiseBinaryOperatorCode, 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, elementwiseBinaryOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + + armnn::TensorInfo inputTensorInfo0 = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor0); + armnn::TensorInfo inputTensorInfo1 = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor1); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); + + + + // Check if we need to expand the dims of the input tensor infos. + // This is required for a few of the backends. + if(inputTensorInfo0.GetNumDimensions() != inputTensorInfo1.GetNumDimensions()) + { + ExpandTensorRankToEqual(inputTensorInfo0, inputTensorInfo1); + } + + auto* tfLiteNodeParameters = reinterpret_cast<TfLiteAddParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); + TfLiteFusedActivation activationType = kTfLiteActNone; + if (tfLiteNodeParameters) + { + activationType = tfLiteNodeParameters->activation; + TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, + tfLiteContext, + outputTensorInfo, + outputTensorInfo, + activationType); + if(activationStatus != kTfLiteOk) + { + return kTfLiteError; + } + } + + 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; + armnnDelegate::MultiLayerFacade multiLayer; + switch(elementwiseBinaryOperatorCode) + { + case kTfLiteBuiltinAdd: + elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( + armnn::BinaryOperation::Add); + break; + case kTfLiteBuiltinDiv: + elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( + armnn::BinaryOperation::Div); + break; + case kTfLiteBuiltinFloorDiv: + { + auto layers = AddFloorDivLayer(delegateData, outputTensorInfo); + multiLayer.AssignValues(layers.first, layers.second); + elementwiseBinaryLayer = &multiLayer; + } + break; + case kTfLiteBuiltinMaximum: + elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( + armnn::BinaryOperation::Maximum); + break; + case kTfLiteBuiltinMinimum: + elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( + armnn::BinaryOperation::Minimum); + break; + case kTfLiteBuiltinMul: + elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( + armnn::BinaryOperation::Mul); + break; + case kTfLiteBuiltinSub: + elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( + armnn::BinaryOperation::Sub); + 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; + } + + if(Connect(elementwiseBinaryLayer, tfLiteContext, tfLiteNode, delegateData) != kTfLiteOk) + { + return kTfLiteError; + } + + if (!tfLiteNodeParameters) + { + // No Activation + return kTfLiteOk; + } + // Check and Create Activation + return FusedActivation(tfLiteContext, tfLiteNode, activationType, elementwiseBinaryLayer, 0, delegateData); +} + +} // namespace armnnOpaqueDelegate |