| // |
| // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <DelegateUtils.hpp> |
| #include <OpaqueDelegateUtils.hpp> |
| |
| namespace armnnOpaqueDelegate |
| { |
| |
| TfLiteStatus VisitConcatenationOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| TfLiteOpaqueNode* tfLiteNode, |
| int nodeIndex, |
| int32_t tfLiteConcatOperatorCode) |
| { |
| auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); |
| if (numInputs < 2) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Minimum number of inputs (%d != %d) in node #%d", |
| 2, numInputs, nodeIndex); |
| return kTfLiteError; |
| } |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| |
| // Gather input indices and use to get input tensor. |
| 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; |
| } |
| |
| std::vector<armnn::TensorInfo> inputTensorInfos; |
| for (int i = 0; i < numInputs; ++i) |
| { |
| const TfLiteOpaqueTensor* inputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[i]); |
| if (!IsValid(tfLiteContext, inputTensor, tfLiteConcatOperatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(inputTensor); |
| inputTensorInfos.emplace_back(inputTensorInfo); |
| } |
| |
| // Convert input tensors to const armnn::TensorInfo* type for FORWARD_LAYER_SUPPORT_FUNC. |
| std::vector<const armnn::TensorInfo*> inputConstTensorInfos; |
| std::transform(inputTensorInfos.begin(), |
| inputTensorInfos.end(), |
| std::back_inserter(inputConstTensorInfos), |
| [](armnn::TensorInfo& t)->const armnn::TensorInfo*{ return &t; }); |
| |
| // 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, tfLiteConcatOperatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| // Setup OriginsDescriptor, axis and view origin |
| auto numConcatView = static_cast<unsigned int>(numInputs); |
| uint32_t inputRank = TfLiteOpaqueTensorNumDims(TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0])); |
| |
| auto* concatenationParameters = |
| reinterpret_cast<TfLiteConcatenationParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| |
| if(!concatenationParameters) |
| { |
| throw armnn::Exception(&"TfLiteArmnnOpaqueDelegate: Concat parameters are null in: " [ nodeIndex ]); |
| } |
| |
| const auto concatDimInput = static_cast<unsigned int>( |
| (static_cast<int>(inputRank) + concatenationParameters->axis) % static_cast<int>(inputRank)); |
| |
| armnn::OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numConcatView), inputRank); |
| concatDescriptor.SetConcatAxis(concatDimInput); |
| |
| unsigned int mergeDimOrigin = 0; |
| for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex) |
| { |
| armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor( |
| TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[viewIndex])); |
| |
| // Sets up concatDescriptor view origin |
| SetupConcatViewOrigin(inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin); |
| } |
| |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| |
| // Verify we support the fused activation before attempting to create a layer |
| TfLiteFusedActivation activationType = concatenationParameters->activation; |
| |
| TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, |
| outputTensorInfo, activationType); |
| if(activationStatus != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| // Check if supported |
| bool isSupported = false; |
| armnn::BackendId setBackend; |
| auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) |
| { |
| FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("CONCATENATION", |
| tfLiteContext, |
| IsConcatSupported, |
| delegateData.m_Backends, |
| isSupported, |
| setBackend, |
| inputConstTensorInfos, |
| outputTensorInfo, |
| concatDescriptor); |
| }; |
| |
| if (!delegateData.m_Network) |
| { |
| validateFunc(outputTensorInfo, isSupported); |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| // Setup layer and connect. |
| armnn::IConnectableLayer* concatenationLayer = delegateData.m_Network->AddConcatLayer(concatDescriptor); |
| concatenationLayer->SetBackendId(setBackend); |
| ARMNN_ASSERT(concatenationLayer != nullptr); |
| |
| // Connect the Constant Inputs |
| auto inputsTensorsProcess = ProcessInputs(concatenationLayer, |
| delegateData, |
| tfLiteContext, |
| tfLiteNode); |
| if (inputsTensorsProcess == kTfLiteError) |
| { |
| return inputsTensorsProcess; |
| } |
| |
| armnn::IOutputSlot& outputSlot = concatenationLayer->GetOutputSlot(0); |
| outputSlot.SetTensorInfo(outputTensorInfo); |
| if(Connect(concatenationLayer, tfLiteContext, tfLiteNode, delegateData) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| if (activationType == kTfLiteActNone) |
| { |
| // No Activation |
| return kTfLiteOk; |
| } |
| |
| // Check and Create activation |
| return FusedActivation(tfLiteContext, tfLiteNode, activationType, concatenationLayer, 0, delegateData); |
| } |
| |
| TfLiteStatus VisitMeanOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| TfLiteOpaqueNode* tfLiteNode, |
| int nodeIndex, |
| int32_t tfLiteMeanOperatorCode) |
| { |
| 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. |
| int numInputs = 0; |
| 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, tfLiteMeanOperatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| // Use input indices to get axis tensor. |
| const TfLiteOpaqueTensor* tfLiteAxisTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); |
| if (!IsValid(tfLiteContext, tfLiteAxisTensor, tfLiteMeanOperatorCode, 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, tfLiteMeanOperatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& axisTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteAxisTensor); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| |
| auto* axisTensorData = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteAxisTensor)); |
| |
| std::vector<int32_t> axis; |
| // Add axis data to vector to be converter to unsigned int and assigned to descriptor axis. |
| for (unsigned int i = 0; i < axisTensorInfo.GetNumElements(); ++i) |
| { |
| axis.emplace_back(axisTensorData[i]); |
| } |
| |
| // Convert the axis to unsigned int and remove duplicates. |
| unsigned int rank = inputTensorInfo.GetNumDimensions(); |
| std::set<unsigned int> uniqueAxis; |
| std::transform(axis.begin(), |
| axis.end(), |
| std::inserter(uniqueAxis, uniqueAxis.begin()), |
| [rank](int i)->unsigned int{ return (i + rank) % rank; }); |
| |
| // Setup MeanDescriptor and assign axis and keepDims |
| armnn::MeanDescriptor desc; |
| desc.m_Axis.assign(uniqueAxis.begin(), uniqueAxis.end()); |
| desc.m_KeepDims = inputTensorInfo.GetNumDimensions() == outputTensorInfo.GetNumDimensions() ? true : false; |
| |
| // Check if supported |
| bool isSupported = false; |
| armnn::BackendId setBackend; |
| auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) |
| { |
| FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("MEAN", |
| tfLiteContext, |
| IsMeanSupported, |
| delegateData.m_Backends, |
| isSupported, |
| setBackend, |
| inputTensorInfo, |
| outputTensorInfo, |
| desc); |
| }; |
| |
| if (!delegateData.m_Network) |
| { |
| validateFunc(outputTensorInfo, isSupported); |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| // Setup layer and connect. |
| armnn::IConnectableLayer* meanLayer = delegateData.m_Network->AddMeanLayer(desc); |
| meanLayer->SetBackendId(setBackend); |
| ARMNN_ASSERT(meanLayer != nullptr); |
| |
| armnn::IOutputSlot& outputSlot = meanLayer->GetOutputSlot(0); |
| outputSlot.SetTensorInfo(outputTensorInfo); |
| |
| // try to connect the Constant Inputs if there are any |
| if(ProcessInputs(meanLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) |
| { |
| return kTfLiteError; |
| } |
| |
| return Connect(meanLayer, tfLiteContext, tfLiteNode, delegateData); |
| } |
| |
| TfLiteStatus VisitControlOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| TfLiteOpaqueNode* tfLiteNode, |
| int nodeIndex, |
| int32_t operatorCode) |
| { |
| switch(operatorCode) |
| { |
| case kTfLiteBuiltinConcatenation: |
| return VisitConcatenationOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); |
| case kTfLiteBuiltinMean: |
| return VisitMeanOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); |
| default: |
| return kTfLiteError; |
| } |
| } |
| |
| } // namespace armnnDelegate |
| |