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Diffstat (limited to 'delegate/classic/src/Control.hpp')
| -rw-r--r-- | delegate/classic/src/Control.hpp | 342 |
1 files changed, 342 insertions, 0 deletions
diff --git a/delegate/classic/src/Control.hpp b/delegate/classic/src/Control.hpp new file mode 100644 index 0000000000..a3ea6e92a7 --- /dev/null +++ b/delegate/classic/src/Control.hpp @@ -0,0 +1,342 @@ +// +// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <armnn/utility/IgnoreUnused.hpp> + +#include <tensorflow/lite/builtin_ops.h> +#include <tensorflow/lite/c/builtin_op_data.h> +#include <tensorflow/lite/c/common.h> +#include <tensorflow/lite/kernels/internal/tensor_ctypes.h> +#include <tensorflow/lite/minimal_logging.h> + +#include <algorithm> +#include <iterator> +#include <string> +#include <vector> + +namespace armnnDelegate +{ + +void SetupConcatViewOrigin(const armnn::TensorInfo& inputTensorInfo, + armnn::OriginsDescriptor& concatDescriptor, + const unsigned int concatAxis, + unsigned int inputIndex, + unsigned int& mergeDimOrigin) +{ + const uint32_t inputRank = concatDescriptor.GetNumDimensions(); + + // double check dimensions of the tensors + if (inputTensorInfo.GetNumDimensions() != inputRank) + { + throw armnn::ParseException("The number of dimensions for input tensors " + "of the concatenation operator should be: " + std::to_string(inputRank)); + } + + for (unsigned int j = 0; j < concatAxis; ++j) + { + concatDescriptor.SetViewOriginCoord(inputIndex, j, 0); + } + + concatDescriptor.SetViewOriginCoord(inputIndex, concatAxis, mergeDimOrigin); + mergeDimOrigin += inputTensorInfo.GetShape()[concatAxis]; + + for (unsigned int j = concatAxis + 1; j < inputRank; ++j) + { + concatDescriptor.SetViewOriginCoord(inputIndex, j, 0); + } +} + +TfLiteStatus VisitConcatenationOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t tfLiteConcatOperatorCode) +{ + unsigned int numInputs = tfLiteNode->inputs->size; + if (numInputs < 2) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", + 2, numInputs, nodeIndex); + return kTfLiteError; + } + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + + std::vector<armnn::TensorInfo> inputTensorInfos; + for (unsigned int i = 0; i < numInputs; ++i) + { + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[i]]; + if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteConcatOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + + armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + 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; }); + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteConcatOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + + // Setup OriginsDescriptor, axis and view origin + unsigned int numConcatView = static_cast<unsigned int>(numInputs); + uint32_t inputRank = tfLiteTensors[tfLiteNode->inputs->data[0]].dims->size; + + auto* concatenationParameters = reinterpret_cast<TfLiteConcatenationParams*>(tfLiteNode->builtin_data); + + if(!concatenationParameters) + { + throw armnn::Exception(&"TfLiteArmnnDelegate: Concat parameters are null in: " [ nodeIndex]); + } + + const unsigned int 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 = GetTensorInfoForTfLiteTensor( + tfLiteTensors[tfLiteNode->inputs->data[viewIndex]]); + + // Sets up concatDescriptor view origin + SetupConcatViewOrigin(inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin); + } + + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(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_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, 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, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t tfLiteMeanOperatorCode) +{ + 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& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if(!IsValid(&tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", + tfLiteMeanOperatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + tfLiteMeanOperatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if(!IsValid(&tfLiteAxisTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid axis tensor in operator #%d node #%d: ", + tfLiteMeanOperatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteAxisTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic axis tensors are not supported in operator #%d node #%d: ", + tfLiteMeanOperatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if(!IsValid(&tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", + tfLiteAxisTensor, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + tfLiteMeanOperatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& axisTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteAxisTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); + + auto* axisTensorData = tflite::GetTensorData<int32_t>(&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_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, tfLiteNode, delegateData); +} + +TfLiteStatus VisitControlOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + armnn::IgnoreUnused(delegateData, + tfLiteContext, + tfLiteNode, + nodeIndex, + 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 |