// // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #pragma once #include "DelegateUtils.hpp" #include #include #include #include #include namespace armnnDelegate { TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData, TfLiteContext* tfLiteContext, TfLiteNode* tfLiteNode, int nodeIndex, int32_t operatorCode) { auto 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)); bool biasEnabled = (numInputs == 3); const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; if(!IsValid(&tfLiteTensors[tfLiteNode->inputs->data[0]])) { TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", operatorCode, nodeIndex); return kTfLiteError; } if (IsDynamicTensor(tfLiteInputTensor)) { TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, "TfLiteArmnnDelegate: Dynamic input tensors are not supported in node #%d: ", 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: ", operatorCode, nodeIndex); return kTfLiteError; } if (IsDynamicTensor(tfLiteOutputTensor)) { TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, "TfLiteArmnnDelegate: Dynamic output tensors are not supported in node #%d: ", nodeIndex); return kTfLiteError; } const TfLiteTensor& tfLiteWeightsTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; if(!IsValid(&tfLiteWeightsTensor)) { TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, "TfLiteArmnnDelegate: Invalid weights tensor in operator #%d node #%d: ", operatorCode, nodeIndex); return kTfLiteError; } if (IsDynamicTensor(tfLiteWeightsTensor)) { TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, "TfLiteArmnnDelegate: Dynamic weight tensors are not supported in node #%d: ", nodeIndex); return kTfLiteError; } const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); armnn::TensorInfo weightsTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteWeightsTensor); // Fully Connected Layer accepts two dimensional weights input int32_t weightsDimension = static_cast(weightsTensorInfo.GetNumDimensions()); if (weightsDimension != 2) { TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, "TfLiteArmnnDelegate: Dimension #$d for Fully Connected weights is not supported by Armnn" " in operator #%d node #%d: ", weightsDimension, operatorCode, nodeIndex); return kTfLiteError; } armnn::TensorInfo biasTensorInfo; if (biasEnabled) { const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; if(!IsValid(&tfLiteBiasTensor)) { TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, "TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ", operatorCode, nodeIndex); return kTfLiteError; } if (IsDynamicTensor(tfLiteBiasTensor)) { TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, "TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ", nodeIndex); return kTfLiteError; } biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); } else { biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); } armnn::FullyConnectedDescriptor descriptor; descriptor.m_TransposeWeightMatrix = true; descriptor.m_BiasEnabled = biasEnabled; bool isSupported = false; auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) { FORWARD_LAYER_SUPPORT_FUNC(__func__, tfLiteContext, IsFullyConnectedSupported, delegateData.m_Backends, isSupported, inputTensorInfo, outputTensorInfo, weightsTensorInfo, biasTensorInfo, descriptor); }; if (!delegateData.m_Network) { validateFunc(outputTensorInfo, isSupported); return isSupported ? kTfLiteOk : kTfLiteError; } auto weightsTensor = CreateConstTensor(&tfLiteWeightsTensor, weightsTensorInfo, armnn::Optional()); armnn::IConnectableLayer* layer = nullptr; if (biasEnabled) { const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo, armnn::Optional()); layer = delegateData.m_Network->AddFullyConnectedLayer(descriptor, weightsTensor, armnn::Optional(biasTensor)); } else { layer = delegateData.m_Network->AddFullyConnectedLayer(descriptor, weightsTensor, armnn::EmptyOptional()); } ARMNN_ASSERT(layer != nullptr); armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); outputSlot.SetTensorInfo(outputTensorInfo); armnn::IConnectableLayer* reshapeLayer = nullptr; if (inputTensorInfo.GetNumDimensions() > 2) { // Add reshape to flatten to 2D [batch_size, input_size] std::vector reshapedDimensions(2); reshapedDimensions[1] = weightsTensorInfo.GetShape()[1]; reshapedDimensions[0] = inputTensorInfo.GetNumElements() / reshapedDimensions[1]; if (inputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0) { TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, "TfLiteArmnnDelegate: Failed to deduce input tensor shape from filter size #%d #%d node #%d: ", reshapedDimensions[1], operatorCode, nodeIndex); return kTfLiteError; } armnn::TensorInfo reshapedTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() }); armnn::ReshapeDescriptor reshapeDescriptor; reshapeDescriptor.m_TargetShape = reshapedTensorInfo.GetShape(); reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor); ARMNN_ASSERT(reshapeLayer != nullptr); reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo); // Connect delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(reshapeLayer->GetInputSlot(0)); reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); delegateData.m_OutputSlotForNode[tfLiteNode->outputs->data[0]] = &outputSlot; } if (reshapeLayer == nullptr) { Connect(layer, tfLiteNode, delegateData); } auto* tfLiteNodeParameters = reinterpret_cast(tfLiteNode->builtin_data); if (!tfLiteNodeParameters) { // No Activation return kTfLiteOk; } // Check Activation TfLiteFusedActivation activationType = tfLiteNodeParameters->activation; return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); } } // namespace armnnDelegate