| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "AdditionOperator.hpp" |
| |
| TosaSerializationBasicBlock* ConvertAdditionToTosaOperator(const Layer* layer, |
| const std::vector<const TensorInfo*>& inputs, |
| const std::vector<const TensorInfo*>& outputs) |
| { |
| std::string input0Name = std::string("input0_"); |
| std::string input1Name = std::string("input1_"); |
| std::string outputName = std::string("output0_"); |
| std::string blockName = std::string("Op_ADD_block_") + GetUniqueTosaMappingID(); |
| |
| // If a layer is present then the block will be used for execution, so input and output names need to be determined |
| // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. |
| if(layer != nullptr) |
| { |
| // Get the layers connected to the input slots and determine unique layer names. |
| Layer& connectedLayer0 = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); |
| input0Name = GenerateUniqueName(connectedLayer0, 0); |
| |
| Layer& connectedLayer1 = layer->GetInputSlot(1).GetConnectedOutputSlot()->GetOwningLayer(); |
| input1Name = GenerateUniqueName(connectedLayer1, 1); |
| |
| // Get the layer connected to the output slot and determine unique layer name. |
| outputName = GenerateUniqueOutputName(*layer, 0); |
| } |
| |
| auto* op = new TosaSerializationOperator(Op_ADD, |
| Attribute_NONE, |
| nullptr, |
| {input0Name, input1Name}, |
| {outputName}); |
| |
| |
| std::vector<TosaSerializationTensor*> tensors; |
| |
| // Only add input tensors if connected layer is an input layer. |
| // As intermediate or constant tensors will be created separately. |
| // There also can't be duplicate tensor. |
| if(input0Name.find("input0_") != std::string::npos) |
| { |
| std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); |
| DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {})); |
| } |
| |
| if(input1Name.find("input1_") != std::string::npos) |
| { |
| std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape()); |
| DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {})); |
| } |
| |
| std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); |
| DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); |
| |
| // operatorInputNames/operatorOutputNames ends up being the same as |
| // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings |
| return new TosaSerializationBasicBlock(blockName, // name |
| {op}, // operators |
| tensors, // tensors |
| {input0Name, input1Name}, // inputs |
| {outputName}); // outputs |
| } |