| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ConcatOperator.hpp" |
| |
| TosaSerializationBasicBlock* ConvertConcatToTosaOperator(const Layer* layer, |
| const std::vector<const TensorInfo*>& inputs, |
| const std::vector<const TensorInfo*>& outputs, |
| const OriginsDescriptor* concatDescriptor) |
| { |
| auto numInputs = inputs.size(); |
| std::vector<std::string> inputNames; |
| inputNames.reserve(numInputs); |
| std::string outputName = std::string("output0_"); |
| std::string blockName = std::string("Op_CONCAT_block_") + GetUniqueTosaMappingID(); |
| |
| // Set input names for validation purposes only. |
| if (layer == nullptr) |
| { |
| for (uint32_t i = 0; i < numInputs; ++i) |
| { |
| inputNames.push_back("input"+ std::to_string(i) +"_"); |
| } |
| } |
| // 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. |
| else |
| { |
| // Get the layers connected to the input slots and determine unique tensor names. |
| for (uint32_t i = 0; i < numInputs; ++i) |
| { |
| Layer& connectedLayer = layer->GetInputSlot(i).GetConnectedOutputSlot()->GetOwningLayer(); |
| |
| std::string inputName = GenerateUniqueName(connectedLayer, i); |
| inputNames.push_back(inputName); |
| } |
| |
| // Determine unique output tensor name. |
| outputName = GenerateUniqueOutputName(*layer, 0); |
| } |
| |
| auto axis = static_cast<int32_t>(concatDescriptor->GetConcatAxis()); |
| TosaAxisAttribute attribute(axis); |
| |
| TosaSerializationOperator* op = new TosaSerializationOperator(Op_CONCAT, |
| Attribute_AxisAttribute, |
| &attribute, |
| inputNames, |
| {outputName}); |
| |
| std::vector<TosaSerializationTensor*> tensors; |
| tensors.reserve(numInputs); |
| |
| for (uint32_t i = 0; i < numInputs; ++i) |
| { |
| // Only add input tensors for validation or when the connected layer is an input layer. |
| // As there can't be duplicate tensors and intermediate or constant tensors are created separately. |
| if(inputNames[i].find("input") != std::string::npos) |
| { |
| std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[i]->GetShape()); |
| DType inputDType = ArmNNToDType(inputs[i]->GetDataType()); |
| tensors.push_back(new TosaSerializationTensor(inputNames[i], inputShape, inputDType, {})); |
| } |
| } |
| |
| std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); |
| DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); |
| |
| TosaSerializationTensor* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}); |
| tensors.push_back(outputTensor0); |
| |
| // operatorInputNames/operatorOutputNames ends up being the same as |
| // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings |
| return new TosaSerializationBasicBlock(blockName, // name |
| mainName, // region name |
| {op}, // operators |
| tensors, // tensors |
| inputNames, // inputs |
| {outputName}); // outputs |
| } |