David Monahan | d7fca09 | 2023-01-12 14:53:34 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "ElementwiseUnaryOperator.hpp" |
| 7 | |
| 8 | TosaSerializationBasicBlock* ConvertElementwiseUnaryOperator(const Layer* layer, |
| 9 | const std::vector<const TensorInfo*>& inputs, |
| 10 | const std::vector<const TensorInfo*>& outputs, |
| 11 | const ElementwiseUnaryDescriptor* unaryDescriptor) |
| 12 | { |
| 13 | std::string input0Name = std::string("input0_"); |
| 14 | std::string outputName = std::string("output0_"); |
| 15 | std::string blockName = std::string("Op_ELEMENTWISEUNARY_block_") + GetUniqueTosaMappingID(); |
| 16 | |
| 17 | |
| 18 | // If a layer is present then the block will be used for execution, so input and output names need to be determined |
| 19 | // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. |
| 20 | if(layer != nullptr) |
| 21 | { |
| 22 | // Get the layer connected to the input slot and determine unique the tensor name. |
| 23 | Layer& connectedLayer0 = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); |
| 24 | input0Name = GenerateUniqueName(connectedLayer0, 0); |
| 25 | |
| 26 | // Determine unique output tensor name. |
| 27 | outputName = GenerateUniqueOutputName(*layer, 0); |
| 28 | } |
| 29 | |
| 30 | TosaSerializationOperator* op = nullptr; |
| 31 | switch(unaryDescriptor->m_Operation) |
| 32 | { |
| 33 | case UnaryOperation::Rsqrt: |
| 34 | { |
| 35 | op = new TosaSerializationOperator(tosa::Op_RSQRT, |
| 36 | Attribute_NONE, |
| 37 | nullptr, |
| 38 | {input0Name}, |
| 39 | {outputName}); |
| 40 | blockName = std::string("Op_RSQRT_block_") + GetUniqueTosaMappingID(); |
| 41 | break; |
| 42 | } |
| 43 | default: |
| 44 | throw armnn::Exception("ConvertElementwiseUnaryToTosaOperator: Unsupported layer type."); |
| 45 | } |
| 46 | |
| 47 | ARMNN_ASSERT(op != nullptr); |
| 48 | |
| 49 | std::vector<TosaSerializationTensor*> tensors; |
| 50 | // Only add input tensor if connected layer is an input layer. |
| 51 | // As intermediate or constant tensors will be created separately. |
| 52 | // There also can't be duplicate tensor. |
| 53 | if(input0Name.find("input0_") != std::string::npos) |
| 54 | { |
| 55 | std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); |
| 56 | DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); |
| 57 | tensors.push_back(new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {})); |
| 58 | } |
| 59 | |
| 60 | std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); |
| 61 | DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); |
| 62 | |
| 63 | tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); |
| 64 | |
| 65 | // operatorInputNames/operatorOutputNames ends up being the same as |
| 66 | // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings |
| 67 | return new TosaSerializationBasicBlock(blockName, // name |
Narumol Prangnawarat | ad323af | 2023-09-29 17:00:38 +0100 | [diff] [blame] | 68 | mainName, // region name |
David Monahan | d7fca09 | 2023-01-12 14:53:34 +0000 | [diff] [blame] | 69 | {op}, // operators |
| 70 | tensors, // tensors |
| 71 | {input0Name}, // inputs |
| 72 | {outputName}); // outputs |
| 73 | } |