Nikhil Raj | 9a33946 | 2022-12-05 11:24:35 +0000 | [diff] [blame^] | 1 | // |
| 2 | // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "ElementwiseBinaryOperator.hpp" |
| 7 | |
| 8 | TosaSerializationBasicBlock* ConvertElementwiseBinaryToTosaOperator(const Layer* layer, |
| 9 | const LayerType type, |
| 10 | const std::vector<const TensorInfo*>& inputs, |
| 11 | const std::vector<const TensorInfo*>& outputs) |
| 12 | { |
| 13 | std::string input0Name = std::string("input0_"); |
| 14 | std::string input1Name = std::string("input1_"); |
| 15 | std::string outputName = std::string("output0_"); |
| 16 | std::string blockName; |
| 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 layers connected to the input slots and determine unique tensor names. |
| 23 | Layer& connectedLayer0 = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); |
| 24 | input0Name = GenerateUniqueName(connectedLayer0, 0); |
| 25 | |
| 26 | Layer& connectedLayer1 = layer->GetInputSlot(1).GetConnectedOutputSlot()->GetOwningLayer(); |
| 27 | input1Name = GenerateUniqueName(connectedLayer1, 1); |
| 28 | |
| 29 | // Determine unique output tensor name. |
| 30 | outputName = GenerateUniqueOutputName(*layer, 0); |
| 31 | } |
| 32 | |
| 33 | TosaSerializationOperator* op = nullptr; |
| 34 | switch(type) |
| 35 | { |
| 36 | case LayerType::Addition: |
| 37 | { |
| 38 | op = new TosaSerializationOperator(Op_ADD, |
| 39 | Attribute_NONE, |
| 40 | nullptr, |
| 41 | {input0Name, input1Name}, |
| 42 | {outputName}); |
| 43 | blockName = std::string("Op_ADD_block_") + GetUniqueTosaMappingID(); |
| 44 | break; |
| 45 | } |
| 46 | case LayerType::Multiplication: |
| 47 | { |
| 48 | int32_t shift = 0; |
| 49 | TosaMulAttribute mulAttribute(shift); |
| 50 | op = new TosaSerializationOperator(Op_MUL, |
| 51 | Attribute_MulAttribute, |
| 52 | &mulAttribute, |
| 53 | {input0Name, input1Name}, |
| 54 | {outputName}); |
| 55 | blockName = std::string("Op_MUL_block_") + GetUniqueTosaMappingID(); |
| 56 | break; |
| 57 | } |
| 58 | case LayerType::Subtraction: |
| 59 | { |
| 60 | op = new TosaSerializationOperator(Op_SUB, |
| 61 | Attribute_NONE, |
| 62 | nullptr, |
| 63 | {input0Name, input1Name}, |
| 64 | {outputName}); |
| 65 | blockName = std::string("Op_SUB_block_") + GetUniqueTosaMappingID(); |
| 66 | break; |
| 67 | } |
| 68 | default: |
| 69 | throw armnn::Exception("ConvertElementwiseBinaryToTosaOperator: Unsupported layer type."); |
| 70 | } |
| 71 | ARMNN_ASSERT(op != nullptr); |
| 72 | |
| 73 | std::vector<TosaSerializationTensor*> tensors; |
| 74 | // Only add input tensors if connected layer is an input layer. |
| 75 | // As intermediate or constant tensors will be created separately. |
| 76 | // There also can't be duplicate tensor. |
| 77 | if(input0Name.find("input0_") != std::string::npos) |
| 78 | { |
| 79 | std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); |
| 80 | DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); |
| 81 | tensors.push_back(new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {})); |
| 82 | } |
| 83 | if(input1Name.find("input1_") != std::string::npos) |
| 84 | { |
| 85 | std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape()); |
| 86 | DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType()); |
| 87 | tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {})); |
| 88 | } |
| 89 | |
| 90 | std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); |
| 91 | DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); |
| 92 | |
| 93 | tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); |
| 94 | |
| 95 | // operatorInputNames/operatorOutputNames ends up being the same as |
| 96 | // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings |
| 97 | return new TosaSerializationBasicBlock(blockName, // name |
| 98 | {op}, // operators |
| 99 | tensors, // tensors |
| 100 | {input0Name, input1Name}, // inputs |
| 101 | {outputName}); // outputs |
| 102 | } |
| 103 | |