diff options
Diffstat (limited to 'src/backends/tosaCommon/operatorMappings/ElementwiseBinaryOperator.cpp')
-rw-r--r-- | src/backends/tosaCommon/operatorMappings/ElementwiseBinaryOperator.cpp | 103 |
1 files changed, 103 insertions, 0 deletions
diff --git a/src/backends/tosaCommon/operatorMappings/ElementwiseBinaryOperator.cpp b/src/backends/tosaCommon/operatorMappings/ElementwiseBinaryOperator.cpp new file mode 100644 index 0000000000..9909e66a7d --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/ElementwiseBinaryOperator.cpp @@ -0,0 +1,103 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ElementwiseBinaryOperator.hpp" + +TosaSerializationBasicBlock* ConvertElementwiseBinaryToTosaOperator(const Layer* layer, + const LayerType type, + 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; + + // 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 tensor names. + Layer& connectedLayer0 = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); + input0Name = GenerateUniqueName(connectedLayer0, 0); + + Layer& connectedLayer1 = layer->GetInputSlot(1).GetConnectedOutputSlot()->GetOwningLayer(); + input1Name = GenerateUniqueName(connectedLayer1, 1); + + // Determine unique output tensor name. + outputName = GenerateUniqueOutputName(*layer, 0); + } + + TosaSerializationOperator* op = nullptr; + switch(type) + { + case LayerType::Addition: + { + op = new TosaSerializationOperator(Op_ADD, + Attribute_NONE, + nullptr, + {input0Name, input1Name}, + {outputName}); + blockName = std::string("Op_ADD_block_") + GetUniqueTosaMappingID(); + break; + } + case LayerType::Multiplication: + { + int32_t shift = 0; + TosaMulAttribute mulAttribute(shift); + op = new TosaSerializationOperator(Op_MUL, + Attribute_MulAttribute, + &mulAttribute, + {input0Name, input1Name}, + {outputName}); + blockName = std::string("Op_MUL_block_") + GetUniqueTosaMappingID(); + break; + } + case LayerType::Subtraction: + { + op = new TosaSerializationOperator(Op_SUB, + Attribute_NONE, + nullptr, + {input0Name, input1Name}, + {outputName}); + blockName = std::string("Op_SUB_block_") + GetUniqueTosaMappingID(); + break; + } + default: + throw armnn::Exception("ConvertElementwiseBinaryToTosaOperator: Unsupported layer type."); + } + ARMNN_ASSERT(op != nullptr); + + 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 +} + |