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Diffstat (limited to 'src/backends/tosaCommon/operatorMappings/LeakyReluOperator.cpp')
| -rw-r--r-- | src/backends/tosaCommon/operatorMappings/LeakyReluOperator.cpp | 213 |
1 files changed, 213 insertions, 0 deletions
diff --git a/src/backends/tosaCommon/operatorMappings/LeakyReluOperator.cpp b/src/backends/tosaCommon/operatorMappings/LeakyReluOperator.cpp new file mode 100644 index 0000000000..4d330818f1 --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/LeakyReluOperator.cpp @@ -0,0 +1,213 @@ +// +// Copyright © 2024 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// +// +// Copyright © 2020 The TensorFlow Authors. All Rights Reserved. +// SPDX-License-Identifier: Apache-2.0 +// + +#include "LeakyReluOperator.hpp" +#include "TosaRescaleOperatorUtils.hpp" + +#include <layers/ActivationLayer.hpp> + +// This function is paraphrased from: +// tensorflow/compiler/mlir/tosa/transforms/legalize_tfl.cc from function ConvertTFLLeakyReluOp +TosaSerializationBasicBlock* ConvertLeakyReluToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, + const std::vector<const TensorInfo*>& outputs, + const ActivationDescriptor* activationDescriptor) +{ + if (inputs.size() != 1) + { + throw armnn::Exception("ConvertLeakyReluToTosaOperator: 1 input tensors required."); + } + + if (outputs.size() != 1) + { + throw armnn::Exception("ConvertLeakyReluToTosaOperator: 1 output tensor required."); + } + + std::string inputName = std::string("input_"); + std::string outputNameAlpha = std::string("intermediate1_") + GetUniqueTosaMappingID(); + std::string outputNameMul = std::string("intermediate2_") + GetUniqueTosaMappingID(); + std::string outputName = std::string("output0_"); + std::string blockName = std::string("Op_LEAKY_RELU_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) + { + inputName = GenerateUniqueInputName(layer->GetInputSlot(0)); + outputName = GenerateUniqueOutputName(*layer); + } + + 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. + std::vector<int32_t> inputShape0; + DType inputDType0 = DType::DType_UNKNOWN; + if(inputName.find("input_") != std::string::npos) + { + inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); + inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); + tensors.push_back(new TosaSerializationTensor(inputName, inputShape0, inputDType0, {})); + } + + std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); + DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); + tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); + + std::string outputNameMAXMIN= std::string("intermediate3_") + GetUniqueTosaMappingID(); + + if (inputDType0 == DType::DType_FP32 || + inputDType0 == DType::DType_FP16) + { + // const_alpha + TosaSerializationOperator* alphaOp = nullptr; + TosaSerializationTensor* alphaTensor = nullptr; + CreateConstTosaOperator<float>(outputNameAlpha, + activationDescriptor->m_A, + inputDType0, + inputShape0, + alphaOp, + alphaTensor); + tensors.push_back(alphaTensor); + + // mul + int32_t shift = 0; + TosaMulAttribute mulAttribute(shift); + TosaSerializationOperator* mulOp = new TosaSerializationOperator(Op_MUL, + Attribute_MulAttribute, + &mulAttribute, + {inputName, outputNameAlpha}, + {outputNameMul}); + tensors.push_back(new TosaSerializationTensor(outputNameMul, inputShape0, inputDType0, {})); + + TosaSerializationOperator* op = nullptr; + if (activationDescriptor->m_A <= 1.0) + { + op = new TosaSerializationOperator(Op_MAXIMUM, + Attribute_NONE, + nullptr, + {inputName, outputNameMul}, + {outputName}); + } + else + { + op = new TosaSerializationOperator(Op_MINIMUM, + Attribute_NONE, + nullptr, + {inputName, outputNameMul}, + {outputName}); + + } + + // 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 + {alphaOp, mulOp, op}, // operators + tensors, // tensors + {inputName}, // inputs + {outputName}); // outputs + } + else + { + std::string outputNameRescaleAlpha = std::string("intermediate3_") + GetUniqueTosaMappingID(); + std::string outputNameRescaleIdentity = std::string("intermediate4_") + GetUniqueTosaMappingID(); + std::string outputNameRescaleMaxMin = std::string("intermediate5_") + GetUniqueTosaMappingID(); + + DType rescale_type = DType::DType_INT32; + float alpha = activationDescriptor->m_A; + double scale_alpha = inputs[0]->GetQuantizationScale() * alpha / outputs[0]->GetQuantizationScale(); + double scale_identity = inputs[0]->GetQuantizationScale() / outputs[0]->GetQuantizationScale(); + int32_t input_zp = inputs[0]->GetQuantizationOffset(); + int32_t output_zp = outputs[0]->GetQuantizationOffset(); + + // Value op_rescale_alpha_in = + // buildRescale(rewriter, op, rescale_type, input, scale_alpha, + // input_qtype.getZeroPoint(), 0, true, true); + TosaSerializationOperator* rescaleAlphaOp = nullptr; + CreateRescaleTosaOperator(inputName, + outputNameRescaleAlpha, + scale_alpha, + input_zp, + 0, + true, + true, + &rescaleAlphaOp); + tensors.push_back(new TosaSerializationTensor(outputNameRescaleAlpha, + GetTosaTensorShape(inputs[0]->GetShape()), + rescale_type, {})); + // Value op_rescale_identity_in = + // buildRescale(rewriter, op, rescale_type, input, scale_identity, + // input_qtype.getZeroPoint(), 0, true, true); + TosaSerializationOperator* rescaleIdentityOp = nullptr; + CreateRescaleTosaOperator(inputName, + outputNameRescaleIdentity, + scale_identity, + input_zp, + 0, + true, + true, + &rescaleIdentityOp); + tensors.push_back(new TosaSerializationTensor(outputNameRescaleIdentity, + GetTosaTensorShape(inputs[0]->GetShape()), + rescale_type, {})); + // Value result_int32; + // if (alpha <= 1.0) { + // auto max_op = CreateOpAndInfer<tosa::MaximumOp>( + // rewriter, op->getLoc(), rescale_type, op_rescale_identity_in, + // op_rescale_alpha_in); + // result_int32 = max_op.getResult(); + // } else { + // auto min_op = CreateOpAndInfer<tosa::MinimumOp>( + // rewriter, op->getLoc(), rescale_type, op_rescale_identity_in, + // op_rescale_alpha_in); + // result_int32 = min_op.getResult(); + // } + TosaSerializationOperator* op = nullptr; + if (alpha <= 1.0) + { + op = new TosaSerializationOperator(Op_MAXIMUM, + Attribute_NONE, + nullptr, + {outputNameRescaleAlpha, outputNameRescaleIdentity}, + {outputNameRescaleMaxMin}); + } + else + { + op = new TosaSerializationOperator(Op_MINIMUM, + Attribute_NONE, + nullptr, + {outputNameRescaleAlpha, outputNameRescaleIdentity}, + {outputNameRescaleMaxMin}); + + } + tensors.push_back(new TosaSerializationTensor(outputNameRescaleMaxMin, + GetTosaTensorShape(inputs[0]->GetShape()), + rescale_type, {})); + + // Value output = buildRescaleFromInt32(rewriter, op, output_type, result_int32, + // 1.0, output_qtype.getZeroPoint()); + TosaSerializationOperator* rescaleOutputOp = nullptr; + CreateFromInt32RescaleTosaOperator(outputNameRescaleMaxMin, + outputName, + 1.0, + output_zp, + &rescaleOutputOp); + + // 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 + {rescaleAlphaOp, rescaleIdentityOp, op, rescaleOutputOp}, // operators + tensors, // tensors + {inputName}, // inputs + {outputName}); // outputs + } +} |