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Diffstat (limited to 'src/backends/tosaCommon/operatorMappings/QuantizeOperator.cpp')
| -rw-r--r-- | src/backends/tosaCommon/operatorMappings/QuantizeOperator.cpp | 139 |
1 files changed, 139 insertions, 0 deletions
diff --git a/src/backends/tosaCommon/operatorMappings/QuantizeOperator.cpp b/src/backends/tosaCommon/operatorMappings/QuantizeOperator.cpp new file mode 100644 index 0000000000..1107add6e9 --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/QuantizeOperator.cpp @@ -0,0 +1,139 @@ +// +// Copyright © 2023 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 "QuantizeOperator.hpp" + +// This function is paraphrased from: +// tensorflow/compiler/mlir/tosa/transforms/legalize_common.cc from function convertQuantizeOp +TosaSerializationBasicBlock* ConvertQuantizeToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, + const std::vector<const TensorInfo*>& outputs) +{ + ARMNN_THROW_INVALIDARG_MSG_IF_FALSE( inputs.size() == 1, + "ConvertQuantizeToTosaOperator: Quantize must have only one input" ); + ARMNN_THROW_INVALIDARG_MSG_IF_FALSE( outputs.size() == 1, + "ConvertQuantizeToTosaOperator: Quantize must have only one output" ); + + std::string inputName = std::string("input0_"); + std::string outputNameZeroPoint = std::string("intermediate0_") + GetUniqueTosaMappingID(); + std::string outputNameScale = std::string("intermediate1_") + GetUniqueTosaMappingID(); + std::string outputNameMul = std::string("intermediate2_") + GetUniqueTosaMappingID(); + std::string outputNameAdd = std::string("intermediate3_") + GetUniqueTosaMappingID(); + std::string outputName = std::string("output0_"); + std::string blockName = std::string("Op_QUANTIZE_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) + { + // Get the layers connected to the input slots and determine unique tensor names. + Layer& connectedLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); + inputName = GenerateUniqueName(connectedLayer, 0); + + // Determine unique output tensor name. + outputName = GenerateUniqueOutputName(*layer, 0); + } + + const TensorInfo inputInfo = *inputs[0]; + const TensorInfo outputInfo = *outputs[0]; + + // Extract quantization detail from Tensor + float zeroPoint = static_cast<float>(outputInfo.GetQuantizationOffset()); + // No per axis support in Tensorflow TOSA code + float scale = outputInfo.GetQuantizationScale(); + + // As per the Tensorflow quantization specification + // Tensorflow TOSA code calculates quantization using multiplication by scale + // Armnn code calculates quantization using division by scale + // Invert scale factor passed from Armnn for tf TOSA code + scale = (scale != 0) ? (1 / scale) : scale; + + 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("input0_") != std::string::npos) + { + inputShape0 = GetTosaTensorShape(inputInfo.GetShape()); + inputDType0 = ArmNNToDType(inputInfo.GetDataType()); + ARMNN_THROW_INVALIDARG_MSG_IF_FALSE( inputDType0 == DType::DType_FP16 || inputDType0 == DType::DType_FP32, + "ConvertQuantizeToTosaOperator: Quantize input must be of type Float" ); + tensors.push_back(new TosaSerializationTensor(inputName, inputShape0, inputDType0, {})); + } + + std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputInfo.GetShape()); + DType outputDType0 = ArmNNToDType(outputInfo.GetDataType()); + + // quantize: + // const_zeroPoint = constant(zeroPoint) + // const_scale = constant(scale) + // out_mul = mul(input, const_scale) + // out_add = add(out_mul, const_zeroPoint) + // output = cast<output_type>(out_add) + + // const_zeroPoint + TosaSerializationOperator* zeroPointOp = nullptr; + TosaSerializationTensor* zeroPointTensor = nullptr; + CreateConstTosaOperator<float>(outputNameZeroPoint, + zeroPoint, + inputDType0, + inputShape0, + zeroPointOp, + zeroPointTensor); + tensors.push_back(zeroPointTensor); + + // const_scale + TosaSerializationOperator *scaleOp = nullptr; + TosaSerializationTensor* scaleTensor = nullptr; + CreateConstTosaOperator<float>(outputNameScale, + scale, + inputDType0, + inputShape0, + scaleOp, + scaleTensor); + tensors.push_back(scaleTensor); + + // mul + int32_t shift = 0; + TosaMulAttribute mulAttribute(shift); + TosaSerializationOperator* mulOp = new TosaSerializationOperator(Op_MUL, + Attribute_MulAttribute, + &mulAttribute, + {inputName, outputNameScale}, + {outputNameMul}); + tensors.push_back(new TosaSerializationTensor(outputNameMul, inputShape0, inputDType0, {})); + + // add + TosaSerializationOperator* addOp = new TosaSerializationOperator(Op_ADD, + Attribute_NONE, + nullptr, + {outputNameMul, outputNameZeroPoint}, + {outputNameAdd}); + tensors.push_back(new TosaSerializationTensor(outputNameAdd, inputShape0, inputDType0, {})); + + // cast + TosaSerializationOperator* castOp = new TosaSerializationOperator(Op_CAST, + Attribute_NONE, + nullptr, + {outputNameAdd}, + {outputName}); + + 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 + mainName, // region name + {zeroPointOp, scaleOp, mulOp, addOp, castOp}, // operators + tensors, // tensors + {inputName}, // inputs + {outputName}); // outputs +} |