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ML examples: - credit_scoring: Credit risk prediction with HMEQ data - disease_prediction: Healthcare ML on encrypted records - cifar: Encrypted image classification - federated_learning: Distributed FHE training - hybrid_model: Mixed FHE/plaintext inference Deployment: - breast_cancer: Cancer detection API - sentiment_analysis: Encrypted NLP - server: FHE inference server Blockchain: - hardhat-template: Solidity + FHE - react-template: Full dApp with FHE
80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
# MIT License
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#
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# Copyright (c) 2019 Xilinx
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# Original file can be found at https://github.com/Xilinx/brevitas/blob/8c3d9de0113528cf6693c6474a13d802a66682c6/src/brevitas_examples/bnn_pynq/models/common.py
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from brevitas.core.bit_width import BitWidthImplType
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from brevitas.core.quant import QuantType
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from brevitas.core.restrict_val import FloatToIntImplType, RestrictValueType
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from brevitas.core.scaling import ScalingImplType
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from brevitas.core.zero_point import ZeroZeroPoint
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from brevitas.inject import ExtendedInjector
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from brevitas.quant import Int8WeightPerTensorFloat
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from brevitas.quant.solver import ActQuantSolver, WeightQuantSolver
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from dependencies import value
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class CommonQuant(ExtendedInjector):
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bit_width_impl_type = BitWidthImplType.CONST
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scaling_impl_type = ScalingImplType.CONST
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restrict_scaling_type = RestrictValueType.FP
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zero_point_impl = ZeroZeroPoint
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float_to_int_impl_type = FloatToIntImplType.ROUND
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scaling_per_output_channel = False
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narrow_range = True
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signed = True
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@value
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def quant_type(bit_width):
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if bit_width is None:
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return QuantType.FP
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elif bit_width == 1:
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return QuantType.BINARY
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else:
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return QuantType.INT
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class CommonIntWeightPerTensorQuant(Int8WeightPerTensorFloat):
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"""
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Common per-tensor weight quantizer with bit-width set to None so that it is forced to be
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specified by each layer.
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"""
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scaling_min_val = 2e-16
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bit_width = None
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class CommonIntWeightPerChannelQuant(CommonIntWeightPerTensorQuant):
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"""
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Common per-channel weight quantizer with bit-width set to None so that it is forced to be
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specified by each layer.
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"""
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scaling_per_output_channel = True
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class CommonWeightQuant(CommonQuant, WeightQuantSolver):
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scaling_const = 1.0
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class CommonActQuant(CommonQuant, ActQuantSolver):
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min_val = -1.0
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max_val = 1.0
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