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CHANGELOG.md

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Changelog

All notable changes to this project will be documented in this file. The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[0.4.1] - 2024/06/13

Changed

  • torchvision warning not shown on import of top level library (error message on usage remains)

[0.4.0] - 2024/06/13

Added

  • ability to load models pretrained on ImageNet
  • Jupyter notebooks for demonstration purposes
  • support for PyTorch version 2.0, Python 3.10

Changed

  • package no longer depends on torchvision directly (only needed for pretrained models from model_hub)

Fixed

  • BEmbedding bag

[0.3.0] - 2023/01/13

Added

  • new models:
  • simple example script for MNIST
  • support for integration of bitorch's inference engine for the following layers
    • QLinear
    • QConv
  • a quantized DLRM version, derived from this implementation
  • example code for training the quantized DLRM model
  • new quantization function: Progressive Sign
  • new features in PyTorch Lightning example:
    • training with Knowledge Distillation
    • improved logging
    • callback to update Progressive Sign module
  • option to integrate custom models, datasets, quantization functions
  • a quantization scheduler which lets you change quantization methods during training
  • a padding layer

Changed

  • requirements changed:
    • code now depends on torch 1.12.x and torchvision 0.13.x
    • requirements for examples are now stored at their respective folders
    • optional requirements now install everything needed to run all examples
  • code is now formatted with the black code formatter
  • using PyTorch's implementation of RAdam
  • renamed the bitwidth attribute of quantization functions to bit_width
  • moved the image datasets out of the bitorch core package into the image classification example

Fixed

  • fix error from updated protobuf package

[0.2.0] - 2022/05/19

Added

  • automatic documentation generation using sphinx
  • more documentation of layers and modules
  • bit-width of quantization functions is now stored
  • new layers:
    • Pact activation function
    • QEmbedding
    • QEmbeddingBag
  • fvbitcore support in the example scripts for flop and model size estimation on operation level

Changed

  • image classification example:
    • script now uses pytorch lightning
    • it includes distributed training capability
    • added wandb metric logging
  • QConv layers can now be pickled
  • Different quantized versions of LeNet available

Fixed

  • a bug where layer input and weight quantization functions could not be set using command line arguments
  • a bug where modules could not be imported in OS that use different path separators than '/'

[0.1.1] - 2022/01/21

Changed

  • make package compatible with python 3.7

[0.1.0] - 2022/01/06

Added

  • basic quantized layers
    • QActivation
    • QConv
    • QLinear
  • several debug layers
  • resnet, lenet
  • various quantization functions
    • approxsign
    • dorefa
    • sign
    • steheaviside
    • swishsign
  • support for cifar10 and mnist
  • general training script for image classification
  • result logger for csv and tensorboard
  • checkpoint manager
  • eta estimator
  • experiment creator
  • model visualization in logs and tensorboard
  • config classes for bitorch layers / quantizations with automated argparse argument creation