Bitorch

Changed

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

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

Added

- new models:
- [MeliusNet](bitorch/models/meliusnet.py)
- [BinaryDenseNet](bitorch/models/densenet.py)
- [QuickNet](bitorch/models/quicknet.py)
- 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](https://github.com/facebookresearch/dlrm) implementation
- example code for training the quantized DLRM model
- new quantization function: [Progressive Sign](bitorch/quantizations/progressive_sign.py)
- 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

Added

- automatic documentation generation using sphinx
- more documentation of layers and modules
- bit-width of quantization functions is now stored
- new layers:
- [Pact](https://arxiv.org/abs/1805.06085) 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](https://www.pytorchlightning.ai/)
- 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 '/'

Changed

- make package compatible with python 3.7

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