Package: bitsandbytes-cuda110
bitsandbytesBitsandbytes is a lightweight wrapper around CUDA custom functions, in particular 8-bit optimizers and quantization functions.Paper--Video--DocsTL;DRInstallation:Note down version:conda list | grep cudatoolkitReplace 111 with the version that you see:pip install bitsandbytes-cuda111Usage:Comment out optimizer:#torch.optim.Adam(....)Add 8-bit optimizer of your choicebnb.optim.Adam8bit(....)(arguments stay the same)Replace embedding layer if necessary:torch.nn.Embedding(..) -> bnb.nn.Embedding(..)Features8-bit Optimizers: Adam, AdamW, RMSProp, LARS, LAMB (saves 75% memory)Stable Embedding Layer: Improved stability through better initialization, and normalization8-bit quantization: Quantile, Linear, and Dynamic quantizationFast quantile estimation: Up to 100x faster than other algorithmsRequirements & InstallationRequirements: anaconda, cudatoolkit, pytorch
Hardware requirements: NVIDIA Maxwell GPU or newer (>=GTX 9XX)
Supported CUDA versions: 9.2 - 11.3The requirements can best be fulfilled by installing pytorch via anaconda. You can install PyTorch by following the"Get Started"instructions on the official website.bitsandbytes is compatible with all major PyTorch releases and cudatoolkit versions, but for now, you need to select the right version manually. To do this run:conda list | grep cudatoolkitand take note of the Cuda version that you have installed. Then you can install bitsandbytes via:# choices: {cuda92, cuda 100, cuda101, cuda102, cuda110, cuda111, cuda113}# replace XXX with the respective numberpipinstallbitsandbytes-cudaXXXTo check if your installation was successful, you can execute the following command, which runs a single bnb Adam update.wget https://gist.githubusercontent.com/TimDettmers/1f5188c6ee6ed69d211b7fe4e381e713/raw/4d17c3d09ccdb57e9ab7eca0171f2ace6e4d2858/check_bnb_install.py && python check_bnb_install.pyUsing bitsandbytesUsing the 8-bit OptimizersWith bitsandbytes 8-bit optimizers can be used by changing a single line of code in your codebase. For NLP models we recommend also to use the StableEmbedding layers (see below) which improves results and helps with stable 8-bit optimization.  To get started with 8-bit optimizers, it is sufficient to replace your old optimizer with the 8-bit optimizer in the following way:importbitsandbytesasbnb# adam = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.995)) # comment out old optimizeradam=bnb.optim.Adam8bit(model.parameters(),lr=0.001,betas=(0.9,0.995))# add bnb optimizeradam=bnb.optim.Adam(model.parameters(),lr=0.001,betas=(0.9,0.995),optim_bits=8)# equivalenttorch.nn.Embedding(...)->bnb.nn.StableEmbedding(...)# recommended for NLP modelsNote that by default all parameter tensors with less than 4096 elements are kept at 32-bit even if you initialize those parameters with 8-bit optimizers. This is done since such small tensors do not save much memory and often contain highly variable parameters (biases) or parameters that require high precision (batch norm, layer norm). You can change this behavior like so:# parameter tensors with less than 16384 values are optimized in 32-bit
# it is recommended to use multiplies of 4096
adam = bnb.optim.Adam8bit(model.parameters(), min_8bit_size=16384)Change Bits and other Hyperparameters for Individual ParametersIf you want to optimize some unstable parameters with 32-bit Adam and others with 8-bit Adam, you can use theGlobalOptimManager. With this, we can also configure specific hyperparameters for particular layers, such as embedding layers. To do that, we need two things: (1) register the parameter while they are still on the CPU, (2) override the config with the new desired hyperparameters (anytime, anywhere). See ourguidefor more detailsFairseq UsersTo use the Stable Embedding Layer, override the respectivebuild_embedding(...)function of your model. Make sure to also use the--no-scale-embeddingflag to disable scaling of the word embedding layer (nor replaced with layer norm). You can use the optimizers by replacing the optimizer in the respective file (adam.pyetc.).Release and Feature HistoryFor upcoming features and changes and full history seePatch Notes.ErrorsRuntimeError: CUDA error: no kernel image is available for execution on the device.Solution_fatbinwrap..SolutionCompile from sourceTo compile from source, please follow thecompile_from_source.mdinstructions.LicenseThe majority of bitsandbytes is licensed under MIT, however portions of the project are available under separate license terms: Pytorch is licensed under the BSD license.We thank Fabio Cannizzo for his work onFastBinarySearchwhich we use for CPU quantization.CitationIf you found this library and 8-bit optimizers or quantization routines useful, please consider citing out work.@misc{dettmers2021optim8bit,
      title={8-bit Optimizers via Block-wise Quantization},
      author={Tim Dettmers and Mike Lewis and Sam Shleifer and Luke Zettlemoyer},
      year={2021},
      eprint={2110.02861},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}