- Ampere, RTX 30 series GPUs now compatible with the library.
- Fixed an error where a
reset_parameters()
call on theStableEmbedding
would lead to an error in older PyTorch versions (from 1.7.0).
Bugs:
- Unified quantization API: each quantization function now returns
Q, S
whereQ
is the quantized tensor andS
the quantization state which may hold absolute max values, a quantization map or more. For dequantization all functions now accept the inputsQ, S
so thatQ
is dequantized with the quantization stateS
. - Fixed an issue where the CUDA 11.1 binary was not compiled with the right headers
API changes:
- Block-wise quantization for optimizers now enabled by default
Features:
- Block-wise quantization routines now support CPU Tensors.
- Fixed a bug where a float/half conversion led to a compilation error for CUDA 11.1 on Turning GPUs.
- removed Apex dependency for bnb LAMB
Features:
- Added
skip_zeros
for block-wise and 32-bit optimizers. This ensures correct updates for sparse gradients and sparse models. - Added support for Kepler GPUs. (#4)
- Added Analysis Adam to track 8-bit vs 32-bit quantization errors over time.
- Make compilation more user friendly.
Bug fixes:
- fixed "undefined symbol: __fatbinwrap_38" error for P100 GPUs on CUDA 10.1 (#5)
Docs:
- Added docs with instructions to compile from source.
Features:
- Added Adagrad (without grad clipping) as 32-bit and 8-bit block-wise optimizer.
- Added AdamW (copy of Adam with weight decay init 1e-2). #10
- Introduced ModuleConfig overrides which can be seamlessly be used at initialization time of a module.
- Added
bnb.nn.Embedding
layer which runs at 32-bit but without the layernorm. This works well if you need to fine-tune pretrained models that do not have a embedding layer norm. #19
Bug fixes:
- Fixed a bug where weight decay was incorrectly applied to 32-bit Adam. #13
- Fixed an unsafe use of eval. #8
- Fixed a bug where the StableEmbedding layer 32-bit optimizer override would not work without registering the whole model first (
bnb.optim.GlobalOptimManager.get_instance().register_parameters(model.parameters())
). #13 #15
Docs:
- Added instructions how to solve "__fatbinwrap_" errors.
Features:
- Added 8-bit matrix multiplication form cuBLAS, and cuBLASLt as well as multiple GEMM kernels (GEMM, GEMMEx, GEMMLt)
- Added 8-bit Linear layers with 8-bit Params that perform memory efficient inference with an option for 8-bit mixed precision matrix decomposition for inference without performance degradation
- Added quantization methods for "fake" quantization as well as optimized kernels vector-wise quantization and equalization as well as optimized cuBLASLt transformations
- CPU only build now available (Thank you, @mryab)
Deprecated:
- Pre-compiled release for CUDA 9.2, 10.0, 10.2 no longer available
Features:
- added direct outlier extraction. This enables outlier extraction without fp16 weights without performance degradation.
- Added automatic CUDA SETUP procedure and packaging all binaries into a single bitsandbytes package.
We added performance enhancements for small models. This makes small models about 2x faster for LLM.int8() inference.
Features:
- Int32 dequantization now supports fused biases.
- Linear8bitLt now uses a fused bias implementation.
- Change
.data.storage().data_ptr()
to.data.data_ptr()
to enhance inference performance.
Bug fixes:
- Now throws and error if LLM.int8() is used on a GPU that is not supported.
- Enhances error messaging if CUDA SETUP fails.
Features:
- CPU quantization now supports a variable
blocksize
variable to enhance quantization speed or precision.
Bug fixes:
- fixed an issue in CPU quantization where tensors with more than 2^31 elements would fail 19a7adca7a6c9bf7061a384d7e9d9b13676a1a88
- fixed a bug where cpu binaries would fail if no GPU would be detected eab4d8232d558f2e6bd7f7cc3d00e2e6e94f4e80
- fixed an issue where cpu binaries cause additional stdout messages 92a3363096e10ad6a5c4e944af898bd1186d806a
- fixed an import of bnb.utils 2e630b55f51d454f3bd723dffda68a07ef93190c
We thank @mryab, @mbrukman, @chessgecko, @dbaranchuk for pull request with bug fixes and new features.
Features:
- Linear8bitLt layer now supports
memory_efficient_backward=True
which enables backprop of gradients through frozen weights.
Bug fixes:
- fixed an issue where too many threads were created in blockwise quantization on the CPU for large tensors
Features:
- CUDA 11.8 support added and binaries added to the PyPI release.
Bug fixes:
- fixed a bug where too long directory names would crash the CUDA SETUP #35 (thank you @tomaarsen)
- fixed a bug where CPU installations on Colab would run into an error #34 (thank you @tomaarsen)
- fixed an issue where the default CUDA version with fast-DreamBooth was not supported #52
Features:
- Added CUDA instruction generator to fix some installations.
Bug fixes:
- Fixed a problem where warning messages would be displayed even though everything worked correctly.
Bug fixes:
- Fixed a bug where the CUDA setup failed due to a wrong function call.
Bug fixes:
- Fixed a bug in the CUDA Setup which led to an incomprehensible error if no GPU was detected.
Bug fixes:
- Fixed a bug in the CUDA Setup failed with the cuda runtime was found, but not the cuda library.
- Fixed a bug where not finding the cuda runtime led to an incomprehensible error.
Features:
- CUDA 11.8 and 12.0 support added
- support for Ada and Hopper GPUs added (compute capability 8.9 and 9.0)
- support for fake k-bit block-wise quantization for Int, Float, quantile quantization, and dynamic exponent data types added
- Added CUDA instruction generator to fix some installations.
- Added additional block sizes for quantization {64, 128, 256, 512, 1024}
- Added SRAM Quantile algorithm to quickly estimate less than 256 quantiles
- Added option to suppress the bitsandbytes welcome message (@Cyberes)
Regression:
- Compute capability 3.0 removed: GTX 600s and 700s series is no longer supported (except GTX 780 and GTX 780 Ti)
Bug fixes:
- fixed a bug where too long directory names would crash the CUDA SETUP #35 (@tomaarsen)
- fixed a bug where CPU installations on Colab would run into an error #34 (@tomaarsen)
- fixed an issue where the default CUDA version with fast-DreamBooth was not supported #52
- fixed a bug where the CUDA setup failed due to a wrong function call.
- fixed a bug in the CUDA Setup which led to an incomprehensible error if no GPU was detected.
- fixed a bug in the CUDA Setup failed with the cuda runtime was found, but not the cuda library.
- fixed a bug where not finding the cuda runtime led to an incomprehensible error.
- fixed a bug where with missing CUDA the default was an error instead of the loading the CPU library
- fixed a bug where the CC version of the GPU was not detected appropriately (@BlackHC)
- fixed a bug in CPU quantization which lead to errors when the input buffer exceeded 2^31 elements
Improvements:
- multiple improvements in formatting, removal of unused imports, and slight performance improvements (@tomaarsen)
- StableEmbedding layer now has device and dtype parameters to make it 1:1 replaceable with regular Embedding layers (@lostmsu)
- runtime performance of block-wise quantization slightly improved
- added error message for the case multiple libcudart.so are installed and bitsandbytes picks the wrong one
Features:
- Int8 MatmulLt now supports backward through inversion of the ColTuring/ColAmpere format. Slow, but memory efficient. Big thanks to @borzunov
- Int8 now supported on all GPUs. On devices with compute capability < 7.5, the Int weights are cast to 16/32-bit for the matrix multiplication. Contributed by @borzunov
Improvements:
- Improved logging for the CUDA detection mechanism.
Features:
- Support for 32 and 8-bit Lion has been added. Thank you @lucidrains
- Support for serialization of Linear8bitLt layers (LLM.int8()). This allows to store and load 8-bit weights directly from the HuggingFace Hub. Thank you @myrab
- New bug report features
python -m bitsandbytes
now gives extensive debugging details to debug CUDA setup failures.
Bug fixes:
- Fixed a bug where some bitsandbytes methods failed in a model-parallel setup on multiple GPUs. Thank you @tonylins
- Fixed a bug where cudart.so libraries could not be found in newer PyTorch releases.
Improvements:
- Improved the CUDA Setup procedure by doing a more extensive search for CUDA libraries
Deprecated:
- Devices with compute capability 3.0 (GTX 700s, K10) and 3.2 (Tegra K1, Jetson TK1) are now deprecated and support will be removed in 0.39.0.
- Support for CUDA 10.0 and 10.2 will be removed in bitsandbytes 0.39.0
Features:
- Added Int8 SwitchBack layers
- Added Fake FP8 layers for research purposes (available under
bnb.research.nn. ...
)
Features:
- 4-bit matrix multiplication for Float4 and NormalFloat4 data types.
- Added 4-bit quantization routines
- Doubled quantization routines for 4-bit quantization
- Paged optimizers for Adam and Lion.
- bfloat16 gradient / weight support for Adam and Lion with 8 or 32-bit states.
Bug fixes:
- Fixed a bug where 8-bit models consumed twice the memory as expected after serialization
Deprecated:
- Kepler binaries (GTX 700s and Tesla K40/K80) are not longer provided via pip and need to be compiled from source. Kepler support might be fully removed in the future.
Features:
- Added 4-bit inference kernels for batch size=1. Currently support are the NF4, FP4 data types.
- Added support for quantizations of bfloat16 input data.
Bug fixes:
- Added
device
variable for bitsandbytes layers to be compatible with PyTorch layers.
Deprecated:
- Binaries for CUDA 11.2, 11.6 no longer ship with
pip install bitsandbytes
and need to be compiled from source.
Features:
- Added precompiled CUDA 11.8 binaries to support H100 GPUs without compilation #571
- CUDA SETUP now no longer looks for libcuda and libcudart and relies PyTorch CUDA libraries. To manually override this behavior see: how_to_use_nonpytorch_cuda.md. Thank you @rapsealk
Bug fixes:
- Fixed a bug where the default type of absmax was undefined which leads to errors if the default type is different than torch.float32. # 553
- Fixed a missing scipy dependency in requirements.txt. #544
- Fixed a bug, where a view operation could cause an error in 8-bit layers.
- Fixed a bug where CPU bitsandbytes would during the import. #593 Thank you @bilelomrani
Documentation:
- Improved documentation for GPUs that do not support 8-bit matmul. #529
- Added description and pointers for the NF4 data type. #543
Bug fixes:
- Fixed a but where a non-existent LD_LIBRARY_PATH variable led to a failure in python -m bitsandbytes #588
- Removed outdated get_cuda_lib_handle calls that lead to errors. #595 Thank you @ihsanturk
- Fixed bug where read-permission was assumed for a file. #497
- Fixed a bug where prefetchAsync lead to errors on GPUs that do not support unified memory but not prefetching (Maxwell, SM52). #470 #451 #453 #477 Thank you @jllllll and @stoperro