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### 0.0.21
- Ampere, RTX 30 series GPUs now compatible with the library.
### 0.0.22:
- Fixed an error where a `reset_parameters()` call on the `StableEmbedding` would lead to an error in older PyTorch versions (from 1.7.0).
### 0.0.23:
Bugs:
- Unified quantization API: each quantization function now returns `Q, S` where `Q` is the quantized tensor and `S` the quantization state which may hold absolute max values, a quantization map or more. For dequantization all functions now accept the inputs `Q, S` so that `Q` is dequantized with the quantization state `S`.
- 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.
### 0.0.24:
- 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
### 0.0.25:
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.
### 0.26.0:
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.
### 0.30.0
#### 8-bit Inference Update
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
### 0.31.0
#### 8-bit Inference and Packaging Update
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.
### 0.32.0
#### 8-bit Inference Performance Enhancements
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.
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