120 lines
4.7 KiB
Markdown
120 lines
4.7 KiB
Markdown
### 0.0.21
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- Ampere, RTX 30 series GPUs now compatible with the library.
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### 0.0.22:
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- Fixed an error where a `reset_parameters()` call on the `StableEmbedding` would lead to an error in older PyTorch versions (from 1.7.0).
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### 0.0.23:
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Bugs:
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- 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`.
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- Fixed an issue where the CUDA 11.1 binary was not compiled with the right headers
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API changes:
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- Block-wise quantization for optimizers now enabled by default
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Features:
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- Block-wise quantization routines now support CPU Tensors.
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### 0.0.24:
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- Fixed a bug where a float/half conversion led to a compilation error for CUDA 11.1 on Turning GPUs.
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- removed Apex dependency for bnb LAMB
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### 0.0.25:
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Features:
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- Added `skip_zeros` for block-wise and 32-bit optimizers. This ensures correct updates for sparse gradients and sparse models.
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- Added support for Kepler GPUs. (#4)
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- Added Analysis Adam to track 8-bit vs 32-bit quantization errors over time.
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- Make compilation more user friendly.
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Bug fixes:
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- fixed "undefined symbol: \_\_fatbinwrap_38" error for P100 GPUs on CUDA 10.1 (#5)
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Docs:
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- Added docs with instructions to compile from source.
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### 0.26.0:
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Features:
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- Added Adagrad (without grad clipping) as 32-bit and 8-bit block-wise optimizer.
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- Added AdamW (copy of Adam with weight decay init 1e-2). #10
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- Introduced ModuleConfig overrides which can be seamlessly be used at initialization time of a module.
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- 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
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Bug fixes:
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- Fixed a bug where weight decay was incorrectly applied to 32-bit Adam. #13
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- Fixed an unsafe use of eval. #8
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- 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
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Docs:
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- Added instructions how to solve "\_\_fatbinwrap_" errors.
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### 0.30.0
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#### 8-bit Inference Update
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Features:
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- Added 8-bit matrix multiplication form cuBLAS, and cuBLASLt as well as multiple GEMM kernels (GEMM, GEMMEx, GEMMLt)
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- 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
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- Added quantization methods for "fake" quantization as well as optimized kernels vector-wise quantization and equalization as well as optimized cuBLASLt transformations
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- CPU only build now available (Thank you, @mryab)
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Deprecated:
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- Pre-compiled release for CUDA 9.2, 10.0, 10.2 no longer available
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### 0.31.0
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#### 8-bit Inference and Packaging Update
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Features:
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- added direct outlier extraction. This enables outlier extraction without fp16 weights without performance degradation.
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- Added automatic CUDA SETUP procedure and packaging all binaries into a single bitsandbytes package.
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### 0.32.0
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#### 8-bit Inference Performance Enhancements
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We added performance enhancements for small models. This makes small models about 2x faster for LLM.int8() inference.
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Features:
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- Int32 dequantization now supports fused biases.
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- Linear8bitLt now uses a fused bias implementation.
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- Change `.data.storage().data_ptr()` to `.data.data_ptr()` to enhance inference performance.
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Bug fixes:
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- Now throws and error if LLM.int8() is used on a GPU that is not supported.
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- Enhances error messaging if CUDA SETUP fails.
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### 0.33.0
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#### Various bug fixes
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Features:
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- CPU quantization now supports a variable `blocksize` variable to enhance quantization speed or precision.
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Bug fixes:
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- fixed an issue in CPU quantization where tensors with more than 2^31 elements would fail 19a7adca7a6c9bf7061a384d7e9d9b13676a1a88
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- fixed a bug where cpu binaries would fail if no GPU would be detected eab4d8232d558f2e6bd7f7cc3d00e2e6e94f4e80
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- fixed an issue where cpu binaries cause additional stdout messages 92a3363096e10ad6a5c4e944af898bd1186d806a
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- fixed an import of bnb.utils 2e630b55f51d454f3bd723dffda68a07ef93190c
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We thank @mryab, @mbrukman, @chessgecko, @dbaranchuk for pull request with bug fixes and new features.
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### 0.34.0
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#### Bug fixes and memory efficient backprop
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Features:
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- Linear8bitLt layer now supports `memory_efficient_backward=True` which enables backprop of gradients through frozen weights.
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Bug fixes:
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- fixed an issue where too many threads were created in blockwise quantization on the CPU for large tensors
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