67 lines
2.8 KiB
Markdown
67 lines
2.8 KiB
Markdown
### 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)
|