37 lines
1.3 KiB
Markdown
37 lines
1.3 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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Bug fixes:
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- fixed "undefined symbol: \_\_fatbinwrap_38" error for P100 GPUs on CUDA 10.1 (#5)
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