build(deps): update bitsandbytes requirement from ~=0.39.0 to ~=0.39.1

[dependabot]

Updates the requirements on bitsandbytes to permit the latest version.

Release notes

Sourced from bitsandbytes's releases.

4-bit QLoRA, Paged Optimizers, and 8-bit Memory Leak Bugfix

This release brings 4-bit quantization support for QLoRA fine-tuning and a critical bugfix that doubled the memory cost of 8-bit models when they were serialized. Furthermore, paged optimizers are introduced, including 8-bit Lion.

0.39.1

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 no longer provided via pip and need to be compiled from source. Kepler support might be fully removed in the future.

Changelog

Sourced from bitsandbytes's changelog.

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

... (truncated)

Commits

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[codecov-commenter]

Codecov Report

Patch and project coverage have no change.

Comparison is base (52bc773) 94.29% compared to head (e45ef35) 94.29%.

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Additional details and impacted files

@@           Coverage Diff           @@
##           master     #217   +/-   ##
=======================================
  Coverage   94.29%   94.29%           
=======================================
  Files           7        7           
  Lines         333      333           
=======================================
  Hits          314      314           
  Misses         19       19           

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