Bitsandbytes-windows

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.

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.

- 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

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.

- Fixed an error where a `reset_parameters()` call on the `StableEmbedding` would lead to an error in older PyTorch versions (from 1.7.0).

- Ampere, RTX 30 series GPUs now compatible with the library.