Fastxtend

- Adds support for bfloat16 mixed precision training via fastxtend's `MixedPrecision` callback
- Adds two callback utilities for callback developers:
- `CallbackScheduler`: a mixin for callback scheduling values during training
- `LogDispatch`: a new default callback for logging values from callbacks to WandBCallback & TensorBoardCallback
- Adds `GradientAccumulation` callback which logs full batches instead of micro-batches
- Adds `GradientAccumulationSchedule` callback which supports batch size warmup via a schedulable accumulation batch size

**Full Changelog**: https://github.com/warner-benjamin/fastxtend/compare/v0.1.3...v0.1.4

Optimizers

- Adds Sophia & StableAdam optimizers
- Adds native fastai support for bitsandbytes 8-bit optimizers
- Reduce ForEach L2 weight decay and RAdam, LAMB, & Ranger optimizer memory usage
- Increase LAMB step speed

Other Features

- Add asynchronous fastai batch transforms to the FFCV Loader
- Add DynamoExplain callback for diagnosing `torch.compile` results

**Full Changelog**: https://github.com/warner-benjamin/fastxtend/compare/v0.1.2...v0.1.3

* Fix issue with `ProgressiveResize` and multiple inputs and/or labels.
* Add experimental `CompilerCallback` and patches to integrate `torch.compile` with fastai

**Full Changelog**: https://github.com/warner-benjamin/fastxtend/compare/v0.1.1...v0.1.2

Bug fixes for profilers and progressive resizing preallocation.

**Full Changelog**: https://github.com/warner-benjamin/fastxtend/compare/v0.1...v0.1.1

What's Changed
* Add the Initial Integration of the FFCV DataLoader
* fastxtend+FFCV Documentation
* Bug fixes for Progressive Resize Callback and updated documentation
* Throughput and Simple Profiler improvements and bug fixes
* An example imagenette.py script and config file which can use of most of fastxtend's features
* Add a conda install script for prerequisites


**Full Changelog**: https://github.com/warner-benjamin/fastxtend/compare/v0.0.19...v0.1

What's Changed
* Progressive Resize Fixes & Improvements
* Losses Documentation Pass and Bug Fix
* TensorBase deepcopy support
* CutMixUpAgment augment_finetune is number of epochs or percent of training to finetunf for


**Full Changelog**: https://github.com/warner-benjamin/fastxtend/compare/v0.0.18...v0.0.19