Sparseml

This is a patch release for 1.0.0 that contains the following changes:

* Quantized ONNX graph folding resolution that prevents and extra quant/dequant pair being added into the residuals for BERT style models. This was causing an accuracy drop after exporting to ONNX of up to 1% and is now fixed.

New Features:
* One-shot and recipe arguments support added for transformers, yolov5, and torchvision.
* Dockerfiles and new build processes created for Docker.
* CLI formats and inclusion standardized on install of SparseML for transformers, yolov5, and torchvision.
* N:M pruning mask creator deployed for use in PyTorch pruning modifiers.
* Masked_language_modeling training CLI added for transformers.
* Documentation additions made across all standard integrations and pathways.
* GitHub action tests running for end-to-end testing of integrations.

Changes:
* Click as a root dependency added as the new preferred route for CLI invocation and arg management.
* Provider parameter added for ONNXRuntime InferenceSessions.
* Moved `onnxruntime` to optional install extra. `onnxruntime` no longer a root dependency and will only be imported when using specific pathways.
* QAT export pipelines improved with better support for QATMatMul and custom operators.

Resolved Issues:
* Incorrect commands and models updated for older docs for transformers, yolov5, and torchvision.
* YOLOv5 issues addressed with data files, configs, and datasets not being easily accessible with the new install pathway. They are now included in the `sparseml` src folder for yolov5.
* An extra batch no longer runs for the PyTorch ModuleRunner.
* None sparsity parameter was being improperly propagated for sparsity in the PyTorch ConstantPruningModifier.
* PyPI dependency conflicts no longer occur with the latest ONNX and Protobuf upgrades.
* When GPUs were not available, yolov5 pathways were not working.
* Transformers export was not working properly when neither `--do_train` or `--do_eval arguments` were passed in.
* Non-string keys now allowed within recipes.
* Numerous fixes applied for pruning modifiers including improper masks casting, improper initialization, and improper arguments passed through for MFAC.
* YOLOv5 export formatting error addressed.
* Missing or incorrect data corrected for logging and recording statements.
* PyTorch DistillationModifier for transformers was ignoring both "self" distillation and "disable" distillation values; instead, normal distillation would be used.
* FP16 not deactivating on QAT start for torchvision.

Known Issues:
* PyTorch > 1.9 quantized ONNX export is broken; waiting on PyTorch resolution and testing.

This is a patch release for 0.12.0 that contains the following changes:

- Protobuf is restricted to version < 4.0 as the newer version breaks ONNX.

This is a patch release for 0.12.0 that contains the following changes:

- Disabling of distillation modifiers no longer crashes Hugging Face Transformers integrations `--distillation_teacher disable`
- Numeric stability is provided for distillation modifiers using log_softmax instead of softmax.
- Accuracy and performance issues were addressed for quantized graphs in image classification and NLP.
- When using mixed precision for a quantized recipe with image classification, crashes no longer occur.

New Features:
* SparseML recipe stages support: recipes can be chained together to enable easier prototyping with compound sparsification.
* SparseML image classification CLIs implemented to enable easy commands for training models like ResNet-50: `sparseml.image_classification.train --help`
* FFCV support provided for PyTorch image classification pipelines.
* Masked language modeling CLI added for Hugging Face transformers integration: `sparseml.transformers.masked_language_modeling --help`
* DistilBERT support provided for Hugging Face transformers integration.

Changes:
* Modifiers logging upgraded to standardize logging across SparseML and integrations with hyperparameter stores like Weights and Biases.
* Hugging Face Transformers integration updated to the latest state from the main upstream branch.
* Ultralytics YOLOv5 Integration updated to the latest state from the main upstream branch.
* Quantization-aware training graphs updated to enable better recovery and to provide optional support for deployment environments like TensorRT.

Resolved Issues:
* MFAC Pruning modifier multiple minor issues addressed that were preventing proper functioning in recipes leading to exceptions.
* Distillation loss for transformers integration was not calculated correctly when inputs were multidimensional.
* Minor fixes made across modifiers and transformers integration.

Known Issues:
* None

This is a patch release for 0.11.0 that contains the following changes:

- Addressed removal of phased, score_type, and global_sparsity flag for PyTorch - GMPruningModifier; rather than crashing, exceptions are only thrown if they are turned on for instances of those modifiers with deprecation notices.
- Crashes no longer occur when using sparseml.transformers training pipelines and distillation modifiers not working without the FP16 training flagged turned on.