SparseML v1.1.0

New Features:

• YOLACT Segmentation native training integration made for SparseML.
• OBSPruning modifier added (https://arxiv.org/abs/2203.07259).
• QAT now supported for MobileBERT.
• Custom module support provided for QAT to enable quantization of layers such as GELU.

Changes:

• Updates made across the repository for new SparseZoo Python APIs.
• Non-string keys are now supported in recipes for layer and module names.
• Native support added for DDP training with pruning in PyTorch pathways.
• YOLOV5p6 models default to their native activations instead of overwriting to Hardswish.
• Transformers eval pathways changed to turn off Amp (fFP16) to give more stable results.
• TensorBoard logger added to transformers integration.
• Python setuptools set as required at 59.5 to avoid installation issues with other packages.
• DDP now works for quantized training of embedding layers where tensors were being placed on incorrect devices and causing training crashes.

Resolved Issues:

• ConstantPruningModifier propagated None in place of the start_epoch value when start_epoch > 0. It now propagates the proper value.
• Quantization of BERT models were dropping accuracy improperly by quantizing the identify branches.
• SparseZoo stubs were not loading model weights for image classification pathways when using DDP training.

Known Issues:

• None