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SparseML v1.0.0

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@jeanniefinks jeanniefinks released this 01 Jul 16:20
· 1 commit to release/1.0 since this release
v1.0.0
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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.
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