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name: model-compression description: "Use NNI model compression for pruning, quantization, distillation, evaluator wrapping, config_list authoring, validation, speedup, and export caveats." disable-model-invocation: true metadata: disco-role: operating license: MIT

Model Compression

Use this sub-skill when the task is about NNI compression for PyTorch models: choosing a pruner, quantizer, distiller, evaluator wrapper, config_list, calibration or training contract, mask speedup, quantization export, or compression-specific troubleshooting.

Route by Task

• Author or validate config_list: read API reference and run scripts/validate_config_list.py on JSON before proposing code.
• Prune a model: use pruning workflow and choose LevelPruner, norm/filter pruners, scheduled pruners, SlimPruner, TaylorPruner, or MovementPruner based on the model and need for training signals.
• Quantize a model: use quantization workflow and choose QAT/PTQ/DoReFa/BNN/LSQ variants by training budget and backend target.
• Distill a model: use distillation workflow for teacher/student layerwise losses with DynamicLayerwiseDistiller or Adaptive1dLayerwiseDistiller.
• Wrap training: use evaluator workflow to select TorchEvaluator, LightningEvaluator, TransformersEvaluator, or DeepspeedTorchEvaluator.
• Make compression physically faster: use speedup and export workflow and distinguish simulated masks/quantization from structural or backend speedup.

Boundaries

• Use this sub-skill for compression algorithm selection, compression API wiring, evaluator contracts, safe config validation, and compression troubleshooting.
• Route HPO experiment launch, tuners, assessors, training services, and nnictl concerns to ../hpo-experiments/.
• Route NAS model space, evaluator, and strategy search design to ../nas/ unless the task is specifically compression evaluator wrapping.
• Route utility-only concrete tracing or feature-engineering helpers to ../feature-engineering-and-utilities/.

Required Safety Checks

• Do not run training, dataset download, notebook execution, TensorRT conversion, DeepSpeed launch, or native examples unless the user explicitly asks and the environment is prepared.
• Validate config_list JSON with the bundled script when the task only needs schema and selection sanity checks.
• Treat nni.compression import errors as optional dependency issues first: compression code requires torch, and some evaluators/backends require Lightning, Transformers, DeepSpeed, ONNX, TensorRT, or PyCUDA.
• Keep any runnable examples in the user's project, not in this skill tree; this sub-skill provides distilled contracts and local validation only.

References

• Compression workflows
• Compression API reference
• Compression troubleshooting