Add score averaging and convergence early stop

[CedricHwong]

What does this PR do?

Overview:
This PR extends GradNAS gradient scoring with
(1) optional score averaging
(2) early stopping when gradient scores converge. New search config keys are added with defaults: average_scores, score_convergence_tol, score_convergence_patience, and score_convergence_min_updates. The gradient estimation loop now exits early once all hparam score trackers converge. Unit tests cover the averaging math, convergence boundaries, and disabled-convergence behavior.

Usage

  import modelopt.torch.prune as mtp

  # Example: enable/adjust score averaging + convergence early stop
  pruned_model, search_history = mtp.prune(
      model=model,
      mode="gradnas",
      constraints={"flops": "90%"},
      dummy_input=dummy_input,
      config={
          "data_loader": train_loader,
          "loss_func": loss_func,
          # New options:
          "average_scores": True,
          "score_convergence_tol": 1e-3,
          "score_convergence_patience": 5,
          "score_convergence_min_updates": 10,
      },
  )

Testing

• pytest tests/unit/torch/prune/test_gradnas.py
• pre-commit run --all-files

Before your PR is "Ready for review"

• Make sure you read and follow Contributor guidelines (https://github.com/NVIDIA/Model-
  Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed.
• Is this change backward compatible?: No Default GradNAS behavior now averages gradient scores and may stop early on convergence, which can change score scale and number of batches processed vs. previous behavior.
• Did you write any new necessary tests?: Yes
• Did you add or update any necessary documentation?: No
• Did you update Changelog (https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: No

Additional Information

N/A

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