fix: validate kd_loss_weight type and reject negative individual weights

[aryan-iconic]

Addresses one item (Bug #4: loss-balancer validation gaps) from #1926.

What does this PR do?

Type of change: Bug fix

This PR addresses two validation gaps in StaticLossBalancer that could lead to crashes or silent algorithmic errors during knowledge distillation.

Root Cause & Rationale

1. Fixes integer scalar input handling:

StaticLossBalancer previously checked specifically for float to wrap scalar inputs into a list. Passing an integer (e.g., 1) bypassed this check, resulting in a raw integer being passed to sum(), which raised TypeError: 'int' object is not iterable.

The check now accepts both int and float so scalar numeric inputs are handled consistently.

- if isinstance(kd_loss_weight, float):
+ if isinstance(kd_loss_weight, (int, float)):

2. Fixes negative weight validation gap:

The balancer validated that the sum of the weights was between 0.0 and 1.0, but did not check the individual elements.

This meant a configuration like [0.5, -0.3] could pass the sum bounds check because the total is 0.2, despite containing an invalid negative loss weight.

An explicit guard is added to reject individual negative weights:

+ if any(w < 0.0 for w in kd_loss_weight):
+     raise ValueError(
+         f"Individual kd_loss_weight values must be non-negative, got {kd_loss_weight}"
+     )

All existing warnings.warn logic and sum-bounds checks remain unchanged.

Usage

from modelopt.torch.distill.loss_balancers import StaticLossBalancer

# Integer scalar inputs are now handled correctly.
balancer = StaticLossBalancer(1)

# Negative individual weights are now rejected.
balancer = StaticLossBalancer([0.5, -0.3])
# Raises ValueError: Individual kd_loss_weight values must be non-negative

Testing

Added a new test suite for distillation loss balancers.

• What I ran: pytest tests/unit/torch/distill/test_loss_balancers.py
• Validation: Both regression tests pass with this change and fail when run against the unmodified main branch.

Before your PR is "Ready for review"

I have read and followed the [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md), and my commits are signed (git commit -s -S).

I have read and followed the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors). This change does not introduce trust_remote_code=True, torch.load(..., weights_only=False), pickle, or similar unsafe patterns.

• Is this change backward compatible?: ✅
• If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
• Did you write any new necessary tests?: ✅
• Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A
• Did you get Claude approval on this PR?: N/A (external contributor)

Additional Information

Related to #1926 (Bug #4).

Summary by CodeRabbit

• Bug Fixes

  • Static loss balancing now accepts integer weighting values.
  • Integer values are converted to the expected single-value format for consistent handling.
  • Negative individual weights are rejected with a validation error before total-weight checks, providing clearer validation behavior.

• Tests

  • Added coverage for integer and floating-point weight conversion.
  • Added validation coverage confirming that negative weights raise an error.

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📝 Walkthrough

Walkthrough

StaticLossBalancer now accepts integer loss weights, converts them to a float list, and rejects negative individual weights. Unit tests cover integer, floating-point, and negative inputs.

Changes

Static loss balancer validation

Layer / File(s)	Summary
Weight normalization and validation modelopt/torch/distill/loss_balancers.py, tests/unit/torch/distill/test_loss_balancers.py	StaticLossBalancer converts scalar integer and floating-point weights to a single-item float list. It raises ValueError for negative individual weights. Parametrized tests cover these behaviors.

Estimated code review effort: 2 (Simple) | ~10 minutes

Merge Risk: ⚪ Minimal · up to 155c5

The change normalizes scalar loss weights and rejects negative individual weights, with regression coverage for the intended validation behavior. No current merge-blocking risk remains.

🚥 Pre-merge checks | ✅ 6

✅ Passed checks (6 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly summarizes both primary changes: kd_loss_weight type handling and rejection of negative individual weights.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 2 functions across 2 files.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Security Anti-Patterns	✅ Passed	The pull request changes only modelopt/torch/distill/loss_balancers.py and adds a unit test. The added code contains no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), h…

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👉 Steps to fix this

Actionable comments posted: 1

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Inline comments:
In `@tests/unit/torch/distill/test_loss_balancers.py`:
- Around line 9-10: Update the StaticLossBalancer scalar-weight regression test
to be parametrized with both an integer and a floating-point value, asserting
each produces the expected _kd_loss_weight. Keep the test focused on scalar
input behavior.

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