fix: allow zero grad norm in dtensor policies for consistency with Megatron

[smahdavi4]

What does this PR do ?

Currently Megatron only accepts float/int for grad norm. To disable grad norm, Dtensor needs None while megatron needs zero. Adding zero to dtensor as well to allow for a consistent grad norm clipping usage.

Summary by CodeRabbit

• Bug Fixes
  • Improved gradient clipping validation to correctly handle edge cases when the maximum gradient norm is configured to zero or negative values, preventing unintended clipping behavior.

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[github-actions]

ℹ️ File Consistency Check

Check based on commit: 8c1ab0a (PR #1618 from allow-zero-grad-norm)

✅ DTensor Policy Worker Synchronization Check

Both DTensor policy worker files were modified in this PR:

• nemo_rl/models/policy/workers/dtensor_policy_worker.py
• nemo_rl/models/policy/workers/dtensor_policy_worker_v2.py

Please ensure that the changes are consistent between both files where applicable.

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_{This check ensures that related file implementations remain synchronized across the codebase. If you believe this warning is incorrect or the files should intentionally differ, please add a comment explaining the reasoning.}

[coderabbitai]

📝 Walkthrough

Walkthrough

Two policy worker files are updated to add an additional guard condition to gradient clipping logic in the train() method, requiring max_grad_norm to be positive (greater than 0) in addition to being non-None.

Changes

Cohort / File(s)	Summary
Gradient clipping guard condition tightened nemo_rl/models/policy/workers/dtensor_policy_worker.py, nemo_rl/models/policy/workers/dtensor_policy_worker_v2.py	Modified the condition for gradient clipping from if max_grad_norm is not None to if max_grad_norm is not None and max_grad_norm > 0 to prevent clipping when max_grad_norm is zero or negative

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Test Results For Major Changes	⚠️ Warning	PR modifies gradient clipping behavior but lacks test results or validation demonstrating no convergence regression.	Add test results or convergence comparison data to PR description showing the change does not cause training regressions.

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.
Title check	✅ Passed	The title clearly and specifically describes the main change: allowing zero grad norm in dtensor policies for consistency with Megatron. It directly matches the core objective of the pull request.

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment

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[terrykong]

@joyang-nv to review