[DEV] fix(megatron-fsdp): preserve non-meta tensors during meta device materialization

[xuwchen]

main PR: #4154

What does this PR do ?

During meta device initialization, the materialization path calls m.to_empty(device, recurse=False) before reset_parameters(). to_empty() unconditionally replaces all tensors (both parameters and buffers) in the module with uninitialized memory, regardless of whether they are actually on the meta device.

This becomes a problem for MoE when --moe-router-enable-expert-bias is enabled: expert_bias is registered with an explicit device=torch.cuda.current_device() [link], so it bypasses the meta context and is correctly initialized to zeros on GPU. But to_empty() clobbers it with uninitialized memory (NaN), and reset_parameters() only reinitializes parameters, not buffers, so expert_bias stays NaN for the entire training run.

The fix here replaces m.to_empty() with a targeted helper that only materializes is_meta=True tensors, leaving non-meta buffers (like expert_bias) untouched.

Contribution process

Pre-checks

• I have added relevant unit tests
• I have added relevant functional tests
• I have added proper typing to my code Typing guidelines
• I have added relevant documentation
• I have run the autoformatter.sh on my PR

Code review

Feel free to message or comment the @mcore-oncall to help accelerate your merge into main. The less complex your PR is, the faster it will be approved and merged!

All PRs start as draft. If you open a non-draft PR, it will be automatically converted to draft.

Step 1: Mark PR as "Ready for Review"

1. When your PR is ready, click Ready for Review.
2. An oncall reviewer is auto-assigned and expert reviewers are notified based on your changes.
   • Some PRs may jump straight to step 2. This is determined by .github/CODEOWNERS.

⚠️ Only mark as ready once merge-conflicts are resolved and the CI is passing.
Final Review might get declined if these requirements are not fulfilled.

Step 2: Final Review

For PRs that change megatron/core, once all expert reviewers have approved, the Final Review label is applied automatically and final reviewers are assigned.

For PRs outside megatron/core, this step is skipped.

Step 3: Approved

Once all required reviewers have approved, the Approved label is applied automatically.

Merge

Any member of mcore-engineers will be able to merge your PR.

For MRs into `dev` branch

The proposed review process for `dev` branch is under active discussion.

MRs are mergable after one approval by either eharper@nvidia.com or zijiey@nvidia.com.

[copy-pr-bot]

This pull request requires additional validation before any workflows can run on NVIDIA's runners.

Pull request vetters can view their responsibilities here.

Contributors can view more details about this message here.

[shjwudp]

Good catch! Some bias parameters are stored as nn.Module buffers (though that’s not a standard PyTorch practice). These buffers weren’t initialized on the meta device and shouldn’t be converted to empty tensors. This PR fixes that issue and also resolves the unexpected NaNs observed in the functional tests with the MoE layer.

[shjwudp]

Please do not reconfigures module parameters, which might cause loss of existing attributes on those parameters or invalidate maps that used these parameters as keys.

[xuwchen]

Updated the implementation to use Module._apply() with an is_meta guard, which is the same mechanism that to_empty() uses internally. For meta→cuda conversion, _apply always creates new Parameter objects; this is a PyTorch behavior, not something we introduced. The existing _reset_parameters already handles dict remapping and attribute copying for this case.

[yaox12]

/ok to test 48b0ff6

[svcnvidia-nemo-ci]

🔄 Merge queue validation started!

You can track the progress here: https://github.com/NVIDIA/Megatron-LM/actions/runs/25646899623