[None][chore] Merge residual+hidden into layer norm at the end of each NemotronH MTP, and remove a % operation

[hnover-nv]

In the prior code, we do hidden + residual, then call the norm-only version of layer norm. But we can call RMSNorm(hidden, residual), and for most implementations that becomes a single kernel that does both the add and the norming.

In theory this should be a tiny bit faster, but I couldn't see a difference in benchmarks. The benchmark runs vary by a few % overall so that's not surprising. I could break out nsys but that seems overkill. Mostly this is cleaner and more in line with surrounding code.

Also, the % in the loop concatenating the layers is unnecessary because the loop index never exceeds the modulus, so I removed it.

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Summary by CodeRabbit

• Refactor
  • Optimized internal residual handling and layer construction patterns in model processing with enhanced efficiency. Public API interfaces remain unchanged and fully backward-compatible.

[hnover-nv]

/bot run

[coderabbitai]

📝 Walkthrough

Walkthrough

This change renames the loop iteration variable from i to step_rel_idx in pattern-based layer construction for improved clarity. Additionally, the final normalization layer is modified to accept and return a residual tuple directly, rather than pre-accumulating the residual into hidden states before normalization.

Changes

Cohort / File(s)	Summary
Residual Handling and Loop Refactoring tensorrt_llm/_torch/models/modeling_nemotron_h.py	Loop variable renamed from i to step_rel_idx for pattern-based layer indexing. Final normalization now receives and handles residual as a tuple via final_layernorm(hidden_states, residual) with residual explicitly reset to None afterward, replacing prior in-place residual accumulation.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

🚥 Pre-merge checks | ✅ 3

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description check	✅ Passed	The PR description provides clear explanation of both changes (RMSNorm fusion and loop modulus removal), acknowledges no performance gains were observed, and includes test coverage information.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Title check	✅ Passed	The title accurately reflects the main changes: merging residual and hidden into layer norm at the end of NemotronH MTP and removing a modulus operation.

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Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

tensorrt_llm/_torch/models/modeling_nemotron_h.py (1)

1-1: ⚠️ Potential issue | 🟡 Minor

Update copyright year to 2026.

The header says 2022-2024 but this file is being modified in 2026. As per coding guidelines, "All source files must contain an NVIDIA copyright header with the year of latest meaningful modification."

Proposed fix

-# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.

[tensorrt-cicd]

PR_Github #35533 [ run ] triggered by Bot. Commit: e480aaa

[tensorrt-cicd]

PR_Github #35533 [ run ] completed with state SUCCESS. Commit: e480aaa
/LLM/main/L0_MergeRequest_PR pipeline #27439 completed with status: 'SUCCESS'
Pipeline passed with automatic retried tests. Check the rerun report for details.

[yechank-nvidia]

LGTM. Can you change the PR title to include this is for NemotronH?

[sunnyqgg]

LGTM

[hnover-nv]

LGTM. Can you change the PR title to include this is for NemotronH?

Done