[#8384][fix] use dict.get() instead of getattr() for rope_scaling dict access

[wojciech-wais]

rope_scaling from HuggingFace PretrainedConfig is a dict, not an object. Using getattr() on a dict doesn't access dict keys - it always returns the default value. This caused YaRN RoPE scaling to never be activated:

• rope_type check always returned None instead of 'yarn'
• factor always returned 1.0 instead of the configured scaling factor

Fixes #8384

Summary by CodeRabbit

• Refactor
  • Improved internal parameter handling for attention mechanisms to enhance code maintainability and robustness.

Description

Test Coverage

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[coderabbitai]

📝 Walkthrough

Walkthrough

Fixes a bug where getattr() was incorrectly used to access dictionary keys in rope_scaling configuration. Replaces attribute access with proper dictionary access using .get() method to correctly retrieve the "factor" and "rope_type" values.

Changes

Cohort / File(s)	Summary
Bug Fix: Dictionary Access Correction tensorrt_llm/_torch/modules/qk_norm_attention.py	Changed rope_scaling attribute access from getattr() to dictionary .get() method for "factor" and "rope_type" keys, ensuring correct retrieval of configuration values.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	The PR successfully addresses issue #8384 by replacing getattr() with dict.get() to correctly read rope_scaling dictionary keys, enabling YaRN RoPE scaling to function as intended.
Out of Scope Changes check	✅ Passed	All changes are directly related to fixing the rope_scaling dict access issue in qk_norm_attention.py. The modification only affects the specific problem identified in issue #8384 with no extraneous changes.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.
Title check	✅ Passed	The title accurately and specifically describes the main change: replacing getattr() with dict.get() for rope_scaling dictionary access, which is the core fix in the PR.
Description check	✅ Passed	PR description clearly explains the issue (dict vs object access), the solution (using dict.get instead of getattr), and references the linked issue #8384. However, the Description and Test Coverage sections are empty.

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[coderabbitai]

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/modules/qk_norm_attention.py (1)

1-1: ⚠️ Potential issue | 🟡 Minor

Update the copyright year to 2026.

The file was meaningfully modified in this PR but the copyright header still reads 2025. As per coding guidelines, the year must be updated on modified files.

📝 Proposed fix

-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.

As per coding guidelines: "Include NVIDIA copyright header on ALL new files and update year on modified files."

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@tensorrt_llm/_torch/modules/qk_norm_attention.py` at line 1, Update the
copyright year in the file header: find the SPDX header line containing
"SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES" and
change the year from 2025 to 2026 so the header reads 2026.

🧹 Nitpick comments (1)

tensorrt_llm/_torch/modules/qk_norm_attention.py (1)

48-49: Redundant None default in .get("rope_type", None).

dict.get() already returns None when the key is absent; passing it explicitly adds noise. rope_scaling.get("rope_type") is idiomatic.

♻️ Proposed nit

-    if rope_scaling is None or rope_scaling.get("rope_type",
-                                                None) != "yarn":
+    if rope_scaling is None or rope_scaling.get("rope_type") != "yarn":

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@tensorrt_llm/_torch/modules/qk_norm_attention.py` around lines 48 - 49, The
condition uses a redundant explicit None default in
rope_scaling.get("rope_type", None); change it to rope_scaling.get("rope_type")
so the check becomes if rope_scaling is None or rope_scaling.get("rope_type") !=
"yarn": — update the expression in the qk_norm_attention logic where
rope_scaling is inspected to remove the unnecessary None argument.

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Outside diff comments:
In `@tensorrt_llm/_torch/modules/qk_norm_attention.py`:
- Line 1: Update the copyright year in the file header: find the SPDX header
line containing "SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION &
AFFILIATES" and change the year from 2025 to 2026 so the header reads 2026.

---

Nitpick comments:
In `@tensorrt_llm/_torch/modules/qk_norm_attention.py`:
- Around line 48-49: The condition uses a redundant explicit None default in
rope_scaling.get("rope_type", None); change it to rope_scaling.get("rope_type")
so the check becomes if rope_scaling is None or rope_scaling.get("rope_type") !=
"yarn": — update the expression in the qk_norm_attention logic where
rope_scaling is inspected to remove the unnecessary None argument.

[karljang]

Thank you for your contribution!
Before we proceed, could you please address the failed checks?

[wojciech-wais]

Thank you for your contribution! Before we proceed, could you please address the failed checks?

They should be addressed now.

[karljang]

There’s a tiny checkbox at the PR description, waiting for you to click. And other PRs as well? 😄

[karljang]

LGTM;
@xxi-nv, could you please review this PR.