[None][fix] AutoDeploy: update nano3 accuracy test

[lucaslie]

@coderabbitai summary

Description

• I have added the correct sharding config to be consistent with the rest but even without the extra sharding config I get correct accuracy results for TP=8.
• I have also removed the warning from the HF logger that gets triggered when loading sharded MoE weights where some weights are missing which spams the entire logging output and makes it really hard to debug MoE issues from prints

accuracy results

accuracy test as is in PR

TP=1, MMLU: 79.020
TP=1, gsm8k: 82.828

TP=4, MMLU: 79.118
TP=4, gsm8k: 83.965

TP=8, MMLU: 79.142
TP=8, gsm8k: 83.851

accuracy test without updated sharding config

TP=8, MMLU: 79.142
TP=8, gsm8k: 84.041

accuracy test with fp32 cache activated

TP=1, fp32 cache, MMLU: 79.264 
TP=1, fp32 cache, gsm8k: **logs missing**

TP=4, fp32 cache, MMLU: 79.142
TP=4, fp32 cache, gsm8k: 84.041

TP=8, fp32 cache, MMLU: 78.972
TP=8, fp32 cache, gsm8k: 83.055

Test Coverage

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[coderabbitai]

📝 Walkthrough

Walkthrough

Changes add enhanced logging control to the HF model state_dict loading patch by switching logger level to ERROR to suppress warnings, and introduce detect_sharding configuration with sharding_source and sharding_dims parameters to test deployment setup.

Changes

Cohort / File(s)	Change Summary
HF Model Loading tensorrt_llm/_torch/auto_deploy/models/hf.py	Enhanced hf_load_state_dict_with_device patch documentation to include logger level switching to ERROR during patching; saves and restores original logger level to suppress HF state_dict warnings. Internal behavior refinement with no public API changes.
Test Configuration tests/integration/defs/accuracy/test_llm_api_autodeploy.py	Added transforms block to TestNemotronMOE.get_default_kwargs() with detect_sharding configuration specifying sharding_source and sharding_dims parameters.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

• Logging control addition is straightforward state management (save/restore pattern)
• Configuration addition to test is a simple dictionary extension with new parameters
• Changes follow predictable patterns with minimal functional complexity

Pre-merge checks and finishing touches

❌ Failed checks (1 warning, 1 inconclusive)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 25.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.
Description check	❓ Inconclusive	PR description partially addresses requirements but lacks clarity on key sections and missing critical information needed for proper review.	Add a proper PR title following the template format, provide a concise explanation of the issue and solution, and list specific test cases that validate the changes.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and specifically describes the main change: updating the nano3 accuracy test in AutoDeploy. It follows the required format with [None][fix] prefix and concisely communicates the primary purpose of the PR.

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

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

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[lucaslie]

/bot run

[tensorrt-cicd]

PR_Github #24120 [ run ] triggered by Bot. Commit: 89be9cb

[tensorrt-cicd]

PR_Github #24120 [ run ] completed with state FAILURE. Commit: 89be9cb
/LLM/main/L0_MergeRequest_PR pipeline #18181 completed with status: 'FAILURE'

[lucaslie]

/bot run

[tensorrt-cicd]

PR_Github #24144 [ run ] triggered by Bot. Commit: a24754a

[tensorrt-cicd]

PR_Github #24144 [ run ] completed with state SUCCESS. Commit: a24754a
/LLM/main/L0_MergeRequest_PR pipeline #18204 completed with status: 'SUCCESS'