[None][autodeploy] fix weight extraction for graph based quantized checkpoints

[Fridah-nv]

Fix of change in #8744 for quantized checkpoints with quantizers

Summary by CodeRabbit

Bug Fixes

• Enhanced weight extraction logic to properly support quantized model graphs, improving robustness of model optimization across different quantization scenarios and graph structures.

Description

Test Coverage

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[coderabbitai]

📝 Walkthrough

Walkthrough

Modified extract_weight_node function to conditionally extract weight nodes from either parametrized node's weight_nodes list or quantized input node. The change consolidates weight extraction and quantization logic into a unified flow, accommodating both regular and quantized graph structures.

Changes

Cohort / File(s)	Change Summary
Weight node extraction logic tensorrt_llm/_torch/auto_deploy/utils/node_utils.py	Refactored extract_weight_node to use conditional path: if weight_nodes exist, take first; independently compute quantizationParams from linear node, then optionally override weight_node with weight_params.input_node if quantization detected. Merged prior direct assignment and separate quantization logic into unified flow.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

• Key focus areas:
  • Verify the conditional logic for extracting weight_node from either weight_nodes[0] or quantization input correctly handles all quantized graph scenarios
  • Confirm the assertion ensuring weight_node is not None remains appropriate with new conditional paths
  • Review interaction between weight_nodes extraction and quantizationParams computation to ensure no edge cases are missed
  • Validate that the merged logic maintains behavior parity with prior separate logic paths

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	Description references PR #8744 regression and mentions quantizers, but omits required sections (issue explanation, solution details, test coverage).	Add detailed explanation of the regression, the implemented solution, and list relevant tests that validate the quantized checkpoint handling.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	Title clearly identifies a specific fix for weight extraction in graph-based quantized checkpoints, directly matching the code change.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

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[coderabbitai]

Actionable comments posted: 1

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/auto_deploy/utils/node_utils.py (1)

109-109: Incorrect return type hint.

The function signature indicates -> int, but the function returns find_get_attr_node(weight_node) (line 150), which returns Optional[Node] based on the implementation at lines 121-126.

Apply this diff to correct the type hint:

-def extract_weight_node(node: Node) -> int:
+def extract_weight_node(node: Node) -> Optional[Node]:

📜 Review details

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📥 Commits

Reviewing files that changed from the base of the PR and between f1d637e and dbf7e5a.

📒 Files selected for processing (1)

• tensorrt_llm/_torch/auto_deploy/utils/node_utils.py (1 hunks)

🧰 Additional context used

📓 Path-based instructions (3)

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Files:

• tensorrt_llm/_torch/auto_deploy/utils/node_utils.py

**/*.{cpp,cxx,cc,h,hpp,hh,hxx,cu,cuh,py}

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• tensorrt_llm/_torch/auto_deploy/utils/node_utils.py

🧠 Learnings (1)

📚 Learning: 2025-10-20T16:54:09.824Z

Learnt from: nvchenghaoz
Repo: NVIDIA/TensorRT-LLM PR: 8469
File: tensorrt_llm/_torch/auto_deploy/custom_ops/rms_norm.py:6-6
Timestamp: 2025-10-20T16:54:09.824Z
Learning: In tensorrt_llm/_torch/auto_deploy/custom_ops/rms_norm.py, the import `from ...modules.mamba.layernorm_gated import _layer_norm_fwd` is correct and should not be changed to modules.fla.layernorm_gated. The _layer_norm_fwd function exists in both modules/mamba/layernorm_gated.py and modules/fla/layernorm_gated.py, but the mamba version is the intended implementation for this use case.

Applied to files:

• tensorrt_llm/_torch/auto_deploy/utils/node_utils.py

[Fridah-nv]

/bot run

[tensorrt-cicd]

PR_Github #24490 [ run ] triggered by Bot. Commit: ae5711c

[tensorrt-cicd]

PR_Github #24490 [ run ] completed with state SUCCESS. Commit: ae5711c
/LLM/main/L0_MergeRequest_PR pipeline #18484 completed with status: 'SUCCESS'