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@JunyiXu-nv JunyiXu-nv commented Feb 9, 2026

…ssue

Summary by CodeRabbit

Release Notes

  • Bug Fixes
    • Improved tokenizer serialization support when using remote code, ensuring better compatibility across distributed environments and preventing potential serialization failures in multi-node setups.

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@JunyiXu-nv JunyiXu-nv requested a review from a team as a code owner February 9, 2026 09:25
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/bot run

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coderabbitai bot commented Feb 9, 2026

📝 Walkthrough

Walkthrough

Introduced cloudpickle-based serialization support for HuggingFace tokenizers to enable cross-node distributed processing. Added helper functions for reconstructing serialized tokenizers and registering dynamic transformer modules when trust_remote_code is enabled.

Changes

Cohort / File(s) Summary
Tokenizer Serialization Enhancement
tensorrt_llm/tokenizer/tokenizer.py
Added __reduce__ method to TransformersTokenizer for cloudpickle by-value serialization, _reconstruct_transformers_tokenizer() helper for deserialization, and maybe_register_transformers_modules_by_value() to register dynamic modules. Enhanced load_hf_tokenizer() to invoke module registration when trust_remote_code=True.

Sequence Diagram(s)

sequenceDiagram
    participant Client
    participant load_hf_tokenizer
    participant TransformersTokenizer
    participant Cloudpickle
    participant HFModules as HF Dynamic Modules

    Client->>load_hf_tokenizer: load_hf_tokenizer(trust_remote_code=True)
    load_hf_tokenizer->>load_hf_tokenizer: maybe_register_transformers_modules_by_value()
    load_hf_tokenizer->>HFModules: Register dynamic modules
    load_hf_tokenizer->>TransformersTokenizer: from_pretrained()
    TransformersTokenizer-->>load_hf_tokenizer: Return tokenizer instance
    Client->>Cloudpickle: pickle(tokenizer)
    Cloudpickle->>TransformersTokenizer: __reduce__()
    TransformersTokenizer-->>Cloudpickle: Return (reconstruct_fn, args)
    Cloudpickle-->>Client: Serialized bytes
    Client->>Cloudpickle: unpickle(bytes)
    Cloudpickle->>Cloudpickle: _reconstruct_transformers_tokenizer(bytes)
    Cloudpickle-->>Client: Deserialized tokenizer
Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

🚥 Pre-merge checks | ✅ 1 | ❌ 2
❌ Failed checks (2 warnings)
Check name Status Explanation Resolution
Docstring Coverage ⚠️ Warning Docstring coverage is 42.86% which is insufficient. The required threshold is 80.00%. Write docstrings for the functions missing them to satisfy the coverage threshold.
Description check ⚠️ Warning PR description lacks critical details: no explanation of the bug, root cause, or implementation approach; all required sections are empty placeholders. Add a clear description of the multi-node hang issue, explain why it occurs with trust_remote_code, describe the solution, list test cases covering the fix, and verify the PR checklist items are appropriately addressed.
✅ Passed checks (1 passed)
Check name Status Explanation
Title check ✅ Passed The title correctly follows the repository template with NVBugs ID and [fix] type, and clearly indicates the fix addresses a multi-node trust_remote_code hang issue.

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PR_Github #35313 [ run ] triggered by Bot. Commit: 3d51f1f

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PR_Github #35313 [ run ] completed with state FAILURE. Commit: 3d51f1f
/LLM/release-1.2/L0_MergeRequest_PR pipeline #339 completed with status: 'FAILURE'

⚠️ Action Required:

  • Please check the failed tests and fix your PR
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  • Once fixed, request an NVIDIA team member to trigger CI again

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/bot run

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PR_Github #35327 [ run ] triggered by Bot. Commit: 3d51f1f

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PR_Github #35327 [ run ] completed with state SUCCESS. Commit: 3d51f1f
/LLM/release-1.2/L0_MergeRequest_PR pipeline #340 completed with status: 'FAILURE'

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#
# See: https://github.com/vllm-project/vllm/pull/6751
try:
import cloudpickle
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why we use local import? And is this a requirements of TRTLLM install? if its already a requirement no need local try/catch I think. If its optional requirement, lets print a better logging for user to install it when import error?

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Upaded the error message.

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/bot run

@JunyiXu-nv JunyiXu-nv force-pushed the dev-junyi-fix-remote-code-issue branch from 224aea4 to f4469af Compare February 12, 2026 13:12
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/bot run

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PR_Github #35783 [ run ] triggered by Bot. Commit: f4469af

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PR_Github #35784 [ ] completed with state ABORTED. Commit: f4469af

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PR_Github #35783 [ run ] completed with state SUCCESS. Commit: f4469af
/LLM/release-1.2/L0_MergeRequest_PR pipeline #351 completed with status: 'FAILURE'

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@JunyiXu-nv JunyiXu-nv force-pushed the dev-junyi-fix-remote-code-issue branch from f4469af to cda3a3a Compare February 13, 2026 03:15
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/bot run

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PR_Github #35866 [ run ] triggered by Bot. Commit: cda3a3a

…ssue

In multi-node scenarios, AutoTokenizer.from_pretrained with trust_remote_code=True
may create dynamic Python modules cached at $HOME/.cache/huggingface/modules/
transformers_modules/. These modules are node-local and won't exist on other nodes.
Standard pickle serializes classes by reference, so deserializing a tokenizer that
uses such a dynamic class fails on other nodes.

To solve this, we use cloudpickle (if available) to serialize the inner tokenizer
by value, embedding the class definition in the serialized bytes. This follows
vLLM PR NVIDIA#6751.

Also adds nosec comments to suppress Bandit security warnings for pickle import
(B403) and pickle.loads (B301). These are safe because the serialized data comes
from our own cloudpickle.dumps() call, not from untrusted external sources.

See: vllm-project/vllm#6751
Signed-off-by: Junyi Xu <[email protected]>
@JunyiXu-nv JunyiXu-nv force-pushed the dev-junyi-fix-remote-code-issue branch from cda3a3a to 0c21fc3 Compare February 13, 2026 04:05
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PR_Github #35866 [ run ] completed with state SUCCESS. Commit: cda3a3a
/LLM/release-1.2/L0_MergeRequest_PR pipeline #363 completed with status: 'FAILURE'

⚠️ Action Required:

  • Please check the failed tests and fix your PR
  • If you cannot view the failures, ask the CI triggerer to share details
  • Once fixed, request an NVIDIA team member to trigger CI again

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