[None][fix] Fix silent MPI failures on models with custom tokenizers

[jthomson04]

Multi-node MPI serving hangs on startup for trust_remote_code=True models (e.g. Kimi K2). The tokenizer is pickled into the MPI payload, but remote workers can't deserialize it because the dynamic transformers_modules namespace doesn't exist on their side. The workers fail silently, and the remaining ranks hang at an MPI barrier.

To fix this, we strip the tokenizer from the MPI payload (proxy.py), and reload it if needed for guided decoding. I also added some logging to prevent these sorts of silent failures.

Summary by CodeRabbit

Release Notes

• Bug Fixes

  • Resolved tokenizer serialization failures in MPI distributed execution environments
  • Enhanced error tracking and logging in MPI session management for improved diagnostics

• Improvements

  • Guided decoding now automatically loads tokenizer from checkpoint when not explicitly provided

Description

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[coderabbitai]

📝 Walkthrough

Walkthrough

These changes implement lazy tokenizer loading for guided decoding, filter tokenizer arguments from MPI worker submissions to prevent serialization failures, and enhance error tracking in the MPI session server with improved failure logging and iteration over futures as they complete.

Changes

Cohort / File(s)	Summary
Guided Decoding Tokenizer Support tensorrt_llm/_torch/pyexecutor/py_executor_creator.py	Adds lazy tokenizer loading from checkpoint directory when guided decoding is requested but no tokenizer is provided. Constructs guided decoding config using the loaded TransformersTokenizer at runtime.
MPI Worker & Session Improvements tensorrt_llm/executor/proxy.py, tensorrt_llm/llmapi/mpi_session.py	Filters tokenizer argument from MPI worker kwargs to avoid pickle failures on remote nodes; enhances error handling in session server by iterating over futures as they complete, tracking failed futures, logging individual failure details, and providing summary failure information.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

🚥 Pre-merge checks | ✅ 1 | ❌ 2

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% 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 explains the issue and solution clearly, but critical sections from the template (PR title, Test Coverage, and PR Checklist items) are missing or incomplete.	Add a proper PR title following the template format (e.g., '[TRTLLM-XXXX][fix] Fix silent MPI failures...'). Complete the Test Coverage section by listing relevant tests. Review all PR Checklist items systematically.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title '[None][fix] Fix silent MPI failures on models with custom tokenizers' clearly and specifically summarizes the main fix being implemented in the pull request.

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[jthomson04]

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