[tianyuxbear]

Dev Engineer Review

TransformersTokenizer.from_pretrained now uses the shared hf_remote_code_lock while calling AutoTokenizer.from_pretrained when trust_remote_code is enabled. When it is disabled, the guard uses a no-op context. The deferred import avoids a module import cycle. Existing exception handling and fallback behavior remain unchanged. The diff also includes formatting and docstring cleanup.

QA Engineer Review

No test changes.

Per-File QA Perspective

• tensorrt_llm/tokenizer/tokenizer.py: Verify tokenizer loading with trust_remote_code enabled and disabled, including concurrent loads that use remote tokenizer code. No public API or default behavior changed. The author reports that pre-commit passed; no unit test was added.

Description

Cherry-pick of #19553 (merged to main on 2026-10-08 as b8af5887) to release/1.3, for https://nvbugs/6782263.

TransformersTokenizer.from_pretrained now holds the existing hf_remote_code_lock around AutoTokenizer.from_pretrained when trust_remote_code is set. AutoTokenizer resolves the tokenizer class through AutoConfig.from_pretrained, so a checkpoint whose config.json has an auto_map (DeepSeek-R1, for example) also copies its .py files into the shared HF_MODULES_CACHE from this path. Without the lock, postprocess workers that build tokenizers at the same time can import a half-written module and fail with AttributeError: module 'transformers_modules...configuration_deepseek' has no attribute 'DeepseekV3Config'. The config and input-processor loads already take this lock; the tokenizer load was the one path left unguarded. With trust_remote_code false, the guard is a no-op.

See #19553 for the full root-cause analysis.

Cherry-pick notes

• Applies with no conflicts. The added and removed lines are identical to b8af5887.
• On release/1.3, TransformersTokenizer.from_pretrained starts with a function-level import tensorrt_llm._torch.configs (from [https://nvbugs/6624972][fix] Preserve Nemotron-H hybrid_override_pattern with MLP layers #18042) that is not in main's copy of this file. It stays above the try:, and the lock still wraps only the AutoTokenizer.from_pretrained call.
• hf_remote_code_lock is on release/1.3 in tensorrt_llm/_torch/model_config.py with the same signature. Its two existing callers (model_config.py:1094, inputs/registry.py:1186) do not reach TransformersTokenizer.from_pretrained, so the non-reentrant lock cannot nest. The callers of TransformersTokenizer.from_pretrained are the same set as on main.
• pre-commit run --files tensorrt_llm/tokenizer/tokenizer.py passes with the release/1.3 hook config.

Test Coverage

No unit test is added, as in #19553: the race fails about 2.5% of ranks, too rare for a deterministic CI gate. #19553 has the offline reproduction (8 processes on one cold HF_MODULES_CACHE, 30 trials: 6/240 ranks failed without the lock, 0/240 with it). That reproduction was not re-run on release/1.3. CI covers the shared load path, since every test that builds a tokenizer goes through TransformersTokenizer.from_pretrained.

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Walkthrough

TransformersTokenizer.from_pretrained now guards the Hugging Face tokenizer load with a no-op context when remote code is not trusted and the shared remote-code lock when it is trusted.

Changes

Tokenizer loading

Layer / File(s)	Summary
Remote-code guard and tokenizer load tensorrt_llm/tokenizer/tokenizer.py	Adds _hf_remote_code_guard and wraps AutoTokenizer.from_pretrained in its returned context. Existing exception handling and fallback behavior remain unchanged.

Priority: ➖ Normal

Estimated code review effort: 2 (Simple) | ~10 minutes

Change: Bug fix

Merge Risk: 🟡 Moderate · up to 2610f

The change addresses a reported tokenizer-load race, but no test protects concurrent trusted loads against regression. Add the required regression test before merging.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 66.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 3 functions across 1 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title follows the required ticket and type format and clearly states that the change serializes Hugging Face remote-code loading during tokenizer construction.
Description check	✅ Passed	The description explains the issue, root cause, solution, cherry-pick context, compatibility notes, test coverage, and checklist status. It also records that no new unit test was added and explains wh…
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 1

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Inline comments:
Review comments at @tensorrt_llm/tokenizer/tokenizer.py:
- Line 329: Add a regression test for concurrent
TransformersTokenizer.from_pretrained calls with trust_remote_code=True, using a
fresh shared HF_MODULES_CACHE and asserting every load succeeds. Ensure the test
exercises concurrent loads and cache writes so it detects removal of the
_hf_remote_code_guard in the from_pretrained path.

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