[https://nvbugs/5936273][fix] Fix bugs of Mistral Large3

[byshiue]

Description

Fix the issue

AttributeError: 'Tekkenizer' object has no attribute 'all_special_tokens'

of Mistral Large 3.

Test Coverage

accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm]
accuracy/test_llm_api_pytorch.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm_eagle]
accuracy/test_llm_api_pytorch_multimodal.py::TestMistralLarge3_675B::test_nvfp4_4gpus[latency_moe_trtllm]

PR Checklist

Please review the following before submitting your PR:

• PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

• PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

• Test cases are provided for new code paths (see test instructions)

• Any new dependencies have been scanned for license and vulnerabilities

• CODEOWNERS updated if ownership changes

• Documentation updated as needed

• Update tava architecture diagram if there is a significant design change in PR.

• The reviewers assigned automatically/manually are appropriate for the PR.

• Please check this after reviewing the above items as appropriate for this PR.

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[coderabbitai]

📝 Walkthrough

Walkthrough

This pull request adds defensive programming to the tokenizer initialization logic by conditionally accessing the all_special_tokens attribute only when it exists, preventing AttributeError on incompatible tokenizers. Additionally, test waive entries are reordered for consistency.

Changes

Cohort / File(s)	Summary
Tokenizer Robustness tensorrt_llm/tokenizer/tokenizer.py	Added conditional check in TransformersTokenizer.init to initialize _all_special_tokens_set only when the underlying tokenizer has an all_special_tokens attribute, preventing AttributeError on tokenizers lacking this property.
Test Waive Reordering tests/integration/test_lists/waives.txt	Reordered test waive entries for MistralLarge3_675B and latency_moe_trtllm configurations across multiple test variants without changing the entries themselves.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~12 minutes

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 warning)

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.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title follows the required format with NVBugs ID and [fix] type tag, and refers to a real change (Mistral Large3 bug fix), but is vague about the specific issue being addressed.
Description check	✅ Passed	The PR description provides the essential information: it clearly identifies the bug (AttributeError with Tekkenizer's all_special_tokens), lists specific test cases covering the fix, and includes the PR checklist.

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[coderabbitai]

Actionable comments posted: 1

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@tensorrt_llm/tokenizer/tokenizer.py`:
- Around line 41-42: The _all_special_tokens_set is only set when self.tokenizer
has all_special_tokens, which causes an AttributeError later when decode() is
called with skip_special_tokens=True; update the Tokenizer initializer to always
create self._all_special_tokens_set (set to
set(self.tokenizer.all_special_tokens) if present, otherwise an empty set) so
any later reads (e.g., in decode/skip_special_tokens logic) are safe and
deterministic.

---

ℹ️ Review info

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Run ID: 9f45cca0-3008-49b0-8b06-769fd658b990

📥 Commits

Reviewing files that changed from the base of the PR and between 527ce26 and b4277d6.

📒 Files selected for processing (2)

• tensorrt_llm/tokenizer/tokenizer.py
• tests/integration/test_lists/waives.txt

💤 Files with no reviewable changes (1)

• tests/integration/test_lists/waives.txt

[byshiue]

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[byshiue]

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