[https://nvbugs/5684820][fix] fix the detokenizer issue for DeepSeek-v3.2

[lfr-0531]

Summary by CodeRabbit

Bug Fixes

• Improved tokenizer stability and reliability by filtering out invalid token entries that may occur during incremental decoding operations. Added diagnostic warning logs to provide enhanced visibility when anomalies are detected during token conversion. Changes ensure consistent and robust behavior across all decoding implementations.

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Description

When benchmarking with random data, it is possible to generate token IDs that fall outside the tokenizer's vocabulary. This causes trtllm_decode_incrementally to yield 'None' tokens. In this PR, we fix this issue by filtering out these 'None' tokens. We have also verified the accuracy:

• NVFP4 model on B200: GSM8k=94.88, GPQA=79.80
• FP8 model on H200: GSM8k=95.79, GPQA=78.28

Test Coverage

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[lfr-0531]

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📝 Walkthrough

Walkthrough

The changes add filtering and warning logic to tokenizer decoding methods to handle None tokens produced by convert_ids_to_tokens. Both trtllm_decode_incrementally and TransformersTokenizer.decode_incrementally now remove None entries from token lists and emit warnings when encountered.

Changes

Cohort / File(s)	Summary
None Token Filtering in Tokenizer Decoding tensorrt_llm/tokenizer/tokenizer.py	Added logic to filter out None tokens in trtllm_decode_incrementally and TransformersTokenizer.decode_incrementally methods; warning is logged when None tokens are encountered before filtering

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

• Straightforward filtering and logging additions applied consistently across two methods
• Logic is contained to a single file with minimal scope
• No structural changes or new APIs introduced

Pre-merge checks and finishing touches

❌ Failed checks (1 warning, 1 inconclusive)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.
Description check	❓ Inconclusive	The description explains the issue and solution, but the Test Coverage section is incomplete and lacks specific test information.	Add specific test cases or test coverage details in the Test Coverage section to document how the fix is validated.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies the fix for the detokenizer issue affecting DeepSeek-v3.2, with proper NVBugs ticket reference and [fix] type tag.

✨ Finishing touches

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📥 Commits

Reviewing files that changed from the base of the PR and between 21a93fb and 83b3ba8.

📒 Files selected for processing (1)

• tensorrt_llm/tokenizer/tokenizer.py (1 hunks)

🧰 Additional context used

📓 Path-based instructions (2)

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Files:

• tensorrt_llm/tokenizer/tokenizer.py

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Files:

• tensorrt_llm/tokenizer/tokenizer.py

🧠 Learnings (4)

📓 Common learnings

Learnt from: samuellees
Repo: NVIDIA/TensorRT-LLM PR: 6974
File: tensorrt_llm/serve/scripts/benchmark_dataset.py:558-566
Timestamp: 2025-08-18T08:42:02.640Z
Learning: In TensorRT-LLM's RandomDataset (tensorrt_llm/serve/scripts/benchmark_dataset.py), when using --random-token-ids option, sequence length accuracy is prioritized over semantic correctness for benchmarking purposes. The encode/decode operations should use skip_special_tokens=True and add_special_tokens=False to ensure exact target token lengths.

📚 Learning: 2025-08-18T08:42:02.640Z

Learnt from: samuellees
Repo: NVIDIA/TensorRT-LLM PR: 6974
File: tensorrt_llm/serve/scripts/benchmark_dataset.py:558-566
Timestamp: 2025-08-18T08:42:02.640Z
Learning: In TensorRT-LLM's RandomDataset (tensorrt_llm/serve/scripts/benchmark_dataset.py), when using --random-token-ids option, sequence length accuracy is prioritized over semantic correctness for benchmarking purposes. The encode/decode operations should use skip_special_tokens=True and add_special_tokens=False to ensure exact target token lengths.

Applied to files:

• tensorrt_llm/tokenizer/tokenizer.py

📚 Learning: 2025-08-14T21:04:50.248Z

Learnt from: thorjohnsen
Repo: NVIDIA/TensorRT-LLM PR: 6910
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-14T21:04:50.248Z
Learning: In KV cache onboarding logic during prefill in cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, when calculating which blocks fall within the attention window, use getTokensPerBlock() to advance token indices rather than block->getUniqueTokens().size(), because the calculation needs to consider the post-prefill state where blocks will be filled to capacity, not their current token count.

Applied to files:

• tensorrt_llm/tokenizer/tokenizer.py

📚 Learning: 2025-08-15T06:46:54.897Z

Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6767
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-15T06:46:54.897Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp addToken function, newly allocated blocks are unshared by design. The beam search path in addToken (when sequence.getNumTokens() > windowSize) is currently broken/non-functional with SWA, so the block allocation doesn't follow a shared-then-unshared pattern.

Applied to files:

• tensorrt_llm/tokenizer/tokenizer.py

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (1)

tensorrt_llm/tokenizer/tokenizer.py (1)

216-222: None token filtering is correctly implemented; both decode paths are now defensive.

The fix for trtllm_decode_incrementally appropriately handles out-of-vocabulary token IDs by filtering None values and logging a clear warning. The hf_decode_incrementally path is already protected via the list comprehension that filters None results from decode_stream.step(), so no additional changes are needed there.

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