[None][feat] add the eos tokens in generation config to stop words in the sampler#10389
[None][feat] add the eos tokens in generation config to stop words in the sampler#10389nv-guomingz merged 3 commits intoNVIDIA:mainfrom
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Signed-off-by: jiant <107457950+JadoTu@users.noreply.github.com>
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📝 WalkthroughWalkthroughThe changes modify stop word handling in the executor and sampling parameters modules. The executor now conditionally supplies stop_words based on the ignore_eos flag, while sampling_params simplifies stop word logic by removing model-specific branches (kimi_k2) and unifying eos_token_id incorporation across models. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
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tensorrt_llm/executor/base_worker.pytensorrt_llm/sampling_params.py
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tensorrt_llm/executor/base_worker.pytensorrt_llm/sampling_params.py
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🧠 Learnings (3)
📓 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: 2025-12-12T03:27:18.859Z
Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 9655
File: tensorrt_llm/_torch/pyexecutor/sampler.py:3031-3031
Timestamp: 2025-12-12T03:27:18.859Z
Learning: In tensorrt_llm/_torch/pyexecutor/sampler.py, when reviewing code that iterates through requests, ensure it does not convert excessive data into Python lists. Instead, the code should use torch.gather or indexing to gather only the data that will be used in the for loop before converting to Python lists. This minimizes data movement and improves performance.
Applied to files:
tensorrt_llm/executor/base_worker.py
📚 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/sampling_params.py
🧬 Code graph analysis (1)
tensorrt_llm/executor/base_worker.py (2)
tests/unittest/_torch/modeling/test_modeling_out_of_tree.py (1)
sampling_params(58-59)tensorrt_llm/sampling_params.py (1)
_get_stop_words(423-436)
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… the sampler (NVIDIA#10389) Signed-off-by: jiant <107457950+JadoTu@users.noreply.github.com> Signed-off-by: Daniil Kulko <kulkodaniil@gmail.com>
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Summary by CodeRabbit
Bug Fixes
Refactor
✏️ Tip: You can customize this high-level summary in your review settings.
Description
Justifications: add_stop_words.pptx
The previous PR#9514 was been reverted because of perf regression.
The CUDA synchronizations in the sampler have been resolved by this PR#10120. Thanks to @yuxianq !
This PR is to bring the functional support of eos tokens in the generation config again, as well as an extension on
ignore eosflag.The perf now has no CUDA Stream Synchronizations also with this PR. The overlap scheduler is not broken.
With this PR:
Output Token Throughput (tokens/sec) │ 4287.60
Without this PR:
Output Token Throughput (tokens/sec) │ 4263.69
The perf metrics are from Qwen3-Next.
Test Coverage
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