[https://nvbugs/5791242][fix] workaround for flashinfer.sampling.sampling_from_logits

[ixlmar]

Description

Avoids https://nvbugs/5791242 by using flashinfer.sampling.sampling_from_probs instead of flashinfer.sampling.sampling_from_logits.

Test Coverage

Unclear, the issue is still being analyzed.

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Summary by CodeRabbit

• Refactor
  • Internal sampling logic refactored to consolidate temperature-based sampling through a unified probabilistic path, maintaining deterministic behavior and backward compatibility.

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[ixlmar]

/bot run

[coderabbitai]

📝 Walkthrough

Walkthrough

The sampling logic for temperature-based operations in the flashinfer utilities was refactored to consolidate multiple code paths through a shared _sample_with_probs helper function. The old logits-based approach is now marked as deprecated, and both standard and temperature-based strategies route through the probabilistic sampling path.

Changes

Cohort / File(s)	Summary
Sampling consolidation tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py	Replaced direct calls to _prepare_logits_with_temperature and flashinfer.sampling.sampling_from_logits with unified _sample_with_probs helper in both StrategyImpl.sample and TemperatureOnlyWithProbs.sample flows. Removed check_nan argument. Added FIXME comment deprecating the old logits-based path.

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 clearly describes the main change: implementing a workaround for a flashinfer sampling issue by switching from sampling_from_logits to sampling_from_probs.
Description check	✅ Passed	The description is minimal but covers the key aspects: it explains what was changed (using sampling_from_probs instead of sampling_from_logits) and why (to avoid nvbugs/5791242). However, test coverage is marked unclear.

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✨ Finishing touches

• 📝 Generate docstrings

---

🧹 Recent nitpick comments

tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py (1)

615-638: Workaround implementation looks correct.

The change correctly routes temperature-only sampling through _sample_with_probs, which uses flashinfer.sampling.sampling_from_probs instead of the buggy sampling_from_logits. The numerical behavior should be equivalent since both paths apply temperature scaling followed by softmax and sampling.

Consider trimming the commented-out block—a brief reference to the bug (e.g., # FIXME: nvbugs/5791242 - revert to sampling_from_logits when fixed) is sufficient, and the original implementation is preserved in git history. Keeping 15 lines of dead code increases maintenance burden.

🔧 Optional: Trim commented code

             new_tokens, _ = self._sample_with_probs(
                 logits,
                 group_logit_indices=group_logit_indices,
                 top_k=None,
                 top_p=None,
                 temperature=self._temperature,
                 generator=generator,
             )
-            # FIXME: https://nvbugs/5791242
-            `#logits` = self._prepare_logits_with_temperature(
-            #    logits, group_logit_indices, self._temperature
-            #)
-            `#new_tokens` = flashinfer.sampling.sampling_from_logits(
-            #    logits,
-            #    # NB: Leveraging 'indices' would require applying temperature+softmax before batching,
-            #    #     because 'flashinfer.sampling.softmax' has no 'indices' argument; but that would
-            #    #     compute unnecessarily softmax also for situations allowing
-            #    #     flashinfer.sampling...._sampling_from_logits.
-            #    # indices=group_logit_indices,
-            #    deterministic=True,
-            #    generator=generator,
-            #    check_nan=self._flashinfer_check_nans(logits),
-            #)
+            # FIXME: nvbugs/5791242 - revert to sampling_from_logits when fixed
             return new_tokens, None

---

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📒 Files selected for processing (1)

• tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py

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• tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py

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• tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py

🧠 Learnings (3)

📚 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/_torch/pyexecutor/sampling_utils_flashinfer.py

📚 Learning: 2025-08-27T15:03:57.149Z

Learnt from: ixlmar
Repo: NVIDIA/TensorRT-LLM PR: 7294
File: tensorrt_llm/_torch/pyexecutor/sampler.py:368-392
Timestamp: 2025-08-27T15:03:57.149Z
Learning: In TensorRT-LLM's sampler.py, int32 usage for softmax_indices and related tensor indexing is intentional and should not be changed to int64. The torch.IntTensor type hint is correct for the sample() function's softmax_indices parameter.

Applied to files:

• tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py

📚 Learning: 2025-12-12T03:27:08.565Z

Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 9655
File: tensorrt_llm/_torch/pyexecutor/sampler.py:3031-3031
Timestamp: 2025-12-12T03:27:08.565Z
Learning: In files under tensorrt_llm/_torch/pyexecutor, avoid accessing torch.Tensor objects inside for-loops when iterating over requests. Convert batched tensors to Python lists beforehand using tensor.tolist(), and then iterate over those lists. This improves performance by reducing tensor-bound operations inside hot loops. Apply this pattern to similar code paths that process batches to access simple Python data structures (lists) inside loops.

Applied to files:

• tensorrt_llm/_torch/pyexecutor/sampling_utils_flashinfer.py

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