[None][fix] Synchronize one-model MTP scheduler draft tokens

[2ez4bz]

Dev Engineer Review

• The change preserves the one-hot draft-probability marker during repeated dynamic draft-length handling.
• One-model speculative decoding initializes zero-filled draft tokens only when sampler draft tokens are unavailable.
• Rejection sampling continues to mark requests for one-hot draft probabilities.
• The implementation is limited to the one-model MTP synchronization path.
• No public API or configuration changes were found.
• Added tests cover draft-token budgets, disaggregated generation, context requests, rejection-sampling zero proposals, and sampler-provided draft tokens.
• No error-handling or performance regressions are indicated.

QA Engineer Review

• Modified test file: tests/unittest/_torch/executor/test_py_executor.py.
• Added or updated MTPDecodingConfig coverage for one-model MTP scheduling.
• Tests cover:
  • Python and C++ draft-token budgets for generation requests.
  • Python and C++ draft-token budgets for disaggregated-generation requests.
  • Unchanged context requests.
  • Rejection-sampling zero proposals.
  • Preservation of sampler-provided draft tokens.
• No corresponding tests/integration/test_lists/ coverage was identified.
• Verdict: needs follow-up.

Description

Test Coverage

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Walkthrough

One-model speculative decoding now preserves sampler-provided draft tokens and one-hot probability markers. Tests cover MTP configuration, draft-length scheduling, rejection sampling, and generation request behavior.

Changes

One-model MTP draft handling

Layer / File(s)	Summary
Preserve draft state during scheduling tensorrt_llm/_torch/pyexecutor/py_executor.py	The executor accumulates the one-hot draft-probability marker. It creates zero-filled draft tokens only when real draft tokens are unavailable.
Validate one-model MTP behavior tests/unittest/_torch/executor/test_py_executor.py	Tests configure one-model MTP scheduling and verify draft-token budgets, rejection-sampling proposals, runtime draft length, one-hot signaling, and pre-existing sampler tokens.

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

Merge Risk: ⚪ Minimal · up to e792b

The change synchronizes draft-token state for one-model MTP scheduling, and no actionable merge-blocking risk remains at the current head; it is merge-ready after normal checks.

Possibly related PRs

• NVIDIA/TensorRT-LLM#17635: Related MTP token-count handling.

Suggested reviewers: schetlur-nv, bowenfu

🚥 Pre-merge checks | ✅ 3 | ❌ 2

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	The description contains only the template and does not explain the issue, solution, or test coverage.	Add a concise issue and solution summary, list the relevant tests, and complete the applicable checklist items.
Docstring Coverage	⚠️ Warning	Docstring coverage is 57.14% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (3 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly identifies the fix for one-model MTP scheduler draft-token synchronization.
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:
In `@tests/unittest/_torch/executor/test_py_executor.py`:
- Around line 3002-3018: Update
test_one_model_mtp_preserves_zero_proposal_signal_for_rejection to initialize
gen.py_needs_onehot_draft_probs to True before _prepare_and_schedule_batch, then
assert it remains true after scheduling and after _handle_dynamic_draft_len. If
preservation depends on batch-level accumulation, include a second request and
verify the existing true marker survives processing it.

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📒 Files selected for processing (2)

• tensorrt_llm/_torch/pyexecutor/py_executor.py
• tests/unittest/_torch/executor/test_py_executor.py

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