[Bugfix] MiniMax M3 reasoning parser: treat non-leading <mm:think> as content

[harjothkhara]

Purpose

Fixes #48663.

MiniMaxM3ReasoningParser recognized the <mm:think> start marker anywhere in the model output. MiniMax M3 delimits reasoning positionally — <mm:think>reasoning</mm:think>content — so a <mm:think> the model emits inside content is literal text, not a block opener. Treating it as an opener splits one valid response across both fields:

• Everything before the marker → content
• Everything after → reasoning
• finish_reason is "stop", so nothing signals a problem

This reliably bites structured-output workloads whose JSON quotes the marker text (logs, transcripts, docs, issue text). In the reporter's case, 2 of 546 documents failed — exactly the two that contained <mm:think>. Concatenating content + reasoning reassembles the original, well-formed JSON: the model complied with the grammar; only the parser was wrong.

Fix

Recognize the start marker only where it delimits a boundary: a leading start marker (or the prefilled template in enabled mode) opens reasoning; a start marker anywhere else is literal content. This mirrors the parser's existing end-marker handling — a non-leading </mm:think> already stays content (test_nonstreaming_non_leading_end_tag_is_content).

Method audit — the fix is scoped to the methods that parse generated output, where a leading start marker (or the prefilled template) opens reasoning:

Method	Path (generated output)	Before → after on {"note": "the <mm:think> marker leaks"}
extract_reasoning	non-streaming response	split → intact content
_visible_segments	streaming response	split → intact content
extract_content_ids	abstract_parser.py:853 → tool parser	[] → full token IDs
count_reasoning_tokens	responses/serving.py:895 → usage.reasoning_tokens	15 → 0

A shared _starts_with_token_sequence / _find_token_sequence helper pair keeps the token-ID checks consistent.

is_reasoning_end is deliberately left unchanged (rightmost-marker semantics). Unlike the methods above, it is called on the full prompt/history to initialize reasoning state (chat_completion/serving.py:353, v1/structured_output/__init__.py:365, abstract_parser.py:815). A prompt never starts with <mm:think>, so applying the leading-only rule there would invert the result — reporting a closed disabled-mode prompt as still-reasoning and an open enabled-mode prompt as ended, misinitializing structured-output grammar. A regression test (test_is_reasoning_end_prompt_state_preserved) locks in that contract. This also keeps the PR clear of #48550's is_reasoning_end_from_prompt() prompt-state work.

Not a duplicate

• No open or recently-closed PR references [Bug]: minimax_m3 reasoning parser splits structured-output JSON at a <mm:think> the model wrote as content #48663 (gh pr list -R vllm-project/vllm --state all --search "48663 in:body" → none).
• Overlap note re [Bugfix] Initialize MiniMax M3 reasoning from prompt mode #48550 (MiniMax M3 reasoning): it rewrites is_reasoning_end_streaming / extract_content_ids for a different defect (prompt-mode initialization / structured-output gating) and does not touch extract_reasoning, _visible_segments, count_reasoning_tokens, or is_reasoning_end. Its extract_content_ids still uses ... or self.start_token in text (containment), so it does not fix this bug. The only file overlap is extract_content_ids; happy to rebase around [Bugfix] Initialize MiniMax M3 reasoning from prompt mode #48550 or coordinate on ordering — flagging so a maintainer can sequence the two.

Known assumption / open question (leading whitespace)

The leading-marker checks are strict: model_output.startswith("<mm:think>"), no lstrip(). This assumes a genuine reasoning block begins with <mm:think> as the first generated token in adaptive/disabled mode (enabled mode is unaffected — the marker is prefilled, so the parser never looks for a leading marker there). If M3's chat template or tokenizer can ever emit a leading space/newline before a real <mm:think>, these checks would treat the whole response as content — the mirror image of this bug.

I could not verify M3's exact template behavior without the model (NVFP4/TP4, no local GPU), and preferred a strict check over hand-rolling whitespace tolerance into the streaming state machine on an unverified assumption. If a maintainer/model author knows M3 can prepend whitespace before the marker, it's a trivial lstrip()-tolerant follow-up — flagging it explicitly rather than guessing.

Test Plan

Added regression tests to tests/reasoning/test_minimax_m3_reasoning_parser.py covering the text and token-ID paths; each fails on unfixed main and passes with the fix (red/green verified by reverting the source):

• test_nonstreaming_non_leading_start_tag_is_content — mirror of the existing end-marker test.
• test_nonstreaming_literal_start_marker_in_json_content — the reporter's exact JSON payload.
• test_nonstreaming_enabled_mode_literal_start_marker_in_reasoning — enabled mode, literal marker inside prefilled reasoning.
• test_streaming_non_leading_start_tag_stays_content — streaming, marker split across deltas.
• test_token_id_helpers_literal_start_marker_is_content / ..._split_tokens — extract_content_ids / count_reasoning_tokens, atomic and text-tokenized markers.
• test_is_reasoning_end_prompt_state_preserved — locks the prompt-state contract for is_reasoning_end (closed prompt → ended; enabled prompt with a fresh open marker → still open).

.venv/bin/python -m pytest tests/reasoning/test_minimax_m3_reasoning_parser.py -v
pre-commit run --files vllm/reasoning/minimax_m3_reasoning_parser.py \
  tests/reasoning/test_minimax_m3_reasoning_parser.py

CPU-only, no GPU required.

Test Result

tests/reasoning/test_minimax_m3_reasoning_parser.py ............. 29 passed

• Red/green: the 6 literal-marker tests fail on unfixed main, pass with the fix. test_is_reasoning_end_prompt_state_preserved passes on main (its behavior is unchanged) and fails against a leading-only variant of is_reasoning_end — a guard proving the prompt contract was preserved.
• Full tests/reasoning/: 456 passed (the test_cohere_command_reasoning_parser collection error is a pre-existing missing optional dependency, cohere_melody, unrelated to this change).
• Lint: ruff check, ruff format, mypy (3.10), SPDX header, and the other pre-commit hooks all pass.

Model eval: not applicable — parser-only fix on the reasoning/content-extraction paths; the split is exercised directly by the added unit tests, and generated model output is unchanged.

Real behavior proof

Behavior addressed: With --reasoning-parser minimax_m3, a <mm:think> string the model emits inside its answer (e.g. structured output whose JSON quotes the marker) was treated as a reasoning-block opener — splitting the response across content/reasoning, dropping content token IDs fed to the tool parser, and over-counting usage.reasoning_tokens. After the fix the marker is literal content on all paths.

Real environment tested: vLLM built from this branch on a local checkout; the parser is resolved through the production ReasoningParserManager.get_reasoning_parser("minimax_m3") dispatch — the exact lookup the OpenAI serving layer performs for --reasoning-parser minimax_m3 — and driven through the real non-streaming (extract_reasoning), streaming (extract_reasoning_streaming), and token-ID (extract_content_ids, count_reasoning_tokens) entry points. Input is the reporter's exact payload {"note": "the <mm:think> marker leaks"}. (MiniMax-M3 is NVFP4/TP4 and cannot run on the test machine, so the model weights themselves are not exercised — see below.)

Exact steps or command run after this patch:

python proof_minimax_m3.py     # response paths, AFTER: this branch
python proof_tokenids.py       # token-ID paths, AFTER: this branch
git show origin/main:vllm/reasoning/minimax_m3_reasoning_parser.py \
  > vllm/reasoning/minimax_m3_reasoning_parser.py   # revert to main
python proof_minimax_m3.py     # BEFORE
python proof_tokenids.py       # BEFORE
git restore vllm/reasoning/minimax_m3_reasoning_parser.py

Evidence after fix:

# response paths
[non-streaming] content: '{"note": "the <mm:think> marker leaks"}'   reasoning: None
[streaming]     content: '{"note": "the <mm:think> marker leaks"}'   reasoning: None
# token-ID paths
extract_content_ids    : '{"note": "the <mm:think> marker leaks"}'  (full payload)
count_reasoning_tokens : 0  (no reasoning)
RESULT: PASS

Same harness on unpatched main:

[non-streaming] content: '{"note": "the '   reasoning: ' marker leaks"}'
[streaming]     content: '{"note": "the '   reasoning: ' marker leaks"}'
extract_content_ids    : ''   (*** TRUNCATED ***)
count_reasoning_tokens : 15   (*** WRONG ***)
RESULT: FAIL

Observed result after fix: On all four paths the full valid JSON is preserved as content, no reasoning is emitted, content token IDs are intact, and reasoning_tokens is 0. On unpatched main the identical input splits the document, drops content token IDs to [], and reports 15 phantom reasoning tokens. Full unit suite: 28 passed; the 6 new tests fail on main, pass here.

What was not tested: End-to-end serving against real MiniMax-M3 weights (NVFP4, TP4) — no GPU available. The reasoning/content split is entirely in the CPU-side parser, driven here through its production dispatch with the reporter's exact payload; the untested surface is only the model generation upstream of the parser, which this change does not touch.

_{AI-assisted (Claude); every line reviewed and tests run locally before submission.}

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