fix(frontend): call the reasoning parser's adjust_request during preprocessing

[yifjiang]

Context: this is one of six fixes from bringing up thinkingmachines/inkling on Dynamo + vLLM (4×GB200, TP=4). inkling's reasoning parser is vLLM's InklingParser (vllm/parser/inkling.py, via InklingParserReasoningAdapter) — a text-grammar parser. The bugs it exposed are general: they hit any model whose parser overrides sampling params through adjust_request(). Sibling PRs: #12510, #12511, #12507, #12543.

Problem

vLLM reasoning parsers expose adjust_request() so they can declare the sampling params they need. Parsers that split thinking from the answer on text markers require skip_special_tokens=False — otherwise the markers are stripped during detokenisation before the parser ever sees them, and the split silently does not happen.

_prepare_request calls the tool parser's adjust_request:

if tool_parser_class and (has_tools or enable_auto_tool_choice):
    if request_for_sampling.tool_choice != "none":
        tool_parser = tool_parser_class(tokenizer, request_for_sampling.tools)
        request_for_sampling = tool_parser.adjust_request(request_for_sampling)

There is no equivalent for the reasoning parser — _prepare_request does not even receive reasoning_parser_class, because the reasoning parser is constructed later, in StreamingPostProcessor.__init__, long after the sampling request is finalised.

Net effect: reasoning_content comes back empty unless the client sends skip_special_tokens: false. Ordinary OpenAI-compatible clients do not, so for such models the reasoning channel silently does nothing on normal traffic. Note the tool path is additionally gated on tool_choice != "none", so a plain tools-free chat request currently gets no adjust_request call from either parser.

Fix

Mirror the tool path: construct the reasoning parser during request preparation and let it adjust the request.

The gating is the load-bearing part

The gate is now single-sourced as _reasoning_parser_enabled(), a TypeGuard shared by _prepare_request, preprocess_chat_request and StreamingPostProcessor. The three must agree — if they drift, the sampling params get adjusted for a parser that then ends up None, and the model's control markers reach the client as visible text. The call sits after chat_template_kwargs is resolved, and is skipped when thinking is disabled (#8636).

This is a no-op for parsers that do not need it — the base ReasoningParser.adjust_request is return request.

⚠️ Two files are required

prepost.py alone is inert: vllm_processor.py must pass reasoning_parser_class into preprocess_chat_request, otherwise the new parameter defaults to None and the gate never fires. Worth knowing for review — a grep-based check on prepost.py cannot distinguish the patched-and-wired case from the patched-but-inert one.

Review feedback (@rmccorm4) — addressed

This can change more than skip_special_tokens. I reproduced a parser converting response_format to a structural tag here: the request carried the tag afterward, but preprocess_chat_request() still forwarded the pre-adjustment JSON schema because assistant_guided_decoding was captured earlier and adjusted guidance is only considered when tool_parser is set. Could we recompute and forward the reasoning parser's guidance independently of the tool parser?

Confirmed and fixed in 9432889. Two things were wrong:

1. parser_guided_decoding was gated on tool_parser is not None, so a reasoning-parser rewrite was never even recomputed.
2. Even recomputed, it could not surface: parser_guided_decoding is consumed only by build_tool_call_guided_decoding, which returns early when the request has no tools.

Now the guidance is recomputed when either parser is active, and a rewrite that only the reasoning parser can have made — no tool parser in the request, so it is attributable — takes precedence over the caller's pre-adjustment copy.

Scoped deliberately. When a tool parser is also active the change is not attributable to either parser, and parser-vs-assistant precedence is already settled there: the caller's explicit constraint wins, pinned by test_assistant_guidance_takes_precedence_over_auto_tool_guidance and the response-format-precedence row of TOOL_GUIDANCE_PARITY_CASES. I tried the unified precedence first and it regressed exactly those three cases, which read as a deliberate policy rather than an oversight — happy to revisit it in a follow-up if you'd rather it change.

Test

TestReasoningParserAdjustRequest — five cases using a stub parser, no real model needed:

• adjust_request is called and the mutation reaches the sampling request
• not called when enable_thinking: False (the gate that protects against marker leakage)
• called when enable_thinking: True
• no parser class ⇒ request untouched
• a pass-through adjust_request leaves the request unchanged (default-parser behaviour)

TestReasoningParserGuidanceForwarding — four cases for the review fix: the rewrite with no tool parser, a pass-through parser, no parser at all, and the thinking-disabled gate. The first fails without this change with the reported symptom — {"json": {"type": "object"}} instead of the structural tag.

Run against main in a dynamo+vLLM container: no regressions in test_vllm_processor_unit.py or tests/frontend/test_prepost.py versus a pristine-main baseline, and no new ruff/mypy findings.

Verified end to end on a real deployment, on a model whose reasoning parser sets skip_special_tokens=False. A 9-probe functional battery, run twice — once with the client sending skip_special_tokens: false and once without:

pass	before	after
client sends skip_special_tokens: false	9/9, reasoning_content 53 chars	9/9 (no regression)
client sends nothing (ordinary request)	7/9, reasoning_content 0 chars	9/9, reasoning_content 53 chars

Thinking-off requests were checked separately and are unaffected: clean visible text, empty reasoning_content, no marker leakage.

It also fixed a worse symptom on the same path: on the default pass, visible content was losing its first token when thinking was on (408 → '', top-left → -left, {"city":… → city":…), scoring 2/9. That does not reproduce after this change, across a 3-cell disambiguation that separately exonerated CUDA graphs and MTP. Note this makes the buggy path unreachable rather than repairing the boundary handling itself — happy to file that separately.

Notes

The behaviour change is server-visible and intentional: skip_special_tokens=False becomes the effective default for requests with reasoning active on models whose parser asks for it, matching what vLLM's own OpenAI server does. Happy to gate it behind a flag instead if that is preferred.

Ordering with #12510. This PR makes skip_special_tokens=False the effective default, which is exactly the condition under which unconsumed control markers leak into visible content — the leak #12510 fixes. #12510 is not in this branch's base. Merging this one alone puts that leak on main, so #12510 should land first or alongside. (main today has _control_markers/_is_control_only_content, but they are used only in the tool-call path as a suppression predicate for all-marker deltas, so mixed content like <|end_message|>408<|end_message|> still leaks.)

Known follow-up, not in this PR: the parser is constructed per request and discarded — measured p50 83.8 ms on a live deployment, which is noise at low rate but a ~40% TTFT hit on short prompts. The cleaner shape is to construct once and thread it through PreprocessResult into StreamingPostProcessor instead of reasoning_parser_class; that touches the postprocessor's signature and all its callers, so I kept it out of this change. Say the word and I'll fold it in.

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[yifjiang]

Additional evidence: this also fixes a content-truncation bug, not just empty reasoning_content.

On a model whose reasoning parser sets skip_special_tokens=False via adjust_request, requests that arrive without the client setting that field (i.e. ordinary traffic) and with thinking enabled were dropping the first token of the visible answer:

expected	got	lost
408	''	all
top-left	-left	top
{"city":"Reykjavik",…	city":"Reykjavik",…	{"

finish_reason was stop throughout and nothing leaked — content was being removed. It appeared only when thinking was ON; a reasoning_effort: "none" request was byte-clean.

Attributed with a single-variable control — two images differing only by this patch, same node, same flags, same model, same checkpoint:

build	default pass (client sends nothing)	with skip_special_tokens: false
without this patch	4/8 — chat, multimodal, reasoning, thinking_toggle all fail	8/8
with this patch	8/8	8/8

Two further cells with the reasoning path exercised but speculative decoding disabled, across two different cudagraph_mode settings, were clean — so this is not a CUDA-graph or speculative-decoding artefact.

Why this is consistent with the mechanism: the drop only occurred on the skip_special_tokens=true path, which is exactly where the content-kind markers are stripped before the text-grammar parser can split on them. The boundary handling there is already doing something delicate, and it loses a token. This patch means that path is no longer taken.

Worth being precise about scope: this makes the buggy path unreachable for models whose parser wires adjust_request; it does not repair the boundary handling itself. Any model in that configuration without the wiring would still hit it — which is an argument for fixing it here in prepost.py rather than per-deployment. If a maintainer would rather I also chase the underlying truncation at the thinking→content boundary, happy to open a separate issue with the reproduction.

[dynamo-ops]

/ok to test d50c741

[coderabbitai]

Walkthrough

The change passes the configured reasoning parser through chat request preprocessing. When thinking is enabled, request preparation applies the parser’s sampling adjustments. Tests cover invocation, gating, absent parsers, and skip_special_tokens.

Changes

Reasoning parser adjustment

Layer / File(s)	Summary
Request preparation and wiring components/src/dynamo/frontend/prepost.py, components/src/dynamo/frontend/vllm_processor.py	Chat preprocessing forwards the reasoning parser class. _prepare_request applies adjust_request when thinking is enabled.
Request adjustment validation tests/frontend/test_prepost.py	Tests validate parser invocation, thinking-mode gating, no-parser behavior, and skip_special_tokens preservation.

Estimated code review effort: 3 (Moderate) | ~20 minutes

Merge Risk: ⚪ Minimal · up to d50c7

The behavior change is localized and supported by targeted tests; no actionable merge-blocking risk remains.

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Title check	✅ Passed	The title clearly and concisely identifies the main change: calling the reasoning parser's adjust_request during frontend preprocessing.
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The description is detailed, on-topic, and covers the problem, implementation, tests, validation, scope, and follow-up work. It does not use the template's explicit reviewer-start heading or complete the required Related Issues selection, but it is otherwise mostly complete.

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Inline comments:
In `@tests/frontend/test_prepost.py`:
- Line 2091: Import ClassVar and update the shared instances registry in
_RecordingReasoningParser to use the ClassVar[list["_RecordingReasoningParser"]]
annotation, resolving Ruff RUF012 while preserving its class-level behavior.

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[devin-ai-integration]

Devin Review found 2 potential issues.

[rmccorm4]

One issue with reasoning-parser guidance propagation.

[dynamo-ops]

/ok to test 9432889

[yifjiang]

Moved into the review thread it answers, so the discussion stays in one place: #12541 (comment)

[dynamo-ops]

/ok to test 2b89671

[dynamo-ops]

/ok to test 6ffa755

[dynamo-ops]

/ok to test 9692a98

[yifjiang]

/ok to test 9692a98

[dynamo-ops]

/ok to test c197a9d

[dynamo-ops]

/ok to test 035378f