[Frontend] Add HyperCLOVAX-SEED-Think reasoning and tool parsers#42366
[Frontend] Add HyperCLOVAX-SEED-Think reasoning and tool parsers#42366ugiugi0823 wants to merge 3 commits into
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Code Review
This pull request introduces reasoning and tool call parsers for the HyperCLOVAX-SEED-Think model, enabling the extraction of reasoning blocks and tool calls in both streaming and non-streaming modes. Feedback identified logic bugs in is_reasoning_end regarding end-marker detection and in extract_tool_calls_streaming concerning the handling of split protocol tags across delta boundaries.
| def is_reasoning_end(self, input_ids: list[int]) -> bool: | ||
| if len(input_ids) > 1: | ||
| n = len(self.think_end_tokens) | ||
| return n > 0 and len(input_ids) >= n and input_ids[-n:] == self.think_end_tokens | ||
| return self.no_reasoning_content or ( | ||
| self.end_token_id is not None and self.end_token_id in input_ids | ||
| ) |
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The current implementation of is_reasoning_end has a logic bug when len(input_ids) > 1. It only returns True if the sequence ends exactly with think_end_tokens. However, for structured decoding to correctly transition from reasoning to content, this method must return True if the reasoning phase has ended at any point in the sequence (i.e., if the end marker or stop token is present anywhere). Additionally, the len(input_ids) > 1 check is unnecessary and potentially incorrect if n=1.
def is_reasoning_end(self, input_ids: list[int]) -> bool:
if self.no_reasoning_content or (self.end_token_id is not None and self.end_token_id in input_ids):
return True
n = len(self.think_end_tokens)
return n > 0 and any(input_ids[i:i + n] == self.think_end_tokens for i in range(len(input_ids) - n + 1))There was a problem hiding this comment.
Thanks — both points are correct.
Fixed in faefc5b:
is_reasoning_endnow scans the whole sequence withany(input_ids[i:i+n] == self.think_end_tokens ...)and drops thelen(input_ids) > 1gate.- Short-circuits on
no_reasoning_contentand the<|im_end|>stop token first, as in your suggestion.
Added regression test test_is_reasoning_end_when_think_end_in_middle.
| if not self.reasoning_ended: | ||
| idx = self.buffer_string.find(THINK_END) | ||
| if idx != -1: | ||
| self.cursor = idx + len(THINK_END) | ||
| while self.cursor < len(self.buffer_string) and self.buffer_string[self.cursor] == "\n": | ||
| self.cursor += 1 | ||
| self.reasoning_ended = True |
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In extract_tool_calls_streaming, self.reasoning_ended is set to True even if THINK_END is not found in the current buffer. If the THINK_END tag is split across delta boundaries, the first delta will trigger reasoning_ended = True without advancing the cursor, causing the second delta to treat the remainder of the protocol tag as content. We should use _partial_prefix_len to wait for the full tag if a partial match is detected at the end of the buffer.
| if not self.reasoning_ended: | |
| idx = self.buffer_string.find(THINK_END) | |
| if idx != -1: | |
| self.cursor = idx + len(THINK_END) | |
| while self.cursor < len(self.buffer_string) and self.buffer_string[self.cursor] == "\n": | |
| self.cursor += 1 | |
| self.reasoning_ended = True | |
| if not self.reasoning_ended: | |
| idx = self.buffer_string.find(THINK_END) | |
| if idx != -1: | |
| self.cursor = idx + len(THINK_END) | |
| while self.cursor < len(self.buffer_string) and self.buffer_string[self.cursor] == "\n": | |
| self.cursor += 1 | |
| self.reasoning_ended = True | |
| elif _partial_prefix_len(self.buffer_string, THINK_END) > 0: | |
| return None | |
| else: | |
| self.reasoning_ended = True |
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Good catch — fixed in faefc5b with exactly the structure you suggested:
if idx != -1:
self.cursor = idx + len(THINK_END)
while self.cursor < len(self.buffer_string) and self.buffer_string[self.cursor] == "\n":
self.cursor += 1
self.reasoning_ended = True
elif _partial_prefix_len(self.buffer_string, THINK_END) > 0:
return None
else:
self.reasoning_ended = TrueThe </think>-straddling case only arises in standalone usage (vLLM's parse_delta normally strips the marker before invoking this parser), but the standalone path is exercised by the unit tests so the fix matters.
Added regression test test_split_think_end_across_deltas_does_not_leak.
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Adds first-class support for `naver-hyperclovax/HyperCLOVAX-SEED-Think-32B`
in the OpenAI-compatible server:
- `vllm/reasoning/hyperclovax_seed_think_reasoning_parser.py`: detects the
`</think>` boundary, supports both `thinking=true` (model emits
reasoning before `</think>`) and `thinking=false` (chat_template embeds
`</think>` in the prompt) paths. Streaming emits `DeltaMessage(reasoning=
..., content=...)` (matching the vLLM convention used by other reasoning
parsers) and lstrips the `\n\n` separator the chat_template inserts
after `</think>` so it does not appear in the user-visible content
stream.
- `vllm/tool_parsers/hyperclovax_seed_think_tool_parser.py`: extracts the
XML `<tool_call>...</tool_call>` format taught by the chat_template's
system prompt, with a JSON-list fallback for `tool_choice="required"`
cases where the structured-output schema forces JSON output. Sets
`supports_required_and_named=False` to route all tool_choice modes
through this parser (consistent with GLM/Qwen3Coder convention).
Registered via the lazy tables in `vllm/reasoning/__init__.py` and
`vllm/tool_parsers/__init__.py`.
Tests (49 cases):
- `tests/reasoning/test_hyperclovax_seed_think_reasoning_parser.py` (26)
- `tests/tool_parsers/test_hyperclovax_seed_think_tool_parser.py` (23)
Plus the e2e matrix (4 reasoning + 8 tool cases, all combinations of
thinking x stream x tool_choice) passes 12/12 against the 32B model.
Usage:
vllm serve naver-hyperclovax/HyperCLOVAX-SEED-Think-32B \
--reasoning-parser hyperclovax_seed_think \
--tool-call-parser hyperclovax_seed_think \
--enable-auto-tool-choice
Co-authored-by: Claude <noreply@anthropic.com>
Signed-off-by: ugiugi0823 <acerghjk@gmail.com>
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Addresses two HIGH-priority issues flagged by gemini-code-assist on vllm-project#42366: 1. `HyperCLOVAXSeedThinkReasoningParser.is_reasoning_end` only matched the `</think>` token sequence at the tail of `input_ids`. Structured decoding asks "has reasoning ended at any point so far?", so the marker must be detected anywhere in the sequence. Also drops the `len(input_ids) > 1` gate that was unnecessary and skipped the n==1 case. 2. `HyperCLOVAXSeedThinkToolParser.extract_tool_calls_streaming` set `self.reasoning_ended = True` unconditionally when `</think>` wasn't found in the current buffer. If `</think>` straddled deltas (standalone usage outside vLLM's `parse_delta` flow), the partial tag tail leaked into emitted content. Now we hold the buffer when a partial prefix is detected and only flip the flag after the full marker is consumed (or confirmed absent). Two new regression tests: - `test_is_reasoning_end_when_think_end_in_middle` - `test_split_think_end_across_deltas_does_not_leak` Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: ugiugi0823 <acerghjk@gmail.com>
- Apply `ruff check --fix` + `ruff format` to the four PR files (Optional -> X | None, Union -> X | Y, import sort, trailing whitespace, line length). No behavior change. - Add HyperCLOVAX-SEED-Think entry to `docs/features/reasoning_outputs.md` (supported-models table + the `thinking` chat_template_kwargs note). - Add a `hyperclovax_seed_think` section to `docs/features/tool_calling.md` describing the XML tool_call format and the recommended flags. 51 unit tests still pass. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: ugiugi0823 <acerghjk@gmail.com>
Status updateThree follow-up commits have been pushed since the initial open:
Local verification done
Known gapThe two parser bug fixes from the gemini review are validated by the new Open items
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Documentation preview: https://vllm--42366.org.readthedocs.build/en/42366/ |
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Hi @chaunceyjiang — gentle ping on this one. I've noticed you've been the most active reviewer on recent tool-parser PRs (e.g. #39243, #39294, #42188, #42292) so apologies if I'm pinging the wrong person; happy to redirect. Quick recap of where it stands:
If the design (dual XML / JSON-list path, |
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Quick follow-up: I've been told #39477 is being withdrawn, so the duplicate-work concern is no longer active. Removed the comparison table from the PR description. |
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Hi @chaunceyjiang @bbrowning @aarnphm @sfeng33 — a gentle follow-up on this PR. 🙏 It's been about three weeks, so I wanted to kindly check in. This adds the reasoning and tool parsers for HyperCLOVAX-SEED-Think, and the PR is in good shape:
Whenever any of you have the bandwidth, I'd really appreciate a review — and if a |
chaunceyjiang
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Sorry, I missed this PR earlier.
Are you part of the team that officially maintains this model?
This model has been available for quite some time, but its adoption appears to be relatively limited. From the implementation, it looks like both its reasoning format and tool-calling format differ significantly from those used by most mainstream models.
My concern is that, after this is merged, there may not be enough users or maintainers to provide ongoing support and maintenance for these model-specific parsers.
Because of that, I would recommend using it through --reasoning-parser-plugin and --tool-parser-plugin instead. This would allow users of the model to enable the custom parsing logic without adding long-term maintenance burden to the core vLLM codebase.
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Hi @chaunceyjiang, thank you for the thoughtful review, and no worries about the timing! To answer your question directly: we are a partner company working closely with NAVER on the HyperCLOVAX models. We're not the upstream model team itself, but we collaborate with them directly — so we have both the motivation and the channel to keep these parsers healthy over the long term. On maintenance: we're committed to maintaining these parsers going forward — responding to issues, keeping them in sync with vLLM's interface changes, and fixing regressions promptly (as we already did for the two bugs flagged in the earlier bot review). The change is also self-contained: it stays within the existing On adoption: HyperCLOVAX is actively used in the Korean LLM ecosystem, and we expect demand for first-class vLLM support to grow as more partners deploy it. That said, we fully understand the maintenance-burden concern. If you'd still prefer the |
Summary
Adds reasoning + tool parsers for the
naver-hyperclovax/HyperCLOVAX-SEED-Think-32Bmodel, targeting the model's actual published chat template (</think>boundary + XML<tool_call>format).Two new parser modules, each registered via the existing lazy-loading tables
(
_REASONING_PARSERS_TO_REGISTER/_TOOL_PARSERS_TO_REGISTER).Files
vllm/reasoning/hyperclovax_seed_think_reasoning_parser.py(new, ~180 lines)vllm/tool_parsers/hyperclovax_seed_think_tool_parser.py(new, ~410 lines)vllm/reasoning/__init__.py(+4 lines, lazy table entry)vllm/tool_parsers/__init__.py(+4 lines, lazy table entry)tests/reasoning/test_hyperclovax_seed_think_reasoning_parser.py(new, 26 unit tests)tests/tool_parsers/test_hyperclovax_seed_think_tool_parser.py(new, 23 unit tests)Model output formats
Reasoning (chat_template controlled by
chat_template_kwargs.thinking)thinking=true→ generation prompt ends with<think>\n; model emits[reasoning]</think>\n\n[content].thinking=false→ prompt already contains<think>\n\n</think>\n\n;model emits
[content]directly.Tool calls (per chat_template's system prompt)
For
tool_choice="required"/ named tool_choice, vLLM's defaultadjust_requestinjects a JSON list schema. Withthinking=truetheschema engages after
</think>and the model outputs[{"name": ..., "parameters": {...}}]instead of the XML form. The toolparser accepts both formats; non-streaming via
extract_tool_calls,streaming via
_emit_json_tool_calls.Why
supports_required_and_named = FalseMirrors the GLM / Qwen3Coder pattern for XML-emitting tool models:
extract_required_tool_call_streaming/TypeAdapter(list[FunctionDefinition])validators reject the XMLoutput produced when the structured-output schema fails to engage
(
thinking=false + requiredcase).formats uniformly.
Test plan
End-to-end validation against the 32B checkpoint covers a 12-case matrix
(reasoning × 2 stream modes × 2 thinking modes, then tool calling × 2
stream × 2 thinking × 2 tool_choice modes):
Test results — 12/12 PASS
All
tool_callcases produce a valid JSONargumentspayload containingthe required
locationfield.Unit tests (49 cases, all pass)
.venv/bin/python -m pytest \ tests/reasoning/test_hyperclovax_seed_think_reasoning_parser.py \ tests/tool_parsers/test_hyperclovax_seed_think_tool_parser.py # 49 passedThe unit tests use
unittest.mock.MagicMocktokenizers (no HF modeldownload), covering: registration,
thinkingflag capture, non-streamingextraction edge cases, partial-tag streaming guards, JSON-list fallback
streaming,
is_reasoning_end/extract_content_idstoken-id helpers,and the
<|im_end|>strip +<tool_call>-straddling-deltas cases.Usage
Request body example:
{ "model": "...", "messages": [...], "tools": [...], "tool_choice": "auto", "chat_template_kwargs": {"thinking": true}, "stream": true }Test plan checklist
thinking=true(streaming + non-streaming)thinking=false(streaming + non-streaming)<tool_call>...</tool_call>)[{"name":...,"parameters":{...}}])tool_choice="required"for both thinking modesAI assistance disclosure (per AGENTS.md §1)
This PR was authored with assistance from Anthropic Claude (Claude Code).
The human submitter (@ugiugi0823) has reviewed every changed line and
executed the full 12-case end-to-end matrix against the 32B checkpoint
locally. Design and parser-behavior decisions (dual XML/JSON path,
supports_required_and_named=False, thechat_template_kwargs.thinkinghandling) were made after empirical inspection of the raw token outputs
of the model under each combination — not inferred from training-time
priors.
Duplicate-work check (per AGENTS.md §1): no overlapping open PRs at the time of this revision.