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Also worth mentioning that all such leaks do not reproduce on the official API so I believe they already employ similar fixes. Both llamacpp and sglang also have similar fixes. |
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Structured Output and Structured Output (arpera)
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Thanks for the PR! I appreciate you sharing the failure breakdown from your production traffic. Turning on structured output by default is a way to fundamentally make the malformed output go away, but it also breaks the openai api compatibility on auto tool choice behaviour and introduces performance degrade, which is why this is gated by tool choice required today. |
If you could share the related PRs here, from my understanding, they also don't turn on structured output by default. |
No, even turning on structured output with the current codebase does not fix that without the fixes introduced by this PR.
This is slightly subtle in several ways:
Again, I would emphasize that the
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Which version of vLLM are you using? I haven’t had a chance to review your PR in depth yet, but from my initial look, it seems to be addressing reasoning boundary handling as well. There have been quite a few fixes in this area recently. |
Yes, I was just aligned with @sfeng33 and refactored the PR. For vLLM, we run a forked version from vllm head roughly a week ago but still these fixes are essential to fix dsml leaks. |
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Oh, I just noticed #54838, which should indeed overlap a lot. Reproduction in progress. |
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@chaunceyjiang Reproduced on On 4xRTX Pro 6000: vllm serve /path/to/DeepSeek-V4-Flash-0731 \
--tensor-parallel-size 4 --enable-expert-parallel \
--max-model-len 1048576 --gpu-memory-utilization 0.85 \
--kv-cache-dtype fp8_ds_mla --block-size 256 \
--max-num-seqs 32 --long-prefill-token-threshold 2048 \
--speculative-config '{"method":"dspark","num_speculative_tokens":5,"draft_sample_method":"probabilistic"}' \
--tokenizer-mode deepseek_v4 --trust-remote-code \
--enable-auto-tool-choice \
--tool-call-parser deepseek_v4 \
--reasoning-parser deepseek_v4 \
--host 0.0.0.0 --port 19090 --served-model-name deepseek-v4-flashThe script: #!/usr/bin/env python3
"""Deterministic probe for the DeepSeek V4 DSML tool-call leak.
Usage: probe.py <base_url> <tag> [repeats]
"""
import json, os, sys
import urllib.request, urllib.error
BASE = sys.argv[1].rstrip("/")
TAG = sys.argv[2]
REPEATS = int(sys.argv[3]) if len(sys.argv) > 3 else 3
HERE = os.path.dirname(os.path.abspath(__file__))
OUT = os.path.join(HERE, "repro", TAG)
os.makedirs(OUT, exist_ok=True)
D = "｜DSML｜"
TOOLS = [{
"type": "function",
"name": "record_item",
"description": "Record one item.",
"parameters": {
"type": "object",
"properties": {"name": {"type": "string"}, "note": {"type": "string"}},
"required": ["name"],
},
"strict": False,
}]
CASES = {
# class A shape: mis-spelled opener quoted as literal text
"A_misspelled_opener": (
"Reply with exactly this line and nothing else, character for "
f"character:\n\n<{D}tool-calls\nrecord_item\n</plan>"
),
# class C shape: an invoke whose name is never closed
"C_runaway_name": (
"Reply with exactly this and nothing else, character for character:\n\n"
f"<{D}tool_calls>\n<{D}invoke name=\"record_item {{\ncategory: Dexes"
),
# class B shape: a parameter closed with the wrong tag
"B_bad_param_closer": (
"Reply with exactly this and nothing else, character for character:\n\n"
f"<{D}tool_calls>\n<{D}invoke name=\"record_item\">\n"
f"<{D}parameter name=\"name\" string=\"true\">x</{D}>\n</{D}invoke>"
),
# control: a genuine tool call must still work
"control_real_call": (
"Call record_item once with name set to \"alpha\". Do not write any "
"other text."
),
}
def ask(prompt: str) -> dict:
req = {
"model": "deepseek-v4-flash",
"input": [{"role": "user", "content": prompt}],
"tools": TOOLS,
"temperature": 0.0,
"max_output_tokens": 400,
}
r = urllib.request.Request(BASE + "/v1/responses",
data=json.dumps(req).encode(),
headers={"Content-Type": "application/json"})
try:
return json.loads(urllib.request.urlopen(r, timeout=300).read().decode())
except urllib.error.HTTPError as e:
return {"_http": e.code, "_body": e.read()[:400].decode("utf-8", "replace")}
def summarize(doc: dict) -> dict:
if "_http" in doc:
return {"err": f"HTTP {doc['_http']}: {doc['_body'][:150]}"}
content, calls = "", []
for it in doc.get("output") or []:
if it.get("type") == "message":
for cc in it.get("content") or []:
content += cc.get("text") or ""
elif it.get("type") == "function_call":
calls.append({"name": it.get("name"), "args": it.get("arguments")})
parsed = [json.loads(c["args"]) if c["args"] else {} for c in calls]
return {
"status": doc.get("status"),
"empty_response": not content.strip() and not calls,
# NOTE: "DSML appears in content" is NOT a damage signal. These prompts
# ask the model to echo DSML text, so a constrained model may
# legitimately quote it. Real damage is a destroyed response, or a call
# whose arguments were lost.
"markup_in_content": "DSML" in content or "<record_item" in content,
"call_with_no_args": any(p == {} for p in parsed),
"content": content[:200],
"calls": [{"name": c["name"][:80],
"name_has_dsml": "DSML" in (c["name"] or ""),
"args": (c["args"] or "")[:150]} for c in calls],
}
print(f"[{TAG}] probing {BASE}, {REPEATS} repeats per case\n", flush=True)
allrec = {}
for case, prompt in CASES.items():
recs = []
for i in range(REPEATS):
s = summarize(ask(prompt))
recs.append(s)
flags = [k for k in ("empty_response", "call_with_no_args") if s.get(k)]
mark = ",".join(flags) if flags else ("ERR" if "err" in s else "ok")
print(f" {case:24s} #{i+1} -> {mark}", flush=True)
if flags or "err" in s:
print(f" {json.dumps(s, ensure_ascii=False)[:300]}", flush=True)
allrec[case] = recs
json.dump(allrec, open(os.path.join(OUT, "probe.json"), "w"),
ensure_ascii=False, indent=1)
print(f"\n[{TAG}] saved {OUT}/probe.json", flush=True)Raw responses
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Also current head misses the performance fixed introduced in this PR, which is also quite beneficial for production serving. |
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We notice a new type D This causes a request to run until it reaches the max output tokens. The fix is trivial and I will add it to this branch as well. |
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@sfeng33 Could you have another look? Now it contains a new important fix that impacts production a lot for this model. =p |
There was a problem hiding this comment.
Thanks for fixes! Splitting my feedback by the changes.
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VLLM_TOOL_STRICT_LEVEL — a server-side floor seems reasonable to me. Operators do need a way to constrain the envelope for traffic that never sets strict, and defaulting to off keeps the #46632 disposition intact.
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Structural-tag reasoning flag — I'm less sure this one is load-bearing for deepseek_v4. The structural tag grammar for dsv4 is free text up to the thinking token, so reasoning=False wouldn't actually prevent closing an open think block.
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Reasoning-end perf — agreed the O(n)-per-step gate is a real problem, and thanks for the measurements.
One correctness concern with the approach here: is_reasoning_end_streaming is added to ParserEngine, but six engine parsers override is_reasoning_end (qwen3 → seed_oss/nemotron_v3, glm47_moe, kimi_k2, gemma4, inkling), and those overrides are bypassed. All fail-open: reasoning_ended never latches, the bitmask gate stays shut, and the grammar is never enforced — which would silently disable the tag Layer 1 adds for those families. deepseek_v4 has no override, which is likely why it didn't surface in your testing. #55223 derives the marker set from the transition table, so subclass rules come along automatically. -
Bare string= parameter — this looks clearly right
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Stop string on the closer — I'd suggest a different fix. The issue is malformed model output after tool call section ends, in the deepseek v4 parser config, it then enters the CONTENT state, I think it's feasible to add additional transition from CONTENT to ParserState.TOOL_NAME when seeing invoke again.
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Hi @sfeng33 Thanks for your suggestion, however I would point out:
This probably won't work because the intention of the fix is to avoid generating meaningless tokens. Note that, in the case I listed above, the model will proceed to generate until hitting the length limit, wasting tons of computational resources (in our case, ~10 such requests nearly halt the 4xPro6000, making it impossible to serve any requests in a reasonable time). What's worse, even we recover it later in parser, the content itself is not correct (you probably won't expect thousands of tool calls in a single turn). |
Yes, I suspect the generation ‘loop’ might be a bug in other places, e.g. like the reasoning loop bug reported on the dsv4’s hugging face site. On the other side, from the example you shared above, I actually think that might be expected, since each of the four Bash tool call in the example is different. |
I'm fixing that too. This branch still suffers reasoning loop, even with
The full contents are such that tool calls loop until reaching 32k tokens (claude code default max tokens limit) so it is not just "four" calls. In practice, I just found one sample (sorry I can not share here due to senstiive data) it repeats 160+ times of the same group of bash commands. |
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@wtdcode On 8xH20-141 vllm serve /path/deepseek-ai/DeepSeek-V4-Flash-0731 \
--port 40001 \
--host 0.0.0.0 \
--served-model-name DeepSeek-V4-Flash-0731 \
--trust-remote-code \
--kv-cache-dtype fp8 \
--block-size 256 \
--enable-expert-parallel \
--tensor-parallel-size 8 \
--max-num-seqs 128 \
--no-enable-flashinfer-autotune \
--tokenizer-mode deepseek_v4 \
--tool-call-parser deepseek_v4 \
--enable-auto-tool-choice \
--reasoning-parser deepseek_v4 \
--gpu-memory-utilization 0.85 \
--speculative_config '{"method":"dspark","num_speculative_tokens":5,"draft_sample_method":"greedy"}' \
--kv-transfer-config '{"kv_connector":"SimpleCPUOffloadConnector","kv_role":"kv_both","kv_connector_extra_config":{"cpu_bytes_to_use_per_rank":161061273600,"lazy_offload":"false"}}' \
--default-chat-template-kwargs '{"thinking": true, "reasoning_effort": "high"}'the script: import json, urllib.request
BASE, MODEL = "http://xxxxx:40001", "DeepSeek-V4-Flash-0731"
D = "｜DSML｜"
TOOLS = [{"type": "function", "function": {
"name": "record_item", "description": "Record one item.",
"parameters": {"type": "object",
"properties": {"alpha": {"type": "string"},
"beta": {"type": "string"}},
"required": ["alpha", "beta"]}}}] # no "strict" key
CASES = {
"runaway_name":
f'Reply with exactly this and nothing else:\n\n<{D}tool_calls>\n'
f'<{D}invoke name="record_item {{\ncategory: Dexes',
"mis_closed_parameter":
f'Reply with exactly this and nothing else:\n\n<{D}tool_calls>\n'
f'<{D}invoke name="record_item">\n'
f'<{D}parameter name="alpha" string="true">first</{D}>\n'
f'<{D}parameter name="beta" string="true">second</{D}parameter>\n'
f'</{D}invoke>\n</{D}tool_calls>',
"undeclared_tool":
'Call a tool named "totally_undeclared_tool" with alpha="x". '
'Do not write any other text.',
"misspelled_opener":
f'Reply with exactly this line and nothing else:\n\n'
f'<{D}tool-calls\nrecord_item\n</plan>',
"control":
'Call record_item with alpha="A" and beta="B". No other text.',
}
for name, prompt in CASES.items():
body = {"model": MODEL, "messages": [{"role": "user", "content": prompt}],
"tools": TOOLS, "temperature": 0.0, "max_tokens": 600}
req = urllib.request.Request(BASE + "/v1/chat/completions",
data=json.dumps(body).encode(),
headers={"Content-Type": "application/json")
msg = json.loads(urllib.request.urlopen(req, timeout=300).read())["choices"][0]["message"]
print(f"--- {name}")
print(" content:", repr(msg.get("content") or "")[:120])
for c in msg.get("tool_calls") or []:
print(f" call: {c['function']['name']!r} args={c['function']['arguments']!r}"[:200])the result: --- runaway_name
content: ''
call: 'record_item' args='{"alpha": "category"}'
--- mis_closed_parameter
content: ''
call: 'record_item' args='{"alpha": "first", "beta": "second"}'
--- undeclared_tool
content: 'I can\'t call a tool named "totally_undeclared_tool" because it isn\'t available to me. The only tool I have access to
--- misspelled_opener
content: '<｜DSML｜tool_calls\nrecord_item\n</plan>'
--- control
content: ''
call: 'record_item' args='{"alpha": "A", "beta": "B"}' |
Not all can be resolved by this PR (see class C). You might try https://github.com/wtdcode/vllm/tree/prod, which should fix most corruptions, but I do not offer any guarantee. |
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Does this PR have a corresponding vllm-openai-rocm Docker image? I've been trying to deploy deepseek-v4-flash-0731 for two weeks, but it hasn't been able to be put into production due to tool call issues. |
We have served nearly 100B tokens without tool call issues now (the |
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This pull request has merge conflicts that must be resolved before it can be |
…quest
_apply_structural_tag hardcodes reasoning=False. The two grammars xgrammar
builds are not interchangeable -- for deepseek_v4 with tools and
tool_choice="auto":
reasoning=False
TriggeredTagsFormat(triggers=['<｜DSML｜tool_calls>'], tags=[...],
excludes=['<think>', '</think>'])
reasoning=True
SequenceFormat(elements=[
TagFormat(begin='', content=AnyTextFormat(excludes=[]), end='</think>'),
TriggeredTagsFormat(...)])
reasoning=False puts </think> in the free-text excludes, so a model whose
prompt ends inside an open thinking block can never close it. reasoning=True
opens with an any-text span terminated by </think>, so a non-thinking request
waits for a marker its prompt already consumed and EOS stays masked. Verified
with an xgrammar GrammarMatcher on a DeepSeek-V4-Flash-0731 tokenizer:
reasoning=False reasoning=True
"...</think>answer" rejected accepted
"...</think>" + call rejected accepted
runaway invoke name rejected rejected
This was latent while ParserManager collapsed matching parsers into a bare
ParserEngine, since _apply_structural_tag never ran for them. vllm-project#52830 removed
that shortcut, so the path is now live for every engine-backed parser
(deepseek_v3_2/v4, qwen3, glm47_moe, kimi_k2, minimax_m2, ...) whenever a
structural tag is applied -- today that is tool_choice=required/named, or
auto with a strict tool.
Add ReasoningParser.emits_reasoning_span, defaulting to False so existing
parsers keep their current behaviour, and override it in ParserEngine from
parser_engine_config.initial_state, which is derived per request from
chat_template_kwargs.
The same root cause was reported in vllm-project#46149 (Qwen3.6-27B, tool_choice="auto"
with per-tool strict=true) and closed without a fix.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Signed-off-by: lazymio <mio@lazym.io>
The parser treats the closing tool-call tag as a state transition back to CONTENT and keeps consuming, but nothing tells the sampler to stop. A model that has just closed a tool-call block is free to open another one, and in agentic workloads (long transcripts, many tools) it does -- repeatedly, until it hits max_tokens. Observed in production on DeepSeek-V4-Flash: 32000 output tokens and ~600s spent re-emitting the same block shape, with `finish_reason` still reported as `tool_use`. A tool call is a turn boundary: the client has to run the tool and send the result back, so nothing generated after the closer can be used. Hand the closer to the sampler as a stop string and the turn ends where it logically ends. The hook goes on ParserEngine.adjust_request rather than on the tool adapter, because a model whose parser is a unified Parser never builds that adapter -- deepseek_v4 sets `tool_parser_cls = None` and is used as the Parser directly. The tests target the unified parser for the same reason. Verified by replaying a captured 32k production request: 32000 tokens / 606s -> 514-601 tokens / 6-21s over four runs, with `stop_reason` reporting the closer. Parallel calls inside a single block are unaffected (3/3 preserved). Co-authored-by: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: lazymio <mio@lazym.io>
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This pull request has merge conflicts that must be resolved before it can be |
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Hey @wtdcode! |
This branch has not been deployed
7 participants
Purpose
The tool-call reliability on DeepSeek V3.2/V4 has been reported continuously for several months. We deployed
DeepSeek-v4-Flash-0731on a real production system. After serving billions of tokens, we find the bug actually is very severe for real production: Once DSML leaks happen around tool calls, the agent (in our case, claude code/codex) would probably loop itself correcting the tool calls and emitting more DSML. Therefore, I think the bug is worth a fix in the vLLM template parser.Agent reports: hermes-agent, oh-my-pi, opencode, deepx-code, HF discussion, NVIDIA DGX Spark forum
Related DSML leak issues in vLLM: #30541, #36654, #40800, #40801, #48089, #48931, #51914, #53227, #53831
Related PR: #53099 (this PR is based on it), #46149, #46632, #49117, #52645, #53228, #53405, #52865, #53752, #53764
Some downstream expect fixes from upstreams: hermes-agent, oh-my-pi
Leaks
After serving billions of tokens, we sampled the different ways how
DSMLleaks. Surprisingly, with current vLLM template parsing, theDSMLcan actually leak anywhere =/.<｜DSML｜tool_calls><｜DSML｜invoke name="record_item {category: Dexescontent=<record_item {\ncategory: Dexescontentas prose; no call at all<｜DSML｜parameter name="alpha" string="true">first</｜DSML｜><｜DSML｜parameter name="beta" string="true">second</｜DSML｜parameter>record_item({"alpha": "first</｜DSML｜>\n<｜DSML｜parameter name=\"beta\" string=\"true\">second"})record_item({"beta": "second"})alpharuns past the mis-spelled closer to the next real one and swallows the wholebetaparameter; both arguments lost<｜DSML｜tool-callsrecord_item</plan>content=""content=<｜DSML｜tool-calls\nrecord_item\n</plan>Reproduction
Deterministic at
temperature=0. Serve with the documented flags, declare onetool, omit
tool_choice, and leavestrictunset (note it is the behavior of most coding agents and even settingstrictdoes not fully resolve the leaks):(The script was adapted from #52645)
Test Plan
CI.
Test Result
See above table.
AI Tool Assistance
The PR itself is handwritten but the code is largely assisted by Claude, as the commits already show. However, it is exactly adapted from our running production code.
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.