fix: route Kimi forced tools through native parser#43155
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Code Review
This pull request implements native tool-call structural tags for the Kimi K2 model, including the registration of model-specific tags and updates to the Kimi K2 tool parser to support forced and required tool choices. It also ensures that the tool call phase is correctly bypassed when 'tool_choice' is set to 'none'. Feedback highlights a redundant condition in the tool call phase logic and suggests a more defensive implementation when updating structured output parameters to prevent accidental loss of existing configurations.
| request.structured_outputs = StructuredOutputsParams( | ||
| structural_tag=json.dumps(structure_tag.model_dump()) | ||
| ) |
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The current implementation overwrites the request.structured_outputs attribute. This is dangerous because it discards any other settings that might have been configured in StructuredOutputsParams, such as enable_in_reasoning or custom regex/json constraints (though the latter are usually mutually exclusive with structural_tag). It is better to update the existing object if it exists, following the defensive pattern established in the base ToolParser class.
| request.structured_outputs = StructuredOutputsParams( | |
| structural_tag=json.dumps(structure_tag.model_dump()) | |
| ) | |
| if request.structured_outputs is None: | |
| request.structured_outputs = StructuredOutputsParams( | |
| structural_tag=json.dumps(structure_tag.model_dump()) | |
| ) | |
| else: | |
| request.structured_outputs.structural_tag = json.dumps( | |
| structure_tag.model_dump() | |
| ) |
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Addressed in the latest revision, but intentionally not with the exact suggested mutation. StructuredOutputsParams treats json, regex, choice, grammar, json_object, and structural_tag as mutually exclusive constraints, so preserving an existing json/regex constraint while setting structural_tag would fail validation later. The Kimi forced-tool path now rebuilds StructuredOutputsParams with structural_tag and carries forward only compatible option fields (disable_any_whitespace, disable_additional_properties, whitespace_pattern). Added a unit test covering replacement of an existing JSON constraint while preserving compatible options.
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| content=SequenceFormat( | ||
| elements=[ | ||
| RegexFormat(pattern=r"\d+"), | ||
| ConstStringFormat(value=argument_begin), |
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One note here - the test_kimi_k2_tool_parser.py the _tool method builds a tool call like this: return f"{TOOL_BEGIN}{tool_id} {ARG_BEGIN}{args}{TOOL_END}". Notice the space between tool_id and ARG_BEGIN. Here, we do not allow for a space with this structural tag definition that I can see.
Do you have an example of actual model output from one or more Kimi K2 models to verify whether it does or does not have a space there? Or whether it can do either? We have to be careful with the structural tag definitions to make sure we don't accidentally cause the model to deviate from its training distribution.
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Good catch. I checked the e2e artifacts, and the current structural tag is too strict here.
The raw native tool-call text is visible in our tool_choice="none" cases because those requests intentionally do not parse native tool calls into OpenAI tool_calls. In multiple Kimi K2.6 samples, the model emitted whitespace around the native markers, for example:
<|tool_calls_section_begin|> <|tool_call_begin|> functions.get_current_weather:0 <|tool_call_argument_begin|> {"location": "Boston, MA", "unit": "fahrenheit"} <|tool_call_end|> <|tool_calls_section_end|>
That also matches the existing parser and tests: KimiK2ToolParser.tool_call_regex already allows \s* after <|tool_call_begin|>, after the :<id>, and after <|tool_call_argument_begin|>, and the test helper emits functions.<name>:0 <|tool_call_argument_begin|>.
I will update the structural tag to allow optional whitespace in the same separator positions the parser already accepts, then add/adjust tests so the constrained format stays aligned with actual Kimi output and the existing parser contract.
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This is a reasonable direction, and it's good to see us clean up the tool_choice=required path for models that don't just emit tools as raw JSON like the Kimi K2 family.
Just as an FYI, there is a VLLM_ENFORCE_STRICT_TOOL_CALLING environment variable that was added with the initial structural tag integration. If that gets set, I believe it means your structural tag returned from get_structural_tag will also get used in the tool_choice=auto path. It looks like the defined structural tag has some support for auto tool choice, but I don't see any tests for that path that verify the right thing is happening.
The guided decoding backends don't support all JSON schema properties typically - see for example has_xgrammar_unsupported_json_features in vllm/v1/structured_output/backend_xgrammar.py. What happens when a user passes in a request using tool_choice=required and an unsupported JSON schema property?
One final note, that could easily be deferred until later, is that technically in function tool definitions of Chat Completions and Responses API each tool can set a strict property to tool or false to control whether the actual params/arguments to that tool call are guided or not.
How much real-world testing were you able to do with this? Thinking on and off, tool_choice auto vs required vs none, that kind of thing? We're obviously doing the wrong thing today for this model with tool_choice=required, so the things I pointed out above are around some of the challenges of doing this right in all scenarios. We don't have to solve all of them now, but are at least worth thinking about and deciding whether to defer or tackle.
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Thanks for the review! I dug through the code paths and ran focused checks against this PR branch. On On per-tool On unsupported schema properties: this found a real gap. Plain For real-world testing, we validated the production-like Kimi K2.6 deployment shape with thinking enabled/disabled and |
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for context, here is a gigantic dump of the raw request/responses and their failure modes Six captured failure scenarios for Summary
Failure Examples1. reasoning-enabled-tool-choice-none - tool_choice="none" returned tool_calls (click to expand)Provider: WandB - moonshotai/kimi-k2.6-20260420 Model: Status: Validation Failed Duration: Variant: Raw Response TextReasoningRaw Full Text PlaceholderFinish Reasons
URLValidation Result{
"__kind": "ERR",
"error": "Expected finish reason to be: stop or length, got tool_calls"
}Usage{
"prompt_tokens": 100,
"completion_tokens": 79,
"total_tokens": 179,
"cost": 0.000411,
"is_byok": false,
"prompt_tokens_details": {
"cached_tokens": 0,
"audio_tokens": 0
},
"cost_details": {
"upstream_inference_cost": 0.000411,
"upstream_inference_prompt_cost": 0.000095,
"upstream_inference_completions_cost": 0.000316
},
"completion_tokens_details": {
"reasoning_tokens": 0,
"audio_tokens": 0
}
}Raw Request (OpenRouter){
"stream": true,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
}
],
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"tool_choice": "none",
"reasoning": {
"enabled": true
}
}Upstream Request (Provider){
"model": "moonshotai/Kimi-K2.6",
"stream": true,
"stream_options": {
"include_usage": true
},
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"max_tokens": 65536,
"temperature": 1,
"top_p": 1,
"repetition_penalty": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"seed": null,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
}
],
"tool_choice": "none",
"chat_template_kwargs": {
"thinking": true,
"enable_thinking": true
}
}2. reasoning-enabled-tool-choice-required - required tool call returned no reasoning (click to expand)Provider: WandB - moonshotai/kimi-k2.6-20260420 Model: Status: Validation Failed Duration: Variant: Raw Response TextRaw Full Text PlaceholderFinish Reasons
URLValidation Result{
"__kind": "ERR",
"error": "Expected reasoning length to be at least 5, got 0"
}Usage{
"prompt_tokens": 76,
"completion_tokens": 35,
"total_tokens": 111,
"cost": 0.0002122,
"is_byok": false,
"prompt_tokens_details": {
"cached_tokens": 0,
"audio_tokens": 0
},
"cost_details": {
"upstream_inference_cost": 0.0002122,
"upstream_inference_prompt_cost": 0.0000722,
"upstream_inference_completions_cost": 0.00014
},
"completion_tokens_details": {
"reasoning_tokens": 0,
"audio_tokens": 0
}
}Raw Request (OpenRouter){
"stream": true,
"tools": [
{
"type": "function",
"function": {
"name": "calculate",
"description": "Perform a mathematical calculation",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "The mathematical expression to evaluate, e.g. 2 + 2"
}
},
"additionalProperties": false,
"required": [
"expression"
]
}
}
}
],
"messages": [
{
"role": "user",
"content": "Hi, how are you?"
}
],
"tool_choice": "required",
"reasoning": {
"enabled": true
}
}Upstream Request (Provider){
"model": "moonshotai/Kimi-K2.6",
"stream": true,
"stream_options": {
"include_usage": true
},
"messages": [
{
"role": "user",
"content": "Hi, how are you?"
}
],
"max_tokens": 65536,
"temperature": 1,
"top_p": 1,
"repetition_penalty": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"seed": null,
"tools": [
{
"type": "function",
"function": {
"name": "calculate",
"description": "Perform a mathematical calculation",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "The mathematical expression to evaluate, e.g. 2 + 2"
}
},
"additionalProperties": false,
"required": [
"expression"
]
}
}
}
],
"tool_choice": "required",
"chat_template_kwargs": {
"thinking": true,
"enable_thinking": true
}
}3. reasoning-enabled-tool-choice-function - forced named tool returned stop (click to expand)Provider: WandB - moonshotai/kimi-k2.6-20260420 Model: Status: Validation Failed Duration: Variant: Raw Response TextRaw Full Text PlaceholderFinish Reasons
URLValidation Result{
"__kind": "ERR",
"error": "Expected finish reason to be: tool_calls, got stop"
}Usage{
"prompt_tokens": 146,
"completion_tokens": 9,
"total_tokens": 155,
"cost": 0.00014942,
"is_byok": false,
"prompt_tokens_details": {
"cached_tokens": 32,
"audio_tokens": 0
},
"cost_details": {
"upstream_inference_cost": 0.00014942,
"upstream_inference_prompt_cost": 0.00011342,
"upstream_inference_completions_cost": 0.000036
},
"completion_tokens_details": {
"reasoning_tokens": 0,
"audio_tokens": 0
}
}Raw Request (OpenRouter){
"stream": true,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
},
{
"type": "function",
"function": {
"name": "calculate",
"description": "Perform a mathematical calculation",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "The mathematical expression to evaluate, e.g. 2 + 2"
}
},
"additionalProperties": false,
"required": [
"expression"
]
}
}
}
],
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"tool_choice": {
"type": "function",
"function": {
"name": "calculate"
}
},
"reasoning": {
"enabled": true
}
}Upstream Request (Provider){
"model": "moonshotai/Kimi-K2.6",
"stream": true,
"stream_options": {
"include_usage": true
},
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"max_tokens": 65536,
"temperature": 1,
"top_p": 1,
"repetition_penalty": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"seed": null,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
},
{
"type": "function",
"function": {
"name": "calculate",
"description": "Perform a mathematical calculation",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "The mathematical expression to evaluate, e.g. 2 + 2"
}
},
"additionalProperties": false,
"required": [
"expression"
]
}
}
}
],
"tool_choice": {
"type": "function",
"function": {
"name": "calculate"
}
},
"chat_template_kwargs": {
"thinking": true,
"enable_thinking": true
}
}4. reasoning-disabled-tool-choice-none - tool_choice="none" returned tool_calls (click to expand)Provider: WandB - moonshotai/kimi-k2.6-20260420 Model: Status: Validation Failed Duration: Variant: Raw Response TextRaw Full Text PlaceholderFinish Reasons
URLValidation Result{
"__kind": "ERR",
"error": "Expected finish reason to be: stop or length, got tool_calls"
}Usage{
"prompt_tokens": 101,
"completion_tokens": 28,
"total_tokens": 129,
"cost": 0.00020795,
"is_byok": false,
"prompt_tokens_details": {
"cached_tokens": 0,
"audio_tokens": 0
},
"cost_details": {
"upstream_inference_cost": 0.00020795,
"upstream_inference_prompt_cost": 0.00009595,
"upstream_inference_completions_cost": 0.000112
},
"completion_tokens_details": {
"reasoning_tokens": 0,
"audio_tokens": 0
}
}Raw Request (OpenRouter){
"stream": true,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
}
],
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"tool_choice": "none",
"reasoning": {
"enabled": false
}
}Upstream Request (Provider){
"model": "moonshotai/Kimi-K2.6",
"stream": true,
"stream_options": {
"include_usage": true
},
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"max_tokens": 65536,
"temperature": 1,
"top_p": 1,
"repetition_penalty": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"seed": null,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
}
],
"tool_choice": "none",
"chat_template_kwargs": {
"thinking": false,
"enable_thinking": false
}
}5. tool-choice-none - tool_choice="none" returned tool_calls (click to expand)Provider: WandB - moonshotai/kimi-k2.6-20260420 Model: Status: Validation Failed Duration: Variant: Raw Response TextReasoningRaw Full Text PlaceholderFinish Reasons
URLValidation Result{
"__kind": "ERR",
"error": "Expected finish reason to be: stop or length, got tool_calls"
}Usage{
"prompt_tokens": 100,
"completion_tokens": 76,
"total_tokens": 176,
"cost": 0.000399,
"is_byok": false,
"prompt_tokens_details": {
"cached_tokens": 0,
"audio_tokens": 0
},
"cost_details": {
"upstream_inference_cost": 0.000399,
"upstream_inference_prompt_cost": 0.000095,
"upstream_inference_completions_cost": 0.000304
},
"completion_tokens_details": {
"reasoning_tokens": 0,
"audio_tokens": 0
}
}Raw Request (OpenRouter){
"stream": true,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
}
],
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"tool_choice": "none"
}Upstream Request (Provider){
"model": "moonshotai/Kimi-K2.6",
"stream": true,
"stream_options": {
"include_usage": true
},
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"max_tokens": 65536,
"temperature": 1,
"top_p": 1,
"repetition_penalty": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"seed": null,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
}
],
"tool_choice": "none",
"chat_template_kwargs": {
"thinking": true,
"enable_thinking": true
}
}6. tool-choice-function - forced named tool returned stop (click to expand)Provider: WandB - moonshotai/kimi-k2.6-20260420 Model: Status: Validation Failed Duration: Variant: Raw Response TextRaw Full Text PlaceholderFinish Reasons
URLValidation Result{
"__kind": "ERR",
"error": "Expected finish reason to be: tool_calls, got stop"
}Usage{
"prompt_tokens": 146,
"completion_tokens": 11,
"total_tokens": 157,
"cost": 0.0001827,
"is_byok": false,
"prompt_tokens_details": {
"cached_tokens": 0,
"audio_tokens": 0
},
"cost_details": {
"upstream_inference_cost": 0.0001827,
"upstream_inference_prompt_cost": 0.0001387,
"upstream_inference_completions_cost": 0.000044
},
"completion_tokens_details": {
"reasoning_tokens": 0,
"audio_tokens": 0
}
}Raw Request (OpenRouter){
"stream": true,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
},
{
"type": "function",
"function": {
"name": "calculate",
"description": "Perform a mathematical calculation",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "The mathematical expression to evaluate, e.g. 2 + 2"
}
},
"additionalProperties": false,
"required": [
"expression"
]
}
}
}
],
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"tool_choice": {
"type": "function",
"function": {
"name": "calculate"
}
}
}Upstream Request (Provider){
"model": "moonshotai/Kimi-K2.6",
"stream": true,
"stream_options": {
"include_usage": true
},
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston, MA in fahrenheit?"
}
],
"max_tokens": 65536,
"temperature": 1,
"top_p": 1,
"repetition_penalty": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"seed": null,
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"additionalProperties": false,
"required": [
"location",
"unit"
]
}
}
},
{
"type": "function",
"function": {
"name": "calculate",
"description": "Perform a mathematical calculation",
"strict": true,
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "The mathematical expression to evaluate, e.g. 2 + 2"
}
},
"additionalProperties": false,
"required": [
"expression"
]
}
}
}
],
"tool_choice": {
"type": "function",
"function": {
"name": "calculate"
}
},
"chat_template_kwargs": {
"thinking": true,
"enable_thinking": true
}
}and the same requests after this PR:
|
|
@alexeldeib I'm a bit confused by the before/after behavior at |
| reasoning=( | ||
| get_enable_structured_outputs_in_reasoning() | ||
| and request.include_reasoning | ||
| and thinking | ||
| ), |
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What's the rationale for gating this on get_enabled_structured_outputs_in_reasoning()? We're not actually applying structured outputs to reasoning here, are we? This just controls whether our grammar allows thinking?
Likewise, why gate it on request.include_reasoning? Whether a client wants reasoning returned to them or not, that's separate from whether the model generates it or not, right?
I do think it's reasonable to gate this on the thinking param in the chat template, but needs confirmation in chat templates themselves that they use this parameter to pre-emptively output empty thinking blocks or something comparable to suppress thinking in the model generation.
More generally, there's some complex interaction with reasoning end detection in our reasoning parsers and the start of applying bitmasks from structural tags and/or grammars. I haven't been able to run this myself yet, so just trying to ensure we're doing the right thing here.
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okay request.include_reasoning is wrong you are correct
I think get_enabled_structured_outputs_in_reasoning is correct: it also controls whether the bitmask is applied.
from some codex exploration:
If enable_in_reasoning=True, the grammar is active from the start of generation, while Kimi may generate reasoning first. Therefore the structural tag must allow free text through before requiring the tool-call section.
If enable_in_reasoning=False, the grammar is inactive during reasoning and starts only after the reasoning parser says reasoning ended. At that point the next constrained token should be the Kimi tool-call section, not an already-consumed reasoning prefix. So a suffix-only structural tag is correct.
Current main has this in StructuredOutputManager.should_fill_bitmask():
reasoner = self._get_reasoner(request)
if reasoner is not None:
if self.enable_in_reasoning:
return True
...
if request.structured_output_request.reasoning_ended is None:
request.structured_output_request.reasoning_ended = (
reasoner.is_reasoning_end(request.prompt_token_ids or [])
)
return request.structured_output_request.reasoning_ended
return True- If self.enable_in_reasoning=True, line 308 returns True unconditionally. Grammar applies from the first generated token.
- If self.enable_in_reasoning=False and a reasoner exists, vLLM asks whether the prompt is already past reasoning. For Kimi thinking prompts, it is not.
- If no reasoner exists, line 320 returns True. That is the fallback, but it is not the Kimi-with-reasoning-parser path.
let me add some tests to clarify this behavior
bleh this is just me trying to do too many things at once and mixing things up, will clean up edit for context: The tool_choice="none" diff was from other validation + an additional private patch for e2e testing. The generic issue is that the streaming Chat Completions path can still invoke DelegatingParser / the configured tool parser after reasoning ends, even when the request says tool_choice="none". If the model emits text matching the parser's tool-call format, streaming can incorrectly surface delta.tool_calls and finish with finish_reason="tool_calls". That affects Kimi because Kimi's native marker format is easy for KimiK2ToolParser to recognize once the parser is invoked. But the bug is not Kimi-specific and is already covered by the narrower generic PRs #42752 and #42868. |
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Thanks for supporting Kimi!
Shall we also exclude <|tool_call_begin|> here? Check out https://github.com/mlc-ai/xgrammar/blob/c4cf39f1baa3fbbc2c349b45315162b7673414d5/python/xgrammar/builtin_structural_tag.py#L639-L643
The strict structural-tag path in `ToolParser.adjust_request` (added in vllm-project#40894, gated by `VLLM_ENFORCE_STRICT_TOOL_CALLING`) installs `structural_tag` on a pre-existing `StructuredOutputsParams` via in-place attribute assignment and returns early without clearing `response_format`. The in-place set bypasses `StructuredOutputsParams.__post_init__`, leaving any prior mutually-exclusive constraint (`json`/`regex`/`choice`/`grammar`/ `json_object`, or one lowered from `response_format`) set alongside the new `structural_tag`. When the params are re-validated downstream this violates the one-constraint invariant, so a strict-mode request that also carries a structured-output constraint or a `response_format` fails: ValueError: You can only use one kind of structured outputs constraint but multiple are specified Rebuild `structured_outputs` with only the structural tag (preserving the whitespace / additional-properties knobs) and null `response_format`, mirroring what Step 2 of the same method already does for the JSON-schema path. Only the strict auto/required/named path is affected; `VLLM_ENFORCE_STRICT_TOOL_CALLING` is off by default. Every parser that installs a structural tag (DeepSeek-V4, Qwen3-Coder, and Kimi via vllm-project#43155) flows through this one base path. The interaction was raised in review on vllm-project#40894 and vllm-project#43155; the Kimi parser in vllm-project#43155 already performs this rebuild for its required/named path. Test plan (real requests, Kimi K2.6 NVFP4 TP=4, VLLM_ENFORCE_STRICT_TOOL_CALLING=1; stock vs this patch applied in place; POST /v1/chat/completions, stream=false, temperature=0; tool get_weather(city)): tool_choice extra constraint stock with patch auto response_format HTTP 400 HTTP 200 tool_call <- fixed auto structured_outputs HTTP 400 HTTP 200 tool_call <- fixed auto (none) HTTP 200 HTTP 200 tool_call (unchanged) required response_format HTTP 200 HTTP 200 tool_call (unchanged; required/named already rebuilds -> the bug is specific to the auto path) Verbatim (auto + response_format): REQUEST {"model":"moonshotai/Kimi-K2.6","tool_choice":"auto", "messages":[{"role":"user","content":"What is the weather in Paris? Call the tool."}], "tools":[{"type":"function","function":{"name":"get_weather","parameters": {"type":"object","properties":{"city":{"type":"string"}},"required":["city"]}}}], "response_format":{"type":"json_schema","json_schema":{"name":"answer","schema": {"type":"object","properties":{"answer":{"type":"string"}},"required":["answer"]}}}} STOCK HTTP 400 {"error":{"message":"1 validation error for StructuredOutputsParams ... You can only use one kind of structured outputs constraint but multiple are specified: {'json': {...}, ..., 'structural_tag': '...'}"}} PATCH HTTP 200 {"finish_reason":"tool_calls","message":{"tool_calls":[{"function": {"name":"get_weather","arguments":"{\"city\":\"Paris\"}"}}]}} Unit regression test: tests/tool_use/test_strict_tool_calling_adjust_request.py asserts adjust_request rebuilds to a single structural_tag constraint, nulls response_format, and preserves user whitespace knobs (fails on the pre-fix code). Signed-off-by: Ace Eldeib <aeldeib@coreweave.com>
ToolParser.adjust_request's strict structural-tag path (added in vllm-project#40894, gated by VLLM_ENFORCE_STRICT_TOOL_CALLING) installs structural_tag on a pre-existing StructuredOutputsParams via in-place attribute assignment and returns without nulling response_format. The in-place set bypasses StructuredOutputsParams.__post_init__, so the params keep a prior mutually-exclusive constraint (json/regex/choice/grammar/json_object, or one lowered from response_format) next to the new structural_tag. On the next re-validation this trips the one-constraint invariant, so a strict-mode request that also carries a structured-output constraint or a response_format fails with: ValueError: You can only use one kind of structured outputs constraint but multiple are specified This affects any parser that installs a structural tag -- currently DeepSeek-V4 and Qwen3-Coder via get_structural_tag. The env var is off by default, and a request with no pre-existing constraint is unaffected. Fix: rebuild structured_outputs with only the structural tag (preserving the whitespace / additional-properties knobs) and null response_format, mirroring Step 2 of the same method. This "tool constraint wins, response_format dropped" resolution already exists in Step 2, the DeepSeek-V3.2 override (vllm-project#41178), and for required/auto in vllm-project#32006 / vllm-project#39969; the in-place-vs-rebuild trade-off was discussed on vllm-project#40894 and vllm-project#43155 (whose Kimi path already rebuilds). Repro / regression test (CPU, no model required): pytest tests/tool_use/test_strict_tool_calling_adjust_request.py The added tests enable strict mode, give a parser a structural tag, and send tools together with a response_format or a structured_outputs.json constraint (tool_choice auto and required). On the pre-fix code adjust_request leaves two constraints, and to_sampling_params raises the ValueError above; with this change structured_outputs holds only the structural tag, response_format is None, and the user's whitespace knobs are preserved. The conflict tests fail without this patch and pass with it; the no-pre-existing-constraint case passes either way. Equivalently over HTTP: with strict mode on, a tool_choice="auto" request that also sets response_format returns HTTP 400 (the error above) before this change and a normal tool call after; a required-tool request is unaffected because that path already rebuilds. Signed-off-by: Ace Eldeib <aeldeib@coreweave.com>
ToolParser.adjust_request's strict structural-tag path (added in vllm-project#40894, gated by VLLM_ENFORCE_STRICT_TOOL_CALLING) installs structural_tag on a pre-existing StructuredOutputsParams via in-place attribute assignment and returns without nulling response_format. The in-place set bypasses StructuredOutputsParams.__post_init__, so the params keep a prior mutually-exclusive constraint (json/regex/choice/grammar/json_object, or one lowered from response_format) next to the new structural_tag. On the next re-validation this trips the one-constraint invariant, so a strict-mode request that also carries a structured-output constraint or a response_format fails with: ValueError: You can only use one kind of structured outputs constraint but multiple are specified This affects any parser that installs a structural tag -- currently DeepSeek-V4 and Qwen3-Coder via get_structural_tag. The env var is off by default, and a request with no pre-existing constraint is unaffected. Fix: rebuild structured_outputs with only the structural tag (preserving the whitespace / additional-properties knobs) and null response_format, mirroring Step 2 of the same method. This "tool constraint wins, response_format dropped" resolution already exists in Step 2 and the DeepSeek-V3.2 override (vllm-project#41178), and is the intent of the open auto-path fix vllm-project#39969; the in-place-vs-rebuild trade-off was discussed on vllm-project#40894 and vllm-project#43155 (whose Kimi path already rebuilds). Repro / regression test (CPU, no model required): pytest tests/tool_use/test_strict_tool_calling_adjust_request.py The added tests enable strict mode, give a parser a structural tag, and send tools together with a response_format or a structured_outputs.json constraint (tool_choice auto and required). On the pre-fix code adjust_request leaves two constraints, and to_sampling_params raises the ValueError above; with this change structured_outputs holds only the structural tag, response_format is None, and the user's whitespace knobs are preserved. The conflict tests fail without this patch and pass with it; the no-pre-existing-constraint case passes either way. Equivalently over HTTP: with strict mode on, a tool_choice="auto" request that also sets response_format returns HTTP 400 (the error above) before this change and a normal tool call after; a required-tool request is unaffected because that path already rebuilds. Signed-off-by: Ace Eldeib <aeldeib@coreweave.com>
ToolParser.adjust_request's strict structural-tag path (added in vllm-project#40894, gated by VLLM_ENFORCE_STRICT_TOOL_CALLING) installs structural_tag on a pre-existing StructuredOutputsParams via in-place attribute assignment and returns without nulling response_format. The in-place set bypasses StructuredOutputsParams.__post_init__, so the params keep a prior mutually-exclusive constraint (json/regex/choice/grammar/json_object, or one lowered from response_format) next to the new structural_tag. On the next re-validation this trips the one-constraint invariant, so a strict-mode request that also carries a structured-output constraint or a response_format fails with: ValueError: You can only use one kind of structured outputs constraint but multiple are specified This affects any parser that installs a structural tag -- currently DeepSeek-V4 and Qwen3-Coder via get_structural_tag. The env var is off by default, and a request with no pre-existing constraint is unaffected. Fix: rebuild structured_outputs with only the structural tag (preserving the whitespace / additional-properties knobs) and null response_format, mirroring Step 2 of the same method. This "tool constraint wins, response_format dropped" resolution already exists in Step 2 and the DeepSeek-V3.2 override (vllm-project#41178), and is the intent of the open auto-path fix vllm-project#39969; the in-place-vs-rebuild trade-off was discussed on vllm-project#40894 and vllm-project#43155 (whose Kimi path already rebuilds). Repro / regression test (CPU, no model required): pytest tests/tool_use/test_strict_tool_calling_adjust_request.py The added tests enable strict mode, give a parser a structural tag, and send tools together with a response_format or a structured_outputs.json constraint (tool_choice auto and required). On the pre-fix code adjust_request leaves two constraints, and to_sampling_params raises the ValueError above; with this change structured_outputs holds only the structural tag, response_format is None, and the user's whitespace knobs are preserved. The conflict tests fail without this patch and pass with it; the no-pre-existing-constraint case passes either way. Equivalently over HTTP: with strict mode on, a tool_choice="auto" request that also sets response_format returns HTTP 400 (the error above) before this change and a normal tool call after; a required-tool request is unaffected because that path already rebuilds. Signed-off-by: Ace Eldeib <aeldeib@coreweave.com> Co-authored-by: OpenAI Codex <codex@openai.com>
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pre-commit run --all-filesThen, commit the changes and push to your branch. For future commits, |
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Documentation preview: https://vllm--43155.org.readthedocs.build/en/43155/ |
Kimi K2 emits tool calls with native structural markers like <|tool_calls_section_begin|> and <|tool_call_begin|> functions.<name>:<id>, not the generic JSON payload used by the default required/named tool-choice path. When forced tool choices are guided and parsed as generic JSON, streamed responses can lose parsed tool calls or prevent visible reasoning before the native tool section. Add a Kimi structural tag so required and named tool choices constrain generation to the same native format that KimiK2ToolParser already understands, and mark the parser as not supporting the generic required/named parser. The tag allows optional whitespace at the separator positions seen in Kimi K2.6 e2e output and already accepted by the parser regex, so guidance does not force the model away from its native distribution. When structured outputs are enabled during reasoning, include a reasoning prefix that allows Kimi to complete its template-opened <think> block before the native tool-call section. Gate that prefix on the engine enable_in_reasoning setting and Kimi's thinking chat-template knob, not include_reasoning, because include_reasoning only controls response visibility. Keep auto/none/no-tool behavior unchanged unless VLLM_ENFORCE_STRICT_TOOL_CALLING routes auto through structural tags, in which case Kimi now uses the same native tag builder as required/named. This change does not address the separate generic streaming parser issue where tool_choice="none" can still enter tool-call parsing; that is covered by vLLM PRs vllm-project#42752 and vllm-project#42868. Preserve strict=false tool definitions by disabling argument-schema guidance for that tool, and reject xgrammar-unsupported JSON schema features before installing the structural tag so unsupported schemas fail consistently with plain JSON structured outputs. Tests cover Kimi structural-tag request adjustment, strict auto routing, strict=false tool schemas, xgrammar-unsupported schema rejection, opt-out from generic required/named parsing, replacement of conflicting structured-output constraints, structural-tag validation, reasoning-prefix gating by bitmask phase and Kimi thinking mode, and include_reasoning visibility not changing the grammar shape. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Ace Eldeib <aeldeib@coreweave.com>
In the Kimi auto tool-choice structural tag, exclude <|tool_call_begin|> from the free-form text before the tool-calls section (alongside the <think>/</think> tokens), so the model cannot emit a bare tool-call marker outside the <|tool_calls_section_begin|>...<|tool_calls_section_end|> envelope. This matches xgrammar's canonical builtin (builtin_structural_tag.py) and the parser, which only recovers tool calls inside the section. Addresses review feedback from @Ubospica. Signed-off-by: Ace Eldeib <aeldeib@coreweave.com>
…asoning) get_structural_tag now sets _grammar_from_tool_parser = not reasoning: - reasoning off (eir off or thinking off) -> suffix-only tag; set the flag so the engine applies the grammar from the first token (immediate forced tool). - reasoning on (enable_in_reasoning & thinking) -> <think> prefix tag; leave the flag unset so enable_in_reasoning drives the grammar from token 0 and the reasoning parser still extracts reasoning (no </think> leak into content). Test asserts the invariant: parser-owned grammar iff no reasoning prefix. Signed-off-by: Ace Eldeib <aeldeib@coreweave.com>
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This pull request has merge conflicts that must be resolved before it can be |
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Superseded by #45003: KimiK2ToolParser now sets |
Purpose
Fix Kimi K2/K2.6 forced-tool routing for Chat Completions requests that use the native
kimi_k2tool parser withtool_choice="required"or a named functiontool_choice.Kimi emits native marker-formatted tool calls, not the generic JSON tool-call array used by vLLM's fallback required/named path. This PR makes
KimiK2ToolParseropt out of that generic helper and installs a Kimi-native structural tag for required/named requests, so constrained generation and parser extraction use the same marker format.Duplicate-work check: open PR searches for
Kimi required named tool_choice structural tag,Kimi forced tools native parser,Kimi supports_required_and_named false,Kimi tool_choice required native markers, andtool_choice required Kimi K2 parserfound one partial overlap: #44934. That PR only setssupports_required_and_named = False; this PR also constrains required/named generation to Kimi's native structural tag. Related required-tool PRs such as #35936 and #44447 target other parser formats.This PR does not change
tool_choice="none". No docs update is needed. AI assistance was used; I reviewed the changed code and test results.Test Plan
Test Result
.venv/bin/python -m pytest tests/tool_parsers/test_kimi_k2_tool_parser.py::TestAdjustRequest -q # 16 passed, 2 warnings in 2.60s.venv/bin/python -m pytest tests/tool_parsers/test_kimi_k2_tool_parser.py -q # 63 passed, 2 warnings in 6.23spre-commit run --files \ vllm/tool_parsers/kimi_k2_tool_parser.py \ vllm/tool_parsers/structural_tag_registry.py \ tests/tool_parsers/test_kimi_k2_tool_parser.py # Passedgit diff --check origin/main...HEAD # PassedCurrent PR checks are green: GitHub pre-commit passed, Buildkite PR CI passed, and ReadTheDocs passed.
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supported_models.mdandexamplesfor a new model. Not applicable.