179 lines
6.6 KiB
Markdown
179 lines
6.6 KiB
Markdown
(reasoning-outputs)=
|
|
|
|
# Reasoning Outputs
|
|
|
|
vLLM offers support for reasoning models like [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), which are designed to generate outputs containing both reasoning steps and final conclusions.
|
|
|
|
Reasoning models return a additional `reasoning_content` field in their outputs, which contains the reasoning steps that led to the final conclusion. This field is not present in the outputs of other models.
|
|
|
|
## Supported Models
|
|
|
|
vLLM currently supports the following reasoning models:
|
|
|
|
- [DeepSeek R1 series](https://huggingface.co/collections/deepseek-ai/deepseek-r1-678e1e131c0169c0bc89728d) (`deepseek_r1`, which looks for `<think> ... </think>`)
|
|
|
|
## Quickstart
|
|
|
|
To use reasoning models, you need to specify the `--enable-reasoning` and `--reasoning-parser` flags when making a request to the chat completion endpoint. The `--reasoning-parser` flag specifies the reasoning parser to use for extracting reasoning content from the model output.
|
|
|
|
```bash
|
|
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
|
|
--enable-reasoning --reasoning-parser deepseek_r1
|
|
```
|
|
|
|
Next, make a request to the model that should return the reasoning content in the response.
|
|
|
|
```python
|
|
from openai import OpenAI
|
|
|
|
# Modify OpenAI's API key and API base to use vLLM's API server.
|
|
openai_api_key = "EMPTY"
|
|
openai_api_base = "http://localhost:8000/v1"
|
|
|
|
client = OpenAI(
|
|
api_key=openai_api_key,
|
|
base_url=openai_api_base,
|
|
)
|
|
|
|
models = client.models.list()
|
|
model = models.data[0].id
|
|
|
|
# Round 1
|
|
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
|
|
response = client.chat.completions.create(model=model, messages=messages)
|
|
|
|
reasoning_content = response.choices[0].message.reasoning_content
|
|
content = response.choices[0].message.content
|
|
|
|
print("reasoning_content:", reasoning_content)
|
|
print("content:", content)
|
|
```
|
|
|
|
The `reasoning_content` field contains the reasoning steps that led to the final conclusion, while the `content` field contains the final conclusion.
|
|
|
|
## Streaming chat completions
|
|
|
|
Streaming chat completions are also supported for reasoning models. The `reasoning_content` field is available in the `delta` field in [chat completion response chunks](https://platform.openai.com/docs/api-reference/chat/streaming).
|
|
|
|
```json
|
|
{
|
|
"id": "chatcmpl-123",
|
|
"object": "chat.completion.chunk",
|
|
"created": 1694268190,
|
|
"model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
|
|
"system_fingerprint": "fp_44709d6fcb",
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {
|
|
"role": "assistant",
|
|
"reasoning_content": "is",
|
|
},
|
|
"logprobs": null,
|
|
"finish_reason": null
|
|
}
|
|
]
|
|
}
|
|
```
|
|
|
|
Please note that it is not compatible with the OpenAI Python client library. You can use the `requests` library to make streaming requests. You could checkout the [example](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/openai_chat_completion_with_reasoning_streaming.py).
|
|
|
|
## Limitations
|
|
|
|
- The reasoning content is only available for online serving's chat completion endpoint (`/v1/chat/completions`).
|
|
- It is not compatible with [`tool_calling`](#tool_calling).
|
|
- The reasoning content is not available for all models. Check the model's documentation to see if it supports reasoning.
|
|
|
|
## How to support a new reasoning model
|
|
|
|
You can add a new `ReasoningParser` similar to `vllm/entrypoints/openai/reasoning_parsers/deepseek_r1_reasoning_parser.py`.
|
|
|
|
```python
|
|
# import the required packages
|
|
|
|
from vllm.entrypoints.openai.reasoning_parsers.abs_reasoning_parsers import (
|
|
ReasoningParser, ReasoningParserManager)
|
|
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
|
|
DeltaMessage)
|
|
|
|
# define a reasoning parser and register it to vllm
|
|
# the name list in register_module can be used
|
|
# in --reasoning-parser.
|
|
@ReasoningParserManager.register_module(["example"])
|
|
class ExampleParser(ReasoningParser):
|
|
def __init__(self, tokenizer: AnyTokenizer):
|
|
super().__init__(tokenizer)
|
|
|
|
def extract_reasoning_content_streaming(
|
|
self,
|
|
previous_text: str,
|
|
current_text: str,
|
|
delta_text: str,
|
|
previous_token_ids: Sequence[int],
|
|
current_token_ids: Sequence[int],
|
|
delta_token_ids: Sequence[int],
|
|
) -> Union[DeltaMessage, None]:
|
|
"""
|
|
Instance method that should be implemented for extracting reasoning
|
|
from an incomplete response; for use when handling reasoning calls and
|
|
streaming. Has to be an instance method because it requires state -
|
|
the current tokens/diffs, but also the information about what has
|
|
previously been parsed and extracted (see constructor)
|
|
"""
|
|
|
|
def extract_reasoning_content(
|
|
self, model_output: str, request: ChatCompletionRequest
|
|
) -> tuple[Optional[str], Optional[str]]:
|
|
"""
|
|
Extract reasoning content from a complete model-generated string.
|
|
|
|
Used for non-streaming responses where we have the entire model response
|
|
available before sending to the client.
|
|
|
|
Parameters:
|
|
model_output: str
|
|
The model-generated string to extract reasoning content from.
|
|
|
|
request: ChatCompletionRequest
|
|
The request object that was used to generate the model_output.
|
|
|
|
Returns:
|
|
tuple[Optional[str], Optional[str]]
|
|
A tuple containing the reasoning content and the content.
|
|
"""
|
|
```
|
|
|
|
Additionally, to enable structured output, you'll need to create a new `Reasoner` similar to the one in `vllm/model_executor/guided_decoding/reasoner/deepseek_reasoner.py`.
|
|
|
|
```python
|
|
@dataclass
|
|
class DeepSeekReasoner(Reasoner):
|
|
"""
|
|
Reasoner for DeepSeek R series models.
|
|
"""
|
|
start_token_id: int
|
|
end_token_id: int
|
|
|
|
start_token: str = "<think>"
|
|
end_token: str = "</think>"
|
|
|
|
@classmethod
|
|
def from_tokenizer(cls, tokenizer: PreTrainedTokenizer) -> Reasoner:
|
|
return cls(start_token_id=tokenizer.encode(
|
|
"<think>", add_special_tokens=False)[0],
|
|
end_token_id=tokenizer.encode("</think>",
|
|
add_special_tokens=False)[0])
|
|
|
|
def is_reasoning_end(self, input_ids: list[int]) -> bool:
|
|
return self.end_token_id in input_ids
|
|
```
|
|
|
|
The structured output engine like xgrammar will use `end_token_id` to check if the reasoning content is present in the model output and skip the structured output if it is the case.
|
|
|
|
Finally, you can enable reasoning for the model by using the `--enable-reasoning` and `--reasoning-parser` flags.
|
|
|
|
```bash
|
|
vllm serve <model_tag> \
|
|
--enable-reasoning --reasoning-parser example
|
|
```
|