[Frontend] Support reasoning content for deepseek r1 (#12473)

Signed-off-by: Ce Gao <cegao@tensorchord.ai>
Co-authored-by: Rafael Vasquez <rafvasq21@gmail.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: Michael Goin <mgoin@redhat.com>

docs/source/features/reasoning_outputs.md

@@ -0,0 +1,151 @@
+(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.
+
+## 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.
+        """
+```
+
+After defining the reasoning parser, you can use it by specifying the `--reasoning-parser` flag when making a request to the chat completion endpoint.
+
+```bash
+vllm serve <model_tag> \
+    --enable-reasoning --reasoning-parser example
+```
+
+## Limitations
+
+- The reasoning content is only available for online serving's chat completion endpoint (`/v1/chat/completions`).
+- It is not compatible with the [`structured_outputs`](#structured_outputs) and [`tool_calling`](#tool_calling) features.
+- The reasoning content is not available for all models. Check the model's documentation to see if it supports reasoning.