[Frontend][Core] Add guidance logits processor for guided decoding

[JC1DA]

Add Guidance backend for guided decoding

This pull request extends guided decoding capabilities

• Add guidance backend
• Process logits in parallel with threadpool

guidance backend supports regex, choice, json and grammar.

relevant: #5245

Usage

• JSON Generation

from pydantic import BaseModel, ConfigDict

model = "Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int4"
llm = LLM(model=model)

class UserProfile(BaseModel):
    name: str
    age: int
    email: str

    model_config = ConfigDict(extra="forbid")

sampling_params = SamplingParams(
    temperature=0.0,
    top_p=0.95,
    max_tokens=512,
    guided_decoding=GuidedDecodingParams(
        json=UserProfile,
        backend="guidance",
    ),
)

outputs = llm.chat(
    messages=[
        [
            CustomChatCompletionMessageParam(
                role="system", content="You are a helpful assistant."
            ),
            CustomChatCompletionMessageParam(
                role="user",
                content="Tell me something about yourself (name, age, email) in JSON format.\n",
            ),
        ],
    ],
    sampling_params=[sampling_params],
)

• Choices Generation

sampling_params = SamplingParams(
    temperature=0.0,
    top_p=0.95,
    max_tokens=512,
    guided_decoding=GuidedDecodingParams(
        choice=["3","4","5","6"],
        backend="guidance",
    ),
)

outputs = llm.chat(
    messages=[
        [
            CustomChatCompletionMessageParam(
                role="system", content="You are a 5 years-old helpful assistant."
            ),
            CustomChatCompletionMessageParam(
                role="user",
                content="How old are you?",
            ),
        ],
    ],
    sampling_params=[sampling_params],
)

• Regex Generation via OpenAI Client

model = "Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int4"
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="NOKEY",
)

completion = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "user",
            "content": "You are a 5 years-old helpful assistant. information.",
        },
        {
            "role": "user",
            "content": """How old are you?""",
        },
    ],
    extra_body={"guided_regex": "\\d+", "guided_decoding_backend": "guidance"}
)

Benchmark

Model: QWEN2.5-7B-GPTQ-INT4
Dataset: GSM8K
Guided Type: JSON

Metric	Outlines	Guidance
Accuracy	1023/1318 (77.62%)	1032/1318 (78.3%)
Average Output tokens	166 (+/- 83)	195 (+/- 69)
Average Latency in ms per Request (1 concurrent req)	2567 (+/- 976)	1799 (+/- 466)
Average Latency in ms per Request (4 concurrent reqs)	8697 (+/- 3866)	3655 (+/- 1154)
Average Latency in ms per Request (8 concurrent reqs)	17370 (+/- 8139)	5997 (+/- 1991)

[github-actions]

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @JC1DA.

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