[Feature]: Support Guided Decoding in `LLM` entrypoint

[simon-mo]

🚀 The feature, motivation and pitch

Currently we support guided decoding of (JSON, Regex, Choice, Grammar, and arbitrary JSON) in OpenAI inference server. It would be great that we expose the same functionality in the offline interface as well.

https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#extra-parameters

Concretely this would mean adding the support here as a new parameter to generate call. Using methods introduced in #2819.

Do make sure to add test and examples.

Alternatives

No response

Additional context

No response

[kevinbu233]

Will work on this.

[BedirT]

Any update on this?

[kevinbu233]

still working on this, sorry about the delay

[maxdebayser]

I'm trying to understand why this support is considered missing if you can already do this:

from vllm import LLM, SamplingParams
from outlines.serve.vllm import JSONLogitsProcessor
from pydantic import BaseModel, conlist
import datetime as dt

class Output(BaseModel):
    names: conlist(str, max_length=5)
    organizations: conlist(str, max_length=5)
    locations: conlist(str, max_length=5)
    miscellanous: conlist(str, max_length=5)

llm = LLM('mistralai/Mistral-7B-v0.1', max_model_len=10_000, gpu_memory_utilization=0.9)
logits_processor = JSONLogitsProcessor(schema=Output, llm=llm.llm_engine)
logits_processor.fsm.vocabulary = list(logits_processor.fsm.vocabulary)
prompt = """
Locate all the names, organizations, locations and other miscellaneous entities in the following sentence: 
"Charles went and saw Anna at the coffee shop Starbucks, which was based in a small town in Germany called Essen."
"""
sampling_params = SamplingParams(max_tokens=128, temperature=0, logits_processors=[logits_processor])

t0 = dt.datetime.now()
llm.generate([prompt] * 256, sampling_params=sampling_params)
time_elapsed = (dt.datetime.now() - t0).total_seconds()
print(f"Generation took {time_elapsed:,} seconds.")

(Example taken from #3087).

Is the point of this issue to make the use of guided decoding more intuitive for the user?

[simon-mo]

Yes just for ease of use, and a way to better reset the processor.

[datalee]

how to use in openai api（[OpenAI Compatible Server]）？

[DarkLight1337]

Please use the extra parameters listed here

[datalee]

Please use the extra parameters listed here

Is there a version limit? Exception, is there a complete call example, thank you.

[DarkLight1337]

Sorry I don't have time to come up with an full example. You can take a look at the tests (e.g. tests/entrypoints/openai/test_chat.py) for some inspiration.

As for the version limit, you can set the version of the docs via the bottom right menu and find the earliest one that documents these parameters.

[datalee]

Sorry I don't have time to come up with an full example. You can take a look at the tests (e.g. tests/entrypoints/openai/test_chat.py) for some inspiration.

As for the version limit, you can set the version of the docs via the bottom right menu and find the earliest one that documents these parameters.

ok,thanks.
If version 0.4.2[https://docs.vllm.ai/en/v0.4.2/serving/openai_compatible_server.html] is used, an error is reported：
{'object': 'error', 'message': "[{'type': 'extra_forbidden', 'loc': ('body', 'extra_body'), 'msg': 'Extra inputs are not permitted', 'input': {'guided_json': {'type': 'object', 'properties': {'name': {'type': 'string'}, 'age': {'type': 'integer'}, 'skills': {'type': 'array', 'items': {'type': 'string', 'maxLength': 10}, 'minItems': 3}, 'work_history': {'type': 'array', 'items': {'type': 'object', 'properties': {'company': {'type': 'string'}, 'duration': {'type': 'number'}, 'position': {'type': 'string'}}, 'required': ['company', 'position']}}}, 'required': ['name', 'age', 'skills', 'work_history']}, 'guided_decoding_backend': 'outlines'}}]", 'type': 'BadRequestError', 'param': None, 'code': 400}

input：
{'model': 'qwen1_5_7b', 'temperature': 0.3, 'top_p': 0.3, 'max_tokens': 1024, 'messages': [{'role': 'system', 'content': 'you are a helpful assistant'}, {'role': 'user', 'content': "Give an example JSON for an employee profile that fits this schema: {'type': 'object', 'properties': {'name': {'type': 'string'}, 'age': {'type': 'integer'}, 'skills': {'type': 'array', 'items': {'type': 'string', 'maxLength': 10}, 'minItems': 3}, 'work_history': {'type': 'array', 'items': {'type': 'object', 'properties': {'company': {'type': 'string'}, 'duration': {'type': 'number'}, 'position': {'type': 'string'}}, 'required': ['company', 'position']}}}, 'required': ['name', 'age', 'skills', 'work_history']}"}], 'extra_body': {'guided_json': {'type': 'object', 'properties': {'name': {'type': 'string'}, 'age': {'type': 'integer'}, 'skills': {'type': 'array', 'items': {'type': 'string', 'maxLength': 10}, 'minItems': 3}, 'work_history': {'type': 'array', 'items': {'type': 'object', 'properties': {'company': {'type': 'string'}, 'duration': {'type': 'number'}, 'position': {'type': 'string'}}, 'required': ['company', 'position']}}}, 'required': ['name', 'age', 'skills', 'work_history']}, 'guided_decoding_backend': 'outlines'}}