Dynamic generation with outlines

[isamu-isozaki]

This is a pr to adapt outlines for exllamav2 dynamic generation. I think this will remove the need for this pr as exllamav2 should do this under the hood.

To run this, you need to install outlines from my branch using

pip uninstall outlines
pip install --no-cache-dir pip install git+https://github.com/isamu-isozaki/outlines.git@exllamav2_filter

This is currently a PR in outlines too here so in the future

pip install outlines 

might be enough.

I started server with

python llm_exl2_dynamic_gen.py --port 5000 --repo_str phi3b --max_context 2048 --total_context 4096 --not_paged

and tested code for

1. Constrained generation for choices
2. Constrained generation for json
3. Constrained generation for regex
4. Checking if stop_at keyword works
5. Making sure server doesn't error on keyboard interrupt and they all seem to work. I'll add the code for these tests in the comments

[isamu-isozaki]

Start client

from langchain.prompts import PromptTemplate
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage, AIMessage
import json
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(temperature=1.0,
                openai_api_base="http://localhost:5000/v1", 
                openai_api_key="Test",
                streaming=True, 
                max_tokens=1024)

Test choices

messages = [
    SystemMessage(
        content="You are a helpful assistant."
    ),
    HumanMessage(
        content="Who is better bob or fred?"
    )
]
for chunk in llm.stream(messages, extra_body={"outlines_type": "choices", "choices": ["bob", "fred"]}):
    print(chunk.content, end="", flush=True)

Output:

bob

Test JSON

from enum import Enum
from pydantic import BaseModel, constr
import json

class Weapon(str, Enum):
    sword = "sword"
    axe = "axe"
    mace = "mace"
    spear = "spear"
    bow = "bow"
    crossbow = "crossbow"

class Armor(str, Enum):
    leather = "leather"
    chainmail = "chainmail"
    plate = "plate"

class Character(BaseModel):
    name: constr(max_length=10)
    age: int
    armor: Armor
    weapon: Weapon
    strength: int
messages = [
    SystemMessage(
        content="You are a helpful assistant."
    ),
    HumanMessage(
        content=f"Give me an interesting character description based on the following schema: {json.dumps(Character.schema())}"
    )
]
for chunk in llm.stream(messages, extra_body={"outlines_type": "json", "json": json.dumps(Character.schema())}):
    print(chunk.content, end="", flush=True)

Output

{ "name": "Eldric the" , "age": 37, "armor": "chainmail", "weapon": "sword", "strength": 87 }

Test Regex

messages = [
    SystemMessage(
        content="You are a helpful assistant."
    ),
    HumanMessage(
        content=f"Choose between bob and fred."
    )
]
for chunk in llm.stream(messages, extra_body={"outlines_type": "regex", "regex": "bob|fred"}):
    print(chunk.content, end="", flush=True)

Output

bob

Test stop_at keyword

messages = [
    SystemMessage(
        content="You are a helpful assistant."
    ),
    HumanMessage(
        content="Instruction: Always answer Questions in the form Question: What is 2+1?\nwith\nAnswer: 2+1=3\nQuestion: What is 21+1?\n"
    )
]
for chunk in llm.stream(messages, extra_body={"outlines_type": "text", "stop_at": "+"}):
    print(chunk.content, end="", flush=True)

For testing keyboard interrupt, I ran the below code, interrupted during generation, and then ran it again. And it seemed to work

messages = [
    SystemMessage(
        content="You are a helpful assistant."
    ),
    HumanMessage(
        content="What is your name?"
    )
]
for chunk in llm.stream(messages):
    print(chunk.content, end="", flush=True)

[isamu-isozaki]

Let me know if there should be more tests!