[Frontend]: Add apply_chat_template method and update generate method in LLM class

[llStringll]

FIX #6416 (Add apply_chat_template method and update generate method in LLM class)

This PR, which is an implementation of the Issue [Feature]#6416 (Add apply_chat_template method and update generate method in LLM class), introduces a new method, apply_chat_template, to the LLM class and integrates it into the existing generate method. The changes add to the flexibility and functionality of the LLM class by allowing for the application of a chat template to input messages, either returning token IDs or raw text based on the user's requirements.

Key Changes:

1. New Method: apply_chat_template

Purpose: Applies a chat template to a list of messages, with options to add a generation prompt and choose between tokenized output or raw text.
Parameters:
messages_list: A list of messages to be processed.
add_generation_prompt: A boolean flag to indicate whether to add a generation prompt.
tokenize: A boolean flag to determine the output format (token IDs or raw text).
Output:
I have kept the output type as Union[Union[List[List[str]], List[str]], Union[List[List[int]],List[int]]] to facilitate usage of the function outside of generate() method to fetch text and prompt_ids separately as:

from vllm import LLM

model = LLM("...")

messages_list = [
    [{"role": "user", "content": "Who are you?"}],
    [{"role": "user", "content": "write a quick sort algorithm in python."}],
    [{"role": "user", "content": "Write a piece of quicksort code in C++."}],
]

# use LLM class to apply chat template to prompts
prompt_ids = model.apply_chat_template(messages_list, add_generation_prompt=True)
text = model.apply_chat_template(messages_list, add_generation_prompt=True, tokenize=False)

2. Update to generate Method
   new parameter : messages_list: I have created this separate from prompts variable as general structure of prompts is different from what we expect in messages_list (as it is a list of dictionary with keys denoting type of prompt, user,system,assistant) . Maybe this could be combined with prompts and we can add those keys automatically.
   Integrated apply_chat_template to process prompts into prompt_token_ids when prompt_token_ids is not provided.
   This allows the generate method to accept raw text prompts and handle them appropriately, converting them to token IDs if necessary.

So now usage becomes:

from vllm import LLM

model = LLM("...")

messages_list = [
    [{"role": "user", "content": "Who are you?"}],
    [{"role": "user", "content": "write a quick sort algorithm in python."}],
    [{"role": "user", "content": "Write a piece of quicksort code in C++."}],
]

# chat template applied internally
outputs = model.generate(messages_list=messages_list)

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[github-actions]

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[mgoin]

Hi @llStringll thanks for PR but I believe we would like to different interface rather than overloading the generate method. Please see this PR: #5049 and this comment by Simon: #5049 (review)
It seems to have been left behind by the original contributor so if you would like to pick it up to satisfy your needs, that would be fine

[DarkLight1337]

The author of #5049 is active again so that PR should be merged soon, sorry for flip-flopping!