[Frontend][Core] Update Outlines Integration from FSM to Guide

[br3no]

This PR updates the Outlines Integration from FSM to the new Guide interface.

Since I'm not sure where to place the change, I added both labels [Frontend] and [Core].

FIX #3715

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[simon-mo]

LGTM.

[simon-mo]

I think the test failure in the entrypoints test might be related, and there's merge conflict. 🙏

[br3no]

I'll have a look at the failing frontend test.

[br3no]

I can reproduce the error in the test on my dev environment. The generation does not stop when it should, generating IP addresses like this: 100.101.102.10319216. I'm investigating why this happens and reached out to @rlouf in the outlines discord.

I have pushed some small improvements to the test code:

• corrected the regex for IPv4 the regex was correct. I reverted the change
• changed the pytest fixture to make sure the same python interpreter is used when starting the vLLM server for the integration test (see the warning box here: https://docs.python.org/3/library/subprocess.html#subprocess.Popen)

[navster888]

hey @br3no @simon-mo any updates on getting this PR merged? The pinned version of outlines is preventing us from picking up a bug fix in included in 0.0.40. Is there anyway we can pull the relaxed version constraint into it's own PR to unblock?

[br3no]

I'm having a call with outlines contributors on Thursday. While there is no guarantee we will have a solution for the problem, I'd propose to wait until then. If there's no progress by the end of the week, I'd open a separate PR for the unpinning.

What do you think?

[br3no]

I have opened #4558 because moving to the Guide API will require outlines-dev/outlines#856 to be fixed first.

[br3no]

I have closed #4558 in favor of this PR. I expect to make progress on this next week. Waiting for outlines-dev/outlines#874.

[rlouf]

outlines-dev/outlines#874 merged, thank you for your patience!

[br3no]

Great, thanks for the support @rlouf!

Can you tell already when you plan to release?

[rlouf]

Next week I think. I need to make sure other downstream libraries have pinned the outlines version to avoid surprises.

[br3no]

@saattrupdan thanks for pointing this out.

I believe this fix will not work in vLLM. The thing is that the logits processors are cached here:

vllm/vllm/model_executor/guided_decoding/outlines_decoding.py

Line 119 in 594392d

def _get_cached_logits_processor(guide: str,

Not resetting the state means that the dictionary will grow with every generation and will never be cleaned.

Or am I missing something?

[br3no]

Let me summarize the issue raised by @saattrupdan:

• when n > 1, all batches share one logits processor. E.g. in a chat completion request

  vllm/vllm/entrypoints/openai/serving_chat.py

  Line 168 in 4238bc8

  await get_guided_decoding_logits_processor(

• the logits processor is stateful, caching the state information for each sequence prefix
• in [vLLM integration bug] Generated output is stopped for all samples in batch outlines-dev/outlines#757 it was observed that for big enough values of n, some sequences break
• because we cache the logits processor, even separate requests (with the same guide and tokenizer, which won't be uncommon) will share the same logits processor
• @saattrupdan's fix was to never reset the state
• never resetting the state will lead to a memory leak, as the cache will increase indefinitely
• but if we want to continue cleaning-up the state, this can and will break in different ways
• we need to find a way to attach the cache's life-cycle to the request. It should be clean on a new request and only be cleared after the request is finished

---

The expensive thing though, is building the Guide for a particular tokenizer and guide specification (regex, json-schema or grammar), not the logits processor itself. So I believe that pushing the cache one level lower (caching only the Guide and not the logits processor), should solve the issue.

@simon-mo should I open a new issue for this, or should I just fix this together with the change to the Guide API, subject of this PR?

[simon-mo]

Same PR works since this is small enough. also cc @njhill I think you mentioned similar issue

[njhill]

Sorry, missed @simon-mo tagging me above. Yes we encountered the same problem and had been thinking to introduce a LogitsProcessorFactory abstract class that can be included in the SamplingParams instead of LogitsProcessors (but allowing those too for backwards compatibility. This could have both create_processor() and return_processor(lp) methods. Latter not required but could be used for pooling.

vLLM would then ensure to call this separately for each sequence. Stateless LP factories can just return a constant LP from the method.

Note this is currently also problem if a list of prompts is passed in the API, and/or if n > 1 like you said (including beam search).

WDYT?

@maxdebayser has started prototyping this.

[br3no]

@njhill I like the direction of your proposal. This would allow us to invert control and get rid of the 10 lines setting up guided decoding in the create_chat_completion method. Is there a PR with @maxdebayser's sketch?

While I think this is the right thing to do on the long run, I believe we should fix this problem ASAP. Would you mind having a look at the code in this PR which handles this issue by pushing the cache one level down? I believe this would work nicely as a band-aid until the refactoring you proposed is implemented.

The core changes are:

• we are no longer caching the LogitProcessors. They are now created on each request anew.
• the LogitsProcessors no longer reset state – so all n sequences can safely share the same state cache in the lifetime of one request.
• the Guide object is cached globally for every guide/tokenizer pair. This is the expensive thing we don't want to recompute on every request. (this is how we were caching the LogitProcessors)

[br3no]

PS: this PR is ready for review @simon-mo

I'm just waiting for outlines to be released, so that we can get rid of the regression in the tests.

[maxdebayser]

Hi @br3no, we also found a problem with the FSM state being shared between sequences. This curl here causes a crash:

curl http://localhost:8000/v1/completions   -H "Content-Type: application/json"   -d '{
    "model": "<MY_MODEL>",
    "prompt": ["An example of a json document: ", "Another example of a json document: "],
    "max_tokens": 100,
    "temperature": 0,
    "guided_decoding_backend": "outlines",
    "response_format": {"type":"json_object"},
    "logit_bias": {"100": -100}
  }'

We have a sketch for a PR here: IBM/vllm#38 . It uses factories like @njhill mentioned, so that each sequence can have it's own logits processor copy.

The changes in our PR solve this particular issue, but I think the CFGLogitsProcessor would still crash if the sequence is preempted with the recompute policy. But I don't know yet how to test this hypothesis.

[maxdebayser]

@br3no , I've tested your changes with the curl command above. The code doesn't crash anymore, but I think the output ends prematurely:

{
  "id": "cmpl-d1e40aa8af734fcc98238fa5e0c2ecac",
  "object": "text_completion",
  "created": 1717517730,
  "choices": [
    {
      "index": 0,
      "text": "\n\n\n{\n",
      "logprobs": null,
      "finish_reason": "stop",
      "stop_reason": null
    },
    {
      "index": 1,
      "text": "\n\n\n{",
      "logprobs": null,
      "finish_reason": "stop",
      "stop_reason": null
    }
  ],
  "usage": {
    "prompt_tokens": 18,
    "total_tokens": 29,
    "completion_tokens": 11
  }
}

So while I do think that your changes are an improvement, I think we need to add the factory PR as well to deal with stateful LogitsProcessors properly. Just for reference, the curl request returns this on our PR:

{
  "id": "cmpl-861186441f9c459c981fcab5abd33f5b",
  "object": "text_completion",
  "created": 1717515348,
  "choices": [
    {
      "index": 0,
      "text": "\n\n\n{\n\"name\" \n: \n\"John Doe\"\n,\n\"age\" \n: 30\n,\n\"city\" \n: \n\"New York\"\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
      "logprobs": null,
      "finish_reason": "length",
      "stop_reason": null
    },
    {
      "index": 1,
      "text": "\n\n\n{\n\"name\" \n: \n\"John Doe\"\n,\n\"age\" \n: 30\n,\n\"city\" \n: \n\"New York\"\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
      "logprobs": null,
      "finish_reason": "length",
      "stop_reason": null
    }
  ],
  "usage": {
    "prompt_tokens": 18,
    "total_tokens": 218,
    "completion_tokens": 200
  }
}

[maxdebayser]

LGTM

[maxdebayser]

Actually, if I run

curl http://localhost:8000/v1/completions   -H "Content-Type: application/json"   -d '{
    "model": "<MY_MODEL>",
    "prompt": ["An example of a json document: ", "Another example of a json document: "],
    "max_tokens": 100,
    "temperature": 0,
    "guided_decoding_backend": "outlines",
    "response_format": {"type":"json_object"},
    "logit_bias": {"100": -100}
  }'

followed by

curl http://localhost:8000/v1/completions   -H "Content-Type: application/json"   -d '{
    "model": "<MY_MODEL>",
    "prompt": ["An example of a json document: "],                                        
    "max_tokens": 100,
    "temperature": 0,
    "guided_decoding_backend": "outlines",
    "response_format": {"type":"json_object"},
    "logit_bias": {"100": -100}
  }' | jq

I get this crash:

    File "/home/develop/.local/lib/python3.11/site-packages/vllm/model_executor/guided_decoding/outlines_logits_processors.py", line 47, in __call__
    instruction = self._guide.get_next_instruction(
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/develop/.local/lib/python3.11/site-packages/outlines/fsm/guide.py", line 349, in get_next_instruction
    interactive.exhaust_lexer()
  File "/opt/vllm/lib/python3.11/site-packages/lark/parsers/lalr_interactive_parser.py", line 52, in exhaust_lexer
    return list(self.iter_parse())
           ^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/vllm/lib/python3.11/site-packages/lark/parsers/lalr_interactive_parser.py", line 43, in iter_parse
    for token in self.lexer_thread.lex(self.parser_state):
  File "/opt/vllm/lib/python3.11/site-packages/lark/lexer.py", line 674, in lex
    raise UnexpectedToken(token, e.allowed, state=parser_state, token_history=[last_token], terminals_by_name=self.root_lexer.terminals_by_name)
lark.exceptions.UnexpectedToken: Unexpected token Token('LBRACE', '{') at line 7, column 2.
Expected one of: 
	* UNESCAPED_STRING
	* RBRACE
Previous tokens: [Token('LBRACE', '{')]

If I restart the server and just send the curl request with a single prompt several times, only the first request generates useful JSON. After that it returns:

{
  "id": "cmpl-948cb1c4033b40f1a07846bed5ac4de9",
  "object": "text_completion",
  "created": 1717518689,
  "model": "/llama_eval_storage/LLaMa/models/hf/7B-F",
  "choices": [
    {
      "index": 0,
      "text": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
      "logprobs": null,
      "finish_reason": "length",
      "stop_reason": null
    }
  ],
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 109,
    "completion_tokens": 100
  }
}

I'm testing with LLama2 7b and outlines==0.0.41

[br3no]

@maxdebayser thanks for looking into it!

we also found a problem with the FSM state being shared between sequences

I have just looked into Outlines and while RegexGuide is thread-safe, CFGGuide is not.

I'll check if I can increase the size of the band-aid a bit to deal with this case...

[br3no]

I have pushed a commit that comes close to what was there before and at the same time does not lead to crashes on n > 1. It's still not correct, though...

Every CFGGuide can only be used by one sequence at a time. We need the factory idea in IBM/vllm#38. Note that the performance will still be horrible, since we will create a new Guide for every sequence. This is unavoidable, unfortunately.

It probably makes sense to pre-build a (large) pool of CFGGuides for the use-case where request.response_format.type == "json_object". I believe this is a pragmatic and useful solution, since this will be the most common case.

To really solve this, Outlines would need to be changed to make the CFGGuide thread-safe. I believe this would require a large effort.

@rlouf, could you give us your expert opinion on this?

[maxdebayser]

@br3no , I can confirm that your latest commit fixes the problem where state from previous single-sequence requests is carried over to new requests.

[simon-mo]

Thank you for doing this. Please let me know when this PR is ready to be merged!

[simon-mo]

Actually it seems complete given @maxdebayser's comment. I will merge now.