Transformers use logits processor

[lapp0]

Fixes dottxt-ai#806

Fixes dottxt-ai#789

Closes dottxt-ai#910

Problem

For outlines.models.transformers, instead of using logits processors which encapsulate automata management, SequenceGenerator directly manages the automata. This different implementation resulted in dottxt-ai#789's bug.

Solution

• Implement Transformers.generate and Transformers.stream which use HF transformers logits_processor argument with outlines.processors.OutlinesLogitsProcessor
• Use SequenceGeneratorAdapter for transformers instead of SequenceGenerator

TODO:

• implement Transformers.generate and Transformers.stream
• implement SequenceGeneratorAdapter version of outlines.models.transformers
• unit tests
• await mlx merge and rebase onto main
• update transformers integration documentation
• revert llamacpp and vllm changes, these will be in a separate PR
• ~~logits processor profiling in benchmarks~
  • will do in logits processor unification PR
• ping people who've requested this

Bonus

This new structure allows us to easily integrate multi-modal models by subclassing models.Transformer. Additionally, we can make models.mamba a Transformer model and just pass model_class=MambaLMHeadModel.

Multi-modal model example:

from outlines.processors import RegexLogitsProcessor
from outlines.models.transformers import TransformerTokenizer

from PIL import Image
import requests
from transformers import AutoProcessor, LlavaForConditionalGeneration, LogitsProcessorList, AutoTokenizer


model_uri = "llava-hf/llava-1.5-7b-hf"

url = "https://www.ilankelman.org/stopsigns/australia.jpg"
prompt = "USER: <image>\nWhat's the content of the image? ASSISTANT:"
output_pattern = r"This is like, totally an image of .*"


model = LlavaForConditionalGeneration.from_pretrained(model_uri, load_in_4bit=True)
llava_processor = AutoProcessor.from_pretrained(model_uri)
regex_logits_processor = RegexLogitsProcessor(
    output_pattern,
    TransformerTokenizer(AutoTokenizer.from_pretrained(model_uri)),
)

inputs = llava_processor(
    text=prompt,
    images=Image.open(requests.get(url, stream=True).raw),
    return_tensors="pt"
)

# Generate
generate_ids = model.generate(
    **inputs,
    logits_processor=LogitsProcessorList([
        regex_logits_processor
    ]),
    max_new_tokens=30
)

result = llava_processor.batch_decode(
    generate_ids,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)[0]
print(result)
# USER:\nWhat's the content of the image? ASSISTANT:This is like, totally an image of a stop sign on a street.