Feat/add transformers integration

examples/transformers_integration.py

@@ -0,0 +1,25 @@
+"""Example of integrating `outlines` with `transformers`."""
+
+from pydantic import BaseModel
+from transformers import pipeline
+
+from outlines.integrations.transformers import JSONPrefixAllowedTokens
+
+
+class Person(BaseModel):
+    first_name: str
+    surname: str
+
+
+pipe = pipeline("text-generation", model="mistralai/Mistral-7B-v0.1")
+prefix_allowed_tokens_fn = JSONPrefixAllowedTokens(
+    schema=Person, tokenizer_or_pipe=pipe, whitespace_pattern=r" ?"
+)
+results = pipe(
+    ["He is Tom Jones", "She saw Linda Smith"],
+    return_full_text=False,
+    do_sample=False,
+    max_new_tokens=50,
+    prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+)
+print(results)

examples/vllm_integration.py

@@ -1,20 +1,24 @@
+"""Example of integrating `outlines` with `vllm`."""
+
 import vllm
 from pydantic import BaseModel
 
-from outlines.serve.vllm import JSONLogitsProcessor
+from outlines.integrations.vllm import JSONLogitsProcessor
 
 
-class User(BaseModel):
-    id: int
-    name: str
+class Person(BaseModel):
+    first_name: str
+    surname: str
 
 
-llm = vllm.LLM(model="openai-community/gpt2")
-logits_processor = JSONLogitsProcessor(schema=User, llm=llm)
+llm = vllm.LLM(model="mistralai/Mistral-7B-v0.1", max_model_len=512)
+logits_processor = JSONLogitsProcessor(schema=Person, llm=llm, whitespace_pattern=r" ?")
 result = llm.generate(
-    ["A prompt", "Another prompt"],
+    ["He is Tom Jones", "She saw Linda Smith"],
     sampling_params=vllm.SamplingParams(
-        max_tokens=100, logits_processors=[logits_processor]
+        temperature=0.0,
+        max_tokens=50,
+        logits_processors=[logits_processor],
     ),
 )
 print(result)

outlines/fsm/guide.py

@@ -62,6 +62,9 @@ def get_next_state(self, state: int, token_id: int) -> int:
     def is_final_state(self, state: int) -> bool:
         ...
 
+    def copy(self) -> "Guide":
+        ...
+
 
 class StopAtEOSGuide(Guide):
     """Guide to generate tokens until the EOS token has been generated."""
@@ -189,7 +192,9 @@ def get_next_state(self, state: int, token_id: int) -> int:
         """
         if token_id == self.eos_token_id:
             return -1
-        elif state in self.final_states:
+        elif (
+            state in self.final_states
+        ):  # Necessary because we keep generating EOS tokens when finished
             return state
 
         last_token_to_end_state = self.states_to_token_maps[state]

outlines/fsm/types.py

@@ -1,5 +1,5 @@
 import datetime
-from typing import Any, Callable, Tuple
+from typing import Protocol, Tuple, Type, Union
 
 INTEGER = r"[+-]?(0|[1-9][0-9]*)"
 BOOLEAN = "(True|False)"
@@ -9,26 +9,49 @@
 DATETIME = rf"({DATE})(\s)({TIME})"
 
 
-def python_types_to_regex(python_type: Any) -> Tuple[str, Callable[[str], Any]]:
+class FormatFunction(Protocol):
+    def __call__(
+        self, sequence: str
+    ) -> Union[int, float, bool, datetime.date, datetime.time, datetime.datetime]:
+        ...
+
+
+def python_types_to_regex(python_type: Type) -> Tuple[str, FormatFunction]:
     if python_type == float:
-        float_format_fn = lambda x: float(x)
+
+        def float_format_fn(sequence: str) -> float:
+            return float(sequence)
+
         return FLOAT, float_format_fn
     elif python_type == int:
-        int_format_fn = lambda x: int(x)
+
+        def int_format_fn(sequence: str) -> int:
+            return int(sequence)
+
         return INTEGER, int_format_fn
     elif python_type == bool:
-        bool_format_fn = lambda x: bool(x)
+
+        def bool_format_fn(sequence: str) -> bool:
+            return bool(sequence)
+
         return BOOLEAN, bool_format_fn
     elif python_type == datetime.date:
-        date_format_fn = lambda s: datetime.datetime.strptime(s, "%Y-%m-%d").date()
+
+        def date_format_fn(sequence: str) -> datetime.date:
+            return datetime.datetime.strptime(sequence, "%Y-%m-%d").date()
+
         return DATE, date_format_fn
     elif python_type == datetime.time:
-        time_format_fn = lambda s: datetime.datetime.strptime(s, "%H:%M:%S").time()
+
+        def time_format_fn(sequence: str) -> datetime.time:
+            return datetime.datetime.strptime(sequence, "%H:%M:%S").time()
+
         return TIME, time_format_fn
     elif python_type == datetime.datetime:
-        datetime_format_fn = lambda s: datetime.datetime.strptime(
-            s, "%Y-%m-%d %H:%M:%S"
-        )
+
+        def datetime_format_fn(sequence: str) -> datetime.datetime:
+            return datetime.datetime.strptime(sequence, "%Y-%m-%d %H:%M:%S")
+
         return DATETIME, datetime_format_fn
     else:
         raise NotImplementedError(

outlines/generate/api.py

@@ -1,9 +1,14 @@
+import datetime
 from typing import Iterator, List, Optional, Union
 
 import torch
 
 from outlines.generate.generator import sequence_generator
 
+FormattedOutput = Union[
+    str, int, float, bool, datetime.date, datetime.time, datetime.datetime
+]
+
 
 class SequenceGenerator:
     def __init__(
@@ -100,7 +105,7 @@ def strip_stop_sequences(
 
         return sequence
 
-    def format_sequence(self, sequence: str) -> str:
+    def format_sequence(self, sequence: str) -> FormattedOutput:
         """Translate the generated sequence to another type.
 
         This method is for instance overridden when generating JSON to either
@@ -124,7 +129,7 @@ def __call__(
         max_tokens: Optional[int] = None,
         stop_at: Optional[Union[str, List[str]]] = None,
         rng: Optional[torch.Generator] = None,
-    ) -> Union[str, List[str], List[List[str]]]:
+    ) -> Union[FormattedOutput, List[FormattedOutput], List[List[FormattedOutput]]]:
         """Generate the full text sequence.
 
         Since `SequenceGenerator.stream` calls the tokenizer at every step this
@@ -148,8 +153,7 @@ def __call__(
 
         Returns
         -------
-        A string or list of strings that contain the generated text.
-
+        The generation(s), potentially cast to another type.
         """
 
         if isinstance(prompts, str):
@@ -222,7 +226,7 @@ def __call__(
         formatted = [self.format_sequence(sequence) for sequence in stripped]
 
         # We reshape the output to (batch_size, sample_size)
-        output = []
+        output: List[List[FormattedOutput]] = list()
         for i in range(batch_size):
             output.append(formatted[i : i + num_samples])
 
@@ -242,7 +246,7 @@ def stream(
         max_tokens: Optional[int] = None,
         stop_at: Optional[Union[str, List[str]]] = None,
         rng: Optional[torch.Generator] = None,
-    ) -> Iterator[Union[List[str], List[List[str]], str]]:
+    ) -> Iterator[Union[List[str], str, List[List[str]]]]:
         """Generate the text sequence one token at a time.
 
         Since `Tokenizer.decode` strips the whitespaces from the tokens we have no
@@ -352,7 +356,7 @@ def token_generator() -> Iterator[Union[List[str], str, List[List[str]]]]:
                     ]
 
                 # We reshape the output to (batch_size, sample_size)
-                output = []
+                output: List[List[str]] = list()
                 for i in range(batch_size):
                     output.append(next_tokens[i : i + num_samples])
 

outlines/generate/regex.py

@@ -2,12 +2,9 @@
 
 from outlines.fsm.guide import RegexGuide
 from outlines.generate.api import SequenceGenerator
+from outlines.integrations.llamacpp import RegexLogitsProcessor
 from outlines.models import OpenAI
-from outlines.models.llamacpp import (
-    LlamaCpp,
-    LlamaSequenceGenerator,
-    RegexLogitsProcessor,
-)
+from outlines.models.llamacpp import LlamaCpp, LlamaSequenceGenerator
 from outlines.samplers import Sampler, multinomial
 
 
@@ -52,8 +49,8 @@ def regex_llamacpp(
             + "than the multinomial sampler."
         )
 
-    logits_processor = RegexLogitsProcessor(regex_str, model.tokenizer)
-    generator = LlamaSequenceGenerator(logits_processor, model)
+    logits_processor = RegexLogitsProcessor(regex_str, llm=model.model)
+    generator = LlamaSequenceGenerator(logits_processor=logits_processor, model=model)
 
     return generator
 

outlines/integrations/__init__.py

@@ -0,0 +1 @@
+"""Utility functions and classes used to integrate `outlines` into other packages."""