Integrate llama.cpp via a logits processor

.gitignore

@@ -4,3 +4,4 @@ __pycache__
 docs/build
 .coverage
 .idea/
+*.gguf

docs/reference/models/llamacpp.md

@@ -11,5 +11,5 @@ Assuming [Phi2's weights](https://huggingface.co/TheBloke/phi-2-GGUF) are in the
 ```python
 from outlines import models, generate
 
-model = models.llamacpp("./phi-2.Q4_K_M.gguf", device="cpu")
+model = models.llamacpp("./phi-2.Q4_K_M.gguf")
 ```

examples/llamacpp_example.py

@@ -30,8 +30,8 @@ class Character(BaseModel):
 
 
 if __name__ == "__main__":
-    # Download model from https://huggingface.co/TheBloke/phi-2-GGUF
-    model = outlines.models.llamacpp("./phi-2.Q3_K_M.gguf", device="cpu")
+    # curl -L -o mistral-7b-instruct-v0.2.Q5_K_M.gguf https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q5_K_M.gguf
+    model = outlines.models.llamacpp("./mistral-7b-instruct-v0.2.Q5_K_M.gguf")
 
     # Construct structured sequence generator
     generator = outlines.generate.json(model, Character)

examples/llamacpp_processor.py

@@ -0,0 +1,50 @@
+from enum import Enum
+
+from llama_cpp import Llama, LogitsProcessorList
+from pydantic import BaseModel, constr
+
+from outlines.generate.processors import JSONLogitsProcessor
+from outlines.models.llamacpp import LlamaCppTokenizer
+
+
+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
+
+
+if __name__ == "__main__":
+    llama = Llama("./phi-2.Q4_K_M.gguf")
+    tokenizer = LlamaCppTokenizer(llama)
+
+    prompt = "Instruct: You are a leading role play gamer. You have seen thousands of different characters and their attributes.\nPlease return a JSON object with common attributes of an RPG character. Give me a character description\nOutput:"
+
+    logits_processor = JSONLogitsProcessor(Character, tokenizer)
+
+    json_str = llama.create_completion(
+        prompt,
+        top_k=40,
+        top_p=0.95,
+        temperature=0.7,
+        max_tokens=100,
+        logits_processor=LogitsProcessorList([logits_processor]),
+    )["choices"][0]["text"]
+
+    print(json_str)

outlines/fsm/fsm.py

@@ -91,7 +91,7 @@ def copy(self) -> "StopAtEosFSM":
 class RegexFSM(FSM):
     """FSM to generate text that is in the language of a regular expression."""
 
-    def __init__(self, regex_string: str, tokenizer: "Tokenizer"):
+    def __init__(self, regex_string: str, tokenizer):
         @cache()
         def create_states_mapping(
             regex_string: str, cacheable_vocabulary: Tuple[Tuple[str, int]]
@@ -190,7 +190,7 @@ def copy(self) -> "RegexFSM":
 class CFGFSM(FSM):
     """FSM to generate text that is in the language of a context-free grammar."""
 
-    def __init__(self, cfg_string: str, tokenizer: "Tokenizer"):
+    def __init__(self, cfg_string: str, tokenizer):
         self.cfg_string = cfg_string
         self.tokenizer = tokenizer
 

outlines/fsm/json_schema.py

@@ -1,10 +1,10 @@
 import inspect
 import json
 import re
-from typing import Callable, Optional, Union
+from typing import Callable, Optional
 
 from jsonschema.protocols import Validator
-from pydantic import BaseModel, create_model
+from pydantic import create_model
 from referencing import Registry, Resource
 from referencing._core import Resolver
 from referencing.jsonschema import DRAFT202012
@@ -38,9 +38,7 @@
 }
 
 
-def build_regex_from_object(
-    object: Union[str, Callable, BaseModel], whitespace_pattern: Optional[str] = None
-):
+def build_regex_from_schema(schema: str, whitespace_pattern: Optional[str] = None):
     """Turn a JSON schema into a regex that matches any JSON object that follows
     this schema.
 
@@ -72,13 +70,7 @@ def build_regex_from_object(
 
     """
 
-    if isinstance(object, type(BaseModel)):
-        schema = object.model_json_schema()
-    elif callable(object):
-        schema = get_schema_from_signature(object)
-    else:
-        schema = json.loads(object)
-
+    schema = json.loads(schema)
     Validator.check_schema(schema)
 
     # Build reference resolver

outlines/generate/cfg.py

@@ -3,6 +3,7 @@
 from outlines.fsm.fsm import CFGFSM
 from outlines.generate.api import SequenceGenerator
 from outlines.models import OpenAI
+from outlines.models.llamacpp import CFGLogitsProcessor, LlamaCpp
 from outlines.samplers import Sampler, multinomial
 
 
@@ -31,6 +32,24 @@ def cfg(model, cfg_str: str, sampler: Sampler = multinomial()) -> SequenceGenera
     return generator
 
 
+@cfg.register(LlamaCpp)
+def cfg_llamacpp(
+    model: LlamaCpp,
+    cfg_str: str,
+    sampler: Sampler = multinomial(),
+):
+    if not isinstance(sampler, multinomial):
+        raise NotImplementedError(
+            r"The llama.cpp integration does not currently support any other sampling algorithm "
+            + "than the multinomial sampler."
+        )
+
+    logits_processor = CFGLogitsProcessor(cfg_str, model.tokenizer)
+    model.logits_processor = logits_processor
+
+    return model
+
+
 @cfg.register(OpenAI)
 def cfg_openai(model, cfg_str: str, sampler: Sampler = multinomial()):
     raise NotImplementedError(

outlines/generate/choice.py

@@ -13,7 +13,11 @@ def choice(
     model, choices: List[str], sampler: Sampler = multinomial()
 ) -> SequenceGenerator:
     regex_str = r"(" + r"|".join(choices) + r")"
-    return regex(model, regex_str, sampler)
+
+    generator = regex(model, regex_str, sampler)
+    generator.format_sequence = lambda x: x
+
+    return generator
 
 
 @choice.register(OpenAI)

outlines/generate/json.py

@@ -4,7 +4,7 @@
 
 from pydantic import BaseModel
 
-from outlines.fsm.json_schema import build_regex_from_object, get_schema_from_signature
+from outlines.fsm.json_schema import build_regex_from_schema, get_schema_from_signature
 from outlines.generate.api import SequenceGenerator
 from outlines.models import OpenAI
 from outlines.samplers import Sampler, multinomial
@@ -45,17 +45,17 @@ def json(
     """
     if isinstance(schema_object, type(BaseModel)):
         schema = pyjson.dumps(schema_object.model_json_schema())
-        regex_str = build_regex_from_object(schema, whitespace_pattern)
+        regex_str = build_regex_from_schema(schema, whitespace_pattern)
         generator = regex(model, regex_str, sampler)
         generator.format_sequence = lambda x: schema_object.parse_raw(x)
     elif callable(schema_object):
         schema = pyjson.dumps(get_schema_from_signature(schema_object))
-        regex_str = build_regex_from_object(schema, whitespace_pattern)
+        regex_str = build_regex_from_schema(schema, whitespace_pattern)
         generator = regex(model, regex_str, sampler)
         generator.format_sequence = lambda x: pyjson.loads(x)
     elif isinstance(schema_object, str):
         schema = schema_object
-        regex_str = build_regex_from_object(schema, whitespace_pattern)
+        regex_str = build_regex_from_schema(schema, whitespace_pattern)
         generator = regex(model, regex_str, sampler)
         generator.format_sequence = lambda x: pyjson.loads(x)
     else:

outlines/generate/regex.py

@@ -3,6 +3,7 @@
 from outlines.fsm.fsm import RegexFSM
 from outlines.generate.api import SequenceGenerator
 from outlines.models import OpenAI
+from outlines.models.llamacpp import LlamaCpp, RegexLogitsProcessor
 from outlines.samplers import Sampler, multinomial
 
 
@@ -35,8 +36,30 @@ def regex(model, regex_str: str, sampler: Sampler = multinomial()):
     return generator
 
 
+@regex.register(LlamaCpp)
+def regex_llamacpp(
+    model: LlamaCpp,
+    regex_str: str,
+    sampler: Sampler = multinomial(),
+):
+    if not isinstance(sampler, multinomial):
+        raise NotImplementedError(
+            r"The llama.cpp integration does not currently support any other sampling algorithm "
+            + "than the multinomial sampler."
+        )
+
+    logits_processor = RegexLogitsProcessor(regex_str, model.tokenizer)
+    model.logits_processor = logits_processor
+
+    return model
+
+
 @regex.register(OpenAI)
-def regex_openai(model, regex_str: str, sampler: Sampler = multinomial()):
+def regex_openai(
+    model: OpenAI,
+    regex_str: str,
+    sampler: Sampler = multinomial(),
+):
     raise NotImplementedError(
         "Cannot use regex-structured generation with an OpenAI model"
         + "due to the limitations of the OpenAI API."

outlines/generate/text.py

@@ -2,7 +2,7 @@
 
 from outlines.fsm.fsm import StopAtEosFSM
 from outlines.generate import SequenceGenerator
-from outlines.models import OpenAI
+from outlines.models import LlamaCpp, OpenAI
 from outlines.samplers import Sampler, multinomial
 
 
@@ -36,12 +36,23 @@ def text(model, sampler: Sampler = multinomial()) -> SequenceGenerator:
     return generator
 
 
+@text.register(LlamaCpp)
+def text_llamacpp(model: LlamaCpp, sampler: Sampler = multinomial()):
+    if not isinstance(sampler, multinomial):
+        raise NotImplementedError(
+            r"The llama.cpp API does not support any other sampling algorithm "
+            + "than the multinomial sampler."
+        )
+
+    return model
+
+
 @text.register(OpenAI)
 def text_openai(model: OpenAI, sampler: Sampler = multinomial()) -> OpenAI:
     if not isinstance(sampler, multinomial):
         raise NotImplementedError(
             r"The OpenAI API does not support any other sampling algorithm "
-            + "that the multinomial sampler."
+            + "than the multinomial sampler."
         )
 
     return model