Merge branch 'main' into better-server-params-and-fields

README.md

@@ -90,7 +90,7 @@ This package is under active development and I welcome any contributions.
 To get started, clone the repository and install the package in development mode:
 
 ```bash
-git clone  --recurse-submodules git@github.com:abetlen/llama-cpp-python.git
+git clone --recurse-submodules git@github.com:abetlen/llama-cpp-python.git
 # Will need to be re-run any time vendor/llama.cpp is updated
 python3 setup.py develop
 ```

llama_cpp/llama.py

@@ -306,7 +306,7 @@ class Llama:
             llama_cpp.llama_sample_typical(
                 ctx=self.ctx,
                 candidates=llama_cpp.ctypes.pointer(candidates),
-                p=llama_cpp.c_float(1.0)
+                p=llama_cpp.c_float(1.0),
             )
             llama_cpp.llama_sample_top_p(
                 ctx=self.ctx,
@@ -637,10 +637,7 @@ class Llama:
                 self.detokenize([token]).decode("utf-8", errors="ignore")
                 for token in all_tokens
             ]
-            all_logprobs = [
-                Llama._logits_to_logprobs(row)
-                for row in self.eval_logits
-            ]
+            all_logprobs = [Llama._logits_to_logprobs(row) for row in self.eval_logits]
             for token, token_str, logprobs_token in zip(
                 all_tokens, all_token_strs, all_logprobs
             ):

llama_cpp/server/__main__.py

@@ -24,10 +24,10 @@ Then visit http://localhost:8000/docs to see the interactive API docs.
 import os
 import uvicorn
 
-from llama_cpp.server.app import app, init_llama
+from llama_cpp.server.app import create_app
 
 if __name__ == "__main__":
-    init_llama()
+    app = create_app()
 
     uvicorn.run(
         app, host=os.getenv("HOST", "localhost"), port=int(os.getenv("PORT", 8000))

llama_cpp/server/app.py

@@ -2,18 +2,18 @@ import os
 import json
 from threading import Lock
 from typing import List, Optional, Union, Iterator, Dict
-from typing_extensions import TypedDict, Literal
+from typing_extensions import TypedDict, Literal, Annotated
 
 import llama_cpp
 
-from fastapi import Depends, FastAPI
+from fastapi import Depends, FastAPI, APIRouter
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel, BaseSettings, Field, create_model_from_typeddict
 from sse_starlette.sse import EventSourceResponse
 
 
 class Settings(BaseSettings):
-    model: str = os.environ.get("MODEL", "null")
+    model: str
     n_ctx: int = 2048
     n_batch: int = 512
     n_threads: int = max((os.cpu_count() or 2) // 2, 1)
@@ -27,25 +27,29 @@ class Settings(BaseSettings):
     vocab_only: bool = False
 
 
-app = FastAPI(
-    title="🦙 llama.cpp Python API",
-    version="0.0.1",
-)
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+router = APIRouter()
 
-llama: llama_cpp.Llama = None
-def init_llama(settings: Settings = None):
+llama: Optional[llama_cpp.Llama] = None
+
+
+def create_app(settings: Optional[Settings] = None):
     if settings is None:
         settings = Settings()
+    app = FastAPI(
+        title="🦙 llama.cpp Python API",
+        version="0.0.1",
+    )
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+    app.include_router(router)
     global llama
     llama = llama_cpp.Llama(
-        settings.model,
+        model_path=settings.model,
         f16_kv=settings.f16_kv,
         use_mlock=settings.use_mlock,
         use_mmap=settings.use_mmap,
@@ -60,8 +64,12 @@ def init_llama(settings: Settings = None):
     if settings.cache:
         cache = llama_cpp.LlamaCache()
         llama.set_cache(cache)
+    return app
+
 
 llama_lock = Lock()
+
+
 def get_llama():
     with llama_lock:
         yield llama
@@ -117,8 +125,6 @@ repeat_penalty_field = Field(
     "Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient."
 )
 
-
-
 class CreateCompletionRequest(BaseModel):
     prompt: Union[str, List[str]] = Field(
         default="",
@@ -162,7 +168,7 @@ class CreateCompletionRequest(BaseModel):
 CreateCompletionResponse = create_model_from_typeddict(llama_cpp.Completion)
 
 
-@app.post(
+@router.post(
     "/v1/completions",
     response_model=CreateCompletionResponse,
 )
@@ -204,7 +210,7 @@ class CreateEmbeddingRequest(BaseModel):
 CreateEmbeddingResponse = create_model_from_typeddict(llama_cpp.Embedding)
 
 
-@app.post(
+@router.post(
     "/v1/embeddings",
     response_model=CreateEmbeddingResponse,
 )
@@ -257,7 +263,7 @@ class CreateChatCompletionRequest(BaseModel):
 CreateChatCompletionResponse = create_model_from_typeddict(llama_cpp.ChatCompletion)
 
 
-@app.post(
+@router.post(
     "/v1/chat/completions",
     response_model=CreateChatCompletionResponse,
 )
@@ -306,7 +312,7 @@ class ModelList(TypedDict):
 GetModelResponse = create_model_from_typeddict(ModelList)
 
 
-@app.get("/v1/models", response_model=GetModelResponse)
+@router.get("/v1/models", response_model=GetModelResponse)
 def get_models() -> ModelList:
     return {
         "object": "list",

tests/test_llama.py

@@ -22,9 +22,11 @@ def test_llama_patch(monkeypatch):
     ## Set up mock function
     def mock_eval(*args, **kwargs):
         return 0
-    
+
     def mock_get_logits(*args, **kwargs):
-        return (llama_cpp.c_float * n_vocab)(*[llama_cpp.c_float(0) for _ in range(n_vocab)])
+        return (llama_cpp.c_float * n_vocab)(
+            *[llama_cpp.c_float(0) for _ in range(n_vocab)]
+        )
 
     monkeypatch.setattr("llama_cpp.llama_cpp.llama_eval", mock_eval)
     monkeypatch.setattr("llama_cpp.llama_cpp.llama_get_logits", mock_get_logits)
@@ -88,6 +90,7 @@ def test_llama_patch(monkeypatch):
 def test_llama_pickle():
     import pickle
     import tempfile
+
     fp = tempfile.TemporaryFile()
     llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True)
     pickle.dump(llama, fp)
@@ -101,6 +104,7 @@ def test_llama_pickle():
 
     assert llama.detokenize(llama.tokenize(text)) == text
 
+
 def test_utf8(monkeypatch):
     llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True)
     n_vocab = int(llama_cpp.llama_n_vocab(llama.ctx))
@@ -110,7 +114,9 @@ def test_utf8(monkeypatch):
         return 0
 
     def mock_get_logits(*args, **kwargs):
-        return (llama_cpp.c_float * n_vocab)(*[llama_cpp.c_float(0) for _ in range(n_vocab)])
+        return (llama_cpp.c_float * n_vocab)(
+            *[llama_cpp.c_float(0) for _ in range(n_vocab)]
+        )
 
     monkeypatch.setattr("llama_cpp.llama_cpp.llama_eval", mock_eval)
     monkeypatch.setattr("llama_cpp.llama_cpp.llama_get_logits", mock_get_logits)
@@ -143,11 +149,13 @@ def test_utf8(monkeypatch):
 
 def test_llama_server():
     from fastapi.testclient import TestClient
-    from llama_cpp.server.app import app, init_llama, Settings
-    s = Settings()
-    s.model = MODEL
-    s.vocab_only = True
-    init_llama(s)
+    from llama_cpp.server.app import create_app, Settings
+
+    settings = Settings(
+        model=MODEL,
+        vocab_only=True,
+    )
+    app = create_app(settings)
     client = TestClient(app)
     response = client.get("/v1/models")
     assert response.json() == {