llama_cpp server: slight refactor to init_llama function

Define an init_llama function that starts llama with supplied settings instead of just doing it in the global context of app.py

This allows the test to be less brittle by not needing to mess with os.environ, then importing the app

Author: Stonelinks

llama_cpp/server/__main__.py

@@ -24,9 +24,10 @@
 import os
 import uvicorn
 
-from llama_cpp.server.app import app
+from llama_cpp.server.app import app, init_llama
 
 if __name__ == "__main__":
+    init_llama()
 
     uvicorn.run(
         app, host=os.getenv("HOST", "localhost"), port=int(os.getenv("PORT", 8000))

llama_cpp/server/app.py

@@ -13,7 +13,7 @@
 
 
 class Settings(BaseSettings):
-    model: str = os.environ["MODEL"]
+    model: str = os.environ.get("MODEL", "null")
     n_ctx: int = 2048
     n_batch: int = 512
     n_threads: int = max((os.cpu_count() or 2) // 2, 1)
@@ -38,31 +38,34 @@ class Settings(BaseSettings):
     allow_methods=["*"],
     allow_headers=["*"],
 )
-settings = Settings()
-llama = llama_cpp.Llama(
-    settings.model,
-    f16_kv=settings.f16_kv,
-    use_mlock=settings.use_mlock,
-    use_mmap=settings.use_mmap,
-    embedding=settings.embedding,
-    logits_all=settings.logits_all,
-    n_threads=settings.n_threads,
-    n_batch=settings.n_batch,
-    n_ctx=settings.n_ctx,
-    last_n_tokens_size=settings.last_n_tokens_size,
-    vocab_only=settings.vocab_only,
-)
-if settings.cache:
-    cache = llama_cpp.LlamaCache()
-    llama.set_cache(cache)
-llama_lock = Lock()
 
+llama: llama_cpp.Llama = None
+def init_llama(settings: Settings = None):
+    if settings is None:
+        settings = Settings()
+    global llama
+    llama = llama_cpp.Llama(
+        settings.model,
+        f16_kv=settings.f16_kv,
+        use_mlock=settings.use_mlock,
+        use_mmap=settings.use_mmap,
+        embedding=settings.embedding,
+        logits_all=settings.logits_all,
+        n_threads=settings.n_threads,
+        n_batch=settings.n_batch,
+        n_ctx=settings.n_ctx,
+        last_n_tokens_size=settings.last_n_tokens_size,
+        vocab_only=settings.vocab_only,
+    )
+    if settings.cache:
+        cache = llama_cpp.LlamaCache()
+        llama.set_cache(cache)
 
+llama_lock = Lock()
 def get_llama():
     with llama_lock:
         yield llama
 
-
 class CreateCompletionRequest(BaseModel):
     prompt: Union[str, List[str]]
     suffix: Optional[str] = Field(None)

tests/test_llama.py

@@ -132,10 +132,11 @@ def mock_sample(*args, **kwargs):
 
 def test_llama_server():
     from fastapi.testclient import TestClient
-    import os
-    os.environ["MODEL"] = MODEL
-    os.environ["VOCAB_ONLY"] = "true"
-    from llama_cpp.server.app import app
+    from llama_cpp.server.app import app, init_llama, Settings
+    s = Settings()
+    s.model = MODEL
+    s.vocab_only = True
+    init_llama(s)
     client = TestClient(app)
     response = client.get("/v1/models")
     assert response.json() == {