config is now backed by pydantic (WIP)

- add models for config options
- add function to regenerate config.yml
- replace references to config with pydantic compatible references
- remove unnecessary unwrap() statements

TODO:

- auto generate env vars
- auto generate argparse
- test loading a model

main.py

@@ -27,8 +27,8 @@ if not do_export_openapi:
 async def entrypoint_async():
     """Async entry function for program startup"""
 
-    host = unwrap(config.network.get("host"), "127.0.0.1")
-    port = unwrap(config.network.get("port"), 5000)
+    host = config.network.host
+    port = config.network.port
 
     # Check if the port is available and attempt to bind a fallback
     if is_port_in_use(port):
@@ -50,16 +50,12 @@ async def entrypoint_async():
             port = fallback_port
 
     # Initialize auth keys
-    load_auth_keys(unwrap(config.network.get("disable_auth"), False))
-
-    # Override the generation log options if given
-    if config.logging:
-        gen_logging.update_from_dict(config.logging)
+    load_auth_keys(config.network.disable_auth)
 
     gen_logging.broadcast_status()
 
     # Set sampler parameter overrides if provided
-    sampling_override_preset = config.sampling.get("override_preset")
+    sampling_override_preset = config.sampling.override_preset
     if sampling_override_preset:
         try:
             sampling.overrides_from_file(sampling_override_preset)
@@ -68,25 +64,23 @@ async def entrypoint_async():
 
     # If an initial model name is specified, create a container
     # and load the model
-    model_name = config.model.get("model_name")
+    model_name = config.model.model_name
     if model_name:
-        model_path = pathlib.Path(unwrap(config.model.get("model_dir"), "models"))
+        model_path = pathlib.Path(config.model.model_dir)
         model_path = model_path / model_name
 
         await model.load_model(model_path.resolve(), **config.model)
 
         # Load loras after loading the model
-        if config.lora.get("loras"):
-            lora_dir = pathlib.Path(unwrap(config.lora.get("lora_dir"), "loras"))
+        if config.lora.loras:
+            lora_dir = pathlib.Path(config.lora.lora_dir)
             await model.container.load_loras(lora_dir.resolve(), **config.lora)
 
     # If an initial embedding model name is specified, create a separate container
     # and load the model
-    embedding_model_name = config.embeddings.get("embedding_model_name")
+    embedding_model_name = config.embeddings.embedding_model_name
     if embedding_model_name:
-        embedding_model_path = pathlib.Path(
-            unwrap(config.embeddings.get("embedding_model_dir"), "models")
-        )
+        embedding_model_path = pathlib.Path(config.embeddings.embedding_model_dir)
         embedding_model_path = embedding_model_path / embedding_model_name
 
         try:
@@ -124,7 +118,7 @@ def entrypoint(arguments: Optional[dict] = None):
     # Check exllamav2 version and give a descriptive error if it's too old
     # Skip if launching unsafely
     print(f"MAIN.PY {config=}")
-    if unwrap(config.developer.get("unsafe_launch"), False):
+    if config.developer.unsafe_launch:
         logger.warning(
             "UNSAFE: Skipping ExllamaV2 version check.\n"
             "If you aren't a developer, please keep this off!"
@@ -133,12 +127,12 @@ def entrypoint(arguments: Optional[dict] = None):
         check_exllama_version()
 
     # Enable CUDA malloc backend
-    if unwrap(config.developer.get("cuda_malloc_backend"), False):
+    if config.developer.cuda_malloc_backend:
         os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "backend:cudaMallocAsync"
         logger.warning("EXPERIMENTAL: Enabled the pytorch CUDA malloc backend.")
 
     # Use Uvloop/Winloop
-    if unwrap(config.developer.get("uvloop"), False):
+    if config.developer.uvloop:
         if platform.system() == "Windows":
             from winloop import install
         else:
@@ -150,7 +144,7 @@ def entrypoint(arguments: Optional[dict] = None):
         logger.warning("EXPERIMENTAL: Running program with Uvloop/Winloop.")
 
     # Set the process priority
-    if unwrap(config.developer.get("realtime_process_priority"), False):
+    if config.developer.realtime_process_priority:
         import psutil
 
         current_process = psutil.Process(os.getpid())