chore: standardize unsupported model error #2517

- llama_stack/exceptions.py: Add UnsupportedModelError class
- remote inference ollama.py and utils/inference/model_registry.py:
Changed ValueError in favor of UnsupportedModelError
- utils/inference/litellm_openai_mixin.py: remote register_model func.
Now uses parent class ModelRegistry's func

Closes #2517

llama_stack/exceptions.py

@@ -0,0 +1,13 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+class UnsupportedModelError(ValueError):
+    """raised when model is not present in the list of supported models"""
+
+    def __init__(self, model_name: str, supported_models_list: list[str]):
+        message = f"'{model_name}' model is not supported. Supported models are: {', '.join(supported_models_list)}"
+        super().__init__(message)

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -51,6 +51,7 @@ from llama_stack.apis.inference.inference import (
     OpenAIResponseFormatParam,
 )
 from llama_stack.apis.models import Model, ModelType
+from llama_stack.exceptions import UnsupportedModelError
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import (
     HealthResponse,
@@ -374,9 +375,7 @@ class OllamaInferenceAdapter(
                     f"Imprecise provider resource id was used but 'latest' is available in Ollama - using '{model.provider_resource_id}:latest'"
                 )
                 return model
-            raise ValueError(
-                f"Model '{model.provider_resource_id}' is not available in Ollama. Available models: {', '.join(available_models)}"
-            )
+            raise UnsupportedModelError(model.provider_resource_id, available_models)
         model.provider_resource_id = provider_resource_id
 
         return model

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -40,7 +40,6 @@ from llama_stack.apis.inference.inference import (
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
 )
-from llama_stack.apis.models.models import Model
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
@@ -91,12 +90,6 @@ class LiteLLMOpenAIMixin(
     async def shutdown(self):
         pass
 
-    async def register_model(self, model: Model) -> Model:
-        model_id = self.get_provider_model_id(model.provider_resource_id)
-        if model_id is None:
-            raise ValueError(f"Unsupported model: {model.provider_resource_id}")
-        return model
-
     def get_litellm_model_name(self, model_id: str) -> str:
         # users may be using openai/ prefix in their model names. the openai/models.py did this by default.
         # model_id.startswith("openai/") is for backwards compatibility.

llama_stack/providers/utils/inference/model_registry.py

@@ -9,6 +9,7 @@ from typing import Any
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.models.models import ModelType
+from llama_stack.exceptions import UnsupportedModelError
 from llama_stack.models.llama.sku_list import all_registered_models
 from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
 from llama_stack.providers.utils.inference import (
@@ -81,9 +82,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
 
     async def register_model(self, model: Model) -> Model:
         if not (supported_model_id := self.get_provider_model_id(model.provider_resource_id)):
-            raise ValueError(
-                f"Model '{model.provider_resource_id}' is not supported. Supported models are: {', '.join(self.alias_to_provider_id_map.keys())}"
-            )
+            raise UnsupportedModelError(model.provider_resource_id, self.alias_to_provider_id_map.keys())
         provider_resource_id = self.get_provider_model_id(model.model_id)
         if model.model_type == ModelType.embedding:
             # embedding models are always registered by their provider model id and does not need to be mapped to a llama model