Fix precommit check after moving to ruff (#927)

Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>

llama_stack/providers/utils/inference/__init__.py

@@ -27,13 +27,10 @@ def supported_inference_models() -> List[Model]:
         m
         for m in all_registered_models()
         if (
-            m.model_family
-            in {ModelFamily.llama3_1, ModelFamily.llama3_2, ModelFamily.llama3_3}
+            m.model_family in {ModelFamily.llama3_1, ModelFamily.llama3_2, ModelFamily.llama3_3}
             or is_supported_safety_model(m)
         )
     ]
 
 
-ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR = {
-    m.huggingface_repo: m.descriptor() for m in all_registered_models()
-}
+ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR = {m.huggingface_repo: m.descriptor() for m in all_registered_models()}

llama_stack/providers/utils/inference/embedding_mixin.py

@@ -28,9 +28,7 @@ class SentenceTransformerEmbeddingMixin:
         contents: List[InterleavedContent],
     ) -> EmbeddingsResponse:
         model = await self.model_store.get_model(model_id)
-        embedding_model = self._load_sentence_transformer_model(
-            model.provider_resource_id
-        )
+        embedding_model = self._load_sentence_transformer_model(model.provider_resource_id)
         embeddings = embedding_model.encode(contents)
         return EmbeddingsResponse(embeddings=embeddings)
 

llama_stack/providers/utils/inference/model_registry.py

@@ -36,9 +36,7 @@ def build_model_alias(provider_model_id: str, model_descriptor: str) -> ModelAli
     )
 
 
-def build_model_alias_with_just_provider_model_id(
-    provider_model_id: str, model_descriptor: str
-) -> ModelAlias:
+def build_model_alias_with_just_provider_model_id(provider_model_id: str, model_descriptor: str) -> ModelAlias:
     return ModelAlias(
         provider_model_id=provider_model_id,
         aliases=[],
@@ -54,16 +52,10 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
             for alias in alias_obj.aliases:
                 self.alias_to_provider_id_map[alias] = alias_obj.provider_model_id
             # also add a mapping from provider model id to itself for easy lookup
-            self.alias_to_provider_id_map[alias_obj.provider_model_id] = (
-                alias_obj.provider_model_id
-            )
+            self.alias_to_provider_id_map[alias_obj.provider_model_id] = alias_obj.provider_model_id
             # ensure we can go from llama model to provider model id
-            self.alias_to_provider_id_map[alias_obj.llama_model] = (
-                alias_obj.provider_model_id
-            )
-            self.provider_id_to_llama_model_map[alias_obj.provider_model_id] = (
-                alias_obj.llama_model
-            )
+            self.alias_to_provider_id_map[alias_obj.llama_model] = alias_obj.provider_model_id
+            self.provider_id_to_llama_model_map[alias_obj.provider_model_id] = alias_obj.llama_model
 
     def get_provider_model_id(self, identifier: str) -> str:
         if identifier in self.alias_to_provider_id_map:
@@ -82,9 +74,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
             # embedding models are always registered by their provider model id and does not need to be mapped to a llama model
             provider_resource_id = model.provider_resource_id
         else:
-            provider_resource_id = self.get_provider_model_id(
-                model.provider_resource_id
-            )
+            provider_resource_id = self.get_provider_model_id(model.provider_resource_id)
         if provider_resource_id:
             model.provider_resource_id = provider_resource_id
         else:
@@ -100,18 +90,13 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
                         f"Provider model id '{model.provider_resource_id}' is already registered to a different llama model: '{existing_llama_model}'"
                     )
             else:
-                if (
-                    model.metadata["llama_model"]
-                    not in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR
-                ):
+                if model.metadata["llama_model"] not in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR:
                     raise ValueError(
                         f"Invalid llama_model '{model.metadata['llama_model']}' specified in metadata. "
                         f"Must be one of: {', '.join(ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR.keys())}"
                     )
                 self.provider_id_to_llama_model_map[model.provider_resource_id] = (
-                    ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[
-                        model.metadata["llama_model"]
-                    ]
+                    ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[model.metadata["llama_model"]]
                 )
 
         return model

llama_stack/providers/utils/inference/openai_compat.py

@@ -135,9 +135,7 @@ def convert_openai_completion_logprobs(
     return None
 
 
-def convert_openai_completion_logprobs_stream(
-    text: str, logprobs: Optional[Union[float, OpenAICompatLogprobs]]
-):
+def convert_openai_completion_logprobs_stream(text: str, logprobs: Optional[Union[float, OpenAICompatLogprobs]]):
     if logprobs is None:
         return None
     if isinstance(logprobs, float):
@@ -148,9 +146,7 @@ def convert_openai_completion_logprobs_stream(
     return None
 
 
-def process_completion_response(
-    response: OpenAICompatCompletionResponse, formatter: ChatFormat
-) -> CompletionResponse:
+def process_completion_response(response: OpenAICompatCompletionResponse, formatter: ChatFormat) -> CompletionResponse:
     choice = response.choices[0]
     # drop suffix <eot_id> if present and return stop reason as end of turn
     if choice.text.endswith("<|eot_id|>"):
@@ -341,17 +337,13 @@ async def process_chat_completion_stream_response(
     )
 
 
-async def convert_message_to_openai_dict(
-    message: Message, download: bool = False
-) -> dict:
+async def convert_message_to_openai_dict(message: Message, download: bool = False) -> dict:
     async def _convert_content(content) -> dict:
         if isinstance(content, ImageContentItem):
             return {
                 "type": "image_url",
                 "image_url": {
-                    "url": await convert_image_content_to_url(
-                        content, download=download
-                    ),
+                    "url": await convert_image_content_to_url(content, download=download),
                 },
             }
         else:

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -119,9 +119,7 @@ async def interleaved_content_convert_to_raw(
                 if image.url.uri.startswith("data"):
                     match = re.match(r"data:image/(\w+);base64,(.+)", image.url.uri)
                     if not match:
-                        raise ValueError(
-                            f"Invalid data URL format, {image.url.uri[:40]}..."
-                        )
+                        raise ValueError(f"Invalid data URL format, {image.url.uri[:40]}...")
                     _, image_data = match.groups()
                     data = base64.b64decode(image_data)
                 elif image.url.uri.startswith("file://"):
@@ -201,19 +199,13 @@ async def convert_image_content_to_url(
 
     content, format = await localize_image_content(media)
     if include_format:
-        return f"data:image/{format};base64," + base64.b64encode(content).decode(
-            "utf-8"
-        )
+        return f"data:image/{format};base64," + base64.b64encode(content).decode("utf-8")
     else:
         return base64.b64encode(content).decode("utf-8")
 
 
-async def completion_request_to_prompt(
-    request: CompletionRequest, formatter: ChatFormat
-) -> str:
-    content = augment_content_with_response_format_prompt(
-        request.response_format, request.content
-    )
+async def completion_request_to_prompt(request: CompletionRequest, formatter: ChatFormat) -> str:
+    content = augment_content_with_response_format_prompt(request.response_format, request.content)
     request.content = content
     request = await convert_request_to_raw(request)
     model_input = formatter.encode_content(request.content)
@@ -223,9 +215,7 @@ async def completion_request_to_prompt(
 async def completion_request_to_prompt_model_input_info(
     request: CompletionRequest, formatter: ChatFormat
 ) -> Tuple[str, int]:
-    content = augment_content_with_response_format_prompt(
-        request.response_format, request.content
-    )
+    content = augment_content_with_response_format_prompt(request.response_format, request.content)
     request.content = content
     request = await convert_request_to_raw(request)
     model_input = formatter.encode_content(request.content)
@@ -288,8 +278,7 @@ def chat_completion_request_to_messages(
         return request.messages
 
     if model.model_family == ModelFamily.llama3_1 or (
-        model.model_family == ModelFamily.llama3_2
-        and is_multimodal(model.core_model_id)
+        model.model_family == ModelFamily.llama3_2 and is_multimodal(model.core_model_id)
     ):
         # llama3.1 and llama3.2 multimodal models follow the same tool prompt format
         messages = augment_messages_for_tools_llama_3_1(request)
@@ -327,9 +316,7 @@ def augment_messages_for_tools_llama_3_1(
     if existing_messages[0].role == Role.system.value:
         existing_system_message = existing_messages.pop(0)
 
-    assert (
-        existing_messages[0].role != Role.system.value
-    ), "Should only have 1 system message"
+    assert existing_messages[0].role != Role.system.value, "Should only have 1 system message"
 
     messages = []
 
@@ -361,9 +348,7 @@ def augment_messages_for_tools_llama_3_1(
         if isinstance(existing_system_message.content, str):
             sys_content += _process(existing_system_message.content)
         elif isinstance(existing_system_message.content, list):
-            sys_content += "\n".join(
-                [_process(c) for c in existing_system_message.content]
-            )
+            sys_content += "\n".join([_process(c) for c in existing_system_message.content])
 
     messages.append(SystemMessage(content=sys_content))
 
@@ -397,9 +382,7 @@ def augment_messages_for_tools_llama_3_2(
     if existing_messages[0].role == Role.system.value:
         existing_system_message = existing_messages.pop(0)
 
-    assert (
-        existing_messages[0].role != Role.system.value
-    ), "Should only have 1 system message"
+    assert existing_messages[0].role != Role.system.value, "Should only have 1 system message"
 
     messages = []
     sys_content = ""
@@ -422,9 +405,7 @@ def augment_messages_for_tools_llama_3_2(
     if custom_tools:
         fmt = request.tool_prompt_format or ToolPromptFormat.python_list
         if fmt != ToolPromptFormat.python_list:
-            raise ValueError(
-                f"Non supported ToolPromptFormat {request.tool_prompt_format}"
-            )
+            raise ValueError(f"Non supported ToolPromptFormat {request.tool_prompt_format}")
 
         tool_gen = PythonListCustomToolGenerator()
         tool_template = tool_gen.gen(custom_tools)
@@ -433,9 +414,7 @@ def augment_messages_for_tools_llama_3_2(
         sys_content += "\n"
 
     if existing_system_message:
-        sys_content += interleaved_content_as_str(
-            existing_system_message.content, sep="\n"
-        )
+        sys_content += interleaved_content_as_str(existing_system_message.content, sep="\n")
 
     messages.append(SystemMessage(content=sys_content))