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/tests/inference/conftest.py

@@ -12,9 +12,7 @@ from .fixtures import INFERENCE_FIXTURES
 
 def pytest_configure(config):
     for model in ["llama_8b", "llama_3b", "llama_vision"]:
-        config.addinivalue_line(
-            "markers", f"{model}: mark test to run only with the given model"
-        )
+        config.addinivalue_line("markers", f"{model}: mark test to run only with the given model")
 
     for fixture_name in INFERENCE_FIXTURES:
         config.addinivalue_line(
@@ -24,12 +22,8 @@ def pytest_configure(config):
 
 
 MODEL_PARAMS = [
-    pytest.param(
-        "meta-llama/Llama-3.1-8B-Instruct", marks=pytest.mark.llama_8b, id="llama_8b"
-    ),
-    pytest.param(
-        "meta-llama/Llama-3.2-3B-Instruct", marks=pytest.mark.llama_3b, id="llama_3b"
-    ),
+    pytest.param("meta-llama/Llama-3.1-8B-Instruct", marks=pytest.mark.llama_8b, id="llama_8b"),
+    pytest.param("meta-llama/Llama-3.2-3B-Instruct", marks=pytest.mark.llama_3b, id="llama_3b"),
 ]
 
 VISION_MODEL_PARAMS = [
@@ -49,9 +43,7 @@ def pytest_generate_tests(metafunc):
         params = []
         inference_models = getattr(test_config, "inference_models", [])
         for model in inference_models:
-            if ("Vision" in cls_name and "Vision" in model) or (
-                "Vision" not in cls_name and "Vision" not in model
-            ):
+            if ("Vision" in cls_name and "Vision" in model) or ("Vision" not in cls_name and "Vision" not in model):
                 params.append(pytest.param(model, id=model))
 
         if not params:
@@ -74,10 +66,7 @@ def pytest_generate_tests(metafunc):
             fixtures = [stack.values[0]["inference"] for stack in filtered_stacks]
         if test_config:
             if custom_fixtures := [
-                (
-                    scenario.fixture_combo_id
-                    or scenario.provider_fixtures.get("inference")
-                )
+                (scenario.fixture_combo_id or scenario.provider_fixtures.get("inference"))
                 for scenario in test_config.scenarios
             ]:
                 fixtures = custom_fixtures

llama_stack/providers/tests/inference/fixtures.py

@@ -47,9 +47,7 @@ def inference_remote() -> ProviderFixture:
 
 @pytest.fixture(scope="session")
 def inference_meta_reference(inference_model) -> ProviderFixture:
-    inference_model = (
-        [inference_model] if isinstance(inference_model, str) else inference_model
-    )
+    inference_model = [inference_model] if isinstance(inference_model, str) else inference_model
     # If embedding dimension is set, use the 8B model for testing
     if os.getenv("EMBEDDING_DIMENSION"):
         inference_model = ["meta-llama/Llama-3.1-8B-Instruct"]
@@ -88,9 +86,7 @@ def inference_cerebras() -> ProviderFixture:
 
 @pytest.fixture(scope="session")
 def inference_ollama(inference_model) -> ProviderFixture:
-    inference_model = (
-        [inference_model] if isinstance(inference_model, str) else inference_model
-    )
+    inference_model = [inference_model] if isinstance(inference_model, str) else inference_model
     if inference_model and "Llama3.1-8B-Instruct" in inference_model:
         pytest.skip("Ollama only supports Llama3.2-3B-Instruct for testing")
 
@@ -99,9 +95,7 @@ def inference_ollama(inference_model) -> ProviderFixture:
             Provider(
                 provider_id="ollama",
                 provider_type="remote::ollama",
-                config=OllamaImplConfig(
-                    host="localhost", port=os.getenv("OLLAMA_PORT", 11434)
-                ).model_dump(),
+                config=OllamaImplConfig(host="localhost", port=os.getenv("OLLAMA_PORT", 11434)).model_dump(),
             )
         ],
     )
@@ -109,9 +103,7 @@ def inference_ollama(inference_model) -> ProviderFixture:
 
 @pytest_asyncio.fixture(scope="session")
 def inference_vllm(inference_model) -> ProviderFixture:
-    inference_model = (
-        [inference_model] if isinstance(inference_model, str) else inference_model
-    )
+    inference_model = [inference_model] if isinstance(inference_model, str) else inference_model
     return ProviderFixture(
         providers=[
             Provider(

llama_stack/providers/tests/inference/groq/test_groq_utils.py

@@ -162,9 +162,7 @@ class TestConvertChatCompletionRequest:
 
     def test_includes_stratgy(self):
         request = self._dummy_chat_completion_request()
-        request.sampling_params.strategy = TopPSamplingStrategy(
-            temperature=0.5, top_p=0.95
-        )
+        request.sampling_params.strategy = TopPSamplingStrategy(temperature=0.5, top_p=0.95)
 
         converted = convert_chat_completion_request(request)
 
@@ -375,9 +373,7 @@ class TestConvertNonStreamChatCompletionResponse:
             choices=[
                 Choice(
                     index=0,
-                    message=ChatCompletionMessage(
-                        role="assistant", content="Hello World"
-                    ),
+                    message=ChatCompletionMessage(role="assistant", content="Hello World"),
                     finish_reason="stop",
                 )
             ],

llama_stack/providers/tests/inference/test_embeddings.py

@@ -29,11 +29,7 @@ class TestEmbeddings:
         assert isinstance(response, EmbeddingsResponse)
         assert len(response.embeddings) > 0
         assert all(isinstance(embedding, list) for embedding in response.embeddings)
-        assert all(
-            isinstance(value, float)
-            for embedding in response.embeddings
-            for value in embedding
-        )
+        assert all(isinstance(value, float) for embedding in response.embeddings for value in embedding)
 
     @pytest.mark.asyncio
     async def test_batch_embeddings(self, inference_model, inference_stack):
@@ -53,11 +49,7 @@ class TestEmbeddings:
         assert isinstance(response, EmbeddingsResponse)
         assert len(response.embeddings) == len(texts)
         assert all(isinstance(embedding, list) for embedding in response.embeddings)
-        assert all(
-            isinstance(value, float)
-            for embedding in response.embeddings
-            for value in embedding
-        )
+        assert all(isinstance(value, float) for embedding in response.embeddings for value in embedding)
 
         embedding_dim = len(response.embeddings[0])
         assert all(len(embedding) == embedding_dim for embedding in response.embeddings)

llama_stack/providers/tests/inference/test_text_inference.py

@@ -44,11 +44,7 @@ from .utils import group_chunks
 
 
 def get_expected_stop_reason(model: str):
-    return (
-        StopReason.end_of_message
-        if ("Llama3.1" in model or "Llama-3.1" in model)
-        else StopReason.end_of_turn
-    )
+    return StopReason.end_of_message if ("Llama3.1" in model or "Llama-3.1" in model) else StopReason.end_of_turn
 
 
 @pytest.fixture
@@ -179,13 +175,9 @@ class TestInference:
             1 <= len(chunks) <= 6
         )  # why 6 and not 5? the response may have an extra closing chunk, e.g. for usage or stop_reason
         for chunk in chunks:
-            if (
-                chunk.delta.type == "text" and chunk.delta.text
-            ):  # if there's a token, we expect logprobs
+            if chunk.delta.type == "text" and chunk.delta.text:  # if there's a token, we expect logprobs
                 assert chunk.logprobs, "Logprobs should not be empty"
-                assert all(
-                    len(logprob.logprobs_by_token) == 3 for logprob in chunk.logprobs
-                )
+                assert all(len(logprob.logprobs_by_token) == 3 for logprob in chunk.logprobs)
             else:  # no token, no logprobs
                 assert not chunk.logprobs, "Logprobs should be empty"
 
@@ -236,9 +228,7 @@ class TestInference:
         assert len(response.completion_message.content) > 0
 
     @pytest.mark.asyncio(loop_scope="session")
-    async def test_structured_output(
-        self, inference_model, inference_stack, common_params
-    ):
+    async def test_structured_output(self, inference_model, inference_stack, common_params):
         inference_impl, _ = inference_stack
 
         class AnswerFormat(BaseModel):
@@ -295,9 +285,7 @@ class TestInference:
             AnswerFormat.model_validate_json(response.completion_message.content)
 
     @pytest.mark.asyncio(loop_scope="session")
-    async def test_chat_completion_streaming(
-        self, inference_model, inference_stack, common_params, sample_messages
-    ):
+    async def test_chat_completion_streaming(self, inference_model, inference_stack, common_params, sample_messages):
         inference_impl, _ = inference_stack
         response = [
             r
@@ -310,9 +298,7 @@ class TestInference:
         ]
 
         assert len(response) > 0
-        assert all(
-            isinstance(chunk, ChatCompletionResponseStreamChunk) for chunk in response
-        )
+        assert all(isinstance(chunk, ChatCompletionResponseStreamChunk) for chunk in response)
         grouped = group_chunks(response)
         assert len(grouped[ChatCompletionResponseEventType.start]) == 1
         assert len(grouped[ChatCompletionResponseEventType.progress]) > 0
@@ -387,9 +373,7 @@ class TestInference:
             )
         ]
         assert len(response) > 0
-        assert all(
-            isinstance(chunk, ChatCompletionResponseStreamChunk) for chunk in response
-        )
+        assert all(isinstance(chunk, ChatCompletionResponseStreamChunk) for chunk in response)
         grouped = group_chunks(response)
         assert len(grouped[ChatCompletionResponseEventType.start]) == 1
         assert len(grouped[ChatCompletionResponseEventType.progress]) > 0
@@ -404,13 +388,10 @@ class TestInference:
 
         if "Llama3.1" in inference_model:
             assert all(
-                chunk.event.delta.type == "tool_call"
-                for chunk in grouped[ChatCompletionResponseEventType.progress]
+                chunk.event.delta.type == "tool_call" for chunk in grouped[ChatCompletionResponseEventType.progress]
             )
             first = grouped[ChatCompletionResponseEventType.progress][0]
-            if not isinstance(
-                first.event.delta.tool_call, ToolCall
-            ):  # first chunk may contain entire call
+            if not isinstance(first.event.delta.tool_call, ToolCall):  # first chunk may contain entire call
                 assert first.event.delta.parse_status == ToolCallParseStatus.started
 
         last = grouped[ChatCompletionResponseEventType.progress][-1]

llama_stack/providers/tests/inference/test_vision_inference.py

@@ -73,9 +73,7 @@ class TestVisionModelInference:
             assert expected_string in response.completion_message.content
 
     @pytest.mark.asyncio
-    async def test_vision_chat_completion_streaming(
-        self, inference_model, inference_stack
-    ):
+    async def test_vision_chat_completion_streaming(self, inference_model, inference_stack):
         inference_impl, _ = inference_stack
 
         images = [
@@ -100,9 +98,7 @@ class TestVisionModelInference:
                         UserMessage(
                             content=[
                                 image,
-                                TextContentItem(
-                                    text="Describe this image in two sentences."
-                                ),
+                                TextContentItem(text="Describe this image in two sentences."),
                             ]
                         ),
                     ],
@@ -112,18 +108,12 @@ class TestVisionModelInference:
             ]
 
             assert len(response) > 0
-            assert all(
-                isinstance(chunk, ChatCompletionResponseStreamChunk)
-                for chunk in response
-            )
+            assert all(isinstance(chunk, ChatCompletionResponseStreamChunk) for chunk in response)
             grouped = group_chunks(response)
             assert len(grouped[ChatCompletionResponseEventType.start]) == 1
             assert len(grouped[ChatCompletionResponseEventType.progress]) > 0
             assert len(grouped[ChatCompletionResponseEventType.complete]) == 1
 
-            content = "".join(
-                chunk.event.delta.text
-                for chunk in grouped[ChatCompletionResponseEventType.progress]
-            )
+            content = "".join(chunk.event.delta.text for chunk in grouped[ChatCompletionResponseEventType.progress])
             for expected_string in expected_strings:
                 assert expected_string in content

llama_stack/providers/tests/inference/utils.py

@@ -10,7 +10,5 @@ import itertools
 def group_chunks(response):
     return {
         event_type: list(group)
-        for event_type, group in itertools.groupby(
-            response, key=lambda chunk: chunk.event.event_type
-        )
+        for event_type, group in itertools.groupby(response, key=lambda chunk: chunk.event.event_type)
     }