chore(lint): update Ruff ignores for project conventions and maintainability (#1184)

- Added new ignores from flake8-bugbear (`B007`, `B008`) - Ignored `C901` (high function complexity) for now, pending review - Maintained PyTorch conventions (`N812`, `N817`) - Allowed `E731` (lambda assignments) for flexibility - Consolidated existing ignores (`E402`, `E501`, `F405`, `C408`, `N812`) - Documented rationale for each ignored rule This keeps our linting aligned with project needs while tracking potential fixes. Signed-off-by: Sébastien Han <seb@redhat.com> Signed-off-by: Sébastien Han <seb@redhat.com>
2025-06-28 02:53:30 +00:00 · 2025-02-28 18:36:49 +01:00 · 2025-02-28 18:36:49 +01:00 · 6fa257b475
commit 6fa257b475
parent 3b57d8ee88
33 changed files with 113 additions and 145 deletions
--- a/llama_stack/cli/stack/_build.py
+++ b/llama_stack/cli/stack/_build.py
@ -141,7 +141,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
                completer=WordCompleter(available_providers),
                complete_while_typing=True,
                validator=Validator.from_callable(
-                    lambda x: x in available_providers,
+                    lambda x: x in available_providers,  # noqa: B023 - see https://github.com/astral-sh/ruff/issues/7847
                    error_message="Invalid provider, use <TAB> to see options",
                ),
            )
--- a/llama_stack/cli/tests/test_stack_config.py
+++ b/llama_stack/cli/tests/test_stack_config.py
@ -112,7 +112,7 @@ def test_parse_and_maybe_upgrade_config_old_format(old_config):

    inference_providers = result.providers["inference"]
    assert len(inference_providers) == 2
-    assert set(x.provider_id for x in inference_providers) == {
+    assert {x.provider_id for x in inference_providers} == {
        "remote::ollama-00",
        "meta-reference-01",
    }
--- a/llama_stack/distribution/distribution.py
+++ b/llama_stack/distribution/distribution.py
@ -13,7 +13,7 @@ from llama_stack.providers.datatypes import Api, ProviderSpec


 def stack_apis() -> List[Api]:
-    return [v for v in Api]
+    return list(Api)


 class AutoRoutedApiInfo(BaseModel):
@ -55,7 +55,7 @@ def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]:


 def providable_apis() -> List[Api]:
-    routing_table_apis = set(x.routing_table_api for x in builtin_automatically_routed_apis())
+    routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()}
    return [api for api in Api if api not in routing_table_apis and api != Api.inspect]


--- a/llama_stack/distribution/resolver.py
+++ b/llama_stack/distribution/resolver.py
@ -115,8 +115,8 @@ async def resolve_impls(
    - flatmaps, sorts and resolves the providers in dependency order
    - for each API, produces either a (local, passthrough or router) implementation
    """
-    routing_table_apis = set(x.routing_table_api for x in builtin_automatically_routed_apis())
-    router_apis = set(x.router_api for x in builtin_automatically_routed_apis())
+    routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()}
+    router_apis = {x.router_api for x in builtin_automatically_routed_apis()}

    providers_with_specs = {}

--- a/llama_stack/distribution/ui/page/playground/rag.py
+++ b/llama_stack/distribution/ui/page/playground/rag.py
@ -134,7 +134,7 @@ def rag_chat_page():
            dict(
                name="builtin::rag/knowledge_search",
                args={
-                    "vector_db_ids": [vector_db_id for vector_db_id in selected_vector_dbs],
+                    "vector_db_ids": list(selected_vector_dbs),
                },
            )
        ],
--- a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
+++ b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
@ -797,10 +797,10 @@ class ChatAgent(ShieldRunnerMixin):
        self, toolgroups_for_turn: Optional[List[AgentToolGroup]] = None
    ) -> Tuple[List[ToolDefinition], Dict[str, str]]:
        # Determine which tools to include
-        agent_config_toolgroups = set(
-            (toolgroup.name if isinstance(toolgroup, AgentToolGroupWithArgs) else toolgroup)
+        agent_config_toolgroups = {
+            toolgroup.name if isinstance(toolgroup, AgentToolGroupWithArgs) else toolgroup
            for toolgroup in self.agent_config.toolgroups
-        )
+        }
        toolgroups_for_turn_set = (
            agent_config_toolgroups
            if toolgroups_for_turn is None
--- a/llama_stack/providers/inline/eval/meta_reference/eval.py
+++ b/llama_stack/providers/inline/eval/meta_reference/eval.py
@ -86,7 +86,6 @@ class MetaReferenceEvalImpl(
    ) -> Job:
        task_def = self.benchmarks[benchmark_id]
        dataset_id = task_def.dataset_id
-        candidate = task_config.eval_candidate
        scoring_functions = task_def.scoring_functions
        dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
        validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.eval.value))
--- a/llama_stack/providers/inline/inference/meta_reference/inference.py
+++ b/llama_stack/providers/inline/inference/meta_reference/inference.py
@ -208,7 +208,6 @@ class MetaReferenceInferenceImpl(
            logprobs = []
            stop_reason = None

-            tokenizer = self.generator.formatter.tokenizer
            for token_result in self.generator.completion(request):
                tokens.append(token_result.token)
                if token_result.text == "<|eot_id|>":
--- a/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py
+++ b/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py
@ -207,7 +207,7 @@ def maybe_parse_message(maybe_json: Optional[str]) -> Optional[ProcessingMessage
        return parse_message(maybe_json)
    except json.JSONDecodeError:
        return None
-    except ValueError as e:
+    except ValueError:
        return None


@ -352,7 +352,7 @@ class ModelParallelProcessGroup:
                if isinstance(obj, TaskResponse):
                    yield obj.result

-        except GeneratorExit as e:
+        except GeneratorExit:
            self.request_socket.send(encode_msg(CancelSentinel()))
            while True:
                obj_json = self.request_socket.send()
--- a/llama_stack/providers/inline/inference/meta_reference/quantization/fp8_txest_disabled.py
+++ b/llama_stack/providers/inline/inference/meta_reference/quantization/fp8_txest_disabled.py
@ -7,6 +7,9 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.

+# The file gets a special treatment for now?
+# ruff: noqa: N803
+
 import unittest

 import torch
--- a/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
+++ b/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
@ -264,7 +264,7 @@ class LoraFinetuningSingleDevice:
            )

        self.adapter_params = get_adapter_params(model)
-        self._is_dora = any(["magnitude" in k for k in self.adapter_params.keys()])
+        self._is_dora = any("magnitude" in k for k in self.adapter_params.keys())

        set_trainable_params(model, self.adapter_params)

--- a/llama_stack/providers/inline/scoring/braintrust/braintrust.py
+++ b/llama_stack/providers/inline/scoring/braintrust/braintrust.py
@ -133,7 +133,7 @@ class BraintrustScoringImpl(
    async def shutdown(self) -> None: ...

    async def list_scoring_functions(self) -> List[ScoringFn]:
-        scoring_fn_defs_list = [x for x in self.supported_fn_defs_registry.values()]
+        scoring_fn_defs_list = list(self.supported_fn_defs_registry.values())
        for f in scoring_fn_defs_list:
            assert f.identifier.startswith("braintrust"), (
                "All braintrust scoring fn must have identifier prefixed with 'braintrust'! "
--- a/llama_stack/providers/remote/inference/nvidia/nvidia.py
+++ b/llama_stack/providers/remote/inference/nvidia/nvidia.py
@ -198,7 +198,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
        tool_config: Optional[ToolConfig] = None,
    ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
        if tool_prompt_format:
-            warnings.warn("tool_prompt_format is not supported by NVIDIA NIM, ignoring")
+            warnings.warn("tool_prompt_format is not supported by NVIDIA NIM, ignoring", stacklevel=2)

        await check_health(self._config)  # this raises errors

--- a/llama_stack/providers/remote/inference/nvidia/openai_utils.py
+++ b/llama_stack/providers/remote/inference/nvidia/openai_utils.py
@ -106,7 +106,7 @@ async def convert_chat_completion_request(
            payload.update(temperature=strategy.temperature)
        elif isinstance(strategy, TopKSamplingStrategy):
            if strategy.top_k != -1 and strategy.top_k < 1:
-                warnings.warn("top_k must be -1 or >= 1")
+                warnings.warn("top_k must be -1 or >= 1", stacklevel=2)
            nvext.update(top_k=strategy.top_k)
        elif isinstance(strategy, GreedySamplingStrategy):
            nvext.update(top_k=-1)
@ -168,7 +168,7 @@ def convert_completion_request(
            payload.update(top_p=request.sampling_params.top_p)
        elif request.sampling_params.strategy == "top_k":
            if request.sampling_params.top_k != -1 and request.sampling_params.top_k < 1:
-                warnings.warn("top_k must be -1 or >= 1")
+                warnings.warn("top_k must be -1 or >= 1", stacklevel=2)
            nvext.update(top_k=request.sampling_params.top_k)
        elif request.sampling_params.strategy == "greedy":
            nvext.update(top_k=-1)
--- a/llama_stack/providers/tests/eval/test_eval.py
+++ b/llama_stack/providers/tests/eval/test_eval.py
@ -39,12 +39,11 @@ class Testeval:

    @pytest.mark.asyncio
    async def test_eval_evaluate_rows(self, eval_stack, inference_model, judge_model):
-        eval_impl, benchmarks_impl, datasetio_impl, datasets_impl, models_impl = (
+        eval_impl, benchmarks_impl, datasetio_impl, datasets_impl = (
            eval_stack[Api.eval],
            eval_stack[Api.benchmarks],
            eval_stack[Api.datasetio],
            eval_stack[Api.datasets],
-            eval_stack[Api.models],
        )

        await register_dataset(datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval")
@ -92,11 +91,10 @@ class Testeval:

    @pytest.mark.asyncio
    async def test_eval_run_eval(self, eval_stack, inference_model, judge_model):
-        eval_impl, benchmarks_impl, datasets_impl, models_impl = (
+        eval_impl, benchmarks_impl, datasets_impl = (
            eval_stack[Api.eval],
            eval_stack[Api.benchmarks],
            eval_stack[Api.datasets],
-            eval_stack[Api.models],
        )

        await register_dataset(datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval")
@ -131,11 +129,10 @@ class Testeval:

    @pytest.mark.asyncio
    async def test_eval_run_benchmark_eval(self, eval_stack, inference_model):
-        eval_impl, benchmarks_impl, datasets_impl, models_impl = (
+        eval_impl, benchmarks_impl, datasets_impl = (
            eval_stack[Api.eval],
            eval_stack[Api.benchmarks],
            eval_stack[Api.datasets],
-            eval_stack[Api.models],
        )

        response = await datasets_impl.list_datasets()
--- a/llama_stack/providers/tests/report.py
+++ b/llama_stack/providers/tests/report.py
@ -18,54 +18,48 @@ from llama_stack.models.llama.sku_list import all_registered_models
 INFERENCE_APIS = ["chat_completion"]
 FUNCTIONALITIES = ["streaming", "structured_output", "tool_calling"]
 SUPPORTED_MODELS = {
-    "ollama": set(
-        [
-            CoreModelId.llama3_1_8b_instruct.value,
-            CoreModelId.llama3_1_8b_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
-            CoreModelId.llama3_2_1b_instruct.value,
-            CoreModelId.llama3_2_1b_instruct.value,
-            CoreModelId.llama3_2_3b_instruct.value,
-            CoreModelId.llama3_2_3b_instruct.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_3_70b_instruct.value,
-            CoreModelId.llama_guard_3_8b.value,
-            CoreModelId.llama_guard_3_1b.value,
-        ]
-    ),
-    "fireworks": set(
-        [
-            CoreModelId.llama3_1_8b_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
-            CoreModelId.llama3_2_1b_instruct.value,
-            CoreModelId.llama3_2_3b_instruct.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_3_70b_instruct.value,
-            CoreModelId.llama_guard_3_8b.value,
-            CoreModelId.llama_guard_3_11b_vision.value,
-        ]
-    ),
-    "together": set(
-        [
-            CoreModelId.llama3_1_8b_instruct.value,
-            CoreModelId.llama3_1_70b_instruct.value,
-            CoreModelId.llama3_1_405b_instruct.value,
-            CoreModelId.llama3_2_3b_instruct.value,
-            CoreModelId.llama3_2_11b_vision_instruct.value,
-            CoreModelId.llama3_2_90b_vision_instruct.value,
-            CoreModelId.llama3_3_70b_instruct.value,
-            CoreModelId.llama_guard_3_8b.value,
-            CoreModelId.llama_guard_3_11b_vision.value,
-        ]
-    ),
+    "ollama": {
+        CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama3_2_1b_instruct.value,
+        CoreModelId.llama3_2_1b_instruct.value,
+        CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama3_2_11b_vision_instruct.value,
+        CoreModelId.llama3_2_11b_vision_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
+        CoreModelId.llama3_3_70b_instruct.value,
+        CoreModelId.llama_guard_3_8b.value,
+        CoreModelId.llama_guard_3_1b.value,
+    },
+    "fireworks": {
+        CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama3_2_1b_instruct.value,
+        CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama3_2_11b_vision_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
+        CoreModelId.llama3_3_70b_instruct.value,
+        CoreModelId.llama_guard_3_8b.value,
+        CoreModelId.llama_guard_3_11b_vision.value,
+    },
+    "together": {
+        CoreModelId.llama3_1_8b_instruct.value,
+        CoreModelId.llama3_1_70b_instruct.value,
+        CoreModelId.llama3_1_405b_instruct.value,
+        CoreModelId.llama3_2_3b_instruct.value,
+        CoreModelId.llama3_2_11b_vision_instruct.value,
+        CoreModelId.llama3_2_90b_vision_instruct.value,
+        CoreModelId.llama3_3_70b_instruct.value,
+        CoreModelId.llama_guard_3_8b.value,
+        CoreModelId.llama_guard_3_11b_vision.value,
+    },
 }


--- a/llama_stack/providers/tests/scoring/test_scoring.py
+++ b/llama_stack/providers/tests/scoring/test_scoring.py
@ -45,13 +45,11 @@ class TestScoring:
            scoring_functions_impl,
            datasetio_impl,
            datasets_impl,
-            models_impl,
        ) = (
            scoring_stack[Api.scoring],
            scoring_stack[Api.scoring_functions],
            scoring_stack[Api.datasetio],
            scoring_stack[Api.datasets],
-            scoring_stack[Api.models],
        )
        scoring_fns_list = await scoring_functions_impl.list_scoring_functions()
        provider_id = scoring_fns_list[0].provider_id
@ -102,13 +100,11 @@ class TestScoring:
            scoring_functions_impl,
            datasetio_impl,
            datasets_impl,
-            models_impl,
        ) = (
            scoring_stack[Api.scoring],
            scoring_stack[Api.scoring_functions],
            scoring_stack[Api.datasetio],
            scoring_stack[Api.datasets],
-            scoring_stack[Api.models],
        )
        await register_dataset(datasets_impl, for_rag=True)
        response = await datasets_impl.list_datasets()
@ -163,13 +159,11 @@ class TestScoring:
            scoring_functions_impl,
            datasetio_impl,
            datasets_impl,
-            models_impl,
        ) = (
            scoring_stack[Api.scoring],
            scoring_stack[Api.scoring_functions],
            scoring_stack[Api.datasetio],
            scoring_stack[Api.datasets],
-            scoring_stack[Api.models],
        )
        await register_dataset(datasets_impl, for_rag=True)
        rows = await datasetio_impl.get_rows_paginated(
--- a/llama_stack/providers/utils/inference/openai_compat.py
+++ b/llama_stack/providers/utils/inference/openai_compat.py
@ -605,7 +605,7 @@ def convert_tool_call(
            tool_name=tool_call.function.name,
            arguments=json.loads(tool_call.function.arguments),
        )
-    except Exception as e:
+    except Exception:
        return UnparseableToolCall(
            call_id=tool_call.id or "",
            tool_name=tool_call.function.name or "",
@ -876,7 +876,9 @@ async def convert_openai_chat_completion_stream(
            # it is possible to have parallel tool calls in stream, but
            # ChatCompletionResponseEvent only supports one per stream
            if len(choice.delta.tool_calls) > 1:
-                warnings.warn("multiple tool calls found in a single delta, using the first, ignoring the rest")
+                warnings.warn(
+                    "multiple tool calls found in a single delta, using the first, ignoring the rest", stacklevel=2
+                )

            if not enable_incremental_tool_calls:
                yield ChatCompletionResponseStreamChunk(
--- a/llama_stack/providers/utils/kvstore/redis/redis.py
+++ b/llama_stack/providers/utils/kvstore/redis/redis.py
@ -36,7 +36,7 @@ class RedisKVStoreImpl(KVStore):
        value = await self.redis.get(key)
        if value is None:
            return None
-        ttl = await self.redis.ttl(key)
+        await self.redis.ttl(key)
        return value

    async def delete(self, key: str) -> None:
--- a/llama_stack/providers/utils/scoring/aggregation_utils.py
+++ b/llama_stack/providers/utils/scoring/aggregation_utils.py
@ -32,7 +32,7 @@ def aggregate_categorical_count(
    scoring_results: List[ScoringResultRow],
 ) -> Dict[str, Any]:
    scores = [str(r["score"]) for r in scoring_results]
-    unique_scores = sorted(list(set(scores)))
+    unique_scores = sorted(set(scores))
    return {"categorical_count": {s: scores.count(s) for s in unique_scores}}


--- a/llama_stack/providers/utils/scoring/base_scoring_fn.py
+++ b/llama_stack/providers/utils/scoring/base_scoring_fn.py
@ -66,7 +66,7 @@ class RegisteredBaseScoringFn(BaseScoringFn):
        return self.__class__.__name__

    def get_supported_scoring_fn_defs(self) -> List[ScoringFn]:
-        return [x for x in self.supported_fn_defs_registry.values()]
+        return list(self.supported_fn_defs_registry.values())

    def register_scoring_fn_def(self, scoring_fn: ScoringFn) -> None:
        if scoring_fn.identifier in self.supported_fn_defs_registry:
--- a/llama_stack/scripts/distro_codegen.py
+++ b/llama_stack/scripts/distro_codegen.py
@ -99,7 +99,7 @@ def collect_template_dependencies(template_dir: Path) -> tuple[str | None, list[
            template = template_func()
            normal_deps, special_deps = get_provider_dependencies(template.providers)
            # Combine all dependencies in order: normal deps, special deps, server deps
-            all_deps = sorted(list(set(normal_deps + SERVER_DEPENDENCIES))) + sorted(list(set(special_deps)))
+            all_deps = sorted(set(normal_deps + SERVER_DEPENDENCIES)) + sorted(set(special_deps))

            return template.name, all_deps
    except Exception: