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>

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

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))

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|>":

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()

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

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)
 

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'! "