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

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- 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>
This commit is contained in:
Sébastien Han 2025-02-28 18:36:49 +01:00 • committed by GitHub
parent 3b57d8ee88
commit 6fa257b475
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GPG key ID: B5690EEEBB952194
33 changed files with 113 additions and 145 deletions
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6
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -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

1
llama_stack/providers/inline/eval/meta_reference/eval.py
View file

@ -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))

1
llama_stack/providers/inline/inference/meta_reference/inference.py
View file

@ -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|>":

4
llama_stack/providers/inline/inference/meta_reference/parallel_utils.py
View file

@ -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()

3
llama_stack/providers/inline/inference/meta_reference/quantization/fp8_txest_disabled.py
View file

@ -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

2
llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
View file

@ -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)

2
llama_stack/providers/inline/scoring/braintrust/braintrust.py
View file

@ -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'! "

2
llama_stack/providers/remote/inference/nvidia/nvidia.py
View file

@ -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

4
llama_stack/providers/remote/inference/nvidia/openai_utils.py
View file

@ -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)

9
llama_stack/providers/tests/eval/test_eval.py
View file

@ -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()

90
llama_stack/providers/tests/report.py
View file

@ -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,
},
}

6
llama_stack/providers/tests/scoring/test_scoring.py
View file

@ -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(

6
llama_stack/providers/utils/inference/openai_compat.py
View file

@ -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(

2
llama_stack/providers/utils/kvstore/redis/redis.py
View file

@ -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:

2
llama_stack/providers/utils/scoring/aggregation_utils.py
View file

@ -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}}

2
llama_stack/providers/utils/scoring/base_scoring_fn.py
View file

@ -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: