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Fix precommit check after moving to ruff (#927)

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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>
This commit is contained in:
Yuan Tang 2025-02-02 09:46:45 -05:00 • committed by GitHub
parent 4773092dd1
commit 34ab7a3b6c
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217 changed files with 981 additions and 2681 deletions
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56
llama_stack/providers/inline/eval/meta_reference/eval.py
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@ -91,14 +91,10 @@ class MetaReferenceEvalImpl(
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)
)
validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.eval.value))
all_rows = await self.datasetio_api.get_rows_paginated(
dataset_id=dataset_id,
rows_in_page=(
-1 if task_config.num_examples is None else task_config.num_examples
),
rows_in_page=(-1 if task_config.num_examples is None else task_config.num_examples),
)
res = await self.evaluate_rows(
task_id=task_id,
@ -127,9 +123,7 @@ class MetaReferenceEvalImpl(
input_messages = [UserMessage(**x) for x in input_messages]
# NOTE: only single-turn agent generation is supported. Create a new session for each input row
session_create_response = await self.agents_api.create_agent_session(
agent_id, f"session-{i}"
)
session_create_response = await self.agents_api.create_agent_session(agent_id, f"session-{i}")
session_id = session_create_response.session_id
turn_request = dict(
@ -138,12 +132,7 @@ class MetaReferenceEvalImpl(
messages=input_messages,
stream=True,
)
turn_response = [
chunk
async for chunk in await self.agents_api.create_agent_turn(
**turn_request
)
]
turn_response = [chunk async for chunk in await self.agents_api.create_agent_turn(**turn_request)]
final_event = turn_response[-1].event.payload
# check if there's a memory retrieval step and extract the context
@ -152,14 +141,10 @@ class MetaReferenceEvalImpl(
if step.step_type == StepType.tool_execution.value:
for tool_response in step.tool_responses:
if tool_response.tool_name == MEMORY_QUERY_TOOL:
memory_rag_context = " ".join(
x.text for x in tool_response.content
)
memory_rag_context = " ".join(x.text for x in tool_response.content)
agent_generation = {}
agent_generation[ColumnName.generated_answer.value] = (
final_event.turn.output_message.content
)
agent_generation[ColumnName.generated_answer.value] = final_event.turn.output_message.content
if memory_rag_context:
agent_generation[ColumnName.context.value] = memory_rag_context
@ -171,9 +156,7 @@ class MetaReferenceEvalImpl(
self, input_rows: List[Dict[str, Any]], task_config: EvalTaskConfig
) -> List[Dict[str, Any]]:
candidate = task_config.eval_candidate
assert (
candidate.sampling_params.max_tokens is not None
), "SamplingParams.max_tokens must be provided"
assert candidate.sampling_params.max_tokens is not None, "SamplingParams.max_tokens must be provided"
generations = []
for x in tqdm(input_rows):
@ -184,15 +167,9 @@ class MetaReferenceEvalImpl(
content=input_content,
sampling_params=candidate.sampling_params,
)
generations.append(
{
ColumnName.generated_answer.value: response.completion_message.content
}
)
generations.append({ColumnName.generated_answer.value: response.completion_message.content})
elif ColumnName.chat_completion_input.value in x:
chat_completion_input_str = str(
x[ColumnName.chat_completion_input.value]
)
chat_completion_input_str = str(x[ColumnName.chat_completion_input.value])
input_messages = eval(chat_completion_input_str)
input_messages = [UserMessage(**x) for x in input_messages]
messages = []
@ -204,11 +181,7 @@ class MetaReferenceEvalImpl(
messages=messages,
sampling_params=candidate.sampling_params,
)
generations.append(
{
ColumnName.generated_answer.value: response.completion_message.content
}
)
generations.append({ColumnName.generated_answer.value: response.completion_message.content})
else:
raise ValueError("Invalid input row")
@ -230,10 +203,7 @@ class MetaReferenceEvalImpl(
raise ValueError(f"Invalid candidate type: {candidate.type}")
# scoring with generated_answer
score_input_rows = [
input_r | generated_r
for input_r, generated_r in zip(input_rows, generations)
]
score_input_rows = [input_r | generated_r for input_r, generated_r in zip(input_rows, generations)]
if task_config.type == "app" and task_config.scoring_params is not None:
scoring_functions_dict = {
@ -241,9 +211,7 @@ class MetaReferenceEvalImpl(
for scoring_fn_id in scoring_functions
}
else:
scoring_functions_dict = {
scoring_fn_id: None for scoring_fn_id in scoring_functions
}
scoring_functions_dict = {scoring_fn_id: None for scoring_fn_id in scoring_functions}
score_response = await self.scoring_api.score(
input_rows=score_input_rows, scoring_functions=scoring_functions_dict