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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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55
llama_stack/providers/remote/inference/nvidia/openai_utils.py
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@ -185,9 +185,7 @@ async def _convert_message(message: Message | Dict) -> OpenAIChatCompletionMessa
return content
elif isinstance(content, ImageContentItem):
return OpenAIChatCompletionContentPartImageParam(
image_url=OpenAIImageURL(
url=await convert_image_content_to_url(content)
),
image_url=OpenAIImageURL(url=await convert_image_content_to_url(content)),
type="image_url",
)
elif isinstance(content, List):
@ -260,12 +258,9 @@ async def convert_chat_completion_request(
# stream -> stream
# logprobs -> logprobs
if request.response_format and not isinstance(
request.response_format, JsonSchemaResponseFormat
):
if request.response_format and not isinstance(request.response_format, JsonSchemaResponseFormat):
raise ValueError(
f"Unsupported response format: {request.response_format}. "
"Only JsonSchemaResponseFormat is supported."
f"Unsupported response format: {request.response_format}. Only JsonSchemaResponseFormat is supported."
)
nvext = {}
@ -286,9 +281,7 @@ async def convert_chat_completion_request(
nvext.update(guided_json=request.response_format.json_schema)
if request.tools:
payload.update(
tools=[_convert_tooldef_to_openai_tool(tool) for tool in request.tools]
)
payload.update(tools=[_convert_tooldef_to_openai_tool(tool) for tool in request.tools])
if request.tool_choice:
payload.update(
tool_choice=request.tool_choice.value
@ -410,11 +403,7 @@ def _convert_openai_logprobs(
return None
return [
TokenLogProbs(
logprobs_by_token={
logprobs.token: logprobs.logprob for logprobs in content.top_logprobs
}
)
TokenLogProbs(logprobs_by_token={logprobs.token: logprobs.logprob for logprobs in content.top_logprobs})
for content in logprobs.content
]
@ -452,17 +441,14 @@ def convert_openai_chat_completion_choice(
end_of_message = "end_of_message"
out_of_tokens = "out_of_tokens"
"""
assert (
hasattr(choice, "message") and choice.message
), "error in server response: message not found"
assert (
hasattr(choice, "finish_reason") and choice.finish_reason
), "error in server response: finish_reason not found"
assert hasattr(choice, "message") and choice.message, "error in server response: message not found"
assert hasattr(choice, "finish_reason") and choice.finish_reason, (
"error in server response: finish_reason not found"
)
return ChatCompletionResponse(
completion_message=CompletionMessage(
content=choice.message.content
or "", # CompletionMessage content is not optional
content=choice.message.content or "", # CompletionMessage content is not optional
stop_reason=_convert_openai_finish_reason(choice.finish_reason),
tool_calls=_convert_openai_tool_calls(choice.message.tool_calls),
),
@ -479,9 +465,7 @@ async def convert_openai_chat_completion_stream(
"""
# generate a stream of ChatCompletionResponseEventType: start -> progress -> progress -> ...
def _event_type_generator() -> (
Generator[ChatCompletionResponseEventType, None, None]
):
def _event_type_generator() -> Generator[ChatCompletionResponseEventType, None, None]:
yield ChatCompletionResponseEventType.start
while True:
yield ChatCompletionResponseEventType.progress
@ -532,18 +516,14 @@ 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")
# NIM only produces fully formed tool calls, so we can assume success
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=next(event_type),
delta=ToolCallDelta(
tool_call=_convert_openai_tool_calls(choice.delta.tool_calls)[
0
],
tool_call=_convert_openai_tool_calls(choice.delta.tool_calls)[0],
parse_status=ToolCallParseStatus.succeeded,
),
logprobs=_convert_openai_logprobs(choice.logprobs),
@ -618,10 +598,7 @@ def convert_completion_request(
nvext.update(top_k=-1)
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
):
if request.sampling_params.top_k != -1 and request.sampling_params.top_k < 1:
warnings.warn("top_k must be -1 or >= 1")
nvext.update(top_k=request.sampling_params.top_k)
elif request.sampling_params.strategy == "greedy":
@ -640,9 +617,7 @@ def _convert_openai_completion_logprobs(
if not logprobs:
return None
return [
TokenLogProbs(logprobs_by_token=logprobs) for logprobs in logprobs.top_logprobs
]
return [TokenLogProbs(logprobs_by_token=logprobs) for logprobs in logprobs.top_logprobs]
def convert_openai_completion_choice(