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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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31
llama_stack/providers/remote/inference/vllm/vllm.py
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@ -147,9 +147,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
r = client.completions.create(**params)
return process_chat_completion_response(r, self.formatter)
async def _stream_chat_completion(
self, request: ChatCompletionRequest, client: OpenAI
) -> AsyncGenerator:
async def _stream_chat_completion(self, request: ChatCompletionRequest, client: OpenAI) -> AsyncGenerator:
params = await self._get_params(request)
# TODO: Can we use client.completions.acreate() or maybe there is another way to directly create an async
@ -163,14 +161,10 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
yield chunk
stream = _to_async_generator()
async for chunk in process_chat_completion_stream_response(
stream, self.formatter
):
async for chunk in process_chat_completion_stream_response(stream, self.formatter):
yield chunk
async def _nonstream_completion(
self, request: CompletionRequest
) -> CompletionResponse:
async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse:
params = await self._get_params(request)
r = self.client.completions.create(**params)
return process_completion_response(r, self.formatter)
@ -199,9 +193,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
)
return model
async def _get_params(
self, request: Union[ChatCompletionRequest, CompletionRequest]
) -> dict:
async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict:
options = get_sampling_options(request.sampling_params)
if "max_tokens" not in options:
options["max_tokens"] = self.config.max_tokens
@ -211,8 +203,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
if isinstance(request, ChatCompletionRequest):
if media_present:
input_dict["messages"] = [
await convert_message_to_openai_dict(m, download=True)
for m in request.messages
await convert_message_to_openai_dict(m, download=True) for m in request.messages
]
else:
input_dict["prompt"] = await chat_completion_request_to_prompt(
@ -221,9 +212,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
self.formatter,
)
else:
assert (
not media_present
), "vLLM does not support media for Completion requests"
assert not media_present, "vLLM does not support media for Completion requests"
input_dict["prompt"] = await completion_request_to_prompt(
request,
self.formatter,
@ -231,9 +220,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
if fmt := request.response_format:
if fmt.type == ResponseFormatType.json_schema.value:
input_dict["extra_body"] = {
"guided_json": request.response_format.json_schema
}
input_dict["extra_body"] = {"guided_json": request.response_format.json_schema}
elif fmt.type == ResponseFormatType.grammar.value:
raise NotImplementedError("Grammar response format not supported yet")
else:
@ -257,9 +244,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
assert model.model_type == ModelType.embedding
assert model.metadata.get("embedding_dimensions")
kwargs["dimensions"] = model.metadata.get("embedding_dimensions")
assert all(
not content_has_media(content) for content in contents
), "VLLM does not support media for embeddings"
assert all(not content_has_media(content) for content in contents), "VLLM does not support media for embeddings"
response = self.client.embeddings.create(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],