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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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86
vllm/entrypoints/openai/serving_pooling.py
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@@ -17,14 +17,20 @@ from vllm.config import VllmConfig
from vllm.engine.protocol import EngineClient
from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
from vllm.entrypoints.logger import RequestLogger
# yapf: disable
from vllm.entrypoints.openai.protocol import (ErrorResponse,
IOProcessorRequest,
IOProcessorResponse,
PoolingChatRequest,
PoolingCompletionRequest,
PoolingRequest, PoolingResponse,
PoolingResponseData, UsageInfo)
from vllm.entrypoints.openai.protocol import (
ErrorResponse,
IOProcessorRequest,
IOProcessorResponse,
PoolingChatRequest,
PoolingCompletionRequest,
PoolingRequest,
PoolingResponse,
PoolingResponseData,
UsageInfo,
)
# yapf: enable
from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
@@ -55,7 +61,6 @@ def _get_data(
class OpenAIServingPooling(OpenAIServing):
def __init__(
self,
engine_client: EngineClient,
@@ -68,11 +73,13 @@ class OpenAIServingPooling(OpenAIServing):
trust_request_chat_template: bool = False,
log_error_stack: bool = False,
) -> None:
super().__init__(engine_client=engine_client,
model_config=vllm_config.model_config,
models=models,
request_logger=request_logger,
log_error_stack=log_error_stack)
super().__init__(
engine_client=engine_client,
model_config=vllm_config.model_config,
models=models,
request_logger=request_logger,
log_error_stack=log_error_stack,
)
self.chat_template = chat_template
self.chat_template_content_format: Final = chat_template_content_format
@@ -110,12 +117,13 @@ class OpenAIServingPooling(OpenAIServing):
if getattr(request, "dimensions", None) is not None:
return self.create_error_response(
"dimensions is currently not supported")
"dimensions is currently not supported"
)
truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens",
None)
truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
truncate_prompt_tokens = _validate_truncation_size(
self.max_model_len, truncate_prompt_tokens)
self.max_model_len, truncate_prompt_tokens
)
if is_io_processor_request:
if self.io_processor is None:
@@ -123,19 +131,20 @@ class OpenAIServingPooling(OpenAIServing):
"No IOProcessor plugin installed. Please refer "
"to the documentation and to the "
"'prithvi_geospatial_mae_io_processor' "
"offline inference example for more details.")
"offline inference example for more details."
)
validated_prompt = self.io_processor.parse_request(request)
engine_prompts = await self.io_processor.pre_process_async(
prompt=validated_prompt, request_id=request_id)
prompt=validated_prompt, request_id=request_id
)
elif isinstance(request, PoolingChatRequest):
error_check_ret = self._validate_chat_template(
request_chat_template=request.chat_template,
chat_template_kwargs=request.chat_template_kwargs,
trust_request_chat_template=self.
trust_request_chat_template,
trust_request_chat_template=self.trust_request_chat_template,
)
if error_check_ret is not None:
return error_check_ret
@@ -148,8 +157,7 @@ class OpenAIServingPooling(OpenAIServing):
tokenizer,
request.messages,
chat_template=request.chat_template or self.chat_template,
chat_template_content_format=self.
chat_template_content_format,
chat_template_content_format=self.chat_template_content_format,
# In pooling requests, we are not generating tokens,
# so there is no need to append extra tokens to the input
add_generation_prompt=False,
@@ -162,8 +170,7 @@ class OpenAIServingPooling(OpenAIServing):
config=self._build_render_config(request),
)
else:
raise ValueError(
f"Unsupported request of type {type(request)}")
raise ValueError(f"Unsupported request of type {type(request)}")
except (ValueError, TypeError, jinja2.TemplateError) as e:
logger.exception("Error in preprocessing prompt inputs")
return self.create_error_response(str(e))
@@ -181,13 +188,18 @@ class OpenAIServingPooling(OpenAIServing):
for i, engine_prompt in enumerate(engine_prompts):
request_id_item = f"{request_id}-{i}"
self._log_inputs(request_id_item,
engine_prompt,
params=pooling_params,
lora_request=lora_request)
self._log_inputs(
request_id_item,
engine_prompt,
params=pooling_params,
lora_request=lora_request,
)
trace_headers = (None if raw_request is None else await
self._get_trace_headers(raw_request.headers))
trace_headers = (
None
if raw_request is None
else await self._get_trace_headers(raw_request.headers)
)
generator = self.engine_client.encode(
engine_prompt,
@@ -213,8 +225,7 @@ class OpenAIServingPooling(OpenAIServing):
)
return self.io_processor.output_to_response(output)
assert isinstance(request,
(PoolingCompletionRequest, PoolingChatRequest))
assert isinstance(request, (PoolingCompletionRequest, PoolingChatRequest))
num_prompts = len(engine_prompts)
# Non-streaming response
@@ -226,8 +237,7 @@ class OpenAIServingPooling(OpenAIServing):
assert all(final_res is not None for final_res in final_res_batch)
final_res_batch_checked = cast(list[PoolingRequestOutput],
final_res_batch)
final_res_batch_checked = cast(list[PoolingRequestOutput], final_res_batch)
response = self.request_output_to_pooling_response(
final_res_batch_checked,
@@ -278,9 +288,9 @@ class OpenAIServingPooling(OpenAIServing):
usage=usage,
)
def _build_render_config(
self, request: PoolingCompletionRequest) -> RenderConfig:
def _build_render_config(self, request: PoolingCompletionRequest) -> RenderConfig:
return RenderConfig(
max_length=self.max_model_len,
truncate_prompt_tokens=request.truncate_prompt_tokens,
add_special_tokens=request.add_special_tokens)
add_special_tokens=request.add_special_tokens,
)