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feat: Improve client perf and error handling #247

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feat: Improve client perf and error handling #247

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98 changes: 57 additions & 41 deletions inference_perf/client/modelserver/openai_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,7 @@ def __init__(
elif not tokenizer_config:
tokenizer_config = CustomTokenizerConfig(pretrained_model_name_or_path=self.model_name)
self.tokenizer = CustomTokenizer(tokenizer_config)
self.session = aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=self.max_tcp_connections))
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@diamondburned diamondburned Nov 10, 2025

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Please correct me if I'm wrong, but isn't openAIModelServerClient shared across multiple asyncio event loops because of multiprocessing? Creating a single ClientSession here might cause issues if the same instance is also being shared to all the multiprocessing workers.

Relevant link: https://stackoverflow.com/questions/62707369/one-aiohttp-clientsession-per-thread


async def process_request(self, data: InferenceAPIData, stage_id: int, scheduled_time: float) -> None:
payload = data.to_payload(
Expand All @@ -85,49 +86,64 @@ async def process_request(self, data: InferenceAPIData, stage_id: int, scheduled

request_data = json.dumps(payload)

async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=self.max_tcp_connections)) as session:
start = time.perf_counter()
try:
async with session.post(self.uri + data.get_route(), headers=headers, data=request_data) as response:
response_info = await data.process_response(
response=response, config=self.api_config, tokenizer=self.tokenizer
)
response_content = await response.text()

end_time = time.perf_counter()
error = None
if response.status != 200:
error = ErrorResponseInfo(error_msg=response_content, error_type="Error response")

self.metrics_collector.record_metric(
RequestLifecycleMetric(
stage_id=stage_id,
request_data=request_data,
response_data=response_content,
info=response_info,
error=error,
start_time=start,
end_time=end_time,
scheduled_time=scheduled_time,
start = time.perf_counter()
response_info = None
error = None
response_content = ""
end_time = None

try:
async with self.session.post(self.uri + data.get_route(), headers=headers, data=request_data) as response:
try:
if response.status == 200:
response_info = await data.process_response(
response=response, config=self.api_config, tokenizer=self.tokenizer
)
response_content = await response.text()
else:
response_content = await response.text()
error = ErrorResponseInfo(
error_msg=response_content, error_type=f"HTTP Error {response.status}"
)
except Exception as e:
logger.error("Error processing response:", exc_info=True)
if not response_content:
try:
response_content = await response.text()
except Exception:
response_content = "Failed to read response text"
error = ErrorResponseInfo(
error_msg=f"Error processing response: {str(e)}",
error_type=type(e).__name__,
)
except Exception as e:
logger.error("error occured during request processing:", exc_info=True)
self.metrics_collector.record_metric(
RequestLifecycleMetric(
stage_id=stage_id,
request_data=request_data,
response_data=response_content if "response_content" in locals() else "",
info=response_info if "response_info" in locals() else InferenceInfo(),
error=ErrorResponseInfo(
error_msg=str(e),
error_type=type(e).__name__,
),
start_time=start,
end_time=time.perf_counter(),
scheduled_time=scheduled_time,
)
)

end_time = time.perf_counter()
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@jjk-g jjk-g Oct 7, 2025

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can move to a finally block


except aiohttp.ClientError as e:
logger.error("Client error during request:", exc_info=True)
error = ErrorResponseInfo(error_msg=str(e), error_type=type(e).__name__)
end_time = time.perf_counter()
except asyncio.TimeoutError:
logger.error("Request timed out:", exc_info=True)
error = ErrorResponseInfo(error_msg="Request timed out", error_type="TimeoutError")
end_time = time.perf_counter()
except Exception as e:
logger.error("Unexpected error during request processing:", exc_info=True)
error = ErrorResponseInfo(error_msg=str(e), error_type=type(e).__name__)
end_time = time.perf_counter()

self.metrics_collector.record_metric(
RequestLifecycleMetric(
stage_id=stage_id,
request_data=request_data,
response_data=response_content,
info=response_info if response_info else InferenceInfo(),
error=error,
start_time=start,
end_time=end_time,
scheduled_time=scheduled_time,
)
)

def get_supported_apis(self) -> List[APIType]:
return []
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