89733111fa
### What this PR does / why we need it?
This patch do some tiny optimization for nightly ci:
1. Polling the frequency with which the service prints logs when it
starts up in order to obtain useful information more quickly.
2. Shorten the timeout for waiting server
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
131 lines
4.4 KiB
Python
131 lines
4.4 KiB
Python
import time
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from typing import Any, List, Optional, Union
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import httpx
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import pytest
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from modelscope import snapshot_download # type: ignore
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from requests.exceptions import ConnectionError, HTTPError, Timeout
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from tests.e2e.conftest import RemoteOpenAIServer
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from tests.e2e.nightly.multi_node.config.multi_node_config import \
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MultiNodeConfig
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from tools.aisbench import run_aisbench_cases
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prompts = [
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"San Francisco is a",
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]
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api_keyword_args = {
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"max_tokens": 10,
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}
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def get_local_model_path_with_retry(
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model: str,
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revision: str = "master",
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max_retries: int = 5,
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delay: int = 5,
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) -> Optional[str]:
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for attempt in range(1, max_retries + 1):
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try:
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local_model_path = snapshot_download(
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model_id=model,
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revision=revision,
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)
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return local_model_path
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except HTTPError:
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continue
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except (ConnectionError, Timeout):
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continue
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if attempt < max_retries:
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time.sleep(delay)
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return None
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async def get_completions(url: str, model: str, prompts: Union[str, List[str]],
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**api_kwargs: Any) -> List[str]:
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"""
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Asynchronously send HTTP requests to endpoint.
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Args:
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url: Full endpoint URL, e.g. "http://localhost:1025/v1/completions"
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model: Model name or local model path
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prompts: A single prompt string or a list of prompts
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**api_kwargs: Additional parameters (e.g., max_tokens, temperature)
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Returns:
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List[str]: A list of generated texts corresponding to each prompt
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"""
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headers = {"Content-Type": "application/json"}
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if isinstance(prompts, str):
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prompts = [prompts]
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results = []
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async with httpx.AsyncClient(timeout=600.0) as client:
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for prompt in prompts:
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payload = {"model": model, "prompt": prompt, **api_kwargs}
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response = await client.post(url, headers=headers, json=payload)
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if response.status_code != 200:
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raise RuntimeError(
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f"Request failed with status {response.status_code}: {response.text}"
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)
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resp_json = response.json()
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choices = resp_json.get("choices", [])
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if not choices or not choices[0].get("text"):
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raise ValueError("Empty response from server")
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results.append(choices[0]["text"])
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return results
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@pytest.mark.asyncio
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async def test_multi_node() -> None:
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config = MultiNodeConfig.from_yaml()
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# To avoid modelscope 400 HttpError, we should download the model with retry
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local_model_path = get_local_model_path_with_retry(config.model)
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config.server_cmd = config.server_cmd.replace(config.model,
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local_model_path)
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assert local_model_path is not None, "can not find any local weight for test"
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env_dict = config.envs
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perf_cmd = config.perf_cmd
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acc_cmd = config.acc_cmd
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nodes_info = config.nodes_info
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disaggregated_prefill = config.disaggregated_prefill
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server_port = config.server_port
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proxy_port = config.proxy_port
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server_host = config.master_ip
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proxy_script = config.envs.get("DISAGGREGATED_PREFILL_PROXY_SCRIPT", \
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'examples/disaggregated_prefill_v1/load_balance_proxy_server_example.py')
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with config.launch_server_proxy(proxy_script):
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with RemoteOpenAIServer(
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model=local_model_path,
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vllm_serve_args=config.server_cmd,
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server_port=server_port,
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server_host=server_host,
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env_dict=env_dict,
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auto_port=False,
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proxy_port=proxy_port,
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disaggregated_prefill=disaggregated_prefill,
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nodes_info=nodes_info,
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max_wait_seconds=1200,
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) as remote_server:
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if config.is_master:
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port = proxy_port if disaggregated_prefill else server_port
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# aisbench test
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aisbench_cases = [acc_cmd, perf_cmd]
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run_aisbench_cases(local_model_path,
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port,
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aisbench_cases,
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host_ip=config.master_ip)
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else:
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# for the nodes except master, should hang until the task complete
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master_url = f"http://{config.master_ip}:{server_port}/health"
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remote_server.hang_until_terminated(master_url)
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