Make AsyncLLMEngine more robust & fix batched abort (#969)
Signed-off-by: Antoni Baum <antoni.baum@protonmail.com> Co-authored-by: Avnish Narayan <38871737+avnishn@users.noreply.github.com>
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86
tests/async_engine/test_api_server.py
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86
tests/async_engine/test_api_server.py
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import subprocess
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import sys
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import time
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from multiprocessing import Pool
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from pathlib import Path
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import pytest
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import requests
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def _query_server(prompt: str) -> dict:
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response = requests.post("http://localhost:8000/generate",
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json={
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"prompt": prompt,
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"max_tokens": 100,
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"temperature": 0,
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"ignore_eos": True
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})
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response.raise_for_status()
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return response.json()
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@pytest.fixture
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def api_server():
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script_path = Path(__file__).parent.joinpath(
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"api_server_async_engine.py").absolute()
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uvicorn_process = subprocess.Popen([
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sys.executable, "-u",
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str(script_path), "--model", "facebook/opt-125m"
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])
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yield
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uvicorn_process.terminate()
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def test_api_server(api_server):
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"""
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Run the API server and test it.
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We run both the server and requests in separate processes.
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We test that the server can handle incoming requests, including
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multiple requests at the same time, and that it can handle requests
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being cancelled without crashing.
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"""
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with Pool(32) as pool:
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# Wait until the server is ready
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prompts = ["Hello world"] * 1
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result = None
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while not result:
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try:
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for result in pool.map(_query_server, prompts):
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break
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except:
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time.sleep(1)
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# Actual tests start here
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# Try with 1 prompt
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for result in pool.map(_query_server, prompts):
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assert result
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num_aborted_requests = requests.get(
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"http://localhost:8000/stats").json()["num_aborted_requests"]
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assert num_aborted_requests == 0
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# Try with 100 prompts
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prompts = ["Hello world"] * 100
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for result in pool.map(_query_server, prompts):
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assert result
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# Cancel requests
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pool.map_async(_query_server, prompts)
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time.sleep(0.01)
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pool.terminate()
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pool.join()
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# check cancellation stats
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num_aborted_requests = requests.get(
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"http://localhost:8000/stats").json()["num_aborted_requests"]
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assert num_aborted_requests > 0
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# check that server still runs after cancellations
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with Pool(32) as pool:
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# Try with 100 prompts
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prompts = ["Hello world"] * 100
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for result in pool.map(_query_server, prompts):
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assert result
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