OpenAI Server refactoring (#2360)

tests/entrypoints/test_openai_server.py

@@ -0,0 +1,193 @@
+import time
+import subprocess
+
+import sys
+import pytest
+import requests
+import ray  # using Ray for overall ease of process management, parallel requests, and debugging.
+import openai  # use the official client for correctness check
+
+MAX_SERVER_START_WAIT_S = 600  # wait for server to start for 60 seconds
+MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"  # any model with a chat template should work here
+
+pytestmark = pytest.mark.asyncio
+
+
+@ray.remote(num_gpus=1)
+class ServerRunner:
+
+    def __init__(self, args):
+        self.proc = subprocess.Popen(
+            ["python3", "-m", "vllm.entrypoints.openai.api_server"] + args,
+            stdout=sys.stdout,
+            stderr=sys.stderr,
+        )
+        self._wait_for_server()
+
+    def ready(self):
+        return True
+
+    def _wait_for_server(self):
+        # run health check
+        start = time.time()
+        while True:
+            try:
+                if requests.get(
+                        "http://localhost:8000/health").status_code == 200:
+                    break
+            except Exception as err:
+                if self.proc.poll() is not None:
+                    raise RuntimeError("Server exited unexpectedly.") from err
+
+                time.sleep(0.5)
+                if time.time() - start > MAX_SERVER_START_WAIT_S:
+                    raise RuntimeError(
+                        "Server failed to start in time.") from err
+
+    def __del__(self):
+        if hasattr(self, "proc"):
+            self.proc.terminate()
+
+
+@pytest.fixture(scope="session")
+def server():
+    ray.init()
+    server_runner = ServerRunner.remote([
+        "--model",
+        MODEL_NAME,
+        "--dtype",
+        "bfloat16",  # use half precision for speed and memory savings in CI environment
+        "--max-model-len",
+        "8192"
+    ])
+    ray.get(server_runner.ready.remote())
+    yield server_runner
+    ray.shutdown()
+
+
+@pytest.fixture(scope="session")
+def client():
+    client = openai.AsyncOpenAI(
+        base_url="http://localhost:8000/v1",
+        api_key="token-abc123",
+    )
+    yield client
+
+
+async def test_single_completion(server, client: openai.AsyncOpenAI):
+    completion = await client.completions.create(model=MODEL_NAME,
+                                                 prompt="Hello, my name is",
+                                                 max_tokens=5,
+                                                 temperature=0.0)
+
+    assert completion.id is not None
+    assert completion.choices is not None and len(completion.choices) == 1
+    assert completion.choices[0].text is not None and len(
+        completion.choices[0].text) >= 5
+    assert completion.choices[0].finish_reason == "length"
+    assert completion.usage == openai.types.CompletionUsage(
+        completion_tokens=5, prompt_tokens=6, total_tokens=11)
+
+
+async def test_single_chat_session(server, client: openai.AsyncOpenAI):
+    messages = [{
+        "role": "system",
+        "content": "you are a helpful assistant"
+    }, {
+        "role": "user",
+        "content": "what is 1+1?"
+    }]
+
+    # test single completion
+    chat_completion = await client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=messages,
+        max_tokens=10,
+    )
+    assert chat_completion.id is not None
+    assert chat_completion.choices is not None and len(
+        chat_completion.choices) == 1
+    assert chat_completion.choices[0].message is not None
+    message = chat_completion.choices[0].message
+    assert message.content is not None and len(message.content) >= 10
+    assert message.role == "assistant"
+    messages.append({"role": "assistant", "content": message.content})
+
+    # test multi-turn dialogue
+    messages.append({"role": "user", "content": "express your result in json"})
+    chat_completion = await client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=messages,
+        max_tokens=10,
+    )
+    message = chat_completion.choices[0].message
+    assert message.content is not None and len(message.content) >= 0
+
+
+async def test_completion_streaming(server, client: openai.AsyncOpenAI):
+    prompt = "What is an LLM?"
+
+    single_completion = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=prompt,
+        max_tokens=5,
+        temperature=0.0,
+    )
+    single_output = single_completion.choices[0].text
+    single_usage = single_completion.usage
+
+    stream = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=prompt,
+        max_tokens=5,
+        temperature=0.0,
+        stream=True,
+    )
+    chunks = []
+    async for chunk in stream:
+        chunks.append(chunk.choices[0].text)
+    assert chunk.choices[0].finish_reason == "length"
+    assert chunk.usage == single_usage
+    assert "".join(chunks) == single_output
+
+
+async def test_chat_streaming(server, client: openai.AsyncOpenAI):
+    messages = [{
+        "role": "system",
+        "content": "you are a helpful assistant"
+    }, {
+        "role": "user",
+        "content": "what is 1+1?"
+    }]
+
+    # test single completion
+    chat_completion = await client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=messages,
+        max_tokens=10,
+        temperature=0.0,
+    )
+    output = chat_completion.choices[0].message.content
+    stop_reason = chat_completion.choices[0].finish_reason
+
+    # test streaming
+    stream = await client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=messages,
+        max_tokens=10,
+        temperature=0.0,
+        stream=True,
+    )
+    chunks = []
+    async for chunk in stream:
+        delta = chunk.choices[0].delta
+        if delta.role:
+            assert delta.role == "assistant"
+        if delta.content:
+            chunks.append(delta.content)
+    assert chunk.choices[0].finish_reason == stop_reason
+    assert "".join(chunks) == output
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])