[Bugfix][Core] Fix get decoding config from ray

tests/async_engine/test_async_llm_engine.py

@@ -91,4 +91,6 @@ async def test_new_requests_event():
     assert engine.engine.step_calls == old_step_calls + 1
 
     engine = MockAsyncLLMEngine(worker_use_ray=True, engine_use_ray=True)
+    assert engine.get_model_config() is not None
     assert engine.get_tokenizer() is not None
+    assert engine.get_decoding_config() is not None

tests/entrypoints/test_openapi_server_ray.py

@@ -0,0 +1,157 @@
+# imports for guided decoding tests
+import os
+import subprocess
+import sys
+import time
+
+import openai  # use the official client for correctness check
+import pytest
+# using Ray for overall ease of process management, parallel requests,
+# and debugging.
+import ray
+import requests
+
+MAX_SERVER_START_WAIT_S = 600  # wait for server to start for 60 seconds
+# any model with a chat template should work here
+MODEL_NAME = "facebook/opt-125m"
+
+
+@ray.remote(num_gpus=1)
+class ServerRunner:
+
+    def __init__(self, args):
+        env = os.environ.copy()
+        env["PYTHONUNBUFFERED"] = "1"
+        self.proc = subprocess.Popen(
+            ["python3", "-m", "vllm.entrypoints.openai.api_server"] + args,
+            env=env,
+            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,
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "float16",
+        "--max-model-len",
+        "2048",
+        "--enforce-eager",
+        "--engine-use-ray"
+    ])
+    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
+
+
+@pytest.mark.asyncio
+async def test_check_models(server, client: openai.AsyncOpenAI):
+    models = await client.models.list()
+    models = models.data
+    served_model = models[0]
+    assert served_model.id == MODEL_NAME
+    assert all(model.root == MODEL_NAME for model in models)
+
+
+@pytest.mark.asyncio
+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)
+
+    # test using token IDs
+    completion = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=[0, 0, 0, 0, 0],
+        max_tokens=5,
+        temperature=0.0,
+    )
+    assert completion.choices[0].text is not None and len(
+        completion.choices[0].text) >= 5
+
+
+@pytest.mark.asyncio
+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,
+                                                           logprobs=True,
+                                                           top_logprobs=5)
+    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
+    assert chat_completion.choices[0].logprobs is not None
+    assert chat_completion.choices[0].logprobs.top_logprobs is not None
+    assert len(chat_completion.choices[0].logprobs.top_logprobs[0]) == 5
+    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

vllm/engine/async_llm_engine.py

@@ -7,7 +7,7 @@
 
 from transformers import PreTrainedTokenizer
 
-from vllm.config import ModelConfig
+from vllm.config import DecodingConfig, ModelConfig
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.llm_engine import LLMEngine
 from vllm.executor.ray_utils import initialize_ray_cluster, ray
@@ -697,6 +697,14 @@ async def get_model_config(self) -> ModelConfig:
         else:
             return self.engine.get_model_config()
 
+    async def get_decoding_config(self) -> DecodingConfig:
+        """Get the decoding configuration of the vLLM engine."""
+        if self.engine_use_ray:
+            return await self.engine.get_decoding_config.remote(  # type: ignore
+            )
+        else:
+            return self.engine.get_decoding_config()
+
     async def do_log_stats(self) -> None:
         if self.engine_use_ray:
             await self.engine.do_log_stats.remote()  # type: ignore

vllm/engine/llm_engine.py

@@ -450,6 +450,10 @@ def get_model_config(self) -> ModelConfig:
         """Gets the model configuration."""
         return self.model_config
 
+    def get_decoding_config(self) -> DecodingConfig:
+        """Gets the decoding configuration."""
+        return self.decoding_config
+
     def get_num_unfinished_requests(self) -> int:
         """Gets the number of unfinished requests."""
         return self.scheduler.get_num_unfinished_seq_groups()

vllm/entrypoints/openai/serving_chat.py

@@ -68,7 +68,7 @@ async def create_chat_completion(
                 request, prompt=prompt)
             sampling_params = request.to_sampling_params()
             lora_request = self._maybe_get_lora(request)
-            decoding_config = self.engine.engine.decoding_config
+            decoding_config = await self.engine.get_decoding_config()
             guided_decoding_backend = request.guided_decoding_backend \
                 or decoding_config.guided_decoding_backend
             guided_decode_logits_processor = (

vllm/entrypoints/openai/serving_completion.py

@@ -88,7 +88,7 @@ async def create_completion(self, request: CompletionRequest,
         try:
             sampling_params = request.to_sampling_params()
             lora_request = self._maybe_get_lora(request)
-            decoding_config = self.engine.engine.decoding_config
+            decoding_config = await self.engine.get_decoding_config()
             guided_decoding_backend = request.guided_decoding_backend \
                 or decoding_config.guided_decoding_backend
             guided_decode_logit_processor = (