[CI/Build] Simplify OpenAI server setup in tests (#5100)

tests/async_engine/test_openapi_server_ray.py

@@ -4,16 +4,22 @@ import pytest
 # and debugging.
 import ray
 
-from ..utils import ServerRunner
+from ..utils import VLLM_PATH, RemoteOpenAIServer
 
 # any model with a chat template should work here
 MODEL_NAME = "facebook/opt-125m"
 
 
 @pytest.fixture(scope="module")
-def server():
+def ray_ctx():
-    ray.init()
+    ray.init(runtime_env={"working_dir": VLLM_PATH})
-    server_runner = ServerRunner.remote([
+    yield
+    ray.shutdown()
+
+
+@pytest.fixture(scope="module")
+def server(ray_ctx):
+    return RemoteOpenAIServer([
         "--model",
         MODEL_NAME,
         # use half precision for speed and memory savings in CI environment
@@ -24,22 +30,15 @@ def server():
         "--enforce-eager",
         "--engine-use-ray"
     ])
-    ray.get(server_runner.ready.remote())
-    yield server_runner
-    ray.shutdown()
 
 
 @pytest.fixture(scope="module")
-def client():
+def client(server):
-    client = openai.AsyncOpenAI(
+    return server.get_async_client()
-        base_url="http://localhost:8000/v1",
-        api_key="token-abc123",
-    )
-    yield client
 
 
 @pytest.mark.asyncio
-async def test_check_models(server, client: openai.AsyncOpenAI):
+async def test_check_models(client: openai.AsyncOpenAI):
     models = await client.models.list()
     models = models.data
     served_model = models[0]
@@ -48,7 +47,7 @@ async def test_check_models(server, client: openai.AsyncOpenAI):
 
 
 @pytest.mark.asyncio
-async def test_single_completion(server, client: openai.AsyncOpenAI):
+async def test_single_completion(client: openai.AsyncOpenAI):
     completion = await client.completions.create(model=MODEL_NAME,
                                                  prompt="Hello, my name is",
                                                  max_tokens=5,
@@ -72,7 +71,7 @@ async def test_single_completion(server, client: openai.AsyncOpenAI):
 
 
 @pytest.mark.asyncio
-async def test_single_chat_session(server, client: openai.AsyncOpenAI):
+async def test_single_chat_session(client: openai.AsyncOpenAI):
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"

tests/entrypoints/test_openai_embedding.py

@@ -0,0 +1,113 @@
+import openai
+import pytest
+import ray
+
+from ..utils import VLLM_PATH, RemoteOpenAIServer
+
+EMBEDDING_MODEL_NAME = "intfloat/e5-mistral-7b-instruct"
+
+pytestmark = pytest.mark.openai
+
+
+@pytest.fixture(scope="module")
+def ray_ctx():
+    ray.init(runtime_env={"working_dir": VLLM_PATH})
+    yield
+    ray.shutdown()
+
+
+@pytest.fixture(scope="module")
+def embedding_server(ray_ctx):
+    return RemoteOpenAIServer([
+        "--model",
+        EMBEDDING_MODEL_NAME,
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "bfloat16",
+        "--enforce-eager",
+        "--max-model-len",
+        "8192",
+        "--enforce-eager",
+    ])
+
+
+@pytest.mark.asyncio
+@pytest.fixture(scope="module")
+def embedding_client(embedding_server):
+    return embedding_server.get_async_client()
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [EMBEDDING_MODEL_NAME],
+)
+async def test_single_embedding(embedding_client: openai.AsyncOpenAI,
+                                model_name: str):
+    input_texts = [
+        "The chef prepared a delicious meal.",
+    ]
+
+    # test single embedding
+    embeddings = await embedding_client.embeddings.create(
+        model=model_name,
+        input=input_texts,
+        encoding_format="float",
+    )
+    assert embeddings.id is not None
+    assert len(embeddings.data) == 1
+    assert len(embeddings.data[0].embedding) == 4096
+    assert embeddings.usage.completion_tokens == 0
+    assert embeddings.usage.prompt_tokens == 9
+    assert embeddings.usage.total_tokens == 9
+
+    # test using token IDs
+    input_tokens = [1, 1, 1, 1, 1]
+    embeddings = await embedding_client.embeddings.create(
+        model=model_name,
+        input=input_tokens,
+        encoding_format="float",
+    )
+    assert embeddings.id is not None
+    assert len(embeddings.data) == 1
+    assert len(embeddings.data[0].embedding) == 4096
+    assert embeddings.usage.completion_tokens == 0
+    assert embeddings.usage.prompt_tokens == 5
+    assert embeddings.usage.total_tokens == 5
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [EMBEDDING_MODEL_NAME],
+)
+async def test_batch_embedding(embedding_client: openai.AsyncOpenAI,
+                               model_name: str):
+    # test List[str]
+    input_texts = [
+        "The cat sat on the mat.", "A feline was resting on a rug.",
+        "Stars twinkle brightly in the night sky."
+    ]
+    embeddings = await embedding_client.embeddings.create(
+        model=model_name,
+        input=input_texts,
+        encoding_format="float",
+    )
+    assert embeddings.id is not None
+    assert len(embeddings.data) == 3
+    assert len(embeddings.data[0].embedding) == 4096
+
+    # test List[List[int]]
+    input_tokens = [[4, 5, 7, 9, 20], [15, 29, 499], [24, 24, 24, 24, 24],
+                    [25, 32, 64, 77]]
+    embeddings = await embedding_client.embeddings.create(
+        model=model_name,
+        input=input_tokens,
+        encoding_format="float",
+    )
+    assert embeddings.id is not None
+    assert len(embeddings.data) == 4
+    assert len(embeddings.data[0].embedding) == 4096
+    assert embeddings.usage.completion_tokens == 0
+    assert embeddings.usage.prompt_tokens == 17
+    assert embeddings.usage.total_tokens == 17

tests/entrypoints/test_openai_server.py

@@ -15,11 +15,10 @@ from openai import BadRequestError
 
 from vllm.transformers_utils.tokenizer import get_tokenizer
 
-from ..utils import ServerRunner
+from ..utils import VLLM_PATH, RemoteOpenAIServer
 
 # any model with a chat template should work here
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
-EMBEDDING_MODEL_NAME = "intfloat/e5-mistral-7b-instruct"
 # technically this needs Mistral-7B-v0.1 as base, but we're not testing
 # generation quality here
 LORA_NAME = "typeof/zephyr-7b-beta-lora"
@@ -80,9 +79,15 @@ def zephyr_lora_files():
 
 
 @pytest.fixture(scope="module")
-def server(zephyr_lora_files):
+def ray_ctx():
-    ray.init()
+    ray.init(runtime_env={"working_dir": VLLM_PATH})
-    server_runner = ServerRunner.remote([
+    yield
+    ray.shutdown()
+
+
+@pytest.fixture(scope="module")
+def server(zephyr_lora_files, ray_ctx):
+    return RemoteOpenAIServer([
         "--model",
         MODEL_NAME,
         # use half precision for speed and memory savings in CI environment
@@ -91,8 +96,6 @@ def server(zephyr_lora_files):
         "--max-model-len",
         "8192",
         "--enforce-eager",
-        "--gpu-memory-utilization",
-        "0.75",
         # lora config below
         "--enable-lora",
         "--lora-modules",
@@ -105,43 +108,14 @@ def server(zephyr_lora_files):
         "--max-num-seqs",
         "128",
     ])
-    ray.get(server_runner.ready.remote())
-    yield server_runner
-    ray.shutdown()
 
 
 @pytest.fixture(scope="module")
-def embedding_server(zephyr_lora_files):
+def client(server):
-    ray.shutdown()
+    return server.get_async_client()
-    ray.init()
-    server_runner = ServerRunner.remote([
-        "--model",
-        EMBEDDING_MODEL_NAME,
-        # use half precision for speed and memory savings in CI environment
-        "--dtype",
-        "bfloat16",
-        "--enforce-eager",
-        "--gpu-memory-utilization",
-        "0.75",
-        "--max-model-len",
-        "8192",
-    ])
-    ray.get(server_runner.ready.remote())
-    yield server_runner
-    ray.shutdown()
 
 
-@pytest.fixture(scope="module")
+async def test_check_models(client: openai.AsyncOpenAI):
-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]
@@ -158,8 +132,7 @@ async def test_check_models(server, client: openai.AsyncOpenAI):
     "model_name",
     [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
 )
-async def test_single_completion(server, client: openai.AsyncOpenAI,
+async def test_single_completion(client: openai.AsyncOpenAI, model_name: str):
-                                 model_name: str):
     completion = await client.completions.create(model=model_name,
                                                  prompt="Hello, my name is",
                                                  max_tokens=5,
@@ -190,8 +163,7 @@ async def test_single_completion(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
 )
-async def test_no_logprobs(server, client: openai.AsyncOpenAI,
+async def test_no_logprobs(client: openai.AsyncOpenAI, model_name: str):
-                           model_name: str):
     # test using token IDs
     completion = await client.completions.create(
         model=MODEL_NAME,
@@ -210,8 +182,7 @@ async def test_no_logprobs(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_zero_logprobs(server, client: openai.AsyncOpenAI,
+async def test_zero_logprobs(client: openai.AsyncOpenAI, model_name: str):
-                             model_name: str):
     # test using token IDs
     completion = await client.completions.create(
         model=MODEL_NAME,
@@ -232,8 +203,7 @@ async def test_zero_logprobs(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_some_logprobs(server, client: openai.AsyncOpenAI,
+async def test_some_logprobs(client: openai.AsyncOpenAI, model_name: str):
-                             model_name: str):
     # test using token IDs
     completion = await client.completions.create(
         model=MODEL_NAME,
@@ -254,7 +224,7 @@ async def test_some_logprobs(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_too_many_completion_logprobs(server, client: openai.AsyncOpenAI,
+async def test_too_many_completion_logprobs(client: openai.AsyncOpenAI,
                                             model_name: str):
 
     with pytest.raises(
@@ -300,8 +270,7 @@ async def test_too_many_completion_logprobs(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
 )
-async def test_no_logprobs_chat(server, client: openai.AsyncOpenAI,
+async def test_no_logprobs_chat(client: openai.AsyncOpenAI, model_name: str):
-                                model_name: str):
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -326,8 +295,7 @@ async def test_no_logprobs_chat(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_zero_logprobs_chat(server, client: openai.AsyncOpenAI,
+async def test_zero_logprobs_chat(client: openai.AsyncOpenAI, model_name: str):
-                                  model_name: str):
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -354,8 +322,7 @@ async def test_zero_logprobs_chat(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_some_logprobs_chat(server, client: openai.AsyncOpenAI,
+async def test_some_logprobs_chat(client: openai.AsyncOpenAI, model_name: str):
-                                  model_name: str):
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -382,7 +349,7 @@ async def test_some_logprobs_chat(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_too_many_chat_logprobs(server, client: openai.AsyncOpenAI,
+async def test_too_many_chat_logprobs(client: openai.AsyncOpenAI,
                                       model_name: str):
     messages = [{
         "role": "system",
@@ -425,7 +392,7 @@ async def test_too_many_chat_logprobs(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_single_chat_session(server, client: openai.AsyncOpenAI,
+async def test_single_chat_session(client: openai.AsyncOpenAI,
                                    model_name: str):
     messages = [{
         "role": "system",
@@ -470,7 +437,7 @@ async def test_single_chat_session(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_completion_streaming(server, client: openai.AsyncOpenAI,
+async def test_completion_streaming(client: openai.AsyncOpenAI,
                                     model_name: str):
     prompt = "What is an LLM?"
 
@@ -505,8 +472,7 @@ async def test_completion_streaming(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_chat_streaming(server, client: openai.AsyncOpenAI,
+async def test_chat_streaming(client: openai.AsyncOpenAI, model_name: str):
-                              model_name: str):
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -555,8 +521,7 @@ async def test_chat_streaming(server, client: openai.AsyncOpenAI,
     "model_name",
     ["HuggingFaceH4/zephyr-7b-beta", "zephyr-lora"],
 )
-async def test_chat_completion_stream_options(server,
+async def test_chat_completion_stream_options(client: openai.AsyncOpenAI,
-                                              client: openai.AsyncOpenAI,
                                               model_name: str):
     messages = [{
         "role": "system",
@@ -626,7 +591,7 @@ async def test_chat_completion_stream_options(server,
     "model_name",
     ["HuggingFaceH4/zephyr-7b-beta", "zephyr-lora"],
 )
-async def test_completion_stream_options(server, client: openai.AsyncOpenAI,
+async def test_completion_stream_options(client: openai.AsyncOpenAI,
                                          model_name: str):
     prompt = "What is the capital of France?"
 
@@ -688,8 +653,7 @@ async def test_completion_stream_options(server, client: openai.AsyncOpenAI,
     "model_name",
     [MODEL_NAME, "zephyr-lora"],
 )
-async def test_batch_completions(server, client: openai.AsyncOpenAI,
+async def test_batch_completions(client: openai.AsyncOpenAI, model_name: str):
-                                 model_name: str):
     # test simple list
     batch = await client.completions.create(
         model=model_name,
@@ -737,7 +701,7 @@ async def test_batch_completions(server, client: openai.AsyncOpenAI,
 
 
 @pytest.mark.asyncio
-async def test_logits_bias(server, client: openai.AsyncOpenAI):
+async def test_logits_bias(client: openai.AsyncOpenAI):
     prompt = "Hello, my name is"
     max_tokens = 5
     tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME)
@@ -786,7 +750,7 @@ async def test_logits_bias(server, client: openai.AsyncOpenAI):
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])
-async def test_guided_json_completion(server, client: openai.AsyncOpenAI,
+async def test_guided_json_completion(client: openai.AsyncOpenAI,
                                       guided_decoding_backend: str):
     completion = await client.completions.create(
         model=MODEL_NAME,
@@ -808,7 +772,7 @@ async def test_guided_json_completion(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])
-async def test_guided_json_chat(server, client: openai.AsyncOpenAI,
+async def test_guided_json_chat(client: openai.AsyncOpenAI,
                                 guided_decoding_backend: str):
     messages = [{
         "role": "system",
@@ -855,7 +819,7 @@ async def test_guided_json_chat(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])
-async def test_guided_regex_completion(server, client: openai.AsyncOpenAI,
+async def test_guided_regex_completion(client: openai.AsyncOpenAI,
                                        guided_decoding_backend: str):
     completion = await client.completions.create(
         model=MODEL_NAME,
@@ -875,7 +839,7 @@ async def test_guided_regex_completion(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])
-async def test_guided_regex_chat(server, client: openai.AsyncOpenAI,
+async def test_guided_regex_chat(client: openai.AsyncOpenAI,
                                  guided_decoding_backend: str):
     messages = [{
         "role": "system",
@@ -913,7 +877,7 @@ async def test_guided_regex_chat(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])
-async def test_guided_choice_completion(server, client: openai.AsyncOpenAI,
+async def test_guided_choice_completion(client: openai.AsyncOpenAI,
                                         guided_decoding_backend: str):
     completion = await client.completions.create(
         model=MODEL_NAME,
@@ -933,7 +897,7 @@ async def test_guided_choice_completion(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])
-async def test_guided_choice_chat(server, client: openai.AsyncOpenAI,
+async def test_guided_choice_chat(client: openai.AsyncOpenAI,
                                   guided_decoding_backend: str):
     messages = [{
         "role": "system",
@@ -972,7 +936,7 @@ async def test_guided_choice_chat(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])
-async def test_guided_decoding_type_error(server, client: openai.AsyncOpenAI,
+async def test_guided_decoding_type_error(client: openai.AsyncOpenAI,
                                           guided_decoding_backend: str):
     with pytest.raises(openai.BadRequestError):
         _ = await client.completions.create(
@@ -1008,7 +972,7 @@ async def test_guided_decoding_type_error(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])
-async def test_guided_choice_chat_logprobs(server, client: openai.AsyncOpenAI,
+async def test_guided_choice_chat_logprobs(client: openai.AsyncOpenAI,
                                            guided_decoding_backend: str):
     messages = [{
         "role": "system",
@@ -1040,7 +1004,7 @@ async def test_guided_choice_chat_logprobs(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend",
                          ["outlines", "lm-format-enforcer"])
-async def test_named_tool_use(server, client: openai.AsyncOpenAI,
+async def test_named_tool_use(client: openai.AsyncOpenAI,
                               guided_decoding_backend: str):
     messages = [{
         "role": "system",
@@ -1131,7 +1095,7 @@ async def test_named_tool_use(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend", ["outlines"])
 async def test_required_tool_use_not_yet_supported(
-        server, client: openai.AsyncOpenAI, guided_decoding_backend: str):
+        client: openai.AsyncOpenAI, guided_decoding_backend: str):
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -1177,7 +1141,7 @@ async def test_required_tool_use_not_yet_supported(
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend", ["outlines"])
 async def test_inconsistent_tool_choice_and_tools(
-        server, client: openai.AsyncOpenAI, guided_decoding_backend: str):
+        client: openai.AsyncOpenAI, guided_decoding_backend: str):
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -1223,7 +1187,7 @@ async def test_inconsistent_tool_choice_and_tools(
 
 
 @pytest.mark.asyncio
-async def test_response_format_json_object(server, client: openai.AsyncOpenAI):
+async def test_response_format_json_object(client: openai.AsyncOpenAI):
     for _ in range(2):
         resp = await client.chat.completions.create(
             model=MODEL_NAME,
@@ -1243,7 +1207,7 @@ async def test_response_format_json_object(server, client: openai.AsyncOpenAI):
 
 
 @pytest.mark.asyncio
-async def test_extra_fields(server, client: openai.AsyncOpenAI):
+async def test_extra_fields(client: openai.AsyncOpenAI):
     with pytest.raises(BadRequestError) as exc_info:
         await client.chat.completions.create(
             model=MODEL_NAME,
@@ -1259,7 +1223,7 @@ async def test_extra_fields(server, client: openai.AsyncOpenAI):
 
 
 @pytest.mark.asyncio
-async def test_complex_message_content(server, client: openai.AsyncOpenAI):
+async def test_complex_message_content(client: openai.AsyncOpenAI):
     resp = await client.chat.completions.create(
         model=MODEL_NAME,
         messages=[{
@@ -1279,7 +1243,7 @@ async def test_complex_message_content(server, client: openai.AsyncOpenAI):
 
 
 @pytest.mark.asyncio
-async def test_custom_role(server, client: openai.AsyncOpenAI):
+async def test_custom_role(client: openai.AsyncOpenAI):
     # Not sure how the model handles custom roles so we just check that
     # both string and complex message content are handled in the same way
 
@@ -1310,7 +1274,7 @@ async def test_custom_role(server, client: openai.AsyncOpenAI):
 
 
 @pytest.mark.asyncio
-async def test_guided_grammar(server, client: openai.AsyncOpenAI):
+async def test_guided_grammar(client: openai.AsyncOpenAI):
     simple_sql_grammar = """
 start: select_statement
 
@@ -1351,7 +1315,7 @@ number: "1" | "2"
     [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
 )
 @pytest.mark.parametrize("logprobs_arg", [1, 0])
-async def test_echo_logprob_completion(server, client: openai.AsyncOpenAI,
+async def test_echo_logprob_completion(client: openai.AsyncOpenAI,
                                        model_name: str, logprobs_arg: int):
     tokenizer = get_tokenizer(tokenizer_name=MODEL_NAME)
     # test using text and token IDs
@@ -1380,7 +1344,7 @@ async def test_echo_logprob_completion(server, client: openai.AsyncOpenAI,
 
 
 @pytest.mark.asyncio
-async def test_long_seed(server, client: openai.AsyncOpenAI):
+async def test_long_seed(client: openai.AsyncOpenAI):
     for seed in [
             torch.iinfo(torch.long).min - 1,
             torch.iinfo(torch.long).max + 1
@@ -1399,81 +1363,5 @@ async def test_long_seed(server, client: openai.AsyncOpenAI):
                 or "less_than_equal" in exc_info.value.message)
 
 
-@pytest.mark.asyncio
-@pytest.mark.parametrize(
-    "model_name",
-    [EMBEDDING_MODEL_NAME],
-)
-async def test_single_embedding(embedding_server, client: openai.AsyncOpenAI,
-                                model_name: str):
-    input_texts = [
-        "The chef prepared a delicious meal.",
-    ]
-
-    # test single embedding
-    embeddings = await client.embeddings.create(
-        model=model_name,
-        input=input_texts,
-        encoding_format="float",
-    )
-    assert embeddings.id is not None
-    assert len(embeddings.data) == 1
-    assert len(embeddings.data[0].embedding) == 4096
-    assert embeddings.usage.completion_tokens == 0
-    assert embeddings.usage.prompt_tokens == 9
-    assert embeddings.usage.total_tokens == 9
-
-    # test using token IDs
-    input_tokens = [1, 1, 1, 1, 1]
-    embeddings = await client.embeddings.create(
-        model=model_name,
-        input=input_tokens,
-        encoding_format="float",
-    )
-    assert embeddings.id is not None
-    assert len(embeddings.data) == 1
-    assert len(embeddings.data[0].embedding) == 4096
-    assert embeddings.usage.completion_tokens == 0
-    assert embeddings.usage.prompt_tokens == 5
-    assert embeddings.usage.total_tokens == 5
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize(
-    "model_name",
-    [EMBEDDING_MODEL_NAME],
-)
-async def test_batch_embedding(embedding_server, client: openai.AsyncOpenAI,
-                               model_name: str):
-    # test List[str]
-    input_texts = [
-        "The cat sat on the mat.", "A feline was resting on a rug.",
-        "Stars twinkle brightly in the night sky."
-    ]
-    embeddings = await client.embeddings.create(
-        model=model_name,
-        input=input_texts,
-        encoding_format="float",
-    )
-    assert embeddings.id is not None
-    assert len(embeddings.data) == 3
-    assert len(embeddings.data[0].embedding) == 4096
-
-    # test List[List[int]]
-    input_tokens = [[4, 5, 7, 9, 20], [15, 29, 499], [24, 24, 24, 24, 24],
-                    [25, 32, 64, 77]]
-    embeddings = await client.embeddings.create(
-        model=model_name,
-        input=input_tokens,
-        encoding_format="float",
-    )
-    assert embeddings.id is not None
-    assert len(embeddings.data) == 4
-    assert len(embeddings.data[0].embedding) == 4096
-    assert embeddings.usage.completion_tokens == 0
-    assert embeddings.usage.prompt_tokens == 17
-    assert embeddings.usage.total_tokens == 17
-
-
 if __name__ == "__main__":
     pytest.main([__file__])

tests/entrypoints/test_openai_vision.py

@@ -8,7 +8,7 @@ import ray
 
 from vllm.multimodal.utils import ImageFetchAiohttp, encode_image_base64
 
-from ..utils import ServerRunner
+from ..utils import VLLM_PATH, RemoteOpenAIServer
 
 MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
 LLAVA_CHAT_TEMPLATE = (Path(__file__).parent.parent.parent /
@@ -25,10 +25,16 @@ TEST_IMAGE_URLS = [
 pytestmark = pytest.mark.openai
 
 
+@pytest.fixture(scope="module")
+def ray_ctx():
+    ray.init(runtime_env={"working_dir": VLLM_PATH})
+    yield
+    ray.shutdown()
+
+
 @pytest.fixture(scope="module")
 def server():
-    ray.init()
+    return RemoteOpenAIServer([
-    server_runner = ServerRunner.remote([
         "--model",
         MODEL_NAME,
         "--dtype",
@@ -47,18 +53,11 @@ def server():
         "--chat-template",
         str(LLAVA_CHAT_TEMPLATE),
     ])
-    ray.get(server_runner.ready.remote())
-    yield server_runner
-    ray.shutdown()
 
 
-@pytest.fixture(scope="session")
+@pytest.fixture(scope="module")
-def client():
+def client(server):
-    client = openai.AsyncOpenAI(
+    return server.get_async_client()
-        base_url="http://localhost:8000/v1",
-        api_key="token-abc123",
-    )
-    yield client
 
 
 @pytest_asyncio.fixture(scope="session")
@@ -73,7 +72,7 @@ async def base64_encoded_image() -> Dict[str, str]:
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 @pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
-async def test_single_chat_session_image(server, client: openai.AsyncOpenAI,
+async def test_single_chat_session_image(client: openai.AsyncOpenAI,
                                          model_name: str, image_url: str):
     messages = [{
         "role":
@@ -126,7 +125,7 @@ async def test_single_chat_session_image(server, client: openai.AsyncOpenAI,
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 @pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
 async def test_single_chat_session_image_base64encoded(
-        server, client: openai.AsyncOpenAI, model_name: str, image_url: str,
+        client: openai.AsyncOpenAI, model_name: str, image_url: str,
         base64_encoded_image: Dict[str, str]):
 
     messages = [{
@@ -180,7 +179,7 @@ async def test_single_chat_session_image_base64encoded(
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 @pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
-async def test_chat_streaming_image(server, client: openai.AsyncOpenAI,
+async def test_chat_streaming_image(client: openai.AsyncOpenAI,
                                     model_name: str, image_url: str):
     messages = [{
         "role":
@@ -237,8 +236,8 @@ async def test_chat_streaming_image(server, client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 @pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
-async def test_multi_image_input(server, client: openai.AsyncOpenAI,
+async def test_multi_image_input(client: openai.AsyncOpenAI, model_name: str,
-                                 model_name: str, image_url: str):
+                                 image_url: str):
 
     messages = [{
         "role":

tests/tensorizer_loader/test_tensorizer.py

@@ -22,11 +22,12 @@ from vllm.model_executor.model_loader.tensorizer import (TensorizerConfig,
                                                          tensorize_vllm_model)
 
 from ..conftest import VllmRunner, cleanup
-from ..utils import ServerRunner
+from ..utils import RemoteOpenAIServer
 
 # yapf conflicts with isort for this docstring
 
 
+
 prompts = [
     "Hello, my name is",
     "The president of the United States is",
@@ -216,18 +217,13 @@ def test_openai_apiserver_with_tensorizer(vllm_runner, tmp_path):
     openai_args = [
         "--model", model_ref, "--dtype", "float16", "--load-format",
         "tensorizer", "--model-loader-extra-config",
-        json.dumps(model_loader_extra_config), "--port", "8000"
+        json.dumps(model_loader_extra_config),
     ]
 
-    server = ServerRunner.remote(openai_args)
+    server = RemoteOpenAIServer(openai_args)
-
-    assert ray.get(server.ready.remote())
     print("Server ready.")
 
-    client = openai.OpenAI(
+    client = server.get_client()
-        base_url="http://localhost:8000/v1",
-        api_key="token-abc123",
-    )
     completion = client.completions.create(model=model_ref,
                                            prompt="Hello, my name is",
                                            max_tokens=5,

tests/utils.py

@@ -4,57 +4,109 @@ import sys
 import time
 import warnings
 from contextlib import contextmanager
+from typing import List
 
+import openai
 import ray
 import requests
 
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment)
+from vllm.entrypoints.openai.cli_args import make_arg_parser
 from vllm.utils import get_open_port
 
 # Path to root of repository so that utilities can be imported by ray workers
 VLLM_PATH = os.path.abspath(os.path.join(__file__, os.pardir, os.pardir))
 
 
-@ray.remote(num_gpus=1)
+class RemoteOpenAIServer:
-class ServerRunner:
+    DUMMY_API_KEY = "token-abc123"  # vLLM's OpenAI server does not need API key
     MAX_SERVER_START_WAIT_S = 600  # wait for server to start for 60 seconds
 
-    def __init__(self, args):
+    @ray.remote(num_gpus=1)
-        env = os.environ.copy()
+    class _RemoteRunner:
-        env["PYTHONUNBUFFERED"] = "1"
+
-        self.proc = subprocess.Popen(
+        def __init__(self, cli_args: List[str], *, wait_url: str,
-            [sys.executable, "-m", "vllm.entrypoints.openai.api_server"] +
+                     wait_timeout: float) -> None:
-            args,
+            env = os.environ.copy()
-            env=env,
+            env["PYTHONUNBUFFERED"] = "1"
-            stdout=sys.stdout,
+            self.proc = subprocess.Popen(
-            stderr=sys.stderr,
+                [
+                    sys.executable, "-m", "vllm.entrypoints.openai.api_server",
+                    *cli_args
+                ],
+                env=env,
+                stdout=sys.stdout,
+                stderr=sys.stderr,
+            )
+
+            self._wait_for_server(url=wait_url, timeout=wait_timeout)
+
+        def ready(self):
+            return True
+
+        def _wait_for_server(self, *, url: str, timeout: float):
+            # run health check
+            start = time.time()
+            while True:
+                try:
+                    if requests.get(url).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 > timeout:
+                        raise RuntimeError(
+                            "Server failed to start in time.") from err
+
+        def __del__(self):
+            if hasattr(self, "proc"):
+                self.proc.terminate()
+
+    def __init__(self, cli_args: List[str], *, auto_port: bool = True) -> None:
+        if auto_port:
+            if "-p" in cli_args or "--port" in cli_args:
+                raise ValueError("You have manually specified the port"
+                                 "when `auto_port=True`.")
+
+            cli_args = cli_args + ["--port", str(get_open_port())]
+
+        parser = make_arg_parser()
+        args = parser.parse_args(cli_args)
+        self.host = str(args.host or 'localhost')
+        self.port = int(args.port)
+
+        self._runner = self._RemoteRunner.remote(
+            cli_args,
+            wait_url=self.url_for("health"),
+            wait_timeout=self.MAX_SERVER_START_WAIT_S)
+
+        self._wait_until_ready()
+
+    @property
+    def url_root(self) -> str:
+        return f"http://{self.host}:{self.port}"
+
+    def url_for(self, *parts: str) -> str:
+        return self.url_root + "/" + "/".join(parts)
+
+    def _wait_until_ready(self) -> None:
+        ray.get(self._runner.ready.remote())
+
+    def get_client(self):
+        return openai.OpenAI(
+            base_url=self.url_for("v1"),
+            api_key=self.DUMMY_API_KEY,
         )
-        self._wait_for_server()
 
-    def ready(self):
+    def get_async_client(self):
-        return True
+        return openai.AsyncOpenAI(
-
+            base_url=self.url_for("v1"),
-    def _wait_for_server(self):
+            api_key=self.DUMMY_API_KEY,
-        # 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 > self.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()
 
 
 def init_test_distributed_environment(