[CI/Build] [3/3] Reorganize entrypoints tests (#5966)

tests/entrypoints/openai/test_models.py

@@ -0,0 +1,69 @@
+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
+# downloading lora to test lora requests
+from huggingface_hub import snapshot_download
+
+from ...utils import RemoteOpenAIServer
+
+# any model with a chat template should work here
+MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+# technically this needs Mistral-7B-v0.1 as base, but we're not testing
+# generation quality here
+LORA_NAME = "typeof/zephyr-7b-beta-lora"
+
+
+@pytest.fixture(scope="module")
+def zephyr_lora_files():
+    return snapshot_download(repo_id=LORA_NAME)
+
+
+@pytest.fixture(scope="module")
+def ray_ctx():
+    ray.init()
+    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
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "8192",
+        "--enforce-eager",
+        # lora config below
+        "--enable-lora",
+        "--lora-modules",
+        f"zephyr-lora={zephyr_lora_files}",
+        f"zephyr-lora2={zephyr_lora_files}",
+        "--max-lora-rank",
+        "64",
+        "--max-cpu-loras",
+        "2",
+        "--max-num-seqs",
+        "128",
+    ])
+
+
+@pytest.fixture(scope="module")
+def client(server):
+    return server.get_async_client()
+
+
+@pytest.mark.asyncio
+async def test_check_models(client: openai.AsyncOpenAI):
+    models = await client.models.list()
+    models = models.data
+    served_model = models[0]
+    lora_models = models[1:]
+    assert served_model.id == MODEL_NAME
+    assert all(model.root == MODEL_NAME for model in models)
+    assert lora_models[0].id == "zephyr-lora"
+    assert lora_models[1].id == "zephyr-lora2"