Sync from v0.13

tests/entrypoints/openai/test_orca_metrics.py

@@ -0,0 +1,126 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import openai
+import pytest
+import pytest_asyncio
+
+from ...utils import RemoteOpenAIServer
+
+# any model with a chat template should work here
+MODEL_NAME = "Qwen/Qwen3-0.6B"
+
+
+@pytest.fixture(scope="module")
+def monkeypatch_module():
+    from _pytest.monkeypatch import MonkeyPatch
+
+    mpatch = MonkeyPatch()
+    yield mpatch
+    mpatch.undo()
+
+
+@pytest.fixture(scope="module", params=[True])
+def server(request, monkeypatch_module):
+    args = [
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "8192",
+        "--enforce-eager",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
+@pytest.mark.asyncio
+async def test_chat_completion_with_orca_header(server: RemoteOpenAIServer):
+    messages = [
+        {"role": "system", "content": "you are a helpful assistant"},
+        {"role": "user", "content": "what is 1+1?"},
+    ]
+
+    client = openai.OpenAI(
+        api_key="EMPTY",
+        base_url=f"http://localhost:{server.port}/v1",
+        default_headers={"endpoint-load-metrics-format": "TEXT"},
+    )
+
+    # 1. Use raw client to get response headers.
+    raw_client = client.with_raw_response
+
+    # 2. Make the API call using the raw_client
+    response_with_raw = raw_client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=messages,
+        extra_headers={"endpoint-load-metrics-format": "TEXT"},
+    )
+
+    # 3. Access the raw httpx.Response object
+    raw_http_response = response_with_raw.http_response
+
+    # 4. Get the headers from the httpx.Response object
+    response_headers = raw_http_response.headers
+
+    assert "endpoint-load-metrics" in response_headers
+
+
+@pytest.mark.asyncio
+async def test_completion_with_orca_header(client: openai.AsyncOpenAI):
+    # 1. Use raw client to get response headers.
+    raw_client = client.with_raw_response
+
+    # 2. Make the API call using the raw_client
+    completion = await raw_client.completions.create(
+        model=MODEL_NAME,
+        prompt="Hello, my name is",
+        max_tokens=5,
+        extra_headers={"endpoint-load-metrics-format": "JSON"},
+    )
+
+    # 3. Access the raw httpx.Response object
+    raw_http_response = completion.http_response
+
+    # 4. Get the headers from the httpx.Response object
+    response_headers = raw_http_response.headers
+
+    assert "endpoint-load-metrics" in response_headers
+
+
+@pytest.mark.asyncio
+async def test_single_completion(client: openai.AsyncOpenAI):
+    completion = await client.completions.create(
+        model=MODEL_NAME,
+        prompt="Hello, my name is",
+        max_tokens=5,
+        extra_headers={"endpoint-load-metrics-format": "JSON"},
+        temperature=0.0,
+    )
+
+    assert completion.id is not None
+    assert completion.choices is not None and len(completion.choices) == 1
+
+    choice = completion.choices[0]
+    assert len(choice.text) >= 5
+    assert choice.finish_reason == "length"
+    # When using Qwen3-0.6B, prompt tokens=[9707, 11, 847, 829, 374]
+    assert completion.usage == openai.types.CompletionUsage(
+        completion_tokens=5, prompt_tokens=5, total_tokens=10
+    )
+
+    # 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 len(completion.choices[0].text) >= 1
+    assert completion.choices[0].prompt_logprobs is None