[Core] Add Support for Default Modality Specific LoRAs [generate / chat completions] (#19126)

Signed-off-by: Alex-Brooks <Alex.Brooks@ibm.com>

tests/entrypoints/openai/test_default_mm_loras.py

@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+
+import openai  # use the official client for correctness check
+import pytest
+import pytest_asyncio
+from huggingface_hub import snapshot_download
+
+from ...conftest import AudioTestAssets
+from ...utils import RemoteOpenAIServer
+
+# NOTE - the tests in this module are currently analogous to test_chat, but are
+# separated to avoid OOM killing due to module-scoped servers, since we
+# need a multimodal model for these tests.
+
+# Contains a modality specific lora alongside the base model
+MULTIMODAL_MODEL_NAME = snapshot_download(
+    "microsoft/Phi-4-multimodal-instruct")
+AUDIO_LORA_PATH = os.path.join(MULTIMODAL_MODEL_NAME, "speech-lora")
+
+ACTIVE_MM_LORA_RESPONSE = "Spoken text: The first words I spoke in the original chronograph, a little piece of practical poetry. Mary had a little lamb, it slept with quite a snow, and everywhere that Mary went, the lamb was sure to go."  # noqa: E501
+
+
+@pytest.fixture(scope="module")
+def monkeypatch_module():
+    from _pytest.monkeypatch import MonkeyPatch
+    mpatch = MonkeyPatch()
+    yield mpatch
+    mpatch.undo()
+
+
+@pytest.fixture(scope="module", params=[False, True])
+def multimodal_server(request, monkeypatch_module):  # noqa: F811
+
+    use_v1 = request.param
+    monkeypatch_module.setenv('VLLM_USE_V1', '1' if use_v1 else '0')
+
+    args = [
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "half",
+        "--max-model-len",
+        "12800",
+        "--enforce-eager",
+        # lora config below
+        "--enable-lora",
+        "--lora-modules",
+        f"speech={AUDIO_LORA_PATH}",
+        "--max-lora-rank",
+        "320",
+        "--max-num-seqs",
+        "2",
+        "--trust-remote-code",
+        "--gpu-memory-utilization",
+        "0.8",
+        "--default-mm-loras",
+        f"{{\"audio\": \"{AUDIO_LORA_PATH}\"}}",
+    ]
+
+    with RemoteOpenAIServer(MULTIMODAL_MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def multi_modal_client(multimodal_server):
+    async with multimodal_server.get_async_client() as async_client:
+        yield async_client
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    # base model with default lora should give the same response as lora model
+    "model_name",
+    [MULTIMODAL_MODEL_NAME, "speech"],
+)
+async def test_default_mm_lora_chat_completions(
+    model_name: str,
+    multi_modal_client: openai.AsyncOpenAI,
+    audio_assets: AudioTestAssets,
+):
+    messages = [{
+        "role":
+        "user",
+        "content": [{
+            "type": "text",
+            "text": "Can you transcribe this audio?",
+        }, {
+            "type": "audio_url",
+            "audio_url": {
+                "url": audio_assets[0].url
+            },
+        }]
+    }]
+
+    chat_completion = await multi_modal_client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        max_completion_tokens=128,
+        temperature=0.0)
+
+    assert len(chat_completion.choices) > 0
+
+    message = chat_completion.choices[0].message
+    assert message.content is not None and len(message.content) >= 0
+    assert message.content == ACTIVE_MM_LORA_RESPONSE