Merge 'rhds/main' into 'rhds/rhoai-2.23'

docs/features/lora.md

@@ -275,3 +275,80 @@ The new format of `--lora-modules` is mainly to support the display of parent mo
         ]
     }
     ```
+
+## Default LoRA Models For Multimodal Models
+
+Some models, e.g., [Granite Speech](https://huggingface.co/ibm-granite/granite-speech-3.3-8b) and [Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) multimodal, contain LoRA adapter(s) that are expected to always be applied when a given modality is present. This can be a bit tedious to manage with the above approaches, as it requires the user to send the `LoRARequest` (offline) or to filter requests between the base model and LoRA model (server) depending on the content of the request's multimodal data.
+
+To this end, we allow registration of default multimodal LoRAs to handle this automatically, where users can map each modality to a LoRA adapter to automatically apply it when the corresponding inputs are present. Note that currently, we only allow one LoRA per prompt; if several modalities are provided, each of which are registered to a given modality, none of them will be applied.
+
+Example usage for offline inference:
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+from vllm.assets.audio import AudioAsset
+
+model_id = "ibm-granite/granite-speech-3.3-2b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def get_prompt(question: str, has_audio: bool):
+    """Build the input prompt to send to vLLM."""
+    if has_audio:
+        question = f"<|audio|>{question}"
+    chat = [
+        {
+            "role": "user",
+            "content": question
+        }
+    ]
+    return tokenizer.apply_chat_template(chat, tokenize=False)
+
+
+model = LLM(
+    model=model_id,
+    enable_lora=True,
+    max_lora_rank=64,
+    max_model_len=2048,
+    limit_mm_per_prompt={"audio": 1},
+    # Will always pass a `LoRARequest` with the `model_id`
+    # whenever audio is contained in the request data.
+    default_mm_loras = {"audio": model_id},
+    enforce_eager=True,
+)
+
+question = "can you transcribe the speech into a written format?"
+prompt_with_audio = get_prompt(
+    question=question,
+    has_audio=True,
+)
+audio = AudioAsset("mary_had_lamb").audio_and_sample_rate
+
+inputs = {
+    "prompt": prompt_with_audio,
+    "multi_modal_data": {
+        "audio": audio,
+    }
+}
+
+
+outputs = model.generate(
+    inputs,
+    sampling_params=SamplingParams(
+        temperature=0.2,
+        max_tokens=64,
+    ),
+)
+```
+
+You can also pass a json dictionary of `--default-mm-loras` mapping modalities to LoRA model IDs. For example, when starting the server:
+
+```bash
+vllm serve ibm-granite/granite-speech-3.3-2b \
+    --max-model-len 2048 \
+    --enable-lora \
+    --default-mm-loras '{"audio":"ibm-granite/granite-speech-3.3-2b"}' \
+    --max-lora-rank 64
+```
+
+Note: Default multimodal LoRAs are currently only available for `.generate` and chat completions.

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

tests/entrypoints/openai/test_serving_models.py

@@ -57,7 +57,8 @@ async def test_load_lora_adapter_success():
     response = await serving_models.load_lora_adapter(request)
     assert response == LORA_LOADING_SUCCESS_MESSAGE.format(lora_name='adapter')
     assert len(serving_models.lora_requests) == 1
-    assert serving_models.lora_requests[0].lora_name == "adapter"
+    assert "adapter" in serving_models.lora_requests
+    assert serving_models.lora_requests["adapter"].lora_name == "adapter"
 
 
 @pytest.mark.asyncio

tests/lora/test_default_mm_loras.py

@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Tests for applying default registered multimodal loras.
+"""
+
+import os
+
+from huggingface_hub import snapshot_download
+
+from vllm.lora.request import LoRARequest
+
+from ..conftest import AudioTestAssets, VllmRunner
+
+MODEL_PATH = snapshot_download("microsoft/Phi-4-multimodal-instruct")
+AUDIO_LORA_PATH = os.path.join(MODEL_PATH, "speech-lora")
+IMAGE_LORA_PATH = os.path.join(MODEL_PATH, "vision-lora")
+
+AUDIO_PROMPT = "<|user|><|audio_1|>Can you transcribe this audio?<|end|><|assistant|>"  # noqa: E501
+
+# Responses are greedy decoded; we just check the end of
+# the generated text. If the lora is inactive, this model
+# generates commentary on the transcription.
+RESPONSE_SUFFIX_WITH_LORA = "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
+RESPONSE_SUFFIX_WITHOUT_LORA = "Certainly! Here is the transcription of the audio you provided:\n\nThe first words I spoke in the original phonograph record: A little piece of practical poetry. Mary had a little lamb; its fleece was white as snow, and everywhere that Mary went, the lamb was sure to go."  # noqa: E501
+
+VLLM_RUNNER_BASE_KWARGS = {
+    "model_name": MODEL_PATH,
+    "dtype": "half",
+    "enable_lora": "True",
+    "max_num_seqs": 2,
+    "max_lora_rank": 320,
+    "max_model_len": 12800,
+    "gpu_memory_utilization": 0.8,
+    "limit_mm_per_prompt": {
+        "audio": 1
+    },
+    "enforce_eager": True,
+}
+
+
+def run_test(vllm_runner, audio_assets, lora_request, expected_suffix,
+             **kwargs):
+    inputs = [([AUDIO_PROMPT], [audio_assets[0].audio_and_sample_rate[0]])]
+
+    # Apply any additional kwargs as overrides to the base kwargs
+    vllm_runner_kwargs = {**VLLM_RUNNER_BASE_KWARGS, **kwargs}
+
+    with vllm_runner(**vllm_runner_kwargs) as vllm_model:
+        vllm_outputs_with_default_lora = [
+            vllm_model.generate_greedy(
+                prompts,
+                max_tokens=128,
+                audios=audios,
+                lora_request=lora_request,
+            ) for prompts, audios in inputs
+        ]
+
+        assert vllm_outputs_with_default_lora[-1][-1][-1].endswith(
+            expected_suffix)
+
+
+def test_active_default_mm_lora(
+    vllm_runner: type[VllmRunner],
+    audio_assets: AudioTestAssets,
+):
+    """Ensure that we can use the default audio lora."""
+    run_test(
+        vllm_runner,
+        audio_assets,
+        lora_request=None,
+        default_mm_loras={"audio": AUDIO_LORA_PATH},
+        expected_suffix=RESPONSE_SUFFIX_WITH_LORA,
+    )
+
+
+def test_inactive_default_mm_lora(
+    vllm_runner: type[VllmRunner],
+    audio_assets: AudioTestAssets,
+):
+    """Ensure that modalities are filtered properly."""
+    # Default image lora won't be active since we only pass audio
+    run_test(
+        vllm_runner,
+        audio_assets,
+        lora_request=None,
+        default_mm_loras={"image": IMAGE_LORA_PATH},
+        expected_suffix=RESPONSE_SUFFIX_WITHOUT_LORA,
+    )
+
+
+def test_default_mm_lora_succeeds_with_redundant_lora_request(
+    vllm_runner: type[VllmRunner],
+    audio_assets: AudioTestAssets,
+):
+    """Ensure that redundantly providing the lora works."""
+    run_test(
+        vllm_runner,
+        audio_assets,
+        lora_request=LoRARequest("audio", 1, AUDIO_LORA_PATH),
+        default_mm_loras={"audio": AUDIO_LORA_PATH},
+        expected_suffix=RESPONSE_SUFFIX_WITH_LORA,
+    )
+
+
+def test_default_mm_lora_fails_with_overridden_lora_request(
+    vllm_runner: type[VllmRunner],
+    audio_assets: AudioTestAssets,
+):
+    """Ensure that if the lora_request conflicts with default_mm_loras,
+    we use the lora_request."""
+    run_test(
+        vllm_runner,
+        audio_assets,
+        lora_request=LoRARequest("speech", 2, AUDIO_LORA_PATH),
+        default_mm_loras={"audio": IMAGE_LORA_PATH},
+        expected_suffix=RESPONSE_SUFFIX_WITH_LORA,
+    )

vllm/config.py

@@ -33,6 +33,7 @@
 from vllm import version
 from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
 from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import (
     ConfigFormat, get_config, get_hf_image_processor_config,
@@ -2964,6 +2965,16 @@ class LoRAConfig:
     trained with those scaling factors to be used at the same time. If not
     specified, only adapters trained with the base model scaling factor are
     allowed."""
+    default_mm_loras: Optional[dict[str, str]] = None
+    """Dictionary mapping specific modalities to LoRA model paths; this field
+    is only applicable to multimodal models and should be leveraged when a
+    model always expects a LoRA to be active when a given modality is present.
+    Note that currently, if a request provides multiple additional
+    modalities, each of which have their own LoRA, we do NOT apply
+    default_mm_loras because we currently only support one lora adapter
+    per prompt. When run in offline mode, the lora IDs for n modalities
+    will be automatically assigned to 1-n with the names of the modalities
+    in alphabetic order."""
     bias_enabled: bool = False
     """Enable bias for LoRA adapters."""
 

vllm/engine/arg_utils.py

@@ -382,6 +382,8 @@ class EngineArgs:
     enable_lora_bias: bool = LoRAConfig.bias_enabled
     max_loras: int = LoRAConfig.max_loras
     max_lora_rank: int = LoRAConfig.max_lora_rank
+    default_mm_loras: Optional[Dict[str, str]] = \
+        LoRAConfig.default_mm_loras
     fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
     max_cpu_loras: Optional[int] = LoRAConfig.max_cpu_loras
     lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
@@ -791,6 +793,8 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
                                 **lora_kwargs["max_cpu_loras"])
         lora_group.add_argument("--fully-sharded-loras",
                                 **lora_kwargs["fully_sharded_loras"])
+        lora_group.add_argument("--default-mm-loras",
+                                **lora_kwargs["default_mm_loras"])
 
         # PromptAdapter related configs
         prompt_adapter_kwargs = get_kwargs(PromptAdapterConfig)
@@ -1245,10 +1249,16 @@ def create_engine_config(
             disable_hybrid_kv_cache_manager,
         )
 
+        if not model_config.is_multimodal_model and self.default_mm_loras:
+            raise ValueError(
+                "Default modality-specific LoRA(s) were provided for a "
+                "non multimodal model")
+
         lora_config = LoRAConfig(
             bias_enabled=self.enable_lora_bias,
             max_lora_rank=self.max_lora_rank,
             max_loras=self.max_loras,
+            default_mm_loras=self.default_mm_loras,
             fully_sharded_loras=self.fully_sharded_loras,
             lora_extra_vocab_size=self.lora_extra_vocab_size,
             long_lora_scaling_factors=self.long_lora_scaling_factors,