[Frontend] Require flag for loading text and image embeds

docs/features/multimodal_inputs.md

@@ -359,13 +359,19 @@ Full example: [examples/offline_inference/audio_language.py](../../examples/offl
 To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
 pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary.
 
+You must enable this feature via `enable_mm_embeds=True`.
+
+!!! warning
+    The vLLM engine may crash if incorrect shape of embeddings is passed.
+    Only enable this flag for trusted users!
+
 ??? code
 
     ```python
     from vllm import LLM
 
     # Inference with image embeddings as input
-    llm = LLM(model="llava-hf/llava-1.5-7b-hf")
+    llm = LLM(model="llava-hf/llava-1.5-7b-hf", enable_mm_embeds=True)
 
     # Refer to the HuggingFace repo for the correct format to use
     prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"
@@ -397,7 +403,11 @@ For Qwen2-VL and MiniCPM-V, we accept additional parameters alongside the embedd
     image_embeds = torch.load(...)
 
     # Qwen2-VL
-    llm = LLM("Qwen/Qwen2-VL-2B-Instruct", limit_mm_per_prompt={"image": 4})
+    llm = LLM(
+        "Qwen/Qwen2-VL-2B-Instruct",
+        limit_mm_per_prompt={"image": 4},
+        enable_mm_embeds=True,
+    )
     mm_data = {
         "image": {
             "image_embeds": image_embeds,
@@ -407,7 +417,12 @@ For Qwen2-VL and MiniCPM-V, we accept additional parameters alongside the embedd
     }
 
     # MiniCPM-V
-    llm = LLM("openbmb/MiniCPM-V-2_6", trust_remote_code=True, limit_mm_per_prompt={"image": 4})
+    llm = LLM(
+        "openbmb/MiniCPM-V-2_6",
+        trust_remote_code=True,
+        limit_mm_per_prompt={"image": 4},
+        enable_mm_embeds=True,
+    )
     mm_data = {
         "image": {
             "image_embeds": image_embeds,
@@ -732,7 +747,13 @@ Full example: [examples/online_serving/openai_chat_completion_client_for_multimo
 ### Embedding Inputs
 
 To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
-pass a tensor of shape to the corresponding field of the multi-modal dictionary.
+pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary.
+
+You must enable this feature via the `--enable-mm-embeds` flag in `vllm serve`.
+
+!!! warning
+    The vLLM engine may crash if incorrect shape of embeddings is passed.
+    Only enable this flag for trusted users!
 
 #### Image Embedding Inputs
 

docs/features/prompt_embeds.md

@@ -20,12 +20,16 @@ You can pass prompt embeddings from Hugging Face Transformers models to the  `'p
 
 ## Online Serving
 
-Our OpenAI-compatible server accepts prompt embeddings inputs via the [Completions API](https://platform.openai.com/docs/api-reference/completions). Prompt embeddings inputs are added via a new `'prompt_embeds'` key in the JSON package.
+Our OpenAI-compatible server accepts prompt embeddings inputs via the [Completions API](https://platform.openai.com/docs/api-reference/completions). Prompt embeddings inputs are added via a new `'prompt_embeds'` key in the JSON package and are enabled by the `--enable-prompt-embeds` flag in `vllm serve`.
 
 When a mixture of `'prompt_embeds'` and `'prompt'` inputs are provided in a single request, the prompt embeds are always returned first.
 
 Prompt embeddings are passed in as base64 encoded torch tensors.
 
+!!! warning
+    The vLLM engine may crash if incorrect shape of embeddings is passed.
+    Only enable this flag for trusted users!
+
 ### Transformers Inputs via OpenAI Client
 
 First, launch the OpenAI-compatible server:

examples/offline_inference/prithvi_geospatial_mae.py

@@ -49,6 +49,7 @@ def __init__(self, model):
             dtype="float16",
             enforce_eager=True,
             model_impl="terratorch",
+            enable_mm_embeds=True,
         )
 
     def run(self, input_data, location_coords):

examples/offline_inference/prithvi_geospatial_mae_io_processor.py

@@ -38,6 +38,7 @@ def main():
         max_num_seqs=32,
         io_processor_plugin="prithvi_to_tiff",
         model_impl="terratorch",
+        enable_mm_embeds=True,
     )
 
     pooling_params = PoolingParams(task="token_classify", activation=False)

examples/online_serving/prithvi_geospatial_mae.py

@@ -19,6 +19,7 @@
 #   --task embed --trust-remote-code
 #   --skip-tokenizer-init --enforce-eager
 #   --io-processor-plugin prithvi_to_tiff
+#   --enable-mm-embeds
 
 
 def main():

tests/entrypoints/llm/test_prompt_validation.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import pytest
+import torch
 
 from vllm import LLM
 
@@ -12,8 +13,22 @@ def test_empty_prompt():
         llm.generate([""])
 
 
-@pytest.mark.skip_v1
 def test_out_of_vocab_token():
     llm = LLM(model="openai-community/gpt2", enforce_eager=True)
     with pytest.raises(ValueError, match="out of vocabulary"):
         llm.generate({"prompt_token_ids": [999999]})
+
+
+def test_require_mm_embeds():
+    llm = LLM(
+        model="llava-hf/llava-1.5-7b-hf",
+        enforce_eager=True,
+        enable_mm_embeds=False,
+    )
+    with pytest.raises(ValueError, match="--enable-mm-embeds"):
+        llm.generate(
+            {
+                "prompt": "<image>",
+                "multi_modal_data": {"image": torch.empty(1, 1, 1)},
+            }
+        )

tests/entrypoints/openai/test_completion_with_prompt_embeds.py

@@ -292,3 +292,16 @@ async def test_prompt_logprobs_raises_error(
             temperature=0.0,
             extra_body={"prompt_embeds": encoded_embeds, "prompt_logprobs": True},
         )
+
+
+@pytest.mark.asyncio
+async def test_empty_prompt_embeds(
+    client_with_prompt_embeds: openai.AsyncOpenAI,
+) -> None:
+    await client_with_prompt_embeds.completions.create(
+        model=MODEL_NAME,
+        prompt="Hello",
+        max_tokens=5,
+        temperature=0.0,
+        extra_body={"prompt_embeds": []},
+    )

tests/entrypoints/openai/test_prompt_validation.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import io
+from unittest.mock import Mock
 
 # imports for structured outputs tests
 import openai
@@ -10,7 +11,8 @@
 import regex as re
 import torch
 
-from vllm.entrypoints.renderer import BaseRenderer
+from vllm.config import ModelConfig
+from vllm.entrypoints.renderer import CompletionRenderer
 
 from ...utils import RemoteOpenAIServer
 
@@ -59,6 +61,10 @@ async def test_out_of_vocab_token_ids():
 def test_load_prompt_embeds(
     dtype: torch.dtype, layout: torch.layout, seq_len: int, hidden_size: int
 ):
+    model_config = Mock(spec=ModelConfig)
+    model_config.enable_prompt_embeds = True
+    renderer = CompletionRenderer(model_config, tokenizer=None)
+
     # construct arbitrary tensors of various dtypes, layouts, and sizes.
     # We need to check against different layouts to make sure that if a user
     # uses sparse tensors to reduce the transmission size of prompt embeddings,
@@ -83,11 +89,30 @@ def test_load_prompt_embeds(
     buffer.seek(0)
     encoded_tensor = pybase64.b64encode(buffer.getvalue())
 
-    loaded_prompt_embeds = BaseRenderer.load_prompt_embeds(encoded_tensor)
+    loaded_prompt_embeds = renderer.load_prompt_embeds(encoded_tensor)
     assert len(loaded_prompt_embeds) == 1
     loaded_tensor = loaded_prompt_embeds[0]["prompt_embeds"]
     assert loaded_tensor.device.type == "cpu"
     assert loaded_tensor.layout == torch.strided
     torch.testing.assert_close(
         loaded_tensor, tensor.to("cpu").to_dense(), equal_nan=True
     )
+
+
+@pytest.mark.parametrize("dtype", [torch.float32])
+@pytest.mark.parametrize("seq_len", [2])
+@pytest.mark.parametrize("hidden_size", [2])
+def test_disable_prompt_embeds(dtype: torch.dtype, seq_len: int, hidden_size: int):
+    model_config = Mock(spec=ModelConfig)
+    model_config.enable_prompt_embeds = False
+    renderer = CompletionRenderer(model_config, tokenizer=None)
+
+    tensor = torch.randn((seq_len, hidden_size), dtype=dtype)
+
+    buffer = io.BytesIO()
+    torch.save(tensor, buffer)
+    buffer.seek(0)
+    encoded_tensor = pybase64.b64encode(buffer.getvalue())
+
+    with pytest.raises(ValueError, match="--enable-prompt-embeds"):
+        renderer.load_prompt_embeds(encoded_tensor)

tests/entrypoints/openai/test_skip_tokenizer.py → tests/entrypoints/openai/test_vision_embeds.py

@@ -15,30 +15,7 @@
 DTYPE = "float16"
 
 
-@pytest.fixture(scope="module")
-def server():
-    args = [
-        "--runner",
-        "pooling",
-        # use half precision for speed and memory savings in CI environment
-        "--dtype",
-        DTYPE,
-        "--enforce-eager",
-        "--trust-remote-code",
-        "--skip-tokenizer-init",
-        "--max-num-seqs",
-        "32",
-        "--model-impl",
-        "terratorch",
-    ]
-
-    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
-        yield remote_server
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_single_request(server: RemoteOpenAIServer, model_name: str):
+def _terratorch_dummy_inputs(model_name: str):
     pixel_values = torch.full((6, 512, 512), 1.0, dtype=torch.float16)
     location_coords = torch.full((1, 2), 1.0, dtype=torch.float16)
 
@@ -54,7 +31,7 @@ async def test_single_request(server: RemoteOpenAIServer, model_name: str):
     binary_data = buffer_coord.read()
     base64_coord_embedding = base64.b64encode(binary_data).decode("utf-8")
 
-    prompt = {
+    return {
         "model": model_name,
         "additional_data": {"prompt_token_ids": [1]},
         "encoding_format": "base64",
@@ -74,12 +51,33 @@ async def test_single_request(server: RemoteOpenAIServer, model_name: str):
         ],
     }
 
-    # test single pooling
-    response = requests.post(server.url_for("pooling"), json=prompt)
-    response.raise_for_status()
 
-    output = response.json()["data"][0]["data"]
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_single_request(model_name: str):
+    args = [
+        "--runner",
+        "pooling",
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        DTYPE,
+        "--enforce-eager",
+        "--trust-remote-code",
+        "--max-num-seqs",
+        "32",
+        "--model-impl",
+        "terratorch",
+        "--skip-tokenizer-init",
+        "--enable-mm-embeds",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as server:
+        prompt = _terratorch_dummy_inputs(model_name)
+
+        # test single pooling
+        response = requests.post(server.url_for("pooling"), json=prompt)
+        response.raise_for_status()
 
-    np_response = np.frombuffer(base64.b64decode(output), dtype=np.float32)
+        output = response.json()["data"][0]["data"]
 
-    assert len(np_response) == 524288
+        np_response = np.frombuffer(base64.b64decode(output), dtype=np.float32)
+        assert len(np_response) == 524288

tests/entrypoints/test_chat_utils.py

@@ -73,6 +73,19 @@ def phi3v_model_config_mm_interleaved():
     )
 
 
+@pytest.fixture(scope="function")
+def phi3v_model_config_image_embeds():
+    return ModelConfig(
+        PHI3V_MODEL_ID,
+        runner="generate",
+        trust_remote_code=True,
+        limit_mm_per_prompt={
+            "image": 2,
+        },
+        enable_mm_embeds=True,
+    )
+
+
 @pytest.fixture(scope="module")
 def phi3v_tokenizer():
     return get_tokenizer(PHI3V_MODEL_ID)
@@ -799,7 +812,7 @@ def test_parse_chat_messages_empty_pil_image_with_uuid(
 
 
 def test_parse_chat_messages_empty_image_embeds_with_uuid(
-    phi3v_model_config,
+    phi3v_model_config_image_embeds,
     phi3v_tokenizer,
 ):
     uuid = "abcd"
@@ -813,7 +826,7 @@ def test_parse_chat_messages_empty_image_embeds_with_uuid(
                 ],
             }
         ],
-        phi3v_model_config,
+        phi3v_model_config_image_embeds,
         phi3v_tokenizer,
         content_format="string",
     )
@@ -832,7 +845,7 @@ def test_parse_chat_messages_empty_image_embeds_with_uuid(
 
 @pytest.mark.asyncio
 async def test_parse_chat_messages_empty_image_embeds_with_uuid_async(
-    phi3v_model_config,
+    phi3v_model_config_image_embeds,
     phi3v_tokenizer,
 ):
     uuid = "abcd"
@@ -846,7 +859,7 @@ async def test_parse_chat_messages_empty_image_embeds_with_uuid_async(
                 ],
             }
         ],
-        phi3v_model_config,
+        phi3v_model_config_image_embeds,
         phi3v_tokenizer,
         content_format="string",
     )

tests/entrypoints/test_renderer.py

@@ -17,6 +17,7 @@
 class MockModelConfig:
     max_model_len: int = 100
     encoder_config: dict | None = None
+    enable_prompt_embeds: bool = True
 
 
 class MockTokenizerResult:

tests/models/multimodal/generation/test_common.py

@@ -109,8 +109,7 @@
                 limit_mm_per_prompt={"image": 4},
             )
         ],
-        # TODO: Revert to "auto" when CPU backend can use torch > 2.6
-        dtype="bfloat16" if current_platform.is_cpu() else "auto",
+        vllm_runner_kwargs={"enable_mm_embeds": True},
         marks=[pytest.mark.core_model, pytest.mark.cpu_model],
     ),
     "qwen2_5_vl": VLMTestInfo(

tests/models/multimodal/generation/test_qwen2_vl.py

@@ -292,6 +292,7 @@ def run_embedding_input_test(
         tensor_parallel_size=tensor_parallel_size,
         distributed_executor_backend=distributed_executor_backend,
         default_torch_num_threads=1,
+        enable_mm_embeds=True,
     ) as vllm_model:
         outputs_per_case_for_original_input = [
             vllm_model.generate_greedy_logprobs(

tests/models/multimodal/pooling/test_prithvi_mae.py

@@ -34,6 +34,7 @@ def _run_test(
         dtype="half",
         enforce_eager=True,
         skip_tokenizer_init=True,
+        enable_mm_embeds=True,
         # Limit the maximum number of sequences to avoid the
         # test going OOM during the warmup run
         max_num_seqs=32,