Add embedding input functionality for disabled modalities [remake] (#32493)

Signed-off-by: Reagan Lee <“reaganjlee@gmail.com”>
Signed-off-by: Reagan Lee <reaganjlee@gmail.com>
Signed-off-by: Reagan Lee <96998476+reaganjlee@users.noreply.github.com>
Co-authored-by: Reagan Lee <“reaganjlee@gmail.com”>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

tests/entrypoints/llm/test_mm_embeds_only.py

@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import weakref
+
+import pytest
+
+from vllm import LLM, SamplingParams
+from vllm.assets.image import ImageAsset
+from vllm.distributed import cleanup_dist_env_and_memory
+
+MODEL = "llava-hf/llava-1.5-7b-hf"
+PROMPT = "USER: <image>\nDescribe this image briefly.\nASSISTANT:"
+TEXT_ONLY_PROMPT = "USER: What is 2 + 2?\nASSISTANT:"
+
+
+@pytest.fixture(scope="module")
+def llm():
+    """LLM with enable_mm_embeds=True and all modality limits zeroed out."""
+    llm = LLM(
+        model=MODEL,
+        max_model_len=2048,
+        enforce_eager=True,
+        gpu_memory_utilization=0.8,
+        enable_mm_embeds=True,
+        limit_mm_per_prompt={"image": 0},
+    )
+
+    yield weakref.proxy(llm)
+
+    del llm
+
+    cleanup_dist_env_and_memory()
+
+
+@pytest.mark.skip_global_cleanup
+def test_generate_with_embedding(llm: LLM):
+    """Pre-computed embedding produces tokens without hanging."""
+    embedding = ImageAsset("stop_sign").image_embeds
+    outputs = llm.generate(
+        {"prompt": PROMPT, "multi_modal_data": {"image": embedding}},
+        sampling_params=SamplingParams(max_tokens=32, temperature=0.0),
+    )
+    assert len(outputs) == 1
+    assert len(outputs[0].outputs[0].text) > 0
+
+
+@pytest.mark.skip_global_cleanup
+def test_raw_image_rejected(llm: LLM):
+    """Raw image input is still rejected when limit=0."""
+    raw_image = ImageAsset("stop_sign").pil_image
+    with pytest.raises(ValueError, match=r"At most 0 image\(s\)"):
+        llm.generate(
+            {"prompt": PROMPT, "multi_modal_data": {"image": raw_image}},
+            sampling_params=SamplingParams(max_tokens=16),
+        )
+
+
+@pytest.mark.skip_global_cleanup
+def test_text_only_prompt(llm: LLM):
+    """Text-only prompts still work under this config."""
+    outputs = llm.generate(
+        TEXT_ONLY_PROMPT,
+        sampling_params=SamplingParams(max_tokens=16, temperature=0.0),
+    )
+    assert len(outputs) == 1
+    assert len(outputs[0].outputs[0].text) > 0