[Model] VLM2Vec, the first multimodal embedding model in vLLM (#9303)

tests/models/embedding/vision_language/test_phi3v.py

@@ -0,0 +1,62 @@
+import pytest
+import torch.nn.functional as F
+
+from ....conftest import IMAGE_ASSETS
+from ..utils import check_embeddings_close
+
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    "stop_sign":
+    "<|image_1|> Select the portion of the image that isolates the object of the given label: The label of the object is stop sign",  # noqa: E501
+    "cherry_blossom":
+    "<|image_1|> Represent the given image with the following question: What is in the image",  # noqa: E501
+})
+
+MODELS = ["TIGER-Lab/VLM2Vec-Full"]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+    # NOTE: take care of the order. run vLLM first, and then run HF.
+    # vLLM needs a fresh new process without cuda initialization.
+    # if we run HF first, the cuda initialization will be done and it
+    # will hurt multiprocessing backend with fork method (the default method).
+    with vllm_runner(model,
+                     max_model_len=4096,
+                     max_num_seqs=2,
+                     dtype=dtype,
+                     enforce_eager=True) as vllm_model:
+        vllm_outputs = vllm_model.encode(example_prompts)
+
+    with hf_runner(model, dtype=dtype) as hf_model:
+        all_inputs = hf_model.get_inputs(example_prompts)
+
+        all_outputs = []
+        for inputs in all_inputs:
+            # Based on: https://github.com/TIGER-AI-Lab/VLM2Vec/blob/db3b951bccabba220c1f53ab46a734e50dd2fc08/src/model.py
+            outputs = hf_model.model(
+                **hf_model.wrap_device(inputs,
+                                       device=hf_model.model.device.type),
+                return_dict=True,
+                output_hidden_states=True,
+            )
+            last_hidden_state = outputs.hidden_states[-1][0]
+            reps = last_hidden_state[inputs.attention_mask[0].sum() - 1]
+            pooled_output = F.normalize(reps, p=2, dim=-1)
+
+            all_outputs.append(pooled_output.tolist())
+
+        hf_outputs = all_outputs
+
+    check_embeddings_close(
+        embeddings_0_lst=hf_outputs,
+        embeddings_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+    )