[CI/Build] Reorganize models tests (#17459)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/models/multimodal/pooling/test_phi3v.py

@@ -0,0 +1,138 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import torch.nn.functional as F
+from PIL import Image
+
+from vllm.assets.base import get_vllm_public_assets
+from vllm.assets.image import VLM_IMAGES_DIR
+
+from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
+from ....utils import large_gpu_test
+from ...utils import check_embeddings_close
+
+HF_TEXT_PROMPTS = [
+    # T -> X
+    "Find me an everyday image that matches the given caption: The label of the object is stop sign",  # noqa: E501
+    # T -> X
+    "Retrieve an image of this caption: cherry blossom",
+]
+
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    # T + I -> X
+    "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
+    # I -> X
+    "cherry_blossom":
+    "<|image_1|> Represent the given image for classification",  # noqa: E501
+})
+
+MODELS = ["TIGER-Lab/VLM2Vec-Full"]
+
+
+def _run_test(
+    hf_runner: type[HfRunner],
+    vllm_runner: type[VllmRunner],
+    input_texts: list[str],
+    input_images: PromptImageInput,
+    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, task="embed", dtype=dtype,
+                     enforce_eager=True) as vllm_model:
+        vllm_outputs = vllm_model.encode(input_texts, images=input_images)
+
+    # use eager mode for hf runner, since phi3_v didn't work with flash_attn
+    hf_model_kwargs = {"_attn_implementation": "eager"}
+    with hf_runner(model, dtype=dtype,
+                   model_kwargs=hf_model_kwargs) as hf_model:
+        all_inputs = hf_model.get_inputs(input_texts, images=input_images)
+
+        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),
+                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",
+    )
+
+
+@pytest.mark.core_model
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_models_text(
+    hf_runner,
+    vllm_runner,
+    image_assets,
+    model: str,
+    dtype: str,
+) -> None:
+    input_texts_images = [(text, None) for text in HF_TEXT_PROMPTS]
+    input_texts = [text for text, _ in input_texts_images]
+    input_images = [image for _, image in input_texts_images]
+
+    _run_test(
+        hf_runner,
+        vllm_runner,
+        input_texts,
+        input_images,  # type: ignore
+        model,
+        dtype=dtype,
+    )
+
+
+@large_gpu_test(min_gb=48)
+@pytest.mark.core_model
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_models_image(
+    hf_runner,
+    vllm_runner,
+    image_assets,
+    model: str,
+    dtype: str,
+) -> None:
+    input_texts_images = [
+        (text, asset.pil_image)
+        for text, asset in zip(HF_IMAGE_PROMPTS, image_assets)
+    ]
+    # add cases for special_tokens
+    input_texts_images.append((
+        "\n<s><|user|>\n <|image_1|>\n\t <s>"
+        "Represent the given image for classification<|end|>"
+        "\n<|assistant|>\n",
+        Image.open(
+            get_vllm_public_assets(filename="cherry_blossom.jpg",
+                                   s3_prefix=VLM_IMAGES_DIR)),
+    ))
+    input_texts = [text for text, _ in input_texts_images]
+    input_images = [image for _, image in input_texts_images]
+
+    _run_test(
+        hf_runner,
+        vllm_runner,
+        input_texts,
+        input_images,
+        model,
+        dtype=dtype,
+    )