[VLM][Model] Support image input for Chameleon (#6633)

tests/models/test_chameleon.py

@@ -0,0 +1,102 @@
+import re
+from typing import List, Optional, Type
+
+import pytest
+
+from vllm.multimodal.utils import rescale_image_size
+
+from ..conftest import IMAGE_ASSETS, VllmRunner, _ImageAssets
+
+pytestmark = pytest.mark.vlm
+
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    "stop_sign":
+    "USER: <image>\nWhat's the content of the image?\nASSISTANT:",
+    "cherry_blossom":
+    "USER: <image>\nWhat is the season?\nASSISTANT:",
+})
+
+models = ["facebook/chameleon-7b"]
+
+
+#TODO (ywang96): Add correctness test when chameleon is
+# available on transformers.
+def run_test(
+    vllm_runner: Type[VllmRunner],
+    image_assets: _ImageAssets,
+    model: str,
+    *,
+    size_factors: List[float],
+    dtype: str,
+    max_tokens: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
+):
+    """Test if the model can generate text given 
+    a batch of images and prompts.
+
+    """
+    images = [asset.pil_image for asset in image_assets]
+
+    inputs_per_image = [(
+        [prompt for _ in size_factors],
+        [rescale_image_size(image, factor) for factor in size_factors],
+    ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+
+    with vllm_runner(model,
+                     max_model_len=4096,
+                     dtype=dtype,
+                     tensor_parallel_size=tensor_parallel_size,
+                     distributed_executor_backend=distributed_executor_backend,
+                     enforce_eager=True) as vllm_model:
+
+        for prompts, images in inputs_per_image:
+            vllm_outputs = vllm_model.generate_greedy(prompts,
+                                                      max_tokens,
+                                                      images=images)
+            for i in range(len(vllm_outputs)):
+
+                # format prompt back to original
+                replacements = {
+                    "<racm3:break>": "",
+                    "<eoss>": "",
+                    "<reserved08706>": ""
+                }
+                pattern = '|'.join(replacements.keys())
+                vllm_result = re.sub(
+                    pattern,
+                    lambda match: replacements[match.group(0)],  #noqa B023
+                    vllm_outputs[i][1])
+                vllm_result = vllm_result.replace("<image>", "", 1023)
+                assert vllm_result[:len(prompts[i])] == prompts[i]
+
+                # assert at least 10 new characters are generated
+                # (to take stop token into account)
+                assert len(vllm_outputs[i][1]) - len(prompts[i]) > 10
+
+
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize(
+    "size_factors",
+    [
+        # Single-scale
+        [1.0],
+        # Single-scale, batched
+        [1.0, 1.0, 1.0],
+        # Multi-scale
+        [0.25, 0.5, 1.0],
+    ],
+)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [128])
+def test_models(vllm_runner, image_assets, model, size_factors, dtype: str,
+                max_tokens: int) -> None:
+    run_test(
+        vllm_runner,
+        image_assets,
+        model,
+        size_factors=size_factors,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        tensor_parallel_size=1,
+    )