[VLM][Model] Support image input for Chameleon

docs/source/models/supported_models.rst

@@ -182,6 +182,10 @@ Vision Language Models
     - Models
     - Example HuggingFace Models
     - :ref:`LoRA <lora>`
+  * - :code:`ChameleonForConditionalGeneration`
+    - Chameleon
+    - :code:`facebook/chameleon-7b` etc.
+    - 
   * - :code:`FuyuForCausalLM`
     - Fuyu
     - :code:`adept/fuyu-8b` etc.

tests/models/test_chameleon.py

@@ -0,0 +1,94 @@
+from typing import List, Optional, Type
+
+import re
+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"]
+
+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,
+):
+    """Inference result should be the same between hf and vllm.
+
+    All the image fixtures for the test is under tests/images.
+    For huggingface runner, we provide the PIL images as input.
+    For vllm runner, we provide MultiModalDataDict objects 
+    and corresponding vision language config as input.
+    Note, the text input is also adjusted to abide by vllm contract.
+    The text output is sanitized to be able to compare with hf.
+    """
+    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
+                replacement = {"<racm3:break>":"", "<eoss>":"", "<reserved08706>":""}
+                pattern = '|'.join(replacement.keys())
+                vllm_result = re.sub(pattern, lambda match: replacement[match.group(0)], 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,
+    )

vllm/entrypoints/chat_utils.py

@@ -105,7 +105,8 @@ def _image_token_str(model_config: ModelConfig,
         return None
     if model_type.startswith("llava"):
         return tokenizer.decode(model_config.hf_config.image_token_index)
-
+    if model_type == "chameleon":
+        return "<image>"
     raise TypeError("Unknown model type: {model_type}")