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

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

+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,
+    )

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}")
 
 

vllm/model_executor/models/__init__.py

@@ -16,9 +16,10 @@ _GENERATION_MODELS = {
     "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),  # baichuan-7b
     "BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"),  # baichuan-13b
     "BloomForCausalLM": ("bloom", "BloomForCausalLM"),
-    "ChameleonForCausalLM":
-    ("chameleon", "ChameleonForConditionalGeneration"
-     ),  #TODO(ywang96): fix model name when huggingface fixes it
+    #TODO(ywang96): remove this when huggingface fixes the model repo
+    "ChameleonForCausalLM": ("chameleon", "ChameleonForConditionalGeneration"),
+    "ChameleonForConditionalGeneration":
+    ("chameleon", "ChameleonForConditionalGeneration"),
     "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),
     "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
     "CohereForCausalLM": ("commandr", "CohereForCausalLM"),

vllm/transformers_utils/configs/__init__.py

-from vllm.transformers_utils.configs.chameleon import ChameleonConfig
+from vllm.transformers_utils.configs.chameleon import (ChameleonConfig,
+                                                       ChameleonVQVAEConfig)
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
 from vllm.transformers_utils.configs.dbrx import DbrxConfig
 # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
@@ -12,6 +13,7 @@ from vllm.transformers_utils.configs.mpt import MPTConfig
 
 __all__ = [
     "ChameleonConfig",
+    "ChameleonVQVAEConfig",
     "ChatGLMConfig",
     "DbrxConfig",
     "MPTConfig",

vllm/transformers_utils/configs/chameleon.py

+from typing import List, Optional
+
 from transformers import PretrainedConfig
 
 
@@ -5,9 +7,7 @@ from transformers import PretrainedConfig
 # transformers once the new release with Chameleon support
 # is available.
 class ChameleonConfig(PretrainedConfig):
-
     model_type = "chameleon"
-    is_composition = True
     keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
@@ -31,7 +31,7 @@ class ChameleonConfig(PretrainedConfig):
         rope_scaling=None,
         attention_bias=False,
         attention_dropout=0.0,
-        qk_layernorm=False,
+        model_parallel_size=1,
         swin_norm=False,
         vq_config=None,
         vocabulary_map=None,
@@ -46,10 +46,6 @@ class ChameleonConfig(PretrainedConfig):
         self.num_attention_heads = num_attention_heads
         self.mlp_bias = mlp_bias
 
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
         self.num_key_value_heads = num_key_value_heads
         self.hidden_act = hidden_act
         self.initializer_range = initializer_range
@@ -60,10 +56,14 @@ class ChameleonConfig(PretrainedConfig):
         self._rope_scaling_validation()
         self.attention_bias = attention_bias
         self.attention_dropout = attention_dropout
-        self.qk_layernorm = qk_layernorm
+        self.model_parallel_size = model_parallel_size
         self.swin_norm = swin_norm
-        # vq config is currently ignored
-        # self.vq_config = ChameleonVQConfig(**vq_config)
+
+        if vq_config is None:
+            vq_config = {}
+
+        self.vq_config = ChameleonVQVAEConfig(**vq_config)
+
         self.vocabulary_map = vocabulary_map
 
         super().__init__(
@@ -99,3 +99,40 @@ class ChameleonConfig(PretrainedConfig):
             raise ValueError(
                 "`rope_scaling`'s factor field must be a float > 1, "
                 f"got {rope_scaling_factor}")
+
+
+class ChameleonVQVAEConfig(PretrainedConfig):
+
+    model_type = "chameleon_vqgan"
+
+    def __init__(
+        self,
+        embed_dim: int = 256,
+        num_embeddings: int = 8192,
+        double_latent: bool = False,
+        latent_channels: int = 256,
+        resolution: int = 512,
+        in_channels: int = 3,
+        base_channels: int = 128,
+        channel_multiplier: List[int] = [1, 1, 2, 2, 4],  #noqa
+        num_res_blocks: int = 2,
+        attn_resolutions: Optional[List[int]] = None,
+        dropout: float = 0.0,
+        attn_type: str = "vanilla",
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.embed_dim = embed_dim
+        self.num_embeddings = num_embeddings
+        self.double_latent = double_latent
+        self.latent_channels = latent_channels
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.base_channels = base_channels
+        self.channel_multiplier = channel_multiplier
+        self.num_res_blocks = num_res_blocks
+        self.attn_resolutions = attn_resolutions
+        self.dropout = dropout
+        self.attn_type = attn_type
+        self.initializer_range = initializer_range