[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

@@ -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,
+    )

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":
+    #TODO(ywang96): remove this when huggingface fixes the model repo
-    ("chameleon", "ChameleonForConditionalGeneration"
+    "ChameleonForCausalLM": ("chameleon", "ChameleonForConditionalGeneration"),
-     ),  #TODO(ywang96): fix model name when huggingface fixes it
+    "ChameleonForConditionalGeneration":
+    ("chameleon", "ChameleonForConditionalGeneration"),
     "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),
     "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
     "CohereForCausalLM": ("commandr", "CohereForCausalLM"),

vllm/model_executor/models/chameleon.py

@@ -1,13 +1,17 @@
 from functools import cached_property
-from typing import Any, Dict, Iterable, List, Optional, Tuple
+from typing import (Any, Dict, Iterable, List, Literal, Optional, Tuple,
+                    TypedDict)
 
 import torch
 import torch.nn.functional as F
+from PIL import Image
 from torch import nn
 
 from vllm.attention import Attention, AttentionMetadata
-from vllm.config import CacheConfig
+from vllm.config import CacheConfig, MultiModalConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
+from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
@@ -22,10 +26,114 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.sequence import IntermediateTensors, SamplerOutput
+from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.transformers_utils.configs import ChameleonConfig
+from vllm.multimodal.image import (cached_get_tokenizer,
+                                   repeat_and_pad_image_tokens)
+from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData
+from vllm.transformers_utils.configs import (ChameleonConfig,
+                                             ChameleonVQVAEConfig)
 from vllm.utils import print_warning_once
 
+from .interfaces import SupportsVision
+
+logger = init_logger(__name__)
+
+# These configs are not part of the model config but the preprocessor
+# and processor files, so we hardcode them in the model file for now.
+CHAMELEON_CROP_SIZE_HEIGHT = CHAMELEON_CROP_SIZE_WIDTH = 512
+CHAMELEON_IMAGE_SEQ_LENGTH = 1024
+CHAMELEON_IMAGE_TOKEN_ID = 8711
+CHAMELEON_IMAGE_START_TOKEN_ID = 8197
+CHAMELEON_IMAGE_END_TOKEN_ID = 8196
+CHAMELEON_SEP_TOKEN_ID = 8710
+
+
+class ChameleonImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: torch.Tensor
+    """Shape: `(batch_size, num_channels, height, width)`"""
+
+
+def get_max_chameleon_image_tokens(ctx: InputContext):
+    return CHAMELEON_IMAGE_SEQ_LENGTH
+
+
+def dummy_seq_data_for_chameleon(
+    seq_len: int,
+    *,
+    image_token_id: int,
+    image_feature_size_override: Optional[int] = None,
+):
+    if image_feature_size_override is None:
+        image_feature_size = CHAMELEON_IMAGE_SEQ_LENGTH
+    else:
+        image_feature_size = image_feature_size_override
+
+    token_ids = [image_token_id] * image_feature_size
+    token_ids += [0] * (seq_len - image_feature_size)
+    return SequenceData(token_ids)
+
+
+def dummy_image_for_chameleon(
+    image_width_override: Optional[int] = None,
+    image_height_override: Optional[int] = None,
+):
+    width = CHAMELEON_CROP_SIZE_WIDTH
+    height = CHAMELEON_CROP_SIZE_HEIGHT
+    if image_width_override is not None:
+        width = image_width_override
+    if image_height_override is not None:
+        height = image_height_override
+
+    image = Image.new("RGB", (width, height), color=0)
+    return {"image": image}
+
+
+def dummy_data_for_chameleon(ctx: InputContext, seq_len: int):
+
+    seq_data = dummy_seq_data_for_chameleon(
+        seq_len,
+        image_token_id=CHAMELEON_IMAGE_TOKEN_ID,
+    )
+
+    mm_data = dummy_image_for_chameleon()
+    return seq_data, mm_data
+
+
+def input_processor_for_chameleon(ctx: InputContext, llm_inputs: LLMInputs):
+
+    """
+    Processing input prompt to insert required tokens for image placeholder.
+
+    See https://github.com/huggingface/transformers/blob/0fdea8607d7e01eb0e38a1ebeb7feee30a22f0cf/src/transformers/models/chameleon/processing_chameleon.py#L58
+    """ # noqa
+
+    multi_modal_data = llm_inputs.get("multi_modal_data")
+    if multi_modal_data is None or "image" not in multi_modal_data:
+        return llm_inputs
+
+    model_config = ctx.model_config
+    tokenizer = cached_get_tokenizer(model_config.tokenizer)
+    new_prompt, new_token_ids = repeat_and_pad_image_tokens(
+        tokenizer,
+        llm_inputs.get("prompt"),
+        llm_inputs["prompt_token_ids"],
+        image_token_id=CHAMELEON_IMAGE_TOKEN_ID,
+        repeat_count=CHAMELEON_IMAGE_SEQ_LENGTH,
+        pad_token_left=CHAMELEON_IMAGE_START_TOKEN_ID,
+        pad_token_right=CHAMELEON_IMAGE_END_TOKEN_ID,
+    )
+
+    # Appending sep token for chat mode to follow default processor
+    # behavior
+    new_prompt += tokenizer.sep_token
+    new_token_ids += [CHAMELEON_SEP_TOKEN_ID]
+
+    # NOTE: Create a defensive copy of the original inputs
+    return LLMInputs(prompt_token_ids=new_token_ids,
+                     prompt=new_prompt,
+                     multi_modal_data=multi_modal_data)
+
 
 class ChameleonLayerNorm(nn.LayerNorm):
 
@@ -318,12 +426,333 @@ class ChameleonSwinDecoderLayer(nn.Module):
         return hidden_states, residual
 
 
+# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEVectorQuantizer #noqa
+class ChameleonVQVAEVectorQuantizer(nn.Module):
+
+    def __init__(self, config: ChameleonVQVAEConfig):
+        super().__init__()
+        self.num_embeddings = config.num_embeddings
+        self.embedding_dim = config.embed_dim
+        self.beta = getattr(config, "beta", 0.25)
+
+        self.embedding = nn.Embedding(self.num_embeddings, self.embedding_dim)
+        self.re_embed = self.num_embeddings
+
+    def forward(self, hidden_state: torch.Tensor):
+        hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
+        hidden_state_flattened = hidden_state.view(-1, self.embedding_dim)
+
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+        distances = (
+            torch.sum(hidden_state_flattened**2, dim=1, keepdim=True) +
+            torch.sum(self.embedding.weight**2, dim=1) -
+            2 * torch.einsum("bd,dn->bn", hidden_state_flattened,
+                             self.embedding.weight.transpose(0, 1)))
+
+        min_encoding_indices = torch.argmin(distances, dim=1)
+        hidden_state_quant = self.embedding(min_encoding_indices).view(
+            hidden_state.shape)
+
+        # compute loss for embedding
+        loss = torch.mean((hidden_state_quant.detach() - hidden_state)**
+                          2) + self.beta * torch.mean(
+                              (hidden_state_quant - hidden_state.detach())**2)
+
+        # preserve gradients
+        hidden_state_quant = hidden_state + (hidden_state_quant -
+                                             hidden_state).detach()
+
+        # reshape back to match original input shape
+        hidden_state_quant = hidden_state_quant.permute(0, 3, 1,
+                                                        2).contiguous()
+
+        return hidden_state_quant, loss, min_encoding_indices
+
+
+# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoderConvDownsample #noqa
+class ChameleonVQVAEEncoderConvDownsample(nn.Module):
+
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels,
+                              in_channels,
+                              kernel_size=3,
+                              stride=2,
+                              padding=0)
+
+    def forward(self, hidden_states: torch.Tensor):
+        # no asymmetric padding in torch conv, must do it ourselves
+        hidden_states = F.pad(hidden_states,
+                              pad=(0, 1, 0, 1),
+                              mode="constant",
+                              value=0)
+        hidden_states = self.conv(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoderResnetBlock #noqa
+class ChameleonVQVAEEncoderResnetBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: ChameleonVQVAEConfig,
+        in_channels: int,
+        out_channels=None,
+        conv_shortcut=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = in_channels if out_channels is None \
+            else out_channels
+        self.use_conv_shortcut = conv_shortcut
+
+        self.norm1 = torch.nn.GroupNorm(num_groups=32,
+                                        num_channels=in_channels,
+                                        eps=1e-6,
+                                        affine=True)
+        self.conv1 = torch.nn.Conv2d(in_channels,
+                                     out_channels,
+                                     kernel_size=3,
+                                     stride=1,
+                                     padding=1)
+        self.norm2 = torch.nn.GroupNorm(num_groups=32,
+                                        num_channels=out_channels,
+                                        eps=1e-6,
+                                        affine=True)
+        self.dropout = torch.nn.Dropout(config.dropout)
+        self.conv2 = torch.nn.Conv2d(out_channels,
+                                     out_channels,
+                                     kernel_size=3,
+                                     stride=1,
+                                     padding=1)
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = torch.nn.Conv2d(in_channels,
+                                                     out_channels,
+                                                     kernel_size=3,
+                                                     stride=1,
+                                                     padding=1)
+            else:
+                self.nin_shortcut = torch.nn.Conv2d(in_channels,
+                                                    out_channels,
+                                                    kernel_size=1,
+                                                    stride=1,
+                                                    padding=0)
+
+    def forward(self, hidden_states: torch.Tensor):
+        residual = hidden_states
+        hidden_states = self.norm1(hidden_states)
+        hidden_states *= torch.sigmoid(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        hidden_states = self.norm2(hidden_states)
+        hidden_states *= torch.sigmoid(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                residual = self.conv_shortcut(residual)
+            else:
+                residual = self.nin_shortcut(residual)
+
+        return residual + hidden_states
+
+
+# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoderAttnBlock #noqa
+class ChameleonVQVAEEncoderAttnBlock(nn.Module):
+
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = torch.nn.GroupNorm(num_groups=32,
+                                       num_channels=in_channels,
+                                       eps=1e-6,
+                                       affine=True)
+        self.q = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.k = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.v = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.proj_out = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=1,
+                                        stride=1,
+                                        padding=0)
+
+    def forward(self, hidden_states: torch.Tensor):
+        residual = hidden_states
+        hidden_states = self.norm(hidden_states)
+        query_states = self.q(hidden_states)
+        key_states = self.k(hidden_states)
+        value_states = self.v(hidden_states)
+
+        # compute attention
+        batch_size, channels, height, width = query_states.shape
+        query_states = query_states.reshape(batch_size, channels,
+                                            height * width).permute(0, 2, 1)
+        key_states = key_states.reshape(batch_size, channels, height * width)
+        attn_weights = torch.bmm(query_states, key_states)
+        attn_weights = attn_weights * (int(channels)**(-0.5))
+        attn_weights = F.softmax(attn_weights, dim=2)
+
+        # attend to values
+        value_states = value_states.reshape(batch_size, channels,
+                                            height * width)
+        attn_weights = attn_weights.permute(0, 2, 1)
+        attn_output = torch.bmm(value_states,
+                                attn_weights).reshape(batch_size, channels,
+                                                      height, width)
+
+        attn_output = self.proj_out(attn_output)
+        return residual + attn_output
+
+
+# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoder #noqa
+class ChameleonVQVAEEncoder(nn.Module):
+
+    def __init__(self, config: ChameleonVQVAEConfig):
+        super().__init__()
+
+        self.num_resolutions = len(config.channel_multiplier)
+        self.num_res_blocks = config.num_res_blocks
+        base_channels = config.base_channels
+        resolution = config.resolution
+        in_channels = config.in_channels
+        double_latent = config.double_latent
+        latent_channels = config.latent_channels
+        channel_multiplier = config.channel_multiplier
+
+        self.conv_in = torch.nn.Conv2d(in_channels,
+                                       base_channels,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        curr_res = resolution
+        in_channel_multiplier = (1, ) + tuple(channel_multiplier)
+        self.in_channel_multiplier = in_channel_multiplier
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = base_channels * in_channel_multiplier[i_level]
+            block_out = base_channels * channel_multiplier[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(
+                    ChameleonVQVAEEncoderResnetBlock(
+                        config=config,
+                        in_channels=block_in,
+                        out_channels=block_out,
+                    ))
+                block_in = block_out
+                if (config.attn_resolutions is not None
+                        and curr_res in config.attn_resolutions
+                        and config.attn_type == "vanilla"):
+                    attn.append(ChameleonVQVAEEncoderAttnBlock(block_in))
+
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = ChameleonVQVAEEncoderConvDownsample(block_in)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        self.mid = nn.Module()
+        self.mid.block_1 = ChameleonVQVAEEncoderResnetBlock(
+            config=config,
+            in_channels=block_in,
+            out_channels=block_in,
+        )
+        self.mid.attn_1 = ChameleonVQVAEEncoderAttnBlock(
+            block_in) if config.attn_type == "vanilla" else nn.Identity()
+        self.mid.block_2 = ChameleonVQVAEEncoderResnetBlock(
+            config=config,
+            in_channels=block_in,
+            out_channels=block_in,
+        )
+
+        self.norm_out = torch.nn.GroupNorm(num_groups=32,
+                                           num_channels=block_in,
+                                           eps=1e-6,
+                                           affine=True)
+        self.conv_out = torch.nn.Conv2d(
+            block_in,
+            2 * latent_channels if double_latent else latent_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+        )
+
+    def forward(self, pixel_values: torch.Tensor):
+        # downsampling
+        hidden_states = [self.conv_in(pixel_values)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                hidden_state = self.down[i_level].block[i_block](
+                    hidden_states[-1], )
+                if len(self.down[i_level].attn) > 0:
+                    hidden_state = self.down[i_level].attn[i_block](
+                        hidden_state)
+                hidden_states.append(hidden_state)
+            if i_level != self.num_resolutions - 1:
+                hidden_states.append(self.down[i_level].downsample(
+                    hidden_states[-1]))
+
+        # middle
+        last_hidden_state = hidden_states[-1]
+        last_hidden_state = self.mid.block_1(last_hidden_state)
+        last_hidden_state = self.mid.attn_1(last_hidden_state)
+        last_hidden_state = self.mid.block_2(last_hidden_state)
+
+        # end
+        last_hidden_state = self.norm_out(last_hidden_state)
+        last_hidden_state *= torch.sigmoid(last_hidden_state)
+        last_hidden_state = self.conv_out(last_hidden_state)
+        return last_hidden_state
+
+
+# Adapted from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAE #noqa
+class ChameleonVQVAE(nn.Module):
+
+    def __init__(self, config: ChameleonVQVAEConfig):
+        super().__init__()
+        self.encoder = ChameleonVQVAEEncoder(config)
+        self.quantize = ChameleonVQVAEVectorQuantizer(config)
+        self.quant_conv = torch.nn.Conv2d(config.latent_channels,
+                                          config.embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(config.embed_dim,
+                                               config.latent_channels, 1)
+        self.eval()  # Chameleon's VQ model is frozen
+
+    def encode(
+        self, pixel_values: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        hidden_states = self.encoder(pixel_values)
+        hidden_states = self.quant_conv(hidden_states)
+        quant, emb_loss, indices = self.quantize(hidden_states)
+        return quant, emb_loss, indices
+
+
+# Copied from transformers.models.chameleon.modeling_chameleon.ChameleonImageVocabularyMapping #noqa
 class ChameleonImageVocabularyMapping:
     """
     A class for mapping discrete image tokens from VQGAN to BPE tokens.
     """
 
-    def __init__(self, vocab_map):
+    def __init__(self, vocab_map: Dict[str, int]):
         self.vocab_map = vocab_map
         self.image_token_id = vocab_map.get("<image>")
 
@@ -401,13 +830,23 @@ class ChameleonModel(nn.Module):
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
+        self.vqmodel = ChameleonVQVAE(config.vq_config)
-        # TODO: Support image input
-        # self.vqmodel = ChameleonVQModel(config.vq_config)
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
+    def get_image_tokens(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        """
+        Tokenizes images into discrete tokens with VQGAN module. Converts
+        obtained image tokens into BPE tokens and wraps with "boi" and "eoi"
+        special tokens.
+        """
+        batch_size = pixel_values.shape[0]
+        _, _, image_toks = self.vqmodel.encode(pixel_values)
+        bpe_toks = self.vocabulary_mapping.convert_img2bpe(image_toks)
+        bpe_toks = bpe_toks.view(batch_size, -1)
+        return bpe_toks
+
     def forward(
         self,
         input_ids: Optional[torch.Tensor],
@@ -434,16 +873,22 @@ class ChameleonModel(nn.Module):
         return hidden_states
 
 
-class ChameleonForConditionalGeneration(nn.Module):
+@MULTIMODAL_REGISTRY.register_image_input_mapper()
+@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_chameleon_image_tokens)
+@INPUT_REGISTRY.register_dummy_data(dummy_data_for_chameleon)
+@INPUT_REGISTRY.register_input_processor(input_processor_for_chameleon)
+class ChameleonForConditionalGeneration(nn.Module, SupportsVision):
 
     def __init__(
         self,
         config: ChameleonConfig,
+        multimodal_config: MultiModalConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
+        self.multimodal_config = multimodal_config
         self.model = ChameleonModel(config, cache_config, quant_config)
         self.unpadded_vocab_size = config.vocab_size
         self.lm_head = ParallelLMHead(
@@ -458,6 +903,36 @@ class ChameleonForConditionalGeneration(nn.Module):
                                                 config.vocab_size, logit_scale)
         self.sampler = Sampler()
 
+    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
+
+        expected_dims = (3, CHAMELEON_CROP_SIZE_HEIGHT,
+                         CHAMELEON_CROP_SIZE_WIDTH)
+        actual_dims = tuple(data.shape[1:])
+
+        if actual_dims != expected_dims:
+            expected_expr = ("batch_size", *map(str, expected_dims))
+            raise ValueError(
+                f"The expected shape of pixel values is {expected_expr}. "
+                f"You supplied {tuple(data.shape)}.")
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[ChameleonImagePixelInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+
+        if pixel_values is None:
+            return None
+
+        if not isinstance(pixel_values, torch.Tensor):
+            raise ValueError("Incorrect type of pixel values. "
+                             f"Got type: {type(pixel_values)}")
+
+        return ChameleonImagePixelInputs(
+            type="pixel_values",
+            data=self._validate_pixel_values(pixel_values),
+        )
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -468,10 +943,17 @@ class ChameleonForConditionalGeneration(nn.Module):
         **kwargs,
     ) -> torch.Tensor:
 
-        # TODO (ywang96): Support image input
+        image_input = self._parse_and_validate_image_input(**kwargs)
-        # image_tokens = self.process_image_input(**kwargs)
+
-        # image_mask = input_ids == self.vocabulary_mapping.image_token_id
+        if image_input is not None:
-        # input_ids[special_image_mask] = image_tokens.flatten().to(input_ids.dtype) #noqa
+            assert self.model.vqmodel is not None
+            image_tokens = self.model.get_image_tokens(image_input["data"].to(
+                self.config.torch_dtype))
+            image_token_id = self.model.vocabulary_mapping.image_token_id
+            special_image_mask = input_ids == image_token_id
+            image_tokens = image_tokens.to(input_ids.device, input_ids.dtype)
+            input_ids = input_ids.masked_scatter(special_image_mask,
+                                                 image_tokens)
 
         hidden_states = self.model(input_ids, positions, kv_caches,
                                    attn_metadata)
@@ -511,43 +993,52 @@ class ChameleonForConditionalGeneration(nn.Module):
             if "rotary_emb.inv_freq" in name:
                 continue
 
-            # Skip loading vqgan
-            # TODO: add support for the vision model
-            if "vqmodel" in name:
-                continue
             if ("rotary_emb.cos_cached" in name
                     or "rotary_emb.sin_cached" in name):
                 # Models trained using ColossalAI may include these tensors in
                 # the checkpoint. Skip them.
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+            use_default_weight_loading = False
-                if weight_name not in name:
+            if "vqmodel" in name:
-                    continue
+                if self.model.vqmodel is not None:
-                name = name.replace(weight_name, param_name)
+                    # We only do sharding for language model and
-                # Skip loading extra bias for GPTQ models.
+                    # not vqvae for now.
-                if name.endswith(".bias") and name not in params_dict:
+                    use_default_weight_loading = True
-                    continue
-                param = params_dict[name]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
-                break
             else:
-                # Skip loading extra bias for GPTQ models.
+                for (param_name, weight_name,
-                if name.endswith(".bias") and name not in params_dict:
+                     shard_id) in stacked_params_mapping:
-                    continue
+                    if weight_name not in name:
-                # Remapping the name of FP8 kv-scale.
-                if name.endswith("kv_scale"):
-                    remapped_kv_scale_name = name.replace(
-                        ".kv_scale", ".attn.kv_scale")
-                    if remapped_kv_scale_name not in params_dict:
-                        print_warning_once(
-                            f"Found kv scale in the checkpoint (e.g. {name}), "
-                            "but not found the expected name in the model "
-                            f"(e.g. {remapped_kv_scale_name}). kv-scale is "
-                            "not loaded.")
                         continue
-                    else:
+                    name = name.replace(weight_name, param_name)
-                        name = remapped_kv_scale_name
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param, loaded_weight, shard_id)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    # Remapping the name of FP8 kv-scale.
+                    if name.endswith("kv_scale"):
+                        remapped_kv_scale_name = name.replace(
+                            ".kv_scale", ".attn.kv_scale")
+                        if remapped_kv_scale_name not in params_dict:
+                            print_warning_once(
+                                "Found kv scale in the checkpoint (e.g. "
+                                f"{name}), but not found the expected name in "
+                                f"the model (e.g. {remapped_kv_scale_name}). "
+                                "kv-scale is not loaded.")
+                            continue
+                        else:
+                            name = remapped_kv_scale_name
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            if use_default_weight_loading and name in params_dict:
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/transformers_utils/configs/__init__.py

@@ -1,4 +1,5 @@
-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

@@ -1,3 +1,5 @@
+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