[VLM] Support multimodal inputs for Florence-2 models (#13320)

docs/source/models/supported_models.md

@@ -715,6 +715,13 @@ See [this page](#generative-models) for more information on how to use generativ
   *
   * ✅︎
   * ✅︎
+- * `Florence2ForConditionalGeneration`
+  * Florence-2
+  * T + I
+  * `microsoft/Florence-2-base`, `microsoft/Florence-2-large` etc.
+  *
+  *
+  *
 - * `FuyuForCausalLM`
   * Fuyu
   * T + I

examples/offline_inference/florence2_inference.py

@@ -1,34 +1,45 @@
 # SPDX-License-Identifier: Apache-2.0
-'''
+"""
 Demonstrate prompting of text-to-text
 encoder/decoder models, specifically Florence-2
-'''
+"""
 # TODO(Isotr0py):
 # Move to offline_inference/vision_language.py
 # after porting vision backbone
 from vllm import LLM, SamplingParams
-
-dtype = "float"
+from vllm.assets.image import ImageAsset
 
 # Create a Florence-2 encoder/decoder model instance
 llm = LLM(
-    model="microsoft/Florence-2-base",
-    tokenizer="facebook/bart-base",
-    dtype=dtype,
+    model="microsoft/Florence-2-large",
+    tokenizer="facebook/bart-large",
+    max_num_seqs=8,
     trust_remote_code=True,
 )
 
 prompts = [
-    "<CAPTION>", "<DETAILED_CAPTION>", "<MORE_DETAILED_CAPTION>",
-    "<CAPTION_TO_PHRASE_GROUNDING>", "<OD>", "<DENSE_REGION_CAPTION>",
-    "<REGION_PROPOSAL>", "<OCR>", "<OCR_WITH_REGION>"
+    {   # implicit prompt with task token
+        "prompt": "<DETAILED_CAPTION>",
+        "multi_modal_data": {
+            "image": ImageAsset("stop_sign").pil_image
+        },
+    },
+    {   # explicit encoder/decoder prompt
+        "encoder_prompt": {
+            "prompt": "Describe in detail what is shown in the image.",
+            "multi_modal_data": {
+                "image": ImageAsset("cherry_blossom").pil_image
+            },
+        },
+        "decoder_prompt": "",
+    },
 ]
 # Create a sampling params object.
 sampling_params = SamplingParams(
     temperature=0,
     top_p=1.0,
     min_tokens=0,
-    max_tokens=20,
+    max_tokens=128,
 )
 
 # Generate output tokens from the prompts. The output is a list of
@@ -38,9 +49,5 @@ outputs = llm.generate(prompts, sampling_params)
 
 # Print the outputs.
 for output in outputs:
-    prompt = output.prompt
-    encoder_prompt = output.encoder_prompt
     generated_text = output.outputs[0].text
-    print(f"Encoder prompt: {encoder_prompt!r}, "
-          f"Decoder prompt: {prompt!r}, "
-          f"Generated text: {generated_text!r}")
+    print(f"Generated text: {generated_text!r}")

examples/offline_inference/vision_language.py

@@ -82,6 +82,22 @@ def run_deepseek_vl2(question: str, modality: str):
     return llm, prompt, stop_token_ids
 
 
+# Florence2
+def run_florence2(question: str, modality: str):
+    assert modality == "image"
+
+    llm = LLM(model="microsoft/Florence-2-large",
+              tokenizer="facebook/bart-large",
+              max_num_seqs=8,
+              trust_remote_code=True,
+              dtype="bfloat16",
+              disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache)
+
+    prompt = "<MORE_DETAILED_CAPTION>"
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
 # Fuyu
 def run_fuyu(question: str, modality: str):
     assert modality == "image"
@@ -571,6 +587,7 @@ model_example_map = {
     "blip-2": run_blip2,
     "chameleon": run_chameleon,
     "deepseek_vl_v2": run_deepseek_vl2,
+    "florence2": run_florence2,
     "fuyu": run_fuyu,
     "glm4v": run_glm4v,
     "h2ovl_chat": run_h2ovl,

tests/conftest.py

@@ -600,8 +600,8 @@ class HfRunner:
             if images is not None and images[i] is not None:
                 processor_kwargs["images"] = images[i]
 
-            encoder_input_ids = self.wrap_device(
-                self.processor(**processor_kwargs).input_ids,
+            encoder_inputs = self.wrap_device(
+                self.processor(**processor_kwargs),
                 device=self.model.device.type,
             )
 
@@ -615,13 +615,13 @@ class HfRunner:
                 )
 
             output = self.model.generate(
-                encoder_input_ids,
                 decoder_input_ids=decoder_input_ids,
                 use_cache=True,
                 do_sample=False,
                 max_new_tokens=max_tokens,
                 output_hidden_states=True,
                 return_dict_in_generate=True,
+                **encoder_inputs,
                 **kwargs,
             )
 

tests/models/decoder_only/audio_language/test_ultravox.py

@@ -15,7 +15,7 @@ from ....conftest import HfRunner, VllmRunner
 from ....utils import RemoteOpenAIServer
 from ...utils import check_logprobs_close
 
-MODEL_NAME = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
+MODEL_NAME = "fixie-ai/ultravox-v0_4"
 
 AudioTuple = Tuple[np.ndarray, int]
 
@@ -187,7 +187,7 @@ def run_multi_audio_test(
 
 
 @pytest.mark.core_model
-@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [128])
 @pytest.mark.parametrize("num_logprobs", [5])
 @pytest.mark.parametrize("vllm_kwargs", [

tests/models/encoder_decoder/vision_language/test_florence2.py

@@ -1,52 +1,59 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from functools import partial
-from typing import List, Optional, Tuple, Type
+from typing import Optional, Type
 
 import pytest
 from PIL import Image
 
-from vllm.inputs.data import ExplicitEncoderDecoderPrompt
+from vllm.inputs.data import ExplicitEncoderDecoderPrompt, TextPrompt
+from vllm.multimodal.image import rescale_image_size
 from vllm.sequence import SampleLogprobs
 
-from ....conftest import HfRunner, VllmRunner
+from ....conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets
 from ...utils import check_logprobs_close
 
-Florence2Prompt = partial(ExplicitEncoderDecoderPrompt,
-                          decoder_prompt=None,
-                          mm_processor_kwargs=None)
-
 MODELS = ["microsoft/Florence-2-base"]
 # Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
 # Therefore, we borrow the BartTokenizer from the original Bart model
 TOKENIZER = "facebook/bart-base"
-PROMPTS = [
-    Florence2Prompt(encoder_prompt="<CAPTION>"),
-    Florence2Prompt(encoder_prompt="<DETAILED_CAPTION>"),
-    Florence2Prompt(encoder_prompt="<MORE_DETAILED_CAPTION>"),
-    Florence2Prompt(encoder_prompt="<CAPTION_TO_PHRASE_GROUNDING>"),
-    Florence2Prompt(encoder_prompt="<DENSE_REGION_CAPTION>"),
-    Florence2Prompt(encoder_prompt="<REGION_PROPOSAL>"),
-    Florence2Prompt(encoder_prompt="<OCR_WITH_REGION>"),
-    Florence2Prompt(encoder_prompt="<OCR>"),
-    Florence2Prompt(encoder_prompt="<OD>"),
-]
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
+    "stop_sign":
+    "<CAPTION>",  # special task token
+    "cherry_blossom":
+    "Describe in detail what is shown in the image.",
+})
 
 
-def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
-                                         Optional[SampleLogprobs]], ):
-    """Sanitize vllm output to be comparable with hf output."""
-    output_ids, output_str, out_logprobs = vllm_output
+def get_hf_images_prompts(
+    prompts_: list[ExplicitEncoderDecoderPrompt[str, TextPrompt]],
+) -> tuple[list[ExplicitEncoderDecoderPrompt[str, str]], list[Image.Image]]:
+    prompts, images = [], []
+    for prompt in prompts_:
+        encoder_prompt = prompt["encoder_prompt"]
+        prompts.append(
+            ExplicitEncoderDecoderPrompt(
+                encoder_prompt=encoder_prompt["prompt"],
+                decoder_prompt=None,
+            ))
+        images.append(encoder_prompt["multi_modal_data"]["image"])
+    return prompts, images
 
-    hf_output_str = "</s><s>" + output_str + "</s>"
 
-    return output_ids, hf_output_str, out_logprobs
+def hf_to_vllm_output(hf_output: tuple[list[int], str,
+                                       Optional[SampleLogprobs]]):
+    """Sanitize hf output to be comparable with vllm output."""
+    output_ids, output_str, out_logprobs = hf_output
+
+    output_str = output_str.replace("</s>", "").replace("<s>", "")
+    output_ids = [ids for ids in output_ids if ids not in [0, 2]]
+
+    return output_ids, output_str, out_logprobs
 
 
 def run_test(
     hf_runner: Type[HfRunner],
     vllm_runner: Type[VllmRunner],
-    prompts: List[ExplicitEncoderDecoderPrompt],
+    inputs: list[list[ExplicitEncoderDecoderPrompt]],
     model: str,
     *,
     dtype: str,
@@ -56,46 +63,76 @@ def run_test(
     distributed_executor_backend: Optional[str] = None,
 ) -> None:
     with vllm_runner(model,
+                     max_num_seqs=8,
                      tokenizer_name=TOKENIZER,
                      dtype=dtype,
                      tensor_parallel_size=tensor_parallel_size,
                      distributed_executor_backend=distributed_executor_backend,
                      enforce_eager=True) as vllm_model:
-        vllm_outputs = vllm_model.generate_encoder_decoder_greedy_logprobs(
-            prompts, max_tokens, num_logprobs)
+        vllm_outputs_per_case = [
+            vllm_model.generate_encoder_decoder_greedy_logprobs(
+                prompts, max_tokens, num_logprobs=num_logprobs)
+            for prompts in inputs
+        ]
+
+    hf_inputs = [get_hf_images_prompts(prompts) for prompts in inputs]
 
-    # Florence-2 processors require image inputs
-    dummy_image = Image.new(mode="RGB", size=(2, 2))
     with hf_runner(model, dtype=dtype, skip_tokenizer_init=True) as hf_model:
         hf_model.model.get_output_embeddings = lambda: \
             hf_model.model.language_model.lm_head
-        hf_outputs = (hf_model.generate_encoder_decoder_greedy_logprobs_limit(
-            prompts,
-            max_tokens,
-            num_logprobs,
-            images=[dummy_image] * len(prompts),
-        ))
+        hf_outputs_per_case = [
+            hf_model.generate_encoder_decoder_greedy_logprobs_limit(
+                prompts, max_tokens, num_logprobs=num_logprobs, images=images)
+            for prompts, images in hf_inputs
+        ]
 
-    check_logprobs_close(
-        outputs_0_lst=hf_outputs,
-        outputs_1_lst=[
-            vllm_to_hf_output(vllm_output) for vllm_output in vllm_outputs
-        ],
-        name_0="hf",
-        name_1="vllm",
-    )
+    for hf_outputs, vllm_outputs in zip(hf_outputs_per_case,
+                                        vllm_outputs_per_case):
+        check_logprobs_close(
+            outputs_0_lst=[hf_to_vllm_output(output) for output in hf_outputs],
+            outputs_1_lst=vllm_outputs,
+            name_0="hf",
+            name_1="vllm",
+        )
 
 
+@pytest.mark.core_model
 @pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float", "bfloat16"])
+@pytest.mark.parametrize(
+    "size_factors",
+    [
+        # No image
+        [],
+        # 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", ["float"])
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])
-def test_models(hf_runner, vllm_runner, model, dtype, max_tokens,
-                num_logprobs) -> None:
+def test_models(hf_runner: Type[HfRunner], vllm_runner: Type[VllmRunner],
+                image_assets: _ImageAssets, model: str,
+                size_factors: list[int], dtype: str, max_tokens: int,
+                num_logprobs: int) -> None:
+    images = [asset.pil_image for asset in image_assets]
+
+    inputs_per_image = [[
+        ExplicitEncoderDecoderPrompt(
+            encoder_prompt=TextPrompt(
+                prompt=prompt,
+                multi_modal_data={"image": rescale_image_size(image, factor)}),
+            decoder_prompt=None,
+        ) for factor in size_factors
+    ] for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+
     run_test(
         hf_runner,
         vllm_runner,
-        PROMPTS,
+        inputs_per_image,
         model,
         dtype=dtype,
         max_tokens=max_tokens,

tests/models/multimodal/processing/test_common.py

@@ -29,8 +29,8 @@ def _test_processing_correctness(
     model_config = ModelConfig(
         model_id,
         task="auto",
-        tokenizer=model_id,
-        tokenizer_mode="auto",
+        tokenizer=model_info.tokenizer or model_id,
+        tokenizer_mode=model_info.tokenizer_mode,
         trust_remote_code=model_info.trust_remote_code,
         seed=0,
         dtype="float16",
@@ -151,6 +151,7 @@ def _test_processing_correctness(
     "Salesforce/blip2-opt-2.7b",
     "facebook/chameleon-7b",
     "deepseek-ai/deepseek-vl2-tiny",
+    "microsoft/Florence-2-base",
     "adept/fuyu-8b",
     "THUDM/glm-4v-9b",
     "h2oai/h2ovl-mississippi-800m",

tests/models/registry.py

@@ -193,11 +193,6 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     # [Encoder-decoder]
     "BartModel": _HfExamplesInfo("facebook/bart-base"),
     "BartForConditionalGeneration": _HfExamplesInfo("facebook/bart-large-cnn"),
-    # Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
-    # Therefore, we borrow the BartTokenizer from the original Bart model
-    "Florence2ForConditionalGeneration": _HfExamplesInfo("microsoft/Florence-2-base",  # noqa: E501
-                                                         tokenizer="facebook/bart-base",
-                                                         trust_remote_code=True),  # noqa: E501
 }
 
 _EMBEDDING_EXAMPLE_MODELS = {
@@ -288,6 +283,11 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                      extras={"v0.5": "fixie-ai/ultravox-v0_5-llama-3_2-1b"},  # noqa: E501
                                      trust_remote_code=True),
     # [Encoder-decoder]
+    # Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
+    # Therefore, we borrow the BartTokenizer from the original Bart model
+    "Florence2ForConditionalGeneration": _HfExamplesInfo("microsoft/Florence-2-base",  # noqa: E501
+                                                         tokenizer="facebook/bart-base",
+                                                         trust_remote_code=True),  # noqa: E501
     "MllamaForConditionalGeneration": _HfExamplesInfo("meta-llama/Llama-3.2-11B-Vision-Instruct"),  # noqa: E501
     "WhisperForConditionalGeneration": _HfExamplesInfo("openai/whisper-large-v3"),  # noqa: E501
 }

vllm/model_executor/models/bart.py

@@ -588,8 +588,12 @@ class BartEncoder(nn.Module):
 
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
 
-    def forward(self, input_ids: torch.Tensor,
-                positions: torch.Tensor) -> torch.Tensor:
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
         r"""
         Args:
             input_ids
@@ -602,7 +606,8 @@ class BartEncoder(nn.Module):
             Decoder output torch.Tensor
         """
         # retrieve input_ids and inputs_embeds
-        inputs_embeds = self.embed_tokens(input_ids)
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
 
         embed_pos = self.embed_positions(positions)
         embed_pos = embed_pos.to(inputs_embeds.device)
@@ -661,9 +666,13 @@ class BartDecoder(nn.Module):
 
         self.layernorm_embedding = nn.LayerNorm(config.d_model)
 
-    def forward(self, decoder_input_ids: torch.Tensor,
-                decoder_positions: torch.Tensor,
-                encoder_hidden_states: Optional[torch.Tensor]) -> torch.Tensor:
+    def forward(
+        self,
+        decoder_input_ids: torch.Tensor,
+        decoder_positions: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
         r"""
         Args:
             decoder_input_ids
@@ -677,8 +686,10 @@ class BartDecoder(nn.Module):
         Returns:
             Decoder output torch.Tensor
         """
-
-        inputs_embeds = self.embed_tokens(decoder_input_ids)
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(decoder_input_ids)
+        else:
+            decoder_positions = inputs_embeds[:, -1]
 
         # embed positions
         embed_pos = self.embed_positions(decoder_positions)

vllm/model_executor/models/florence2.py

@@ -1,10 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import math
-from typing import Iterable, Optional, Set, Tuple
+from functools import cached_property
+from typing import (Iterable, List, Literal, Mapping, Optional, OrderedDict,
+                    Set, Tuple, TypedDict, Union)
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import BatchFeature, PretrainedConfig
 
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -14,11 +19,567 @@ from vllm.model_executor.models.bart import (BartDecoder, BartEncoder,
                                              BartParallelLMHead,
                                              BartScaledWordEmbedding)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY, NestedTensors
+from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargs
+from vllm.multimodal.parse import MultiModalDataDict, MultiModalDataItems
+from vllm.multimodal.processing import (BaseProcessingInfo,
+                                        EncDecMultiModalProcessor,
+                                        PromptReplacement,
+                                        PromptReplacementDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 
-from .utils import AutoWeightsLoader
+from .interfaces import SupportsMultiModal
+from .utils import AutoWeightsLoader, flatten_bn, merge_multimodal_embeddings
 
 
+class Florence2ImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: torch.Tensor
+    """Shape: (batch_size, num_channel, height, width)"""
+
+
+# ViT implementation are all copied from
+# https://huggingface.co/microsoft/Florence-2-base/blob/main/modeling_florence2.py
+class LearnedAbsolutePositionEmbedding2D(nn.Module):
+    """
+    This module learns positional embeddings up to a fixed maximum size.
+    """
+
+    def __init__(self, embedding_dim=256, num_pos=50):
+        super().__init__()
+        self.row_embeddings = nn.Embedding(num_pos, embedding_dim // 2)
+        self.column_embeddings = nn.Embedding(
+            num_pos, embedding_dim - (embedding_dim // 2))
+
+    def forward(self, pixel_values):
+        """
+        pixel_values: (batch_size, height, width, num_channels) 
+        returns: (batch_size, height, width, embedding_dim * 2)
+        """
+        if len(pixel_values.shape) != 4:
+            raise ValueError('pixel_values must be a 4D tensor')
+        height, width = pixel_values.shape[1:3]
+        width_values = torch.arange(width, device=pixel_values.device)
+        height_values = torch.arange(height, device=pixel_values.device)
+        x_emb = self.column_embeddings(width_values)
+        y_emb = self.row_embeddings(height_values)
+        # (height, width, embedding_dim * 2)
+        pos = torch.cat([
+            x_emb.unsqueeze(0).repeat(height, 1, 1),
+            y_emb.unsqueeze(1).repeat(1, width, 1)
+        ],
+                        dim=-1)
+        # (embedding_dim * 2, height, width)
+        pos = pos.permute(2, 0, 1)
+        pos = pos.unsqueeze(0)
+        # (batch_size, embedding_dim * 2, height, width)
+        pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
+        # (batch_size, height, width, embedding_dim * 2)
+        pos = pos.permute(0, 2, 3, 1)
+        return pos
+
+
+class PositionalEmbeddingCosine1D(nn.Module):
+    """
+    This class implements a very simple positional encoding. It follows closely
+    the encoder from the link below:
+    https://pytorch.org/tutorials/beginner/translation_transformer.html
+    Args:
+        embed_dim: The dimension of the embeddings.
+        dropout_prob: The dropout probability.
+        max_seq_len: The maximum length to precompute the positional encodings.
+    """
+
+    def __init__(self, embed_dim: int = 512, max_seq_len: int = 1024) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.max_seq_len = max_seq_len
+        # Generate the sinusoidal arrays.
+        factor = math.log(10000)
+        denominator = torch.exp(-factor * torch.arange(0, self.embed_dim, 2) /
+                                self.embed_dim)
+        # Matrix where rows correspond to a positional embedding as a function
+        # of the position index (i.e., the row index).
+        frequencies = \
+            torch.arange(0, self.max_seq_len) \
+            .reshape(self.max_seq_len, 1) * denominator
+        pos_idx_to_embed = torch.zeros((self.max_seq_len, self.embed_dim))
+        # Populate uneven entries.
+        pos_idx_to_embed[:, 0::2] = torch.sin(frequencies)
+        pos_idx_to_embed[:, 1::2] = torch.cos(frequencies)
+        # Save the positional embeddings in a constant buffer.
+        # self.register_buffer("pos_idx_to_embed", pos_idx_to_embed)
+        self.pos_idx_to_embed = nn.Parameter(pos_idx_to_embed,
+                                             requires_grad=False)
+
+    def forward(self, seq_embeds: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            seq_embeds: The sequence embeddings in order. Allowed size:
+                1. [T, D], where T is the length of the sequence, and D is the
+                frame embedding dimension.
+                2. [B, T, D], where B is the batch size and T and D are the
+                same as above.
+        Returns a tensor of with the same dimensions as the input: i.e.,
+        [1, T, D] or [T, D].
+        """
+        shape_len = len(seq_embeds.shape)
+        assert 2 <= shape_len <= 3
+        len_seq = seq_embeds.size(-2)
+        assert len_seq <= self.max_seq_len
+        pos_embeds = self.pos_idx_to_embed[0:seq_embeds.size(-2), :]
+        # Adapt pre-computed positional embeddings to the input.
+        if shape_len == 3:
+            pos_embeds = pos_embeds.view(
+                (1, pos_embeds.size(0), pos_embeds.size(1)))
+        return pos_embeds
+
+
+class MySequential(nn.Sequential):
+
+    def forward(self, *inputs):
+        for module in self._modules.values():
+            if isinstance(inputs, tuple):
+                inputs = module(*inputs)
+            else:
+                inputs = module(inputs)
+        return inputs
+
+
+class PreNorm(nn.Module):
+
+    def __init__(self, norm, fn):
+        super().__init__()
+        self.norm = norm
+        self.fn = fn
+
+    def forward(self, x, *args, **kwargs):
+        shortcut = x
+        if self.norm is not None:
+            x, size = self.fn(self.norm(x), *args, **kwargs)
+        else:
+            x, size = self.fn(x, *args, **kwargs)
+
+        x = shortcut + x
+
+        return x, size
+
+
+class Mlp(nn.Module):
+
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.GELU,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.net = nn.Sequential(
+            OrderedDict([("fc1", nn.Linear(in_features, hidden_features)),
+                         ("act", act_layer()),
+                         ("fc2", nn.Linear(hidden_features, out_features))]))
+
+    def forward(self, x, size):
+        return self.net(x), size
+
+
+class DepthWiseConv2d(nn.Module):
+
+    def __init__(
+        self,
+        dim_in,
+        kernel_size,
+        padding,
+        stride,
+        bias=True,
+    ):
+        super().__init__()
+        self.dw = nn.Conv2d(dim_in,
+                            dim_in,
+                            kernel_size=kernel_size,
+                            padding=padding,
+                            groups=dim_in,
+                            stride=stride,
+                            bias=bias)
+
+    def forward(self, x, size):
+        B, N, C = x.shape
+        H, W = size
+        assert N == H * W
+
+        x = self.dw(x.transpose(1, 2).view(B, C, H, W))
+        size = (x.size(-2), x.size(-1))
+        x = x.flatten(2).transpose(1, 2)
+        return x, size
+
+
+class ConvEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+
+    def __init__(self,
+                 patch_size=7,
+                 in_chans=3,
+                 embed_dim=64,
+                 stride=4,
+                 padding=2,
+                 norm_layer=None,
+                 pre_norm=True):
+        super().__init__()
+        self.patch_size = patch_size
+
+        self.proj = nn.Conv2d(in_chans,
+                              embed_dim,
+                              kernel_size=patch_size,
+                              stride=stride,
+                              padding=padding)
+
+        dim_norm = in_chans if pre_norm else embed_dim
+        self.norm = norm_layer(dim_norm) if norm_layer else None
+
+        self.pre_norm = pre_norm
+
+    def forward(self, x, size):
+        H, W = size
+        if len(x.size()) == 3:
+            if self.norm and self.pre_norm:
+                x = self.norm(x)
+            x = rearrange(x, 'b (h w) c -> b c h w', h=H, w=W)
+
+        x = self.proj(x)
+
+        _, _, H, W = x.shape
+        x = rearrange(x, 'b c h w -> b (h w) c')
+        if self.norm and not self.pre_norm:
+            x = self.norm(x)
+
+        return x, (H, W)
+
+
+class ChannelAttention(nn.Module):
+
+    def __init__(self, dim, groups=8, qkv_bias=True):
+        super().__init__()
+
+        self.groups = groups
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x, size):
+        B, N, C = x.shape
+
+        qkv = self.qkv(x).reshape(B, N, 3, self.groups,
+                                  C // self.groups).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        q = q * (float(N)**-0.5)
+        attention = q.transpose(-1, -2) @ k
+        attention = attention.softmax(dim=-1)
+        x = (attention @ v.transpose(-1, -2)).transpose(-1, -2)
+        x = x.transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        return x, size
+
+
+class ChannelBlock(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 groups,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 drop_path_rate=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm,
+                 conv_at_attn=True,
+                 conv_at_ffn=True):
+        super().__init__()
+
+        self.conv1 = PreNorm(None, DepthWiseConv2d(
+            dim, 3, 1, 1)) if conv_at_attn else None
+        self.channel_attn = PreNorm(
+            norm_layer(dim),
+            ChannelAttention(dim, groups=groups, qkv_bias=qkv_bias),
+        )
+        self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1,
+                                                   1)) if conv_at_ffn else None
+        self.ffn = PreNorm(
+            norm_layer(dim),
+            Mlp(in_features=dim,
+                hidden_features=int(dim * mlp_ratio),
+                act_layer=act_layer),
+        )
+
+    def forward(self, x, size):
+        if self.conv1:
+            x, size = self.conv1(x, size)
+        x, size = self.channel_attn(x, size)
+
+        if self.conv2:
+            x, size = self.conv2(x, size)
+        x, size = self.ffn(x, size)
+
+        return x, size
+
+
+def window_partition(x, window_size: int):
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size,
+               C)
+    windows = x.permute(0, 1, 3, 2, 4,
+                        5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, batch_size: int, window_size: int, H: int, W: int):
+    B = batch_size
+
+    x = windows.view(B, H // window_size, W // window_size, window_size,
+                     window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+
+    def __init__(self, dim, num_heads, window_size, qkv_bias=True):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = float(head_dim)**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, size):
+
+        H, W = size
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        x = window_partition(x, self.window_size)
+        x = x.view(-1, self.window_size * self.window_size, C)
+
+        # W-MSA/SW-MSA
+        # attn_windows = self.attn(x_windows)
+
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
+                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+        attn = self.softmax(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+
+        # merge windows
+        x = x.view(-1, self.window_size, self.window_size, C)
+        x = window_reverse(x, B, self.window_size, Hp, Wp)
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        return x, size
+
+
+class SpatialBlock(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 window_size,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 drop_path_rate=0.,
+                 act_layer=nn.GELU,
+                 norm_layer=nn.LayerNorm,
+                 conv_at_attn=True,
+                 conv_at_ffn=True):
+        super().__init__()
+
+        self.conv1 = PreNorm(None, DepthWiseConv2d(
+            dim, 3, 1, 1)) if conv_at_attn else None
+        self.window_attn = PreNorm(
+            norm_layer(dim),
+            WindowAttention(dim, num_heads, window_size, qkv_bias=qkv_bias),
+        )
+        self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1,
+                                                   1)) if conv_at_ffn else None
+        self.ffn = PreNorm(
+            norm_layer(dim),
+            Mlp(in_features=dim,
+                hidden_features=int(dim * mlp_ratio),
+                act_layer=act_layer),
+        )
+
+    def forward(self, x, size):
+        if self.conv1:
+            x, size = self.conv1(x, size)
+        x, size = self.window_attn(x, size)
+
+        if self.conv2:
+            x, size = self.conv2(x, size)
+        x, size = self.ffn(x, size)
+        return x, size
+
+
+class DaViT(nn.Module):
+
+    def __init__(
+        self,
+        in_chans=3,
+        num_classes=1000,
+        depths=(1, 1, 3, 1),
+        patch_size=(7, 2, 2, 2),
+        patch_stride=(4, 2, 2, 2),
+        patch_padding=(3, 0, 0, 0),
+        patch_prenorm=(False, False, False, False),
+        embed_dims=(64, 128, 192, 256),
+        num_heads=(3, 6, 12, 24),
+        num_groups=(3, 6, 12, 24),
+        window_size=7,
+        mlp_ratio=4.,
+        qkv_bias=True,
+        drop_path_rate=0.1,
+        norm_layer=nn.LayerNorm,
+        enable_checkpoint=False,
+        conv_at_attn=True,
+        conv_at_ffn=True,
+    ):
+        super().__init__()
+
+        self.num_classes = num_classes
+        self.embed_dims = embed_dims
+        self.num_heads = num_heads
+        self.num_groups = num_groups
+        self.num_stages = len(self.embed_dims)
+        self.enable_checkpoint = enable_checkpoint
+        assert self.num_stages == len(self.num_heads) == len(self.num_groups)
+
+        num_stages = len(embed_dims)
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate,
+                                             sum(depths) * 2)
+        ]
+
+        depth_offset = 0
+        convs = []
+        blocks = []
+        for i in range(num_stages):
+            conv_embed = ConvEmbed(
+                patch_size=patch_size[i],
+                stride=patch_stride[i],
+                padding=patch_padding[i],
+                in_chans=in_chans if i == 0 else self.embed_dims[i - 1],
+                embed_dim=self.embed_dims[i],
+                norm_layer=norm_layer,
+                pre_norm=patch_prenorm[i])
+            convs.append(conv_embed)
+
+            block = MySequential(*[
+                MySequential(
+                    OrderedDict([('spatial_block',
+                                  SpatialBlock(
+                                      embed_dims[i],
+                                      num_heads[i],
+                                      window_size,
+                                      drop_path_rate=dpr[depth_offset + j * 2],
+                                      qkv_bias=qkv_bias,
+                                      mlp_ratio=mlp_ratio,
+                                      conv_at_attn=conv_at_attn,
+                                      conv_at_ffn=conv_at_ffn,
+                                  )),
+                                 ('channel_block',
+                                  ChannelBlock(
+                                      embed_dims[i],
+                                      num_groups[i],
+                                      drop_path_rate=dpr[depth_offset + j * 2 +
+                                                         1],
+                                      qkv_bias=qkv_bias,
+                                      mlp_ratio=mlp_ratio,
+                                      conv_at_attn=conv_at_attn,
+                                      conv_at_ffn=conv_at_ffn,
+                                  ))])) for j in range(depths[i])
+            ])
+            blocks.append(block)
+            depth_offset += depths[i] * 2
+
+        self.convs = nn.ModuleList(convs)
+        self.blocks = nn.ModuleList(blocks)
+
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+
+    @property
+    def dim_out(self):
+        return self.embed_dims[-1]
+
+    def forward_features_unpool(self, x):
+        """
+        forward until avg pooling 
+        Args:
+            x (_type_): input image tensor
+        """
+        input_size = (x.size(2), x.size(3))
+        for conv, block in zip(self.convs, self.blocks):
+            x, input_size = conv(x, input_size)
+            x, input_size = block(x, input_size)
+        return x
+
+    def forward_features(self, x):
+        x = self.forward_features_unpool(x)
+
+        # (batch_size, num_tokens, token_dim)
+        x = self.avgpool(x.transpose(1, 2))
+        # (batch_size, 1, num_tokens)
+        x = torch.flatten(x, 1)
+        x = self.norms(x)
+
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+        return x
+
+    @classmethod
+    def from_config(cls, config):
+        return cls(
+            depths=config.depths,
+            embed_dims=config.dim_embed,
+            num_heads=config.num_heads,
+            num_groups=config.num_groups,
+            patch_size=config.patch_size,
+            patch_stride=config.patch_stride,
+            patch_padding=config.patch_padding,
+            patch_prenorm=config.patch_prenorm,
+            drop_path_rate=config.drop_path_rate,
+            window_size=config.window_size,
+        )
+
+
+# Language backbone and processor implementation
 class Florence2LanguageModel(nn.Module):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@@ -47,9 +608,14 @@ class Florence2LanguageModel(nn.Module):
             self.encoder.embed_tokens.weight = self.shared.weight
             self.decoder.embed_tokens.weight = self.shared.weight
 
-    def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
-                encoder_input_ids: torch.Tensor,
-                encoder_positions: torch.Tensor) -> torch.Tensor:
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        encoder_input_ids: torch.Tensor,
+        encoder_positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
         r"""
         Args:
             input_ids
@@ -68,11 +634,12 @@ class Florence2LanguageModel(nn.Module):
 
         encoder_hidden_states = None
 
-        if encoder_input_ids.numel() > 0:
+        if inputs_embeds is not None or encoder_input_ids.numel() > 0:
             # Run encoder attention if a non-zero number of encoder tokens
             # are provided as input
             encoder_hidden_states = self.encoder(input_ids=encoder_input_ids,
-                                                 positions=encoder_positions)
+                                                 positions=encoder_positions,
+                                                 inputs_embeds=inputs_embeds)
 
         # decoder outputs consists of
         # (dec_features, past_key_value, dec_hidden, dec_attn)
@@ -112,6 +679,7 @@ class Florence2LanguageForConditionalGeneration(nn.Module):
         positions: torch.Tensor,
         encoder_input_ids: torch.Tensor,
         encoder_positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> torch.Tensor:
         r"""
@@ -127,8 +695,15 @@ class Florence2LanguageForConditionalGeneration(nn.Module):
         Returns:
             Output torch.Tensor
         """
-        return self.model(input_ids, positions, encoder_input_ids,
-                          encoder_positions)
+
+        return self.model(input_ids,
+                          positions,
+                          encoder_input_ids,
+                          encoder_positions,
+                          inputs_embeds=inputs_embeds)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.encoder.embed_tokens(input_ids)
 
     def compute_logits(
         self,
@@ -177,21 +752,312 @@ class Florence2LanguageForConditionalGeneration(nn.Module):
         return loaded_params
 
 
-class Florence2ForConditionalGeneration(nn.Module):
+class Florence2ProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config()
+
+    def get_hf_processor(self):
+        return self.ctx.get_hf_processor()
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": 1}
+
+    def get_max_image_tokens(self) -> int:
+        processor_config = self.ctx.get_hf_image_processor_config()
+        return processor_config["image_seq_length"]
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        return {"image": self.get_max_image_tokens()}
+
+
+class Florence2DummyInputsBuilder(
+        BaseDummyInputsBuilder[Florence2ProcessingInfo]):
+
+    def get_dummy_processor_inputs(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        num_images = mm_counts.get("image", 0)
+
+        target_width = target_height = self.info.get_hf_config().projection_dim
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+        return ProcessorInputs(
+            prompt_text="",
+            mm_data=mm_data,
+        )
+
+
+class Florence2MultiModalProcessor(
+        EncDecMultiModalProcessor[Florence2ProcessingInfo]):
+
+    def _hf_processor_applies_repl(
+        self,
+        prompt_text: str,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> bool:
+        return False
+
+    def create_encoder_prompt(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+    ) -> Union[str, list[int]]:
+        return prompt
+
+    def create_decoder_prompt(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+    ) -> Union[str, list[int]]:
+        return [self.info.get_hf_config().eos_token_id]
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if mm_data:
+            processed_outputs = super()._call_hf_processor(
+                prompt, mm_data, mm_kwargs)
+        else:
+            hf_processor = self.info.get_hf_processor()
+            tokenizer = hf_processor.tokenizer
+            prompt = hf_processor._construct_prompts([prompt])[0]
+            processed_outputs = tokenizer(prompt,
+                                          add_special_tokens=True,
+                                          return_tensors="pt")
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(pixel_values=MultiModalFieldConfig.batched("image"))
+
+    def _get_prompt_replacements(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> list[PromptReplacement]:
+        hf_config = self.info.get_hf_config()
+        pad_token_id = hf_config.pad_token_id
+        bos_token_id = hf_config.bos_token_id
+        num_image_tokens = self.info.get_max_image_tokens()
+        image_tokens = [pad_token_id] * num_image_tokens
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=[bos_token_id],
+                replacement=PromptReplacementDetails(
+                    full=image_tokens + [bos_token_id],
+                    features=image_tokens,
+                ),
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Florence2MultiModalProcessor,
+    info=Florence2ProcessingInfo,
+    dummy_inputs=Florence2DummyInputsBuilder)
+class Florence2ForConditionalGeneration(nn.Module, SupportsMultiModal):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
+        processor_config = vllm_config.model_config.hf_image_processor_config
 
-        # TODO(Isotr0py): Add vision backbone
+        self.config = config
+        self.vision_config = config.vision_config
+        self.processor_config = processor_config
+        assert config.vision_config.model_type == 'davit', (
+            'only DaViT is supported for now')
+        self.vision_tower = DaViT.from_config(config=config.vision_config)
+        self._build_image_projection_layers(config)
         self.language_model = Florence2LanguageForConditionalGeneration(
             vllm_config=vllm_config.with_hf_config(config.text_config),
             prefix=f"{prefix}.language_model",
         )
+        self.pad_token_id = config.pad_token_id
 
-    @property
+    def _build_image_projection_layers(self, config: PretrainedConfig):
+        image_dim_out = config.vision_config.dim_embed[-1]
+        dim_projection = config.vision_config.projection_dim
+        self.image_projection = nn.Parameter(
+            torch.empty(image_dim_out, dim_projection))
+        self.image_proj_norm = nn.LayerNorm(dim_projection)
+        image_pos_embed_config = config.vision_config.image_pos_embed
+        if image_pos_embed_config['type'] == 'learned_abs_2d':
+            self.image_pos_embed = LearnedAbsolutePositionEmbedding2D(
+                embedding_dim=image_dim_out,
+                num_pos=image_pos_embed_config['max_pos_embeddings'])
+        else:
+            raise NotImplementedError("Florence2 only supports learned_abs_2d "
+                                      "as image position embedding.")
+
+        self.image_feature_source = config.vision_config.image_feature_source
+
+        # temporal embedding
+        visual_temporal_embedding_config = (
+            self.vision_config.visual_temporal_embedding)
+        if visual_temporal_embedding_config['type'] == 'COSINE':
+            self.visual_temporal_embed = PositionalEmbeddingCosine1D(
+                embed_dim=image_dim_out,
+                max_seq_len=visual_temporal_embedding_config[
+                    'max_temporal_embeddings'])
+        else:
+            raise NotImplementedError(
+                'Florence2 only supports COSINE as temporal embedding.')
+
+    @cached_property
     def sampler(self):
-        return self.language_model.sampler
+        if hasattr(self.language_model, "sampler"):
+            return self.language_model.sampler
+        return get_sampler()
+
+    def _validate_pixel_values(
+        self, data: Union[torch.Tensor, List[torch.Tensor]]
+    ) -> Union[torch.Tensor, List[torch.Tensor]]:
+
+        size = self.processor_config["size"]
+        h, w = size["height"], size["width"]
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = tuple(d.shape)
+
+            if actual_dims != expected_dims:
+                expected_expr = tuple(*map(str, expected_dims))
+                raise ValueError(
+                    "The expected shape of pixel values per batch "
+                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _parse_and_validate_image_input(self, **kwargs: object):
+        pixel_values: Optional[Union[List[List[torch.Tensor]],
+                                     List[torch.Tensor],
+                                     torch.Tensor]] = kwargs.pop(
+                                         "pixel_values", None)
+        image_embeds: Optional[Union[List[List[torch.Tensor]],
+                                     List[torch.Tensor],
+                                     torch.Tensor]] = kwargs.pop(
+                                         "image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None and image_embeds is not None:
+            raise ValueError(
+                "Both pixel values and image embeds are provided.")
+
+        if pixel_values is not None:
+            return Florence2ImagePixelInputs(
+                type="pixel_values",
+                data=self._validate_pixel_values(
+                    flatten_bn(pixel_values, concat=True)),
+            )
+
+        if image_embeds is not None:
+            raise NotImplementedError
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _encode_image(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        dtype = next(self.vision_tower.parameters()).dtype
+        pixel_values = pixel_values.to(dtype)
+
+        batch_size, T = pixel_values.size(0), 1
+        x = self.vision_tower.forward_features_unpool(pixel_values)
+        if self.image_pos_embed is not None:
+            x = x.view(batch_size * T, -1, x.shape[-1])
+            num_tokens = x.shape[-2]
+            h, w = int(num_tokens**0.5), int(num_tokens**0.5)
+            assert h * w == num_tokens, (
+                'only support square feature maps for now')
+            x = x.view(batch_size * T, h, w, x.shape[-1])
+            pos_embed = self.image_pos_embed(x)
+            x = x + pos_embed
+            x = x.view(batch_size, T * h * w, x.shape[-1])
+
+        if self.visual_temporal_embed is not None:
+            visual_temporal_embed = self.visual_temporal_embed(
+                x.view(batch_size, T, -1, x.shape[-1])[:, :, 0])
+            x = x.view(batch_size, T, -1,
+                       x.shape[-1]) + visual_temporal_embed.view(
+                           1, T, 1, x.shape[-1])
+
+        x_feat_dict = {}
+
+        spatial_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=2)
+        x_feat_dict['spatial_avg_pool'] = spatial_avg_pool_x
+
+        temporal_avg_pool_x = x.view(batch_size, T, -1,
+                                     x.shape[-1]).mean(dim=1)
+        x_feat_dict['temporal_avg_pool'] = temporal_avg_pool_x
+
+        x = x.view(batch_size, T, -1, x.shape[-1])[:, -1]
+        x_feat_dict['last_frame'] = x
+
+        new_x = []
+        for _image_feature_source in self.image_feature_source:
+            if _image_feature_source not in x_feat_dict:
+                raise ValueError('invalid image feature source: {}'.format(
+                    _image_feature_source))
+            new_x.append(x_feat_dict[_image_feature_source])
+
+        x = torch.cat(new_x, dim=1)
+
+        x = x @ self.image_projection
+        x = self.image_proj_norm(x)
+
+        return x
+
+    def _process_image_input(
+            self, image_input: Florence2ImagePixelInputs) -> torch.Tensor:
+        assert image_input["type"] == "pixel_values"
+        pixel_values = image_input["data"]
+        return self._encode_image(pixel_values)
+
+    def get_multimodal_embeddings(self, **kwargs: object) -> torch.Tensor:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.pad_token_id)
+        return inputs_embeds
 
     def forward(
         self,
@@ -216,8 +1082,19 @@ class Florence2ForConditionalGeneration(nn.Module):
         Returns:
             Output torch.Tensor
         """
-        return self.language_model(input_ids, positions, encoder_input_ids,
-                                   encoder_positions)
+        vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+        if encoder_input_ids.numel() > 0 or vision_embeddings is not None:
+            inputs_embeds = self.get_input_embeddings(encoder_input_ids,
+                                                      vision_embeddings)
+        else:
+            inputs_embeds = None
+
+        hidden_states = self.language_model(input_ids,
+                                            positions,
+                                            encoder_input_ids,
+                                            encoder_positions,
+                                            inputs_embeds=inputs_embeds)
+        return hidden_states
 
     def compute_logits(
         self,
@@ -236,9 +1113,5 @@ class Florence2ForConditionalGeneration(nn.Module):
 
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
-        skip_prefixes = [
-            'image_projection', "vision_tower", "image_proj_norm",
-            "image_pos_embed", "visual_temporal_embed"
-        ]
-        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
+        loader = AutoWeightsLoader(self)
         return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -105,7 +105,6 @@ _TEXT_GENERATION_MODELS = {
     # [Encoder-decoder]
     "BartModel": ("bart", "BartForConditionalGeneration"),
     "BartForConditionalGeneration": ("bart", "BartForConditionalGeneration"),
-    "Florence2ForConditionalGeneration": ("florence2", "Florence2ForConditionalGeneration"),  # noqa: E501
 }
 
 _EMBEDDING_MODELS = {
@@ -182,6 +181,7 @@ _MULTIMODAL_MODELS = {
     "Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"),  # noqa: E501
     "UltravoxModel": ("ultravox", "UltravoxModel"),
     # [Encoder-decoder]
+    "Florence2ForConditionalGeneration": ("florence2", "Florence2ForConditionalGeneration"),  # noqa: E501
     "MllamaForConditionalGeneration": ("mllama", "MllamaForConditionalGeneration"),  # noqa: E501
     "WhisperForConditionalGeneration": ("whisper", "WhisperForConditionalGeneration"),  # noqa: E501
 }

vllm/multimodal/processing.py

@@ -1303,6 +1303,14 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
         """
         raise NotImplementedError
 
+    def create_decoder_prompt(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+    ) -> Union[str, list[int]]:
+        """Create input prompt for the decoder."""
+        return prompt
+
     def apply(
         self,
         prompt: Union[str, list[int]],
@@ -1323,17 +1331,15 @@ class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
             hf_processor_mm_kwargs,
         )
 
-        # We assumed the decoder prompt text is copied from
-        # the original encoder prompt without extra process
         tokenizer = self.info.get_tokenizer()
-        if isinstance(prompt, str):
-            decoder_prompt = prompt
+        decoder_prompt = self.create_decoder_prompt(prompt, mm_data)
+        if isinstance(decoder_prompt, str):
             decoder_prompt_ids = encode_tokens(tokenizer,
-                                               prompt,
+                                               decoder_prompt,
                                                add_special_tokens=False)
         else:
-            decoder_prompt = decode_tokens(tokenizer, prompt)
-            decoder_prompt_ids = prompt
+            decoder_prompt_ids = decoder_prompt
+            decoder_prompt = decode_tokens(tokenizer, decoder_prompt)
 
         mm_inputs = MultiModalEncDecInputs(
             encoder_prompt=encoder_inputs["prompt"],

vllm/multimodal/profiling.py

@@ -204,9 +204,11 @@ class MultiModalProfiler(Generic[_I]):
                     "and/or reduce `mm_counts`.", seq_len, total_len,
                     total_placeholders_by_modality)
 
+            num_tokens_to_pad = max(total_len, seq_len) - total_len
+            prompt_token_ids.extend([0] * num_tokens_to_pad)
+
             return DummyData(
-                seq_data=SequenceData.from_prompt_token_counts(
-                    (0, max(seq_len, total_len))),
+                seq_data=SequenceData.from_seqs(prompt_token_ids),
                 multi_modal_data=None,
                 multi_modal_placeholders=None,
             )