[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

 # 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

 # 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 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
 
 
-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 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
 
-    hf_output_str = "</s><s>" + output_str + "</s>"
+    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, hf_output_str, out_logprobs
+    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
+        ]
+
+    for hf_outputs, vllm_outputs in zip(hf_outputs_per_case,
+                                        vllm_outputs_per_case):
         check_logprobs_close(
-        outputs_0_lst=hf_outputs,
-        outputs_1_lst=[
-            vllm_to_hf_output(vllm_output) for vllm_output in vllm_outputs
-        ],
+            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
@@ -602,6 +606,7 @@ class BartEncoder(nn.Module):
             Decoder output torch.Tensor
         """
         # retrieve input_ids and inputs_embeds
+        if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
         embed_pos = self.embed_positions(positions)
@@ -661,9 +666,13 @@ class BartDecoder(nn.Module):
 
         self.layernorm_embedding = nn.LayerNorm(config.d_model)
 
-    def forward(self, decoder_input_ids: torch.Tensor,
+    def forward(
+        self,
+        decoder_input_ids: torch.Tensor,
         decoder_positions: torch.Tensor,
-                encoder_hidden_states: Optional[torch.Tensor]) -> 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
         """
-
+        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/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,
             )