[VLM] Implement merged multimodal processor for Mllama (#11427)

tests/models/encoder_decoder/vision_language/test_mllama.py

@@ -7,11 +7,11 @@ import torch
 from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer,
                           BatchEncoding)
 
+from vllm import LLM, SamplingParams
 from vllm.attention.backends.flash_attn import FlashAttentionMetadata
 from vllm.attention.selector import (_Backend, _cached_get_attn_backend,
                                      global_force_attn_backend_context_manager)
-from vllm.model_executor.models.mllama import (MLLAMA_IMAGE_TOKEN_ID,
+from vllm.model_executor.models.mllama import MllamaForConditionalGeneration
-                                               MllamaForConditionalGeneration)
 from vllm.multimodal.image import rescale_image_size
 from vllm.sequence import SampleLogprobs
 
@@ -21,6 +21,7 @@ from ....utils import large_gpu_test
 from ...utils import check_logprobs_close
 
 _LIMIT_IMAGE_PER_PROMPT = 3
+MLLAMA_IMAGE_TOKEN_ID = 128256
 
 LIST_ENC_DEC_SUPPORTED_BACKENDS = [_Backend.XFORMERS, _Backend.FLASH_ATTN]
 
@@ -396,6 +397,64 @@ def test_models_interleaved_images(hf_runner, vllm_runner, image_assets, model,
         )
 
 
+@large_gpu_test(min_gb=48)
+@pytest.mark.core_model
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [32])
+def test_explicit_implicit_prompt(
+    image_assets: _ImageAssets,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+):
+    stop_sign = image_assets[0].pil_image
+    # yapf: disable
+    prompts = [
+        # explicit prompt
+        {
+            "encoder_prompt": {
+                "prompt": "<|image|>",
+                "multi_modal_data": {"image": stop_sign},
+            },
+            "decoder_prompt": {
+                "prompt_token_ids": [128000, 791, 2262, 315, 279, 2217, 220, 128256, 374],  # noqa: E501
+            }
+        },
+        {
+            "encoder_prompt": "Not <|image|>",
+            "decoder_prompt": "The color of the sky is blue but sometimes it can also be",  # noqa: E501
+        },
+        # implicit prompt
+        {
+            "prompt": "<|begin_of_text|>The content of the image <|image|> is", # noqa: E501
+            "multi_modal_data": {"image": stop_sign},
+        },
+        {
+            "prompt": "The color of the sky is blue but sometimes it can also be",  # noqa: E501
+        },
+    ]
+    # yapf: enable
+    llm = LLM(
+        model=model,
+        dtype=dtype,
+        max_model_len=4096,
+        max_num_seqs=2,
+        tensor_parallel_size=1,
+        enforce_eager=True,
+    )
+    sampling_params = SamplingParams(
+        temperature=0,
+        max_tokens=max_tokens,
+    )
+    outputs = llm.generate(prompts, sampling_params)
+    n_prompts = len(prompts)
+    explicit_outputs = outputs[:n_prompts // 2]
+    implicit_outputs = outputs[n_prompts // 2:]
+    for exp_output, imp_output in zip(explicit_outputs, implicit_outputs):
+        assert exp_output.outputs[0].text == imp_output.outputs[0].text
+
+
 @large_gpu_test(min_gb=48)
 @pytest.mark.core_model
 @pytest.mark.parametrize("model", models)
@@ -458,6 +517,10 @@ def test_regression(vllm_runner, image_assets, model, dtype, max_tokens,
                                             images=images)
 
 
+class DummyModel:
+    image_token_id = MLLAMA_IMAGE_TOKEN_ID
+
+
 @pytest.mark.core_model
 @pytest.mark.parametrize(
     "input_indices_and_output",
@@ -499,7 +562,7 @@ def test_get_cross_attention_mask(input_indices_and_output) -> None:
         use_cuda_graph=False,
     )
 
-    dummy: dict[str, str] = {}
+    dummy = DummyModel()
 
     cross_attention_mask, kv_range_for_decode = MllamaForConditionalGeneration\
         .get_cross_attention_mask(dummy,
@@ -556,7 +619,7 @@ def test_get_full_text_row_masked_out_mask(input_indices) -> None:
         use_cuda_graph=False,
     )
 
-    dummy: dict[str, str] = {}
+    dummy = DummyModel()
 
     full_text_row_masked_out_mask = MllamaForConditionalGeneration\
         .get_full_text_row_masked_out_mask(dummy,

tests/models/multimodal/processing/test_common.py

@@ -85,6 +85,14 @@ def _test_processing_correctness(
         partial(random_audio, rng, min_len=512, max_len=1024, sr=16000),
     }
 
+    tokenizer_encode_kwargs = {}
+    if model_config.hf_config.model_type == "mllama":
+        # For Mllama, tokenizer will always add bos_token at the beginning of
+        # prompt by default, causing hf_processor outputs incorrect token ids.
+        # So we need use `add_special_tokens=False` here to leave bos_token
+        # to be added by the processor.
+        tokenizer_encode_kwargs = {"add_special_tokens": False}
+
     for batch_idx in range(num_batches):
         mm_data = {
             k:
@@ -122,7 +130,7 @@ def _test_processing_correctness(
             f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
 
         baseline_tokenized_result = baseline_processor.apply(
-            tokenizer.encode(prompt),
+            tokenizer.encode(prompt, **tokenizer_encode_kwargs),
             mm_data=mm_data,
             hf_processor_mm_kwargs={},
         )
@@ -131,7 +139,7 @@ def _test_processing_correctness(
             f"Failed ({batch_idx=}, {prompt=}, {mm_data=})")
 
         cached_tokenized_result = cached_processor.apply(
-            tokenizer.encode(prompt),
+            tokenizer.encode(prompt, **tokenizer_encode_kwargs),
             mm_data=mm_data,
             hf_processor_mm_kwargs={},
         )
@@ -155,6 +163,7 @@ def _test_processing_correctness(
     "llava-hf/llava-v1.6-mistral-7b-hf",
     "llava-hf/LLaVA-NeXT-Video-7B-hf",
     "llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+    "meta-llama/Llama-3.2-11B-Vision-Instruct",
     "TIGER-Lab/Mantis-8B-siglip-llama3",
     "mistral-community/pixtral-12b",
     "openbmb/MiniCPM-o-2_6",

vllm/inputs/preprocess.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import asyncio
-from typing import List, Mapping, Optional, Union
+from typing import List, Mapping, Optional, Tuple, Union, cast
 
 from typing_extensions import assert_never
 
@@ -9,7 +9,8 @@ from vllm.config import ModelConfig
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
-from vllm.multimodal.inputs import MultiModalDataDict, MultiModalInputs
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalEncDecInputs,
+                                    MultiModalInputs)
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
 
@@ -495,6 +496,51 @@ class InputPreprocessor:
             decoder=decoder_inputs,
         )
 
+    def _separate_enc_dec_inputs_from_mm_processor_outputs(
+        self,
+        inputs: SingletonInputs,
+        decoder_inputs_to_override: Optional[SingletonInputs] = None,
+    ) -> Tuple[SingletonInputs, SingletonInputs]:
+        """
+        For encoder/decoder models only:
+        Separate Encoder/Decoder inputs from a MultiModalEncDecInputs
+        """
+        encoder_inputs: SingletonInputs
+        decoder_inputs: SingletonInputs
+        if inputs["type"] == "multimodal":
+            # Multimodal data inputs
+            assert ("encoder_prompt" in inputs
+                    and "encoder_prompt_token_ids" in inputs)
+            inputs = cast(MultiModalEncDecInputs, inputs)
+            encoder_inputs = token_inputs(
+                prompt=inputs["encoder_prompt"],
+                prompt_token_ids=inputs["encoder_prompt_token_ids"],
+            )
+            if decoder_inputs_to_override is not None:
+                decoder_inputs = MultiModalInputs(
+                    type="multimodal",
+                    prompt=decoder_inputs_to_override.get("prompt", ""),
+                    prompt_token_ids=decoder_inputs_to_override[
+                        "prompt_token_ids"],
+                    mm_kwargs=inputs["mm_kwargs"],
+                    mm_placeholders=inputs["mm_placeholders"],
+                )
+            else:
+                decoder_inputs = MultiModalInputs(
+                    type="multimodal",
+                    prompt=inputs["prompt"],
+                    prompt_token_ids=inputs["prompt_token_ids"],
+                    mm_kwargs=inputs["mm_kwargs"],
+                    mm_placeholders=inputs["mm_placeholders"],
+                )
+        elif inputs["type"] == "token":
+            # Text-only inputs
+            encoder_inputs = token_inputs(prompt="", prompt_token_ids=[])
+            decoder_inputs = decoder_inputs_to_override or inputs
+        else:
+            assert_never(inputs)  # type: ignore[arg-type]
+        return encoder_inputs, decoder_inputs
+
     def _process_encoder_decoder_prompt(
         self,
         prompt: PromptType,
@@ -539,7 +585,6 @@ class InputPreprocessor:
                 prompt["encoder_prompt"],
                 request_id=request_id,
             )
-
             if (decoder_input := prompt["decoder_prompt"]) is None:
                 decoder_inputs = None
             else:
@@ -547,11 +592,26 @@ class InputPreprocessor:
                     decoder_input,
                     request_id=request_id,
                 )
+            # For multimodal model, override decoder prompt from processor
+            # with explicit decoder prompt.
+            if self.model_config.is_multimodal_model and (
+                    self._can_process_multimodal()):
+                encoder_inputs, decoder_inputs = (
+                    self._separate_enc_dec_inputs_from_mm_processor_outputs(
+                        encoder_inputs, decoder_inputs))
         else:
-            encoder_inputs = self._prompt_to_llm_inputs(
+            inputs = self._prompt_to_llm_inputs(
                 prompt,
                 request_id=request_id,
             )
+            if self.model_config.is_multimodal_model and (
+                    self._can_process_multimodal()):
+                # Encoder-Decoder Multimodal model
+                encoder_inputs, decoder_inputs = (
+                    self._separate_enc_dec_inputs_from_mm_processor_outputs(
+                        inputs))
+            else:
+                encoder_inputs = inputs
 
                 decoder_inputs = None
 
@@ -583,11 +643,27 @@ class InputPreprocessor:
 
                 encoder_inputs, decoder_inputs = await asyncio.gather(
                     encoder_task, decoder_task)
+
+            # For multimodal model, override decoder prompt from processor
+            # with explicit decoder prompt.
+            if self.model_config.is_multimodal_model and (
+                    self._can_process_multimodal()):
+                encoder_inputs, decoder_inputs = (
+                    self._separate_enc_dec_inputs_from_mm_processor_outputs(
+                        encoder_inputs, decoder_inputs))
         else:
-            encoder_inputs = await self._prompt_to_llm_inputs_async(
+            inputs = await self._prompt_to_llm_inputs_async(
                 prompt,
                 request_id=request_id,
             )
+            if self.model_config.is_multimodal_model and (
+                    self._can_process_multimodal()):
+                # Encoder-Decoder Multimodal model
+                encoder_inputs, decoder_inputs = (
+                    self._separate_enc_dec_inputs_from_mm_processor_outputs(
+                        inputs))
+            else:
+                encoder_inputs = inputs
 
                 decoder_inputs = None
 

vllm/inputs/registry.py

@@ -350,7 +350,8 @@ class InputRegistry:
             )
             processor = mm_registry.create_processor(model_config, tokenizer)
             profiler = MultiModalProfiler(processor)
-            dummy_data = profiler.get_dummy_data(seq_len)
+            dummy_data = profiler.get_dummy_data(
+                seq_len, is_encoder_data=is_encoder_data)
         else:
             model_cls, _ = get_model_architecture(model_config)
             if is_encoder_data:

vllm/model_executor/models/mllama.py

@@ -23,14 +23,15 @@ import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
 import transformers.models.mllama.configuration_mllama as config_mllama
-from PIL import Image
+from PIL.Image import Image
 from torch import nn
+from transformers import BatchFeature, MllamaConfig
 from transformers.modeling_outputs import (BaseModelOutput,
                                            CausalLMOutputWithPast)
 from transformers.models.mllama.image_processing_mllama import (
     get_optimal_tiled_canvas)
 from transformers.models.mllama.processing_mllama import (
-    get_cross_attention_token_mask)
+    MllamaProcessor, get_cross_attention_token_mask)
 
 import vllm.distributed.parallel_state as ps
 from vllm.attention import Attention, AttentionMetadata, AttentionType
@@ -38,8 +39,6 @@ from vllm.attention.ops.paged_attn import PagedAttention
 from vllm.attention.selector import _Backend
 from vllm.config import VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
-from vllm.inputs import (INPUT_REGISTRY, DummyData, EncoderDecoderInputs,
-                         InputContext, TokenInputs, token_inputs)
 from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -54,8 +53,13 @@ from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.sequence import SequenceData
+from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargs
-from vllm.utils import is_list_of
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataDict, MultiModalDataItems)
+from vllm.multimodal.processing import (BaseProcessingInfo,
+                                        EncDecMultiModalProcessor,
+                                        PromptReplacement)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 
 from .clip import CLIPMLP
 from .interfaces import SupportsMultiModal
@@ -63,8 +67,6 @@ from .llama import LlamaDecoderLayer, LlamaMLP
 from .utils import maybe_prefix
 
 logger = init_logger(__name__)
-MLLAMA_IMAGE_TOKEN_ID = 128256
-MLLAMA_IMAGE_TOKEN = "<|image|>"
 
 
 class MllamaImagePixelInputs(TypedDict):
@@ -81,25 +83,113 @@ class MllamaImagePixelInputs(TypedDict):
 # TODO: support LlamaImageEmbeddingInputs
 
 
-def _get_num_image_in_last_group(prompt_token_ids: List[int]) -> int:
+def calc_token_per_chunk(image_size: int) -> int:
-    num_images = 0
+    assert image_size % 14 == 0, "chunk size should be multiple of 14"
-    for token_id in prompt_token_ids[::-1]:
+    token_per_chunk = (image_size // 14)**2 + 1
-        if token_id == MLLAMA_IMAGE_TOKEN_ID:
+    return token_per_chunk
-            num_images += 1
-        elif num_images > 0:
-            break
-    return num_images
 
 
-def input_processor_for_mllama(
+class MllamaProcessingInfo(BaseProcessingInfo):
-    ctx: InputContext,
+
-    inputs: EncoderDecoderInputs,
+    def get_hf_config(self) -> MllamaConfig:
-) -> EncoderDecoderInputs:
+        return self.ctx.get_hf_config(MllamaConfig)
-    # Example input to processor:
+
+    def get_hf_processor(self) -> MllamaProcessor:
+        return self.ctx.get_hf_processor(MllamaProcessor)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_token_per_chunk_from_config(self) -> int:
+        image_size = self.get_hf_config().vision_config.image_size
+        return calc_token_per_chunk(image_size)
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        vision_config = self.get_hf_config().vision_config
+        token_per_chunk = self.get_token_per_chunk_from_config()
+        mm_max_tokens = vision_config.max_num_tiles * token_per_chunk
+        return {"image": mm_max_tokens}
+
+    def get_num_tiles_per_image(self, image_height: int,
+                                image_width: int) -> int:
+        vision_config = self.get_hf_config().vision_config
+        max_num_tiles = vision_config.max_num_tiles
+        image_size = vision_config.image_size
+        tiled_height, tiled_width = get_optimal_tiled_canvas(
+            image_height,
+            image_width,
+            max_num_tiles,
+            tile_size=image_size,
+        )
+        num_tiles_height = tiled_height // image_size
+        num_tiles_width = tiled_width // image_size
+        return num_tiles_height * num_tiles_width
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        vision_config = self.get_hf_config().vision_config
+        image_size = vision_config.image_size
+        max_num_tiles = vision_config.max_num_tiles
+        # Result in the max possible feature size (h:w = 16:1)
+        return ImageSize(height=max_num_tiles * image_size, width=image_size)
+
+
+class MllamaDummyInputsBuilder(BaseDummyInputsBuilder[MllamaProcessingInfo]):
+
+    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_image_size_with_most_features()
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+        hf_processor = self.info.get_hf_processor()
+        image_token: str = hf_processor.image_token
+
+        return ProcessorInputs(
+            prompt_text=image_token * num_images,
+            mm_data=mm_data,
+        )
+
+
+class MllamaMultiModalProcessor(EncDecMultiModalProcessor[MllamaProcessingInfo]
+                                ):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        tokenizer = self.info.get_tokenizer()
+        if mm_data:
+            num_tiles = [
+                self.info.get_num_tiles_per_image(img.height, img.width)
+                for img in mm_data["images"]
+            ]
+            processed_outputs = super()._call_hf_processor(
+                prompt, mm_data, mm_kwargs)
+            processed_outputs["num_tiles"] = torch.tensor(num_tiles)
+            for k in ('pixel_values', 'aspect_ratio_ids', "aspect_ratio_mask"):
+                processed_outputs[k] = processed_outputs[k].squeeze(0)
+            # Example input to encoder and decoder:
             # {
             #     'encoder': {
             #         'type': 'token',
-    #         'prompt_token_ids': [128000, 128256, 128000, 3923, 374, 279, 2262, 315, 420, 2217, 30],  # noqa: E501
+            #         'prompt_token_ids': [128256, 128000, 3923, 374, 279, 2262, 315, 420, 2217, 30],  # noqa: E501
             #         'prompt': '<|image|><|begin_of_text|>What is the content of this image?',  # noqa: E501
             #         'multi_modal_data': {'image': <PIL.Image.Image image mode=RGB size=1770x1180 at 0x7FDE2C624880>},  # noqa: E501
             #     },
@@ -108,131 +198,76 @@ def input_processor_for_mllama(
             #         'prompt_token_ids': [128000],
             #     },
             # }
+            processed_token_ids = processed_outputs.pop("input_ids")
+            start_idx, end_idx = 0, processed_token_ids.size(1)
+            processed_prompt_text = tokenizer.decode(processed_token_ids[0])
 
-    # move encoder prompt to decoder
+            hf_processor = self.info.get_hf_processor()
-    dec_inputs = TokenInputs(**inputs["encoder"])
+            bos_token = hf_processor.bos_token
+            # Remove the bos_token from the start of prompt,
+            # because we all know there would be image_token.
+            if processed_prompt_text.startswith(bos_token):
+                start_idx += 1
+            # Remove the bos_token from the end of prompt,
+            # because text is empty in this case.
+            if processed_prompt_text.endswith(bos_token):
+                end_idx -= 1
+            processed_outputs[
+                "input_ids"] = processed_token_ids[:, start_idx:end_idx]
+        else:
+            processed_outputs = tokenizer(prompt,
+                                          add_special_tokens=False,
+                                          return_tensors="pt")
+        return processed_outputs
 
-    multi_modal_data = dec_inputs.get("multi_modal_data")
+    def _get_mm_fields_config(
-    if multi_modal_data is None or "image" not in multi_modal_data:
+        self,
-        # text-only
+        hf_inputs: BatchFeature,
-        return EncoderDecoderInputs(
+        hf_processor_mm_kwargs: Mapping[str, object],
-            encoder=token_inputs([]),
+    ) -> Mapping[str, MultiModalFieldConfig]:
-            decoder=dec_inputs,
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            aspect_ratio_ids=MultiModalFieldConfig.batched("image"),
+            aspect_ratio_mask=MultiModalFieldConfig.batched("image"),
+            num_tiles=MultiModalFieldConfig.batched("image"),
         )
 
-    image_data = multi_modal_data["image"]
+    def create_encoder_prompt(
-    if isinstance(image_data, Image.Image):
+        self,
-        image_data = [image_data]
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+    ) -> Union[str, list[int]]:
+        data = mm_data.get("image", [])
+        num_images = 1 if isinstance(data, Image) else len(data)
+        image_token_id = self.info.get_hf_config().image_token_index
+        return [image_token_id] * num_images
 
-    assert is_list_of(image_data, Image.Image)
+    def _get_prompt_replacements(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> list[PromptReplacement]:
+        token_per_chunk = self.info.get_token_per_chunk_from_config()
+        image_token_id = self.info.get_hf_config().image_token_index
 
-    num_image_tokens = dec_inputs['prompt_token_ids'].count(
+        def get_replacement_mllama(item_idx):
-        MLLAMA_IMAGE_TOKEN_ID)
+            images = mm_items.get_items("image", ImageProcessorItems)
-    if num_image_tokens != len(image_data):
+            image_size = images.get_image_size(item_idx)
-        raise ValueError(
+            num_tile = self.info.get_num_tiles_per_image(
-            f"The number of image tokens ({num_image_tokens}) must be"
+                image_height=image_size.height,
-            f" the same as the number of images ({len(image_data)})")
+                image_width=image_size.width,
-
-    # Since only the last group of consecutive images
-    # are attended by the decoded tokens, we only need to
-    # get the number of tiles for those images.
-    num_decode_images = _get_num_image_in_last_group(
-        dec_inputs["prompt_token_ids"])
-
-    hf_config = ctx.model_config.hf_config
-    vision_config = hf_config.vision_config
-
-    num_tiles = 0
-    for image in image_data[::-1]:
-        width, height = image.size
-        tile_size = vision_config.image_size
-        canvas_height, canvas_width = get_optimal_tiled_canvas(
-            image_height=height,
-            image_width=width,
-            max_image_tiles=vision_config.max_num_tiles,
-            tile_size=tile_size,
             )
-        num_tiles_height = canvas_height // tile_size
+            num_tokens = num_tile * token_per_chunk
-        num_tiles_width = canvas_width // tile_size
+            return [image_token_id] * num_tokens
-        num_tiles += num_tiles_height * num_tiles_width
-        num_decode_images -= 1
-        if num_decode_images == 0:
-            break
 
-    # Set encoder prompt length based on the number of tiles.
+        return [
-    # This tells the block manager to allocate correct number
+            PromptReplacement(
-    # of slots for encoder tokens.
+                modality="image",
-    assert vision_config.image_size % 14 == 0, \
+                target=[image_token_id],
-        "chunk size should be multiple of 14"
+                replacement=get_replacement_mllama,
-    token_per_chunk = (vision_config.image_size // 14)**2 + 1
-    num_tokens = num_tiles * token_per_chunk
-
-    # Example output from processor:
-    # {
-    #     'encoder': {
-    #         'type': 'token',
-    #         'prompt_token_ids': [128256, 128256, ..., 128256],
-    #         'prompt': '<|image|><|image|>...<|image|>',
-    #         'multi_modal_data': {'image': <PIL.Image.Image image mode=RGB size=1770x1180 at 0x7FDE2C624880>},  # noqa: E501
-    #     },
-    #     'decoder': {
-    #         'type': 'token',
-    #         'prompt_token_ids': [128000, 128256, 128000, 3923, 374, 279, 2262, 315, 420, 2217, 30],  # noqa: E501
-    #         'prompt': '<|image|><|begin_of_text|>What is the content of this image?',  # noqa: E501
-    #         'multi_modal_data': {'image': <PIL.Image.Image image mode=RGB size=1770x1180 at 0x7FDE2C624880>},  # noqa: E501
-    #     },
-    # }
-    return EncoderDecoderInputs(
-        encoder=token_inputs(
-            prompt_token_ids=[MLLAMA_IMAGE_TOKEN_ID] * num_tokens,
-            prompt=MLLAMA_IMAGE_TOKEN * num_tokens,
-            multi_modal_data=multi_modal_data,
-        ),
-        decoder=dec_inputs,
             )
-
+        ]
-
-def get_max_mllama_image_tokens(ctx: InputContext) -> int:
-    hf_config = ctx.model_config.hf_config
-    token_per_chunk = (hf_config.vision_config.image_size // 14)**2 + 1
-    return hf_config.vision_config.max_num_tiles * token_per_chunk
-
-
-def dummy_decoder_seq_data(seq_len: int, num_images: int):
-    # <|image|> * num_images + 0 * (seq_len - num_images)
-    assert seq_len >= num_images, \
-        "seq_len should be greater than or equal to num_images"
-
-    return SequenceData.from_prompt_token_counts(
-        (MLLAMA_IMAGE_TOKEN_ID, num_images),
-        (0, seq_len - num_images),
-    )
-
-
-def dummy_encoder_seq_data(ctx: InputContext, num_images: int):
-    num_tokens = get_max_mllama_image_tokens(ctx) * num_images
-
-    return SequenceData.from_prompt_token_counts(
-        (MLLAMA_IMAGE_TOKEN_ID, num_tokens))
-
-
-def dummy_image(num_images: int, ):
-    width = height = 1024
-    image = Image.new("RGB", (width, height), color=0)
-    return {"image": image if num_images == 1 else [image] * num_images}
-
-
-def dummy_decoder_data_for_mllama(ctx: InputContext, seq_len: int,
-                                  mm_counts: Mapping[str, int]):
-    num_images = mm_counts["image"]
-    return DummyData(dummy_decoder_seq_data(seq_len, num_images))
-
-
-def dummy_encoder_data_for_mllama(ctx: InputContext, seq_len: int,
-                                  mm_counts: Mapping[str, int]):
-    num_images = mm_counts["image"]
-    return DummyData(dummy_encoder_seq_data(ctx, num_images),
-                     dummy_image(num_images))
 
 
 def _prepare_aspect_ratio_attention_mask(
@@ -1107,11 +1142,9 @@ class MllamaForCausalLM(nn.Module):
         return hidden_states
 
 
-@MULTIMODAL_REGISTRY.register_image_input_mapper()
+@MULTIMODAL_REGISTRY.register_processor(MllamaMultiModalProcessor,
-@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_mllama_image_tokens)
+                                        info=MllamaProcessingInfo,
-@INPUT_REGISTRY.register_dummy_data(dummy_decoder_data_for_mllama)
+                                        dummy_inputs=MllamaDummyInputsBuilder)
-@INPUT_REGISTRY.register_dummy_encoder_data(dummy_encoder_data_for_mllama)
-@INPUT_REGISTRY.register_input_processor(input_processor_for_mllama)
 class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
@@ -1120,7 +1153,7 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
-        config = vllm_config.model_config.hf_config
+        config: MllamaConfig = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         self.quant_config = quant_config
         self.vocab_size = config.text_config.vocab_size
@@ -1130,6 +1163,7 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
         self.pad_token_id = \
             config.pad_token_id if config.pad_token_id is not None else -1
         self.image_size = config.vision_config.image_size
+        self.image_token_id = config.image_token_index
 
         self.vision_model = MllamaVisionModel(config.vision_config,
                                               quant_config,
@@ -1204,48 +1238,12 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
         if pixel_values is not None:
             assert aspect_ratio_ids is not None
             assert aspect_ratio_mask is not None
-            max_num_images = max([len(x[0]) for x in pixel_values])
-            if max_num_images == 0:
-                raise ValueError("No images provided.")
-            max_num_tiles = max(
-                max([len(x) for x in y[0]]) for y in pixel_values)
-            device = next(self.multi_modal_projector.parameters()).device
-            bsz = len(pixel_values)
-            out_num_tiles = []
-            out_images = torch.zeros(
-                bsz,
-                max_num_images,
-                max_num_tiles,
-                3,
-                self.image_size,
-                self.image_size,
-                dtype=torch.float32,
-                device=device,
-            )
-            out_ar_ids = torch.ones(bsz,
-                                    max_num_images,
-                                    dtype=torch.int64,
-                                    device=device)
-            out_ar_mask = torch.zeros(bsz,
-                                      max_num_images,
-                                      max_num_tiles,
-                                      dtype=torch.int64,
-                                      device=device)
-            for b in range(len(pixel_values)):
-                _num_tiles = []
-                for i in range(len(pixel_values[b][0])):
-                    img = pixel_values[b][0][i]
-                    out_images[b, i, :img.shape[0]] = img
-                    out_ar_ids[b, i] = aspect_ratio_ids[b][0][i]
-                    out_ar_mask[b, i] = aspect_ratio_mask[b][0][i]
-                    _num_tiles.append(img.shape[0])
-                out_num_tiles.append(_num_tiles)
 
             return MllamaImagePixelInputs(
                 type="pixel_values",
-                data=out_images,
+                data=pixel_values,
-                aspect_ratio_ids=out_ar_ids,
+                aspect_ratio_ids=aspect_ratio_ids,
-                aspect_ratio_mask=out_ar_mask,
+                aspect_ratio_mask=aspect_ratio_mask,
             )
 
         if image_embeds is not None:
@@ -1312,7 +1310,7 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
             batch_token_ids.append(token_ids[start:start + seq_len])
             start += seq_len
         sparse_mask = [
-            get_cross_attention_token_mask(t, MLLAMA_IMAGE_TOKEN_ID)
+            get_cross_attention_token_mask(t, self.image_token_id)
             for t in batch_token_ids
         ]
 
@@ -1384,8 +1382,8 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
             # block manager to allocate blocks for those images only.
             # See input_processor_for_mllama() for more details.
             num_tiles_tensor = kwargs.pop("num_tiles")
-            num_tiles = [t[0].tolist() for t in num_tiles_tensor]
+            num_tiles = [t.tolist() for t in num_tiles_tensor]
-            num_tokens_per_tile = (self.image_size // 14)**2 + 1
+            num_tokens_per_tile = calc_token_per_chunk(self.image_size)
             actual_encoder_seq_lens = [
                 sum(num_tile) * num_tokens_per_tile for num_tile in num_tiles
             ]

vllm/multimodal/inputs.py

@@ -739,3 +739,19 @@ class MultiModalInputs(TypedDict):
     For each modality, information about the placeholder tokens in
     :code:`prompt_token_ids`.
     """
+
+
+class MultiModalEncDecInputs(MultiModalInputs):
+    """
+    Represents the outputs of :class:`vllm.multimodal.EncDecMultiModalProcessor`
+    ready to be passed to vLLM internals.
+    """
+
+    encoder_prompt: str
+    """The processed encoder prompt text."""
+
+    encoder_prompt_token_ids: list[int]
+    """The processed token IDs of the encoder prompt."""
+
+    encoder_token_type_ids: NotRequired[list[int]]
+    """The token type IDs of the encoder prompt."""

vllm/multimodal/processing.py

@@ -20,9 +20,9 @@ from vllm.transformers_utils.tokenizer import (AnyTokenizer, decode_tokens,
 from vllm.utils import LRUCache, flatten_2d_lists, full_groupby
 
 from .hasher import MultiModalHasher
-from .inputs import (MultiModalDataDict, MultiModalFieldConfig,
+from .inputs import (MultiModalDataDict, MultiModalEncDecInputs,
-                     MultiModalInputs, MultiModalKwargs, MultiModalKwargsItem,
+                     MultiModalFieldConfig, MultiModalInputs, MultiModalKwargs,
-                     PlaceholderRange)
+                     MultiModalKwargsItem, PlaceholderRange)
 from .parse import MultiModalDataItems, MultiModalDataParser
 
 if TYPE_CHECKING:
@@ -1293,3 +1293,57 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
             mm_hashes=mm_hashes,
             mm_placeholders=mm_placeholder_ranges,
         )
+
+
+class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
+
+    @abstractmethod
+    def create_encoder_prompt(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+    ) -> Union[str, list[int]]:
+        """Create input prompt for the encoder."""
+        raise NotImplementedError
+
+    def apply(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> MultiModalEncDecInputs:
+        """
+        Process multi-modal inputs to be used in vLLM.
+        The main processing steps are modified to fit encoder-decoder model:
+        1. Create encoder prompt from input prompt text.
+        2. Apply the HF processor on encoder prompt.
+        3. Copy the input prompt text as decoder prompt inputs.
+        """
+        encoder_prompt = self.create_encoder_prompt(prompt, mm_data)
+        encoder_inputs = super().apply(
+            encoder_prompt,
+            mm_data,
+            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_ids = encode_tokens(tokenizer,
+                                               prompt,
+                                               add_special_tokens=False)
+        else:
+            decoder_prompt = decode_tokens(tokenizer, prompt)
+            decoder_prompt_ids = prompt
+
+        mm_inputs = MultiModalEncDecInputs(
+            encoder_prompt=encoder_inputs["prompt"],
+            encoder_prompt_token_ids=encoder_inputs["prompt_token_ids"],
+            **encoder_inputs)
+        mm_inputs.update({
+            "prompt": decoder_prompt,
+            "prompt_token_ids": decoder_prompt_ids
+        })
+        return mm_inputs

vllm/multimodal/profiling.py

@@ -144,7 +144,11 @@ class MultiModalProfiler(Generic[_I]):
             hf_processor_mm_kwargs=processor_inputs.hf_processor_mm_kwargs,
         )
 
-    def get_dummy_data(self, seq_len: int) -> DummyData:
+    def get_dummy_data(
+        self,
+        seq_len: int,
+        is_encoder_data: bool = False,
+    ) -> DummyData:
         # Avoid circular import
         from vllm.sequence import SequenceData
 
@@ -183,16 +187,18 @@ class MultiModalProfiler(Generic[_I]):
         total_len = len(prompt_token_ids)
 
         # V0 does not support chunked prefill.
-        if total_len > seq_len and not envs.VLLM_USE_V1:
+        if (total_len > seq_len and not envs.VLLM_USE_V1) or is_encoder_data:
+            if total_len > seq_len:
                 logger.warning(
                     "The context length (%d) of the model is too short "
                     "to hold the multi-modal embeddings in the worst case "
                     "(%d tokens in total, out of which %s are reserved for "
-                "multi-modal embeddings). This may cause certain multi-modal "
+                    "multi-modal embeddings). This may cause certain "
-                "inputs to fail during inference, even when the input text is "
+                    "multi-modal inputs to fail during inference, even when "
-                "short. To avoid this, you should increase `max_model_len`, "
+                    "the input text is short. To avoid this, you should "
-                "reduce `max_num_seqs`, and/or reduce `mm_counts`.", seq_len,
+                    "increase `max_model_len`, reduce `max_num_seqs`, "
-                total_len, total_placeholders_by_modality)
+                    "and/or reduce `mm_counts`.", seq_len, total_len,
+                    total_placeholders_by_modality)
 
             return DummyData(
                 seq_data=SequenceData.from_prompt_token_counts((0, seq_len)),