[VLM][Core] Support profiling with multiple multi-modal inputs per prompt (#7126)

docs/source/dev/input_processing/input_processing_pipeline.rst

@@ -17,4 +17,4 @@ Input Processing Pipeline
 
 6. If the data contains multi-modal data, convert it into keyword arguments using :meth:`MULTIMODAL_REGISTRY.map_input <vllm.multimodal.MultiModalRegistry.map_input>`.
 
-   - For example, convert a :class:`PIL.Image.Image` input to its pixel values for a vision language model.
+   - For example, convert a :class:`PIL.Image.Image` input to its pixel values for a vision model.

docs/source/dev/multimodal/multimodal_index.rst

@@ -15,6 +15,9 @@ by following :ref:`this guide <adding_multimodal_plugin>`.
 
 Looking to add your own multi-modal model? Please follow the instructions listed :ref:`here <enabling_multimodal_inputs>`.
 
+..
+  TODO: Add usage of --limit-mm-per-prompt when multi-image input is officially supported
+
 Guides
 ++++++
 

docs/source/models/enabling_multimodal_inputs.rst

@@ -66,7 +66,7 @@ A default mapper is available for each modality in the core vLLM library. This i
 3. Register maximum number of multi-modal tokens
 ------------------------------------------------
 
-For each modality type that the model accepts as input, calculate the maximum possible number of tokens
+For each modality type that the model accepts as input, calculate the maximum possible number of tokens per data instance
 and register it via :meth:`INPUT_REGISTRY.register_dummy_data <vllm.inputs.registry.InputRegistry.register_max_multimodal_tokens>`.
 
 .. code-block:: diff

tests/engine/test_arg_utils.py

@@ -0,0 +1,24 @@
+import pytest
+
+from vllm.engine.arg_utils import EngineArgs
+from vllm.utils import FlexibleArgumentParser
+
+
+@pytest.mark.parametrize(("arg", "expected"), [
+    (None, None),
+    ("image=16", {
+        "image": 16
+    }),
+    ("image=16,video=2", {
+        "image": 16,
+        "video": 2
+    }),
+])
+def test_limit_mm_per_prompt_parser(arg, expected):
+    parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
+    if arg is None:
+        args = parser.parse_args([])
+    else:
+        args = parser.parse_args(["--limit-mm-per-prompt", arg])
+
+    assert args.limit_mm_per_prompt == expected

tests/models/test_blip2.py

@@ -59,7 +59,7 @@ def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
     For vllm runner, we provide MultiModalData objects and corresponding
-    vision language config as input.
+    MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
     """

tests/models/test_fuyu.py

@@ -49,7 +49,7 @@ def run_test(
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
     For vllm runner, we provide MultiModalDataDict objects 
-    and corresponding vision language config as input.
+    and corresponding MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
     """

tests/models/test_internvl.py

@@ -117,7 +117,7 @@ def run_test(
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
     For vllm runner, we provide MultiModalDataDict objects 
-    and corresponding vision language config as input.
+    and corresponding MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
     """

tests/models/test_llava.py

@@ -69,7 +69,7 @@ def run_test(
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
     For vllm runner, we provide MultiModalDataDict objects 
-    and corresponding vision language config as input.
+    and corresponding MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
     """

tests/models/test_llava_next.py

@@ -177,7 +177,7 @@ def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
     For vllm runner, we provide MultiModalDataDict objects 
-    and corresponding vision language config as input.
+    and corresponding MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
     """

tests/models/test_minicpmv.py

@@ -61,7 +61,7 @@ def run_test(
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
     For vllm runner, we provide MultiModalDataDict objects 
-    and corresponding vision language config as input.
+    and corresponding MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
     """
@@ -176,7 +176,7 @@ def run_multi_image_test(
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
     For vllm runner, we provide MultiModalDataDict objects 
-    and corresponding vision language config as input.
+    and corresponding MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
     """
@@ -197,6 +197,7 @@ def run_multi_image_test(
     with vllm_runner(model,
                      max_model_len=4096,
                      max_num_seqs=1,
+                     limit_mm_per_prompt={"image": len(images)},
                      dtype=dtype,
                      tensor_parallel_size=tensor_parallel_size,
                      distributed_executor_backend=distributed_executor_backend,

tests/models/test_paligemma.py

@@ -72,7 +72,7 @@ def run_test(
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
     For vllm runner, we provide MultiModalDataDict objects 
-    and corresponding vision language config as input.
+    and corresponding MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
     """

tests/models/test_phi3v.py

@@ -73,7 +73,7 @@ def run_test(
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
     For vllm runner, we provide MultiModalDataDict objects 
-    and corresponding vision language config as input.
+    and corresponding MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
     """

tests/multimodal/test_mapper.py

@@ -1,15 +1,22 @@
+from contextlib import nullcontext
+
 import numpy as np
 import pytest
 from transformers import CLIPImageProcessor, LlavaNextImageProcessor
 
-from vllm.config import ModelConfig
+from vllm.config import ModelConfig, MultiModalConfig
-from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal import MultiModalRegistry
 from vllm.multimodal.utils import rescale_image_size
 
 
+@pytest.fixture
+def mm_registry():
+    return MultiModalRegistry()
+
+
 @pytest.mark.parametrize("dtype", ["half", "float"])
 @pytest.mark.parametrize("size_factor", [0.25, 0.5, 1.0])
-def test_clip_image_processor(image_assets, dtype, size_factor):
+def test_clip_image_processor(image_assets, mm_registry, dtype, size_factor):
     MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
 
     hf_processor = CLIPImageProcessor.from_pretrained(MODEL_NAME)
@@ -24,6 +31,9 @@ def test_clip_image_processor(image_assets, dtype, size_factor):
         dtype=dtype,
         revision=None,
     )
+    mm_config = MultiModalConfig(limit_per_prompt={"image": 1})
+
+    mm_registry.init_mm_limits_per_prompt(model_config, mm_config)
 
     for asset in image_assets:
         image = rescale_image_size(asset.pil_image, size_factor)
@@ -32,7 +42,7 @@ def test_clip_image_processor(image_assets, dtype, size_factor):
             image,
             return_tensors="pt",
         )
-        vllm_result = MULTIMODAL_REGISTRY.map_input(
+        vllm_result = mm_registry.map_input(
             model_config,
             {"image": image},
         )
@@ -48,7 +58,8 @@ def test_clip_image_processor(image_assets, dtype, size_factor):
 
 @pytest.mark.parametrize("dtype", ["half", "float"])
 @pytest.mark.parametrize("size_factor", [0.25, 0.5, 1.0])
-def test_llava_next_image_processor(image_assets, dtype, size_factor):
+def test_llava_next_image_processor(image_assets, mm_registry, dtype,
+                                    size_factor):
     MODEL_NAME = "llava-hf/llava-v1.6-vicuna-7b-hf"
 
     hf_processor = LlavaNextImageProcessor.from_pretrained(MODEL_NAME)
@@ -63,6 +74,9 @@ def test_llava_next_image_processor(image_assets, dtype, size_factor):
         dtype=dtype,
         revision=None,
     )
+    mm_config = MultiModalConfig(limit_per_prompt={"image": 1})
+
+    mm_registry.init_mm_limits_per_prompt(model_config, mm_config)
 
     for asset in image_assets:
         image = rescale_image_size(asset.pil_image, size_factor)
@@ -71,7 +85,7 @@ def test_llava_next_image_processor(image_assets, dtype, size_factor):
             image,
             return_tensors="pt",
         )
-        vllm_result = MULTIMODAL_REGISTRY.map_input(
+        vllm_result = mm_registry.map_input(
             model_config,
             {"image": image},
         )
@@ -83,3 +97,61 @@ def test_llava_next_image_processor(image_assets, dtype, size_factor):
 
             assert hf_arr.shape == vllm_arr.shape, f"Failed for key={key}"
             assert np.allclose(hf_arr, vllm_arr), f"Failed for key={key}"
+
+
+@pytest.mark.parametrize(
+    ("num_images", "limit", "is_valid"),
+    [(0, 0, True), (0, 1, True), (1, 0, False), (1, 1, True), (1, 2, True),
+     (2, 1, False), (2, 2, True)],
+)
+def test_mm_limits(image_assets, mm_registry, num_images, limit, is_valid):
+    MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
+
+    model_config = ModelConfig(
+        model=MODEL_NAME,
+        tokenizer=MODEL_NAME,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        seed=0,
+        dtype="half",
+        revision=None,
+    )
+    mm_config = MultiModalConfig(limit_per_prompt={"image": limit})
+
+    mm_registry.init_mm_limits_per_prompt(model_config, mm_config)
+
+    image = image_assets[0].pil_image
+    if num_images == 0:
+        mm_inputs = {}
+    elif num_images == 1:
+        mm_inputs = {"image": image}
+    else:
+        mm_inputs = {"image": [image] * num_images}
+
+    with nullcontext() if is_valid else pytest.raises(ValueError):
+        mm_registry.map_input(model_config, mm_inputs)
+
+
+# NOTE: We don't test zero images since the HF processor doesn't support it
+@pytest.mark.parametrize("num_images", [1, 2])
+def test_image_mapper_multi(image_assets, mm_registry, num_images):
+    MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
+
+    model_config = ModelConfig(
+        model=MODEL_NAME,
+        tokenizer=MODEL_NAME,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        seed=0,
+        dtype="half",
+        revision=None,
+    )
+    mm_config = MultiModalConfig(limit_per_prompt={"image": num_images})
+
+    mm_registry.init_mm_limits_per_prompt(model_config, mm_config)
+
+    image = image_assets[0].pil_image
+    mm_inputs = {"image": [image] * num_images}
+
+    mapped_inputs = mm_registry.map_input(model_config, mm_inputs)
+    assert len(mapped_inputs["pixel_values"]) == num_images

vllm/config.py

@@ -1,7 +1,8 @@
 import enum
 import json
 from dataclasses import dataclass, field, fields
-from typing import TYPE_CHECKING, ClassVar, List, Optional, Tuple, Type, Union
+from typing import (TYPE_CHECKING, ClassVar, List, Mapping, Optional, Tuple,
+                    Type, Union)
 
 import torch
 from transformers import PretrainedConfig
@@ -1429,10 +1430,15 @@ class PromptAdapterConfig:
 
 @dataclass
 class MultiModalConfig:
-    """Configs the input data format and how models should run for
+    """Controls the behavior of multimodal models."""
-    multimodal models."""
+
+    limit_per_prompt: Mapping[str, int]
+    """
+    The maximum number of multi-modal input instances allowed per prompt
+    for each :class:`~vllm.multimodal.MultiModalPlugin`.
+    """
+
     # TODO: Add configs to init vision tower or not.
-    pass
 
 
 _STR_DTYPE_TO_TORCH_DTYPE = {

vllm/engine/arg_utils.py

@@ -2,7 +2,8 @@ import argparse
 import dataclasses
 import json
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, List, Optional, Tuple, Type, Union
+from typing import (TYPE_CHECKING, Dict, List, Mapping, Optional, Tuple, Type,
+                    Union)
 
 from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
                          EngineConfig, LoadConfig, LoRAConfig, ModelConfig,
@@ -15,8 +16,7 @@ from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.utils import FlexibleArgumentParser
 
 if TYPE_CHECKING:
-    from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import (
+    from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
-        BaseTokenizerGroup)
 
 logger = init_logger(__name__)
 
@@ -29,11 +29,32 @@ def nullable_str(val: str):
     return val
 
 
+def nullable_kvs(val: str) -> Optional[Mapping[str, int]]:
+    if len(val) == 0:
+        return None
+
+    out_dict: Dict[str, int] = {}
+    for item in val.split(","):
+        try:
+            key, value = item.split("=")
+        except TypeError as exc:
+            msg = "Each item should be in the form KEY=VALUE"
+            raise ValueError(msg) from exc
+
+        try:
+            out_dict[key] = int(value)
+        except ValueError as exc:
+            msg = f"Failed to parse value of item {key}={value}"
+            raise ValueError(msg) from exc
+
+    return out_dict
+
+
 @dataclass
 class EngineArgs:
     """Arguments for vLLM engine."""
     model: str = 'facebook/opt-125m'
-    served_model_name: Optional[Union[List[str]]] = None
+    served_model_name: Optional[Union[str, List[str]]] = None
     tokenizer: Optional[str] = None
     skip_tokenizer_init: bool = False
     tokenizer_mode: str = 'auto'
@@ -81,6 +102,7 @@ class EngineArgs:
     # notice.
     tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]] = "ray"
     tokenizer_pool_extra_config: Optional[dict] = None
+    limit_mm_per_prompt: Optional[Mapping[str, int]] = None
     enable_lora: bool = False
     max_loras: int = 1
     max_lora_rank: int = 16
@@ -435,6 +457,21 @@ class EngineArgs:
                             'This should be a JSON string that will be '
                             'parsed into a dictionary. Ignored if '
                             'tokenizer_pool_size is 0.')
+
+        # Multimodal related configs
+        parser.add_argument(
+            '--limit-mm-per-prompt',
+            type=nullable_kvs,
+            default=EngineArgs.limit_mm_per_prompt,
+            # The default value is given in
+            # MultiModalRegistry.init_mm_limits_per_prompt
+            help=('For each multimodal plugin, limit how many '
+                  'input instances to allow for each prompt. '
+                  'Expects a comma-separated list of items, '
+                  'e.g.: `image=16,video=2` allows a maximum of 16 '
+                  'images and 2 videos per prompt. Defaults to 1 for '
+                  'each modality.'))
+
         # LoRA related configs
         parser.add_argument('--enable-lora',
                             action='store_true',
@@ -709,7 +746,8 @@ class EngineArgs:
             "CPU offload space must be non-negative"
             f", but got {self.cpu_offload_gb}")
 
-        multimodal_config = MultiModalConfig()
+        multimodal_config = MultiModalConfig(
+            limit_per_prompt=self.limit_mm_per_prompt or {})
 
         device_config = DeviceConfig(device=self.device)
         model_config = ModelConfig(

vllm/engine/llm_engine.py

@@ -24,8 +24,9 @@ from vllm.engine.output_processor.stop_checker import StopChecker
 from vllm.engine.output_processor.util import create_output_by_sequence_group
 from vllm.executor.executor_base import ExecutorBase
 from vllm.executor.ray_utils import initialize_ray_cluster
-from vllm.inputs import (INPUT_REGISTRY, EncoderDecoderLLMInputs, LLMInputs,
+from vllm.inputs import (INPUT_REGISTRY, EncoderDecoderLLMInputs,
-                         PromptInputs, SingletonPromptInputs)
+                         InputRegistry, LLMInputs, PromptInputs,
+                         SingletonPromptInputs)
 from vllm.inputs.parse import is_explicit_encoder_decoder_prompt
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
@@ -180,6 +181,7 @@ class LLMEngine:
         log_stats: bool,
         usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
         stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
+        input_registry: InputRegistry = INPUT_REGISTRY,
     ) -> None:
         logger.info(
             "Initializing an LLM engine (v%s) with config: "
@@ -265,8 +267,9 @@ class LLMEngine:
         self.generation_config_fields = _load_generation_config_dict(
             model_config)
 
-        self.input_processor = INPUT_REGISTRY.create_input_processor(
+        self.input_registry = input_registry
-            self.model_config)
+        self.input_processor = input_registry.create_input_processor(
+            model_config)
 
         self.model_executor = executor_class(
             model_config=model_config,

vllm/inputs/registry.py

@@ -1,6 +1,8 @@
 import functools
+from collections import UserDict
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Callable, Dict, Optional, Tuple, Type
+from typing import (TYPE_CHECKING, Callable, Dict, Mapping, Optional, Protocol,
+                    Tuple, Type)
 
 from torch import nn
 from transformers import PretrainedConfig
@@ -12,7 +14,7 @@ from .data import LLMInputs
 
 if TYPE_CHECKING:
     from vllm.config import ModelConfig, MultiModalConfig
-    from vllm.multimodal import MultiModalDataDict
+    from vllm.multimodal import MultiModalDataDict, MultiModalRegistry
     from vllm.sequence import SequenceData
 
 logger = init_logger(__name__)
@@ -65,15 +67,38 @@ class InputContext:
 
 N = TypeVar("N", bound=Type[nn.Module])
 
-DummyDataFactory = Callable[[InputContext, int],
-                            Tuple["SequenceData",
-                                  Optional["MultiModalDataDict"]]]
-"""
-Create dummy data to be inputted into the model.
 
-Note:
+class DummyDataFactory(Protocol):
-    :data:`InputProcessor` is not applied to the dummy data.
+
-"""
+    def __call__(
+        self,
+        ctx: InputContext,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Tuple["SequenceData", Optional["MultiModalDataDict"]]:
+        """
+        Create dummy data to be inputted into the model.
+
+        Note:
+            :data:`InputProcessor` is not applied to the dummy data.
+        """
+        ...
+
+
+class _MultiModalCounts(UserDict):
+    """
+    Wraps `mm_counts` for a more informative error message
+    when attempting to access a plugin that does not exist.
+    """
+
+    def __getitem__(self, key: str) -> int:
+        try:
+            return super().__getitem__(key)
+        except KeyError as exc:
+            msg = (f"There is no multi-modal plugin with the key: {key}. "
+                   f"Available keys: {set(self.keys())}")
+            raise KeyError(msg) from exc
+
 
 InputProcessor = Callable[[InputContext, LLMInputs], LLMInputs]
 """Preprocess the inputs to the model."""
@@ -95,6 +120,7 @@ class InputRegistry:
         self,
         ctx: InputContext,
         seq_len: int,
+        mm_counts: Mapping[str, int],
     ) -> Tuple["SequenceData", Optional["MultiModalDataDict"]]:
         """
         The default dummy data factory represents the longest possible text
@@ -133,8 +159,12 @@ class InputRegistry:
 
         return wrapper
 
-    def dummy_data_for_profiling(self, model_config: "ModelConfig",
+    def dummy_data_for_profiling(
-                                 seq_len: int):
+        self,
+        model_config: "ModelConfig",
+        seq_len: int,
+        mm_registry: "MultiModalRegistry",
+    ) -> Tuple["SequenceData", Optional["MultiModalDataDict"]]:
         """
         Create dummy data for profiling the memory usage of a model.
 
@@ -142,6 +172,10 @@ class InputRegistry:
 
         See also:
             :ref:`enabling_multimodal_inputs`
+
+        Note:
+            This should be called after
+            :meth:`~MultiModalRegistry.init_mm_limits_per_prompt`.
         """
         # Avoid circular import
         from vllm.model_executor.model_loader import get_model_architecture
@@ -149,8 +183,29 @@ class InputRegistry:
         model_cls, _ = get_model_architecture(model_config)
         dummy_factory = self._dummy_factories_by_model_type \
             .get(model_cls, self._default_dummy_data_factory)
+        mm_counts = mm_registry.get_mm_limits_per_prompt(model_config)
 
-        return dummy_factory(InputContext(model_config), seq_len)
+        seq_data, mm_data = dummy_factory(
+            InputContext(model_config),
+            seq_len,
+            _MultiModalCounts(mm_counts),
+        )
+
+        # Having more tokens is over-conservative but otherwise fine
+        num_tokens = seq_data.prompt_token_ids
+        assert len(num_tokens) >= seq_len, (
+            f"Expected at least {seq_len} dummy tokens for profiling, "
+            f"but found {len(num_tokens)} tokens instead.")
+
+        if mm_data is not None:
+            for k, v in mm_data.items():
+                num_items = len(v) if isinstance(v, list) else 1
+                num_expected = mm_counts[k]
+                assert num_items >= num_expected, (
+                    f"Expected at least {num_expected} dummy '{k}' instances "
+                    f"for profiling, but found {num_items} instances instead.")
+
+        return seq_data, mm_data
 
     def _default_input_processor(self, ctx: InputContext,
                                  inputs: LLMInputs) -> LLMInputs:

vllm/model_executor/model_loader/loader.py

@@ -133,7 +133,7 @@ def _get_model_initialization_kwargs(
 
     if supports_multimodal(model_class):
         if multimodal_config is None:
-            raise ValueError("Provide vision related configurations "
+            raise ValueError("Provide multi-modal related configurations "
                              "through LLM entrypoint or engine arguments.")
 
         extra_kwargs["multimodal_config"] = multimodal_config

vllm/model_executor/models/blip.py

@@ -31,13 +31,13 @@ def get_blip_num_patches(*, image_size: int, patch_size: int) -> int:
 
 
 def get_blip_image_feature_size(
-    hf_config: Union[BlipVisionConfig, Blip2VisionConfig], ) -> int:
+        hf_config: Union[BlipVisionConfig, Blip2VisionConfig]) -> int:
     return get_blip_num_patches(image_size=hf_config.image_size,
                                 patch_size=hf_config.patch_size)
 
 
 def get_max_blip_image_tokens(
-    hf_config: Union[BlipVisionConfig, Blip2VisionConfig], ) -> int:
+        hf_config: Union[BlipVisionConfig, Blip2VisionConfig]) -> int:
     return get_blip_image_feature_size(hf_config)
 
 
@@ -60,6 +60,7 @@ def dummy_seq_data_for_blip(
 
 def dummy_image_for_blip(
     hf_config: Union[BlipVisionConfig, Blip2VisionConfig],
+    num_images: int,
     *,
     image_width_override: Optional[int] = None,
     image_height_override: Optional[int] = None,
@@ -71,7 +72,7 @@ def dummy_image_for_blip(
         height = image_height_override
 
     image = Image.new("RGB", (width, height), color=0)
-    return {"image": image}
+    return {"image": image if num_images == 1 else [image] * num_images}
 
 
 def input_processor_for_blip(

vllm/model_executor/models/blip2.py

@@ -1,4 +1,5 @@
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
+from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
+                    TypedDict, Union)
 
 import torch
 import torch.nn as nn
@@ -413,17 +414,39 @@ def get_max_blip2_image_tokens(ctx: InputContext):
     raise NotImplementedError(msg)
 
 
-def dummy_data_for_blip2(ctx: InputContext, seq_len: int):
+def dummy_seq_data_for_blip2(
+    hf_config: Blip2Config,
+    seq_len: int,
+    num_images: int,
+    *,
+    image_token_id: int,
+    image_feature_size_override: Optional[int] = None,
+):
+    if image_feature_size_override is None:
+        image_feature_size = get_blip2_image_feature_size(hf_config)
+    else:
+        image_feature_size = image_feature_size_override
+
+    token_ids = [image_token_id] * image_feature_size * num_images
+    token_ids += [0] * (seq_len - image_feature_size * num_images)
+    return SequenceData(token_ids)
+
+
+def dummy_data_for_blip2(ctx: InputContext, seq_len: int,
+                         mm_counts: Mapping[str, int]):
     hf_config = ctx.get_hf_config(Blip2Config)
     vision_config = hf_config.vision_config
+    num_images = mm_counts["image"]
 
-    image_feature_size = get_blip2_image_feature_size(hf_config)
+    seq_data = dummy_seq_data_for_blip2(
-    token_ids = [BLIP2_IMAGE_TOKEN_ID] * image_feature_size
+        hf_config,
-    token_ids += [0] * (seq_len - image_feature_size)
+        seq_len,
-    seq_data = SequenceData(token_ids)
+        num_images,
+        image_token_id=BLIP2_IMAGE_TOKEN_ID,
+    )
 
     if isinstance(vision_config, Blip2VisionConfig):
-        mm_data = dummy_image_for_blip(vision_config)
+        mm_data = dummy_image_for_blip(vision_config, num_images)
 
         return seq_data, mm_data
 

vllm/model_executor/models/chameleon.py

@@ -1,6 +1,6 @@
 from functools import cached_property
-from typing import (Any, Dict, Iterable, List, Literal, Optional, Tuple,
+from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional,
-                    TypedDict)
+                    Tuple, TypedDict)
 
 import torch
 import torch.nn.functional as F
@@ -19,8 +19,7 @@ from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.base_config import (
+from vllm.model_executor.layers.quantization import QuantizationConfig
-    QuantizationConfig)
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -61,6 +60,7 @@ def get_max_chameleon_image_tokens(ctx: InputContext):
 
 def dummy_seq_data_for_chameleon(
     seq_len: int,
+    num_images: int,
     *,
     image_token_id: int,
     image_feature_size_override: Optional[int] = None,
@@ -70,12 +70,14 @@ def dummy_seq_data_for_chameleon(
     else:
         image_feature_size = image_feature_size_override
 
-    token_ids = [image_token_id] * image_feature_size
+    token_ids = [image_token_id] * image_feature_size * num_images
-    token_ids += [0] * (seq_len - image_feature_size)
+    token_ids += [0] * (seq_len - image_feature_size * num_images)
     return SequenceData(token_ids)
 
 
 def dummy_image_for_chameleon(
+    num_images: int,
+    *,
     image_width_override: Optional[int] = None,
     image_height_override: Optional[int] = None,
 ):
@@ -87,17 +89,20 @@ def dummy_image_for_chameleon(
         height = image_height_override
 
     image = Image.new("RGB", (width, height), color=0)
-    return {"image": image}
+    return {"image": image if num_images == 1 else [image] * num_images}
 
 
-def dummy_data_for_chameleon(ctx: InputContext, seq_len: int):
+def dummy_data_for_chameleon(ctx: InputContext, seq_len: int,
+                             mm_counts: Mapping[str, int]):
+    num_images = mm_counts["image"]
 
     seq_data = dummy_seq_data_for_chameleon(
         seq_len,
+        num_images,
         image_token_id=CHAMELEON_IMAGE_TOKEN_ID,
     )
 
-    mm_data = dummy_image_for_chameleon()
+    mm_data = dummy_image_for_chameleon(num_images)
     return seq_data, mm_data
 
 

vllm/model_executor/models/clip.py

@@ -43,6 +43,7 @@ def get_max_clip_image_tokens(hf_config: CLIPVisionConfig) -> int:
 def dummy_seq_data_for_clip(
     hf_config: CLIPVisionConfig,
     seq_len: int,
+    num_images: int,
     *,
     image_token_id: int,
     image_feature_size_override: Optional[int] = None,
@@ -52,13 +53,14 @@ def dummy_seq_data_for_clip(
     else:
         image_feature_size = image_feature_size_override
 
-    token_ids = [image_token_id] * image_feature_size
+    token_ids = [image_token_id] * image_feature_size * num_images
-    token_ids += [0] * (seq_len - image_feature_size)
+    token_ids += [0] * (seq_len - image_feature_size * num_images)
     return SequenceData(token_ids)
 
 
 def dummy_image_for_clip(
     hf_config: CLIPVisionConfig,
+    num_images: int,
     *,
     image_width_override: Optional[int] = None,
     image_height_override: Optional[int] = None,
@@ -70,7 +72,7 @@ def dummy_image_for_clip(
         height = image_height_override
 
     image = Image.new("RGB", (width, height), color=0)
-    return {"image": image}
+    return {"image": image if num_images == 1 else [image] * num_images}
 
 
 def input_processor_for_clip(

vllm/model_executor/models/fuyu.py

@@ -16,7 +16,7 @@
 # limitations under the License.
 """ PyTorch Fuyu model."""
 import math
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict
+from typing import Iterable, List, Literal, Mapping, Optional, Tuple, TypedDict
 
 import torch
 import torch.nn as nn
@@ -29,8 +29,7 @@ from vllm.config import CacheConfig, MultiModalConfig
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import ColumnParallelLinear
-from vllm.model_executor.layers.quantization.base_config import (
+from vllm.model_executor.layers.quantization import QuantizationConfig
-    QuantizationConfig)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.persimmon import PersimmonForCausalLM
 from vllm.model_executor.sampling_metadata import SamplingMetadata
@@ -94,27 +93,33 @@ def get_max_fuyu_image_tokens(ctx: InputContext):
     return (ncol + 1) * nrow
 
 
-def dummy_seq_data_for_fuyu(ctx: InputContext, seq_len: int):
+def dummy_seq_data_for_fuyu(ctx: InputContext, seq_len: int, num_images: int):
     ncol, nrow = get_max_fuyu_image_feature_size()
     image_feature_size = get_max_fuyu_image_tokens(ctx)
 
-    token_ids = ([_IMAGE_TOKEN_ID] * ncol + [_NEWLINE_TOKEN_ID]) * nrow
+    image_token_ids = ([_IMAGE_TOKEN_ID] * ncol + [_NEWLINE_TOKEN_ID]) * nrow
-    token_ids += [0] * (seq_len - image_feature_size)
+    token_ids = image_token_ids * num_images
+    token_ids += [0] * (seq_len - image_feature_size * num_images)
     return SequenceData(token_ids)
 
 
 def dummy_image_for_fuyu(
+    num_images: int,
+    *,
     image_width: int,
     image_height: int,
 ):
     image = Image.new("RGB", (image_width, image_height), color=0)
-    return {"image": image}
+    return {"image": image if num_images == 1 else [image] * num_images}
 
 
-def dummy_data_for_fuyu(ctx: InputContext, seq_len: int):
+def dummy_data_for_fuyu(ctx: InputContext, seq_len: int,
-    seq_data = dummy_seq_data_for_fuyu(ctx, seq_len)
+                        mm_counts: Mapping[str, int]):
-    mm_data = dummy_image_for_fuyu(MAX_IMAGE_FEATURE_SIZE_WIDTH,
+    num_images = mm_counts["image"]
-                                   MAX_IMAGE_FEATURE_SIZE_HEIGHT)
+    seq_data = dummy_seq_data_for_fuyu(ctx, seq_len, num_images)
+    mm_data = dummy_image_for_fuyu(num_images,
+                                   image_width=MAX_IMAGE_FEATURE_SIZE_WIDTH,
+                                   image_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT)
     return seq_data, mm_data
 
 

vllm/model_executor/models/interfaces.py

@@ -11,14 +11,11 @@ logger = init_logger(__name__)
 
 @runtime_checkable
 class SupportsMultiModal(Protocol):
-    """
+    """The interface required for all multi-modal models."""
-    The interface required for all multimodal (vision or audio) language
-    models.
-    """
 
     supports_multimodal: ClassVar[Literal[True]] = True
     """
-    A flag that indicates this model supports multimodal inputs.
+    A flag that indicates this model supports multi-modal inputs.
 
     Note:
         There is no need to redefine this flag if this class is in the

vllm/model_executor/models/internvl.py

@@ -5,7 +5,8 @@
 # Licensed under The MIT License [see LICENSE for details]
 # --------------------------------------------------------
 import itertools
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
+from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
+                    TypedDict, Union)
 
 import torch
 import torch.nn as nn
@@ -230,7 +231,7 @@ def input_processor_for_internvl(ctx: InputContext, llm_inputs: LLMInputs):
 
 
 def input_mapper_for_internvl(ctx: InputContext, data: object):
-    hf_config = ctx.get_hf_config(PretrainedConfig)
+    hf_config = ctx.get_hf_config()
 
     use_thumbnail = hf_config.use_thumbnail
     min_num = hf_config.min_dynamic_patch
@@ -256,7 +257,9 @@ def input_mapper_for_internvl(ctx: InputContext, data: object):
     })
 
 
-def dummy_data_for_internvl(ctx: InputContext, seq_len: int):
+def dummy_data_for_internvl(ctx: InputContext, seq_len: int,
+                            mm_counts: Mapping[str, int]):
+    num_images = mm_counts["image"]
 
     image_feature_size = get_max_internvl_image_tokens(ctx)
     model_config = ctx.model_config
@@ -268,6 +271,7 @@ def dummy_data_for_internvl(ctx: InputContext, seq_len: int):
     seq_data = dummy_seq_data_for_clip(
         vision_config,
         seq_len,
+        num_images,
         image_token_id=tokenizer.encode(IMG_CONTEXT,
                                         add_special_tokens=False)[0],
         image_feature_size_override=image_feature_size,
@@ -281,6 +285,7 @@ def dummy_data_for_internvl(ctx: InputContext, seq_len: int):
 
     mm_data = dummy_image_for_clip(
         vision_config,
+        num_images,
         image_width_override=max_image_width,
         image_height_override=max_image_height,
     )

vllm/model_executor/models/llava.py

@@ -1,5 +1,6 @@
 import itertools
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
+from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
+                    TypedDict, Union)
 
 import torch
 import torch.nn as nn
@@ -9,8 +10,7 @@ from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, MultiModalConfig
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
 from vllm.model_executor.layers.activation import get_act_fn
-from vllm.model_executor.layers.quantization.base_config import (
+from vllm.model_executor.layers.quantization import QuantizationConfig
-    QuantizationConfig)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -88,9 +88,11 @@ def get_max_llava_image_tokens(ctx: InputContext):
         raise ValueError(f"Unexpected select feature strategy: {strategy}")
 
 
-def dummy_data_for_llava(ctx: InputContext, seq_len: int):
+def dummy_data_for_llava(ctx: InputContext, seq_len: int,
+                         mm_counts: Mapping[str, int]):
     hf_config = ctx.get_hf_config(LlavaConfig)
     vision_config = hf_config.vision_config
+    num_images = mm_counts["image"]
 
     image_feature_size = get_max_llava_image_tokens(ctx)
 
@@ -98,21 +100,23 @@ def dummy_data_for_llava(ctx: InputContext, seq_len: int):
         seq_data = dummy_seq_data_for_clip(
             vision_config,
             seq_len,
+            num_images,
             image_token_id=hf_config.image_token_index,
             image_feature_size_override=image_feature_size,
         )
 
-        mm_data = dummy_image_for_clip(vision_config)
+        mm_data = dummy_image_for_clip(vision_config, num_images)
         return seq_data, mm_data
     elif isinstance(vision_config, SiglipVisionConfig):
         seq_data = dummy_seq_data_for_siglip(
             vision_config,
             seq_len,
+            num_images,
             image_token_id=hf_config.image_token_index,
             image_feature_size_override=image_feature_size,
         )
 
-        mm_data = dummy_image_for_siglip(vision_config)
+        mm_data = dummy_image_for_siglip(vision_config, num_images)
         return seq_data, mm_data
 
     msg = f"Unsupported vision config: {type(vision_config)}"

vllm/model_executor/models/llava_next.py

@@ -1,5 +1,6 @@
 import itertools
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
+from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
+                    TypedDict, Union)
 
 import torch
 import torch.nn as nn
@@ -13,8 +14,7 @@ from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, MultiModalConfig
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
 from vllm.logger import init_logger
-from vllm.model_executor.layers.quantization.base_config import (
+from vllm.model_executor.layers.quantization import QuantizationConfig
-    QuantizationConfig)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -158,9 +158,11 @@ def get_max_llava_next_image_tokens(ctx: InputContext):
     )
 
 
-def dummy_data_for_llava_next(ctx: InputContext, seq_len: int):
+def dummy_data_for_llava_next(ctx: InputContext, seq_len: int,
+                              mm_counts: Mapping[str, int]):
     hf_config = ctx.get_hf_config(LlavaNextConfig)
     vision_config = hf_config.vision_config
+    num_images = mm_counts["image"]
 
     image_feature_size = get_max_llava_next_image_tokens(ctx)
 
@@ -168,12 +170,14 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int):
         seq_data = dummy_seq_data_for_clip(
             vision_config,
             seq_len,
+            num_images,
             image_token_id=hf_config.image_token_index,
             image_feature_size_override=image_feature_size,
         )
 
         mm_data = dummy_image_for_clip(
             vision_config,
+            num_images,
             image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH,
             image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
         )
@@ -183,12 +187,14 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int):
         seq_data = dummy_seq_data_for_siglip(
             vision_config,
             seq_len,
+            num_images,
             image_token_id=hf_config.image_token_index,
             image_feature_size_override=image_feature_size,
         )
 
         mm_data = dummy_image_for_siglip(
             vision_config,
+            num_images,
             image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH,
             image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
         )

vllm/model_executor/models/minicpmv.py

@@ -24,8 +24,8 @@
 import math
 import re
 from functools import partial
-from typing import (Any, Callable, Iterable, List, Optional, Tuple, TypedDict,
+from typing import (Any, Callable, Iterable, List, Mapping, Optional, Tuple,
-                    Union)
+                    TypedDict, Union)
 
 import numpy as np
 import torch
@@ -42,8 +42,7 @@ from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.base_config import (
+from vllm.model_executor.layers.quantization import QuantizationConfig
-    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.utils import set_default_torch_dtype
@@ -408,22 +407,24 @@ def get_max_minicpmv_image_tokens(ctx: InputContext):
     return getattr(hf_config, "query_num", 64)
 
 
-def dummy_seq_data_for_minicpmv(seq_len: int):
+def dummy_seq_data_for_minicpmv(seq_len: int, num_images: int):
     token_ids = [0] * seq_len
     return SequenceData(token_ids)
 
 
-def dummy_image_for_minicpmv(hf_config: PretrainedConfig):
+def dummy_image_for_minicpmv(hf_config: PretrainedConfig, num_images: int):
     width = height = hf_config.image_size
     image = Image.new("RGB", (width, height), color=0)
-    return {"image": image}
+    return {"image": image if num_images == 1 else [image] * num_images}
 
 
-def dummy_data_for_minicpmv(ctx: InputContext, seq_len: int):
+def dummy_data_for_minicpmv(ctx: InputContext, seq_len: int,
+                            mm_counts: Mapping[str, int]):
     hf_config = ctx.get_hf_config()
+    num_images = mm_counts["image"]
 
-    seq_data = dummy_seq_data_for_minicpmv(seq_len)
+    seq_data = dummy_seq_data_for_minicpmv(seq_len, num_images)
-    mm_data = dummy_image_for_minicpmv(hf_config)
+    mm_data = dummy_image_for_minicpmv(hf_config, num_images)
 
     return seq_data, mm_data
 

vllm/model_executor/models/paligemma.py

@@ -1,4 +1,5 @@
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
+from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
+                    TypedDict, Union)
 
 import torch
 from torch import nn
@@ -9,8 +10,7 @@ from vllm.config import CacheConfig, MultiModalConfig
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
 from vllm.logger import init_logger
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.base_config import (
+from vllm.model_executor.layers.quantization import QuantizationConfig
-    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.gemma import GemmaModel
@@ -57,17 +57,20 @@ def get_max_paligemma_image_tokens(ctx: InputContext):
     return get_max_siglip_image_tokens(vision_config)
 
 
-def dummy_data_for_paligemma(ctx: InputContext, seq_len: int):
+def dummy_data_for_paligemma(ctx: InputContext, seq_len: int,
+                             mm_counts: Mapping[str, int]):
     hf_config = ctx.get_hf_config(PaliGemmaConfig)
     vision_config = hf_config.vision_config
+    num_images = mm_counts["image"]
 
     seq_data = dummy_seq_data_for_siglip(
         vision_config,
         seq_len,
+        num_images,
         image_token_id=hf_config.image_token_index,
     )
 
-    mm_data = dummy_image_for_siglip(vision_config)
+    mm_data = dummy_image_for_siglip(vision_config, num_images)
     return seq_data, mm_data
 
 

vllm/model_executor/models/phi3v.py

@@ -15,7 +15,8 @@
 # limitations under the License.
 import re
 from functools import lru_cache
-from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
+from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
+                    TypedDict, Union)
 
 import numpy as np
 import torch
@@ -28,8 +29,7 @@ from vllm.config import CacheConfig, ModelConfig, MultiModalConfig
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
 from vllm.logger import init_logger
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.base_config import (
+from vllm.model_executor.layers.quantization import QuantizationConfig
-    QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -347,18 +347,22 @@ def get_max_phi3v_image_tokens(ctx: InputContext):
     )
 
 
-def dummy_data_for_phi3v(ctx: InputContext, seq_len: int):
+def dummy_data_for_phi3v(ctx: InputContext, seq_len: int,
+                         mm_counts: Mapping[str, int]):
+    num_images = mm_counts["image"]
 
     image_feature_size = get_max_phi3v_image_tokens(ctx)
 
     seq_data = dummy_seq_data_for_clip(
         CLIP_VIT_LARGE_PATCH14_336_CONFIG,
         seq_len,
+        num_images,
         image_token_id=_IMAGE_TOKEN_ID,
         image_feature_size_override=image_feature_size,
     )
     mm_data = dummy_image_for_clip(
         CLIP_VIT_LARGE_PATCH14_336_CONFIG,
+        num_images,
         image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH,
         image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
     )

vllm/model_executor/models/siglip.py

@@ -52,6 +52,7 @@ def get_max_siglip_image_tokens(hf_config: SiglipVisionConfig) -> int:
 def dummy_seq_data_for_siglip(
     hf_config: SiglipVisionConfig,
     seq_len: int,
+    num_images: int,
     *,
     image_token_id: int,
     image_feature_size_override: Optional[int] = None,
@@ -61,13 +62,14 @@ def dummy_seq_data_for_siglip(
     else:
         image_feature_size = image_feature_size_override
 
-    token_ids = [image_token_id] * image_feature_size
+    token_ids = [image_token_id] * image_feature_size * num_images
-    token_ids += [0] * (seq_len - image_feature_size)
+    token_ids += [0] * (seq_len - image_feature_size * num_images)
     return SequenceData(token_ids)
 
 
 def dummy_image_for_siglip(
     hf_config: SiglipVisionConfig,
+    num_images: int,
     *,
     image_width_override: Optional[int] = None,
     image_height_override: Optional[int] = None,
@@ -79,7 +81,7 @@ def dummy_image_for_siglip(
         height = image_height_override
 
     image = Image.new("RGB", (width, height), color=0)
-    return {"image": image}
+    return {"image": image if num_images == 1 else [image] * num_images}
 
 
 def input_processor_for_siglip(

vllm/multimodal/base.py

@@ -1,9 +1,9 @@
 import sys
 from abc import ABC, abstractmethod
 from collections import UserDict, defaultdict
-from typing import Any, Callable, Dict, List, Optional
+from typing import Callable, Dict, List, Mapping, Optional
 from typing import Sequence as GenericSequence
-from typing import Tuple, Type, TypedDict, TypeVar, Union, cast
+from typing import Tuple, Type, TypedDict, TypeVar, Union, cast, final
 
 import numpy as np
 import torch
@@ -116,17 +116,30 @@ class MultiModalInputs(_MultiModalInputsBase):
                                batched_inputs)
 
 
+_T = TypeVar("_T")
+
+MultiModalData: TypeAlias = Union[_T, List[_T]]
+"""
+Either a single data instance, or a list of data instances.
+
+The number of data instances allowed per modality is restricted by
+`--limit-mm-per-prompt`.
+"""
+
+
+@final
 class MultiModalDataBuiltins(TypedDict, total=False):
     """Modality types that are predefined by vLLM."""
 
-    image: Image.Image
+    image: MultiModalData[Image.Image]
-    """The input image."""
+    """The input image(s)."""
 
-    audio: Tuple[np.ndarray, Union[int, float]]
+    audio: MultiModalData[Tuple[np.ndarray, Union[int, float]]]
-    """The input audio and its sampling rate."""
+    """The input audio item(s) and corresponding sampling rate(s)."""
 
 
-MultiModalDataDict = Union[MultiModalDataBuiltins, Dict[str, Any]]
+MultiModalDataDict = Union[MultiModalDataBuiltins,
+                           Mapping[str, MultiModalData[object]]]
 """
 A dictionary containing an item for each modality type to input.
 
@@ -137,7 +150,8 @@ Note:
     Read more on that :ref:`here <adding_multimodal_plugin>`.
 """
 
-MultiModalInputMapper = Callable[[InputContext, object], MultiModalInputs]
+MultiModalInputMapper = Callable[[InputContext, MultiModalData[object]],
+                                 MultiModalInputs]
 """
 Return a dictionary to be passed as keyword arguments to
 :meth:`~torch.nn.Module.forward`. This is similar in concept to tokenizers
@@ -181,8 +195,11 @@ class MultiModalPlugin(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def _default_input_mapper(self, ctx: InputContext,
+    def _default_input_mapper(
-                              data: object) -> MultiModalInputs:
+        self,
+        ctx: InputContext,
+        data: MultiModalData[object],
+    ) -> MultiModalInputs:
         """
         Return a dictionary to be passed as keyword arguments to
         :meth:`~torch.nn.Module.forward`. This is similar in concept to
@@ -225,7 +242,7 @@ class MultiModalPlugin(ABC):
         return wrapper
 
     def map_input(self, model_config: ModelConfig,
-                  data: object) -> MultiModalInputs:
+                  data: MultiModalData[object]) -> MultiModalInputs:
         """
         Transform the data into a dictionary of model inputs using the
         input mapper registered for that model.
@@ -254,8 +271,8 @@ class MultiModalPlugin(ABC):
     @abstractmethod
     def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
         """
-        Calculate the maximum number of multimodal tokens input to the language
+        Calculate the maximum number of tokens, corresponding to a single
-        model. This does not include tokens that correspond to the input text.
+        instance of multimodal data, that are passed to the language model.
         """
         raise NotImplementedError
 
@@ -269,8 +286,9 @@ class MultiModalPlugin(ABC):
         max_mm_tokens: Optional[MultiModalTokensCalc] = None,
     ):
         """
-        Register the maximum number of multi-modal tokens input to the
+        Register the maximum number of tokens, corresponding to a single
-        language model for a model class.
+        instance of multimodal data, that are passed to the language model
+        for a model class.
 
         If `None` is provided, then the default calculation is used instead.
 

vllm/multimodal/image.py

@@ -11,7 +11,7 @@ from vllm.transformers_utils.image_processor import get_image_processor
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer
 from vllm.utils import is_list_of
 
-from .base import MultiModalInputs, MultiModalPlugin
+from .base import MultiModalData, MultiModalInputs, MultiModalPlugin
 
 logger = init_logger(__name__)
 
@@ -110,8 +110,11 @@ class ImagePlugin(MultiModalPlugin):
             model_config.model,
             trust_remote_code=model_config.trust_remote_code)
 
-    def _default_input_mapper(self, ctx: InputContext,
+    def _default_input_mapper(
-                              data: object) -> MultiModalInputs:
+        self,
+        ctx: InputContext,
+        data: MultiModalData[object],
+    ) -> MultiModalInputs:
         model_config = ctx.model_config
 
         # PIL image

vllm/multimodal/registry.py

@@ -1,19 +1,33 @@
 import functools
-from typing import Dict, Optional, Sequence
+from collections import UserDict
+from typing import Dict, Mapping, Optional, Sequence
 
-import torch
+from vllm.config import ModelConfig, MultiModalConfig
-
-from vllm.config import ModelConfig
 from vllm.logger import init_logger
 
 from .audio import AudioPlugin
 from .base import (MultiModalDataDict, MultiModalInputMapper, MultiModalInputs,
-                   MultiModalPlugin, MultiModalTokensCalc)
+                   MultiModalPlugin, MultiModalTokensCalc, NestedTensors)
 from .image import ImagePlugin
 
 logger = init_logger(__name__)
 
 
+class _MultiModalLimits(UserDict):
+    """
+    Wraps `_limits_by_model` for a more informative error message
+    when attempting to access a model that does not exist.
+    """
+
+    def __getitem__(self, key: ModelConfig) -> Dict[str, int]:
+        try:
+            return super().__getitem__(key)
+        except KeyError as exc:
+            msg = (f"Cannot find `mm_limits` for model={key.model}. Did you "
+                   "forget to call `init_mm_limits_per_prompt`?")
+            raise KeyError(msg) from exc
+
+
 class MultiModalRegistry:
     """
     A registry that dispatches data processing to the
@@ -28,6 +42,11 @@ class MultiModalRegistry:
             plugins: Sequence[MultiModalPlugin] = DEFAULT_PLUGINS) -> None:
         self._plugins = {p.get_data_key(): p for p in plugins}
 
+        # This is used for non-multimodal models
+        self._disabled_limits_per_plugin = {k: 0 for k in self._plugins}
+
+        self._limits_by_model = _MultiModalLimits()
+
     def register_plugin(self, plugin: MultiModalPlugin) -> None:
         """
         Register a multi-modal plugin so it can be recognized by vLLM.
@@ -86,13 +105,24 @@ class MultiModalRegistry:
         via the input mapper registered for that model.
 
         See :meth:`MultiModalPlugin.map_input` for more details.
+
+        Note:
+            This should be called after :meth:`init_mm_limits_per_prompt`.
         """
-        merged_dict: Dict[str, torch.Tensor] = {}
+        merged_dict: Dict[str, NestedTensors] = {}
 
         for data_key, data_value in data.items():
-            input_dict = self._get_plugin(data_key) \
+            plugin = self._get_plugin(data_key)
-                .map_input(model_config, data_value)
 
+            num_items = len(data_value) if isinstance(data_value, list) else 1
+            max_items = self._limits_by_model[model_config][data_key]
+            if num_items > max_items:
+                raise ValueError(
+                    f"You set {data_key}={max_items} (or defaulted to 1) in "
+                    f"`--limit-mm-per-prompt`, but found {num_items} items "
+                    "in the same prompt.")
+
+            input_dict = plugin.map_input(model_config, data_value)
             for input_key, input_tensor in input_dict.items():
                 if input_key in merged_dict:
                     raise ValueError(f"The input mappers (keys={set(data)}) "
@@ -115,8 +145,9 @@ class MultiModalRegistry:
         max_mm_tokens: Optional[MultiModalTokensCalc] = None,
     ):
         """
-        Register the maximum number of tokens, belonging to a
+        Register the maximum number of tokens, corresponding to a single
-        specific modality, input to the language model for a model class.
+        instance of multimodal data belonging to a specific modality, that are
+        passed to the language model for a model class.
         """
         return self._get_plugin(data_type_key) \
             .register_max_multimodal_tokens(max_mm_tokens)
@@ -126,8 +157,8 @@ class MultiModalRegistry:
         max_mm_tokens: Optional[MultiModalTokensCalc] = None,
     ):
         """
-        Register the maximum number of image tokens
+        Register the maximum number of image tokens, corresponding to a single
-        input to the language model for a model class.
+        image, that are passed to the language model for a model class.
         """
         return self.register_max_multimodal_tokens("image", max_mm_tokens)
 
@@ -135,9 +166,63 @@ class MultiModalRegistry:
         """
         Get the maximum number of multi-modal tokens
         for profiling the memory usage of a model.
-        
+
         See :meth:`MultiModalPlugin.get_max_multimodal_tokens` for more details.
+
+        Note:
+            This should be called after :meth:`init_mm_limits_per_prompt`.
         """
-        return sum(
+        limits_per_plugin = self._limits_by_model[model_config]
-            plugin.get_max_multimodal_tokens(model_config)
+
-            for plugin in self._plugins.values())
+        return sum((limits_per_plugin[key] *
+                    plugin.get_max_multimodal_tokens(model_config))
+                   for key, plugin in self._plugins.items())
+
+    def init_mm_limits_per_prompt(
+        self,
+        model_config: ModelConfig,
+        multimodal_config: Optional[MultiModalConfig],
+    ) -> None:
+        """
+        Initialize the maximum number of multi-modal input instances for each
+        modality that are allowed per prompt for a model class.
+        """
+        if model_config in self._limits_by_model:
+            logger.warning(
+                "`mm_limits` has already been set for model=%s, and will "
+                "be overwritten by the new values.", model_config.model)
+
+        if multimodal_config is None:
+            limits_per_plugin = self._disabled_limits_per_plugin
+        else:
+            config_limits_per_plugin = multimodal_config.limit_per_prompt
+
+            extra_keys = config_limits_per_plugin.keys() - self._plugins.keys()
+            if extra_keys:
+                logger.warning(
+                    "Detected extra keys in `--limit-mm-per-prompt` which "
+                    "are not registered as multi-modal plugins: %s. "
+                    "They will be ignored.", extra_keys)
+
+            # NOTE: Currently the default is set to 1 for each plugin
+            # TODO: Automatically determine the limits based on budget
+            # once more models support multi-image inputs
+            limits_per_plugin = {
+                key: config_limits_per_plugin.get(key, 1)
+                for key in self._plugins
+            }
+
+        self._limits_by_model[model_config] = limits_per_plugin
+
+    def get_mm_limits_per_prompt(
+        self,
+        model_config: ModelConfig,
+    ) -> Mapping[str, int]:
+        """
+        Get the maximum number of multi-modal input instances for each modality
+        that are allowed per prompt for a model class.
+
+        Note:
+            This should be called after :meth:`init_mm_limits_per_prompt`.
+        """
+        return self._limits_by_model[model_config]

vllm/utils.py

@@ -13,7 +13,6 @@ import threading
 import uuid
 import warnings
 from asyncio import FIRST_COMPLETED, ensure_future
-from collections import defaultdict
 from functools import lru_cache, partial, wraps
 from platform import uname
 from typing import (Any, AsyncGenerator, Awaitable, Callable, Dict, Generic,
@@ -760,16 +759,6 @@ class CudaMemoryProfiler:
         gc.collect()
 
 
-def str_to_int_tuple(s: str) -> Tuple[int, ...]:
-    """Convert a string to a tuple of integers."""
-    try:
-        return tuple(map(int, s.split(",")))
-    except ValueError as e:
-        raise ValueError(
-            "String must be a series of integers separated by commas "
-            f"(e.g., 1, 2, 3). Given input: {s}") from e
-
-
 def make_ndarray_with_pad(
     x: List[List[T]],
     pad: T,
@@ -863,23 +852,6 @@ def is_list_of(
     assert_never(check)
 
 
-def merge_dicts(dict1: Dict[K, List[T]],
-                dict2: Dict[K, List[T]]) -> Dict[K, List[T]]:
-    """Merge 2 dicts that have key -> List of items.
-
-    When a key conflicts, the values in dict1 is prioritized.
-    """
-    merged_dict: Dict[K, List[T]] = defaultdict(list)
-
-    for key, value in dict1.items():
-        merged_dict[key].extend(value)
-
-    for key, value in dict2.items():
-        merged_dict[key].extend(value)
-
-    return dict(merged_dict)
-
-
 JSONTree = Union[Dict[str, "JSONTree[T]"], List["JSONTree[T]"],
                  Tuple["JSONTree[T]", ...], T]
 """A nested JSON structure where the leaves need not be JSON-serializable."""

vllm/worker/enc_dec_model_runner.py

@@ -12,9 +12,10 @@ from vllm.attention.selector import (_Backend, get_env_variable_attn_backend,
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ObservabilityConfig,
                          ParallelConfig, PromptAdapterConfig, SchedulerConfig)
-from vllm.inputs import INPUT_REGISTRY
+from vllm.inputs import INPUT_REGISTRY, InputRegistry
 from vllm.logger import init_logger
 from vllm.model_executor import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import (IntermediateTensors, PoolerOutput, SamplerOutput,
                            SequenceGroupMetadata)
@@ -83,6 +84,8 @@ class EncoderDecoderModelRunner(GPUModelRunnerBase[EncoderDecoderModelInput]):
         prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         multimodal_config: Optional[MultiModalConfig] = None,
         observability_config: Optional[ObservabilityConfig] = None,
+        input_registry: InputRegistry = INPUT_REGISTRY,
+        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
     ):
         '''
         EncoderDecoderModelRunner constructor.
@@ -271,6 +274,16 @@ class EncoderDecoderModelRunner(GPUModelRunnerBase[EncoderDecoderModelInput]):
         seqs: List[SequenceGroupMetadata] = []
 
         model_config = self.model_config
+        mm_config = self.multimodal_config
+
+        input_registry = self.input_registry
+        mm_registry = self.mm_registry
+        mm_registry.init_mm_limits_per_prompt(model_config, mm_config)
+
+        max_mm_tokens = mm_registry.get_max_multimodal_tokens(model_config)
+        if max_mm_tokens > 0:
+            raise NotImplementedError(
+                "Multi-modal encoder-decoder models are not supported yet")
 
         batch_size = 0
         for group_id in range(max_num_seqs):
@@ -278,8 +291,8 @@ class EncoderDecoderModelRunner(GPUModelRunnerBase[EncoderDecoderModelInput]):
                        (group_id < max_num_batched_tokens % max_num_seqs))
             batch_size += seq_len
 
-            seq_data, _ = INPUT_REGISTRY \
+            seq_data, _ = input_registry \
-                .dummy_data_for_profiling(model_config, seq_len)
+                .dummy_data_for_profiling(model_config, seq_len, mm_registry)
 
             # Having more tokens is over-conservative but otherwise fine
             assert len(seq_data.prompt_token_ids) >= seq_len, (

vllm/worker/model_runner.py

@@ -31,7 +31,7 @@ from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ParallelConfig, PromptAdapterConfig, SchedulerConfig)
 from vllm.distributed import get_pp_group
 from vllm.distributed.parallel_state import graph_capture
-from vllm.inputs import INPUT_REGISTRY
+from vllm.inputs import INPUT_REGISTRY, InputRegistry
 from vllm.logger import init_logger
 from vllm.lora.layers import LoRAMapping
 from vllm.lora.request import LoRARequest
@@ -43,7 +43,7 @@ from vllm.model_executor.models.interfaces import (supports_lora,
                                                    supports_multimodal)
 from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
-                             MultiModalInputs)
+                             MultiModalInputs, MultiModalRegistry)
 from vllm.prompt_adapter.layers import PromptAdapterMapping
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.prompt_adapter.worker_manager import (
@@ -807,6 +807,8 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         multimodal_config: Optional[MultiModalConfig] = None,
         return_hidden_states: bool = False,
         observability_config: Optional[ObservabilityConfig] = None,
+        input_registry: InputRegistry = INPUT_REGISTRY,
+        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
     ):
         self.model_config = model_config
         self.parallel_config = parallel_config
@@ -860,8 +862,10 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         ) if num_attn_heads else None
 
         # Multi-modal data support
-        self.multi_modal_input_mapper = MULTIMODAL_REGISTRY \
+        self.input_registry = input_registry
-            .create_input_mapper(self.model_config)
+        self.mm_registry = mm_registry
+        self.multi_modal_input_mapper = mm_registry \
+            .create_input_mapper(model_config)
 
         # Lazy initialization
         self.model: nn.Module  # Set after load_model
@@ -902,7 +906,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
             assert supports_lora(self.model), "Model does not support LoRA"
             assert not supports_multimodal(
                 self.model
-            ), "To be tested: multimodal language model with LoRA settings."
+            ), "To be tested: Multi-modal model with LoRA settings."
 
             self.lora_manager = LRUCacheWorkerLoRAManager(
                 self.scheduler_config.max_num_seqs,
@@ -1046,17 +1050,21 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         # Profile memory usage with max_num_sequences sequences and the total
         # number of tokens equal to max_num_batched_tokens.
         seqs: List[SequenceGroupMetadata] = []
-        # Additional GPU memory may be needed for vision encoding, which needs
+        # Additional GPU memory may be needed for multi-modal encoding, which
-        # to be accounted for when calculating the GPU blocks for
+        # needs to be accounted for when calculating the GPU blocks for
         # vLLM blocker manager.
         # To exercise the worst scenario for GPU memory consumption,
         # the number of seqs (batch_size) is chosen to maximize the number
         # of images processed.
         model_config = self.model_config
+        mm_config = self.multimodal_config
 
-        if supports_multimodal(self.model):
+        input_registry = self.input_registry
-            max_mm_tokens = MULTIMODAL_REGISTRY \
+        mm_registry = self.mm_registry
-                .get_max_multimodal_tokens(model_config)
+        mm_registry.init_mm_limits_per_prompt(model_config, mm_config)
+
+        max_mm_tokens = mm_registry.get_max_multimodal_tokens(model_config)
+        if max_mm_tokens > 0:
             max_num_seqs_orig = max_num_seqs
             max_num_seqs = min(max_num_seqs,
                                max_num_batched_tokens // max_mm_tokens)
@@ -1074,13 +1082,8 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                        (group_id < max_num_batched_tokens % max_num_seqs))
             batch_size += seq_len
 
-            seq_data, dummy_multi_modal_data = INPUT_REGISTRY \
+            seq_data, dummy_multi_modal_data = input_registry \
-                .dummy_data_for_profiling(model_config, seq_len)
+                .dummy_data_for_profiling(model_config, seq_len, mm_registry)
-
-            # Having more tokens is over-conservative but otherwise fine
-            assert len(seq_data.prompt_token_ids) >= seq_len, (
-                f"Expected at least {seq_len} dummy tokens for profiling, "
-                f"but got: {len(seq_data.prompt_token_ids)}")
 
             seq = SequenceGroupMetadata(
                 request_id=str(group_id),

vllm/worker/xpu_model_runner.py

@@ -9,12 +9,11 @@ from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
                          PromptAdapterConfig, SchedulerConfig)
 from vllm.distributed import broadcast_tensor_dict
-from vllm.inputs import INPUT_REGISTRY
+from vllm.inputs import INPUT_REGISTRY, InputRegistry
 from vllm.logger import init_logger
 from vllm.model_executor.model_loader import get_model
-from vllm.model_executor.models.interfaces import supports_multimodal
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
-                             MultiModalInputs)
+                             MultiModalInputs, MultiModalRegistry)
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import (IntermediateTensors, SamplerOutput,
                            SequenceGroupMetadata)
@@ -89,6 +88,8 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPU]):
         kv_cache_dtype: Optional[str] = "auto",
         prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         is_driver_worker: bool = False,
+        input_registry: InputRegistry = INPUT_REGISTRY,
+        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
         *args,
         **kwargs,
     ):
@@ -120,8 +121,10 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPU]):
         )
 
         # Multi-modal data support
-        self.multi_modal_input_mapper = MULTIMODAL_REGISTRY \
+        self.input_registry = input_registry
-            .create_input_mapper(self.model_config)
+        self.mm_registry = mm_registry
+        self.multi_modal_input_mapper = mm_registry \
+            .create_input_mapper(model_config)
 
         # Lazy initialization.
         self.model: nn.Module  # Set after init_Model
@@ -157,17 +160,21 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPU]):
         # Profile memory usage with max_num_sequences sequences and the total
         # number of tokens equal to max_num_batched_tokens.
         seqs: List[SequenceGroupMetadata] = []
-        # Additional GPU memory may be needed for vision encoding, which needs
+        # Additional GPU memory may be needed for multi-modal encoding, which
-        # to be accounted for when calculating the GPU blocks for
+        # needs to be accounted for when calculating the GPU blocks for
         # vLLM blocker manager.
         # To exercise the worst scenario for GPU memory consumption,
         # the number of seqs (batch_size) is chosen to maximize the number
         # of images processed.
         model_config = self.model_config
+        mm_config = self.multimodal_config
 
-        if supports_multimodal(self.model):
+        input_registry = self.input_registry
-            max_mm_tokens = MULTIMODAL_REGISTRY \
+        mm_registry = self.mm_registry
-                .get_max_multimodal_tokens(model_config)
+        mm_registry.init_mm_limits_per_prompt(model_config, mm_config)
+
+        max_mm_tokens = mm_registry.get_max_multimodal_tokens(model_config)
+        if max_mm_tokens > 0:
             max_num_seqs_orig = max_num_seqs
             max_num_seqs = min(max_num_seqs,
                                max_num_batched_tokens // max_mm_tokens)
@@ -183,13 +190,8 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPU]):
             seq_len = (max_num_batched_tokens // max_num_seqs +
                        (group_id < max_num_batched_tokens % max_num_seqs))
 
-            seq_data, dummy_multi_modal_data = INPUT_REGISTRY \
+            seq_data, dummy_multi_modal_data = input_registry \
-                .dummy_data_for_profiling(model_config, seq_len)
+                .dummy_data_for_profiling(model_config, seq_len, mm_registry)
-
-            # Having more tokens is over-conservative but otherwise fine
-            assert len(seq_data.prompt_token_ids) >= seq_len, (
-                f"Expected at least {seq_len} dummy tokens for profiling, "
-                f"but got: {len(seq_data.prompt_token_ids)}")
 
             seq = SequenceGroupMetadata(
                 request_id=str(group_id),