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

vllm/model_executor/models/chameleon.py

 from functools import cached_property
-from typing import (Any, Dict, Iterable, List, Literal, Optional, Tuple,
-                    TypedDict)
+from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional,
+                    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 (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import 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 += [0] * (seq_len - image_feature_size)
+    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_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 += [0] * (seq_len - image_feature_size)
+    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_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 (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import 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
-    token_ids += [0] * (seq_len - image_feature_size)
+    image_token_ids = ([_IMAGE_TOKEN_ID] * ncol + [_NEWLINE_TOKEN_ID]) * nrow
+    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):
-    seq_data = dummy_seq_data_for_fuyu(ctx, seq_len)
-    mm_data = dummy_image_for_fuyu(MAX_IMAGE_FEATURE_SIZE_WIDTH,
-                                   MAX_IMAGE_FEATURE_SIZE_HEIGHT)
+def dummy_data_for_fuyu(ctx: InputContext, seq_len: int,
+                        mm_counts: Mapping[str, int]):
+    num_images = mm_counts["image"]
+    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 multimodal (vision or audio) language
-    models.
-    """
+    """The interface required for all multi-modal 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

 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 (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import 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

 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 (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import 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,
-                    Union)
+from typing import (Any, Callable, Iterable, List, Mapping, Optional, Tuple,
+                    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 (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import 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)
-    mm_data = dummy_image_for_minicpmv(hf_config)
+    seq_data = dummy_seq_data_for_minicpmv(seq_len, num_images)
+    mm_data = dummy_image_for_minicpmv(hf_config, num_images)
 
     return seq_data, mm_data
 

vllm/model_executor/models/paligemma.py

-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 (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import 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 (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import 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 += [0] * (seq_len - image_feature_size)
+    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_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

 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
-    """The input image."""
+    image: MultiModalData[Image.Image]
+    """The input image(s)."""
 
-    audio: Tuple[np.ndarray, Union[int, float]]
-    """The input audio and its sampling rate."""
+    audio: MultiModalData[Tuple[np.ndarray, Union[int, float]]]
+    """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,
-                              data: object) -> MultiModalInputs:
+    def _default_input_mapper(
+        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
-        model. This does not include tokens that correspond to the input text.
+        Calculate the maximum number of tokens, corresponding to a single
+        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
-        language model for a model class.
+        Register the maximum number of tokens, corresponding to a single
+        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,
-                              data: object) -> MultiModalInputs:
+    def _default_input_mapper(
+        self,
+        ctx: InputContext,
+        data: MultiModalData[object],
+    ) -> MultiModalInputs:
         model_config = ctx.model_config
 
         # PIL image

vllm/multimodal/registry.py

 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
+from vllm.config import ModelConfig, MultiModalConfig
 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) \
-                .map_input(model_config, data_value)
+            plugin = self._get_plugin(data_key)
 
+            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
-        specific modality, input to the language model for a model class.
+        Register the maximum number of tokens, corresponding to a single
+        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
-        input to the language model for a model class.
+        Register the maximum number of image tokens, corresponding to a single
+        image, that are passed to the language model for a model class.
         """
         return self.register_max_multimodal_tokens("image", max_mm_tokens)
 
@@ -137,7 +168,61 @@ class MultiModalRegistry:
         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`.
+        """
+        limits_per_plugin = self._limits_by_model[model_config]
+
+        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 sum(
-            plugin.get_max_multimodal_tokens(model_config)
-            for plugin in self._plugins.values())
+        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 \
-                .dummy_data_for_profiling(model_config, seq_len)
+            seq_data, _ = input_registry \
+                .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 \
-            .create_input_mapper(self.model_config)
+        self.input_registry = input_registry
+        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
-        # to be accounted for when calculating the GPU blocks for
+        # Additional GPU memory may be needed for multi-modal encoding, which
+        # 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):
-            max_mm_tokens = MULTIMODAL_REGISTRY \
-                .get_max_multimodal_tokens(model_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:
             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 \
-                .dummy_data_for_profiling(model_config, seq_len)
-
-            # 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_data, dummy_multi_modal_data = input_registry \
+                .dummy_data_for_profiling(model_config, seq_len, mm_registry)
 
             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 \
-            .create_input_mapper(self.model_config)
+        self.input_registry = input_registry
+        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
-        # to be accounted for when calculating the GPU blocks for
+        # Additional GPU memory may be needed for multi-modal encoding, which
+        # 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):
-            max_mm_tokens = MULTIMODAL_REGISTRY \
-                .get_max_multimodal_tokens(model_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:
             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 \
-                .dummy_data_for_profiling(model_config, seq_len)
-
-            # 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_data, dummy_multi_modal_data = input_registry \
+                .dummy_data_for_profiling(model_config, seq_len, mm_registry)
 
             seq = SequenceGroupMetadata(
                 request_id=str(group_id),