[VLM][Core] Support profiling with multiple multi-modal inputs per prompt

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.multimodal import MULTIMODAL_REGISTRY
+from vllm.config import ModelConfig, MultiModalConfig
+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 @@ def verify_with_model_config(self, model_config: ModelConfig):
 
 @dataclass
 class MultiModalConfig:
-    """Configs the input data format and how models should run for
-    multimodal models."""
+    """Controls the behavior of 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 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.utils import FlexibleArgumentParser
 
 if TYPE_CHECKING:
-    from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import (
-        BaseTokenizerGroup)
+    from vllm.transformers_utils.tokenizer_group import 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 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
                             '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 @@ def create_engine_config(self, ) -> EngineConfig:
             "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(