[Bugfix] Clean up multi-modal processors (#14417)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/config.py

@@ -2405,6 +2405,15 @@ class MultiModalConfig:
         hash_str = hashlib.md5(str(factors).encode()).hexdigest()
         return hash_str
 
+    def get_limit_per_prompt(self, modality: str) -> int:
+        """
+        Get the maximum number of input items allowed per prompt
+        for the given modality.
+
+        If not set by the user, this defaults to `1`.
+        """
+        return self.limit_per_prompt.get(modality, 1)
+
     # TODO: Add configs to init vision tower or not.
 
 

vllm/model_executor/models/deepseek_vl2.py

@@ -14,7 +14,6 @@ from einops import rearrange, repeat
 from transformers import BatchFeature
 
 from vllm.config import VllmConfig
-from vllm.logger import init_logger
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
@@ -25,8 +24,8 @@ from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    ImageSize, MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, ProcessingCache,
+                                        BaseProcessingInfo, PromptReplacement,
-                                        PromptReplacement, PromptUpdate)
+                                        PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
@@ -42,8 +41,6 @@ from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                     init_vllm_registered_model, maybe_prefix,
                     merge_multimodal_embeddings)
 
-logger = init_logger(__name__)
-
 # The image token id may be various
 _IMAGE_TOKEN = "<image>"
 
@@ -216,30 +213,6 @@ class DeepseekVL2DummyInputsBuilder(
 class DeepseekVL2MultiModalProcessor(
         BaseMultiModalProcessor[DeepseekVL2ProcessingInfo]):
 
-    def __init__(
-            self,
-            info: DeepseekVL2ProcessingInfo,
-            dummy_inputs: "BaseDummyInputsBuilder[DeepseekVL2ProcessingInfo]",
-            *,
-            cache: Optional[ProcessingCache] = None,
-            enable_sanity_checks: bool = True) -> None:
-        super().__init__(
-            info,
-            dummy_inputs,
-            cache=cache,
-            enable_sanity_checks=enable_sanity_checks,
-        )
-
-        mm_limit = self.info.ctx.model_config.multimodal_config.limit_per_prompt
-        if self.cache is not None and mm_limit["image"] > 2:
-            # The processor output depends on the number of images passed,
-            # making it incompatible with processing cache which is supposed
-            # to be invariant of how many images are passed per prompt
-            self.cache = None
-            logger.warning_once(
-                f"{type(self).__name__} does not support processing cache with "
-                "image limit larger than 2.")
-
     def _call_hf_processor(
         self,
         prompt: str,
@@ -316,6 +289,31 @@ class DeepseekVL2MultiModalProcessor(
             )
         ]
 
+    def _cached_apply_hf_processor(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> tuple[list[int], MultiModalKwargs, bool]:
+        # The processor logic is different for len(images) <= 2 vs > 2
+        # Since the processing cache assumes that the processor output is
+        # invariant of how many images are passed per prompt, we only
+        # perform caching for the most common case
+        if mm_data_items.get_count("image", strict=False) > 2:
+            # This code path corresponds to the cache being disabled
+            return self._apply_hf_processor_main(
+                prompt=prompt,
+                mm_items=mm_data_items,
+                hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+                enable_hf_prompt_update=True,
+            )
+
+        return super()._cached_apply_hf_processor(
+            prompt=prompt,
+            mm_data_items=mm_data_items,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+        )
+
 
 @MULTIMODAL_REGISTRY.register_processor(
     DeepseekVL2MultiModalProcessor,

vllm/model_executor/models/h2ovl.py

@@ -8,21 +8,19 @@
 # Licensed under Apache 2.0 License [see LICENSE for details]
 # --------------------------------------------------------
 from collections.abc import Mapping, Sequence
-from typing import Optional
+from typing import Optional, Union
 
 import torch
 from PIL import Image
 from transformers import PretrainedConfig
 
-from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalKwargs
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    MultiModalDataItems)
-from vllm.multimodal.processing import (ProcessingCache, PromptReplacement,
+from vllm.multimodal.processing import (PromptReplacement, PromptUpdate,
-                                        PromptUpdate, PromptUpdateDetails)
+                                        PromptUpdateDetails)
-from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 from .intern_vit import InternVisionModel
@@ -32,8 +30,6 @@ from .internvl import (IMG_CONTEXT, IMG_END, IMG_START,
                        InternVLMultiModalProcessor, build_transform,
                        find_closest_aspect_ratio, get_internvl_target_ratios)
 
-logger = init_logger(__name__)
-
 
 def resolve_h2ovl_min_max_num(
     *,
@@ -465,29 +461,6 @@ class H2OVLProcessingInfo(BaseInternVLProcessingInfo):
 class H2OVLMultiModalProcessor(InternVLMultiModalProcessor[H2OVLProcessingInfo]
                                ):
 
-    def __init__(self,
-                 info: H2OVLProcessingInfo,
-                 dummy_inputs: "BaseDummyInputsBuilder[H2OVLProcessingInfo]",
-                 *,
-                 cache: Optional[ProcessingCache] = None,
-                 enable_sanity_checks: bool = True) -> None:
-        super().__init__(
-            info,
-            dummy_inputs,
-            cache=cache,
-            enable_sanity_checks=enable_sanity_checks,
-        )
-
-        mm_limit = self.info.ctx.model_config.multimodal_config.limit_per_prompt
-        if self.cache is not None and mm_limit["image"] >= 2:
-            # The processor output depends on the number of images passed,
-            # making it incompatible with processing cache which is supposed
-            # to be invariant of how many images are passed per prompt
-            self.cache = None
-            logger.warning_once(
-                f"{type(self).__name__} does not support processing cache with "
-                "multi-image support enabled.")
-
     def _get_prompt_updates(
         self,
         mm_items: MultiModalDataItems,
@@ -543,6 +516,31 @@ class H2OVLMultiModalProcessor(InternVLMultiModalProcessor[H2OVLProcessingInfo]
             )
         ]
 
+    def _cached_apply_hf_processor(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> tuple[list[int], MultiModalKwargs, bool]:
+        # The processor logic is different for len(images) <= 1 vs > 1
+        # Since the processing cache assumes that the processor output is
+        # invariant of how many images are passed per prompt, we only
+        # perform caching for the most common case
+        if mm_data_items.get_count("image", strict=False) > 1:
+            # This code path corresponds to the cache being disabled
+            return self._apply_hf_processor_main(
+                prompt=prompt,
+                mm_items=mm_data_items,
+                hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+                enable_hf_prompt_update=True,
+            )
+
+        return super()._cached_apply_hf_processor(
+            prompt=prompt,
+            mm_data_items=mm_data_items,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+        )
+
 
 @MULTIMODAL_REGISTRY.register_processor(
     H2OVLMultiModalProcessor,

vllm/model_executor/models/llava_next_video.py

@@ -133,7 +133,7 @@ class LlavaNextVideoProcessingInfo(BaseProcessingInfo):
 
     def get_num_frames_with_most_features(self, seq_len: int) -> int:
         mm_config = self.ctx.get_mm_config()
-        max_videos = mm_config.limit_per_prompt.get("video", 1)
+        max_videos = mm_config.get_limit_per_prompt("video")
 
         max_total_frames = self._get_max_video_frames(seq_len)
 

vllm/model_executor/models/llava_onevision.py

@@ -206,8 +206,8 @@ class LlavaOnevisionProcessingInfo(LlavaNextProcessingInfo):
 
     def get_num_frames_with_most_features(self, seq_len: int) -> int:
         mm_config = self.ctx.get_mm_config()
-        max_images = mm_config.limit_per_prompt.get("image", 1)
+        max_images = mm_config.get_limit_per_prompt("image")
-        max_videos = mm_config.limit_per_prompt.get("video", 1)
+        max_videos = mm_config.get_limit_per_prompt("video")
 
         max_image_tokens = self.get_max_image_tokens() * max_images
         max_total_frames = self._get_max_video_frames(seq_len -

vllm/model_executor/models/minicpmo.py

@@ -201,9 +201,9 @@ class MiniCPMOProcessingInfo(MiniCPMVProcessingInfo):
 
     def get_num_frames_with_most_features(self, seq_len: int) -> int:
         mm_config = self.ctx.get_mm_config()
-        max_images = mm_config.limit_per_prompt.get("image", 1)
+        max_images = mm_config.get_limit_per_prompt("image")
-        max_videos = mm_config.limit_per_prompt.get("video", 1)
+        max_videos = mm_config.get_limit_per_prompt("video")
-        max_audios = mm_config.limit_per_prompt.get("audio", 1)
+        max_audios = mm_config.get_limit_per_prompt("audio")
 
         # count <image_idx></image_idx> tokens
         # which are not in get_max_image_tokens

vllm/model_executor/models/minicpmv.py

@@ -446,8 +446,8 @@ class MiniCPMVProcessingInfo(BaseProcessingInfo):
 
     def get_num_frames_with_most_features(self, seq_len: int) -> int:
         mm_config = self.ctx.get_mm_config()
-        max_images = mm_config.limit_per_prompt.get("image", 1)
+        max_images = mm_config.get_limit_per_prompt("image")
-        max_videos = mm_config.limit_per_prompt.get("video", 1)
+        max_videos = mm_config.get_limit_per_prompt("video")
 
         # count <image_idx></image_idx> tokens
         # which are not in get_max_image_tokens

vllm/model_executor/models/pixtral.py

@@ -68,7 +68,7 @@ def dummy_data_for_pixtral(ctx: InputContext, seq_len: int,
     image_token_id = mm_encoder.special_ids.img
 
     mm_config = ctx.get_mm_config()
-    num_images = mm_config.limit_per_prompt.get("image", 1)
+    num_images = mm_config.get_limit_per_prompt("image")
 
     # dummy size
     size = 256

vllm/model_executor/models/qwen2_vl.py

@@ -911,8 +911,8 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
 
     def get_num_frames_with_most_features(self, seq_len: int) -> int:
         mm_config = self.ctx.get_mm_config()
-        max_images = mm_config.limit_per_prompt.get("image", 1)
+        max_images = mm_config.get_limit_per_prompt("image")
-        max_videos = mm_config.limit_per_prompt.get("video", 1)
+        max_videos = mm_config.get_limit_per_prompt("video")
 
         max_image_tokens = self.get_max_image_tokens() * max_images
         max_total_frames = self._get_max_video_frames(seq_len -

vllm/multimodal/processing.py

@@ -984,10 +984,10 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
         before passing them to :meth:`_get_hf_mm_data`.
         """
         mm_items = self.data_parser.parse_mm_data(mm_data)
+        mm_config = self.info.ctx.get_mm_config()
 
-        mm_limits = self.info.ctx.get_mm_config().limit_per_prompt
         for modality, items in mm_items.items():
-            limit = mm_limits.get(modality, 1)
+            limit = mm_config.get_limit_per_prompt(modality)
             if len(items) > limit:
                 raise ValueError(
                     f"You set {modality}={limit} (or defaulted to 1) in "

vllm/multimodal/profiling.py

@@ -110,12 +110,10 @@ class MultiModalProfiler(Generic[_I]):
 
     def get_mm_limits(self) -> Mapping[str, int]:
         mm_config = self.processing_info.ctx.get_mm_config()
-        mm_limit_per_prompt = mm_config.limit_per_prompt
-
         supported_mm_limits = self.processing_info.get_supported_mm_limits()
 
         mm_limits = {
-            modality: mm_limit_per_prompt.get(modality, 1)
+            modality: mm_config.get_limit_per_prompt(modality)
             for modality in supported_mm_limits
         }
 

vllm/multimodal/registry.py

@@ -355,7 +355,7 @@ class MultiModalRegistry:
             # 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)
+                key: multimodal_config.get_limit_per_prompt(key)
                 for key in self._plugins
             }