refactor: unify names of the feature field of MultimodalDataItem (#8075)

python/sglang/srt/managers/multimodal_processors/qwen_audio.py

@@ -78,7 +78,7 @@ class Qwen2AudioMultimodalProcessor(BaseMultimodalProcessor):
             output_lengths = (input_lengths - 2) // 2 + 1
 
             item = MultimodalDataItem(
-                audio_features=res["input_features"],
+                feature=res["input_features"],
                 audio_feature_lens=output_lengths,
                 audio_offsets=audio_offsets,
                 modality=Modality.AUDIO,

python/sglang/srt/managers/schedule_batch.py

@@ -207,13 +207,12 @@ class MultimodalDataItem:
     modality: Modality
     hash: int = None
     pad_value: int = None
-    image_sizes: Tuple[int, int] = None
     offsets: Optional[list] = None
+    # the raw features returned by processor, e.g. pixel_values or audio_features
+    feature: Union[torch.Tensor, np.ndarray] = None
+
+    image_sizes: Tuple[int, int] = None
 
-    # the real data, pixel_values or audio_features
-    # data: Union[List[torch.Tensor], List[np.ndarray]]
-    pixel_values: Union[torch.Tensor, np.ndarray, "PIL.Image"] = None
-    audio_features: Union[torch.Tensor, np.ndarray] = None
     audio_feature_lens: Optional[List[torch.Tensor]] = None
     audio_offsets: Optional[List[Tuple[int, int]]] = None
     precomputed_features: Optional[Union[torch.Tensor, np.ndarray]] = None
@@ -238,7 +237,6 @@ class MultimodalDataItem:
     image_grid_hws: Optional[List[torch.Tensor]] = None
 
     # For gemma3n
-    input_features: Optional[torch.Tensor] = None
     input_features_mask: Optional[torch.Tensor] = None
 
     @staticmethod
@@ -254,18 +252,11 @@ class MultimodalDataItem:
         from sglang.srt.managers.mm_utils import hash_feature
 
         if self.hash is None:
-            if self.precomputed_features is not None:
+            if self.feature is not None:
-                self.hash = hash_feature(self.precomputed_features)
+                hashed_feature = self.feature
-            elif self.is_audio():
-                if self.audio_features is not None:
-                    self.hash = hash_feature(self.audio_features)
-                elif self.input_features is not None:
-                    self.hash = hash_feature(self.input_features)
-            elif self.is_video():
-                self.hash = hash_feature(self.pixel_values_videos)
             else:
-                self.hash = hash_feature(self.pixel_values)
+                hashed_feature = self.precomputed_features
-
+            self.hash = hash_feature(hashed_feature)
         assert self.hash is not None
         self.pad_value = self.hash % (1 << 30)
 
@@ -275,8 +266,7 @@ class MultimodalDataItem:
     def is_audio(self):
         return (self.modality == Modality.AUDIO) and (
             self.precomputed_features is not None
-            or not MultimodalDataItem.is_empty_list(self.audio_features)
+            or not MultimodalDataItem.is_empty_list(self.feature)
-            or not MultimodalDataItem.is_empty_list(self.input_features)
         )
 
     def is_image(self):
@@ -284,13 +274,13 @@ class MultimodalDataItem:
             self.is_modality(Modality.IMAGE) or self.is_modality(Modality.MULTI_IMAGES)
         ) and (
             self.precomputed_features is not None
-            or not MultimodalDataItem.is_empty_list(self.pixel_values)
+            or not MultimodalDataItem.is_empty_list(self.feature)
         )
 
     def is_video(self):
         return (self.modality == Modality.VIDEO) and (
             self.precomputed_features is not None
-            or not MultimodalDataItem.is_empty_list(self.pixel_values_videos)
+            or not MultimodalDataItem.is_empty_list(self.feature)
         )
 
     def is_valid(self) -> bool:
@@ -311,7 +301,7 @@ class MultimodalDataItem:
         return ret
 
     def merge(self, other):
-        self.pixel_values += other.pixel_values
+        self.feature += other.feature
         self.image_sizes += other.image_sizes
         self.image_offsets += other.image_offsets
         self.hash = hash((self.hash, other.hash))
@@ -354,7 +344,6 @@ class MultimodalInputs:
 
         assert isinstance(ret.mm_items, list)
         ret.mm_items = [item for item in ret.mm_items if item.is_valid()]
-
         for item in ret.mm_items:
             item.set_pad_value()
 
@@ -1278,11 +1267,9 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             if mm_input is None:
                 continue
             for mm_item in mm_input.mm_items:
-                pixel_values = getattr(mm_item, "pixel_values", None)
+                pixel_values = getattr(mm_item, "feature", None)
                 if isinstance(pixel_values, torch.Tensor):
-                    mm_item.pixel_values = pixel_values.to(
+                    mm_item.feature = pixel_values.to(self.device, non_blocking=True)
-                        self.device, non_blocking=True
-                    )
         self.multimodal_inputs = multimodal_inputs
         self.token_type_ids = token_type_ids_tensor
         self.seq_lens_sum = sum(seq_lens)

python/sglang/srt/models/clip.py

@@ -463,7 +463,7 @@ class CLIPModel(nn.Module):
         if forward_batch.mm_inputs is not None:
             mm_inputs = forward_batch.mm_inputs
         pixel_values_list = [
-            item.pixel_values
+            item.feature
             for item in flatten_nested_list(
                 [mm_input.mm_items for mm_input in mm_inputs if mm_input is not None]
             )

python/sglang/srt/models/deepseek_janus_pro.py

@@ -1960,7 +1960,7 @@ class MultiModalityCausalLM(MultiModalityPreTrainedModel):
         self.logits_processor = LogitsProcessor(config)
 
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
-        pixel_values = torch.concat([item.pixel_values for item in items], dim=0)
+        pixel_values = torch.concat([item.feature for item in items], dim=0)
         bs, n = pixel_values.shape[0:2]
         pixel_values = pixel_values.to(
             device=self.vision_model.device, dtype=self.vision_model.dtype

python/sglang/srt/models/deepseek_vl2.py

@@ -268,9 +268,9 @@ class DeepseekVL2ForCausalLM(nn.Module):
         # TODO: can it be batched ?
         images_in_this_batch = []
         for item in items:
-            assert item.pixel_values.dim() == 4
+            assert item.feature.dim() == 4
             image_feature = self.vision.forward_features(
-                item.pixel_values.type(next(self.vision.parameters()).dtype).to(
+                item.feature.type(next(self.vision.parameters()).dtype).to(
                     device=next(self.vision.parameters()).device
                 )
             )

python/sglang/srt/models/gemma3_mm.py

@@ -283,7 +283,7 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
             image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
         """
         # Process images one by one to handle flatten_batch=True constraint in vision_tower
-        all_pixel_values = flatten_nested_list([item.pixel_values for item in items])
+        all_pixel_values = flatten_nested_list([item.feature for item in items])
         vision_outputs_list = []
 
         for pixel_values_batch in all_pixel_values:

python/sglang/srt/models/gemma3n_mm.py

@@ -265,7 +265,7 @@ class Gemma3nForConditionalGeneration(PreTrainedModel):
             image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
         """
         # Process images one by one to handle flatten_batch=True constraint in vision_tower
-        all_pixel_values = flatten_nested_list([item.pixel_values for item in items])
+        all_pixel_values = flatten_nested_list([item.feature for item in items])
         vision_outputs_list = []
 
         for pixel_values_batch in all_pixel_values:
@@ -316,9 +316,7 @@ class Gemma3nForConditionalGeneration(PreTrainedModel):
             audio_features (`torch.Tensor`): Audio feature tensor of shape `(num_audios, audio_length, embed_dim)`).
         """
         # Extract audio features and masks from items
-        all_input_features = flatten_nested_list(
+        all_input_features = flatten_nested_list([item.feature for item in items])
-            [item.input_features for item in items]
-        )
         all_input_features_mask = flatten_nested_list(
             [~item.input_features_mask for item in items]
         )  # Note(Xinyuan): reverse the mask according to the HF implementation

python/sglang/srt/models/internvl.py

@@ -510,7 +510,7 @@ class InternVLChatModel(nn.Module):
         Returns:
             image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
         """
-        pixel_values = torch.cat([item.pixel_values for item in items])
+        pixel_values = torch.cat([item.feature for item in items])
         image_features = self.extract_feature(pixel_values)
         return image_features
 

python/sglang/srt/models/kimi_vl.py

@@ -144,7 +144,7 @@ class KimiVLForConditionalGeneration(nn.Module):
 
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         pixel_values = (
-            torch.cat([item.pixel_values for item in items], dim=0)
+            torch.cat([item.feature for item in items], dim=0)
             .type(self.vision_tower.dtype)
             .to(self.vision_tower.device)
         )

python/sglang/srt/models/llava.py

@@ -186,7 +186,7 @@ class LlavaBaseForCausalLM(nn.Module):
                 bs = forward_batch.batch_size
                 pixel_values = flatten_nested_list(
                     [
-                        [item.pixel_values for item in image_inputs[i].mm_items]
+                        [item.feature for item in image_inputs[i].mm_items]
                         for i in range(bs)
                         if need_vision[i]
                     ]
@@ -753,7 +753,7 @@ class LlavaForConditionalGeneration(LlavaBaseForCausalLM):
         features = []
         for item in items:
             # in each item, we assume pixel_values is always batched
-            pixel_values, image_sizes = item.pixel_values, item.image_sizes
+            pixel_values, image_sizes = item.feature, item.image_sizes
             image_outputs = self.vision_tower(
                 pixel_values, image_sizes, output_hidden_states=True
             )

python/sglang/srt/models/llavavid.py

@@ -135,7 +135,7 @@ class LlavaVidForCausalLM(nn.Module):
             if need_vision.any():
                 pixel_values = flatten_nested_list(
                     [
-                        [item.pixel_values for item in image_inputs[i].mm_items]
+                        [item.feature for item in image_inputs[i].mm_items]
                         for i in range(bs)
                         if need_vision[i]
                     ]

python/sglang/srt/models/minicpmo.py

@@ -1552,9 +1552,7 @@ class MiniCPMO(MiniCPMBaseModel):
         Returns:
             List[List[torch.Tensor]]: audio embeddings
         """
-        wavforms = flatten_nested_list(
+        wavforms = flatten_nested_list([item.feature for item in items if item.feature])
-            [item.audio_features for item in items if item.audio_features]
-        )
         # list, [[x1, x2], [y1], [z1]]
         audio_feature_lens_raw = flatten_nested_list(
             [item.audio_feature_lens for item in items if item.audio_feature_lens]
@@ -1659,9 +1657,7 @@ class MiniCPMO(MiniCPMBaseModel):
             List[List[torch.Tensor]]: audio embeddings
         """
         # (bs, 80, frames) or [], multi audios need filled in advance
-        wavforms = flatten_nested_list(
+        wavforms = flatten_nested_list([item.feature for item in items if item.feature])
-            [item.audio_features for item in items if item.audio_features]
-        )
         # list, [[x1, x2], [y1], [z1]]
         audio_feature_lens_raw = flatten_nested_list(
             [item.audio_feature_lens for item in items if item.audio_feature_lens]
@@ -1778,7 +1774,7 @@ class MiniCPMO(MiniCPMBaseModel):
 
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         # list of tensors
-        pixel_values = flatten_nested_list([item.pixel_values for item in items])
+        pixel_values = flatten_nested_list([item.feature for item in items])
         tgt_sizes = torch.stack(
             flatten_nested_list([item.tgt_size for item in items]), dim=0
         )

python/sglang/srt/models/minicpmv.py

@@ -724,7 +724,7 @@ class MiniCPMV2_6(MiniCPMBaseModel):
 
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         # list of tensors
-        pixel_values = flatten_nested_list([item.pixel_values for item in items])
+        pixel_values = flatten_nested_list([item.feature for item in items])
         tgt_sizes = torch.stack(
             flatten_nested_list([item.tgt_size for item in items]), dim=0
         )

python/sglang/srt/models/mistral.py

@@ -56,7 +56,7 @@ class Mistral3ForConditionalGeneration:
         features = []
         for item in items:
             # in each item, we assume pixel_values is always batched
-            pixel_values, image_sizes = item.pixel_values, item.image_sizes
+            pixel_values, image_sizes = item.feature, item.image_sizes
             image_outputs = self.vision_tower(
                 pixel_values, image_sizes, output_hidden_states=True
             )

python/sglang/srt/models/mllama.py

@@ -838,9 +838,7 @@ class MllamaForConditionalGeneration(nn.Module):
         self.logits_processor = LogitsProcessor(config.text_config)
 
     def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
-        pixel_values = torch.cat(
+        pixel_values = torch.cat([item.feature for item in mm_inputs.mm_items], dim=0)
-            [item.pixel_values for item in mm_inputs.mm_items], dim=0
-        )
         pad_values = [item.pad_value for item in mm_inputs.mm_items]
 
         num_concurrent_media, num_tiles = pixel_values.shape[1:3]
@@ -862,7 +860,7 @@ class MllamaForConditionalGeneration(nn.Module):
 
             if not forward_batch.encoder_cached[i] and mm_input is not None:
                 pixel_values = torch.cat(
-                    [item.pixel_values for item in mm_input.mm_items], dim=0
+                    [item.feature for item in mm_input.mm_items], dim=0
                 )
                 max_num_images = max(max_num_images, pixel_values.shape[1])
 
@@ -897,7 +895,7 @@ class MllamaForConditionalGeneration(nn.Module):
 
                 encoder_lens_need.append(forward_batch.encoder_lens[k])
                 pixel_values = torch.cat(
-                    [item.pixel_values for item in mm_input.mm_items], dim=0
+                    [item.feature for item in mm_input.mm_items], dim=0
                 )
                 for j in range(pixel_values.shape[1]):
                     img = pixel_values[0, j]

python/sglang/srt/models/mllama4.py

@@ -147,7 +147,7 @@ class Llama4ForConditionalGeneration(nn.Module):
             raise ValueError("Vision model not available for text-only checkpoint")
 
         pixel_values = (
-            torch.concat([item.pixel_values for item in items])
+            torch.concat([item.feature for item in items])
             .to(next(self.vision_model.parameters()).device)
             .type(next(self.vision_model.parameters()).dtype)
         )

python/sglang/srt/models/phi4mm.py

@@ -422,9 +422,7 @@ class Phi4MMForCausalLM(nn.Module):
 
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         dtype = next(self.vision_encoder.parameters()).dtype
-        pixel_values = torch.cat([item.pixel_values for item in items], dim=0).type(
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(dtype)
-            dtype
-        )
         image_attention_mask = torch.cat([item.image_emb_mask for item in items], dim=0)
         image_sizes = torch.cat([item.image_sizes for item in items], dim=0)
         image_embeds = self.vision_encoder(

python/sglang/srt/models/qwen2_5_vl.py

@@ -497,7 +497,7 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
 
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         # in qwen-vl, last dim is the same
-        pixel_values = torch.cat([item.pixel_values for item in items], dim=0).type(
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
             self.visual.dtype
         )
         image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
@@ -508,9 +508,9 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
 
     def get_video_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         # in qwen-vl, last dim is the same
-        pixel_values = torch.cat(
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
-            [getattr(item, "pixel_values_videos") for item in items], dim=0
+            self.visual.dtype
-        ).type(self.visual.dtype)
+        )
         video_grid_thw = torch.concat([item.video_grid_thw for item in items], dim=0)
         assert pixel_values.dim() == 2, pixel_values.dim()
         assert video_grid_thw.dim() == 2, video_grid_thw.dim()

python/sglang/srt/models/qwen2_audio.py

@@ -118,7 +118,7 @@ class Qwen2AudioForConditionalGeneration(nn.Module):
 
     def get_audio_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         # Extract audio features from input items
-        input_features = torch.cat([item.audio_features for item in items], dim=0).type(
+        input_features = torch.cat([item.feature for item in items], dim=0).type(
             self.audio_tower.dtype
         )
 

python/sglang/srt/models/qwen2_vl.py

@@ -484,7 +484,7 @@ class Qwen2VLForConditionalGeneration(nn.Module):
 
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         # in qwen-vl, last dim is the same
-        pixel_values = torch.cat([item.pixel_values for item in items], dim=0).type(
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
             self.visual.dtype
         )
         image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
@@ -495,9 +495,9 @@ class Qwen2VLForConditionalGeneration(nn.Module):
 
     def get_video_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
         # in qwen-vl, last dim is the same
-        pixel_values = torch.cat(
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
-            [item.pixel_values_videos for item in items], dim=0
+            self.visual.dtype
-        ).type(self.visual.dtype)
+        )
         video_grid_thw = torch.concat([item.video_grid_thw for item in items], dim=0)
         assert pixel_values.dim() == 2, pixel_values.dim()
         assert video_grid_thw.dim() == 2, video_grid_thw.dim()

python/sglang/srt/models/vila.py

@@ -237,7 +237,7 @@ class VILAForConditionalGeneration(nn.Module):
         return cast(LogitsProcessorOutput, output)
 
     def get_image_feature(self, mm_input: List[MultimodalDataItem]) -> Tensor:
-        pixel_values = cast(Tensor, mm_input[0].pixel_values)
+        pixel_values = cast(Tensor, mm_input[0].feature)
 
         ##### BEGIN COPY modeling_vila.py #####
 

python/sglang/srt/multimodal/processors/base_processor.py

@@ -5,7 +5,6 @@ import multiprocessing as mp
 import os
 import re
 from abc import ABC, abstractmethod
-from functools import lru_cache
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -156,6 +155,10 @@ class BaseMultimodalProcessor(ABC):
             # "precomputed_features" - handled specially as it can be any modality
         }
 
+        # name of the feature filed
+        # TODO: pass from processors
+        self.FEATURE_NAMES = ["pixel_values", "pixel_values_videos", "audio_features"]
+
     def process_mm_data(
         self, input_text, images=None, videos=None, audios=None, **kwargs
     ):
@@ -524,6 +527,9 @@ class BaseMultimodalProcessor(ABC):
                 if modality not in items:
                     items[modality] = MultimodalDataItem(modality=modality)
 
+                if attr_name in self.FEATURE_NAMES:
+                    attr_name = "feature"
+
                 # Set attribute
                 setattr(items[modality], attr_name, value)
 

python/sglang/srt/multimodal/processors/clip.py

@@ -26,7 +26,7 @@ class ClipImageProcessor(BaseMultimodalProcessor):
         image_inputs["input_ids"] = image_inputs["input_ids"].tolist()[0]
         image_inputs["mm_items"] = [
             MultimodalDataItem(
-                pixel_values=image_inputs["pixel_values"], modality=Modality.IMAGE
+                feature=image_inputs["pixel_values"], modality=Modality.IMAGE
             )
         ]
 

python/sglang/srt/multimodal/processors/deepseek_vl_v2.py

@@ -68,7 +68,7 @@ class DeepseekVL2ImageProcessor(BaseMultimodalProcessor):
             input_ids=input_ids, mm_token_id=self._processor.image_token_id
         )
         item = MultimodalDataItem(
-            pixel_values=res["images"],
+            feature=res["images"],
             offsets=image_offsets,
             modality=Modality.IMAGE,
             image_emb_mask=images_seq_mask,

python/sglang/srt/multimodal/processors/internvl.py

@@ -223,7 +223,7 @@ class InternVLImageProcessor(BaseMultimodalProcessor):
         )
         items = [
             MultimodalDataItem(
-                pixel_values=pixel_values,
+                feature=pixel_values,
                 modality=Modality.IMAGE,
                 offsets=image_offsets,
             )

python/sglang/srt/multimodal/processors/janus_pro.py

@@ -47,7 +47,7 @@ class JanusProImageProcessor(BaseMultimodalProcessor):
         return {
             "mm_items": [
                 MultimodalDataItem(
-                    pixel_values=res["pixel_values"],
+                    feature=res["pixel_values"],
                     image_emb_mask=res["images_emb_mask"],
                     offsets=image_offsets,
                     modality=Modality.IMAGE,

python/sglang/srt/multimodal/processors/llava.py

@@ -158,7 +158,7 @@ class LlavaImageProcessor(BaseMultimodalProcessor):
         return {
             "mm_items": [
                 MultimodalDataItem(
-                    pixel_values=pixel_values,
+                    feature=pixel_values,
                     image_sizes=image_sizes,
                     modality=modality,
                 )

python/sglang/srt/multimodal/processors/minicpm.py

@@ -114,7 +114,7 @@ class MiniCPMMultimodalProcessor(BaseMultimodalProcessor):
 
         if len(pixel_values) != 0:
             item = MultimodalDataItem(
-                pixel_values=pixel_values,
+                feature=pixel_values,
                 offsets=image_offsets,
                 tgt_size=tgt_sizes_flat,
                 modality=Modality.IMAGE,
@@ -135,7 +135,7 @@ class MiniCPMMultimodalProcessor(BaseMultimodalProcessor):
             else:
                 audio_offsets = None
             item = MultimodalDataItem(
-                audio_features=[res["audio_features"]],
+                feature=[res["audio_features"]],
                 audio_feature_lens=res["audio_feature_lens"],
                 offsets=audio_offsets,
                 modality=Modality.AUDIO,

python/sglang/srt/multimodal/processors/mlama.py

@@ -24,7 +24,7 @@ class MllamaImageProcessor(BaseMultimodalProcessor):
         image_inputs["input_ids"] = image_inputs["input_ids"].tolist()[0]
         image_inputs["mm_items"] = [
             MultimodalDataItem(
-                pixel_values=image_inputs["pixel_values"],
+                feature=image_inputs["pixel_values"],
                 aspect_ratio_id=image_inputs["aspect_ratio_ids"],
                 aspect_ratio_mask=image_inputs["aspect_ratio_mask"],
                 modality=Modality.IMAGE,

python/sglang/srt/multimodal/processors/mllama4.py

@@ -142,7 +142,7 @@ class Mllama4ImageProcessor(BaseMultimodalProcessor):
         # Add metadata for image processing
         processor_output["mm_items"] = [
             MultimodalDataItem(
-                pixel_values=processor_output["pixel_values"],
+                feature=processor_output["pixel_values"],
                 modality=Modality.IMAGE,
                 offsets=image_offsets,
             )

python/sglang/srt/multimodal/processors/phi4mm.py

@@ -62,7 +62,7 @@ class Phi4MMImageProcessor(BaseMultimodalProcessor):
 
         items = [
             MultimodalDataItem(
-                pixel_values=res["input_image_embeds"],
+                feature=res["input_image_embeds"],
                 image_sizes=res["image_sizes"],
                 image_emb_mask=res["image_attention_mask"],
                 offsets=image_offsets,

python/sglang/srt/multimodal/processors/pixtral.py

@@ -103,7 +103,7 @@ class PixtralProcessor(BaseMultimodalProcessor):
             )
             mm_items = [
                 MultimodalDataItem(
-                    pixel_values=processor_output["pixel_values"],
+                    feature=processor_output["pixel_values"],
                     image_sizes=processor_output["image_sizes"],
                     modality=Modality.IMAGE,
                     offsets=image_offsets,

test/srt/test_vlm_accuracy.py

@@ -245,7 +245,7 @@ class TestMiniCPMVLogits(VisionLLMLogitsBase):
                     MultimodalInputs(
                         mm_items=[
                             MultimodalDataItem(
-                                pixel_values=pixel_values_flat,
+                                feature=pixel_values_flat,
                                 offsets=image_offsets,
                                 tgt_size=tgt_sizes_flat,
                                 modality=Modality.IMAGE,