[V1][Bugfix] Fix data item ordering in mixed-modality inference (#12259)

Signed-off-by: Roger Wang <ywang@roblox.com>

vllm/multimodal/utils.py

 from functools import lru_cache
+from itertools import groupby
 from pathlib import Path
 from typing import TYPE_CHECKING, Optional, TypeVar, Union
 from urllib.parse import ParseResult, urlparse
@@ -26,7 +27,7 @@ _M = TypeVar("_M")
 
 if TYPE_CHECKING:
     from .hasher import MultiModalHashDict
-    from .inputs import MultiModalPlaceholderDict
+    from .inputs import MultiModalKwargs, MultiModalPlaceholderDict
 
 
 class MediaConnector:
@@ -477,3 +478,34 @@ def merge_and_sort_multimodal_metadata(
         merged_hashes = None
 
     return sorted_modalities, merged_placeholders, merged_hashes
+
+
+def group_mm_inputs_by_modality(
+        mm_inputs: list["MultiModalKwargs"]) -> list[list["MultiModalKwargs"]]:
+    """Group consecutive MultiModalKwargs from mm_inputs with the same modality 
+    together into the same list for batching purpose. For MultiModalKwargs with 
+    multiple modalities, put them into their own list.
+
+    Args:
+        mm_inputs: List of MultiModalKwargs.
+
+    Returns:
+        list[list[MultiModalKwargs]]: List of list of MultiModalKwargs, each 
+        inner list contains consecutive MultiModalKwargs with same modality, or
+        one with multimodal modalities.
+    """
+    if not mm_inputs:
+        return []
+
+    def modality_group_func(mm_input: "MultiModalKwargs") -> Union[str, int]:
+        # If the input has multiple modalities, return a id as the unique key
+        # for the mm_input input.
+        if len(mm_input.modalities) > 1:
+            return id(mm_input)
+
+        # Otherwise return the modality string
+        return list(mm_input.modalities)[0]
+
+    return [
+        list(group) for _, group in groupby(mm_inputs, key=modality_group_func)
+    ]

vllm/v1/worker/gpu_model_runner.py

@@ -17,6 +17,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
 from vllm.model_executor.model_loader import get_model
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
+from vllm.multimodal.utils import group_mm_inputs_by_modality
 from vllm.sampling_params import SamplingType
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
                         LayerBlockType, cdiv, is_pin_memory_available)
@@ -629,20 +630,35 @@ class GPUModelRunner:
             for input_id in encoder_input_ids:
                 mm_inputs.append(req_state.mm_inputs[input_id])
                 req_input_ids.append((req_id, input_id))
-        batched_mm_inputs = MultiModalKwargs.batch(mm_inputs)
+
+        # Batch mm inputs as much as we can: if a request in the batch has
+        # multiple modalities or a different modality than the previous one,
+        # we process it separately to preserve item order.
+        # FIXME(ywang96): This is a hacky way to deal with multiple modalities
+        # in the same batch while still being able to benefit from batching
+        # multimodal inputs. The proper solution should be reordering the
+        # encoder outputs.
+        grouped_mm_inputs_list = group_mm_inputs_by_modality(mm_inputs)
+
+        encoder_outputs = []
+        for grouped_mm_inputs in grouped_mm_inputs_list:
+            batched_mm_inputs = MultiModalKwargs.batch(grouped_mm_inputs)
             batched_mm_inputs = MultiModalKwargs.as_kwargs(batched_mm_inputs,
                                                            device=self.device)
 
             # Run the encoder.
-        # `encoder_outputs` is either of the following:
-        # 1. A tensor of shape [num_images, feature_size, hidden_size]
-        # in case when feature_size is fixed across all images.
-        # 2. A list (length: num_images) of tensors, each of shape
-        # [feature_size, hidden_size] in case when the feature size is
-        # dynamic depending on input images.
-        encoder_outputs = self.model.get_multimodal_embeddings(
+            # `curr_group_outputs` is either of the following:
+            # 1. A tensor of shape (num_items, feature_size, hidden_size)
+            # in case feature_size is fixed across all multimodal items.
+            # 2. A list or tuple (length: num_items) of tensors, each of shape
+            # (feature_size, hidden_size) in case the feature size is dynamic
+            # depending on the input multimodal items.
+            curr_group_outputs = self.model.get_multimodal_embeddings(
                 **batched_mm_inputs)
 
+            for output in curr_group_outputs:
+                encoder_outputs.append(output)
+
         # Cache the encoder outputs.
         for (req_id, input_id), output in zip(req_input_ids, encoder_outputs):
             if req_id not in self.encoder_cache: