[Misc] Rename `group_mm_kwargs_by_modality -> group_and_batch_mm_kwargs` (#36158)

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

tests/models/multimodal/processing/test_tensor_schema.py

@@ -27,7 +27,7 @@ from vllm.distributed import (
 from vllm.model_executor.models.interfaces import supports_multimodal
 from vllm.multimodal import MULTIMODAL_REGISTRY, BatchedTensorInputs
 from vllm.multimodal.processing import BaseMultiModalProcessor, InputProcessingContext
-from vllm.multimodal.utils import group_mm_kwargs_by_modality
+from vllm.multimodal.utils import group_and_batch_mm_kwargs
 from vllm.platforms import current_platform
 from vllm.tokenizers import cached_tokenizer_from_config
 from vllm.utils.collection_utils import is_list_of
@@ -114,7 +114,7 @@ def create_batched_mm_kwargs(
         hf_processor_mm_kwargs=processor_inputs.hf_processor_mm_kwargs,
     )["mm_kwargs"].require_data()
 
-    return group_mm_kwargs_by_modality(
+    return group_and_batch_mm_kwargs(
         [
             (modality, item)
             for modality in supported_mm_limits

vllm/multimodal/utils.py

@@ -10,6 +10,7 @@ from typing import TYPE_CHECKING, Any
 import numpy as np
 import numpy.typing as npt
 from PIL import Image
+from typing_extensions import deprecated
 
 from vllm.utils.import_utils import LazyLoader
 
@@ -207,7 +208,7 @@ def group_and_batch_mm_items(
     assert start_idx == len(items)
 
 
-def group_mm_kwargs_by_modality(
+def group_and_batch_mm_kwargs(
     mm_kwargs: list[tuple[str, MultiModalKwargsItem]],
     *,
     device: torch.types.Device = None,
@@ -246,6 +247,19 @@ def group_mm_kwargs_by_modality(
             yield modality, num_items, mm_kwargs_batch
 
 
+@deprecated(
+    "`group_mm_kwargs_by_modality` has been renamed to `group_and_batch_mm_kwargs`. "
+    "The old name will be removed in v0.19."
+)
+def group_mm_kwargs_by_modality(
+    mm_kwargs: list[tuple[str, MultiModalKwargsItem]],
+    *,
+    device: torch.types.Device = None,
+    pin_memory: bool = False,
+) -> Generator[tuple[str, int, BatchedTensorInputs], None, None]:
+    return group_and_batch_mm_kwargs(mm_kwargs, device=device, pin_memory=pin_memory)
+
+
 def fetch_audio(
     audio_url: str,
     audio_io_kwargs: dict[str, Any] | None = None,

vllm/v1/worker/gpu/mm/encoder_runner.py

@@ -5,7 +5,7 @@ import torch
 
 from vllm.model_executor.models.interfaces import SupportsMultiModal
 from vllm.multimodal.inputs import MultiModalKwargsItem
-from vllm.multimodal.utils import group_mm_kwargs_by_modality
+from vllm.multimodal.utils import group_and_batch_mm_kwargs
 from vllm.v1.worker.gpu.mm.encoder_cache import EncoderCache
 from vllm.v1.worker.utils import sanity_check_mm_encoder_outputs
 
@@ -53,14 +53,12 @@ class EncoderRunner:
         mm_kwargs: list[tuple[str, MultiModalKwargsItem]],
     ) -> list[torch.Tensor]:
         encoder_outputs: list[torch.Tensor] = []
-        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
+        for modality, num_items, mm_kwargs_batch in group_and_batch_mm_kwargs(
             mm_kwargs, device=self.device, pin_memory=False
         ):
-            curr_group_outputs = self.model.embed_multimodal(**mm_kwargs_group)
-            sanity_check_mm_encoder_outputs(
-                curr_group_outputs, expected_num_items=num_items
-            )
-            encoder_outputs.extend(curr_group_outputs)
+            batch_outputs = self.model.embed_multimodal(**mm_kwargs_batch)
+            sanity_check_mm_encoder_outputs(batch_outputs, expected_num_items=num_items)
+            encoder_outputs.extend(batch_outputs)
         return encoder_outputs
 
     def gather_mm_embeddings(

vllm/v1/worker/gpu_model_runner.py

@@ -93,7 +93,7 @@ from vllm.multimodal.inputs import (
     MultiModalKwargsItem,
     PlaceholderRange,
 )
-from vllm.multimodal.utils import group_mm_kwargs_by_modality
+from vllm.multimodal.utils import group_and_batch_mm_kwargs
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingType
 from vllm.sequence import IntermediateTensors
@@ -1311,12 +1311,12 @@ class GPUModelRunner(
 
         # Input all modalities at once
         mm_kwargs_combined: BatchedTensorInputs = {}
-        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
+        for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
             mm_kwargs,
             device=self.device,
             pin_memory=self.pin_memory,
         ):
-            mm_kwargs_combined.update(mm_kwargs_group)
+            mm_kwargs_combined.update(mm_kwargs_batch)
 
         return mm_kwargs_combined
 
@@ -2446,12 +2446,12 @@ class GPUModelRunner(
         encoder_outputs: list[torch.Tensor] = []
         # Track the current index in mm_kwargs/mm_lora_refs to map groups to request IDs
         current_item_idx = 0
-        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
+        for modality, num_items, mm_kwargs_batch in group_and_batch_mm_kwargs(
             mm_kwargs,
             device=self.device,
             pin_memory=self.pin_memory,
         ):
-            curr_group_outputs: MultiModalEmbeddings
+            batch_outputs: MultiModalEmbeddings
 
             # EVS-related change.
             # (ekhvedchenia): Temporary hack to limit peak memory usage when
@@ -2467,14 +2467,14 @@ class GPUModelRunner(
                 and modality == "video"
                 and num_items > 1
             ):
-                curr_group_outputs_lst = list[torch.Tensor]()
+                batch_outputs_lst = list[torch.Tensor]()
                 for video_idx in range(num_items):
                     video_mm_kwargs_item = mm_kwargs[current_item_idx + video_idx]
                     with self.timed_encoder_operation(
                         should_time, mm_lora_refs, current_item_idx + video_idx, 1
                     ):
                         _, _, micro_batch_mm_inputs = next(
-                            group_mm_kwargs_by_modality(
+                            group_and_batch_mm_kwargs(
                                 [video_mm_kwargs_item],
                                 device=self.device,
                                 pin_memory=self.pin_memory,
@@ -2485,12 +2485,12 @@ class GPUModelRunner(
                             **micro_batch_mm_inputs
                         )
 
-                        curr_group_outputs_lst.extend(micro_batch_outputs)
+                        batch_outputs_lst.extend(micro_batch_outputs)
 
-                curr_group_outputs = curr_group_outputs_lst
+                batch_outputs = batch_outputs_lst
             else:
                 # Run the encoder.
-                # `curr_group_outputs` is either of the following:
+                # `batch_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,
@@ -2500,13 +2500,10 @@ class GPUModelRunner(
                 with self.timed_encoder_operation(
                     should_time, mm_lora_refs, current_item_idx, num_items
                 ):
-                    curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)
+                    batch_outputs = model.embed_multimodal(**mm_kwargs_batch)
 
-            sanity_check_mm_encoder_outputs(
-                curr_group_outputs,
-                expected_num_items=num_items,
-            )
-            encoder_outputs.extend(curr_group_outputs)
+            sanity_check_mm_encoder_outputs(batch_outputs, expected_num_items=num_items)
+            encoder_outputs.extend(batch_outputs)
 
             current_item_idx += num_items
 
@@ -4707,8 +4704,8 @@ class GPUModelRunner(
         assert dummy_mm_item is not None, "Item should not already be cached"
 
         return next(
-            mm_kwargs_group
-            for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
+            mm_kwargs_batch
+            for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
                 [(modality, dummy_mm_item)] * max_items_per_batch,
                 device=self.device,
                 pin_memory=self.pin_memory,