[0/N] Rename MultiModalInputs to MultiModalKwargs (vllm-project#1…

…0040)

Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: Loc Huynh <[email protected]>

docs/source/design/multimodal/multimodal_index.rst

@@ -53,7 +53,7 @@ Base Classes
 
 .. autodata:: vllm.multimodal.MultiModalDataDict
 
-.. autoclass:: vllm.multimodal.MultiModalInputs
+.. autoclass:: vllm.multimodal.MultiModalKwargs
     :members:
     :show-inheritance:
 

tests/models/decoder_only/vision_language/mm_processor_kwargs/test_qwen.py

@@ -6,7 +6,7 @@
 from PIL.Image import Image
 
 from vllm.inputs import InputContext, token_inputs
-from vllm.multimodal.base import MultiModalInputs
+from vllm.multimodal.base import MultiModalKwargs
 from vllm.multimodal.utils import cached_get_tokenizer
 
 from .....conftest import IMAGE_ASSETS
@@ -96,7 +96,7 @@ def test_input_mapper_valid_mm_data(input_mapper_for_qwen,
     mapped_img_data = input_mapper_for_qwen(qwen_vl_context, img_data)
     # Ensure that we get the appropriately shaped pixel_values
     # for images and image embeddings, respectively.
-    assert isinstance(mapped_img_data, MultiModalInputs)
+    assert isinstance(mapped_img_data, MultiModalKwargs)
     assert "pixel_values" in mapped_img_data
     assert mapped_img_data["pixel_values"].shape == expected_shape
 

tests/multimodal/test_base.py

@@ -1,6 +1,6 @@
 import torch
 
-from vllm.multimodal.base import MultiModalInputs, NestedTensors
+from vllm.multimodal.base import MultiModalKwargs, NestedTensors
 
 
 def assert_nested_tensors_equal(expected: NestedTensors,
@@ -13,40 +13,40 @@ def assert_nested_tensors_equal(expected: NestedTensors,
             assert_nested_tensors_equal(expected_item, actual_item)
 
 
-def assert_multimodal_inputs_equal(expected: MultiModalInputs,
-                                   actual: MultiModalInputs):
+def assert_multimodal_inputs_equal(expected: MultiModalKwargs,
+                                   actual: MultiModalKwargs):
     assert set(expected.keys()) == set(actual.keys())
     for key in expected:
         assert_nested_tensors_equal(expected[key], actual[key])
 
 
 def test_multimodal_input_batch_single_tensor():
     t = torch.rand([1, 2])
-    result = MultiModalInputs.batch([{"image": t}])
+    result = MultiModalKwargs.batch([{"image": t}])
     assert_multimodal_inputs_equal(result, {"image": t.unsqueeze(0)})
 
 
 def test_multimodal_input_batch_multiple_tensors():
     a = torch.rand([1, 1, 2])
     b = torch.rand([1, 1, 2])
     c = torch.rand([1, 1, 2])
-    result = MultiModalInputs.batch([{"image": a}, {"image": b}, {"image": c}])
+    result = MultiModalKwargs.batch([{"image": a}, {"image": b}, {"image": c}])
     assert_multimodal_inputs_equal(result, {"image": torch.stack([a, b, c])})
 
 
 def test_multimodal_input_batch_multiple_heterogeneous_tensors():
     a = torch.rand([1, 2, 2])
     b = torch.rand([1, 3, 2])
     c = torch.rand([1, 4, 2])
-    result = MultiModalInputs.batch([{"image": a}, {"image": b}, {"image": c}])
+    result = MultiModalKwargs.batch([{"image": a}, {"image": b}, {"image": c}])
     assert_multimodal_inputs_equal(result, {"image": [a, b, c]})
 
 
 def test_multimodal_input_batch_nested_tensors():
     a = torch.rand([2, 3])
     b = torch.rand([2, 3])
     c = torch.rand([2, 3])
-    result = MultiModalInputs.batch([{
+    result = MultiModalKwargs.batch([{
         "image": [a]
     }, {
         "image": [b]
@@ -65,7 +65,7 @@ def test_multimodal_input_batch_heterogeneous_lists():
     a = torch.rand([1, 2, 3])
     b = torch.rand([1, 2, 3])
     c = torch.rand([1, 2, 3])
-    result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c]}])
+    result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c]}])
     assert_multimodal_inputs_equal(
         result,
         {"image": [torch.stack([a, b]), c.unsqueeze(0)]})
@@ -76,7 +76,7 @@ def test_multimodal_input_batch_multiple_batchable_lists():
     b = torch.rand([1, 2, 3])
     c = torch.rand([1, 2, 3])
     d = torch.rand([1, 2, 3])
-    result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c, d]}])
+    result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c, d]}])
     assert_multimodal_inputs_equal(
         result,
         {"image": torch.stack([torch.stack([a, b]),
@@ -88,8 +88,8 @@ def test_multimodal_input_batch_mixed_stacking_depths():
     b = torch.rand([1, 3, 3])
     c = torch.rand([1, 4, 3])
 
-    result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c]}])
+    result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c]}])
     assert_multimodal_inputs_equal(result, {"image": [[a, b], c.unsqueeze(0)]})
 
-    result = MultiModalInputs.batch([{"image": [a]}, {"image": [b, c]}])
+    result = MultiModalKwargs.batch([{"image": [a]}, {"image": [b, c]}])
     assert_multimodal_inputs_equal(result, {"image": [a.unsqueeze(0), [b, c]]})

vllm/model_executor/models/chatglm.py

@@ -30,7 +30,7 @@
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.glm4_vision_encoder import EVA2CLIPModel
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalInputs
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
 from vllm.multimodal.base import MultiModalData
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
@@ -74,7 +74,7 @@ def mm_input_mapper_for_glmv(
         raise
     pixel_values = raw_batch_data['images']
 
-    return MultiModalInputs({'pixel_values': pixel_values})
+    return MultiModalKwargs({'pixel_values': pixel_values})
 
 
 def merge_glm_vision_embeddings(

vllm/model_executor/models/fuyu.py

@@ -34,7 +34,7 @@
 from vllm.model_executor.models.persimmon import PersimmonForCausalLM
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.base import MultiModalInputs
+from vllm.multimodal.base import MultiModalKwargs
 from vllm.multimodal.image import cached_get_image_processor
 from vllm.multimodal.utils import (cached_get_tokenizer,
                                    consecutive_placeholder_ranges)
@@ -218,7 +218,7 @@ def input_mapper_for_fuyu(ctx: InputContext, data: object):
         ])
 
     # image has been processed with prompt in input processor
-    return MultiModalInputs({"pixel_values": data})
+    return MultiModalKwargs({"pixel_values": data})
 
 
 @MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_fuyu)

vllm/model_executor/models/h2ovl.py

@@ -16,7 +16,7 @@
                          token_inputs)
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.base import MultiModalInputs
+from vllm.multimodal.base import MultiModalKwargs
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.utils import is_list_of
 
@@ -324,12 +324,12 @@ def input_mapper(
         data: object,
         *,
         max_dynamic_patch: Optional[int] = None,
-    ) -> MultiModalInputs:
+    ) -> MultiModalKwargs:
 
         # NOTE: Preprocessing for the image data is done in the
         # 'input_processor' function during actual inference.
         if isinstance(data, dict):
-            return MultiModalInputs(data)
+            return MultiModalKwargs(data)
 
         # The section below is only used with dummy data during
         # memory profiling.
@@ -347,7 +347,7 @@ def input_mapper(
             pixel_values = [image_pixel_values_mapper(img) for img in data]
 
         else:
-            return MultiModalInputs({"image_embeds": data})
+            return MultiModalKwargs({"image_embeds": data})
         model_config = ctx.model_config
         tokenizer = cached_get_tokenizer(
             model_config.tokenizer,
@@ -359,7 +359,7 @@ def input_mapper(
             return_tensors="pt",
         )[0]
 
-        return MultiModalInputs({
+        return MultiModalKwargs({
             "pixel_values": pixel_values,
             "image_token_id": image_token_id
         })

vllm/model_executor/models/idefics3.py

@@ -36,7 +36,7 @@
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalInputs
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
 from vllm.multimodal.image import cached_get_image_processor
 from vllm.sequence import IntermediateTensors, SequenceData
 from vllm.transformers_utils.processor import cached_get_processor
@@ -127,7 +127,7 @@ def input_mapper_for_idefics3(
         logger.error("Failed to process image (%s)", data)
         raise
 
-    return MultiModalInputs(batch_data)
+    return MultiModalKwargs(batch_data)
 
 
 def _resize_output_size(height: int,

vllm/model_executor/models/internvl.py

@@ -26,7 +26,7 @@
                                                    InternVisionPatchModel)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.base import MultiModalInputs
+from vllm.multimodal.base import MultiModalKwargs
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors
 from vllm.utils import is_list_of
@@ -346,7 +346,7 @@ def input_mapper(
             # we can't stack here because images may have different num_patches
             data = [image_pixel_values_mapper(img) for img in data]
         else:
-            return MultiModalInputs({"image_embeds": data})
+            return MultiModalKwargs({"image_embeds": data})
         model_config = ctx.model_config
         tokenizer = cached_get_tokenizer(
             model_config.tokenizer,
@@ -355,7 +355,7 @@ def input_mapper(
                                           add_special_tokens=False,
                                           return_tensors="pt")[0]
 
-        return MultiModalInputs({
+        return MultiModalKwargs({
             "pixel_values": data,
             "image_token_id": image_token_id
         })

vllm/model_executor/models/minicpmv.py

@@ -52,7 +52,7 @@
 from vllm.model_executor.models.utils import LLMWrapper
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.base import MultiModalInputs
+from vllm.multimodal.base import MultiModalKwargs
 from vllm.multimodal.image import cached_get_image_processor
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors, SequenceData
@@ -374,7 +374,7 @@ def input_mapper_for_minicpmv(ctx: InputContext, data: object):
             batch_data["slice_start_id"] = data[0]["slice_start_id"]
             batch_data["slice_end_id"] = data[0]["slice_end_id"]
 
-    return MultiModalInputs(batch_data)
+    return MultiModalKwargs(batch_data)
 
 
 class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):

vllm/model_executor/models/mllama.py

@@ -1162,7 +1162,7 @@ def sample(
 
     def _parse_and_validate_image_input(self, **kwargs: object):
         # tensor with the same shape will be batched together by
-        # MultiModalInputs.batch, so pixel_values here can be:
+        # MultiModalKwargs.batch, so pixel_values here can be:
         #   - List[List[torch.Tensor]]:
         #       with shape (num_tiles, 3, image_res, image_res)
         #   - List[torch.Tensor]:

vllm/model_executor/models/molmo.py

@@ -37,7 +37,7 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalInputs
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
                            SequenceData)
@@ -866,7 +866,7 @@ def image_input_mapper_for_molmo(
     ctx: InputContext,
     data: object,
 ):
-    return MultiModalInputs(data)
+    return MultiModalKwargs(data)
 
 
 def dummy_data_for_molmo(ctx: InputContext, seq_len: int,

vllm/model_executor/models/pixtral.py

@@ -30,7 +30,7 @@
 from vllm.model_executor.models.utils import merge_multimodal_embeddings
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.base import MultiModalInputs
+from vllm.multimodal.base import MultiModalKwargs
 from vllm.multimodal.utils import (cached_get_tokenizer,
                                    consecutive_placeholder_ranges)
 from vllm.sequence import IntermediateTensors, SequenceData
@@ -94,16 +94,16 @@ def dummy_data_for_pixtral(ctx: InputContext, seq_len: int,
 
 
 def input_mapper_for_pixtral(ctx: InputContext,
-                             data: object) -> MultiModalInputs:
-    """Maps the input data to its MultiModalInputs (if any).
+                             data: object) -> MultiModalKwargs:
+    """Maps the input data to its MultiModalKwargs (if any).
 
     Args:
         ctx: Context of the loaded model.
         data: data potentially containing image/image embeddings to be mapped
             to pixel_values in .forward() for a visual QWenLMHeadModel model.
 
     Returns:
-        MultiModalInputs containing the stacked normalized images tensor or
+        MultiModalKwargs containing the stacked normalized images tensor or
         image embeddings.
     """
     # Early exit if we have provided an image to a language only Qwen model
@@ -121,7 +121,7 @@ def input_mapper_for_pixtral(ctx: InputContext,
                                                     dtype=torch.float16)
         images.append(image)
 
-    return MultiModalInputs({"images": images})
+    return MultiModalKwargs({"images": images})
 
 
 def input_processor_for_pixtral(ctx: InputContext, inputs: DecoderOnlyInputs):

vllm/model_executor/models/qwen.py

@@ -43,7 +43,7 @@
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.base import MultiModalInputs
+from vllm.multimodal.base import MultiModalKwargs
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors, SequenceData
 from vllm.utils import is_list_of
@@ -722,16 +722,16 @@ def input_processor_for_qwen(ctx: InputContext,
                         multi_modal_data=multi_modal_data)
 
 
-def input_mapper_for_qwen(ctx: InputContext, data: object) -> MultiModalInputs:
-    """Maps the input data to its MultiModalInputs (if any).
+def input_mapper_for_qwen(ctx: InputContext, data: object) -> MultiModalKwargs:
+    """Maps the input data to its MultiModalKwargs (if any).
 
     Args:
         ctx: Context of the loaded model.
         data: data potentially containing image/image embeddings to be mapped
             to pixel_values in .forward() for a visual QWenLMHeadModel model.
 
     Returns:
-        MultiModalInputs containing the stacked normalized images tensor or
+        MultiModalKwargs containing the stacked normalized images tensor or
         image embeddings.
     """
     # Early exit if we have provided an image to a language only Qwen model
@@ -740,7 +740,7 @@ def input_mapper_for_qwen(ctx: InputContext, data: object) -> MultiModalInputs:
         logger.warning(
             "Images were provided but this model has no visual config; "
             "multimodal inputs will not be forwarded to the model.")
-        return MultiModalInputs()
+        return MultiModalKwargs()
 
     model_config = ctx.model_config
     tokenizer = cached_get_tokenizer(
@@ -784,7 +784,7 @@ def input_mapper_for_qwen(ctx: InputContext, data: object) -> MultiModalInputs:
             data = [data]
         transformed_images = [transform(datum) for datum in data]
         pixel_values = torch.stack(transformed_images, dim=0)
-    return MultiModalInputs({"pixel_values": pixel_values})
+    return MultiModalKwargs({"pixel_values": pixel_values})
 
 
 def build_normalization_transform(image_size: int) -> transforms.Compose:

vllm/model_executor/models/qwen2_audio.py

@@ -42,7 +42,7 @@
     default_weight_loader, maybe_remap_kv_scale_name)
 from vllm.model_executor.models.qwen2 import Qwen2Model
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalInputs
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
 from vllm.multimodal.utils import consecutive_placeholder_ranges
 from vllm.sequence import IntermediateTensors, SequenceData
 
@@ -221,13 +221,13 @@ def input_processor_for_qwen2_audio(
 def input_mapper_for_qwen2_audio(
     ctx: InputContext,
     multi_modal_data: Union[np.ndarray, List[np.ndarray]],
-) -> MultiModalInputs:
+) -> MultiModalKwargs:
     """Input mapper for Qwen2-Audio."""
     if not isinstance(multi_modal_data, list):
         multi_modal_data = [multi_modal_data]
 
     if len(multi_modal_data) == 0:
-        return MultiModalInputs()
+        return MultiModalKwargs()
 
     processor = cached_get_processor(ctx.model_config.model)
     audio_feature_extractor = processor.feature_extractor
@@ -254,7 +254,7 @@ def input_mapper_for_qwen2_audio(
         logger.error("Failed to process audio (%s)", multi_modal_data)
         raise
 
-    return MultiModalInputs(batch_data)
+    return MultiModalKwargs(batch_data)
 
 
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_qwen2_audio)