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[Misc] Clean up type annotation for SupportsMultiModal (#14794)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-03-14 15:59:56 +08:00
committed by GitHub
parent 09269b3127
commit 601bd3268e
27 changed files with 121 additions and 141 deletions
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10
vllm/model_executor/models/chameleon.py
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@@ -30,8 +30,7 @@ from vllm.model_executor.model_loader.weight_utils import (
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.model_executor.utils import set_weight_attrs
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
NestedTensors)
from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargs
from vllm.multimodal.parse import MultiModalDataItems
from vllm.multimodal.processing import (BaseMultiModalProcessor,
BaseProcessingInfo, PromptReplacement,
@@ -39,7 +38,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsMultiModal, SupportsPP
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix, merge_multimodal_embeddings)
@@ -986,8 +985,7 @@ class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal,
)
def get_multimodal_embeddings(
self, **kwargs
) -> Union[list[torch.Tensor], torch.Tensor, tuple[torch.Tensor, ...]]:
self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
image_input = self._parse_and_validate_image_input(**kwargs)
if image_input is None:
return None
@@ -1000,7 +998,7 @@ class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal,
def get_input_embeddings(
self,
input_ids: torch.Tensor,
multimodal_embeddings: Optional[NestedTensors] = None,
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
) -> torch.Tensor:
inputs_embeds = self.model.get_input_embeddings(input_ids)