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[0/N] Rename MultiModalInputs to MultiModalKwargs (#10040)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2024-11-09 11:31:02 +08:00
committed by GitHub
parent d7edca1dee
commit e0191a95d8
32 changed files with 151 additions and 121 deletions
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12
vllm/model_executor/models/qwen.py
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@@ -43,7 +43,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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
@@ -722,8 +722,8 @@ 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.
@@ -731,7 +731,7 @@ def input_mapper_for_qwen(ctx: InputContext, data: object) -> MultiModalInputs:
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: