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Migrate Interns1 inputs to TensorSchema (#23510)

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Signed-off-by: Benji Beck <benjibeck@meta.com>
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Benji Beck 2025-09-01 21:35:45 -07:00 committed by GitHub
parent 7be0cb8e9e
commit 56d04089ef
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1 changed files with 50 additions and 51 deletions
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101
vllm/model_executor/models/interns1.py
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@ -7,7 +7,7 @@
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from collections.abc import Iterable, Mapping, Sequence
from typing import Literal, Optional, TypedDict, Union
from typing import Annotated, Literal, Optional, Union
import regex as re
import torch
@ -32,6 +32,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
PromptUpdate, PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.utils.tensor_schema import TensorSchema, TensorShape
from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
SupportsMultiModal, SupportsPP)
@ -62,51 +63,60 @@ class InternS1MultiModalProjector(nn.Module):
return hidden_states
class InternS1ImagePixelInputs(TypedDict):
type: Literal["pixel_values"]
pixel_values: torch.Tensor
class InternS1ImagePixelInputs(TensorSchema):
"""
Shape:
`(batch_size * num_images * (1 + num_patches), num_channels, height, width)`
Dimensions:
- bnp: Batch size * number of images * (1 + num_patches)
- c: Number of channels (3)
- h: Height
- w: Width
- bn: Batch size * number of images
"""
type: Literal["pixel_values"] = "pixel_values"
pixel_values: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w")]
num_patches: Annotated[torch.Tensor, TensorShape("bn")]
class InternS1ImageEmbeddingInputs(TypedDict):
type: Literal["image_embeds"]
data: Union[torch.Tensor, list[torch.Tensor]]
class InternS1ImageEmbeddingInputs(TensorSchema):
"""
A tensor of shape `(num_images, total_image_feature_size, hidden_size)`
or a list of tensors of shape `(total_image_feature_size, hidden_size)`
`hidden_size` must match the hidden size of language model backbone.
Dimensions:
- ni: Number of images
- tifs: Total image feature size
- hs: Hidden size (must match language model backbone)
"""
type: Literal["image_embeds"] = "image_embeds"
data: Annotated[Union[torch.Tensor, list[torch.Tensor]],
TensorShape("ni", "tifs", "hs")]
InternS1ImageInputs = Union[InternS1ImagePixelInputs,
InternS1ImageEmbeddingInputs]
class InternS1VideoPixelInputs(TypedDict):
type: Literal["pixel_values_videos"]
pixel_values: torch.Tensor
class InternS1VideoPixelInputs(TensorSchema):
"""
Shape:
`(batch_size * num_video * num_frames, num_channels, height, width)`
Dimensions:
- bnv: Batch size * number of videos * number of frames
- bn: Batch size * number of images
- c: Number of channels (3)
- h: Height
- w: Width
"""
num_patches: torch.Tensor
"""Shape: `(batch_size * num_images)`"""
type: Literal["pixel_values_videos"] = "pixel_values_videos"
pixel_values: Annotated[torch.Tensor, TensorShape("bnv", 3, "h", "w")]
num_patches: Annotated[torch.Tensor, TensorShape("bn")]
class InternS1VideoEmbeddingInputs(TypedDict):
type: Literal["video_embeds"]
data: Union[torch.Tensor, list[torch.Tensor]]
class InternS1VideoEmbeddingInputs(TensorSchema):
"""
A tensor of shape `(num_videos, total_video_feature_size, hidden_size)`
or a list of tensors of shape `(total_video_feature_size, hidden_size)`
`hidden_size` must match the hidden size of language model backbone.
Dimensions:
- nv: Number of videos
- tvfs: Total video feature size
- hs: Hidden size (must match language model backbone)
"""
type: Literal["video_embeds"] = "video_embeds"
data: Annotated[Union[torch.Tensor, list[torch.Tensor]],
TensorShape("nv", "tvfs", "hs")]
InternS1VideoInputs = Union[InternS1VideoPixelInputs,
@ -572,26 +582,6 @@ class InternS1ForConditionalGeneration(nn.Module, SupportsMultiModal,
vit_embeds = self.multi_modal_projector(vit_embeds)
return vit_embeds
def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
h, w = self.config.vision_config.image_size
expected_dims = (3, h, w)
def _validate_shape(d: torch.Tensor):
actual_dims = tuple(d.shape)
if actual_dims != expected_dims:
expected_expr = str(expected_dims)
raise ValueError(
"The expected shape of pixel values per image per batch "
f" per patch is {expected_expr}. "
f"You supplied {tuple(d.shape)}.")
for d in data:
_validate_shape(d)
return data
def _parse_and_validate_image_input(
self, **kwargs: object) -> Optional[InternS1ImageInputs]:
pixel_values = kwargs.pop("pixel_values", None)
@ -627,10 +617,15 @@ class InternS1ForConditionalGeneration(nn.Module, SupportsMultiModal,
pixel_values = flatten_bn(pixel_values, concat=True)
image_num_patches = flatten_bn(image_num_patches, concat=True)
h, w = self.config.vision_config.image_size
return InternS1ImagePixelInputs(
type="pixel_values",
pixel_values=self._validate_pixel_values(pixel_values),
pixel_values=pixel_values,
num_patches=image_num_patches,
resolve_bindings={
"h": h,
"w": w,
},
)
raise AssertionError("This line should be unreachable.")
@ -671,11 +666,15 @@ class InternS1ForConditionalGeneration(nn.Module, SupportsMultiModal,
concat=True)
video_num_patches = flatten_bn(video_num_patches, concat=True)
h, w = self.config.vision_config.image_size
return InternS1VideoPixelInputs(
type="pixel_values_videos",
pixel_values=self._validate_pixel_values(
pixel_values_flat_video),
num_patches=video_num_patches,
pixel_values=pixel_values_flat_video,
resolve_bindings={
"h": h,
"w": w,
},
)
raise AssertionError("This line should be unreachable.")