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[1/2] Move InternVL-based processors (#37260)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-03-17 21:50:56 +08:00
committed by GitHub
parent 2660b9289c
commit f340324335
20 changed files with 3252 additions and 3099 deletions
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585
vllm/model_executor/models/internvl.py
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@@ -7,16 +7,13 @@
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from abc import ABC, abstractmethod
from abc import abstractmethod
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Any, Literal, TypeAlias, TypeVar
from typing import Annotated, Literal, TypeAlias, TypeVar
import numpy.typing as npt
import torch
import torch.nn as nn
import torchvision.transforms as T
from PIL import Image
from transformers import BatchFeature, PretrainedConfig, TensorType
from transformers import BatchFeature, PretrainedConfig
from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
@@ -28,7 +25,6 @@ from vllm.model_executor.models.intern_vit import (
)
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import convert_image_mode
from vllm.multimodal.inputs import (
MultiModalDataDict,
MultiModalFieldConfig,
@@ -46,10 +42,12 @@ from vllm.multimodal.processing import (
BaseProcessingInfo,
PromptReplacement,
PromptUpdate,
PromptUpdateDetails,
)
from vllm.sequence import IntermediateTensors
from vllm.tokenizers import TokenizerLike
from vllm.transformers_utils.processors.internvl import (
BaseInternVLProcessor,
InternVLProcessor,
)
from vllm.utils.tensor_schema import TensorSchema, TensorShape
from .interfaces import (
@@ -60,13 +58,6 @@ from .interfaces import (
)
from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
IMG_START = "<img>"
IMG_END = "</img>"
IMG_CONTEXT = "<IMG_CONTEXT>"
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
class InternVLImagePixelInputs(TensorSchema):
"""
@@ -128,568 +119,6 @@ class InternVLVideoEmbeddingInputs(TensorSchema):
InternVLVideoInputs: TypeAlias = InternVLVideoPixelInputs | InternVLVideoEmbeddingInputs
# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
def build_transform(input_size: int):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose(
[
T.Lambda(lambda img: convert_image_mode(img, "RGB")),
T.Resize(
(input_size, input_size), interpolation=T.InterpolationMode.BICUBIC
),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD),
]
)
return transform
# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
def find_closest_aspect_ratio(
aspect_ratio: float,
target_ratios: list[tuple[int, int]],
*,
width: int,
height: int,
image_size: int,
) -> tuple[int, int]:
best_ratio_diff = float("inf")
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def resolve_internvl_min_max_num(
*,
min_dynamic_patch: int,
max_dynamic_patch: int,
dynamic_image_size: bool,
use_thumbnail: bool,
) -> tuple[int, int]:
min_dynamic_patch = min_dynamic_patch if dynamic_image_size else 1
max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
if use_thumbnail and max_dynamic_patch != 1:
max_dynamic_patch += 1
return min_dynamic_patch, max_dynamic_patch
def get_internvl_target_ratios(
min_num: int,
max_num: int,
) -> list[tuple[int, int]]:
target_ratios = {
(i, j)
for n in range(min_num, max_num + 1)
for i in range(1, n + 1)
for j in range(1, n + 1)
if min_num <= i * j <= max_num
}
return sorted(target_ratios, key=lambda x: x[0] * x[1])
def calculate_internvl_targets(
*,
orig_width: int,
orig_height: int,
target_ratios: list[tuple[int, int]],
image_size: int,
use_thumbnail: bool,
) -> tuple[int, int, int]:
aspect_ratio = orig_width / orig_height
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio,
target_ratios,
width=orig_width,
height=orig_height,
image_size=image_size,
)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# add thumbnail image if num_blocks != 1
if use_thumbnail and blocks != 1:
blocks += 1
return blocks, target_width, target_height
# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
def dynamic_preprocess_internvl(
image: Image.Image,
*,
target_ratios: list[tuple[int, int]],
image_size: int,
use_thumbnail: bool,
) -> list[Image.Image]:
orig_width, orig_height = image.size
# calculate the number of blocks without thumbnail
blocks, target_width, target_height = calculate_internvl_targets(
orig_width=orig_width,
orig_height=orig_height,
target_ratios=target_ratios,
image_size=image_size,
use_thumbnail=False,
)
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size,
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
def image_to_pixel_values_internvl(
image: Image.Image,
*,
input_size: int,
min_num: int,
max_num: int,
use_thumbnail: bool,
) -> torch.Tensor:
target_ratios = get_internvl_target_ratios(min_num, max_num)
transform = build_transform(input_size=input_size)
images = dynamic_preprocess_internvl(
image,
target_ratios=target_ratios,
image_size=input_size,
use_thumbnail=use_thumbnail,
)
pixel_values = torch.stack([transform(image) for image in images])
return pixel_values
# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
def video_to_pixel_values_internvl(
video: npt.NDArray,
*,
input_size: int,
min_num: int,
max_num: int,
use_thumbnail: bool,
) -> torch.Tensor:
target_ratios = get_internvl_target_ratios(min_num, max_num)
transform = build_transform(input_size=input_size)
frames_list = list[Image.Image]()
for frame in video:
pil_frame = dynamic_preprocess_internvl(
Image.fromarray(frame, mode="RGB"),
target_ratios=target_ratios,
image_size=input_size,
use_thumbnail=use_thumbnail,
)
assert len(pil_frame) == 1
frames_list.extend(pil_frame)
pixel_values = torch.stack([transform(image) for image in frames_list])
return pixel_values
class BaseInternVLProcessor(ABC):
"""
This model doesn't define its own HF processor,
so we implement our own one here.
The code to insert image tokens is based on:
https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py#L252
"""
def __init__(
self,
config: PretrainedConfig,
tokenizer: TokenizerLike,
*,
min_dynamic_patch: int | None = None,
max_dynamic_patch: int | None = None,
dynamic_image_size: bool | None = None,
) -> None:
super().__init__()
self.config = config
self.tokenizer = tokenizer
image_size: int = config.vision_config.image_size
patch_size: int = config.vision_config.patch_size
if min_dynamic_patch is None:
min_dynamic_patch = config.min_dynamic_patch
assert isinstance(min_dynamic_patch, int)
if max_dynamic_patch is None:
max_dynamic_patch = config.max_dynamic_patch
assert isinstance(max_dynamic_patch, int)
if dynamic_image_size is None:
dynamic_image_size = config.dynamic_image_size
assert isinstance(dynamic_image_size, bool)
self.num_image_token = int(
(image_size // patch_size) ** 2 * (config.downsample_ratio**2)
)
self.image_size = image_size
self.min_dynamic_patch = min_dynamic_patch
self.max_dynamic_patch = max_dynamic_patch
self.dynamic_image_size = dynamic_image_size
self.use_thumbnail: bool = config.use_thumbnail
@property
@abstractmethod
def image_token_id(self) -> int:
raise NotImplementedError
@abstractmethod
def get_image_repl(
self,
feature_size: int,
num_patches: int | None,
) -> PromptUpdateDetails[str]:
raise NotImplementedError
def resolve_min_max_num(
self,
*,
min_dynamic_patch: int | None = None,
max_dynamic_patch: int | None = None,
dynamic_image_size: bool | None = None,
use_thumbnail: bool | None = None,
) -> tuple[int, int]:
min_dynamic_patch = (
self.min_dynamic_patch if min_dynamic_patch is None else min_dynamic_patch
)
max_dynamic_patch = (
self.max_dynamic_patch if max_dynamic_patch is None else max_dynamic_patch
)
dynamic_image_size = (
self.dynamic_image_size
if dynamic_image_size is None
else dynamic_image_size
)
use_thumbnail = self.use_thumbnail if use_thumbnail is None else use_thumbnail
return resolve_internvl_min_max_num(
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
use_thumbnail=use_thumbnail,
)
def resolve_target_ratios(
self,
*,
min_dynamic_patch: int | None = None,
max_dynamic_patch: int | None = None,
dynamic_image_size: bool | None = None,
use_thumbnail: bool | None = None,
) -> list[tuple[int, int]]:
min_num, max_num = self.resolve_min_max_num(
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
use_thumbnail=use_thumbnail,
)
return get_internvl_target_ratios(min_num, max_num)
def get_num_image_tokens(
self,
*,
image_width: int,
image_height: int,
) -> int:
target_ratios = self.resolve_target_ratios(
use_thumbnail=False, # Applied in calculate_targets
)
num_patches, _, _ = calculate_internvl_targets(
orig_width=image_width,
orig_height=image_height,
image_size=self.image_size,
target_ratios=target_ratios,
use_thumbnail=self.use_thumbnail,
)
return num_patches * self.num_image_token
def _images_to_pixel_values_lst(
self,
images: list[Image.Image],
min_dynamic_patch: int | None = None,
max_dynamic_patch: int | None = None,
dynamic_image_size: bool | None = None,
) -> list[torch.Tensor]:
min_num, max_num = self.resolve_min_max_num(
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
use_thumbnail=False, # Applied in image_to_pixel_values
)
return [
image_to_pixel_values_internvl(
image,
input_size=self.image_size,
min_num=min_num,
max_num=max_num,
use_thumbnail=self.use_thumbnail,
)
for image in images
]
def _preprocess_image(
self,
text: list[str],
images: list[Image.Image],
min_dynamic_patch: int | None = None,
max_dynamic_patch: int | None = None,
dynamic_image_size: bool | None = None,
) -> tuple[list[str], dict[str, torch.Tensor]]:
if len(images) == 0:
image_inputs = {}
else:
pixel_values_lst = self._images_to_pixel_values_lst(
images,
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
)
image_inputs = {
"pixel_values_flat": torch.cat(pixel_values_lst),
"image_num_patches": torch.tensor(
[len(item) for item in pixel_values_lst]
),
}
for pixel_values in pixel_values_lst:
num_patches = pixel_values.shape[0]
feature_size = num_patches * self.num_image_token
image_repl = self.get_image_repl(feature_size, num_patches)
text = [t.replace("<image>", image_repl.full, 1) for t in text]
return text, image_inputs
def _make_batch_input(self, input_item: Any | list[Any] | None = None):
if input_item is None:
input_item = []
if not isinstance(input_item, list):
input_item = [input_item]
return input_item
def __call__(
self,
text: str | list[str] | None = None,
images: Image.Image | list[Image.Image] | None = None,
min_dynamic_patch: int | None = None,
max_dynamic_patch: int | None = None,
dynamic_image_size: bool | None = None,
return_tensors: str | TensorType | None = None,
) -> BatchFeature:
text, images = [self._make_batch_input(x) for x in (text, images)]
text, image_inputs = self._preprocess_image(
text=text,
images=images,
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
)
text_inputs = self.tokenizer(text)
combined_outputs = {**text_inputs, **image_inputs}
return BatchFeature(combined_outputs, tensor_type=return_tensors)
class InternVLProcessor(BaseInternVLProcessor):
"""
HF Processor for InternVLChatModel with extended video processing logic.
Code for video processing is adapted from video example:
https://huggingface.co/OpenGVLab/InternVL3-1B#inference-with-transformers
"""
def __init__(
self,
config: PretrainedConfig,
tokenizer: TokenizerLike,
*,
min_dynamic_patch: int | None = None,
max_dynamic_patch: int | None = None,
dynamic_image_size: bool | None = None,
video_token: str | None = None,
) -> None:
super().__init__(
config=config,
tokenizer=tokenizer,
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
)
# add extra video token for video processing
self.video_token = video_token
@property
def image_token_id(self) -> int:
return self.tokenizer.get_vocab()[IMG_CONTEXT]
@property
def video_token_id(self) -> int | None:
if self.video_token is None:
return None
return self.tokenizer.get_vocab().get(self.video_token, None)
@property
def supports_video(self) -> bool:
return self.video_token_id is not None
def _videos_to_pixel_values_lst(
self,
videos: list[npt.NDArray],
dynamic_image_size: bool | None = None,
) -> list[torch.Tensor]:
min_num, max_num = self.resolve_min_max_num(
min_dynamic_patch=1,
max_dynamic_patch=1,
dynamic_image_size=dynamic_image_size,
use_thumbnail=False, # Applied in image_to_pixel_values
)
return [
video_to_pixel_values_internvl(
video,
input_size=self.image_size,
min_num=min_num,
max_num=max_num,
use_thumbnail=False,
)
for video in videos
]
def _preprocess_video(
self,
text: list[str],
videos: list[npt.NDArray],
dynamic_image_size: bool | None = None,
):
if len(videos) == 0 or not self.supports_video:
video_inputs = {}
else:
pixel_values_lst_video = self._videos_to_pixel_values_lst(
videos,
dynamic_image_size=dynamic_image_size,
)
video_inputs = {
"pixel_values_flat_video": torch.cat(pixel_values_lst_video),
"video_num_patches": torch.tensor(
[len(item) for item in pixel_values_lst_video]
),
}
for pixel_values in pixel_values_lst_video:
num_patches = pixel_values.shape[0]
video_repl = self.get_video_repl(
self.num_image_token, num_patches, self.video_token
)
text = [t.replace("<video>", video_repl.full, 1) for t in text]
return text, video_inputs
def __call__(
self,
text: str | list[str] | None = None,
images: Image.Image | list[Image.Image] | None = None,
videos: npt.NDArray | list[npt.NDArray] | None = None,
min_dynamic_patch: int | None = None,
max_dynamic_patch: int | None = None,
dynamic_image_size: bool | None = None,
return_tensors: str | TensorType | None = None,
) -> BatchFeature:
text, images, videos = [
self._make_batch_input(x) for x in (text, images, videos)
]
text, image_inputs = self._preprocess_image(
text=text,
images=images,
min_dynamic_patch=min_dynamic_patch,
max_dynamic_patch=max_dynamic_patch,
dynamic_image_size=dynamic_image_size,
)
text, video_inputs = self._preprocess_video(
text=text,
videos=videos,
dynamic_image_size=dynamic_image_size,
)
text_inputs = self.tokenizer(text)
combined_outputs = {**text_inputs, **image_inputs, **video_inputs}
return BatchFeature(combined_outputs, tensor_type=return_tensors)
def get_image_repl(
self,
feature_size: int,
num_patches: int | None,
) -> PromptUpdateDetails[str]:
repl_features = IMG_CONTEXT * feature_size
repl_full = IMG_START + repl_features + IMG_END
return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
def get_video_repl(
self,
feature_size: int,
num_patches: int | None = None,
video_context_token: str = IMG_CONTEXT,
) -> PromptUpdateDetails[str]:
repl_features = video_context_token * self.num_image_token
repl_features_with_sep = IMG_START + repl_features + IMG_END
# num_patches is equal to num_frames
repl_full = "".join(
[f"Frame{i + 1}: {repl_features_with_sep}" for i in range(num_patches)]
)
return PromptUpdateDetails.select_text(repl_full, video_context_token)
class BaseInternVLProcessingInfo(BaseProcessingInfo):
"""Basic image-only ProcessingInfo for InternVL-style models."""