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vlm: support video as an input modality (#5888)

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This commit is contained in:
Mick
2025-07-10 14:48:35 +08:00
committed by GitHub
parent 4ed57807c2
commit b5e3d6031c
42 changed files with 887 additions and 524 deletions
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328
python/sglang/srt/multimodal/processors/base_processor.py
View File

@@ -5,7 +5,7 @@ import multiprocessing as mp
import os
import re
from abc import ABC, abstractmethod
from enum import Enum
from functools import lru_cache
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
@@ -14,7 +14,7 @@ from PIL import Image
from transformers import BaseImageProcessorFast
from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
from sglang.srt.utils import encode_video, load_audio, load_image
from sglang.srt.utils import load_audio, load_image, load_video, logger
@dataclasses.dataclass
@@ -25,14 +25,22 @@ class BaseMultiModalProcessorOutput:
# frames loaded from image and video, in given order
images: Optional[list[Union[Image.Image, dict]]] = None
# videos
videos: Optional[list[Union[torch.Tensor, dict]]] = None
# audios
audios: Optional[list[Union[np.ndarray, dict]]] = None
def normalize(self):
for field_name in ["images", "audios"]:
field = getattr(self, field_name, None)
if field is not None and isinstance(field, list) and len(field) == 0:
setattr(self, field_name, None)
def organize_results(self) -> List[Tuple[Modality, Any]]:
"""
:return: a list of results, with their corresponding modalities
"""
return (
[(Modality.IMAGE, data) for data in self.images]
+ [(Modality.VIDEO, data) for data in self.videos]
+ [(Modality.AUDIO, data) for data in self.audios]
)
@dataclasses.dataclass
@@ -41,6 +49,10 @@ class MultimodalSpecialTokens:
video_token: Optional[Union[int, str, List[str]]] = None
audio_token: Optional[Union[int, str, List[str]]] = None
image_token_regex: Optional[re.Pattern] = None
video_token_regex: Optional[re.Pattern] = None
audio_token_regex: Optional[re.Pattern] = None
def convert_to_str(self, token: Union[str, int], processor) -> str:
if token is None:
return token
@@ -53,11 +65,29 @@ class MultimodalSpecialTokens:
self.video_token = self.convert_to_str(self.video_token, processor)
self.audio_token = self.convert_to_str(self.audio_token, processor)
image_token_regex: Optional[re.Pattern] = None
video_token_regex: Optional[re.Pattern] = None
audio_token_regex: Optional[re.Pattern] = None
def get_modality_of_token(self, token) -> Optional[Modality]:
"""
:return: the modality associated with the given token, if the token is a special_token or matches with the multimodal token regex
"""
modality = {
self.image_token: Modality.IMAGE,
self.video_token: Modality.VIDEO,
self.audio_token: Modality.AUDIO,
}.get(token)
if modality:
return modality
def __post_init__(self):
for regex, modality in [
(self.image_token_regex, Modality.IMAGE),
(self.video_token_regex, Modality.VIDEO),
(self.audio_token_regex, Modality.AUDIO),
]:
if regex and regex.match(token):
return modality
return None
def parse_regex(self):
if self.image_token_regex is None and self.image_token is not None:
self.image_token_regex = re.compile(re.escape(self.image_token))
if self.video_token_regex is None and self.video_token is not None:
@@ -65,7 +95,7 @@ class MultimodalSpecialTokens:
if self.audio_token_regex is None and self.audio_token is not None:
self.audio_token_regex = re.compile(re.escape(self.audio_token))
def collect(self) -> re.Pattern:
def combine_regex(self) -> re.Pattern:
tokens = [
self.image_token_regex,
self.video_token_regex,
@@ -105,6 +135,7 @@ class BaseMultimodalProcessor(ABC):
self.ATTR_NAME_TO_MODALITY = {
# Image-related attributes
"pixel_values": Modality.IMAGE,
"pixel_values_videos": Modality.VIDEO,
"image_sizes": Modality.IMAGE,
"image_grid_thw": Modality.IMAGE,
"image_emb_mask": Modality.IMAGE,
@@ -120,7 +151,7 @@ class BaseMultimodalProcessor(ABC):
"input_features": Modality.AUDIO,
"input_features_mask": Modality.AUDIO,
# Video-related attributes
"video_grid_thws": Modality.VIDEO,
"video_grid_thw": Modality.VIDEO,
# Generic attributes that could apply to multiple modalities
# "precomputed_features" - handled specially as it can be any modality
}
@@ -196,20 +227,25 @@ class BaseMultimodalProcessor(ABC):
@staticmethod
def _load_single_item(
data, is_video, is_audio, frame_count_limit=None, discard_alpha_channel=True
data, modality: Modality, frame_count_limit=None, discard_alpha_channel=True
):
"""Static method that can be pickled for multiprocessing"""
"""
Load a single multimodal data.
If data is precomputed, returns directly.
Static method that can be pickled for multiprocessing"""
if isinstance(data, dict):
return data
try:
if is_audio:
return load_audio(data)
elif is_video:
path = data[len("video:") :]
return encode_video(path, frame_count_limit)
else:
if modality == Modality.IMAGE:
img, _ = load_image(data)
return img.convert("RGB") if discard_alpha_channel else img
elif modality == Modality.VIDEO:
return load_video(data, frame_count_limit)
elif modality == Modality.AUDIO:
return load_audio(data)
except Exception as e:
raise RuntimeError(f"Error while loading data {data}: {e}")
@@ -217,75 +253,78 @@ class BaseMultimodalProcessor(ABC):
self,
text_parts: List[str],
multimodal_tokens: MultimodalSpecialTokens,
image_data: Optional[list] = None,
audio_data: Optional[list] = None,
data_iterators: dict,
discard_alpha_channel: bool = True,
):
image_estimated_frames_iter: Optional[iter] = None,
image_scaling_factor: float = 1.0,
max_image_frames: int = 30,
) -> Tuple[List, List]:
"""
load multimodal data parallelly
load multimodal data parallelly using iterators.
"""
# TODO(mick): load from server_args, env, or sampling_params
MAX_NUM_FRAMES = 30
estimated_frames_list = self.get_estimated_frames_list(image_data=image_data)
total_frame_count = sum(estimated_frames_list)
# a heuristic value, suggesting the maximum fraction of frames to embed from all visual inputs.
# e.g., 0.1 suggests that 1 frame out of 10 input frames should be used
scaling_factor = min(1.0, MAX_NUM_FRAMES / max(1, total_frame_count))
assert len(image_data) == len(estimated_frames_list)
# Submit all tasks
futures = []
task_info = []
image_index, audio_index = 0, 0
for text_part in text_parts:
if (
multimodal_tokens.image_token_regex
and multimodal_tokens.image_token_regex.match(text_part)
):
data = image_data[image_index]
is_video = isinstance(data, str) and data.startswith("video:")
estimated_frames = estimated_frames_list[image_index]
frame_count_limit = max(1, int(estimated_frames * scaling_factor))
modality = multimodal_tokens.get_modality_of_token(text_part)
if modality is not None:
data_iterator = data_iterators.get(modality)
if data_iterator is None:
raise ValueError(f"No data iterator found for token: {text_part}")
try:
data = next(data_iterator)
except StopIteration:
raise ValueError(
f"Mismatch: More '{text_part}' tokens found than corresponding data items provided."
)
frame_count_limit = None
if modality == Modality.IMAGE and image_estimated_frames_iter:
try:
estimated_frames = next(image_estimated_frames_iter)
# Use the pre-calculated scaling factor and max frames
frame_count_limit = max(
1, int(estimated_frames * image_scaling_factor)
)
# Ensure we don't exceed the absolute max (redundant if scaling_factor handles it)
# frame_count_limit = min(frame_count_limit, max_image_frames)
except StopIteration:
raise ValueError(
"Mismatch between image tokens and estimated frame counts."
)
futures.append(
self.io_executor.submit(
BaseMultimodalProcessor._load_single_item,
data,
is_video,
False,
modality,
frame_count_limit,
discard_alpha_channel,
)
)
task_info.append((Modality.IMAGE, data, frame_count_limit))
image_index += 1
elif (
multimodal_tokens.audio_token_regex
and multimodal_tokens.audio_token_regex.match(text_part)
):
data = audio_data[audio_index]
futures.append(
self.io_executor.submit(
BaseMultimodalProcessor._load_single_item,
data,
False,
True,
None,
discard_alpha_channel,
)
task_info.append((modality, data, frame_count_limit))
for modality, iterator in data_iterators.items():
try:
next(iterator)
logger.warning(
f"Warning: More {modality.name.lower()} data items provided than corresponding tokens found in the prompt."
)
task_info.append((Modality.AUDIO, data, None))
audio_index += 1
except StopIteration:
pass
except Exception:
pass
return futures, task_info
def load_mm_data(
self,
prompt: str | List[int],
prompt: str,
multimodal_tokens: MultimodalSpecialTokens,
max_req_input_len: int,
image_data: Optional[list] = None,
video_data: Optional[list] = None,
audio_data: Optional[list] = None,
return_text: Optional[bool] = True,
discard_alpha_channel: bool = True,
@@ -299,14 +338,9 @@ class BaseMultimodalProcessor(ABC):
discard_alpha_channel: if True, discards the alpha channel in the returned images
"""
if not return_text:
raise NotImplementedError()
if image_data is None:
image_data = []
multimodal_tokens.convert_to_strs(self._processor)
multimodal_tokens_pattern = multimodal_tokens.collect()
multimodal_tokens.parse_regex()
multimodal_tokens_pattern = multimodal_tokens.combine_regex()
if isinstance(prompt, list) and return_text:
assert len(prompt) and isinstance(prompt[0], int)
prompt = self._processor.tokenizer.decode(prompt)
@@ -317,59 +351,84 @@ class BaseMultimodalProcessor(ABC):
# split text into list of normal text and special tokens
text_parts = re.split(multimodal_tokens_pattern, prompt)
# collect all data
data_iterators = {}
if multimodal_tokens.image_token and image_data:
data_iterators[Modality.IMAGE] = iter(image_data)
if multimodal_tokens.video_token and video_data:
data_iterators[Modality.VIDEO] = iter(video_data)
if multimodal_tokens.audio_token and audio_data:
data_iterators[Modality.AUDIO] = iter(audio_data)
# futures: the futures of loaded data
# task_info: modality, raw_data, and other metadata of each data
futures, task_info = self.submit_data_loading_tasks(
text_parts=text_parts,
multimodal_tokens=multimodal_tokens,
image_data=image_data,
audio_data=audio_data,
data_iterators=data_iterators,
discard_alpha_channel=discard_alpha_channel,
)
task_info_iter = iter(task_info)
futures_iter = iter(futures)
# Process results
images, audios = [], []
new_text = ""
task_ptr = 0
images, videos, audios = [], [], []
new_text_parts = []
for text_part in text_parts:
if multimodal_tokens_pattern.match(text_part):
task_type, data, frame_limit = task_info[task_ptr]
result = futures[task_ptr].result()
task_ptr += 1
try:
if multimodal_tokens_pattern.match(text_part):
modality, raw_data, frame_limit = next(task_info_iter)
is_precomputed = isinstance(raw_data, dict)
result = next(futures_iter).result()
if task_type == Modality.IMAGE:
# If data is already processed it will be a
# dictionary. In this case we want to keep the
# expanded tokens in text_part. Otherwise, we will
# call the processor code, so keep only a single image
# token.
mm_tokens = (
text_part
if isinstance(data, dict)
else multimodal_tokens.image_token
)
frames = [result] if not isinstance(result, list) else result
if frames:
images += frames
new_text += mm_tokens * len(frames)
elif task_type == Modality.AUDIO:
# audio
mm_tokens = (
text_part
if isinstance(data, dict)
else multimodal_tokens.audio_token
)
audios.append(result)
new_text += mm_tokens
# TODO: handle video
else:
new_text += text_part
if modality == Modality.IMAGE:
# If data is already processed it will be a
# dictionary(precomputed). In this case we want to keep the
# expanded tokens in text_part. Otherwise, we will
# call the processor code, so keep only a single image
# token.
mm_tokens = (
text_part
if is_precomputed
else multimodal_tokens.image_token
)
frames = [result] if not isinstance(result, list) else result
if frames:
# only for minicpmv
images += frames
new_text_parts += mm_tokens * len(frames)
elif modality == Modality.VIDEO:
# load as video
mm_tokens = (
text_part
if is_precomputed
else multimodal_tokens.video_token
)
videos += [result]
new_text_parts += mm_tokens
elif modality == Modality.AUDIO:
# audio
mm_tokens = (
text_part
if is_precomputed
else multimodal_tokens.audio_token
)
audios += [result]
new_text_parts += mm_tokens
else:
# normal text
new_text_parts += [text_part]
out = BaseMultiModalProcessorOutput(
input_text=new_text,
except Exception as e:
raise RuntimeError(
f"An exception occurred while loading multimodal data: {e}"
)
return BaseMultiModalProcessorOutput(
images=images,
audios=audios,
videos=videos,
input_text="".join(new_text_parts),
)
out.normalize()
return out
@staticmethod
def get_mm_items_offset(
@@ -460,21 +519,19 @@ class BaseMultimodalProcessor(ABC):
)
except ValueError:
modality = Modality.IMAGE
if modality:
# Create item if needed
if modality not in items:
items[modality] = MultimodalDataItem(modality=modality)
# Set attribute
if hasattr(items[modality], attr_name):
setattr(items[modality], attr_name, value)
setattr(items[modality], attr_name, value)
return list(items.values())
def _process_and_collect_mm_items(
self, input_text: str, images=None, audios=None, videos=None, **kwargs
) -> Tuple[List[MultimodalDataItem], torch.Tensor]:
) -> Tuple[List[MultimodalDataItem], torch.Tensor, dict]:
"""
Helper method to process multimodal data and create mm_items in one step.
@@ -488,11 +545,11 @@ class BaseMultimodalProcessor(ABC):
input_ids = ret["input_ids"].flatten()
collected_items = self.collect_mm_items_from_processor_output(ret)
return collected_items, input_ids
return collected_items, input_ids, ret
def process_and_combine_mm_data(
self, base_output: BaseMultiModalProcessorOutput
) -> Tuple[List[MultimodalDataItem], torch.Tensor]:
) -> Tuple[List[MultimodalDataItem], torch.Tensor, dict]:
"""
Process multimodal data and return the combined multimodal items and input_ids.
Supports mixed modalities (images and audio in the same request).
@@ -501,8 +558,7 @@ class BaseMultimodalProcessor(ABC):
Tuple of (list of mm_items, input_ids)
"""
# Collect all items and categorize them
all_items = (base_output.images or []) + (base_output.audios or [])
all_items = base_output.organize_results()
# Handle text-only case
if not all_items:
input_ids = self._processor.tokenizer(
@@ -510,19 +566,20 @@ class BaseMultimodalProcessor(ABC):
return_tensors="pt",
add_special_tokens=True,
).input_ids.flatten()
return [], input_ids
return [], input_ids, {}
dict_items, raw_images, raw_audios = [], [], []
for item in all_items:
dict_items, raw_images, raw_audios, raw_videos = [], [], [], []
for modality, item in all_items:
if isinstance(item, dict):
dict_items.append(item)
elif isinstance(item, Image.Image):
elif modality == Modality.IMAGE:
raw_images.append(item)
elif isinstance(item, np.ndarray):
elif modality == Modality.AUDIO:
raw_audios.append(item)
elif modality == Modality.VIDEO:
raw_videos.append(item)
else:
raise ValueError(f"Unknown multimodal item type: {type(item)}")
# Process items and get input_ids
all_collected_items = []
input_ids = None
@@ -534,13 +591,16 @@ class BaseMultimodalProcessor(ABC):
)
# Handle raw items (need processing)
if raw_images or raw_audios:
collected_items, input_ids = self._process_and_collect_mm_items(
if raw_images or raw_audios or raw_videos:
collected_items, input_ids, ret = self._process_and_collect_mm_items(
input_text=base_output.input_text,
images=raw_images,
audios=raw_audios,
videos=raw_videos,
)
all_collected_items.extend(collected_items)
else:
ret = None
# Fallback tokenization if no raw items were processed
if input_ids is None:
@@ -553,21 +613,21 @@ class BaseMultimodalProcessor(ABC):
# Add offsets to all items
for mm_item in all_collected_items:
if mm_item.modality in [Modality.IMAGE, Modality.MULTI_IMAGES]:
mm_item.image_offsets = self.get_mm_items_offset(
mm_item.offsets = self.get_mm_items_offset(
input_ids=input_ids,
mm_token_id=self.IM_TOKEN_ID,
)
elif mm_item.modality == Modality.AUDIO:
mm_item.audio_offsets = self.get_mm_items_offset(
mm_item.offsets = self.get_mm_items_offset(
input_ids=input_ids,
mm_token_id=self.AUDIO_TOKEN_ID,
)
elif mm_item.modality == Modality.VIDEO:
mm_item.video_offsets = self.get_mm_items_offset(
mm_item.offsets = self.get_mm_items_offset(
input_ids=input_ids,
mm_token_id=self.VIDEO_TOKEN_ID,
)
else:
raise ValueError(f"Unknown modality: {mm_item.modality}")
return all_collected_items, input_ids
return all_collected_items, input_ids, ret

2
python/sglang/srt/multimodal/processors/deepseek_vl_v2.py
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@@ -69,7 +69,7 @@ class DeepseekVL2ImageProcessor(BaseMultimodalProcessor):
)
item = MultimodalDataItem(
pixel_values=res["images"],
image_offsets=image_offsets,
offsets=image_offsets,
modality=Modality.IMAGE,
image_emb_mask=images_seq_mask,
image_spatial_crop=batched_images_spatial_crop,

6
python/sglang/srt/multimodal/processors/gemma3.py
View File

@@ -36,6 +36,7 @@ class Gemma3SGLangImageProcessor(SGLangBaseProcessor):
*args,
**kwargs,
):
print(f"{image_data=}")
base_output = self.load_mm_data(
prompt=input_text,
image_data=image_data,
@@ -46,8 +47,9 @@ class Gemma3SGLangImageProcessor(SGLangBaseProcessor):
discard_alpha_channel=True,
)
mm_items, input_ids = self.process_and_combine_mm_data(base_output)
mm_items, input_ids, _ = self.process_and_combine_mm_data(base_output)
print(f"{base_output=}")
print(f"{mm_items=}")
return {
"input_ids": input_ids.tolist(),
"mm_items": mm_items,

2
python/sglang/srt/multimodal/processors/gemma3n.py
View File

@@ -72,7 +72,7 @@ class Gemma3nSGLangProcessor(SGLangBaseProcessor):
),
)
mm_items, input_ids = self.process_and_combine_mm_data(base_output)
mm_items, input_ids, _ = self.process_and_combine_mm_data(base_output)
return {
"input_ids": input_ids.tolist(),

2
python/sglang/srt/multimodal/processors/internvl.py
View File

@@ -225,7 +225,7 @@ class InternVLImageProcessor(BaseMultimodalProcessor):
MultimodalDataItem(
pixel_values=pixel_values,
modality=Modality.IMAGE,
image_offsets=image_offsets,
offsets=image_offsets,
)
]

2
python/sglang/srt/multimodal/processors/janus_pro.py
View File

@@ -49,7 +49,7 @@ class JanusProImageProcessor(BaseMultimodalProcessor):
MultimodalDataItem(
pixel_values=res["pixel_values"],
image_emb_mask=res["images_emb_mask"],
image_offsets=image_offsets,
offsets=image_offsets,
modality=Modality.IMAGE,
)
],

2
python/sglang/srt/multimodal/processors/kimi_vl.py
View File

@@ -39,7 +39,7 @@ class KimiVLImageProcessor(SGLangBaseProcessor):
max_req_input_len=max_req_input_len,
)
mm_items, input_ids = self.process_and_combine_mm_data(base_output)
mm_items, input_ids, _ = self.process_and_combine_mm_data(base_output)
return {
"input_ids": input_ids.tolist(),

7
python/sglang/srt/multimodal/processors/minicpm.py
View File

@@ -19,6 +19,7 @@ class MiniCPMMultimodalProcessor(BaseMultimodalProcessor):
super().__init__(hf_config, server_args, _processor)
self.image_token = "(<image>./</image>)"
self.audio_token = "(<audio>./</audio>)"
self.video_token = "(<video>./</video>)"
async def process_mm_data_async(
self,
@@ -36,6 +37,7 @@ class MiniCPMMultimodalProcessor(BaseMultimodalProcessor):
image_data=image_data,
multimodal_tokens=MultimodalSpecialTokens(
image_token=self.image_token,
video_token=self.video_token,
audio_token=self.audio_token,
),
)
@@ -113,7 +115,7 @@ class MiniCPMMultimodalProcessor(BaseMultimodalProcessor):
if len(pixel_values) != 0:
item = MultimodalDataItem(
pixel_values=pixel_values,
image_offsets=image_offsets,
offsets=image_offsets,
tgt_size=tgt_sizes_flat,
modality=Modality.IMAGE,
)
@@ -135,11 +137,10 @@ class MiniCPMMultimodalProcessor(BaseMultimodalProcessor):
item = MultimodalDataItem(
audio_features=[res["audio_features"]],
audio_feature_lens=res["audio_feature_lens"],
audio_offsets=audio_offsets,
offsets=audio_offsets,
modality=Modality.AUDIO,
)
items += [item]
return {
"mm_items": items,
"input_ids": input_ids.tolist(),

2
python/sglang/srt/multimodal/processors/mllama4.py
View File

@@ -144,7 +144,7 @@ class Mllama4ImageProcessor(BaseMultimodalProcessor):
MultimodalDataItem(
pixel_values=processor_output["pixel_values"],
modality=Modality.IMAGE,
image_offsets=image_offsets,
offsets=image_offsets,
)
]

2
python/sglang/srt/multimodal/processors/phi4mm.py
View File

@@ -65,7 +65,7 @@ class Phi4MMImageProcessor(BaseMultimodalProcessor):
pixel_values=res["input_image_embeds"],
image_sizes=res["image_sizes"],
image_emb_mask=res["image_attention_mask"],
image_offsets=image_offsets,
offsets=image_offsets,
modality=Modality.IMAGE,
)
]

2
python/sglang/srt/multimodal/processors/pixtral.py
View File

@@ -106,7 +106,7 @@ class PixtralProcessor(BaseMultimodalProcessor):
pixel_values=processor_output["pixel_values"],
image_sizes=processor_output["image_sizes"],
modality=Modality.IMAGE,
image_offsets=image_offsets,
offsets=image_offsets,
)
]

283
python/sglang/srt/multimodal/processors/qwen_vl.py
View File

@@ -1,9 +1,13 @@
import asyncio
import math
import os
import re
from typing import Dict, List, Union
from typing import List, Union
import torch
import torchvision
from PIL import Image
from torchvision.transforms import InterpolationMode
from sglang.srt.layers.rotary_embedding import MRotaryEmbedding
from sglang.srt.models.qwen2_5_vl import Qwen2_5_VLForConditionalGeneration
@@ -12,6 +16,185 @@ from sglang.srt.multimodal.processors.base_processor import (
BaseMultimodalProcessor as SGLangBaseProcessor,
)
from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
from sglang.utils import logger
IMAGE_FACTOR = 28
MIN_PIXELS = 4 * 28 * 28
MAX_PIXELS = 16384 * 28 * 28
MAX_RATIO = 200
VIDEO_TOTAL_PIXELS = int(
float(os.environ.get("VIDEO_MAX_PIXELS", 128000 * 28 * 28 * 0.9))
)
VIDEO_MIN_PIXELS = 128 * 28 * 28
VIDEO_MAX_PIXELS = 768 * 28 * 28
FRAME_FACTOR = 2
FPS = 2.0
FPS_MIN_FRAMES = 4
FPS_MAX_FRAMES = 768
def smart_resize(
height: int,
width: int,
factor: int = IMAGE_FACTOR,
min_pixels: int = MIN_PIXELS,
max_pixels: int = MAX_PIXELS,
) -> tuple[int, int]:
"""
Rescales the image so that the following conditions are met:
1. Both dimensions (height and width) are divisible by 'factor'.
2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
3. The aspect ratio of the image is maintained as closely as possible.
"""
if max(height, width) / min(height, width) > MAX_RATIO:
raise ValueError(
f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
)
h_bar = max(factor, round_by_factor(height, factor))
w_bar = max(factor, round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = floor_by_factor(height / beta, factor)
w_bar = floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = ceil_by_factor(height * beta, factor)
w_bar = ceil_by_factor(width * beta, factor)
return h_bar, w_bar
def resize_image(image, size_factor: int = IMAGE_FACTOR) -> Image.Image:
width, height = image.size
min_pixels = MIN_PIXELS
max_pixels = MAX_PIXELS
resized_height, resized_width = smart_resize(
height,
width,
factor=size_factor,
min_pixels=min_pixels,
max_pixels=max_pixels,
)
image = image.resize((resized_width, resized_height))
return image
def round_by_factor(number: int, factor: int) -> int:
"""Returns the closest integer to 'number' that is divisible by 'factor'."""
return round(number / factor) * factor
def ceil_by_factor(number: int, factor: int) -> int:
"""Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
return math.ceil(number / factor) * factor
def floor_by_factor(number: int, factor: int) -> int:
"""Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
return math.floor(number / factor) * factor
async def resize_image_async(image):
return resize_image(image)
def smart_nframes(
ele: dict,
total_frames: int,
video_fps: int | float,
) -> int:
"""calculate the number of frames for video used for model inputs.
Args:
ele (dict): a dict contains the configuration of video.
support either `fps` or `nframes`:
- nframes: the number of frames to extract for model inputs.
- fps: the fps to extract frames for model inputs.
- min_frames: the minimum number of frames of the video, only used when fps is provided.
- max_frames: the maximum number of frames of the video, only used when fps is provided.
total_frames (int): the original total number of frames of the video.
video_fps (int | float): the original fps of the video.
Raises:
ValueError: nframes should in interval [FRAME_FACTOR, total_frames].
Returns:
int: the number of frames for video used for model inputs.
"""
assert not (
"fps" in ele and "nframes" in ele
), "Only accept either `fps` or `nframes`"
if "nframes" in ele:
nframes = round_by_factor(ele["nframes"], FRAME_FACTOR)
else:
fps = ele.get("fps", FPS)
min_frames = ceil_by_factor(ele.get("min_frames", FPS_MIN_FRAMES), FRAME_FACTOR)
max_frames = floor_by_factor(
ele.get("max_frames", min(FPS_MAX_FRAMES, total_frames)), FRAME_FACTOR
)
nframes = total_frames / video_fps * fps
if nframes > total_frames:
logger.warning(
f"smart_nframes: nframes[{nframes}] > total_frames[{total_frames}]"
)
nframes = min(min(max(nframes, min_frames), max_frames), total_frames)
nframes = floor_by_factor(nframes, FRAME_FACTOR)
if not (FRAME_FACTOR <= nframes and nframes <= total_frames):
raise ValueError(
f"nframes should in interval [{FRAME_FACTOR}, {total_frames}], but got {nframes}."
)
return nframes
# process video, qwen-specific
async def preprocess_video(
vr,
image_factor: int = IMAGE_FACTOR,
# vr: VideoReader, image_factor: int = IMAGE_FACTOR
) -> torch.Tensor:
ele = {}
total_frames, video_fps = len(vr), vr.get_avg_fps()
nframes = smart_nframes({}, total_frames=total_frames, video_fps=video_fps)
idx = torch.linspace(0, total_frames - 1, nframes).round().long().tolist()
video = vr.get_batch(idx).asnumpy()
video = torch.tensor(video).permute(0, 3, 1, 2) # Convert to TCHW format
nframes, _, height, width = video.shape
min_pixels = ele.get("min_pixels", VIDEO_MIN_PIXELS)
total_pixels = ele.get("total_pixels", VIDEO_TOTAL_PIXELS)
max_pixels = max(
min(VIDEO_MAX_PIXELS, total_pixels / nframes * FRAME_FACTOR),
int(min_pixels * 1.05),
)
max_pixels_supposed = ele.get("max_pixels", max_pixels)
if max_pixels_supposed > max_pixels:
logger.warning(
f"The given max_pixels[{max_pixels_supposed}] exceeds limit[{max_pixels}]."
)
max_pixels = min(max_pixels_supposed, max_pixels)
if "resized_height" in ele and "resized_width" in ele:
resized_height, resized_width = smart_resize(
ele["resized_height"],
ele["resized_width"],
factor=image_factor,
)
else:
resized_height, resized_width = smart_resize(
height,
width,
factor=image_factor,
min_pixels=min_pixels,
max_pixels=max_pixels,
)
video = torchvision.transforms.functional.resize(
video,
[resized_height, resized_width],
interpolation=InterpolationMode.BICUBIC,
antialias=True,
).float()
return video
# Compatible with Qwen2VL and Qwen2_5VL
@@ -37,104 +220,44 @@ class Qwen2_5VLImageProcessor(SGLangBaseProcessor):
self.MIN_PIXELS = 4 * 28 * 28
self.MAX_PIXELS = 16384 * 28 * 28
self.MAX_RATIO = 200
# TODO(mick): move all MultimodalSpecialTokens initializations into processor init
self.mm_special_tokens = MultimodalSpecialTokens(
image_token=self.IMAGE_TOKEN,
image_token_regex=self.IMAGE_TOKEN_REGEX,
video_token=self.VIDEO_TOKEN_ID,
)
async def process_mm_data_async(
self,
image_data: List[Union[str, bytes, Dict]],
image_data: List[Union[str, bytes]],
input_text,
request_obj,
max_req_input_len,
*args,
**kwargs,
):
base_output = self.load_mm_data(
prompt=input_text,
image_data=image_data,
multimodal_tokens=MultimodalSpecialTokens(
image_token=self.IMAGE_TOKEN,
image_token_regex=self.IMAGE_TOKEN_REGEX,
),
video_data=request_obj.video_data,
multimodal_tokens=self.mm_special_tokens,
max_req_input_len=max_req_input_len,
)
def smart_resize(
height: int,
width: int,
factor: int = self.IMAGE_FACTOR,
min_pixels: int = self.MIN_PIXELS,
max_pixels: int = self.MAX_PIXELS,
) -> tuple[int, int]:
"""
Rescales the image so that the following conditions are met:
1. Both dimensions (height and width) are divisible by 'factor'.
2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
3. The aspect ratio of the image is maintained as closely as possible.
"""
if max(height, width) / min(height, width) > self.MAX_RATIO:
raise ValueError(
f"absolute aspect ratio must be smaller than {self.MAX_RATIO}, got {max(height, width) / min(height, width)}"
)
h_bar = max(factor, round_by_factor(height, factor))
w_bar = max(factor, round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = floor_by_factor(height / beta, factor)
w_bar = floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = ceil_by_factor(height * beta, factor)
w_bar = ceil_by_factor(width * beta, factor)
return h_bar, w_bar
def resize_image(image, size_factor: int = self.IMAGE_FACTOR) -> Image.Image:
width, height = image.size
min_pixels = self.MIN_PIXELS
max_pixels = self.MAX_PIXELS
resized_height, resized_width = smart_resize(
height,
width,
factor=size_factor,
min_pixels=min_pixels,
max_pixels=max_pixels,
)
image = image.resize((resized_width, resized_height))
return image
def round_by_factor(number: int, factor: int) -> int:
"""Returns the closest integer to 'number' that is divisible by 'factor'."""
return round(number / factor) * factor
def ceil_by_factor(number: int, factor: int) -> int:
"""Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
return math.ceil(number / factor) * factor
def floor_by_factor(number: int, factor: int) -> int:
"""Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
return math.floor(number / factor) * factor
async def resize_image_async(image):
return resize_image(image)
# Qwen-specific: resize images if they are raw Image objects
if base_output.images and isinstance(base_output.images[0], Image.Image):
resize_tasks = [resize_image_async(image) for image in base_output.images]
base_output.images = await asyncio.gather(*resize_tasks)
video_grid_thw = None # TODO
if base_output.videos:
base_output.videos = [
await preprocess_video(video) for video in base_output.videos
]
mm_items, input_ids = self.process_and_combine_mm_data(base_output)
if not mm_items:
# Note(Xinyuan): This is the case where image loading fails.
return None
combined_mm_item = mm_items[0] # only image is supported for now
video_grid_thw = None # TODO
second_per_grid_ts = getattr(combined_mm_item, "second_per_grid_ts", None)
mm_items, input_ids, ret = self.process_and_combine_mm_data(base_output)
input_ids = input_ids.flatten()
mrope_positions, mrope_position_delta = MRotaryEmbedding.get_rope_index(
spatial_merge_size=self.hf_config.vision_config.spatial_merge_size,
image_token_id=self.IM_TOKEN_ID,
@@ -145,9 +268,9 @@ class Qwen2_5VLImageProcessor(SGLangBaseProcessor):
self.hf_config.vision_config, "tokens_per_second", None
),
input_ids=input_ids.unsqueeze(0),
image_grid_thw=combined_mm_item.image_grid_thw,
video_grid_thw=video_grid_thw,
second_per_grid_ts=second_per_grid_ts,
image_grid_thw=getattr(ret, "image_grid_thw", None),
video_grid_thw=getattr(ret, "video_grid_thw", None),
second_per_grid_ts=getattr(ret, "second_per_grid_ts", None),
)
mrope_positions = mrope_positions.squeeze(1)

2
python/sglang/srt/multimodal/processors/vila.py
View File

@@ -57,7 +57,7 @@ class VILAMultimodalProcessor(BaseMultimodalProcessor):
image_data=image_data,
)
mm_items, input_ids = self.process_and_combine_mm_data(base_output)
mm_items, input_ids, _ = self.process_and_combine_mm_data(base_output)
return {
"input_ids": input_ids.tolist(),