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838c455 23b5fcc 838c455 23b5fcc 838c455 23b5fcc 838c455 23b5fcc 838c455 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | """Vision-Asym (BQA prefill + BKA decode) Processor for Qwen3-VL.
The processor only reports MODALITY: it emits ``input_ids`` + pixel tensors +
``visual_token_mask`` (True on image/video tokens). The always-keep/selectable
partition (sink + recent window) is a MODEL decision made at cache-finalize time
from the model config — the processor makes no assumptions about where system
text or instructions live and adds no padding tokens.
The video/image processors still align the visual grid so h/merge and w/merge are
multiples of ``focus_size`` (= R): each frame is a whole number of R x R spatial
regions, which the model's post-prefill region fold requires.
"""
from typing import Any, Optional, Union
import torch
import numpy as np
from transformers.image_utils import ImageInput
from transformers.processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import logging
from transformers.video_utils import VideoInput
logger = logging.get_logger(__name__)
class MMFeature(dict):
def __init__(self, data, tensor_type: str | None = None):
super().__init__(data)
self.tensor_type = tensor_type
self.convert_to_tensor()
def convert_to_tensor(self) -> "MMFeature":
if self.tensor_type is None:
return self
match self.tensor_type:
case "pt":
for k, v in self.items():
if not isinstance(v, torch.Tensor):
try:
self[k] = torch.tensor(v)
except Exception:
pass
case "np":
for k, v in self.items():
if not isinstance(v, np.ndarray):
try:
self[k] = np.array(v)
except Exception:
pass
return self
def to(self, target: Any) -> "MMFeature":
for k, v in self.items():
if isinstance(v, torch.Tensor):
self[k] = v.to(target)
return self
class Qwen3VLVideosProcessorKwargs(VideosKwargs, total=False):
pass
class Qwen3VLImagesKwargs(ImagesKwargs):
min_pixels: Optional[int]
max_pixels: Optional[int]
patch_size: Optional[int]
temporal_patch_size: Optional[int]
merge_size: Optional[int]
class Qwen3VLProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: Qwen3VLImagesKwargs # type: ignore
videos_kwargs: Qwen3VLVideosProcessorKwargs # type: ignore
_defaults = { # type: ignore
"text_kwargs": {
"padding": False,
"return_token_type_ids": False,
"return_mm_token_type_ids": False,
},
"videos_kwargs": {"return_metadata": True},
}
class VisionAsymQwen3VLProcessor(ProcessorMixin):
"""Processor for Vision-Asym (BQA prefill + BKA decode) Qwen3-VL.
Emits ``input_ids`` + pixel tensors + ``visual_token_mask`` (True on
image/video tokens). Modality only — the always/selectable partition is the
model's decision (config sink_size / recent_window), not the processor's.
"""
attributes = ["image_processor", "tokenizer", "video_processor"]
image_processor_class = "AutoImageProcessor"
video_processor_class = "AutoVideoProcessor"
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None,
**kwargs):
super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
self.image_token_id = (
tokenizer.image_token_id
if getattr(tokenizer, "image_token_id", None)
else tokenizer.convert_tokens_to_ids(self.image_token)
)
self.video_token_id = (
tokenizer.video_token_id
if getattr(tokenizer, "video_token_id", None)
else tokenizer.convert_tokens_to_ids(self.video_token)
)
self.vision_start_token = (
"<|vision_start|>" if not hasattr(tokenizer, "vision_start_token") else tokenizer.vision_start_token
)
self.vision_end_token = (
"<|vision_end|>" if not hasattr(tokenizer, "vision_end_token") else tokenizer.vision_end_token
)
self.vision_start_token_id = tokenizer.convert_tokens_to_ids(self.vision_start_token)
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
videos: VideoInput = None,
**kwargs: Unpack[Qwen3VLProcessorKwargs],
) -> MMFeature:
output_kwargs = self._merge_kwargs(
Qwen3VLProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if images is not None:
image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
image_grid_thw = image_inputs["image_grid_thw"]
else:
image_inputs = {}
image_grid_thw = None
if videos is not None:
videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
video_grid_thw = videos_inputs["video_grid_thw"]
video_metadata = videos_inputs.pop("video_metadata", None)
else:
videos_inputs = {}
video_grid_thw = None
if not isinstance(text, list):
text = [text]
text = text.copy()
if image_grid_thw is not None:
merge_length = self.image_processor.merge_size**2
index = 0
for i in range(len(text)):
while self.image_token in text[i]:
num_image_tokens = image_grid_thw[index].prod() // merge_length
text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
index += 1
text[i] = text[i].replace("<|placeholder|>", self.image_token)
if video_grid_thw is not None:
merge_length = self.video_processor.merge_size**2
index = 0
for i in range(len(text)):
while self.video_token in text[i]:
metadata = video_metadata[index]
if metadata.fps is None:
metadata.fps = 24
# Calculate timestamps
indices = metadata.frames_indices
if not isinstance(indices, list):
indices = indices.tolist()
ms = self.video_processor.merge_size
if len(indices) % ms != 0:
indices.extend(indices[-1] for _ in range(ms - len(indices) % ms))
timestamps = [idx / metadata.fps for idx in indices]
timestamps = [
(timestamps[j] + timestamps[j + ms - 1]) / 2
for j in range(0, len(timestamps), ms)
]
# Pad timestamps to match grid_t (video processor may pad frames)
grid_t = int(video_grid_thw[index][0])
while len(timestamps) < grid_t:
timestamps.append(timestamps[-1])
video_placeholder = ""
frame_seqlen = video_grid_thw[index][1:].prod() // merge_length
for frame_idx in range(grid_t):
curr_time = timestamps[frame_idx]
video_placeholder += f"<{curr_time:.1f} seconds>"
video_placeholder += (
self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token
)
if f"{self.vision_start_token}{self.video_token}{self.vision_end_token}" in text[i]:
text[i] = text[i].replace(
f"{self.vision_start_token}{self.video_token}{self.vision_end_token}", video_placeholder, 1
)
else:
text[i] = text[i].replace(self.video_token, video_placeholder, 1)
index += 1
text[i] = text[i].replace("<|placeholder|>", self.video_token)
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
ids = np.asarray(text_inputs["input_ids"])
visual_token_mask = ((ids == self.image_token_id) | (ids == self.video_token_id)).astype(bool)
if visual_token_mask.shape[0] == 1:
visual_token_mask = visual_token_mask[0]
return MMFeature(data={
**text_inputs,
**image_inputs,
**videos_inputs,
"visual_token_mask": visual_token_mask,
}, tensor_type=return_tensors)
def post_process_image_text_to_text(self, generated_outputs, skip_special_tokens=True,
clean_up_tokenization_spaces=False, **kwargs):
return self.tokenizer.batch_decode(
generated_outputs,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
**kwargs,
)
__all__ = ["VisionAsymQwen3VLProcessor"]
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