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7344bef | 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 | import math
import torch
from typing import Optional
from PIL import Image
PREDEFINED_RESOLUTIONS = [
(2048, 2048),
(2304, 1728),
(1728, 2304),
(2560, 1440),
(1440, 2560),
(2496, 1664),
(1664, 2496),
(3104, 1312),
(1312, 3104),
(2304, 1792),
(1792, 2304),
]
def find_closest_resolution(width, height):
img_ratio = width / height
best_res = None
min_diff = float("inf")
for w, h in PREDEFINED_RESOLUTIONS:
ratio = w / h
diff = abs(ratio - img_ratio)
if diff < min_diff:
min_diff = diff
best_res = (w, h)
return best_res
def resize_pilimage(pil_image, image_size, patch_size=16, resampler=Image.BICUBIC):
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
m = patch_size
width, height = pil_image.width, pil_image.height
S_max = image_size * image_size
scale = S_max / (width * height)
scale = math.sqrt(scale)
new_sizes = [
(round(width * scale) // m * m, round(height * scale) // m * m),
(round(width * scale) // m * m, math.floor(height * scale) // m * m),
(math.floor(width * scale) // m * m, round(height * scale) // m * m),
(math.floor(width * scale) // m * m, math.floor(height * scale) // m * m),
]
new_sizes = sorted(new_sizes, key=lambda x: x[0] * x[1], reverse=True)
for new_size in new_sizes:
if new_size[0] * new_size[1] <= S_max:
break
s1 = width / new_size[0]
s2 = height / new_size[1]
if s1 < s2:
pil_image = pil_image.resize([new_size[0], round(height / s1)], resample=resampler)
top = (round(height / s1) - new_size[1]) // 2
pil_image = pil_image.crop((0, top, new_size[0], top + new_size[1]))
else:
pil_image = pil_image.resize([round(width / s2), new_size[1]], resample=resampler)
left = (round(width / s2) - new_size[0]) // 2
pil_image = pil_image.crop((left, 0, left + new_size[0], new_size[1]))
return pil_image
def calculate_dimensions(max_size, ratio):
width = math.sqrt(max_size * max_size * ratio)
height = width / ratio
width = int(width / 32) * 32
height = int(height / 32) * 32
return width, height
def get_rope_index_fix_point(
spatial_merge_size,
image_token_id,
video_token_id,
vision_start_token_id,
input_ids: Optional[torch.LongTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
skip_vision_start_token=None,
fix_point=4096,
) -> tuple[torch.Tensor, torch.Tensor]:
if video_grid_thw is not None:
video_grid_thw = torch.repeat_interleave(video_grid_thw, video_grid_thw[:, 0], dim=0)
video_grid_thw[:, 0] = 1
mrope_position_deltas = []
if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None):
total_input_ids = input_ids
if attention_mask is None:
attention_mask = torch.ones_like(total_input_ids)
position_ids = torch.ones(
3,
input_ids.shape[0],
input_ids.shape[1],
dtype=input_ids.dtype,
device=input_ids.device,
)
image_index, video_index = 0, 0
attention_mask = attention_mask.to(total_input_ids.device)
for i, input_ids in enumerate(total_input_ids):
input_ids = input_ids[attention_mask[i] == 1]
image_nums, video_nums = 0, 0
vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1)
vision_tokens = input_ids[vision_start_indices + 1]
image_nums = (vision_tokens == image_token_id).sum()
video_nums = (vision_tokens == video_token_id).sum()
input_tokens = input_ids.tolist()
llm_pos_ids_list: list = []
st = 0
remain_images, remain_videos = image_nums, video_nums
for _ in range(image_nums + video_nums):
if image_token_id in input_tokens and remain_images > 0:
ed_image = input_tokens.index(image_token_id, st)
else:
ed_image = len(input_tokens) + 1
if video_token_id in input_tokens and remain_videos > 0:
ed_video = input_tokens.index(video_token_id, st)
else:
ed_video = len(input_tokens) + 1
if ed_image < ed_video:
t, h, w = (
image_grid_thw[image_index][0],
image_grid_thw[image_index][1],
image_grid_thw[image_index][2],
)
image_index += 1
remain_images -= 1
ed = ed_image
else:
t, h, w = (
video_grid_thw[video_index][0],
video_grid_thw[video_index][1],
video_grid_thw[video_index][2],
)
video_index += 1
remain_videos -= 1
ed = ed_video
llm_grid_t, llm_grid_h, llm_grid_w = (
t.item(),
h.item() // spatial_merge_size,
w.item() // spatial_merge_size,
)
text_len = ed - st
text_len -= skip_vision_start_token[image_index - 1]
text_len = max(0, text_len)
st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten()
h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten()
w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten()
if skip_vision_start_token[image_index - 1]:
if fix_point > 0:
fix_point = fix_point - st_idx
llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + fix_point + st_idx)
fix_point = 0
else:
llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
st = ed + llm_grid_t * llm_grid_h * llm_grid_w
if st < len(input_tokens):
st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
text_len = len(input_tokens) - st
llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device)
mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i]))
mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1)
return position_ids, mrope_position_deltas
else:
if attention_mask is not None:
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device)
max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
else:
position_ids = (
torch.arange(input_ids.shape[1], device=input_ids.device)
.view(1, 1, -1)
.expand(3, input_ids.shape[0], -1)
)
mrope_position_deltas = torch.zeros(
[input_ids.shape[0], 1],
device=input_ids.device,
dtype=input_ids.dtype,
)
return position_ids, mrope_position_deltas
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