Spaces:
Running
on
Zero
Running
on
Zero
File size: 9,376 Bytes
18d050b |
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 |
import os
from typing import Dict, List
import torch
from PIL import Image
from torchvision import transforms
from transformers import AutoTokenizer
from huggingface_hub import snapshot_download
import numpy as np
def crop_arr(pil_image, max_image_size):
while min(*pil_image.size) >= 2 * max_image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
if max(*pil_image.size) > max_image_size:
scale = max_image_size / max(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
if min(*pil_image.size) < 16:
scale = 16 / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
arr = np.array(pil_image)
crop_y1 = (arr.shape[0] % 16) // 2
crop_y2 = arr.shape[0] % 16 - crop_y1
crop_x1 = (arr.shape[1] % 16) // 2
crop_x2 = arr.shape[1] % 16 - crop_x1
arr = arr[crop_y1:arr.shape[0] - crop_y2, crop_x1:arr.shape[1] - crop_x2]
return Image.fromarray(arr)
class OmniGenProcessor:
def __init__(self, max_image_size: int = 1024):
self.max_image_size = max_image_size
self.image_transform = transforms.Compose([
transforms.Lambda(lambda pil_image: crop_arr(pil_image, max_image_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
self.collator = OmniGenCollator()
self.separate_collator = OmniGenSeparateCollator()
@classmethod
def from_pretrained(cls, model_name):
if not os.path.exists(model_name):
cache_folder = os.getenv('HF_HUB_CACHE')
model_name = snapshot_download(repo_id=model_name,
cache_dir=cache_folder,
allow_patterns="*.json")
text_tokenizer = AutoTokenizer.from_pretrained(model_name)
return cls(text_tokenizer)
def process_image(self, image):
image = Image.open(image).convert('RGB')
return self.image_transform(image)
def __call__(self,
context_hidden_state: List[torch.tensor],
neg_context_hidden_state: List[torch.tensor],
height: int = 1024,
width: int = 1024,
separate_cfg_input: bool = False,
) -> Dict:
input_data = []
for i in range(len(context_hidden_state)):
cur_context_hidden_state = context_hidden_state[i]
cur_neg_context_hidden_state = neg_context_hidden_state[i]
input_data.append((cur_context_hidden_state, cur_neg_context_hidden_state, [height, width]))
if separate_cfg_input:
return self.separate_collator(input_data)
return self.collator(input_data)
class OmniGenCollator:
def __init__(self, pad_token_id=2, llm_pad_token_id=151643, hidden_size=3072):
self.llm_pad_token_id = llm_pad_token_id
self.pad_token_id = pad_token_id
self.hidden_size = hidden_size
def create_position(self, attention_mask, num_tokens_for_output_images):
position_ids = []
text_length = attention_mask.size(-1)
img_length = max(num_tokens_for_output_images)
for mask in attention_mask:
temp_l = torch.sum(mask)
temp_position = [0] * (text_length - temp_l) + [i for i in range(temp_l + img_length + 1)] # we add a time embedding into the sequence, so add one more token
position_ids.append(temp_position)
return torch.LongTensor(position_ids)
def create_connector_position(self, llm_2d_attention_mask):
position_ids = []
text_length = llm_2d_attention_mask.size(-1)
for batch_idx, mask in enumerate(llm_2d_attention_mask):
temp_l = torch.sum(llm_2d_attention_mask[batch_idx])
temp_position = [0] * (text_length - temp_l) + [i for i in range(temp_l)] # only condition for mllm like qwen
position_ids.append(temp_position)
return torch.LongTensor(position_ids)
def create_mask(self, attention_mask, num_tokens_for_output_images):
extended_mask = []
padding_images = []
text_length = attention_mask.size(-1)
img_length = max(num_tokens_for_output_images)
seq_len = text_length + img_length + 1 # we add a time embedding into the sequence, so add one more token
inx = 0
for mask in attention_mask:
temp_l = torch.sum(mask)
pad_l = text_length - temp_l
temp_mask = torch.tril(torch.ones(size=(temp_l + 1, temp_l + 1)))
image_mask = torch.zeros(size=(temp_l + 1, img_length))
temp_mask = torch.cat([temp_mask, image_mask], dim=-1)
image_mask = torch.ones(size=(img_length, temp_l + img_length + 1))
temp_mask = torch.cat([temp_mask, image_mask], dim=0)
if pad_l > 0:
pad_mask = torch.zeros(size=(temp_l + 1 + img_length, pad_l))
temp_mask = torch.cat([pad_mask, temp_mask], dim=-1)
pad_mask = torch.ones(size=(pad_l, seq_len))
temp_mask = torch.cat([pad_mask, temp_mask], dim=0)
true_img_length = num_tokens_for_output_images[inx]
pad_img_length = img_length - true_img_length
if pad_img_length > 0:
temp_mask[:, -pad_img_length:] = 0
temp_padding_imgs = torch.zeros(size=(1, pad_img_length, self.hidden_size))
else:
temp_padding_imgs = None
extended_mask.append(temp_mask.unsqueeze(0))
padding_images.append(temp_padding_imgs)
inx += 1
return torch.cat(extended_mask, dim=0), padding_images
def adjust_attention_for_input_images(self, attention_mask, image_sizes):
for b_inx in image_sizes.keys():
for start_inx, end_inx in image_sizes[b_inx]:
attention_mask[b_inx][start_inx:end_inx, start_inx:end_inx] = 1
return attention_mask
def pad_input(self, context_hidden_state):
# pad_token_id = self.llm_pad_token_id # 151642 <|endoftext|> in qwen2.5vl
max_l = max([x.shape[1] for x in context_hidden_state])
attention_mask = []
for i in range(len(context_hidden_state)):
temp_hidden = context_hidden_state[i]
temp_l = temp_hidden.shape[1]
pad_l = max_l - temp_l
if pad_l == 0:
attention_mask.append([1] * max_l)
else:
attention_mask.append([0] * pad_l + [1] * temp_l)
return torch.LongTensor(attention_mask)
def process_mllm_input(self, context_hidden_state, target_img_size):
num_tokens_for_output_images = []
for img_size in target_img_size:
num_tokens_for_output_images.append(img_size[0] * img_size[1] // 16 // 16)
llm_2d_attention_mask = self.pad_input(context_hidden_state)
connector_position_ids = self.create_connector_position(llm_2d_attention_mask)
llm_position_ids = self.create_position(llm_2d_attention_mask, num_tokens_for_output_images)
llm_attention_mask, _ = self.create_mask(llm_2d_attention_mask, num_tokens_for_output_images)
return llm_2d_attention_mask, connector_position_ids, llm_attention_mask, llm_position_ids
class OmniGenSeparateCollator(OmniGenCollator):
def __call__(self, features):
context_hidden_state = [f[0] for f in features]
neg_context_hidden_state = [f[1] for f in features]
target_img_size = [f[2] for f in features]
all_context_hidden_state, all_connector_attention_mask, all_connector_position_ids, all_llm_attention_mask, all_llm_position_ids = [], [], [], [], []
connector_attention_mask, connector_position_ids, llm_attention_mask, llm_position_ids = self.process_mllm_input(context_hidden_state, target_img_size)
all_context_hidden_state.append(context_hidden_state[0])
all_connector_attention_mask.append(connector_attention_mask)
all_connector_position_ids.append(connector_position_ids)
all_llm_attention_mask.append(llm_attention_mask)
all_llm_position_ids.append(llm_position_ids)
if neg_context_hidden_state[0] is not None:
connector_attention_mask, connector_position_ids, llm_attention_mask, llm_position_ids = self.process_mllm_input(neg_context_hidden_state, target_img_size)
all_context_hidden_state.append(neg_context_hidden_state[0])
all_connector_attention_mask.append(connector_attention_mask)
all_connector_position_ids.append(connector_position_ids)
all_llm_attention_mask.append(llm_attention_mask)
all_llm_position_ids.append(llm_position_ids)
data = {
"context_hidden_state": all_context_hidden_state,
"connector_attention_mask": all_connector_attention_mask,
"connector_position_ids": all_connector_position_ids,
"llm_attention_mask": all_llm_attention_mask,
"llm_position_ids": all_llm_position_ids,
}
return data
|