File size: 12,885 Bytes
032e687 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 | import os
import glob
import torch
import json
import jsonlines
import numpy as np
import pandas as pd
from PIL import Image
import torch.distributed
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm
from typing import Optional
from einops import rearrange
from data.templates import PROMPT_TEMPLATE
from data.utils import (
load_image_to_base64,
download_image_to_base64,
load_base64_to_PILImage,
convert_image_base64_to_patches,
visualize_patches,
load_image_bytes_to_base64
)
IGNORE_INDEX = -100
def read_json(file_path):
with open(file_path, "r") as f:
data = json.load(f)
return data
def read_jsonlines(file_path):
with jsonlines.open(file_path) as reader:
data = [obj for obj in reader]
return data
def prepare_image_textual_seq(h, w, tokenizer, add_cls=True):
seq = ""
tok_len = 0
seq += tokenizer.vis_beg_tok
tok_len += 1
for _ in range(h-1):
seq += tokenizer.vis_patch_tok * w + tokenizer.vis_rsep_tok
tok_len += (w + 1)
seq += tokenizer.vis_patch_tok * w + tokenizer.vis_end_tok
tok_len += (w + 1)
if add_cls:
seq += tokenizer.vis_cls_tok
tok_len += 1
return seq, tok_len
def prepare_image_textual_seq_norowsep(h, w, tokenizer, add_cls=True):
seq = ""
tok_len = 0
seq += tokenizer.vis_beg_tok
tok_len += 1
seq += tokenizer.vis_patch_tok * (w * h)
tok_len += (w * h)
seq += tokenizer.vis_end_tok
tok_len += 1
if add_cls:
seq += tokenizer.vis_cls_tok
tok_len += 1
return seq, tok_len
def create_single_prefix_mask(prefix_len, max_len):
attn_mask = torch.zeros(max_len, max_len)
attn_mask[:prefix_len, :prefix_len] = 1
causal_mask = torch.tril(torch.ones(max_len, max_len))
attn_mask = attn_mask.bool() | causal_mask.bool()
return attn_mask
def generate_mm_pos_ids_singleit(input_ids, vpatch_id, h, w):
input_ids_pt = torch.Tensor(input_ids).int()
vpatch_pos = torch.argwhere(input_ids_pt == vpatch_id)
vpatch_start_pos = vpatch_pos[0].item()
nt = len(input_ids) - (h*w) + 1
# v_pos
t_indices = torch.arange(1)
h_indices = torch.arange(h)
w_indices = torch.arange(w)
v_pos_id = torch.stack(torch.meshgrid(t_indices, h_indices, w_indices, indexing='ij'), dim=0)
v_pos_id = rearrange(v_pos_id, "d t h w -> (t h w) d") # [h*w, 3]
v_pos_id += vpatch_start_pos
position_id = torch.cat(
[
torch.arange(vpatch_start_pos).unsqueeze(-1).repeat(1,3),
v_pos_id,
torch.arange(nt-vpatch_start_pos-1).unsqueeze(-1).repeat(1,3) + v_pos_id.max() + 1,
],
dim=0
)
assert len(input_ids) == position_id.size(0)
position_id = rearrange(position_id, "slen d -> d slen").long()
return position_id
class SFTModule():
def prepare_inputs_img(self, images, inputs, tokenizer):
end_token = tokenizer.eos_token if tokenizer.eos_token is not None else tokenizer.pad_token
bos_token = tokenizer.bos_token if tokenizer.bos_token is not None else ''
NON_VISION_TOKEN = -1
tokens = []
vision_patch_indices = []
vision_patches = []
labels = []
patches = images
n_rows, n_cols = patches.shape[:2]
n_patches = n_rows * n_cols
patches = patches.view(n_patches, -1)
# ---
image_text_seq, image_tok_len = prepare_image_textual_seq_norowsep(n_rows, n_cols, tokenizer, getattr(self.config, "add_cls", False))
# ---
cur_tokens_pt = tokenizer(image_text_seq, add_special_tokens=False, return_tensors="pt").input_ids[0]
cur_patch_indices = torch.full_like(cur_tokens_pt, fill_value=NON_VISION_TOKEN)
assert (cur_tokens_pt==tokenizer.vis_patch_tok_id).sum() == n_patches
cur_patch_indices[cur_tokens_pt==tokenizer.vis_patch_tok_id] = torch.arange(n_patches)
cur_tokens = cur_tokens_pt.cpu().numpy().tolist()
cur_patch_indices = cur_patch_indices.cpu().numpy().tolist()
assert len(cur_tokens) == len(cur_patch_indices), f"{len(cur_tokens)} != {len(cur_patch_indices)}"
tokens.extend(cur_tokens)
labels.extend([-100] * len(cur_tokens))
vision_patch_indices.extend(cur_patch_indices)
vision_patches.extend(patches.numpy().astype(np.float16))
for idx, i in enumerate(inputs):
if idx % 2 == 0:
# system/user part
if idx == 0:
i = i.replace("<image>\n", '').replace("\n<image>", '')
c_new = bos_token + self.template['INSTRUCTION'].format(input=i.strip())
else:
c_new = self.template['INSTRUCTION'].format(input=i.strip())
_tokenized = tokenizer(c_new, return_tensors="pt", add_special_tokens=False)
cur_tokens = _tokenized["input_ids"].squeeze(0)
tokens.extend(cur_tokens)
labels.extend([-100] * len(cur_tokens))
vision_patch_indices.extend([NON_VISION_TOKEN] * len(cur_tokens))
else:
# assistant part
i = i + end_token
_tokenized = tokenizer(i, return_tensors="pt", add_special_tokens=False)
cur_tokens = _tokenized["input_ids"].squeeze(0)
tokens.extend(cur_tokens)
labels.extend(cur_tokens)
vision_patch_indices.extend([NON_VISION_TOKEN] * len(cur_tokens))
position_ids = generate_mm_pos_ids_singleit(tokens, tokenizer.vis_patch_tok_id, n_rows, n_cols) # [3, slen]
attention_masks = create_single_prefix_mask(image_tok_len, len(tokens)).unsqueeze(0) # [1, slen, slen]
tokens = torch.Tensor(tokens).long()
labels = torch.Tensor(labels).long()
vision_patch_indices = torch.Tensor(vision_patch_indices).long()
if len(vision_patches) > 0:
# convert vision patches to numpy
vision_patches = np.array(vision_patches)
vision_patches = torch.Tensor(vision_patches).bfloat16()
else:
vision_patches = None
if len(tokens) > self.max_position_embeddings:
tokens = tokens[:self.max_position_embeddings]
labels = labels[:self.max_position_embeddings]
position_ids = position_ids[:, :self.max_position_embeddings]
attention_masks = attention_masks[:, :self.max_position_embeddings, :self.max_position_embeddings]
vision_patch_indices = vision_patch_indices[:self.max_position_embeddings]
vision_patches = vision_patches[:self.max_position_embeddings]
return tokens, position_ids, attention_masks, vision_patches, vision_patch_indices, labels
def prepare_inputs(self, inputs, flag, tokenizer):
end_token = tokenizer.eos_token if tokenizer.eos_token is not None else tokenizer.pad_token
bos_token = tokenizer.bos_token if tokenizer.bos_token is not None else ''
NON_VISION_TOKEN = -1
tokens = []
attention_masks = []
vision_patch_indices = []
vision_patches = []
labels = []
for idx, i in enumerate(inputs):
if idx % 2 == 0:
if idx == 0:
c_new = bos_token + self.template['INSTRUCTION'][0].format(input=i.strip())
else:
c_new = self.template['INSTRUCTION'][1].format(input=i.strip())
_tokenized = tokenizer(c_new, return_tensors="pt", add_special_tokens=False)
cur_tokens = _tokenized["input_ids"].squeeze(0)
cur_attention_mask = _tokenized["attention_mask"].squeeze(0)
tokens.extend(cur_tokens)
labels.extend([-100] * len(cur_tokens))
attention_masks.extend(cur_attention_mask)
vision_patch_indices.extend([NON_VISION_TOKEN] * len(cur_tokens))
else:
i = i + end_token
_tokenized = tokenizer(i, return_tensors="pt", add_special_tokens=False)
cur_tokens = _tokenized["input_ids"].squeeze(0)
cur_attention_mask = _tokenized["attention_mask"].squeeze(0)
tokens.extend(cur_tokens)
labels.extend(cur_tokens)
attention_masks.extend(cur_attention_mask)
vision_patch_indices.extend([NON_VISION_TOKEN] * len(cur_tokens))
if len(tokens) > self.max_position_embeddings:
tokens = tokens[:self.max_position_embeddings]
labels = labels[:self.max_position_embeddings]
attention_masks = attention_masks[:self.max_position_embeddings]
vision_patch_indices = vision_patch_indices[:self.max_position_embeddings]
tokens = torch.Tensor(tokens).long()
labels = torch.Tensor(labels).long()
attention_masks = torch.Tensor(attention_masks).long()
vision_patches = None
vision_patch_indices = torch.Tensor(vision_patch_indices).long()
position_ids = torch.arange(len(tokens)).unsqueeze(0).expand(3,-1).clone().long()
return tokens, position_ids, attention_masks, vision_patches, vision_patch_indices, labels
def collate_fn(self, batch):
try:
assert len(batch) == 1
for i, tgt_item in enumerate(batch):
conversation_li = []
conversations = tgt_item['conversations']
for item in conversations:
if type(item) is str:
conversation_li.append(item)
else:
conversation_li.append(item['value'])
if 'image' in tgt_item:
# orig_img_path = os.path.join(self.img_dir, tgt_item['image'])
orig_img_path = tgt_item['image']
img_base64 = load_image_to_base64(orig_img_path)
img_patches = convert_image_base64_to_patches(img_base64, patch_size=self.patch_size,fix_resolution=self.fix_resolution)
tokens, position_ids, attention_masks, vision_patches, vision_patch_indices, labels = self.prepare_inputs_img(img_patches, conversation_li, self.tokenizer)
else:
tokens, position_ids, attention_masks, vision_patches, vision_patch_indices, labels = self.prepare_inputs(conversation_li, 0, self.tokenizer)
return {
"input_ids": tokens.unsqueeze(0),
"position_ids": position_ids.unsqueeze(1),
"attention_mask": attention_masks.unsqueeze(0),
"vision_patches": vision_patches,
"vision_patch_indices": vision_patch_indices.unsqueeze(0),
"labels": labels.unsqueeze(0)
}
except Exception as e:
print(e)
return None
def train_dataloader(self):
return torch.utils.data.DataLoader(
self.all_data,
batch_size=self.config["data"]["batch_size"],
shuffle=True,
num_workers=self.config["data"]["num_workers"],
collate_fn=self.collate_fn,
pin_memory=True
)
def __init__(self, config: dict, tokenizer):
super().__init__()
self.tokenizer = tokenizer
self.config = config
self.max_position_embeddings = config["data"]["max_position_embeddings"]
self.img_dir = config["data"]["img_dir"]
template_name = config["data"].get("template", "mistral")
self.template = PROMPT_TEMPLATE[template_name]
ann_file = config["data"]["train_data"]
all_data = []
ann_files = ann_file
img_dirs = self.img_dir
for sub_ann_file, sub_img_dir in zip(ann_files,img_dirs):
if sub_ann_file.endswith(".jsonl"):
data_dict = read_jsonlines(sub_ann_file)
elif sub_ann_file.endswith(".json"):
data_dict = json.load(open(sub_ann_file))
for i in range(len(data_dict)):
if 'image' in data_dict[i]:
data_dict[i]['image'] = os.path.join(sub_img_dir, data_dict[i]['image'])
all_data.extend(data_dict)
self.all_data = all_data
self.patch_size = config["data"].get("patch_size", 32)
self.fix_resolution = config["data"].get("fix_resolution", False)
def get_data(config, tokenizer):
return SFTModule(config, tokenizer)
|