File size: 15,654 Bytes
8fa1f84 |
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 |
from abc import ABC, abstractmethod
import torch
from .multimodal_encoder.builder import build_vision_tower
from .multimodal_projector.builder import build_vision_projector
from seagull.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
class SeagullMetaModel:
def __init__(self, config):
super(SeagullMetaModel, self).__init__(config)
if hasattr(config, "mm_vision_tower"):
self.vision_tower = build_vision_tower(config, delay_load=False)
self.mm_projector = build_vision_projector(config)
def get_vision_tower(self):
vision_tower = getattr(self, 'vision_tower', None)
if type(vision_tower) is list:
vision_tower = vision_tower[0]
return vision_tower
def initialize_vision_modules(self, model_args, fsdp=None):
vision_tower = model_args.vision_tower
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
if not hasattr(self.config, "mm_vision_tower"):
self.config.mm_vision_tower = vision_tower
vision_tower = build_vision_tower(model_args)
if fsdp is not None and len(fsdp) > 0:
self.vision_tower = [self.vision_tower]
else:
self.vision_tower = vision_tower
self.config.use_mm_proj = True
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
self.mm_projector = build_vision_projector(self.config)
if pretrain_mm_mlp_adapter is not None:
print("***********load projector_weights********")
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
def get_w(weights, keyword):
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
class SeagullMetaForCausalLM(ABC):
def __init__(self):
super(SeagullMetaForCausalLM, self).__init__()
@abstractmethod
def get_model(self):
pass
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def encode_images(self, images):
image_features, image_features_dict = self.get_model().get_vision_tower()(images)
self.get_model().mm_projector.to(device=image_features.device, dtype=image_features.dtype)
image_features = self.get_model().mm_projector(image_features)
return image_features, image_features_dict
def prepare_inputs_labels_for_multimodal(
self, input_ids, masks, attention_mask, past_key_values, labels, images, preprocessed_img_dict=None, cropped_img=None
):
vision_tower = self.get_vision_tower()
if vision_tower is None or images is None or input_ids.shape[1] == 1:
if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
return input_ids, attention_mask, past_key_values, None, labels
if preprocessed_img_dict is not None:
image_features, image_features_dict = images, preprocessed_img_dict
else:
if type(images) is list or images.ndim == 5:
concat_images = torch.cat([image for image in images], dim=0)
image_features, image_features_dict = self.encode_images(concat_images)
split_sizes = [image.shape[0] for image in images]
image_features = torch.split(image_features, split_sizes, dim=0)
image_features = [x.flatten(0, 1).to(concat_images.device) for x in image_features]
else:
image_features, image_features_dict = self.encode_images(images)
mask_feats, pos_feats = self.mask_extractor(image_features_dict, masks, cropped_img=cropped_img)
new_input_embeds = []
new_labels = [] if labels is not None else None
cur_image_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
# multimodal LLM, but the current sample is not multimodal
# FIXME: this is a hacky fix, for deepspeed zero3 to work
half_len = cur_input_ids.shape[0] // 2
cur_image_features = image_features[cur_image_idx]
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0)
new_input_embeds.append(cur_input_embeds)
if labels is not None:
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
cur_new_input_embeds = []
if labels is not None:
cur_labels = labels[batch_idx]
cur_new_labels = []
assert cur_labels.shape == cur_input_ids.shape
while image_token_indices.numel() > 0:
cur_image_features = image_features[cur_image_idx]
image_token_start = image_token_indices[0]
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start-1]).detach())
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start-1:image_token_start]))
cur_new_input_embeds.append(cur_image_features)
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start+1:image_token_start+2]))
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_new_labels.append(cur_labels[image_token_start:image_token_start+1])
cur_labels = cur_labels[image_token_start+2:]
else:
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start]))
cur_new_input_embeds.append(cur_image_features)
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_labels = cur_labels[image_token_start+1:]
cur_image_idx += 1
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
cur_input_ids = cur_input_ids[image_token_start+2:]
else:
cur_input_ids = cur_input_ids[image_token_start+1:]
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
if cur_input_ids.numel() > 0:
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
mask_idx = torch.nonzero(cur_input_ids==self.tokenizer.convert_tokens_to_ids(['<global>'])[0])
_l = 0
for i, idx in enumerate(mask_idx):
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[_l:idx[0]]).detach())
## mask
cur_new_input_embeds.append(mask_feats[batch_idx][i:i+1].detach())
## pos
cur_new_input_embeds.append(pos_feats[batch_idx][i:i+1].detach())
if labels is not None:
cur_labels[idx[0]:idx[0]+2] = torch.full((2,), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)
_l = idx[0]+2
if _l< len(cur_input_ids):
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[_l:]).detach())
else:
mask_idx = torch.nonzero(cur_input_ids==self.tokenizer.convert_tokens_to_ids(['<global>'])[0])
assert len(mask_idx) == len(mask_feats[batch_idx]), "mask num not equal to mask feats"
_l = 0
for i, idx in enumerate(mask_idx):
cur_raw_new_input_embeds = self.get_model().embed_tokens(cur_input_ids[_l:idx[0]])
cur_new_input_embeds.append(cur_raw_new_input_embeds)
## mask
cur_new_input_embeds.append(mask_feats[batch_idx][i:i+1].to(cur_raw_new_input_embeds.dtype))
## pos
cur_new_input_embeds.append(pos_feats[batch_idx][i:i+1].to(cur_raw_new_input_embeds.dtype))
if labels is not None:
cur_labels[idx[0]:idx[0]+2] = torch.full((2,), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)
_l = idx[0]+2
if _l< len(cur_input_ids):
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[_l:]))
if labels is not None:
cur_new_labels.append(cur_labels)
cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds]
cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
new_input_embeds.append(cur_new_input_embeds)
if labels is not None:
cur_new_labels = torch.cat(cur_new_labels, dim=0)
new_labels.append(cur_new_labels)
if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
max_len = max(x.shape[0] for x in new_input_embeds)
new_input_embeds_align = []
for cur_new_embed in new_input_embeds:
cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
new_input_embeds_align.append(cur_new_embed)
new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
if labels is not None:
new_labels_align = []
_new_labels = new_labels
for cur_new_label in new_labels:
cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0)
new_labels_align.append(cur_new_label)
new_labels = torch.stack(new_labels_align, dim=0)
if attention_mask is not None:
new_attention_mask = []
for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device)
new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device)
cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
new_attention_mask.append(cur_new_attention_mask)
attention_mask = torch.stack(new_attention_mask, dim=0)
assert attention_mask.shape == new_labels.shape
else:
new_input_embeds = torch.stack(new_input_embeds, dim=0)
if labels is not None:
new_labels = torch.stack(new_labels, dim=0)
if attention_mask is not None:
new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device)
attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
assert attention_mask.shape == new_input_embeds.shape[:2]
return None, attention_mask, past_key_values, new_input_embeds, new_labels
def initialize_vision_tokenizer(self, model_args, tokenizer):
if model_args.mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
mask_tokens = ['<global>', '<pos>']
num_new_tokens = tokenizer.add_tokens(mask_tokens, special_tokens=True)
if model_args.mm_use_im_start_end:
num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = self.get_input_embeddings().weight.data
output_embeddings = self.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = True
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
if model_args.pretrain_mm_mlp_adapter:
mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
assert num_new_tokens == 2
if input_embeddings.shape == embed_tokens_weight.shape:
input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
elif embed_tokens_weight.shape[0] == num_new_tokens:
input_embeddings[-num_new_tokens:] = embed_tokens_weight
else:
raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
elif model_args.mm_use_im_patch_token:
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = False
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
for m in self.modules():
m.tokenizer = tokenizer |