File size: 21,083 Bytes
638aec8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 |
# --------------------------------------------------------
# InternVL
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from functools import partial
from typing import Optional
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from peft import LoraConfig, get_peft_model
from timm.models.layers import DropPath
from torch import nn
from transformers import GenerationConfig
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
from .configuration_internvl import InternVLConfig
from .modeling_intern_vit import (InternVisionEmbeddings, InternVisionEncoder,
InternVisionModel)
from .modeling_qllama import LlamaForCausalLM, _expand_mask, _make_causal_mask
try:
from .flash_attention import FlashAttention # v1/v2
except:
print('FlashAttention is not installed.')
logger = logging.get_logger(__name__)
class InternVLPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = InternVLConfig
base_model_prefix = 'internvl'
supports_gradient_checkpointing = True
_keys_to_ignore_on_load_missing = [
r'position_ids',
]
_no_split_modules = ['InternAttention', 'LlamaDecoderLayer', 'LlamaForCausalLM']
_skip_keys_device_placement = 'past_key_values'
_keep_in_fp32_modules = ['wo']
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_range
if isinstance(module, nn.Conv2d) or isinstance(module, nn.Embedding) or isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=factor)
if hasattr(module, 'bias') and module.bias is not None:
module.bias.data.zero_()
if isinstance(module, InternVisionEmbeddings):
if hasattr(self.config, 'vision_config'):
factor = self.config.vision_config.initializer_range
nn.init.trunc_normal_(module.position_embedding, mean=0.0, std=factor)
nn.init.trunc_normal_(module.class_embedding, mean=0.0, std=factor)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, InternVisionModel):
module.gradient_checkpointing = value
if isinstance(module, InternVisionEncoder):
module.gradient_checkpointing = value
class CrossAttention(nn.Module):
def __init__(
self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
proj_drop=0., attn_head_dim=None, out_dim=None):
super().__init__()
if out_dim is None:
out_dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
if attn_head_dim is not None:
head_dim = attn_head_dim
all_head_dim = head_dim * self.num_heads
self.scale = qk_scale or head_dim ** -0.5
assert all_head_dim == dim
self.q = nn.Linear(dim, all_head_dim, bias=False)
self.k = nn.Linear(dim, all_head_dim, bias=False)
self.v = nn.Linear(dim, all_head_dim, bias=False)
if qkv_bias:
self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
self.k_bias = nn.Parameter(torch.zeros(all_head_dim))
self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
else:
self.q_bias = None
self.k_bias = None
self.v_bias = None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(all_head_dim, out_dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, k=None, v=None):
B, N, C = x.shape
N_k = k.shape[1]
N_v = v.shape[1]
q_bias, k_bias, v_bias = None, None, None
if self.q_bias is not None:
q_bias = self.q_bias
k_bias = self.k_bias
v_bias = self.v_bias
q = F.linear(input=x, weight=self.q.weight, bias=q_bias)
q = q.reshape(B, N, 1, self.num_heads, -1).permute(2, 0, 3, 1, 4).squeeze(0) # (B, N_head, N_q, dim)
k = F.linear(input=k, weight=self.k.weight, bias=k_bias)
k = k.reshape(B, N_k, 1, self.num_heads, -1).permute(2, 0, 3, 1, 4).squeeze(0)
v = F.linear(input=v, weight=self.v.weight, bias=v_bias)
v = v.reshape(B, N_v, 1, self.num_heads, -1).permute(2, 0, 3, 1, 4).squeeze(0)
q = q * self.scale
attn = (q @ k.transpose(-2, -1)) # (B, N_head, N_q, N_k)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
x = self.proj(x)
x = self.proj_drop(x)
return x
class AttentiveBlock(nn.Module):
def __init__(self, dim, num_heads, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., norm_layer=nn.LayerNorm, attn_head_dim=None, out_dim=None):
super().__init__()
self.norm1_q = norm_layer(dim)
self.norm1_k = norm_layer(dim)
self.norm1_v = norm_layer(dim)
self.cross_attn = CrossAttention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
proj_drop=drop, attn_head_dim=attn_head_dim, out_dim=out_dim)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x_q, x_kv, pos_q, pos_k, bool_masked_pos, rel_pos_bias=None):
x_q = self.norm1_q(x_q + pos_q)
x_k = self.norm1_k(x_kv + pos_k)
x_v = self.norm1_v(x_kv)
x = self.cross_attn(x_q, k=x_k, v=x_v)
return x
class AttentionPoolingBlock(AttentiveBlock):
def forward(self, x):
x_q = x.mean(1, keepdim=True)
x_kv, pos_q, pos_k = x, 0, 0
x = super().forward(x_q, x_kv, pos_q, pos_k, bool_masked_pos=None, rel_pos_bias=None)
x = x.squeeze(1)
return x
class InternVLModel(InternVLPreTrainedModel):
config_class = InternVLConfig
main_input_name = 'pixel_values'
def __init__(self, config: InternVLConfig):
super().__init__(config)
text_hidden_size = config.qllama_config.hidden_size
vision_hidden_size = config.vision_config.hidden_size
clip_embed_dim = config.clip_embed_dim
attn_pool_num_heads = config.attn_pool_num_heads
config.qllama_config.num_query_token = config.num_query_token
self.num_query_token = config.num_query_token
self.label_smoothing = config.label_smoothing
self.vision_model = InternVisionModel(config.vision_config) # frozen
self.qllama = LlamaForCausalLM(config.qllama_config) # frozen
self.query_tokens = nn.Parameter( # trainable
torch.zeros(1, config.num_query_token, text_hidden_size)
)
self.text_projection = nn.Parameter(torch.empty(text_hidden_size, clip_embed_dim)) # frozen
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) # trainable
self.clip_projector = AttentionPoolingBlock( # frozen
dim=vision_hidden_size, num_heads=attn_pool_num_heads, qkv_bias=True, qk_scale=None,
drop=0., attn_drop=0., norm_layer=partial(nn.LayerNorm, eps=1e-5), out_dim=clip_embed_dim)
self.clip_projector2 = AttentionPoolingBlock( # trainable
dim=text_hidden_size, num_heads=attn_pool_num_heads, qkv_bias=True, qk_scale=None,
drop=0., attn_drop=0., norm_layer=partial(nn.LayerNorm, eps=1e-5), out_dim=clip_embed_dim)
self.itm_head = nn.Linear(text_hidden_size, 2) # trainable
self.gradient_checkpointing = True
# Initialize weights and apply final processing
# self.post_init()
if config.use_backbone_lora:
self.wrap_backbone_lora(r=config.use_backbone_lora)
if config.use_qllama_lora:
self.wrap_qllama_lora(r=config.use_qllama_lora)
if config.force_image_size:
self.vision_model.resize_pos_embeddings(
old_size=config.vision_config.image_size,
new_size=config.force_image_size,
patch_size=config.vision_config.patch_size
)
def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
lora_config = LoraConfig(
r=r,
target_modules=['attn.qkv', 'attn.proj', 'mlp.fc1', 'mlp.fc2'],
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
)
self.vision_model = get_peft_model(self.vision_model, lora_config)
self.vision_model.print_trainable_parameters()
def wrap_qllama_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
lora_config = LoraConfig(
r=r,
target_modules=['self_attn.q_proj', 'self_attn.k_proj', 'self_attn.v_proj', 'self_attn.o_proj',
'mlp.gate_proj', 'mlp.down_proj', 'mlp.up_proj'],
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
)
self.qllama = get_peft_model(self.qllama, lora_config)
self.qllama.print_trainable_parameters()
def get_input_embeddings(self):
return self.qllama.get_input_embeddings()
def set_input_embeddings(self, value):
self.qllama.set_input_embeddings(value)
def set_output_embeddings(self, new_embeddings):
self.qllama.set_output_embeddings(new_embeddings)
def get_output_embeddings(self) -> nn.Module:
return self.qllama.get_output_embeddings()
@torch.no_grad()
def generate(
self,
pixel_values: torch.FloatTensor,
input_ids: torch.FloatTensor,
attention_mask: torch.LongTensor,
generation_config: Optional[GenerationConfig] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**generate_kwargs,
) -> torch.LongTensor:
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict)
image_embeds = vision_outputs[0]
batch_size = image_embeds.shape[0]
input_embeds = self.get_input_embeddings()(input_ids)
query_tokens = self.query_tokens.repeat(batch_size, 1, 1)
input_embeds = torch.cat([query_tokens, input_embeds], dim=1)
image_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
attention_mask = torch.cat([image_attention_mask, attention_mask], dim=1)
outputs = self.qllama.generate(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
vision_hidden_states=image_embeds,
generation_config=generation_config,
use_zero_attention_mask=True,
**generate_kwargs,
)
return outputs
def get_text_features(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Returns:
text_outputs (`CausalLMOutputWithPast`, or `tuple(torch.FloatTensor)` if `return_dict=False`):
The language model outputs. If `return_dict=True`, the output is a [`CausalLMOutputWithPast`] that
contains the language model logits, the past key values and the hidden states if
`output_hidden_states=True`.
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
input_embeds = self.get_input_embeddings()(input_ids)
attention_mask = _expand_mask(attention_mask, input_embeds.dtype).to(
input_embeds.device) # [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask += _make_causal_mask(
(attention_mask.shape[0], attention_mask.shape[2]),
input_embeds.dtype,
device=input_embeds.device
)
if type(self.qllama.model) == LlamaForCausalLM:
outputs = self.qllama.model.model.forward_train(
inputs_embeds=input_embeds,
vision_hidden_states=None,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
).last_hidden_state
else:
outputs = self.qllama.model.forward_train(
inputs_embeds=input_embeds,
vision_hidden_states=None,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
).last_hidden_state
return outputs
def get_image_features(
self,
pixel_values: torch.FloatTensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict)
image_embeds = vision_outputs[0]
backbone_embeds = image_embeds
batch_size = image_embeds.shape[0]
input_embeds = self.query_tokens.repeat(batch_size, 1, 1)
attention_mask = torch.ones(input_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)
attention_mask = _expand_mask(attention_mask, input_embeds.dtype).to(
input_embeds.device) # [bsz, 1, tgt_seq_len, src_seq_len]
if type(self.qllama.model) == LlamaForCausalLM:
outputs = self.qllama.model.model.forward_train(
inputs_embeds=input_embeds,
vision_hidden_states=image_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
).last_hidden_state
else:
outputs = self.qllama.model.forward_train(
inputs_embeds=input_embeds,
vision_hidden_states=image_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
).last_hidden_state
return backbone_embeds, outputs
def encode_image(self, image, mode):
if mode == 'InternVL-C':
vision_outputs = self.vision_model(
pixel_values=image,
output_hidden_states=False,
return_dict=True)
image_embeds = vision_outputs[0]
image_embeds = self.clip_projector(image_embeds)
elif mode == 'InternVL-G':
backbone_embeds, image_embeds = self.get_image_features(
pixel_values=image,
output_hidden_states=False,
return_dict=True,
)
backbone_embeds = self.clip_projector(backbone_embeds)
image_embeds = self.clip_projector2(image_embeds)
# ensemble
backbone_embeds = backbone_embeds / backbone_embeds.norm(dim=1, keepdim=True)
image_embeds = image_embeds / image_embeds.norm(dim=1, keepdim=True)
image_embeds = image_embeds + backbone_embeds
else:
raise NotImplementedError
return image_embeds
def encode_text(self, text):
attention_mask = text > 0
text_embeds = self.get_text_features(
input_ids=text,
attention_mask=attention_mask,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
text_embeds = text_embeds[torch.arange(text_embeds.shape[0]), attention_mask.sum(1) - 1]
text_embeds = text_embeds @ self.text_projection
return text_embeds
def forward(self, image, text, mode='InternVL-C'):
assert mode in ['InternVL-C', 'InternVL-G'], 'mode must be InternVL-C or InternVL-G'
image_features = self.encode_image(image, mode)
text_features = self.encode_text(text)
# normalized features
image_features = image_features / image_features.norm(dim=1, keepdim=True)
text_features = text_features / text_features.norm(dim=1, keepdim=True)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_image = logit_scale * image_features @ text_features.t()
logits_per_text = logits_per_image.t()
return logits_per_image, logits_per_text
class InternVL_C(InternVLModel):
def encode_image(self, image):
vision_outputs = self.vision_model(
pixel_values=image,
output_hidden_states=False,
return_dict=True)
image_embeds = vision_outputs[0]
image_embeds = self.clip_projector(image_embeds)
return image_embeds
def encode_text(self, text):
attention_mask = text > 0
text_embeds = self.get_text_features(
input_ids=text,
attention_mask=attention_mask,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
text_embeds = text_embeds[torch.arange(text_embeds.shape[0]), attention_mask.sum(1) - 1]
text_embeds = text_embeds @ self.text_projection
return text_embeds
def forward(self, image, text):
image_features = self.encode_image(image)
text_features = self.encode_text(text)
# normalized features
image_features = image_features / image_features.norm(dim=1, keepdim=True)
text_features = text_features / text_features.norm(dim=1, keepdim=True)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_image = logit_scale * image_features @ text_features.t()
logits_per_text = logits_per_image.t()
return logits_per_image, logits_per_text
class InternVL_G(InternVLModel):
def encode_image(self, image):
backbone_embeds, image_embeds = self.get_image_features(
pixel_values=image,
output_hidden_states=False,
return_dict=True,
)
backbone_embeds = self.clip_projector(backbone_embeds)
image_embeds = self.clip_projector2(image_embeds)
# ensemble
backbone_embeds = backbone_embeds / backbone_embeds.norm(dim=1, keepdim=True)
image_embeds = image_embeds / image_embeds.norm(dim=1, keepdim=True)
image_embeds = image_embeds + backbone_embeds
return image_embeds
def encode_text(self, text):
attention_mask = text > 0
text_embeds = self.get_text_features(
input_ids=text,
attention_mask=attention_mask,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
text_embeds = text_embeds[torch.arange(text_embeds.shape[0]), attention_mask.sum(1) - 1]
text_embeds = text_embeds @ self.text_projection
return text_embeds
def forward(self, image, text):
image_features = self.encode_image(image)
text_features = self.encode_text(text)
# normalized features
image_features = image_features / image_features.norm(dim=1, keepdim=True)
text_features = text_features / text_features.norm(dim=1, keepdim=True)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_image = logit_scale * image_features @ text_features.t()
logits_per_text = logits_per_image.t()
return logits_per_image, logits_per_text
|