File size: 13,804 Bytes
56b1f4f |
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 |
import torch
import torch.nn.functional as F
from typing import List, Optional, Tuple, Type, Union
from functools import partial
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from typing import Type
from torchvision import transforms
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from torchvision.transforms.functional import InterpolationMode
from transformers import (
Qwen2Config,
Qwen2Model,
Qwen2ForCausalLM,
)
from .configuration_gex import GexConfig
LayerNorm = partial(nn.LayerNorm, eps=1e-6)
class GexImageEvalProcessor:
def __init__(self, image_size=1024, mean=None, std=None):
if mean is None:
mean = (0.48145466, 0.4578275, 0.40821073)
if std is None:
std = (0.26862954, 0.26130258, 0.27577711)
self.normalize = transforms.Normalize(mean, std)
self.transform = transforms.Compose(
[
transforms.Resize(
(image_size, image_size), interpolation=InterpolationMode.BICUBIC
),
transforms.ToTensor(),
self.normalize,
]
)
def __call__(self, item):
return self.transform(item)
class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.num_channels = num_channels
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x.permute(0, 2, 3, 1)
return torch.nn.functional.layer_norm(
x,
normalized_shape=(self.num_channels,),
weight=self.weight,
bias=self.bias,
eps=self.eps,
).permute(0, 3, 1, 2)
class PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
kernel_size: Tuple[int, int] = (16, 16),
stride: Tuple[int, int] = (16, 16),
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
"""
Args:
kernel_size (Tuple): kernel size of the projection layer.
stride (Tuple): stride of the projection layer.
padding (Tuple): padding size of the projection layer.
in_chans (int): Number of input image channels.
embed_dim (int): Patch embedding dimension.
"""
super().__init__()
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=kernel_size, stride=stride
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.proj(x)
# B C H W -> B H W C
x = x.permute(0, 2, 3, 1)
return x
class Attention(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
input_size: Optional[Tuple[int, int]] = None,
) -> None:
super().__init__()
self.num_heads = num_heads
self.head_dim = 64
self.scale = 64**-0.5
self.seq_len = input_size[0] * input_size[1]
self.input_size = input_size
self.qkv = nn.Linear(dim, dim * 3, bias=True)
self.proj = nn.Linear(dim, dim)
# self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, self.head_dim))
# self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, self.head_dim))
self.rel_pos_h = nn.Parameter(torch.zeros(input_size[0],input_size[0], self.head_dim))
self.rel_pos_w = nn.Parameter(torch.zeros(input_size[1],input_size[1], self.head_dim))
def init_rel_pos(self):
q_size, k_size = self.input_size
q_coords = torch.arange(q_size)[:, None]
k_coords = torch.arange(k_size)[None, :]
relative_coords = (q_coords - k_coords) + (k_size - 1)
self.rel_pos_h = nn.Parameter(self.rel_pos_h.data[relative_coords.long()])
self.rel_pos_w = nn.Parameter(self.rel_pos_w.data[relative_coords.long()])
def get_attn_bias(self, q: torch.Tensor):
q = q.view(-1, *self.input_size, 64)
rel_h = torch.einsum("bhwc,hkc->bhwk", q, self.rel_pos_h)
rel_w = torch.einsum("bhwc,wkc->bhwk", q, self.rel_pos_w)
return (rel_h.unsqueeze(-1) + rel_w.unsqueeze(-2)).reshape(
-1, self.num_heads, self.seq_len, self.seq_len
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
qkv = torch.split(
self.qkv(x).view(-1, self.seq_len, 3 * 768),
768,
dim=2,
)
q, k, v = (
i.unflatten(-1, (self.num_heads, -1)).transpose(1, 2).contiguous()
for i in qkv
)
attn_bias = self.get_attn_bias(q)
attn_output = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=attn_bias, is_causal=False
)
attn_output = attn_output.transpose(1, 2).flatten(-2)
x = self.proj(attn_output)
return x.view(-1, *self.input_size, 768)
class MLP(nn.Module):
def __init__(
self,
):
super().__init__()
self.lin1 = nn.Linear(768, 4 * 768)
self.lin2 = nn.Linear(4 * 768, 768)
self.act = nn.GELU()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.lin2(self.act(self.lin1(x)))
class Block(nn.Module):
def __init__(self, idx: int, window_size: int = 14):
super().__init__()
self.idx = idx
self.window_size = window_size
self.norm1 = LayerNorm(768)
self.attn = Attention(
dim=768,
num_heads=12,
input_size=(64, 64) if window_size == 0 else (14, 14),
)
self.norm2 = LayerNorm(768)
self.mlp = MLP()
@staticmethod
def window_partition(x: torch.Tensor) -> torch.Tensor:
x = F.pad(x, (0, 0, 0, 6, 0, 6))
x = (
x.view(-1, 5, 14, 5, 14, 768)
.permute(0, 1, 3, 2, 4, 5)
.contiguous()
.view(-1, 14, 14, 768)
)
return x
@staticmethod
def window_unpartition(x: torch.Tensor) -> torch.Tensor:
x = (
x.view(-1, 5, 5, 14, 14, 768)
.permute(0, 1, 3, 2, 4, 5)
.contiguous()
.view(-1, 70, 70, 768)
)
return x[:, :64, :64, :].contiguous()
def forward(self, x: torch.Tensor) -> torch.Tensor:
shortcut = x
x = self.norm1(x)
if self.window_size > 0:
x = self.window_partition(x)
x = self.attn(x)
if self.window_size > 0:
x = self.window_unpartition(x)
x = shortcut + x
x = x + self.mlp(self.norm2(x))
return x
class GexVit(nn.Module):
def __init__(self, global_attn_indexes=[2, 5, 8, 11], **kwargs):
super().__init__()
self.global_attn_indexes = global_attn_indexes
self.patch_embed = PatchEmbed()
self.pos_embed = nn.Parameter(torch.zeros(1, 64, 64, 768))
self.blocks = nn.ModuleList(
[
Block(idx=i, window_size=14 if i not in global_attn_indexes else 0)
for i in range(12)
]
)
self.neck = nn.ModuleList(
[
nn.Conv2d(
768,
256,
kernel_size=1,
bias=False,
),
LayerNorm2d(256),
nn.Conv2d(
256,
256,
kernel_size=3,
padding=1,
bias=False,
),
LayerNorm2d(256),
]
)
self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
self.net_3 = nn.Conv2d(
512, 1024, kernel_size=3, stride=2, padding=1, bias=False
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.patch_embed(x)
x = x + self.pos_embed
for blk in self.blocks:
x = blk(x)
x = x.permute(0, 3, 1, 2)
for m in self.neck:
x = m(x)
x = self.net_2(x)
x = self.net_3(x)
return x
class GexQwenModel(Qwen2Model):
config_class = GexConfig
def __init__(self, config: Qwen2Config):
super().__init__(config)
self.vit = GexVit()
self.vit.eval()
self.vit_proj = nn.Linear(1024, 1024)
self.vit_proj.eval()
for param in self.vit.parameters():
param.requires_grad = False
for param in self.vit_proj.parameters():
param.requires_grad = False
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple, BaseModelOutputWithPast]:
if images is not None:
assert input_ids is None, input_ids
input_ids = None
attention_mask = None
kwargs["is_causal"] = True
with torch.no_grad():
vit_feature = self.vit_proj(
self.vit(images).flatten(2).permute(0, 2, 1)
)
inputs_embeds = vit_feature
# print(input_ids, images)
if inputs_embeds is None and input_ids is not None:
inputs_embeds = self.embed_tokens(input_ids)
return super().forward(
input_ids=None,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
class GexQwenForCausalLM(Qwen2ForCausalLM):
config_class = GexConfig
# supports_gradient_checkpointing = True
def __init__(self, config):
super().__init__(config)
self.model = GexQwenModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
self.has_image = False
self.image_preprocess = GexImageEvalProcessor()
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
images: Optional[torch.FloatTensor] = None,
**kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
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
if self.has_image:
input_ids = None
self.has_image = False
else:
images = None
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
images=images,
**kwargs,
)
hidden_states = outputs[0]
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@torch.no_grad
def generate(self,*args,**kwargs):
self.has_image = True
res = super().generate(*args, **kwargs)
self.has_image = False
return res
|