File size: 13,719 Bytes
c5a246d | 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 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from .align_layers import build_align_layer
from .common_layers import BasePreTrainedModel
from .configuration_glint import GLINTConfig
from .losses import LossFunctions
from .text_encoders import build_text_encoder
from .vision_encoders import build_vision_encoder
logger = logging.getLogger("glint")
class GLINTModel(BasePreTrainedModel):
config_class = GLINTConfig
# only relevant when vision_only=True drops these submodules; in VLM mode every key loads
_keys_to_ignore_on_load_unexpected = [
r"^text_model\.",
r"^align_layer\.",
r"^loss_fns\.",
r"^dfr_module\.",
r"^vision_layer_norm\.",
r"^text_layer_norm\.",
]
def build_vision_model(self, config):
vision_config = config.vision_config
vision_model = build_vision_encoder(vision_config)
return vision_model
def build_text_model(self, config):
text_config = config.text_config
text_model = build_text_encoder(text_config)
return text_model
def build_align_layer_model(self, config):
align_layer_config = config.align_layer_config
align_layer = build_align_layer(align_layer_config)
return align_layer
def build_dfr_module(self, config):
from .dfr import DFRModule
dfr_module = DFRModule(config.dfr_config)
return dfr_module
def __init__(self, config: GLINTConfig):
super().__init__(config)
logger.info("Build vision model ...")
self.vision_model = self.build_vision_model(config)
if getattr(config, "vision_only", False):
logger.info("vision_only=True: skipping text model, align layer, losses and DFR module.")
return
logger.info("Build text model ...")
self.text_model = self.build_text_model(config)
logger.info("Build align layer model ...")
self.align_layer = self.build_align_layer_model(config)
logger.info("Build loss functions ...")
loss_cfg = config.kwargs["loss"]
self.loss_fns = LossFunctions(loss_cfg)
if hasattr(config, "dfr_config") and config.dfr_config is not None:
logger.info("Build dfr module ...")
self.dfr_module = self.build_dfr_module(config)
self.vision_layer_norm = (
nn.LayerNorm(config.align_layer_config.hidden_size) if config.use_vision_layer_norm else None
)
self.text_layer_norm = nn.LayerNorm(config.text_config.hidden_size) if config.use_text_layer_norm else None
self.compute_logits_type = config.kwargs.get("compute_logits_type")
self.module_to_update = config.kwargs.get("module_to_update")
def forward_vision_model(self, pixel_values, output_hidden_states=False):
vision_model_type = self.config.vision_config.model_type
vision_hidden_states = None
use_cls_token = getattr(self.config.vision_config, "use_cls_token", False)
if vision_model_type == "dinov2":
vision_outputs = self.vision_model(pixel_values, output_hidden_states=output_hidden_states)
vision_tokens = vision_outputs["last_hidden_state"]
if not use_cls_token:
vision_tokens = vision_tokens[:, 1:, :]
vision_hidden_states = vision_outputs.get("hidden_states", None)
elif vision_model_type == "dinov3_vit":
vision_outputs = self.vision_model(pixel_values, output_hidden_states=output_hidden_states)
original_output = vision_outputs["last_hidden_state"]
patch_tokens = original_output[:, 1 + self.config.vision_config.num_register_tokens :, :]
if use_cls_token:
cls_token = original_output[:, :1, :]
vision_tokens = torch.cat([cls_token, patch_tokens], dim=1)
else:
vision_tokens = patch_tokens
vision_hidden_states = vision_outputs.get("hidden_states", None)
elif vision_model_type == "vjepa2":
vision_outputs = self.vision_model(
pixel_values, skip_predictor=True, output_hidden_states=output_hidden_states
)
vision_tokens = vision_outputs["last_hidden_state"]
vision_hidden_states = vision_outputs.get("hidden_states", None)
elif vision_model_type == "vjepa2.1":
vision_outputs = self.vision_model(pixel_values, output_hidden_states=output_hidden_states)
vision_tokens = vision_outputs["last_hidden_state"]
vision_hidden_states = vision_outputs.get("hidden_states", None)
else:
raise NotImplementedError
outputs = {}
vision_tokens = self.align_layer(vision_tokens)
if self.vision_layer_norm is not None:
vision_tokens = self.vision_layer_norm(vision_tokens)
outputs["vision_tokens"] = vision_tokens
outputs["vision_hidden_states"] = vision_hidden_states
return outputs
def forward_text_model(self, encoded_input):
text_outputs = {}
text_model_type = self.config.text_config.model_type
if text_model_type == "mpnet":
model_output = self.text_model(encoded_input["input_ids"], attention_mask=encoded_input["attention_mask"])
token_embeddings = model_output[0]
else:
raise NotImplementedError
if self.config.text_config.use_cls_token:
text_features = token_embeddings[:, 0, :]
else:
input_mask_expanded = (
encoded_input["attention_mask"].unsqueeze(-1).expand(token_embeddings.size()).to(token_embeddings.dtype)
)
text_features = torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
if self.text_layer_norm is not None:
text_features = self.text_layer_norm(text_features)
text_outputs["text_features"] = text_features
text_outputs["text_features_norm"] = F.normalize(text_features, p=2, dim=1)
return text_outputs
def forward(
self,
pixel_values=None,
enc_sents=None,
neg_enc_sents=None,
return_loss=True,
forward_accum_outputs=False,
forward_accum_loss=False,
**kwargs,
):
if forward_accum_outputs:
return self.forward_accum_outputs(
pixel_values=pixel_values,
enc_sents=enc_sents,
neg_enc_sents=neg_enc_sents,
**kwargs,
)
if forward_accum_loss:
return self.forward_accum_loss(**kwargs)
if getattr(self.config, "vision_only", False):
return self.vision_model(pixel_values)
outputs = {}
use_dfr_module = hasattr(self, "dfr_module") and self.dfr_module is not None
vision_outputs = self.forward_vision_model(pixel_values, output_hidden_states=use_dfr_module)
if use_dfr_module:
dfr_outputs = self.dfr_module(
pixel_values=pixel_values,
student_hidden_states=vision_outputs["vision_hidden_states"],
cached_teacher_features=kwargs.get("cached_teacher_features"),
)
outputs.update(dfr_outputs)
outputs.update(vision_outputs)
if return_loss:
loss = 0
losses = {}
for loss_type, loss_fn in self.loss_fns.loss_dict.items():
loss_ratio = self.loss_fns.loss_ratio[loss_type]
if loss_fn.__class__.__name__ == "AlignmentLoss":
loss_outputs = loss_fn(
enc_sents=enc_sents,
neg_enc_sents=neg_enc_sents,
vision_tokens=outputs["vision_tokens"],
forward_text_model=self.forward_text_model,
)
_losses = loss_outputs["losses"]
loop_loss = _losses.pop("loss")
losses.update(_losses)
elif loss_fn.__class__.__name__ == "DFRLoss":
loss_outputs = loss_fn(
proj_student_features=outputs["proj_student_features"],
teacher_features=outputs["teacher_features"],
)
_losses = loss_outputs["losses"]
loop_loss = _losses.pop("loss")
losses.update(_losses)
else:
raise NotImplementedError
loss += loop_loss * loss_ratio
losses["loss"] = loss
outputs["loss"] = loss
outputs["losses"] = losses
return outputs
def compute_logits(
self,
pixel_values,
enc_sents,
**kwargs,
):
vision_outputs = self.forward_vision_model(pixel_values)
outputs = {}
if self.compute_logits_type == "vl_alignment":
loss_fn = self.loss_fns.loss_dict["AlignmentLoss"]
splited_sentences = [
{
"input_ids": enc_sents[0]["input_ids"][i : i + 1],
"attention_mask": enc_sents[0]["attention_mask"][i : i + 1],
}
for i in range(enc_sents[0]["input_ids"].size(0))
]
loss_outputs = loss_fn(
splited_sentences,
vision_outputs["vision_tokens"],
self.forward_text_model,
ddp_gather=False,
need_attn_weights=True,
compute_loss=False,
)
outputs.update(loss_outputs)
similarity_scores = loss_outputs["attn_weights"][0]
if getattr(self.config.vision_config, "use_cls_token", False):
similarity_scores = similarity_scores[:, :, 1:]
outputs["similarity_scores"] = similarity_scores
if "sim_components" in loss_outputs:
comp = loss_outputs["sim_components"]
if getattr(self.config.vision_config, "use_cls_token", False):
comp = {k: v[:, :, 1:] for k, v in comp.items()}
outputs["sim_components"] = comp
logits = loss_outputs["logits"]
logits = logits.T
else:
raise NotImplementedError
outputs["logits"] = logits
return outputs
def forward_accum_outputs(
self,
pixel_values,
enc_sents=None,
neg_enc_sents=None,
**kwargs,
):
use_dfr_module = hasattr(self, "dfr_module") and self.dfr_module is not None
outputs = {}
vision_outputs = self.forward_vision_model(pixel_values, output_hidden_states=use_dfr_module)
outputs.update(vision_outputs)
if use_dfr_module:
dfr_outputs = self.dfr_module(
pixel_values=pixel_values,
student_hidden_states=vision_outputs["vision_hidden_states"],
cached_teacher_features=kwargs.get("cached_teacher_features"),
)
outputs.update(dfr_outputs)
text_outputs = []
for kp in enc_sents:
text_outputs.append(self.forward_text_model(kp)["text_features"])
text_outputs = {"text_features": text_outputs}
outputs.update(text_outputs)
if neg_enc_sents is not None:
negative_text_outputs = []
for kp in neg_enc_sents:
if isinstance(kp["input_ids"], torch.Tensor):
negative_text_outputs.append(self.forward_text_model(kp)["text_features"])
else:
negative_text_outputs.append(
torch.zeros((0, self.config.text_config.hidden_size)).to(pixel_values.device)
)
negative_text_outputs = {"negative_text_features": negative_text_outputs}
outputs.update(negative_text_outputs)
if "ids_mask" in kwargs:
outputs["ids_mask"] = kwargs["ids_mask"]
outputs["mask"] = kwargs["mask"]
return outputs
def forward_accum_loss(
self,
vision_tokens,
text_features,
negative_text_features=None,
**kwargs,
):
outputs = {}
losses = {}
loss = 0
for loss_type, loss_fn in self.loss_fns.loss_dict.items():
loss_ratio = self.loss_fns.loss_ratio[loss_type]
if loss_fn.__class__.__name__ == "AlignmentLoss":
loss_outputs = loss_fn(
enc_sents=None,
neg_enc_sents=None,
vision_tokens=vision_tokens,
forward_text_model=None,
text_feats_list=text_features,
neg_text_feats_list=negative_text_features,
)
_losses = loss_outputs["losses"]
loop_loss = _losses.pop("loss")
losses.update(_losses)
elif loss_fn.__class__.__name__ == "DFRLoss":
loss_outputs = loss_fn(
proj_student_features=kwargs.get("proj_student_features"),
teacher_features=kwargs.get("teacher_features"),
)
_losses = loss_outputs["losses"]
loop_loss = _losses.pop("loss")
losses.update(_losses)
else:
raise NotImplementedError
loss += loop_loss * loss_ratio
losses["loss"] = loss
outputs["loss"] = loss
outputs["losses"] = losses
return outputs
|