File size: 14,313 Bytes
89b38b0 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from typing import Optional, Tuple, Union, Dict, Any, List
from transformers.modeling_outputs import SequenceClassifierOutputWithPast
from transformers.models.qwen2_vl.modeling_qwen2_vl import (
Qwen2VLPreTrainedModel,
Qwen2VLModel,
Qwen2VisionTransformerPretrainedModel,
)
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
Qwen2_5_VLPreTrainedModel,
Qwen2_5_VLModel
)
from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLConfig
from train.monkey_patch_vision import replace_qwen2_5_vision
replace_qwen2_5_vision()
class Qwen2VLForSequenceClassification(Qwen2VLPreTrainedModel):
_checkpoint_conversion_mapping = {
"^visual": "model.visual",
r"^model(?!\.(language_model|visual))": "model.language_model",
}
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
bridge_h = config.mlp_head_hidden_dim
bridge_p = config.mlp_head_dropout
self.model = Qwen2VLModel(config)
self.bridge = None
in_dim = config.hidden_size
if bridge_h > 0:
self.bridge = nn.Sequential(
nn.Linear(config.hidden_size, bridge_h),
nn.GELU(),
nn.Dropout(bridge_p),
)
nn.init.xavier_uniform_(self.bridge[0].weight, gain=1.0)
nn.init.zeros_(self.bridge[0].bias)
in_dim = bridge_h
self.score = nn.Linear(in_dim, self.num_labels, bias=False)
nn.init.normal_(self.score.weight, std=1e-3)
self.loss_fn = None
self.post_init()
def get_input_embeddings(self):
return self.model.get_input_embeddings()
def set_input_embeddings(self, value):
self.model.set_input_embeddings(value)
def set_decoder(self, decoder):
self.model.set_decoder(decoder)
def get_decoder(self):
return self.model.get_decoder()
def get_video_features(
self, pixel_values_videos: torch.FloatTensor, video_grid_thw: Optional[torch.LongTensor] = None
):
return self.model.get_video_features(pixel_values_videos, video_grid_thw)
def get_image_features(self, pixel_values: torch.FloatTensor, image_grid_thw: Optional[torch.LongTensor] = None):
return self.model.get_image_features(pixel_values, image_grid_thw)
@property
def language_model(self):
return self.model.language_model
@property
def visual(self):
return self.model.visual
def forward(
self,
input_ids: Optional[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,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None,
pixel_values_videos: Optional[torch.FloatTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
cache_position: Optional[torch.LongTensor] = None,
rope_deltas: Optional[torch.LongTensor] = None,
**kwargs,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
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
)
outputs = self.model(
input_ids=input_ids,
pixel_values=pixel_values,
pixel_values_videos=pixel_values_videos,
image_grid_thw=image_grid_thw,
video_grid_thw=video_grid_thw,
position_ids=position_ids,
attention_mask=attention_mask,
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=True,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
feats = self.bridge(hidden_states) if self.bridge is not None else hidden_states
if input_ids is not None:
batch_size, _ = input_ids.shape[:2]
else:
batch_size, _ = inputs_embeds.shape[:2]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError(
"Cannot handle batch sizes > 1 if no padding token is defined."
)
if self.config.pad_token_id is None:
sequence_lengths = torch.full((batch_size,), -1, device=feats.device)
else:
if input_ids is not None:
non_pad_mask = (input_ids != self.config.pad_token_id).to(feats.device)
token_indices = torch.arange(
input_ids.size(-1), device=feats.device, dtype=torch.long
)
sequence_lengths = (token_indices * non_pad_mask).argmax(dim=-1)
else:
sequence_lengths = torch.full((batch_size,), -1, device=feats.device)
pooled_feats = feats[torch.arange(batch_size, device=feats.device), sequence_lengths]
pooled_logits = self.score(pooled_feats)
loss: Optional[torch.Tensor] = None
if labels is not None:
labels = labels.to(pooled_logits.device)
if self.config.problem_type is None:
# automatically infer
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and labels.dtype in (
torch.long,
torch.int,
):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
elif self.config.problem_type == "single_label_classification":
if hasattr(self, "loss_fn") and self.loss_fn is not None:
loss_fct = self.loss_fn
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
else: # multi-label
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class Qwen2_5_VLForSequenceClassification(Qwen2_5_VLPreTrainedModel):
_checkpoint_conversion_mapping = {
"^visual": "model.visual",
r"^model(?!\.(language_model|visual))": "model.language_model",
}
accepts_loss_kwargs = False
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
bridge_h = config.mlp_head_hidden_dim
bridge_p = config.mlp_head_dropout
self.model = Qwen2_5_VLModel(config)
self.bridge = None
in_dim = config.hidden_size
if bridge_h > 0:
self.bridge = nn.Sequential(
nn.Linear(config.hidden_size, bridge_h),
nn.GELU(),
nn.Dropout(bridge_p),
)
nn.init.xavier_uniform_(self.bridge[0].weight, gain=1.0)
nn.init.zeros_(self.bridge[0].bias)
in_dim = bridge_h
self.score = nn.Linear(in_dim, self.num_labels, bias=False)
nn.init.normal_(self.score.weight, std=1e-3)
self.loss_fn = None
self.post_init()
def get_input_embeddings(self):
return self.model.get_input_embeddings()
def set_input_embeddings(self, value):
self.model.set_input_embeddings(value)
def set_decoder(self, decoder):
self.model.set_decoder(decoder)
def get_decoder(self):
return self.model.get_decoder()
def get_video_features(
self, pixel_values_videos: torch.FloatTensor, video_grid_thw: Optional[torch.LongTensor] = None
):
return self.model.get_video_features(pixel_values_videos, video_grid_thw)
def get_image_features(self, pixel_values: torch.FloatTensor, image_grid_thw: Optional[torch.LongTensor] = None):
return self.model.get_image_features(pixel_values, image_grid_thw)
# Make modules available through conditional class for BC
@property
def language_model(self):
return self.model.language_model
@property
def visual(self):
return self.model.visual
def forward(
self,
input_ids: Optional[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,
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,
pixel_values: Optional[torch.Tensor] = None,
pixel_values_videos: Optional[torch.FloatTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
rope_deltas: Optional[torch.LongTensor] = None,
cache_position: Optional[torch.LongTensor] = None,
second_per_grid_ts: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
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
)
outputs = self.model(
input_ids=input_ids,
pixel_values=pixel_values,
pixel_values_videos=pixel_values_videos,
image_grid_thw=image_grid_thw,
video_grid_thw=video_grid_thw,
second_per_grid_ts=second_per_grid_ts,
position_ids=position_ids,
attention_mask=attention_mask,
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=True,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
feats = self.bridge(hidden_states) if self.bridge is not None else hidden_states
if input_ids is not None:
batch_size, _ = input_ids.shape[:2]
else:
batch_size, _ = inputs_embeds.shape[:2]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError(
"Cannot handle batch sizes > 1 if no padding token is defined."
)
if self.config.pad_token_id is None:
sequence_lengths = torch.full((batch_size,), -1, device=feats.device)
else:
if input_ids is not None:
non_pad_mask = (input_ids != self.config.pad_token_id).to(feats.device)
token_indices = torch.arange(
input_ids.size(-1), device=feats.device, dtype=torch.long
)
sequence_lengths = (token_indices * non_pad_mask).argmax(dim=-1)
else:
sequence_lengths = torch.full((batch_size,), -1, device=feats.device)
pooled_feats = feats[torch.arange(batch_size, device=feats.device), sequence_lengths]
pooled_logits = self.score(pooled_feats)
loss: Optional[torch.Tensor] = None
if labels is not None:
labels = labels.to(pooled_logits.device)
if self.config.problem_type is None:
# automatically infer
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and labels.dtype in (
torch.long,
torch.int,
):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
elif self.config.problem_type == "single_label_classification":
if hasattr(self, "loss_fn") and self.loss_fn is not None:
loss_fct = self.loss_fn
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
else: # multi-label
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
|