File size: 14,438 Bytes
08b3cf0 f3588fe 08b3cf0 |
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 |
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
from torch.nn import CrossEntropyLoss
from transformers import Qwen2Config, Qwen2Model, Qwen2ForCausalLM, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast, ModelOutput
from torch import nn
import torch.nn.functional as F
from .configuration_aimv2 import MonoConfig
from .modeling_aimv2 import AIMv2Model, PixelShuffleConnector
from transformers.generation import GenerationMixin
"""
Simple arch of Mono, used for pretrain vision encoder.
"""
@dataclass
class MonoCausalLMOutputWithPast(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
class MonoPretrainedModel(PreTrainedModel):
config_class = MonoConfig
base_model_prefix = "mono"
# main_input_name = "pixel_values"
_supports_sdpa = True
_supports_flash_attn_2 = True
_supports_cache_class = True
supports_gradient_checkpointing = True
# class MonoForConditionalGeneration(MonoPretrainedModel, Qwen2ForCausalLM):
class MonoForConditionalGeneration(MonoPretrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: MonoConfig):
# super().__init__(config)
MonoPretrainedModel.__init__(self, config)
# super(Qwen2ForCausalLM, self).__init__(config)
self.vision_tower = AIMv2Model(config=config.vision_config)
self._attn_implementation = config._attn_implementation
self._build_image_projection_layers(config)
self.model = Qwen2Model(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.pad_token_id = config.pad_token_id
print(f"==> pad_token_id: {self.pad_token_id}")
self.post_init()
def _build_image_projection_layers(self, config):
image_dim_out = config.vision_config.hidden_size
dim_projection = config.hidden_size
# self.mm_projector = nn.Linear(image_dim_out, dim_projection)
self.mm_projector = PixelShuffleConnector(image_dim_out, dim_projection)
print(f"==> build mm_projector: {image_dim_out} -> {dim_projection}")
def get_vision_tower(self):
return self.vision_tower
def get_input_embeddings(self):
return self.model.get_input_embeddings()
def resize_token_embeddings(
self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None
) -> nn.Embedding:
model_embeds = self.model.resize_token_embeddings(
new_num_tokens, pad_to_multiple_of
)
# update vocab size
self.config.text_config.vocab_size = model_embeds.num_embeddings
self.config.vocab_size = model_embeds.num_embeddings
self.vocab_size = model_embeds.num_embeddings
return model_embeds
def _encode_image(self, pixel_values):
# print(f"pixel_values: {pixel_values}")
batch_size, C, H, W = pixel_values.shape
x = self.vision_tower(pixel_values, output_hidden_states=True)
x = x.hidden_states[-2]
# print(x)
x = self.mm_projector(x)
# print(f"image features: {x}")
return x
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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=None,
) -> 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
)
image_features = None
if inputs_embeds is None:
if pixel_values is not None:
# (batch_size, num_image_tokens, hidden_size)
image_features = self._encode_image(pixel_values)
if input_ids is not None:
inputs_embeds, attention_mask, labels = (
self._get_input_embeds_with_image(input_ids, image_features, labels)
)
# print(f'before inputs_embeds: {inputs_embeds.shape}')
# print(f'before labels: {labels.shape}')
# padding all to normal sequence length only train
# if labels is not None:
# input_length = inputs_embeds.shape[1]
# label_length = labels.shape[1]
# if labels is not None:
# labels = F.pad(labels, (input_length, 0), value=-100)
# if inputs_embeds is not None:
# # append embeds and attn_mask to labels length
# padding = torch.zeros(
# inputs_embeds.shape[0],
# label_length,
# inputs_embeds.shape[2],
# dtype=inputs_embeds.dtype,
# device=inputs_embeds.device,
# )
# inputs_embeds = torch.cat([inputs_embeds, padding], dim=1)
# attention_mask = attention_mask.to(inputs_embeds.dtype)
# attention_mask = F.pad(attention_mask, (0, label_length), value=0)
# if position_ids is None:
# position_ids = torch.arange(
# input_length + label_length, device=inputs_embeds.device
# )
# position_ids = position_ids.unsqueeze(0).expand(
# inputs_embeds.shape[0], -1
# )
# position_ids[input_length:] = 0
# print(f"position_ids {position_ids}")
# print(f"labels {labels.shape}")
# print(f"labels {labels}")
# print(f"inputs_embeds {inputs_embeds.shape}")
# print(f"inputs_embeds {inputs_embeds}")
# print(f"attention_mask {attention_mask.shape}")
# print(f"attention_mask {attention_mask}")
outputs = self.model(
input_ids=None,
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,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Upcast to float if we need to compute the loss to avoid potential precision issues
logits = logits.float()
labels = labels.to(logits.device)
# Shift so that tokens < n predict n
if attention_mask is not None:
# we use the input attention mask to shift the logits and labels, because it is 2D.
# we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(
logits.device
)
shift_logits = logits[..., :-1, :][
shift_attention_mask != 0
].contiguous()
# print(f"shift_logits: {shift_logits.shape}")
shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous()
# print(f"shift_labels: {shift_labels.shape}")
else:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return MonoCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def _get_input_embeds_with_image(self, input_ids, image_features, labels=None):
# 1. replace image token with features; 2. replace -100 in input_ids into zeroes
# 3. handling right attention_mask
# not complicated, you can understand.
batch_size = input_ids.size(0)
processed_embeds = []
processed_masks = []
labels_ignored_im = []
max_seq_len = 0
for idx in range(batch_size):
seq = input_ids[idx]
im_pos = (seq == -200).nonzero(as_tuple=True)[0]
if im_pos.numel() > 0:
im_pos = im_pos.item()
before = seq[:im_pos]
after = seq[im_pos + 1 :]
# Exclude -100 tokens (maybe, input_ids padding with -100 intentionly)
before = before[before != -100]
after = after[after != -100]
# Get embeddings for before and after
before_embed = self.get_input_embeddings()(before)
after_embed = self.get_input_embeddings()(after)
# Concatenate before, image features, and after
seq_embed = torch.cat(
[before_embed, image_features[idx], after_embed], dim=0
)
new_seq_len = seq_embed.size(0)
# if labels not None, change image token into -100, keep image tokens length
if labels is not None:
image_token_ignore = torch.full(
(image_features[idx].shape[0],),
-100,
dtype=torch.long,
device=labels.device,
)
labels_ignored_im.append(
torch.cat(
(
labels[idx][:im_pos],
image_token_ignore,
labels[idx][im_pos + 1 :],
),
dim=0,
)
)
else:
# Exclude -100 tokens
valid_tokens = seq[seq != -100]
seq_embed = self.get_input_embeddings()(valid_tokens)
new_seq_len = seq_embed.size(0)
# Update the maximum sequence length
if new_seq_len > max_seq_len:
max_seq_len = new_seq_len
processed_embeds.append(seq_embed)
attn_mask = torch.ones(new_seq_len, dtype=torch.bool, device=seq.device)
processed_masks.append(attn_mask)
# rest embedding is 0, rest mask is False, just padding it
inputs_embeds = torch.nn.utils.rnn.pad_sequence(
processed_embeds, batch_first=True, padding_value=0.0
)
attn_masks = torch.nn.utils.rnn.pad_sequence(
processed_masks, batch_first=True, padding_value=0
)
if labels is not None:
labels_ignored_im = torch.stack(labels_ignored_im, dim=0)
return inputs_embeds, attn_masks, labels_ignored_im
return inputs_embeds, attn_masks, None
@torch.no_grad()
def generate(self, input_ids, pixel_values=None, **kwargs):
# print(input_ids)
# print(f"pixel_values {pixel_values}")
if pixel_values is not None:
image_features = self._encode_image(pixel_values)
# print(f"image_features {image_features}")
inputs_embeds, attention_mask, _ = self._get_input_embeds_with_image(
input_ids, image_features
)
else:
if input_ids is not None:
inputs_embeds = self.get_input_embeddings()(input_ids)
attention_mask = torch.ones(
inputs_embeds.size(0),
inputs_embeds.size(1),
dtype=torch.bool,
device=inputs_embeds.device,
)
# print(f"inputs_embeds: {inputs_embeds}")
return super().generate(
input_ids=None,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
**kwargs,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
inputs_embeds=None,
attention_mask=None,
**kwargs,
):
# cut input_ids if past_key_values is used
# if past_key_values is not None:
# past_length = past_key_values[0][0].shape[2]
# # Some generation methods already pass only the last input ID
# if input_ids.shape[1] > past_length:
# input_ids = input_ids[:, -1:]
# elif input_ids.shape[1] == 1:
# pass
# else:
# # Default to old behavior: keep only final ID
# input_ids = input_ids[:, -1:]
model_inputs = super().prepare_inputs_for_generation(
input_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
**kwargs,
)
return model_inputs
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return self.model.shift_tokens_right(labels)
def _reorder_cache(self, *args, **kwargs):
return self.model._reorder_cache(*args, **kwargs)
|