File size: 36,762 Bytes
256a361 f3440bb 256a361 f3440bb 256a361 f3440bb 256a361 f3440bb 256a361 f3440bb 256a361 f3440bb 256a361 f3440bb 256a361 f3440bb 256a361 f3440bb 256a361 f3440bb 256a361 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 |
"""largely copy from llama and adapt for cogvlm"""
import warnings
from typing import TYPE_CHECKING, Optional, Tuple, List, Union, Literal, Dict, Any
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import CrossEntropyLoss
from torchvision import transforms
from einops import rearrange
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.utils.logging import get_logger
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from .configuration_cogvlm import CogVLMConfig
from .visual import EVA2CLIPModel
if TYPE_CHECKING:
from transformers.utils import ModelOutput
logger = get_logger(__name__)
LANGUAGE_TOKEN_TYPE = 0
VISION_TOKEN_TYPE = 1
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return (self.weight * hidden_states).to(input_dtype)
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
def get_expert_mask(token_type_ids: "torch.LongTensor(B, L)") -> "[torch.BoolTensor(B, L), torch.BoolTensor(B, L)]":
vision_token_mask = torch.zeros_like(token_type_ids, dtype=torch.bool)
vision_token_mask[:, :-1] = (token_type_ids[:, :-1] == VISION_TOKEN_TYPE) & (token_type_ids[:, 1:] == VISION_TOKEN_TYPE)
language_token_mask = ~vision_token_mask
return vision_token_mask, language_token_mask
class VisionExpertMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.language_mlp = MLP(config)
self.vision_mlp = MLP(config)
def forward(self, hidden_states: "torch.Tensor(B, L, D)", token_type_ids: "torch.LongTensor(B, L)"):
output = torch.empty(hidden_states.shape, dtype=hidden_states.dtype, device=hidden_states.device)
vision_token_mask, language_token_mask = get_expert_mask(token_type_ids)
output[vision_token_mask] = self.vision_mlp(hidden_states[vision_token_mask])
output[language_token_mask] = self.language_mlp(hidden_states[language_token_mask])
return output
def attention_fn(
query_layer: "torch.tensor(B, H, L, HD)",
key_layer: "torch.tensor(B, H, L, HD)",
value_layer: "torch.tensor(B, H, L, HD)",
attention_mask: "torch.tensor(B, H, L, HD)",
*,
scaling_attention_score: bool = True,
attention_dropout: nn.Module = None
):
attention_mask_bool = (attention_mask == 0)
is_low_triangle = (attention_mask_bool == torch.ones_like(attention_mask_bool, dtype=torch.float).tril()).all()
is_full = (attention_mask_bool > 0).all()
if not (int(torch.__version__.split('.')[0]) >= 2):
warnings.warn("It's recommended to use torch2.0 or higher.")
if int(torch.__version__.split('.')[0]) >= 2 and scaling_attention_score and (is_full or is_low_triangle):
dropout_p = 0. if attention_dropout is None or not attention_dropout.training else attention_dropout.p
return torch.nn.functional.scaled_dot_product_attention(
query_layer, key_layer, value_layer,
attn_mask=None,
dropout_p=dropout_p,
is_causal=not is_full
)
else:
if scaling_attention_score:
query_layer = query_layer / math.sqrt(query_layer.shape[-1])
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores + attention_mask
attention_scores = nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32).to(query_layer.dtype)
if attention_dropout is not None:
attention_scores = attention_dropout(attention_scores)
context_layer = torch.matmul(attention_scores, value_layer)
return context_layer
class RotaryEmbedding(torch.nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = self._compute_inv_freq(device)
self.register_buffer("inv_freq", inv_freq)
self.max_seq_len_cached = 0
def _compute_inv_freq(self, device=None):
return 1.0 / (
self.base
** (torch.arange(0, self.dim, 2, device=device) / self.dim)
)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos()[:, None, :].to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin()[:, None, :].to(dtype), persistent=False)
def forward(self, x, seq_len):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
return (
self.cos_cached[:seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:seq_len, ...].to(dtype=x.dtype),
)
def rotate_half(x):
x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=x1.ndim - 1)
def apply_rotary_pos_emb_index_bhs(q, k, cos, sin, position_id):
# batch_size, num_head, seq_len, hidden_size
cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(1), \
F.embedding(position_id, sin.squeeze(1)).unsqueeze(1)
q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
return q, k
class VisionExpertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.max_position_embeddings = config.max_position_embeddings
self.rotary_emb = RotaryEmbedding(self.head_dim)
self.vision_expert_query_key_value = nn.Linear(self.hidden_size, self.hidden_size * 3, bias=False)
self.vision_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.language_expert_query_key_value = nn.Linear(self.hidden_size, self.hidden_size * 3, bias=False)
self.language_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
def _transpose_for_scores(self, tensor):
"""Transpose a 3D tensor [B, L, H*HD] into a 4D tensor with size [B H L HD]."""
new_tensor_shape = tensor.size()[:-1] + (self.num_heads, self.head_dim)
tensor = tensor.view(*new_tensor_shape)
return tensor.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
token_type_ids: torch.LongTensor,
position_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
vision_token_mask, language_token_mask = get_expert_mask(token_type_ids)
shape = list(hidden_states.shape)
shape[-1] = shape[-1] * 3
mixed_raw_layer = torch.empty(shape, dtype=hidden_states.dtype, device=hidden_states.device)
mixed_raw_layer[vision_token_mask] = self.vision_expert_query_key_value(hidden_states[vision_token_mask])
mixed_raw_layer[language_token_mask] = self.language_expert_query_key_value(hidden_states[language_token_mask])
query_states, key_states, value_states = torch.split(mixed_raw_layer, self.hidden_size, dim=-1)
query_states = self._transpose_for_scores(query_states) # B, H, L, HD
key_states = self._transpose_for_scores(key_states) # B, H, L, HD
value_states = self._transpose_for_scores(value_states) # B, H, L, HD
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=position_ids.max() + 1)
query_states, key_states = apply_rotary_pos_emb_index_bhs(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
context_layer = attention_fn(
query_layer=query_states, key_layer=key_states, value_layer=value_states, attention_mask=attention_mask,
scaling_attention_score=True, attention_dropout=None)
if context_layer.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {context_layer.size()}"
)
context_layer = context_layer.transpose(1, 2).contiguous().reshape(bsz, q_len, self.hidden_size)
attn_output = torch.empty(context_layer.shape, dtype=hidden_states.dtype, device=hidden_states.device)
attn_output[vision_token_mask] = self.vision_expert_dense(context_layer[vision_token_mask])
attn_output[language_token_mask] = self.language_expert_dense(context_layer[language_token_mask])
if output_attentions:
warnings.warn("output_attentions is not implemented.")
return attn_output, None, past_key_value
class CogVLMDecoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = VisionExpertAttention(config=config)
self.mlp = VisionExpertMLP(config)
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
token_type_ids: torch.LongTensor,
position_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states, token_type_ids=token_type_ids)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs # type: ignore
class CogVLMPreTrainedModel(PreTrainedModel):
config_class = CogVLMConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
_no_split_modules = ["CogVLMDecoderLayer", "TransformerLayer"]
_skip_keys_device_placement = "past_key_values"
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def is_empty(images_list: Optional[List[List[torch.Tensor]]]):
if images_list is None or len(images_list) == 0:
return True
for image_list in images_list:
if len(image_list):
return False
return True
def build_position_ids(x: "torch.BoolTensor(B, L)", attention_mask: Optional["torch.BoolTensor(B, L)"] = None) -> "torch.LongTensor(B, L)":
if attention_mask is not None:
tmp = x.clone()
tmp[~(attention_mask.bool())] = -1
else:
tmp = x.clone()
# image boi eoi token as LANGUAGE_TOKEN_TYPE
is_boi_eoi = torch.zeros_like(x, dtype=torch.bool)
is_boi_eoi[:, 1:] |= (tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE)
is_boi_eoi[:, 0] |= (tmp[:, 0] == VISION_TOKEN_TYPE)
is_boi_eoi[:, :-1] |= (tmp[:, :-1] == VISION_TOKEN_TYPE) & (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE)
is_boi_eoi[:, -1] |= (tmp[:, -1] == VISION_TOKEN_TYPE)
tmp[is_boi_eoi] = LANGUAGE_TOKEN_TYPE
# final position ids
y = torch.zeros_like(x, dtype=torch.long)
y[:, 1:] = (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE) | ((tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE))
y = y.cumsum(dim=-1)
return y
class CogVLMModel(CogVLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList([CogVLMDecoderLayer(config) for _ in range(config.num_hidden_layers)])
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.vision = EVA2CLIPModel(config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def encode_images(self, images: List[List[torch.Tensor]]) -> torch.Tensor:
images_list, images = images, []
images = []
for image_list in images_list:
for image in image_list:
images.append(image)
images = torch.stack(images)
images_features = self.vision(images)
return images_features
def forward(
self,
input_ids: torch.LongTensor = None,
images: List[List[torch.Tensor]] = None,
token_type_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,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
"""take care of image_encode, token_type_ids, position_ids and (attention_mask = None is fine)"""
if past_key_values is not None:
pass # generate mode with past_key_values. the image features are already mapped
else:
# not allow for inputs_embeds, because we want to process image feature
assert input_ids is not None and inputs_embeds is None, f"{input_ids} {inputs_embeds}"
if not is_empty(images): # multi-modality
assert token_type_ids is not None, f"multi-modality requires `token_type_ids`!"
assert len(input_ids) == len(images), f"{len(input_ids)} {len(images)}"
inputs_embeds = self.embed_tokens(input_ids)
images_features = self.encode_images(images)
images_features = rearrange(images_features, 'b n d -> (b n) d')
images_features = images_features.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
inputs_embeds = inputs_embeds.index_put([token_type_ids == VISION_TOKEN_TYPE], images_features)
else: # single-modality
if token_type_ids is None:
token_type_ids = torch.ones_like(input_ids, dtype=torch.long, device=input_ids.device) * LANGUAGE_TOKEN_TYPE
assert not (token_type_ids == VISION_TOKEN_TYPE).any(), f"{(token_type_ids == VISION_TOKEN_TYPE).sum()}"
inputs_embeds = self.embed_tokens(input_ids)
if position_ids is None:
position_ids = build_position_ids(token_type_ids, attention_mask)
input_ids = None
return self.llm_forward(
input_ids=input_ids,
token_type_ids=token_type_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,
)
def llm_forward(
self,
input_ids: torch.LongTensor = None,
token_type_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,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
"""largely copy from llama forward and adapt for cogvlm with `token_type_ids`"""
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
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
layer_outputs = decoder_layer(
hidden_states,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
# noinspection PyMethodMayBeStatic
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def _history_to_prompt(signal_type, history, query):
if signal_type == 'base':
return query
elif signal_type == 'vqa':
answer_format = 'Short answer:'
elif signal_type == 'chat':
answer_format = 'Answer:'
else:
assert False, f"Unknown signal type {signal_type}"
prompt = ''
for i, (old_query, response) in enumerate(history):
prompt += 'Question: ' + old_query + " {} ".format(answer_format) + response + "\n"
prompt += 'Question: {} {}'.format(query, answer_format)
return prompt
class CogVLMForCausalLM(CogVLMPreTrainedModel):
_auto_class = "AutoModelForCausalLM"
def __init__(self, config):
super().__init__(config)
self.model = CogVLMModel(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()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
images: List[List[torch.Tensor]] = None,
token_type_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,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = 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
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
images=images,
token_type_ids=token_type_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,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
logits = logits.float()
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
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,
)
def _prepare_attention_mask_for_generation(
self,
inputs: torch.Tensor,
pad_token_id: Optional[int],
eos_token_id: Optional[Union[int, List[int]]],
) -> torch.LongTensor:
return torch.ones(inputs.shape[:2], dtype=torch.long, device=inputs.device) # type: ignore
def prepare_inputs_for_generation(
self, input_ids, token_type_ids, images=None, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
# build position_ids if needed
position_ids = kwargs.get("position_ids", None)
if position_ids is None:
position_ids = build_position_ids(token_type_ids, attention_mask)
if past_key_values:
input_ids = input_ids[:, -1:]
token_type_ids = token_type_ids[:, -1:]
position_ids = position_ids[:, -1:]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"token_type_ids": token_type_ids,
"images": images,
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs
def _update_model_kwargs_for_generation(
self,
outputs: "ModelOutput",
model_kwargs: Dict[str, Any],
is_encoder_decoder: bool = False,
standardize_cache_format: bool = False,
) -> Dict[str, Any]:
# update past_key_values
model_kwargs["past_key_values"] = self._extract_past_from_model_output(
outputs, standardize_cache_format=standardize_cache_format
)
if getattr(outputs, "state", None) is not None:
model_kwargs["state"] = outputs.state
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
new_token_type_ids = torch.ones(size=(token_type_ids.shape[0], 1), dtype=token_type_ids.dtype,
device=token_type_ids.device) * LANGUAGE_TOKEN_TYPE
model_kwargs["token_type_ids"] = torch.cat([token_type_ids, new_token_type_ids], dim=-1)
if not is_encoder_decoder:
# update attention mask
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
)
else:
# update decoder attention mask
if "decoder_attention_mask" in model_kwargs:
decoder_attention_mask = model_kwargs["decoder_attention_mask"]
model_kwargs["decoder_attention_mask"] = torch.cat(
[decoder_attention_mask, decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1))],
dim=-1,
)
return model_kwargs
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
def build_conversation_input_ids(
self,
tokenizer: "PreTrainedTokenizer",
*,
query: str,
history: Optional[List[Tuple[str, str]]] = None,
images: Optional[List["PIL.Image"]] = None,
template_version: Optional[Literal["base", "chat", "vqa"]] = None,
):
image_size: int = self.config.vision_config['image_size']
patch_size: int = self.config.vision_config['patch_size']
template_version = template_version or self.config.template_version
assert images is None or len(images) <= 1, f"not support multi images by now."
history = history or []
text = _history_to_prompt(template_version, history, query)
input_ids = [tokenizer.bos_token_id]
token_type_ids = [LANGUAGE_TOKEN_TYPE]
if images is not None and len(images) == 1:
# vision
transform = transforms.Compose(
[
transforms.Resize(
(image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC
),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
]
)
images = [transform(images[0])]
# language
vision_token_num = (image_size // patch_size) * (image_size // patch_size) + 2
input_ids += [tokenizer.pad_token_id] * vision_token_num
token_type_ids += [VISION_TOKEN_TYPE] * vision_token_num
text_ids = tokenizer.encode(text, add_special_tokens=False)
input_ids += text_ids
token_type_ids += [LANGUAGE_TOKEN_TYPE] * len(text_ids)
attention_mask = [1] * len(input_ids)
return {
'input_ids': torch.tensor(input_ids, dtype=torch.long),
'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),
'attention_mask': torch.tensor(attention_mask, dtype=torch.long),
'images': images,
}
|