File size: 12,242 Bytes
a277bb8 |
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 |
# ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
import torch
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from torch import Tensor, nn
from torchvision.ops.boxes import nms
from transformers import BertConfig, BertModel, BertPreTrainedModel
from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
class BertModelWarper(nn.Module):
def __init__(self, bert_model):
super().__init__()
# self.bert = bert_modelc
self.config = bert_model.config
self.embeddings = bert_model.embeddings
self.encoder = bert_model.encoder
self.pooler = bert_model.pooler
self.get_extended_attention_mask = bert_model.get_extended_attention_mask
self.invert_attention_mask = bert_model.invert_attention_mask
self.get_head_mask = bert_model.get_head_mask
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
"""
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.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
batch_size, seq_length = input_shape
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size, seq_length = input_shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = (
past_key_values[0][0].shape[2] if past_key_values is not None else 0
)
if attention_mask is None:
attention_mask = torch.ones(
((batch_size, seq_length + past_key_values_length)), device=device
)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
# import ipdb; ipdb.set_trace()
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
class TextEncoderShell(nn.Module):
def __init__(self, text_encoder):
super().__init__()
self.text_encoder = text_encoder
self.config = self.text_encoder.config
def forward(self, **kw):
# feed into text encoder
return self.text_encoder(**kw)
def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
"""Generate attention mask between each pair of special tokens
Args:
input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
special_tokens_mask (list): special tokens mask.
Returns:
torch.Tensor: attention mask between each special tokens.
"""
input_ids = tokenized["input_ids"]
bs, num_token = input_ids.shape
# special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
for special_token in special_tokens_list:
special_tokens_mask |= input_ids == special_token
# idxs: each row is a list of indices of special tokens
idxs = torch.nonzero(special_tokens_mask)
# generate attention mask and positional ids
attention_mask = (
torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
)
position_ids = torch.zeros((bs, num_token), device=input_ids.device)
previous_col = 0
for i in range(idxs.shape[0]):
row, col = idxs[i]
if (col == 0) or (col == num_token - 1):
attention_mask[row, col, col] = True
position_ids[row, col] = 0
else:
attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
position_ids[row, previous_col + 1 : col + 1] = torch.arange(
0, col - previous_col, device=input_ids.device
)
previous_col = col
# # padding mask
# padding_mask = tokenized['attention_mask']
# attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
return attention_mask, position_ids.to(torch.long)
def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
"""Generate attention mask between each pair of special tokens
Args:
input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
special_tokens_mask (list): special tokens mask.
Returns:
torch.Tensor: attention mask between each special tokens.
"""
input_ids = tokenized["input_ids"]
bs, num_token = input_ids.shape
# special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
for special_token in special_tokens_list:
special_tokens_mask |= input_ids == special_token
# idxs: each row is a list of indices of special tokens
idxs = torch.nonzero(special_tokens_mask)
# generate attention mask and positional ids
attention_mask = (
torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
)
position_ids = torch.zeros((bs, num_token), device=input_ids.device)
cate_to_token_mask_list = [[] for _ in range(bs)]
previous_col = 0
for i in range(idxs.shape[0]):
row, col = idxs[i]
if (col == 0) or (col == num_token - 1):
attention_mask[row, col, col] = True
position_ids[row, col] = 0
else:
attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
position_ids[row, previous_col + 1 : col + 1] = torch.arange(
0, col - previous_col, device=input_ids.device
)
c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
c2t_maski[previous_col + 1 : col] = True
cate_to_token_mask_list[row].append(c2t_maski)
previous_col = col
cate_to_token_mask_list = [
torch.stack(cate_to_token_mask_listi, dim=0)
for cate_to_token_mask_listi in cate_to_token_mask_list
]
# # padding mask
# padding_mask = tokenized['attention_mask']
# attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list
|