File size: 10,583 Bytes
f8db31b c36a6f3 f8db31b c36a6f3 |
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 |
import torch
import numpy as np
import random
import os
# 1. Set random seeds
seed = 2025
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
# 2. Disable dropout & training randomness
torch.use_deterministic_algorithms(True, warn_only=True)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
from transformers.modeling_outputs import TokenClassifierOutput
import torch
import torch.nn as nn
from transformers import PreTrainedModel, AutoModel, AutoConfig, BertConfig
from torch.nn import CrossEntropyLoss
from typing import Optional, Tuple, Union
import logging, json, os
from .configuration_stacked import ImpressoConfig
logger = logging.getLogger(__name__)
def get_info(label_map):
num_token_labels_dict = {task: len(labels) for task, labels in label_map.items()}
return num_token_labels_dict
class ExtendedMultitaskTimeModelForTokenClassification(PreTrainedModel):
config_class = ImpressoConfig
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config, temporal_fusion_strategy="baseline", num_years=327):
super().__init__(config)
self.num_token_labels_dict = get_info(config.label_map)
self.config = config
self.temporal_fusion_strategy = temporal_fusion_strategy
self.model = AutoModel.from_pretrained(
config.pretrained_config["_name_or_path"], config=config.pretrained_config
)
self.model.config.use_cache = False
self.model.config.pretraining_tp = 1
self.num_years = num_years
classifier_dropout = getattr(config, "classifier_dropout", 0.1) or config.hidden_dropout_prob
self.dropout = nn.Dropout(classifier_dropout)
self.temporal_fusion = TemporalFusion(config.hidden_size, strategy=self.temporal_fusion_strategy,
num_years=num_years)
# Additional transformer layers
self.transformer_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=config.hidden_size, nhead=config.num_attention_heads
),
num_layers=2,
)
self.token_classifiers = nn.ModuleDict({
task: nn.Linear(config.hidden_size, num_labels)
for task, num_labels in self.num_token_labels_dict.items()
})
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
token_labels: Optional[dict] = None,
date_indices: Optional[torch.Tensor] = None,
year_index: Optional[torch.Tensor] = None,
decade_index: Optional[torch.Tensor] = None,
century_index: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if inputs_embeds is None:
inputs_embeds = self.model.embeddings(input_ids)
# Early cross-attention fusion
if self.temporal_fusion_strategy == "early-cross-attention":
year_emb = self.temporal_fusion.compute_time_embedding(year_index) # (B, H)
inputs_embeds = self.temporal_fusion.cross_attn(inputs_embeds, year_emb)
bert_kwargs = {
"inputs_embeds": inputs_embeds if self.temporal_fusion_strategy == "early-cross-attention" else None,
"input_ids": input_ids if self.temporal_fusion_strategy != "early-cross-attention" else None,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"position_ids": position_ids,
"head_mask": head_mask,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
if any(keyword in self.config.name_or_path.lower() for keyword in ["llama", "deberta"]):
bert_kwargs.pop("token_type_ids", None)
bert_kwargs.pop("head_mask", None)
outputs = self.model(**bert_kwargs)
token_output = self.dropout(outputs[0]) # (B, T, H)
hidden_states = list(outputs.hidden_states) if output_hidden_states else None
# Pass through additional transformer layers
token_output = self.transformer_encoder(token_output.transpose(0, 1)).transpose(
0, 1
)
# Apply fusion after transformer if needed
if self.temporal_fusion_strategy not in ["baseline", "early-cross-attention"]:
token_output = self.temporal_fusion(token_output, year_index)
if output_hidden_states:
hidden_states.append(token_output) # add the final fused state
task_logits = {}
total_loss = 0
for task, classifier in self.token_classifiers.items():
logits = classifier(token_output)
task_logits[task] = logits
if token_labels and task in token_labels:
loss_fct = CrossEntropyLoss()
loss = loss_fct(
logits.view(-1, self.num_token_labels_dict[task]),
token_labels[task].view(-1),
)
total_loss += loss
if not return_dict:
output = (task_logits,) + outputs[2:]
return ((total_loss,) + output) if total_loss != 0 else output
return TokenClassifierOutput(
loss=total_loss,
logits=task_logits,
hidden_states=tuple(hidden_states) if hidden_states is not None else None,
attentions=outputs.attentions if output_attentions else None,
)
class TemporalFusion(nn.Module):
def __init__(self, hidden_size, strategy="add", num_years=327, min_year=1700):
super().__init__()
self.strategy = strategy
self.hidden_size = hidden_size
self.min_year = min_year
self.max_year = min_year + num_years - 1
self.year_emb = nn.Embedding(num_years, hidden_size)
if strategy == "concat":
self.concat_proj = nn.Linear(hidden_size * 2, hidden_size)
elif strategy == "film":
self.film_gamma = nn.Linear(hidden_size, hidden_size)
self.film_beta = nn.Linear(hidden_size, hidden_size)
elif strategy == "adapter":
self.adapter = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, hidden_size),
)
elif strategy == "relative":
self.relative_encoder = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.SiLU(),
nn.LayerNorm(hidden_size),
)
self.film_gamma = nn.Linear(hidden_size, hidden_size)
self.film_beta = nn.Linear(hidden_size, hidden_size)
elif strategy == "multiscale":
self.decade_emb = nn.Embedding(1000, hidden_size)
self.century_emb = nn.Embedding(100, hidden_size)
elif strategy in ["early-cross-attention", "late-cross-attention"]:
self.year_encoder = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.SiLU()
)
self.cross_attn = TemporalCrossAttention(hidden_size)
def compute_time_embedding(self, year_index):
if self.strategy in ["early-cross-attention", "late-cross-attention"]:
return self.year_encoder(self.year_emb(year_index))
elif self.strategy == "multiscale":
year_index = year_index.long()
year = year_index + self.min_year
decade = (year // 10).long()
century = (year // 100).long()
return (
self.year_emb(year_index) +
self.decade_emb(decade) +
self.century_emb(century)
)
else:
return self.year_emb(year_index)
def forward(self, token_output, year_index):
B, T, H = token_output.size()
if self.strategy == "baseline":
return token_output
year_emb = self.compute_time_embedding(year_index)
if self.strategy == "concat":
expanded_year = year_emb.unsqueeze(1).repeat(1, T, 1)
fused = torch.cat([token_output, expanded_year], dim=-1)
return self.concat_proj(fused)
elif self.strategy == "film":
gamma = self.film_gamma(year_emb).unsqueeze(1)
beta = self.film_beta(year_emb).unsqueeze(1)
return gamma * token_output + beta
elif self.strategy == "adapter":
return token_output + self.adapter(year_emb).unsqueeze(1)
elif self.strategy == "add":
expanded_year = year_emb.unsqueeze(1).repeat(1, T, 1)
return token_output + expanded_year
elif self.strategy == "relative":
encoded = self.relative_encoder(year_emb)
gamma = self.film_gamma(encoded).unsqueeze(1)
beta = self.film_beta(encoded).unsqueeze(1)
return gamma * token_output + beta
elif self.strategy == "multiscale":
expanded_year = year_emb.unsqueeze(1).expand(-1, T, -1)
return token_output + expanded_year
elif self.strategy == "late-cross-attention":
return self.cross_attn(token_output, year_emb)
else:
raise ValueError(f"Unknown fusion strategy: {self.strategy}")
class TemporalCrossAttention(nn.Module):
def __init__(self, hidden_size, num_heads=4):
super().__init__()
self.attn = nn.MultiheadAttention(embed_dim=hidden_size, num_heads=num_heads, batch_first=True)
def forward(self, token_output, time_embedding):
# token_output: (B, T, H), time_embedding: (B, H)
time_as_seq = time_embedding.unsqueeze(1) # (B, 1, H)
attn_output, _ = self.attn(token_output, time_as_seq, time_as_seq)
return token_output + attn_output
|