File size: 8,284 Bytes
239ee0c |
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 |
from typing import Any, Dict, Optional
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModel, PreTrainedModel
from transformers.modeling_outputs import (
BaseModelOutputWithPooling,
MaskedLMOutput,
BaseModelOutput,
SequenceClassifierOutput,
)
from enum import Enum
from .config import ILKTConfig
def cls_pooling(last_hidden_state, attention_mask):
return last_hidden_state[:, 0, :]
def create_head_blocks(
hidden_size: int,
n_dense: int,
use_batch_norm: bool,
use_layer_norm: bool,
dropout: float,
**kwargs,
) -> nn.Module:
blocks = []
for _ in range(n_dense):
blocks.append(nn.Linear(hidden_size, hidden_size))
if use_batch_norm:
blocks.append(nn.BatchNorm1d(hidden_size))
elif use_layer_norm:
blocks.append(nn.LayerNorm(hidden_size))
blocks.append(nn.ReLU())
if dropout > 0:
blocks.append(nn.Dropout(dropout))
return nn.Sequential(*blocks)
class SentenceEmbeddingHead(nn.Module):
def __init__(
self, backbone_hidden_size: int, embedding_head_config: Dict[str, Any]
):
super().__init__()
self.config = embedding_head_config
self.head = nn.Sequential(
*[
create_head_blocks(backbone_hidden_size, **embedding_head_config),
]
)
def forward(
self, backbone_output: BaseModelOutput, attention_mask: torch.Tensor, **kwargs
) -> BaseModelOutputWithPooling:
if self.config["pool_type"] == "cls":
embeddings = cls_pooling(backbone_output.last_hidden_state, attention_mask)
else:
raise NotImplementedError(
f"Pooling type {self.config['pool_type']} not implemented"
)
if self.config["normalize_embeddings"]:
embeddings = nn.functional.normalize(embeddings, p=2, dim=-1)
return BaseModelOutputWithPooling(
last_hidden_state=backbone_output.last_hidden_state,
pooler_output=embeddings, # type: ignore
)
class MLMHead(nn.Module):
def __init__(
self,
backbone_hidden_size: int,
vocab_size: int,
mlm_head_config: Dict[str, Any],
):
super().__init__()
self.config = mlm_head_config
self.head = nn.Sequential(
*[
create_head_blocks(backbone_hidden_size, **mlm_head_config),
nn.Linear(backbone_hidden_size, vocab_size),
]
)
def forward(
self,
backbone_output: BaseModelOutput,
attention_mask: torch.Tensor,
labels: Optional[torch.Tensor] = None,
**kwargs,
) -> MaskedLMOutput:
prediction_scores = self.head(backbone_output.last_hidden_state)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(
prediction_scores.view(-1, prediction_scores.size(-1)),
labels.view(-1),
)
return MaskedLMOutput(loss=loss, logits=prediction_scores)
class CLSHead(nn.Module):
def __init__(
self,
backbone_hidden_size: int,
n_classes: int,
cls_head_config: Dict[str, Any],
):
super().__init__()
self.config = cls_head_config
self.head = nn.Sequential(
*[
create_head_blocks(backbone_hidden_size, **cls_head_config),
nn.Linear(backbone_hidden_size, n_classes),
]
)
def forward(
self,
backbone_output: BaseModelOutput,
attention_mask: torch.Tensor,
labels: Optional[torch.Tensor] = None,
**kwargs,
) -> SequenceClassifierOutput:
if self.config["pool_type"] == "cls":
embeddings = cls_pooling(backbone_output.last_hidden_state, attention_mask)
else:
raise NotImplementedError(
f"Pooling type {self.config['pool_type']} not implemented"
)
prediction_scores = self.head(embeddings)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(
prediction_scores.view(-1, prediction_scores.size(-1)),
labels.view(-1),
)
return SequenceClassifierOutput(loss=loss, logits=prediction_scores)
class ForwardRouting(Enum):
GET_SENTENCE_EMBEDDING = "get_sentence_embedding"
GET_MLM_OUTPUT = "get_mlm_output"
GET_CLS_OUTPUT = "get_cls_output"
class ILKTModel(PreTrainedModel):
config_class = ILKTConfig
def __init__(self, config: ILKTConfig):
super().__init__(config)
backbone_config = AutoConfig.from_pretrained(**config.backbone_config)
pretrained_model_name_or_path = config.backbone_config[
"pretrained_model_name_or_path"
]
self.backbone = AutoModel.from_pretrained(
pretrained_model_name_or_path, config=backbone_config
)
backbone_hidden_size = backbone_config.hidden_size
self.config.hidden_size = backbone_hidden_size
backbone_vocab_size = backbone_config.vocab_size
self.embedding_head = SentenceEmbeddingHead(
backbone_hidden_size, config.embedding_head_config
)
self.mlm_head = MLMHead(
backbone_hidden_size, backbone_vocab_size, config.mlm_head_config
)
self.cls_heads = nn.ModuleDict(
dict(
[
(
name,
CLSHead(
backbone_hidden_size, n_classes, config.cls_head_config
),
)
for n_classes, name in config.cls_heads
]
)
)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
token_type_ids: Optional[torch.Tensor] = None,
forward_routing: ForwardRouting = ForwardRouting.GET_SENTENCE_EMBEDDING,
**kwargs,
):
if forward_routing == ForwardRouting.GET_SENTENCE_EMBEDDING:
return self.get_sentence_embedding(
input_ids, attention_mask, token_type_ids=token_type_ids
)
elif forward_routing == ForwardRouting.GET_MLM_OUTPUT:
return self.get_mlm_output(
input_ids, attention_mask, token_type_ids=token_type_ids, **kwargs
)
elif forward_routing == ForwardRouting.GET_CLS_OUTPUT:
return self.get_cls_output(
input_ids, attention_mask, token_type_ids=token_type_ids, **kwargs
)
else:
raise ValueError(f"Unknown forward routing {forward_routing}")
def get_sentence_embedding(
self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **kwargs
):
backbone_output: BaseModelOutput = self.backbone(
input_ids=input_ids, attention_mask=attention_mask, **kwargs
)
embedding_output = self.embedding_head(
backbone_output, attention_mask, **kwargs
)
return embedding_output
def get_mlm_output(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
labels: Optional[torch.Tensor] = None,
**kwargs,
):
backbone_output: BaseModelOutput = self.backbone(
input_ids=input_ids, attention_mask=attention_mask, **kwargs
)
mlm_output = self.mlm_head(backbone_output, attention_mask, labels, **kwargs)
return mlm_output
def get_cls_output(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
head_name: str,
labels: Optional[torch.Tensor] = None,
**kwargs,
):
backbone_output: BaseModelOutput = self.backbone(
input_ids=input_ids, attention_mask=attention_mask, **kwargs
)
if head_name not in self.cls_heads:
raise ValueError(f"Head {head_name} not found in model")
cls_output = self.cls_heads[head_name](
backbone_output, attention_mask, labels, **kwargs
)
return cls_output |