187 lines
7.1 KiB
Python
187 lines
7.1 KiB
Python
import logging
|
|
from typing import Optional, Tuple, Union
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import MSELoss, CrossEntropyLoss, BCEWithLogitsLoss
|
|
from transformers import GPT2Model, GPT2ForSequenceClassification
|
|
from transformers.modeling_outputs import SequenceClassifierOutputWithPast
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class GPT2ClassificationHeadCustomFIX(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
hidden_size = config.n_embd
|
|
self.dense_1_input = nn.Linear(hidden_size, 2 * hidden_size)
|
|
self.dense_1_hidden = nn.Linear(hidden_size, 2 * hidden_size)
|
|
|
|
self.dense_2 = nn.Linear(2 * hidden_size, 2 * hidden_size)
|
|
self.dense_2_hidden = nn.Linear(hidden_size, 2 * hidden_size)
|
|
|
|
self.dense_3 = nn.Linear(2 * hidden_size, 2 * hidden_size)
|
|
self.dense_3_hidden = nn.Linear(hidden_size, 2 * hidden_size)
|
|
|
|
self.dense_4 = nn.Linear(2 * hidden_size, hidden_size)
|
|
|
|
self.dropout = nn.Dropout(config.resid_pdrop)
|
|
self.out_proj = nn.Linear(hidden_size, config.num_labels, bias=False)
|
|
|
|
def forward(self, x, **kwargs):
|
|
if 'hidden_states' in kwargs and kwargs['hidden_states'] is not None:
|
|
hidden_a = torch.cat(kwargs['hidden_states'][-2:])
|
|
else:
|
|
hidden_a = torch.zeros(x.size(), dtype=x.dtype, device=x.device)
|
|
|
|
x = self.dense_1_input(x)
|
|
x = torch.relu(x)
|
|
x = self.dropout(x)
|
|
|
|
hidden = self.dense_1_hidden(hidden_a)
|
|
hidden = torch.relu(hidden)
|
|
hidden = self.dropout(hidden)
|
|
|
|
x = torch.cat((x, hidden))
|
|
|
|
x = self.dense_2(x)
|
|
x = torch.relu(x)
|
|
x = self.dropout(x)
|
|
|
|
hidden = self.dense_2_hidden(hidden_a)
|
|
hidden = torch.relu(hidden)
|
|
hidden = self.dropout(hidden)
|
|
|
|
x = torch.cat((x, hidden))
|
|
|
|
x = self.dense_3(x)
|
|
x = torch.relu(x)
|
|
x = self.dropout(x)
|
|
|
|
hidden = self.dense_3_hidden(hidden_a)
|
|
hidden = torch.relu(hidden)
|
|
hidden = self.dropout(hidden)
|
|
|
|
x = torch.cat((x, hidden))
|
|
|
|
x = self.dense_4(x)
|
|
x = torch.relu(x)
|
|
x = self.dropout(x)
|
|
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
|
|
class GPT2ForSequenceClassificationCustomFIX(GPT2ForSequenceClassification):
|
|
_keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.transformer = GPT2Model(config)
|
|
self.score = GPT2ClassificationHeadCustomFIX(config)
|
|
|
|
# Model parallel
|
|
self.model_parallel = False
|
|
self.device_map = None
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = 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,
|
|
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states or self.config.use_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
logits = self.score(hidden_states, hidden_states=transformer_outputs.hidden_states)
|
|
|
|
if input_ids is not None:
|
|
batch_size, sequence_length = input_ids.shape[:2]
|
|
else:
|
|
batch_size, sequence_length = inputs_embeds.shape[:2]
|
|
|
|
assert (
|
|
self.config.pad_token_id is not None or batch_size == 1
|
|
), "Cannot handle batch sizes > 1 if no padding token is defined."
|
|
if self.config.pad_token_id is None:
|
|
sequence_lengths = -1
|
|
else:
|
|
if input_ids is not None:
|
|
sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
|
|
else:
|
|
sequence_lengths = -1
|
|
logger.warning(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(pooled_logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(pooled_logits, labels)
|
|
if not return_dict:
|
|
output = (pooled_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|