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from transformers import AutoModel, AutoTokenizer, AutoConfig, PreTrainedModel
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from dataclasses import dataclass
from .configuration import GECToRConfig
from typing import List, Union, Optional, Tuple
import os
import json
from huggingface_hub import snapshot_download, ModelCard
@dataclass
class GECToROutput:
loss: torch.Tensor = None
loss_d: torch.Tensor = None
loss_labels: torch.Tensor = None
logits_d: torch.Tensor = None
logits_labels: torch.Tensor = None
accuracy: torch.Tensor = None
accuracy_d: torch.Tensor = None
@dataclass
class GECToRPredictionOutput:
probability_labels: torch.Tensor = None
probability_d: torch.Tensor = None
pred_labels: List[List[str]] = None
pred_label_ids: torch.Tensor = None
max_error_probability: torch.Tensor = None
class GECToR(PreTrainedModel):
config_class = GECToRConfig
def __init__(
self,
config: GECToRConfig
):
super().__init__(config)
self.config = config
self.tokenizer = AutoTokenizer.from_pretrained(
self.config.model_id
)
if self.config.has_add_pooling_layer:
self.bert = AutoModel.from_pretrained(
self.config.model_id,
add_pooling_layer=False
)
else:
self.bert = AutoModel.from_pretrained(
self.config.model_id
)
# +1 is for $START token
self.bert.resize_token_embeddings(self.bert.config.vocab_size + 1)
self.label_proj_layer = nn.Linear(
self.bert.config.hidden_size,
self.config.num_labels - 1
) # -1 is for <PAD>
self.d_proj_layer = nn.Linear(
self.bert.config.hidden_size,
self.config.d_num_labels - 1
)
self.dropout = nn.Dropout(self.config.p_dropout)
self.loss_fn = CrossEntropyLoss(
label_smoothing=self.config.label_smoothing
)
self.post_init()
self.tune_bert(False)
def init_weight(self) -> None:
self._init_weights(self.label_proj_layer)
self._init_weights(self.d_proj_layer)
def _init_weights(self, module) -> None:
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(
mean=0.0,
std=self.config.initializer_range
)
if module.bias is not None:
module.bias.data.zero_()
return
def tune_bert(self, tune=True):
# If tune=False, only classifier layers will be tuned.
for param in self.bert.parameters():
param.requires_grad = tune
return
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,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
d_labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
word_masks: Optional[torch.Tensor] = None,
) -> GECToROutput:
bert_logits = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
).last_hidden_state
logits_d = self.d_proj_layer(bert_logits)
logits_labels = self.label_proj_layer(self.dropout(bert_logits))
loss_d, loss_labels, loss = None, None, None
accuracy, accuracy_d = None, None
if d_labels is not None and labels is not None:
pad_id = self.config.label2id[self.config.label_pad_token]
# -100 is the default ignore_idx of CrossEntropyLoss
labels[labels == pad_id] = -100
d_labels[labels == -100] = -100
loss_d = self.loss_fn(
logits_d.view(-1, self.config.d_num_labels - 1), # -1 for <PAD>
d_labels.view(-1)
)
loss_labels = self.loss_fn(
logits_labels.view(-1, self.config.num_labels - 1),
labels.view(-1)
)
loss = loss_d + loss_labels
pred_labels = torch.argmax(logits_labels, dim=-1)
accuracy = torch.sum(
(labels == pred_labels) * word_masks
) / torch.sum(word_masks)
pred_d = torch.argmax(logits_d, dim=-1)
accuracy_d = torch.sum(
(d_labels == pred_d) * word_masks
) / torch.sum(word_masks)
return GECToROutput(
loss=loss,
loss_d=loss_d,
loss_labels=loss_labels,
logits_d=logits_d,
logits_labels=logits_labels,
accuracy=accuracy,
accuracy_d=accuracy_d
)
def predict(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
word_masks: torch.Tensor,
keep_confidence: float=0,
min_error_prob: float=0
):
with torch.no_grad():
outputs = self.forward(
input_ids,
attention_mask
)
probability_labels = F.softmax(outputs.logits_labels, dim=-1)
probability_d = F.softmax(outputs.logits_d, dim=-1)
# Get actual labels considering inference parameters.
keep_index = self.config.label2id[self.config.keep_label]
probability_labels[:, :, keep_index] += keep_confidence
incor_idx = self.config.d_label2id[self.config.incorrect_label]
probability_d = probability_d[:, :, incor_idx]
max_error_probability = torch.max(probability_d * word_masks, dim=-1)[0]
probability_labels[max_error_probability < min_error_prob, :, keep_index] \
= float('inf')
pred_label_ids = torch.argmax(probability_labels, dim=-1)
def convert_ids_to_labels(ids, id2label):
labels = []
for id in ids.tolist():
labels.append(id2label[id])
return labels
pred_labels = []
for ids in pred_label_ids:
labels = convert_ids_to_labels(
ids,
self.config.id2label
)
pred_labels.append(labels)
return GECToRPredictionOutput(
probability_labels=probability_labels,
probability_d=probability_d,
pred_labels=pred_labels,
pred_label_ids=pred_label_ids,
max_error_probability=max_error_probability
)
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