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import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer, PreTrainedModel
from .config import SRLModelConfig
class SRLModel(PreTrainedModel):
config_class = SRLModelConfig
def __init__(self, config):
super().__init__(config)
print(config.num_labels, config.bert_model_name, config.embedding_dropout)
# Load pre-trained transformer-based model and tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(config.bert_model_name)
self.transformer = AutoModel.from_pretrained(
config.bert_model_name,
num_labels=config.num_labels,
output_hidden_states=True,
)
self.transformer.config.id2label = config.id2label
self.transformer.config.label2id = config.label2id
# The roberta models do not have token_type_embeddings
# (the type_vocab_size is 1)
# but we use this to pass the verb's position
# so we need to change the model and initialize the embeddings randomly
if "xlm" in config.bert_model_name or "roberta" in config.bert_model_name:
self.transformer.config.type_vocab_size = 2
# Create a new Embeddings layer, with 2 possible segments IDs instead of 1
self.transformer.embeddings.token_type_embeddings = nn.Embedding(
2, self.transformer.config.hidden_size
)
# Initialize it
self.transformer.embeddings.token_type_embeddings.weight.data.normal_(
mean=0.0, std=self.transformer.config.initializer_range
)
# Linear layer for tag projection
self.tag_projection_layer = nn.Linear(
self.transformer.config.hidden_size, config.num_labels
)
# Dropout layer for embeddings
self.embedding_dropout = nn.Dropout(p=config.embedding_dropout)
# Number of labels
self.num_labels = config.num_labels
def forward(self, input_ids, attention_mask, token_type_ids, labels=None):
# print("FORWARD")
# print(labels)
# Forward pass through the transformer model
# Returns BaseModelOutputWithPoolingAndCrossAttentions
outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
)
# Extract the [CLS] token representation
# cls_output = outputs.pooler_output
bert_embedding = outputs.last_hidden_state
# Apply dropout to the embeddings
embedded_text_input = self.embedding_dropout(bert_embedding)
# Project to tag space
logits = self.tag_projection_layer(embedded_text_input)
reshaped_log_probs = logits.view(-1, self.num_labels)
class_probabilities = F.softmax(reshaped_log_probs, dim=-1).view(
logits.size(0), logits.size(1), -1
)
output_dict = {"logits": logits, "class_probabilities": class_probabilities}
output_dict["attention_mask"] = attention_mask
output_dict["input_ids"] = input_ids
# output_dict["start_offsets"] = start_offsets
if labels is not None:
# print("Input", logits.view(-1, self.num_labels).size())
# print("Target", labels.view(-1).size())
# print("C", self.num_labels)
# print("AllenNLP function", logits.size(-1))
# Could consider passing ignore_index as 0 (pad index) for minor optimization
loss = nn.CrossEntropyLoss(ignore_index=-100)(
logits.view(-1, self.num_labels), labels.view(-1)
)
output_dict["loss"] = loss
return output_dict
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