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import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import BertConfig, BertModel, BertPreTrainedModel, RobertaConfig
# from transformers.modeling_bert import BertLayerNorm, BertOnlyMLMHead
from transformers.models.bert.modeling_bert import BertOnlyMLMHead
BertLayerNorm = torch.nn.LayerNorm
logger = logging.getLogger(__name__)
LAYOUTLMV1_PRETRAINED_MODEL_ARCHIVE_MAP = {}
LAYOUTLMV1_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class Layoutlmv1Config_roberta(RobertaConfig):
pretrained_config_archive_map = LAYOUTLMV1_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "bert"
def __init__(self, max_2d_position_embeddings=1024, add_linear=False, **kwargs):
super().__init__(**kwargs)
self.max_2d_position_embeddings = max_2d_position_embeddings
self.add_linear = add_linear # determine whether to add an additional mapping
class Layoutlmv1Config(BertConfig):
pretrained_config_archive_map = LAYOUTLMV1_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "bert"
def __init__(self, max_2d_position_embeddings=1024, add_linear=False, **kwargs):
super().__init__(**kwargs)
self.max_2d_position_embeddings = max_2d_position_embeddings
self.add_linear = add_linear # determine whether to add an additional mapping
class WebConfig:
max_depth = 50
xpath_unit_hidden_size = 32
hidden_size = 768
hidden_dropout_prob = 0.1
layer_norm_eps = 1e-12
max_xpath_tag_unit_embeddings = 256
max_xpath_subs_unit_embeddings = 1024
class XPathEmbeddings(nn.Module):
"""Construct the embddings from xpath -- tag and subscript"""
# we drop tree-id in this version, as its info can be covered by xpath
def __init__(self, config):
super(XPathEmbeddings, self).__init__()
config = WebConfig()
self.max_depth = config.max_depth
self.xpath_unitseq2_embeddings = nn.Linear(
config.xpath_unit_hidden_size * self.max_depth, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.activation = nn.ReLU()
self.xpath_unitseq2_inner = nn.Linear(config.xpath_unit_hidden_size * self.max_depth, 4 * config.hidden_size)
self.inner2emb = nn.Linear(4 * config.hidden_size, config.hidden_size)
self.xpath_tag_sub_embeddings = nn.ModuleList(
[nn.Embedding(config.max_xpath_tag_unit_embeddings, config.xpath_unit_hidden_size) for _ in
range(self.max_depth)])
self.xpath_subs_sub_embeddings = nn.ModuleList(
[nn.Embedding(config.max_xpath_subs_unit_embeddings, config.xpath_unit_hidden_size) for _ in
range(self.max_depth)])
def forward(self,
xpath_tags_seq=None,
xpath_subs_seq=None):
xpath_tags_embeddings = []
xpath_subs_embeddings = []
for i in range(self.max_depth):
xpath_tags_embeddings.append(self.xpath_tag_sub_embeddings[i](xpath_tags_seq[:, :, i]))
xpath_subs_embeddings.append(self.xpath_subs_sub_embeddings[i](xpath_subs_seq[:, :, i]))
xpath_tags_embeddings = torch.cat(xpath_tags_embeddings, dim=-1)
xpath_subs_embeddings = torch.cat(xpath_subs_embeddings, dim=-1)
xpath_embeddings = xpath_tags_embeddings + xpath_subs_embeddings
xpath_embeddings = self.inner2emb(
self.dropout(self.activation(self.xpath_unitseq2_inner(xpath_embeddings))))
return xpath_embeddings
class Layoutlmv1Embeddings(nn.Module):
def __init__(self, config):
super(Layoutlmv1Embeddings, self).__init__()
self.config = config
self.word_embeddings = nn.Embedding(
config.vocab_size, config.hidden_size, padding_idx=0
)
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size
)
self.x_position_embeddings = nn.Embedding(
config.max_2d_position_embeddings, config.hidden_size
)
self.y_position_embeddings = nn.Embedding(
config.max_2d_position_embeddings, config.hidden_size
)
self.h_position_embeddings = nn.Embedding(
config.max_2d_position_embeddings, config.hidden_size
)
self.w_position_embeddings = nn.Embedding(
config.max_2d_position_embeddings, config.hidden_size
)
self.token_type_embeddings = nn.Embedding(
config.type_vocab_size, config.hidden_size
)
# for web extension
self.xpath_embeddings = XPathEmbeddings(config)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.doc_linear1 = nn.Linear(config.hidden_size, config.hidden_size)
self.doc_linear2 = nn.Linear(config.hidden_size, config.hidden_size)
self.web_linear1 = nn.Linear(config.hidden_size, config.hidden_size)
self.web_linear2 = nn.Linear(config.hidden_size, config.hidden_size)
self.web_linear3 = nn.Linear(config.hidden_size, config.hidden_size)
self.web_linear4 = nn.Linear(config.hidden_size, config.hidden_size)
self.relu = nn.ReLU()
def forward(
self,
input_ids,
bbox=None,
xpath_tags_seq=None,
xpath_subs_seq=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
embedding_mode=None
):
seq_length = input_ids.size(1)
if position_ids is None:
position_ids = torch.arange(
seq_length, dtype=torch.long, device=input_ids.device
)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
words_embeddings = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
if embedding_mode != None and embedding_mode == 'box' : # doc entry
bbox = torch.clamp(bbox, 0, self.config.max_2d_position_embeddings-1)
left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
embeddings = (
words_embeddings
+ position_embeddings
+ left_position_embeddings
+ upper_position_embeddings
# + right_position_embeddings
# + lower_position_embeddings
# + h_position_embeddings
# + w_position_embeddings
+ token_type_embeddings
)
elif embedding_mode != None and embedding_mode == 'html+box' : # doc entry
bbox = torch.clamp(bbox, 0, self.config.max_2d_position_embeddings-1)
left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
xpath_embeddings = self.xpath_embeddings(xpath_tags_seq, xpath_subs_seq)
embeddings = (
words_embeddings
+ position_embeddings
+ left_position_embeddings
+ upper_position_embeddings
+ xpath_embeddings
# + right_position_embeddings
# + lower_position_embeddings
# + h_position_embeddings
# + w_position_embeddings
+ token_type_embeddings
)
else: # web entry
if not self.config.add_linear:
xpath_embeddings = self.xpath_embeddings(xpath_tags_seq, xpath_subs_seq)
embeddings = (
words_embeddings
+ position_embeddings
+ token_type_embeddings
+ xpath_embeddings
)
else:
xpath_embeddings = self.xpath_embeddings(xpath_tags_seq, xpath_subs_seq)
temp_embeddings = self.web_linear2(self.relu(self.web_linear1(
xpath_embeddings
)))
embeddings = (
words_embeddings
+ position_embeddings
+ token_type_embeddings
+ temp_embeddings
)
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class Layoutlmv1Model(BertModel):
config_class = Layoutlmv1Config
pretrained_model_archive_map = LAYOUTLMV1_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "bert"
def __init__(self, config):
super(Layoutlmv1Model, self).__init__(config)
self.embeddings = Layoutlmv1Embeddings(config)
self.init_weights()
def forward(
self,
input_ids,
bbox=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
xpath_tags_seq=None,
xpath_subs_seq=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
embedding_mode=None,
):
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = extended_attention_mask.to(
dtype=torch.float32
# dtype=next(self.parameters()).dtype # this will trigger error when using high version torch
) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = (
head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
)
head_mask = head_mask.expand(
self.config.num_hidden_layers, -1, -1, -1, -1
)
elif head_mask.dim() == 2:
head_mask = (
head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
) # We can specify head_mask for each layer
head_mask = head_mask.to(
dtype=next(self.parameters()).dtype
) # switch to fload if need + fp16 compatibility
else:
head_mask = [None] * self.config.num_hidden_layers
embedding_output = self.embeddings(
input_ids, bbox=bbox, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, position_ids=position_ids, token_type_ids=token_type_ids, embedding_mode=embedding_mode
)
encoder_outputs = self.encoder(
embedding_output, extended_attention_mask, head_mask=head_mask
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
outputs = (sequence_output, pooled_output) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions)
class Layoutlmv1ForTokenClassification(BertPreTrainedModel):
config_class = Layoutlmv1Config
pretrained_model_archive_map = LAYOUTLMV1_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "bert"
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = Layoutlmv1Model(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(
self,
input_ids,
bbox=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
):
outputs = self.bert(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[
2:
] # add hidden states and attention if they are here
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)[active_loss]
active_labels = labels.view(-1)[active_loss]
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), scores, (hidden_states), (attentions)
class Layoutlmv1ForMaskedLM(BertPreTrainedModel):
config_class = Layoutlmv1Config
pretrained_model_archive_map = LAYOUTLMV1_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "bert"
def __init__(self, config):
super().__init__(config)
self.bert = Layoutlmv1Model(config)
self.cls = BertOnlyMLMHead(config)
self.init_weights()
def get_input_embeddings(self):
return self.bert.embeddings.word_embeddings
def get_output_embeddings(self):
return self.cls.predictions.decoder
def forward(
self,
input_ids,
bbox=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
masked_lm_labels=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
lm_labels=None,
xpath_tags_seq=None,
xpath_subs_seq=None,
):
outputs = self.bert(
input_ids,
bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
outputs = (prediction_scores,) + outputs[
2:
] # Add hidden states and attention if they are here
# Although this may seem awkward, BertForMaskedLM supports two scenarios:
# 1. If a tensor that contains the indices of masked labels is provided,
# the cross-entropy is the MLM cross-entropy that measures the likelihood
# of predictions for masked words.
# 2. If `lm_labels` is provided we are in a causal scenario where we
# try to predict the next token for each input in the decoder.
if masked_lm_labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(
prediction_scores.view(-1, self.config.vocab_size),
masked_lm_labels.view(-1),
)
outputs = (masked_lm_loss,) + outputs
return (
outputs
) # (masked_lm_loss), (ltr_lm_loss), prediction_scores, (hidden_states), (attentions)
class Layoutlmv1ForMaskedLM_roberta(BertPreTrainedModel):
config_class = Layoutlmv1Config
pretrained_model_archive_map = LAYOUTLMV1_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "bert"
def __init__(self, config):
super().__init__(config)
self.roberta = Layoutlmv1Model(config)
self.cls = BertOnlyMLMHead(config)
self.init_weights()
def get_input_embeddings(self):
return self.roberta.embeddings.word_embeddings
def get_output_embeddings(self):
return self.cls.predictions.decoder
def forward(
self,
input_ids,
bbox=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
masked_lm_labels=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
lm_labels=None,
xpath_tags_seq=None,
xpath_subs_seq=None,
):
outputs = self.roberta(
input_ids,
bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
outputs = (prediction_scores,) + outputs[
2:
] # Add hidden states and attention if they are here
# Although this may seem awkward, BertForMaskedLM supports two scenarios:
# 1. If a tensor that contains the indices of masked labels is provided,
# the cross-entropy is the MLM cross-entropy that measures the likelihood
# of predictions for masked words.
# 2. If `lm_labels` is provided we are in a causal scenario where we
# try to predict the next token for each input in the decoder.
if masked_lm_labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(
prediction_scores.view(-1, self.config.vocab_size),
masked_lm_labels.view(-1),
)
outputs = (masked_lm_loss,) + outputs
return (
outputs
) # (masked_lm_loss), (ltr_lm_loss), prediction_scores, (hidden_states), (attentions)
class Layoutlmv1ForQuestionAnswering(BertPreTrainedModel):
config_class = Layoutlmv1Config
pretrained_model_archive_map = LAYOUTLMV1_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "bert"
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = Layoutlmv1Model(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(
self,
input_ids,
bbox=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
# inputs_embeds=None,
start_positions=None,
end_positions=None,
# output_attentions=None,
# output_hidden_states=None,
# return_dict=None,
xpath_tags_seq=None,
xpath_subs_seq=None,
embedding_mode=None,
):
r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
"""
# return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids=input_ids,
bbox=bbox,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
embedding_mode=embedding_mode
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
# if not return_dict:
# output = (start_logits, end_logits) + outputs[2:]
# return ((total_loss,) + output) if total_loss is not None else output
#
# return QuestionAnsweringModelOutput(
# loss=total_loss,
# start_logits=start_logits,
# end_logits=end_logits,
# hidden_states=outputs.hidden_states,
# attentions=outputs.attentions,
# )
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
class Layoutlmv1ForQuestionAnswering_roberta(BertPreTrainedModel):
config_class = Layoutlmv1Config
pretrained_model_archive_map = LAYOUTLMV1_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "bert"
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = Layoutlmv1Model(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(
self,
input_ids,
bbox=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
# inputs_embeds=None,
start_positions=None,
end_positions=None,
# output_attentions=None,
# output_hidden_states=None,
# return_dict=None,
xpath_tags_seq=None,
xpath_subs_seq=None,
embedding_mode=None,
):
r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
"""
# return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids=input_ids,
bbox=bbox,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
embedding_mode=embedding_mode
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
# if not return_dict:
# output = (start_logits, end_logits) + outputs[2:]
# return ((total_loss,) + output) if total_loss is not None else output
#
# return QuestionAnsweringModelOutput(
# loss=total_loss,
# start_logits=start_logits,
# end_logits=end_logits,
# hidden_states=outputs.hidden_states,
# attentions=outputs.attentions,
# )
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
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