Upload BERTWithCRF.py

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	#code adapted form https://github.com/Louis-udm/NER-BERT-CRF/blob/master/NER_BERT_CRF.py
	import torch
	from transformers import BertModel, BertConfig ##### import these guys -important otherwise config error and you spend an hour figuring out!
	from transformers.models.bert.modeling_bert import BertPreTrainedModel
	from torch import nn
	from torch.nn import CrossEntropyLoss, BCELoss, LayerNorm
	from transformers.modeling_outputs import TokenClassifierOutput

	# Hack to guarantee backward-compatibility.
	BertLayerNorm = LayerNorm


	def log_sum_exp_batch(log_Tensor, axis=-1): # shape (batch_size,n,m)
	return torch.max(log_Tensor, axis)[0]+torch.log(torch.exp(log_Tensor-torch.max(log_Tensor, axis)[0].view(log_Tensor.shape[0],-1,1)).sum(axis))


	class BERT_CRF_NER(BertPreTrainedModel):
	_keys_to_ignore_on_load_unexpected = [r"pooler"]

	def __init__(self, config):
	super().__init__(config)
	self.hidden_size = 768
	self.start_label_id = config.start_label_id
	self.stop_label_id = config.stop_label_id
	self.num_labels = config.num_classes
	# self.max_seq_length = max_seq_length
	self.batch_size = config.batch_size

	# use pretrainded BertModel
	self.bert = BertModel(config, add_pooling_layer=False)

	self.dropout = torch.nn.Dropout(0.2)
	# Maps the output of the bert into label space.
	self.hidden2label = nn.Linear(self.hidden_size, self.num_labels)

	# Matrix of transition parameters. Entry i,j is the score of transitioning to i from j.
	self.transitions = nn.Parameter(
	torch.randn(self.num_labels, self.num_labels))

	# These two statements enforce the constraint that we never transfer to the start tag(or label),
	# and we never transfer from the stop label (the model would probably learn this anyway,
	# so this enforcement is likely unimportant)

	self.transitions.data[self.start_label_id, :] = -10000
	self.transitions.data[:, self.stop_label_id] = -10000

	nn.init.xavier_uniform_(self.hidden2label.weight)
	nn.init.constant_(self.hidden2label.bias, 0.0)
	# self.apply(self.init_bert_weights)

	def init_bert_weights(self, module):
	""" Initialize the weights.
	"""
	if isinstance(module, (nn.Linear, nn.Embedding)):
	# 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)
	elif isinstance(module, BertLayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)
	if isinstance(module, nn.Linear) and module.bias is not None:
	module.bias.data.zero_()

	def _forward_alg(self, feats):
	"""
	this also called alpha-recursion or forward recursion, to calculate log_prob of all barX
	"""

	# T = self.max_seq_length
	T = feats.shape[1]
	batch_size = feats.shape[0]

	# alpha_recursion,forward, alpha(zt)=p(zt,bar_x_1:t)
	log_alpha = torch.Tensor(batch_size, 1, self.num_labels).fill_(-10000.).to(self.device)
	# normal_alpha_0 : alpha[0]=Ot[0]*self.PIs
	# self.start_label has all of the score. it is log,0 is p=1
	log_alpha[:, 0, self.start_label_id] = 0

	# feats: sentances -> word embedding -> lstm -> MLP -> feats
	# feats is the probability of emission, feat.shape=(1,tag_size)
	for t in range(1, T):
	log_alpha = (log_sum_exp_batch(self.transitions + log_alpha, axis=-1) + feats[:, t]).unsqueeze(1)

	# log_prob of all barX
	log_prob_all_barX = log_sum_exp_batch(log_alpha)
	return log_prob_all_barX

	def _get_bert_features(self, input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	head_mask,
	inputs_embeds,
	output_attentions,
	output_hidden_states,
	return_dict):
	"""
	sentences -> word embedding -> lstm -> MLP -> feats
	"""
	bert_seq_out = self.bert(input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict) # output_all_encoded_layers=False removed

	bert_seq_out_last = bert_seq_out[0]
	bert_seq_out_last = self.dropout(bert_seq_out_last)
	bert_feats = self.hidden2label(bert_seq_out_last)
	return bert_feats, bert_seq_out

	def _score_sentence(self, feats, label_ids):
	"""
	Gives the score of a provided label sequence
	p(X=w1:t,Zt=tag1:t)=...p(Zt=tag_t\|Zt-1=tag_t-1)p(xt\|Zt=tag_t)...
	"""

	# T = self.max_seq_length
	T = feats.shape[1]
	batch_size = feats.shape[0]

	batch_transitions = self.transitions.expand(batch_size, self.num_labels, self.num_labels)
	batch_transitions = batch_transitions.flatten(1)

	score = torch.zeros((feats.shape[0], 1)).to(self.device)
	# the 0th node is start_label->start_word, the probability of them=1. so t begins with 1.
	for t in range(1, T):

	score = score + \
	batch_transitions.gather(-1, (label_ids[:, t] * self.num_labels + label_ids[:, t-1]).view(-1, 1)) + \
	feats[:, t].gather(-1, label_ids[:, t].view(-1, 1)).view(-1, 1)
	return score

	def _viterbi_decode(self, feats):
	"""
	Max-Product Algorithm or viterbi algorithm, argmax(p(z_0:t\|x_0:t))
	"""

	# T = self.max_seq_length
	# feats=feats[0]#added
	T = feats.shape[1]
	batch_size = feats.shape[0]

	# batch_transitions=self.transitions.expand(batch_size,self.num_labels,self.num_labels)

	log_delta = torch.Tensor(batch_size, 1, self.num_labels).fill_(-10000.).to(self.device)
	log_delta[:, 0, self.start_label_id] = 0

	# psi is for the value of the last latent that make P(this_latent) maximum.
	psi = torch.zeros((batch_size, T, self.num_labels), dtype=torch.long).to(self.device) # psi[0]=0000 useless
	for t in range(1, T):
	# delta[t][k]=max_z1:t-1( p(x1,x2,...,xt,z1,z2,...,zt-1,zt=k\|theta) )
	# delta[t] is the max prob of the path from z_t-1 to z_t[k]
	log_delta, psi[:, t] = torch.max(self.transitions + log_delta, -1)
	# psi[t][k]=argmax_z1:t-1( p(x1,x2,...,xt,z1,z2,...,zt-1,zt=k\|theta) )
	# psi[t][k] is the path chosen from z_t-1 to z_t[k],the value is the z_state(is k) index of z_t-1

	log_delta = (log_delta + feats[:, t]).unsqueeze(1)

	# trace back
	path = torch.zeros((batch_size, T), dtype=torch.long).to(self.device)

	# max p(z1:t,all_x\|theta)
	max_logLL_allz_allx, path[:, -1] = torch.max(log_delta.squeeze(), -1)

	for t in range(T-2, -1, -1):
	# choose the state of z_t according the state chosen of z_t+1.
	path[:, t] = psi[:, t+1].gather(-1, path[:, t+1].view(-1, 1)).squeeze()

	return max_logLL_allz_allx, path

	def neg_log_likelihood(self, input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	head_mask,
	inputs_embeds,
	output_attentions,
	output_hidden_states,
	return_dict,
	label_ids):

	bert_feats, _ = self._get_bert_features(input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	head_mask,
	inputs_embeds,
	output_attentions,
	output_hidden_states,
	return_dict)

	forward_score = self._forward_alg(bert_feats)
	# p(X=w1:t,Zt=tag1:t)=...p(Zt=tag_t\|Zt-1=tag_t-1)p(xt\|Zt=tag_t)...
	gold_score = self._score_sentence(bert_feats, label_ids)
	# - log[ p(X=w1:t,Zt=tag1:t)/p(X=w1:t) ] = - log[ p(Zt=tag1:t\|X=w1:t) ]
	return torch.mean(forward_score - gold_score)

	# this forward is just for predict, not for train
	# dont confuse this with _forward_alg above.
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	inference_mode=False,
	):
	# Get the emission scores from the BiLSTM
	bert_feats, bert_out = self._get_bert_features(input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	head_mask,
	inputs_embeds,
	output_attentions,
	output_hidden_states,
	return_dict)

	# Find the best path, given the features.
	score, label_seq_ids = self._viterbi_decode(bert_feats)

	if not inference_mode:
	neg_log_likelihood = self.neg_log_likelihood(input_ids,
	attention_mask,
	token_type_ids,
	position_ids,
	head_mask,
	inputs_embeds,
	output_attentions,
	output_hidden_states,
	return_dict,
	labels)

	return TokenClassifierOutput(
	loss=neg_log_likelihood,
	logits=label_seq_ids,
	hidden_states=bert_out.hidden_states,
	attentions=bert_out.attentions,
	)
	else:
	neg_log_likelihood = None
	return TokenClassifierOutput(
	loss=neg_log_likelihood,
	logits=label_seq_ids,
	hidden_states=bert_out.hidden_states,
	attentions=bert_out.attentions,
	)