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bert_nli.py
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bert_nli.py
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import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import os
import numpy as np
from tqdm import tqdm
from transformers import *
from utils.utils import build_batch
class BertNLIModel(nn.Module):
"""Performs prediction, given the input of BERT embeddings.
"""
def __init__(self,model_path=None,gpu=True,bert_type='bert-base',label_num=3,batch_size=8,reinit_num=0,freeze_layers=False):
super(BertNLIModel, self).__init__()
self.bert_type = bert_type
if 'bert-base' in bert_type:
self.bert = BertModel.from_pretrained('bert-base-uncased')
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
elif 'bert-large' in bert_type:
self.bert = BertModel.from_pretrained('bert-large-uncased')
self.tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
elif 'albert' in bert_type:
self.bert = AlbertModel.from_pretrained(bert_type)
self.tokenizer = AlbertTokenizer.from_pretrained(bert_type)
else:
print('illegal bert type {}!'.format(bert_type))
self.num_hidden_layers = self.bert.config.num_hidden_layers
self.vdim = self.bert.config.hidden_size
self.nli_head = nn.Linear(self.vdim,label_num)
self.gpu = gpu
self.batch_size=batch_size
self.sm = nn.Softmax(dim=1)
self.reinit(layer_num=reinit_num, freeze=freeze_layers)
# load trained model
if model_path is not None:
if gpu:
sdict = torch.load(model_path)
self.load_state_dict(sdict)
self.to('cuda')
else:
sdict = torch.load(model_path,map_location=lambda storage, loc: storage)
self.load_state_dict(sdict)
else:
if self.gpu: self.to('cuda')
def reinit(self, layer_num, freeze):
"""Reinitialise parameters of last N layers and freeze all others"""
if freeze:
for _, pp in self.bert.named_parameters():
pp.requires_grad = False
if layer_num >= 0:
layer_idx = [self.num_hidden_layers-1-i for i in range(layer_num)]
layer_names = ['encoder.layer.{}'.format(j) for j in layer_idx]
for pn, pp in self.bert.named_parameters():
if any([ln in pn for ln in layer_names]) or 'pooler.' in pn:
pp.data = torch.randn(pp.shape)*0.02
pp.requires_grad = True
def load_model(self, sdict):
if self.gpu:
self.load_state_dict(sdict)
self.to('cuda')
else:
self.load_state_dict(sdict)
def forward(self, sent_pair_list, checkpoint=True, bs=None):
all_probs = None
if bs is None:
bs = self.batch_size
no_prog_bar = True
else: no_prog_bar = False
for batch_idx in tqdm(range(0,len(sent_pair_list),bs), disable=no_prog_bar,desc='evaluate'):
probs = self.ff(sent_pair_list[batch_idx:batch_idx+bs],checkpoint)[1].data.cpu().numpy()
if all_probs is None:
all_probs = probs
else:
all_probs = np.append(all_probs, probs, axis=0)
labels = []
for pp in all_probs:
ll = np.argmax(pp)
if ll==0:
labels.append('contradiction')
elif ll==1:
labels.append('entail')
else:
assert ll==2
labels.append('neutral')
return labels, all_probs
def step_bert_encode(self, module, hidden_states, attention_mask=None, head_mask=None):
all_hidden_states = ()
all_attentions = ()
for i, layer_module in enumerate(module.layer):
if module.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = checkpoint.checkpoint(layer_module, hidden_states, attention_mask, head_mask[i])
hidden_states = layer_outputs[0]
if module.output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if module.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if module.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if module.output_attentions:
outputs = outputs + (all_attentions,)
return outputs # last-layer hidden state, (all hidden states), (all attentions)
def step_checkpoint_bert(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
modules = [module for k, module in self.bert._modules.items()]
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)
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
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.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.num_hidden_layers
embedding_output = modules[0](input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
encoder_outputs = self.step_bert_encode(modules[1], embedding_output,extended_attention_mask,head_mask)
sequence_output = encoder_outputs[0]
pooled_output = modules[2](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)
def ff(self,sent_pair_list,checkpoint):
ids, types, masks = build_batch(self.tokenizer, sent_pair_list, self.bert_type)
if ids is None: return None
ids_tensor = torch.tensor(ids)
types_tensor = torch.tensor(types)
masks_tensor = torch.tensor(masks)
if self.gpu:
ids_tensor = ids_tensor.to('cuda')
types_tensor = types_tensor.to('cuda')
masks_tensor = masks_tensor.to('cuda')
if checkpoint:
cls_vecs = self.step_checkpoint_bert(input_ids=ids_tensor, token_type_ids=types_tensor, attention_mask=masks_tensor)[1]
else:
cls_vecs = self.bert(input_ids=ids_tensor, token_type_ids=types_tensor, attention_mask=masks_tensor)[1]
logits = self.nli_head(cls_vecs)
probs = self.sm(logits)
# to reduce gpu memory usage
# del ids_tensor
# del types_tensor
# del masks_tensor
# torch.cuda.empty_cache() # releases all unoccupied cached memory
return logits, probs
def save(self, output_path, config_dic=None, acc=None):
if acc is None:
model_name = 'nli_model.state_dict'
else:
model_name = 'nli_model_acc{}.state_dict'.format(acc)
opath = os.path.join(output_path, model_name)
if config_dic is None:
torch.save(self.state_dict(),opath)
else:
torch.save(config_dic,opath)
@staticmethod
def load(input_path,gpu=True,bert_type='base',label_num=3,batch_size=16):
if gpu:
sdict = torch.load(input_path)
else:
sdict = torch.load(input_path,map_location=lambda storage, loc: storage)
model = BertNLIModel(gpu,bert_type,label_num,batch_size)
model.load_state_dict(sdict)
return model