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run_re.py
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run_re.py
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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""
from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
from collections import defaultdict
import re
import shutil
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tensorboardX import SummaryWriter
from tqdm import tqdm, trange
import time
from transformers import (WEIGHTS_NAME, BertConfig,
BertTokenizer,
RobertaConfig,
RobertaTokenizer,
get_linear_schedule_with_warmup,
AdamW,
BertForACEBothOneDropoutSub,
AlbertForACEBothSub,
AlbertConfig,
AlbertTokenizer,
AlbertForACEBothOneDropoutSub,
BertForACEBothOneDropoutSubNoNer,
)
from transformers import AutoTokenizer
from torch.utils.data import TensorDataset, Dataset
import json
import pickle
import numpy as np
import unicodedata
import itertools
import timeit
from tqdm import tqdm
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, AlbertConfig)), ())
MODEL_CLASSES = {
'bertsub': (BertConfig, BertForACEBothOneDropoutSub, BertTokenizer),
'bertnonersub': (BertConfig, BertForACEBothOneDropoutSubNoNer, BertTokenizer),
'albertsub': (AlbertConfig, AlbertForACEBothOneDropoutSub, AlbertTokenizer),
}
task_ner_labels = {
'ace04': ['FAC', 'WEA', 'LOC', 'VEH', 'GPE', 'ORG', 'PER'],
'ace05': ['FAC', 'WEA', 'LOC', 'VEH', 'GPE', 'ORG', 'PER'],
'scierc': ['Method', 'OtherScientificTerm', 'Task', 'Generic', 'Material', 'Metric'],
}
task_rel_labels = {
'ace04': ['PER-SOC', 'OTHER-AFF', 'ART', 'GPE-AFF', 'EMP-ORG', 'PHYS'],
'ace05': ['PER-SOC', 'ART', 'ORG-AFF', 'GEN-AFF', 'PHYS', 'PART-WHOLE'],
'scierc': ['PART-OF', 'USED-FOR', 'FEATURE-OF', 'CONJUNCTION', 'EVALUATE-FOR', 'HYPONYM-OF', 'COMPARE'],
}
class ACEDataset(Dataset):
def __init__(self, tokenizer, args=None, evaluate=False, do_test=False, max_pair_length=None):
if not evaluate:
file_path = os.path.join(args.data_dir, args.train_file)
else:
if do_test:
if args.test_file.find('models')==-1:
file_path = os.path.join(args.data_dir, args.test_file)
else:
file_path = args.test_file
else:
if args.dev_file.find('models')==-1:
file_path = os.path.join(args.data_dir, args.dev_file)
else:
file_path = args.dev_file
assert os.path.isfile(file_path)
self.file_path = file_path
self.tokenizer = tokenizer
self.max_seq_length = args.max_seq_length
self.max_pair_length = max_pair_length
self.max_entity_length = self.max_pair_length*2
self.evaluate = evaluate
self.use_typemarker = args.use_typemarker
self.local_rank = args.local_rank
self.args = args
self.model_type = args.model_type
self.no_sym = args.no_sym
if args.data_dir.find('ace05')!=-1:
self.ner_label_list = ['NIL', 'FAC', 'WEA', 'LOC', 'VEH', 'GPE', 'ORG', 'PER']
if args.no_sym:
label_list = ['PER-SOC', 'ART', 'ORG-AFF', 'GEN-AFF', 'PHYS', 'PART-WHOLE']
self.sym_labels = ['NIL']
self.label_list = self.sym_labels + label_list
else:
label_list = ['ART', 'ORG-AFF', 'GEN-AFF', 'PHYS', 'PART-WHOLE']
self.sym_labels = ['NIL', 'PER-SOC']
self.label_list = self.sym_labels + label_list
elif args.data_dir.find('ace04')!=-1:
self.ner_label_list = ['NIL', 'FAC', 'WEA', 'LOC', 'VEH', 'GPE', 'ORG', 'PER']
if args.no_sym:
label_list = ['PER-SOC', 'OTHER-AFF', 'ART', 'GPE-AFF', 'EMP-ORG', 'PHYS']
self.sym_labels = ['NIL']
self.label_list = self.sym_labels + label_list
else:
label_list = ['OTHER-AFF', 'ART', 'GPE-AFF', 'EMP-ORG', 'PHYS']
self.sym_labels = ['NIL', 'PER-SOC']
self.label_list = self.sym_labels + label_list
elif args.data_dir.find('scierc')!=-1:
self.ner_label_list = ['NIL', 'Method', 'OtherScientificTerm', 'Task', 'Generic', 'Material', 'Metric']
if args.no_sym:
label_list = ['CONJUNCTION', 'COMPARE', 'PART-OF', 'USED-FOR', 'FEATURE-OF', 'EVALUATE-FOR', 'HYPONYM-OF']
self.sym_labels = ['NIL']
self.label_list = self.sym_labels + label_list
else:
label_list = ['PART-OF', 'USED-FOR', 'FEATURE-OF', 'EVALUATE-FOR', 'HYPONYM-OF']
self.sym_labels = ['NIL', 'CONJUNCTION', 'COMPARE']
self.label_list = self.sym_labels + label_list
else:
assert (False)
self.global_predicted_ners = {}
self.initialize()
def initialize(self):
tokenizer = self.tokenizer
vocab_size = tokenizer.vocab_size
max_num_subwords = self.max_seq_length - 4 # for two marker
label_map = {label: i for i, label in enumerate(self.label_list)}
ner_label_map = {label: i for i, label in enumerate(self.ner_label_list)}
def tokenize_word(text):
if (
isinstance(tokenizer, RobertaTokenizer)
and (text[0] != "'")
and (len(text) != 1 or not self.is_punctuation(text))
):
return tokenizer.tokenize(text, add_prefix_space=True)
return tokenizer.tokenize(text)
f = open(self.file_path, "r", encoding='utf-8')
self.ner_tot_recall = 0
self.tot_recall = 0
self.data = []
self.ner_golden_labels = set([])
self.golden_labels = set([])
self.golden_labels_withner = set([])
maxR = 0
maxL = 0
for l_idx, line in enumerate(f):
data = json.loads(line)
if self.args.output_dir.find('test')!=-1:
if len(self.data) > 100:
break
sentences = data['sentences']
if 'predicted_ner' in data: # e2e predict
ners = data['predicted_ner']
else:
ners = data['ner']
std_ners = data['ner']
relations = data['relations']
for sentence_relation in relations:
for x in sentence_relation:
if x[4] in self.sym_labels[1:]:
self.tot_recall += 2
else:
self.tot_recall += 1
sentence_boundaries = [0]
words = []
L = 0
for i in range(len(sentences)):
L += len(sentences[i])
sentence_boundaries.append(L)
words += sentences[i]
tokens = [tokenize_word(w) for w in words]
subwords = [w for li in tokens for w in li]
maxL = max(maxL, len(subwords))
subword2token = list(itertools.chain(*[[i] * len(li) for i, li in enumerate(tokens)]))
token2subword = [0] + list(itertools.accumulate(len(li) for li in tokens))
subword_start_positions = frozenset(token2subword)
subword_sentence_boundaries = [sum(len(li) for li in tokens[:p]) for p in sentence_boundaries]
for n in range(len(subword_sentence_boundaries) - 1):
sentence_ners = ners[n]
sentence_relations = relations[n]
std_ner = std_ners[n]
std_entity_labels = {}
self.ner_tot_recall += len(std_ner)
for start, end, label in std_ner:
std_entity_labels[(start, end)] = label
self.ner_golden_labels.add( ((l_idx, n), (start, end), label) )
self.global_predicted_ners[(l_idx, n)] = list(sentence_ners)
doc_sent_start, doc_sent_end = subword_sentence_boundaries[n : n + 2]
left_length = doc_sent_start
right_length = len(subwords) - doc_sent_end
sentence_length = doc_sent_end - doc_sent_start
half_context_length = int((max_num_subwords - sentence_length) / 2)
if sentence_length < max_num_subwords:
if left_length < right_length:
left_context_length = min(left_length, half_context_length)
right_context_length = min(right_length, max_num_subwords - left_context_length - sentence_length)
else:
right_context_length = min(right_length, half_context_length)
left_context_length = min(left_length, max_num_subwords - right_context_length - sentence_length)
doc_offset = doc_sent_start - left_context_length
target_tokens = subwords[doc_offset : doc_sent_end + right_context_length]
target_tokens = [tokenizer.cls_token] + target_tokens[ : self.max_seq_length - 4] + [tokenizer.sep_token]
assert(len(target_tokens) <= self.max_seq_length - 2)
pos2label = {}
for x in sentence_relations:
pos2label[(x[0],x[1],x[2],x[3])] = label_map[x[4]]
self.golden_labels.add(((l_idx, n), (x[0],x[1]), (x[2],x[3]), x[4]))
self.golden_labels_withner.add(((l_idx, n), (x[0],x[1], std_entity_labels[(x[0], x[1])]), (x[2],x[3], std_entity_labels[(x[2], x[3])]), x[4]))
if x[4] in self.sym_labels[1:]:
self.golden_labels.add(((l_idx, n), (x[2],x[3]), (x[0],x[1]), x[4]))
self.golden_labels_withner.add(((l_idx, n), (x[2],x[3], std_entity_labels[(x[2], x[3])]), (x[0],x[1], std_entity_labels[(x[0], x[1])]), x[4]))
entities = list(sentence_ners)
for x in sentence_relations:
w = (x[2],x[3],x[0],x[1])
if w not in pos2label:
if x[4] in self.sym_labels[1:]:
pos2label[w] = label_map[x[4]] # bug
else:
pos2label[w] = label_map[x[4]] + len(label_map) - len(self.sym_labels)
if not self.evaluate:
entities.append((10000, 10000, 'NIL')) # only for NER
for sub in entities:
cur_ins = []
if sub[0] < 10000:
sub_s = token2subword[sub[0]] - doc_offset + 1
sub_e = token2subword[sub[1]+1] - doc_offset
sub_label = ner_label_map[sub[2]]
if self.use_typemarker:
l_m = '[unused%d]' % ( 2 + sub_label )
r_m = '[unused%d]' % ( 2 + sub_label + len(self.ner_label_list) )
else:
l_m = '[unused0]'
r_m = '[unused1]'
sub_tokens = target_tokens[:sub_s] + [l_m] + target_tokens[sub_s:sub_e+1] + [r_m] + target_tokens[sub_e+1: ]
sub_e += 2
else:
sub_s = len(target_tokens)
sub_e = len(target_tokens)+1
sub_tokens = target_tokens + ['[unused0]', '[unused1]']
sub_label = -1
if sub_e >= self.max_seq_length-1:
continue
# assert(sub_e < self.max_seq_length)
for start, end, obj_label in sentence_ners:
if self.model_type.endswith('nersub'):
if start==sub[0] and end==sub[1]:
continue
doc_entity_start = token2subword[start]
doc_entity_end = token2subword[end+1]
left = doc_entity_start - doc_offset + 1
right = doc_entity_end - doc_offset
obj = (start, end)
if obj[0] >= sub[0]:
left += 1
if obj[0] > sub[1]:
left += 1
if obj[1] >= sub[0]:
right += 1
if obj[1] > sub[1]:
right += 1
label = pos2label.get((sub[0], sub[1], obj[0], obj[1]), 0)
if right >= self.max_seq_length-1:
continue
cur_ins.append(((left, right, ner_label_map[obj_label]), label, obj))
maxR = max(maxR, len(cur_ins))
dL = self.max_pair_length
if self.args.shuffle:
np.random.shuffle(cur_ins)
for i in range(0, len(cur_ins), dL):
examples = cur_ins[i : i + dL]
item = {
'index': (l_idx, n),
'sentence': sub_tokens,
'examples': examples,
'sub': (sub, (sub_s, sub_e), sub_label), #(sub[0], sub[1], sub_label),
}
self.data.append(item)
logger.info('maxR: %s', maxR)
logger.info('maxL: %s', maxL)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
entry = self.data[idx]
sub, sub_position, sub_label = entry['sub']
input_ids = self.tokenizer.convert_tokens_to_ids(entry['sentence'])
L = len(input_ids)
input_ids += [self.tokenizer.pad_token_id] * (self.max_seq_length - len(input_ids))
attention_mask = torch.zeros((self.max_entity_length+self.max_seq_length, self.max_entity_length+self.max_seq_length), dtype=torch.int64)
attention_mask[:L, :L] = 1
if self.model_type.startswith('albert'):
input_ids = input_ids + [30002] * (len(entry['examples'])) + [self.tokenizer.pad_token_id] * (self.max_pair_length - len(entry['examples']))
input_ids = input_ids + [30003] * (len(entry['examples'])) + [self.tokenizer.pad_token_id] * (self.max_pair_length - len(entry['examples'])) # for debug
else:
input_ids = input_ids + [3] * (len(entry['examples'])) + [self.tokenizer.pad_token_id] * (self.max_pair_length - len(entry['examples']))
input_ids = input_ids + [4] * (len(entry['examples'])) + [self.tokenizer.pad_token_id] * (self.max_pair_length - len(entry['examples'])) # for debug
labels = []
ner_labels = []
mention_pos = []
mention_2 = []
position_ids = list(range(self.max_seq_length)) + [0] * self.max_entity_length
num_pair = self.max_pair_length
for x_idx, obj in enumerate(entry['examples']):
m2 = obj[0]
label = obj[1]
mention_pos.append((m2[0], m2[1]))
mention_2.append(obj[2])
w1 = x_idx
w2 = w1 + num_pair
w1 += self.max_seq_length
w2 += self.max_seq_length
position_ids[w1] = m2[0]
position_ids[w2] = m2[1]
for xx in [w1, w2]:
for yy in [w1, w2]:
attention_mask[xx, yy] = 1
attention_mask[xx, :L] = 1
labels.append(label)
ner_labels.append(m2[2])
if self.use_typemarker:
l_m = '[unused%d]' % ( 2 + m2[2] + len(self.ner_label_list)*2 )
r_m = '[unused%d]' % ( 2 + m2[2] + len(self.ner_label_list)*3 )
l_m = self.tokenizer._convert_token_to_id(l_m)
r_m = self.tokenizer._convert_token_to_id(r_m)
input_ids[w1] = l_m
input_ids[w2] = r_m
pair_L = len(entry['examples'])
if self.args.att_left:
attention_mask[self.max_seq_length : self.max_seq_length+pair_L, self.max_seq_length : self.max_seq_length+pair_L] = 1
if self.args.att_right:
attention_mask[self.max_seq_length+num_pair : self.max_seq_length+num_pair+pair_L, self.max_seq_length+num_pair : self.max_seq_length+num_pair+pair_L] = 1
mention_pos += [(0, 0)] * (num_pair - len(mention_pos))
labels += [-1] * (num_pair - len(labels))
ner_labels += [-1] * (num_pair - len(ner_labels))
item = [torch.tensor(input_ids),
attention_mask,
torch.tensor(position_ids),
torch.tensor(sub_position),
torch.tensor(mention_pos),
torch.tensor(labels, dtype=torch.int64),
torch.tensor(ner_labels, dtype=torch.int64),
torch.tensor(sub_label, dtype=torch.int64)
]
if self.evaluate:
item.append(entry['index'])
item.append(sub)
item.append(mention_2)
return item
@staticmethod
def collate_fn(batch):
fields = [x for x in zip(*batch)]
num_metadata_fields = 3
stacked_fields = [torch.stack(field) for field in fields[:-num_metadata_fields]] # don't stack metadata fields
stacked_fields.extend(fields[-num_metadata_fields:]) # add them as lists not torch tensors
return stacked_fields
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def _rotate_checkpoints(args, checkpoint_prefix, use_mtime=False):
if not args.save_total_limit:
return
if args.save_total_limit <= 0:
return
# Check if we should delete older checkpoint(s)
glob_checkpoints = glob.glob(os.path.join(args.output_dir, '{}-*'.format(checkpoint_prefix)))
if len(glob_checkpoints) <= args.save_total_limit:
return
ordering_and_checkpoint_path = []
for path in glob_checkpoints:
if use_mtime:
ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
else:
regex_match = re.match('.*{}-([0-9]+)'.format(checkpoint_prefix), path)
if regex_match and regex_match.groups():
ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
checkpoints_sorted = sorted(ordering_and_checkpoint_path)
checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)
checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
for checkpoint in checkpoints_to_be_deleted:
logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
shutil.rmtree(checkpoint)
def train(args, model, tokenizer):
""" Train the model """
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter("logs/"+args.data_dir[max(args.data_dir.rfind('/'),0):]+"_re_logs/"+args.output_dir[args.output_dir.rfind('/'):])
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_dataset = ACEDataset(tokenizer=tokenizer, args=args, max_pair_length=args.max_pair_length)
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, num_workers=4*int(args.output_dir.find('test')==-1))
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
if args.warmup_steps==-1:
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=int(0.1*t_total), num_training_steps=t_total
)
else:
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# ori_model = model
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
tr_ner_loss, logging_ner_loss = 0.0, 0.0
tr_re_loss, logging_re_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
set_seed(args) # Added here for reproductibility (even between python 2 and 3)
best_f1 = -1
for _ in train_iterator:
if args.shuffle and _ > 0:
train_dataset.initialize()
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'position_ids': batch[2],
'labels': batch[5],
'ner_labels': batch[6],
}
inputs['sub_positions'] = batch[3]
if args.model_type.find('span')!=-1:
inputs['mention_pos'] = batch[4]
if args.model_type.endswith('bertonedropoutnersub'):
inputs['sub_ner_labels'] = batch[7]
outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc)
re_loss = outputs[1]
ner_loss = outputs[2]
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
re_loss = re_loss / args.gradient_accumulation_steps
ner_loss = ner_loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if re_loss > 0:
tr_re_loss += re_loss.item()
if ner_loss > 0:
tr_ner_loss += ner_loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.max_grad_norm > 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
# if args.model_type.endswith('rel') :
# ori_model.bert.encoder.layer[args.add_coref_layer].attention.self.relative_attention_bias.weight.data[0].zero_() # 可以手动乘个mask
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
logging_loss = tr_loss
tb_writer.add_scalar('RE_loss', (tr_re_loss - logging_re_loss)/args.logging_steps, global_step)
logging_re_loss = tr_re_loss
tb_writer.add_scalar('NER_loss', (tr_ner_loss - logging_ner_loss)/args.logging_steps, global_step)
logging_ner_loss = tr_ner_loss
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # valid for bert/spanbert
update = True
# Save model checkpoint
if args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer)
f1 = results['f1_with_ner']
tb_writer.add_scalar('f1_with_ner', f1, global_step)
if f1 > best_f1:
best_f1 = f1
print ('Best F1', best_f1)
else:
update = False
if update:
checkpoint_prefix = 'checkpoint'
output_dir = os.path.join(args.output_dir, '{}-{}'.format(checkpoint_prefix, global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
_rotate_checkpoints(args, checkpoint_prefix)
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
if args.local_rank in [-1, 0]:
tb_writer.close()
return global_step, tr_loss / global_step, best_f1
def to_list(tensor):
return tensor.detach().cpu().tolist()
def evaluate(args, model, tokenizer, prefix="", do_test=False):
eval_output_dir = args.output_dir
eval_dataset = ACEDataset(tokenizer=tokenizer, args=args, evaluate=True, do_test=do_test, max_pair_length=args.max_pair_length)
golden_labels = set(eval_dataset.golden_labels)
golden_labels_withner = set(eval_dataset.golden_labels_withner)
label_list = list(eval_dataset.label_list)
sym_labels = list(eval_dataset.sym_labels)
tot_recall = eval_dataset.tot_recall
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
scores = defaultdict(dict)
# ner_pred = not args.model_type.endswith('noner')
example_subs = set([])
num_label = len(label_list)
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=ACEDataset.collate_fn, num_workers=4*int(args.output_dir.find('test')==-1))
# Eval!
logger.info(" Num examples = %d", len(eval_dataset))
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
indexs = batch[-3]
subs = batch[-2]
batch_m2s = batch[-1]
ner_labels = batch[6]
batch = tuple(t.to(args.device) for t in batch[:-3])
with torch.no_grad():
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'position_ids': batch[2],
# 'labels': batch[4],
# 'ner_labels': batch[5],
}
inputs['sub_positions'] = batch[3]
if args.model_type.find('span')!=-1:
inputs['mention_pos'] = batch[4]
outputs = model(**inputs)
logits = outputs[0]
if args.eval_logsoftmax: # perform a bit better
logits = torch.nn.functional.log_softmax(logits, dim=-1)
elif args.eval_softmax:
logits = torch.nn.functional.softmax(logits, dim=-1)
if args.use_ner_results or args.model_type.endswith('nonersub'):
ner_preds = ner_labels
else:
ner_preds = torch.argmax(outputs[1], dim=-1)
logits = logits.cpu().numpy()
ner_preds = ner_preds.cpu().numpy()
for i in range(len(indexs)):
index = indexs[i]
sub = subs[i]
m2s = batch_m2s[i]
example_subs.add(((index[0], index[1]), (sub[0], sub[1])))
for j in range(len(m2s)):
obj = m2s[j]
ner_label = eval_dataset.ner_label_list[ner_preds[i,j]]
scores[(index[0], index[1])][( (sub[0], sub[1]), (obj[0], obj[1]))] = (logits[i, j].tolist(), ner_label)
cor = 0
tot_pred = 0
cor_with_ner = 0
global_predicted_ners = eval_dataset.global_predicted_ners
ner_golden_labels = eval_dataset.ner_golden_labels
ner_cor = 0
ner_tot_pred = 0
ner_ori_cor = 0
tot_output_results = defaultdict(list)
if not args.eval_unidirect: # eval_unidrect is for ablation study
# print (len(scores))
for example_index, pair_dict in sorted(scores.items(), key=lambda x:x[0]):
visited = set([])
sentence_results = []
for k1, (v1, v2_ner_label) in pair_dict.items():
if k1 in visited:
continue
visited.add(k1)
if v2_ner_label=='NIL':
continue
v1 = list(v1)
m1 = k1[0]
m2 = k1[1]
if m1 == m2:
continue
k2 = (m2, m1)
v2s = pair_dict.get(k2, None)
if v2s is not None:
visited.add(k2)
v2, v1_ner_label = v2s
v2 = v2[ : len(sym_labels)] + v2[num_label:] + v2[len(sym_labels) : num_label]
for j in range(len(v2)):
v1[j] += v2[j]
else:
assert ( False )
if v1_ner_label=='NIL':
continue
pred_label = np.argmax(v1)
if pred_label>0:
if pred_label >= num_label:
pred_label = pred_label - num_label + len(sym_labels)
m1, m2 = m2, m1
v1_ner_label, v2_ner_label = v2_ner_label, v1_ner_label
pred_score = v1[pred_label]
sentence_results.append( (pred_score, m1, m2, pred_label, v1_ner_label, v2_ner_label) )
sentence_results.sort(key=lambda x: -x[0])
no_overlap = []
def is_overlap(m1, m2):
if m2[0]<=m1[0] and m1[0]<=m2[1]:
return True
if m1[0]<=m2[0] and m2[0]<=m1[1]:
return True
return False
output_preds = []
for item in sentence_results:
m1 = item[1]
m2 = item[2]
overlap = False
for x in no_overlap:
_m1 = x[1]
_m2 = x[2]
# same relation type & overlap subject & overlap object --> delete
if item[3]==x[3] and (is_overlap(m1, _m1) and is_overlap(m2, _m2)):
overlap = True
break
pred_label = label_list[item[3]]
if not overlap:
no_overlap.append(item)
pos2ner = {}
for item in no_overlap:
m1 = item[1]
m2 = item[2]
pred_label = label_list[item[3]]
tot_pred += 1
if pred_label in sym_labels:
tot_pred += 1 # duplicate
if (example_index, m1, m2, pred_label) in golden_labels or (example_index, m2, m1, pred_label) in golden_labels:
cor += 2
else:
if (example_index, m1, m2, pred_label) in golden_labels:
cor += 1
if m1 not in pos2ner:
pos2ner[m1] = item[4]
if m2 not in pos2ner:
pos2ner[m2] = item[5]
output_preds.append((m1, m2, pred_label))
if pred_label in sym_labels:
if (example_index, (m1[0], m1[1], pos2ner[m1]), (m2[0], m2[1], pos2ner[m2]), pred_label) in golden_labels_withner \
or (example_index, (m2[0], m2[1], pos2ner[m2]), (m1[0], m1[1], pos2ner[m1]), pred_label) in golden_labels_withner:
cor_with_ner += 2
else:
if (example_index, (m1[0], m1[1], pos2ner[m1]), (m2[0], m2[1], pos2ner[m2]), pred_label) in golden_labels_withner:
cor_with_ner += 1
if do_test:
#output_w.write(json.dumps(output_preds) + '\n')
tot_output_results[example_index[0]].append((example_index[1], output_preds))
# refine NER results
ner_results = list(global_predicted_ners[example_index])
for i in range(len(ner_results)):
start, end, label = ner_results[i]
if (example_index, (start, end), label) in ner_golden_labels:
ner_ori_cor += 1
if (start, end) in pos2ner:
label = pos2ner[(start, end)]
if (example_index, (start, end), label) in ner_golden_labels:
ner_cor += 1
ner_tot_pred += 1
else:
for example_index, pair_dict in sorted(scores.items(), key=lambda x:x[0]):
sentence_results = []
for k1, (v1, v2_ner_label) in pair_dict.items():
if v2_ner_label=='NIL':
continue
v1 = list(v1)
m1 = k1[0]
m2 = k1[1]
if m1 == m2:
continue
pred_label = np.argmax(v1)
if pred_label>0 and pred_label < num_label:
pred_score = v1[pred_label]
sentence_results.append( (pred_score, m1, m2, pred_label, None, v2_ner_label) )
sentence_results.sort(key=lambda x: -x[0])
no_overlap = []
def is_overlap(m1, m2):
if m2[0]<=m1[0] and m1[0]<=m2[1]:
return True
if m1[0]<=m2[0] and m2[0]<=m1[1]:
return True
return False
output_preds = []
for item in sentence_results:
m1 = item[1]
m2 = item[2]
overlap = False
for x in no_overlap:
_m1 = x[1]
_m2 = x[2]
if item[3]==x[3] and (is_overlap(m1, _m1) and is_overlap(m2, _m2)):
overlap = True
break
pred_label = label_list[item[3]]
output_preds.append((m1, m2, pred_label))
if not overlap:
no_overlap.append(item)
pos2ner = {}
predpos2ner = {}
ner_results = list(global_predicted_ners[example_index])
for start, end, label in ner_results:
predpos2ner[(start, end)] = label
for item in no_overlap:
m1 = item[1]
m2 = item[2]
pred_label = label_list[item[3]]
tot_pred += 1
if (example_index, m1, m2, pred_label) in golden_labels:
cor += 1
if m1 not in pos2ner:
pos2ner[m1] = predpos2ner[m1]#item[4]
if m2 not in pos2ner:
pos2ner[m2] = item[5]
# if pred_label in sym_labels:
# if (example_index, (m1[0], m1[1], pos2ner[m1]), (m2[0], m2[1], pos2ner[m2]), pred_label) in golden_labels_withner \
# or (example_index, (m2[0], m2[1], pos2ner[m2]), (m1[0], m1[1], pos2ner[m1]), pred_label) in golden_labels_withner:
# cor_with_ner += 2
# else:
if (example_index, (m1[0], m1[1], pos2ner[m1]), (m2[0], m2[1], pos2ner[m2]), pred_label) in golden_labels_withner:
cor_with_ner += 1
# refine NER results
ner_results = list(global_predicted_ners[example_index])
for i in range(len(ner_results)):
start, end, label = ner_results[i]
if (example_index, (start, end), label) in ner_golden_labels:
ner_ori_cor += 1
if (start, end) in pos2ner:
label = pos2ner[(start, end)]
if (example_index, (start, end), label) in ner_golden_labels:
ner_cor += 1
ner_tot_pred += 1
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f example per second)", evalTime, len(global_predicted_ners) / evalTime)
if do_test:
output_w = open(os.path.join(args.output_dir, 'pred_results.json'), 'w')
json.dump(tot_output_results, output_w)
ner_p = ner_cor / ner_tot_pred if ner_tot_pred > 0 else 0
ner_r = ner_cor / len(ner_golden_labels)
ner_f1 = 2 * (ner_p * ner_r) / (ner_p + ner_r) if ner_cor > 0 else 0.0