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"""
This script provides an example to wrap TencentPretrain for NER.
"""
import sys
import os
import random
import argparse
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.append(tencentpretrain_dir)
from tencentpretrain.embeddings import *
from tencentpretrain.encoders import *
from tencentpretrain.utils.config import load_hyperparam
from tencentpretrain.utils.optimizers import *
from tencentpretrain.utils.constants import *
from tencentpretrain.utils.vocab import Vocab
from tencentpretrain.utils.seed import set_seed
from tencentpretrain.utils.logging import init_logger
from tencentpretrain.utils.tokenizers import *
from tencentpretrain.model_saver import save_model
from tencentpretrain.opts import finetune_opts
from finetune.run_classifier import build_optimizer, load_or_initialize_parameters
class NerTagger(nn.Module):
def __init__(self, args):
super(NerTagger, self).__init__()
self.embedding = Embedding(args)
for embedding_name in args.embedding:
tmp_emb = str2embedding[embedding_name](args, len(args.tokenizer.vocab))
self.embedding.update(tmp_emb, embedding_name)
self.encoder = str2encoder[args.encoder](args)
self.labels_num = args.labels_num
self.output_layer = nn.Linear(args.hidden_size, self.labels_num)
self.crf_target = args.crf_target
if args.crf_target:
from torchcrf import CRF
self.crf = CRF(self.labels_num, batch_first=True)
self.seq_length = args.seq_length
def forward(self, src, tgt, seg):
"""
Args:
src: [batch_size x seq_length]
tgt: [batch_size x seq_length]
seg: [batch_size x seq_length]
Returns:
loss: Sequence labeling loss.
logits: Output logits.
"""
# Embedding.
emb = self.embedding(src, seg)
# Encoder.
output = self.encoder(emb, seg)
# Target.
logits = self.output_layer(output)
if self.crf_target:
tgt_mask = seg.type(torch.uint8)
pred = self.crf.decode(logits, mask=tgt_mask)
for j in range(len(pred)):
while len(pred[j]) < self.seq_length:
pred[j].append(self.labels_num - 1)
pred = torch.tensor(pred).contiguous().view(-1)
if tgt is not None:
loss = -self.crf(F.log_softmax(logits, 2), tgt, mask=tgt_mask, reduction='mean')
return loss, pred
else:
return None, pred
else:
tgt_mask = seg.contiguous().view(-1).float()
logits = logits.contiguous().view(-1, self.labels_num)
pred = logits.argmax(dim=-1)
if tgt is not None:
tgt = tgt.contiguous().view(-1, 1)
one_hot = torch.zeros(tgt.size(0), self.labels_num). \
to(torch.device(tgt.device)). \
scatter_(1, tgt, 1.0)
numerator = -torch.sum(nn.LogSoftmax(dim=-1)(logits) * one_hot, 1)
numerator = torch.sum(tgt_mask * numerator)
denominator = torch.sum(tgt_mask) + 1e-6
loss = numerator / denominator
return loss, pred
else:
return None, pred
def read_dataset(args, path):
dataset, columns = [], {}
with open(path, mode="r", encoding="utf-8") as f:
for line_id, line in enumerate(f):
if line_id == 0:
for i, column_name in enumerate(line.rstrip("\r\n").split("\t")):
columns[column_name] = i
continue
line = line.rstrip("\r\n").split("\t")
labels = line[columns["label"]]
tgt = [args.l2i[l] for l in labels.split(" ")]
text_a = line[columns["text_a"]]
src = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_a))
seg = [1] * len(src)
if len(src) > args.seq_length:
src = src[: args.seq_length]
tgt = tgt[: args.seq_length]
seg = seg[: args.seq_length]
PAD_ID = args.tokenizer.convert_tokens_to_ids([PAD_TOKEN])[0]
while len(src) < args.seq_length:
src.append(PAD_ID)
tgt.append(args.labels_num - 1)
seg.append(0)
dataset.append([src, tgt, seg])
return dataset
def batch_loader(batch_size, src, tgt, seg):
instances_num = src.size()[0]
for i in range(instances_num // batch_size):
src_batch = src[i * batch_size : (i + 1) * batch_size, :]
tgt_batch = tgt[i * batch_size : (i + 1) * batch_size, :]
seg_batch = seg[i * batch_size : (i + 1) * batch_size, :]
yield src_batch, tgt_batch, seg_batch
if instances_num > instances_num // batch_size * batch_size:
src_batch = src[instances_num // batch_size * batch_size :, :]
tgt_batch = tgt[instances_num // batch_size * batch_size :, :]
seg_batch = seg[instances_num // batch_size * batch_size :, :]
yield src_batch, tgt_batch, seg_batch
def train(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch):
model.zero_grad()
src_batch = src_batch.to(args.device)
tgt_batch = tgt_batch.to(args.device)
seg_batch = seg_batch.to(args.device)
loss, _ = model(src_batch, tgt_batch, seg_batch)
if torch.cuda.device_count() > 1:
loss = torch.mean(loss)
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
optimizer.step()
scheduler.step()
return loss
def evaluate(args, dataset):
src = torch.LongTensor([sample[0] for sample in dataset])
tgt = torch.LongTensor([sample[1] for sample in dataset])
seg = torch.LongTensor([sample[2] for sample in dataset])
instances_num = src.size(0)
batch_size = args.batch_size
correct, gold_entities_num, pred_entities_num = 0, 0, 0
args.model.eval()
for i, (src_batch, tgt_batch, seg_batch) in enumerate(batch_loader(batch_size, src, tgt, seg)):
src_batch = src_batch.to(args.device)
tgt_batch = tgt_batch.to(args.device)
seg_batch = seg_batch.to(args.device)
loss, pred = args.model(src_batch, tgt_batch, seg_batch)
gold = tgt_batch.contiguous().view(-1, 1)
for j in range(gold.size()[0]):
if gold[j].item() in args.begin_ids:
gold_entities_num += 1
for j in range(pred.size()[0]):
if pred[j].item() in args.begin_ids and gold[j].item() != args.l2i["[PAD]"]:
pred_entities_num += 1
pred_entities_pos, gold_entities_pos = set(), set()
for j in range(gold.size()[0]):
if gold[j].item() in args.begin_ids:
start = j
for k in range(j + 1, gold.size()[0]):
if gold[k].item() == args.l2i["[PAD]"] or gold[k].item() == args.l2i["O"] or gold[k].item() in args.begin_ids:
end = k - 1
break
else:
end = gold.size()[0] - 1
gold_entities_pos.add((start, end))
for j in range(pred.size()[0]):
if pred[j].item() in args.begin_ids and gold[j].item() != args.l2i["[PAD]"]:
start = j
for k in range(j + 1, pred.size()[0]):
if pred[k].item() == args.l2i["[PAD]"] or pred[k].item() == args.l2i["O"] or pred[k].item() in args.begin_ids:
end = k - 1
break
else:
end = pred.size()[0] - 1
pred_entities_pos.add((start, end))
for entity in pred_entities_pos:
if entity not in gold_entities_pos:
continue
for j in range(entity[0], entity[1] + 1):
if gold[j].item() != pred[j].item():
break
else:
correct += 1
args.logger.info("Report precision, recall, and f1:")
eps = 1e-9
p = correct / (pred_entities_num + eps)
r = correct / (gold_entities_num + eps)
f1 = 2 * p * r / (p + r + eps)
args.logger.info("{:.3f}, {:.3f}, {:.3f}".format(p, r, f1))
return f1
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
finetune_opts(parser)
parser.add_argument("--vocab_path", default=None, type=str,
help="Path of the vocabulary file.")
parser.add_argument("--spm_model_path", default=None, type=str,
help="Path of the sentence piece model.")
parser.add_argument("--label2id_path", type=str, required=True,
help="Path of the label2id file.")
parser.add_argument("--crf_target", action="store_true",
help="Use CRF loss as the target function or not, default False.")
args = parser.parse_args()
# Load the hyperparameters of the config file.
args = load_hyperparam(args)
# Get logger.
args.logger = init_logger(args)
set_seed(args.seed)
args.begin_ids = []
with open(args.label2id_path, mode="r", encoding="utf-8") as f:
l2i = json.load(f)
args.logger.info("Labels: " + str(l2i))
l2i["[PAD]"] = len(l2i)
for label in l2i:
if label.startswith("B"):
args.begin_ids.append(l2i[label])
args.l2i = l2i
args.labels_num = len(l2i)
args.tokenizer = SpaceTokenizer(args)
# Build sequence labeling model.
model = NerTagger(args)
# Load or initialize parameters.
load_or_initialize_parameters(args, model)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(args.device)
# Training phase.
instances = read_dataset(args, args.train_path)
instances_num = len(instances)
batch_size = args.batch_size
args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1
args.logger.info("Batch size: {}".format(batch_size))
args.logger.info("The number of training instances: {}".format(instances_num))
optimizer, scheduler = build_optimizer(args, model)
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)
if torch.cuda.device_count() > 1:
args.logger.info("{} GPUs are available. Let's use them.".format(torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
args.model = model
total_loss, f1, best_f1 = 0.0, 0.0, 0.0
args.logger.info("Start training.")
for epoch in range(1, args.epochs_num + 1):
random.shuffle(instances)
src = torch.LongTensor([ins[0] for ins in instances])
tgt = torch.LongTensor([ins[1] for ins in instances])
seg = torch.LongTensor([ins[2] for ins in instances])
model.train()
for i, (src_batch, tgt_batch, seg_batch) in enumerate(batch_loader(batch_size, src, tgt, seg)):
loss = train(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch)
total_loss += loss.item()
if (i + 1) % args.report_steps == 0:
args.logger.info("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".format(epoch, i + 1, total_loss / args.report_steps))
total_loss = 0.0
f1 = evaluate(args, read_dataset(args, args.dev_path))
if f1 > best_f1:
best_f1 = f1
save_model(model, args.output_model_path)
else:
continue
# Evaluation phase.
if args.test_path is not None:
args.logger.info("Test set evaluation.")
if torch.cuda.device_count() > 1:
args.model.module.load_state_dict(torch.load(args.output_model_path))
else:
args.model.load_state_dict(torch.load(args.output_model_path))
evaluate(args, read_dataset(args, args.test_path))
if __name__ == "__main__":
main()
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