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train.py
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train.py
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"""Train and evaluate the model"""
import argparse
import random
import logging
import os
import torch
import transformers
import torch.nn as nn
from torch.optim.lr_scheduler import LambdaLR
from transformers.optimization import AdamW
from tqdm import trange
from sequence_tagger import BertOnlyForSequenceTagging as BertForSequenceTagging
from data_loader import DataLoader
from evaluate import evaluate
import utils
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='conll',
help="Directory containing the dataset")
parser.add_argument('--seed', type=int, default=2019,
help="random seed for initialization")
parser.add_argument('--restore_dir', default=None,
help="Optional, name of the directory containing weights to reload before training, e.g., 'experiments/conll/'")
parser.add_argument('--multi_gpu', default=False, action='store_true',
help="Whether to use multiple GPUs if available")
parser.add_argument('--fp16', default=False, action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--loss_scale', type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
def train(model, data_iterator, optimizer, scheduler, params):
"""Train the model on `steps` batches"""
# set model to training mode
model.train()
# scheduler.step()
# a running average object for loss
loss_avg = utils.RunningAverage()
# Use tqdm for progress bar
one_epoch = trange(params.train_steps)
for step, batch in zip(one_epoch, data_iterator):
# fetch the next training batch
input_ids, label_ids, attention_mask, sentence_ids, label_mask = batch
# compute model output and loss
loss = model(input_ids, token_type_ids=sentence_ids,
attention_mask=attention_mask, labels=label_ids, label_masks=label_mask)[0]
if params.n_gpu > 1 and args.multi_gpu:
loss = loss.mean() # mean() to average on multi-gpu
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
# gradient clipping
nn.utils.clip_grad_norm_(
parameters=model.parameters(), max_norm=params.clip_grad)
# performs updates using calculated gradients
optimizer.step()
scheduler.step()
# clear previous gradients, compute gradients of all variables wrt loss
model.zero_grad()
# update the average loss
loss_avg.update(loss.item())
one_epoch.set_postfix(loss='{:05.3f}'.format(loss_avg()))
def train_and_evaluate(model, train_data, val_data, optimizer, scheduler, params, model_dir, restore_dir=None):
"""Train the model and evaluate every epoch."""
# reload weights from restore_dir if specified
if restore_dir is not None:
model = BertForSequenceTagging.from_pretrained(tagger_model_dir)
best_val_f1 = 0.0
patience_counter = 0
for epoch in range(1, params.epoch_num + 1):
# Run one epoch
logging.info("Epoch {}/{}".format(epoch, params.epoch_num))
# Compute number of batches in one epoch
params.train_steps = params.train_size // params.batch_size
params.val_steps = params.val_size // params.batch_size
# data iterator for training
train_data_iterator = data_loader.data_iterator(
train_data, shuffle=True)
# Train for one epoch on training set
train(model, train_data_iterator, optimizer, scheduler, params)
# data iterator for evaluation
# train_data_iterator = data_loader.data_iterator(train_data, shuffle=False)
val_data_iterator = data_loader.data_iterator(val_data, shuffle=False)
# Evaluate for one epoch on training set and validation set
# params.eval_steps = params.train_steps
# train_metrics = evaluate(model, train_data_iterator, params, mark='Train')
params.eval_steps = params.val_steps
val_metrics = evaluate(model, val_data_iterator, params, mark='Val')
val_f1 = val_metrics['f1']
improve_f1 = val_f1 - best_val_f1
if improve_f1 > 0:
logging.info("- Found new best F1")
best_val_f1 = val_f1
model.save_pretrained(model_dir)
if improve_f1 < params.patience:
patience_counter += 1
else:
patience_counter = 0
else:
patience_counter += 1
# Early stopping and logging best f1
if (patience_counter >= params.patience_num and epoch > params.min_epoch_num) or epoch == params.epoch_num:
logging.info("Best val f1: {:05.2f}".format(best_val_f1))
break
if __name__ == '__main__':
args = parser.parse_args()
tagger_model_dir = 'experiments/' + args.dataset
# Load the parameters from json file
json_path = os.path.join(tagger_model_dir, 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = utils.Params(json_path)
# Use GPUs if available
params.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
params.n_gpu = torch.cuda.device_count()
params.multi_gpu = args.multi_gpu
# Set the random seed for reproducible experiments
random.seed(args.seed)
torch.manual_seed(args.seed)
if params.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed) # set random seed for all GPUs
params.seed = args.seed
# Set the logger
utils.set_logger(os.path.join(tagger_model_dir, 'train.log'))
logging.info(
"device: {}, n_gpu: {}, 16-bits training: {}".format(params.device, params.n_gpu, args.fp16))
# Create the input data pipeline
logging.info("Loading the datasets...")
# Initialize the DataLoader
data_dir = 'data/' + args.dataset
if args.dataset in ["conll"]:
bert_model_dir = 'pretrained_bert_models/bert-base-cased/'
elif args.dataset in ["msra"]:
bert_model_dir = 'pretrained_bert_models/bert-base-chinese/'
data_loader = DataLoader(data_dir, bert_model_dir, params)
# Load training data and test data
train_data = data_loader.load_data('train')
val_data = data_loader.load_data('val')
# Specify the training and validation dataset sizes
params.train_size = train_data.__len__()
params.val_size = val_data.__len__()
# Prepare model
model = BertForSequenceTagging.from_pretrained(
bert_model_dir, num_labels=len(params.tag2idx))
model.to(params.device)
if args.fp16:
model.half()
if params.n_gpu > 1 and args.multi_gpu:
model = torch.nn.DataParallel(model)
# Prepare optimizer
if params.full_finetuning:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay': params.weight_decay},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay': 0.0}
]
else: # only finetune the head classifier
param_optimizer = list(model.classifier.named_parameters())
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer]}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"lease install apex from https://www.github.com/nvidia/apex to use fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=params.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
scheduler = LambdaLR(
optimizer, lr_lambda=lambda epoch: 1 / (1 + 0.05 * epoch))
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(
optimizer, static_loss_scale=args.loss_scale)
else:
# optimizer = AdamW(model.parameters(), lr=params.learning_rate, correct_bias=False)
optimizer = AdamW(optimizer_grouped_parameters,
lr=params.learning_rate)
train_steps_per_epoch = params.train_size // params.batch_size
warmup_steps = int(params.warmup_proportion * params.epoch_num * train_steps_per_epoch)
scheduler = transformers.get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=warmup_steps, num_training_steps=params.epoch_num * train_steps_per_epoch)
# Train and evaluate the model
logging.info("Starting training for {} epoch(s)".format(params.epoch_num))
train_and_evaluate(model, train_data, val_data, optimizer,
scheduler, params, tagger_model_dir, args.restore_dir)