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train.py
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train.py
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"""Train a model on SQuAD.
Author:
Chris Chute ([email protected])
"""
import numpy as np
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as sched
import torch.utils.data as data
import util
from args import get_train_args
from collections import OrderedDict
from json import dumps
from models import *
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from ujson import load as json_load
from util import collate_fn, SQuAD
import faulthandler
def main(args):
# Set up faulthandler
faulthandler.enable()
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info(f'Using random seed {args.seed}...')
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
char_vectors = util.torch_from_json(args.char_emb_file)
# Get model
log.info('Building model...')
model_params = {'word_vectors':word_vectors, 'char_vectors':char_vectors, 'args':args}
model = get_model(args.model, model_params)
print('Model size: {:f} MB'.format(sum(p.nelement()*p.element_size() for p in model.parameters())/(1024*1024)))
# model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info(f'Loading checkpoint from {args.load_path}...')
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
optimizer = optim.Adadelta(model.parameters(), args.lr,
weight_decay=args.l2_wd)
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# Get data loader
log.info('Building dataset...')
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
while epoch != args.num_epochs:
epoch += 1
log.info(f'Starting epoch {epoch}...')
with torch.enable_grad(), \
tqdm(total=len(train_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
progress_bar.set_description('Batch data_loading finished'.ljust(30))
progress_bar.refresh()
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
progress_bar.set_description('Batch initialization finished'.ljust(30))
progress_bar.refresh()
# Forward
faulthandler.dump_traceback_later(timeout=3)
log_p1, log_p2 = model(cw_idxs, cc_idxs, qw_idxs, qc_idxs)
faulthandler.cancel_dump_traceback_later()
progress_bar.set_description('Batch forward finished'.ljust(30))
progress_bar.refresh()
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
# Backward
faulthandler.dump_traceback_later(timeout=3)
loss.backward()
faulthandler.cancel_dump_traceback_later()
progress_bar.set_description('Batch backward finished'.ljust(30))
progress_bar.refresh()
nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
progress_bar.set_description('Optimization finished'.ljust(30))
progress_bar.refresh()
scheduler.step()
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch,
NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR',
optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info(f'Evaluating at step {step}...')
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
progress_bar.set_description('Evaluation finished'.ljust(30))
progress_bar.refresh()
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join(f'{k}: {v:05.2f}' for k, v in results.items())
log.info(f'Dev {results_str}')
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar(f'dev/{k}', v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
def evaluate(model, data_loader, device, eval_file, max_len, use_squad_v2):
nll_meter = util.AverageMeter()
model.eval()
pred_dict = {}
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(data_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
batch_size = cw_idxs.size(0)
# Forward
log_p1, log_p2 = model(cw_idxs, cc_idxs, qw_idxs, qc_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
p1, p2 = log_p1.exp(), log_p2.exp()
starts, ends = util.discretize(p1, p2, max_len, use_squad_v2)
# Log info
progress_bar.update(batch_size)
progress_bar.set_postfix(NLL=nll_meter.avg)
preds, _ = util.convert_tokens(gold_dict,
ids.tolist(),
starts.tolist(),
ends.tolist(),
use_squad_v2)
pred_dict.update(preds)
model.train()
results = util.eval_dicts(gold_dict, pred_dict, use_squad_v2)
results_list = [('NLL', nll_meter.avg),
('F1', results['F1']),
('EM', results['EM'])]
if use_squad_v2:
results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
return results, pred_dict
def get_model(model_name, model_params):
if model_name == 'BiDAF_W':
args = model_params['args']
return BiDAF_W(model_params['word_vectors'], args.hidden_size, args.drop_prob)
elif model_name == 'BiDAF_CW':
args = model_params['args']
return BiDAF_CW(model_params['word_vectors'], model_params['char_vectors'], args.char_cnn_o_size, args.hidden_size, args.drop_prob)
elif model_name == 'Coattention_W':
args = model_params['args']
return DCN_W(model_params['word_vectors'], args.hidden_size, args.drop_prob)
if __name__ == '__main__':
main(get_train_args())