def main(args):
# 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('Args: {}'.format(dumps(vars(args), indent=4, sort_keys=True)))
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info('Using random seed {}...'.format(args.seed))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get embeddings
# Args: word_vectors: word vector tensor of dimension [vocab_size * wemb_dim]
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 = QANet(word_vectors,
char_vectors,
args.para_limit,
args.ques_limit,
args.f_model,
num_head=args.num_head,
train_cemb = (not args.pretrained_char))
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info('Loading checkpoint from {}...'.format(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
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adam(
params=parameters,
lr=args.lr,
betas=(args.beta1, args.beta2),
eps=1e-8,
weight_decay=3e-7)
cr = 1.0 / math.log(args.lr_warm_up_num)
scheduler = optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lambda ee: cr * math.log(ee + 1)
if ee < args.lr_warm_up_num else 1)
loss_f = torch.nn.CrossEntropyLoss()
# 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('Starting epoch {}...'.format(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:
# 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()
# Forward
log_p1, log_p2 = model(cw_idxs, cc_idxs, qw_idxs, qc_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = torch.mean(loss_f(log_p1, y1) + loss_f(log_p2, y2))
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step(step // batch_size)
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('Evaluating at step {}...'.format(step))
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('Dev {}'.format(results_str))
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar('dev/{}'.format(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 main(args):
# 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)
np.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 = BiDAF(word_vectors=word_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
"""
'''
model = charBiDAF(word_vectors=word_vectors,
char_vectors=char_vectors,
emb_size=char_vectors.size(1),
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
'''
model = QANet(word_vectors=word_vectors,
char_vectors=char_vectors,
emb_size=char_vectors.size(1),
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
print(
"YOU ARE ENTERING THE QA NET MODEL TRAINING_____________________________________________________________________"
)
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()
#print("Model status - ", cuda_or_cpu(model))
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)
#torch.utils.data._utils.MP_STATUS_CHECK_INTERVAL = 300
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=0,
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:
# Setup for forward
cw_idxs = cw_idxs.to(device)
cc_idxs = cc_idxs.to(device)
qw_idxs = qw_idxs.to(device)
qc_idxs = qc_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# 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)
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
#print(torch.cuda.memory_allocated(device=None))
scheduler.step(step // batch_size)
ema(model, step // batch_size)
#torch.cuda.empty_cache()
# 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}...')
# print('Memory 1: ', torch.cuda.memory_allocated())
ema.assign(model)
# print('Memory 2: ', torch.cuda.memory_allocated())
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
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 train(config):
from models import QANet
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.char_emb_file, "r") as fh:
char_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
train_log = open(config.train_log, "w")
dev_total = meta["total"]
print("Building model...")
train_dataset = SQuADDataset(config.train_record_file, config.num_steps,
config.batch_size)
dev_dataset = SQuADDataset(config.dev_record_file, config.val_num_batches,
config.batch_size)
lr = config.learning_rate
model = QANet(word_mat, char_mat).to(device)
model.train()
parameters = filter(lambda param: param.requires_grad, model.parameters())
optimizer = optim.Adam(betas=(0.8, 0.999),
eps=1e-7,
weight_decay=3e-7,
params=parameters)
crit = lr / math.log2(1000)
scheduler = optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lambda ee: crit * math.log2(ee + 1)
if ee + 1 <= 1000 else lr)
for ep in tqdm(range(config.num_steps), total=config.num_steps):
model.zero_grad()
(Cwid, Ccid, Qwid, Qcid, y1, y2, ids) = train_dataset[ep]
Cwid, Ccid, Qwid, Qcid = Cwid.to(device), Ccid.to(device), Qwid.to(
device), Qcid.to(device)
p1, p2 = model(Cwid, Ccid, Qwid, Qcid)
y1, y2 = y1.to(device), y2.to(device)
loss1 = F.cross_entropy(p1, y1)
loss2 = F.cross_entropy(p2, y2)
loss = loss1 + loss2
loss.backward(retain_graph=True)
scheduler.step()
del loss, p1, p2
if (ep + 1) % config.checkpoint == 0:
del Cwid, Ccid, Qwid, Qcid, y1, y2, p1, p2, loss
torch.cuda.empty_cache()
metric = evaluate_batch(model, dev_eval_file, dev_dataset)
log_ = "EPOCH {:8d} loss {:8f} F1 {:8f} EM {:8f}\n".format(
ep, metric["loss"], metric["f1"], metric["exact_match"])
train_log.write(log_)
train_log.flush()
dev_f1 = metric["f1"]
dev_em = metric["exact_match"]
if dev_f1 < best_f1 and dev_em < best_em:
patience += 1
if patience > config.early_stop:
break
else:
patience = 0
best_em = max(best_em, dev_em)
best_f1 = max(best_f1, dev_f1)
fn = os.path.join(config.save_dir, "model_{}.ckpt".format(ep))
torch.save(model, fn, pickle_protocol=False)
def main(args):
# 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('Args: {}'.format(dumps(vars(args), indent=4, sort_keys=True)))
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info('Using random seed {}...'.format(args.seed))
random.seed(args.seed)
np.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)
# setup_args = get_setup_args()
with open(args.char2idx_file, "r") as f:
char2idx = json_load(f)
# Get model
log.info('Building model...')
model = QANet(word_vectors=word_vectors, char2idx=char2idx)
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info('Loading checkpoint from {}...'.format(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)
# optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.8, 0.999))
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# Get data loader
log.info('Building dataset...')
dataset1 = np.load(args.train_record_file)
outfile = '/content/dataset/train_light.npz'
n_row = 75000
np.savez(outfile,
context_idxs=dataset1['context_idxs'][:n_row],
context_char_idxs=dataset1['context_char_idxs'][:n_row],
ques_idxs=dataset1['ques_idxs'][:n_row],
ques_char_idxs=dataset1['ques_char_idxs'][:n_row],
y1s=dataset1['y1s'][:n_row],
y2s=dataset1['y2s'][:n_row],
ids=dataset1['ids'][:n_row])
# train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_dataset = SQuAD(outfile, 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('Starting epoch {}...'.format(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:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# 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)
loss_val = loss.item()
if step % 10000 == 0:
print('loss val: {}'.format(loss_val))
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step(step // batch_size)
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('Evaluating at step {}...'.format(step))
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('Dev {}'.format(results_str))
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar('dev/{}'.format(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 main(args):
# 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('Args: {}'.format(dumps(vars(args), indent=4, sort_keys=True)))
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info('Using random seed {}...'.format(args.seed))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get word embeddings
log.info('Loading word embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
### start our code:
# Get char-embeddings
log.info('Loading char-embeddings...')
char_vectors = util.torch_from_json(args.char_emb_file)
### end our code
# Get model
log.info('Building model...')
# model = BiDAF(word_vectors=word_vectors,
# hidden_size=args.hidden_size,
# char_vectors = char_vectors,
# drop_prob=args.drop_prob)
### start our code: (QANet)
model = QANet(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
kernel_size=7,
filters=128,
drop_prob=args.drop_prob)
### end our code
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info('Loading checkpoint from {}...'.format(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
# https://pytorch.org/docs/stable/optim.html
# Original:
# optimizer = optim.Adadelta(model.parameters(), args.lr,
# weight_decay=args.l2_wd)
# # default: lr=0.5, rho=0.9, eps=1e-06, weight_decay=0
# scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# A function which computes a multiplicative factor given an integer parameter epoch,
# or a list of such functions, one for each group in optimizer.param_groups
### start our code:
optimizer = optim.Adam(model.parameters(),
lr=1e-3,
betas=(0.8, 0.999),
eps=1e-7,
weight_decay=3e-7,
amsgrad=False)
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# Adabound
# optimizer = adabound.AdaBound(model.parameters(), lr=1e-3, final_lr=0.1)
# scheduler = sched.LambdaLR(optimizer, lambda s: 1.)
### end our code
# 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('Starting epoch {}...'.format(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:
# print("cc_idxs: ", cc_idxs)
# print("qc_idxs: ", qc_idxs)
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
### start our code:
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
### end our code
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# Forward
# log_p1, log_p2 = model(cw_idxs, qw_idxs) # original
### start our code:
log_p1, log_p2 = model(cw_idxs, qw_idxs, cc_idxs, qc_idxs)
### end our code
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
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
### start our code:
# optimizer = LR_decay(optimizer, epoch, lr = 0.8) # initial learning rate 0.8
if step < 1000:
optimizer = LR_warmup(optimizer, step, lr=0.)
### end our code
optimizer.step()
scheduler.step(step // batch_size)
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('Evaluating at step {}...'.format(step))
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('Dev {}'.format(results_str))
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar('dev/{}'.format(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)
