def clean_memory(device_name):
utils.sync_out_streams()
print('\nClearning memory device:', device_name)
utils.sync_out_streams()
gc.collect()
if device_name != DEVICE_CPU:
with torch.cuda.device(device_name):
torch.cuda.empty_cache()
def validate(model, train_params, dataset, run, qrelf, epoch, model_out_dir):
rerank_run = run_model(model, train_params, dataset, run)
eval_metric = train_params.eval_metric
utils.sync_out_streams()
print(f'Evaluating run with QREL file {qrelf} using metric {eval_metric}')
utils.sync_out_streams()
runf = os.path.join(model_out_dir, f'{epoch}.run')
return getEvalResults(train_params.use_external_eval, eval_metric,
rerank_run, runf, qrelf)
def validate(model, train_params, dataset, orig_run, qrelf, run_filename):
"""Model validation step:
1. Re-rank a given run
2. Save the re-ranked run
3. Evaluate results
:param model: a model reference.
:param train_params: training parameters
:param dataset: validation dataset
:param orig_run: a run to re-rank
:param qrelf: QREL files
:param run_filename: a file name to store the *RE-RANKED* run
:return:
"""
utils.sync_out_streams()
rerank_run = run_model(model, train_params, dataset, orig_run)
eval_metric = train_params.eval_metric
utils.sync_out_streams()
print(
f'\nEvaluating run with QREL file {qrelf} using metric {eval_metric}')
utils.sync_out_streams()
# Let us always save the run
return getEvalResults(useExternalEval=train_params.use_external_eval,
evalMetric=eval_metric,
rerankRun=rerank_run,
qrelFile=qrelf,
runFile=run_filename)
def main_cli():
parser = argparse.ArgumentParser('CEDR model training and validation')
model_init_utils.add_model_init_basic_args(parser, True)
parser.add_argument('--datafiles',
metavar='data files',
help='data files: docs & queries',
type=argparse.FileType('rt'),
nargs='+',
required=True)
parser.add_argument('--qrels',
metavar='QREL file',
help='QREL file',
type=argparse.FileType('rt'),
required=True)
parser.add_argument('--train_pairs',
metavar='paired train data',
help='paired train data',
type=argparse.FileType('rt'),
required=True)
parser.add_argument('--valid_run',
metavar='validation file',
help='validation file',
type=argparse.FileType('rt'),
required=True)
parser.add_argument('--model_out_dir',
metavar='model out dir',
help='an output directory for the trained model',
required=True)
parser.add_argument('--epoch_qty',
metavar='# of epochs',
help='# of epochs',
type=int,
default=10)
parser.add_argument('--no_cuda', action='store_true')
parser.add_argument('--warmup_pct',
metavar='warm-up fraction',
default=None,
type=float,
help='use a warm-up/cool-down learning-reate schedule')
parser.add_argument('--device_qty',
type=int,
metavar='# of device for multi-GPU training',
default=1,
help='# of GPUs for multi-GPU training')
parser.add_argument(
'--batch_sync_qty',
metavar='# of batches before model sync',
type=int,
default=4,
help=
'Model syncronization frequency for multi-GPU trainig in the # of batche'
)
parser.add_argument('--master_port',
type=int,
metavar='pytorch master port',
default=None,
help='pytorch master port for multi-GPU training')
parser.add_argument('--print_grads',
action='store_true',
help='print gradient norms of parameters')
parser.add_argument('--save_epoch_snapshots',
action='store_true',
help='save model after each epoch')
parser.add_argument(
'--save_last_snapshot_every_k_batch',
metavar='debug: save latest snapshot every k batch',
type=int,
default=None,
help='debug option: save latest snapshot every k batch')
parser.add_argument('--seed',
metavar='random seed',
help='random seed',
type=int,
default=42)
parser.add_argument('--optim',
metavar='optimizer',
choices=[OPT_SGD, OPT_ADAMW],
default=OPT_ADAMW,
help='Optimizer')
parser.add_argument('--loss_margin',
metavar='loss margin',
help='Margin in the margin loss',
type=float,
default=1)
parser.add_argument(
'--init_lr',
metavar='init learn. rate',
type=float,
default=0.001,
help='Initial learning rate for BERT-unrelated parameters')
parser.add_argument('--momentum',
metavar='SGD momentum',
type=float,
default=0.9,
help='SGD momentum')
parser.add_argument('--init_bert_lr',
metavar='init BERT learn. rate',
type=float,
default=0.00005,
help='Initial learning rate for BERT parameters')
parser.add_argument('--epoch_lr_decay',
metavar='epoch LR decay',
type=float,
default=1.0,
help='Per-epoch learning rate decay')
parser.add_argument('--weight_decay',
metavar='weight decay',
type=float,
default=0.0,
help='optimizer weight decay')
parser.add_argument('--batch_size',
metavar='batch size',
type=int,
default=32,
help='batch size')
parser.add_argument('--batch_size_val',
metavar='val batch size',
type=int,
default=32,
help='validation batch size')
parser.add_argument('--backprop_batch_size',
metavar='backprop batch size',
type=int,
default=1,
help='batch size for each backprop step')
parser.add_argument(
'--batches_per_train_epoch',
metavar='# of rand. batches per epoch',
type=int,
default=0,
help='# of random batches per epoch: 0 tells to use all data')
parser.add_argument(
'--max_query_val',
metavar='max # of val queries',
type=int,
default=0,
help='max # of validation queries: 0 tells to use all data')
parser.add_argument('--no_shuffle_train',
action='store_true',
help='disabling shuffling of training data')
parser.add_argument('--use_external_eval',
action='store_true',
help='use external eval tools: gdeval or trec_eval')
parser.add_argument('--eval_metric',
choices=METRIC_LIST,
default=METRIC_LIST[0],
help='Metric list: ' + ','.join(METRIC_LIST),
metavar='eval metric')
parser.add_argument('--loss_func',
choices=LOSS_FUNC_LIST,
default=PairwiseSoftmaxLoss.name(),
help='Loss functions: ' + ','.join(LOSS_FUNC_LIST))
parser.add_argument(
'--json_conf',
metavar='JSON config',
type=str,
default=None,
help=
'a JSON config (simple-dictionary): keys are the same as args, takes precedence over command line args'
)
parser.add_argument(
'--valid_run_dir',
metavar='',
type=str,
default=None,
help='directory to store predictions on validation set')
parser.add_argument('--valid_checkpoints',
metavar='',
type=str,
default=None,
help='validation checkpoints (in # of batches)')
args = parser.parse_args()
print(args)
utils.sync_out_streams()
all_arg_names = vars(args).keys()
if args.json_conf is not None:
conf_file = args.json_conf
print(f'Reading configuration variables from {conf_file}')
add_conf = utils.read_json(conf_file)
for arg_name, arg_val in add_conf.items():
if arg_name not in all_arg_names:
print(f'Invalid option in the configuration file: {arg_name}')
sys.exit(1)
arg_default = getattr(args, arg_name)
exp_type = type(arg_default)
if arg_default is not None and type(arg_val) != exp_type:
print(
f'Invalid type in the configuration file: {arg_name} expected type: '
+ str(type(exp_type)) + f' default {arg_default}')
sys.exit(1)
print(f'Using {arg_name} from the config')
setattr(args, arg_name, arg_val)
# This hack copies max query and document length parameters to the model space parameters
# maybe some other approach is more elegant, but this one should at least work
setattr(args, f'{MODEL_PARAM_PREF}max_query_len', args.max_query_len)
setattr(args, f'{MODEL_PARAM_PREF}max_doc_len', args.max_doc_len)
if args.save_last_snapshot_every_k_batch is not None and args.save_last_snapshot_every_k_batch < 2:
print('--save_last_snapshot_every_k_batch should be > 1')
sys.exit(1)
utils.set_all_seeds(args.seed)
loss_name = args.loss_func
if loss_name == PairwiseSoftmaxLoss.name():
loss_obj = PairwiseSoftmaxLoss()
elif loss_name == MarginRankingLossWrapper.name():
loss_obj = MarginRankingLossWrapper(margin=args.loss_margin)
else:
print('Unsupported loss: ' + loss_name)
sys.exit(1)
# If we have the complete model, we just load it,
# otherwise we first create a model and load *SOME* of its weights.
# For example, if we start from an original BERT model, which has
# no extra heads, it we will load only the respective weights and
# initialize the weights of the head randomly.
if args.init_model is not None:
print('Loading a complete model from:', args.init_model.name)
model = torch.load(args.init_model.name, map_location='cpu')
elif args.init_model_weights is not None:
model = model_init_utils.create_model_from_args(args)
print('Loading model weights from:', args.init_model_weights.name)
model.load_state_dict(torch.load(args.init_model_weights.name,
map_location='cpu'),
strict=False)
else:
print('Creating the model from scratch!')
model = model_init_utils.create_model_from_args(args)
os.makedirs(args.model_out_dir, exist_ok=True)
print(model)
utils.sync_out_streams()
model.set_grad_checkpoint_param(args.grad_checkpoint_param)
dataset = data.read_datafiles(args.datafiles)
qrelf = args.qrels.name
qrels = readQrelsDict(qrelf)
train_pairs_all = data.read_pairs_dict(args.train_pairs)
valid_run = readRunDict(args.valid_run.name)
max_query_val = args.max_query_val
query_ids = list(valid_run.keys())
if max_query_val > 0:
query_ids = query_ids[0:max_query_val]
valid_run = {k: valid_run[k] for k in query_ids}
print('# of eval. queries:', len(query_ids), ' in the file',
args.valid_run.name)
device_qty = args.device_qty
master_port = args.master_port
if device_qty > 1:
if master_port is None:
print('Specify a master port for distributed training!')
sys.exit(1)
processes = []
is_distr_train = device_qty > 1
qids = []
if is_distr_train:
qids = list(train_pairs_all.keys())
sync_barrier = Barrier(device_qty)
# We must go in the reverse direction, b/c
# rank == 0 trainer is in the same process and
# we call the function do_train in the same process,
# i.e., this call is blocking processing and
# prevents other processes from starting.
for rank in range(device_qty - 1, -1, -1):
if is_distr_train:
device_name = f'cuda:{rank}'
else:
device_name = args.device_name
if args.no_cuda:
device_name = DEVICE_CPU
# When we have only a single GPP, the main process is its own master
is_master_proc = rank == 0
train_params = TrainParams(
init_lr=args.init_lr,
init_bert_lr=args.init_bert_lr,
momentum=args.momentum,
warmup_pct=args.warmup_pct,
batch_sync_qty=args.batch_sync_qty,
epoch_lr_decay=args.epoch_lr_decay,
weight_decay=args.weight_decay,
backprop_batch_size=args.backprop_batch_size,
batches_per_train_epoch=args.batches_per_train_epoch,
save_epoch_snapshots=args.save_epoch_snapshots,
save_last_snapshot_every_k_batch=args.
save_last_snapshot_every_k_batch,
batch_size=args.batch_size,
batch_size_val=args.batch_size_val,
max_query_len=args.max_query_len,
max_doc_len=args.max_doc_len,
epoch_qty=args.epoch_qty,
device_name=device_name,
use_external_eval=args.use_external_eval,
eval_metric=args.eval_metric.lower(),
print_grads=args.print_grads,
shuffle_train=not args.no_shuffle_train,
optim=args.optim)
train_pair_qty = len(train_pairs_all)
if is_distr_train or train_pair_qty < device_qty:
tpart_qty = int((train_pair_qty + device_qty - 1) / device_qty)
train_start = rank * tpart_qty
train_end = min(train_start + tpart_qty, len(qids))
train_pairs = {
k: train_pairs_all[k]
for k in qids[train_start:train_end]
}
else:
train_pairs = train_pairs_all
print('Process rank %d device %s using %d training pairs out of %d' %
(rank, device_name, len(train_pairs), train_pair_qty))
valid_checkpoints = [] if args.valid_checkpoints is None \
else list(map(int, args.valid_checkpoints.split(',')))
param_dict = {
'sync_barrier': sync_barrier,
'device_qty': device_qty,
'master_port': master_port,
'rank': rank,
'is_master_proc': is_master_proc,
'dataset': dataset,
'qrels': qrels,
'qrel_file_name': qrelf,
'train_pairs': train_pairs,
'valid_run': valid_run,
'valid_run_dir': args.valid_run_dir,
'valid_checkpoints': valid_checkpoints,
'model_out_dir': args.model_out_dir,
'model': model,
'loss_obj': loss_obj,
'train_params': train_params
}
if is_distr_train and not is_master_proc:
p = Process(target=do_train, kwargs=param_dict)
p.start()
processes.append(p)
else:
do_train(**param_dict)
for p in processes:
utils.join_and_check_stat(p)
if device_qty > 1:
dist.destroy_process_group()
def do_train(sync_barrier, device_qty, master_port, rank, is_master_proc,
dataset, qrels, qrel_file_name, train_pairs, valid_run,
valid_run_dir, valid_checkpoints, model_out_dir, model, loss_obj,
train_params):
if device_qty > 1:
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = str(master_port)
dist.init_process_group(utils.PYTORCH_DISTR_BACKEND,
rank=rank,
world_size=device_qty)
device_name = train_params.device_name
if is_master_proc:
print('Training parameters:')
print(train_params)
print('Loss function:', loss_obj.name())
print('Device name:', device_name)
model.to(device_name)
lr = train_params.init_lr
bert_lr = train_params.init_bert_lr
epoch_lr_decay = train_params.epoch_lr_decay
weight_decay = train_params.weight_decay
momentum = train_params.momentum
top_valid_score = None
train_stat = {}
validation_timer = ValidationTimer(valid_checkpoints)
valid_scores_holder = dict()
for epoch in range(train_params.epoch_qty):
params = [(k, v) for k, v in model.named_parameters()
if v.requires_grad]
non_bert_params = {
'params': [v for k, v in params if not k.startswith('bert.')]
}
bert_params = {
'params': [v for k, v in params if k.startswith('bert.')],
'lr': bert_lr
}
if train_params.optim == OPT_ADAMW:
optimizer = torch.optim.AdamW([non_bert_params, bert_params],
lr=lr,
weight_decay=weight_decay)
elif train_params.optim == OPT_SGD:
optimizer = torch.optim.SGD([non_bert_params, bert_params],
lr=lr,
weight_decay=weight_decay,
momentum=momentum)
else:
raise Exception('Unsupported optimizer: ' + train_params.optim)
bpte = train_params.batches_per_train_epoch
max_train_qty = data.train_item_qty(
train_pairs) if bpte <= 0 else bpte * train_params.batch_size
lr_steps = int(math.ceil(max_train_qty / train_params.batch_size))
scheduler = None
if train_params.warmup_pct:
if is_master_proc:
print('Using a scheduler with a warm-up for %f steps' %
train_params.warmup_pct)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
total_steps=lr_steps,
max_lr=[lr, bert_lr],
anneal_strategy='linear',
pct_start=train_params.warmup_pct)
if is_master_proc:
print('Optimizer', optimizer)
start_time = time.time()
loss = train_iteration(model=model,
sync_barrier=sync_barrier,
is_master_proc=is_master_proc,
device_qty=device_qty,
loss_obj=loss_obj,
train_params=train_params,
max_train_qty=max_train_qty,
valid_run=valid_run,
valid_qrel_filename=qrel_file_name,
optimizer=optimizer,
scheduler=scheduler,
dataset=dataset,
train_pairs=train_pairs,
qrels=qrels,
validation_timer=validation_timer,
valid_run_dir=valid_run_dir,
valid_scores_holder=valid_scores_holder,
save_last_snapshot_every_k_batch=train_params.
save_last_snapshot_every_k_batch,
model_out_dir=model_out_dir)
end_time = time.time()
if is_master_proc:
if train_params.save_epoch_snapshots:
print('Saving the model epoch snapshot')
torch.save(model, os.path.join(model_out_dir,
f'model.{epoch}'))
os.makedirs(model_out_dir, exist_ok=True)
print(
f'train epoch={epoch} loss={loss:.3g} lr={lr:g} bert_lr={bert_lr:g}'
)
utils.sync_out_streams()
valid_score = validate(model,
train_params,
dataset,
valid_run,
qrelf=qrel_file_name,
run_filename=os.path.join(
model_out_dir, f'{epoch}.run'))
utils.sync_out_streams()
print(f'validation epoch={epoch} score={valid_score:.4g}')
train_stat[epoch] = {
'loss': loss,
'score': valid_score,
'lr': lr,
'bert_lr': bert_lr,
'train_time': end_time - start_time
}
utils.save_json(os.path.join(model_out_dir, 'train_stat.json'),
train_stat)
if top_valid_score is None or valid_score > top_valid_score:
top_valid_score = valid_score
print('new top validation score, saving the whole model')
torch.save(model, os.path.join(model_out_dir, 'model.best'))
# We must sync here or else non-master processes would start training and they
# would timeout on the model averaging barrier. However, the wait time here
# can be much longer. This is actually quite lame, because validation
# should instead be split accross GPUs, but validation is usually pretty quick
# and this should work as a (semi-)temporary fix
if device_qty > 1:
try:
sync_barrier.wait(BARRIER_WAIT_VALIDATION_TIMEOUT)
except BrokenBarrierError:
raise Exception('A model parameter synchronization timeout!')
lr *= epoch_lr_decay
bert_lr *= epoch_lr_decay
def train_iteration(model, sync_barrier, is_master_proc, device_qty, loss_obj,
train_params, max_train_qty, valid_run,
valid_qrel_filename, optimizer, scheduler, dataset,
train_pairs, qrels, validation_timer, valid_run_dir,
valid_scores_holder, save_last_snapshot_every_k_batch,
model_out_dir):
clean_memory(train_params.device_name)
model.train()
total_loss = 0.
total_prev_qty = total_qty = 0. # This is a total number of records processed, it can be different from
# the total number of training pairs
batch_size = train_params.batch_size
optimizer.zero_grad()
if train_params.print_grads:
print('Gradient sums before training')
for k, v in model.named_parameters():
print(
k, 'None' if v.grad is None else torch.sum(
torch.norm(v.grad, dim=-1, p=2)))
lr_desc = get_lr_desc(optimizer)
batch_id = 0
snap_id = 0
if is_master_proc:
utils.sync_out_streams()
pbar = tqdm('training',
total=max_train_qty,
ncols=80,
desc=None,
leave=False)
else:
pbar = None
for record in data.iter_train_pairs(model, train_params.device_name,
dataset, train_pairs,
train_params.shuffle_train, qrels,
train_params.backprop_batch_size,
train_params.max_query_len,
train_params.max_doc_len):
scores = model(record['query_tok'], record['query_mask'],
record['doc_tok'], record['doc_mask'])
count = len(record['query_id']) // 2
scores = scores.reshape(count, 2)
loss = loss_obj.compute(scores)
loss.backward()
total_qty += count
if train_params.print_grads:
print(f'Records processed {total_qty} Gradient sums:')
for k, v in model.named_parameters():
print(
k, 'None' if v.grad is None else torch.sum(
torch.norm(v.grad, dim=-1, p=2)))
total_loss += loss.item()
if total_qty - total_prev_qty >= batch_size:
if is_master_proc:
validation_timer.increment(total_qty - total_prev_qty)
#print(total, 'optimizer step!')
optimizer.step()
optimizer.zero_grad()
total_prev_qty = total_qty
# Scheduler must make a step in each batch! *AFTER* the optimizer makes an update!
if scheduler is not None:
scheduler.step()
lr_desc = get_lr_desc(optimizer)
# This must be done in every process, not only in the master process
if device_qty > 1:
if batch_id % train_params.batch_sync_qty == 0:
try:
sync_barrier.wait(BARRIER_WAIT_MODEL_AVERAGE_TIMEOUT)
except BrokenBarrierError:
raise Exception(
'A model parameter synchronization timeout!')
avg_model_params(model)
batch_id += 1
# We will surely skip batch_id == 0
if save_last_snapshot_every_k_batch is not None and batch_id % save_last_snapshot_every_k_batch == 0:
if is_master_proc:
os.makedirs(model_out_dir, exist_ok=True)
out_tmp = os.path.join(model_out_dir,
f'model.last.{snap_id}')
torch.save(model, out_tmp)
snap_id += 1
if pbar is not None:
pbar.update(count)
pbar.refresh()
utils.sync_out_streams()
pbar.set_description('%s train loss %.5f' %
(lr_desc, total_loss / float(total_qty)))
while is_master_proc and validation_timer.is_time(
) and valid_run_dir is not None:
model.eval()
os.makedirs(valid_run_dir, exist_ok=True)
run_file_name = os.path.join(
valid_run_dir,
f'batch_{validation_timer.last_checkpoint()}.run')
pbar.refresh()
utils.sync_out_streams()
score = validate(model,
train_params,
dataset,
valid_run,
qrelf=valid_qrel_filename,
run_filename=run_file_name)
pbar.refresh()
utils.sync_out_streams()
print(
f'\n# of batches={validation_timer.total_steps} score={score:.4g}'
)
valid_scores_holder[
f'batch_{validation_timer.last_checkpoint()}'] = score
utils.save_json(os.path.join(valid_run_dir, "scores.log"),
valid_scores_holder)
model.train()
if total_qty >= max_train_qty:
break
# Final model averaging in the end.
if device_qty > 1:
try:
sync_barrier.wait(BARRIER_WAIT_MODEL_AVERAGE_TIMEOUT)
except BrokenBarrierError:
raise Exception('A model parameter synchronization timeout!')
avg_model_params(model)
if pbar is not None:
pbar.close()
utils.sync_out_streams()
return total_loss / float(total_qty)
def do_train(sync_barrier, device_qty, master_port, rank, is_master_proc,
dataset, qrels, qrel_file_name, train_pairs, valid_run,
model_out_dir, model, loss_obj, train_params):
if device_qty > 1:
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = str(master_port)
dist.init_process_group(utils.PYTORCH_DISTR_BACKEND,
rank=rank,
world_size=device_qty)
device_name = train_params.device_name
if is_master_proc:
print('Training parameters:')
print(train_params)
print('Loss function:', loss_obj.name())
print('Device name:', device_name)
model.to(device_name)
lr = train_params.init_lr
bert_lr = train_params.init_bert_lr
epoch_lr_decay = train_params.epoch_lr_decay
weight_decay = train_params.weight_decay
momentum = train_params.momentum
top_valid_score = None
train_stat = {}
for epoch in range(train_params.epoch_qty):
params = [(k, v) for k, v in model.named_parameters()
if v.requires_grad]
non_bert_params = {
'params': [v for k, v in params if not k.startswith('bert.')]
}
bert_params = {
'params': [v for k, v in params if k.startswith('bert.')],
'lr': bert_lr
}
if train_params.optim == OPT_ADAMW:
optimizer = torch.optim.AdamW([non_bert_params, bert_params],
lr=lr,
weight_decay=weight_decay)
elif train_params.optim == OPT_SGD:
optimizer = torch.optim.SGD([non_bert_params, bert_params],
lr=lr,
weight_decay=weight_decay,
momentum=momentum)
else:
raise Exception('Unsupported optimizer: ' + train_params.optim)
bpte = train_params.batches_per_train_epoch
max_train_qty = data.train_item_qty(
train_pairs) if bpte <= 0 else bpte * train_params.batch_size
lr_steps = int(math.ceil(max_train_qty / train_params.batch_size))
scheduler = None
if train_params.warmup_pct:
if is_master_proc:
print('Using a scheduler with a warm-up for %f steps' %
train_params.warmup_pct)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
total_steps=lr_steps,
max_lr=[lr, bert_lr],
anneal_strategy='linear',
pct_start=train_params.warmup_pct)
if is_master_proc:
print('Optimizer', optimizer)
start_time = time.time()
loss = train_iteration(model=model,
sync_barrier=sync_barrier,
is_master_proc=is_master_proc,
device_qty=device_qty,
loss_obj=loss_obj,
train_params=train_params,
max_train_qty=max_train_qty,
optimizer=optimizer,
scheduler=scheduler,
dataset=dataset,
train_pairs=train_pairs,
qrels=qrels,
save_last_snapshot_every_k_batch=train_params.
save_last_snapshot_every_k_batch,
model_out_dir=model_out_dir)
end_time = time.time()
if is_master_proc:
if train_params.save_epoch_snapshots:
print('Saving the model epoch snapshot')
torch.save(model, os.path.join(model_out_dir,
f'model.{epoch}'))
os.makedirs(model_out_dir, exist_ok=True)
print(
f'train epoch={epoch} loss={loss:.3g} lr={lr:g} bert_lr={bert_lr:g}'
)
utils.sync_out_streams()
valid_score = validate(model, train_params, dataset, valid_run,
qrel_file_name, epoch, model_out_dir)
utils.sync_out_streams()
print(f'validation epoch={epoch} score={valid_score:.4g}')
train_stat[epoch] = {
'loss': loss,
'score': valid_score,
'lr': lr,
'bert_lr': bert_lr,
'train_time': end_time - start_time
}
utils.save_json(os.path.join(model_out_dir, 'train_stat.json'),
train_stat)
if top_valid_score is None or valid_score > top_valid_score:
top_valid_score = valid_score
print('new top validation score, saving the whole model')
torch.save(model, os.path.join(model_out_dir, 'model.best'))
lr *= epoch_lr_decay
bert_lr *= epoch_lr_decay
