def main(opts):
hvd.init()
n_gpu = hvd.size()
device = torch.device("cuda", hvd.local_rank())
torch.cuda.set_device(hvd.local_rank())
opts.n_gpu = n_gpu
LOGGER.info("device: {} n_gpu: {}, rank: {}, "
"16-bits training: {}".format(device, n_gpu, hvd.rank(),
opts.fp16))
if hvd.rank() != 0:
LOGGER.disabled = True
set_random_seed(opts.seed)
# train_examples = None
LOGGER.info(f"Loading the whole video dataset {opts.sub_txt_db}, "
f"{opts.vfeat_db}")
if opts.task != "didemo_video_only":
video_db = load_video_sub_dataset(opts.vfeat_db, opts.sub_txt_db,
opts.vfeat_interval, opts)
else:
txt_meta = load_json(join(opts.train_query_txt_db, "meta.json"))
video_db = load_video_only_dataset(opts.vfeat_db, txt_meta,
opts.vfeat_interval, opts)
# data loaders
# train
video_ids = get_video_ids(opts.train_query_txt_db)
train_q_txt_db = QueryTokLmdb(opts.train_query_txt_db, opts.max_txt_len)
train_dataloaders = build_downstream_dataloaders([opts.task],
video_db,
video_ids,
True,
opts,
shuffle=True,
q_txt_db=train_q_txt_db)
meta_loader = MetaLoader(train_dataloaders,
accum_steps=opts.gradient_accumulation_steps,
distributed=n_gpu > 1)
meta_loader = PrefetchLoader(meta_loader)
# val
video_ids = get_video_ids(opts.val_query_txt_db)
val_q_txt_db = QueryTokLmdb(opts.val_query_txt_db, -1)
val_dataloaders = build_downstream_dataloaders([opts.task],
video_db,
video_ids,
False,
opts,
q_txt_db=val_q_txt_db)
if opts.task != "didemo_video_only":
inf_dataset = VcmrFullEvalDataset
else:
inf_dataset = VcmrVideoOnlyFullEvalDataset
LOGGER.info(f"Loading Inference Dataset {opts.val_query_txt_db} (val)")
val_dset = inf_dataset(video_ids,
video_db,
val_q_txt_db,
distributed=opts.distributed_eval)
inf_loader_val = DataLoader(val_dset,
batch_size=opts.vcmr_eval_q_batch_size,
num_workers=opts.n_workers,
pin_memory=opts.pin_mem,
collate_fn=vcmr_full_eval_collate)
inf_loader_val = PrefetchLoader(inf_loader_val)
if opts.test_query_txt_db:
LOGGER.info(
f"Loading Inference Dataset {opts.test_query_txt_db} (test)")
video_ids = get_video_ids(opts.test_query_txt_db)
test_q_txt_db = QueryTokLmdb(opts.test_query_txt_db, -1)
test_dset = inf_dataset(video_ids,
video_db,
test_q_txt_db,
distributed=opts.distributed_eval)
inf_loader_test = DataLoader(test_dset,
batch_size=opts.vcmr_eval_q_batch_size,
num_workers=opts.n_workers,
pin_memory=opts.pin_mem,
collate_fn=vcmr_full_eval_collate)
inf_loader_test = PrefetchLoader(inf_loader_test)
# Prepare model
if opts.checkpoint:
checkpoint = torch.load(opts.checkpoint)
else:
checkpoint = {}
img_pos_embed_weight_key = "v_encoder.f_encoder.img_embeddings" +\
".position_embeddings.weight"
if img_pos_embed_weight_key in checkpoint:
max_frm_seq_len = len(checkpoint[img_pos_embed_weight_key])
else:
max_frm_seq_len = MAX_FRM_SEQ_LEN
model = HeroForVcmr.from_pretrained(
opts.model_config,
state_dict=checkpoint,
vfeat_dim=VFEAT_DIM,
max_frm_seq_len=max_frm_seq_len,
lw_neg_ctx=opts.lw_neg_ctx,
lw_neg_q=opts.lw_neg_q,
lw_st_ed=0,
ranking_loss_type=opts.ranking_loss_type,
use_hard_negative=False,
hard_pool_size=opts.hard_pool_size,
margin=opts.margin,
use_all_neg=opts.use_all_neg,
drop_svmr_prob=opts.drop_svmr_prob)
model.to(device)
# make sure every process has same model parameters in the beginning
broadcast_tensors([p.data for p in model.parameters()], 0)
set_dropout(model, opts.dropout)
# Prepare optimizer
optimizer = build_optimizer(model, opts)
task2scaler = {t: i for i, t in enumerate(train_dataloaders.keys())}
model, optimizer = amp.initialize(model,
optimizer,
num_losses=len(task2scaler),
enabled=opts.fp16,
opt_level='O2')
restorer = TrainingRestorer(opts, model, optimizer)
global_step = restorer.global_step
TB_LOGGER.global_step = global_step
if hvd.rank() == 0:
save_training_meta(opts)
TB_LOGGER.create(join(opts.output_dir, 'log'))
pbar = tqdm(total=opts.num_train_steps)
model_saver = ModelSaver(join(opts.output_dir, 'ckpt'))
if not exists(join(opts.output_dir, 'results')):
# store tvr predictions
os.makedirs(join(opts.output_dir, 'results'))
if opts.nms_thd != -1:
# store tvr-nms predictions
if not exists(join(opts.output_dir, 'results_nms')):
os.makedirs(join(opts.output_dir, 'results_nms'))
add_log_to_file(join(opts.output_dir, 'log', 'log.txt'))
else:
pbar = NoOp()
model_saver = NoOp()
restorer = NoOp()
if global_step > 0:
pbar.update(global_step)
LOGGER.info(f"***** Running training with {n_gpu} GPUs *****")
LOGGER.info(" Batch size = %d", opts.train_batch_size)
LOGGER.info(" Accumulate steps = %d", opts.gradient_accumulation_steps)
LOGGER.info(" Num steps = %d", opts.num_train_steps)
task2loss = {
task: RunningMeter(f'loss/{task}')
for task in train_dataloaders.keys()
}
for obj in (f'{opts.task}_st_ed', f'{opts.task}_neg_ctx',
f'{opts.task}_neg_q'):
task2loss[obj] = RunningMeter(f'loss/{obj}')
model.train()
n_examples = defaultdict(int)
start = time()
# quick hack for amp delay_unscale bug
optimizer.zero_grad()
if global_step == 0:
optimizer.step()
for step, (task, batch) in enumerate(meta_loader):
if len(opts.hard_negtiave_start_step) > 0:
for i, hn_step in enumerate(opts.hard_negtiave_start_step):
if global_step >= hn_step and hn_step != -1:
model.set_hard_negative(True, opts.hard_pool_size[i],
opts.hard_neg_weights[i])
if opts.train_span_start_step != -1 and\
global_step >= opts.train_span_start_step:
model.set_train_st_ed(opts.lw_st_ed)
n_examples[task] += opts.train_batch_size
loss = model(batch, task=task, compute_loss=True)
loss_st_ed, loss_neg_ctx, loss_neg_q = loss
loss = loss_st_ed + loss_neg_ctx + loss_neg_q
for n, ls, w in (('st_ed', loss_st_ed, opts.lw_st_ed),
('neg_ctx', loss_neg_ctx, opts.lw_neg_ctx),
('neg_q', loss_neg_q, opts.lw_neg_q)):
ls = ls.item()
if w:
ls /= w
task2loss[f'{task}_{n}'](ls)
loss = loss.mean()
task2loss[task](loss.item())
delay_unscale = (step + 1) % opts.gradient_accumulation_steps != 0
with amp.scale_loss(loss,
optimizer,
delay_unscale=delay_unscale,
loss_id=task2scaler[task]) as scaled_loss:
scaled_loss.backward()
if not delay_unscale:
# gather gradients from every processes
# do this before unscaling to make sure every process uses
# the same gradient scale
grads = [
p.grad.data for p in model.parameters()
if p.requires_grad and p.grad is not None
]
all_reduce_and_rescale_tensors(grads, float(1))
if (step + 1) % opts.gradient_accumulation_steps == 0:
global_step += 1
# learning rate scheduling
lr_this_step = get_lr_sched(global_step, opts)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
TB_LOGGER.add_scalar('lr', lr_this_step, global_step)
# log loss
TB_LOGGER.log_scaler_dict({
temp_loss.name: temp_loss.val
for temp_loss in task2loss.values()
if temp_loss.val is not None
})
TB_LOGGER.step()
# update model params
if opts.grad_norm != -1:
grad_norm = clip_grad_norm_(amp.master_params(optimizer),
opts.grad_norm)
TB_LOGGER.add_scalar('grad_norm', grad_norm, global_step)
optimizer.step()
optimizer.zero_grad()
pbar.update(1)
if global_step % 100 == 0:
# monitor training throughput
LOGGER.info('-------------------------------------------')
LOGGER.info(f'Step {global_step}:')
for t in train_dataloaders.keys():
tot_ex = sum(all_gather_list(n_examples[t]))
ex_per_sec = int(tot_ex / (time() - start))
LOGGER.info(f'{t}: {tot_ex} examples trained at '
f'{ex_per_sec} ex/s')
TB_LOGGER.add_scalar(f'perf/{t}_ex_per_s', ex_per_sec,
global_step)
if global_step % opts.valid_steps == 0:
LOGGER.info('===========================================')
LOGGER.info(f"Step {global_step}: start running validation")
validate(model, val_dataloaders, opts)
if hvd.rank() == 0 or opts.distributed_eval:
log, results = validate_full_vcmr(model,
inf_loader_val,
'val',
opts,
model_opts=opts)
save_json(
results, f'{opts.output_dir}/results/'
f'val_results_{global_step}_rank{hvd.rank()}.json')
TB_LOGGER.log_scaler_dict(log)
if opts.test_query_txt_db:
log, results = validate_full_vcmr(model,
inf_loader_test,
'test',
opts,
model_opts=opts)
save_json(
results, f'{opts.output_dir}/results/'
f'test_results_{global_step}_rank{hvd.rank()}.json'
)
TB_LOGGER.log_scaler_dict(log)
LOGGER.info('===========================================')
model_saver.save(model, global_step)
# step restorer in the end to prevent missing validation checkpoint
restorer.step()
if global_step >= opts.num_train_steps:
break
LOGGER.info('===========================================')
if global_step % opts.valid_steps != 0:
if hvd.rank() == 0 or opts.distributed_eval:
log, results = validate_full_vcmr(model,
inf_loader_val,
'val',
opts,
model_opts=opts)
save_json(
results, f'{opts.output_dir}/results/'
f'val_results_{global_step}'
f'_rank{hvd.rank()}_final.json')
TB_LOGGER.log_scaler_dict(log)
if opts.test_query_txt_db:
log, results = validate_full_vcmr(model,
inf_loader_test,
'test',
opts,
model_opts=opts)
save_json(
results, f'{opts.output_dir}/results/'
f'test_results_{global_step}_rank{hvd.rank()}.json')
TB_LOGGER.log_scaler_dict(log)
model_saver.save(model, f'{global_step}_final')
def main(opts):
hvd.init()
n_gpu = hvd.size()
device = torch.device("cuda", hvd.local_rank())
torch.cuda.set_device(hvd.local_rank())
rank = hvd.rank()
opts.rank = rank
LOGGER.info("device: {} n_gpu: {}, rank: {}, "
"16-bits training: {}".format(
device, n_gpu, hvd.rank(), opts.fp16))
if opts.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, "
"should be >= 1".format(
opts.gradient_accumulation_steps))
set_random_seed(opts.seed)
if rank == 0:
save_training_meta(opts)
TB_LOGGER.create(join(opts.output_dir, 'log'))
pbar = tqdm(total=opts.num_train_steps)
model_saver = ModelSaver(join(args.output_dir, 'ckpt'))
add_log_to_file(join(opts.output_dir, 'log', 'log.txt'))
else:
LOGGER.disabled = True
pbar = NoOp()
model_saver = NoOp()
all_dbs = [db for datasets in [opts.train_datasets, opts.val_datasets]
for dset in datasets for db in dset['db']]
tokenizer = json.load(open(f'{all_dbs[0]}/meta.json'))['bert']
assert all(tokenizer == json.load(open(f'{db}/meta.json'))['bert']
for db in all_dbs)
# build data loaders
train_dataloaders, all_img_dbs = create_dataloaders(
opts.train_datasets, True, opts)
val_dataloaders, _ = create_dataloaders(
opts.val_datasets, False, opts, all_img_dbs)
meta_loader = MetaLoader(train_dataloaders,
accum_steps=opts.gradient_accumulation_steps,
distributed=n_gpu > 1)
meta_loader = PrefetchLoader(meta_loader)
# Prepare model
if opts.checkpoint:
checkpoint = torch.load(opts.checkpoint)
else:
checkpoint = {}
model = UniterForPretraining.from_pretrained(
opts.model_config, checkpoint,
img_dim=IMG_DIM, img_label_dim=IMG_LABEL_DIM)
model.to(device)
model.train()
# make sure every process has same model parameters in the beginning
broadcast_tensors([p.data for p in model.parameters()], 0)
set_dropout(model, opts.dropout)
# Prepare optimizer
optimizer = build_optimizer(model, opts)
task2scaler = {t: i for i, t in enumerate(train_dataloaders.keys())}
model, optimizer = amp.initialize(model, optimizer,
num_losses=len(task2scaler),
enabled=opts.fp16, opt_level='O2')
global_step = 0
LOGGER.info(f"***** Running training with {n_gpu} GPUs *****")
LOGGER.info(" Batch size = %d", opts.train_batch_size)
LOGGER.info(" Accumulate steps = %d", opts.gradient_accumulation_steps)
LOGGER.info(" Num steps = %d", opts.num_train_steps)
# to compute training statistics
task2loss = {task: RunningMeter(f'loss/{task}')
for task in train_dataloaders.keys()}
# ITM w/ OT
if opts.itm_ot_lambda > 0:
for task in train_dataloaders.keys():
if task.startswith('itm'):
task2loss[f'{task}_xe'] = RunningMeter(f'loss/{task}_xe')
task2loss[f'{task}_ot'] = RunningMeter(f'loss/{task}_ot')
task2loss[f'{task}_ot_pos'] = RunningMeter(
f'loss/{task}_ot_pos')
task2loss[f'{task}_ot_neg'] = RunningMeter(
f'loss/{task}_ot_neg')
n_examples = defaultdict(int)
n_in_units = defaultdict(int)
n_loss_units = defaultdict(int)
grad_norm = 0
start = time()
# quick hack for amp delay_unscale bug
optimizer.zero_grad()
optimizer.step()
for step, (name, batch) in enumerate(meta_loader):
# forward pass
n_examples[name] += batch['input_ids'].size(0)
n_in_units[name] += (batch['attn_masks'] == 1).sum().item()
task = name.split('_')[0]
loss = model(batch, task=task, compute_loss=True)
if task.startswith('itm'):
# OT
itm_loss, ot_loss = loss
n_loss_units[name] += itm_loss.size(0)
itm_loss = itm_loss.mean()
if ot_loss is not None:
ot_pos, ot_neg = ot_loss
ot_loss = (ot_pos.sum() - ot_neg.sum()
) / (ot_pos.size(0) + ot_neg.size(0))
# NOTE: be ware of empty tensor
ot_pos = ot_pos.mean().item()
if not math.isnan(ot_pos):
task2loss[f'{name}_ot_pos'](ot_pos)
ot_neg = ot_neg.mean().item()
if not math.isnan(ot_neg):
task2loss[f'{name}_ot_neg'](ot_neg)
loss = itm_loss + opts.itm_ot_lambda * ot_loss
task2loss[f'{name}_xe'](itm_loss.item())
task2loss[f'{name}_ot'](ot_loss.item())
else:
loss = itm_loss
else:
n_loss_units[name] += loss.size(0)
loss = loss.mean() # loss is not normalized in model
# backward pass
delay_unscale = (step+1) % opts.gradient_accumulation_steps != 0
with amp.scale_loss(loss, optimizer, delay_unscale=delay_unscale,
loss_id=task2scaler[name]) as scaled_loss:
scaled_loss.backward()
if not delay_unscale:
# gather gradients from every processes
# do this before unscaling to make sure every process uses
# the same gradient scale
grads = [p.grad.data for p in model.parameters()
if p.requires_grad and p.grad is not None]
all_reduce_and_rescale_tensors(grads, float(1))
task2loss[name](loss.item())
# optimizer update and logging
if (step + 1) % opts.gradient_accumulation_steps == 0:
global_step += 1
# learning rate scheduling
lr_this_step = get_lr_sched(global_step, opts)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
TB_LOGGER.add_scalar('lr', lr_this_step, global_step)
# log loss
# NOTE: not gathered across GPUs for efficiency
TB_LOGGER.log_scaler_dict({ll.name: ll.val
for ll in task2loss.values()
if ll.val is not None})
TB_LOGGER.step()
# update model params
if opts.grad_norm != -1:
grad_norm = clip_grad_norm_(amp.master_params(optimizer),
opts.grad_norm)
TB_LOGGER.add_scalar('grad_norm', grad_norm, global_step)
optimizer.step()
optimizer.zero_grad()
pbar.update(1)
if global_step % 100 == 0:
# monitor training throughput
LOGGER.info(f'==============Step {global_step}===============')
for t in train_dataloaders.keys():
assert all(tt == t for tt in all_gather_list(t))
tot_ex = sum(all_gather_list(n_examples[t]))
ex_per_sec = int(tot_ex / (time()-start))
tot_in = sum(all_gather_list(n_in_units[t]))
in_per_sec = int(tot_in / (time()-start))
tot_l = sum(all_gather_list(n_loss_units[t]))
l_per_sec = int(tot_l / (time()-start))
LOGGER.info(f'{t}: {tot_ex} examples trained at '
f'{ex_per_sec} ex/s')
TB_LOGGER.add_scalar(f'perf/{t}_ex_per_s', ex_per_sec,
global_step)
TB_LOGGER.add_scalar(f'perf/{t}_in_per_s', in_per_sec,
global_step)
TB_LOGGER.add_scalar(f'perf/{t}_loss_per_s', l_per_sec,
global_step)
LOGGER.info('===============================================')
if global_step % opts.valid_steps == 0:
LOGGER.info(f'Step {global_step}: start validation')
validate(model, val_dataloaders)
model_saver.save(model, global_step)
if global_step >= opts.num_train_steps:
break
if global_step % opts.valid_steps != 0:
LOGGER.info(f'Step {global_step}: start validation')
validate(model, val_dataloaders)
model_saver.save(model, global_step)
def main(opts):
hvd.init()
n_gpu = hvd.size()
device = torch.device("cuda", hvd.local_rank())
torch.cuda.set_device(hvd.local_rank())
opts.n_gpu = n_gpu
LOGGER.info("device: {} n_gpu: {}, rank: {}, "
"16-bits training: {}".format(device, n_gpu, hvd.rank(),
opts.fp16))
if hvd.rank() != 0:
LOGGER.disabled = True
set_random_seed(opts.seed)
# data loaders
train_dataloaders = {}
val_dataloaders = {}
for target, t_r in zip(opts.targets, opts.targets_ratio):
train_loaders, val_loaders = build_target_loaders(
target, t_r,
opts) # -> choose which task and get corrsponding task dataloder
train_dataloaders.update(train_loaders)
val_dataloaders.update(val_loaders)
meta_loader = MetaLoader(train_dataloaders,
accum_steps=opts.gradient_accumulation_steps,
distributed=n_gpu > 1)
meta_loader = PrefetchLoader(meta_loader)
# Prepare model
if opts.checkpoint:
checkpoint = torch.load(opts.checkpoint)
else:
checkpoint = {}
img_pos_embed_weight_key = "v_encoder.f_encoder.img_embeddings" +\
".position_embeddings.weight"
if img_pos_embed_weight_key in checkpoint:
max_frm_seq_len = len(checkpoint[img_pos_embed_weight_key])
else:
max_frm_seq_len = MAX_FRM_SEQ_LEN
if opts.load_partial_pretrained:
# from roberta
model = HeroForPretraining(VideoModelConfig(opts.model_config),
vfeat_dim=VFEAT_DIM,
max_frm_seq_len=max_frm_seq_len,
lw_neg_ctx=opts.lw_neg_ctx,
lw_neg_q=opts.lw_neg_q,
lw_st_ed=0,
ranking_loss_type=opts.ranking_loss_type,
use_hard_negative=False,
hard_pool_size=opts.hard_pool_size,
margin=opts.margin,
use_all_neg=opts.use_all_neg,
drop_svmr_prob=opts.drop_svmr_prob)
model.load_partial_pretrained(checkpoint,
VFEAT_DIM,
max_frm_seq_len,
skip_layers=opts.skip_layer_loading)
else:
# continue training
model = HeroForPretraining.from_pretrained(
opts.model_config,
state_dict=checkpoint,
vfeat_dim=VFEAT_DIM,
max_frm_seq_len=max_frm_seq_len,
lw_neg_ctx=opts.lw_neg_ctx,
lw_neg_q=opts.lw_neg_q,
lw_st_ed=0,
ranking_loss_type=opts.ranking_loss_type,
use_hard_negative=False,
hard_pool_size=opts.hard_pool_size,
margin=opts.margin,
use_all_neg=opts.use_all_neg,
drop_svmr_prob=opts.drop_svmr_prob)
model.to(device)
# make sure every process has same model parameters in the beginning
broadcast_tensors([p.data for p in model.parameters()], 0)
set_dropout(model, opts.dropout)
# Prepare optimizer
optimizer = build_optimizer(model, opts)
task2scaler = {t: i for i, t in enumerate(train_dataloaders.keys())}
model, optimizer = amp.initialize(model,
optimizer,
num_losses=len(task2scaler),
enabled=opts.fp16,
opt_level='O2')
restorer = TrainingRestorer(opts, model, optimizer)
all_gather_list(None) # sync to prevent slower rank to read training meta
global_step = restorer.global_step
TB_LOGGER.global_step = global_step
if hvd.rank() == 0:
save_training_meta(opts)
TB_LOGGER.create(join(opts.output_dir, 'log'))
pbar = tqdm(total=opts.num_train_steps)
model_saver = ModelSaver(join(opts.output_dir, 'ckpt'))
add_log_to_file(join(opts.output_dir, 'log', 'log.txt'))
else:
pbar = NoOp()
model_saver = NoOp()
restorer = NoOp()
if global_step > 0:
pbar.update(global_step)
LOGGER.info(f"***** Running training with {n_gpu} GPUs *****")
LOGGER.info(" Batch size = %d", opts.train_batch_size)
LOGGER.info(" Accumulate steps = %d", opts.gradient_accumulation_steps)
LOGGER.info(" Num steps = %d", opts.num_train_steps)
task2loss = {
task: RunningMeter(f'loss/{task}')
for task in train_dataloaders.keys()
}
for task in train_dataloaders.keys():
if task.startswith('vsm'):
for obj in ('st_ed', 'neg_ctx', 'neg_q'):
task2loss[f"{task}_{obj}"] = RunningMeter(f'loss/{task}_{obj}')
model.train()
n_examples = defaultdict(int)
start = time()
# quick hack for amp delay_unscale bug
optimizer.zero_grad()
if global_step == 0:
optimizer.step()
assert all(global_step == s for s in all_gather_list(global_step))
for step, (task, batch) in enumerate(meta_loader):
LOGGER.debug(f"Task: {task}")
# hard negative in VSM
if len(opts.hard_negtiave_start_step) > 0:
for i, hn_step in enumerate(opts.hard_negtiave_start_step):
if global_step >= hn_step and hn_step != -1:
model.set_hard_negative(True, opts.hard_pool_size[i],
opts.hard_neg_weights[i])
# start-end loss
if opts.train_span_start_step != -1 and\
global_step >= opts.train_span_start_step:
model.set_train_st_ed(opts.lw_st_ed)
train_task = task.split('_')[0]
n_examples[task] += opts.train_batch_size
loss = model(batch, task=train_task, compute_loss=True)
if train_task == 'vsm':
loss_st_ed, loss_neg_ctx, loss_neg_q = loss
loss = loss_st_ed + loss_neg_ctx + loss_neg_q
for n, ls, w in (('st_ed', loss_st_ed, opts.lw_st_ed),
('neg_ctx', loss_neg_ctx, opts.lw_neg_ctx),
('neg_q', loss_neg_q, opts.lw_neg_q)):
ls = ls.item()
if w:
ls /= w
task2loss[f'{task}_{n}'](ls)
elif train_task == "mffr":
loss = torch.sqrt(loss.sum(dim=1))
loss = loss.mean()
task2loss[task](loss.item())
delay_unscale = (step + 1) % opts.gradient_accumulation_steps != 0
with amp.scale_loss(loss,
optimizer,
delay_unscale=delay_unscale,
loss_id=task2scaler[task]) as scaled_loss:
scaled_loss.backward()
if not delay_unscale:
# gather gradients from every processes
# do this before unscaling to make sure every process uses
# the same gradient scale
grads = [
p.grad.data for p in model.parameters()
if p.requires_grad and p.grad is not None
]
LOGGER.debug("before reduce grad")
all_reduce_and_rescale_tensors(grads, float(1))
LOGGER.debug("after reduce grad")
if (step + 1) % opts.gradient_accumulation_steps == 0:
global_step += 1
# learning rate scheduling
lr_this_step = get_lr_sched(global_step, opts)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
TB_LOGGER.add_scalar('lr', lr_this_step, global_step)
# log loss
# NOTE: only consider rank 0 for speed
TB_LOGGER.log_scaler_dict({
ll.name: ll.val
for ll in task2loss.values() if ll.val is not None
})
TB_LOGGER.step()
LOGGER.debug("before norm grad")
# update model params
if opts.grad_norm != -1:
grad_norm = clip_grad_norm_(amp.master_params(optimizer),
opts.grad_norm)
TB_LOGGER.add_scalar('grad_norm', grad_norm, global_step)
LOGGER.debug("after norm grad")
LOGGER.debug("before optim step")
optimizer.step()
optimizer.zero_grad()
pbar.update(1)
LOGGER.debug("after optim step")
if global_step % 100 == 0:
LOGGER.debug("after gather stats")
# monitor training throughput
LOGGER.info('-------------------------------------------')
LOGGER.info(f'Step {global_step}:')
for t in train_dataloaders.keys():
tot_ex = sum(all_gather_list(n_examples[t]))
ex_per_sec = int(tot_ex / (time() - start))
LOGGER.info(f'{t}: {tot_ex} examples trained at '
f'{ex_per_sec} ex/s')
TB_LOGGER.add_scalar(f'perf/{t}_ex_per_s', ex_per_sec,
global_step)
LOGGER.debug("after gather stats")
if global_step % opts.valid_steps == 0:
LOGGER.info('===========================================')
LOGGER.info(f"Step {global_step}: start running validation")
validate(model, val_dataloaders, opts)
LOGGER.info('===========================================')
model_saver.save(model, global_step)
# step restorer in the end to prevent missing validation checkpoint
restorer.step()
if global_step >= opts.num_train_steps:
break
LOGGER.info('===========================================')
if global_step % opts.valid_steps != 0:
LOGGER.info('===========================================')
LOGGER.info(f"Step {global_step}: start running validation")
validate(model, val_dataloaders, opts)
LOGGER.info('===========================================')
model_saver.save(model, global_step)
def main(opts):
hvd.init()
n_gpu = hvd.size()
device = torch.device("cuda", hvd.local_rank())
torch.cuda.set_device(hvd.local_rank())
opts.n_gpu = n_gpu
LOGGER.info("device: {} n_gpu: {}, rank: {}, "
"16-bits training: {}".format(device, n_gpu, hvd.rank(),
opts.fp16))
if hvd.rank() != 0:
LOGGER.disabled = True
set_random_seed(opts.seed)
# train_examples = None
LOGGER.info(f"Loading the whole video dataset {opts.sub_txt_db}, "
f"{opts.vfeat_db}")
video_db = load_video_sub_dataset(opts.vfeat_db, opts.sub_txt_db,
opts.vfeat_interval, opts)
# data loaders
# train
LOGGER.info(f"Loading the train QA dataset {opts.train_query_txt_db}")
video_ids = get_video_ids(opts.train_query_txt_db)
train_q_txt_db = QaQueryTokLmdb(opts.train_query_txt_db, opts.max_txt_len)
train_dataloaders = build_downstream_dataloaders([opts.task],
video_db,
video_ids,
True,
opts,
q_txt_db=train_q_txt_db,
shuffle=True)
meta_loader = MetaLoader(train_dataloaders,
accum_steps=opts.gradient_accumulation_steps,
distributed=n_gpu > 1)
meta_loader = PrefetchLoader(meta_loader)
# val
LOGGER.info(f"Loading the val QA dataset {opts.val_query_txt_db}")
video_ids = get_video_ids(opts.val_query_txt_db)
val_q_txt_db = QaQueryTokLmdb(opts.val_query_txt_db, -1)
val_dataloaders = build_downstream_dataloaders([opts.task],
video_db,
video_ids,
False,
opts,
q_txt_db=val_q_txt_db)
if opts.test_query_txt_db:
LOGGER.info(f"Loading the test QA dataset {opts.test_query_txt_db}")
video_ids = get_video_ids(opts.test_query_txt_db)
test_q_txt_db = QaQueryTokLmdb(opts.test_query_txt_db, -1)
test_dataloaders = build_downstream_dataloaders([opts.task],
video_db,
video_ids,
False,
opts,
q_txt_db=test_q_txt_db)
# Prepare model
if opts.checkpoint:
checkpoint = torch.load(opts.checkpoint)
else:
checkpoint = {}
img_pos_embed_weight_key = "v_encoder.f_encoder.img_embeddings" +\
".position_embeddings.weight"
if img_pos_embed_weight_key in checkpoint:
max_frm_seq_len = len(checkpoint[img_pos_embed_weight_key])
else:
max_frm_seq_len = MAX_FRM_SEQ_LEN
model = HeroForVideoQA.from_pretrained(opts.model_config,
state_dict=checkpoint,
vfeat_dim=VFEAT_DIM,
max_frm_seq_len=max_frm_seq_len)
model.to(device)
# make sure every process has same model parameters in the beginning
broadcast_tensors([p.data for p in model.parameters()], 0)
set_dropout(model, opts.dropout)
# Prepare optimizer
optimizer = build_optimizer(model, opts)
task2scaler = {t: i for i, t in enumerate(train_dataloaders.keys())}
model, optimizer = amp.initialize(model,
optimizer,
num_losses=len(task2scaler),
enabled=opts.fp16,
opt_level='O2')
restorer = TrainingRestorer(opts, model, optimizer)
global_step = restorer.global_step
TB_LOGGER.global_step = global_step
if hvd.rank() == 0:
save_training_meta(opts)
TB_LOGGER.create(join(opts.output_dir, 'log'))
pbar = tqdm(total=opts.num_train_steps)
model_saver = ModelSaver(join(opts.output_dir, 'ckpt'))
if not exists(join(opts.output_dir, 'results')):
# store tvqa predictions
os.makedirs(join(opts.output_dir, 'results'))
add_log_to_file(join(opts.output_dir, 'log', 'log.txt'))
else:
LOGGER.disabled = True
pbar = NoOp()
model_saver = NoOp()
restorer = NoOp()
if global_step > 0:
pbar.update(global_step)
LOGGER.info(f"***** Running training with {n_gpu} GPUs *****")
LOGGER.info(" Batch size = %d", opts.train_batch_size)
LOGGER.info(" Accumulate steps = %d", opts.gradient_accumulation_steps)
LOGGER.info(" Num steps = %d", opts.num_train_steps)
task2loss = {
task: RunningMeter(f'loss/{task}')
for task in train_dataloaders.keys()
}
for obj in (f'{opts.task}_qa', f'{opts.task}_st_ed'):
task2loss[obj] = RunningMeter(f'loss/{obj}')
model.train()
n_examples = defaultdict(int)
start = time()
# quick hack for amp delay_unscale bug
optimizer.zero_grad()
if global_step == 0:
optimizer.step()
for step, (task, batch) in enumerate(meta_loader):
n_examples[task] += opts.train_batch_size
loss = model(batch, task=task, compute_loss=True)
loss_qa, loss_st_ed = loss
loss = loss_qa + opts.lw_st_ed * loss_st_ed
for n, ls in (('st_ed', loss_st_ed), ('qa', loss_qa)):
ls = ls.item()
task2loss[f'{task}_{n}'](ls)
loss = loss.mean()
task2loss[task](loss.item())
delay_unscale = (step + 1) % opts.gradient_accumulation_steps != 0
with amp.scale_loss(loss,
optimizer,
delay_unscale=delay_unscale,
loss_id=task2scaler[task]) as scaled_loss:
scaled_loss.backward()
if not delay_unscale:
# gather gradients from every processes
# do this before unscaling to make sure every process uses
# the same gradient scale
grads = [
p.grad.data for p in model.parameters()
if p.requires_grad and p.grad is not None
]
all_reduce_and_rescale_tensors(grads, float(1))
if (step + 1) % opts.gradient_accumulation_steps == 0:
global_step += 1
# learning rate scheduling
lr_this_step = get_lr_sched(global_step, opts)
for i, param_group in enumerate(optimizer.param_groups):
if i == 0 or i == 1:
param_group['lr'] = lr_this_step * opts.lr_mul
elif i == 2 or i == 3:
param_group['lr'] = lr_this_step
else:
raise ValueError()
TB_LOGGER.add_scalar('lr', lr_this_step, global_step)
TB_LOGGER.log_scaler_dict({
temp_loss.name: temp_loss.val
for temp_loss in task2loss.values()
if temp_loss.val is not None
})
TB_LOGGER.step()
# update model params
if opts.grad_norm != -1:
grad_norm = clip_grad_norm_(amp.master_params(optimizer),
opts.grad_norm)
TB_LOGGER.add_scalar('grad_norm', grad_norm, global_step)
optimizer.step()
optimizer.zero_grad()
restorer.step()
pbar.update(1)
if global_step % 100 == 0:
# monitor training throughput
LOGGER.info('-------------------------------------------')
LOGGER.info(f'Step {global_step}:')
for t in train_dataloaders.keys():
tot_ex = sum(all_gather_list(n_examples[t]))
ex_per_sec = int(tot_ex / (time() - start))
LOGGER.info(f'{t}: {tot_ex} examples trained at '
f'{ex_per_sec} ex/s')
TB_LOGGER.add_scalar(f'perf/{t}_ex_per_s', ex_per_sec,
global_step)
if global_step % opts.valid_steps == 0:
LOGGER.info('===========================================')
LOGGER.info(f"Step {global_step}: start running validation")
validate(model,
val_dataloaders,
"val",
opts,
global_step=global_step)
if opts.test_query_txt_db:
validate(model,
test_dataloaders,
"test",
opts,
global_step=global_step)
LOGGER.info('===========================================')
model_saver.save(model, global_step)
if global_step >= opts.num_train_steps:
break
LOGGER.info('===========================================')
if global_step % opts.valid_steps != 0:
LOGGER.info('===========================================')
LOGGER.info(f"Step {global_step}: start running validation")
validate(model, val_dataloaders, "val", opts, global_step=global_step)
if opts.test_query_txt_db:
validate(model,
test_dataloaders,
"test",
opts,
global_step=global_step)
LOGGER.info('===========================================')
model_saver.save(model, f'{global_step}_final')
def main(opts):
hvd.init()
n_gpu = hvd.size()
device = torch.device("cuda", hvd.local_rank())
torch.cuda.set_device(hvd.local_rank())
rank = hvd.rank()
opts.rank = rank
LOGGER.info("device: {} n_gpu: {}, rank: {}, "
"16-bits training: {}".format(
device, n_gpu, hvd.rank(), opts.fp16))
if opts.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, "
"should be >= 1".format(
opts.gradient_accumulation_steps))
set_random_seed(opts.seed)
if rank == 0:
save_training_meta(opts)
TB_LOGGER.create(join(opts.output_dir, 'log'))
pbar = tqdm(total=opts.num_train_steps)
model_saver = ModelSaver(join(opts.output_dir, 'ckpt'))
add_log_to_file(join(opts.output_dir, 'log', 'log.txt'))
else:
LOGGER.disabled = True
pbar = NoOp()
model_saver = NoOp()
all_dbs = [db for datasets in [opts.train_datasets, opts.val_datasets]
for dset in datasets for db in dset['db']]
tokenizer = json.load(open(f'{all_dbs[0]}/meta.json'))['bert']
#print(tokenizer)
# assert all(tokenizer == json.load(open(f'{db}/meta.json'))['bert']
# for db in all_dbs)
# build data loaders
train_dataloaders, all_img_dbs = create_dataloaders(
opts.train_datasets, True, opts)
val_dataloaders, _ = create_dataloaders(
opts.val_datasets, False, opts, all_img_dbs)
meta_loader = MetaLoader(train_dataloaders,
accum_steps=opts.gradient_accumulation_steps,
distributed=n_gpu > 1)
meta_loader = PrefetchLoader(meta_loader)
# Prepare model
if opts.checkpoint:
checkpoint = torch.load(opts.checkpoint)
else:
checkpoint = {}
if opts.rename_checkpoints:
rename_checkpoint(checkpoint)
#Include early_adaptation
if opts.early_adaptation:
early_adaptation_checkpoint = torch.load(opts.early_adaptation_checkpoint)
checkpoint['roberta.img_embeddings.img_linear.weight'] = early_adaptation_checkpoint['v2w_linear.weight']
checkpoint['roberta.img_embeddings.img_linear.bias'] = early_adaptation_checkpoint['v2w_linear.bias']
model = VLXLMRForPretraining.from_pretrained(
opts.model_config, checkpoint,
img_dim=IMG_DIM, img_label_dim=IMG_LABEL_DIM,
nce_temp=opts.nce_temp, ot_pos_only=opts.ot_pos_only)
# model = UniterForPretraining.from_pretrained(
# opts.model_config, checkpoint,
# img_dim=IMG_DIM, img_label_dim=IMG_LABEL_DIM,
# nce_temp=opts.nce_temp, ot_pos_only=opts.ot_pos_only)
model.pad_vocab() # tensor core padding for vocabulary
model.to(device)
model.train()
# make sure every process has same model parameters in the beginning
broadcast_tensors([p.data for p in model.parameters()], 0)
set_dropout(model, opts.dropout)
# Prepare optimizer
optimizer = build_optimizer(model, opts)
task2scaler = {t: i for i, t in enumerate(train_dataloaders.keys())}
model, optimizer = amp.initialize(model, optimizer,
num_losses=len(task2scaler),
enabled=opts.fp16, opt_level='O2')
#global_step = 0
#Initialize the TrainingRestorer
restorer = TrainingRestorer(opts, model, optimizer)
global_step = restorer.global_step
TB_LOGGER._global_step = global_step
if hvd.rank() !=0:
restorer = NoOp() #Added for Restoring the Checkpoints
if global_step > 0:
pbar.update(global_step)
LOGGER.info(f"***** Running training with {n_gpu} GPUs *****")
LOGGER.info(" Batch size = %d", opts.train_batch_size)
LOGGER.info(" Accumulate steps = %d", opts.gradient_accumulation_steps)
LOGGER.info(" Num steps = %d", opts.num_train_steps)
# to compute training statistics
task2loss = {task: RunningMeter(f'loss/{task}')
for task in train_dataloaders.keys()}
# ITM w/ OT
if opts.itm_ot_lambda > 0:
for task in train_dataloaders.keys():
if task.startswith('itm'):
task2loss[f'{task}_xe'] = RunningMeter(f'loss/{task}_xe')
task2loss[f'{task}_ot'] = RunningMeter(f'loss/{task}_ot')
if not opts.ot_pos_only:
task2loss[f'{task}_ot_pos'] = RunningMeter(
f'loss/{task}_ot_pos')
task2loss[f'{task}_ot_neg'] = RunningMeter(
f'loss/{task}_ot_neg')
n_examples = defaultdict(int)
n_in_units = defaultdict(int)
n_loss_units = defaultdict(int)
n_neg_nce = defaultdict(int)
grad_norm = 0
start = time()
#Added by Mingyang to debug the training procedure
# debug_start = torch.cuda.Event(enable_timing=True)
# debug_end = torch.cuda.Event(enable_timing=True)
# quick hack for amp delay_unscale bug
optimizer.zero_grad()
optimizer.step()
#Added by Mingyang Zhou
# debug_start.record()
for step, (name, batch) in enumerate(meta_loader):
# forward pass
assert all(name == n for n in all_gather_list(name))
n_examples[name] += batch['input_ids'].size(0)
n_in_units[name] += (batch['attn_masks'] == 1).sum().item()
if 'nce' in name:
n_neg_nce[name] += batch['neg_feats'].size(0)
task = name.split('_')[0]
loss = model(batch, task=task, compute_loss=True)
if task.startswith('itm'):
# OT
itm_loss, ot_loss = loss
n_loss_units[name] += itm_loss.size(0)
itm_loss = itm_loss.mean()
if ot_loss is not None:
if not opts.ot_pos_only:
ot_pos, ot_neg = ot_loss
ot_loss = (ot_pos.sum() - ot_neg.sum()
) / (ot_pos.size(0) + ot_neg.size(0))
# NOTE: be ware of empty tensor
ot_pos = ot_pos.mean().item()
if not math.isnan(ot_pos):
task2loss[f'{name}_ot_pos'](ot_pos)
ot_neg = ot_neg.mean().item()
if not math.isnan(ot_neg):
task2loss[f'{name}_ot_neg'](ot_neg)
else:
ot_loss = ot_loss.mean()
loss = itm_loss + opts.itm_ot_lambda * ot_loss
task2loss[f'{name}_xe'](itm_loss.item())
task2loss[f'{name}_ot'](ot_loss.item())
else:
loss = itm_loss
elif task.startswith('vmlm-soft'):
loss = 1000*loss.mean()
else:
n_loss_units[name] += loss.size(0)
loss = loss.mean() # loss is not normalized in model
# backward pass
delay_unscale = (step+1) % opts.gradient_accumulation_steps != 0
with amp.scale_loss(loss, optimizer, delay_unscale=delay_unscale,
loss_id=task2scaler[name]) as scaled_loss:
scaled_loss.backward()
if not delay_unscale:
# gather gradients from every processes
# do this before unscaling to make sure every process uses
# the same gradient scale
grads = [p.grad.data for p in model.parameters()
if p.requires_grad and p.grad is not None]
all_reduce_and_rescale_tensors(grads, float(1))
task2loss[name](loss.item())
# optimizer update and logging
if (step + 1) % opts.gradient_accumulation_steps == 0:
global_step += 1
# learning rate scheduling
lr_this_step = get_lr_sched(global_step, opts)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
TB_LOGGER.add_scalar('lr', lr_this_step, global_step)
# log loss
# for t, l in task2loss.items():
# loss = sum(v for v in all_gather_list(l.val)
# if v is not None) / hvd.size()
# task2loss[t] = RunningMeter(f'loss/{t}', loss)
TB_LOGGER.log_scaler_dict({l.name: l.val
for l in task2loss.values()
if l.val is not None})
TB_LOGGER.step()
# update model params
if opts.grad_norm != -1:
'''
if global_step % 10 == 0 and not opts.fp16:
bias = model.bert.img_embeddings.img_linear.bias
weight = model.bert.img_embeddings.img_linear.weight
print(f"bnorm: {bias.norm()}")
print(f"wnorm: {weight.norm()}")
print(f"bgnorm: {bias.grad.norm()}")
print(f"wgnorm: {weight.grad.norm()}")
mask = model.bert.img_embeddings.mask_embedding.weight
print(f"mnorm: {mask.norm()}")
print(f"mgnorm: {mask.grad.norm()}")
print([(n, p.grad.norm().item())
for n, p in model.named_parameters()
if p.grad is not None
and p.grad.norm().item() > grad_norm/10])
'''
grad_norm = clip_grad_norm_(amp.master_params(optimizer),
opts.grad_norm)
TB_LOGGER.add_scalar('grad_norm', grad_norm, global_step)
optimizer.step()
optimizer.zero_grad()
pbar.update(1)
if global_step % 100 == 0:
# monitor training throughput
LOGGER.info(f'==============Step {global_step}===============')
for t in train_dataloaders.keys():
assert all(tt == t for tt in all_gather_list(t))
tot_ex = sum(all_gather_list(n_examples[t]))
ex_per_sec = int(tot_ex / (time()-start))
tot_in = sum(all_gather_list(n_in_units[t]))
in_per_sec = int(tot_in / (time()-start))
tot_l = sum(all_gather_list(n_loss_units[t]))
l_per_sec = int(tot_l / (time()-start))
LOGGER.info(f'{t}: {tot_ex} examples trained at '
f'{ex_per_sec} ex/s')
TB_LOGGER.add_scalar(f'perf/{t}_ex_per_s', ex_per_sec,
global_step)
TB_LOGGER.add_scalar(f'perf/{t}_in_per_s', in_per_sec,
global_step)
TB_LOGGER.add_scalar(f'perf/{t}_loss_per_s', l_per_sec,
global_step)
if 'nce' in t:
avg_neg = sum(all_gather_list(n_neg_nce[t])
) / hvd.size() // step
LOGGER.info(f'{t}: averaging '
f'{avg_neg} negative samples')
LOGGER.info(f'===============================================')
if global_step % opts.valid_steps == 0:
LOGGER.info(f'Step {global_step}: start validation')
validate(model, val_dataloaders)
#os.makedir('/'.join([opts.output_dir, "ckpt")
model_saver.save(model, global_step, optimizer)
restorer.step()
if global_step >= opts.num_train_steps:
break
if global_step % opts.valid_steps != 0:
LOGGER.info(f'Step {global_step}: start validation')
validate(model, val_dataloaders)
model_saver.save(model, global_step)