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()
LOGGER.info("device: {} n_gpu: {}, rank: {}, "
"16-bits training: {}".format(
device, n_gpu, hvd.rank(), opts.fp16))
if rank != 0:
LOGGER.disabled = True
hps_file = f'{opts.output_dir}/log/hps.json'
model_opts = Struct(json.load(open(hps_file)))
assert opts.split in opts.img_db and opts.split in opts.txt_db
# load DBs and image dirs
eval_img_db, eval_img_db_gt = load_img_feat(opts.img_db, model_opts)
eval_txt_db = VcrTxtTokLmdb(opts.txt_db, -1)
eval_dataset = VcrEvalDataset(
"test", eval_txt_db, img_db=eval_img_db,
img_db_gt=eval_img_db_gt)
# Prepare model
model = UniterForVisualCommonsenseReasoning.from_pretrained(
f'{opts.output_dir}/log/model.json', state_dict={},
img_dim=IMG_DIM)
model.init_type_embedding()
model.init_word_embedding(NUM_SPECIAL_TOKENS)
if exists(opts.checkpoint):
ckpt_file = opts.checkpoint
else:
ckpt_file = f'{opts.output_dir}/ckpt/model_step_{opts.checkpoint}.pt'
checkpoint = torch.load(ckpt_file)
state_dict = checkpoint.get('model_state', checkpoint)
matched_state_dict = {}
unexpected_keys = set()
missing_keys = set()
for name, param in model.named_parameters():
missing_keys.add(name)
for key, data in state_dict.items():
if key in missing_keys:
matched_state_dict[key] = data
missing_keys.remove(key)
else:
unexpected_keys.add(key)
LOGGER.info(f"Unexpected_keys: {list(unexpected_keys)}")
LOGGER.info(f"Missing_keys: {list(missing_keys)}")
model.load_state_dict(matched_state_dict, strict=False)
model.to(device)
if opts.fp16:
model = amp.initialize(model, enabled=True, opt_level='O2')
eval_dataloader = DataLoader(eval_dataset,
batch_size=opts.batch_size,
num_workers=opts.n_workers,
pin_memory=opts.pin_mem,
shuffle=False,
collate_fn=vcr_eval_collate)
eval_dataloader = PrefetchLoader(eval_dataloader)
_, results = evaluate(model, eval_dataloader)
result_dir = f'{opts.output_dir}/results_{opts.split}'
if not exists(result_dir) and rank == 0:
os.makedirs(result_dir)
all_results = {}
for id2res in all_gather_list(results):
all_results.update(id2res)
if hvd.rank() == 0:
with open(f'{result_dir}/'
f'results_{opts.checkpoint}_all.json', 'w') as f:
json.dump(all_results, f)
probs_df = save_for_submission(
f'{result_dir}/results_{opts.checkpoint}_all.json')
probs_df.to_csv(f'{result_dir}/results_{opts.checkpoint}_all.csv')
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()
LOGGER.info("device: {} n_gpu: {}, rank: {}, "
"16-bits training: {}".format(device, n_gpu, hvd.rank(),
opts.fp16))
if rank != 0:
LOGGER.disabled = True
hps_file = f'{opts.output_dir}/log/hps.json'
model_opts = Struct(json.load(open(hps_file)))
assert opts.split in opts.img_db and opts.split in opts.txt_db
# load DBs and image dirs
eval_img_db, eval_img_db_gt = load_img_feat(opts.img_db, model_opts)
eval_txt_db = VcrTxtTokLmdb(opts.txt_db, -1)
eval_dataset = VcrEvalDataset("val",
eval_txt_db,
img_db=eval_img_db,
img_db_gt=eval_img_db_gt)
# Prepare model
model = UniterForVisualCommonsenseReasoning.from_pretrained(
f'{opts.output_dir}/log/model.json', state_dict={}, img_dim=IMG_DIM)
model.init_type_embedding()
model.init_type_embedding_know()
model.init_word_embedding(NUM_SPECIAL_TOKENS)
if exists(opts.checkpoint):
ckpt_file = opts.checkpoint
else:
ckpt_file = f'{opts.output_dir}/ckpt/model_step_{opts.checkpoint}.pt'
checkpoint = torch.load(ckpt_file)
state_dict = checkpoint.get('model_state', checkpoint)
matched_state_dict = {}
unexpected_keys = set()
missing_keys = set()
for name, param in model.named_parameters():
missing_keys.add(name)
for key, data in state_dict.items():
if key in missing_keys:
matched_state_dict[key] = data
missing_keys.remove(key)
else:
unexpected_keys.add(key)
LOGGER.info(f"Unexpected_keys: {list(unexpected_keys)}")
LOGGER.info(f"Missing_keys: {list(missing_keys)}")
model.load_state_dict(matched_state_dict, strict=False)
model.to(device)
if opts.fp16:
model = amp.initialize(model, enabled=True, opt_level='O2')
eval_dataloader = DataLoader(eval_dataset,
batch_size=opts.batch_size,
num_workers=opts.n_workers,
pin_memory=opts.pin_mem,
shuffle=False,
collate_fn=vcr_eval_collate)
eval_dataloader = PrefetchLoader(eval_dataloader)
results = evaluate(model, eval_dataloader)
output = '/src/vlkaf.json'
before_json = ""
for i, item in enumerate(results):
jstring = json.dumps(item)
before_json += jstring + '\n'
f = open(output, "w")
f.write(before_json)
f.close()
'''
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)
# load DBs and image dirs
all_img_dbs = ImageLmdbGroup(opts.conf_th, opts.max_bb, opts.min_bb,
opts.num_bb, opts.compressed_db)
# train
LOGGER.info(f"Loading Train Dataset "
f"{opts.train_txt_dbs}, {opts.train_img_dbs}")
train_datasets = []
for txt_path, img_path in zip(opts.train_txt_dbs, opts.train_img_dbs):
img_db, img_db_gt = load_img_feat(img_path, all_img_dbs, opts)
qa_txt_db = VcrTxtTokLmdb(txt_path, opts.max_txt_len, task="qa")
qar_txt_db = VcrTxtTokLmdb(txt_path, opts.max_txt_len, task="qar")
train_datasets.append(
VcrDataset(qa_txt_db, img_db_gt=img_db_gt, img_db=img_db))
train_datasets.append(
VcrDataset(qar_txt_db, img_db_gt=img_db_gt, img_db=img_db))
train_dataset = ConcatDatasetWithLens(train_datasets)
train_dataloader = build_dataloader(train_dataset, vcr_collate, True, opts)
# val
LOGGER.info(f"Loading Val Dataset {opts.val_txt_db}, {opts.val_img_db}")
val_img_db, val_img_db_gt = load_img_feat(opts.val_img_db, all_img_dbs, opts)
val_txt_db = VcrTxtTokLmdb(opts.val_txt_db, -1, task="qa")
val_dataset = VcrEvalDataset(
"val", val_txt_db, img_db=val_img_db, img_db_gt=val_img_db_gt)
val_final_dataset = VcrEvalDataset(
##"test"
"val", val_txt_db, img_db=val_img_db, img_db_gt=val_img_db_gt)
val_dataloader = build_dataloader(val_dataset, vcr_eval_collate,
False, opts)
val_final_dataloader = build_dataloader(
val_final_dataset, vcr_eval_collate,
False, opts)
# Prepare model
if opts.checkpoint and opts.checkpoint_from == "pretrain":
checkpoint = torch.load(opts.checkpoint)
else:
checkpoint = {}
all_dbs = opts.train_txt_dbs + [opts.val_txt_db]
toker = json.load(open(f'{all_dbs[0]}/meta.json'))['bert']
assert all(toker == json.load(open(f'{db}/meta.json'))['bert']
for db in all_dbs)
model = UniterForVisualCommonsenseReasoning.from_pretrained(
opts.model_config, checkpoint, img_dim=IMG_DIM)
model.init_type_embedding()
model.init_type_embedding_know()
model.init_word_embedding(NUM_SPECIAL_TOKENS)
if opts.checkpoint_from == "vcr_pretrain":
checkpoint = torch.load(opts.checkpoint)
state_dict = checkpoint.get('model_state', checkpoint)
matched_state_dict = {}
unexpected_keys = set()
missing_keys = set()
for name, param in model.named_parameters():
missing_keys.add(name)
for key, data in state_dict.items():
if key in missing_keys:
matched_state_dict[key] = data
missing_keys.remove(key)
else:
unexpected_keys.add(key)
print("Unexpected_keys:", list(unexpected_keys))
print("Missing_keys:", list(missing_keys))
model.load_state_dict(matched_state_dict, strict=False)
del checkpoint
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)
model, optimizer = amp.initialize(model, optimizer,
enabled=opts.fp16, opt_level='O2')
global_step = 0
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'))
os.makedirs(join(opts.output_dir, 'results')) # store VQA predictions
add_log_to_file(join(opts.output_dir, 'log', 'log.txt'))
else:
LOGGER.disabled = True
pbar = NoOp()
model_saver = NoOp()
LOGGER.info(f"***** Running training with {n_gpu} GPUs *****")
LOGGER.info(" Num examples = %d", len(train_dataset) * hvd.size())
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)
running_loss = RunningMeter('loss')
model.train()
n_examples = 0
n_epoch = 0
start = time()
# quick hack for amp delay_unscale bug
optimizer.zero_grad()
optimizer.step()
while True:
for step, batch in enumerate(train_dataloader):
n_examples += batch['input_ids'].size(0)
loss = model(batch, compute_loss=True)
loss = loss.mean()
delay_unscale = (step+1) % opts.gradient_accumulation_steps != 0
with amp.scale_loss(loss, optimizer, delay_unscale=delay_unscale
) 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))
running_loss(loss.item())
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)
# log loss
# NOTE: not gathered across GPUs for efficiency
TB_LOGGER.add_scalar('loss', running_loss.val, global_step)
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}=============')
tot_ex = sum(all_gather_list(n_examples))
ex_per_sec = int(tot_ex / (time()-start))
LOGGER.info(f'{tot_ex} examples trained at '
f'{ex_per_sec} ex/s')
TB_LOGGER.add_scalar('perf/ex_per_s',
ex_per_sec, global_step)
LOGGER.info(f'===========================================')
if global_step % opts.valid_steps == 0:
val_log, results = validate(
model, val_dataloader)
TB_LOGGER.log_scaler_dict(val_log)
model_saver.save(model, global_step)
if global_step >= opts.num_train_steps:
break
if global_step >= opts.num_train_steps:
break
n_epoch += 1
LOGGER.info(f"finished {n_epoch} epochs")
if global_step % opts.valid_steps != 0:
val_log, results = validate(
model, val_dataloader)
TB_LOGGER.log_scaler_dict(val_log)
val_log, results = validate(model, val_final_dataloader)
with open(f'{opts.output_dir}/results/'
f'results_{global_step}_final_qa_qar_'
f'rank{rank}.json', 'w') as f:
json.dump(results, f)
TB_LOGGER.log_scaler_dict(val_log)
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())
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)
# load DBs and image dirs
all_img_dbs = ImageLmdbGroup(opts.conf_th, opts.max_bb, opts.min_bb,
opts.num_bb, opts.compressed_db)
# train
LOGGER.info(f"Loading Train Dataset "
f"{opts.train_txt_dbs}, {opts.train_img_dbs}")
train_datasets = []
for txt_path, img_path in zip(opts.train_txt_dbs, opts.train_img_dbs):
img_db, img_db_gt = load_img_feat(img_path, all_img_dbs, opts)
qa_txt_db = VcrTxtTokLmdb(txt_path, opts.max_txt_len, task="qa")
qar_txt_db = VcrTxtTokLmdb(txt_path, opts.max_txt_len, task="qar")
train_datasets.append(
VcrDataset(qa_txt_db, img_db_gt=img_db_gt, img_db=img_db))
train_datasets.append(
VcrDataset(qar_txt_db, img_db_gt=img_db_gt, img_db=img_db))
train_dataset = ConcatDatasetWithLens(train_datasets)
train_dataloader = build_dataloader(train_dataset, vcr_collate, True, opts)
# val
LOGGER.info(f"Loading Val Dataset {opts.val_txt_db}, {opts.val_img_db}")
val_img_db, val_img_db_gt = load_img_feat(opts.val_img_db, all_img_dbs,
opts)
val_txt_db = VcrTxtTokLmdb(opts.val_txt_db, -1)
val_dataset = VcrEvalDataset("val",
val_txt_db,
img_db=val_img_db,
img_db_gt=val_img_db_gt)
val_final_dataset = VcrEvalDataset("test",
val_txt_db,
img_db=val_img_db,
img_db_gt=val_img_db_gt)
val_dataloader = build_dataloader(val_dataset, vcr_eval_collate, False,
opts)
val_final_dataloader = build_dataloader(val_final_dataset,
vcr_eval_collate, False, opts)
# Prepare model
if opts.checkpoint and opts.checkpoint_from == "pretrain":
ckpt = torch.load(opts.checkpoint)
checkpoint = {k.replace('bert', 'uniter'): v for k, v in ckpt.items()}
else:
checkpoint = {}
all_dbs = opts.train_txt_dbs + [opts.val_txt_db]
toker = json.load(open(f'{all_dbs[0]}/meta.json'))['bert']
assert all(toker == json.load(open(f'{db}/meta.json'))['bert']
for db in all_dbs)
model = UniterForVisualCommonsenseReasoning.from_pretrained(
opts.model_config, checkpoint, img_dim=IMG_DIM)
model.init_type_embedding()
model.init_word_embedding(NUM_SPECIAL_TOKENS)
if opts.checkpoint_from == "vcr_pretrain":
ckpt = torch.load(opts.checkpoint)
checkpoint = {k.replace('bert', 'uniter'): v for k, v in ckpt.items()}
state_dict = checkpoint.get('model_state', checkpoint)
matched_state_dict = {}
unexpected_keys = set()
missing_keys = set()
for name, param in model.named_parameters():
missing_keys.add(name)
for key, data in state_dict.items():
if key in missing_keys:
matched_state_dict[key] = data
missing_keys.remove(key)
else:
unexpected_keys.add(key)
print("Unexpected_keys:", list(unexpected_keys))
print("Missing_keys:", list(missing_keys))
model.load_state_dict(matched_state_dict, strict=False)
del checkpoint
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)
model, optimizer = amp.initialize(model,
optimizer,
enabled=opts.fp16,
opt_level='O2')
global_step = 0
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'))
os.makedirs(join(opts.output_dir, 'results')) # store VQA predictions
add_log_to_file(join(opts.output_dir, 'log', 'log.txt'))
else:
LOGGER.disabled = True
pbar = NoOp()
model_saver = NoOp()
LOGGER.info(f"***** Running training with {n_gpu} GPUs *****")
LOGGER.info(" Num examples = %d", len(train_dataset) * hvd.size())
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)
running_loss = RunningMeter('loss')
model.train()
n_examples = 0
n_epoch = 0
start = time()
# quick hack for amp delay_unscale bug
optimizer.zero_grad()
optimizer.step()
while True:
for step, batch in enumerate(train_dataloader):
n_examples += batch['input_ids'].size(0)
# ============= Code for adversarial training =============
if opts.adv_training:
# initialize delta
txt_embeds_init = model.uniter.embeddings.word_embeddings(
batch['input_ids'])
img_embeds_init = batch['img_feat']
# for simplicity, we initialize the delta as zero vectors, which performs
# very simliar as initializing randomly using norm or uniform distributions
txt_delta = torch.zeros_like(txt_embeds_init)
img_delta = torch.zeros_like(img_embeds_init)
# calculate the prob. scores for clean samples
gt_answer_scores = model(batch, compute_loss=False)
gt_answer_prob = F.softmax(gt_answer_scores, dim=1)
gt_answer_logprob = F.log_softmax(gt_answer_scores, dim=1)
# the main loop
for astep in range(opts.adv_steps):
# (0) forward
if opts.adv_modality == ["text"]:
txt_delta.requires_grad_()
img_delta = torch.zeros_like(img_embeds_init)
elif opts.adv_modality == ["image"]:
img_delta.requires_grad_()
txt_delta = torch.zeros_like(txt_embeds_init)
else:
txt_delta.requires_grad_()
img_delta.requires_grad_()
if "alter" not in opts.adv_modality:
answer_scores = model(batch,
adv_training=True,
adv_modality=opts.adv_modality,
adv_delta_txt=txt_delta,
adv_delta_img=img_delta,
compute_loss=False)
# CE loss
ce_loss = F.cross_entropy(answer_scores,
batch['targets'].squeeze(-1),
reduction='mean')
# KL loss
answer_prob = F.softmax(answer_scores, dim=1)
answer_logprob = F.log_softmax(answer_scores, dim=1)
kl_loss = F.kl_div(
answer_logprob, gt_answer_prob, reduction='none') + \
F.kl_div(
gt_answer_logprob, answer_prob,
reduction='none')
kl_loss = kl_loss.mean()
# (1) backward
loss = (ce_loss +
opts.adv_kl_weight * kl_loss) / opts.adv_steps
else:
answer_scores_1 = model(batch,
adv_training=True,
adv_modality=["text"],
adv_delta_txt=txt_delta,
adv_delta_img=None,
compute_loss=False)
# CE loss
ce_loss_1 = F.cross_entropy(
answer_scores,
batch['targets'].squeeze(-1),
reduction='mean')
answer_scores_2 = model(batch,
adv_training=True,
adv_modality=["image"],
adv_delta_txt=None,
adv_delta_img=img_delta,
compute_loss=False)
# CE loss
ce_loss_2 = F.cross_entropy(
answer_scores,
batch['targets'].squeeze(-1),
reduction='mean')
# KL loss
answer_prob_1 = F.softmax(answer_scores_1, dim=1)
answer_logprob_1 = F.log_softmax(answer_scores_1,
dim=1)
answer_prob_2 = F.softmax(answer_scores_2, dim=1)
answer_logprob_2 = F.log_softmax(answer_scores_2,
dim=1)
kl_loss_1 = F.kl_div(
answer_logprob_1, gt_answer_prob, reduction='none') + \
F.kl_div(
gt_answer_logprob, answer_prob_1,
reduction='none')
kl_loss_1 = kl_loss_1.mean()
kl_loss_2 = F.kl_div(
answer_logprob_2, gt_answer_prob, reduction='none') + \
F.kl_div(
gt_answer_logprob, answer_prob_2,
reduction='none')
kl_loss_2 = kl_loss_2.mean()
# (1) backward
loss = (ce_loss_1 + ce_loss_2 + opts.adv_kl_weight *
(kl_loss_1 + kl_loss_2)) / (opts.adv_steps * 2)
delay_unscale = (
(step + 1) % opts.gradient_accumulation_steps !=
0) or ((astep + 1) % opts.adv_steps != 0)
with amp.scale_loss(
loss, optimizer,
delay_unscale=delay_unscale) as scaled_loss:
scaled_loss.backward(retain_graph=True)
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))
running_loss(loss.item())
if astep == opts.adv_steps - 1:
# further updates on delta
break
# (2) get gradient on delta
# fix fp16 problem
amp_scale = scaled_loss.item() // loss.item()
if "text" in opts.adv_modality:
txt_delta_grad = txt_delta.grad.clone().detach()
txt_delta_grad = txt_delta_grad.float() / amp_scale
if "image" in opts.adv_modality:
img_delta_grad = img_delta.grad.clone().detach()
img_delta_grad = img_delta_grad.float() / amp_scale
# (3) update and clip for txt delta
if "text" in opts.adv_modality:
if opts.norm_type == "l2":
denorm = torch.norm(txt_delta_grad.view(
txt_delta_grad.size(0), -1),
dim=1).view(-1, 1, 1)
denorm = torch.clamp(denorm, min=1e-8)
txt_delta_step = (opts.adv_lr_txt *
txt_delta_grad /
denorm).to(txt_delta)
txt_delta = (txt_delta + txt_delta_step).detach()
if opts.adv_max_norm > 0:
delta_norm = torch.norm(txt_delta.view(
txt_delta.size(0), -1),
p=2,
dim=1).detach()
exceed_mask = (delta_norm > opts.adv_max_norm
).to(txt_embeds_init)
reweights = (opts.adv_max_norm / delta_norm *
exceed_mask +
(1 - exceed_mask)).view(-1, 1, 1)
txt_delta = (txt_delta * reweights).detach()
elif opts.norm_type == "linf":
denorm = torch.norm(txt_delta_grad.view(
txt_delta_grad.size(0), -1),
dim=1,
p=float("inf")).view(-1, 1, 1)
denorm = torch.clamp(denorm, min=1e-8)
txt_delta_step = (opts.adv_lr_txt *
txt_delta_grad /
denorm).to(txt_delta)
txt_delta = (txt_delta + txt_delta_step).detach()
if opts.adv_max_norm > 0:
txt_delta = torch.clamp(
txt_delta, -opts.adv_max_norm,
opts.adv_max_norm).detach()
# (4) update and clip for image delta
if "image" in opts.adv_modality:
if opts.norm_type == "l2":
denorm = torch.norm(img_delta_grad.view(
img_delta_grad.size(0), -1),
dim=1).view(-1, 1, 1)
denorm = torch.clamp(denorm, min=1e-8)
img_delta_step = (opts.adv_lr_img *
img_delta_grad /
denorm).to(img_delta)
img_delta = (img_delta + img_delta_step).detach()
if opts.adv_max_norm > 0:
delta_norm = torch.norm(img_delta.view(
img_delta.size(0), -1),
p=2,
dim=1).detach()
exceed_mask = (delta_norm > opts.adv_max_norm
).to(img_embeds_init)
reweights = (opts.adv_max_norm / delta_norm *
exceed_mask +
(1 - exceed_mask)).view(-1, 1, 1)
img_delta = (img_delta * reweights).detach()
elif opts.norm_type == "linf":
denorm = torch.norm(img_delta_grad.view(
img_delta_grad.size(0), -1),
dim=1,
p=float("inf")).view(-1, 1, 1)
denorm = torch.clamp(denorm, min=1e-8)
img_delta_step = (opts.adv_lr_img *
img_delta_grad /
denorm).to(img_delta)
img_delta = (img_delta + img_delta_step).detach()
if opts.adv_max_norm > 0:
img_delta = torch.clamp(
img_delta, -opts.adv_max_norm,
opts.adv_max_norm).detach()
else:
loss = model(batch, compute_loss=True)
loss = loss.mean()
delay_unscale = ((step + 1) % opts.gradient_accumulation_steps
!= 0)
with amp.scale_loss(
loss, optimizer,
delay_unscale=delay_unscale) 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))
running_loss(loss.item())
# ============================ End ==========================
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)
# log loss
# NOTE: not gathered across GPUs for efficiency
TB_LOGGER.add_scalar('loss', running_loss.val, global_step)
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}=============')
tot_ex = sum(all_gather_list(n_examples))
ex_per_sec = int(tot_ex / (time() - start))
LOGGER.info(f'{tot_ex} examples trained at '
f'{ex_per_sec} ex/s')
TB_LOGGER.add_scalar('perf/ex_per_s', ex_per_sec,
global_step)
LOGGER.info('===========================================')
if global_step % opts.valid_steps == 0:
val_log, results = validate(model, val_dataloader)
TB_LOGGER.log_scaler_dict(val_log)
model_saver.save(model, global_step)
if global_step >= opts.num_train_steps:
break
if global_step >= opts.num_train_steps:
break
n_epoch += 1
LOGGER.info(f"finished {n_epoch} epochs")
if global_step % opts.valid_steps != 0:
val_log, results = validate(model, val_dataloader)
TB_LOGGER.log_scaler_dict(val_log)
val_log, results = validate(model, val_final_dataloader)
with open(
f'{opts.output_dir}/results/'
f'results_{global_step}_final_qa_qar_'
f'rank{rank}.json', 'w') as f:
json.dump(results, f)
TB_LOGGER.log_scaler_dict(val_log)
model_saver.save(model, global_step)