def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--bert_model",
default="bert-base-uncased",
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
)
parser.add_argument(
"--from_pretrained",
default="bert-base-uncased",
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.",
)
parser.add_argument(
"--output_dir",
default="save",
type=str,
help="The output directory where the model checkpoints will be written.",
)
parser.add_argument(
"--config_file",
default="config/bert_base_6layer_6conect.json",
type=str,
help="The config file which specified the model details.",
)
parser.add_argument(
"--num_train_epochs",
default=20,
type=int,
help="Total number of training epochs to perform.",
)
parser.add_argument(
"--train_iter_multiplier",
default=1.0,
type=float,
help="multiplier for the multi-task training.",
)
parser.add_argument(
"--train_iter_gap",
default=4,
type=int,
help="forward every n iteration is the validation score is not improving over the last 3 epoch, -1 means will stop",
)
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.",
)
parser.add_argument(
"--no_cuda", action="store_true", help="Whether not to use CUDA when available"
)
parser.add_argument(
"--do_lower_case",
default=True,
type=bool,
help="Whether to lower case the input text. True for uncased models, False for cased models.",
)
parser.add_argument(
"--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus",
)
parser.add_argument(
"--seed", type=int, default=0, help="random seed for initialization"
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumualte before performing a backward/update pass.",
)
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit float precision instead of 32-bit",
)
parser.add_argument(
"--loss_scale",
type=float,
default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n",
)
parser.add_argument(
"--num_workers",
type=int,
default=16,
help="Number of workers in the dataloader.",
)
parser.add_argument(
"--save_name", default="", type=str, help="save name for training."
)
parser.add_argument(
"--in_memory",
default=False,
type=bool,
help="whether use chunck for parallel training.",
)
parser.add_argument(
"--optim", default="AdamW", type=str, help="what to use for the optimization."
)
parser.add_argument(
"--freeze",
default=-1,
type=int,
help="till which layer of textual stream of vilbert need to fixed.",
)
parser.add_argument(
"--vision_scratch",
action="store_true",
help="whether pre-trained the image or not.",
)
parser.add_argument(
"--evaluation_interval", default=1, type=int, help="evaluate very n epoch."
)
parser.add_argument(
"--lr_scheduler",
default="mannul",
type=str,
help="whether use learning rate scheduler.",
)
parser.add_argument(
"--baseline", action="store_true", help="whether use single stream baseline."
)
parser.add_argument(
"--resume_file", default="", type=str, help="Resume from checkpoint"
)
parser.add_argument(
"--dynamic_attention",
action="store_true",
help="whether use dynamic attention.",
)
parser.add_argument(
"--clean_train_sets",
default=True,
type=bool,
help="whether clean train sets for multitask data.",
)
parser.add_argument(
"--visual_target",
default=0,
type=int,
help="which target to use for visual branch. \
0: soft label, \
1: regress the feature, \
2: NCE loss.",
)
args = parser.parse_args()
with open("task_config.yml", "r") as f:
task_cfg = edict(yaml.safe_load(f))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.baseline:
from pytorch_transformers.modeling_bert import BertConfig
from src.models.basebert import BaseBertForVLTasks
else:
from src.models.vilbert import BertConfig
from src.models.vilbert import VILBertForVLTasks
name = task_cfg["name"]
task_lr = task_cfg["lr"]
base_lr = task_lr
loss_scale = task_lr / base_lr
if args.save_name:
prefix = "-" + args.save_name
else:
prefix = ""
timeStamp = (
args.config_file.split("/")[1].split(".")[0]
+ prefix
)
savePath = os.path.join(args.output_dir, timeStamp)
bert_weight_name = json.load(
open("config/" + args.bert_model + "_weight_name.json", "r")
)
if args.local_rank == -1 or args.no_cuda:
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"
)
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
torch.distributed.init_process_group(backend="nccl")
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16
)
)
default_gpu = False
if dist.is_available() and args.local_rank != -1:
rank = dist.get_rank()
if rank == 0:
default_gpu = True
else:
default_gpu = True
if default_gpu:
if not os.path.exists(savePath):
os.makedirs(savePath)
config = BertConfig.from_json_file(args.config_file)
if default_gpu:
# save all the hidden parameters.
with open(os.path.join(savePath, "command.txt"), "w") as f:
print(args, file=f) # Python 3.x
print("\n", file=f)
print(config, file=f)
# load dataset
task_batch_size, task_num_iters, task_datasets_train, task_datasets_val, task_dataloader_train, task_dataloader_val = LoadDatasets(
args, task_cfg
)
logdir = os.path.join(savePath, "logs")
tbLogger = utils.tbLogger(
logdir,
savePath,
task_num_iters,
args.gradient_accumulation_steps,
)
if args.visual_target == 0:
config.v_target_size = 1601
config.visual_target = args.visual_target
else:
config.v_target_size = 2048
config.visual_target = args.visual_target
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_ave_iter = {}
task_stop_controller = {}
task_ave_iter = int(
task_cfg["num_epoch"]
* task_num_iters
* args.train_iter_multiplier
/ args.num_train_epochs
)
task_stop_controller = utils.TaskStopOnPlateau(
mode="max",
patience=1,
continue_threshold=0.005,
cooldown=1,
threshold=0.001,
)
median_num_iter = task_ave_iter
num_train_optimization_steps = (
median_num_iter * args.num_train_epochs // args.gradient_accumulation_steps
)
num_labels = task_datasets_train.num_labels
if args.dynamic_attention:
config.dynamic_attention = True
model = VILBertForVLTasks.from_pretrained(
args.from_pretrained,
config=config,
num_labels=num_labels,
default_gpu=default_gpu,
)
task_losses = LoadLosses(args, task_cfg)
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
if args.freeze != -1:
bert_weight_name_filtered = []
for name in bert_weight_name:
if "embeddings" in name:
bert_weight_name_filtered.append(name)
elif "encoder" in name:
layer_num = name.split(".")[2]
if int(layer_num) <= args.freeze:
bert_weight_name_filtered.append(name)
optimizer_grouped_parameters = []
for key, value in dict(model.named_parameters()).items():
if key[12:] in bert_weight_name_filtered:
value.requires_grad = False
if default_gpu:
print("filtered weight")
print(bert_weight_name_filtered)
optimizer_grouped_parameters = []
for key, value in dict(model.named_parameters()).items():
if value.requires_grad:
if "vil_" in key:
lr = 1e-4
else:
if args.vision_scratch:
if key[12:] in bert_weight_name:
lr = base_lr
else:
lr = 1e-4
else:
lr = base_lr
if any(nd in key for nd in no_decay):
optimizer_grouped_parameters += [
{"params": [value], "lr": lr, "weight_decay": 0.0}
]
if not any(nd in key for nd in no_decay):
optimizer_grouped_parameters += [
{"params": [value], "lr": lr, "weight_decay": 0.01}
]
if default_gpu:
print(len(list(model.named_parameters())), len(optimizer_grouped_parameters))
# choose optimizer
if args.optim == "AdamW":
optimizer = AdamW(optimizer_grouped_parameters, lr=base_lr, correct_bias=False)
elif args.optim == "RAdam":
optimizer = RAdam(optimizer_grouped_parameters, lr=base_lr)
# choose scheduler
warmpu_steps = args.warmup_proportion * num_train_optimization_steps
if args.lr_scheduler == "warmup_linear":
warmup_scheduler = WarmupLinearSchedule(
optimizer, warmup_steps=warmpu_steps, t_total=num_train_optimization_steps
)
else:
warmup_scheduler = WarmupConstantSchedule(optimizer, warmup_steps=warmpu_steps)
lr_reduce_list = np.array([5, 7])
if args.lr_scheduler == "automatic":
lr_scheduler = ReduceLROnPlateau(
optimizer, mode="max", factor=0.2, patience=1, cooldown=1, threshold=0.001
)
elif args.lr_scheduler == "cosine":
lr_scheduler = CosineAnnealingLR(
optimizer, T_max=median_num_iter * args.num_train_epochs
)
elif args.lr_scheduler == "cosine_warm":
lr_scheduler = CosineAnnealingWarmRestarts(
optimizer, T_0=median_num_iter * args.num_train_epochs
)
elif args.lr_scheduler == "mannul":
def lr_lambda_fun(epoch):
return pow(0.2, np.sum(lr_reduce_list <= epoch))
lr_scheduler = LambdaLR(optimizer, lr_lambda=lr_lambda_fun)
startIterID = 0
global_step = 0
start_epoch = 0
if args.resume_file != "" and os.path.exists(args.resume_file):
checkpoint = torch.load(args.resume_file, map_location="cpu")
new_dict = {}
for attr in checkpoint["model_state_dict"]:
if attr.startswith("module."):
new_dict[attr.replace("module.", "", 1)] = checkpoint[
"model_state_dict"
][attr]
else:
new_dict[attr] = checkpoint["model_state_dict"][attr]
model.load_state_dict(new_dict)
warmup_scheduler.load_state_dict(checkpoint["warmup_scheduler_state_dict"])
# lr_scheduler.load_state_dict(checkpoint['lr_scheduler_state_dict'])
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
global_step = checkpoint["global_step"]
start_epoch = int(checkpoint["epoch_id"]) + 1
task_stop_controller = checkpoint["task_stop_controller"]
tbLogger = checkpoint["tb_logger"]
del checkpoint
model.to(device)
print("`==============`MODEL=============")
print(next(model.parameters()).is_cuda)#False
for state in optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model, delay_allreduce=True)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
if default_gpu:
print("***** Running training *****")
print(" Num Iters: ", task_num_iters)
print(" Batch size: ", task_batch_size)
print(" Num steps: %d" % num_train_optimization_steps)
task_iter_train = None
task_count = 0
for epochId in tqdm(range(start_epoch, args.num_train_epochs), desc="Epoch", ncols=100):
model.train()
for step in range(median_num_iter):
iterId = startIterID + step + (epochId * median_num_iter)
first_task = True
is_forward = False
if (not task_stop_controller.in_stop) or (
iterId % args.train_iter_gap == 0
):
is_forward = True
if is_forward:
loss, score = ForwardModelsTrain(
args,
task_cfg,
device,
task_count,
task_iter_train,
task_dataloader_train,
model,
task_losses,
)
loss = loss * loss_scale
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_optimization_steps,
args.warmup_proportion,
)
for param_group in optimizer.param_groups:
param_group["lr"] = lr_this_step
if first_task and (
global_step < warmpu_steps
or args.lr_scheduler == "warmup_linear"
):
warmup_scheduler.step()
optimizer.step()
model.zero_grad()
if first_task:
global_step += 1
first_task = False
if default_gpu:
tbLogger.step_train(
epochId,
iterId,
float(loss),
float(score),
optimizer.param_groups[0]["lr"],
"train",
)
if "cosine" in args.lr_scheduler and global_step > warmpu_steps:
lr_scheduler.step()
if (
step % (20 * args.gradient_accumulation_steps) == 0
and step != 0
and default_gpu
):
tbLogger.showLossTrain()
# decided whether to evaluate on SNLI tasks.
if (iterId != 0 and iterId % task_num_iters == 0) or (
epochId == args.num_train_epochs - 1 and step == median_num_iter - 1
):
evaluate(
args,
task_dataloader_val,
task_stop_controller,
task_cfg,
device,
model,
task_losses,
epochId,
default_gpu,
tbLogger,
)
if args.lr_scheduler == "automatic":
lr_scheduler.step(sum(val_scores.values()))
logger.info("best average score is %3f" % lr_scheduler.best)
elif args.lr_scheduler == "mannul":
lr_scheduler.step()
if epochId in lr_reduce_list:
# reset the task_stop_controller once the lr drop
task_stop_controller._reset()
if default_gpu:
# Save a trained model
logger.info("** ** * Saving fine - tuned model ** ** * ")
model_to_save = (
model.module if hasattr(model, "module") else model
) # Only save the model it-self
output_model_file = os.path.join(
savePath, "pytorch_model_" + str(epochId) + ".bin"
)
output_checkpoint = os.path.join(savePath, "pytorch_ckpt_latest.tar")
torch.save(model_to_save.state_dict(), output_model_file)
torch.save(
{
"model_state_dict": model_to_save.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"warmup_scheduler_state_dict": warmup_scheduler.state_dict(),
# 'lr_scheduler_state_dict': lr_scheduler.state_dict(),
"global_step": global_step,
"epoch_id": epochId,
"task_stop_controller": task_stop_controller,
"tb_logger": tbLogger,
},
output_checkpoint,
)
tbLogger.txt_close()
pretrained_dict = pretrained_dict['model_state_dict']
model_dict = dialog_encoder.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
print("number of keys transferred", len(pretrained_dict))
assert len(pretrained_dict.keys()) > 0
model_dict.update(pretrained_dict)
dialog_encoder.load_state_dict(model_dict)
del pretrained_dict, model_dict \
else:
model_dict = dialog_encoder.state_dict()
optimizer_dict = optimizer.state_dict()
pretrained_dict_model = pretrained_dict['model_state_dict']
pretrained_dict_optimizer = pretrained_dict['optimizer_state_dict']
pretrained_dict_scheduler = pretrained_dict['scheduler_state_dict']
pretrained_dict_model = {
k: v
for k, v in pretrained_dict_model.items() if k in model_dict
}
pretrained_dict_optimizer = {
k: v
for k, v in pretrained_dict_optimizer.items()
if k in optimizer_dict
}
model_dict.update(pretrained_dict_model)
optimizer_dict.update(pretrained_dict_optimizer)
dialog_encoder.load_state_dict(model_dict)
best = True
else:
patience += 1
if patience > 5:
print("Patience reached...")
break
print("Saving model...")
torch.save({
'epoch': epoch,
'val_loss': val_loss,
'train_loss': train_loss,
'state_dict': discriminator.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'patience': patience,
}, os.path.join(config.model_path, 'last_model_discriminator.pth'))
if best:
copyfile(os.path.join(config.model_path, 'last_model_discriminator.pth') , os.path.join(config.model_path, 'best_model_discriminator.pth'))
# for idx, (captions, image_features, image_name, label) in enumerate(train_data_loader):
# captions = captions.to(device) # (N, L), long
# image_features = image_features.to(device) # (N, L), long
# num_img_tokens = image_features.size(1)
# seq_length = captions.size(1)
optimizer = AdamW(optimizer_grouped_parameters, lr=params['lr'])
scheduler = WarmupLinearScheduleNonZero(optimizer, warmup_steps=10000, t_total=200000)
start_iter_id = 0
if params['start_path']:
pretrained_dict = torch.load(params['start_path'])
if not params['continue']:
model_dict = dialog_encoder.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
print("pretrained dict", pretrained_dict)
assert len(pretrained_dict.keys()) > 0
model_dict.update(pretrained_dict)
dialog_encoder.load_state_dict(model_dict)
else:
model_dict = dialog_encoder.state_dict()
optimizer_dict = optimizer.state_dict()
pretrained_dict_model = pretrained_dict['model_state_dict']
pretrained_dict_optimizer = pretrained_dict['optimizer_state_dict']
pretrained_dict_scheduler = pretrained_dict['scheduler_state_dict']
pretrained_dict_model = {k: v for k, v in pretrained_dict_model.items() if k in model_dict}
pretrained_dict_optimizer = {k: v for k, v in pretrained_dict_optimizer.items() if k in optimizer_dict}
model_dict.update(pretrained_dict_model)
optimizer_dict.update(pretrained_dict_optimizer)
dialog_encoder.load_state_dict(model_dict)
optimizer.load_state_dict(optimizer_dict)
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
scheduler = WarmupLinearScheduleNonZero(optimizer, warmup_steps=10000, \
t_total=200000, last_epoch=pretrained_dict["iterId"])
def train(args):
save_path = join(args.save_path, 'ckpt')
if not os.path.exists(save_path):
os.makedirs(save_path)
args.train_batch_size = args.bs * max(1, args.n_gpu)
train_loader, val_loader = batcher(args.path, args.train_batch_size)
t_total = len(train_loader) // args.gradient_accumulation_steps * args.num_train_epochs
print(t_total / args.num_train_epochs)
tb_writer = SummaryWriter(log_dir=join(args.save_path, 'tensorboard'))
model = Bert_choice()
if args.cuda:
model = model.cuda()
model.train()
optimizer = AdamW(model.parameters(), lr=1e-5)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=0, t_total=t_total)
if args.fp16:
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
train_iterator = trange(int(args.num_train_epochs), desc="Epoch")
global_ccr = 0
global_step = 0
for _ in train_iterator:
epoch_iterator = tqdm(train_loader, desc="Iteration")
tr_loss, logging_loss = 0, 0
tr_ccr = 0
for step, batch in enumerate(epoch_iterator):
#questions, contexts, choicess = batch
_inputs = batch.to(args.device)
bs, cn, length = _inputs.size()
labels = torch.tensor([0 for _ in range(bs)]).to(args.device)
loss, _ids = model(_inputs, labels)
ccr = sum([1 if _id == 0 else 0 for _id in _ids]) / len(_ids)
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
tr_loss += loss.item()
tr_ccr += ccr / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), 2)
optimizer.step()
scheduler.step()
model.zero_grad()
global_ccr = global_ccr * 0.01 + tr_ccr
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', (tr_loss - logging_loss), global_step)
tb_writer.add_scalar('ccr', global_ccr, global_step)
global_step += 1
#print('loss: {:.4f} ccr {:.4f}\r'.format(tr_loss, ccr), end='')
logging_loss = tr_loss
tr_ccr = 0
if global_step % args.ckpt == 0:
total_ccr, total_loss = evaluate(model, val_loader, args)
name = 'ckpt-{:4f}-{:4f}-{}'.format(total_loss, total_ccr, global_step)
save_dict = {}
save_dict['state_dict'] = model.state_dict()
save_dict['optimizer'] = optimizer.state_dict()
torch.save(save_dict, join(save_path, name))
