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=0,
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("--tasks",
default="",
type=str,
help="1-2-3... training task separate by -")
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.",
)
parser.add_argument(
"--task_specific_tokens",
action="store_true",
help="whether to use task specific tokens for the multi-task learning.",
)
args = parser.parse_args()
with open("vilbert_tasks.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)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if args.baseline:
from pytorch_transformers.modeling_bert import BertConfig
from vilbert.basebert import BaseBertForVLTasks
else:
from vilbert.vilbert import BertConfig
from vilbert.vilbert import VILBertForVLTasks
task_names = []
task_lr = []
for i, task_id in enumerate(args.tasks.split("-")):
task = "TASK" + task_id
name = task_cfg[task]["name"]
task_names.append(name)
task_lr.append(task_cfg[task]["lr"])
base_lr = min(task_lr)
loss_scale = {}
for i, task_id in enumerate(args.tasks.split("-")):
task = "TASK" + task_id
loss_scale[task] = task_lr[i] / base_lr
if args.save_name:
prefix = "-" + args.save_name
else:
prefix = ""
timeStamp = ("-".join(task_names) + "_" +
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)
task_batch_size, task_num_iters, task_ids, task_datasets_train, task_datasets_val, task_dataloader_train, task_dataloader_val = LoadDatasets(
args, task_cfg, args.tasks.split("-"))
logdir = os.path.join(savePath, "logs")
tbLogger = utils.tbLogger(
logdir,
savePath,
task_names,
task_ids,
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 args.task_specific_tokens:
config.task_specific_tokens = True
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_ave_iter = {}
task_stop_controller = {}
for task_id, num_iter in task_num_iters.items():
task_ave_iter[task_id] = int(task_cfg[task]["num_epoch"] * num_iter *
args.train_iter_multiplier /
args.num_train_epochs)
task_stop_controller[task_id] = utils.MultiTaskStopOnPlateau(
mode="max",
patience=1,
continue_threshold=0.005,
cooldown=1,
threshold=0.001,
)
task_ave_iter_list = sorted(task_ave_iter.values())
median_num_iter = task_ave_iter_list[-1]
num_train_optimization_steps = (median_num_iter * args.num_train_epochs //
args.gradient_accumulation_steps)
num_labels = max(
[dataset.num_labels for dataset in task_datasets_train.values()])
if args.dynamic_attention:
config.dynamic_attention = True
if "roberta" in args.bert_model:
config.model = "roberta"
if args.baseline:
model = BaseBertForVLTasks.from_pretrained(
args.from_pretrained,
config=config,
num_labels=num_labels,
default_gpu=default_gpu,
)
else:
model = VILBertForVLTasks.from_pretrained(
args.from_pretrained,
config=config,
num_labels=num_labels,
default_gpu=default_gpu,
)
task_losses = LoadLosses(args, task_cfg, args.tasks.split("-"))
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))
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)
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)
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 = {name: None for name in task_ids}
task_count = {name: 0 for name in task_ids}
for epochId in tqdm(range(start_epoch, args.num_train_epochs),
desc="Epoch"):
model.train()
torch.autograd.set_detect_anomaly(True)
for step in range(median_num_iter):
iterId = startIterID + step + (epochId * median_num_iter)
first_task = True
for task_id in task_ids:
is_forward = False
if (not task_stop_controller[task_id].in_stop) or (
iterId % args.train_iter_gap == 0):
is_forward = True
if is_forward:
loss, score = ForwardModelsTrain(
args,
task_cfg,
device,
task_id,
task_count,
task_iter_train,
task_dataloader_train,
model,
task_losses,
)
loss = loss * loss_scale[task_id]
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
optimizer.step()
model.zero_grad()
if first_task and (global_step < warmpu_steps
or args.lr_scheduler
== "warmup_linear"):
warmup_scheduler.step()
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"],
task_id,
"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 each tasks.
for task_id in task_ids:
if (iterId != 0 and iterId % task_num_iters[task_id]
== 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,
task_id,
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:
for task_id in task_ids:
# reset the task_stop_controller once the lr drop
task_stop_controller[task_id]._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()
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_config.json",
type=str,
help="The config file which specified the model details.",
)
parser.add_argument("--learning_rate",
default=2e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument(
"--num_train_epochs",
default=20,
type=int,
help="Total number of training epochs to perform.",
)
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("--use_chunk",
default=0,
type=float,
help="whether use chunck for parallel training.")
parser.add_argument("--in_memory",
default=False,
type=bool,
help="whether use chunck for parallel training.")
parser.add_argument("--optimizer",
default='BertAdam',
type=str,
help="whether use chunck for parallel training.")
parser.add_argument("--tasks",
default='',
type=str,
help="1-2-3... training task separate by -")
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("--compact",
action="store_true",
help="whether use compact vilbert model.")
parser.add_argument("--debug",
action="store_true",
help="whether in debug mode.")
parser.add_argument(
"--tensorboard_dir",
default="tensorboard_log",
type=str,
help="The output directory where tensorboard log will be written.",
)
parser.add_argument(
"--batch_size",
default=-1,
type=int,
help="Custom Batch size for task.",
)
parser.add_argument(
"--data_root",
default="",
type=str,
help="The data root of the task.",
)
args = parser.parse_args()
with open('vlbert_tasks.yml', 'r') as f:
task_cfg = edict(yaml.load(f))
# random.seed(args.seed)
# np.random.seed(args.seed)
# torch.manual_seed(args.seed)
if args.baseline:
from pytorch_pretrained_bert.modeling import BertConfig
from vilbert.basebert import BaseBertForVLTasks
elif args.compact:
from vilbert.vilbert_compact import BertConfig
from vilbert.vilbert_compact import VILBertForVLTasks
else:
from vilbert.vilbert import BertConfig
from vilbert.vilbert import VILBertForVLTasks
task_names = []
task_lr = []
for i, task_id in enumerate(args.tasks.split('-')):
task = 'TASK' + task_id
name = task_cfg[task]['name']
task_names.append(name)
task_lr.append(task_cfg[task]['lr'])
base_lr = min(task_lr)
loss_scale = {}
for i, task_id in enumerate(args.tasks.split('-')):
task = 'TASK' + task_id
loss_scale[task] = task_lr[i] / base_lr
if args.save_name:
prefix = '-' + args.save_name
else:
prefix = ''
timeStamp = '-'.join(task_names) + '_' + args.config_file.split(
'/')[1].split('.')[0] + prefix
savePath = os.path.join(args.output_dir, timeStamp)
logPath = os.path.join(args.tensorboard_dir, timeStamp)
if os.path.isdir(logPath):
logger.error('Tensorboard Log path exists. Overwriting.')
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
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
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)
if args.batch_size != -1:
for i, task_id in enumerate(args.tasks.split('-')):
task = 'TASK' + task_id
task_cfg[task]['batch_size'] = args.batch_size
if args.data_root != "":
for i, task_id in enumerate(args.tasks.split('-')):
data_root = args.data_root
task = 'TASK' + task_id
task_cfg[task]['dataroot'] = data_root
task_cfg[task]['features_h5path1'] = os.path.join(
data_root, task_cfg[task]['features_h5path1'].split('/')[-1])
task_cfg[task]['features_h5path2'] = os.path.join(
data_root, task_cfg[task]['features_h5path2'].split('/')[-1])
task_cfg[task]['train_annotations_jsonpath'] = os.path.join(
data_root,
task_cfg[task]['train_annotations_jsonpath'].split('/')[-1])
task_cfg[task]['val_annotations_jsonpath'] = os.path.join(
data_root,
task_cfg[task]['val_annotations_jsonpath'].split('/')[-1])
# Done it for VCR Dataset only, need to put this train_100.jsonl for other datasets
if args.debug:
for i, task_id in enumerate(args.tasks.split('-')):
task = 'TASK' + task_id
task_cfg[task]['train_annotations_jsonpath'] = '/'.join(
task_cfg[task]['train_annotations_jsonpath'].split('/')[:-1] +
['train_100.jsonl'])
task_cfg[task]['val_annotations_jsonpath'] = '/'.join(
task_cfg[task]['val_annotations_jsonpath'].split('/')[:-1] +
['val_100.jsonl'])
task_cfg[task]['batch_size'] = 2
# Have added args.debug to only VCR Datasets (vcr_dataset.py) will need to add it to other dataset too.
task_batch_size, task_num_iters, task_ids, task_datasets_train, task_datasets_val, \
task_dataloader_train, task_dataloader_val = LoadDatasets(args, task_cfg, args.tasks.split('-'), args.debug)
tbLogger = utils.tbLogger(logPath, savePath, task_names, task_ids,
task_num_iters, args.gradient_accumulation_steps)
# if n_gpu > 0:
# torch.cuda.manual_seed_all(args.seed)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
num_train_optimization_steps = max(task_num_iters.values(
)) * args.num_train_epochs // args.gradient_accumulation_steps
num_labels = max(
[dataset.num_labels for dataset in task_datasets_train.values()])
task_start_iter = {}
task_interval = {}
for task_id, num_iter in task_num_iters.items():
task_start_iter[task_id] = num_train_optimization_steps - (
task_cfg[task]['num_epoch'] * num_iter //
args.gradient_accumulation_steps)
task_interval[task_id] = num_train_optimization_steps // (
task_cfg[task]['num_epoch'] * num_iter //
args.gradient_accumulation_steps)
if args.baseline:
model = BaseBertForVLTasks.from_pretrained(args.from_pretrained,
config,
num_labels=num_labels,
default_gpu=default_gpu)
else:
model = VILBertForVLTasks.from_pretrained(args.from_pretrained,
config,
num_labels=num_labels,
default_gpu=default_gpu)
task_losses = LoadLosses(args, task_cfg, args.tasks.split('-'))
model.to(device)
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)
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 = []
lr = args.learning_rate
for key, value in dict(model.named_parameters()).items():
if value.requires_grad:
if 'vil_prediction' in key:
# if args.learning_rate <= 2e-5:
lr = 1e-4
else:
if args.vision_scratch:
if key[12:] in bert_weight_name:
lr = args.learning_rate
else:
lr = 1e-4
else:
lr = args.learning_rate
if any(nd in key for nd in no_decay):
optimizer_grouped_parameters += [{
"params": [value],
"lr": lr,
"weight_decay": 0.01
}]
if not any(nd in key for nd in no_decay):
optimizer_grouped_parameters += [{
"params": [value],
"lr": lr,
"weight_decay": 0.0
}]
if default_gpu:
print(len(list(model.named_parameters())),
len(optimizer_grouped_parameters))
max_num_iter = max(task_num_iters.values())
max_batch_size = max(task_batch_size.values())
if args.optimizer == 'BertAdam':
optimizer = BertAdam(
optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps,
schedule='warmup_constant',
)
elif args.optimizer == 'Adam':
optimizer = Adam(
optimizer_grouped_parameters,
lr=base_lr,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps,
schedule='warmup_constant',
)
elif args.optimizer == 'Adamax':
optimizer = Adamax(
optimizer_grouped_parameters,
lr=base_lr,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps,
schedule='warmup_constant',
)
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 == 'mannul':
lr_reduce_list = np.array([12, 16])
# lr_reduce_list = np.array([6, 8, 10])
def lr_lambda_fun(epoch):
return pow(0.1, np.sum(lr_reduce_list <= epoch))
lr_scheduler = LambdaLR(optimizer, lr_lambda=lr_lambda_fun)
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)
startIterID = 0
# initialize the data iteration.
task_iter_train = {name: None for name in task_ids}
task_count = {name: 0 for name in task_ids}
for epochId in tqdm(range(args.num_train_epochs), desc="Epoch"):
model.train()
for step in range(max_num_iter):
iterId = startIterID + step + (epochId * max_num_iter)
for task_id in task_ids:
if iterId >= task_start_iter[task_id]:
# if iterId % task_interval[task_id] == 0:
loss, score = ForwardModelsTrain(args, task_cfg, device,
task_id, task_count,
task_iter_train,
task_dataloader_train,
model, task_losses,
task_start_iter)
loss = loss * loss_scale[task_id]
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
model.zero_grad()
if default_gpu:
tbLogger.step_train(epochId, iterId, float(loss),
float(score),
optimizer.show_lr(), task_id,
'train')
if step % (20 * args.gradient_accumulation_steps
) == 0 and step != 0 and default_gpu:
tbLogger.showLossTrain()
model.eval()
# when run evaluate, we run each task sequentially.
for task_id in task_ids:
for i, batch in enumerate(task_dataloader_val[task_id]):
loss, score, batch_size = ForwardModelsVal(
args, task_cfg, device, task_id, batch, model, task_losses)
tbLogger.step_val(epochId, float(loss), float(score), task_id,
batch_size, 'val')
if default_gpu:
sys.stdout.write('%d/%d\r' %
(i, len(task_dataloader_val[task_id])))
sys.stdout.flush()
ave_score = tbLogger.showLossVal()
if args.lr_scheduler == 'automatic':
lr_scheduler.step(ave_score)
logger.info("best average score is %3f" % lr_scheduler.best)
else:
lr_scheduler.step()
if default_gpu:
# Save a trained model
logger.info("** ** * Saving fine - tuned model on " + logPath +
"** ** * ")
model_to_save = (
model.module if hasattr(model, "module") else model
) # Only save the model it-self
if not os.path.exists(savePath):
os.makedirs(savePath)
output_model_file = os.path.join(
savePath, "pytorch_model_" + str(epochId) + ".bin")
torch.save(model_to_save.state_dict(), output_model_file)
tbLogger.txt_close()
