def eval_model(model, dev_dataloader, device_num, args):
model.eval()
uid_list = []
y_list = []
pred_list = []
logits_list = []
with torch.no_grad():
for i, batch in enumerate(dev_dataloader, 0):
batch = move_to_device(batch, device_num)
if args.model_class_name in ["distilbert", "bart-large"]:
outputs = model(batch['input_ids'],
attention_mask=batch['attention_mask'],
labels=None)
else:
outputs = model(batch['input_ids'],
attention_mask=batch['attention_mask'],
token_type_ids=batch['token_type_ids'],
labels=None)
# print(outputs)
logits = outputs[0]
uid_list.extend(list(batch['uid']))
y_list.extend(batch['y'].tolist())
pred_list.extend(torch.max(logits, 1)[1].view(logits.size(0)).tolist())
logits_list.extend(logits.tolist())
assert len(pred_list) == len(logits_list)
assert len(pred_list) == len(logits_list)
result_items_list = []
for i in range(len(uid_list)):
r_item = dict()
r_item['uid'] = uid_list[i]
r_item['logits'] = logits_list[i]
r_item['probability'] = softmax(r_item['logits'])
r_item['predicted_label'] = id2label[pred_list[i]]
result_items_list.append(r_item)
return result_items_list
def train(local_rank, args):
# debug = False
# print("GPU:", gpu)
# world_size = args.world_size
args.global_rank = args.node_rank * args.gpus_per_node + local_rank
args.local_rank = local_rank
# args.warmup_steps = 20
debug_count = 1000
num_epoch = args.epochs
actual_train_batch_size = args.world_size * args.per_gpu_train_batch_size * args.gradient_accumulation_steps
args.actual_train_batch_size = actual_train_batch_size
set_seed(args.seed)
num_labels = 3 # we are doing NLI so we set num_labels = 3, for other task we can change this value.
max_length = args.max_length
model_class_item = MODEL_CLASSES[args.model_class_name]
model_name = model_class_item['model_name']
do_lower_case = model_class_item[
'do_lower_case'] if 'do_lower_case' in model_class_item else False
tokenizer = model_class_item['tokenizer'].from_pretrained(
model_name,
cache_dir=str(config.PRO_ROOT / "trans_cache"),
do_lower_case=do_lower_case)
model = model_class_item['sequence_classification'].from_pretrained(
model_name,
cache_dir=str(config.PRO_ROOT / "trans_cache"),
num_labels=num_labels)
padding_token_value = tokenizer.convert_tokens_to_ids(
[tokenizer.pad_token])[0]
padding_segement_value = model_class_item["padding_segement_value"]
padding_att_value = model_class_item["padding_att_value"]
left_pad = model_class_item[
'left_pad'] if 'left_pad' in model_class_item else False
batch_size_per_gpu_train = args.per_gpu_train_batch_size
batch_size_per_gpu_eval = args.per_gpu_eval_batch_size
if not args.cpu and not args.single_gpu:
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=args.world_size,
rank=args.global_rank)
train_data_str = args.train_data
train_data_weights_str = args.train_weights
eval_data_str = args.eval_data
train_data_name = []
train_data_path = []
train_data_list = []
train_data_weights = []
eval_data_name = []
eval_data_path = []
eval_data_list = []
train_data_named_path = train_data_str.split(',')
weights_str = train_data_weights_str.split(
',') if train_data_weights_str is not None else None
eval_data_named_path = eval_data_str.split(',')
for named_path in train_data_named_path:
ind = named_path.find(':')
name = named_path[:ind]
path = name[ind + 1:]
if name in registered_path:
d_list = common.load_jsonl(registered_path[name])
else:
d_list = common.load_jsonl(path)
train_data_name.append(name)
train_data_path.append(path)
train_data_list.append(d_list)
if weights_str is not None:
for weights in weights_str:
train_data_weights.append(float(weights))
else:
for i in range(len(train_data_list)):
train_data_weights.append(1)
for named_path in eval_data_named_path:
ind = named_path.find(':')
name = named_path[:ind]
path = name[ind + 1:]
if name in registered_path:
d_list = common.load_jsonl(registered_path[name])
else:
d_list = common.load_jsonl(path)
eval_data_name.append(name)
eval_data_path.append(path)
eval_data_list.append(d_list)
assert len(train_data_weights) == len(train_data_list)
batching_schema = {
'uid':
RawFlintField(),
'y':
LabelFlintField(),
'input_ids':
ArrayIndexFlintField(pad_idx=padding_token_value, left_pad=left_pad),
'token_type_ids':
ArrayIndexFlintField(pad_idx=padding_segement_value,
left_pad=left_pad),
'attention_mask':
ArrayIndexFlintField(pad_idx=padding_att_value, left_pad=left_pad),
}
data_transformer = NLITransform(model_name, tokenizer, max_length)
# data_transformer = NLITransform(model_name, tokenizer, max_length, with_element=True)
eval_data_loaders = []
for eval_d_list in eval_data_list:
d_dataset, d_sampler, d_dataloader = build_eval_dataset_loader_and_sampler(
eval_d_list, data_transformer, batching_schema,
batch_size_per_gpu_eval)
eval_data_loaders.append(d_dataloader)
# Estimate the training size:
training_list = []
for i in range(len(train_data_list)):
print("Build Training Data ...")
train_d_list = train_data_list[i]
train_d_name = train_data_name[i]
train_d_weight = train_data_weights[i]
cur_train_list = sample_data_list(
train_d_list, train_d_weight
) # change later # we can apply different sample strategy here.
print(
f"Data Name:{train_d_name}; Weight: {train_d_weight}; "
f"Original Size: {len(train_d_list)}; Sampled Size: {len(cur_train_list)}"
)
training_list.extend(cur_train_list)
estimated_training_size = len(training_list)
print("Estimated training size:", estimated_training_size)
# Estimate the training size ends:
# t_total = estimated_training_size // args.gradient_accumulation_steps * num_epoch
t_total = estimated_training_size * num_epoch // args.actual_train_batch_size
if args.warmup_steps <= 0: # set the warmup steps to 0.1 * total step if the given warmup step is -1.
args.warmup_steps = int(t_total * 0.1)
if not args.cpu:
torch.cuda.set_device(args.local_rank)
model.cuda(args.local_rank)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
"weight_decay":
args.weight_decay,
},
{
"params": [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
"weight_decay":
0.0
},
]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
if not args.cpu and not args.single_gpu:
model = nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True)
args_dict = dict(vars(args))
file_path_prefix = '.'
if args.global_rank in [-1, 0]:
print("Total Steps:", t_total)
args.total_step = t_total
print("Warmup Steps:", args.warmup_steps)
print("Actual Training Batch Size:", actual_train_batch_size)
print("Arguments", pp.pprint(args))
# Let build the logger and log everything before the start of the first training epoch.
if args.global_rank in [
-1, 0
]: # only do logging if we use cpu or global_rank=0
if not args.debug_mode:
file_path_prefix, date = save_tool.gen_file_prefix(
f"{args.experiment_name}")
# # # Create Log File
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name),
'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# Save option file
common.save_json(args_dict,
os.path.join(file_path_prefix, "args.json"))
checkpoints_path = Path(file_path_prefix) / "checkpoints"
if not checkpoints_path.exists():
checkpoints_path.mkdir()
prediction_path = Path(file_path_prefix) / "predictions"
if not prediction_path.exists():
prediction_path.mkdir()
global_step = 0
# print(f"Global Rank:{args.global_rank} ### ", 'Init!')
for epoch in tqdm(range(num_epoch),
desc="Epoch",
disable=args.global_rank not in [-1, 0]):
# Let's build up training dataset for this epoch
training_list = []
for i in range(len(train_data_list)):
print("Build Training Data ...")
train_d_list = train_data_list[i]
train_d_name = train_data_name[i]
train_d_weight = train_data_weights[i]
cur_train_list = sample_data_list(
train_d_list, train_d_weight
) # change later # we can apply different sample strategy here.
print(
f"Data Name:{train_d_name}; Weight: {train_d_weight}; "
f"Original Size: {len(train_d_list)}; Sampled Size: {len(cur_train_list)}"
)
training_list.extend(cur_train_list)
random.shuffle(training_list)
train_dataset = NLIDataset(training_list, data_transformer)
train_sampler = SequentialSampler(train_dataset)
if not args.cpu and not args.single_gpu:
print("Use distributed sampler.")
train_sampler = DistributedSampler(train_dataset,
args.world_size,
args.global_rank,
shuffle=True)
train_dataloader = DataLoader(
dataset=train_dataset,
batch_size=batch_size_per_gpu_train,
shuffle=False, #
num_workers=0,
pin_memory=True,
sampler=train_sampler,
collate_fn=BaseBatchBuilder(batching_schema)) #
# training build finished.
print(debug_node_info(args), "epoch: ", epoch)
if not args.cpu and not args.single_gpu:
train_sampler.set_epoch(
epoch
) # setup the epoch to ensure random sampling at each epoch
for forward_step, batch in enumerate(
tqdm(train_dataloader,
desc="Iteration",
disable=args.global_rank not in [-1, 0]), 0):
model.train()
batch = move_to_device(batch, local_rank)
# print(batch['input_ids'], batch['y'])
if args.model_class_name in ["distilbert", "bart-large"]:
outputs = model(batch['input_ids'],
attention_mask=batch['attention_mask'],
labels=batch['y'])
else:
outputs = model(batch['input_ids'],
attention_mask=batch['attention_mask'],
token_type_ids=batch['token_type_ids'],
labels=batch['y'])
loss, logits = outputs[:2]
# print(debug_node_info(args), loss, logits, batch['uid'])
# print(debug_node_info(args), loss, batch['uid'])
# Accumulated loss
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
# if this forward step need model updates
# handle fp16
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Gradient clip: if max_grad_norm < 0
if (forward_step + 1) % args.gradient_accumulation_steps == 0:
if args.max_grad_norm > 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.global_rank in [
-1, 0
] and args.eval_frequency > 0 and global_step % args.eval_frequency == 0:
r_dict = dict()
# Eval loop:
for i in range(len(eval_data_name)):
cur_eval_data_name = eval_data_name[i]
cur_eval_data_list = eval_data_list[i]
cur_eval_dataloader = eval_data_loaders[i]
# cur_eval_raw_data_list = eval_raw_data_list[i]
evaluation_dataset(args,
cur_eval_dataloader,
cur_eval_data_list,
model,
r_dict,
eval_name=cur_eval_data_name)
# saving checkpoints
current_checkpoint_filename = \
f'e({epoch})|i({global_step})'
for i in range(len(eval_data_name)):
cur_eval_data_name = eval_data_name[i]
current_checkpoint_filename += \
f'|{cur_eval_data_name}#({round(r_dict[cur_eval_data_name]["acc"], 4)})'
if not args.debug_mode:
# save model:
model_output_dir = checkpoints_path / current_checkpoint_filename
if not model_output_dir.exists():
model_output_dir.mkdir()
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
torch.save(model_to_save.state_dict(),
str(model_output_dir / "model.pt"))
torch.save(optimizer.state_dict(),
str(model_output_dir / "optimizer.pt"))
torch.save(scheduler.state_dict(),
str(model_output_dir / "scheduler.pt"))
# save prediction:
if not args.debug_mode and args.save_prediction:
cur_results_path = prediction_path / current_checkpoint_filename
if not cur_results_path.exists():
cur_results_path.mkdir(parents=True)
for key, item in r_dict.items():
common.save_jsonl(
item['predictions'],
cur_results_path / f"{key}.jsonl")
# avoid saving too many things
for key, item in r_dict.items():
del r_dict[key]['predictions']
common.save_json(r_dict,
cur_results_path /
"results_dict.json",
indent=2)
# End of epoch evaluation.
if args.global_rank in [-1, 0]:
r_dict = dict()
# Eval loop:
for i in range(len(eval_data_name)):
cur_eval_data_name = eval_data_name[i]
cur_eval_data_list = eval_data_list[i]
cur_eval_dataloader = eval_data_loaders[i]
# cur_eval_raw_data_list = eval_raw_data_list[i]
evaluation_dataset(args,
cur_eval_dataloader,
cur_eval_data_list,
model,
r_dict,
eval_name=cur_eval_data_name)
# saving checkpoints
current_checkpoint_filename = \
f'e({epoch})|i({global_step})'
for i in range(len(eval_data_name)):
cur_eval_data_name = eval_data_name[i]
current_checkpoint_filename += \
f'|{cur_eval_data_name}#({round(r_dict[cur_eval_data_name]["acc"], 4)})'
if not args.debug_mode:
# save model:
model_output_dir = checkpoints_path / current_checkpoint_filename
if not model_output_dir.exists():
model_output_dir.mkdir()
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
torch.save(model_to_save.state_dict(),
str(model_output_dir / "model.pt"))
torch.save(optimizer.state_dict(),
str(model_output_dir / "optimizer.pt"))
torch.save(scheduler.state_dict(),
str(model_output_dir / "scheduler.pt"))
# save prediction:
if not args.debug_mode and args.save_prediction:
cur_results_path = prediction_path / current_checkpoint_filename
if not cur_results_path.exists():
cur_results_path.mkdir(parents=True)
for key, item in r_dict.items():
common.save_jsonl(item['predictions'],
cur_results_path / f"{key}.jsonl")
# avoid saving too many things
for key, item in r_dict.items():
del r_dict[key]['predictions']
common.save_json(r_dict,
cur_results_path / "results_dict.json",
indent=2)