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pre_train_t5.py
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pre_train_t5.py
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import argparse
import torch
import torch.nn as nn
from config import Config
import os, sys, math
from pre_train_dataset import PreTrainDataset, get_data_loader
import t5_model
import numpy as np
import random
import utils
from checkpointing import CheckpointManager
from tqdm import tqdm
def evaluation(_C, eval_data, model, device):
model.eval()
loss_list = []
with torch.no_grad():
for batch in tqdm(eval_data):
for n in batch:
batch[n] = batch[n].to(device)
outputs = model(
input_ids=batch['encoder_input_ids'],
attention_mask=batch['encoder_mask'],
labels=batch['decoder_input_ids'],
)
loss = outputs.loss
loss_list.append(loss.item())
final_loss = sum(loss_list) / len(loss_list)
print("EVAL LOSS %.2f" % final_loss)
return -1 * final_loss
parser = argparse.ArgumentParser("Train a MT5 for Machine Translation")
parser.add_argument(
"--config", required=True, help="Path to a config file with all configuration parameters."
)
parser.add_argument(
"--config-override",
default=[],
nargs="*",
help="A sequence of key-value pairs specifying certain config arguments (with dict-like "
"nesting) using a dot operator. The actual config will be updated and recorded in "
"the serialization directory.",
)
parser.add_argument(
"--serialization-dir",
default=None,
help="Path to a (non-existent) directory for serializing checkpoints and tensorboard logs.",
)
parser.add_argument(
"--start-from-checkpoint",
default=None,
help="Path to load checkpoint and continue training [only supported for module_training].",
)
parser.add_argument(
"--output-path",
default=None,
help="Path to save output captions",
)
group = parser.add_mutually_exclusive_group()
group.add_argument('--train', action='store_true')
group.add_argument('--validation', action='store_true')
group.add_argument('--test', action='store_true')
if __name__ == "__main__":
_A = parser.parse_args()
_C = Config(_A.config, _A.config_override)
np.random.seed(_C.random_seed)
random.seed(_C.random_seed)
torch.manual_seed(_C.random_seed)
torch.cuda.manual_seed_all(_C.random_seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if _C.enable_full_finetune:
tokenizer, model = t5_model.get_full_finetune_t5_model(_C)
elif _C.enable_full_pretrain:
tokenizer, model = t5_model.get_full_pretrain_t5_model(_C)
else:
tokenizer, model = t5_model.get_t5_model(_C)
total_parameter_count = 0
trainable_parameter_count = 0
for p in model.parameters():
total_parameter_count += p.numel()
if p.requires_grad:
trainable_parameter_count += p.numel()
print('Total Parameter Count %d' % total_parameter_count)
print('Trainable Parameter Count %d' % trainable_parameter_count)
print(_C)
for arg in vars(_A):
print("{:<20}: {}".format(arg, getattr(_A, arg)))
dev_data = PreTrainDataset(_C, tokenizer, _C.dev_path)
dev_loader = get_data_loader(_C, dev_data, _C.batch_size, shuffle=False)
test_data = PreTrainDataset(_C, tokenizer, _C.dev_path)
test_loader = get_data_loader(_C, test_data, _C.batch_size, shuffle=False)
if _A.validation or _A.test:
if torch.cuda.is_available():
model.load_state_dict(torch.load(os.path.join(_A.start_from_checkpoint, 'model-best.pth'))['model'], strict=False)
else:
model.load_state_dict(torch.load(os.path.join(_A.start_from_checkpoint, 'model-best.pth'), map_location=torch.device('cpu'))['model'], strict=False)
selected_data = dev_loader if _A.validation else test_loader
evaluation(_C, selected_data, model, device)
if _A.train:
train_data = PreTrainDataset(_C, tokenizer, _C.train_path)
train_loader = get_data_loader(_C, train_data, _C.batch_size, shuffle=True)
train_iter = iter(train_loader)
if _C.num_training_steps == 0:
_C.num_training_steps = int(len(train_iter) * _C.max_epoch / _C.gradient_accumulation_steps)
epoch_num = math.ceil(_C.num_training_steps / _C.checkpoint_every_step)
if _C.enable_full_finetune:
optimizer = utils.build_t5_finetune_optimizer(_C, model)
elif _C.enable_full_pretrain:
optimizer = utils.build_t5_pretraining_optimizer(_C, model)
elif _C.enable_adam_opt:
optimizer = utils.build_optimizer(_C, model)
else:
optimizer = utils.build_t5_optimizer(_C, model)
os.makedirs(_A.serialization_dir, exist_ok=True)
_C.dump(os.path.join(_A.serialization_dir, "config.yml"))
checkpoint_manager = CheckpointManager(model, _A.serialization_dir, mode="max")
eval_every = _C.checkpoint_every_step * _C.gradient_accumulation_steps
total_step = 0
total_oom = 0
best_test_performance = 0
model.parallelize()
for epoch in range(epoch_num):
print('EPOCH %d / %d' % (epoch + 1, epoch_num))
run_step = eval_every if total_step + eval_every < _C.num_training_steps * _C.gradient_accumulation_steps else _C.num_training_steps * _C.gradient_accumulation_steps - total_step
model.train()
with tqdm(total=math.ceil(run_step / _C.gradient_accumulation_steps), file=sys.stdout) as pbar:
for step in range(run_step):
try:
batch = next(train_iter)
except:
train_iter = iter(train_loader)
batch = next(train_iter)
for n in batch:
batch[n] = batch[n].to(device)
total_step += 1
try:
outputs = model(
input_ids=batch['encoder_input_ids'],
attention_mask=batch['encoder_mask'],
labels=batch['decoder_input_ids'],
)
loss = outputs.loss
loss = loss / _C.gradient_accumulation_steps
loss.backward()
except RuntimeError:
torch.cuda.empty_cache()
total_oom += 1
pbar.set_description("OOM %d loss -" % (total_oom))
pbar.update(1)
continue
if (step + 1) % _C.gradient_accumulation_steps == 0:
optimizer.step()
if torch.cuda.is_initialized():
torch.cuda.synchronize()
pbar.set_description("OOM %d loss %.2f" % (total_oom, loss.item() * _C.gradient_accumulation_steps))
pbar.update(1)
optimizer.zero_grad()
_score = evaluation(_C, dev_loader, model, device)
checkpoint_manager.step(_score)