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train_sl.py
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import os
import sys
import torch
import torch.nn as nn
from tqdm.auto import tqdm
from datetime import datetime
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from src.sl.dataset import GECDataset
from src.sl.utils import process_data, collate_func
from src.utils import load_yaml, load_text, write_json, freeze_params, load_model
os.environ["TOKENIZERS_PARALLELISM"] = "false"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@torch.cuda.amp.autocast()
def evaluate(model, batch, criterion):
labels = torch.from_numpy(batch['labels']).to(device)
logits = model(tokens=batch['tokens'])
batch_size, seq_size, labels_size = logits.shape
loss = criterion(logits.view(-1, labels_size), labels.view(-1))
loss = loss.view(batch_size, seq_size).sum(dim=-1)
return loss
def load_data(datasets, data_type, only_solvable=False, data_limit=0):
data = []
filename = "data.gector" if data_type == "train" else "dev.gector"
if only_solvable:
filename = filename.replace(".gector", "_filtered.gector")
for dataset in tqdm(datasets, desc=f"Loading {data_type} datasets", total=len(datasets)):
data_path = f"data/processed/{dataset}/{filename}"
data.extend(load_text(data_path))
if (data_limit > 0) and (len(data) > data_limit):
print(f"Truncating amount of data from {len(data)} to {data_limit}")
data = data[:data_limit]
print(f"Total number of sentences: {len(data)}")
return data
def load_and_process_data(
datasets,
label_vocab,
batch_size,
accumulation_size,
keep_corrects=True,
num_workers=None,
data_limit=0,
only_solvable=False,
):
train_data = load_data(datasets, "train", data_limit=data_limit, only_solvable=only_solvable)
val_data = load_data(datasets, "validation")
train_tokens, train_labels = process_data(train_data, label_vocab, keep_corrects=keep_corrects)
val_tokens, val_labels = process_data(val_data, label_vocab, keep_corrects=True)
label2index = {label: i for i, label in enumerate(label_vocab)}
train_dataset = GECDataset(train_tokens, train_labels, label2index)
val_dataset = GECDataset(val_tokens, val_labels, label2index)
train_batch_size = int(batch_size / accumulation_size)
train_loader = DataLoader(
train_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=num_workers,
collate_fn=collate_func,
)
val_loader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
collate_fn=collate_func,
)
return train_loader, val_loader
def main(
cold_lr,
warm_lr,
dropout,
num_epochs,
cold_epochs,
patience,
batch_size,
accumulation_size,
num_workers,
data_limit,
keep_corrects,
datasets,
label_path,
model_path,
log_dir,
meta_data,
only_solvable=False,
):
train_type = "pretrain" if model_path is None else "finetune"
current_datetime = datetime.now().strftime("%d_%m_%Y_%H:%M")
exp_log_dir = os.path.join(log_dir, f"{train_type}_sl_{current_datetime}")
# Load labels
label_vocab = load_text(label_path)
# Load and process datasets
train_loader, val_loader = load_and_process_data(
datasets,
label_vocab,
batch_size,
accumulation_size,
keep_corrects=keep_corrects,
num_workers=num_workers,
data_limit=data_limit,
only_solvable=only_solvable,
)
# Load model
model_name = "roberta-base"
tokenizer_config = {"use_fast": True}
transformer_config = {"output_attentions": False}
model = load_model(
model_name=model_name,
model_path=model_path,
num_labels=len(label_vocab),
tokenizer_config=tokenizer_config,
transformer_config=transformer_config,
local_files_only=True,
).to(device)
freeze_params(model.encoder, requires_grad=False) # Freeze encoder model
model.train()
# Load loss function, optimizer and gradient scaler
lr = cold_lr
criterion = nn.CrossEntropyLoss(reduction="none")
optim = torch.optim.Adam(model.parameters(), lr=lr)
grad_scaler = torch.cuda.amp.GradScaler()
writer = SummaryWriter(log_dir=exp_log_dir)
write_json(os.path.join(exp_log_dir, "meta.json"), meta_data)
# Log hyperparameters
writer.add_scalar("hyperparameters/dropout", dropout, 0)
writer.add_scalar("hyperparameters/patience", patience, 0)
writer.add_scalar("hyperparameters/batch_size", batch_size, 0)
writer.add_scalar("hyperparameters/cold_epochs", cold_epochs, 0)
writer.add_scalar("hyperparameters/keep_corrects", int(keep_corrects), 0)
writer.add_scalar("hyperparameters/accumulation_size", accumulation_size, 0)
writer.add_scalar("hyperparameters/uses_CE_loss", int(isinstance(criterion, nn.CrossEntropyLoss)), 0)
# Evaluate the model
with torch.no_grad():
val_losses = [evaluate(model, batch, criterion) for batch in val_loader]
val_loss = torch.cat(val_losses).mean()
writer.add_scalar("sl/validation_loss", val_loss, 0)
epochs_since_improvement = 0
best_val_score = val_loss
train_size = len(train_loader)
for epoch in tqdm(range(num_epochs), desc="Training", total=num_epochs):
if epoch == cold_epochs: # Unfreeze encoder model at the end of the cold epoch
lr = warm_lr
freeze_params(model.encoder, requires_grad=True, optim=optim, lr=lr)
step_offset = epoch * train_size
for i, batch in tqdm(enumerate(train_loader), desc=f"Epoch {epoch + 1}", total=len(train_loader)):
loss = evaluate(model, batch, criterion)
loss = loss.mean()
grad_scaler.scale(loss / accumulation_size).backward()
if ((i + 1) % accumulation_size) == 0:
grad_scaler.step(optim)
grad_scaler.update()
optim.zero_grad()
torch.cuda.empty_cache()
current_step = step_offset + i
writer.add_scalar("sl/lr", lr, current_step)
writer.add_scalar("sl/train_loss", loss, current_step)
# Evaluate model
with torch.no_grad():
val_losses = [evaluate(model, batch, criterion) for batch in val_loader]
val_loss = torch.cat(val_losses).mean()
writer.add_scalar("sl/validation_loss", val_loss, epoch+1)
if val_loss <= best_val_score:
best_val_score = val_loss
epochs_since_improvement = 0
torch.save(model.state_dict(), os.path.join(exp_log_dir, "model-best.pt")) # Save best model
else:
epochs_since_improvement += 1
if epochs_since_improvement >= patience:
print("Early stopping!")
break
torch.save(model.state_dict(), os.path.join(exp_log_dir, "model-last.pt"))
if __name__ == "__main__":
config_path = sys.argv[1]
params = load_yaml(config_path)
main(**params)