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import torch, torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
model_ckpt = "distilbert-base-uncased"
batch_size = 16
n_epochs = 3
learning_rate = 1e-4
RANK = 4
ALPHA = 4
"""
---- Device ----
"""
if torch.cuda.is_available():
device = torch.device('cuda')
print("Using CUDA (GPU)")
elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
device = torch.device('mps')
print("Using MPS (Apple Silicon GPU)")
else:
device = torch.device('cpu')
print("Using device's CPU")
from baseline import tokenized_datasets
"""
tokenized_datasets:
DatasetDict({
train: Dataset({
features: ['labels', 'input_ids', 'attention_mask'],
num_rows: 25000
})
test: Dataset({
features: ['labels', 'input_ids', 'attention_mask'],
num_rows: 25000
})
unsupervised: Dataset({
features: ['labels', 'input_ids', 'attention_mask'],
num_rows: 50000
})
})
"""
train_dataloader = DataLoader(tokenized_datasets["train"], shuffle=True, batch_size=batch_size)
eval_dataloader = DataLoader(tokenized_datasets["test"], batch_size=batch_size)
from loraLinear import model
model.to(device)
print(f"INFO: Moved model to {device}")
trainable_params = [p for p in model.parameters() if p.requires_grad] # len: 24
optimizer = optim.AdamW(trainable_params, lr=learning_rate)
for epoch in range(n_epochs):
model.train()
print(f"\n--- Starting Epoch {epoch+1}/{n_epochs} ---")
for batch in tqdm(train_dataloader, desc=f"Training Epoch {epoch+1}"):
batch = {k: v.to(device) for k, v in batch.items()}
optimizer.zero_grad()
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
model.eval()
num_correct = 0
num_samples = 0
with torch.no_grad():
for batch in tqdm(eval_dataloader, desc=f"Evaluating Epoch {epoch+1}"):
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
predictions = torch.argmax(outputs.logits, dim=-1)
num_correct += (predictions == batch["labels"]).sum().item()
num_samples += batch["labels"].size(0)
accuracy = num_correct / num_samples
print(f"--- Epoch {epoch+1} Validation Accuracy: {accuracy:.4f} ---")
print("\nFine-tuning complete.")
torch.save(model.state_dict(), "DISTILBERT_WITH_LORA.pth")
print("Trained LoRA model saved.") |