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e4721a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
import os
def train_model(model, train_loader, val_loader, config, tokenizer):
model = model.to(config.DEVICE)
optimizer = optim.AdamW(model.parameters(), lr=config.LEARNING_RATE)
criterion = nn.CrossEntropyLoss(ignore_index=tokenizer.pad_token_id)
best_loss = float('inf')
for epoch in range(config.NUM_EPOCHS):
model.train()
train_loss = 0
loop = tqdm(train_loader, total=len(train_loader), leave=True)
for images, input_ids, attention_mask in loop:
images = images.to(config.DEVICE)
input_ids = input_ids.to(config.DEVICE)
attention_mask = attention_mask.to(config.DEVICE)
optimizer.zero_grad()
# Forward pass
# Logits: [batch, seq_len+1, vocab_size]
logits = model(images, input_ids, attention_mask)
# Shift logits and labels for next-token prediction
# We want to predict input_ids based on previous context
# The model output at index i corresponds to prediction for token at i+1
# Input sequence to model: [Image, T1, T2, T3, ...]
# Output logits: [P1, P2, P3, P4, ...]
# Targets: [T1, T2, T3, T4, ...]
# We discard the last logit because we don't have a target for it
shift_logits = logits[:, :-1, :].contiguous()
# We use input_ids as targets
shift_labels = input_ids.contiguous()
loss = criterion(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
loss.backward()
optimizer.step()
train_loss += loss.item()
loop.set_description(f"Epoch [{epoch+1}/{config.NUM_EPOCHS}]")
loop.set_postfix(loss=loss.item())
avg_train_loss = train_loss / len(train_loader)
print(f"Epoch {epoch+1} Loss: {avg_train_loss:.4f}")
# Save checkpoint
if avg_train_loss < best_loss:
best_loss = avg_train_loss
torch.save(model.state_dict(), os.path.join(config.MODEL_SAVE_DIR, "best_model_llm.pth"))
print("Saved Best Model!")
torch.save(model.state_dict(), os.path.join(config.MODEL_SAVE_DIR, "last_checkpoint_llm.pth"))
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