gpt_train.py

import matplotlib.pyplot as plt
import os
import torch
import urllib.request
import tiktoken



# Import from local files
from previous_chapters import GPTModel, create_dataloader_v1, generate_text_simple


def text_to_token_ids(text, tokenizer):
    encoded = tokenizer.encode(text)
    encoded_tensor = torch.tensor(encoded).unsqueeze(0)  # add batch dimension
    return encoded_tensor


def token_ids_to_text(token_ids, tokenizer):
    flat = token_ids.squeeze(0)  # remove batch dimension
    return tokenizer.decode(flat.tolist())


def calc_loss_batch(input_batch, target_batch, model, device):
    input_batch, target_batch = input_batch.to(device), target_batch.to(device)
    logits = model(input_batch)
    loss = torch.nn.functional.cross_entropy(logits.flatten(0, 1), target_batch.flatten())
    return loss


def calc_loss_loader(data_loader, model, device, num_batches=None):
    total_loss = 0.
    if len(data_loader) == 0:
        return float("nan")
    elif num_batches is None:
        num_batches = len(data_loader)
    else:
        num_batches = min(num_batches, len(data_loader))
    for i, (input_batch, target_batch) in enumerate(data_loader):
        if i < num_batches:
            loss = calc_loss_batch(input_batch, target_batch, model, device)
            total_loss += loss.item()
        else:
            break
    return total_loss / num_batches


def evaluate_model(model, train_loader, val_loader, device, eval_iter):
    model.eval()
    with torch.no_grad():
        train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
        val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
    model.train()
    return train_loss, val_loss


def generate_and_print_sample(model, tokenizer, device, start_context):
    model.eval()
    context_size = model.pos_emb.weight.shape[0]
    encoded = text_to_token_ids(start_context, tokenizer).to(device)
    with torch.no_grad():
        token_ids = generate_text_simple(
            model=model, idx=encoded,
            max_new_tokens=50, context_size=context_size
        )
        decoded_text = token_ids_to_text(token_ids, tokenizer)
        print(decoded_text.replace("\n", " "))  # Compact print format
    model.train()


def train_model_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
                    eval_freq, eval_iter, start_context, tokenizer):
    # Initialize lists to track losses and tokens seen
    train_losses, val_losses, track_tokens_seen = [], [], []
    tokens_seen = 0
    global_step = -1

    # Main training loop
    for epoch in range(num_epochs):
        model.train()  # Set model to training mode

        for input_batch, target_batch in train_loader:
            optimizer.zero_grad()  # Reset loss gradients from previous epoch
            loss = calc_loss_batch(input_batch, target_batch, model, device)
            loss.backward()  # Calculate loss gradients
            optimizer.step()  # Update model weights using loss gradients
            tokens_seen += input_batch.numel()
            global_step += 1

            # Optional evaluation step
            if global_step % eval_freq == 0:
                train_loss, val_loss = evaluate_model(
                    model, train_loader, val_loader, device, eval_iter)
                train_losses.append(train_loss)
                val_losses.append(val_loss)
                track_tokens_seen.append(tokens_seen)
                print(f"Ep {epoch+1} (Step {global_step:06d}): "
                    f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")

        # Print a sample text after each epoch
        generate_and_print_sample(
            model, tokenizer, device, start_context
        )

    return train_losses, val_losses, track_tokens_seen


def plot_losses(epochs_seen, tokens_seen, train_losses, val_losses):
    fig, ax1 = plt.subplots()

    # Plot training and validation loss against epochs
    ax1.plot(epochs_seen, train_losses, label="Training loss")
    ax1.plot(epochs_seen, val_losses, linestyle="-.", label="Validation loss")
    ax1.set_xlabel("Epochs")
    ax1.set_ylabel("Loss")
    ax1.legend(loc="upper right")

    # Create a second x-axis for tokens seen
    ax2 = ax1.twiny()  # Create a second x-axis that shares the same y-axis
    ax2.plot(tokens_seen, train_losses, alpha=0)  # Invisible plot for aligning ticks
    ax2.set_xlabel("Tokens seen")

    fig.tight_layout()  # Adjust layout to make room
    # plt.show()


def main(gpt_config, settings):

    torch.manual_seed(123)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    ##############################
    # Download data if necessary
    ##############################

    file_path = "data/Frank Herbert - Dune Messiah.txt"
    with open(file_path, "r", encoding="utf-8") as file:
            text_data = file.read()

    ##############################
    # Initialize model
    ##############################

    model = GPTModel(gpt_config)
    model.to(device)  # no assignment model = model.to(device) necessary for nn.Module classes
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=settings["learning_rate"], weight_decay=settings["weight_decay"]
    )

    ##############################
    # Set up dataloaders
    ##############################

    # Train/validation ratio
    train_ratio = 0.90
    split_idx = int(train_ratio * len(text_data))

    train_loader = create_dataloader_v1(
        text_data[:split_idx],
        batch_size=settings["batch_size"],
        max_length=gpt_config["context_length"],
        stride=gpt_config["context_length"],
        drop_last=True,
        shuffle=True,
        num_workers=0
    )

    val_loader = create_dataloader_v1(
        text_data[split_idx:],
        batch_size=settings["batch_size"],
        max_length=gpt_config["context_length"],
        stride=gpt_config["context_length"],
        drop_last=False,
        shuffle=False,
        num_workers=0
    )

    ##############################
    # Train model
    ##############################

    tokenizer = tiktoken.get_encoding("gpt2")

    train_losses, val_losses, tokens_seen = train_model_simple(
        model, train_loader, val_loader, optimizer, device,
        num_epochs=settings["num_epochs"], eval_freq=5, eval_iter=1,
        start_context="Every effort moves you", tokenizer=tokenizer
    )

    return train_losses, val_losses, tokens_seen, model


if __name__ == "__main__":

    GPT_CONFIG_124M = {
        "vocab_size": 50257,    # Vocabulary size
        "context_length": 256,  # Shortened context length (orig: 1024)
        "emb_dim": 768,         # Embedding dimension
        "n_heads": 12,          # Number of attention heads
        "n_layers": 12,         # Number of layers
        "drop_rate": 0.1,       # Dropout rate
        "qkv_bias": False       # Query-key-value bias
    }

    OTHER_SETTINGS = {
        "learning_rate": 5e-4,
        "num_epochs": 10,
        "batch_size": 2,
        "weight_decay": 0.1
    }

    ###########################
    # Initiate training
    ###########################

    train_losses, val_losses, tokens_seen, model = main(GPT_CONFIG_124M, OTHER_SETTINGS)

    ###########################
    # After training
    ###########################

    # Plot results
    epochs_tensor = torch.linspace(0, OTHER_SETTINGS["num_epochs"], len(train_losses))
    plot_losses(epochs_tensor, tokens_seen, train_losses, val_losses)
    plt.savefig("loss.pdf")

    # Save and load model
    torch.save(model.state_dict(), "model.pth")
    model = GPTModel(GPT_CONFIG_124M)
    model.load_state_dict(torch.load("model.pth"))