run.py

"""
Credits: Code modified from Andrej Karpathy Walkthrough:
https://www.youtube.com/watch?v=kCc8FmEb1nY
"""

import torch
import torch.nn as nn
from torch.nn import functional as F
import wandb
import matplotlib.pyplot as plt
import argparse

# hyperparameters
batch_size = 16 # how many independent sequences will we process in parallel?
block_size = 32 # what is the maximum context length for predictions?
max_iters = 5000
eval_interval = 200
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_embd = 256
n_head = 16
n_layer = 16
dropout = 0.0


"""
For shorter training time, use these parameters instead
by uncommenting the code. Otherwise, for a better model, 
keep as-is (commented out).
"""
# eval_iters = 100
# n_embd = 64
# n_head = 8
# n_layer = 8




torch.manual_seed(1337)

# Load and preprocess data
with open('formatted_comments.txt', 'r', encoding='utf-8') as f:
    text = f.read()

chars = sorted(list(set(text)))
vocab_size = len(chars)
stoi = { ch:i for i,ch in enumerate(chars) }
itos = { i:ch for i,ch in enumerate(chars) }
encode = lambda s: [stoi[c] for c in s]
decode = lambda l: ''.join([itos[i] for i in l])

data = torch.tensor(encode(text), dtype=torch.long)
n = int(0.9*len(data))
train_data = data[:n]
val_data = data[n:]

# Data loading function
def get_batch(split):
    data = train_data if split == 'train' else val_data
    ix = torch.randint(len(data) - block_size, (batch_size,))
    x = torch.stack([data[i:i+block_size] for i in ix])
    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
    return x.to(device), y.to(device)

@torch.no_grad()
def estimate_loss():
    out = {}
    model.eval()
    for split in ['train', 'val']:
        losses = torch.zeros(eval_iters)
        for k in range(eval_iters):
            X, Y = get_batch(split)
            logits, loss = model(X, Y)
            losses[k] = loss.item()
        out[split] = losses.mean()
    model.train()
    return out

class Head(nn.Module):
    """ one head of self-attention """

    def __init__(self, head_size):
        super().__init__()
        self.key = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))

        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        B,T,C = x.shape
        k = self.key(x)   # (B,T,C)
        q = self.query(x) # (B,T,C)
        # compute attention scores ("affinities")
        wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T)
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
        wei = F.softmax(wei, dim=-1) # (B, T, T)
        wei = self.dropout(wei)
        # perform the weighted aggregation of the values
        v = self.value(x) # (B,T,C)
        out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)
        return out

class MultiHeadAttention(nn.Module):
    """ multiple heads of self-attention in parallel """

    def __init__(self, num_heads, head_size):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
        self.proj = nn.Linear(n_embd, n_embd)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)
        out = self.dropout(self.proj(out))
        return out

class FeedFoward(nn.Module):
    """ a simple linear layer followed by a non-linearity """

    def __init__(self, n_embd):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(n_embd, 4 * n_embd),
            nn.ReLU(),
            nn.Linear(4 * n_embd, n_embd),
            nn.Dropout(dropout),
        )

    def forward(self, x):
        return self.net(x)

class Block(nn.Module):
    """ Transformer block: communication followed by computation """

    def __init__(self, n_embd, n_head):
        # n_embd: embedding dimension, n_head: the number of heads we'd like
        super().__init__()
        head_size = n_embd // n_head
        self.sa = MultiHeadAttention(n_head, head_size)
        self.ffwd = FeedFoward(n_embd)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)

    def forward(self, x):
        x = x + self.sa(self.ln1(x))
        x = x + self.ffwd(self.ln2(x))
        return x

class BigramLanguageModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)
        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)
        self.lm_head = nn.Linear(n_embd, vocab_size)

    def forward(self, idx, targets=None):
        B, T = idx.shape
        tok_emb = self.token_embedding_table(idx)
        pos_emb = self.position_embedding_table(torch.arange(T, device=device))
        x = tok_emb + pos_emb
        x = self.blocks(x)
        x = self.ln_f(x)
        logits = self.lm_head(x)

        if targets is None:
            loss = None
        else:
            B, T, C = logits.shape
            logits = logits.view(B*T, C)
            targets = targets.view(B*T)
            loss = F.cross_entropy(logits, targets)

        return logits, loss

    def generate(self, idx, max_new_tokens):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -block_size:]
            logits, loss = self(idx_cond)
            logits = logits[:, -1, :]
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
        return idx

def train_model():
    global model
    print(sum(p.numel() for p in model.parameters())/1e6, 'M parameters')
    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)

    wandb.init(project="student-language-model", config={
        "batch_size": batch_size,
        "block_size": block_size,
        "max_iters": max_iters,
        "learning_rate": learning_rate,
        "n_embd": n_embd,
        "n_head": n_head,
        "n_layer": n_layer,
        "dropout": dropout
    })

    train_losses = []
    val_losses = []

    for iter in range(max_iters):
        if iter % eval_interval == 0:
            losses = estimate_loss()
            print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
            wandb.log({
                "train_loss": losses['train'],
                "val_loss": losses['val'],
                "step": iter
            })
            train_losses.append(losses['train'])
            val_losses.append(losses['val'])

        xb, yb = get_batch('train')
        logits, loss = model(xb, yb)
        optimizer.zero_grad(set_to_none=True)
        loss.backward()
        optimizer.step()

    torch.save(model.state_dict(), 'model.pth')
    print("Model saved successfully.")

    plt.figure(figsize=(10, 5))
    plt.plot(range(0, max_iters, eval_interval), train_losses, label='Train Loss')
    plt.plot(range(0, max_iters, eval_interval), val_losses, label='Validation Loss')
    plt.xlabel('Iterations')
    plt.ylabel('Loss')
    plt.title('Training and Validation Loss')
    plt.legend()
    plt.savefig('loss_curve.png')
    plt.close()

    wandb.log({"loss_curve": wandb.Image('loss_curve.png')})
    wandb.finish()

def load_and_generate(model_path, prompt="", max_new_tokens=500):
    loaded_model = BigramLanguageModel().to(device)
    loaded_model.load_state_dict(torch.load(model_path))
    loaded_model.eval()
    
    if prompt:
        context = torch.tensor(encode(prompt), dtype=torch.long, device=device).unsqueeze(0)
    else:
        context = torch.zeros((1, 1), dtype=torch.long, device=device)
    
    generated_text = decode(loaded_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist())
    return generated_text

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Train or generate text with the language model")
    parser.add_argument('--mode', type=str, choices=['train', 'generate'], required=True,
                        help="Choose 'train' to train the model or 'generate' to generate text")
    parser.add_argument('--prompt', type=str, default="To be or not to be",
                        help="Prompt for text generation (only used in generate mode)")
    parser.add_argument('--max_new_tokens', type=int, default=200,
                        help="Maximum number of new tokens to generate (only used in generate mode)")
    args = parser.parse_args()

    model = BigramLanguageModel().to(device)

    if args.mode == 'train':
        train_model()
    elif args.mode == 'generate':
        try:
            model.load_state_dict(torch.load('model.pth'))
            print("Model loaded successfully.")
        except FileNotFoundError:
            print("No saved model found. Training a new model...")
            train_model()

        generated_text = load_and_generate('model.pth', prompt=args.prompt, max_new_tokens=args.max_new_tokens)
        print("Generated text:")
        print(generated_text)