train_shakespeare.py

import os
import math
import time
import inspect
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F

# Set MPS memory management
os.environ['PYTORCH_MPS_HIGH_WATERMARK_RATIO'] = '0.0'
os.environ['PYTORCH_MPS_LOW_WATERMARK_RATIO'] = '0.5'


class CausalSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        # key, query, value projections for all heads, but in a batch
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        # output projection
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        self.c_proj.NANGPT_SCALE_INIT = 1
        # regularization
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
                           .view(1, 1, config.block_size, config.block_size))

    def forward(self, x):
        B, T, C = x.size()
        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.n_embd, dim=2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)

        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
        att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
        att = F.softmax(att, dim=-1)
        y = att @ v

        y = y.transpose(1, 2).contiguous().view(B, T, C)
        y = self.c_proj(y)
        return y

class MLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
        self.gelu = nn.GELU(approximate='tanh')
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = 1

    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        return x

class Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln_1 = nn.LayerNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd)
        self.mlp = MLP(config)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x

@dataclass
class GPTConfig:
    block_size: int = 1024
    vocab_size: int = 50257
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.transformer = nn.ModuleDict(dict(
            wte = nn.Embedding(config.vocab_size, config.n_embd),
            wpe = nn.Embedding(config.block_size, config.n_embd),
            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
            ln_f = nn.LayerNorm(config.n_embd),
        ))
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.transformer.wte.weight = self.lm_head.weight
        self.apply(self._init_weights)

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            std = 0.02
            if hasattr(module, 'NANGPT_SCALE_INIT'):
                std *= (2 * self.config.n_layer) ** -0.5
            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, idx, targets=None):
        B, T = idx.size()
        assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
        tok_emb = self.transformer.wte(idx)
        pos_emb = self.transformer.wpe(pos)
        x = tok_emb + pos_emb
        
        for block in self.transformer.h:
            x = block(x)
        
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x)
        
        loss = None
        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
        
        return logits, loss

class DataLoaderLite:
    def __init__(self, B, T):
        self.B = B
        self.T = T
        with open('src/input.txt', 'r') as f:
            text = f.read()
        enc = tiktoken.get_encoding('gpt2')
        tokens = enc.encode(text)
        self.tokens = torch.tensor(tokens)
        print(f'loaded {len(self.tokens)} tokens')
        print(f'1 epoch = {len(self.tokens) // (B * T)} batches')
        self.current_position = 0
    
    def next_batch(self):
        B, T = self.B, self.T
        buf = self.tokens[self.current_position: self.current_position + B * T + 1]
        x = (buf[:-1]).view(B, T)
        y = (buf[1:]).view(B, T)
        self.current_position += B*T
        if self.current_position + (B * T + 1) > len(self.tokens):
            self.current_position = 0
        return x, y


# write the main block

if __name__ == "__main__":

    # Device configuration
    device = 'cpu'
    if torch.cuda.is_available():
        device = 'cuda'
    elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
        device = "mps"
    print(f"using device: {device}")

    # Set random seed
    torch.manual_seed(1337)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(1337)

    # Initialize model and move to device
    model = GPT(GPTConfig())
    model.to(device)

    # Initialize data loader
    train_loader = DataLoaderLite(B=4, T=32)

    # Training settings
    learning_rate = 3e-4
    num_iters = 100000  # Increased to 100000
    eval_interval = 50   # Evaluate every 50 iterations
    best_loss = float('inf')
    checkpoint_dir = 'checkpoints'
    os.makedirs(checkpoint_dir, exist_ok=True)

    # Initialize optimizer
    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)

    print(f"\n=== Starting Training ===")
    print(f"Total iterations: {num_iters}")
    print(f"Evaluation interval: {eval_interval}")
    print(f"Learning rate: {learning_rate}")

    # Training loop
    for iter in range(num_iters):
        # Get batch
        x, y = train_loader.next_batch()
        x, y = x.to(device), y.to(device)
        
        # Forward pass
        optimizer.zero_grad()
        logits, loss = model(x, y)
        
        # Backward pass
        loss.backward()
        optimizer.step()
        
        # Log progress every 50 iterations
        if iter % eval_interval == 0:
            current_loss = loss.item()
            print(f'step {iter}, loss: {current_loss:.4f}')
            
            # Save if this is the best model so far
            if current_loss < best_loss:
                best_loss = current_loss
                checkpoint_path = os.path.join(checkpoint_dir, f'model_step_{iter}_loss_{current_loss:.4f}.pt')
                torch.save({
                    'iter': iter,
                    'model_state_dict': model.state_dict(),
                    'optimizer_state_dict': optimizer.state_dict(),
                    'loss': current_loss,
                    'best_loss': best_loss,
                }, checkpoint_path)
                print(f'New best model saved! Loss: {current_loss:.4f}')
                
                # Also save as best model
                torch.save({
                    'iter': iter,
                    'model_state_dict': model.state_dict(),
                    'optimizer_state_dict': optimizer.state_dict(),
                    'loss': current_loss,
                    'best_loss': best_loss,
                }, 'best_model.pt')

    print("\n=== Training Complete ===")
    print(f"Best loss achieved: {best_loss:.4f}")

    # Save final model
    final_path = os.path.join(checkpoint_dir, 'model_final.pt')
    torch.save({
        'iter': num_iters-1,
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'loss': loss.item(),
        'best_loss': best_loss,
    }, final_path)