Create train_optimized.py

train_optimized.py

@@ -0,0 +1,298 @@
+import os
+import math
+import time
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from dataclasses import dataclass
+from torch.nn.parallel import DistributedDataParallel as DDP
+import numpy as np
+from datetime import datetime
+
+# Hyperparameters
+learning_rate = 3e-4  # Peak learning rate
+min_lr = 3e-5  # Minimum learning rate at the end of training
+warmup_iters = 2000  # Linear warmup over warmup_iters
+lr_decay_iters = 800000  # Cosine decay over lr_decay_iters
+weight_decay = 0.1  # AdamW weight decay
+beta1 = 0.9  # AdamW beta1
+beta2 = 0.95  # AdamW beta2
+grad_clip = 1.0  # Clip gradients at this value
+decay_lr = True  # Whether to decay learning rate
+batch_size = 64  # Training batch size
+block_size = 256  # Maximum sequence length
+eval_interval = 500  # How often to evaluate
+eval_iters = 200  # Number of iterations to use for evaluation
+log_interval = 10  # How often to print training progress
+
+# Model architecture
+@dataclass
+class GPTConfig:
+    block_size: int = block_size
+    vocab_size: int = 50304
+    n_layer: int = 12
+    n_head: int = 16
+    n_embd: int = 1024
+    dropout: float = 0.1
+    bias: bool = False
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+
+    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)
+        
+        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = self.resid_dropout(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, bias=config.bias)
+        self.gelu = nn.GELU()
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(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
+
+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),
+            drop = nn.Dropout(config.dropout),
+            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
+        
+        # Initialize weights
+        self.apply(self._init_weights)
+        for pn, p in self.named_parameters():
+            if pn.endswith('c_proj.weight'):
+                torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            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):
+        device = idx.device
+        b, t = idx.size()
+        pos = torch.arange(0, t, dtype=torch.long, device=device)
+        
+        tok_emb = self.transformer.wte(idx)
+        pos_emb = self.transformer.wpe(pos)
+        x = self.transformer.drop(tok_emb + pos_emb)
+        
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        
+        if targets is not None:
+            logits = self.lm_head(x)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+        else:
+            logits = self.lm_head(x[:, [-1], :])
+            loss = None
+            
+        return logits, loss
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        for _ in range(max_new_tokens):
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')
+            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 get_batch(data, block_size, batch_size):
+    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+1+block_size] for i in ix])
+    return x, y
+
+def get_lr(it):
+    # 1) Linear warmup for warmup_iters steps
+    if it < warmup_iters:
+        return learning_rate * it / warmup_iters
+    # 2) If it > lr_decay_iters, return min learning rate
+    if it > lr_decay_iters:
+        return min_lr
+    # 3) In between, use cosine decay down to min learning rate
+    decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
+    assert 0 <= decay_ratio <= 1
+    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
+    return min_lr + coeff * (learning_rate - min_lr)
+
+def save_training_log(log_entry, filename='training_logs.md'):
+    """Save training logs in markdown format"""
+    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    with open(filename, 'a') as f:
+        if not f.tell():  # If file is empty, write header
+            f.write('# Training Logs\n\n')
+            f.write('| Timestamp | Iteration | Training Loss | Learning Rate |\n')
+            f.write('|-----------|------------|---------------|---------------|\n')
+        f.write(f'| {timestamp} | {log_entry["iter"]:10d} | {log_entry["train_loss"]:.6f} | {log_entry["lr"]:.2e} |\n')
+
+def save_model(model, optimizer, iter_num, loss, filename):
+    """Save model checkpoint with error handling"""
+    try:
+        # First save to a temporary file
+        tmp_filename = filename + '.tmp'
+        checkpoint = {
+            'model_state_dict': model.state_dict(),
+            'optimizer_state_dict': optimizer.state_dict(),
+            'iter_num': iter_num,
+            'loss': loss,
+        }
+        
+        # Use torch.save with zip compression
+        torch.save(checkpoint, tmp_filename, _use_new_zipfile_serialization=True)
+        
+        # If save was successful, rename tmp file to final filename
+        if os.path.exists(filename):
+            os.remove(filename)  # Remove old file if it exists
+        os.rename(tmp_filename, filename)
+        return True
+    except Exception as e:
+        print(f"Error saving model to {filename}: {str(e)}")
+        # Clean up temp file if it exists
+        if os.path.exists(tmp_filename):
+            try:
+                os.remove(tmp_filename)
+            except:
+                pass
+        return False
+
+def main():
+    torch.manual_seed(1337)
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print(f"Using device: {device}")
+    
+    # Create checkpoint directory
+    os.makedirs('checkpoints', exist_ok=True)
+    
+    # Load the data
+    with open('input.txt', 'r') 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]
+    data = torch.tensor(encode(text), dtype=torch.long)
+    n = int(0.9 * len(data))
+    train_data = data[:n]
+    val_data = data[n:]
+    
+    # Initialize the model
+    model = GPT(GPTConfig(vocab_size=vocab_size))
+    model = model.to(device)
+    print(f"Model parameters: {sum(p.numel() for p in model.parameters())/1e6:.2f}M")
+    
+    # Initialize optimizer
+    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, betas=(beta1, beta2), weight_decay=weight_decay)
+    
+    # Training loop
+    best_train_loss = float('inf')
+    iter_num = 0
+    
+    while True:
+        # Get batch and learning rate
+        xb, yb = get_batch(train_data, block_size, batch_size)
+        xb, yb = xb.to(device), yb.to(device)
+        lr = get_lr(iter_num) if decay_lr else learning_rate
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = lr
+            
+        # Forward pass
+        logits, loss = model(xb, yb)
+        optimizer.zero_grad(set_to_none=True)
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
+        optimizer.step()
+        
+        # Logging and model saving
+        if iter_num % log_interval == 0:
+            train_loss = loss.item()
+            print(f"iter {iter_num}: loss {train_loss:.4f}, lr {lr:e}")
+            save_training_log({
+                "iter": iter_num,
+                "train_loss": train_loss,
+                "lr": lr
+            })
+            
+            # Save model if loss improved
+            if train_loss < best_train_loss:
+                best_train_loss = train_loss
+                print(f"Saving model with training loss: {best_train_loss:.6f}")
+                
+                # Save the latest model
+                save_model(
+                    model, 
+                    optimizer, 
+                    iter_num, 
+                    best_train_loss, 
+                    os.path.join('checkpoints', 'latest_model.pt')
+                )
+                
+                if best_train_loss < 0.099999:
+                    print(f"Achieved target loss of {best_train_loss:.6f}")
+                    break
+                    
+        iter_num += 1
+        if iter_num > lr_decay_iters:
+            break
+
+if __name__ == '__main__':
+    main()