Upload hf_free_training.py with huggingface_hub

hf_free_training.py

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+"""
+Free H200 Training Script for Nano-Coder
+Optimized for HF's free 4-minute daily H200 access
+"""
+
+import os
+import time
+import math
+import pickle
+from contextlib import nullcontext
+
+import numpy as np
+import torch
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.distributed import init_process_group, destroy_process_group
+
+from model import GPTConfig, GPT
+
+# Hugging Face specific imports
+from huggingface_hub import HfApi, login
+import wandb
+
+# -----------------------------------------------------------------------------
+# Configuration optimized for FREE H200 (4 minutes daily)
+# I/O
+out_dir = 'out-nano-coder-free'
+eval_interval = 50  # Very frequent evaluation for short runs
+log_interval = 2
+eval_iters = 10  # Fewer eval iterations
+eval_only = False
+always_save_checkpoint = True
+init_from = 'scratch'
+
+# wandb logging - enabled for HF
+wandb_log = True
+wandb_project = 'nano-coder-free'
+wandb_run_name = 'nano-coder-h200-free'
+
+# data
+dataset = 'python-codes-25k'
+gradient_accumulation_steps = 1 * 8  # Minimal for H200
+batch_size = 64  # Larger batch size for H200 efficiency
+block_size = 512  # Smaller context for faster training
+
+# model - smaller for free tier
+n_layer = 6  # Reduced from 12
+n_head = 6   # Reduced from 12
+n_embd = 384 # Reduced from 768
+dropout = 0.1
+bias = False
+
+# optimizer - optimized for H200
+learning_rate = 1e-3  # Higher learning rate for faster convergence
+max_iters = 1000  # Limited iterations for 4-minute runs
+weight_decay = 1e-1
+beta1 = 0.9
+beta2 = 0.95
+grad_clip = 1.0
+
+# learning rate decay - faster for short runs
+decay_lr = True
+warmup_iters = 100  # Shorter warmup
+lr_decay_iters = 1000
+min_lr = 1e-4
+
+# DDP settings
+backend = 'nccl'
+
+# system
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16'
+compile = True
+
+# HF specific
+hf_repo_id = "mlopez6132/nano-coder-free"  # Free tier repo
+push_to_hub = True
+
+# Time tracking for 4-minute limit
+start_time = time.time()
+MAX_TRAINING_TIME = 3.5 * 60  # 3.5 minutes to be safe
+
+# -----------------------------------------------------------------------------
+config_keys = [k for k,v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))]
+exec(open('configurator.py').read())
+config = {k: globals()[k] for k in config_keys}
+
+# -----------------------------------------------------------------------------
+
+# HF setup
+if push_to_hub:
+    login()  # Will use HF_TOKEN environment variable
+    api = HfApi()
+
+# various inits, derived attributes, I/O setup
+ddp = int(os.environ.get('RANK', -1)) != -1
+if ddp:
+    init_process_group(backend=backend)
+    ddp_rank = int(os.environ['RANK'])
+    ddp_local_rank = int(os.environ['LOCAL_RANK'])
+    ddp_world_size = int(os.environ['WORLD_SIZE'])
+    device = f'cuda:{ddp_local_rank}'
+    torch.cuda.set_device(device)
+    master_process = ddp_rank == 0
+    seed_offset = ddp_rank
+    assert gradient_accumulation_steps % ddp_world_size == 0
+    gradient_accumulation_steps //= ddp_world_size
+else:
+    master_process = True
+    seed_offset = 0
+    ddp_world_size = 1
+
+tokens_per_iter = gradient_accumulation_steps * ddp_world_size * batch_size * block_size
+print(f"tokens per iteration will be: {tokens_per_iter:,}")
+print(f"FREE H200 TRAINING - MAX TIME: {MAX_TRAINING_TIME/60:.1f} minutes")
+
+if master_process:
+    os.makedirs(out_dir, exist_ok=True)
+
+torch.manual_seed(1337 + seed_offset)
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+device_type = 'cuda' if 'cuda' in device else 'cpu'
+ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
+ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
+
+# data loader
+data_dir = os.path.join('data', dataset)
+def get_batch(split):
+    if split == 'train':
+        data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
+    else:
+        data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')
+    ix = torch.randint(len(data) - block_size, (batch_size,))
+    x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
+    y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
+    if device_type == 'cuda':
+        x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True)
+    else:
+        x, y = x.to(device), y.to(device)
+    return x, y
+
+# init these up here, can override if init_from='resume'
+iter_num = 0
+best_val_loss = 1e9
+
+# attempt to derive vocab_size from the dataset
+meta_path = os.path.join(data_dir, 'meta.pkl')
+meta_vocab_size = None
+if os.path.exists(meta_path):
+    with open(meta_path, 'rb') as f:
+        meta = pickle.load(f)
+    meta_vocab_size = meta['vocab_size']
+    print(f"found vocab_size = {meta_vocab_size} (inside {meta_path})")
+
+# model init
+model_args = dict(n_layer=n_layer, n_head=n_head, n_embd=n_embd, block_size=block_size,
+                  bias=bias, vocab_size=None, dropout=dropout)
+
+if init_from == 'scratch':
+    print("Initializing a new nano-coder model from scratch (FREE TIER)")
+    if meta_vocab_size is None:
+        print("defaulting to vocab_size of GPT-2 to 50304")
+    model_args['vocab_size'] = meta_vocab_size if meta_vocab_size is not None else 50304
+    gptconf = GPTConfig(**model_args)
+    model = GPT(gptconf)
+elif init_from == 'resume':
+    print(f"Resuming training from {out_dir}")
+    ckpt_path = os.path.join(out_dir, 'ckpt.pt')
+    checkpoint = torch.load(ckpt_path, map_location=device)
+    checkpoint_model_args = checkpoint['model_args']
+    for k in ['n_layer', 'n_head', 'n_embd', 'block_size', 'bias', 'vocab_size']:
+        model_args[k] = checkpoint_model_args[k]
+    gptconf = GPTConfig(**model_args)
+    model = GPT(gptconf)
+    state_dict = checkpoint['model']
+    unwanted_prefix = '_orig_mod.'
+    for k,v in list(state_dict.items()):
+        if k.startswith(unwanted_prefix):
+            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+    model.load_state_dict(state_dict)
+    iter_num = checkpoint['iter_num']
+    best_val_loss = checkpoint['best_val_loss']
+elif init_from.startswith('gpt2'):
+    print(f"Initializing from OpenAI GPT-2 weights: {init_from}")
+    override_args = dict(dropout=dropout)
+    model = GPT.from_pretrained(init_from, override_args)
+    for k in ['n_layer', 'n_head', 'n_embd', 'block_size', 'bias', 'vocab_size']:
+        model_args[k] = getattr(model.config, k)
+
+if block_size < model.config.block_size:
+    model.crop_block_size(block_size)
+    model_args['block_size'] = block_size
+
+model.to(device)
+
+# initialize a GradScaler
+scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16'))
+
+# optimizer
+optimizer = model.configure_optimizers(weight_decay, learning_rate, (beta1, beta2), device_type)
+if init_from == 'resume':
+    optimizer.load_state_dict(checkpoint['optimizer'])
+checkpoint = None
+
+# compile the model
+if compile:
+    print("compiling the model... (takes a ~minute)")
+    unoptimized_model = model
+    model = torch.compile(model)
+
+# wrap model into DDP container
+if ddp:
+    model = DDP(model, device_ids=[ddp_local_rank])
+
+# helps estimate an arbitrarily accurate loss over either split using many batches
+@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)
+            with ctx:
+                logits, loss = model(X, Y)
+            losses[k] = loss.item()
+        out[split] = losses.mean()
+    model.train()
+    return out
+
+# learning rate decay scheduler (cosine with warmup)
+def get_lr(it):
+    if it < warmup_iters:
+        return learning_rate * (it + 1) / (warmup_iters + 1)
+    if it > lr_decay_iters:
+        return min_lr
+    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)
+
+# logging
+if wandb_log and master_process:
+    wandb.init(project=wandb_project, name=wandb_run_name, config=config)
+
+# HF checkpoint upload function
+def upload_checkpoint_to_hf(checkpoint_path, iter_num):
+    if push_to_hub and master_process:
+        try:
+            # Create a unique filename
+            filename = f"checkpoint_iter_{iter_num}.pt"
+            file_path = os.path.join(out_dir, filename)
+            
+            # Copy checkpoint with new name
+            import shutil
+            shutil.copy2(checkpoint_path, file_path)
+            
+            # Upload to HF
+            api.upload_file(
+                path_or_fileobj=file_path,
+                path_in_repo=filename,
+                repo_id=hf_repo_id,
+                repo_type="model"
+            )
+            print(f"Uploaded checkpoint to HF: {filename}")
+            
+            # Clean up local copy
+            os.remove(file_path)
+        except Exception as e:
+            print(f"Failed to upload checkpoint: {e}")
+
+# training loop
+print("Starting FREE H200 nano-coder training...")
+X, Y = get_batch('train')
+t0 = time.time()
+local_iter_num = 0
+raw_model = model.module if ddp else model
+running_mfu = -1.0
+
+while True:
+    # Check time limit
+    elapsed_time = time.time() - start_time
+    if elapsed_time > MAX_TRAINING_TIME:
+        print(f"\n⏰ TIME LIMIT REACHED! Training stopped after {elapsed_time/60:.1f} minutes")
+        break
+    
+    # determine and set the learning rate for this iteration
+    lr = get_lr(iter_num) if decay_lr else learning_rate
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+    # evaluate the loss on train/val sets and write checkpoints
+    if iter_num % eval_interval == 0 and master_process:
+        losses = estimate_loss()
+        remaining_time = MAX_TRAINING_TIME - elapsed_time
+        print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}, time left: {remaining_time/60:.1f}min")
+        if wandb_log:
+            wandb.log({
+                "iter": iter_num,
+                "train/loss": losses['train'],
+                "val/loss": losses['val'],
+                "lr": lr,
+                "mfu": running_mfu*100,
+                "elapsed_time": elapsed_time,
+                "remaining_time": remaining_time,
+            })
+        if losses['val'] < best_val_loss or always_save_checkpoint:
+            best_val_loss = losses['val']
+            if iter_num > 0:
+                checkpoint = {
+                    'model': raw_model.state_dict(),
+                    'optimizer': optimizer.state_dict(),
+                    'model_args': model_args,
+                    'iter_num': iter_num,
+                    'best_val_loss': best_val_loss,
+                    'config': config,
+                }
+                checkpoint_path = os.path.join(out_dir, 'ckpt.pt')
+                print(f"saving checkpoint to {out_dir}")
+                torch.save(checkpoint, checkpoint_path)
+                
+                # Upload to HF every 200 iterations (frequent for short runs)
+                if iter_num % 200 == 0:
+                    upload_checkpoint_to_hf(checkpoint_path, iter_num)
+    if iter_num == 0 and eval_only:
+        break
+
+    # forward backward update
+    for micro_step in range(gradient_accumulation_steps):
+        if ddp:
+            model.require_backward_grad_sync = (micro_step == gradient_accumulation_steps - 1)
+        with ctx:
+            logits, loss = model(X, Y)
+            loss = loss / gradient_accumulation_steps
+        X, Y = get_batch('train')
+        scaler.scale(loss).backward()
+    
+    # clip the gradient
+    if grad_clip != 0.0:
+        scaler.unscale_(optimizer)
+        torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
+    
+    # step the optimizer and scaler
+    scaler.step(optimizer)
+    scaler.update()
+    optimizer.zero_grad(set_to_none=True)
+
+    # timing and logging
+    t1 = time.time()
+    dt = t1 - t0
+    t0 = t1
+    if iter_num % log_interval == 0 and master_process:
+        lossf = loss.item() * gradient_accumulation_steps
+        if local_iter_num >= 5:
+            mfu = raw_model.estimate_mfu(batch_size * gradient_accumulation_steps, dt)
+            running_mfu = mfu if running_mfu == -1.0 else 0.9*running_mfu + 0.1*mfu
+        remaining_time = MAX_TRAINING_TIME - elapsed_time
+        print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms, mfu {running_mfu*100:.2f}%, remaining: {remaining_time/60:.1f}min")
+    iter_num += 1
+    local_iter_num += 1
+
+    # termination conditions
+    if iter_num > max_iters:
+        break
+
+if ddp:
+    destroy_process_group()
+
+# Final upload
+if push_to_hub and master_process:
+    upload_checkpoint_to_hf(os.path.join(out_dir, 'ckpt.pt'), 'final')
+
+total_time = time.time() - start_time
+print(f"\n🎉 FREE H200 TRAINING COMPLETED!")
+print(f"Total training time: {total_time/60:.1f} minutes")
+print(f"Total iterations: {iter_num}")
+print(f"Final validation loss: {best_val_loss:.4f}")
+print(f"Model saved to: {out_dir}")