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 out_dir = 'out' eval_interval = 2000 log_interval = 1 eval_iters = 200 eval_only = False always_save_checkpoint = True init_from = 'scratch' wandb_log = False wandb_project = 'owt' wandb_run_name = 'gpt2' dataset = 'openwebtext' gradient_accumulation_steps = 5 * 8 batch_size = 12 block_size = 1024 n_layer = 12 n_head = 12 n_embd = 768 dropout = 0.0 bias = False learning_rate = 6e-4 max_iters = 600000 weight_decay = 1e-1 beta1 = 0.9 beta2 = 0.95 grad_clip = 1.0 decay_lr = True warmup_iters = 2000 lr_decay_iters = 600000 min_lr = 6e-5 backend = 'nccl' device = 'cuda' dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' compile = True 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} 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:,}") 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_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 iter_num = 0 best_val_loss = 1e9 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_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 model from scratch") if meta_vocab_size is None: print("defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency)") 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) scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) optimizer = model.configure_optimizers(weight_decay, learning_rate, (beta1, beta2), device_type) if init_from == 'resume': optimizer.load_state_dict(checkpoint['optimizer']) checkpoint = None if compile: print("compiling the model... (takes a ~minute)") unoptimized_model = model model = torch.compile(model) if ddp: model = DDP(model, device_ids=[ddp_local_rank]) @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 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) if wandb_log and master_process: import wandb wandb.init(project=wandb_project, name=wandb_run_name, config=config) 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: lr = get_lr(iter_num) if decay_lr else learning_rate for param_group in optimizer.param_groups: param_group['lr'] = lr if iter_num % eval_interval == 0 and master_process: losses = estimate_loss() print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}") if wandb_log: wandb.log({ "iter": iter_num, "train/loss": losses['train'], "val/loss": losses['val'], "lr": lr, "mfu": running_mfu*100, }) 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, } print(f"saving checkpoint to {out_dir}") torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) if iter_num == 0 and eval_only: break 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() if grad_clip != 0.0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip) scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) 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 print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms, mfu {running_mfu*100:.2f}%") iter_num += 1 local_iter_num += 1 if iter_num > max_iters: break if ddp: destroy_process_group()