""" This training script can be run both on a single gpu in debug mode, and also in a larger training run with distributed data parallel (ddp). To run in debug mode example: $ python train.py --batch_size=32 --other=args To run DDP on 4 gpus on one node, example: $ torchrun --standalone --nproc_per_node=4 train.py """ import os import sys import time import math from ast import literal_eval import wandb 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 # ----------------------------------------------------------------------------- # default config values # I/O out_dir = 'out' eval_interval = 500 log_interval = 1 eval_iters = 50 eval_only = False # if True, script exits right after the first eval # wandb logging wandb_log = False # disabled by default wandb_entity = 'karpathy' wandb_project = 'owt' wandb_run_name = 'gpt2' # 'run' + str(time.time()) # data dataset = 'openwebtext' batch_size = 8 block_size = 1024 # model device = 'cuda:0' init_from = 'scratch' # 'scratch' or 'resume' or 'gpt2*' dropout = 0.1 n_layer = 12 n_head = 12 n_embd = 768 # adamw optimizer learning_rate = 2.5e-4 # max learning rate max_iters = 500000 # total number of training iterations weight_decay = 1e-2 betas = (0.9, 0.95) # learning rate decay settings decay_lr = True # whether to decay the learning rate warmup_iters = 2000 # how many steps to warm up for lr_decay_iters = 320000 # how many steps to decay the learning rate for min_lr = 1e-5 # minimum learning rate # DDP settings backend = 'nccl' # 'nccl', 'gloo', etc. # ----------------------------------------------------------------------------- # poor man's Configurator. Potentially a bad idea. Example usage: # $ python train.py override_file --batch_size=32 # this will first run config/override_file.py, then override batch_size to 32 for arg in sys.argv[1:]: if '=' not in arg: # assume it's the name of a config file assert not arg.startswith('--') config_file = os.path.join('config', arg + '.py') print(f"Overriding config with {config_file}:") with open(config_file) as f: print(f.read()) exec(open(config_file).read()) else: # assume it's a --key=value argument assert arg.startswith('--') key, val = arg.split('=') key = key[2:] if key in globals(): try: # attempt to eval it it (e.g. if bool, number, or etc) attempt = literal_eval(val) except (SyntaxError, ValueError): # if that goes wrong, just use the string attempt = val # ensure the types match ok assert type(attempt) == type(globals()[key]) # cross fingers print(f"Overriding: {key} = {attempt}") globals()[key] = attempt else: raise ValueError(f"Unknown config key: {key}") # ----------------------------------------------------------------------------- ddp = int(os.environ.get('LOCAL_RANK', -1)) != -1 # is this a ddp run? if ddp: init_process_group(backend=backend) gpu_id = int(os.environ["LOCAL_RANK"]) device = f"cuda:{gpu_id}" else: gpu_id = 0 # gpu_id 0 means this is the (single) master process, basically if gpu_id == 0: os.makedirs(out_dir, exist_ok=True) torch.manual_seed(1337 + gpu_id) # note: each worker gets a different seed torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn # poor man's data loader, TODO evaluate need for actual DataLoader data_dir = os.path.join('data', dataset) train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r') val_data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r') 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([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]) x, y = x.to(device), y.to(device) return x, y # model init model_args = dict(n_layer = n_layer, n_head = n_head, n_embd = n_embd, block_size = block_size, dropout = dropout) if init_from == 'scratch': # init a new model from scratch gptconf = GPTConfig(**model_args) model = GPT(gptconf) elif init_from == 'resume': # resume training from a checkpoint. # TODO: should we also resume iter_num and best_val_loss? 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, v in model_args.items(): assert checkpoint_model_args[k] == v, "for now" gptconf = GPTConfig(**model_args) model = GPT(gptconf) model.load_state_dict(checkpoint['model']) elif init_from.startswith('gpt2'): # initialize from OpenAI GPT-2 weights model = GPT.from_pretrained(init_from) # crop down the model block size if desired if block_size < model.block_size: model.crop_block_size(block_size) model.to(device) # optimizer optimizer = model.configure_optimizers(weight_decay, learning_rate, betas) if init_from == 'resume': optimizer.load_state_dict(checkpoint['optimizer']) # wrap model into DDP container if ddp: model = DDP(model, device_ids=[gpu_id]) @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 torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): 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(iter): # 1) linear warmup for warmup_iters steps if iter < warmup_iters: return learning_rate * iter / warmup_iters # 2) if iter > lr_decay_iters, return min learning rate if iter > lr_decay_iters: return min_lr # 3) in between, use cosine decay down to min learning rate decay_ratio = (iter - warmup_iters) / (lr_decay_iters - warmup_iters) assert 0 <= decay_ratio <= 1 coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff ranges 0..1 return min_lr + coeff * (learning_rate - min_lr) # logging if wandb_log and gpu_id == 0: wandb.init(project=wandb_project, entity=wandb_entity, name=wandb_run_name) wandb.config = { "batch_size": batch_size, "block_size": block_size, "learning_rate": learning_rate, # TODO log everything else too } # training loop iter_num = 0 best_val_loss = 1e9 t0 = time.time() while True: # determine the learning rate for this iteration if decay_lr: lr = get_lr(iter_num) for param_group in optimizer.param_groups: param_group['lr'] = lr else: lr = learning_rate if iter_num % eval_interval == 0 and gpu_id == 0: 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, }) if losses['val'] < best_val_loss: best_val_loss = losses['val'] raw_model = model.module if ddp else model 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, } torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt')) if iter_num == 0 and eval_only: break X, Y = get_batch('train') with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16): logits, loss = model(X, Y) optimizer.zero_grad(set_to_none=True) loss.backward() # TODO: gradient clipping evaluate need for optimizer.step() t1 = time.time() dt = t1 - t0 t0 = t1 if iter_num % log_interval == 0 and gpu_id == 0: lossf = loss.item() # loss as float. TODO CPU-GPU sync: profile, make sure not slow af print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms") iter_num += 1 # termination conditions if iter_num >= max_iters: break destroy_process_group()