""" 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 on a single GPU, example: $ python train.py --batch_size=32 --compile=False To run with DDP on 4 gpus on 1 node, example: $ torchrun --standalone --nproc_per_node=4 train.py To run with DDP on 4 gpus across 2 nodes, example: - Run on the first (master) node with example IP 123.456.123.456: $ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py - Run on the worker node: $ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=123.456.123.456 --master_port=1234 train.py (If your cluster does not have Infiniband interconnect prepend NCCL_IB_DISABLE=1) """ 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 # ----------------------------------------------------------------------------- # default config values designed to train a gpt2 (124M) on OpenWebText # I/O out_dir = 'out' eval_interval = 25 log_interval = 1 eval_iters = 2 eval_only = False # if True, script exits right after the first eval always_save_checkpoint = True # if True, always save a checkpoint after each eval init_from = 'scratch' # 'scratch' or 'resume' or 'gpt2*' # wandb logging wandb_log = False # disabled by default wandb_project = 'owt' wandb_run_name = 'gpt2' # 'run' + str(time.time()) # data # dataset = 'openwebtext' gradient_accumulation_steps = 5 * 8 # used to simulate larger batch sizes batch_size = 12 # if gradient_accumulation_steps > 1, this is the micro-batch size block_size = 1024 # model n_layer = 24 n_head = 24 n_embd = 768 dropout = 0.0 # for pretraining 0 is good, for finetuning try 0.1+ bias = False # do we use bias inside LayerNorm and Linear layers? # adamw optimizer learning_rate = 5e-4 # max learning rate max_iters = 100 # total number of training iterations weight_decay = 1e-1 beta1 = 0.9 beta2 = 0.95 grad_clip = 1.0 # clip gradients at this value, or disable if == 0.0 # 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 = 600000 # should be ~= max_iters per Chinchilla min_lr = 5e-5 # minimum learning rate, should be ~= learning_rate/10 per Chinchilla # DDP settings backend = 'nccl' # 'nccl', 'gloo', etc. # system device = 'cuda' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1' etc., or try 'mps' on macbooks dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or 'float16', the latter will auto implement a GradScaler compile = True # use PyTorch 2.0 to compile the model to be faster # ----------------------------------------------------------------------------- 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()) # overrides from command line or config file config = {k: globals()[k] for k in config_keys} # will be useful for logging # ----------------------------------------------------------------------------- # various inits, derived attributes, I/O setup ddp = int(os.environ.get('RANK', -1)) != -1 # is this a ddp run? 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 # this process will do logging, checkpointing etc. seed_offset = ddp_rank # each process gets a different seed # world_size number of processes will be training simultaneously, so we can scale # down the desired gradient accumulation iterations per process proportionally assert gradient_accumulation_steps % ddp_world_size == 0 gradient_accumulation_steps //= ddp_world_size else: # if not ddp, we are running on a single gpu, and one process 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 # allow tf32 on matmul torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast # note: float16 data type will automatically use a GradScaler 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) # poor man's data loader data_dir = '/teamspace/studios/this_studio/nanoGPT/data/dataset' def get_batch(split): # We recreate np.memmap every batch to avoid a memory leak, as per # https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-iterate-once/61472122#61472122 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': # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True) 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' (i.e. from a checkpoint) 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) # start with model_args from command line if init_from == 'scratch': # init a new model from scratch print("Initializing a new model from scratch") # determine the vocab size we'll use for from-scratch training 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}") # resume training from a checkpoint. ckpt_path = os.path.join(out_dir, 'ckpt.pt') checkpoint = torch.load(ckpt_path, map_location=device) checkpoint_model_args = checkpoint['model_args'] # force these config attributes to be equal otherwise we can't even resume training # the rest of the attributes (e.g. dropout) can stay as desired from command line for k in ['n_layer', 'n_head', 'n_embd', 'block_size', 'bias', 'vocab_size']: model_args[k] = checkpoint_model_args[k] # create the model gptconf = GPTConfig(**model_args) model = GPT(gptconf) state_dict = checkpoint['model'] # fix the keys of the state dictionary :( # honestly no idea how checkpoints sometimes get this prefix, have to debug more 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}") # initialize from OpenAI GPT-2 weights override_args = dict(dropout=dropout) model = GPT.from_pretrained(init_from, override_args) # read off the created config params, so we can store them into checkpoint correctly for k in ['n_layer', 'n_head', 'n_embd', 'block_size', 'bias', 'vocab_size']: model_args[k] = getattr(model.config, k) # crop down the model block size if desired, using model surgery if block_size < model.config.block_size: model.crop_block_size(block_size) model_args['block_size'] = block_size # so that the checkpoint will have the right value model.to(device) # initialize a GradScaler. If enabled=False scaler is a no-op 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 # free up memory # compile the model if compile: print("compiling the model... (takes a ~minute)") unoptimized_model = model model = torch.compile(model) # requires PyTorch 2.0 # 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): # 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)) # coeff ranges 0..1 return min_lr + coeff * (learning_rate - min_lr) # logging if wandb_log and master_process: import wandb wandb.init(project=wandb_project, name=wandb_run_name, config=config) # training loop X, Y = get_batch('train') # fetch the very first batch t0 = time.time() local_iter_num = 0 # number of iterations in the lifetime of this process raw_model = model.module if ddp else model # unwrap DDP container if needed running_mfu = -1.0 while True: # 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() 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, # convert to percentage }) 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 # forward backward update, with optional gradient accumulation to simulate larger batch size # and using the GradScaler if data type is float16 for micro_step in range(gradient_accumulation_steps): if ddp: # in DDP training we only need to sync gradients at the last micro step. # the official way to do this is with model.no_sync() context manager, but # I really dislike that this bloats the code and forces us to repeat code # looking at the source of that context manager, it just toggles this variable model.require_backward_grad_sync = (micro_step == gradient_accumulation_steps - 1) with ctx: logits, loss = model(X, Y) loss = loss / gradient_accumulation_steps # scale the loss to account for gradient accumulation # immediately async prefetch next batch while model is doing the forward pass on the GPU X, Y = get_batch('train') # backward pass, with gradient scaling if training in fp16 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 if training in fp16 scaler.step(optimizer) scaler.update() # flush the gradients as soon as we can, no need for this memory anymore 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: # get loss as float. note: this is a CPU-GPU sync point # scale up to undo the division above, approximating the true total loss (exact would have been a sum) lossf = loss.item() * gradient_accumulation_steps if local_iter_num >= 5: # let the training loop settle a bit 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 # termination conditions if iter_num > max_iters: break if ddp: destroy_process_group()