""" Prepare the Shakespeare dataset for character-level language modeling. So instead of encoding with GPT-2 BPE tokens, we just map characters to ints. Will save train.bin, val.bin containing the ids, and meta.pkl containing the encoder and decoder and some other related info. """ import os import pickle import requests import numpy as np # download the tiny shakespeare dataset input_file_path = os.path.join(os.path.dirname(__file__), 'input.txt') if not os.path.exists(input_file_path): data_url = 'https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt' with open(input_file_path, 'w') as f: f.write(requests.get(data_url).text) with open(input_file_path, 'r') as f: data = f.read() print(f"length of dataset in characters: {len(data):,}") # get all the unique characters that occur in this text chars = sorted(list(set(data))) vocab_size = len(chars) print("all the unique characters:", ''.join(chars)) print(f"vocab size: {vocab_size:,}") # create a mapping from characters to integers stoi = { ch:i for i,ch in enumerate(chars) } itos = { i:ch for i,ch in enumerate(chars) } def encode(s): return [stoi[c] for c in s] # encoder: take a string, output a list of integers def decode(l): return ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string # create the train and test splits n = len(data) train_data = data[:int(n*0.9)] val_data = data[int(n*0.9):] # encode both to integers train_ids = encode(train_data) val_ids = encode(val_data) print(f"train has {len(train_ids):,} tokens") print(f"val has {len(val_ids):,} tokens") # export to bin files train_ids = np.array(train_ids, dtype=np.uint16) val_ids = np.array(val_ids, dtype=np.uint16) train_ids.tofile(os.path.join(os.path.dirname(__file__), 'train.bin')) val_ids.tofile(os.path.join(os.path.dirname(__file__), 'val.bin')) # save the meta information as well, to help us encode/decode later meta = { 'vocab_size': vocab_size, 'itos': itos, 'stoi': stoi, } with open(os.path.join(os.path.dirname(__file__), 'meta.pkl'), 'wb') as f: pickle.dump(meta, f) # length of dataset in characters: 1115394 # all the unique characters: # !$&',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz # vocab size: 65 # train has 1003854 tokens # val has 111540 tokens """ 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 = 2000 log_interval = 1 eval_iters = 200 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 = 12 n_head = 12 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 = 6e-4 # max learning rate max_iters = 600000 # 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 = 6e-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' # '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 assert gradient_accumulation_steps % torch.cuda.device_count() == 0 gradient_accumulation_steps //= torch.cuda.device_count() 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.cuda.amp.autocast(dtype=torch.float16) # poor man's data loader 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]) 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()