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Diffstat (limited to 'train.py')
| -rw-r--r-- | train.py | 336 |
1 files changed, 336 insertions, 0 deletions
diff --git a/train.py b/train.py new file mode 100644 index 0000000..de57850 --- /dev/null +++ b/train.py @@ -0,0 +1,336 @@ +""" +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' 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 = os.path.join('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 + 1) / (warmup_iters + 1) + # 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() |