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