mirror of
https://github.com/osmarks/nanogpt-experiments.git
synced 2024-09-21 03:39:44 +00:00
260 lines
8.9 KiB
Python
260 lines
8.9 KiB
Python
"""
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This training script can be run both on a single gpu in debug mode,
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and also in a larger training run with distributed data parallel (ddp).
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To run in debug mode example:
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$ python train.py --batch_size=32 --other=args
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To run DDP on 4 gpus on one node, example:
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$ torchrun --standalone --nproc_per_node=4 train.py
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"""
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import os
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import sys
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import time
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import math
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from ast import literal_eval
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import wandb
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import numpy as np
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import torch
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.distributed import init_process_group, destroy_process_group
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from model import GPTConfig, GPT
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# -----------------------------------------------------------------------------
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# default config values
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# I/O
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out_dir = 'out'
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eval_interval = 500
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log_interval = 1
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eval_iters = 50
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eval_only = False # if True, script exits right after the first eval
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# wandb logging
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wandb_log = False # disabled by default
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wandb_entity = 'karpathy'
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wandb_project = 'owt'
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wandb_run_name = 'gpt2' # 'run' + str(time.time())
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# data
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dataset = 'openwebtext'
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batch_size = 8
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block_size = 1024
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# model
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device = 'cuda:0'
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init_from = 'scratch' # 'scratch' or 'resume' or 'gpt2*'
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dropout = 0.1
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n_layer = 12
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n_head = 12
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n_embd = 768
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# adamw optimizer
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learning_rate = 2.5e-4 # max learning rate
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max_iters = 500000 # total number of training iterations
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weight_decay = 1e-2
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betas = (0.9, 0.95)
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# learning rate decay settings
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decay_lr = True # whether to decay the learning rate
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warmup_iters = 2000 # how many steps to warm up for
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lr_decay_iters = 320000 # how many steps to decay the learning rate for
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min_lr = 1e-5 # minimum learning rate
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# DDP settings
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backend = 'nccl' # 'nccl', 'gloo', etc.
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# -----------------------------------------------------------------------------
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# poor man's Configurator. Potentially a bad idea. Example usage:
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# $ python train.py override_file --batch_size=32
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# this will first run config/override_file.py, then override batch_size to 32
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for arg in sys.argv[1:]:
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if '=' not in arg:
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# assume it's the name of a config file
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assert not arg.startswith('--')
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config_file = os.path.join('config', arg + '.py')
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print(f"Overriding config with {config_file}:")
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with open(config_file) as f:
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print(f.read())
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exec(open(config_file).read())
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else:
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# assume it's a --key=value argument
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assert arg.startswith('--')
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key, val = arg.split('=')
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key = key[2:]
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if key in globals():
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try:
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# attempt to eval it it (e.g. if bool, number, or etc)
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attempt = literal_eval(val)
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except (SyntaxError, ValueError):
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# if that goes wrong, just use the string
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attempt = val
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# ensure the types match ok
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assert type(attempt) == type(globals()[key])
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# cross fingers
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print(f"Overriding: {key} = {attempt}")
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globals()[key] = attempt
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else:
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raise ValueError(f"Unknown config key: {key}")
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# -----------------------------------------------------------------------------
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ddp = int(os.environ.get('LOCAL_RANK', -1)) != -1 # is this a ddp run?
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if ddp:
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init_process_group(backend=backend)
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gpu_id = int(os.environ["LOCAL_RANK"])
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device = f"cuda:{gpu_id}"
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else:
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gpu_id = 0 # gpu_id 0 means this is the (single) master process, basically
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if gpu_id == 0:
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os.makedirs(out_dir, exist_ok=True)
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torch.manual_seed(1337 + gpu_id) # note: each worker gets a different seed
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torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
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torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
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# poor man's data loader, TODO evaluate need for actual DataLoader
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data_dir = os.path.join('data', dataset)
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train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
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val_data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')
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def get_batch(split):
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data = train_data if split == 'train' else val_data
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ix = torch.randint(len(data) - block_size, (batch_size,))
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x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
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y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
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x, y = x.to(device), y.to(device)
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return x, y
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# init these up here, can override if init_from='resume' (i.e. from a checkpoint)
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iter_num = 0
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best_val_loss = 1e9
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# model init
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model_args = dict(n_layer = n_layer, n_head = n_head, n_embd = n_embd, block_size = block_size, dropout = dropout)
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if init_from == 'scratch':
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# init a new model from scratch
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print("Initializing a new model from scratch")
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gptconf = GPTConfig(**model_args)
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model = GPT(gptconf)
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elif init_from == 'resume':
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print(f"Resuming training from {out_dir}")
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# resume training from a checkpoint.
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ckpt_path = os.path.join(out_dir, 'ckpt.pt')
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checkpoint = torch.load(ckpt_path, map_location=device)
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checkpoint_model_args = checkpoint['model_args']
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for k, v in model_args.items():
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assert checkpoint_model_args[k] == v, "for now"
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gptconf = GPTConfig(**model_args)
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model = GPT(gptconf)
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model.load_state_dict(checkpoint['model'])
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iter_num = checkpoint['iter_num']
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best_val_loss = checkpoint['best_val_loss']
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elif init_from.startswith('gpt2'):
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print(f"Initializing from OpenAI GPT-2 weights: {init_from}")
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# initialize from OpenAI GPT-2 weights
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model = GPT.from_pretrained(init_from)
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# crop down the model block size if desired
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if block_size < model.block_size:
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model.crop_block_size(block_size)
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model.to(device)
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# optimizer
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optimizer = model.configure_optimizers(weight_decay, learning_rate, betas)
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if init_from == 'resume':
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optimizer.load_state_dict(checkpoint['optimizer'])
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# wrap model into DDP container
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if ddp:
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model = DDP(model, device_ids=[gpu_id])
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@torch.no_grad()
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def estimate_loss():
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out = {}
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model.eval()
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for split in ['train', 'val']:
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losses = torch.zeros(eval_iters)
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for k in range(eval_iters):
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X, Y = get_batch(split)
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with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
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logits, loss = model(X, Y)
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losses[k] = loss.item()
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out[split] = losses.mean()
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model.train()
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return out
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# learning rate decay scheduler (cosine with warmup)
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def get_lr(iter):
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# 1) linear warmup for warmup_iters steps
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if iter < warmup_iters:
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return learning_rate * iter / warmup_iters
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# 2) if iter > lr_decay_iters, return min learning rate
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if iter > lr_decay_iters:
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return min_lr
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# 3) in between, use cosine decay down to min learning rate
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decay_ratio = (iter - warmup_iters) / (lr_decay_iters - warmup_iters)
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assert 0 <= decay_ratio <= 1
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coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff ranges 0..1
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return min_lr + coeff * (learning_rate - min_lr)
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# logging
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if wandb_log and gpu_id == 0:
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wandb.init(project=wandb_project, entity=wandb_entity, name=wandb_run_name)
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wandb.config = {
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"batch_size": batch_size,
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"block_size": block_size,
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"learning_rate": learning_rate, # TODO log everything else too
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}
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# training loop
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t0 = time.time()
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while True:
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# determine the learning rate for this iteration
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if decay_lr:
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lr = get_lr(iter_num)
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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else:
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lr = learning_rate
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if iter_num % eval_interval == 0 and gpu_id == 0:
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losses = estimate_loss()
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print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
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if wandb_log:
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wandb.log({
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"iter": iter_num,
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"train/loss": losses['train'],
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"val/loss": losses['val'],
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"lr": lr,
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})
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if losses['val'] < best_val_loss:
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best_val_loss = losses['val']
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raw_model = model.module if ddp else model
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if iter_num > 0:
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checkpoint = {
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'model': raw_model.state_dict(),
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'optimizer': optimizer.state_dict(),
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'model_args': model_args,
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'iter_num': iter_num,
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'best_val_loss': best_val_loss,
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}
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torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt'))
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if iter_num == 0 and eval_only:
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break
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X, Y = get_batch('train')
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with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
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logits, loss = model(X, Y)
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optimizer.zero_grad(set_to_none=True)
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loss.backward()
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# TODO: gradient clipping evaluate need for
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optimizer.step()
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t1 = time.time()
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dt = t1 - t0
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t0 = t1
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if iter_num % log_interval == 0 and gpu_id == 0:
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lossf = loss.item() # loss as float. TODO CPU-GPU sync: profile, make sure not slow af
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print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms")
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iter_num += 1
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# termination conditions
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if iter_num >= max_iters:
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break
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destroy_process_group()
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