File size: 7,106 Bytes
24b1807 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """DeltaLens training script.
Usage:
python train.py --data_path /path/to/train.pt --val_path /path/to/val.pt
Data format: torch tensor of shape (num_sequences, seq_len) with token IDs.
"""
import sys, os, math, time, glob, argparse, signal
import torch
import wandb
_SHOULD_STOP = False
def _sigterm_handler(signum, frame):
global _SHOULD_STOP
print(f"\n[SIGTERM] Saving checkpoint and exiting...")
_SHOULD_STOP = True
signal.signal(signal.SIGTERM, _sigterm_handler)
def get_lr(step, total_steps, warmup_steps, lr_max, lr_min):
if step < warmup_steps:
return lr_max * step / warmup_steps
progress = (step - warmup_steps) / max(1, total_steps - warmup_steps)
return lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos(math.pi * progress))
def save_checkpoint(model, optimizer, step, global_tokens, path):
torch.save({
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"step": step,
"global_tokens": global_tokens,
}, path)
size_mb = os.path.getsize(path) / 1e6
print(f" Checkpoint: {path} ({size_mb:.0f}MB, step={step})")
@torch.no_grad()
def evaluate(model, val_data, max_docs=200):
model.eval()
total_loss = 0.0
total_tokens = 0
for i in range(min(len(val_data), max_docs)):
input_ids = val_data[i:i+1].long().cuda()
out = model(input_ids=input_ids, labels=input_ids)
n = input_ids.numel()
total_loss += out.loss.item() * n
total_tokens += n
model.train()
return math.exp(total_loss / total_tokens), total_loss / total_tokens
def main():
global _SHOULD_STOP
parser = argparse.ArgumentParser()
parser.add_argument("--exp_id", default="DeltaLens-1.3B")
parser.add_argument("--data_path", required=True)
parser.add_argument("--val_path", required=True)
parser.add_argument("--ckpt_dir", default="./checkpoints")
parser.add_argument("--total_tokens", type=int, default=1_000_000_000)
parser.add_argument("--micro_bs", type=int, default=2)
parser.add_argument("--grad_accum", type=int, default=256)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--lr_min", type=float, default=3e-5)
parser.add_argument("--d_model", type=int, default=2048)
parser.add_argument("--d_state", type=int, default=512)
parser.add_argument("--n_layers", type=int, default=24)
parser.add_argument("--n_heads", type=int, default=16)
parser.add_argument("--vocab_size", type=int, default=32000)
args = parser.parse_args()
SEQ_LEN = 2048
EFFECTIVE_BS = args.micro_bs * args.grad_accum
TOKENS_PER_STEP = EFFECTIVE_BS * SEQ_LEN
TOTAL_STEPS = args.total_tokens // TOKENS_PER_STEP
WARMUP_RATIO = 0.03
os.makedirs(args.ckpt_dir, exist_ok=True)
print(f"=== {args.exp_id} ===")
print(f" Total: {args.total_tokens/1e9:.0f}B tokens, {TOTAL_STEPS} steps")
print(f" Effective BS: {EFFECTIVE_BS}")
from deltalens_layer import DeltaLensModel
model = DeltaLensModel(
vocab_size=args.vocab_size,
d_model=args.d_model,
n_layers=args.n_layers,
d_state=args.d_state,
n_heads=args.n_heads,
max_seq_len=SEQ_LEN,
).to(torch.bfloat16).cuda()
total_params = sum(p.numel() for p in model.parameters())
print(f" Params: {total_params:,} ({total_params*2/1e9:.2f}GB)")
print("\nLoading data...")
train_data = torch.load(args.data_path, mmap=True)
val_data = torch.load(args.val_path, mmap=True)
print(f" Train: {len(train_data):,}, Val: {len(val_data):,}")
optimizer = torch.optim.AdamW(
model.parameters(), lr=args.lr, weight_decay=0.01,
betas=(0.9, 0.95), eps=1e-8,
)
warmup_steps = max(1, int(TOTAL_STEPS * WARMUP_RATIO))
# Resume
start_step = 0
global_tokens = 0
ckpts = sorted(glob.glob(os.path.join(args.ckpt_dir, "ckpt_*.pt")))
if ckpts:
print(f"Resuming from {ckpts[-1]}")
ckpt = torch.load(ckpts[-1], map_location="cpu")
model.load_state_dict(ckpt["model_state"])
optimizer.load_state_dict(ckpt["optimizer_state"])
start_step = ckpt["step"] + 1
global_tokens = ckpt["global_tokens"]
del ckpt
wandb.init(project="deltalens", name=args.exp_id,
config=vars(args), resume="allow")
model.train()
EVAL_EVERY = args.total_tokens // 10 // TOKENS_PER_STEP
step_time_start = time.time()
for step in range(start_step, TOTAL_STEPS):
optimizer.zero_grad(set_to_none=True)
step_loss = 0.0
for micro in range(args.grad_accum):
seq_idx = step * EFFECTIVE_BS + micro * args.micro_bs
input_ids = train_data[seq_idx : seq_idx + args.micro_bs].long().cuda()
out = model(input_ids=input_ids, labels=input_ids)
loss = out.loss / args.grad_accum
loss.backward()
step_loss += loss.item()
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0).item()
optimizer.step()
lr = get_lr(step, TOTAL_STEPS, warmup_steps, args.lr, args.lr_min)
for pg in optimizer.param_groups:
pg["lr"] = lr
global_tokens += TOKENS_PER_STEP
if step % 10 == 0:
elapsed = time.time() - step_time_start
tps = (10 * TOKENS_PER_STEP) / max(elapsed, 1) if step > start_step else 0
wandb.log({"train/loss": step_loss, "train/lr": lr,
"train/tokens": global_tokens, "train/grad_norm": grad_norm,
"train/tokens_per_sec": tps, "step": step})
print(f" step {step}/{TOTAL_STEPS} | loss {step_loss:.4f} | "
f"lr {lr:.2e} | gnorm {grad_norm:.3f} | {tps:.0f} tok/s", flush=True)
step_time_start = time.time()
if step > 0 and step % EVAL_EVERY == 0:
ppl, eval_loss = evaluate(model, val_data)
wandb.log({"eval/val_ppl": ppl, "eval/val_loss": eval_loss, "step": step})
print(f" [EVAL] step {step} | val_ppl {ppl:.2f}", flush=True)
if step > 0 and step % 100 == 0:
ckpt_path = os.path.join(args.ckpt_dir, f"ckpt_s{step:06d}.pt")
save_checkpoint(model, optimizer, step, global_tokens, ckpt_path)
ckpts = sorted(glob.glob(os.path.join(args.ckpt_dir, "ckpt_*.pt")))
for old in ckpts[:-2]:
os.remove(old)
if _SHOULD_STOP:
ckpt_path = os.path.join(args.ckpt_dir, f"ckpt_s{step:06d}.pt")
save_checkpoint(model, optimizer, step, global_tokens, ckpt_path)
wandb.finish()
return
# Final save
print("\n=== Training complete! ===")
ckpt_path = os.path.join(args.ckpt_dir, f"ckpt_s{TOTAL_STEPS:06d}_final.pt")
save_checkpoint(model, optimizer, TOTAL_STEPS, global_tokens, ckpt_path)
ppl, eval_loss = evaluate(model, val_data)
wandb.log({"eval/val_ppl": ppl, "step": TOTAL_STEPS})
print(f"[FINAL] val_ppl {ppl:.2f}")
wandb.finish()
if __name__ == "__main__":
main()
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