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Diffstat (limited to 'ep_run/casc_bp_train.py')
| -rw-r--r-- | ep_run/casc_bp_train.py | 94 |
1 files changed, 94 insertions, 0 deletions
diff --git a/ep_run/casc_bp_train.py b/ep_run/casc_bp_train.py new file mode 100644 index 0000000..8e039ec --- /dev/null +++ b/ep_run/casc_bp_train.py @@ -0,0 +1,94 @@ +"""BP-train a small cascade-form standard transformer (L distinct blocks), saving ckpts +every --save_every for the A0.2 on-trajectory gradient gate (cascade_probe.py --ckpt). +Plain LLM training — this is also the BP twin for the C-tier money runs.""" +import argparse, math, pickle, time, json +import numpy as np, torch, torch.nn as nn, torch.nn.functional as F +from pathlib import Path + +ap = argparse.ArgumentParser() +ap.add_argument('--tag', default='casc_bp6') +ap.add_argument('--L', type=int, default=6); ap.add_argument('--C', type=int, default=256) +ap.add_argument('--H', type=int, default=8); ap.add_argument('--T', type=int, default=256) +ap.add_argument('--B', type=int, default=24); ap.add_argument('--steps', type=int, default=4000) +ap.add_argument('--lr', type=float, default=3e-4); ap.add_argument('--warmup', type=int, default=200) +ap.add_argument('--seed', type=int, default=0) +ap.add_argument('--save_every', type=int, default=500); ap.add_argument('--log', type=int, default=200) +ap.add_argument('--wandb', default=''); ap.add_argument('--wandb_run', default='') +args = ap.parse_args() +torch.manual_seed(args.seed) +dev = 'cuda' if torch.cuda.is_available() else 'cpu' + +DD = Path('/home/yurenh2/ept/ep_run/data/tinystories_bpe') +vocab = pickle.load(open(DD / 'meta.pkl', 'rb'))['vocab_size'] + +def get_batch(split): + data = np.memmap(DD / ('train.bin' if split == 'train' else 'val.bin'), dtype=np.uint16, mode='r') + ix = torch.randint(len(data) - args.T - 1, (args.B,)) + x = torch.stack([torch.from_numpy(data[i:i + args.T].astype(np.int64)) for i in ix]) + y = torch.stack([torch.from_numpy(data[i + 1:i + 1 + args.T].astype(np.int64)) for i in ix]) + return x.to(dev), y.to(dev) + +class Block(nn.Module): + def __init__(self, C, H): + super().__init__() + self.ln1, self.ln2 = nn.LayerNorm(C), nn.LayerNorm(C) + self.attn = nn.MultiheadAttention(C, H, batch_first=True) + self.ff = nn.Sequential(nn.Linear(C, 4 * C), nn.GELU(), nn.Linear(4 * C, C)) + def forward(self, z, mask): + h = self.ln1(z); a, _ = self.attn(h, h, h, attn_mask=mask, need_weights=False) + z = z + a; return z + self.ff(self.ln2(z)) + +tok = nn.Embedding(vocab, args.C).to(dev) +pos = nn.Embedding(args.T, args.C).to(dev) +blocks = nn.ModuleList([Block(args.C, args.H) for _ in range(args.L)]).to(dev) +mask = torch.triu(torch.full((args.T, args.T), float('-inf'), device=dev), 1) +params = list(tok.parameters()) + list(pos.parameters()) + list(blocks.parameters()) +opt = torch.optim.AdamW(params, lr=args.lr, weight_decay=1e-4) +sched = torch.optim.lr_scheduler.LambdaLR(opt, lambda s: min(1.0, (s + 1) / max(args.warmup, 1))) + +def fwd(x): + z = tok(x) + pos(torch.arange(args.T, device=dev))[None] + for b in blocks: z = b(z, mask) + return z @ tok.weight.t() + +@torch.no_grad() +def evaluate(nb=6): + tot = 0.0 + for _ in range(nb): + x, y = get_batch('val') + tot += F.cross_entropy(fwd(x).reshape(-1, vocab), y.reshape(-1)).item() + return tot / nb + +wb = None +if args.wandb: + try: + import wandb as _w + wb = _w.init(project=args.wandb, name=args.wandb_run or args.tag, id=args.wandb_run or args.tag, + resume='allow', config=vars(args)) + except Exception as e: + print(f'[wandb] disabled ({e})', flush=True) + +n = sum(p.numel() for p in params) +print(f'[{args.tag}] cascade-BP L{args.L} C{args.C} H{args.H} T{args.T} | {n/1e6:.2f}M params | {dev}', flush=True) +best, t0 = 1e9, time.time() +outdir = Path('runs'); outdir.mkdir(exist_ok=True) +for step in range(args.steps + 1): + x, y = get_batch('train') + loss = F.cross_entropy(fwd(x).reshape(-1, vocab), y.reshape(-1)) + opt.zero_grad(set_to_none=True); loss.backward() + torch.nn.utils.clip_grad_norm_(params, 1.0) + opt.step(); sched.step() + if step % args.log == 0: + val = evaluate(); best = min(best, val) + print(f'step {step:5d}/{args.steps} | train {loss.item():.4f} val {val:.4f} (best {best:.4f}) ' + f'| {step/max(time.time()-t0,1e-9):.2f} it/s', flush=True) + if wb is not None: + try: wb.log({'train_ce': loss.item(), 'val_ce': val, 'best': best}, step=step) + except Exception: pass + if step % args.save_every == 0: + torch.save({'tok': tok.state_dict(), 'pos': pos.state_dict(), 'blocks': blocks.state_dict(), + 'step': step, 'val': best, 'config': vars(args)}, outdir / f'{args.tag}_s{step}.pt') +print(f'[{args.tag}] DONE best val CE {best:.4f} (random ln({vocab})={math.log(vocab):.3f})', flush=True) +if wb is not None: + try: wb.summary['best_val_ce'] = best; wb.finish() + except Exception: pass |