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a216fa7 | 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 | import argparse, math, os, random
import numpy as np
import torch
import torch.nn.functional as F
from torch_predictive_attn import ppmi_embed, learn_map, doc_index, apply_stack, features, log
def iter_chunks(tokens, eos, args, max_tokens, shuffle=False):
xnp = tokens[:max_tokens]
starts = np.flatnonzero(np.r_[True, xnp[:-1] == eos])
ids = list(range(0, len(starts), args.chunk_docs))
if shuffle:
random.shuffle(ids)
for i in ids:
lo = starts[i]
hi = starts[i + args.chunk_docs] if i + args.chunk_docs < len(starts) else len(xnp)
yield xnp[lo:hi]
def chunk_features(xnp, E, Ps, Bs, eos, args, device):
x = torch.tensor(xnp.astype(np.int64), device=device)
seg, within = doc_index(x, eos)
H, phis = apply_stack(x, E, Ps, Bs, within, args)
Phi = features(H, within, phis, args.extra_context)
y = torch.empty(len(x), device=device, dtype=torch.long)
y[:-1] = x[1:]; y[-1] = eos
m = torch.ones(len(x), device=device, dtype=torch.bool)
m[-1] = False; m[:-1] &= seg[1:].eq(seg[:-1]); m &= x.ne(eos)
return Phi[m].float(), y[m]
def eval_ppl(tokens, E, Ps, Bs, W, b, eos, args, device):
nll = 0.0; n = 0
with torch.no_grad():
for xnp in iter_chunks(tokens, eos, args, args.eval_tokens, shuffle=False):
X, y = chunk_features(xnp, E, Ps, Bs, eos, args, device)
for i in range(0, len(y), args.batch):
logits = X[i:i+args.batch] @ W + b
nll += float(F.cross_entropy(logits, y[i:i+args.batch], reduction="sum"))
n += len(y[i:i+args.batch])
return math.exp(nll / max(1, n))
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--data", default="/workspace/glm")
ap.add_argument("--spm_model", default="/workspace/glm/glm16k.model")
ap.add_argument("--train_bin", default="/workspace/glm/glm_train.bin")
ap.add_argument("--valid_bin", default="/workspace/glm/glm_valid.bin")
ap.add_argument("--vocab", type=int, default=8192)
ap.add_argument("--d", type=int, default=896)
ap.add_argument("--r", type=int, default=320)
ap.add_argument("--layers", type=int, default=10)
ap.add_argument("--att_window", type=int, default=10)
ap.add_argument("--temp", type=float, default=0.28)
ap.add_argument("--window", type=int, default=10)
ap.add_argument("--extra_context", type=int, default=1)
ap.add_argument("--res_scale", type=float, default=0.07)
ap.add_argument("--pred_scale", type=float, default=0.035)
ap.add_argument("--pred_schedule", default="late")
ap.add_argument("--orth_delta", type=int, default=1)
ap.add_argument("--pred_norm", type=int, default=1)
ap.add_argument("--pred_features", type=int, default=1)
ap.add_argument("--map_lam", type=float, default=0.001)
ap.add_argument("--cooc_tokens", type=int, default=3_600_000)
ap.add_argument("--proj_tokens", type=int, default=3_600_000)
ap.add_argument("--fit_tokens", type=int, default=3_600_000)
ap.add_argument("--eval_tokens", type=int, default=159_631)
ap.add_argument("--chunk_docs", type=int, default=8)
ap.add_argument("--value_mode", default="dual_ridge_delta")
ap.add_argument("--ridge_lam", type=float, default=10.0)
ap.add_argument("--init_scale", type=float, default=0.05)
ap.add_argument("--steps", type=int, default=800)
ap.add_argument("--batch", type=int, default=2048)
ap.add_argument("--lr", type=float, default=0.003)
ap.add_argument("--wd", type=float, default=1e-4)
ap.add_argument("--eval_every", type=int, default=100)
ap.add_argument("--save", default="")
ap.add_argument("--resume", default="")
args = ap.parse_args()
import sentencepiece as spm
sp = spm.SentencePieceProcessor(model_file=args.spm_model)
eos = sp.eos_id(); V = sp.get_piece_size()
device = "cuda" if torch.cuda.is_available() else "cpu"
train = np.fromfile(args.train_bin, dtype=np.uint16)
valid = np.fromfile(args.valid_bin, dtype=np.uint16)
log("STREAM_CE device", device, "V", V, "train", len(train), "valid", len(valid))
E = ppmi_embed(train, V, args.d, args.window, args.cooc_tokens, device)
Ps, Bs = [], []
for _ in range(args.layers):
P, B = learn_map(train, E, Ps, Bs, eos, args, device)
Ps.append(P); Bs.append(B)
# Build ridge init streaming stats only.
A = G = None
for xnp in iter_chunks(train, eos, args, args.fit_tokens, shuffle=False):
X, y = chunk_features(xnp, E, Ps, Bs, eos, args, device)
if A is None:
D = X.shape[1]
A = torch.zeros((D, D), device=device, dtype=torch.float64)
G = torch.zeros((D, V), device=device, dtype=torch.float64)
Xd = X.double()
A += Xd.T @ Xd
G.index_add_(1, y, Xd.T)
diag = torch.trace(A) / A.shape[0]
W0 = torch.linalg.solve(A + args.ridge_lam * diag * torch.eye(A.shape[0], device=device, dtype=torch.float64), G).float()
W = (args.init_scale * W0).detach().clone()
b = torch.zeros(V, device=device)
if args.resume and os.path.exists(args.resume):
ck = torch.load(args.resume, map_location=device)
W = ck["W"].to(device)
b = ck["b"].to(device)
log("resumed", args.resume, "ppl", ck.get("ppl"))
W = W.requires_grad_(True)
b = b.requires_grad_(True)
opt = torch.optim.AdamW([W, b], lr=args.lr, weight_decay=args.wd)
log("init_eval_start D", W.shape[0])
best = eval_ppl(valid, E, Ps, Bs, W, b, eos, args, device)
log(f"STREAM_CE init_ppl={best:.2f}")
step = 0
while step < args.steps:
for xnp in iter_chunks(train, eos, args, args.fit_tokens, shuffle=True):
X, y = chunk_features(xnp, E, Ps, Bs, eos, args, device)
if len(y) == 0:
continue
idx = torch.randint(0, len(y), (min(args.batch, len(y)),), device=device)
loss = F.cross_entropy(X[idx] @ W + b, y[idx])
opt.zero_grad(set_to_none=True); loss.backward(); opt.step()
step += 1
if step % args.eval_every == 0:
ppl = eval_ppl(valid, E, Ps, Bs, W, b, eos, args, device)
if ppl < best:
best = ppl
if args.save:
torch.save({"W": W.detach().cpu(), "b": b.detach().cpu(), "ppl": best, "args": vars(args)}, args.save)
log(f"step={step} loss={float(loss):.4f} ppl={ppl:.2f} best={best:.2f}")
if step >= args.steps:
break
log(f"STREAM_CE best_ppl={best:.2f}")
if args.save:
torch.save({"W": W.detach().cpu(), "b": b.detach().cpu(), "ppl": best, "args": vars(args)}, args.save)
if __name__ == "__main__":
main()
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