File size: 5,086 Bytes
c6be157 | 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 | """Four GNOMON components, each in ~20 lines, all jitted."""
import os
import sys
import time
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
import jax
import jax.numpy as jnp
import numpy as np
from gnomon import (
ternarize,
compile_ldc,
init_gnomon, init_mode_state, make_forward_jit,
compute_bids, vickrey_topk,
init_ib, ib_forward, ib_loss_terms,
)
def demo_ternary():
print("[1/4] Ternary weights (BitNet b1.58)")
w = jax.random.normal(jax.random.PRNGKey(0), (128, 128))
w_q, scale = ternarize(w)
uniq = np.unique(np.asarray(w_q / scale).round(3))
print(f" distinct values after ternarize: {sorted(uniq.tolist())}")
print(f" storage: 32 bits → 2 bits per weight (16x smaller)")
print()
def demo_ldc():
print("[2/4] Lipschitz Decision Circuit")
rng = np.random.default_rng(0)
N, D, C = 20_000, 32, 3
# Three Gaussian clusters — linearly separable enough that LDA is optimal.
centers = rng.standard_normal((C, D)).astype(np.float32) * 2.0
labels = rng.integers(0, C, N).astype(np.int32)
X = (centers[labels] + rng.standard_normal((N, D))).astype(np.float32)
Y = np.eye(C, dtype=np.float32)[labels]
X_tr, Y_tr = X[:15_000], Y[:15_000]
X_te, y_te = X[15_000:], labels[15_000:]
t0 = time.perf_counter()
ldc = compile_ldc(X_tr, Y_tr, k=C - 1, max_leaves=128)
dt = time.perf_counter() - t0
x_test = jnp.asarray(X_te)
logits = ldc.predict_jit(x_test)
jax.block_until_ready(logits)
t0 = time.perf_counter()
logits = ldc.predict_jit(x_test)
jax.block_until_ready(logits)
infer_dt = time.perf_counter() - t0
preds = np.asarray(logits).argmax(-1)
acc = (preds == y_te).mean()
eig_str = ", ".join(f"{e:.2f}" for e in ldc.lda_eigenvalues[:4])
print(f" LDA eigenvalues: [{eig_str}]")
print(f" compiled in {dt:.3f}s → {ldc.n_leaves} leaves, "
f"{ldc.n_internal} internal")
print(f" test accuracy: {acc:.4f}")
print(f" throughput: {len(X_te) / infer_dt:,.0f} queries/s (jitted)")
print()
def demo_ib():
print("[3/4] Information-Bottleneck cache")
params = init_ib(jax.random.PRNGKey(0), d_enc_in=64, d_dec_out=32,
d_hidden=128, k=16)
x = jax.random.normal(jax.random.PRNGKey(1), (2, 32, 64))
kv_true = jax.random.normal(jax.random.PRNGKey(2), (2, 32, 32))
s, mu, log_sigma, kv_hat = ib_forward(params, x, jax.random.PRNGKey(3), S=32)
loss, aux = ib_loss_terms(mu, log_sigma, kv_hat, kv_true)
print(f" input KV state: 2 × 32 × 32 = 2048 floats")
print(f" IB compressed state: 2 × 16 = 32 floats")
print(f" compression ratio: {2048 / 32:.0f}x")
print(f" initial KL: {float(aux['kl']):.3f}")
print(f" initial recon error: {float(aux['recon']):.3f}")
print()
def demo_vickrey():
print("[4/4] Vickrey auction slot selection")
S, D = 512, 32
K = jax.random.normal(jax.random.PRNGKey(0), (S, D))
proj = jax.random.normal(jax.random.PRNGKey(1), (D, 16))
importance = jnp.linspace(1.0, 5.0, S)
bids = compute_bids(K, proj, importance)
for k in (32, 128, 256):
mask, price = vickrey_topk(bids, k)
kept = int(mask.sum())
top_quartile = int(mask[int(0.75 * S):].sum())
print(f" budget={k:4d} kept={kept:4d} "
f"price={float(price):.3f} "
f"top-quartile positions kept: {top_quartile}/{S // 4}")
print()
def demo_gnomon_forward():
print("[bonus] GNOMON forward pass (all four layers wired together)")
params = init_gnomon(
jax.random.PRNGKey(0),
vocab_size=64, d_model=64, n_layers=2, n_heads=4,
ib_k=16, seq_len=16, d_signal=16,
)
ids = jax.random.randint(jax.random.PRNGKey(1), (1, 16), 0, 64)
mode_sig = jax.random.normal(jax.random.PRNGKey(2), (1, 16, 16))
mode = init_mode_state(d_mod=4)
forward = make_forward_jit(n_heads=4)
_ = forward(params, ids, mode, mode_sig, jax.random.PRNGKey(4))
jax.block_until_ready(_["logits"])
t0 = time.perf_counter()
out = forward(params, ids, mode, mode_sig, jax.random.PRNGKey(3))
jax.block_until_ready(out["logits"])
dt = time.perf_counter() - t0
ib_total = sum(float(c["loss"]) for c in out["cache_logs"])
print(f" forward pass: {dt * 1000:.2f} ms (jitted, post-warmup)")
print(f" output logits: {out['logits'].shape}")
print(f" IB layers active: {len(out['cache_logs'])}")
print(f" total IB loss: {ib_total:.4f}")
print()
if __name__ == "__main__":
print("=" * 60)
print("GNOMON component showcase")
print("=" * 60)
print()
demo_ternary()
demo_ldc()
demo_ib()
demo_vickrey()
demo_gnomon_forward()
print("=" * 60)
print("All four components operational.")
print("=" * 60) |