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bdfb884 | 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 | """Shared helpers (same as the neural-DDR project): bit<->int, MLP, verify, train."""
from __future__ import annotations
import torch
import torch.nn as nn
DEV = "cuda" if torch.cuda.is_available() else "cpu"
def bits_of(v: int, n: int) -> list[int]:
return [(v >> k) & 1 for k in range(n)]
def int_of(bits) -> int:
return sum((1 << k) for k, b in enumerate(bits) if b > 0)
def pm(bits) -> torch.Tensor:
return torch.tensor([1.0 if b else -1.0 for b in bits], dtype=torch.float32)
def mlp(inp: int, out: int, h: int = 256, layers: int = 2) -> nn.Sequential:
mods = [nn.Linear(inp, h), nn.GELU()]
for _ in range(layers - 1):
mods += [nn.Linear(h, h), nn.GELU()]
mods += [nn.Linear(h, out)]
return nn.Sequential(*mods)
@torch.no_grad()
def verify(net, X, Ybits) -> tuple[int, int]:
net = net.to("cpu")
pred = (net(X) > 0).int()
ok = (pred == Ybits.int()).all(dim=1).sum().item()
return ok, X.shape[0]
def train(net, X, Ybits, steps=8000, lr=2e-3, tag="", report=2000):
net = net.to(DEV)
Xd, Yd = X.to(DEV), Ybits.to(DEV)
opt = torch.optim.Adam(net.parameters(), lr=lr)
sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=steps)
lossfn = nn.BCEWithLogitsLoss()
for e in range(steps):
opt.zero_grad()
loss = lossfn(net(Xd), Yd)
loss.backward(); opt.step(); sch.step()
if e % report == 0 or e == steps - 1:
ok, tot = verify(net, X, Ybits); net.to(DEV)
print(f" [{tag}] epoch {e:5d} loss {loss.item():.2e} verified {ok}/{tot}")
if ok == tot:
print(f" [{tag}] -> N/N"); break
return net.to("cpu")
def run8(net, v: int) -> int:
return int_of((net(pm(bits_of(v, 8)).unsqueeze(0))[0] > 0).int().tolist())
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