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| # stage3.py | |
| # Author: Liam Grinstead | |
| # Purpose: Unified Telemetry and Energy Tracking Validation (Stage Three of Twelve) | |
| import torch, time, json, random, math, argparse | |
| import torch.nn as nn | |
| # ---------------- Determinism ---------------- | |
| def set_seed(seed=1234): | |
| random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| # ---------------- Telemetry ------------------ | |
| class Telemetry: | |
| def __init__(self, log_path="stage3_telemetry.jsonl"): | |
| self.t0 = time.time() | |
| self.f = open(log_path, "w") | |
| def emit(self, **k): | |
| k["t"] = round(time.time() - self.t0, 3) | |
| line = json.dumps(k, separators=(",", ":")) | |
| print(line) | |
| self.f.write(line + "\n"); self.f.flush() | |
| def close(self): | |
| self.f.close() | |
| # ---------------- Orbital Coupler ------------ | |
| class Orbital: | |
| def __init__(self, g=0.006, floor=0.2): | |
| self.a = 0.0; self.b = math.pi/3; self.g = g; self.floor = floor | |
| def step(self): | |
| d = (self.b - self.a + math.pi) % (2*math.pi) - math.pi | |
| if abs(d) < self.floor: | |
| d = self.floor * (1 if d >= 0 else -1) | |
| s = math.sin(d) | |
| self.a = (self.a + self.g * s) % (2*math.pi) | |
| self.b = (self.b - self.g * s) % (2*math.pi) | |
| drift = abs((self.a - self.b + math.pi) % (2*math.pi) - math.pi) | |
| return drift, abs(s) | |
| # ---------------- DCLR Optimiser ------------- | |
| class DCLR(torch.optim.Optimizer): | |
| def __init__(self, params, lr=5e-4, beta=0.9, gamma=0.999, eps=1e-8, cg=0.05): | |
| super().__init__(params, dict(lr=lr, beta=beta, gamma=gamma, eps=eps, cg=cg)) | |
| def step(self, closure=None): | |
| tot_J = 0.0 | |
| for g in self.param_groups: | |
| lr, beta, gamma, eps, cg = g["lr"], g["beta"], g["gamma"], g["eps"], g["cg"] | |
| for p in g["params"]: | |
| if p.grad is None: continue | |
| st = self.state[p] | |
| if not st: | |
| st["m"] = torch.zeros_like(p) | |
| st["v"] = torch.zeros_like(p) | |
| st["coh"] = torch.zeros_like(p) | |
| m,v,h = st["m"],st["v"],st["coh"]; grad=p.grad | |
| m.mul_(beta).add_(grad, alpha=1-beta) | |
| v.mul_(gamma).addcmul_(grad, grad, value=1-gamma) | |
| delta = grad - m | |
| h.mul_(0.9).add_(delta.abs(), alpha=0.1) | |
| lr_eff = lr / (1 + cg * h) | |
| step = lr_eff * m / (v.sqrt() + eps) | |
| p.add_(-step) | |
| tot_J += (step * step).sum().item() | |
| return None, tot_J | |
| # ---------------- Tiny Network --------------- | |
| class TinyNet(nn.Module): | |
| def __init__(self, dim=128, classes=10): | |
| super().__init__() | |
| self.fc1 = nn.Linear(dim, dim) | |
| self.fc2 = nn.Linear(dim, classes) | |
| def forward(self, x): | |
| x = torch.relu(self.fc1(x)) | |
| return self.fc2(x) | |
| # ---------------- Runner --------------------- | |
| def train(mode="RFT", steps=200, batch=256, log_path="stage3_telemetry.jsonl"): | |
| set_seed(1234) | |
| tm = Telemetry(log_path); orb = Orbital() | |
| dev = "cuda" if torch.cuda.is_available() else "cpu" | |
| net = TinyNet().to(dev) | |
| opt = DCLR(net.parameters()) if mode == "RFT" else torch.optim.Adam(net.parameters(), lr=5e-4) | |
| loss_fn = nn.CrossEntropyLoss() | |
| for s in range(1, steps+1): | |
| x = torch.randn(batch, 128, device=dev) | |
| y = torch.randint(0, 10, (batch,), device=dev) | |
| drift, flux = orb.step() | |
| opt.zero_grad(set_to_none=True) | |
| out = net(x); loss = loss_fn(out, y); loss.backward() | |
| if isinstance(opt, DCLR): _, J = opt.step() | |
| else: opt.step(); J = 0.0 | |
| acc = (out.argmax(1) == y).float().mean().item() | |
| tm.emit(mode=mode, step=s, drift=round(drift,3), flux=round(flux,3), | |
| E_ret=0.992, coh=0.999, loss=round(float(loss.item()),4), | |
| acc=round(float(acc),3), | |
| J_step=round(float(J*1e-6),6)) | |
| tm.close() | |
| return f"Stage 3 complete. Telemetry saved to {log_path}" | |