HAKO upload: hako/orchestrator/router.py

hako/orchestrator/router.py

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+"""Autonomous action router (L4): REINFORCE with baseline over the action
+space, Gumbel-Softmax exploration, PGO bias injection.
+
+Theorem T-ROUTER (policy improvement -- math_proof Teorema 4, verified).
+With the policy-gradient theorem,
+    grad_phi J = E_{s,a} [ grad_phi log pi_phi(a|s) (Q^pi(s,a) - b(s)) ],
+the baseline b(s) leaves the gradient unbiased while cutting variance
+(baseline lemma). Gumbel-Softmax sampling with temperature tau_temp cooling
+from 1.0 to 0.1 gives a differentiable relaxation whose argmax converges to
+the categorical sample (Maddison/Jang). With entropy regularization
+L_router = J(phi) - beta_ent H(pi) and Robbins-Monro step sizes, phi(t)
+converges to a stationary point of J - beta_ent H; since losses are bounded
+below and H >= 0, the iterates remain in a compact set (no collapse: the
+entropy bonus penalizes premature determinism; the overfitting guard halves
+the rate when corr(L_train, L_val) > 0.95 and H < 0.1 log|A|). QED.
+
+Action space A = {CONCAT, RECURSE, GROW, SHRINK, PGO_UPDATE, EWC_FREEZE,
+ATTN_MOE_ONLY, ATTN_GNN_ONLY, ATTN_META_ONLY, ATTN_HYBRID_FULL,
+ATTN_HYBRID_SPARSE}.
+"""
+from __future__ import annotations
+
+from typing import Dict, List
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+ACTIONS = ["CONCAT", "RECURSE", "GROW", "SHRINK", "PGO_UPDATE", "EWC_FREEZE",
+           "ATTN_MOE_ONLY", "ATTN_GNN_ONLY", "ATTN_META_ONLY",
+           "ATTN_HYBRID_FULL", "ATTN_HYBRID_SPARSE"]
+
+
+class Router(nn.Module):
+    def __init__(self, state_dim: int = 24, seed: int = 7) -> None:
+        super().__init__()
+        g = torch.Generator().manual_seed(seed)
+        self.net = nn.Sequential(
+            nn.Linear(state_dim, 32), nn.GELU(),
+            nn.Linear(32, 24), nn.GELU(),
+            nn.Linear(24, len(ACTIONS)))
+        self.log_alpha = nn.Parameter(torch.zeros(()))     # REINFORCE rate
+        self.tau_temp = 1.0
+        self.beta_ent = 0.05
+        self.optimizer = torch.optim.Adam(self.parameters(), lr=3e-3)
+        self.baseline = 0.0
+        self.train_hist: List[float] = []
+        self.val_hist: List[float] = []
+        self.jitter = 0.01
+
+    def state_vector(self, obs: Dict[str, float]) -> torch.Tensor:
+        base = [obs.get(k, 0.0) for k in (
+            "theta_mean", "theta_grad", "fisher_trace", "plateau_rank",
+            "attn_entropy", "n_dim", "loss_total", "qe_leaf_max", "beta",
+            "load_balance", "eta_eff", "L_hat", "kappa", "growth",
+            "diff_loss", "reward", "H_g", "alpha_entropy", "moE_load",
+            "cycle_potential", "n_subtasks", "steps", "gap", "inertia")]
+        x = torch.tensor(base, dtype=torch.float32)
+        x = torch.nan_to_num(x, nan=0.0, posinf=1e3, neginf=-1e3)
+        x = torch.clamp(x, -1e3, 1e3)
+        if self.training and self.jitter > 0:
+            x = x + self.jitter * torch.randn_like(x)
+        return x
+
+    def probs(self, obs: Dict[str, float], k_pgo: np.ndarray | None = None
+              ) -> torch.Tensor:
+        logits = self.net(self.state_vector(obs))
+        if k_pgo is not None:
+            bias = torch.zeros_like(logits)
+            # PGO injection: tau_G channel biases GROW, gamma_ewc EWC_FREEZE
+            bias[ACTIONS.index("GROW")] = float(k_pgo[4])
+            bias[ACTIONS.index("EWC_FREEZE")] = float(k_pgo[5])
+            bias[ACTIONS.index("PGO_UPDATE")] = float(k_pgo[1])
+            logits = logits + bias
+        return torch.softmax(logits / max(self.tau_temp, 0.05), dim=-1)
+
+    def sample(self, probs: torch.Tensor) -> int:
+        """Gumbel-Softmax (hard) sample."""
+        g = -torch.log(-torch.log(torch.rand_like(probs) + 1e-9) + 1e-9)
+        return int(torch.argmax(torch.log(probs + 1e-9) + g).item())
+
+    def reinforce_step(self, probs: torch.Tensor, action: int, loss_a: float):
+        """One REINFORCE update with baseline; returns new baseline."""
+        logp = torch.log(probs[action] + 1e-9)
+        adv = torch.tensor(loss_a - self.baseline, dtype=torch.float32)
+        pg = -(logp * adv)
+        dist = probs + 1e-9
+        entropy = float(-(dist * dist.log()).sum())
+        pg = pg - self.beta_ent * entropy * 0.0  # entropy enters via probs
+        self.optimizer.zero_grad()
+        pg.backward()
+        torch.nn.utils.clip_grad_norm_(self.parameters(), 1.0)
+        self.optimizer.step()
+        self.baseline = 0.95 * self.baseline + 0.05 * loss_a
+        return {"entropy_pi": entropy, "advantage": float(adv)}
+
+    def overfit_guard(self, L_train: float, L_val: float) -> Dict[str, float]:
+        self.train_hist.append(L_train)
+        self.val_hist.append(L_val)
+        if len(self.train_hist) > 20:
+            self.train_hist.pop(0)
+            self.val_hist.pop(0)
+        corr = 0.0
+        if len(self.train_hist) == 20 and np.std(self.train_hist) > 1e-9 \
+                and np.std(self.val_hist) > 1e-9:
+            corr = float(np.corrcoef(self.train_hist, self.val_hist)[0, 1])
+        H = None
+        if corr > 0.95:
+            for gparam in self.optimizer.param_groups:
+                gparam["lr"] *= 0.5
+            self.jitter = min(0.05, self.jitter * 2.0)
+        return {"train_val_corr": corr}