baseline: add unamortized node perturbation

2 files changed, 58 insertions, 2 deletions

diff --git a/experiments/run.py b/experiments/run.py
index 3b0f961..ea19366 100644
--- a/experiments/run.py
+++ b/experiments/run.py
@@ -222,6 +222,15 @@ def build(args, device):
             p_update_on_neutral=bool(args.p_neutral),
             normalize_delta=bool(args.normalize_delta),
             raw_scale_control=args.raw_scale_control)
+    elif args.mode == "nodepert":
+        if args.pert_every != 1:
+            raise ValueError("direct node perturbation requires --pert_every 1")
+        cfg = SDILConfig(
+            eta=args.eta, use_residual=False, learn_A=False, learn_P=False,
+            pert_sigma=args.pert_sigma, pert_every=args.pert_every,
+            pert_ndirs=args.pert_ndirs,
+            pert_mode=args.pert_mode, momentum=args.momentum,
+            normalize_delta=bool(args.normalize_delta), direct_node_pert=True)
     else:
         raise ValueError(args.mode)
     return net, cfg
@@ -366,7 +375,7 @@ def train(args):
             else:
                 loss, aux = sdil_step(net, x, y, yoh, cfg, step, prev_error=prev_error)
                 prev_error = aux["error"]
-                if args.mode == "sdil" and aux["did_pert"]:
+                if args.mode in ("sdil", "nodepert") and aux["did_pert"]:
                     event = calibration_work_per_event(net, cfg)
                     perturbation_events += 1
                     calibration_batch_loss_evaluations += event["batch_loss_evaluations"]
@@ -387,6 +396,8 @@ def train(args):
                     diagnostics_t0 = time.time()
                 if args.mode == "fa" and inline_diagnostics:
                     rec.update(probes.fa_alignment_report(net, px, py, poh))
+                elif args.mode == "nodepert" and inline_diagnostics:
+                    rec.update(probes.nodepert_alignment_report(net, px, py, cfg))
                 elif args.mode != "bp" and inline_diagnostics:
                     al = probes.alignment_report(net, px, py, poh, cfg)
                     rec["cos_r_negg"] = al["cos_r_negg"]
@@ -442,6 +453,10 @@ def train(args):
                 al = probes.fa_alignment_report(net, px, py, poh)
                 meancos = sum(al["cos_fa_negg"]) / len(al["cos_fa_negg"])
                 msg += f" mean_cos(fa,-g) {meancos:+.3f} per-layer {['%.2f'%v for v in al['cos_fa_negg']]}"
+            elif args.mode == "nodepert":
+                al = probes.nodepert_alignment_report(net, px, py, cfg)
+                meancos = sum(al["cos_q_negg"]) / len(al["cos_q_negg"])
+                msg += f" mean_cos(q,-g) {meancos:+.3f} per-layer {['%.2f'%v for v in al['cos_q_negg']]}"
             elif args.mode != "bp":
                 al = probes.alignment_report(net, px, py, poh, cfg)
                 meancos = sum(al["cos_r_negg"]) / len(al["cos_r_negg"])
@@ -468,6 +483,12 @@ def train(args):
         log["final"].update(probes.fa_alignment_report(net, px, py, poh))
         device_sync(device)
         diagnostics_wall_s += time.time() - diagnostics_t0
+    elif args.mode == "nodepert" and args.diagnostics != "none":
+        device_sync(device)
+        diagnostics_t0 = time.time()
+        log["final"].update(probes.nodepert_alignment_report(net, px, py, cfg))
+        device_sync(device)
+        diagnostics_wall_s += time.time() - diagnostics_t0
     elif args.mode != "bp" and args.diagnostics != "none":
         device_sync(device)
         diagnostics_t0 = time.time()
@@ -534,7 +555,8 @@ def train(args):
 
 def get_args():
     p = argparse.ArgumentParser()
-    p.add_argument("--mode", default="sdil", choices=["bp", "fa", "dfa", "sdil"])
+    p.add_argument("--mode", default="sdil",
+                   choices=["bp", "fa", "dfa", "sdil", "nodepert"])
     p.add_argument("--dataset", default="mnist",
                    choices=list(REAL_DATASETS + SYNTHETIC_DATASETS))
     p.add_argument("--depth", type=int, default=3)          # hidden layers
diff --git a/experiments/smoke.py b/experiments/smoke.py
index fcc0e9c..a49e4c6 100644
--- a/experiments/smoke.py
+++ b/experiments/smoke.py
@@ -155,6 +155,39 @@ def check_state_conditioned_vectorizers():
     print("CHECK0e state-conditioned vectorizers: zero-init and local calibration passed")
 
 
+def check_direct_node_perturbation():
+    """Unamortized q must update hidden weights without using A."""
+    torch.manual_seed(19)
+    x = torch.randn(32, 5)
+    y = torch.randint(0, 3, (32,))
+    yoh = onehot(y, 3)
+    net = SDILNet([5, 7, 7, 3], act="tanh", device="cpu", seed=10,
+                  residual=True)
+    for weight in net.A:
+        weight.zero_()
+    weights_before = [weight.clone() for weight in net.W]
+    apical_before = [weight.clone() for weight in net.A]
+    rng_state = torch.get_rng_state()
+    fwd = net.forward(x)
+    targets = simultaneous_node_perturbation_targets(
+        net, x, y, sigma=0.01, n_dirs=2)
+    expected = []
+    for layer, target in enumerate(targets):
+        delta = target * net.act_prime(fwd["u"][layer])
+        if layer >= 1:
+            delta = net.res_alpha * delta
+        expected.append(delta.t() @ fwd["h"][layer] / x.shape[0])
+    torch.set_rng_state(rng_state)
+    cfg = SDILConfig(eta=0.01, learn_A=False, learn_P=False, pert_every=1,
+                     pert_ndirs=2, pert_mode="simultaneous", direct_node_pert=True)
+    sdil_step(net, x, y, yoh, cfg, step=0)
+    for layer in range(net.L - 1):
+        observed = net.W[layer] - weights_before[layer]
+        assert torch.allclose(observed, cfg.eta * expected[layer], atol=1e-6, rtol=1e-5)
+    assert all(torch.equal(before, after) for before, after in zip(apical_before, net.A))
+    print("CHECK0f direct node perturbation: exact q update; A untouched")
+
+
 def main():
     torch.manual_seed(0)
     dev = "cpu"
@@ -163,6 +196,7 @@ def main():
     check_topdown_predictor()
     check_traffic_seed_isolation()
     check_state_conditioned_vectorizers()
+    check_direct_node_perturbation()
     print("loading MNIST subset...")
     tr, te, n_in, n_out = get_dataset("mnist", batch_size=128, device=dev)
     xb, yb = next(iter(tr))