diff options
Diffstat (limited to 'experiments')
| -rw-r--r-- | experiments/run.py | 6 | ||||
| -rw-r--r-- | experiments/smoke.py | 30 |
2 files changed, 34 insertions, 2 deletions
diff --git a/experiments/run.py b/experiments/run.py index ced7585..0019c67 100644 --- a/experiments/run.py +++ b/experiments/run.py @@ -223,7 +223,9 @@ def build(args, device): kappa=args.kappa, feedback=args.feedback, p_update_on_neutral=bool(args.p_neutral), normalize_delta=bool(args.normalize_delta), - raw_scale_control=args.raw_scale_control) + raw_scale_control=args.raw_scale_control, + vectorizer_optimizer=args.vectorizer_optimizer, + vectorizer_eps=args.vectorizer_eps) elif args.mode == "nodepert": if args.pert_every != 1: raise ValueError("direct node perturbation requires --pert_every 1") @@ -679,6 +681,8 @@ def get_args(): p.add_argument("--predictor_mode", default="diagonal", choices=["diagonal", "full"]) p.add_argument("--vectorizer_mode", default="linear", choices=["linear", "soma_gated", "context_gated"]) + p.add_argument("--vectorizer_optimizer", default="sgd", choices=["sgd", "nlms"]) + p.add_argument("--vectorizer_eps", type=float, default=1e-6) p.add_argument("--normalize_delta", type=int, default=0) p.add_argument("--settle_steps", type=int, default=0) p.add_argument("--kappa", type=float, default=0.0) diff --git a/experiments/smoke.py b/experiments/smoke.py index 9971800..37c38e3 100644 --- a/experiments/smoke.py +++ b/experiments/smoke.py @@ -8,7 +8,7 @@ import torch sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from sdil.core import (SDILNet, SDILConfig, apical_calibration_step, sdil_step, node_perturbation_targets, simultaneous_node_perturbation_targets, - neutral_p_update, teaching_signal) + neutral_p_update, teaching_signal, _update_apical_vectorizer) from sdil.baselines import dfa_config from sdil import probes from sdil.data import get_dataset, onehot @@ -215,6 +215,33 @@ def check_feedback_first_calibration(): print("CHECK0g feedback-first calibration: A changed; W/readout frozen") +def check_vectorizer_nlms(): + """NLMS must divide the joint linear/context update by feature power.""" + torch.manual_seed(29) + batch = 16 + net = SDILNet([5, 7, 7, 3], act="relu", device="cpu", seed=12, + residual=True, vectorizer_mode="context_gated") + for weight in net.A: + weight.zero_() + for weight in net.A_gate: + weight.zero_() + h = net.forward(torch.randn(batch, 5))["h"] + c = torch.randn(batch, 3) + qs = [torch.randn_like(hidden) for hidden in h[1:-1]] + residuals = [torch.zeros_like(target) for target in qs] + cfg = SDILConfig(eta_A=0.03, vectorizer_optimizer="nlms", vectorizer_eps=1e-6) + features = (c.unsqueeze(2) * torch.tanh(h[-2]).unsqueeze(1)).flatten(1) + denominator = (c.square().sum(1, keepdim=True) + + features.square().sum(1, keepdim=True)).clamp_min(cfg.vectorizer_eps) + expected_a0 = cfg.eta_A * ((qs[0] / denominator).t() @ c / batch) + expected_gate0 = cfg.eta_A * ((qs[0] / denominator).t() @ features / batch) + _update_apical_vectorizer(net, h, c, residuals, qs, cfg) + assert torch.allclose(net.A[0], expected_a0, atol=1e-7, rtol=1e-6) + assert torch.allclose(net.A_gate[0], expected_gate0, atol=1e-7, rtol=1e-6) + assert torch.isfinite(net.A[0]).all() and torch.isfinite(net.A_gate[0]).all() + print("CHECK0h vectorizer NLMS: exact joint-feature normalization") + + def main(): torch.manual_seed(0) dev = "cpu" @@ -225,6 +252,7 @@ def main(): check_state_conditioned_vectorizers() check_direct_node_perturbation() check_feedback_first_calibration() + check_vectorizer_nlms() print("loading MNIST subset...") tr, te, n_in, n_out = get_dataset("mnist", batch_size=128, device=dev) xb, yb = next(iter(tr)) |