feat: transfer released physical drift profile to Rain EP

3 files changed, 138 insertions, 0 deletions

diff --git a/experiments/rain_ep_bias_train.py b/experiments/rain_ep_bias_train.py
index fa62137..2ba3dd0 100644
--- a/experiments/rain_ep_bias_train.py
+++ b/experiments/rain_ep_bias_train.py
@@ -19,6 +19,7 @@ ROOT = Path(__file__).resolve().parents[1]
 sys.path.insert(0, str(ROOT))
 
 from sdil.rain_ep_adapter import (  # noqa: E402
+    DillavouBiasProfile,
     DillavouUpdateCorrector,
     RainGradientCorrector,
     RainLayerStateCorrector,
@@ -55,6 +56,9 @@ def parse_args() -> argparse.Namespace:
         "--dillavou-drift-ratio", type=float, default=0.0,
         help="zero is the exact fixed update-offset model from Dillavou et al.")
     parser.add_argument("--dillavou-calibration-steps", type=int, default=1)
+    parser.add_argument(
+        "--dillavou-profile-json", type=Path,
+        help="released physical state-dependence report; omitted means constant B")
     parser.add_argument("--predictor-rate", type=float, default=0.1)
     parser.add_argument(
         "--neutral-cadence", type=int, default=1,
@@ -230,12 +234,18 @@ def main() -> None:
             raise ValueError(
                 "Dillavou calibration probes the local update circuit and "
                 "does not require equilibrium batches")
+        empirical_profile = None
+        if args.dillavou_profile_json is not None:
+            profile_path = args.dillavou_profile_json.resolve()
+            empirical_profile = DillavouBiasProfile.from_state_dependence_report(
+                json.loads(profile_path.read_text()), source=str(profile_path))
         corrector = DillavouUpdateCorrector(
             mode=args.mode,
             bias_ratio=args.bias_ratio,
             predictor_rate=args.predictor_rate,
             neutral_cadence=args.neutral_cadence,
             drift_ratio=args.dillavou_drift_ratio,
+            empirical_profile=empirical_profile,
             seed=args.seed + 1729,
         )
         attach_dillavou_to_rain_estimator(estimator, corrector)
@@ -354,6 +364,10 @@ def main() -> None:
             "bias_ratio": args.bias_ratio,
             "dillavou_drift_ratio": args.dillavou_drift_ratio,
             "dillavou_calibration_steps": args.dillavou_calibration_steps,
+            "dillavou_profile": (
+                None if args.adapter != "dillavou"
+                or corrector.empirical_profile is None
+                else corrector.empirical_profile.as_dict()),
             "predictor_rate": args.predictor_rate,
             "neutral_cadence": args.neutral_cadence,
             "layer_calibration_steps": args.layer_calibration_steps,
diff --git a/experiments/rain_ep_dillavou_smoke.py b/experiments/rain_ep_dillavou_smoke.py
index 9519210..39bf5fd 100644
--- a/experiments/rain_ep_dillavou_smoke.py
+++ b/experiments/rain_ep_dillavou_smoke.py
@@ -13,6 +13,7 @@ ROOT = Path(__file__).resolve().parents[1]
 sys.path.insert(0, str(ROOT))
 
 from sdil.rain_ep_adapter import (  # noqa: E402
+    DillavouBiasProfile,
     DillavouUpdateCorrector,
     attach_dillavou_to_rain_estimator,
 )
@@ -21,6 +22,9 @@ from sdil.rain_ep_adapter import (  # noqa: E402
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
     parser.add_argument("--author-root", type=Path, required=True)
+    parser.add_argument(
+        "--profile-json", type=Path,
+        default=ROOT / "results/physical_bias/p0_state_dependence.json")
     return parser.parse_args()
 
 
@@ -92,6 +96,58 @@ def main() -> None:
     assert constant.debiaser.neutral_observations == 1
     assert innovation.debiaser.neutral_observations == 1
 
+    # Build the state-dependent shape only from the committed analysis of the
+    # released physical traces. With matched neutral observations, an affine
+    # predictor must generalize across local parameter states better than an
+    # intercept-only predictor.
+    import json
+    profile = DillavouBiasProfile.from_state_dependence_report(
+        json.loads(args.profile_json.read_text()),
+        source=str(args.profile_json.resolve()),
+    )
+    profile_constant = DillavouUpdateCorrector(
+        mode="constant", bias_ratio=0.2, predictor_rate=0.2,
+        calibration_steps=1, neutral_cadence=1,
+        empirical_profile=profile, seed=67)
+    profile_innovation = DillavouUpdateCorrector(
+        mode="innovation", bias_ratio=0.2, predictor_rate=0.2,
+        calibration_steps=1, neutral_cadence=1,
+        empirical_profile=profile, seed=67)
+    parameter_scale = [
+        value.square().mean().sqrt().clamp_min(1e-6)
+        for value in parameters_a
+    ]
+    for _ in range(12):
+        for displacement in torch.linspace(-1.0, 1.0, 21):
+            state = [
+                value + displacement * scale
+                for value, scale in zip(parameters_a, parameter_scale)
+            ]
+            profile_constant.apply(clean_a, state)
+            profile_innovation.apply(clean_a, state)
+    assert (
+        profile_constant.debiaser.neutral_observations
+        == profile_innovation.debiaser.neutral_observations
+    )
+    profile_constant.neutral_cadence = 0
+    profile_innovation.neutral_cadence = 0
+    held_state = [
+        value - 0.55 * scale
+        for value, scale in zip(parameters_a, parameter_scale)
+    ]
+    held_constant = profile_constant.apply(clean_b, held_state)
+    held_innovation = profile_innovation.apply(clean_b, held_state)
+    held_constant_error = sum(
+        float((actual - target).square().sum())
+        for actual, target in zip(held_constant, clean_b)
+    )
+    held_innovation_error = sum(
+        float((actual - target).square().sum())
+        for actual, target in zip(held_innovation, clean_b)
+    )
+    assert held_innovation_error < 0.05 * held_constant_error, (
+        held_innovation_error, held_constant_error)
+
     # Integration check: the corruption is attached after Rain's hand-written
     # local EP estimator and introduces no autograd graph.
     energy, network, cost, augmented, minimizer, estimator = build_estimator(
@@ -122,6 +178,11 @@ def main() -> None:
         "innovation_relative_error": innovation_error,
         "integrated_bias_to_clean_update_rms": observed_ratio,
         "neutral_observations": constant.debiaser.neutral_observations,
+        "released_profile_normalized_offsets": profile.normalized_offsets,
+        "released_profile_normalized_state_variations": (
+            profile.normalized_state_variations),
+        "released_profile_heldout_mse_ratio_affine_over_constant": (
+            held_innovation_error / held_constant_error),
         "autodiff_used_for_learning": False,
     })
 
diff --git a/experiments/rain_ep_released_profile_r0.sh b/experiments/rain_ep_released_profile_r0.sh
new file mode 100755
index 0000000..7529fc5
--- /dev/null
+++ b/experiments/rain_ep_released_profile_r0.sh
@@ -0,0 +1,63 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT=/home/yurenh2/sdil
+AUTHOR=/scratch/yurenh2/energy-based-learning
+PYTHON=/scratch/yurenh2/venvs/burstccn/bin/python
+PROFILE="$ROOT/results/physical_bias/p0_state_dependence.json"
+OUT="$ROOT/results/ep_bias/released_profile_r0"
+mkdir -p "$OUT"
+cd "$AUTHOR"
+
+run_cell() {
+  local gpu=$1
+  local tag=$2
+  local mode=$3
+  local cadence=$4
+  CUBLAS_WORKSPACE_CONFIG=:4096:8 CUDA_VISIBLE_DEVICES="$gpu" "$PYTHON" \
+    "$ROOT/experiments/rain_ep_bias_train.py" \
+    --author-root "$AUTHOR" --device cuda \
+    --adapter dillavou --network-protocol comparative32 \
+    --beta-policy fixed_positive --beta-value 0.25 \
+    --mode "$mode" --bias-ratio 1 \
+    --dillavou-drift-ratio 0 --dillavou-profile-json "$PROFILE" \
+    --predictor-rate 1 --dillavou-calibration-steps 1 \
+    --neutral-cadence "$cadence" --epochs 1 --schedule-epochs 100 \
+    --train-limit 10000 --test-limit 2000 --batch-size 128 \
+    --training-iterations 15 --inference-iterations 60 \
+    --evaluation-split train_holdout --data-seed 6200 \
+    --seed 1988 --beta-seed 7100 --deterministic \
+    --output "$OUT/$tag.json" > "$OUT/$tag.log" 2>&1
+}
+
+run_cell 0 pep_clean clean 0 &
+run_cell 1 profile_raw raw 0 &
+run_cell 2 intercept_initial constant 0 &
+run_cell 3 sdil_initial innovation 0 &
+run_cell 4 intercept_c10 constant 10 &
+run_cell 5 sdil_c10 innovation 10 &
+run_cell 6 intercept_c50 constant 50 &
+run_cell 7 sdil_c50 innovation 50 &
+wait
+
+"$PYTHON" - "$OUT" <<'PY'
+import json
+from pathlib import Path
+import sys
+
+root = Path(sys.argv[1])
+summary = {}
+for path in sorted(root.glob("*.json")):
+    if path.name == "summary.json":
+        continue
+    report = json.loads(path.read_text())
+    summary[path.stem] = {
+        "test_accuracy": report["final"]["test_accuracy"],
+        "test_cost": report["final"]["test_cost"],
+        "finite": report["final"]["finite"],
+        "wall_seconds": report["final"]["wall_seconds"],
+        "corrector": report["final"]["corrector"],
+    }
+(root / "summary.json").write_text(json.dumps(summary, indent=2) + "\n")
+print(json.dumps(summary, indent=2))
+PY