diff options
Diffstat (limited to 'experiments/analyze_residual_mirror_short.py')
| -rwxr-xr-x | experiments/analyze_residual_mirror_short.py | 141 |
1 files changed, 141 insertions, 0 deletions
diff --git a/experiments/analyze_residual_mirror_short.py b/experiments/analyze_residual_mirror_short.py new file mode 100755 index 0000000..da50fce --- /dev/null +++ b/experiments/analyze_residual_mirror_short.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +"""Audit and gate the frozen RRM-2 short accuracy screen.""" +import argparse +import glob +import json +import math +import os + + +RATES = (0.03, 0.1) +BP_ACCURACY = 0.7494 +SPLIT_HASH = "8328b206a97c420e49e54e3eca4abe3274c4756b084355784ea3fb8059e4515b" + + +def load(path): + with open(path) as handle: + record = json.load(handle) + args = record["args"] + expected = { + "mode": "rrm", "depth": 20, "width": 16, "seed": 0, + "loader_seed": 0, "epochs": 20, "train_limit": 10000, + "val_examples": 5000, "split_seed": 2027, + "eval_split": "validation", "eval_every": 0, + "augment_train": 1, "lr_schedule": "cosine", "warmup_epochs": 0, + "momentum": 0.9, "weight_decay": 1e-4, + "normalization": "batchnorm", "output_lr": 0.1, + "a_scale": 1.0, "alignment_probe": 32, + "mirror_warmup_steps": 20, "mirror_every": 16, + "mirror_batch_size": 1, "mirror_eta": 0.1, + "mirror_noise_std": 1.0, "mirror_seed": 3000, + } + for key, value in expected.items(): + if args.get(key) != value: + raise ValueError(f"{path}: {key} drift") + rate = float(args["lr"]) + if rate not in RATES: + raise ValueError(f"{path}: unregistered hidden rate") + if record["provenance"]["git_tracked_dirty"]: + raise ValueError(f"tracked-dirty result: {path}") + if record["split"]["validation_index_sha256"] != SPLIT_HASH: + raise ValueError(f"split drift: {path}") + if record["evaluation_protocol"]["test_evaluations"]: + raise ValueError(f"test touched: {path}") + if record.get("calibration_metric_space") != ( + "local_parent_child_response_residual"): + raise ValueError(f"residual-mirror metric-space drift: {path}") + accuracy = float(record["final"]["accuracy"]) + loss = float(record["final"]["loss"]) + diagnostics = record["diagnostics"] + early = float(diagnostics["early_third_mean"]) + values = diagnostics["teaching_negative_gradient_cosine"] + all_layer = sum(values) / len(values) + feedback_cosines = diagnostics["feedback_forward_cosine"] + norm_ratios = diagnostics["feedback_forward_norm_ratio"] + finite_values = [ + accuracy, loss, early, all_layer, *feedback_cosines, *norm_ratios] + finite = (bool(record["final"]["finite"]) + and all(math.isfinite(value) for value in finite_values)) + return { + "path": path, "lr": rate, "accuracy": accuracy, "loss": loss, + "early_third_alignment": early, "all_layer_alignment": all_layer, + "mean_feedback_forward_cosine": ( + sum(feedback_cosines) / len(feedback_cosines)), + "min_feedback_forward_norm_ratio": min(norm_ratios), + "max_feedback_forward_norm_ratio": max(norm_ratios), + "finite": finite, + "total_macs": int(record["work"]["total_macs_estimate"]), + "logical_batch_loss_queries": int( + record["work"]["logical_batch_loss_queries"]), + "peak_memory_allocated_bytes": int( + record["hardware"]["peak_memory_allocated_bytes"]), + "wall_s": float(record["timing"]["total_timed_wall_s"]), + "mirror_events": int(record["counters"]["mirror_events"]), + "source_commit": record["provenance"]["git_commit"], + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="results/residual_mirror_short") + parser.add_argument("--bp", default="results/oral_a_short/bp_lr0.1.json") + parser.add_argument("--out", default="results/residual_mirror_short_gate.json") + args = parser.parse_args() + rows = [load(path) for path in sorted( + glob.glob(os.path.join(args.input, "*.json")))] + if len(rows) != len(RATES) or {row["lr"] for row in rows} != set(RATES): + raise ValueError("incomplete RRM-2 rate grid") + if len({row["source_commit"] for row in rows}) != 1: + raise ValueError("RRM-2 source commits differ") + with open(args.bp) as handle: + bp = json.load(handle) + if float(bp["final"]["accuracy"]) != BP_ACCURACY: + raise ValueError("matched BP endpoint drift") + bp_macs = int(bp["work"]["total_macs_estimate"]) + finite = [row for row in rows if row["finite"]] + finite.sort(key=lambda row: (-row["accuracy"], row["total_macs"], row["lr"])) + selected = finite[0] if finite else None + checks = { + "both_records_finite": all(row["finite"] for row in rows), + "candidate_selected": selected is not None, + } + if selected is None: + checks.update({ + "accuracy_at_least_0.65": False, + "within_10_points_of_bp": False, + "early_alignment_at_least_0.50": False, + "zero_task_loss_queries": False, + "macs_at_most_1.15x_bp": False, + }) + else: + checks.update({ + "accuracy_at_least_0.65": selected["accuracy"] >= 0.65, + "within_10_points_of_bp": ( + selected["accuracy"] >= BP_ACCURACY - 0.10), + "early_alignment_at_least_0.50": ( + selected["early_third_alignment"] >= 0.50), + "zero_task_loss_queries": all( + row["logical_batch_loss_queries"] == 0 for row in rows), + "macs_at_most_1.15x_bp": selected["total_macs"] <= 1.15 * bp_macs, + }) + output = { + "protocol": "residual_response_mirror_short_v1", + "status": "passed" if all(checks.values()) else "failed", + "checks": checks, "rows": rows, "selected_rrm": selected, + "matched_bp": {"accuracy": BP_ACCURACY, "total_macs": bp_macs}, + "confirmation_test_seeds_touched": False, + "review_score_before": 5, "review_score_after": 5, + "score_change_rule": "inherited short baseline cannot raise score", + } + os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True) + with open(args.out, "w") as handle: + json.dump(output, handle, indent=2, sort_keys=True) + handle.write("\n") + print(json.dumps({ + "status": output["status"], "checks": checks, + "rows": rows, "selected_rrm": selected, + }, indent=2)) + + +if __name__ == "__main__": + main() |