1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
|
#!/usr/bin/env python3
"""Frozen single-run S0 screen from SHARED_FEEDBACK.md."""
import argparse
import json
import os
from pathlib import Path
import subprocess
import sys
import time
import torch
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from sdil.shared_feedback import (
CONDITIONS, SharedFeedbackConfig, SharedFeedbackNet,
conditional_selector_data, evaluate_shared_feedback, shared_feedback_step,
)
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_OUT = ROOT / "results" / "shared_feedback" / "s0.json"
def git_output(*args):
return subprocess.run(
["git", *args], cwd=ROOT, check=True, capture_output=True,
text=True).stdout.strip()
def train_condition(condition, base, train, validation, neutral, device):
net = base.clone()
x_train, z_train, y_train = train
x_val, z_val, y_val = validation
x_neutral, z_neutral = neutral
shuffle = torch.Generator(device="cpu").manual_seed(3101)
epoch_losses = []
predictor_reports = []
first_nonfinite_epoch = None
started = time.time()
for epoch in range(40):
if condition in ("innovation", "matched_raw"):
predictor_reports = net.fit_neutral_predictor(x_neutral, z_neutral)
permutation = torch.randperm(x_train.shape[0], generator=shuffle)
losses = []
for start in range(0, x_train.shape[0], 128):
indices = permutation[start:start + 128].to(device)
loss, _ = shared_feedback_step(
net, x_train[indices], z_train[indices], y_train[indices], condition)
losses.append(loss)
mean_loss = sum(losses) / len(losses)
epoch_losses.append(mean_loss)
if not torch.isfinite(torch.tensor(mean_loss)):
first_nonfinite_epoch = epoch
break
if condition not in ("innovation", "matched_raw"):
predictor_reports = net.fit_neutral_predictor(x_neutral, z_neutral)
endpoint = evaluate_shared_feedback(net, x_val, z_val, y_val)
lesion = evaluate_shared_feedback(
net, x_val, z_val, y_val, context_enabled=False)
return {
"condition": condition,
"epochs_completed": len(epoch_losses),
"epoch_train_loss": epoch_losses,
"first_nonfinite_epoch": first_nonfinite_epoch,
"finite": first_nonfinite_epoch is None,
"validation": endpoint,
"context_lesion_validation": lesion,
"predictor": predictor_reports,
"wall_seconds": time.time() - started,
}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--device", default="cpu")
parser.add_argument("--out", type=Path, default=DEFAULT_OUT)
args = parser.parse_args()
if git_output("status", "--porcelain", "--untracked-files=no"):
raise RuntimeError("S0 requires clean tracked source")
device = torch.device(args.device)
torch.manual_seed(3101)
if device.type == "cpu":
torch.set_num_threads(1)
config = SharedFeedbackConfig()
base = SharedFeedbackNet(config, seed=3101, device=device)
train = conditional_selector_data(8192, 3101, device)
validation = conditional_selector_data(2048, 3102, device)
neutral = (train[0][:512], train[1][:512])
records = [train_condition(
condition, base, train, validation, neutral, device)
for condition in CONDITIONS]
by_name = {row["condition"]: row for row in records}
oracle = 100.0 * by_name["oracle"]["validation"]["accuracy"]
raw = 100.0 * by_name["raw_shared"]["validation"]["accuracy"]
innovation = 100.0 * by_name["innovation"]["validation"]["accuracy"]
matched = 100.0 * by_name["matched_raw"]["validation"]["accuracy"]
lesion_drop = 100.0 * (
by_name["oracle"]["validation"]["accuracy"]
- by_name["oracle"]["context_lesion_validation"]["accuracy"])
predictor = by_name["innovation"]["predictor"]
checks = {
"oracle_at_least_90": oracle >= 90.0,
"oracle_context_lesion_drop_at_least_10": lesion_drop >= 10.0,
"nonzero_context_every_layer": all(
row["context_rms"] > 0 for row in predictor),
"raw_below_oracle_by_5_or_nonfinite": (
oracle - raw >= 5.0 or not by_name["raw_shared"]["finite"]),
"innovation_above_raw_by_5": innovation - raw >= 5.0,
"innovation_within_3_of_oracle": oracle - innovation <= 3.0,
"innovation_within_2_of_exact_subtraction": abs(
innovation - oracle) <= 2.0,
"matched_raw_below_innovation_by_3": innovation - matched >= 3.0,
"predictor_mean_r2_at_least_0p8": (
sum(row["mean_per_cell_r2"] for row in predictor)
/ len(predictor) >= 0.8),
"predictor_residual_ratio_at_most_0p25": max(
row["residual_context_rms_ratio"] for row in predictor) <= 0.25,
"zero_instruction_observations": max(
row["instruction_observations"] for record in records
for row in record["predictor"]) == 0,
}
report = {
"stage": "shared_feedback_s0",
"gate": "pass" if all(checks.values()) else "fail",
"checks": checks,
"config": config.__dict__,
"data": {
"train_examples": 8192, "validation_examples": 2048,
"neutral_examples_per_epoch": 512, "batch_size": 128,
"epochs": 40, "data_seed": 3101,
"validation_seed": 3102, "test_generated": False,
},
"records": records,
"summary": {
"oracle_validation_accuracy_percent": oracle,
"raw_validation_accuracy_percent": raw,
"innovation_validation_accuracy_percent": innovation,
"matched_raw_validation_accuracy_percent": matched,
"oracle_context_lesion_drop_points": lesion_drop,
},
"provenance": {
"git_commit": git_output("rev-parse", "HEAD"),
"git_dirty_tracked": False,
"device": str(device), "torch_version": torch.__version__,
"cuda_device_name": (
torch.cuda.get_device_name(device) if device.type == "cuda" else None),
"cuda_visible_devices": os.environ.get("CUDA_VISIBLE_DEVICES"),
},
}
args.out.parent.mkdir(parents=True, exist_ok=True)
with open(args.out, "w", encoding="utf-8") as handle:
json.dump(report, handle, indent=2, sort_keys=True)
handle.write("\n")
print(json.dumps({"gate": report["gate"], **report["summary"],
"checks": checks}, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
|
