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#!/usr/bin/env python3
"""Neutral-only capacity screen for the original population P_l h_l map."""
import argparse
import json
import os
from pathlib import Path
import subprocess
import sys
import numpy as np
import torch
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from sdil.babyai_shared import (
BabyAISharedConfig, BabyAISharedNet, build_history_index,
fit_population_predictor, history_input_dim, manual_step,
population_predictor_metrics,
)
from babyai_shared_run import encode_compact_batch, load_data
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_PICKUP_DATA = ROOT / "data" / "babyai_shared" / "pickup_loc_p0.npz"
AUDIT_EPOCHS = (0, 1, 5, 10, 20, 40)
def git_output(*args):
return subprocess.run(
["git", *args], cwd=ROOT, check=True, capture_output=True,
text=True).stdout.strip()
@torch.no_grad()
def audit_predictor(net, calibration_features, calibration_missions,
holdout_features, holdout_missions, ridge):
calibration = net.forward_features(
calibration_features, calibration_missions)
holdout = net.forward_features(holdout_features, holdout_missions)
layers = []
for layer in range(net.config.hidden_layers):
coefficient, intercept = fit_population_predictor(
calibration["h"][layer + 1], calibration["context"][layer], ridge)
train_metrics = population_predictor_metrics(
calibration["h"][layer + 1], calibration["context"][layer],
coefficient, intercept)
holdout_metrics = population_predictor_metrics(
holdout["h"][layer + 1], holdout["context"][layer],
coefficient, intercept)
layers.append({
"layer": layer,
"calibration": train_metrics,
"holdout": holdout_metrics,
"action_observations": 0,
"teaching_observations": 0,
})
return layers
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data", type=Path, default=DEFAULT_PICKUP_DATA)
parser.add_argument("--model-seed", type=int, required=True)
parser.add_argument("--shuffle-seed", type=int)
parser.add_argument("--ridge", type=float, default=1e-3)
parser.add_argument("--device", default="cuda")
parser.add_argument("--out", type=Path, required=True)
args = parser.parse_args()
if git_output("status", "--porcelain", "--untracked-files=no"):
raise RuntimeError("population predictor screen requires clean tracked source")
shuffle_seed = (args.model_seed if args.shuffle_seed is None
else args.shuffle_seed)
device = torch.device(args.device)
if device.type == "cuda":
device_index = (torch.cuda.current_device()
if device.index is None else device.index)
torch.cuda.set_device(device_index)
device = torch.device("cuda", device_index)
else:
torch.set_num_threads(1)
metadata, train, _, _ = load_data(args.data)
cardinalities = (
metadata["object_cardinality"], metadata["color_cardinality"],
metadata["state_cardinality"])
history_steps = 4
config = BabyAISharedConfig(
input_dim=history_input_dim(*cardinalities, history_steps),
mission_dim=len(metadata["vocabulary"]), width=256, hidden_layers=4,
learning_rate=0.03, reciprocal_learning_rate=0.03,
context_gain=1.0)
net = BabyAISharedNet(config, seed=args.model_seed, device=device)
history = build_history_index(
train["episode_offset"], train["action"], history_steps)
calibration_indices = np.arange(0, 4096)
holdout_indices = np.arange(4096, 8192)
calibration_features = encode_compact_batch(
train, calibration_indices, cardinalities, net, history)
holdout_features = encode_compact_batch(
train, holdout_indices, cardinalities, net, history)
calibration_missions = torch.as_tensor(
train["mission_bow"][calibration_indices],
device=device, dtype=net.dtype)
holdout_missions = torch.as_tensor(
train["mission_bow"][holdout_indices],
device=device, dtype=net.dtype)
shuffle = torch.Generator(device="cpu").manual_seed(shuffle_seed)
audits = [{
"epoch": 0,
"layers": audit_predictor(
net, calibration_features, calibration_missions,
holdout_features, holdout_missions, args.ridge),
}]
epoch_losses = []
for epoch in range(1, 41):
permutation = torch.randperm(
len(train["action"]), generator=shuffle).numpy()
losses = []
for start in range(0, len(permutation), 256):
indices = permutation[start:start + 256]
features = encode_compact_batch(
train, indices, cardinalities, net, history)
missions = torch.as_tensor(
train["mission_bow"][indices],
device=device, dtype=net.dtype)
actions = torch.as_tensor(
train["action"][indices], device=device, dtype=torch.long)
loss, _ = manual_step(
net, features, missions, actions, "clean_kp")
losses.append(loss)
epoch_losses.append(float(np.mean(losses)))
if epoch in AUDIT_EPOCHS:
audits.append({
"epoch": epoch,
"layers": audit_predictor(
net, calibration_features, calibration_missions,
holdout_features, holdout_missions, args.ridge),
})
all_holdout = [layer["holdout"]
for audit in audits for layer in audit["layers"]]
checks = {
"holdout_r2_at_least_0p8_every_epoch_and_layer": all(
row["mean_per_cell_r2"] >= 0.8 for row in all_holdout),
"holdout_residual_ratio_at_most_0p25_every_epoch_and_layer": all(
row["residual_context_rms_ratio"] <= 0.25
for row in all_holdout),
"zero_action_or_teaching_observations": all(
layer["action_observations"] == 0
and layer["teaching_observations"] == 0
for audit in audits for layer in audit["layers"]),
}
report = {
"stage": "babyai_population_predictor_capacity_screen",
"gate": "pass" if all(checks.values()) else "fail",
"checks": checks,
"model_seed": args.model_seed,
"shuffle_seed": shuffle_seed,
"config": config.to_dict(),
"ridge_relative_to_mean_soma_variance": args.ridge,
"calibration_examples": 4096,
"holdout_examples": 4096,
"audits": audits,
"epoch_clean_kp_train_loss": epoch_losses,
"raw_sdil_rollout_or_test_outcomes_read": False,
"provenance": {
"git_commit": git_output("rev-parse", "HEAD"),
"torch_version": torch.__version__,
"device": str(device),
"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"], "checks": checks,
"minimum_holdout_r2": min(
row["mean_per_cell_r2"] for row in all_holdout),
"maximum_holdout_residual_ratio": max(
row["residual_context_rms_ratio"] for row in all_holdout),
"out": str(args.out),
}, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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