File size: 7,267 Bytes
3425207 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | from __future__ import annotations
import itertools
import json
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import trackio
from model import NeuralModelMachine, parameter_count
from safetensors.torch import save_file
from schema import ACTIONS, CAPABILITY_NAMES, STATE_NAMES, reference_transition
from torch.nn import functional as F
PROJECT_DIR = Path(__file__).resolve().parent
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "kernelmind-model-machine"
DATA_DIR = PROJECT_DIR / "data"
SEED = 2087
def build_rows() -> list[dict]:
rows = []
for state in itertools.product([0, 1], repeat=8):
for capabilities in itertools.product([0, 1], repeat=4):
for action_index, action in enumerate(ACTIONS):
next_state, blocked = reference_transition(
list(state), list(capabilities), action
)
rows.append(
{
"state": list(state),
"capabilities": list(capabilities),
"action": action,
"action_index": action_index,
"next_state": next_state,
"blocked": blocked,
}
)
return rows
def tensors(rows: list[dict]) -> tuple[torch.Tensor, ...]:
return (
torch.tensor([row["state"] for row in rows], dtype=torch.float32),
torch.tensor([row["capabilities"] for row in rows], dtype=torch.float32),
torch.tensor([row["action_index"] for row in rows]),
torch.tensor(
[row["next_state"] + [row["blocked"]] for row in rows],
dtype=torch.float32,
),
)
@torch.inference_mode()
def evaluate(model: NeuralModelMachine, rows: list[dict]) -> dict:
state, capabilities, action, targets = tensors(rows)
probabilities = torch.sigmoid(model(state, capabilities, action))
predictions = (probabilities >= 0.5).float()
exact = (predictions == targets).all(1)
return {
"exact_transition_accuracy": float(exact.float().mean()),
"state_bit_accuracy": float(
(predictions[:, :8] == targets[:, :8]).float().mean()
),
"blocked_accuracy": float(
(predictions[:, 8] == targets[:, 8]).float().mean()
),
"transitions": len(rows),
}
@torch.inference_mode()
def rollout_audit(model: NeuralModelMachine, samples: int = 2_000) -> dict:
rng = np.random.default_rng(SEED + 1)
exact_steps = 0
exact_rollouts = 0
for _ in range(samples):
state = rng.integers(0, 2, size=8).tolist()
capabilities = rng.integers(0, 2, size=4).tolist()
learned = torch.tensor([state], dtype=torch.float32)
reference = state
rollout_exact = True
for action_index in rng.integers(0, len(ACTIONS), size=6):
action = ACTIONS[action_index]
reference, reference_blocked = reference_transition(
reference, capabilities, action
)
predicted, blocked_probability = model.transition(
learned,
torch.tensor([capabilities], dtype=torch.float32),
torch.tensor([action_index]),
)
learned = predicted
step_exact = learned[0].int().tolist() == reference
step_exact = step_exact and (
int(blocked_probability[0] >= 0.5) == reference_blocked
)
exact_steps += int(step_exact)
rollout_exact = rollout_exact and step_exact
exact_rollouts += int(rollout_exact)
return {
"rollouts": samples,
"steps_per_rollout": 6,
"exact_step_fraction": exact_steps / (samples * 6),
"exact_six_step_rollout_fraction": exact_rollouts / samples,
}
def main() -> None:
torch.manual_seed(SEED)
torch.set_num_threads(1)
rows = build_rows()
rng = np.random.default_rng(SEED)
order = rng.permutation(len(rows))
train_end = int(0.8 * len(rows))
validation_end = int(0.9 * len(rows))
train_rows = [rows[index] for index in order[:train_end]]
validation_rows = [rows[index] for index in order[train_end:validation_end]]
test_rows = [rows[index] for index in order[validation_end:]]
state, capabilities, action, targets = tensors(train_rows)
model = NeuralModelMachine()
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=1e-5)
best = -1.0
best_state = None
best_epoch = 0
trackio.init(
project="kernelmind-model-machine",
name="neural-os-transition-runtime-v1",
config={
"parameters": parameter_count(model),
"training_transitions": len(train_rows),
"state_bits": len(STATE_NAMES),
"capability_bits": len(CAPABILITY_NAMES),
},
)
for epoch in range(1, 81):
permutation = torch.randperm(len(state))
model.train()
for start in range(0, len(state), 512):
indexes = permutation[start : start + 512]
logits = model(state[indexes], capabilities[indexes], action[indexes])
loss = F.binary_cross_entropy_with_logits(logits, targets[indexes])
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
validation = evaluate(model, validation_rows)
if validation["exact_transition_accuracy"] > best:
best = validation["exact_transition_accuracy"]
best_epoch = epoch
best_state = {
name: value.detach().cpu().clone()
for name, value in model.state_dict().items()
}
if epoch == 1 or epoch % 10 == 0:
trackio.log({"epoch": epoch, **validation})
if best == 1.0 and epoch >= 20:
break
assert best_state is not None
model.load_state_dict(best_state)
test = evaluate(model, test_rows)
rollouts = rollout_audit(model)
report = {
"model": "KernelMind Neural Model Machine",
"parameters": parameter_count(model),
"training_transitions": len(train_rows),
"heldout_transitions": len(test_rows),
"best_epoch": best_epoch,
"test": test,
"multi_step_audit": rollouts,
"boundary": "Models an eight-bit virtual state; it does not control the host OS",
}
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
DATA_DIR.mkdir(parents=True, exist_ok=True)
save_file(model.state_dict(), ARTIFACT_DIR / "model_machine.safetensors")
(ARTIFACT_DIR / "evaluation.json").write_text(
json.dumps(report, indent=2), encoding="utf-8"
)
pd.DataFrame(
{
"state": [row["state"] for row in rows],
"capabilities": [row["capabilities"] for row in rows],
"action": [row["action"] for row in rows],
"next_state": [row["next_state"] for row in rows],
"blocked": [row["blocked"] for row in rows],
}
).to_parquet(DATA_DIR / "transitions.parquet", index=False)
trackio.log({**test, **rollouts})
trackio.finish()
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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