source/train.py

from __future__ import annotations

import copy
import json
from pathlib import Path

import numpy as np
import torch
import trackio
from data import generate_bindings
from model import FastWeightProgrammer, GRUControl, parameter_count
from safetensors.torch import save_file
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

PROJECT_DIR = Path(__file__).resolve().parent
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "fast-weight-time-machine"
DATA_DIR = PROJECT_DIR / "data"


def seed_everything(seed: int) -> None:
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.set_num_threads(1)


def make_loader(
    dataset: tuple[np.ndarray, ...],
    *,
    shuffle: bool,
    seed: int,
) -> DataLoader:
    keys, values, writes, targets = dataset
    return DataLoader(
        TensorDataset(
            torch.from_numpy(keys),
            torch.from_numpy(values),
            torch.from_numpy(writes),
            torch.from_numpy(targets),
        ),
        batch_size=256,
        shuffle=shuffle,
        generator=torch.Generator().manual_seed(seed),
    )


@torch.inference_mode()
def evaluate(model: nn.Module, loader: DataLoader) -> dict:
    model.eval()
    correct = 0
    total = 0
    losses = []
    for keys, values, writes, targets in loader:
        logits = model(keys, values, writes)
        losses.append(float(nn.functional.cross_entropy(logits, targets)))
        correct += int((logits.argmax(1) == targets).sum())
        total += len(targets)
    return {"accuracy": correct / total, "cross_entropy": float(np.mean(losses))}


def train_variant(
    name: str,
    model: nn.Module,
    train_loader: DataLoader,
    validation_loader: DataLoader,
) -> tuple[nn.Module, list[dict]]:
    optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=1e-5)
    best = copy.deepcopy(model.state_dict())
    best_accuracy = 0.0
    stale = 0
    history = []
    for epoch in range(1, 61):
        model.train()
        losses = []
        for keys, values, writes, targets in train_loader:
            logits = model(keys, values, writes)
            loss = nn.functional.cross_entropy(logits, targets)
            optimizer.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            optimizer.step()
            losses.append(float(loss.detach()))
        validation = evaluate(model, validation_loader)
        record = {
            "variant": name,
            "epoch": epoch,
            "training_loss": float(np.mean(losses)),
            "validation_accuracy": validation["accuracy"],
        }
        history.append(record)
        if epoch % 5 == 0:
            trackio.log(record)
        if validation["accuracy"] > best_accuracy + 1e-4:
            best_accuracy = validation["accuracy"]
            best = copy.deepcopy(model.state_dict())
            stale = 0
        else:
            stale += 1
        if stale >= 10 and epoch >= 20:
            break
    model.load_state_dict(best)
    return model, history


def main() -> None:
    seed_everything(2043)
    train_data = generate_bindings(16_000, pairs=4, distractors=12, seed=2043)
    validation_data = generate_bindings(
        2_000, pairs=4, distractors=12, seed=3043
    )
    evaluation_data = {
        "four_pairs_12_distractors": generate_bindings(
            4_000, 4, 12, seed=4043
        ),
        "eight_pairs_12_distractors": generate_bindings(
            4_000, 8, 12, seed=5043
        ),
        "four_pairs_64_distractors": generate_bindings(
            4_000, 4, 64, seed=6043
        ),
        "eight_pairs_64_distractors": generate_bindings(
            4_000, 8, 64, seed=7043
        ),
    }
    train_loader = make_loader(train_data, shuffle=True, seed=2043)
    validation_loader = make_loader(
        validation_data, shuffle=False, seed=3043
    )
    variants = {
        "fast_weight": FastWeightProgrammer(),
        "gru": GRUControl(),
    }
    trackio.init(
        project="fast-weight-time-machine",
        name="temporary-variable-binding-v1",
        config={
            "training_examples": len(train_data[0]),
            "training_pairs": 4,
            "training_distractors": 12,
            "parameters": {
                name: parameter_count(model) for name, model in variants.items()
            },
        },
    )
    ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
    results = {}
    histories = {}
    for name, model in variants.items():
        trained, history = train_variant(
            name, model, train_loader, validation_loader
        )
        histories[name] = history
        results[name] = {
            "parameters": parameter_count(trained),
            "training_epochs": len(history),
            **{
                condition: evaluate(
                    trained, make_loader(dataset, shuffle=False, seed=8043)
                )
                for condition, dataset in evaluation_data.items()
            },
        }
        save_file(trained.state_dict(), ARTIFACT_DIR / f"{name}.safetensors")
    report = {
        "benchmark": "Temporary variable binding",
        "training_examples": len(train_data[0]),
        "results": results,
        "training_history": histories,
    }
    (ARTIFACT_DIR / "evaluation.json").write_text(
        json.dumps(report, indent=2), encoding="utf-8"
    )
    DATA_DIR.mkdir(parents=True, exist_ok=True)
    np.savez_compressed(
        DATA_DIR / "binding_test.npz",
        keys=evaluation_data["four_pairs_12_distractors"][0],
        values=evaluation_data["four_pairs_12_distractors"][1],
        writes=evaluation_data["four_pairs_12_distractors"][2],
        targets=evaluation_data["four_pairs_12_distractors"][3],
    )
    trackio.log(
        {
            "fast_weight_accuracy": results["fast_weight"][
                "four_pairs_12_distractors"
            ]["accuracy"],
            "gru_accuracy": results["gru"]["four_pairs_12_distractors"][
                "accuracy"
            ],
            "fast_weight_long_accuracy": results["fast_weight"][
                "eight_pairs_64_distractors"
            ]["accuracy"],
            "gru_long_accuracy": results["gru"][
                "eight_pairs_64_distractors"
            ]["accuracy"],
        }
    )
    trackio.finish()
    print(json.dumps(report, indent=2))


if __name__ == "__main__":
    main()