main.py

import time
from argparse import ArgumentParser
from functools import partial

import mlx.core as mx
import mlx.nn as nn
import mlx.optimizers as optim
from datasets import download_cora, load_data, train_val_test_mask
from mlx.nn.losses import cross_entropy
from mlx.utils import tree_flatten

from gcn import GCN


def loss_fn(y_hat, y, weight_decay=0.0, parameters=None):
    l = mx.mean(nn.losses.cross_entropy(y_hat, y))

    if weight_decay != 0.0:
        assert parameters != None, "Model parameters missing for L2 reg."

        l2_reg = sum(mx.sum(p[1] ** 2) for p in tree_flatten(parameters)).sqrt()
        return l + weight_decay * l2_reg
    return l


def eval_fn(x, y):
    return mx.mean(mx.argmax(x, axis=1) == y)


def forward_fn(gcn, x, adj, y, train_mask, weight_decay):
    y_hat = gcn(x, adj)
    loss = loss_fn(y_hat[train_mask], y[train_mask], weight_decay, gcn.parameters())
    return loss, y_hat


def main(args):
    # Data loading
    x, y, adj = load_data(args)
    train_mask, val_mask, test_mask = train_val_test_mask()

    gcn = GCN(
        x_dim=x.shape[-1],
        h_dim=args.hidden_dim,
        out_dim=args.nb_classes,
        nb_layers=args.nb_layers,
        dropout=args.dropout,
        bias=args.bias,
    )
    mx.eval(gcn.parameters())

    optimizer = optim.Adam(learning_rate=args.lr)

    state = [gcn.state, optimizer.state, mx.random.state]

    @partial(mx.compile, inputs=state, outputs=state)
    def step():
        loss_and_grad_fn = nn.value_and_grad(gcn, forward_fn)
        (loss, y_hat), grads = loss_and_grad_fn(
            gcn, x, adj, y, train_mask, args.weight_decay
        )
        optimizer.update(gcn, grads)
        return loss, y_hat

    best_val_loss = float("inf")
    cnt = 0

    # Training loop
    for epoch in range(args.epochs):
        tic = time.time()
        loss, y_hat = step()
        mx.eval(state)

        # Validation
        val_loss = loss_fn(y_hat[val_mask], y[val_mask])
        val_acc = eval_fn(y_hat[val_mask], y[val_mask])
        toc = time.time()

        # Early stopping
        if val_loss < best_val_loss:
            best_val_loss = val_loss
            cnt = 0
        else:
            cnt += 1
            if cnt == args.patience:
                break

        print(
            " | ".join(
                [
                    f"Epoch: {epoch:3d}",
                    f"Train loss: {loss.item():.3f}",
                    f"Val loss: {val_loss.item():.3f}",
                    f"Val acc: {val_acc.item():.2f}",
                    f"Time: {1e3*(toc - tic):.3f} (ms)",
                ]
            )
        )

    # Test
    test_y_hat = gcn(x, adj)
    test_loss = loss_fn(y_hat[test_mask], y[test_mask])
    test_acc = eval_fn(y_hat[test_mask], y[test_mask])

    print(f"Test loss: {test_loss.item():.3f}  |  Test acc: {test_acc.item():.2f}")


if __name__ == "__main__":
    parser = ArgumentParser()
    parser.add_argument("--nodes_path", type=str, default="cora/cora.content")
    parser.add_argument("--edges_path", type=str, default="cora/cora.cites")
    parser.add_argument("--hidden_dim", type=int, default=20)
    parser.add_argument("--dropout", type=float, default=0.5)
    parser.add_argument("--nb_layers", type=int, default=2)
    parser.add_argument("--nb_classes", type=int, default=7)
    parser.add_argument("--bias", type=bool, default=True)
    parser.add_argument("--lr", type=float, default=0.001)
    parser.add_argument("--weight_decay", type=float, default=0.0)
    parser.add_argument("--patience", type=int, default=20)
    parser.add_argument("--epochs", type=int, default=100)
    args = parser.parse_args()

    main(args)