# Buggy code
def main(args):
    # load and preprocess dataset
    data = load_data(args)
    features = mx.nd.array(data.features)
    labels = mx.nd.array(data.labels)
    train_mask = mx.nd.array(data.train_mask)
    val_mask = mx.nd.array(data.val_mask)
    test_mask = mx.nd.array(data.test_mask)
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_edges()
    print(
        """----Data statistics------'
      #Edges %d
      #Classes %d
      #Train samples %d
      #Val samples %d
      #Test samples %d"""
        % (
            n_edges,
            n_classes,
            train_mask.sum().asscalar(),
            val_mask.sum().asscalar(),
            test_mask.sum().asscalar(),
        )
    )

    if args.gpu < 0:
        cuda = False
        ctx = mx.cpu(0)
    else:
        cuda = True
        ctx = mx.gpu(args.gpu)

    features = features.as_in_context(ctx)
    labels = labels.as_in_context(ctx)
    train_mask = train_mask.as_in_context(ctx)
    val_mask = val_mask.as_in_context(ctx)
    test_mask = test_mask.as_in_context(ctx)

    # create GCN model
    g = data.graph
    if args.self_loop:
        g.remove_edges_from(g.selfloop_edges())
        g.add_edges_from(zip(g.nodes(), g.nodes()))
    g = DGLGraph(g)
    # normalization
    degs = g.in_degrees().astype("float32")
    norm = mx.nd.power(degs, -0.5)
    if cuda:
        norm = norm.as_in_context(ctx)
    g.ndata["norm"] = mx.nd.expand_dims(norm, 1)

    model = GCN(
        g, in_feats, args.n_hidden, n_classes, args.n_layers, mx.nd.relu, args.dropout
    )
    model.initialize(ctx=ctx)
    n_train_samples = train_mask.sum().asscalar()
    loss_fcn = gluon.loss.SoftmaxCELoss()

    # use optimizer
    print(model.collect_params())
    trainer = gluon.Trainer(
        model.collect_params(),
        "adam",
        {"learning_rate": args.lr, "wd": args.weight_decay},
    )

    # initialize graph
    dur = []
    for epoch in range(args.n_epochs):
        if epoch >= 3:
            t0 = time.time()
        # forward
        with mx.autograd.record():
            pred = model(features)
            loss = loss_fcn(pred, labels, mx.nd.expand_dims(train_mask, 1))
            loss = loss.sum() / n_train_samples

        loss.backward()
        trainer.step(batch_size=1)

        if epoch >= 3:
            loss.asscalar()
            dur.append(time.time() - t0)
            acc = evaluate(model, features, labels, val_mask)
            print(
                "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
                "ETputs(KTEPS) {:.2f}".format(
                    epoch,
                    np.mean(dur),
                    loss.asscalar(),
                    acc,
                    n_edges / np.mean(dur) / 1000,
                )
            )

    # test set accuracy
    acc = evaluate(model, features, labels, test_mask)
    print("Test accuracy {:.2%}".format(acc))


# Traceback (AssertionError)
test_shared_mem_store.test_init ... /var/jenkins_home/workspace/DGL_PR-752@2/python/dgl/base.py:18: UserWarning: Initializer is not set. Use zero initializer instead. To suppress this warning, use `set_initializer` to explicitly specify which initializer to use.
warnings.warn(msg, warn_type)
/var/jenkins_home/workspace/DGL_PR-752@2/python/dgl/base.py:18: UserWarning: It may not be safe to access node data of all nodes.It's recommended to node data of a subset of nodes directly.
warnings.warn(msg, warn_type)
/var/jenkins_home/workspace/DGL_PR-752@2/python/dgl/base.py:18: UserWarning: It may not be safe to access edge data of all edges.It's recommended to edge data of a subset of edges directly.
warnings.warn(msg, warn_type)
[06:09:05] /var/jenkins_home/workspace/DGL_PR-752@2/src/runtime/shared_mem.cc:32: remove /test_graph1_node_feat for shared memory
[06:09:05] /var/jenkins_home/workspace/DGL_PR-752@2/src/runtime/shared_mem.cc:32: remove /test_graph1_node_test4 for shared memory
[06:09:05] /var/jenkins_home/workspace/DGL_PR-752@2/src/runtime/shared_mem.cc:32: remove /test_graph1_in for shared memory
[06:09:05] /var/jenkins_home/workspace/DGL_PR-752@2/src/runtime/shared_mem.cc:32: remove /test_graph1_edge_feat for shared memory
[06:09:05] /var/jenkins_home/workspace/DGL_PR-752@2/src/runtime/shared_mem.cc:32: remove /test_graph1_edge_test4 for shared memory
/var/jenkins_home/workspace/DGL_PR-752@2/python/dgl/base.py:18: UserWarning: Initializer is not set. Use zero initializer instead. To suppress this warning, use `set_initializer` to explicitly specify which initializer to use.
warnings.warn(msg, warn_type)
/var/jenkins_home/workspace/DGL_PR-752@2/python/dgl/base.py:18: UserWarning: It may not be safe to access node data of all nodes.It's recommended to node data of a subset of nodes directly.
warnings.warn(msg, warn_type)
/var/jenkins_home/workspace/DGL_PR-752@2/python/dgl/base.py:18: UserWarning: It may not be safe to access edge data of all edges.It's recommended to edge data of a subset of edges directly.
warnings.warn(msg, warn_type)

Arrays are not almost equal to 7 decimals

(mismatch 100.0%)
x: array([20., 20., 20., 20., 20., 20., 20., 20., 20., 20.], dtype=float32)
y: array(11)
Traceback (most recent call last):
File "/var/jenkins_home/workspace/DGL_PR-752@2/tests/distributed/test_shared_mem_store.py", line 65, in check_init_func
check_array_shared_memory(g, worker_id, [g.nodes[:].data['test4'], g.edges[:].data['test4']])
File "/var/jenkins_home/workspace/DGL_PR-752@2/tests/distributed/test_shared_mem_store.py", line 28, in check_array_shared_memory
assert_almost_equal(F.asnumpy(arr[0]), i + 10)
File "/usr/local/lib/python3.5/dist-packages/numpy/testing/nose_tools/utils.py", line 567, in assert_almost_equal
return assert_array_almost_equal(actual, desired, decimal, err_msg)
File "/usr/local/lib/python3.5/dist-packages/numpy/testing/nose_tools/utils.py", line 965, in assert_array_almost_equal
precision=decimal)
File "/usr/local/lib/python3.5/dist-packages/numpy/testing/nose_tools/utils.py", line 781, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 7 decimals

(mismatch 100.0%)
x: array([20., 20., 20., 20., 20., 20., 20., 20., 20., 20.], dtype=float32)
y: array(11)
FAIL

# Fixed code
def main(args):
    # load and preprocess dataset
    data = load_data(args)
    features = mx.nd.array(data.features)
    labels = mx.nd.array(data.labels)
    train_mask = mx.nd.array(data.train_mask)
    val_mask = mx.nd.array(data.val_mask)
    test_mask = mx.nd.array(data.test_mask)
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_edges()
    print(
        """----Data statistics------'
      #Edges %d
      #Classes %d
      #Train samples %d
      #Val samples %d
      #Test samples %d"""
        % (
            n_edges,
            n_classes,
            train_mask.sum().asscalar(),
            val_mask.sum().asscalar(),
            test_mask.sum().asscalar(),
        )
    )

    if args.gpu < 0:
        cuda = False
        ctx = mx.cpu(0)
    else:
        cuda = True
        ctx = mx.gpu(args.gpu)

    features = features.as_in_context(ctx)
    labels = labels.as_in_context(ctx)
    train_mask = train_mask.as_in_context(ctx)
    val_mask = val_mask.as_in_context(ctx)
    test_mask = test_mask.as_in_context(ctx)

    # create GCN model
    g = data.graph
    if args.self_loop:
        g.remove_edges_from(nx.selfloop_edges(g))
        g.add_edges_from(zip(g.nodes(), g.nodes()))
    g = DGLGraph(g)
    # normalization
    degs = g.in_degrees().astype("float32")
    norm = mx.nd.power(degs, -0.5)
    if cuda:
        norm = norm.as_in_context(ctx)
    g.ndata["norm"] = mx.nd.expand_dims(norm, 1)

    model = GCN(
        g, in_feats, args.n_hidden, n_classes, args.n_layers, mx.nd.relu, args.dropout
    )
    model.initialize(ctx=ctx)
    n_train_samples = train_mask.sum().asscalar()
    loss_fcn = gluon.loss.SoftmaxCELoss()

    # use optimizer
    print(model.collect_params())
    trainer = gluon.Trainer(
        model.collect_params(),
        "adam",
        {"learning_rate": args.lr, "wd": args.weight_decay},
    )

    # initialize graph
    dur = []
    for epoch in range(args.n_epochs):
        if epoch >= 3:
            t0 = time.time()
        # forward
        with mx.autograd.record():
            pred = model(features)
            loss = loss_fcn(pred, labels, mx.nd.expand_dims(train_mask, 1))
            loss = loss.sum() / n_train_samples

        loss.backward()
        trainer.step(batch_size=1)

        if epoch >= 3:
            loss.asscalar()
            dur.append(time.time() - t0)
            acc = evaluate(model, features, labels, val_mask)
            print(
                "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
                "ETputs(KTEPS) {:.2f}".format(
                    epoch,
                    np.mean(dur),
                    loss.asscalar(),
                    acc,
                    n_edges / np.mean(dur) / 1000,
                )
            )

    # test set accuracy
    acc = evaluate(model, features, labels, test_mask)
    print("Test accuracy {:.2%}".format(acc))