gat_batch.py

"""
Graph Attention Networks
Paper: https://arxiv.org/abs/1710.10903
Code: https://github.com/PetarV-/GAT

GAT with batch processing
"""

import argparse
import numpy as np
import time
import mxnet as mx
from mxnet import gluon
from dgl import DGLGraph
from dgl.data import register_data_args, load_data


def elu(data):
    return mx.nd.LeakyReLU(data, act_type='elu')


def gat_message(edges):
    return {'ft': edges.src['ft'], 'a2': edges.src['a2']}


class GATReduce(gluon.Block):
    def __init__(self, attn_drop):
        super(GATReduce, self).__init__()
        if attn_drop:
            self.attn_drop = gluon.nn.Dropout(attn_drop)
        else:
            self.attn_drop = 0

    def forward(self, nodes):
        a1 = mx.nd.expand_dims(nodes.data['a1'], 1)  # shape (B, 1, 1)
        a2 = nodes.mailbox['a2']  # shape (B, deg, 1)
        ft = nodes.mailbox['ft']  # shape (B, deg, D)
        # attention
        a = a1 + a2  # shape (B, deg, 1)
        e = mx.nd.softmax(mx.nd.LeakyReLU(a))
        if self.attn_drop != 0.0:
            e = self.attn_drop(e)
        return {'accum': mx.nd.sum(e * ft, axis=1)}  # shape (B, D)


class GATFinalize(gluon.Block):
    def __init__(self, headid, indim, hiddendim, activation, residual):
        super(GATFinalize, self).__init__()
        self.headid = headid
        self.activation = activation
        self.residual = residual
        self.residual_fc = None
        if residual:
            if indim != hiddendim:
                self.residual_fc = gluon.nn.Dense(hiddendim, use_bias=False)

    def forward(self, nodes):
        ret = nodes.data['accum']
        if self.residual:
            if self.residual_fc is not None:
                ret = self.residual_fc(nodes.data['h']) + ret
            else:
                ret = nodes.data['h'] + ret
        return {'head%d' % self.headid : self.activation(ret)}


class GATPrepare(gluon.Block):
    def __init__(self, indim, hiddendim, drop):
        super(GATPrepare, self).__init__()
        self.fc = gluon.nn.Dense(hiddendim)
        if drop:
            self.drop = gluon.nn.Dropout(drop)
        else:
            self.drop = 0
        self.attn_l = gluon.nn.Dense(1, use_bias=False)
        self.attn_r = gluon.nn.Dense(1, use_bias=False)

    def forward(self, feats):
        h = feats
        if self.drop != 0.0:
            h = self.drop(h)
        ft = self.fc(h)
        a1 = self.attn_l(ft)
        a2 = self.attn_r(ft)
        return {'h': h, 'ft': ft, 'a1': a1, 'a2': a2}


class GAT(gluon.Block):
    def __init__(self,
                 g,
                 num_layers,
                 in_dim,
                 num_hidden,
                 num_classes,
                 num_heads,
                 activation,
                 in_drop,
                 attn_drop,
                 residual):
        super(GAT, self).__init__()
        self.g = g
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.prp = gluon.nn.Sequential()
        self.red = gluon.nn.Sequential()
        self.fnl = gluon.nn.Sequential()
        # input projection (no residual)
        for hid in range(num_heads):
            self.prp.add(GATPrepare(in_dim, num_hidden, in_drop))
            self.red.add(GATReduce(attn_drop))
            self.fnl.add(GATFinalize(hid, in_dim, num_hidden, activation, False))
        # hidden layers
        for l in range(num_layers - 1):
            for hid in range(num_heads):
                # due to multi-head, the in_dim = num_hidden * num_heads
                self.prp.add(GATPrepare(num_hidden * num_heads, num_hidden, in_drop))
                self.red.add(GATReduce(attn_drop))
                self.fnl.add(GATFinalize(hid, num_hidden * num_heads,
                                         num_hidden, activation, residual))
        # output projection
        self.prp.add(GATPrepare(num_hidden * num_heads, num_classes, in_drop))
        self.red.add(GATReduce(attn_drop))
        self.fnl.add(GATFinalize(0, num_hidden * num_heads,
                                 num_classes, activation, residual))
        # sanity check
        assert len(self.prp) == self.num_layers * self.num_heads + 1
        assert len(self.red) == self.num_layers * self.num_heads + 1
        assert len(self.fnl) == self.num_layers * self.num_heads + 1

    def forward(self, features):
        last = features
        for l in range(self.num_layers):
            for hid in range(self.num_heads):
                i = l * self.num_heads + hid
                # prepare
                self.g.set_n_repr(self.prp[i](last))
                # message passing
                self.g.update_all(gat_message, self.red[i], self.fnl[i])
            # merge all the heads
            last = mx.nd.concat(
                    *[self.g.pop_n_repr('head%d' % hid) for hid in range(self.num_heads)],
                    dim=1)
        # output projection
        self.g.set_n_repr(self.prp[-1](last))
        self.g.update_all(gat_message, self.red[-1], self.fnl[-1])
        return self.g.pop_n_repr('head0')


def evaluate(model, features, labels, mask):
    logits = model(features)
    logits = logits[mask].asnumpy().squeeze()
    val_labels = labels[mask].asnumpy().squeeze()
    max_index = np.argmax(logits, axis=1)
    accuracy = np.sum(np.where(max_index == val_labels, 1, 0)) / len(val_labels)
    return accuracy


def main(args):
    # load and preprocess dataset
    data = load_data(args)

    features = mx.nd.array(data.features)
    labels = mx.nd.array(data.labels)
    mask = mx.nd.array(np.where(data.train_mask == 1))
    test_mask = mx.nd.array(np.where(data.test_mask == 1))
    val_mask = mx.nd.array(np.where(data.val_mask == 1))
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_edges()

    if args.gpu < 0:
        ctx = mx.cpu()
    else:
        ctx = mx.gpu(args.gpu)
        features = features.as_in_context(ctx)
        labels = labels.as_in_context(ctx)
        mask = mask.as_in_context(ctx)
        test_mask = test_mask.as_in_context(ctx)
        val_mask = val_mask.as_in_context(ctx)
    # create graph
    g = data.graph
    # add self-loop
    g.remove_edges_from(g.selfloop_edges())
    g = DGLGraph(g)
    g.add_edges(g.nodes(), g.nodes())
    # create model
    model = GAT(g,
                args.num_layers,
                in_feats,
                args.num_hidden,
                n_classes,
                args.num_heads,
                elu,
                args.in_drop,
                args.attn_drop,
                args.residual)

    model.initialize(ctx=ctx)

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

    dur = []
    for epoch in range(args.epochs):
        if epoch >= 3:
            t0 = time.time()
        # forward
        with mx.autograd.record():
            logits = model(features)
            loss = mx.nd.softmax_cross_entropy(logits[mask].squeeze(), labels[mask].squeeze())
            loss.backward()
        trainer.step(mask.shape[0])

        if epoch >= 3:
            dur.append(time.time() - t0)
        print("Epoch {:05d} | Loss {:.4f} | Time(s) {:.4f} | ETputs(KTEPS) {:.2f}".format(
            epoch, loss.asnumpy()[0], np.mean(dur), n_edges / np.mean(dur) / 1000))
        if epoch % 100 == 0:
            val_accuracy = evaluate(model, features, labels, val_mask)
            print("Validation Accuracy {:.4f}".format(val_accuracy))

    test_accuracy = evaluate(model, features, labels, test_mask)
    print("Test Accuracy {:.4f}".format(test_accuracy))


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='GAT')
    register_data_args(parser)
    parser.add_argument("--gpu", type=int, default=-1,
            help="Which GPU to use. Set -1 to use CPU.")
    parser.add_argument("--epochs", type=int, default=1000,
            help="number of training epochs")
    parser.add_argument("--num-heads", type=int, default=8,
            help="number of attentional heads to use")
    parser.add_argument("--num-layers", type=int, default=1,
            help="number of hidden layers")
    parser.add_argument("--num-hidden", type=int, default=8,
            help="size of hidden units")
    parser.add_argument("--residual", action="store_false",
            help="use residual connection")
    parser.add_argument("--in-drop", type=float, default=.6,
            help="input feature dropout")
    parser.add_argument("--attn-drop", type=float, default=.6,
            help="attention dropout")
    parser.add_argument("--lr", type=float, default=0.005,
            help="learning rate")
    args = parser.parse_args()
    print(args)

    main(args)