train.py

"""
Graph Attention Networks in DGL using SPMV optimization.
Multiple heads are also batched together for faster training.

Compared with the original paper, this code does not implement
early stopping.

References
----------
Paper: https://arxiv.org/abs/1710.10903
Author's code: https://github.com/PetarV-/GAT
Pytorch implementation: https://github.com/Diego999/pyGAT
"""

import argparse
import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from dgl import DGLGraph
from dgl.data import register_data_args, load_data
import dgl.function as fn

class GraphAttention(nn.Module):
    def __init__(self,
                 g,
                 in_dim,
                 out_dim,
                 num_heads,
                 feat_drop,
                 attn_drop,
                 alpha,
                 residual=False):
        super(GraphAttention, self).__init__()
        self.g = g
        self.num_heads = num_heads
        self.fc = nn.Linear(in_dim, num_heads * out_dim, bias=False)
        if feat_drop:
            self.feat_drop = nn.Dropout(feat_drop)
        else:
            self.feat_drop = None
        if attn_drop:
            self.attn_drop = nn.Dropout(attn_drop)
        else:
            self.attn_drop = None
        self.attn_l = nn.Parameter(torch.Tensor(size=(num_heads, out_dim, 1)))
        self.attn_r = nn.Parameter(torch.Tensor(size=(num_heads, out_dim, 1)))
        nn.init.xavier_normal_(self.fc.weight.data, gain=1.414)
        nn.init.xavier_normal_(self.attn_l.data, gain=1.414)
        nn.init.xavier_normal_(self.attn_r.data, gain=1.414)
        self.leaky_relu = nn.LeakyReLU(alpha)
        self.residual = residual
        if residual:
            if in_dim != out_dim:
                self.res_fc = nn.Linear(in_dim, num_heads * out_dim, bias=False)
                nn.init.xavier_normal_(self.res_fc.weight.data, gain=1.414)
            else:
                self.res_fc = None

    def forward(self, inputs):
        # prepare
        h = inputs  # NxD
        if self.feat_drop:
            h = self.feat_drop(h)
        ft = self.fc(h).reshape((h.shape[0], self.num_heads, -1))  # NxHxD'
        head_ft = ft.transpose(0, 1)  # HxNxD'
        a1 = torch.bmm(head_ft, self.attn_l).transpose(0, 1)  # NxHx1
        a2 = torch.bmm(head_ft, self.attn_r).transpose(0, 1)  # NxHx1
        if self.feat_drop:
            ft = self.feat_drop(ft)
        self.g.ndata.update({'ft' : ft, 'a1' : a1, 'a2' : a2})
        # 1. compute edge attention
        self.g.apply_edges(self.edge_attention)
        # 2. compute two results: one is the node features scaled by the dropped,
        # unnormalized attention values; another is the normalizer of the attention values.
        self.g.update_all([fn.src_mul_edge('ft', 'a_drop', 'ft'), fn.copy_edge('a', 'a')],
                          [fn.sum('ft', 'ft'), fn.sum('a', 'z')])
        # 3. apply normalizer
        ret = self.g.ndata['ft'] / self.g.ndata['z']  # NxHxD'
        # 4. residual
        if self.residual:
            if self.res_fc is not None:
                resval = self.res_fc(h).reshape((h.shape[0], self.num_heads, -1))  # NxHxD'
            else:
                resval = torch.unsqueeze(h, 1)  # Nx1xD'
            ret = resval + ret
        return ret

    def edge_attention(self, edges):
        # an edge UDF to compute unnormalized attention values from src and dst
        a = self.leaky_relu(edges.src['a1'] + edges.dst['a2'])
        a = torch.exp(a).clamp(-10, 10)  # use clamp to avoid overflow
        if self.attn_drop:
            a_drop = self.attn_drop(a)
        return {'a' : a, 'a_drop' : a_drop}

class GAT(nn.Module):
    def __init__(self,
                 g,
                 num_layers,
                 in_dim,
                 num_hidden,
                 num_classes,
                 heads,
                 activation,
                 feat_drop,
                 attn_drop,
                 alpha,
                 residual):
        super(GAT, self).__init__()
        self.g = g
        self.num_layers = num_layers
        self.gat_layers = nn.ModuleList()
        self.activation = activation
        # input projection (no residual)
        self.gat_layers.append(GraphAttention(
            g, in_dim, num_hidden, heads[0], feat_drop, attn_drop, alpha, False))
        # hidden layers
        for l in range(1, num_layers):
            # due to multi-head, the in_dim = num_hidden * num_heads
            self.gat_layers.append(GraphAttention(
                g, num_hidden * heads[l-1], num_hidden, heads[l],
                feat_drop, attn_drop, alpha, residual))
        # output projection
        self.gat_layers.append(GraphAttention(
            g, num_hidden * heads[-2], num_classes, heads[-1],
            feat_drop, attn_drop, alpha, residual))

    def forward(self, inputs):
        h = inputs
        for l in range(self.num_layers):
            h = self.gat_layers[l](h).flatten(1)
            h = self.activation(h)
        # output projection
        logits = self.gat_layers[-1](h).mean(1)
        return logits

def accuracy(logits, labels):
    _, indices = torch.max(logits, dim=1)
    correct = torch.sum(indices == labels)
    return correct.item() * 1.0 / len(labels)

def evaluate(model, features, labels, mask):
    model.eval()
    with torch.no_grad():
        logits = model(features)
        logits = logits[mask]
        labels = labels[mask]
        return accuracy(logits, labels)

def main(args):
    # load and preprocess dataset
    data = load_data(args)
    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    train_mask = torch.ByteTensor(data.train_mask)
    val_mask = torch.ByteTensor(data.val_mask)
    test_mask = torch.ByteTensor(data.test_mask)
    num_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().item(),
           val_mask.sum().item(),
           test_mask.sum().item()))

    if args.gpu < 0:
        cuda = False
    else:
        cuda = True
        torch.cuda.set_device(args.gpu)
        features = features.cuda()
        labels = labels.cuda()
        train_mask = train_mask.cuda()
        val_mask = val_mask.cuda()
        test_mask = test_mask.cuda()

    # create DGL graph
    g = DGLGraph(data.graph)
    n_edges = g.number_of_edges()
    # add self loop
    g.add_edges(g.nodes(), g.nodes())
    # create model
    heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
    model = GAT(g,
                args.num_layers,
                num_feats,
                args.num_hidden,
                n_classes,
                heads,
                F.elu,
                args.in_drop,
                args.attn_drop,
                args.alpha,
                args.residual)
    print(model)
    if cuda:
        model.cuda()
    loss_fcn = torch.nn.CrossEntropyLoss()

    # use optimizer
    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)

    # initialize graph
    dur = []
    for epoch in range(args.epochs):
        model.train()
        if epoch >= 3:
            t0 = time.time()
        # forward
        logits = model(features)
        loss = loss_fcn(logits[train_mask], labels[train_mask])

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if epoch >= 3:
            dur.append(time.time() - t0)

        train_acc = accuracy(logits[train_mask], labels[train_mask])

        if args.fastmode:
            val_acc = accuracy(logits[val_mask], labels[val_mask])
        else:
            val_acc = evaluate(model, features, labels, val_mask)

        print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | TrainAcc {:.4f} |"
              " ValAcc {:.4f} | ETputs(KTEPS) {:.2f}".
              format(epoch, np.mean(dur), loss.item(), train_acc,
                     val_acc, n_edges / np.mean(dur) / 1000))

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

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=300,
                        help="number of training epochs")
    parser.add_argument("--num-heads", type=int, default=8,
                        help="number of hidden attention heads")
    parser.add_argument("--num-out-heads", type=int, default=1,
                        help="number of output attention heads")
    parser.add_argument("--num-layers", type=int, default=1,
                        help="number of hidden layers")
    parser.add_argument("--num-hidden", type=int, default=8,
                        help="number of hidden units")
    parser.add_argument("--residual", action="store_true", default=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")
    parser.add_argument('--weight-decay', type=float, default=5e-4,
                        help="weight decay")
    parser.add_argument('--alpha', type=float, default=0.2,
                        help="the negative slop of leaky relu")
    parser.add_argument('--fastmode', action="store_true", default=False,
                        help="skip re-evaluate the validation set")
    args = parser.parse_args()
    print(args)

    main(args)