wehub-resource-sync
383 lines
12 KiB
Python
383 lines
12 KiB
Python
import argparse
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import time
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from functools import partial
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import dgl
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import dgl.nn.pytorch as dglnn
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import numpy as np
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import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import tqdm
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from ogb.nodeproppred import DglNodePropPredDataset
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from sampler import ClusterIter, subgraph_collate_fn
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from torch.utils.data import DataLoader
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class GAT(nn.Module):
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def __init__(
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self,
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in_feats,
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num_heads,
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n_hidden,
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n_classes,
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n_layers,
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activation,
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dropout=0.0,
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):
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super().__init__()
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self.n_layers = n_layers
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self.n_hidden = n_hidden
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self.n_classes = n_classes
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self.layers = nn.ModuleList()
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self.num_heads = num_heads
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self.layers.append(
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dglnn.GATConv(
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in_feats,
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n_hidden,
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num_heads=num_heads,
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feat_drop=dropout,
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attn_drop=dropout,
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activation=activation,
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negative_slope=0.2,
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)
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)
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for i in range(1, n_layers - 1):
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self.layers.append(
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dglnn.GATConv(
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n_hidden * num_heads,
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n_hidden,
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num_heads=num_heads,
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feat_drop=dropout,
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attn_drop=dropout,
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activation=activation,
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negative_slope=0.2,
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)
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)
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self.layers.append(
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dglnn.GATConv(
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n_hidden * num_heads,
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n_classes,
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num_heads=num_heads,
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feat_drop=dropout,
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attn_drop=dropout,
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activation=None,
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negative_slope=0.2,
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)
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)
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def forward(self, g, x):
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h = x
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for l, conv in enumerate(self.layers):
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h = conv(g, h)
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if l < len(self.layers) - 1:
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h = h.flatten(1)
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h = h.mean(1)
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return h.log_softmax(dim=-1)
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def inference(self, g, x, batch_size, device):
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"""
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Inference with the GAT model on full neighbors (i.e. without neighbor sampling).
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g : the entire graph.
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x : the input of entire node set.
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The inference code is written in a fashion that it could handle any number of nodes and
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layers.
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"""
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num_heads = self.num_heads
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for l, layer in enumerate(self.layers):
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if l < self.n_layers - 1:
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y = th.zeros(
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g.num_nodes(),
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self.n_hidden * num_heads
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if l != len(self.layers) - 1
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else self.n_classes,
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)
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else:
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y = th.zeros(
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g.num_nodes(),
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self.n_hidden
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if l != len(self.layers) - 1
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else self.n_classes,
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)
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sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
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dataloader = dgl.dataloading.DataLoader(
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g,
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th.arange(g.num_nodes()),
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sampler,
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batch_size=batch_size,
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shuffle=False,
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drop_last=False,
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num_workers=args.num_workers,
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)
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with dataloader.enable_cpu_affinity():
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for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
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block = blocks[0].int().to(device)
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h = x[input_nodes].to(device)
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if l < self.n_layers - 1:
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h = layer(block, h).flatten(1)
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else:
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h = layer(block, h)
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h = h.mean(1)
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h = h.log_softmax(dim=-1)
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y[output_nodes] = h.cpu()
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x = y
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return y
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def compute_acc(pred, labels):
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"""
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Compute the accuracy of prediction given the labels.
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"""
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return (th.argmax(pred, dim=1) == labels).float().sum() / len(pred)
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def evaluate(model, g, nfeat, labels, val_nid, test_nid, batch_size, device):
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"""
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Evaluate the model on the validation set specified by ``val_mask``.
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g : The entire graph.
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inputs : The features of all the nodes.
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labels : The labels of all the nodes.
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val_mask : A 0-1 mask indicating which nodes do we actually compute the accuracy for.
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batch_size : Number of nodes to compute at the same time.
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device : The GPU device to evaluate on.
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"""
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model.eval()
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with th.no_grad():
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pred = model.inference(g, nfeat, batch_size, device)
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model.train()
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labels_cpu = labels.to(th.device("cpu"))
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return (
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compute_acc(pred[val_nid], labels_cpu[val_nid]),
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compute_acc(pred[test_nid], labels_cpu[test_nid]),
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pred,
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)
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def model_param_summary(model):
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"""Count the model parameters"""
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cnt = sum(p.numel() for p in model.parameters() if p.requires_grad)
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print("Total Params {}".format(cnt))
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#### Entry point
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def run(args, device, data, nfeat):
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# Unpack data
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(
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train_nid,
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val_nid,
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test_nid,
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in_feats,
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labels,
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n_classes,
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g,
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cluster_iterator,
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) = data
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labels = labels.to(device)
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# Define model and optimizer
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model = GAT(
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in_feats,
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args.num_heads,
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args.num_hidden,
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n_classes,
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args.num_layers,
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F.relu,
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args.dropout,
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)
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model_param_summary(model)
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model = model.to(device)
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optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
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# Training loop
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avg = 0
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best_eval_acc = 0
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best_test_acc = 0
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for epoch in range(args.num_epochs):
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iter_load = 0
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iter_far = 0
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iter_back = 0
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tic = time.time()
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# Loop over the dataloader to sample the computation dependency graph as a list of
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# blocks.
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tic_start = time.time()
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for step, cluster in enumerate(cluster_iterator):
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mask = cluster.ndata.pop("train_mask")
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if mask.sum() == 0:
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continue
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cluster.edata.pop(dgl.EID)
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cluster = cluster.int().to(device)
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input_nodes = cluster.ndata[dgl.NID]
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batch_inputs = nfeat[input_nodes]
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batch_labels = labels[input_nodes]
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tic_step = time.time()
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# Compute loss and prediction
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batch_pred = model(cluster, batch_inputs)
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batch_pred = batch_pred[mask]
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batch_labels = batch_labels[mask]
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loss = nn.functional.nll_loss(batch_pred, batch_labels)
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optimizer.zero_grad()
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tic_far = time.time()
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loss.backward()
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optimizer.step()
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tic_back = time.time()
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iter_load += tic_step - tic_start
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iter_far += tic_far - tic_step
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iter_back += tic_back - tic_far
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if step % args.log_every == 0:
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acc = compute_acc(batch_pred, batch_labels)
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gpu_mem_alloc = (
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th.cuda.max_memory_allocated() / 1000000
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if th.cuda.is_available()
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else 0
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)
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print(
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"Epoch {:05d} | Step {:05d} | Loss {:.4f} | Train Acc {:.4f} | GPU {:.1f} MB".format(
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epoch, step, loss.item(), acc.item(), gpu_mem_alloc
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)
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)
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tic_start = time.time()
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toc = time.time()
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print(
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"Epoch Time(s): {:.4f} Load {:.4f} Forward {:.4f} Backward {:.4f}".format(
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toc - tic, iter_load, iter_far, iter_back
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)
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)
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if epoch >= 5:
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avg += toc - tic
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if epoch % args.eval_every == 0 and epoch != 0:
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eval_acc, test_acc, pred = evaluate(
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model,
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g,
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nfeat,
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labels,
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val_nid,
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test_nid,
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args.val_batch_size,
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device,
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)
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model = model.to(device)
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if args.save_pred:
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np.savetxt(
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args.save_pred + "%02d" % epoch,
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pred.argmax(1).cpu().numpy(),
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"%d",
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)
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print("Eval Acc {:.4f}".format(eval_acc))
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if eval_acc > best_eval_acc:
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best_eval_acc = eval_acc
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best_test_acc = test_acc
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print(
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"Best Eval Acc {:.4f} Test Acc {:.4f}".format(
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best_eval_acc, best_test_acc
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)
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)
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if epoch >= 5:
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print("Avg epoch time: {}".format(avg / (epoch - 4)))
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return best_test_acc.to(th.device("cpu"))
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if __name__ == "__main__":
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argparser = argparse.ArgumentParser("multi-gpu training")
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argparser.add_argument(
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"--gpu",
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type=int,
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default=0,
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help="GPU device ID. Use -1 for CPU training",
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)
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argparser.add_argument("--num_epochs", type=int, default=20)
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argparser.add_argument("--num_hidden", type=int, default=128)
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argparser.add_argument("--num_layers", type=int, default=3)
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argparser.add_argument("--num_heads", type=int, default=8)
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argparser.add_argument("--batch_size", type=int, default=32)
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argparser.add_argument("--val_batch_size", type=int, default=2000)
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argparser.add_argument("--log_every", type=int, default=20)
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argparser.add_argument("--eval_every", type=int, default=1)
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argparser.add_argument("--lr", type=float, default=0.001)
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argparser.add_argument("--dropout", type=float, default=0.5)
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argparser.add_argument("--save_pred", type=str, default="")
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argparser.add_argument("--wd", type=float, default=0)
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argparser.add_argument("--num_partitions", type=int, default=15000)
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argparser.add_argument("--num_workers", type=int, default=4)
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argparser.add_argument(
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"--data_cpu",
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action="store_true",
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help="By default the script puts all node features and labels "
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"on GPU when using it to save time for data copy. This may "
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"be undesired if they cannot fit in GPU memory at once. "
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"This flag disables that.",
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)
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args = argparser.parse_args()
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if args.gpu >= 0:
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device = th.device("cuda:%d" % args.gpu)
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else:
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device = th.device("cpu")
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# load ogbn-products data
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data = DglNodePropPredDataset(name="ogbn-products")
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splitted_idx = data.get_idx_split()
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train_idx, val_idx, test_idx = (
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splitted_idx["train"],
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splitted_idx["valid"],
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splitted_idx["test"],
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)
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graph, labels = data[0]
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labels = labels[:, 0]
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print("Total edges before adding self-loop {}".format(graph.num_edges()))
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graph = dgl.remove_self_loop(graph)
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graph = dgl.add_self_loop(graph)
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print("Total edges after adding self-loop {}".format(graph.num_edges()))
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num_nodes = train_idx.shape[0] + val_idx.shape[0] + test_idx.shape[0]
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assert num_nodes == graph.num_nodes()
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mask = th.zeros(num_nodes, dtype=th.bool)
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mask[train_idx] = True
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graph.ndata["train_mask"] = mask
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graph.in_degrees(0)
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graph.out_degrees(0)
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graph.find_edges(0)
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cluster_iter_data = ClusterIter(
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"ogbn-products", graph, args.num_partitions, args.batch_size
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)
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cluster_iterator = DataLoader(
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cluster_iter_data,
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batch_size=args.batch_size,
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shuffle=True,
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pin_memory=True,
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num_workers=args.num_workers,
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collate_fn=partial(subgraph_collate_fn, graph),
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)
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in_feats = graph.ndata["feat"].shape[1]
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n_classes = (labels.max() + 1).item()
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# Pack data
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data = (
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train_idx,
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val_idx,
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test_idx,
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in_feats,
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labels,
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n_classes,
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graph,
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cluster_iterator,
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)
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# Run 10 times
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test_accs = []
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nfeat = graph.ndata.pop("feat").to(device)
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for i in range(10):
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test_accs.append(run(args, device, data, nfeat))
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print("Average test accuracy:", np.mean(test_accs), "±", np.std(test_accs))
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