/
ogb_example.py
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/
ogb_example.py
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"""
This is modified version of: https://github.com/dmlc/dgl/blob/master/examples/pytorch/ogb/ogbn-products/graphsage/main.py
"""
import argparse
import time
import dgl
import dgl.nn.pytorch as dglnn
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import tqdm
from ogb.nodeproppred import DglNodePropPredDataset
class SAGE(nn.Module):
def __init__(
self, in_feats, n_hidden, n_classes, n_layers, activation, dropout
):
super().__init__()
self.n_layers = n_layers
self.n_hidden = n_hidden
self.n_classes = n_classes
self.layers = nn.ModuleList()
self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, "mean"))
for i in range(1, n_layers - 1):
self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, "mean"))
self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, "mean"))
self.dropout = nn.Dropout(dropout)
self.activation = activation
def forward(self, blocks, x):
h = x
for l, (layer, block) in enumerate(zip(self.layers, blocks)):
# We need to first copy the representation of nodes on the RHS from the
# appropriate nodes on the LHS.
# Note that the shape of h is (num_nodes_LHS, D) and the shape of h_dst
# would be (num_nodes_RHS, D)
h_dst = h[: block.num_dst_nodes()]
# Then we compute the updated representation on the RHS.
# The shape of h now becomes (num_nodes_RHS, D)
h = layer(block, (h, h_dst))
if l != len(self.layers) - 1:
h = self.activation(h)
h = self.dropout(h)
return h
def inference(self, g, x, device):
"""
Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling).
g : the entire graph.
x : the input of entire node set.
The inference code is written in a fashion that it could handle any number of nodes and
layers.
"""
# During inference with sampling, multi-layer blocks are very inefficient because
# lots of computations in the first few layers are repeated.
# Therefore, we compute the representation of all nodes layer by layer. The nodes
# on each layer are of course splitted in batches.
# TODO: can we standardize this?
for l, layer in enumerate(self.layers):
y = th.zeros(
g.num_nodes(),
self.n_hidden if l != len(self.layers) - 1 else self.n_classes,
).to(device)
sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
dataloader = dgl.dataloading.DataLoader(
g,
th.arange(g.num_nodes()),
sampler,
batch_size=args.batch_size,
shuffle=True,
drop_last=False,
num_workers=args.num_workers,
)
for input_nodes, output_nodes, blocks in tqdm.tqdm(
dataloader, disable=None
):
block = blocks[0].int().to(device)
h = x[input_nodes]
h_dst = h[: block.num_dst_nodes()]
h = layer(block, (h, h_dst))
if l != len(self.layers) - 1:
h = self.activation(h)
h = self.dropout(h)
y[output_nodes] = h
x = y
return y
def compute_acc(pred, labels):
"""
Compute the accuracy of prediction given the labels.
"""
return (th.argmax(pred, dim=1) == labels).float().sum() / len(pred)
def evaluate(model, g, nfeat, labels, val_nid, test_nid, device):
"""
Evaluate the model on the validation set specified by ``val_mask``.
g : The entire graph.
inputs : The features of all the nodes.
labels : The labels of all the nodes.
val_mask : A 0-1 mask indicating which nodes do we actually compute the accuracy for.
device : The GPU device to evaluate on.
"""
model.eval()
with th.no_grad():
pred = model.inference(g, nfeat, device)
model.train()
return (
compute_acc(pred[val_nid], labels[val_nid]),
compute_acc(pred[test_nid], labels[test_nid]),
pred,
)
def load_subtensor(nfeat, labels, seeds, input_nodes):
"""
Extracts features and labels for a set of nodes.
"""
batch_inputs = nfeat[input_nodes]
batch_labels = labels[seeds]
return batch_inputs, batch_labels
#### Entry point
def train(args, device, data):
# Unpack data
train_nid, val_nid, test_nid, in_feats, labels, n_classes, nfeat, g = data
# Create PyTorch DataLoader for constructing blocks
sampler = dgl.dataloading.MultiLayerNeighborSampler(
[int(fanout) for fanout in args.fan_out.split(",")]
)
dataloader = dgl.dataloading.DataLoader(
g,
train_nid,
sampler,
batch_size=args.batch_size,
shuffle=True,
drop_last=False,
num_workers=args.num_workers,
)
# Define model and optimizer
model = SAGE(
in_feats,
args.num_hidden,
n_classes,
args.num_layers,
F.relu,
args.dropout,
)
model = model.to(device)
loss_fcn = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
# Training loop
avg = 0
iter_tput = []
best_eval_acc = 0
best_test_acc = 0
with dataloader.enable_cpu_affinity():
for epoch in range(args.num_epochs):
tic = time.time()
# Loop over the dataloader to sample the computation dependency graph as a list of
# blocks.
for step, (input_nodes, seeds, blocks) in enumerate(dataloader):
tic_step = time.time()
# copy block to gpu
blocks = [blk.int().to(device) for blk in blocks]
# Load the input features as well as output labels
batch_inputs, batch_labels = load_subtensor(
nfeat, labels, seeds, input_nodes
)
# Compute loss and prediction
batch_pred = model(blocks, batch_inputs)
loss = loss_fcn(batch_pred, batch_labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
iter_tput.append(len(seeds) / (time.time() - tic_step))
if step % args.log_every == 0 and step != 0:
acc = compute_acc(batch_pred, batch_labels)
print(
"Step {:05d} | Loss {:.4f} | Train Acc {:.4f} | Speed (samples/sec) {:.4f}".format(
step,
loss.item(),
acc.item(),
np.mean(iter_tput[3:]),
)
)
toc = time.time()
print("Epoch Time(s): {:.4f}".format(toc - tic))
avg += toc - tic
if epoch % args.eval_every == 0 and epoch != 0:
eval_acc, test_acc, pred = evaluate(
model, g, nfeat, labels, val_nid, test_nid, device
)
if args.save_pred:
np.savetxt(
args.save_pred + "%02d" % epoch,
pred.argmax(1).cpu().numpy(),
"%d",
)
print("Eval Acc {:.4f}".format(eval_acc))
if eval_acc > best_eval_acc:
best_eval_acc = eval_acc
best_test_acc = test_acc
print(
"Best Eval Acc {:.4f} Test Acc {:.4f}".format(
best_eval_acc, best_test_acc
)
)
print("Avg epoch time: {}".format(avg / args.num_epochs))
return best_test_acc
if __name__ == "__main__":
argparser = argparse.ArgumentParser("multi-gpu training")
argparser.add_argument(
"--gpu",
type=int,
default=0,
help="GPU device ID. Use -1 for CPU training",
)
argparser.add_argument("--num-epochs", type=int, default=20)
argparser.add_argument("--num-hidden", type=int, default=256)
argparser.add_argument("--num-layers", type=int, default=3)
argparser.add_argument("--fan-out", type=str, default="5,10,15")
argparser.add_argument("--batch-size", type=int, default=1000)
argparser.add_argument("--val-batch-size", type=int, default=10000)
argparser.add_argument("--log-every", type=int, default=20)
argparser.add_argument("--eval-every", type=int, default=1)
argparser.add_argument("--lr", type=float, default=0.003)
argparser.add_argument("--dropout", type=float, default=0.5)
argparser.add_argument(
"--dataset",
type=str,
default="ogbn-products",
choices=["ogbn-papers100M", "ogbn-products"],
)
argparser.add_argument(
"--num-workers",
type=int,
default=4,
help="Number of sampling processes. Use 0 for no extra process.",
)
argparser.add_argument("--save-pred", type=str, default="")
argparser.add_argument("--wd", type=float, default=0)
args = argparser.parse_args()
device = th.device("cpu")
# load ogbn-products data
data = DglNodePropPredDataset(args.dataset)
splitted_idx = data.get_idx_split()
train_idx, val_idx, test_idx = (
splitted_idx["train"],
splitted_idx["valid"],
splitted_idx["test"],
)
graph, labels = data[0]
nfeat = graph.ndata.pop("feat").to(device)
labels = labels[:, 0].to(device)
in_feats = nfeat.shape[1]
n_classes = (labels.max() + 1).item()
# Create csr/coo/csc formats before launching sampling processes
# This avoids creating certain formats in each data loader process, which saves momory and CPU.
graph.create_formats_()
# Pack data
data = (
train_idx,
val_idx,
test_idx,
in_feats,
labels,
n_classes,
nfeat,
graph,
)
test_acc = train(args, device, data).cpu().numpy()
print("Test accuracy:", test_acc)