wehub-resource-sync
316 lines
9.8 KiB
Python
316 lines
9.8 KiB
Python
import argparse
|
|
import time
|
|
import traceback
|
|
from functools import partial
|
|
|
|
import dgl
|
|
import dgl.function as fn
|
|
import dgl.nn.pytorch as dglnn
|
|
|
|
import numpy as np
|
|
import torch as th
|
|
import torch.multiprocessing as mp
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torch.optim as optim
|
|
import tqdm
|
|
from dgl.data import RedditDataset
|
|
from ogb.nodeproppred import DglNodePropPredDataset
|
|
from sampler import ClusterIter, subgraph_collate_fn
|
|
from torch.utils.data import DataLoader
|
|
|
|
#### Neighbor sampler
|
|
|
|
|
|
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, g, x):
|
|
h = x
|
|
for l, conv in enumerate(self.layers):
|
|
h = conv(g, h)
|
|
if l != len(self.layers) - 1:
|
|
h = self.activation(h)
|
|
h = self.dropout(h)
|
|
return h
|
|
|
|
def inference(self, g, x, batch_size, 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?
|
|
h = x
|
|
for l, conv in enumerate(self.layers):
|
|
h = conv(g, h)
|
|
if l != len(self.layers) - 1:
|
|
h = self.activation(h)
|
|
|
|
return h
|
|
|
|
|
|
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, labels, val_nid, test_nid, batch_size, 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.
|
|
batch_size : Number of nodes to compute at the same time.
|
|
device : The GPU device to evaluate on.
|
|
"""
|
|
model.eval()
|
|
with th.no_grad():
|
|
inputs = g.ndata["feat"]
|
|
model = model.cpu()
|
|
pred = model.inference(g, inputs, batch_size, device)
|
|
model.train()
|
|
return (
|
|
compute_acc(pred[val_nid], labels[val_nid]),
|
|
compute_acc(pred[test_nid], labels[test_nid]),
|
|
pred,
|
|
)
|
|
|
|
|
|
def load_subtensor(g, labels, seeds, input_nodes, device):
|
|
"""
|
|
Copys features and labels of a set of nodes onto GPU.
|
|
"""
|
|
batch_inputs = g.ndata["feat"][input_nodes].to(device)
|
|
batch_labels = labels[seeds].to(device)
|
|
return batch_inputs, batch_labels
|
|
|
|
|
|
#### Entry point
|
|
def run(args, device, data):
|
|
# Unpack data
|
|
(
|
|
train_nid,
|
|
val_nid,
|
|
test_nid,
|
|
in_feats,
|
|
labels,
|
|
n_classes,
|
|
g,
|
|
cluster_iterator,
|
|
) = data
|
|
|
|
# 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()
|
|
loss_fcn = loss_fcn.to(device)
|
|
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
|
|
for epoch in range(args.num_epochs):
|
|
iter_load = 0
|
|
iter_far = 0
|
|
iter_back = 0
|
|
iter_tl = 0
|
|
tic = time.time()
|
|
|
|
# Loop over the dataloader to sample the computation dependency graph as a list of
|
|
# blocks.
|
|
tic_start = time.time()
|
|
for step, cluster in enumerate(cluster_iterator):
|
|
cluster = cluster.int().to(device)
|
|
mask = cluster.ndata["train_mask"].to(device)
|
|
if mask.sum() == 0:
|
|
continue
|
|
feat = cluster.ndata["feat"].to(device)
|
|
batch_labels = cluster.ndata["labels"].to(device)
|
|
tic_step = time.time()
|
|
|
|
batch_pred = model(cluster, feat)
|
|
batch_pred = batch_pred[mask]
|
|
batch_labels = batch_labels[mask]
|
|
loss = loss_fcn(batch_pred, batch_labels)
|
|
optimizer.zero_grad()
|
|
tic_far = time.time()
|
|
loss.backward()
|
|
optimizer.step()
|
|
tic_back = time.time()
|
|
iter_load += tic_step - tic_start
|
|
iter_far += tic_far - tic_step
|
|
iter_back += tic_back - tic_far
|
|
|
|
tic_start = time.time()
|
|
if step % args.log_every == 0:
|
|
acc = compute_acc(batch_pred, batch_labels)
|
|
gpu_mem_alloc = (
|
|
th.cuda.max_memory_allocated() / 1000000
|
|
if th.cuda.is_available()
|
|
else 0
|
|
)
|
|
print(
|
|
"Epoch {:05d} | Step {:05d} | Loss {:.4f} | Train Acc {:.4f} | GPU {:.1f} MB".format(
|
|
epoch, step, loss.item(), acc.item(), gpu_mem_alloc
|
|
)
|
|
)
|
|
|
|
toc = time.time()
|
|
print(
|
|
"Epoch Time(s): {:.4f} Load {:.4f} Forward {:.4f} Backward {:.4f}".format(
|
|
toc - tic, iter_load, iter_far, iter_back
|
|
)
|
|
)
|
|
if epoch >= 5:
|
|
avg += toc - tic
|
|
|
|
if epoch % args.eval_every == 0 and epoch != 0:
|
|
eval_acc, test_acc, pred = evaluate(
|
|
model, g, labels, val_nid, test_nid, args.val_batch_size, device
|
|
)
|
|
model = model.to(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 / (epoch - 4)))
|
|
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=30)
|
|
argparser.add_argument("--num-hidden", type=int, default=256)
|
|
argparser.add_argument("--num-layers", type=int, default=3)
|
|
argparser.add_argument("--batch-size", type=int, default=32)
|
|
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=0.001)
|
|
argparser.add_argument("--dropout", type=float, default=0.5)
|
|
argparser.add_argument("--save-pred", type=str, default="")
|
|
argparser.add_argument("--wd", type=float, default=0)
|
|
argparser.add_argument("--num_partitions", type=int, default=15000)
|
|
args = argparser.parse_args()
|
|
|
|
if args.gpu >= 0:
|
|
device = th.device("cuda:%d" % args.gpu)
|
|
else:
|
|
device = th.device("cpu")
|
|
|
|
# load ogbn-products data
|
|
data = DglNodePropPredDataset(name="ogbn-products")
|
|
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]
|
|
labels = labels[:, 0]
|
|
num_nodes = train_idx.shape[0] + val_idx.shape[0] + test_idx.shape[0]
|
|
assert num_nodes == graph.num_nodes()
|
|
graph.ndata["labels"] = labels
|
|
mask = th.zeros(num_nodes, dtype=th.bool)
|
|
mask[train_idx] = True
|
|
graph.ndata["train_mask"] = mask
|
|
mask = th.zeros(num_nodes, dtype=th.bool)
|
|
mask[val_idx] = True
|
|
graph.ndata["valid_mask"] = mask
|
|
mask = th.zeros(num_nodes, dtype=th.bool)
|
|
mask[test_idx] = True
|
|
graph.ndata["test_mask"] = mask
|
|
|
|
graph.in_degrees(0)
|
|
graph.out_degrees(0)
|
|
graph.find_edges(0)
|
|
|
|
cluster_iter_data = ClusterIter(
|
|
"ogbn-products",
|
|
graph,
|
|
args.num_partitions,
|
|
args.batch_size,
|
|
th.cat([train_idx, val_idx, test_idx]),
|
|
)
|
|
idx = th.arange(args.num_partitions // args.batch_size)
|
|
cluster_iterator = DataLoader(
|
|
cluster_iter_data,
|
|
batch_size=32,
|
|
shuffle=True,
|
|
pin_memory=True,
|
|
num_workers=4,
|
|
collate_fn=partial(subgraph_collate_fn, graph),
|
|
)
|
|
|
|
in_feats = graph.ndata["feat"].shape[1]
|
|
print(in_feats)
|
|
n_classes = (labels.max() + 1).item()
|
|
# Pack data
|
|
data = (
|
|
train_idx,
|
|
val_idx,
|
|
test_idx,
|
|
in_feats,
|
|
labels,
|
|
n_classes,
|
|
graph,
|
|
cluster_iterator,
|
|
)
|
|
|
|
# Run 10 times
|
|
test_accs = []
|
|
for i in range(10):
|
|
test_accs.append(run(args, device, data))
|
|
print(
|
|
"Average test accuracy:", np.mean(test_accs), "±", np.std(test_accs)
|
|
)
|