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Unverified Commit fce96140 authored by Chang Liu's avatar Chang Liu Committed by GitHub
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[Example][Refactor] Refactor graphsage multigpu and full-graph example (#4430) 

* Add refactors for multi-gpu and full-graph example

* Fix format

* Update

* Update

* Update
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examples/pytorch/graphsage/train_full.py
View file @ fce96140
"""
Inductive Representation Learning on Large Graphs
Paper: http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf
Code: https://github.com/williamleif/graphsage-simple
Simple reference implementation of GraphSAGE.
"""
import argparse
import time
import numpy as np
import networkx as nx
import torch import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import dgl import dgl.nn as dglnn
from dgl import DGLGraph from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset
from dgl.data import register_data_args, load_data from dgl import AddSelfLoop
from dgl.nn.pytorch.conv import SAGEConv import argparse
class GraphSAGE(nn.Module): class SAGE(nn.Module):
def __init__(self, def __init__(self, in_size, hid_size, out_size):
in_feats, super().__init__()
n_hidden,
n_classes,
n_layers,
activation,
dropout,
aggregator_type):
super(GraphSAGE, self).__init__()
self.layers = nn.ModuleList() self.layers = nn.ModuleList()
self.dropout = nn.Dropout(dropout) # two-layer GraphSAGE-mean
self.activation = activation self.layers.append(dglnn.SAGEConv(in_size, hid_size, 'gcn'))
self.layers.append(dglnn.SAGEConv(hid_size, out_size, 'gcn'))
# input layer self.dropout = nn.Dropout(0.5)
self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type))
# hidden layers
for i in range(n_layers - 1):
self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type))
# output layer
self.layers.append(SAGEConv(n_hidden, n_classes, aggregator_type)) # activation None
def forward(self, graph, inputs): def forward(self, graph, x):
h = self.dropout(inputs) h = self.dropout(x)
for l, layer in enumerate(self.layers): for l, layer in enumerate(self.layers):
h = layer(graph, h) h = layer(graph, h)
if l != len(self.layers) - 1: if l != len(self.layers) - 1:
h = self.activation(h) h = F.relu(h)
h = self.dropout(h) h = self.dropout(h)
return h return h
def evaluate(g, features, labels, mask, model):
def evaluate(model, graph, features, labels, nid):
model.eval() model.eval()
with torch.no_grad(): with torch.no_grad():
logits = model(graph, features) logits = model(g, features)
logits = logits[nid] logits = logits[mask]
labels = labels[nid] labels = labels[mask]
_, indices = torch.max(logits, dim=1) _, indices = torch.max(logits, dim=1)
correct = torch.sum(indices == labels) correct = torch.sum(indices == labels)
return correct.item() * 1.0 / len(labels) return correct.item() * 1.0 / len(labels)
def main(args): def train(g, features, labels, masks, model):
# load and preprocess dataset # define train/val samples, loss function and optimizer
data = load_data(args) train_mask, val_mask = masks
g = data[0] loss_fcn = nn.CrossEntropyLoss()
features = g.ndata['feat'] optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)
labels = g.ndata['label']
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
in_feats = features.shape[1]
n_classes = data.num_classes
n_edges = g.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.int().sum().item(),
val_mask.int().sum().item(),
test_mask.int().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()
print("use cuda:", args.gpu)
train_nid = train_mask.nonzero().squeeze()
val_nid = val_mask.nonzero().squeeze()
test_nid = test_mask.nonzero().squeeze()
# graph preprocess and calculate normalization factor
g = dgl.remove_self_loop(g)
n_edges = g.number_of_edges()
if cuda:
g = g.int().to(args.gpu)
# create GraphSAGE model
model = GraphSAGE(in_feats,
args.n_hidden,
n_classes,
args.n_layers,
F.relu,
args.dropout,
args.aggregator_type)
if cuda:
model.cuda()
# use optimizer # training loop
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) for epoch in range(200):
# initialize graph
dur = []
for epoch in range(args.n_epochs):
model.train() model.train()
if epoch >= 3:
t0 = time.time()
# forward
logits = model(g, features) logits = model(g, features)
loss = F.cross_entropy(logits[train_nid], labels[train_nid]) loss = loss_fcn(logits[train_mask], labels[train_mask])
optimizer.zero_grad() optimizer.zero_grad()
loss.backward() loss.backward()
optimizer.step() optimizer.step()
acc = evaluate(g, features, labels, val_mask, model)
if epoch >= 3: print("Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} "
dur.append(time.time() - t0) . format(epoch, loss.item(), acc))
acc = evaluate(model, g, features, labels, val_nid)
print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
"ETputs(KTEPS) {:.2f}".format(epoch, np.mean(dur), loss.item(),
acc, n_edges / np.mean(dur) / 1000))
print()
acc = evaluate(model, g, features, labels, test_nid)
print("Test Accuracy {:.4f}".format(acc))
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser(description='GraphSAGE') parser = argparse.ArgumentParser(description='GraphSAGE')
register_data_args(parser) parser.add_argument("--dataset", type=str, default="cora",
parser.add_argument("--dropout", type=float, default=0.5, help="Dataset name ('cora', 'citeseer', 'pubmed')")
help="dropout probability")
parser.add_argument("--gpu", type=int, default=-1,
help="gpu")
parser.add_argument("--lr", type=float, default=1e-2,
help="learning rate")
parser.add_argument("--n-epochs", type=int, default=200,
help="number of training epochs")
parser.add_argument("--n-hidden", type=int, default=16,
help="number of hidden gcn units")
parser.add_argument("--n-layers", type=int, default=1,
help="number of hidden gcn layers")
parser.add_argument("--weight-decay", type=float, default=5e-4,
help="Weight for L2 loss")
parser.add_argument("--aggregator-type", type=str, default="gcn",
help="Aggregator type: mean/gcn/pool/lstm")
args = parser.parse_args() args = parser.parse_args()
print(args) print(f'Training with DGL built-in GraphSage module')
main(args) # load and preprocess dataset
transform = AddSelfLoop() # by default, it will first remove self-loops to prevent duplication
if args.dataset == 'cora':
data = CoraGraphDataset(transform=transform)
elif args.dataset == 'citeseer':
data = CiteseerGraphDataset(transform=transform)
elif args.dataset == 'pubmed':
data = PubmedGraphDataset(transform=transform)
else:
raise ValueError('Unknown dataset: {}'.format(args.dataset))
g = data[0]
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
g = g.int().to(device)
features = g.ndata['feat']
labels = g.ndata['label']
masks = g.ndata['train_mask'], g.ndata['val_mask']
# create GraphSAGE model
in_size = features.shape[1]
out_size = data.num_classes
model = SAGE(in_size, 16, out_size).to(device)
# model training
print('Training...')
train(g, features, labels, masks, model)
# test the model
print('Testing...')
acc = evaluate(g, features, labels, g.ndata['test_mask'], model)
print("Test accuracy {:.4f}".format(acc))
examples/pytorch/multigpu/multi_gpu_node_classification.py
View file @ fce96140
import os
import torch import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import torch.distributed as dist
import torch.distributed.optim
import torchmetrics.functional as MF import torchmetrics.functional as MF
import dgl import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
import torch.multiprocessing as mp
import dgl.nn as dglnn import dgl.nn as dglnn
from dgl.multiprocessing import shared_tensor from dgl.multiprocessing import shared_tensor
import time from dgl.data import AsNodePredDataset
import numpy as np from dgl.dataloading import DataLoader, NeighborSampler, MultiLayerFullNeighborSampler
from ogb.nodeproppred import DglNodePropPredDataset from ogb.nodeproppred import DglNodePropPredDataset
import tqdm import tqdm
import argparse
class SAGE(nn.Module): class SAGE(nn.Module):
def __init__(self, in_feats, n_hidden, n_classes): def __init__(self, in_size, hid_size, out_size):
super().__init__() super().__init__()
self.layers = nn.ModuleList() self.layers = nn.ModuleList()
self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, 'mean')) # three-layer GraphSAGE-mean
self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, 'mean')) self.layers.append(dglnn.SAGEConv(in_size, hid_size, 'mean'))
self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, 'mean')) self.layers.append(dglnn.SAGEConv(hid_size, hid_size, 'mean'))
self.layers.append(dglnn.SAGEConv(hid_size, out_size, 'mean'))
self.dropout = nn.Dropout(0.5) self.dropout = nn.Dropout(0.5)
self.n_hidden = n_hidden self.hid_size = hid_size
self.n_classes = n_classes self.out_size = out_size
def _forward_layer(self, l, block, x):
h = self.layers[l](block, x)
if l != len(self.layers) - 1:
h = F.relu(h)
h = self.dropout(h)
return h
def forward(self, blocks, x): def forward(self, blocks, x):
h = x h = x
for l, (layer, block) in enumerate(zip(self.layers, blocks)): for l, (layer, block) in enumerate(zip(self.layers, blocks)):
h = self._forward_layer(l, blocks[l], h) h = layer(block, h)
if l != len(self.layers) - 1:
h = F.relu(h)
h = self.dropout(h)
return h return h
def inference(self, g, device, batch_size): def inference(self, g, device, batch_size, use_uva):
"""
Perform inference in layer-major order rather than batch-major order.
That is, infer the first layer for the entire graph, and store the
intermediate values h_0, before infering the second layer to generate
h_1. This is done for two reasons: 1) it limits the effect of node
degree on the amount of memory used as it only proccesses 1-hop
neighbors at a time, and 2) it reduces the total amount of computation
required as each node is only processed once per layer.
Parameters
----------
g : DGLGraph
The graph to perform inference on.
device : context
The device this process should use for inference
batch_size : int
The number of items to collect in a batch.
Returns
-------
tensor
The predictions for all nodes in the graph.
"""
g.ndata['h'] = g.ndata['feat'] g.ndata['h'] = g.ndata['feat']
sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1, prefetch_node_feats=['h']) sampler = MultiLayerFullNeighborSampler(1, prefetch_node_feats=['h'])
for l, layer in enumerate(self.layers): for l, layer in enumerate(self.layers):
dataloader = dgl.dataloading.DataLoader( dataloader = DataLoader(
g, torch.arange(g.num_nodes(), device=device), sampler, device=device, g, torch.arange(g.num_nodes(), device=device), sampler, device=device,
batch_size=batch_size, shuffle=False, drop_last=False, batch_size=batch_size, shuffle=False, drop_last=False,
num_workers=0, use_ddp=True, use_uva=True) num_workers=0, use_ddp=True, use_uva=use_uva)
# in order to prevent running out of GPU memory, we allocate a # in order to prevent running out of GPU memory, allocate a
# shared output tensor 'y' in host memory # shared output tensor 'y' in host memory
y = shared_tensor( y = shared_tensor(
(g.num_nodes(), self.n_hidden if l != len(self.layers) - 1 else self.n_classes)) (g.num_nodes(), self.hid_size if l != len(self.layers) - 1 else self.out_size))
for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader) \ for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader) \
if dist.get_rank() == 0 else dataloader: if dist.get_rank() == 0 else dataloader:
x = blocks[0].srcdata['h'] x = blocks[0].srcdata['h']
h = self._forward_layer(l, blocks[0], x) h = layer(blocks[0], x) # len(blocks) = 1
y[output_nodes] = h.to(y.device) if l != len(self.layers) - 1:
h = F.relu(h)
h = self.dropout(h)
# non_blocking (with pinned memory) to accelerate data transfer
y[output_nodes] = h.to(y.device, non_blocking=True)
# make sure all GPUs are done writing to 'y' # make sure all GPUs are done writing to 'y'
dist.barrier() dist.barrier()
if l + 1 < len(self.layers): g.ndata['h'] = y if use_uva else y.to(device)
# assign the output features of this layer as the new input
# features for the next layer
g.ndata['h'] = y
else:
# remove the intermediate data from the graph
g.ndata.pop('h')
return y
g.ndata.pop('h')
return y
def train(rank, world_size, graph, num_classes, split_idx): def evaluate(model, g, dataloader):
torch.cuda.set_device(rank) model.eval()
dist.init_process_group('nccl', 'tcp://127.0.0.1:12347', world_size=world_size, rank=rank) ys = []
y_hats = []
for it, (input_nodes, output_nodes, blocks) in enumerate(dataloader):
with torch.no_grad():
x = blocks[0].srcdata['feat']
ys.append(blocks[-1].dstdata['label'])
y_hats.append(model(blocks, x))
return MF.accuracy(torch.cat(y_hats), torch.cat(ys))
model = SAGE(graph.ndata['feat'].shape[1], 256, num_classes).cuda() def layerwise_infer(proc_id, device, g, nid, model, use_uva, batch_size = 2**16):
model = nn.parallel.DistributedDataParallel(model, device_ids=[rank], output_device=rank) model.eval()
with torch.no_grad():
pred = model.module.inference(g, device, batch_size, use_uva)
pred = pred[nid]
labels = g.ndata['label'][nid].to(pred.device)
if proc_id == 0:
acc = MF.accuracy(pred, labels)
print("Test Accuracy {:.4f}".format(acc.item()))
def train(proc_id, nprocs, device, g, train_idx, val_idx, model, use_uva):
sampler = NeighborSampler([10, 10, 10],
prefetch_node_feats=['feat'],
prefetch_labels=['label'])
train_dataloader = DataLoader(g, train_idx, sampler, device=device,
batch_size=1024, shuffle=True,
drop_last=False, num_workers=0,
use_ddp=True, use_uva=use_uva)
val_dataloader = DataLoader(g, val_idx, sampler, device=device,
batch_size=1024, shuffle=True,
drop_last=False, num_workers=0,
use_ddp=True, use_uva=use_uva)
opt = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4) opt = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4)
for epoch in range(10):
train_idx, valid_idx, test_idx = split_idx['train'], split_idx['valid'], split_idx['test']
# move ids to GPU
train_idx = train_idx.to('cuda')
valid_idx = valid_idx.to('cuda')
# For training, each process/GPU will get a subset of the
# train_idx/valid_idx, and generate mini-batches indepednetly. This allows
# the only communication neccessary in training to be the all-reduce for
# the gradients performed by the DDP wrapper (created above).
sampler = dgl.dataloading.NeighborSampler(
[15, 10, 5], prefetch_node_feats=['feat'], prefetch_labels=['label'])
train_dataloader = dgl.dataloading.DataLoader(
graph, train_idx, sampler,
device='cuda', batch_size=1024, shuffle=True, drop_last=False,
num_workers=0, use_ddp=True, use_uva=True)
valid_dataloader = dgl.dataloading.DataLoader(
graph, valid_idx, sampler, device='cuda', batch_size=1024, shuffle=True,
drop_last=False, num_workers=0, use_ddp=True,
use_uva=True)
durations = []
for _ in range(10):
model.train() model.train()
t0 = time.time() total_loss = 0
for it, (input_nodes, output_nodes, blocks) in enumerate(train_dataloader): for it, (input_nodes, output_nodes, blocks) in enumerate(train_dataloader):
x = blocks[0].srcdata['feat'] x = blocks[0].srcdata['feat']
y = blocks[-1].dstdata['label'][:, 0] y = blocks[-1].dstdata['label']
y_hat = model(blocks, x) y_hat = model(blocks, x)
loss = F.cross_entropy(y_hat, y) loss = F.cross_entropy(y_hat, y)
opt.zero_grad() opt.zero_grad()
loss.backward() loss.backward()
opt.step() opt.step()
if it % 20 == 0 and rank == 0: total_loss += loss
acc = MF.accuracy(y_hat, y) acc = evaluate(model, g, val_dataloader).to(device) / nprocs
mem = torch.cuda.max_memory_allocated() / 1000000
print('Loss', loss.item(), 'Acc', acc.item(), 'GPU Mem', mem, 'MB')
tt = time.time()
if rank == 0:
print(tt - t0)
durations.append(tt - t0)
model.eval()
ys = []
y_hats = []
for it, (input_nodes, output_nodes, blocks) in enumerate(valid_dataloader):
with torch.no_grad():
x = blocks[0].srcdata['feat']
ys.append(blocks[-1].dstdata['label'])
y_hats.append(model.module(blocks, x))
acc = MF.accuracy(torch.cat(y_hats), torch.cat(ys)) / world_size
dist.reduce(acc, 0) dist.reduce(acc, 0)
if rank == 0: if (proc_id == 0):
print('Validation acc:', acc.item()) print("Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} "
dist.barrier() .format(epoch, total_loss / (it+1), acc.item()))
if rank == 0: def run(proc_id, nprocs, devices, g, data, mode):
print(np.mean(durations[4:]), np.std(durations[4:])) # find corresponding device for my rank
model.eval() device = devices[proc_id]
with torch.no_grad(): torch.cuda.set_device(device)
# since we do 1-layer at a time, use a very large batch size # initialize process group and unpack data for sub-processes
pred = model.module.inference(graph, device='cuda', batch_size=2**16) dist.init_process_group(backend="nccl", init_method='tcp://127.0.0.1:12345',
if rank == 0: world_size=nprocs, rank=proc_id)
acc = MF.accuracy(pred[test_idx], graph.ndata['label'][test_idx]) out_size, train_idx, val_idx, test_idx = data
print('Test acc:', acc.item()) train_idx = train_idx.to(device)
val_idx = val_idx.to(device)
g = g.to(device if mode == 'puregpu' else 'cpu')
# create GraphSAGE model (distributed)
in_size = g.ndata['feat'].shape[1]
model = SAGE(in_size, 256, out_size).to(device)
model = DistributedDataParallel(model, device_ids=[device], output_device=device)
# training + testing
use_uva = (mode == 'mixed')
train(proc_id, nprocs, device, g, train_idx, val_idx, model, use_uva)
layerwise_infer(proc_id, device, g, test_idx, model, use_uva)
# cleanup process group
dist.destroy_process_group()
if __name__ == '__main__': if __name__ == '__main__':
dataset = DglNodePropPredDataset('ogbn-products') parser = argparse.ArgumentParser()
graph, labels = dataset[0] parser.add_argument("--mode", default='mixed', choices=['mixed', 'puregpu'],
graph.ndata['label'] = labels help="Training mode. 'mixed' for CPU-GPU mixed training, "
graph.create_formats_() # must be called before mp.spawn(). "'puregpu' for pure-GPU training.")
split_idx = dataset.get_idx_split() parser.add_argument("--gpu", type=str, default='0',
num_classes = dataset.num_classes help="GPU(s) in use. Can be a list of gpu ids for multi-gpu training,"
# use all available GPUs " e.g., 0,1,2,3.")
n_procs = torch.cuda.device_count() args = parser.parse_args()
devices = list(map(int, args.gpu.split(',')))
# Tested with mp.spawn and fork. Both worked and got 4s per epoch with 4 GPUs nprocs = len(devices)
# and 3.86s per epoch with 8 GPUs on p2.8x, compared to 5.2s from official examples. assert torch.cuda.is_available(), f"Must have GPUs to enable multi-gpu training."
import torch.multiprocessing as mp print(f'Training in {args.mode} mode using {nprocs} GPU(s)')
mp.spawn(train, args=(n_procs, graph, num_classes, split_idx), nprocs=n_procs)
# load and preprocess dataset
print('Loading data')
dataset = AsNodePredDataset(DglNodePropPredDataset('ogbn-products'))
g = dataset[0]
# avoid creating certain graph formats in each sub-process to save momory
g.create_formats_()
# thread limiting to avoid resource competition
os.environ['OMP_NUM_THREADS'] = str(mp.cpu_count() // 2 // nprocs)
data = dataset.num_classes, dataset.train_idx, dataset.val_idx, dataset.test_idx
mp.spawn(run, args=(nprocs, devices, g, data, args.mode), nprocs=nprocs)
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