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Delete train_sampling_unsupervised.py (#5161)

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import os
import dgl
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 torch.multiprocessing as mp
import dgl.function as fn
import dgl.nn.pytorch as dglnn
import time
import argparse
from torch.nn.parallel import DistributedDataParallel
from model import SAGE, compute_acc_unsupervised as compute_acc
from negative_sampler import NegativeSampler
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from load_graph import load_reddit, load_ogb
class CrossEntropyLoss(nn.Module):
def forward(self, block_outputs, pos_graph, neg_graph):
with pos_graph.local_scope():
pos_graph.ndata['h'] = block_outputs
pos_graph.apply_edges(fn.u_dot_v('h', 'h', 'score'))
pos_score = pos_graph.edata['score']
with neg_graph.local_scope():
neg_graph.ndata['h'] = block_outputs
neg_graph.apply_edges(fn.u_dot_v('h', 'h', 'score'))
neg_score = neg_graph.edata['score']
score = th.cat([pos_score, neg_score])
label = th.cat([th.ones_like(pos_score), th.zeros_like(neg_score)]).long()
loss = F.binary_cross_entropy_with_logits(score, label.float())
return loss
def evaluate(model, g, nfeat, labels, train_nids, val_nids, test_nids, device, args):
"""
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():
# single gpu
if isinstance(model, SAGE):
pred = model.inference(g, nfeat, device, args.batch_size, args.num_workers)
# multi gpu
else:
pred = model.module.inference(g, nfeat, device, args.batch_size, args.num_workers)
model.train()
return compute_acc(pred, labels, train_nids, val_nids, test_nids)
#### Entry point
def run(proc_id, n_gpus, args, devices, data):
# Unpack data
device = th.device(devices[proc_id])
if n_gpus > 0:
th.cuda.set_device(device)
if n_gpus > 1:
dist_init_method = 'tcp://{master_ip}:{master_port}'.format(
master_ip='127.0.0.1', master_port='12345')
world_size = n_gpus
th.distributed.init_process_group(backend="nccl",
init_method=dist_init_method,
world_size=world_size,
rank=proc_id)
train_nid, val_nid, test_nid, n_classes, g, nfeat, labels = data
if args.data_device == 'gpu':
nfeat = nfeat.to(device)
elif args.data_device == 'uva':
nfeat = nfeat.pin_memory()
in_feats = nfeat.shape[1]
# Create PyTorch DataLoader for constructing blocks
n_edges = g.num_edges()
train_seeds = th.arange(n_edges)
if args.graph_device == 'gpu':
train_seeds = train_seeds.to(device)
g = g.to(device)
args.num_workers = 0
elif args.graph_device == 'uva':
train_seeds = train_seeds.to(device)
g.pin_memory_()
args.num_workers = 0
# Create sampler
sampler = dgl.dataloading.NeighborSampler(
[int(fanout) for fanout in args.fan_out.split(',')])
sampler = dgl.dataloading.as_edge_prediction_sampler(
sampler, exclude='reverse_id',
# For each edge with ID e in Reddit dataset, the reverse edge is e ± |E|/2.
reverse_eids=th.cat([
th.arange(n_edges // 2, n_edges),
th.arange(0, n_edges // 2)]).to(train_seeds),
negative_sampler=NegativeSampler(g, args.num_negs, args.neg_share,
device if args.graph_device == 'uva' else None))
dataloader = dgl.dataloading.DataLoader(
g, train_seeds, sampler,
device=device,
use_ddp=n_gpus > 1,
batch_size=args.batch_size,
shuffle=True,
drop_last=False,
num_workers=args.num_workers,
use_uva=args.graph_device == 'uva')
# Define model and optimizer
model = SAGE(in_feats, args.num_hidden, args.num_hidden, args.num_layers, F.relu, args.dropout)
model = model.to(device)
if n_gpus > 1:
model = DistributedDataParallel(model, device_ids=[device], output_device=device)
loss_fcn = CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# Training loop
avg = 0
iter_pos = []
iter_neg = []
iter_d = []
iter_t = []
best_eval_acc = 0
best_test_acc = 0
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.
tic_step = time.time()
for step, (input_nodes, pos_graph, neg_graph, blocks) in enumerate(dataloader):
input_nodes = input_nodes.to(device)
batch_inputs = dgl.utils.gather_pinned_tensor_rows(nfeat, input_nodes)
blocks = [block.int() for block in blocks]
d_step = time.time()
# Compute loss and prediction
batch_pred = model(blocks, batch_inputs)
loss = loss_fcn(batch_pred, pos_graph, neg_graph)
optimizer.zero_grad()
loss.backward()
optimizer.step()
t = time.time()
pos_edges = pos_graph.num_edges()
neg_edges = neg_graph.num_edges()
iter_pos.append(pos_edges / (t - tic_step))
iter_neg.append(neg_edges / (t - tic_step))
iter_d.append(d_step - tic_step)
iter_t.append(t - d_step)
if step % args.log_every == 0 and proc_id == 0:
gpu_mem_alloc = th.cuda.max_memory_allocated() / 1000000 if th.cuda.is_available() else 0
print('[{}]Epoch {:05d} | Step {:05d} | Loss {:.4f} | Speed (samples/sec) {:.4f}|{:.4f} | Load {:.4f}| train {:.4f} | GPU {:.1f} MB'.format(
proc_id, epoch, step, loss.item(), np.mean(iter_pos[3:]), np.mean(iter_neg[3:]), np.mean(iter_d[3:]), np.mean(iter_t[3:]), gpu_mem_alloc))
tic_step = time.time()
toc = time.time()
if proc_id == 0:
print('Epoch Time(s): {:.4f}'.format(toc - tic))
if epoch >= 5:
avg += toc - tic
if (epoch + 1) % args.eval_every == 0:
eval_acc, test_acc = evaluate(model, g, nfeat, labels, train_nid, val_nid, test_nid, device, args)
print('Eval Acc {:.4f} Test Acc {:.4f}'.format(eval_acc, test_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))
if n_gpus > 1:
th.distributed.barrier()
if proc_id == 0:
print('Avg epoch time: {}'.format(avg / (epoch - 4)))
def main(args):
devices = list(map(int, args.gpu.split(',')))
n_gpus = len(devices)
# load dataset
if args.dataset == 'reddit':
g, n_classes = load_reddit(self_loop=False)
elif args.dataset == 'ogbn-products':
g, n_classes = load_ogb('ogbn-products')
else:
raise Exception('unknown dataset')
train_nid = g.ndata.pop('train_mask').nonzero().squeeze()
val_nid = g.ndata.pop('val_mask').nonzero().squeeze()
test_nid = g.ndata.pop('test_mask').nonzero().squeeze()
nfeat = g.ndata.pop('features')
labels = g.ndata.pop('labels')
# Create csr/coo/csc formats before launching training processes with multi-gpu.
# This avoids creating certain formats in each sub-process, which saves memory and CPU.
g.create_formats_()
# this to avoid competition overhead on machines with many cores.
# Change it to a proper number on your machine, especially for multi-GPU training.
os.environ['OMP_NUM_THREADS'] = str(mp.cpu_count() // 2 // n_gpus)
# Pack data
data = train_nid, val_nid, test_nid, n_classes, g, nfeat, labels
if devices[0] == -1:
assert args.graph_device == 'cpu', \
f"Must have GPUs to enable {args.graph_device} sampling."
assert args.data_device == 'cpu', \
f"Must have GPUs to enable {args.data_device} feature storage."
run(0, 0, args, ['cpu'], data)
elif n_gpus == 1:
run(0, n_gpus, args, devices, data)
else:
mp.spawn(run, args=(n_gpus, args, devices, data), nprocs=n_gpus)
if __name__ == '__main__':
argparser = argparse.ArgumentParser("multi-gpu training")
argparser.add_argument("--gpu", type=str, default='0',
help="GPU, can be a list of gpus for multi-gpu training,"
" e.g., 0,1,2,3; -1 for CPU")
argparser.add_argument('--dataset', type=str, default='reddit',
choices=('reddit', 'ogbn-products'))
argparser.add_argument('--num-epochs', type=int, default=20)
argparser.add_argument('--num-hidden', type=int, default=16)
argparser.add_argument('--num-layers', type=int, default=2)
argparser.add_argument('--num-negs', type=int, default=1)
argparser.add_argument('--neg-share', default=False, action='store_true',
help="sharing neg nodes for positive nodes")
argparser.add_argument('--fan-out', type=str, default='10,25')
argparser.add_argument('--batch-size', type=int, default=10000)
argparser.add_argument('--log-every', type=int, default=20)
argparser.add_argument('--eval-every', type=int, default=5)
argparser.add_argument('--lr', type=float, default=0.003)
argparser.add_argument('--dropout', type=float, default=0.5)
argparser.add_argument('--num-workers', type=int, default=0,
help="Number of sampling processes. Use 0 for no extra process.")
argparser.add_argument('--graph-device', choices=('cpu', 'gpu', 'uva'), default='cpu',
help="Device to perform the sampling. "
"Must have 0 workers for 'gpu' and 'uva'")
argparser.add_argument('--data-device', choices=('cpu', 'gpu', 'uva'), default='gpu',
help="By default the script puts all node features and labels "
"on GPU when using it to save time for data copy. This may "
"be undesired if they cannot fit in GPU memory at once. "
"Use 'cpu' to keep the features on host memory and "
"'uva' to enable UnifiedTensor (GPU zero-copy access on "
"pinned host memory).")
args = argparser.parse_args()
main(args)
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