chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,476 @@
|
||||
import argparse
|
||||
import time
|
||||
from contextlib import contextmanager
|
||||
|
||||
import dgl
|
||||
import dgl.distributed
|
||||
import dgl.function as fn
|
||||
import dgl.nn.pytorch as dglnn
|
||||
|
||||
import numpy as np
|
||||
import sklearn.linear_model as lm
|
||||
import sklearn.metrics as skm
|
||||
import torch as th
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optim
|
||||
import tqdm
|
||||
|
||||
|
||||
class DistSAGE(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 i, (layer, block) in enumerate(zip(self.layers, blocks)):
|
||||
h = layer(block, h)
|
||||
if i != 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?
|
||||
nodes = dgl.distributed.node_split(
|
||||
np.arange(g.num_nodes()),
|
||||
g.get_partition_book(),
|
||||
force_even=True,
|
||||
)
|
||||
y = dgl.distributed.DistTensor(
|
||||
(g.num_nodes(), self.n_hidden),
|
||||
th.float32,
|
||||
"h",
|
||||
persistent=True,
|
||||
)
|
||||
for i, layer in enumerate(self.layers):
|
||||
if i == len(self.layers) - 1:
|
||||
y = dgl.distributed.DistTensor(
|
||||
(g.num_nodes(), self.n_classes),
|
||||
th.float32,
|
||||
"h_last",
|
||||
persistent=True,
|
||||
)
|
||||
# Create sampler
|
||||
sampler = dgl.dataloading.NeighborSampler([-1])
|
||||
# Create dataloader
|
||||
dataloader = dgl.distributed.DistNodeDataLoader(
|
||||
g,
|
||||
nodes,
|
||||
sampler,
|
||||
batch_size=batch_size,
|
||||
shuffle=False,
|
||||
drop_last=False,
|
||||
)
|
||||
|
||||
for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
|
||||
block = blocks[0].to(device)
|
||||
h = x[input_nodes].to(device)
|
||||
h_dst = h[: block.number_of_dst_nodes()]
|
||||
h = layer(block, (h, h_dst))
|
||||
if i != len(self.layers) - 1:
|
||||
h = self.activation(h)
|
||||
h = self.dropout(h)
|
||||
|
||||
y[output_nodes] = h.cpu()
|
||||
|
||||
x = y
|
||||
g.barrier()
|
||||
return y
|
||||
|
||||
@contextmanager
|
||||
def join(self):
|
||||
"""dummy join for standalone"""
|
||||
yield
|
||||
|
||||
|
||||
def load_subtensor(g, input_nodes, device):
|
||||
"""
|
||||
Copys features and labels of a set of nodes onto GPU.
|
||||
"""
|
||||
batch_inputs = g.ndata["features"][input_nodes].to(device)
|
||||
return batch_inputs
|
||||
|
||||
|
||||
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 generate_emb(model, g, inputs, batch_size, device):
|
||||
"""
|
||||
Generate embeddings for each node
|
||||
g : The entire graph.
|
||||
inputs : The features of all the nodes.
|
||||
batch_size : Number of nodes to compute at the same time.
|
||||
device : The GPU device to evaluate on.
|
||||
"""
|
||||
model.eval()
|
||||
with th.no_grad():
|
||||
pred = model.inference(g, inputs, batch_size, device)
|
||||
|
||||
return pred
|
||||
|
||||
|
||||
def compute_acc(emb, labels, train_nids, val_nids, test_nids):
|
||||
"""
|
||||
Compute the accuracy of prediction given the labels.
|
||||
|
||||
We will fist train a LogisticRegression model using the trained embeddings,
|
||||
the training set, validation set and test set is provided as the arguments.
|
||||
|
||||
The final result is predicted by the lr model.
|
||||
|
||||
emb: The pretrained embeddings
|
||||
labels: The ground truth
|
||||
train_nids: The training set node ids
|
||||
val_nids: The validation set node ids
|
||||
test_nids: The test set node ids
|
||||
"""
|
||||
|
||||
emb = emb[np.arange(labels.shape[0])].cpu().numpy()
|
||||
train_nids = train_nids.cpu().numpy()
|
||||
val_nids = val_nids.cpu().numpy()
|
||||
test_nids = test_nids.cpu().numpy()
|
||||
labels = labels.cpu().numpy()
|
||||
|
||||
emb = (emb - emb.mean(0, keepdims=True)) / emb.std(0, keepdims=True)
|
||||
lr = lm.LogisticRegression(multi_class="multinomial", max_iter=10000)
|
||||
lr.fit(emb[train_nids], labels[train_nids])
|
||||
|
||||
pred = lr.predict(emb)
|
||||
eval_acc = skm.accuracy_score(labels[val_nids], pred[val_nids])
|
||||
test_acc = skm.accuracy_score(labels[test_nids], pred[test_nids])
|
||||
return eval_acc, test_acc
|
||||
|
||||
|
||||
def run(args, device, data):
|
||||
# Unpack data
|
||||
(
|
||||
train_eids,
|
||||
train_nids,
|
||||
in_feats,
|
||||
g,
|
||||
global_train_nid,
|
||||
global_valid_nid,
|
||||
global_test_nid,
|
||||
labels,
|
||||
) = data
|
||||
# Create sampler
|
||||
neg_sampler = dgl.dataloading.negative_sampler.Uniform(args.num_negs)
|
||||
sampler = dgl.dataloading.NeighborSampler(
|
||||
[int(fanout) for fanout in args.fan_out.split(",")]
|
||||
)
|
||||
# Create dataloader
|
||||
exclude = "reverse_id" if args.remove_edge else None
|
||||
reverse_eids = th.arange(g.num_edges()) if args.remove_edge else None
|
||||
dataloader = dgl.distributed.DistEdgeDataLoader(
|
||||
g,
|
||||
train_eids,
|
||||
sampler,
|
||||
negative_sampler=neg_sampler,
|
||||
exclude=exclude,
|
||||
reverse_eids=reverse_eids,
|
||||
batch_size=args.batch_size,
|
||||
shuffle=True,
|
||||
drop_last=False,
|
||||
)
|
||||
# Define model and optimizer
|
||||
model = DistSAGE(
|
||||
in_feats,
|
||||
args.num_hidden,
|
||||
args.num_hidden,
|
||||
args.num_layers,
|
||||
F.relu,
|
||||
args.dropout,
|
||||
)
|
||||
model = model.to(device)
|
||||
if not args.standalone:
|
||||
if args.num_gpus == -1:
|
||||
model = th.nn.parallel.DistributedDataParallel(model)
|
||||
else:
|
||||
dev_id = g.rank() % args.num_gpus
|
||||
model = th.nn.parallel.DistributedDataParallel(
|
||||
model, device_ids=[dev_id], output_device=dev_id
|
||||
)
|
||||
loss_fcn = CrossEntropyLoss()
|
||||
loss_fcn = loss_fcn.to(device)
|
||||
optimizer = optim.Adam(model.parameters(), lr=args.lr)
|
||||
|
||||
# Training loop
|
||||
epoch = 0
|
||||
for epoch in range(args.num_epochs):
|
||||
num_seeds = 0
|
||||
num_inputs = 0
|
||||
|
||||
step_time = []
|
||||
sample_t = []
|
||||
feat_copy_t = []
|
||||
forward_t = []
|
||||
backward_t = []
|
||||
update_t = []
|
||||
iter_tput = []
|
||||
|
||||
start = time.time()
|
||||
with model.join():
|
||||
# Loop over the dataloader to sample the computation dependency
|
||||
# graph as a list of blocks.
|
||||
for step, (input_nodes, pos_graph, neg_graph, blocks) in enumerate(
|
||||
dataloader
|
||||
):
|
||||
if args.debug:
|
||||
# Verify exclude_edges functionality.
|
||||
for block in blocks:
|
||||
current_eids = block.edata[dgl.EID]
|
||||
seed_eids = pos_graph.edata[dgl.EID]
|
||||
if exclude is None:
|
||||
assert th.any(th.isin(current_eids, seed_eids))
|
||||
elif exclude == "self":
|
||||
assert not th.any(th.isin(current_eids, seed_eids))
|
||||
elif exclude == "reverse_id":
|
||||
assert not th.any(th.isin(current_eids, seed_eids))
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported exclude type: {exclude}"
|
||||
)
|
||||
tic_step = time.time()
|
||||
sample_t.append(tic_step - start)
|
||||
|
||||
copy_t = time.time()
|
||||
pos_graph = pos_graph.to(device)
|
||||
neg_graph = neg_graph.to(device)
|
||||
blocks = [block.to(device) for block in blocks]
|
||||
batch_inputs = load_subtensor(g, input_nodes, device)
|
||||
copy_time = time.time()
|
||||
feat_copy_t.append(copy_time - copy_t)
|
||||
|
||||
# Compute loss and prediction
|
||||
batch_pred = model(blocks, batch_inputs)
|
||||
loss = loss_fcn(batch_pred, pos_graph, neg_graph)
|
||||
forward_end = time.time()
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
compute_end = time.time()
|
||||
forward_t.append(forward_end - copy_time)
|
||||
backward_t.append(compute_end - forward_end)
|
||||
|
||||
# Aggregate gradients in multiple nodes.
|
||||
optimizer.step()
|
||||
update_t.append(time.time() - compute_end)
|
||||
|
||||
pos_edges = pos_graph.num_edges()
|
||||
|
||||
step_t = time.time() - start
|
||||
step_time.append(step_t)
|
||||
iter_tput.append(pos_edges / step_t)
|
||||
num_seeds += pos_edges
|
||||
if step % args.log_every == 0:
|
||||
print(
|
||||
"[{}] Epoch {:05d} | Step {:05d} | Loss {:.4f} | Speed "
|
||||
"(samples/sec) {:.4f} | time {:.3f}s | sample {:.3f} | "
|
||||
"copy {:.3f} | forward {:.3f} | backward {:.3f} | "
|
||||
"update {:.3f}".format(
|
||||
g.rank(),
|
||||
epoch,
|
||||
step,
|
||||
loss.item(),
|
||||
np.mean(iter_tput[3:]),
|
||||
np.sum(step_time[-args.log_every :]),
|
||||
np.sum(sample_t[-args.log_every :]),
|
||||
np.sum(feat_copy_t[-args.log_every :]),
|
||||
np.sum(forward_t[-args.log_every :]),
|
||||
np.sum(backward_t[-args.log_every :]),
|
||||
np.sum(update_t[-args.log_every :]),
|
||||
)
|
||||
)
|
||||
start = time.time()
|
||||
|
||||
print(
|
||||
"[{}]Epoch Time(s): {:.4f}, sample: {:.4f}, data copy: {:.4f}, "
|
||||
"forward: {:.4f}, backward: {:.4f}, update: {:.4f}, #seeds: {}, "
|
||||
"#inputs: {}".format(
|
||||
g.rank(),
|
||||
np.sum(step_time),
|
||||
np.sum(sample_t),
|
||||
np.sum(feat_copy_t),
|
||||
np.sum(forward_t),
|
||||
np.sum(backward_t),
|
||||
np.sum(update_t),
|
||||
num_seeds,
|
||||
num_inputs,
|
||||
)
|
||||
)
|
||||
epoch += 1
|
||||
|
||||
# evaluate the embedding using LogisticRegression
|
||||
pred = generate_emb(
|
||||
model if args.standalone else model.module,
|
||||
g,
|
||||
g.ndata["features"],
|
||||
args.batch_size_eval,
|
||||
device,
|
||||
)
|
||||
if g.rank() == 0:
|
||||
eval_acc, test_acc = compute_acc(
|
||||
pred, labels, global_train_nid, global_valid_nid, global_test_nid
|
||||
)
|
||||
print("eval acc {:.4f}; test acc {:.4f}".format(eval_acc, test_acc))
|
||||
|
||||
# sync for eval and test
|
||||
if not args.standalone:
|
||||
th.distributed.barrier()
|
||||
|
||||
if not args.standalone:
|
||||
g._client.barrier()
|
||||
|
||||
# save features into file
|
||||
if g.rank() == 0:
|
||||
th.save(pred, "emb.pt")
|
||||
else:
|
||||
th.save(pred, "emb.pt")
|
||||
|
||||
|
||||
def main(args):
|
||||
print("--- Distributed node classification with GraphSAGE unsuperised ---")
|
||||
dgl.distributed.initialize(args.ip_config)
|
||||
if not args.standalone:
|
||||
th.distributed.init_process_group(backend="gloo")
|
||||
g = dgl.distributed.DistGraph(args.graph_name, part_config=args.part_config)
|
||||
print("rank:", g.rank())
|
||||
print("number of edges", g.num_edges())
|
||||
|
||||
train_eids = dgl.distributed.edge_split(
|
||||
th.ones((g.num_edges(),), dtype=th.bool),
|
||||
g.get_partition_book(),
|
||||
force_even=True,
|
||||
)
|
||||
train_nids = dgl.distributed.node_split(
|
||||
th.ones((g.num_nodes(),), dtype=th.bool), g.get_partition_book()
|
||||
)
|
||||
global_train_nid = th.LongTensor(
|
||||
np.nonzero(g.ndata["train_mask"][np.arange(g.num_nodes())])
|
||||
)
|
||||
global_valid_nid = th.LongTensor(
|
||||
np.nonzero(g.ndata["val_mask"][np.arange(g.num_nodes())])
|
||||
)
|
||||
global_test_nid = th.LongTensor(
|
||||
np.nonzero(g.ndata["test_mask"][np.arange(g.num_nodes())])
|
||||
)
|
||||
labels = g.ndata["labels"][np.arange(g.num_nodes())]
|
||||
if args.num_gpus == -1:
|
||||
device = th.device("cpu")
|
||||
else:
|
||||
dev_id = g.rank() % args.num_gpus
|
||||
device = th.device("cuda:" + str(dev_id))
|
||||
|
||||
# Pack data
|
||||
in_feats = g.ndata["features"].shape[1]
|
||||
global_train_nid = global_train_nid.squeeze()
|
||||
global_valid_nid = global_valid_nid.squeeze()
|
||||
global_test_nid = global_test_nid.squeeze()
|
||||
print("number of train {}".format(global_train_nid.shape[0]))
|
||||
print("number of valid {}".format(global_valid_nid.shape[0]))
|
||||
print("number of test {}".format(global_test_nid.shape[0]))
|
||||
data = (
|
||||
train_eids,
|
||||
train_nids,
|
||||
in_feats,
|
||||
g,
|
||||
global_train_nid,
|
||||
global_valid_nid,
|
||||
global_test_nid,
|
||||
labels,
|
||||
)
|
||||
run(args, device, data)
|
||||
print("parent ends")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="GCN")
|
||||
parser.add_argument("--graph_name", type=str, help="graph name")
|
||||
parser.add_argument("--id", type=int, help="the partition id")
|
||||
parser.add_argument(
|
||||
"--ip_config", type=str, help="The file for IP configuration"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--part_config", type=str, help="The path to the partition config file"
|
||||
)
|
||||
parser.add_argument("--n_classes", type=int, help="the number of classes")
|
||||
parser.add_argument(
|
||||
"--num_gpus",
|
||||
type=int,
|
||||
default=-1,
|
||||
help="the number of GPU device. Use -1 for CPU training",
|
||||
)
|
||||
parser.add_argument("--num_epochs", type=int, default=20)
|
||||
parser.add_argument("--num_hidden", type=int, default=16)
|
||||
parser.add_argument("--num-layers", type=int, default=2)
|
||||
parser.add_argument("--fan_out", type=str, default="10,25")
|
||||
parser.add_argument("--batch_size", type=int, default=1000)
|
||||
parser.add_argument("--batch_size_eval", type=int, default=100000)
|
||||
parser.add_argument("--log_every", type=int, default=20)
|
||||
parser.add_argument("--eval_every", type=int, default=5)
|
||||
parser.add_argument("--lr", type=float, default=0.003)
|
||||
parser.add_argument("--dropout", type=float, default=0.5)
|
||||
parser.add_argument(
|
||||
"--local_rank", type=int, help="get rank of the process"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--standalone", action="store_true", help="run in the standalone mode"
|
||||
)
|
||||
parser.add_argument("--num_negs", type=int, default=1)
|
||||
parser.add_argument(
|
||||
"--remove_edge",
|
||||
default=False,
|
||||
action="store_true",
|
||||
help="whether to remove edges during sampling",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--debug",
|
||||
default=False,
|
||||
action="store_true",
|
||||
help="whether to verify functionality of remove edges",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
print(args)
|
||||
main(args)
|
||||
Reference in New Issue
Block a user