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Unverified Commit f3f35098 authored by xiang song(charlie.song)'s avatar xiang song(charlie.song) Committed by GitHub
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Fix (#2051) 


Co-authored-by: default avatarUbuntu <ubuntu@ip-172-31-63-129.ec2.internal>
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examples/mxnet/appnp/README.md
View file @ f3f35098
......@@ -22,11 +22,11 @@ Results
Run with following (available dataset: "cora", "citeseer", "pubmed")
```bash
DGLBACKEND=mxnet python3 train.py --dataset cora --gpu 0
DGLBACKEND=mxnet python3 appnp.py --dataset cora --gpu 0
```
* cora: 0.8370 (paper: 0.850)
* citeseer: 0.713 (paper: 0.757)
* pubmed: 0.798 (paper: 0.797)
Experiments were done on dgl datasets (GCN settings) which are different from those used in the original implementation. (discrepancies are detailed in experimental section of the original paper)
\ No newline at end of file
Experiments were done on dgl datasets (GCN settings) which are different from those used in the original implementation. (discrepancies are detailed in experimental section of the original paper)
examples/mxnet/sse/README.md deleted 100644 → 0
View file @ 0052f121
Benchmark SSE on multi-GPUs
=======================
Paper link:
[http://proceedings.mlr.press/v80/dai18a/dai18a.pdf](http://proceedings.mlr.press/v80/dai18a/dai18a.pdf)
Dependencies
-------------
* MXNet nightly build
* requests
```bash
pip install mxnet --pre
pip install requests
```
Use a small embedding
---------------------
```bash
DGLBACKEND=mxnet python3 sse_batch.py --graph-file ../../data/5_5_csr.nd \
--n-epochs 1 \
--lr 0.0005 \
--batch-size 1024 \
--use-spmv \
--num-parallel-subgraphs 32 \
--gpu 1 \
--num-feats 100 \
--n-hidden 100
```
Test convergence
----------------
```bash
DGLBACKEND=mxnet python3 sse_batch.py --dataset "pubmed" \
--n-epochs 1000 \
--lr 0.001 \
--batch-size 30 \
--use-spmv \
--neigh-expand 8 \
--n-hidden 16
```
examples/mxnet/sse/sse_batch.py deleted 100644 → 0
View file @ 0052f121
"""
Learning Steady-States of Iterative Algorithms over Graphs
Paper: http://proceedings.mlr.press/v80/dai18a.html
"""
import argparse
import random
import numpy as np
import time
import math
import mxnet as mx
from mxnet import gluon
import dgl
import dgl.function as fn
from dgl import DGLGraph
from dgl.data import register_data_args, load_data
def gcn_msg(edges):
return {'m': mx.nd.concat(edges.src['in'], edges.src['h'], dim=1)}
def gcn_reduce(nodes):
return {'accum': mx.nd.sum(nodes.mailbox['m'], 1) / nodes.mailbox['m'].shape[1]}
class NodeUpdate(gluon.Block):
def __init__(self, out_feats, activation=None, alpha=0.1, **kwargs):
super(NodeUpdate, self).__init__(**kwargs)
self.linear1 = gluon.nn.Dense(out_feats, activation=activation)
self.linear2 = gluon.nn.Dense(out_feats)
self.alpha = alpha
def forward(self, in_data, hidden_data, accum):
tmp = mx.nd.concat(in_data, accum, dim=1)
hidden = self.linear2(self.linear1(tmp))
return hidden_data * (1 - self.alpha) + self.alpha * hidden
class DGLNodeUpdate(gluon.Block):
def __init__(self, update):
super(DGLNodeUpdate, self).__init__()
self.update = update
def forward(self, node):
return {'h1': self.update(node.data['in'], node.data['h'], node.data['accum'])}
class DGLSSEUpdateHiddenInfer(gluon.Block):
def __init__(self,
n_hidden,
activation,
dropout,
use_spmv,
inference,
**kwargs):
super(DGLSSEUpdateHiddenInfer, self).__init__(**kwargs)
with self.name_scope():
self.layer = DGLNodeUpdate(NodeUpdate(n_hidden, activation))
self.dropout = dropout
self.use_spmv = use_spmv
self.inference = inference
def forward(self, g, vertices):
if self.use_spmv:
feat = g.ndata['in']
g.ndata['cat'] = mx.nd.concat(feat, g.ndata['h'], dim=1)
msg_func = fn.copy_src(src='cat', out='m')
reduce_func = fn.sum(msg='m', out='accum')
else:
msg_func = gcn_msg
reduce_func = gcn_reduce
deg = mx.nd.expand_dims(g.in_degrees(), 1).astype(np.float32)
if vertices is None:
g.update_all(msg_func, reduce_func, None)
if self.use_spmv:
g.ndata.pop('cat')
g.ndata['accum'] = g.ndata['accum'] / deg
batch_size = 100000
num_batches = int(math.ceil(g.number_of_nodes() / batch_size))
for i in range(num_batches):
vs = mx.nd.arange(i * batch_size, min((i + 1) * batch_size, g.number_of_nodes()), dtype=np.int64)
g.apply_nodes(self.layer, vs, inplace=self.inference)
g.ndata.pop('accum')
return g.get_n_repr()['h1']
else:
# We don't need dropout for inference.
if self.dropout:
g.ndata['h'] = mx.nd.Dropout(g.ndata['h'], p=self.dropout)
g.update_all(msg_func, reduce_func, None)
ctx = g.ndata['accum'].context
if self.use_spmv:
g.ndata.pop('cat')
deg = deg.as_in_context(ctx)
g.ndata['accum'] = g.ndata['accum'] / deg
g.apply_nodes(self.layer, vertices, inplace=self.inference)
g.ndata.pop('accum')
return mx.nd.take(g.ndata['h1'], vertices.as_in_context(ctx))
class DGLSSEUpdateHiddenTrain(gluon.Block):
def __init__(self,
n_hidden,
activation,
dropout,
use_spmv,
inference,
**kwargs):
super(DGLSSEUpdateHiddenTrain, self).__init__(**kwargs)
with self.name_scope():
self.update = DGLNodeUpdate(NodeUpdate(n_hidden, activation))
self.dropout = dropout
self.use_spmv = use_spmv
self.inference = inference
def forward(self, subg, vertices):
assert vertices is not None
if self.use_spmv:
feat = subg.layers[0].data['in']
subg.layers[0].data['cat'] = mx.nd.concat(feat, subg.layers[0].data['h'],
dim=1)
msg_func = fn.copy_src(src='cat', out='m')
reduce_func = fn.sum(msg='m', out='accum')
else:
msg_func = gcn_msg
reduce_func = gcn_reduce
deg = mx.nd.expand_dims(subg.layer_in_degree(1), 1).astype(np.float32)
# We don't need dropout for inference.
if self.dropout:
subg.layers[0].data['h'] = mx.nd.Dropout(subg.layers[0].data['h'], p=self.dropout)
subg.block_compute(0, msg_func, reduce_func, None)
ctx = subg.layers[1].data['accum'].context
if self.use_spmv:
subg.layers[0].data.pop('cat')
deg = deg.as_in_context(ctx)
subg.layers[1].data['accum'] = subg.layers[1].data['accum'] / deg
subg.apply_layer(1, self.update, inplace=self.inference)
subg.layers[1].data.pop('accum')
return subg.layers[1].data['h1']
class SSEPredict(gluon.Block):
def __init__(self, update_hidden, out_feats, dropout, **kwargs):
super(SSEPredict, self).__init__(**kwargs)
with self.name_scope():
self.linear1 = gluon.nn.Dense(out_feats, activation='relu')
self.linear2 = gluon.nn.Dense(out_feats)
self.update_hidden = update_hidden
self.dropout = dropout
def forward(self, g, vertices):
hidden = self.update_hidden(g, vertices)
if self.dropout:
hidden = mx.nd.Dropout(hidden, p=self.dropout)
return self.linear2(self.linear1(hidden))
def copy_to_gpu(subg, ctx):
for i in range(subg.num_layers):
frame = subg.layers[i].data
for key in frame:
subg.layers[i].data[key] = frame[key].as_in_context(ctx)
class CachedSubgraphLoader(object):
def __init__(self, loader, shuffle):
self._loader = loader
self._cached = []
self._shuffle = shuffle
def restart(self):
self._subgraphs = self._cached
self._gen_subgraph = len(self._subgraphs) == 0
random.shuffle(self._subgraphs)
self._cached = []
def __iter__(self):
return self
def __next__(self):
if len(self._subgraphs) > 0:
subg = self._subgraphs.pop(0)
elif self._gen_subgraph:
subg = self._loader.__next__()
else:
raise StopIteration
# We can't cache the input subgraph because it contains node frames
# and data frames.
subg = dgl.NodeFlow(subg._parent, subg._graph)
self._cached.append(subg)
return subg
def main(args, data):
if isinstance(data.features, mx.nd.NDArray):
features = data.features
else:
features = mx.nd.array(data.features)
if isinstance(data.labels, mx.nd.NDArray):
labels = data.labels
else:
labels = mx.nd.array(data.labels)
if data.train_mask is not None:
train_vs = mx.nd.array(np.nonzero(data.train_mask)[0], dtype='int64')
eval_vs = mx.nd.array(np.nonzero(data.train_mask == 0)[0], dtype='int64')
else:
train_size = len(labels) * args.train_percent
train_vs = mx.nd.arange(0, train_size, dtype='int64')
eval_vs = mx.nd.arange(train_size, len(labels), dtype='int64')
print("train size: " + str(len(train_vs)))
print("eval size: " + str(len(eval_vs)))
eval_labels = mx.nd.take(labels, eval_vs)
in_feats = features.shape[1]
n_edges = data.graph.number_of_edges()
# create the SSE model
try:
graph = data.graph.get_graph()
except AttributeError:
graph = data.graph
g = DGLGraph(graph, readonly=True)
g.ndata['in'] = features
g.ndata['h'] = mx.nd.random.normal(shape=(g.number_of_nodes(), args.n_hidden),
ctx=mx.cpu(0))
update_hidden_infer = DGLSSEUpdateHiddenInfer(args.n_hidden, 'relu',
args.update_dropout, args.use_spmv,
inference=True, prefix='sse')
update_hidden_train = DGLSSEUpdateHiddenTrain(args.n_hidden, 'relu',
args.update_dropout, args.use_spmv,
inference=False, prefix='sse')
model_train = SSEPredict(update_hidden_train, args.n_hidden, args.predict_dropout, prefix='app')
model_infer = SSEPredict(update_hidden_infer, args.n_hidden, args.predict_dropout, prefix='app')
model_infer.initialize(ctx=mx.cpu(0))
if args.gpu <= 0:
model_train.initialize(ctx=mx.cpu(0))
else:
train_ctxs = []
for i in range(args.gpu):
train_ctxs.append(mx.gpu(i))
model_train.initialize(ctx=train_ctxs)
# use optimizer
num_batches = int(g.number_of_nodes() / args.batch_size)
scheduler = mx.lr_scheduler.CosineScheduler(args.n_epochs * num_batches,
args.lr * 10, 0, 0, args.lr/5)
trainer = gluon.Trainer(model_train.collect_params(), 'adam', {'learning_rate': args.lr,
'lr_scheduler': scheduler}, kvstore=mx.kv.create('device'))
# compute vertex embedding.
all_hidden = update_hidden_infer(g, None)
g.ndata['h'] = all_hidden
rets = []
rets.append(all_hidden)
if args.neigh_expand <= 0:
neigh_expand = g.number_of_nodes()
else:
neigh_expand = args.neigh_expand
# initialize graph
dur = []
sampler = dgl.contrib.sampling.NeighborSampler(g, args.batch_size, neigh_expand,
neighbor_type='in', num_workers=args.num_parallel_subgraphs, seed_nodes=train_vs,
shuffle=True)
if args.cache_subgraph:
sampler = CachedSubgraphLoader(sampler, shuffle=True)
for epoch in range(args.n_epochs):
t0 = time.time()
train_loss = 0
i = 0
num_batches = len(train_vs) / args.batch_size
start1 = time.time()
for subg in sampler:
seeds = subg.layer_parent_nid(-1)
subg_seeds = subg.layer_nid(-1)
subg.copy_from_parent()
losses = []
if args.gpu > 0:
ctx = mx.gpu(i % args.gpu)
copy_to_gpu(subg, ctx)
with mx.autograd.record():
logits = model_train(subg, subg_seeds)
batch_labels = mx.nd.take(labels, seeds).as_in_context(logits.context)
loss = mx.nd.softmax_cross_entropy(logits, batch_labels)
loss.backward()
losses.append(loss)
i += 1
if args.gpu <= 0:
trainer.step(seeds.shape[0])
train_loss += loss.asnumpy()[0]
losses = []
elif i % args.gpu == 0:
trainer.step(len(seeds) * len(losses))
for loss in losses:
train_loss += loss.asnumpy()[0]
losses = []
if i % args.num_parallel_subgraphs == 0:
end1 = time.time()
print("process " + str(args.num_parallel_subgraphs)
+ " subgraphs takes " + str(end1 - start1))
start1 = end1
if args.cache_subgraph:
sampler.restart()
else:
sampler = dgl.contrib.sampling.NeighborSampler(g, args.batch_size, neigh_expand,
neighbor_type='in',
num_workers=args.num_parallel_subgraphs,
seed_nodes=train_vs, shuffle=True)
# test set accuracy
logits = model_infer(g, eval_vs)
y_bar = mx.nd.argmax(logits, axis=1)
y = eval_labels
accuracy = mx.nd.sum(y_bar == y) / len(y)
accuracy = accuracy.asnumpy()[0]
# update the inference model.
infer_params = model_infer.collect_params()
for key in infer_params:
idx = trainer._param2idx[key]
trainer._kvstore.pull(idx, out=infer_params[key].data())
# Update node embeddings.
all_hidden = update_hidden_infer(g, None)
g.ndata['h'] = all_hidden
rets.append(all_hidden)
dur.append(time.time() - t0)
print("Epoch {:05d} | Train Loss {:.4f} | Test Accuracy {:.4f} | Time(s) {:.4f} | ETputs(KTEPS) {:.2f}".format(
epoch, train_loss, accuracy, np.mean(dur), n_edges / np.mean(dur) / 1000))
return rets
class MXNetGraph(object):
"""A simple graph object that uses scipy matrix."""
def __init__(self, mat):
self._mat = mat
def get_graph(self):
return self._mat
def number_of_nodes(self):
return self._mat.shape[0]
def number_of_edges(self):
return mx.nd.contrib.getnnz(self._mat).asnumpy()[0]
class GraphData:
def __init__(self, csr, num_feats):
num_edges = mx.nd.contrib.getnnz(csr).asnumpy()[0]
edge_ids = mx.nd.arange(0, num_edges, step=1, repeat=1, dtype=np.int64)
csr = mx.nd.sparse.csr_matrix((edge_ids, csr.indices, csr.indptr), shape=csr.shape, dtype=np.int64)
self.graph = MXNetGraph(csr)
self.features = mx.nd.random.normal(shape=(csr.shape[0], num_feats))
self.labels = mx.nd.floor(mx.nd.random.uniform(low=0, high=10, shape=(csr.shape[0])))
self.train_mask = None
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='GCN')
register_data_args(parser)
parser.add_argument("--graph-file", type=str, default="",
help="graph file")
parser.add_argument("--num-feats", type=int, default=10,
help="the number of features")
parser.add_argument("--gpu", type=int, default=-1,
help="gpu")
parser.add_argument("--lr", type=float, default=1e-3,
help="learning rate")
parser.add_argument("--batch-size", type=int, default=128,
help="number of vertices in a batch")
parser.add_argument("--n-epochs", type=int, default=20,
help="number of training epochs")
parser.add_argument("--n-hidden", type=int, default=16,
help="number of hidden gcn units")
parser.add_argument("--warmup", type=int, default=10,
help="number of iterations to warm up with large learning rate")
parser.add_argument("--update-dropout", type=float, default=0,
help="the dropout rate for updating vertex embedding")
parser.add_argument("--predict-dropout", type=float, default=0,
help="the dropout rate for prediction")
parser.add_argument("--train_percent", type=float, default=0.5,
help="the percentage of data used for training")
parser.add_argument("--use-spmv", action="store_true",
help="use SpMV for faster speed.")
parser.add_argument("--cache-subgraph", default=False, action="store_false")
parser.add_argument("--num-parallel-subgraphs", type=int, default=1,
help="the number of subgraphs to construct in parallel.")
parser.add_argument("--neigh-expand", type=int, default=16,
help="the number of neighbors to sample.")
args = parser.parse_args()
print("cache: " + str(args.cache_subgraph))
# load and preprocess dataset
if args.graph_file != '':
csr = mx.nd.load(args.graph_file)[0]
data = GraphData(csr, args.num_feats)
csr = None
else:
data = load_data(args)
rets1 = main(args, data)
rets2 = main(args, data)
for hidden1, hidden2 in zip(rets1, rets2):
print("hidden: " + str(mx.nd.sum(mx.nd.abs(hidden1 - hidden2)).asnumpy()))
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