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Unverified Commit 89a4cc4d authored by Hongzhi (Steve), Chen's avatar Hongzhi (Steve), Chen Committed by GitHub
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[Misc] Black auto fix. (#4694) 


Co-authored-by: default avatarSteve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>
parent 303b150f
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tests/mxnet/test_geometry.py
View file @ 89a4cc4d
import backend as F
import mxnet as mx
import numpy as np
from dgl.geometry import farthest_point_sampler
import backend as F
import numpy as np
def test_fps():
N = 1000
batch_size = 5
sample_points = 10
x = mx.nd.array(np.random.uniform(size=(batch_size, int(N/batch_size), 3)))
x = mx.nd.array(
np.random.uniform(size=(batch_size, int(N / batch_size), 3))
)
ctx = F.ctx()
if F.gpu_ctx():
x = x.as_in_context(ctx)
......@@ -17,5 +20,6 @@ def test_fps():
assert res.shape[1] == sample_points
assert res.sum() > 0
if __name__ == '__main__':
if __name__ == "__main__":
test_fps()
tests/mxnet/test_nn.py
View file @ 89a4cc4d
import backend as F
import mxnet as mx
import networkx as nx
import numpy as np
import scipy as sp
import pytest
import scipy as sp
from mxnet import autograd, gluon, nd
from test_utils import parametrize_idtype
from test_utils.graph_cases import (
get_cases,
random_bipartite,
random_dglgraph,
random_graph,
)
import dgl
import dgl.nn.mxnet as nn
import dgl.function as fn
import backend as F
from test_utils.graph_cases import get_cases, random_graph, random_bipartite, random_dglgraph
from test_utils import parametrize_idtype
from mxnet import autograd, gluon, nd
import dgl.nn.mxnet as nn
def check_close(a, b):
assert np.allclose(a.asnumpy(), b.asnumpy(), rtol=1e-4, atol=1e-4)
def _AXWb(A, X, W, b):
X = mx.nd.dot(X, W.data(X.context))
Y = mx.nd.dot(A, X.reshape(X.shape[0], -1)).reshape(X.shape)
return Y + b.data(X.context)
@parametrize_idtype
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_graph_conv(idtype, out_dim):
g = dgl.from_networkx(nx.path_graph(3))
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
adj = g.adjacency_matrix(transpose=True, ctx=ctx)
conv = nn.GraphConv(5, out_dim, norm='none', bias=True)
conv = nn.GraphConv(5, out_dim, norm="none", bias=True)
conv.initialize(ctx=ctx)
# test#1: basic
h0 = F.ones((3, 5))
......@@ -77,14 +86,18 @@ def test_graph_conv(idtype, out_dim):
assert len(g.ndata) == 1
assert len(g.edata) == 0
assert "h" in g.ndata
check_close(g.ndata['h'], 2 * F.ones((3, 1)))
check_close(g.ndata["h"], 2 * F.ones((3, 1)))
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'], exclude=['zero-degree', 'dglgraph']))
@pytest.mark.parametrize('norm', ['none', 'both', 'right', 'left'])
@pytest.mark.parametrize('weight', [True, False])
@pytest.mark.parametrize('bias', [False])
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize(
"g",
get_cases(["homo", "block-bipartite"], exclude=["zero-degree", "dglgraph"]),
)
@pytest.mark.parametrize("norm", ["none", "both", "right", "left"])
@pytest.mark.parametrize("weight", [True, False])
@pytest.mark.parametrize("bias", [False])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_graph_conv2(idtype, g, norm, weight, bias, out_dim):
g = g.astype(idtype).to(F.ctx())
conv = nn.GraphConv(5, out_dim, norm=norm, weight=weight, bias=bias)
......@@ -99,12 +112,15 @@ def test_graph_conv2(idtype, g, norm, weight, bias, out_dim):
h_out = conv(g, h, ext_w)
assert h_out.shape == (ndst, out_dim)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['bipartite'], exclude=['zero-degree', 'dglgraph']))
@pytest.mark.parametrize('norm', ['none', 'both', 'right'])
@pytest.mark.parametrize('weight', [True, False])
@pytest.mark.parametrize('bias', [False])
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize(
"g", get_cases(["bipartite"], exclude=["zero-degree", "dglgraph"])
)
@pytest.mark.parametrize("norm", ["none", "both", "right"])
@pytest.mark.parametrize("weight", [True, False])
@pytest.mark.parametrize("bias", [False])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_graph_conv2_bi(idtype, g, norm, weight, bias, out_dim):
g = g.astype(idtype).to(F.ctx())
conv = nn.GraphConv(5, out_dim, norm=norm, weight=weight, bias=bias)
......@@ -120,6 +136,7 @@ def test_graph_conv2_bi(idtype, g, norm, weight, bias, out_dim):
h_out = conv(g, (h, h_dst), ext_w)
assert h_out.shape == (ndst, out_dim)
def _S2AXWb(A, N, X, W, b):
X1 = X * N
X1 = mx.nd.dot(A, X1.reshape(X1.shape[0], -1))
......@@ -132,12 +149,13 @@ def _S2AXWb(A, N, X, W, b):
return Y + b
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_tagconv(out_dim):
g = dgl.from_networkx(nx.path_graph(3)).to(F.ctx())
ctx = F.ctx()
adj = g.adjacency_matrix(transpose=True, ctx=ctx)
norm = mx.nd.power(g.in_degrees().astype('float32'), -0.5)
norm = mx.nd.power(g.in_degrees().astype("float32"), -0.5)
conv = nn.TAGConv(5, out_dim, bias=True)
conv.initialize(ctx=ctx)
......@@ -151,7 +169,9 @@ def test_tagconv(out_dim):
shp = norm.shape + (1,) * (h0.ndim - 1)
norm = norm.reshape(shp).as_in_context(h0.context)
assert F.allclose(h1, _S2AXWb(adj, norm, h0, conv.lin.data(ctx), conv.h_bias.data(ctx)))
assert F.allclose(
h1, _S2AXWb(adj, norm, h0, conv.lin.data(ctx), conv.h_bias.data(ctx))
)
conv = nn.TAGConv(5, out_dim)
conv.initialize(ctx=ctx)
......@@ -161,10 +181,13 @@ def test_tagconv(out_dim):
h1 = conv(g, h0)
assert h1.shape[-1] == out_dim
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'], exclude=['zero-degree']))
@pytest.mark.parametrize('out_dim', [1, 20])
@pytest.mark.parametrize('num_heads', [1, 5])
@pytest.mark.parametrize(
"g", get_cases(["homo", "block-bipartite"], exclude=["zero-degree"])
)
@pytest.mark.parametrize("out_dim", [1, 20])
@pytest.mark.parametrize("num_heads", [1, 5])
def test_gat_conv(g, idtype, out_dim, num_heads):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
......@@ -182,25 +205,30 @@ def test_gat_conv(g, idtype, out_dim, num_heads):
gat.initialize(ctx=ctx)
h = gat(g, feat)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['bipartite'], exclude=['zero-degree']))
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize('num_heads', [1, 4])
@pytest.mark.parametrize("g", get_cases(["bipartite"], exclude=["zero-degree"]))
@pytest.mark.parametrize("out_dim", [1, 2])
@pytest.mark.parametrize("num_heads", [1, 4])
def test_gat_conv_bi(g, idtype, out_dim, num_heads):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
gat = nn.GATConv(5, out_dim, num_heads)
gat.initialize(ctx=ctx)
feat = (F.randn((g.number_of_src_nodes(), 5)), F.randn((g.number_of_dst_nodes(), 5)))
feat = (
F.randn((g.number_of_src_nodes(), 5)),
F.randn((g.number_of_dst_nodes(), 5)),
)
h = gat(g, feat)
assert h.shape == (g.number_of_dst_nodes(), num_heads, out_dim)
_, a = gat(g, feat, True)
assert a.shape == (g.number_of_edges(), num_heads, 1)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite']))
@pytest.mark.parametrize('aggre_type', ['mean', 'pool', 'gcn'])
@pytest.mark.parametrize('out_dim', [1, 10])
@pytest.mark.parametrize("g", get_cases(["homo", "block-bipartite"]))
@pytest.mark.parametrize("aggre_type", ["mean", "pool", "gcn"])
@pytest.mark.parametrize("out_dim", [1, 10])
def test_sage_conv(idtype, g, aggre_type, out_dim):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
......@@ -210,36 +238,41 @@ def test_sage_conv(idtype, g, aggre_type, out_dim):
h = sage(g, feat)
assert h.shape[-1] == out_dim
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['bipartite']))
@pytest.mark.parametrize('aggre_type', ['mean', 'pool', 'gcn'])
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize("g", get_cases(["bipartite"]))
@pytest.mark.parametrize("aggre_type", ["mean", "pool", "gcn"])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_sage_conv_bi(idtype, g, aggre_type, out_dim):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
dst_dim = 5 if aggre_type != 'gcn' else 10
dst_dim = 5 if aggre_type != "gcn" else 10
sage = nn.SAGEConv((10, dst_dim), out_dim, aggre_type)
feat = (F.randn((g.number_of_src_nodes(), 10)), F.randn((g.number_of_dst_nodes(), dst_dim)))
feat = (
F.randn((g.number_of_src_nodes(), 10)),
F.randn((g.number_of_dst_nodes(), dst_dim)),
)
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == out_dim
assert h.shape[0] == g.number_of_dst_nodes()
@parametrize_idtype
@pytest.mark.parametrize('aggre_type', ['mean', 'pool', 'gcn'])
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize("aggre_type", ["mean", "pool", "gcn"])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_sage_conv_bi2(idtype, aggre_type, out_dim):
# Test the case for graphs without edges
g = dgl.heterograph({('_U', '_E', '_V'): ([], [])}, {'_U': 5, '_V': 3})
g = dgl.heterograph({("_U", "_E", "_V"): ([], [])}, {"_U": 5, "_V": 3})
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
sage = nn.SAGEConv((3, 3), out_dim, 'gcn')
sage = nn.SAGEConv((3, 3), out_dim, "gcn")
feat = (F.randn((5, 3)), F.randn((3, 3)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == out_dim
assert h.shape[0] == 3
for aggre_type in ['mean', 'pool']:
for aggre_type in ["mean", "pool"]:
sage = nn.SAGEConv((3, 1), out_dim, aggre_type)
feat = (F.randn((5, 3)), F.randn((3, 1)))
sage.initialize(ctx=ctx)
......@@ -247,6 +280,7 @@ def test_sage_conv_bi2(idtype, aggre_type, out_dim):
assert h.shape[-1] == out_dim
assert h.shape[0] == 3
def test_gg_conv():
g = dgl.from_networkx(nx.erdos_renyi_graph(20, 0.3)).to(F.ctx())
ctx = F.ctx()
......@@ -261,7 +295,8 @@ def test_gg_conv():
h1 = gg_conv(g, h0, etypes)
assert h1.shape == (20, 20)
@pytest.mark.parametrize('out_dim', [1, 20])
@pytest.mark.parametrize("out_dim", [1, 20])
def test_cheb_conv(out_dim):
g = dgl.from_networkx(nx.erdos_renyi_graph(20, 0.3)).to(F.ctx())
ctx = F.ctx()
......@@ -275,8 +310,11 @@ def test_cheb_conv(out_dim):
h1 = cheb(g, h0)
assert h1.shape == (20, out_dim)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'], exclude=['zero-degree']))
@pytest.mark.parametrize(
"g", get_cases(["homo", "block-bipartite"], exclude=["zero-degree"])
)
def test_agnn_conv(g, idtype):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
......@@ -287,18 +325,23 @@ def test_agnn_conv(g, idtype):
h = agnn_conv(g, feat)
assert h.shape == (g.number_of_dst_nodes(), 10)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['bipartite'], exclude=['zero-degree']))
@pytest.mark.parametrize("g", get_cases(["bipartite"], exclude=["zero-degree"]))
def test_agnn_conv_bi(g, idtype):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
agnn_conv = nn.AGNNConv(0.1, True)
agnn_conv.initialize(ctx=ctx)
print(agnn_conv)
feat = (F.randn((g.number_of_src_nodes(), 5)), F.randn((g.number_of_dst_nodes(), 5)))
feat = (
F.randn((g.number_of_src_nodes(), 5)),
F.randn((g.number_of_dst_nodes(), 5)),
)
h = agnn_conv(g, feat)
assert h.shape == (g.number_of_dst_nodes(), 5)
def test_appnp_conv():
g = dgl.from_networkx(nx.erdos_renyi_graph(20, 0.3)).to(F.ctx())
ctx = F.ctx()
......@@ -312,69 +355,70 @@ def test_appnp_conv():
h1 = appnp_conv(g, h0)
assert h1.shape == (20, 10)
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_dense_cheb_conv(out_dim):
for k in range(1, 4):
ctx = F.ctx()
g = dgl.from_scipy(sp.sparse.random(100, 100, density=0.3)).to(F.ctx())
adj = g.adjacency_matrix(transpose=True, ctx=ctx).tostype('default')
adj = g.adjacency_matrix(transpose=True, ctx=ctx).tostype("default")
cheb = nn.ChebConv(5, out_dim, k)
dense_cheb = nn.DenseChebConv(5, out_dim, k)
cheb.initialize(ctx=ctx)
dense_cheb.initialize(ctx=ctx)
for i in range(len(cheb.fc)):
dense_cheb.fc[i].weight.set_data(
cheb.fc[i].weight.data())
dense_cheb.fc[i].weight.set_data(cheb.fc[i].weight.data())
if cheb.bias is not None:
dense_cheb.bias.set_data(
cheb.bias.data())
dense_cheb.bias.set_data(cheb.bias.data())
feat = F.randn((100, 5))
out_cheb = cheb(g, feat, [2.0])
out_dense_cheb = dense_cheb(adj, feat, 2.0)
assert F.allclose(out_cheb, out_dense_cheb)
@parametrize_idtype
@pytest.mark.parametrize('norm_type', ['both', 'right', 'none'])
@pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'], exclude=['zero-degree']))
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize("norm_type", ["both", "right", "none"])
@pytest.mark.parametrize(
"g", get_cases(["homo", "block-bipartite"], exclude=["zero-degree"])
)
@pytest.mark.parametrize("out_dim", [1, 2])
def test_dense_graph_conv(idtype, g, norm_type, out_dim):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
adj = g.adjacency_matrix(transpose=True, ctx=ctx).tostype('default')
adj = g.adjacency_matrix(transpose=True, ctx=ctx).tostype("default")
conv = nn.GraphConv(5, out_dim, norm=norm_type, bias=True)
dense_conv = nn.DenseGraphConv(5, out_dim, norm=norm_type, bias=True)
conv.initialize(ctx=ctx)
dense_conv.initialize(ctx=ctx)
dense_conv.weight.set_data(
conv.weight.data())
dense_conv.bias.set_data(
conv.bias.data())
dense_conv.weight.set_data(conv.weight.data())
dense_conv.bias.set_data(conv.bias.data())
feat = F.randn((g.number_of_src_nodes(), 5))
out_conv = conv(g, feat)
out_dense_conv = dense_conv(adj, feat)
assert F.allclose(out_conv, out_dense_conv)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo', 'bipartite', 'block-bipartite']))
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize(
"g", get_cases(["homo", "bipartite", "block-bipartite"])
)
@pytest.mark.parametrize("out_dim", [1, 2])
def test_dense_sage_conv(idtype, g, out_dim):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
adj = g.adjacency_matrix(transpose=True, ctx=ctx).tostype('default')
sage = nn.SAGEConv(5, out_dim, 'gcn')
adj = g.adjacency_matrix(transpose=True, ctx=ctx).tostype("default")
sage = nn.SAGEConv(5, out_dim, "gcn")
dense_sage = nn.DenseSAGEConv(5, out_dim)
sage.initialize(ctx=ctx)
dense_sage.initialize(ctx=ctx)
dense_sage.fc.weight.set_data(
sage.fc_neigh.weight.data())
dense_sage.fc.bias.set_data(
sage.fc_neigh.bias.data())
dense_sage.fc.weight.set_data(sage.fc_neigh.weight.data())
dense_sage.fc.bias.set_data(sage.fc_neigh.bias.data())
if len(g.ntypes) == 2:
feat = (
F.randn((g.number_of_src_nodes(), 5)),
F.randn((g.number_of_dst_nodes(), 5))
F.randn((g.number_of_dst_nodes(), 5)),
)
else:
feat = F.randn((g.number_of_nodes(), 5))
......@@ -383,9 +427,12 @@ def test_dense_sage_conv(idtype, g, out_dim):
out_dense_sage = dense_sage(adj, feat)
assert F.allclose(out_sage, out_dense_sage)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'], exclude=['zero-degree']))
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize(
"g", get_cases(["homo", "block-bipartite"], exclude=["zero-degree"])
)
@pytest.mark.parametrize("out_dim", [1, 2])
def test_edge_conv(g, idtype, out_dim):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
......@@ -397,9 +444,10 @@ def test_edge_conv(g, idtype, out_dim):
h1 = edge_conv(g, h0)
assert h1.shape == (g.number_of_dst_nodes(), out_dim)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['bipartite'], exclude=['zero-degree']))
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize("g", get_cases(["bipartite"], exclude=["zero-degree"]))
@pytest.mark.parametrize("out_dim", [1, 2])
def test_edge_conv_bi(g, idtype, out_dim):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
......@@ -412,9 +460,10 @@ def test_edge_conv_bi(g, idtype, out_dim):
h1 = edge_conv(g, (h0, x0))
assert h1.shape == (g.number_of_dst_nodes(), out_dim)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite']))
@pytest.mark.parametrize('aggregator_type', ['mean', 'max', 'sum'])
@pytest.mark.parametrize("g", get_cases(["homo", "block-bipartite"]))
@pytest.mark.parametrize("aggregator_type", ["mean", "max", "sum"])
def test_gin_conv(g, idtype, aggregator_type):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
......@@ -428,9 +477,10 @@ def test_gin_conv(g, idtype, aggregator_type):
h = gin_conv(g, feat)
assert h.shape == (g.number_of_dst_nodes(), 5)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['bipartite']))
@pytest.mark.parametrize('aggregator_type', ['mean', 'max', 'sum'])
@pytest.mark.parametrize("g", get_cases(["bipartite"]))
@pytest.mark.parametrize("aggregator_type", ["mean", "max", "sum"])
def test_gin_conv_bi(g, idtype, aggregator_type):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
......@@ -440,29 +490,35 @@ def test_gin_conv_bi(g, idtype, aggregator_type):
print(gin_conv)
# test #2: bipartite
feat = (F.randn((g.number_of_src_nodes(), 5)), F.randn((g.number_of_dst_nodes(), 5)))
feat = (
F.randn((g.number_of_src_nodes(), 5)),
F.randn((g.number_of_dst_nodes(), 5)),
)
h = gin_conv(g, feat)
return h.shape == (g.number_of_dst_nodes(), 5)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite'], exclude=['zero-degree']))
@pytest.mark.parametrize(
"g", get_cases(["homo", "block-bipartite"], exclude=["zero-degree"])
)
def test_gmm_conv(g, idtype):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
gmm_conv = nn.GMMConv(5, 2, 5, 3, 'max')
gmm_conv = nn.GMMConv(5, 2, 5, 3, "max")
gmm_conv.initialize(ctx=ctx)
h0 = F.randn((g.number_of_src_nodes(), 5))
pseudo = F.randn((g.number_of_edges(), 5))
h1 = gmm_conv(g, h0, pseudo)
assert h1.shape == (g.number_of_dst_nodes(), 2)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['bipartite'], exclude=['zero-degree']))
@pytest.mark.parametrize("g", get_cases(["bipartite"], exclude=["zero-degree"]))
def test_gmm_conv_bi(g, idtype):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
gmm_conv = nn.GMMConv((5, 4), 2, 5, 3, 'max')
gmm_conv = nn.GMMConv((5, 4), 2, 5, 3, "max")
gmm_conv.initialize(ctx=ctx)
# test #1: basic
h0 = F.randn((g.number_of_src_nodes(), 5))
......@@ -471,12 +527,13 @@ def test_gmm_conv_bi(g, idtype):
h1 = gmm_conv(g, (h0, hd), pseudo)
assert h1.shape == (g.number_of_dst_nodes(), 2)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo', 'block-bipartite']))
@pytest.mark.parametrize("g", get_cases(["homo", "block-bipartite"]))
def test_nn_conv(g, idtype):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
nn_conv = nn.NNConv(5, 2, gluon.nn.Embedding(3, 5 * 2), 'max')
nn_conv = nn.NNConv(5, 2, gluon.nn.Embedding(3, 5 * 2), "max")
nn_conv.initialize(ctx=ctx)
# test #1: basic
h0 = F.randn((g.number_of_src_nodes(), 5))
......@@ -484,12 +541,13 @@ def test_nn_conv(g, idtype):
h1 = nn_conv(g, h0, etypes)
assert h1.shape == (g.number_of_dst_nodes(), 2)
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['bipartite']))
@pytest.mark.parametrize("g", get_cases(["bipartite"]))
def test_nn_conv_bi(g, idtype):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
nn_conv = nn.NNConv((5, 4), 2, gluon.nn.Embedding(3, 5 * 2), 'max')
nn_conv = nn.NNConv((5, 4), 2, gluon.nn.Embedding(3, 5 * 2), "max")
nn_conv.initialize(ctx=ctx)
# test #1: basic
h0 = F.randn((g.number_of_src_nodes(), 5))
......@@ -498,7 +556,8 @@ def test_nn_conv_bi(g, idtype):
h1 = nn_conv(g, (h0, hd), etypes)
assert h1.shape == (g.number_of_dst_nodes(), 2)
@pytest.mark.parametrize('out_dim', [1, 2])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_sg_conv(out_dim):
g = dgl.from_networkx(nx.erdos_renyi_graph(20, 0.3)).to(F.ctx())
g = dgl.add_self_loop(g)
......@@ -513,6 +572,7 @@ def test_sg_conv(out_dim):
h1 = sgc(g, h0)
assert h1.shape == (g.number_of_nodes(), out_dim)
def test_set2set():
g = dgl.from_networkx(nx.path_graph(10)).to(F.ctx())
ctx = F.ctx()
......@@ -532,6 +592,7 @@ def test_set2set():
h1 = s2s(bg, h0)
assert h1.shape[0] == 3 and h1.shape[1] == 10 and h1.ndim == 2
def test_glob_att_pool():
g = dgl.from_networkx(nx.path_graph(10)).to(F.ctx())
ctx = F.ctx()
......@@ -550,6 +611,7 @@ def test_glob_att_pool():
h1 = gap(bg, h0)
assert h1.shape[0] == 4 and h1.shape[1] == 10 and h1.ndim == 2
def test_simple_pool():
g = dgl.from_networkx(nx.path_graph(15)).to(F.ctx())
......@@ -575,33 +637,43 @@ def test_simple_pool():
bg = dgl.batch([g, g_, g, g_, g])
h0 = F.randn((bg.number_of_nodes(), 5))
h1 = sum_pool(bg, h0)
truth = mx.nd.stack(F.sum(h0[:15], 0),
truth = mx.nd.stack(
F.sum(h0[:15], 0),
F.sum(h0[15:20], 0),
F.sum(h0[20:35], 0),
F.sum(h0[35:40], 0),
F.sum(h0[40:55], 0), axis=0)
F.sum(h0[40:55], 0),
axis=0,
)
check_close(h1, truth)
h1 = avg_pool(bg, h0)
truth = mx.nd.stack(F.mean(h0[:15], 0),
truth = mx.nd.stack(
F.mean(h0[:15], 0),
F.mean(h0[15:20], 0),
F.mean(h0[20:35], 0),
F.mean(h0[35:40], 0),
F.mean(h0[40:55], 0), axis=0)
F.mean(h0[40:55], 0),
axis=0,
)
check_close(h1, truth)
h1 = max_pool(bg, h0)
truth = mx.nd.stack(F.max(h0[:15], 0),
truth = mx.nd.stack(
F.max(h0[:15], 0),
F.max(h0[15:20], 0),
F.max(h0[20:35], 0),
F.max(h0[35:40], 0),
F.max(h0[40:55], 0), axis=0)
F.max(h0[40:55], 0),
axis=0,
)
check_close(h1, truth)
h1 = sort_pool(bg, h0)
assert h1.shape[0] == 5 and h1.shape[1] == 10 * 5 and h1.ndim == 2
@pytest.mark.parametrize('O', [1, 2, 8])
@pytest.mark.parametrize("O", [1, 2, 8])
def test_rgcn(O):
ctx = F.ctx()
etype = []
......@@ -654,6 +726,7 @@ def test_rgcn(O):
h_new = rgc_basis(g, h, r)
assert list(h_new.shape) == [100, O]
def test_sequential():
ctx = F.ctx()
# test single graph
......@@ -663,11 +736,11 @@ def test_sequential():
def forward(self, graph, n_feat, e_feat):
graph = graph.local_var()
graph.ndata['h'] = n_feat
graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
n_feat += graph.ndata['h']
graph.apply_edges(fn.u_add_v('h', 'h', 'e'))
e_feat += graph.edata['e']
graph.ndata["h"] = n_feat
graph.update_all(fn.copy_u("h", "m"), fn.sum("m", "h"))
n_feat += graph.ndata["h"]
graph.apply_edges(fn.u_add_v("h", "h", "e"))
e_feat += graph.edata["e"]
return n_feat, e_feat
g = dgl.graph(([], [])).to(F.ctx())
......@@ -691,9 +764,9 @@ def test_sequential():
def forward(self, graph, n_feat):
graph = graph.local_var()
graph.ndata['h'] = n_feat
graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
n_feat += graph.ndata['h']
graph.ndata["h"] = n_feat
graph.update_all(fn.copy_u("h", "m"), fn.sum("m", "h"))
n_feat += graph.ndata["h"]
return n_feat.reshape(graph.number_of_nodes() // 2, 2, -1).sum(1)
g1 = dgl.from_networkx(nx.erdos_renyi_graph(32, 0.05)).to(F.ctx())
......@@ -709,58 +782,75 @@ def test_sequential():
n_feat = net([g1, g2, g3], n_feat)
assert n_feat.shape == (4, 4)
def myagg(alist, dsttype):
rst = alist[0]
for i in range(1, len(alist)):
rst = rst + (i + 1) * alist[i]
return rst
@parametrize_idtype
@pytest.mark.parametrize('agg', ['sum', 'max', 'min', 'mean', 'stack', myagg])
@pytest.mark.parametrize("agg", ["sum", "max", "min", "mean", "stack", myagg])
def test_hetero_conv(agg, idtype):
g = dgl.heterograph({
('user', 'follows', 'user'): ([0, 0, 2, 1], [1, 2, 1, 3]),
('user', 'plays', 'game'): ([0, 0, 0, 1, 2], [0, 2, 3, 0, 2]),
('store', 'sells', 'game'): ([0, 0, 1, 1], [0, 3, 1, 2])},
idtype=idtype, device=F.ctx())
conv = nn.HeteroGraphConv({
'follows': nn.GraphConv(2, 3, allow_zero_in_degree=True),
'plays': nn.GraphConv(2, 4, allow_zero_in_degree=True),
'sells': nn.GraphConv(3, 4, allow_zero_in_degree=True)},
agg)
g = dgl.heterograph(
{
("user", "follows", "user"): ([0, 0, 2, 1], [1, 2, 1, 3]),
("user", "plays", "game"): ([0, 0, 0, 1, 2], [0, 2, 3, 0, 2]),
("store", "sells", "game"): ([0, 0, 1, 1], [0, 3, 1, 2]),
},
idtype=idtype,
device=F.ctx(),
)
conv = nn.HeteroGraphConv(
{
"follows": nn.GraphConv(2, 3, allow_zero_in_degree=True),
"plays": nn.GraphConv(2, 4, allow_zero_in_degree=True),
"sells": nn.GraphConv(3, 4, allow_zero_in_degree=True),
},
agg,
)
conv.initialize(ctx=F.ctx())
print(conv)
uf = F.randn((4, 2))
gf = F.randn((4, 4))
sf = F.randn((2, 3))
h = conv(g, {'user': uf, 'store': sf, 'game': gf})
assert set(h.keys()) == {'user', 'game'}
if agg != 'stack':
assert h['user'].shape == (4, 3)
assert h['game'].shape == (4, 4)
h = conv(g, {"user": uf, "store": sf, "game": gf})
assert set(h.keys()) == {"user", "game"}
if agg != "stack":
assert h["user"].shape == (4, 3)
assert h["game"].shape == (4, 4)
else:
assert h['user'].shape == (4, 1, 3)
assert h['game'].shape == (4, 2, 4)
block = dgl.to_block(g.to(F.cpu()), {'user': [0, 1, 2, 3], 'game': [0, 1, 2, 3], 'store': []}).to(F.ctx())
h = conv(block, ({'user': uf, 'game': gf, 'store': sf}, {'user': uf, 'game': gf, 'store': sf[0:0]}))
assert set(h.keys()) == {'user', 'game'}
if agg != 'stack':
assert h['user'].shape == (4, 3)
assert h['game'].shape == (4, 4)
assert h["user"].shape == (4, 1, 3)
assert h["game"].shape == (4, 2, 4)
block = dgl.to_block(
g.to(F.cpu()), {"user": [0, 1, 2, 3], "game": [0, 1, 2, 3], "store": []}
).to(F.ctx())
h = conv(
block,
(
{"user": uf, "game": gf, "store": sf},
{"user": uf, "game": gf, "store": sf[0:0]},
),
)
assert set(h.keys()) == {"user", "game"}
if agg != "stack":
assert h["user"].shape == (4, 3)
assert h["game"].shape == (4, 4)
else:
assert h['user'].shape == (4, 1, 3)
assert h['game'].shape == (4, 2, 4)
h = conv(block, {'user': uf, 'game': gf, 'store': sf})
assert set(h.keys()) == {'user', 'game'}
if agg != 'stack':
assert h['user'].shape == (4, 3)
assert h['game'].shape == (4, 4)
assert h["user"].shape == (4, 1, 3)
assert h["game"].shape == (4, 2, 4)
h = conv(block, {"user": uf, "game": gf, "store": sf})
assert set(h.keys()) == {"user", "game"}
if agg != "stack":
assert h["user"].shape == (4, 3)
assert h["game"].shape == (4, 4)
else:
assert h['user'].shape == (4, 1, 3)
assert h['game'].shape == (4, 2, 4)
assert h["user"].shape == (4, 1, 3)
assert h["game"].shape == (4, 2, 4)
# test with mod args
class MyMod(mx.gluon.nn.Block):
......@@ -769,39 +859,46 @@ def test_hetero_conv(agg, idtype):
self.carg1 = 0
self.s1 = s1
self.s2 = s2
def forward(self, g, h, arg1=None): # mxnet does not support kwargs
if arg1 is not None:
self.carg1 += 1
return F.zeros((g.number_of_dst_nodes(), self.s2))
mod1 = MyMod(2, 3)
mod2 = MyMod(2, 4)
mod3 = MyMod(3, 4)
conv = nn.HeteroGraphConv({
'follows': mod1,
'plays': mod2,
'sells': mod3},
agg)
conv = nn.HeteroGraphConv(
{"follows": mod1, "plays": mod2, "sells": mod3}, agg
)
conv.initialize(ctx=F.ctx())
mod_args = {'follows' : (1,), 'plays' : (1,)}
h = conv(g, {'user' : uf, 'store' : sf, 'game': gf}, mod_args)
mod_args = {"follows": (1,), "plays": (1,)}
h = conv(g, {"user": uf, "store": sf, "game": gf}, mod_args)
assert mod1.carg1 == 1
assert mod2.carg1 == 1
assert mod3.carg1 == 0
#conv on graph without any edges
# conv on graph without any edges
for etype in g.etypes:
g = dgl.remove_edges(g, g.edges(form='eid', etype=etype), etype=etype)
g = dgl.remove_edges(g, g.edges(form="eid", etype=etype), etype=etype)
assert g.num_edges() == 0
h = conv(g, {'user': uf, 'game': gf, 'store': sf})
assert set(h.keys()) == {'user', 'game'}
h = conv(g, {"user": uf, "game": gf, "store": sf})
assert set(h.keys()) == {"user", "game"}
block = dgl.to_block(
g.to(F.cpu()), {"user": [0, 1, 2, 3], "game": [0, 1, 2, 3], "store": []}
).to(F.ctx())
h = conv(
block,
(
{"user": uf, "game": gf, "store": sf},
{"user": uf, "game": gf, "store": sf[0:0]},
),
)
assert set(h.keys()) == {"user", "game"}
block = dgl.to_block(g.to(F.cpu()), {'user': [0, 1, 2, 3], 'game': [
0, 1, 2, 3], 'store': []}).to(F.ctx())
h = conv(block, ({'user': uf, 'game': gf, 'store': sf},
{'user': uf, 'game': gf, 'store': sf[0:0]}))
assert set(h.keys()) == {'user', 'game'}
if __name__ == '__main__':
if __name__ == "__main__":
test_graph_conv()
test_gat_conv()
test_sage_conv()
......
tests/pytorch/mock_sparse/test_elementwise_op_sp.py
View file @ 89a4cc4d
import operator
import numpy as np
import pytest
import dgl
import torch
import operator
import dgl
from dgl.mock_sparse import SparseMatrix, diag
parametrize_idtype = pytest.mark.parametrize(
......
tests/pytorch/test_dist_optim.py
View file @ 89a4cc4d
import os
os.environ['OMP_NUM_THREADS'] = '1'
import dgl
os.environ["OMP_NUM_THREADS"] = "1"
import multiprocessing as mp
import pickle
import random
import socket
import sys
import numpy as np
import time
import socket
from scipy import sparse as spsp
import unittest
import backend as F
import numpy as np
import torch as th
from scipy import sparse as spsp
from dgl.distributed import DistGraphServer, DistGraph
from dgl.distributed import partition_graph, load_partition_book
import multiprocessing as mp
import dgl
from dgl import function as fn
import backend as F
import unittest
import pickle
import random
from dgl.distributed import DistEmbedding
from dgl.distributed import (
DistEmbedding,
DistGraph,
DistGraphServer,
load_partition_book,
partition_graph,
)
from dgl.distributed.optim import SparseAdagrad, SparseAdam
def create_random_graph(n):
arr = (spsp.random(n, n, density=0.001, format='coo', random_state=100) != 0).astype(np.int64)
arr = (
spsp.random(n, n, density=0.001, format="coo", random_state=100) != 0
).astype(np.int64)
return dgl.from_scipy(arr)
def get_local_usable_addr():
"""Get local usable IP and port
......@@ -34,10 +44,10 @@ def get_local_usable_addr():
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
try:
# doesn't even have to be reachable
sock.connect(('10.255.255.255', 1))
sock.connect(("10.255.255.255", 1))
ip_addr = sock.getsockname()[0]
except ValueError:
ip_addr = '127.0.0.1'
ip_addr = "127.0.0.1"
finally:
sock.close()
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
......@@ -46,40 +56,62 @@ def get_local_usable_addr():
port = sock.getsockname()[1]
sock.close()
return ip_addr + ' ' + str(port)
return ip_addr + " " + str(port)
def prepare_dist():
ip_config = open("optim_ip_config.txt", "w")
ip_addr = get_local_usable_addr()
ip_config.write('{}\n'.format(ip_addr))
ip_config.write("{}\n".format(ip_addr))
ip_config.close()
def run_server(graph_name, server_id, server_count, num_clients, shared_mem):
g = DistGraphServer(server_id, "optim_ip_config.txt", num_clients, server_count,
'/tmp/dist_graph/{}.json'.format(graph_name),
disable_shared_mem=not shared_mem)
print('start server', server_id)
g = DistGraphServer(
server_id,
"optim_ip_config.txt",
num_clients,
server_count,
"/tmp/dist_graph/{}.json".format(graph_name),
disable_shared_mem=not shared_mem,
)
print("start server", server_id)
g.start()
def initializer(shape, dtype):
arr = th.zeros(shape, dtype=dtype)
th.manual_seed(0)
th.nn.init.uniform_(arr, 0, 1.0)
return arr
def run_client(graph_name, cli_id, part_id, server_count):
device=F.ctx()
device = F.ctx()
time.sleep(5)
os.environ['DGL_NUM_SERVER'] = str(server_count)
os.environ["DGL_NUM_SERVER"] = str(server_count)
dgl.distributed.initialize("optim_ip_config.txt")
gpb, graph_name, _, _ = load_partition_book('/tmp/dist_graph/{}.json'.format(graph_name),
part_id, None)
gpb, graph_name, _, _ = load_partition_book(
"/tmp/dist_graph/{}.json".format(graph_name), part_id, None
)
g = DistGraph(graph_name, gpb=gpb)
policy = dgl.distributed.PartitionPolicy('node', g.get_partition_book())
policy = dgl.distributed.PartitionPolicy("node", g.get_partition_book())
num_nodes = g.number_of_nodes()
emb_dim = 4
dgl_emb = DistEmbedding(num_nodes, emb_dim, name='optim', init_func=initializer, part_policy=policy)
dgl_emb_zero = DistEmbedding(num_nodes, emb_dim, name='optim-zero', init_func=initializer, part_policy=policy)
dgl_emb = DistEmbedding(
num_nodes,
emb_dim,
name="optim",
init_func=initializer,
part_policy=policy,
)
dgl_emb_zero = DistEmbedding(
num_nodes,
emb_dim,
name="optim-zero",
init_func=initializer,
part_policy=policy,
)
dgl_adam = SparseAdam(params=[dgl_emb, dgl_emb_zero], lr=0.01)
dgl_adam._world_size = 1
dgl_adam._rank = 0
......@@ -91,11 +123,13 @@ def run_client(graph_name, cli_id, part_id, server_count):
th.manual_seed(0)
th.nn.init.uniform_(torch_emb_zero.weight, 0, 1.0)
torch_adam = th.optim.SparseAdam(
list(torch_emb.parameters()) + list(torch_emb_zero.parameters()), lr=0.01)
list(torch_emb.parameters()) + list(torch_emb_zero.parameters()),
lr=0.01,
)
labels = th.ones((4,)).long()
idx = th.randint(0, num_nodes, size=(4,))
dgl_value = dgl_emb(idx, device).to(th.device('cpu'))
dgl_value = dgl_emb(idx, device).to(th.device("cpu"))
torch_value = torch_emb(idx)
torch_adam.zero_grad()
torch_loss = th.nn.functional.cross_entropy(torch_value, labels)
......@@ -107,7 +141,10 @@ def run_client(graph_name, cli_id, part_id, server_count):
dgl_loss.backward()
dgl_adam.step()
assert F.allclose(dgl_emb.weight[0 : num_nodes//2], torch_emb.weight[0 : num_nodes//2])
assert F.allclose(
dgl_emb.weight[0 : num_nodes // 2], torch_emb.weight[0 : num_nodes // 2]
)
def check_sparse_adam(num_trainer=1, shared_mem=True):
prepare_dist()
......@@ -116,23 +153,27 @@ def check_sparse_adam(num_trainer=1, shared_mem=True):
num_clients = num_trainer
num_parts = 1
graph_name = 'dist_graph_test'
partition_graph(g, graph_name, num_parts, '/tmp/dist_graph')
graph_name = "dist_graph_test"
partition_graph(g, graph_name, num_parts, "/tmp/dist_graph")
# let's just test on one partition for now.
# We cannot run multiple servers and clients on the same machine.
serv_ps = []
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for serv_id in range(num_servers):
p = ctx.Process(target=run_server, args=(graph_name, serv_id, num_servers,
num_clients, shared_mem))
p = ctx.Process(
target=run_server,
args=(graph_name, serv_id, num_servers, num_clients, shared_mem),
)
serv_ps.append(p)
p.start()
cli_ps = []
for cli_id in range(num_clients):
print('start client', cli_id)
p = ctx.Process(target=run_client, args=(graph_name, cli_id, 0, num_servers))
print("start client", cli_id)
p = ctx.Process(
target=run_client, args=(graph_name, cli_id, 0, num_servers)
)
p.start()
cli_ps.append(p)
......@@ -142,12 +183,14 @@ def check_sparse_adam(num_trainer=1, shared_mem=True):
for p in serv_ps:
p.join()
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
def test_sparse_opt():
os.environ['DGL_DIST_MODE'] = 'distributed'
os.environ["DGL_DIST_MODE"] = "distributed"
check_sparse_adam(1, True)
check_sparse_adam(1, False)
if __name__ == '__main__':
os.makedirs('/tmp/dist_graph', exist_ok=True)
if __name__ == "__main__":
os.makedirs("/tmp/dist_graph", exist_ok=True)
test_sparse_opt()
tests/pytorch/test_geometry.py
View file @ 89a4cc4d
import backend as F
import dgl.nn
import dgl
import numpy as np
import pytest
import torch as th
from dgl import DGLError
from dgl.base import DGLWarning
from dgl.geometry import neighbor_matching, farthest_point_sampler
from test_utils import parametrize_idtype
from test_utils.graph_cases import get_cases
import dgl
import dgl.nn
from dgl import DGLError
from dgl.base import DGLWarning
from dgl.geometry import farthest_point_sampler, neighbor_matching
def test_fps():
N = 1000
batch_size = 5
sample_points = 10
x = th.tensor(np.random.uniform(size=(batch_size, int(N/batch_size), 3)))
x = th.tensor(np.random.uniform(size=(batch_size, int(N / batch_size), 3)))
ctx = F.ctx()
if F.gpu_ctx():
x = x.to(ctx)
......@@ -29,17 +30,18 @@ def test_fps_start_idx():
N = 1000
batch_size = 5
sample_points = 10
x = th.tensor(np.random.uniform(size=(batch_size, int(N/batch_size), 3)))
x = th.tensor(np.random.uniform(size=(batch_size, int(N / batch_size), 3)))
ctx = F.ctx()
if F.gpu_ctx():
x = x.to(ctx)
res = farthest_point_sampler(x, sample_points, start_idx=0)
assert th.any(res[:, 0] == 0)
def _test_knn_common(device, algorithm, dist, exclude_self):
x = th.randn(8, 3).to(device)
kg = dgl.nn.KNNGraph(3)
if dist == 'euclidean':
if dist == "euclidean":
d = th.cdist(x, x).to(F.cpu())
else:
x = x + th.randn(1).item()
......@@ -55,7 +57,14 @@ def _test_knn_common(device, algorithm, dist, exclude_self):
assert len(src) == k
if check_indices:
i = v - start
src_ans = set(th.topk(d[start:end, start:end][i], k + (1 if exclude_self else 0), largest=False)[1].numpy() + start)
src_ans = set(
th.topk(
d[start:end, start:end][i],
k + (1 if exclude_self else 0),
largest=False,
)[1].numpy()
+ start
)
if exclude_self:
# remove self
src_ans.remove(v)
......@@ -63,7 +72,9 @@ def _test_knn_common(device, algorithm, dist, exclude_self):
def check_batch(g, k, expected_batch_info):
assert F.array_equal(g.batch_num_nodes(), F.tensor(expected_batch_info))
assert F.array_equal(g.batch_num_edges(), k*F.tensor(expected_batch_info))
assert F.array_equal(
g.batch_num_edges(), k * F.tensor(expected_batch_info)
)
# check knn with 2d input
g = kg(x, algorithm, dist, exclude_self)
......@@ -152,16 +163,20 @@ def _test_knn_common(device, algorithm, dist, exclude_self):
check_batch(g, 3, [4, 7, 5, 4])
@pytest.mark.parametrize('algorithm', ['bruteforce-blas', 'bruteforce', 'kd-tree'])
@pytest.mark.parametrize('dist', ['euclidean', 'cosine'])
@pytest.mark.parametrize('exclude_self', [False, True])
@pytest.mark.parametrize(
"algorithm", ["bruteforce-blas", "bruteforce", "kd-tree"]
)
@pytest.mark.parametrize("dist", ["euclidean", "cosine"])
@pytest.mark.parametrize("exclude_self", [False, True])
def test_knn_cpu(algorithm, dist, exclude_self):
_test_knn_common(F.cpu(), algorithm, dist, exclude_self)
@pytest.mark.parametrize('algorithm', ['bruteforce-blas', 'bruteforce', 'bruteforce-sharemem'])
@pytest.mark.parametrize('dist', ['euclidean', 'cosine'])
@pytest.mark.parametrize('exclude_self', [False, True])
@pytest.mark.parametrize(
"algorithm", ["bruteforce-blas", "bruteforce", "bruteforce-sharemem"]
)
@pytest.mark.parametrize("dist", ["euclidean", "cosine"])
@pytest.mark.parametrize("exclude_self", [False, True])
def test_knn_cuda(algorithm, dist, exclude_self):
if not th.cuda.is_available():
return
......@@ -169,9 +184,9 @@ def test_knn_cuda(algorithm, dist, exclude_self):
@parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph']))
@pytest.mark.parametrize('weight', [True, False])
@pytest.mark.parametrize('relabel', [True, False])
@pytest.mark.parametrize("g", get_cases(["homo"], exclude=["dglgraph"]))
@pytest.mark.parametrize("weight", [True, False])
@pytest.mark.parametrize("relabel", [True, False])
def test_edge_coarsening(idtype, g, weight, relabel):
num_nodes = g.num_nodes()
g = dgl.to_bidirected(g)
......@@ -205,7 +220,7 @@ def test_edge_coarsening(idtype, g, weight, relabel):
assert g.has_edges_between(u, v)
if __name__ == '__main__':
if __name__ == "__main__":
test_fps()
test_fps_start_idx()
test_knn()
tests/pytorch/test_ipc.py
View file @ 89a4cc4d
import dgl
import torch as th
import torch.multiprocessing as mp
import os
import unittest
import torch as th
import torch.multiprocessing as mp
import dgl
def sub_ipc(g):
print(g)
return g
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
def test_torch_ipc():
g = dgl.graph(([0, 1, 2], [1, 2, 3]))
ctx = mp.get_context("spawn")
p = ctx.Process(target=sub_ipc, args=(g, ))
p = ctx.Process(target=sub_ipc, args=(g,))
p.start()
p.join()
......
tests/pytorch/test_optim.py
View file @ 89a4cc4d
import os
import time
import torch.multiprocessing as mp
import unittest, os
import pytest
import unittest
import torch as th
import backend as F
import pytest
import torch as th
import torch.multiprocessing as mp
from dgl.nn import NodeEmbedding
from dgl.optim import SparseAdam, SparseAdagrad
from dgl.optim import SparseAdagrad, SparseAdam
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@pytest.mark.parametrize('emb_dim', [1, 4, 101, 1024])
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@pytest.mark.parametrize("emb_dim", [1, 4, 101, 1024])
def test_sparse_adam(emb_dim):
num_embs = 10
device=F.ctx()
dgl_emb = NodeEmbedding(num_embs, emb_dim, 'test')
device = F.ctx()
dgl_emb = NodeEmbedding(num_embs, emb_dim, "test")
torch_emb = th.nn.Embedding(num_embs, emb_dim, sparse=True)
th.manual_seed(0)
th.nn.init.uniform_(torch_emb.weight, 0, 1.0)
......@@ -26,7 +28,7 @@ def test_sparse_adam(emb_dim):
# first step
idx = th.randint(0, num_embs, size=(4,))
dgl_value = dgl_emb(idx, device).to(th.device('cpu'))
dgl_value = dgl_emb(idx, device).to(th.device("cpu"))
torch_value = torch_emb(idx)
labels = th.zeros((4,)).long()
print("dgl_value = {}".format(dgl_value))
......@@ -47,17 +49,18 @@ def test_sparse_adam(emb_dim):
# Pytorch sparseAdam maintains a global step
# DGL sparseAdam use a per embedding step
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@pytest.mark.parametrize('use_uva', [False, True, None])
@pytest.mark.parametrize('emb_dim', [1, 4, 101, 1024])
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@pytest.mark.parametrize("use_uva", [False, True, None])
@pytest.mark.parametrize("emb_dim", [1, 4, 101, 1024])
def test_sparse_adam_uva(use_uva, emb_dim):
if F.ctx().type == 'cpu' and use_uva == True:
if F.ctx().type == "cpu" and use_uva == True:
# we want to only test values of False and None when not using GPU
pytest.skip("UVA cannot be used without GPUs.")
num_embs = 10
device=F.ctx()
dgl_emb = NodeEmbedding(num_embs, emb_dim, 'test_uva{}'.format(use_uva))
device = F.ctx()
dgl_emb = NodeEmbedding(num_embs, emb_dim, "test_uva{}".format(use_uva))
torch_emb = th.nn.Embedding(num_embs, emb_dim, sparse=True)
th.manual_seed(0)
th.nn.init.uniform_(torch_emb.weight, 0, 1.0)
......@@ -69,7 +72,7 @@ def test_sparse_adam_uva(use_uva, emb_dim):
# first step
idx = th.randint(0, num_embs, size=(4,))
dgl_value = dgl_emb(idx, device).to(th.device('cpu'))
dgl_value = dgl_emb(idx, device).to(th.device("cpu"))
torch_value = torch_emb(idx)
labels = th.zeros((4,)).long()
......@@ -88,13 +91,14 @@ def test_sparse_adam_uva(use_uva, emb_dim):
# Pytorch sparseAdam maintains a global step
# DGL sparseAdam use a per embedding step
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@pytest.mark.parametrize('dtype', [th.float32, th.float16])
@pytest.mark.parametrize('emb_dim', [1, 4, 101, 1024])
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@pytest.mark.parametrize("dtype", [th.float32, th.float16])
@pytest.mark.parametrize("emb_dim", [1, 4, 101, 1024])
def test_sparse_adam_dtype(dtype, emb_dim):
num_embs = 10
device=F.ctx()
dgl_emb = NodeEmbedding(num_embs, emb_dim, 'test_dtype{}'.format(dtype))
device = F.ctx()
dgl_emb = NodeEmbedding(num_embs, emb_dim, "test_dtype{}".format(dtype))
torch_emb = th.nn.Embedding(num_embs, emb_dim, sparse=True)
th.manual_seed(0)
th.nn.init.uniform_(torch_emb.weight, 0, 1.0)
......@@ -106,7 +110,7 @@ def test_sparse_adam_dtype(dtype, emb_dim):
# first step
idx = th.randint(0, num_embs, size=(4,))
dgl_value = dgl_emb(idx, device).to(th.device('cpu'))
dgl_value = dgl_emb(idx, device).to(th.device("cpu"))
torch_value = torch_emb(idx)
labels = th.zeros((4,)).long()
......@@ -126,15 +130,14 @@ def test_sparse_adam_dtype(dtype, emb_dim):
# DGL sparseAdam use a per embedding step
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
def test_sparse_adam_zero_step():
num_embs = 10
emb_dim = 4
device=F.ctx()
dgl_emb = NodeEmbedding(num_embs, emb_dim, 'test')
device = F.ctx()
dgl_emb = NodeEmbedding(num_embs, emb_dim, "test")
torch_emb = th.nn.Embedding(num_embs, emb_dim, sparse=True)
dgl_emb_zero = NodeEmbedding(num_embs, emb_dim, 'test2')
dgl_emb_zero = NodeEmbedding(num_embs, emb_dim, "test2")
torch_emb_zero = th.nn.Embedding(num_embs, emb_dim, sparse=True)
th.manual_seed(0)
th.nn.init.uniform_(torch_emb.weight, 0, 1.0)
......@@ -145,11 +148,13 @@ def test_sparse_adam_zero_step():
dgl_adam = SparseAdam(params=[dgl_emb, dgl_emb_zero], lr=0.01)
torch_adam = th.optim.SparseAdam(
list(torch_emb.parameters()) + list(torch_emb_zero.parameters()), lr=0.01)
list(torch_emb.parameters()) + list(torch_emb_zero.parameters()),
lr=0.01,
)
# first step
idx = th.randint(0, num_embs, size=(4,))
dgl_value = dgl_emb(idx, device).to(th.device('cpu'))
dgl_value = dgl_emb(idx, device).to(th.device("cpu"))
torch_value = torch_emb(idx)
labels = th.ones((4,)).long()
......@@ -164,33 +169,51 @@ def test_sparse_adam_zero_step():
torch_adam.step()
assert F.allclose(dgl_emb.weight, torch_emb.weight)
def initializer(emb):
th.manual_seed(0)
emb.uniform_(-1.0, 1.0)
return emb
def start_sparse_adam_worker(rank, device, world_size, weight, tensor_dev='cpu', has_zero_grad=False,
backend='gloo', num_embs=128, emb_dim=10):
print('start sparse worker for adam {}'.format(rank))
dist_init_method = 'tcp://{master_ip}:{master_port}'.format(
master_ip='127.0.0.1', master_port='12345')
if device.type == 'cuda':
def start_sparse_adam_worker(
rank,
device,
world_size,
weight,
tensor_dev="cpu",
has_zero_grad=False,
backend="gloo",
num_embs=128,
emb_dim=10,
):
print("start sparse worker for adam {}".format(rank))
dist_init_method = "tcp://{master_ip}:{master_port}".format(
master_ip="127.0.0.1", master_port="12345"
)
if device.type == "cuda":
th.cuda.set_device(device)
th.distributed.init_process_group(backend=backend,
th.distributed.init_process_group(
backend=backend,
init_method=dist_init_method,
world_size=world_size,
rank=rank)
rank=rank,
)
init_weight = th.empty((num_embs, emb_dim))
th.manual_seed(0)
th.nn.init.uniform_(init_weight, -1.0, 1.0)
dgl_emb = NodeEmbedding(num_embs, emb_dim, 'test', init_func=initializer, device=tensor_dev)
dgl_emb = NodeEmbedding(
num_embs, emb_dim, "test", init_func=initializer, device=tensor_dev
)
dgl_emb.all_set_embedding(init_weight)
if has_zero_grad:
dgl_emb_zero = NodeEmbedding(num_embs, emb_dim, 'zero', init_func=initializer, device=tensor_dev)
dgl_emb_zero = NodeEmbedding(
num_embs, emb_dim, "zero", init_func=initializer, device=tensor_dev
)
dgl_adam = SparseAdam(params=[dgl_emb, dgl_emb_zero], lr=0.01)
else:
dgl_adam = SparseAdam(params=[dgl_emb], lr=0.01)
......@@ -215,17 +238,22 @@ def start_sparse_adam_worker(rank, device, world_size, weight, tensor_dev='cpu',
weight[:] = dgl_weight[:]
th.distributed.barrier()
def start_torch_adam_worker(rank, world_size, weight, has_zero_grad=False,
num_embs=128, emb_dim=10):
print('start sparse worker for adam {}'.format(rank))
dist_init_method = 'tcp://{master_ip}:{master_port}'.format(
master_ip='127.0.0.1', master_port='12345')
backend='gloo'
th.distributed.init_process_group(backend=backend,
def start_torch_adam_worker(
rank, world_size, weight, has_zero_grad=False, num_embs=128, emb_dim=10
):
print("start sparse worker for adam {}".format(rank))
dist_init_method = "tcp://{master_ip}:{master_port}".format(
master_ip="127.0.0.1", master_port="12345"
)
backend = "gloo"
th.distributed.init_process_group(
backend=backend,
init_method=dist_init_method,
world_size=world_size,
rank=rank)
rank=rank,
)
torch_emb = th.nn.Embedding(num_embs, emb_dim, sparse=True)
th.manual_seed(0)
......@@ -238,10 +266,14 @@ def start_torch_adam_worker(rank, world_size, weight, has_zero_grad=False,
th.nn.init.uniform_(torch_emb_zero.weight, -1.0, 1.0)
torch_emb_zero = th.nn.parallel.DistributedDataParallel(torch_emb_zero)
torch_adam = th.optim.SparseAdam(
list(torch_emb.module.parameters()) + list(torch_emb_zero.module.parameters()),
lr=0.01)
list(torch_emb.module.parameters())
+ list(torch_emb_zero.module.parameters()),
lr=0.01,
)
else:
torch_adam = th.optim.SparseAdam(list(torch_emb.module.parameters()), lr=0.01)
torch_adam = th.optim.SparseAdam(
list(torch_emb.module.parameters()), lr=0.01
)
start = (num_embs // world_size) * rank
end = (num_embs // world_size) * (rank + 1)
......@@ -259,20 +291,31 @@ def start_torch_adam_worker(rank, world_size, weight, has_zero_grad=False,
weight[:] = torch_emb.module.weight.cpu()[:]
th.distributed.barrier()
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(F.ctx().type != 'cpu', reason='cpu only test')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@unittest.skipIf(F.ctx().type != "cpu", reason="cpu only test")
@pytest.mark.parametrize("num_workers", [2, 4])
def test_multiprocess_cpu_sparse_adam(num_workers):
backend = 'gloo'
backend = "gloo"
worker_list = []
num_embs=128
emb_dim=10
num_embs = 128
emb_dim = 10
dgl_weight = th.empty((num_embs, emb_dim))
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for i in range(num_workers):
device = F.ctx()
p = ctx.Process(target=start_sparse_adam_worker,
args=(i, device, num_workers, dgl_weight, th.device('cpu'), True, backend))
p = ctx.Process(
target=start_sparse_adam_worker,
args=(
i,
device,
num_workers,
dgl_weight,
th.device("cpu"),
True,
backend,
),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -281,8 +324,10 @@ def test_multiprocess_cpu_sparse_adam(num_workers):
worker_list = []
torch_weight = th.empty((num_embs, emb_dim))
for i in range(num_workers):
p = ctx.Process(target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False))
p = ctx.Process(
target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -290,26 +335,37 @@ def test_multiprocess_cpu_sparse_adam(num_workers):
assert F.allclose(dgl_weight, torch_weight)
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(F.ctx().type == 'cpu', reason='gpu only test')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@unittest.skipIf(F.ctx().type == "cpu", reason="gpu only test")
@pytest.mark.parametrize("num_workers", [2, 4, 8])
@pytest.mark.parametrize("backend", ['nccl', 'gloo'])
@pytest.mark.parametrize("backend", ["nccl", "gloo"])
def test_multiprocess_sparse_adam(num_workers, backend):
if F.ctx().type == 'cuda' and th.cuda.device_count() < num_workers:
if F.ctx().type == "cuda" and th.cuda.device_count() < num_workers:
pytest.skip("Not enough GPUs to run test.")
worker_list = []
num_embs=128
emb_dim=10
num_embs = 128
emb_dim = 10
dgl_weight = th.empty((num_embs, emb_dim))
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for i in range(num_workers):
device = F.ctx()
if device.type == 'cuda':
if device.type == "cuda":
# make sure each process has a unique GPU
device = th.device(i)
p = ctx.Process(target=start_sparse_adam_worker,
args=(i, device, num_workers, dgl_weight, th.device('cpu'), True, backend))
p = ctx.Process(
target=start_sparse_adam_worker,
args=(
i,
device,
num_workers,
dgl_weight,
th.device("cpu"),
True,
backend,
),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -318,8 +374,10 @@ def test_multiprocess_sparse_adam(num_workers, backend):
worker_list = []
torch_weight = th.empty((num_embs, emb_dim))
for i in range(num_workers):
p = ctx.Process(target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False))
p = ctx.Process(
target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -327,25 +385,30 @@ def test_multiprocess_sparse_adam(num_workers, backend):
assert F.allclose(dgl_weight, torch_weight)
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(F.ctx().type == 'cpu', reason='cuda tensor is not supported for cpu')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@unittest.skipIf(
F.ctx().type == "cpu", reason="cuda tensor is not supported for cpu"
)
@pytest.mark.parametrize("num_workers", [2, 4, 8])
def test_multiprocess_sparse_adam_cuda_tensor(num_workers):
if F.ctx().type == 'cpu':
if F.ctx().type == "cpu":
pytest.skip("Do not test CPU")
if F.ctx().type == 'cuda' and th.cuda.device_count() < num_workers:
if F.ctx().type == "cuda" and th.cuda.device_count() < num_workers:
pytest.skip("Not enough GPUs to run test.")
backend = 'nccl'
backend = "nccl"
worker_list = []
num_embs=128
emb_dim=10
num_embs = 128
emb_dim = 10
dgl_weight = th.empty((num_embs, emb_dim))
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for i in range(num_workers):
device = th.device(i)
p = ctx.Process(target=start_sparse_adam_worker,
args=(i, device, num_workers, dgl_weight, device, False, backend))
p = ctx.Process(
target=start_sparse_adam_worker,
args=(i, device, num_workers, dgl_weight, device, False, backend),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -354,8 +417,10 @@ def test_multiprocess_sparse_adam_cuda_tensor(num_workers):
worker_list = []
torch_weight = th.empty((num_embs, emb_dim))
for i in range(num_workers):
p = ctx.Process(target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False))
p = ctx.Process(
target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -363,21 +428,32 @@ def test_multiprocess_sparse_adam_cuda_tensor(num_workers):
assert F.allclose(dgl_weight, torch_weight)
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(F.ctx().type != 'cpu', reason='cpu only test')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@unittest.skipIf(F.ctx().type != "cpu", reason="cpu only test")
@pytest.mark.parametrize("num_workers", [2, 4])
def test_multiprocess_sparse_adam_cpu_zero_step(num_workers):
backend = 'gloo'
backend = "gloo"
worker_list = []
num_embs=128
emb_dim=10
num_embs = 128
emb_dim = 10
dgl_weight = th.empty((num_embs, emb_dim))
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for i in range(num_workers):
device = F.ctx()
p = ctx.Process(target=start_sparse_adam_worker,
args=(i, device, num_workers, dgl_weight, th.device('cpu'), True, backend))
p = ctx.Process(
target=start_sparse_adam_worker,
args=(
i,
device,
num_workers,
dgl_weight,
th.device("cpu"),
True,
backend,
),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -386,8 +462,10 @@ def test_multiprocess_sparse_adam_cpu_zero_step(num_workers):
worker_list = []
torch_weight = th.empty((num_embs, emb_dim))
for i in range(num_workers):
p = ctx.Process(target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False))
p = ctx.Process(
target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -395,26 +473,37 @@ def test_multiprocess_sparse_adam_cpu_zero_step(num_workers):
assert F.allclose(dgl_weight, torch_weight)
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(F.ctx().type == 'cpu', reason='gpu only test')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@unittest.skipIf(F.ctx().type == "cpu", reason="gpu only test")
@pytest.mark.parametrize("num_workers", [2, 4, 8])
@pytest.mark.parametrize("backend", ['nccl', 'gloo'])
@pytest.mark.parametrize("backend", ["nccl", "gloo"])
def test_multiprocess_sparse_adam_zero_step(num_workers, backend):
if F.ctx().type == 'cuda' and th.cuda.device_count() < num_workers:
if F.ctx().type == "cuda" and th.cuda.device_count() < num_workers:
pytest.skip("Not enough GPUs to run test.")
worker_list = []
num_embs=128
emb_dim=10
num_embs = 128
emb_dim = 10
dgl_weight = th.empty((num_embs, emb_dim))
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for i in range(num_workers):
device = F.ctx()
if device.type == 'cuda':
if device.type == "cuda":
# make sure each process has a unique GPU
device = th.device(i)
p = ctx.Process(target=start_sparse_adam_worker,
args=(i, device, num_workers, dgl_weight, th.device('cpu'), True, backend))
p = ctx.Process(
target=start_sparse_adam_worker,
args=(
i,
device,
num_workers,
dgl_weight,
th.device("cpu"),
True,
backend,
),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -423,8 +512,10 @@ def test_multiprocess_sparse_adam_zero_step(num_workers, backend):
worker_list = []
torch_weight = th.empty((num_embs, emb_dim))
for i in range(num_workers):
p = ctx.Process(target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False))
p = ctx.Process(
target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -432,23 +523,28 @@ def test_multiprocess_sparse_adam_zero_step(num_workers, backend):
assert F.allclose(dgl_weight, torch_weight)
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(F.ctx().type == 'cpu', reason='cuda tensor is not supported for cpu')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@unittest.skipIf(
F.ctx().type == "cpu", reason="cuda tensor is not supported for cpu"
)
@pytest.mark.parametrize("num_workers", [2, 4, 8])
def test_multiprocess_sparse_adam_zero_step_cuda_tensor(num_workers):
if F.ctx().type == 'cuda' and th.cuda.device_count() < num_workers:
if F.ctx().type == "cuda" and th.cuda.device_count() < num_workers:
pytest.skip("Not enough GPUs to run test.")
backend = 'nccl'
backend = "nccl"
worker_list = []
num_embs=128
emb_dim=10
num_embs = 128
emb_dim = 10
dgl_weight = th.empty((num_embs, emb_dim))
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for i in range(num_workers):
device = th.device(i)
p = ctx.Process(target=start_sparse_adam_worker,
args=(i, device, num_workers, dgl_weight, device, True, backend))
p = ctx.Process(
target=start_sparse_adam_worker,
args=(i, device, num_workers, dgl_weight, device, True, backend),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -457,8 +553,10 @@ def test_multiprocess_sparse_adam_zero_step_cuda_tensor(num_workers):
worker_list = []
torch_weight = th.empty((num_embs, emb_dim))
for i in range(num_workers):
p = ctx.Process(target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False))
p = ctx.Process(
target=start_torch_adam_worker,
args=(i, num_workers, torch_weight, False),
)
p.start()
worker_list.append(p)
for p in worker_list:
......@@ -466,7 +564,8 @@ def test_multiprocess_sparse_adam_zero_step_cuda_tensor(num_workers):
assert F.allclose(dgl_weight, torch_weight)
if __name__ == '__main__':
if __name__ == "__main__":
test_sparse_adam(1)
test_sparse_adam(4)
test_sparse_adam(101)
......@@ -478,15 +577,15 @@ if __name__ == '__main__':
test_multiprocess_cpu_sparse_adam(8)
test_multiprocess_sparse_adam_cpu_zero_step(2)
test_multiprocess_sparse_adam(2, backend='gloo')
test_multiprocess_sparse_adam(4, backend='gloo')
test_multiprocess_sparse_adam(8, backend='gloo')
test_multiprocess_sparse_adam(2, backend='nccl')
test_multiprocess_sparse_adam(4, backend='nccl')
test_multiprocess_sparse_adam(8, backend='nccl')
test_multiprocess_sparse_adam(2, backend="gloo")
test_multiprocess_sparse_adam(4, backend="gloo")
test_multiprocess_sparse_adam(8, backend="gloo")
test_multiprocess_sparse_adam(2, backend="nccl")
test_multiprocess_sparse_adam(4, backend="nccl")
test_multiprocess_sparse_adam(8, backend="nccl")
test_multiprocess_sparse_adam_zero_step(2, backend='gloo')
test_multiprocess_sparse_adam_zero_step(4, backend='nccl')
test_multiprocess_sparse_adam_zero_step(2, backend="gloo")
test_multiprocess_sparse_adam_zero_step(4, backend="nccl")
test_multiprocess_sparse_adam_cuda_tensor(2)
test_multiprocess_sparse_adam_zero_step_cuda_tensor(4)
tests/pytorch/test_pickle.py
View file @ 89a4cc4d
import io
import pickle
import networkx as nx
import dgl
import torch
import pickle
import io
import dgl
def _reconstruct_pickle(obj):
f = io.BytesIO()
......@@ -12,15 +15,17 @@ def _reconstruct_pickle(obj):
f.close()
return obj
def test_pickling_batched_graph():
# NOTE: this is a test for a wierd bug mentioned in
# https://github.com/dmlc/dgl/issues/438
glist = [nx.path_graph(i + 5) for i in range(5)]
glist = [dgl.DGLGraph(g) for g in glist]
bg = dgl.batch(glist)
bg.ndata['x'] = torch.randn((35, 5))
bg.edata['y'] = torch.randn((60, 3))
bg.ndata["x"] = torch.randn((35, 5))
bg.edata["y"] = torch.randn((60, 3))
new_bg = _reconstruct_pickle(bg)
if __name__ == '__main__':
if __name__ == "__main__":
test_pickling_batched_graph()
tests/pytorch/test_pin_memory.py
View file @ 89a4cc4d
import backend as F
import dgl
import pytest
import torch
@pytest.mark.skipif(F._default_context_str == 'cpu', reason="Need gpu for this test.")
import dgl
@pytest.mark.skipif(
F._default_context_str == "cpu", reason="Need gpu for this test."
)
def test_pin_noncontiguous():
t = torch.empty([10, 100]).transpose(0, 1)
......@@ -13,7 +17,10 @@ def test_pin_noncontiguous():
with pytest.raises(dgl.DGLError):
dgl.utils.pin_memory_inplace(t)
@pytest.mark.skipif(F._default_context_str == 'cpu', reason="Need gpu for this test.")
@pytest.mark.skipif(
F._default_context_str == "cpu", reason="Need gpu for this test."
)
def test_pin_view():
t = torch.empty([100, 10])
v = t[10:20]
......@@ -24,7 +31,10 @@ def test_pin_view():
with pytest.raises(dgl.DGLError):
dgl.utils.pin_memory_inplace(v)
@pytest.mark.skipif(F._default_context_str == 'cpu', reason='Need gpu for this test.')
@pytest.mark.skipif(
F._default_context_str == "cpu", reason="Need gpu for this test."
)
def test_unpin_automatically():
# run a sufficient number of iterations such that the memory pool should be
# re-used
......@@ -38,26 +48,32 @@ def test_unpin_automatically():
assert not F.is_pinned(t)
del t
@pytest.mark.skipif(F._default_context_str == 'cpu', reason='Need gpu for this test.')
@pytest.mark.skipif(
F._default_context_str == "cpu", reason="Need gpu for this test."
)
def test_pin_unpin_column():
g = dgl.graph(([1, 2, 3, 4], [0, 0, 0, 0]))
g.ndata['x'] = torch.randn(g.num_nodes())
g.ndata["x"] = torch.randn(g.num_nodes())
g.pin_memory_()
assert g.is_pinned()
assert g.ndata['x'].is_pinned()
assert g.ndata["x"].is_pinned()
for col in g._node_frames[0].values():
assert col.pinned_by_dgl
assert col._data_nd is not None
g.ndata['x'] = torch.randn(g.num_nodes()) # unpin the old ndata['x']
g.ndata["x"] = torch.randn(g.num_nodes()) # unpin the old ndata['x']
assert g.is_pinned()
for col in g._node_frames[0].values():
assert not col.pinned_by_dgl
assert col._data_nd is None
assert not g.ndata['x'].is_pinned()
assert not g.ndata["x"].is_pinned()
@pytest.mark.skipif(F._default_context_str == 'cpu', reason='Need gpu for this test.')
@pytest.mark.skipif(
F._default_context_str == "cpu", reason="Need gpu for this test."
)
def test_pin_empty():
t = torch.tensor([])
assert not t.is_pinned()
......@@ -68,6 +84,7 @@ def test_pin_empty():
nd = dgl.utils.pin_memory_inplace(t)
assert not t.is_pinned()
if __name__ == "__main__":
test_pin_noncontiguous()
test_pin_view()
......
tests/pytorch/test_sparse_emb.py
View file @ 89a4cc4d
import multiprocessing as mp
import unittest, os
import pytest
import os
import unittest
import torch as th
import backend as F
import pytest
import torch as th
from dgl.nn import NodeEmbedding
......@@ -13,46 +14,54 @@ def initializer(emb):
emb.uniform_(-1.0, 1.0)
return emb
def check_all_set_all_get_func(device, init_emb):
num_embs = init_emb.shape[0]
emb_dim = init_emb.shape[1]
dgl_emb = NodeEmbedding(num_embs, emb_dim, 'test', device=device)
dgl_emb = NodeEmbedding(num_embs, emb_dim, "test", device=device)
dgl_emb.all_set_embedding(init_emb)
out_emb = dgl_emb.all_get_embedding()
assert F.allclose(init_emb, out_emb)
def start_sparse_worker(rank, world_size, test, args):
print('start sparse worker {}'.format(rank))
dist_init_method = 'tcp://{master_ip}:{master_port}'.format(
master_ip='127.0.0.1', master_port='12345')
backend = 'gloo'
print("start sparse worker {}".format(rank))
dist_init_method = "tcp://{master_ip}:{master_port}".format(
master_ip="127.0.0.1", master_port="12345"
)
backend = "gloo"
device = F.ctx()
if device.type == 'cuda':
if device.type == "cuda":
device = th.device(rank)
th.cuda.set_device(device)
th.distributed.init_process_group(backend=backend,
th.distributed.init_process_group(
backend=backend,
init_method=dist_init_method,
world_size=world_size,
rank=rank)
rank=rank,
)
test(device, *args)
th.distributed.barrier()
@unittest.skipIf(os.name == 'nt', reason='Do not support windows yet')
@unittest.skipIf(os.name == "nt", reason="Do not support windows yet")
@pytest.mark.parametrize("num_workers", [1, 2, 3])
def test_multiprocess_sparse_emb_get_set(num_workers):
if F.ctx().type == 'cuda' and th.cuda.device_count() < num_workers:
if F.ctx().type == "cuda" and th.cuda.device_count() < num_workers:
pytest.skip("Not enough GPUs to run test.")
worker_list = []
init_emb = th.rand([1000, 8])
ctx = mp.get_context('spawn')
ctx = mp.get_context("spawn")
for i in range(num_workers):
p = ctx.Process(target=start_sparse_worker,
args=(i, num_workers, check_all_set_all_get_func, (init_emb,)))
p = ctx.Process(
target=start_sparse_worker,
args=(i, num_workers, check_all_set_all_get_func, (init_emb,)),
)
p.start()
worker_list.append(p)
......@@ -62,7 +71,7 @@ def test_multiprocess_sparse_emb_get_set(num_workers):
assert p.exitcode == 0
if __name__ == '__main__':
if __name__ == "__main__":
test_sparse_emb_get_set(1)
test_sparse_emb_get_set(2)
test_sparse_emb_get_set(3)
tests/pytorch/test_stream.py
View file @ 89a4cc4d
from statistics import mean
import unittest
from statistics import mean
import backend as F
import numpy as np
import torch
import dgl
import dgl.ndarray as nd
from dgl import rand_graph
import dgl.ops as OPS
from dgl._ffi.streams import to_dgl_stream_handle, _dgl_get_stream
from dgl import rand_graph
from dgl._ffi.streams import _dgl_get_stream, to_dgl_stream_handle
from dgl.utils import to_dgl_context
import backend as F
# borrowed from PyTorch, torch/testing/_internal/common_utils.py
def _get_cycles_per_ms() -> float:
"""Measure and return approximate number of cycles per millisecond for torch.cuda._sleep
"""
"""Measure and return approximate number of cycles per millisecond for torch.cuda._sleep"""
def measure() -> float:
start = torch.cuda.Event(enable_timing=True)
......@@ -36,7 +38,10 @@ def _get_cycles_per_ms() -> float:
vals = sorted(vals)
return mean(vals[2 : num - 2])
@unittest.skipIf(F._default_context_str == 'cpu', reason="stream only runs on GPU.")
@unittest.skipIf(
F._default_context_str == "cpu", reason="stream only runs on GPU."
)
def test_basics():
g = rand_graph(10, 20, device=F.cpu())
x = torch.ones(g.num_nodes(), 10)
......@@ -57,22 +62,31 @@ def test_basics():
s.synchronize()
assert torch.equal(OPS.copy_u_sum(gg, xx), result)
@unittest.skipIf(F._default_context_str == 'cpu', reason="stream only runs on GPU.")
@unittest.skipIf(
F._default_context_str == "cpu", reason="stream only runs on GPU."
)
def test_set_get_stream():
current_stream = torch.cuda.current_stream()
# test setting another stream
s = torch.cuda.Stream(device=F.ctx())
torch.cuda.set_stream(s)
assert to_dgl_stream_handle(s).value == _dgl_get_stream(to_dgl_context(F.ctx())).value
assert (
to_dgl_stream_handle(s).value
== _dgl_get_stream(to_dgl_context(F.ctx())).value
)
# revert to default stream
torch.cuda.set_stream(current_stream)
@unittest.skipIf(F._default_context_str == 'cpu', reason="stream only runs on GPU.")
@unittest.skipIf(
F._default_context_str == "cpu", reason="stream only runs on GPU."
)
# borrowed from PyTorch, test/test_cuda.py: test_record_stream()
def test_record_stream_ndarray():
cycles_per_ms = _get_cycles_per_ms()
t = nd.array(np.array([1., 2., 3., 4.], dtype=np.float32), ctx=nd.cpu())
t = nd.array(np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32), ctx=nd.cpu())
t.pin_memory_()
result = nd.empty([4], ctx=nd.gpu(0))
stream = torch.cuda.Stream()
......@@ -84,25 +98,33 @@ def test_record_stream_ndarray():
tmp = t.copyto(nd.gpu(0))
ptr[0] = F.from_dgl_nd(tmp).data_ptr()
torch.cuda.current_stream().wait_stream(stream)
tmp.record_stream(
to_dgl_stream_handle(torch.cuda.current_stream()))
tmp.record_stream(to_dgl_stream_handle(torch.cuda.current_stream()))
torch.cuda._sleep(int(50 * cycles_per_ms)) # delay the copy
result.copyfrom(tmp)
perform_copy()
with torch.cuda.stream(stream):
tmp2 = nd.empty([4], ctx=nd.gpu(0))
assert F.from_dgl_nd(tmp2).data_ptr() != ptr[0], 'allocation re-used too soon'
assert (
F.from_dgl_nd(tmp2).data_ptr() != ptr[0]
), "allocation re-used too soon"
assert torch.equal(F.from_dgl_nd(result).cpu(), torch.tensor([1., 2., 3., 4.]))
assert torch.equal(
F.from_dgl_nd(result).cpu(), torch.tensor([1.0, 2.0, 3.0, 4.0])
)
# Check that the block will be re-used after the main stream finishes
torch.cuda.current_stream().synchronize()
with torch.cuda.stream(stream):
tmp3 = nd.empty([4], ctx=nd.gpu(0))
assert F.from_dgl_nd(tmp3).data_ptr() == ptr[0], 'allocation not re-used'
assert (
F.from_dgl_nd(tmp3).data_ptr() == ptr[0]
), "allocation not re-used"
@unittest.skipIf(F._default_context_str == 'cpu', reason="stream only runs on GPU.")
@unittest.skipIf(
F._default_context_str == "cpu", reason="stream only runs on GPU."
)
def test_record_stream_graph_positive():
cycles_per_ms = _get_cycles_per_ms()
......@@ -133,7 +155,10 @@ def test_record_stream_graph_positive():
torch.cuda.current_stream().synchronize()
assert torch.equal(result, results2)
@unittest.skipIf(F._default_context_str == 'cpu', reason="stream only runs on GPU.")
@unittest.skipIf(
F._default_context_str == "cpu", reason="stream only runs on GPU."
)
def test_record_stream_graph_negative():
cycles_per_ms = _get_cycles_per_ms()
......@@ -165,7 +190,8 @@ def test_record_stream_graph_negative():
torch.cuda.current_stream().synchronize()
assert not torch.equal(result, results2)
if __name__ == '__main__':
if __name__ == "__main__":
test_basics()
test_set_get_stream()
test_record_stream_ndarray()
......
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