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[Misc] Mofidy cugraph tests to avoid import dgl #7075

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[Misc] Mofidy cugraph tests to avoid import dgl #7075

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7 changes: 4 additions & 3 deletions tests/cugraph/cugraph-ops/test_cugraph_gatconv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,9 +2,10 @@
from collections import OrderedDict
from itertools import product

import dgl
import pytest
import torch

from dgl import graph, to_block as dgl_to_block
from dgl.nn import CuGraphGATConv, GATConv

options = OrderedDict(
Expand All @@ -20,7 +21,7 @@
def generate_graph():
u = torch.tensor([0, 1, 0, 2, 3, 0, 4, 0, 5, 0, 6, 7, 0, 8, 9])
v = torch.tensor([1, 9, 2, 9, 9, 4, 9, 5, 9, 6, 9, 9, 8, 9, 0])
g = dgl.graph((u, v))
g = graph((u, v))
return g


Expand All @@ -34,7 +35,7 @@ def test_gatconv_equality(idtype_int, max_in_degree, num_heads, to_block):
if idtype_int:
g = g.int()
if to_block:
g = dgl.to_block(g)
g = dgl_to_block(g)
feat = torch.rand(g.num_src_nodes(), in_feat).to(device)

torch.manual_seed(0)
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13 changes: 7 additions & 6 deletions tests/cugraph/cugraph-ops/test_cugraph_relgraphconv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,9 +2,10 @@
from collections import OrderedDict
from itertools import product

import dgl
import pytest
import torch

from dgl import ETYPE, graph, to_block as dgl_to_block
from dgl.nn import CuGraphRelGraphConv, RelGraphConv

# TODO(tingyu66): Re-enable the following tests after updating cuGraph CI image.
Expand All @@ -23,7 +24,7 @@
def generate_graph():
u = torch.tensor([0, 1, 0, 2, 3, 0, 4, 0, 5, 0, 6, 7, 0, 8, 9])
v = torch.tensor([1, 9, 2, 9, 9, 4, 9, 5, 9, 6, 9, 9, 8, 9, 0])
g = dgl.graph((u, v))
g = graph((u, v))
return g


Expand All @@ -41,11 +42,11 @@ def test_relgraphconv_equality(
"self_loop": self_loop,
}
g = generate_graph().to(device)
g.edata[dgl.ETYPE] = torch.randint(num_rels, (g.num_edges(),)).to(device)
g.edata[ETYPE] = torch.randint(num_rels, (g.num_edges(),)).to(device)
if idtype_int:
g = g.int()
if to_block:
g = dgl.to_block(g)
g = dgl_to_block(g)
feat = torch.rand(g.num_src_nodes(), in_feat).to(device)

torch.manual_seed(0)
Expand All @@ -55,8 +56,8 @@ def test_relgraphconv_equality(
kwargs["apply_norm"] = False
conv2 = CuGraphRelGraphConv(*args, **kwargs).to(device)

out1 = conv1(g, feat, g.edata[dgl.ETYPE])
out2 = conv2(g, feat, g.edata[dgl.ETYPE], max_in_degree=max_in_degree)
out1 = conv1(g, feat, g.edata[ETYPE])
out2 = conv2(g, feat, g.edata[ETYPE], max_in_degree=max_in_degree)
assert torch.allclose(out1, out2, atol=1e-06)

grad_out = torch.rand_like(out1)
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7 changes: 4 additions & 3 deletions tests/cugraph/cugraph-ops/test_cugraph_sageconv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,9 +2,10 @@
from collections import OrderedDict
from itertools import product

import dgl
import pytest
import torch

from dgl import graph, to_block as dgl_to_block
from dgl.nn import CuGraphSAGEConv, SAGEConv

options = OrderedDict(
Expand All @@ -19,7 +20,7 @@
def generate_graph():
u = torch.tensor([0, 1, 0, 2, 3, 0, 4, 0, 5, 0, 6, 7, 0, 8, 9])
v = torch.tensor([1, 9, 2, 9, 9, 4, 9, 5, 9, 6, 9, 9, 8, 9, 0])
g = dgl.graph((u, v))
g = graph((u, v))
return g


Expand All @@ -32,7 +33,7 @@ def test_SAGEConv_equality(idtype_int, max_in_degree, to_block):
if idtype_int:
g = g.int()
if to_block:
g = dgl.to_block(g)
g = dgl_to_block(g)
feat = torch.rand(g.num_src_nodes(), in_feat).to(device)

torch.manual_seed(0)
Expand Down
8 changes: 4 additions & 4 deletions tests/cugraph/test_basics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
import cugraph # usort: skip
import backend as F

import dgl
from dgl import from_cugraph, graph


def test_dummy():
Expand All @@ -12,7 +12,7 @@ def test_dummy():


def test_to_cugraph_conversion():
g = dgl.graph((F.tensor([0, 1, 2, 3]), F.tensor([1, 0, 3, 2]))).to("cuda")
g = graph((F.tensor([0, 1, 2, 3]), F.tensor([1, 0, 3, 2]))).to("cuda")
cugraph_g = g.to_cugraph()

assert cugraph_g.number_of_nodes() == g.num_nodes()
Expand All @@ -33,7 +33,7 @@ def test_from_cugraph_conversion():

cugraph_g.from_cudf_edgelist(df)

g = dgl.from_cugraph(cugraph_g)
g = from_cugraph(cugraph_g)

assert g.device.type == "cuda"
assert g.num_nodes() == cugraph_g.number_of_nodes()
Expand All @@ -52,7 +52,7 @@ def test_from_cugraph_conversion():

cugraph_g.from_cudf_edgelist(df)

g = dgl.from_cugraph(cugraph_g)
g = from_cugraph(cugraph_g)

assert g.device.type == "cuda"
assert g.num_nodes() == cugraph_g.number_of_nodes()
Expand Down
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