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[Mics] Provide options for bidirectional edge #6566

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Nov 19, 2023
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[Mics] Provide options for bidirectional edge #6566

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Skeleton003 Nov 14, 2023
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5 changes: 3 additions & 2 deletions tests/python/pytorch/graphbolt/gb_test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,9 +9,10 @@
import torch


def rand_csc_graph(N, density):
def rand_csc_graph(N, density, bidirection_edge=False):
adj = sp.random(N, N, density)
adj = adj + adj.T
if bidirection_edge:
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adj = adj + adj.T
adj = adj.tocsc()

indptr = torch.LongTensor(adj.indptr)
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4 changes: 2 additions & 2 deletions tests/python/pytorch/graphbolt/impl/test_negative_sampler.py
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Expand Up @@ -32,7 +32,7 @@ def test_NegativeSampler_invoke():

def test_UniformNegativeSampler_invoke():
# Instantiate graph and required datapipes.
graph = gb_test_utils.rand_csc_graph(100, 0.05)
graph = gb_test_utils.rand_csc_graph(100, 0.05, bidirection_edge=True)
num_seeds = 30
item_set = gb.ItemSet(
torch.arange(0, 2 * num_seeds).reshape(-1, 2), names="node_pairs"
Expand Down Expand Up @@ -69,7 +69,7 @@ def _verify(negative_sampler):
@pytest.mark.parametrize("negative_ratio", [1, 5, 10, 20])
def test_Uniform_NegativeSampler(negative_ratio):
# Construct FusedCSCSamplingGraph.
graph = gb_test_utils.rand_csc_graph(100, 0.05)
graph = gb_test_utils.rand_csc_graph(100, 0.05, bidirection_edge=True)
num_seeds = 30
item_set = gb.ItemSet(
torch.arange(0, num_seeds * 2).reshape(-1, 2), names="node_pairs"
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2 changes: 1 addition & 1 deletion tests/python/pytorch/graphbolt/test_base.py
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Expand Up @@ -29,7 +29,7 @@ def test_CopyToWithMiniBatches():
N = 16
B = 2
itemset = gb.ItemSet(torch.arange(N), names="seed_nodes")
graph = gb_test_utils.rand_csc_graph(100, 0.15)
graph = gb_test_utils.rand_csc_graph(100, 0.15, bidirection_edge=True)

features = {}
keys = [("node", None, "a"), ("node", None, "b")]
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6 changes: 3 additions & 3 deletions tests/python/pytorch/graphbolt/test_feature_fetcher.py
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Expand Up @@ -8,7 +8,7 @@

def test_FeatureFetcher_invoke():
# Prepare graph and required datapipes.
graph = gb_test_utils.rand_csc_graph(20, 0.15)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True)
a = torch.tensor(
[[random.randint(0, 10)] for _ in range(graph.total_num_nodes)]
)
Expand Down Expand Up @@ -40,7 +40,7 @@ def test_FeatureFetcher_invoke():


def test_FeatureFetcher_homo():
graph = gb_test_utils.rand_csc_graph(20, 0.15)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True)
a = torch.tensor(
[[random.randint(0, 10)] for _ in range(graph.total_num_nodes)]
)
Expand All @@ -65,7 +65,7 @@ def test_FeatureFetcher_homo():


def test_FeatureFetcher_with_edges_homo():
graph = gb_test_utils.rand_csc_graph(20, 0.15)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True)
a = torch.tensor(
[[random.randint(0, 10)] for _ in range(graph.total_num_nodes)]
)
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2 changes: 1 addition & 1 deletion tests/python/pytorch/graphbolt/test_minibatch_transformer.py
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Expand Up @@ -7,7 +7,7 @@ def test_dgl_minibatch_converter():
N = 32
B = 4
itemset = gb.ItemSet(torch.arange(N), names="seed_nodes")
graph = gb_test_utils.rand_csc_graph(200, 0.15)
graph = gb_test_utils.rand_csc_graph(200, 0.15, bidirection_edge=True)

features = {}
keys = [("node", None, "a"), ("node", None, "b")]
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2 changes: 1 addition & 1 deletion tests/python/pytorch/graphbolt/test_multi_process_dataloader.py
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Expand Up @@ -14,7 +14,7 @@ def test_DataLoader():
N = 40
B = 4
itemset = dgl.graphbolt.ItemSet(torch.arange(N), names="seed_nodes")
graph = gb_test_utils.rand_csc_graph(200, 0.15)
graph = gb_test_utils.rand_csc_graph(200, 0.15, bidirection_edge=True)
features = {}
keys = [("node", None, "a"), ("node", None, "b")]
features[keys[0]] = dgl.graphbolt.TorchBasedFeature(torch.randn(200, 4))
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2 changes: 1 addition & 1 deletion tests/python/pytorch/graphbolt/test_single_process_dataloader.py
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Expand Up @@ -11,7 +11,7 @@ def test_DataLoader():
N = 32
B = 4
itemset = dgl.graphbolt.ItemSet(torch.arange(N), names="seed_nodes")
graph = gb_test_utils.rand_csc_graph(200, 0.15)
graph = gb_test_utils.rand_csc_graph(200, 0.15, bidirection_edge=True)

features = {}
keys = [("node", None, "a"), ("node", None, "b")]
Expand Down
10 changes: 5 additions & 5 deletions tests/python/pytorch/graphbolt/test_subgraph_sampler.py
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Expand Up @@ -22,7 +22,7 @@ def test_SubgraphSampler_invoke():

@pytest.mark.parametrize("labor", [False, True])
def test_NeighborSampler_invoke(labor):
graph = gb_test_utils.rand_csc_graph(20, 0.15)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True)
itemset = gb.ItemSet(torch.arange(10), names="seed_nodes")
item_sampler = gb.ItemSampler(itemset, batch_size=2)
num_layer = 2
Expand All @@ -43,7 +43,7 @@ def test_NeighborSampler_invoke(labor):

@pytest.mark.parametrize("labor", [False, True])
def test_NeighborSampler_fanouts(labor):
graph = gb_test_utils.rand_csc_graph(20, 0.15)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True)
itemset = gb.ItemSet(torch.arange(10), names="seed_nodes")
item_sampler = gb.ItemSampler(itemset, batch_size=2)
num_layer = 2
Expand All @@ -67,7 +67,7 @@ def test_NeighborSampler_fanouts(labor):

@pytest.mark.parametrize("labor", [False, True])
def test_SubgraphSampler_Node(labor):
graph = gb_test_utils.rand_csc_graph(20, 0.15)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True)
itemset = gb.ItemSet(torch.arange(10), names="seed_nodes")
item_sampler = gb.ItemSampler(itemset, batch_size=2)
num_layer = 2
Expand All @@ -84,7 +84,7 @@ def to_link_batch(data):

@pytest.mark.parametrize("labor", [False, True])
def test_SubgraphSampler_Link(labor):
graph = gb_test_utils.rand_csc_graph(20, 0.15)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True)
itemset = gb.ItemSet(torch.arange(0, 20).reshape(-1, 2), names="node_pairs")
item_sampler = gb.ItemSampler(itemset, batch_size=2)
num_layer = 2
Expand All @@ -96,7 +96,7 @@ def test_SubgraphSampler_Link(labor):

@pytest.mark.parametrize("labor", [False, True])
def test_SubgraphSampler_Link_With_Negative(labor):
graph = gb_test_utils.rand_csc_graph(20, 0.15)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True)
itemset = gb.ItemSet(torch.arange(0, 20).reshape(-1, 2), names="node_pairs")
item_sampler = gb.ItemSampler(itemset, batch_size=2)
num_layer = 2
Expand Down