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Unverified Commit 6309483d authored by Xinyu Yao's avatar Xinyu Yao Committed by GitHub
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[GraphBolt] Remove old version `subgraph sampler`. (#7305) 


Co-authored-by: default avatarUbuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal>
parent 65272a53
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Showing with 137 additions and 1161 deletions
python/dgl/graphbolt/subgraph_sampler.py
View file @ 6309483d
......@@ -57,20 +57,7 @@ class SubgraphSampler(MiniBatchTransformer):
@staticmethod
def _preprocess(minibatch):
if minibatch.node_pairs is not None:
(
seeds,
seeds_timestamp,
minibatch.compacted_node_pairs,
minibatch.compacted_negative_srcs,
minibatch.compacted_negative_dsts,
) = SubgraphSampler._node_pairs_preprocess(minibatch)
elif minibatch.seed_nodes is not None:
seeds = minibatch.seed_nodes
seeds_timestamp = (
minibatch.timestamp if hasattr(minibatch, "timestamp") else None
)
elif minibatch.seeds is not None:
if minibatch.seeds is not None:
(
seeds,
seeds_timestamp,
......@@ -78,144 +65,12 @@ class SubgraphSampler(MiniBatchTransformer):
) = SubgraphSampler._seeds_preprocess(minibatch)
else:
raise ValueError(
f"Invalid minibatch {minibatch}: One of `node_pairs`, "
"`seed_nodes` and `seeds` should have a value."
f"Invalid minibatch {minibatch}: `seeds` should have a value."
)
minibatch._seed_nodes = seeds
minibatch._seeds_timestamp = seeds_timestamp
return minibatch
@staticmethod
def _node_pairs_preprocess(minibatch):
use_timestamp = hasattr(minibatch, "timestamp")
node_pairs = minibatch.node_pairs
neg_src, neg_dst = minibatch.negative_srcs, minibatch.negative_dsts
has_neg_src = neg_src is not None
has_neg_dst = neg_dst is not None
is_heterogeneous = isinstance(node_pairs, Dict)
if is_heterogeneous:
has_neg_src = has_neg_src and all(
item is not None for item in neg_src.values()
)
has_neg_dst = has_neg_dst and all(
item is not None for item in neg_dst.values()
)
# Collect nodes from all types of input.
nodes = defaultdict(list)
nodes_timestamp = None
if use_timestamp:
nodes_timestamp = defaultdict(list)
for etype, (src, dst) in node_pairs.items():
src_type, _, dst_type = etype_str_to_tuple(etype)
nodes[src_type].append(src)
nodes[dst_type].append(dst)
if use_timestamp:
nodes_timestamp[src_type].append(minibatch.timestamp[etype])
nodes_timestamp[dst_type].append(minibatch.timestamp[etype])
if has_neg_src:
for etype, src in neg_src.items():
src_type, _, _ = etype_str_to_tuple(etype)
nodes[src_type].append(src.view(-1))
if use_timestamp:
nodes_timestamp[src_type].append(
minibatch.timestamp[etype].repeat_interleave(
src.shape[-1]
)
)
if has_neg_dst:
for etype, dst in neg_dst.items():
_, _, dst_type = etype_str_to_tuple(etype)
nodes[dst_type].append(dst.view(-1))
if use_timestamp:
nodes_timestamp[dst_type].append(
minibatch.timestamp[etype].repeat_interleave(
dst.shape[-1]
)
)
# Unique and compact the collected nodes.
if use_timestamp:
seeds, nodes_timestamp, compacted = compact_temporal_nodes(
nodes, nodes_timestamp
)
else:
seeds, compacted = unique_and_compact(nodes)
nodes_timestamp = None
(
compacted_node_pairs,
compacted_negative_srcs,
compacted_negative_dsts,
) = ({}, {}, {})
# Map back in same order as collect.
for etype, _ in node_pairs.items():
src_type, _, dst_type = etype_str_to_tuple(etype)
src = compacted[src_type].pop(0)
dst = compacted[dst_type].pop(0)
compacted_node_pairs[etype] = (src, dst)
if has_neg_src:
for etype, _ in neg_src.items():
src_type, _, _ = etype_str_to_tuple(etype)
compacted_negative_srcs[etype] = compacted[src_type].pop(0)
compacted_negative_srcs[etype] = compacted_negative_srcs[
etype
].view(neg_src[etype].shape)
if has_neg_dst:
for etype, _ in neg_dst.items():
_, _, dst_type = etype_str_to_tuple(etype)
compacted_negative_dsts[etype] = compacted[dst_type].pop(0)
compacted_negative_dsts[etype] = compacted_negative_dsts[
etype
].view(neg_dst[etype].shape)
else:
# Collect nodes from all types of input.
nodes = list(node_pairs)
nodes_timestamp = None
if use_timestamp:
# Timestamp for source and destination nodes are the same.
nodes_timestamp = [minibatch.timestamp, minibatch.timestamp]
if has_neg_src:
nodes.append(neg_src.view(-1))
if use_timestamp:
nodes_timestamp.append(
minibatch.timestamp.repeat_interleave(neg_src.shape[-1])
)
if has_neg_dst:
nodes.append(neg_dst.view(-1))
if use_timestamp:
nodes_timestamp.append(
minibatch.timestamp.repeat_interleave(neg_dst.shape[-1])
)
# Unique and compact the collected nodes.
if use_timestamp:
seeds, nodes_timestamp, compacted = compact_temporal_nodes(
nodes, nodes_timestamp
)
else:
seeds, compacted = unique_and_compact(nodes)
nodes_timestamp = None
# Map back in same order as collect.
compacted_node_pairs = tuple(compacted[:2])
compacted = compacted[2:]
if has_neg_src:
compacted_negative_srcs = compacted.pop(0)
# Since we need to calculate the neg_ratio according to the
# compacted_negatvie_srcs shape, we need to reshape it back.
compacted_negative_srcs = compacted_negative_srcs.view(
neg_src.shape
)
if has_neg_dst:
compacted_negative_dsts = compacted.pop(0)
# Same as above.
compacted_negative_dsts = compacted_negative_dsts.view(
neg_dst.shape
)
return (
seeds,
nodes_timestamp,
compacted_node_pairs,
compacted_negative_srcs if has_neg_src else None,
compacted_negative_dsts if has_neg_dst else None,
)
def _sample(self, minibatch):
(
minibatch.input_nodes,
......
tests/python/pytorch/graphbolt/test_subgraph_sampler.py
View file @ 6309483d
......@@ -46,159 +46,6 @@ def _get_sampler(sampler_type):
)
def test_SubgraphSampler_invoke_nodes():
itemset = gb.ItemSet(torch.arange(10), names="seed_nodes")
item_sampler = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
# Invoke via class constructor.
datapipe = gb.SubgraphSampler(item_sampler)
with pytest.raises(NotImplementedError):
next(iter(datapipe))
# Invokde via functional form.
datapipe = item_sampler.sample_subgraph()
with pytest.raises(NotImplementedError):
next(iter(datapipe))
@pytest.mark.parametrize("labor", [False, True])
def test_NeighborSampler_invoke_nodes(labor):
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True).to(
F.ctx()
)
itemset = gb.ItemSet(torch.arange(10), names="seed_nodes")
item_sampler = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
# Invoke via class constructor.
Sampler = gb.LayerNeighborSampler if labor else gb.NeighborSampler
datapipe = Sampler(item_sampler, graph, fanouts)
assert len(list(datapipe)) == 5
# Invokde via functional form.
if labor:
datapipe = item_sampler.sample_layer_neighbor(graph, fanouts)
else:
datapipe = item_sampler.sample_neighbor(graph, fanouts)
assert len(list(datapipe)) == 5
@pytest.mark.parametrize("labor", [False, True])
def test_NeighborSampler_fanouts_nodes(labor):
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True).to(
F.ctx()
)
itemset = gb.ItemSet(torch.arange(10), names="seed_nodes")
item_sampler = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
# `fanouts` is a list of tensors.
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
if labor:
datapipe = item_sampler.sample_layer_neighbor(graph, fanouts)
else:
datapipe = item_sampler.sample_neighbor(graph, fanouts)
assert len(list(datapipe)) == 5
# `fanouts` is a list of integers.
fanouts = [2 for _ in range(num_layer)]
if labor:
datapipe = item_sampler.sample_layer_neighbor(graph, fanouts)
else:
datapipe = item_sampler.sample_neighbor(graph, fanouts)
assert len(list(datapipe)) == 5
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_Node_seed_nodes(sampler_type):
_check_sampler_type(sampler_type)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True).to(
F.ctx()
)
items = torch.arange(10)
names = "seed_nodes"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {"timestamp": torch.arange(20).to(F.ctx())}
graph.edge_attributes = {
"timestamp": torch.arange(len(graph.indices)).to(F.ctx())
}
items = (items, torch.arange(10))
names = ("seed_nodes", "timestamp")
itemset = gb.ItemSet(items, names=names)
item_sampler = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
sampler_dp = sampler(item_sampler, graph, fanouts)
_check_sampler_len(sampler_dp, 5)
def to_link_batch(data):
block = gb.MiniBatch(node_pairs=data)
return block
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_Link_node_pairs(sampler_type):
_check_sampler_type(sampler_type)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True).to(
F.ctx()
)
items = torch.arange(20).reshape(-1, 2)
names = "node_pairs"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {"timestamp": torch.arange(20).to(F.ctx())}
graph.edge_attributes = {
"timestamp": torch.arange(len(graph.indices)).to(F.ctx())
}
items = (items, torch.arange(10))
names = ("node_pairs", "timestamp")
itemset = gb.ItemSet(items, names=names)
datapipe = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
_check_sampler_len(datapipe, 5)
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_Link_With_Negative_node_pairs(sampler_type):
_check_sampler_type(sampler_type)
graph = gb_test_utils.rand_csc_graph(20, 0.15, bidirection_edge=True).to(
F.ctx()
)
items = torch.arange(20).reshape(-1, 2)
names = "node_pairs"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {"timestamp": torch.arange(20).to(F.ctx())}
graph.edge_attributes = {
"timestamp": torch.arange(len(graph.indices)).to(F.ctx())
}
items = (items, torch.arange(10))
names = ("node_pairs", "timestamp")
itemset = gb.ItemSet(items, names=names)
datapipe = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
datapipe = gb.UniformNegativeSampler(datapipe, graph, 1)
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
_check_sampler_len(datapipe, 5)
def get_hetero_graph():
# COO graph:
# [0, 0, 1, 1, 2, 2, 3, 3, 4, 4]
......@@ -217,742 +64,52 @@ def get_hetero_graph():
node_type_offset=node_type_offset,
type_per_edge=type_per_edge,
node_type_to_id=ntypes,
edge_type_to_id=etypes,
)
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_Node_seed_nodes_Hetero(sampler_type):
_check_sampler_type(sampler_type)
graph = get_hetero_graph().to(F.ctx())
items = torch.arange(3)
names = "seed_nodes"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {
"timestamp": torch.arange(graph.csc_indptr.numel() - 1).to(F.ctx())
}
graph.edge_attributes = {
"timestamp": torch.arange(graph.indices.numel()).to(F.ctx())
}
items = (items, torch.randint(0, 10, (3,)))
names = (names, "timestamp")
itemset = gb.ItemSetDict({"n2": gb.ItemSet(items, names=names)})
item_sampler = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
sampler_dp = sampler(item_sampler, graph, fanouts)
_check_sampler_len(sampler_dp, 2)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
for minibatch in sampler_dp:
assert len(minibatch.sampled_subgraphs) == num_layer
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_Link_Hetero_node_pairs(sampler_type):
_check_sampler_type(sampler_type)
graph = get_hetero_graph().to(F.ctx())
first_items = torch.LongTensor([[0, 0, 1, 1], [0, 2, 0, 1]]).T
first_names = "node_pairs"
second_items = torch.LongTensor([[0, 0, 1, 1, 2, 2], [0, 1, 1, 0, 0, 1]]).T
second_names = "node_pairs"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {
"timestamp": torch.arange(graph.csc_indptr.numel() - 1).to(F.ctx())
}
graph.edge_attributes = {
"timestamp": torch.arange(graph.indices.numel()).to(F.ctx())
}
first_items = (first_items, torch.randint(0, 10, (4,)))
first_names = (first_names, "timestamp")
second_items = (second_items, torch.randint(0, 10, (6,)))
second_names = (second_names, "timestamp")
itemset = gb.ItemSetDict(
{
"n1:e1:n2": gb.ItemSet(
first_items,
names=first_names,
),
"n2:e2:n1": gb.ItemSet(
second_items,
names=second_names,
),
}
)
datapipe = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
_check_sampler_len(datapipe, 5)
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_Link_Hetero_With_Negative_node_pairs(sampler_type):
_check_sampler_type(sampler_type)
graph = get_hetero_graph().to(F.ctx())
first_items = torch.LongTensor([[0, 0, 1, 1], [0, 2, 0, 1]]).T
first_names = "node_pairs"
second_items = torch.LongTensor([[0, 0, 1, 1, 2, 2], [0, 1, 1, 0, 0, 1]]).T
second_names = "node_pairs"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {
"timestamp": torch.arange(graph.csc_indptr.numel() - 1).to(F.ctx())
}
graph.edge_attributes = {
"timestamp": torch.arange(graph.indices.numel()).to(F.ctx())
}
first_items = (first_items, torch.randint(0, 10, (4,)))
first_names = (first_names, "timestamp")
second_items = (second_items, torch.randint(0, 10, (6,)))
second_names = (second_names, "timestamp")
itemset = gb.ItemSetDict(
{
"n1:e1:n2": gb.ItemSet(
first_items,
names=first_names,
),
"n2:e2:n1": gb.ItemSet(
second_items,
names=second_names,
),
}
)
datapipe = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
datapipe = gb.UniformNegativeSampler(datapipe, graph, 1)
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
_check_sampler_len(datapipe, 5)
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_Link_Hetero_Unknown_Etype_node_pairs(sampler_type):
_check_sampler_type(sampler_type)
graph = get_hetero_graph().to(F.ctx())
first_items = torch.LongTensor([[0, 0, 1, 1], [0, 2, 0, 1]]).T
first_names = "node_pairs"
second_items = torch.LongTensor([[0, 0, 1, 1, 2, 2], [0, 1, 1, 0, 0, 1]]).T
second_names = "node_pairs"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {
"timestamp": torch.arange(graph.csc_indptr.numel() - 1).to(F.ctx())
}
graph.edge_attributes = {
"timestamp": torch.arange(graph.indices.numel()).to(F.ctx())
}
first_items = (first_items, torch.randint(0, 10, (4,)))
first_names = (first_names, "timestamp")
second_items = (second_items, torch.randint(0, 10, (6,)))
second_names = (second_names, "timestamp")
# "e11" and "e22" are not valid edge types.
itemset = gb.ItemSetDict(
{
"n1:e11:n2": gb.ItemSet(
first_items,
names=first_names,
),
"n2:e22:n1": gb.ItemSet(
second_items,
names=second_names,
),
}
)
datapipe = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
_check_sampler_len(datapipe, 5)
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_Link_Hetero_With_Negative_Unknown_Etype_node_pairs(
sampler_type,
):
_check_sampler_type(sampler_type)
graph = get_hetero_graph().to(F.ctx())
first_items = torch.LongTensor([[0, 0, 1, 1], [0, 2, 0, 1]]).T
first_names = "node_pairs"
second_items = torch.LongTensor([[0, 0, 1, 1, 2, 2], [0, 1, 1, 0, 0, 1]]).T
second_names = "node_pairs"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {
"timestamp": torch.arange(graph.csc_indptr.numel() - 1).to(F.ctx())
}
graph.edge_attributes = {
"timestamp": torch.arange(graph.indices.numel()).to(F.ctx())
}
first_items = (first_items, torch.randint(0, 10, (4,)))
first_names = (first_names, "timestamp")
second_items = (second_items, torch.randint(0, 10, (6,)))
second_names = (second_names, "timestamp")
# "e11" and "e22" are not valid edge types.
itemset = gb.ItemSetDict(
{
"n1:e11:n2": gb.ItemSet(
first_items,
names=first_names,
),
"n2:e22:n1": gb.ItemSet(
second_items,
names=second_names,
),
}
)
datapipe = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
datapipe = gb.UniformNegativeSampler(datapipe, graph, 1)
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
_check_sampler_len(datapipe, 5)
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
@pytest.mark.parametrize(
"replace",
[False, True],
)
def test_SubgraphSampler_Random_Hetero_Graph_seed_ndoes(sampler_type, replace):
_check_sampler_type(sampler_type)
if F._default_context_str == "gpu" and replace == True:
pytest.skip("Sampling with replacement not yet supported on GPU.")
num_nodes = 5
num_edges = 9
num_ntypes = 3
num_etypes = 3
(
csc_indptr,
indices,
node_type_offset,
type_per_edge,
node_type_to_id,
edge_type_to_id,
) = gb_test_utils.random_hetero_graph(
num_nodes, num_edges, num_ntypes, num_etypes
)
node_attributes = {}
edge_attributes = {
"A1": torch.randn(num_edges),
"A2": torch.randn(num_edges),
}
if sampler_type == SamplerType.Temporal:
node_attributes["timestamp"] = torch.randint(0, 10, (num_nodes,))
edge_attributes["timestamp"] = torch.randint(0, 10, (num_edges,))
graph = gb.fused_csc_sampling_graph(
csc_indptr,
indices,
node_type_offset=node_type_offset,
type_per_edge=type_per_edge,
node_type_to_id=node_type_to_id,
edge_type_to_id=edge_type_to_id,
node_attributes=node_attributes,
edge_attributes=edge_attributes,
).to(F.ctx())
first_items = torch.tensor([0])
first_names = "seed_nodes"
second_items = torch.tensor([0])
second_names = "seed_nodes"
if sampler_type == SamplerType.Temporal:
first_items = (first_items, torch.randint(0, 10, (1,)))
first_names = (first_names, "timestamp")
second_items = (second_items, torch.randint(0, 10, (1,)))
second_names = (second_names, "timestamp")
itemset = gb.ItemSetDict(
{
"n2": gb.ItemSet(first_items, names=first_names),
"n1": gb.ItemSet(second_items, names=second_names),
}
)
item_sampler = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
sampler_dp = sampler(item_sampler, graph, fanouts, replace=replace)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
for data in sampler_dp:
for sampledsubgraph in data.sampled_subgraphs:
for _, value in sampledsubgraph.sampled_csc.items():
for idx in [value.indices, value.indptr]:
assert torch.equal(
torch.ge(idx, torch.zeros(len(idx)).to(F.ctx())),
torch.ones(len(idx)).to(F.ctx()),
)
node_ids = [
sampledsubgraph.original_column_node_ids,
sampledsubgraph.original_row_node_ids,
]
for ids in node_ids:
for _, value in ids.items():
assert torch.equal(
torch.ge(
value, torch.zeros(len(value)).to(F.ctx())
),
torch.ones(len(value)).to(F.ctx()),
)
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_without_deduplication_Homo_seed_nodes(sampler_type):
_check_sampler_type(sampler_type)
graph = dgl.graph(
([5, 0, 1, 5, 6, 7, 2, 2, 4], [0, 1, 2, 2, 2, 2, 3, 4, 4])
)
graph = gb.from_dglgraph(graph, True).to(F.ctx())
seed_nodes = torch.LongTensor([0, 3, 4])
items = seed_nodes
names = "seed_nodes"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {
"timestamp": torch.zeros(graph.csc_indptr.numel() - 1).to(F.ctx())
}
graph.edge_attributes = {
"timestamp": torch.zeros(graph.indices.numel()).to(F.ctx())
}
items = (items, torch.randint(1, 10, (3,)))
names = (names, "timestamp")
itemset = gb.ItemSet(items, names=names)
item_sampler = gb.ItemSampler(itemset, batch_size=len(seed_nodes)).copy_to(
F.ctx()
)
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
if sampler_type == SamplerType.Temporal:
datapipe = sampler(item_sampler, graph, fanouts)
else:
datapipe = sampler(item_sampler, graph, fanouts, deduplicate=False)
length = [17, 7]
compacted_indices = [
(torch.arange(0, 10) + 7).to(F.ctx()),
(torch.arange(0, 4) + 3).to(F.ctx()),
]
indptr = [
torch.tensor([0, 1, 2, 4, 4, 6, 8, 10]).to(F.ctx()),
torch.tensor([0, 1, 2, 4]).to(F.ctx()),
]
seeds = [
torch.tensor([0, 2, 2, 3, 4, 4, 5]).to(F.ctx()),
torch.tensor([0, 3, 4]).to(F.ctx()),
]
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
assert (
len(sampled_subgraph.original_row_node_ids) == length[step]
)
assert torch.equal(
sampled_subgraph.sampled_csc.indices,
compacted_indices[step],
)
assert torch.equal(
sampled_subgraph.sampled_csc.indptr, indptr[step]
)
assert torch.equal(
torch.sort(sampled_subgraph.original_column_node_ids)[0],
seeds[step],
)
def _assert_hetero_values(
datapipe, original_row_node_ids, original_column_node_ids, csc_formats
):
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
for ntype in ["n1", "n2"]:
assert torch.equal(
sampled_subgraph.original_row_node_ids[ntype],
original_row_node_ids[step][ntype].to(F.ctx()),
)
assert torch.equal(
sampled_subgraph.original_column_node_ids[ntype],
original_column_node_ids[step][ntype].to(F.ctx()),
)
for etype in ["n1:e1:n2", "n2:e2:n1"]:
assert torch.equal(
sampled_subgraph.sampled_csc[etype].indices,
csc_formats[step][etype].indices.to(F.ctx()),
)
assert torch.equal(
sampled_subgraph.sampled_csc[etype].indptr,
csc_formats[step][etype].indptr.to(F.ctx()),
)
def _assert_homo_values(
datapipe, original_row_node_ids, compacted_indices, indptr, seeds
):
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
assert torch.equal(
sampled_subgraph.original_row_node_ids,
original_row_node_ids[step],
)
assert torch.equal(
sampled_subgraph.sampled_csc.indices, compacted_indices[step]
)
assert torch.equal(
sampled_subgraph.sampled_csc.indptr, indptr[step]
)
assert torch.equal(
sampled_subgraph.original_column_node_ids, seeds[step]
)
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_without_deduplication_Hetero_seed_nodes(sampler_type):
_check_sampler_type(sampler_type)
graph = get_hetero_graph().to(F.ctx())
items = torch.arange(2)
names = "seed_nodes"
if sampler_type == SamplerType.Temporal:
graph.node_attributes = {
"timestamp": torch.zeros(graph.csc_indptr.numel() - 1).to(F.ctx())
}
graph.edge_attributes = {
"timestamp": torch.zeros(graph.indices.numel()).to(F.ctx())
}
items = (items, torch.randint(1, 10, (2,)))
names = (names, "timestamp")
itemset = gb.ItemSetDict({"n2": gb.ItemSet(items, names=names)})
item_sampler = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
if sampler_type == SamplerType.Temporal:
datapipe = sampler(item_sampler, graph, fanouts)
else:
datapipe = sampler(item_sampler, graph, fanouts, deduplicate=False)
csc_formats = [
{
"n1:e1:n2": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4]),
indices=torch.tensor([4, 5, 6, 7]),
),
"n2:e2:n1": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4, 6, 8]),
indices=torch.tensor([2, 3, 4, 5, 6, 7, 8, 9]),
),
},
{
"n1:e1:n2": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4]),
indices=torch.tensor([0, 1, 2, 3]),
),
"n2:e2:n1": gb.CSCFormatBase(
indptr=torch.tensor([0]),
indices=torch.tensor([], dtype=torch.int64),
),
},
]
original_column_node_ids = [
{
"n1": torch.tensor([0, 1, 1, 0]),
"n2": torch.tensor([0, 1]),
},
{
"n1": torch.tensor([], dtype=torch.int64),
"n2": torch.tensor([0, 1]),
},
]
original_row_node_ids = [
{
"n1": torch.tensor([0, 1, 1, 0, 0, 1, 1, 0]),
"n2": torch.tensor([0, 1, 0, 2, 0, 1, 0, 1, 0, 2]),
},
{
"n1": torch.tensor([0, 1, 1, 0]),
"n2": torch.tensor([0, 1]),
},
]
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
_assert_hetero_values(
datapipe,
original_row_node_ids,
original_column_node_ids,
csc_formats,
)
@unittest.skipIf(
F._default_context_str == "gpu",
reason="Fails due to different result on the GPU.",
)
@pytest.mark.parametrize("labor", [False, True])
def test_SubgraphSampler_unique_csc_format_Homo_cpu_seed_nodes(labor):
torch.manual_seed(1205)
graph = dgl.graph(([5, 0, 6, 7, 2, 2, 4], [0, 1, 2, 2, 3, 4, 4]))
graph = gb.from_dglgraph(graph, True).to(F.ctx())
seed_nodes = torch.LongTensor([0, 3, 4])
itemset = gb.ItemSet(seed_nodes, names="seed_nodes")
item_sampler = gb.ItemSampler(itemset, batch_size=len(seed_nodes)).copy_to(
F.ctx()
)
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
Sampler = gb.LayerNeighborSampler if labor else gb.NeighborSampler
datapipe = Sampler(
item_sampler,
graph,
fanouts,
deduplicate=True,
)
original_row_node_ids = [
torch.tensor([0, 3, 4, 5, 2, 6, 7]).to(F.ctx()),
torch.tensor([0, 3, 4, 5, 2]).to(F.ctx()),
]
compacted_indices = [
torch.tensor([3, 4, 4, 2, 5, 6]).to(F.ctx()),
torch.tensor([3, 4, 4, 2]).to(F.ctx()),
]
indptr = [
torch.tensor([0, 1, 2, 4, 4, 6]).to(F.ctx()),
torch.tensor([0, 1, 2, 4]).to(F.ctx()),
]
seeds = [
torch.tensor([0, 3, 4, 5, 2]).to(F.ctx()),
torch.tensor([0, 3, 4]).to(F.ctx()),
]
_assert_homo_values(
datapipe, original_row_node_ids, compacted_indices, indptr, seeds
)
@unittest.skipIf(
F._default_context_str == "cpu",
reason="Fails due to different result on the CPU.",
)
@pytest.mark.parametrize("labor", [False, True])
def test_SubgraphSampler_unique_csc_format_Homo_gpu_seed_nodes(labor):
torch.manual_seed(1205)
graph = dgl.graph(([5, 0, 7, 7, 2, 4], [0, 1, 2, 2, 3, 4]))
graph = gb.from_dglgraph(graph, is_homogeneous=True).to(F.ctx())
seed_nodes = torch.LongTensor([0, 3, 4])
itemset = gb.ItemSet(seed_nodes, names="seed_nodes")
item_sampler = gb.ItemSampler(itemset, batch_size=len(seed_nodes)).copy_to(
F.ctx()
)
num_layer = 2
fanouts = [torch.LongTensor([-1]) for _ in range(num_layer)]
Sampler = gb.LayerNeighborSampler if labor else gb.NeighborSampler
datapipe = Sampler(
item_sampler,
graph,
fanouts,
deduplicate=True,
)
if torch.cuda.get_device_capability()[0] < 7:
original_row_node_ids = [
torch.tensor([0, 3, 4, 2, 5, 7]).to(F.ctx()),
torch.tensor([0, 3, 4, 2, 5]).to(F.ctx()),
]
compacted_indices = [
torch.tensor([4, 3, 2, 5, 5]).to(F.ctx()),
torch.tensor([4, 3, 2]).to(F.ctx()),
]
indptr = [
torch.tensor([0, 1, 2, 3, 5, 5]).to(F.ctx()),
torch.tensor([0, 1, 2, 3]).to(F.ctx()),
]
seeds = [
torch.tensor([0, 3, 4, 2, 5]).to(F.ctx()),
torch.tensor([0, 3, 4]).to(F.ctx()),
]
else:
original_row_node_ids = [
torch.tensor([0, 3, 4, 5, 2, 7]).to(F.ctx()),
torch.tensor([0, 3, 4, 5, 2]).to(F.ctx()),
]
compacted_indices = [
torch.tensor([3, 4, 2, 5, 5]).to(F.ctx()),
torch.tensor([3, 4, 2]).to(F.ctx()),
]
indptr = [
torch.tensor([0, 1, 2, 3, 3, 5]).to(F.ctx()),
torch.tensor([0, 1, 2, 3]).to(F.ctx()),
]
seeds = [
torch.tensor([0, 3, 4, 5, 2]).to(F.ctx()),
torch.tensor([0, 3, 4]).to(F.ctx()),
]
_assert_homo_values(
datapipe, original_row_node_ids, compacted_indices, indptr, seeds
edge_type_to_id=etypes,
)
@pytest.mark.parametrize("labor", [False, True])
def test_SubgraphSampler_unique_csc_format_Hetero_seed_nodes(labor):
graph = get_hetero_graph().to(F.ctx())
itemset = gb.ItemSetDict(
{"n2": gb.ItemSet(torch.arange(2), names="seed_nodes")}
def _assert_hetero_values(
datapipe, original_row_node_ids, original_column_node_ids, csc_formats
):
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
for ntype in ["n1", "n2"]:
assert torch.equal(
sampled_subgraph.original_row_node_ids[ntype],
original_row_node_ids[step][ntype].to(F.ctx()),
)
item_sampler = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
Sampler = gb.LayerNeighborSampler if labor else gb.NeighborSampler
datapipe = Sampler(
item_sampler,
graph,
fanouts,
deduplicate=True,
assert torch.equal(
sampled_subgraph.original_column_node_ids[ntype],
original_column_node_ids[step][ntype].to(F.ctx()),
)
csc_formats = [
{
"n1:e1:n2": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4]),
indices=torch.tensor([0, 1, 1, 0]),
),
"n2:e2:n1": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4]),
indices=torch.tensor([0, 2, 0, 1]),
),
},
{
"n1:e1:n2": gb.CSCFormatBase(
indptr=torch.tensor([0, 2, 4]),
indices=torch.tensor([0, 1, 1, 0]),
),
"n2:e2:n1": gb.CSCFormatBase(
indptr=torch.tensor([0]),
indices=torch.tensor([], dtype=torch.int64),
),
},
]
original_column_node_ids = [
{
"n1": torch.tensor([0, 1]),
"n2": torch.tensor([0, 1]),
},
{
"n1": torch.tensor([], dtype=torch.int64),
"n2": torch.tensor([0, 1]),
},
]
original_row_node_ids = [
{
"n1": torch.tensor([0, 1]),
"n2": torch.tensor([0, 1, 2]),
},
{
"n1": torch.tensor([0, 1]),
"n2": torch.tensor([0, 1]),
},
]
_assert_hetero_values(
datapipe, original_row_node_ids, original_column_node_ids, csc_formats
for etype in ["n1:e1:n2", "n2:e2:n1"]:
assert torch.equal(
sampled_subgraph.sampled_csc[etype].indices,
csc_formats[step][etype].indices.to(F.ctx()),
)
assert torch.equal(
sampled_subgraph.sampled_csc[etype].indptr,
csc_formats[step][etype].indptr.to(F.ctx()),
)
@pytest.mark.parametrize(
"sampler_type",
[SamplerType.Normal, SamplerType.Layer, SamplerType.Temporal],
)
def test_SubgraphSampler_Hetero_multifanout_per_layer_seed_nodes(sampler_type):
_check_sampler_type(sampler_type)
graph = get_hetero_graph().to(F.ctx())
items_n1 = torch.tensor([0])
items_n2 = torch.tensor([1])
names = "seed_nodes"
item_length = 2
if sampler_type == SamplerType.Temporal:
item_length = 3
graph.node_attributes = {
"timestamp": torch.arange(graph.csc_indptr.numel() - 1).to(F.ctx())
}
graph.edge_attributes = {
"timestamp": torch.arange(graph.indices.numel()).to(F.ctx())
}
# All edges can be sampled.
items_n1 = (items_n1, torch.tensor([10]))
items_n2 = (items_n2, torch.tensor([10]))
names = (names, "timestamp")
itemset = gb.ItemSetDict(
{
"n1": gb.ItemSet(items=items_n1, names=names),
"n2": gb.ItemSet(items=items_n2, names=names),
}
def _assert_homo_values(
datapipe, original_row_node_ids, compacted_indices, indptr, seeds
):
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
assert torch.equal(
sampled_subgraph.original_row_node_ids,
original_row_node_ids[step],
)
item_sampler = gb.ItemSampler(itemset, batch_size=2).copy_to(F.ctx())
num_layer = 2
# The number of edges to be sampled for each edge types of each node.
fanouts = [torch.LongTensor([2, 1]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
sampler_dp = sampler(item_sampler, graph, fanouts)
indices_len = [
{
"n1:e1:n2": 4,
"n2:e2:n1": item_length,
},
{
"n1:e1:n2": 2,
"n2:e2:n1": 1,
},
]
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
for minibatch in sampler_dp:
for step, sampled_subgraph in enumerate(
minibatch.sampled_subgraphs
):
assert (
len(sampled_subgraph.sampled_csc["n1:e1:n2"].indices)
== indices_len[step]["n1:e1:n2"]
assert torch.equal(
sampled_subgraph.sampled_csc.indices, compacted_indices[step]
)
assert (
len(sampled_subgraph.sampled_csc["n2:e2:n1"].indices)
== indices_len[step]["n2:e2:n1"]
assert torch.equal(
sampled_subgraph.sampled_csc.indptr, indptr[step]
)
assert torch.equal(
sampled_subgraph.original_column_node_ids, seeds[step]
)
......@@ -1044,7 +201,7 @@ def test_SubgraphSampler_Node(sampler_type):
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
sampler_dp = sampler(item_sampler, graph, fanouts)
assert len(list(sampler_dp)) == 5
_check_sampler_len(sampler_dp, 5)
@pytest.mark.parametrize(
......@@ -1072,7 +229,7 @@ def test_SubgraphSampler_Link(sampler_type):
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
assert len(list(datapipe)) == 5
_check_sampler_len(datapipe, 5)
for data in datapipe:
assert torch.equal(
data.compacted_seeds, torch.tensor([[0, 1], [2, 3]]).to(F.ctx())
......@@ -1105,7 +262,7 @@ def test_SubgraphSampler_Link_With_Negative(sampler_type):
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
assert len(list(datapipe)) == 5
_check_sampler_len(datapipe, 5)
@pytest.mark.parametrize(
......@@ -1132,8 +289,10 @@ def test_SubgraphSampler_Node_Hetero(sampler_type):
fanouts = [torch.LongTensor([2]) for _ in range(num_layer)]
sampler = _get_sampler(sampler_type)
sampler_dp = sampler(item_sampler, graph, fanouts)
assert len(list(sampler_dp)) == 2
for step, minibatch in enumerate(sampler_dp):
_check_sampler_len(sampler_dp, 2)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
for minibatch in sampler_dp:
assert len(minibatch.sampled_subgraphs) == num_layer
......@@ -1178,7 +337,7 @@ def test_SubgraphSampler_Link_Hetero(sampler_type):
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
assert len(list(datapipe)) == 5
_check_sampler_len(datapipe, 5)
for data in datapipe:
for compacted_seeds in data.compacted_seeds.values():
if sampler_type == SamplerType.Temporal:
......@@ -1234,7 +393,7 @@ def test_SubgraphSampler_Link_Hetero_With_Negative(sampler_type):
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
assert len(list(datapipe)) == 5
_check_sampler_len(datapipe, 5)
@pytest.mark.parametrize(
......@@ -1279,7 +438,7 @@ def test_SubgraphSampler_Link_Hetero_Unknown_Etype(sampler_type):
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
assert len(list(datapipe)) == 5
_check_sampler_len(datapipe, 5)
@pytest.mark.parametrize(
......@@ -1325,7 +484,7 @@ def test_SubgraphSampler_Link_Hetero_With_Negative_Unknown_Etype(sampler_type):
sampler = _get_sampler(sampler_type)
datapipe = sampler(datapipe, graph, fanouts)
datapipe = datapipe.transform(partial(gb.exclude_seed_edges))
assert len(list(datapipe)) == 5
_check_sampler_len(datapipe, 5)
@pytest.mark.parametrize(
......@@ -1395,6 +554,8 @@ def test_SubgraphSampler_Random_Hetero_Graph(sampler_type, replace):
sampler_dp = sampler(item_sampler, graph, fanouts, replace=replace)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
for data in sampler_dp:
for sampledsubgraph in data.sampled_subgraphs:
for _, value in sampledsubgraph.sampled_csc.items():
......@@ -1407,11 +568,15 @@ def test_SubgraphSampler_Random_Hetero_Graph(sampler_type, replace):
)
assert torch.equal(
torch.ge(
value.indptr, torch.zeros(len(value.indptr)).to(F.ctx())
value.indptr,
torch.zeros(len(value.indptr)).to(F.ctx()),
),
torch.ones(len(value.indptr)).to(F.ctx()),
)
for _, value in sampledsubgraph.original_column_node_ids.items():
for (
_,
value,
) in sampledsubgraph.original_column_node_ids.items():
assert torch.equal(
torch.ge(value, torch.zeros(len(value)).to(F.ctx())),
torch.ones(len(value)).to(F.ctx()),
......@@ -1472,11 +637,16 @@ def test_SubgraphSampler_without_deduplication_Homo_Node(sampler_type):
torch.tensor([0, 2, 2, 3, 4, 4, 5]).to(F.ctx()),
torch.tensor([0, 3, 4]).to(F.ctx()),
]
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
assert len(sampled_subgraph.original_row_node_ids) == length[step]
assert (
len(sampled_subgraph.original_row_node_ids) == length[step]
)
assert torch.equal(
sampled_subgraph.sampled_csc.indices, compacted_indices[step]
sampled_subgraph.sampled_csc.indices,
compacted_indices[step],
)
assert torch.equal(
sampled_subgraph.sampled_csc.indptr, indptr[step]
......@@ -1557,25 +727,13 @@ def test_SubgraphSampler_without_deduplication_Hetero_Node(sampler_type):
},
]
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
for ntype in ["n1", "n2"]:
assert torch.equal(
sampled_subgraph.original_row_node_ids[ntype],
original_row_node_ids[step][ntype].to(F.ctx()),
)
assert torch.equal(
sampled_subgraph.original_column_node_ids[ntype],
original_column_node_ids[step][ntype].to(F.ctx()),
)
for etype in ["n1:e1:n2", "n2:e2:n1"]:
assert torch.equal(
sampled_subgraph.sampled_csc[etype].indices,
csc_formats[step][etype].indices.to(F.ctx()),
)
assert torch.equal(
sampled_subgraph.sampled_csc[etype].indptr,
csc_formats[step][etype].indptr.to(F.ctx()),
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
_assert_hetero_values(
datapipe,
original_row_node_ids,
original_column_node_ids,
csc_formats,
)
......@@ -1621,20 +779,8 @@ def test_SubgraphSampler_unique_csc_format_Homo_Node_cpu(labor):
torch.tensor([0, 3, 4, 5, 2]).to(F.ctx()),
torch.tensor([0, 3, 4]).to(F.ctx()),
]
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
assert torch.equal(
sampled_subgraph.original_row_node_ids,
original_row_node_ids[step],
)
assert torch.equal(
sampled_subgraph.sampled_csc.indices, compacted_indices[step]
)
assert torch.equal(
sampled_subgraph.sampled_csc.indptr, indptr[step]
)
assert torch.equal(
sampled_subgraph.original_column_node_ids, seeds[step]
_assert_homo_values(
datapipe, original_row_node_ids, compacted_indices, indptr, seeds
)
......@@ -1699,20 +845,8 @@ def test_SubgraphSampler_unique_csc_format_Homo_Node_gpu(labor):
torch.tensor([0, 3, 4]).to(F.ctx()),
]
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
assert torch.equal(
sampled_subgraph.original_row_node_ids,
original_row_node_ids[step],
)
assert torch.equal(
sampled_subgraph.sampled_csc.indices, compacted_indices[step]
)
assert torch.equal(
sampled_subgraph.sampled_csc.indptr, indptr[step]
)
assert torch.equal(
sampled_subgraph.original_column_node_ids, seeds[step]
_assert_homo_values(
datapipe, original_row_node_ids, compacted_indices, indptr, seeds
)
......@@ -1773,25 +907,8 @@ def test_SubgraphSampler_unique_csc_format_Hetero_Node(labor):
},
]
for data in datapipe:
for step, sampled_subgraph in enumerate(data.sampled_subgraphs):
for ntype in ["n1", "n2"]:
assert torch.equal(
sampled_subgraph.original_row_node_ids[ntype],
original_row_node_ids[step][ntype].to(F.ctx()),
)
assert torch.equal(
sampled_subgraph.original_column_node_ids[ntype],
original_column_node_ids[step][ntype].to(F.ctx()),
)
for etype in ["n1:e1:n2", "n2:e2:n1"]:
assert torch.equal(
sampled_subgraph.sampled_csc[etype].indices,
csc_formats[step][etype].indices.to(F.ctx()),
)
assert torch.equal(
sampled_subgraph.sampled_csc[etype].indptr,
csc_formats[step][etype].indptr.to(F.ctx()),
_assert_hetero_values(
datapipe, original_row_node_ids, original_column_node_ids, csc_formats
)
......@@ -1850,8 +967,12 @@ def test_SubgraphSampler_Hetero_multifanout_per_layer(sampler_type):
"n2:e2:n1": 1,
},
]
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
for minibatch in sampler_dp:
for step, sampled_subgraph in enumerate(minibatch.sampled_subgraphs):
for step, sampled_subgraph in enumerate(
minibatch.sampled_subgraphs
):
assert (
len(sampled_subgraph.sampled_csc["n1:e1:n2"].indices)
== indices_len[step]["n1:e1:n2"]
......
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