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Unverified Commit afca1114 authored by peizhou001's avatar peizhou001 Committed by GitHub
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[Graphbolt]Fix negative sampler (#6933) 


Co-authored-by: default avatarUbuntu <ubuntu@ip-172-31-21-218.ap-northeast-1.compute.internal>
parent 3ff7ad9d
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Showing with 98 additions and 151 deletions
graphbolt/include/graphbolt/fused_csc_sampling_graph.h
View file @ afca1114
......@@ -359,32 +359,6 @@ class FusedCSCSamplingGraph : public torch::CustomClassHolder {
torch::optional<std::string> node_timestamp_attr_name,
torch::optional<std::string> edge_timestamp_attr_name) const;
/**
* @brief Sample negative edges by randomly choosing negative
* source-destination pairs according to a uniform distribution. For each edge
* ``(u, v)``, it is supposed to generate `negative_ratio` pairs of negative
* edges ``(u, v')``, where ``v'`` is chosen uniformly from all the nodes in
* the graph.
*
* @param node_pairs A tuple of two 1D tensors that represent the source and
* destination of positive edges, with 'positive' indicating that these edges
* are present in the graph. It's important to note that within the context of
* a heterogeneous graph, the ids in these tensors signify heterogeneous ids.
* @param negative_ratio The ratio of the number of negative samples to
* positive samples.
* @param max_node_id The maximum ID of the node to be selected. It
* should correspond to the number of nodes of a specific type.
*
* @return A tuple consisting of two 1D tensors represents the source and
* destination of negative edges. In the context of a heterogeneous
* graph, both the input nodes and the selected nodes are represented
* by heterogeneous IDs. Note that negative refers to false negatives,
* which means the edge could be present or not present in the graph.
*/
std::tuple<torch::Tensor, torch::Tensor> SampleNegativeEdgesUniform(
const std::tuple<torch::Tensor, torch::Tensor>& node_pairs,
int64_t negative_ratio, int64_t max_node_id) const;
/**
* @brief Copy the graph to shared memory.
* @param shared_memory_name The name of the shared memory.
......
graphbolt/src/fused_csc_sampling_graph.cc
View file @ afca1114
......@@ -712,18 +712,6 @@ FusedCSCSamplingGraph::TemporalSampleNeighbors(
}
}
std::tuple<torch::Tensor, torch::Tensor>
FusedCSCSamplingGraph::SampleNegativeEdgesUniform(
const std::tuple<torch::Tensor, torch::Tensor>& node_pairs,
int64_t negative_ratio, int64_t max_node_id) const {
torch::Tensor pos_src;
std::tie(pos_src, std::ignore) = node_pairs;
auto neg_len = pos_src.size(0) * negative_ratio;
auto neg_src = pos_src.repeat(negative_ratio);
auto neg_dst = torch::randint(0, max_node_id, {neg_len}, pos_src.options());
return std::make_tuple(neg_src, neg_dst);
}
static c10::intrusive_ptr<FusedCSCSamplingGraph>
BuildGraphFromSharedMemoryHelper(SharedMemoryHelper&& helper) {
helper.InitializeRead();
......
graphbolt/src/python_binding.cc
View file @ afca1114
......@@ -52,9 +52,6 @@ TORCH_LIBRARY(graphbolt, m) {
.def(
"temporal_sample_neighbors",
&FusedCSCSamplingGraph::TemporalSampleNeighbors)
.def(
"sample_negative_edges_uniform",
&FusedCSCSamplingGraph::SampleNegativeEdgesUniform)
.def("copy_to_shared_memory", &FusedCSCSamplingGraph::CopyToSharedMemory)
.def_pickle(
// __getstate__
......
python/dgl/graphbolt/impl/fused_csc_sampling_graph.py
View file @ afca1114
......@@ -876,7 +876,8 @@ class FusedCSCSamplingGraph(SamplingGraph):
pairs according to a uniform distribution. For each edge ``(u, v)``,
it is supposed to generate `negative_ratio` pairs of negative edges
``(u, v')``, where ``v'`` is chosen uniformly from all the nodes in
the graph.
the graph. As ``u`` is exactly same as the corresponding positive edges,
it returns None for negative sources.
Parameters
----------
......@@ -903,23 +904,22 @@ class FusedCSCSamplingGraph(SamplingGraph):
`edge_type`. Note that negative refers to false negatives, which
means the edge could be present or not present in the graph.
"""
if edge_type is not None:
assert (
self.node_type_offset is not None
), "The 'node_type_offset' array is necessary for performing \
negative sampling by edge type."
_, _, dst_node_type = etype_str_to_tuple(edge_type)
dst_node_type_id = self.node_type_to_id[dst_node_type]
offset = self._node_type_offset_list
max_node_id = (
offset[dst_node_type_id + 1] - offset[dst_node_type_id]
)
if edge_type:
_, _, dst_ntype = etype_str_to_tuple(edge_type)
max_node_id = self.num_nodes[dst_ntype]
else:
max_node_id = self.total_num_nodes
return self._c_csc_graph.sample_negative_edges_uniform(
node_pairs,
negative_ratio,
pos_src, _ = node_pairs
num_negative = pos_src.size(0) * negative_ratio
return (
None,
torch.randint(
0,
max_node_id,
(num_negative,),
dtype=pos_src.dtype,
device=pos_src.device,
),
)
def copy_to_shared_memory(self, shared_memory_name: str):
......
python/dgl/graphbolt/impl/uniform_negative_sampler.py
View file @ afca1114
......@@ -32,20 +32,23 @@ class UniformNegativeSampler(NegativeSampler):
Examples
--------
>>> from dgl import graphbolt as gb
>>> indptr = torch.LongTensor([0, 2, 4, 5])
>>> indices = torch.LongTensor([1, 2, 0, 2, 0])
>>> indptr = torch.LongTensor([0, 1, 2, 3, 4])
>>> indices = torch.LongTensor([1, 2, 3, 0])
>>> graph = gb.fused_csc_sampling_graph(indptr, indices)
>>> node_pairs = (torch.tensor([0, 1]), torch.tensor([1, 2]))
>>> node_pairs = torch.tensor([[0, 1], [1, 2], [2, 3], [3, 0]])
>>> item_set = gb.ItemSet(node_pairs, names="node_pairs")
>>> item_sampler = gb.ItemSampler(
... item_set, batch_size=1,)
... item_set, batch_size=4,)
>>> neg_sampler = gb.UniformNegativeSampler(
... item_sampler, graph, 2)
>>> for minibatch in neg_sampler:
... print(minibatch.negative_srcs)
... print(minibatch.negative_dsts)
(tensor([0, 0, 0]), tensor([1, 1, 2]), tensor([1, 0, 0]))
(tensor([1, 1, 1]), tensor([2, 1, 2]), tensor([1, 0, 0]))
None
tensor([[2, 1],
[2, 1],
[3, 2],
[1, 3]])
"""
def __init__(
......
tests/python/pytorch/graphbolt/impl/test_negative_sampler.py
View file @ afca1114
......@@ -46,8 +46,7 @@ def test_UniformNegativeSampler_invoke():
def _verify(negative_sampler):
for data in negative_sampler:
# Assertation
assert data.negative_srcs.size(0) == batch_size
assert data.negative_srcs.size(1) == negative_ratio
assert data.negative_srcs is None
assert data.negative_dsts.size(0) == batch_size
assert data.negative_dsts.size(1) == negative_ratio
......@@ -90,12 +89,9 @@ def test_Uniform_NegativeSampler(negative_ratio):
# Assertation
assert len(pos_src) == batch_size
assert len(pos_dst) == batch_size
assert len(neg_src) == batch_size
assert len(neg_dst) == batch_size
assert neg_src.numel() == batch_size * negative_ratio
assert neg_src is None
assert neg_dst.numel() == batch_size * negative_ratio
expected_src = pos_src.repeat(negative_ratio).view(-1, negative_ratio)
assert torch.equal(expected_src, neg_src)
def get_hetero_graph():
......
tests/python/pytorch/graphbolt/test_integration.py
View file @ afca1114
......@@ -48,7 +48,7 @@ def test_integration_link_prediction():
}
feature_store = gb.BasicFeatureStore(features)
datapipe = gb.ItemSampler(item_set, batch_size=4)
datapipe = datapipe.sample_uniform_negative(graph, 1)
datapipe = datapipe.sample_uniform_negative(graph, 2)
fanouts = torch.LongTensor([1])
datapipe = datapipe.sample_neighbor(graph, [fanouts, fanouts], replace=True)
datapipe = datapipe.transform(gb.exclude_seed_edges)
......@@ -62,23 +62,23 @@ def test_integration_link_prediction():
str(
"""MiniBatch(seed_nodes=None,
sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1, 1, 1, 1, 2]),
indices=tensor([5, 4]),
indices=tensor([0, 4]),
),
original_row_node_ids=tensor([5, 3, 1, 2, 0, 4]),
original_edge_ids=None,
original_column_node_ids=tensor([5, 3, 1, 2, 0, 4]),
),
SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1, 1, 1, 1]),
indices=tensor([5]),
SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1, 1, 1, 1, 2]),
indices=tensor([5, 4]),
),
original_row_node_ids=tensor([5, 3, 1, 2, 0, 4]),
original_edge_ids=None,
original_column_node_ids=tensor([5, 3, 1, 2, 0]),
original_column_node_ids=tensor([5, 3, 1, 2, 0, 4]),
)],
positive_node_pairs=(tensor([0, 1, 1, 1]),
tensor([2, 3, 3, 1])),
node_pairs_with_labels=((tensor([0, 1, 1, 1, 0, 1, 1, 1]), tensor([2, 3, 3, 1, 4, 4, 1, 4])),
tensor([1., 1., 1., 1., 0., 0., 0., 0.])),
node_pairs_with_labels=((tensor([0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1]), tensor([2, 3, 3, 1, 4, 4, 1, 4, 0, 1, 1, 5])),
tensor([1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.])),
node_pairs=(tensor([5, 3, 3, 3]),
tensor([1, 2, 2, 3])),
node_features={'feat': tensor([[0.5160, 0.2486],
......@@ -87,131 +87,120 @@ def test_integration_link_prediction():
[0.2109, 0.1089],
[0.9634, 0.2294],
[0.5503, 0.8223]])},
negative_srcs=tensor([[5],
[3],
[3],
[3]]),
negative_node_pairs=(tensor([0, 1, 1, 1]),
tensor([4, 4, 1, 4])),
negative_dsts=tensor([[0],
[0],
[3],
[0]]),
negative_srcs=None,
negative_node_pairs=(tensor([0, 0, 1, 1, 1, 1, 1, 1]),
tensor([4, 4, 1, 4, 0, 1, 1, 5])),
negative_dsts=tensor([[0, 0],
[3, 0],
[5, 3],
[3, 4]]),
labels=None,
input_nodes=tensor([5, 3, 1, 2, 0, 4]),
edge_features=[{},
{}],
compacted_node_pairs=(tensor([0, 1, 1, 1]),
tensor([2, 3, 3, 1])),
compacted_negative_srcs=tensor([[0],
[1],
[1],
[1]]),
compacted_negative_dsts=tensor([[4],
[4],
[1],
[4]]),
compacted_negative_srcs=None,
compacted_negative_dsts=tensor([[4, 4],
[1, 4],
[0, 1],
[1, 5]]),
blocks=[Block(num_src_nodes=6, num_dst_nodes=6, num_edges=2),
Block(num_src_nodes=6, num_dst_nodes=5, num_edges=1)],
Block(num_src_nodes=6, num_dst_nodes=6, num_edges=2)],
)"""
),
str(
"""MiniBatch(seed_nodes=None,
sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 1, 2]),
indices=tensor([1, 3]),
sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 1, 2, 3]),
indices=tensor([4, 1, 0]),
),
original_row_node_ids=tensor([3, 4, 0, 5, 1]),
original_row_node_ids=tensor([3, 4, 0, 1, 5, 2]),
original_edge_ids=None,
original_column_node_ids=tensor([3, 4, 0, 5, 1]),
original_column_node_ids=tensor([3, 4, 0, 1, 5, 2]),
),
SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 1, 2]),
indices=tensor([1, 3]),
SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 1, 2, 3]),
indices=tensor([4, 4, 0]),
),
original_row_node_ids=tensor([3, 4, 0, 5, 1]),
original_row_node_ids=tensor([3, 4, 0, 1, 5, 2]),
original_edge_ids=None,
original_column_node_ids=tensor([3, 4, 0, 5, 1]),
original_column_node_ids=tensor([3, 4, 0, 1, 5, 2]),
)],
positive_node_pairs=(tensor([0, 1, 1, 2]),
tensor([0, 0, 1, 1])),
node_pairs_with_labels=((tensor([0, 1, 1, 2, 0, 1, 1, 2]), tensor([0, 0, 1, 1, 1, 1, 3, 4])),
tensor([1., 1., 1., 1., 0., 0., 0., 0.])),
node_pairs_with_labels=((tensor([0, 1, 1, 2, 0, 0, 1, 1, 1, 1, 2, 2]), tensor([0, 0, 1, 1, 3, 4, 5, 4, 1, 0, 3, 4])),
tensor([1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.])),
node_pairs=(tensor([3, 4, 4, 0]),
tensor([3, 3, 4, 4])),
node_features={'feat': tensor([[0.8672, 0.2276],
[0.5503, 0.8223],
[0.9634, 0.2294],
[0.6172, 0.7865],
[0.5160, 0.2486],
[0.6172, 0.7865]])},
negative_srcs=tensor([[3],
[4],
[4],
[0]]),
negative_node_pairs=(tensor([0, 1, 1, 2]),
tensor([1, 1, 3, 4])),
negative_dsts=tensor([[4],
[4],
[5],
[1]]),
[0.2109, 0.1089]])},
negative_srcs=None,
negative_node_pairs=(tensor([0, 0, 1, 1, 1, 1, 2, 2]),
tensor([3, 4, 5, 4, 1, 0, 3, 4])),
negative_dsts=tensor([[1, 5],
[2, 5],
[4, 3],
[1, 5]]),
labels=None,
input_nodes=tensor([3, 4, 0, 5, 1]),
input_nodes=tensor([3, 4, 0, 1, 5, 2]),
edge_features=[{},
{}],
compacted_node_pairs=(tensor([0, 1, 1, 2]),
tensor([0, 0, 1, 1])),
compacted_negative_srcs=tensor([[0],
[1],
[1],
[2]]),
compacted_negative_dsts=tensor([[1],
[1],
[3],
[4]]),
blocks=[Block(num_src_nodes=5, num_dst_nodes=5, num_edges=2),
Block(num_src_nodes=5, num_dst_nodes=5, num_edges=2)],
compacted_negative_srcs=None,
compacted_negative_dsts=tensor([[3, 4],
[5, 4],
[1, 0],
[3, 4]]),
blocks=[Block(num_src_nodes=6, num_dst_nodes=6, num_edges=3),
Block(num_src_nodes=6, num_dst_nodes=6, num_edges=3)],
)"""
),
str(
"""MiniBatch(seed_nodes=None,
sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1]),
indices=tensor([1]),
sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1, 1, 2]),
indices=tensor([1, 0]),
),
original_row_node_ids=tensor([5, 4]),
original_row_node_ids=tensor([5, 4, 0, 1]),
original_edge_ids=None,
original_column_node_ids=tensor([5, 4]),
original_column_node_ids=tensor([5, 4, 0, 1]),
),
SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1]),
indices=tensor([1]),
SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1, 1, 2]),
indices=tensor([1, 0]),
),
original_row_node_ids=tensor([5, 4]),
original_row_node_ids=tensor([5, 4, 0, 1]),
original_edge_ids=None,
original_column_node_ids=tensor([5, 4]),
original_column_node_ids=tensor([5, 4, 0, 1]),
)],
positive_node_pairs=(tensor([0, 1]),
tensor([0, 0])),
node_pairs_with_labels=((tensor([0, 1, 0, 1]), tensor([0, 0, 0, 0])),
tensor([1., 1., 0., 0.])),
node_pairs_with_labels=((tensor([0, 1, 0, 0, 1, 1]), tensor([0, 0, 2, 1, 2, 3])),
tensor([1., 1., 0., 0., 0., 0.])),
node_pairs=(tensor([5, 4]),
tensor([5, 5])),
node_features={'feat': tensor([[0.5160, 0.2486],
[0.5503, 0.8223]])},
negative_srcs=tensor([[5],
[4]]),
negative_node_pairs=(tensor([0, 1]),
tensor([0, 0])),
negative_dsts=tensor([[5],
[5]]),
[0.5503, 0.8223],
[0.9634, 0.2294],
[0.6172, 0.7865]])},
negative_srcs=None,
negative_node_pairs=(tensor([0, 0, 1, 1]),
tensor([2, 1, 2, 3])),
negative_dsts=tensor([[0, 4],
[0, 1]]),
labels=None,
input_nodes=tensor([5, 4]),
input_nodes=tensor([5, 4, 0, 1]),
edge_features=[{},
{}],
compacted_node_pairs=(tensor([0, 1]),
tensor([0, 0])),
compacted_negative_srcs=tensor([[0],
[1]]),
compacted_negative_dsts=tensor([[0],
[0]]),
blocks=[Block(num_src_nodes=2, num_dst_nodes=2, num_edges=1),
Block(num_src_nodes=2, num_dst_nodes=2, num_edges=1)],
compacted_negative_srcs=None,
compacted_negative_dsts=tensor([[2, 1],
[2, 3]]),
blocks=[Block(num_src_nodes=4, num_dst_nodes=4, num_edges=2),
Block(num_src_nodes=4, num_dst_nodes=4, num_edges=2)],
)"""
),
]
......
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