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Enable trim_to_layer with hetero CSR flow #7514

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merged 6 commits into from
Jun 5, 2023
Merged

Enable trim_to_layer with hetero CSR flow #7514

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33d76ac
Enable trim to layer with hetero CSR flow
mszarma Jun 5, 2023
abdc4dd
Refactor
mszarma Jun 5, 2023
1f5080c
update
rusty1s Jun 5, 2023
323f385
update
rusty1s Jun 5, 2023
1345c8b
Merge branch 'master' into enable_csr_trim_to_layer
rusty1s Jun 5, 2023
ce837bc
update
rusty1s Jun 5, 2023
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2 changes: 1 addition & 1 deletion test/utils/test_trim_to_layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ def test_trim_sparse_tensor():
edge_index = torch.tensor([[0, 0, 1, 2], [1, 2, 3, 4]])
adj = SparseTensor.from_edge_index(edge_index, sparse_sizes=[5, 5])

adj = trim_sparse_tensor(adj, num_nodes=3, num_seed_nodes=1)
adj = trim_sparse_tensor(adj, size=(3, 3), num_seed_nodes=1)

row, col, _ = adj.coo()
assert row.tolist() == [0, 0]
Expand Down
41 changes: 29 additions & 12 deletions torch_geometric/utils/trim_to_layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,8 +54,13 @@ def trim_to_layer(
for k, v in x.items()
}
edge_index = {
k: trim_adj(v, layer, num_sampled_nodes_per_hop[k[-1]],
num_sampled_edges_per_hop[k])
k: trim_adj(
v,
layer,
num_sampled_nodes_per_hop[k[0]],
num_sampled_nodes_per_hop[k[-1]],
num_sampled_edges_per_hop[k],
)
for k, v in edge_index.items()
}
if edge_attr is not None:
Expand All @@ -66,8 +71,13 @@ def trim_to_layer(
return x, edge_index, edge_attr

x = trim_feat(x, layer, num_sampled_nodes_per_hop)
edge_index = trim_adj(edge_index, layer, num_sampled_nodes_per_hop,
num_sampled_edges_per_hop)
edge_index = trim_adj(
edge_index,
layer,
num_sampled_nodes_per_hop,
num_sampled_nodes_per_hop,
num_sampled_edges_per_hop,
)

if edge_attr is not None:
edge_attr = trim_feat(edge_attr, layer, num_sampled_edges_per_hop)
Expand Down Expand Up @@ -125,7 +135,8 @@ def trim_feat(x: Tensor, layer: int, num_samples_per_hop: List[int]) -> Tensor:
def trim_adj(
edge_index: Adj,
layer: int,
num_sampled_nodes_per_hop: List[int],
num_sampled_src_nodes_per_hop: List[int],
num_sampled_dst_nodes_per_hop: List[int],
num_sampled_edges_per_hop: List[int],
) -> Adj:

Expand All @@ -140,14 +151,19 @@ def trim_adj(
)

elif isinstance(edge_index, SparseTensor):
num_nodes = edge_index.size(0) - num_sampled_nodes_per_hop[-layer]
num_seed_nodes = num_nodes - num_sampled_nodes_per_hop[-(layer + 1)]
return trim_sparse_tensor(edge_index, num_nodes, num_seed_nodes)
size = (
edge_index.size(0) - num_sampled_dst_nodes_per_hop[-layer],
edge_index.size(1) - num_sampled_src_nodes_per_hop[-layer],
)

num_seed_nodes = size[0] - num_sampled_dst_nodes_per_hop[-(layer + 1)]

return trim_sparse_tensor(edge_index, size, num_seed_nodes)

raise ValueError(f"Unsupported 'edge_index' type '{type(edge_index)}'")


def trim_sparse_tensor(src: SparseTensor, num_nodes: int,
def trim_sparse_tensor(src: SparseTensor, size: Tuple[int, int],
num_seed_nodes: None) -> SparseTensor:
r"""Trims a :class:`SparseTensor` along both dimensions to only contain
the upper :obj:`num_nodes` in both dimensions.
Expand All @@ -157,13 +173,14 @@ def trim_sparse_tensor(src: SparseTensor, num_nodes: int,

Args:
src (SparseTensor): The sparse tensor.
num_nodes (int): The number of first nodes to keep.
size (Tuple[int, int]): The number of source and destination nodes to
keep.
num_seed_nodes (int): The number of seed nodes to compute
representations.
"""
rowptr, col, value = src.csr()

rowptr = torch.narrow(rowptr, 0, 0, num_nodes + 1).clone()
rowptr = torch.narrow(rowptr, 0, 0, size[0] + 1).clone()
rowptr[num_seed_nodes + 1:] = rowptr[num_seed_nodes]

col = torch.narrow(col, 0, 0, rowptr[-1])
Expand All @@ -180,7 +197,7 @@ def trim_sparse_tensor(src: SparseTensor, num_nodes: int,
rowptr=rowptr,
col=col,
value=value,
sparse_sizes=(num_nodes, num_nodes),
sparse_sizes=size,
rowcount=None,
colptr=None,
colcount=None,
Expand Down