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Pytorch Sparse Tensor support: PointConv, PointGNNConv, PointTransformerConv, PPFConv, and ResGatedGraphConv #6937

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Merged
rusty1s merged 10 commits into from
Mar 19, 2023
Merged

Pytorch Sparse Tensor support: PointConv, PointGNNConv, PointTransformerConv, PPFConv, and ResGatedGraphConv #6937

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840dd00
PointConv
EdisonLeeeee Mar 17, 2023
2258bd5
PointGNNConv
EdisonLeeeee Mar 17, 2023
2f28f5e
PointTransformerConv
EdisonLeeeee Mar 17, 2023
096814e
PPFConv
EdisonLeeeee Mar 17, 2023
e739c38
ResGatedGraphConv
EdisonLeeeee Mar 17, 2023
f329752
changelog
EdisonLeeeee Mar 17, 2023
70b39a3
Merge branch 'master' into sparse_tensor3
wsad1 Mar 18, 2023
e17b547
Merge branch 'master' into sparse_tensor3
rusty1s Mar 19, 2023
01b8b70
changelog
rusty1s Mar 19, 2023
4baa40d
changelog
rusty1s Mar 19, 2023
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2 changes: 1 addition & 1 deletion CHANGELOG.md
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Expand Up @@ -95,7 +95,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Added `Data.edge_subgraph` and `HeteroData.edge_subgraph` functionalities ([#6193](https://github.com/pyg-team/pytorch_geometric/pull/6193))
- Added `input_time` option to `LightningNodeData` and `transform_sampler_output` to `NodeLoader` and `LinkLoader` ([#6187](https://github.com/pyg-team/pytorch_geometric/pull/6187))
- Added `summary` for PyG/PyTorch models ([#5859](https://github.com/pyg-team/pytorch_geometric/pull/5859), [#6161](https://github.com/pyg-team/pytorch_geometric/pull/6161))
- Started adding `torch.sparse` support to PyG ([#5906](https://github.com/pyg-team/pytorch_geometric/pull/5906), [#5944](https://github.com/pyg-team/pytorch_geometric/pull/5944), [#6003](https://github.com/pyg-team/pytorch_geometric/pull/6003), [#6033](https://github.com/pyg-team/pytorch_geometric/pull/6033), [#6514](https://github.com/pyg-team/pytorch_geometric/pull/6514), [#6532](https://github.com/pyg-team/pytorch_geometric/pull/6532), [#6748](https://github.com/pyg-team/pytorch_geometric/pull/6748), [#6847](https://github.com/pyg-team/pytorch_geometric/pull/6847), [#6868](https://github.com/pyg-team/pytorch_geometric/pull/6868), [#6874](https://github.com/pyg-team/pytorch_geometric/pull/6874), [#6897](https://github.com/pyg-team/pytorch_geometric/pull/6897))
- Started adding `torch.sparse` support to PyG ([#5906](https://github.com/pyg-team/pytorch_geometric/pull/5906), [#5944](https://github.com/pyg-team/pytorch_geometric/pull/5944), [#6003](https://github.com/pyg-team/pytorch_geometric/pull/6003), [#6033](https://github.com/pyg-team/pytorch_geometric/pull/6033), [#6514](https://github.com/pyg-team/pytorch_geometric/pull/6514), [#6532](https://github.com/pyg-team/pytorch_geometric/pull/6532), [#6748](https://github.com/pyg-team/pytorch_geometric/pull/6748), [#6847](https://github.com/pyg-team/pytorch_geometric/pull/6847), [#6868](https://github.com/pyg-team/pytorch_geometric/pull/6868), [#6874](https://github.com/pyg-team/pytorch_geometric/pull/6874), [#6897](https://github.com/pyg-team/pytorch_geometric/pull/6897), [#6937](https://github.com/pyg-team/pytorch_geometric/pull/6937))
- Add `inputs_channels` back in training benchmark ([#6154](https://github.com/pyg-team/pytorch_geometric/pull/6154))
- Added support for dropping nodes in `utils.to_dense_batch` in case `max_num_nodes` is smaller than the number of nodes ([#6124](https://github.com/pyg-team/pytorch_geometric/pull/6124))
- Added the RandLA-Net architecture as an example ([#5117](https://github.com/pyg-team/pytorch_geometric/pull/5117))
Expand Down
20 changes: 13 additions & 7 deletions test/nn/conv/test_point_conv.py
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Expand Up @@ -14,7 +14,8 @@ def test_point_net_conv():
pos2 = torch.randn(2, 3)
edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
row, col = edge_index
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
adj1 = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
adj2 = adj1.to_torch_sparse_coo_tensor()

local_nn = Seq(Lin(16 + 3, 32), ReLU(), Lin(32, 32))
global_nn = Seq(Lin(32, 32))
Expand All @@ -29,7 +30,8 @@ def test_point_net_conv():
'))')
out = conv(x1, pos1, edge_index)
assert out.size() == (4, 32)
assert torch.allclose(conv(x1, pos1, adj.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, pos1, adj1.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, pos1, adj2.t()), out, atol=1e-6)

if is_full_test():
t = '(OptTensor, Tensor, Tensor) -> Tensor'
Expand All @@ -38,14 +40,18 @@ def test_point_net_conv():

t = '(OptTensor, Tensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x1, pos1, adj.t()), out, atol=1e-6)
assert torch.allclose(jit(x1, pos1, adj1.t()), out, atol=1e-6)

adj = adj.sparse_resize((4, 2))
adj1 = adj1.sparse_resize((4, 2))
adj2 = adj1.to_torch_sparse_coo_tensor()
out = conv(x1, (pos1, pos2), edge_index)
assert out.size() == (2, 32)
assert conv((x1, None), (pos1, pos2), edge_index).tolist() == out.tolist()
assert torch.allclose(conv(x1, (pos1, pos2), adj.t()), out, atol=1e-6)
assert torch.allclose(conv((x1, None), (pos1, pos2), adj.t()), out,
assert torch.allclose(conv(x1, (pos1, pos2), adj1.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, (pos1, pos2), adj2.t()), out, atol=1e-6)
assert torch.allclose(conv((x1, None), (pos1, pos2), adj1.t()), out,
atol=1e-6)
assert torch.allclose(conv((x1, None), (pos1, pos2), adj2.t()), out,
atol=1e-6)

if is_full_test():
Expand All @@ -56,5 +62,5 @@ def test_point_net_conv():

t = '(PairOptTensor, PairTensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit((x1, None), (pos1, pos2), adj.t()), out,
assert torch.allclose(jit((x1, None), (pos1, pos2), adj1.t()), out,
atol=1e-6)
8 changes: 5 additions & 3 deletions test/nn/conv/test_point_gnn_conv.py
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Expand Up @@ -10,7 +10,8 @@ def test_pointgnn_conv():
pos = torch.randn(6, 3)
edge_index = torch.tensor([[0, 1, 1, 1, 2, 5], [1, 2, 3, 4, 3, 4]])
row, col = edge_index
adj = SparseTensor(row=row, col=col, sparse_sizes=(6, 6))
adj1 = SparseTensor(row=row, col=col, sparse_sizes=(6, 6))
adj2 = adj1.to_torch_sparse_coo_tensor()

conv = PointGNNConv(
mlp_h=MLP([8, 16, 3]),
Expand All @@ -25,7 +26,8 @@ def test_pointgnn_conv():

out = conv(x, pos, edge_index)
assert out.size() == (6, 8)
assert torch.allclose(conv(x, pos, adj.t()), out)
assert torch.allclose(conv(x, pos, adj1.t()), out)
assert torch.allclose(conv(x, pos, adj2.t()), out)

if is_full_test():
t = '(Tensor, Tensor, Tensor) -> Tensor'
Expand All @@ -34,4 +36,4 @@ def test_pointgnn_conv():

t = '(Tensor, Tensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x, pos, adj.t()), out)
assert torch.allclose(jit(x, pos, adj1.t()), out)
25 changes: 16 additions & 9 deletions test/nn/conv/test_point_transformer_conv.py
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Expand Up @@ -13,46 +13,53 @@ def test_point_transformer_conv():
pos2 = torch.randn(2, 3)
edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
row, col = edge_index
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
adj1 = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
adj2 = adj1.to_torch_sparse_coo_tensor()

conv = PointTransformerConv(in_channels=16, out_channels=32)
assert str(conv) == 'PointTransformerConv(16, 32)'

out = conv(x1, pos1, edge_index)
assert out.size() == (4, 32)
assert torch.allclose(conv(x1, pos1, adj.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, pos1, adj1.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, pos1, adj2.t()), out, atol=1e-6)

if is_full_test():
t = '(Tensor, Tensor, Tensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, pos1, edge_index).tolist() == out.tolist()
assert torch.allclose(jit(x1, pos1, edge_index), out, atol=1e-6)

t = '(Tensor, Tensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x1, pos1, adj.t()), out, atol=1e-6)
assert torch.allclose(jit(x1, pos1, adj1.t()), out, atol=1e-6)

pos_nn = Sequential(Linear(3, 16), ReLU(), Linear(16, 32))
attn_nn = Sequential(Linear(32, 32), ReLU(), Linear(32, 32))
conv = PointTransformerConv(16, 32, pos_nn, attn_nn)

out = conv(x1, pos1, edge_index)
assert out.size() == (4, 32)
assert torch.allclose(conv(x1, pos1, adj.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, pos1, adj1.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, pos1, adj2.t()), out, atol=1e-6)

conv = PointTransformerConv((16, 8), 32)
adj = adj.sparse_resize((4, 2))
adj1 = adj1.sparse_resize((4, 2))
adj2 = adj1.to_torch_sparse_coo_tensor()

out = conv((x1, x2), (pos1, pos2), edge_index)
assert out.size() == (2, 32)
assert torch.allclose(conv((x1, x2), (pos1, pos2), adj.t()), out,
assert torch.allclose(conv((x1, x2), (pos1, pos2), adj1.t()), out,
atol=1e-6)
assert torch.allclose(conv((x1, x2), (pos1, pos2), adj2.t()), out,
atol=1e-6)

if is_full_test():
t = '(PairTensor, PairTensor, Tensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), (pos1, pos2), edge_index).tolist() == out.tolist()
assert torch.allclose(jit((x1, x2), (pos1, pos2), edge_index), out,
atol=1e-6)

t = '(PairTensor, PairTensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit((x1, x2), (pos1, pos2), adj.t()), out,
assert torch.allclose(jit((x1, x2), (pos1, pos2), adj1.t()), out,
atol=1e-6)
34 changes: 21 additions & 13 deletions test/nn/conv/test_ppf_conv.py
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Expand Up @@ -17,7 +17,8 @@ def test_ppf_conv():
n2 = F.normalize(torch.rand(2, 3), dim=-1)
edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
row, col = edge_index
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
adj1 = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
adj2 = adj1.to_torch_sparse_coo_tensor()

local_nn = Seq(Lin(16 + 4, 32), ReLU(), Lin(32, 32))
global_nn = Seq(Lin(32, 32))
Expand All @@ -32,34 +33,41 @@ def test_ppf_conv():
'))')
out = conv(x1, pos1, n1, edge_index)
assert out.size() == (4, 32)
assert torch.allclose(conv(x1, pos1, n1, adj.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, pos1, n1, adj1.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, pos1, n1, adj2.t()), out, atol=1e-6)

if is_full_test():
t = '(OptTensor, Tensor, Tensor, Tensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, pos1, n1, edge_index).tolist() == out.tolist()
assert torch.allclose(jit(x1, pos1, n1, edge_index), out, atol=1e-6)

t = '(OptTensor, Tensor, Tensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit(x1, pos1, n1, adj.t()), out, atol=1e-6)
assert torch.allclose(jit(x1, pos1, n1, adj1.t()), out, atol=1e-6)

adj = adj.sparse_resize((4, 2))
adj1 = adj1.sparse_resize((4, 2))
adj2 = adj1.to_torch_sparse_coo_tensor()
out = conv(x1, (pos1, pos2), (n1, n2), edge_index)
assert out.size() == (2, 32)
assert conv((x1, None), (pos1, pos2), (n1, n2),
edge_index).tolist() == out.tolist()
assert torch.allclose(conv(x1, (pos1, pos2), (n1, n2), adj.t()), out,
assert torch.allclose(conv((x1, None), (pos1, pos2), (n1, n2), edge_index),
out, atol=1e-6)
assert torch.allclose(conv(x1, (pos1, pos2), (n1, n2), adj1.t()), out,
atol=1e-6)
assert torch.allclose(conv(x1, (pos1, pos2), (n1, n2), adj2.t()), out,
atol=1e-6)
assert torch.allclose(conv((x1, None), (pos1, pos2), (n1, n2), adj.t()),
assert torch.allclose(conv((x1, None), (pos1, pos2), (n1, n2), adj1.t()),
out, atol=1e-6)
assert torch.allclose(conv((x1, None), (pos1, pos2), (n1, n2), adj2.t()),
out, atol=1e-6)

if is_full_test():
t = '(PairOptTensor, PairTensor, PairTensor, Tensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, None), (pos1, pos2), (n1, n2),
edge_index).tolist() == out.tolist()
assert torch.allclose(
jit((x1, None), (pos1, pos2), (n1, n2), edge_index), out,
atol=1e-6)

t = '(PairOptTensor, PairTensor, PairTensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert torch.allclose(jit((x1, None), (pos1, pos2), (n1, n2), adj.t()),
out, atol=1e-6)
assert torch.allclose(
jit((x1, None), (pos1, pos2), (n1, n2), adj1.t()), out, atol=1e-6)
20 changes: 12 additions & 8 deletions test/nn/conv/test_res_gated_graph_conv.py
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Expand Up @@ -10,35 +10,39 @@ def test_res_gated_graph_conv():
x2 = torch.randn(2, 32)
edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
row, col = edge_index
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
adj1 = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
adj2 = adj1.to_torch_sparse_coo_tensor()

conv = ResGatedGraphConv(8, 32)
assert str(conv) == 'ResGatedGraphConv(8, 32)'
out = conv(x1, edge_index)
assert out.size() == (4, 32)
assert conv(x1, adj.t()).tolist() == out.tolist()
assert torch.allclose(conv(x1, adj1.t()), out, atol=1e-6)
assert torch.allclose(conv(x1, adj2.t()), out, atol=1e-6)

if is_full_test():
t = '(Tensor, Tensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, edge_index).tolist() == out.tolist()
assert torch.allclose(jit(x1, edge_index), out, atol=1e-6)

t = '(Tensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit(x1, adj.t()).tolist() == out.tolist()
assert torch.allclose(jit(x1, adj1.t()), out, atol=1e-6)

adj = adj.sparse_resize((4, 2))
adj1 = adj1.sparse_resize((4, 2))
adj2 = adj1.to_torch_sparse_coo_tensor()
conv = ResGatedGraphConv((8, 32), 32)
assert str(conv) == 'ResGatedGraphConv((8, 32), 32)'
out = conv((x1, x2), edge_index)
assert out.size() == (2, 32)
assert conv((x1, x2), adj.t()).tolist() == out.tolist()
assert torch.allclose(conv((x1, x2), adj1.t()), out, atol=1e-6)
assert torch.allclose(conv((x1, x2), adj2.t()), out, atol=1e-6)

if is_full_test():
t = '(PairTensor, Tensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), edge_index).tolist() == out.tolist()
assert torch.allclose(jit((x1, x2), edge_index), out, atol=1e-6)

t = '(PairTensor, SparseTensor) -> Tensor'
jit = torch.jit.script(conv.jittable(t))
assert jit((x1, x2), adj.t()).tolist() == out.tolist()
assert torch.allclose(jit((x1, x2), adj1.t()), out, atol=1e-6)