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Fuse compiled single-system periodic shifts #2142

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14 changes: 10 additions & 4 deletions src/fairchem/core/models/uma/escn_md.py
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Original file line number Diff line number Diff line change
Expand Up @@ -665,10 +665,16 @@ def _generate_graph(self, data_dict):

# Compute shifts from cell offsets
if len(data_dict["natoms"]) == 1:
# Single system: use matmul (compile-friendly, no data-dependent ops)
shifts = data_dict["cell_offsets"].to(
data_dict["cell"].dtype
) @ data_dict["cell"].squeeze(0)
offsets = data_dict["cell_offsets"].to(data_dict["cell"].dtype)
if torch.compiler.is_compiling():
cell = data_dict["cell"][0]
shifts = (
offsets[:, 0:1] * cell[0]
+ offsets[:, 1:2] * cell[1]
+ offsets[:, 2:3] * cell[2]
)
else:
shifts = offsets @ data_dict["cell"].squeeze(0)
else:
# Batched: need repeat_interleave for variable edges per system
cell_per_edge = data_dict["cell"].repeat_interleave(
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29 changes: 29 additions & 0 deletions tests/core/models/uma/test_compile.py
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Expand Up @@ -153,6 +153,35 @@ def test_compile_batched_external_graph(compile_reset_state):
torch.testing.assert_close(actual["edge_distance"], expected["edge_distance"])


@pytest.mark.gpu
@pytest.mark.compile_gpu
def test_compile_single_system_external_graph(compile_reset_state):
model = get_escn_md_backbone(cutoff=6.0, otf_graph=False, device="cuda")
data = get_diamond_tg_data(neighbors=300, cutoff=6.0, size=1, device="cuda")
data.cell = torch.tensor(
[
[
[35.8, 0.12345, 0.06789],
[0.3333, 35.819, 0.0147],
[0.011, 0.027, 35.769],
]
],
device="cuda",
)
data.edge_index = torch.tensor([[0, 1], [1, 2]], device="cuda")
data.cell_offsets = torch.tensor(
[[-2, -1, 0], [1, 2, -2]], dtype=torch.float32, device="cuda"
)
data.nedges = torch.tensor([2], device="cuda")

expected = model._generate_graph(data)
actual = torch.compile(model._generate_graph, fullgraph=True, dynamic=False)(data)

torch.testing.assert_close(
actual["edge_distance_vec"], expected["edge_distance_vec"], rtol=0, atol=0
)


# compile tests take a long time
@pytest.mark.skip()
@pytest.mark.gpu()
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