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Merged
merged 7 commits into from
Aug 25, 2025
Merged

fix(pt,pd): remove redundant tensor handling to eliminate tensor construction warnings #4907

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7492ef6
Initial plan
Copilot Aug 23, 2025
afe7af5
Fix torch.tensor warnings by using clone().detach() for existing tensors
Copilot Aug 23, 2025
5eade9b
Fix PaddlePaddle tensor construction warnings and enhance PyTorch to_…
Copilot Aug 23, 2025
a5536ab
Remove redundant tensor handling in to_torch_tensor and fix test tens…
Copilot Aug 23, 2025
0e9ae51
Remove redundant tensor handling in to_paddle_tensor function
Copilot Aug 23, 2025
4f4bd3f
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Aug 23, 2025
a42db43
Fix TypeError: change place= to device= in tensor.to() method calls
Copilot Aug 23, 2025
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58 changes: 46 additions & 12 deletions source/tests/pd/common.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,12 @@ def eval_model(
if spins is not None:
assert isinstance(spins, paddle.Tensor), err_msg
assert isinstance(atom_types, paddle.Tensor) or isinstance(atom_types, list)
atom_types = paddle.to_tensor(atom_types, dtype=paddle.int32, place=DEVICE)
if isinstance(atom_types, paddle.Tensor):
atom_types = (
atom_types.clone().detach().to(dtype=paddle.int32, device=DEVICE)
)
else:
atom_types = paddle.to_tensor(atom_types, dtype=paddle.int32, place=DEVICE)
elif isinstance(coords, np.ndarray):
if cells is not None:
assert isinstance(cells, np.ndarray), err_msg
Expand All @@ -101,28 +106,57 @@ def eval_model(
else:
natoms = len(atom_types[0])

coord_input = paddle.to_tensor(
coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE
)
spin_input = None
if spins is not None:
spin_input = paddle.to_tensor(
spins.reshape([-1, natoms, 3]),
if isinstance(coords, paddle.Tensor):
coord_input = (
coords.reshape([-1, natoms, 3])
.clone()
.detach()
.to(dtype=GLOBAL_PD_FLOAT_PRECISION, device=DEVICE)
)
else:
coord_input = paddle.to_tensor(
coords.reshape([-1, natoms, 3]),
dtype=GLOBAL_PD_FLOAT_PRECISION,
place=DEVICE,
)
spin_input = None
if spins is not None:
if isinstance(spins, paddle.Tensor):
spin_input = (
spins.reshape([-1, natoms, 3])
.clone()
.detach()
.to(dtype=GLOBAL_PD_FLOAT_PRECISION, device=DEVICE)
)
else:
spin_input = paddle.to_tensor(
spins.reshape([-1, natoms, 3]),
dtype=GLOBAL_PD_FLOAT_PRECISION,
place=DEVICE,
)
has_spin = getattr(model, "has_spin", False)
if callable(has_spin):
has_spin = has_spin()
type_input = paddle.to_tensor(atom_types, dtype=paddle.int64, place=DEVICE)
if isinstance(atom_types, paddle.Tensor):
type_input = atom_types.clone().detach().to(dtype=paddle.int64, device=DEVICE)
else:
type_input = paddle.to_tensor(atom_types, dtype=paddle.int64, place=DEVICE)
box_input = None
if cells is None:
pbc = False
else:
pbc = True
box_input = paddle.to_tensor(
cells.reshape([-1, 3, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE
)
if isinstance(cells, paddle.Tensor):
box_input = (
cells.reshape([-1, 3, 3])
.clone()
.detach()
.to(dtype=GLOBAL_PD_FLOAT_PRECISION, device=DEVICE)
)
else:
box_input = paddle.to_tensor(
cells.reshape([-1, 3, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE
)
num_iter = int((nframes + infer_batch_size - 1) / infer_batch_size)

for ii in range(num_iter):
Expand Down
60 changes: 48 additions & 12 deletions source/tests/pt/common.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,12 @@ def eval_model(
if spins is not None:
assert isinstance(spins, torch.Tensor), err_msg
assert isinstance(atom_types, torch.Tensor) or isinstance(atom_types, list)
atom_types = torch.tensor(atom_types, dtype=torch.int32, device=DEVICE)
if isinstance(atom_types, torch.Tensor):
atom_types = (
atom_types.clone().detach().to(dtype=torch.int32, device=DEVICE)
)
else:
atom_types = torch.tensor(atom_types, dtype=torch.int32, device=DEVICE)
elif isinstance(coords, np.ndarray):
if cells is not None:
assert isinstance(cells, np.ndarray), err_msg
Expand All @@ -101,28 +106,59 @@ def eval_model(
else:
natoms = len(atom_types[0])

coord_input = torch.tensor(
coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
spin_input = None
if spins is not None:
spin_input = torch.tensor(
spins.reshape([-1, natoms, 3]),
if isinstance(coords, torch.Tensor):
coord_input = (
coords.reshape([-1, natoms, 3])
.clone()
.detach()
.to(dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE)
)
else:
coord_input = torch.tensor(
coords.reshape([-1, natoms, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
spin_input = None
if spins is not None:
if isinstance(spins, torch.Tensor):
spin_input = (
spins.reshape([-1, natoms, 3])
.clone()
.detach()
.to(dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE)
)
else:
spin_input = torch.tensor(
spins.reshape([-1, natoms, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
has_spin = getattr(model, "has_spin", False)
if callable(has_spin):
has_spin = has_spin()
type_input = torch.tensor(atom_types, dtype=torch.long, device=DEVICE)
if isinstance(atom_types, torch.Tensor):
type_input = atom_types.clone().detach().to(dtype=torch.long, device=DEVICE)
else:
type_input = torch.tensor(atom_types, dtype=torch.long, device=DEVICE)
box_input = None
if cells is None:
pbc = False
else:
pbc = True
box_input = torch.tensor(
cells.reshape([-1, 3, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
if isinstance(cells, torch.Tensor):
box_input = (
cells.reshape([-1, 3, 3])
.clone()
.detach()
.to(dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE)
)
else:
box_input = torch.tensor(
cells.reshape([-1, 3, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
num_iter = int((nframes + infer_batch_size - 1) / infer_batch_size)

for ii in range(num_iter):
Expand Down
28 changes: 18 additions & 10 deletions source/tests/pt/test_calculator.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -64,14 +64,18 @@ def test_calculator(self) -> None:
atomic_numbers = [1, 1, 1, 8, 8]
idx_perm = [1, 0, 4, 3, 2]

# Convert tensors to numpy for ASE compatibility
cell_np = cell.numpy()
coord_np = coord.numpy()

prec = 1e-10
low_prec = 1e-4

ase_atoms0 = Atoms(
numbers=atomic_numbers,
positions=coord,
positions=coord_np,
# positions=[tuple(item) for item in coordinate],
cell=cell,
cell=cell_np,
calculator=self.calculator,
pbc=True,
)
Expand All @@ -83,9 +87,9 @@ def test_calculator(self) -> None:

ase_atoms1 = Atoms(
numbers=[atomic_numbers[i] for i in idx_perm],
positions=coord[idx_perm, :],
positions=coord_np[idx_perm, :],
# positions=[tuple(item) for item in coordinate],
cell=cell,
cell=cell_np,
calculator=self.calculator,
pbc=True,
)
Expand Down Expand Up @@ -141,19 +145,23 @@ def test_calculator(self) -> None:
generator = torch.Generator(device="cpu").manual_seed(GLOBAL_SEED)
coord = torch.rand([natoms, 3], dtype=dtype, device="cpu", generator=generator)
coord = torch.matmul(coord, cell)
fparam = torch.IntTensor([1, 2])
aparam = torch.IntTensor([[1], [0], [2], [1], [0]])
fparam = torch.IntTensor([1, 2]).numpy()
aparam = torch.IntTensor([[1], [0], [2], [1], [0]]).numpy()
atomic_numbers = [1, 1, 1, 8, 8]
idx_perm = [1, 0, 4, 3, 2]

# Convert tensors to numpy for ASE compatibility
cell_np = cell.numpy()
coord_np = coord.numpy()

prec = 1e-10
low_prec = 1e-4

ase_atoms0 = Atoms(
numbers=atomic_numbers,
positions=coord,
positions=coord_np,
# positions=[tuple(item) for item in coordinate],
cell=cell,
cell=cell_np,
calculator=self.calculator,
pbc=True,
)
Expand All @@ -166,9 +174,9 @@ def test_calculator(self) -> None:

ase_atoms1 = Atoms(
numbers=[atomic_numbers[i] for i in idx_perm],
positions=coord[idx_perm, :],
positions=coord_np[idx_perm, :],
# positions=[tuple(item) for item in coordinate],
cell=cell,
cell=cell_np,
calculator=self.calculator,
pbc=True,
)
Expand Down