Skip to content

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

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
njzjz merged 7 commits into from
Aug 25, 2025
+112 −34
Merged

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

Show file tree
Hide file tree
Changes from 3 commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
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
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 3 additions & 0 deletions deepmd/pd/utils/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -267,6 +267,9 @@ def to_paddle_tensor(
if xx is None:
return None
assert xx is not None
# Handle PaddlePaddle tensors - clone and move to target device/dtype if needed
if isinstance(xx, paddle.Tensor):
return xx.clone().detach().to(device=DEVICE)
if not isinstance(xx, np.ndarray):
return xx
# Create a reverse mapping of NP_PRECISION_DICT
Expand Down
3 changes: 3 additions & 0 deletions deepmd/pt/utils/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -244,6 +244,9 @@ def to_torch_tensor(
if xx is None:
return None
assert xx is not None
# Handle PyTorch tensors - clone and move to target device/dtype if needed
if isinstance(xx, torch.Tensor):
return xx.clone().detach().to(device=DEVICE)
Comment thread
njzjz marked this conversation as resolved.
Outdated
if not isinstance(xx, np.ndarray):
return xx
# Create a reverse mapping of NP_PRECISION_DICT
Expand Down
41 changes: 28 additions & 13 deletions source/tests/pd/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,10 @@ 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, place=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 +104,40 @@ 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
)
if isinstance(coords, paddle.Tensor):
coord_input = coords.reshape([-1, natoms, 3]).clone().detach().to(dtype=GLOBAL_PD_FLOAT_PRECISION, place=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:
spin_input = paddle.to_tensor(
spins.reshape([-1, natoms, 3]),
dtype=GLOBAL_PD_FLOAT_PRECISION,
place=DEVICE,
)
if isinstance(spins, paddle.Tensor):
spin_input = spins.reshape([-1, natoms, 3]).clone().detach().to(dtype=GLOBAL_PD_FLOAT_PRECISION, place=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, place=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, place=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
41 changes: 28 additions & 13 deletions source/tests/pt/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,10 @@ 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 +104,40 @@ 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
)
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:
spin_input = torch.tensor(
spins.reshape([-1, natoms, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
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