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Fix concatenation of states in InFlightAutoBatcher #219

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Merged
merged 1 commit into from
Aug 8, 2025
Merged

Fix concatenation of states in InFlightAutoBatcher #219

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2 changes: 1 addition & 1 deletion tests/test_autobatching.py
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Original file line number Diff line number Diff line change
Expand Up @@ -490,7 +490,7 @@ def convergence_fn(state: ts.SimState) -> bool:
break

# run 10 steps, arbitrary number
for _ in range(10):
for _ in range(5):
state = fire_update(state)
convergence_tensor = convergence_fn(state)

Expand Down
44 changes: 31 additions & 13 deletions torch_sim/optimizers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -589,7 +589,9 @@ def fire_init(
atomic_numbers=state.atomic_numbers.clone(),
system_idx=state.system_idx.clone(),
pbc=state.pbc,
velocities=None,
velocities=torch.full(
state.positions.shape, torch.nan, device=device, dtype=dtype
),
forces=forces,
energy=energy,
# Optimization attributes
Expand Down Expand Up @@ -862,13 +864,17 @@ def fire_init(
atomic_numbers=state.atomic_numbers.clone(),
system_idx=state.system_idx.clone(),
pbc=state.pbc,
velocities=None,
velocities=torch.full(
state.positions.shape, torch.nan, device=device, dtype=dtype
),
Comment on lines -865 to +869
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Why not just initialize to zeros?

forces=forces,
energy=energy,
stress=stress,
# Cell attributes
cell_positions=torch.zeros(n_systems, 3, 3, device=device, dtype=dtype),
cell_velocities=None,
cell_velocities=torch.full(
cell_forces.shape, torch.nan, device=device, dtype=dtype
),
cell_forces=cell_forces,
cell_masses=cell_masses,
# Optimization attributes
Expand Down Expand Up @@ -1161,13 +1167,17 @@ def fire_init(
atomic_numbers=state.atomic_numbers,
system_idx=state.system_idx,
pbc=state.pbc,
velocities=None,
velocities=torch.full(
state.positions.shape, torch.nan, device=device, dtype=dtype
),
forces=forces,
energy=energy,
stress=stress,
# Cell attributes
cell_positions=cell_positions,
cell_velocities=None,
cell_velocities=torch.full(
cell_forces.shape, torch.nan, device=device, dtype=dtype
),
cell_forces=cell_forces,
cell_masses=cell_masses,
# Optimization attributes
Expand Down Expand Up @@ -1244,15 +1254,19 @@ def _vv_fire_step( # noqa: C901, PLR0915
dtype = state.positions.dtype
deform_grad_new: torch.Tensor | None = None

if state.velocities is None:
state.velocities = torch.zeros_like(state.positions)
nan_velocities = state.velocities.isnan().any(dim=1)
if nan_velocities.any():
state.velocities[nan_velocities] = torch.zeros_like(
state.positions[nan_velocities]
)
if is_cell_optimization:
if not isinstance(state, AnyFireCellState):
raise ValueError(
f"Cell optimization requires one of {get_args(AnyFireCellState)}."
)
state.cell_velocities = torch.zeros(
(n_systems, 3, 3), device=device, dtype=dtype
nan_cell_velocities = state.cell_velocities.isnan().any(dim=(1, 2))
state.cell_velocities[nan_cell_velocities] = torch.zeros_like(
state.cell_positions[nan_cell_velocities]
)

alpha_start_system = torch.full(
Expand Down Expand Up @@ -1461,16 +1475,20 @@ def _ase_fire_step( # noqa: C901, PLR0915

cur_deform_grad = None # Initialize cur_deform_grad to prevent UnboundLocalError

if state.velocities is None:
state.velocities = torch.zeros_like(state.positions)
nan_velocities = state.velocities.isnan().any(dim=1)
if nan_velocities.any():
state.velocities[nan_velocities] = torch.zeros_like(
state.positions[nan_velocities]
)
forces = state.forces
if is_cell_optimization:
if not isinstance(state, AnyFireCellState):
raise ValueError(
f"Cell optimization requires one of {get_args(AnyFireCellState)}."
)
state.cell_velocities = torch.zeros(
(n_systems, 3, 3), device=device, dtype=dtype
nan_cell_velocities = state.cell_velocities.isnan().any(dim=(1, 2))
state.cell_velocities[nan_cell_velocities] = torch.zeros_like(
state.cell_positions[nan_cell_velocities]
)
cur_deform_grad = state.deform_grad()
else:
Expand Down
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