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Allowing torchani to use GPU #327

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merged 6 commits into from
Nov 10, 2021
Merged

Allowing torchani to use GPU #327

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afc11be
Update config.py
kexul Nov 2, 2021
a4dec42
Update torchani.py
kexul Nov 2, 2021
2c21ac5
Update torchani.py
kexul Nov 3, 2021
fccceeb
Update torchani.py
kexul Nov 3, 2021
d30a735
Update config.py
kexul Nov 3, 2021
d7ced82
code format
kexul Nov 10, 2021
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2 changes: 1 addition & 1 deletion qcengine/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -158,7 +158,7 @@ class TaskConfig(pydantic.BaseModel):
scratch_messy: bool = pydantic.Field(
False, description="Leave scratch directory and contents on disk after completion."
)

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Introducing the whitespace here probably caused a problem with our linter.

Could you run make format and issue a commit with the changed files?

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Done by d7ced82 😉

class Config:
extra = "forbid"

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11 changes: 6 additions & 5 deletions qcengine/programs/torchani.py
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Expand Up @@ -100,7 +100,7 @@ def compute(self, input_data: "AtomicInput", config: "TaskConfig") -> "AtomicRes
import torch
import torchani

device = torch.device("cpu")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Failure flag
ret_data = {"success": False}
Expand All @@ -126,7 +126,8 @@ def compute(self, input_data: "AtomicInput", config: "TaskConfig") -> "AtomicRes
# Build coord array
geom_array = input_data.molecule.geometry.reshape(1, -1, 3) * ureg.conversion_factor("bohr", "angstrom")
coordinates = torch.tensor(geom_array.tolist(), requires_grad=True, device=device)

model.to(device)

_, energy_array = model((species, coordinates))
energy = energy_array.mean()
ensemble_std = energy_array.std()
Expand All @@ -139,11 +140,11 @@ def compute(self, input_data: "AtomicInput", config: "TaskConfig") -> "AtomicRes
elif input_data.driver == "gradient":
derivative = torch.autograd.grad(energy.sum(), coordinates)[0].squeeze()
ret_data["return_result"] = (
np.asarray(derivative * ureg.conversion_factor("angstrom", "bohr")).ravel().tolist()
np.asarray(derivative.cpu() * ureg.conversion_factor("angstrom", "bohr")).ravel().tolist()
)
elif input_data.driver == "hessian":
hessian = torchani.utils.hessian(coordinates, energies=energy)
ret_data["return_result"] = np.asarray(hessian)
ret_data["return_result"] = np.asarray(hessian.cpu())
else:
raise InputError(
f"TorchANI can only compute energy, gradient, and hessian driver methods. Found {input_data.driver}."
Expand Down Expand Up @@ -172,7 +173,7 @@ def compute(self, input_data: "AtomicInput", config: "TaskConfig") -> "AtomicRes
ret_data["extras"] = input_data.extras.copy()
ret_data["extras"].update(
{
"ensemble_energies": energy_array.detach().numpy(),
"ensemble_energies": energy_array.cpu().detach().numpy(),
"ensemble_energy_avg": energy.item(),
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"ensemble_energy_std": ensemble_std.item(),
"ensemble_per_root_atom_disagreement": ensemble_scaled_std.item(),
Expand Down