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Enable Energy and Force fitting on a GPU #72

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merged 3 commits into from
Oct 8, 2024
Merged

Enable Energy and Force fitting on a GPU #72

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f578b6e
make sure energy and force predictions are on the same device to allo…
jthorton Aug 29, 2024
fbcc620
fix tests dtype
jthorton Aug 30, 2024
9a8c446
Merge branch 'main' into energy_update
jthorton Oct 8, 2024
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29 changes: 20 additions & 9 deletions descent/targets/energy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import datasets.table
import pyarrow
import smee
import smee.utils
import torch

DATA_SCHEMA = pyarrow.schema(
Expand Down Expand Up @@ -110,12 +111,13 @@ def predict(
energy_ref = entry["energy"]
forces_ref = entry["forces"].reshape(len(energy_ref), -1, 3)

coords = (
entry["coords"]
.reshape(len(energy_ref), -1, 3)
.detach()
.requires_grad_(True)
coords_flat = smee.utils.tensor_like(
entry["coords"], force_field.potentials[0].parameters
)

coords = (
coords_flat.reshape(len(energy_ref), -1, 3)
).detach().requires_grad_(True)
topology = topologies[smiles]

energy_pred = smee.compute_energy(topology, force_field, coords)
Expand Down Expand Up @@ -150,9 +152,18 @@ def predict(
energy_pred_all.append(scale_energy * (energy_pred - energy_pred_0))
forces_pred_all.append(scale_forces * forces_pred.reshape(-1, 3))

energy_pred_all = torch.cat(energy_pred_all)
forces_pred_all = torch.cat(forces_pred_all)

energy_ref_all = torch.cat(energy_ref_all)
energy_ref_all = smee.utils.tensor_like(energy_ref_all, energy_pred_all)

forces_ref_all = torch.cat(forces_ref_all)
forces_ref_all = smee.utils.tensor_like(forces_ref_all, forces_pred_all)

return (
torch.cat(energy_ref_all),
torch.cat(energy_pred_all),
torch.cat(forces_ref_all),
torch.cat(forces_pred_all),
energy_ref_all,
energy_pred_all,
forces_ref_all,
forces_pred_all,
)
8 changes: 4 additions & 4 deletions descent/tests/targets/test_energy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,7 @@ def test_extract_smiles(mock_meoh_entry, mock_hoh_entry):
[9.0, 10.0, 11.0],
[12.0, 13.0, 14.0],
[15.0, 16.0, 17.0],
]
], dtype=torch.float64
)
/ math.sqrt(6.0 * 3.0),
torch.tensor([7.899425506591797, -7.89942741394043]) / math.sqrt(2.0),
Expand All @@ -90,7 +90,7 @@ def test_extract_smiles(mock_meoh_entry, mock_hoh_entry):
[0.0, -137.45770263671875, 0.0],
[102.62999725341797, 68.72884368896484, 0.0],
[-102.62999725341797, 68.72884368896484, 0.0],
]
], dtype=torch.float64
)
/ math.sqrt(6.0 * 3.0),
),
Expand All @@ -106,7 +106,7 @@ def test_extract_smiles(mock_meoh_entry, mock_hoh_entry):
[9.0, 10.0, 11.0],
[12.0, 13.0, 14.0],
[15.0, 16.0, 17.0],
]
], dtype=torch.float64
),
torch.tensor([0.0, -15.798852920532227]),
-torch.tensor(
Expand All @@ -117,7 +117,7 @@ def test_extract_smiles(mock_meoh_entry, mock_hoh_entry):
[0.0, -137.45770263671875, 0.0],
[102.62999725341797, 68.72884368896484, 0.0],
[-102.62999725341797, 68.72884368896484, 0.0],
]
], dtype=torch.float64
),
),
],
Expand Down
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