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awvwgk merged 1 commit into from
Apr 22, 2026
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Update ftorch example for Skala-1.1 #49

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38 changes: 19 additions & 19 deletions docs/ftorch.rst
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Expand Up @@ -220,7 +220,7 @@ The output for the H2 molecule with the def2-QZVP basis set should look like thi
[3] Preparing input dictionary
[4] Running model inference
[5] Computing XC energy = sum(exc * grid_weights)
-> E_xc = -6.23580775902096E-0
-> E_xc = -6.26480858787919E-01

The ``get_exc_vxc`` procedure computes the exchange-correlation energy and potential, which we can then access from the returned dictionary.
The potential terms are stored under the same keys as the input features and can be extracted as tensors.
Expand Down Expand Up @@ -271,35 +271,35 @@ In the output we can see the computed exchange-correlation energy as well as the
[3] Preparing input dictionary
[4] Running model inference
[5] Computing XC energy = sum(exc * grid_weights)
-> E_xc = -6.23580775902096E-01
-> E_xc = -6.26480858787919E-01
[6] Extracting vxc components
[7] Gradient means (dexc/dx)
-> mean(dexc/d_density) = -5.22209689934698E-03
-> mean(dexc/d_grad) = -5.63822012469897E-12
-> mean(dexc/d_kin) = -5.74746008322732E-04
-> mean(dexc/d_grid_coords) = -2.86873728034485E-14
-> mean(dexc/d_grid_weights) = -3.47603697347615E-02
-> mean(dexc/d_coarse_0_atomic_coords) = 2.81365752618854E-10
-> mean(dexc/d_density) = -3.61391813525080E-03
-> mean(dexc/d_grad) = -2.32959324675360E-13
-> mean(dexc/d_kin) = -8.83645473913423E-04
-> mean(dexc/d_grid_coords) = 4.28210805205588E-15
-> mean(dexc/d_grid_weights) = -3.07264776522541E-02
-> mean(dexc/d_coarse_0_atomic_coords) = -4.19989157107543E-11
[8] Accessing tensor data as Fortran arrays
-> exc: shape = (19616)
[ -1.40859407629398E-11 -5.02191290297704E-14 -4.82271929094491E-19 ...]
[ -1.70762973238251E-01 -1.70762851641779E-01 -1.70761433376289E-01 ...]
-> dexc/d_density: shape = (19616, 2)
[[ -1.41238001293684E-02 -7.07236361510544E-03 -1.22373184625014E-03 ...]
[ -1.41238001293684E-02 -7.07236361510544E-03 -1.22373184625014E-03 ...]]
[[ -6.91094160750184E-15 -2.68068796422506E-12 -8.75755568871490E-11 ...]
[ -6.91094160750184E-15 -2.68068796422506E-12 -8.75755568871490E-11 ...]]
-> dexc/d_grad: shape = (19616, 3, 2)
[[[ 2.54671362468241E-15 3.18318623003652E-19 2.04989634182288E-27 ...]
[ 2.54671362468241E-15 3.18318623003652E-19 2.04989634182288E-27 ...]]
[[[ -3.53224861366702E-20 -1.25883699330863E-16 -1.50302693127561E-14 ...]
[ 2.45810484635441E-32 9.53375368531974E-30 3.11056983360779E-28 ...]]
[[ ... ]]]
-> dexc/d_kin: shape = (19616, 2)
[[ -2.80759310169539E-07 -2.05221067626510E-09 -6.12585093687525E-14 ...]
[ -2.80759310169539E-07 -2.05221067626510E-09 -6.12585093687525E-14 ...]]
[[ 8.31256652855356E-16 3.22438658440735E-13 1.05346827546379E-11 ...]
[ 8.31256652855356E-16 3.22438658440735E-13 1.05346827546379E-11 ...]]
-> dexc/d_grid_coords: shape = (3, 19616)
[[ 0.00000000000000E+00 0.00000000000000E+00 0.00000000000000E+00 ...]
[ 0.00000000000000E+00 0.00000000000000E+00 0.00000000000000E+00 ...]]
[[ -1.10244039021109E-20 -3.46793732833479E-29 1.44417263443964E-21 ...]
[ -3.92941562081365E-17 -1.23603204621552E-25 5.14711125999810E-18 ...]]
-> dexc/d_grid_weights: shape = (19616)
[ -1.40859407629398E-11 -5.02191290297704E-14 -4.82271929094491E-19 ...]
[ -1.70762973238251E-01 -1.70762851641779E-01 -1.70761433376289E-01 ...]
-> dexc/d_coarse_0_atomic_coords: shape = (3, 2)
[[ -4.21657770765388E-10 1.95481841380307E-10 -4.14780685154337E-03]
[[ 5.94466622059813E-12 -3.78104062375730E-12 4.36839713298186E-04]
[ ...]]

Summary
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11 changes: 7 additions & 4 deletions examples/fortran/ftorch_integration/src/skala_ftorch.cxx
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Expand Up @@ -16,7 +16,7 @@ typedef enum SkalaFeature {
Feature_Kin = 3,
Feature_GridCoords = 4,
Feature_GridWeights = 5,
Feature_Coarse0AtomicCoords = 6
Feature_Coarse0AtomicCoords = 6,
Feature_AtomicGridWeights = 7,
Feature_AtomicGridSizes = 8,
Feature_AtomicGridSizeBoundShape = 9
Expand Down Expand Up @@ -169,13 +169,16 @@ skala_model_get_exc_and_vxc(torch_jit_script_module_t module, skala_dict_t input
c10::Dict<std::string, at::Tensor> features_with_grad;
for (const auto& entry : *dict) {
std::string key = entry.key();
bool requires_grad = (key == "density" || key == "grad" || key == "kin" || key == "coarse_0_atomic_coords" || key == "grid_coords" || key == "grid_weights");
const auto& values = entry.value();
std::vector<at::Tensor> tensors;
for (const auto &value : values) {
auto tensor_with_grad = value.clone().requires_grad_(true);
auto tensor_with_grad = value.clone().requires_grad_(requires_grad);
tensors.push_back(tensor_with_grad);
input_tensors.push_back(tensor_with_grad);
tensor_keys.push_back(key);
if (requires_grad) {
input_tensors.push_back(tensor_with_grad);
tensor_keys.push_back(key);
}
}
features_with_grad.insert(key, torch::concat(tensors));
}
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6 changes: 6 additions & 0 deletions examples/fortran/ftorch_integration/src/skala_ftorch.f90
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Expand Up @@ -349,6 +349,12 @@ subroutine skala_feature_key(feature, key)
key = "grid_weights"
case (skala_feature%coarse_0_atomic_coords)
key = "coarse_0_atomic_coords"
case (skala_feature%atomic_grid_weights)
key = "atomic_grid_weights"
case (skala_feature%atomic_grid_sizes)
key = "atomic_grid_sizes"
case (skala_feature%atomic_grid_size_bound_shape)
key = "atomic_grid_size_bound_shape"
end select
end subroutine skala_feature_key

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