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# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
import torch
import torch.nn as nn
from e3nn import o3
from nequip.data import AtomicDataDict
from nequip.nn import GraphModuleMixin
from gto import GTOBasis
class AuxdensityHead(nn.Module):
def __init__(
self,
irreps_in: str | o3.Irreps,
type_names: list[str],
biases: bool = False,
auxbasis: str = "def2-universal-jfit",
):
super().__init__()
self.auxbasis = GTOBasis.from_basis_name(auxbasis, elements=type_names)
self.species_list = list(self.auxbasis.per_element_irreps.keys())
self.per_species_modules = nn.ModuleDict(
{
species: o3.Linear(
irreps_in=irreps_in, irreps_out=irreps_out, biases=biases
)
for species, irreps_out in self.auxbasis.per_element_irreps.items()
}
)
def forward(
self, atom_features: torch.Tensor, species_indices: dict[str, torch.Tensor]
):
outputs = {}
for species in self.species_list:
outputs[species] = self.per_species_modules[species](
atom_features[species_indices[species]]
)
return outputs
class AuxdensityHeadForNequip(GraphModuleMixin, torch.nn.Module):
def __init__(
self,
type_names: list[str],
auxbasis: str = "def2-universal-jfit",
field: str = AtomicDataDict.NODE_FEATURES_KEY,
out_field: str | None = None,
biases: bool = True,
irreps_in: str | o3.Irreps = None,
):
super().__init__()
self.field = field
out_field = out_field if out_field is not None else field
self.out_field = out_field
self._init_irreps(
irreps_in=irreps_in,
required_irreps_in=[field],
# we do not init irreps_out here because this module should be the final layer
)
self.layer = AuxdensityHead(
irreps_in=irreps_in[field],
type_names=type_names,
auxbasis=auxbasis,
biases=biases,
)
def forward(self, data):
data[self.out_field] = self.layer(data[self.field], data["species_indices"])
return data
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