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# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
import os
import lmdb
import pickle
from functools import lru_cache
import numpy as np
import torch
from torch.utils.data import Dataset
from gto import (
element_to_atomic_number,
GTOBasis,
GTOAuxDensityHelper,
GTOProductBasisHelper,
)
def compute_edge_index(coords, r_max, remove_self_loops=True):
from scipy.spatial import distance_matrix
dist = distance_matrix(coords, coords)
edge_index = np.stack(np.nonzero(dist < r_max), axis=0)
if remove_self_loops:
edge_index = edge_index[:, edge_index[0] != edge_index[1]]
return edge_index
class ShardedLMDBDataset(Dataset):
def __init__(self, data_root: str):
super().__init__()
self.data_root = data_root
if os.path.isfile(os.path.join(data_root, "data.lmdb")):
self.shards = ["."]
else:
self.shards = sorted(os.listdir(data_root))
envs = self.get_envs()
self.env_lengths = [env.stat()["entries"] for env in envs]
self.env_boundaries = np.cumsum(self.env_lengths)
self.len = self.env_boundaries[-1]
self.envs = None # postpone env intitialization until ddp is intitialized
def get_envs(self):
return [
lmdb.Environment(
os.path.join(self.data_root, shard, "data.lmdb"),
map_size=(1024**3) * 256,
subdir=False,
readonly=True,
readahead=True,
meminit=False,
lock=False,
)
for shard in self.shards
]
def __len__(self):
return self.len
def __getitem__(self, index: int):
if self.envs is None:
self.envs = self.get_envs()
if index < 0 or index >= self.len:
raise IndexError
env_idx = np.searchsorted(self.env_boundaries, index, "right")
data_idx = index - (self.env_boundaries[env_idx - 1] if env_idx != 0 else 0)
x = pickle.loads(
self.envs[env_idx].begin(write=False).get(f"{data_idx}".encode())
)
return x
class MultipartLMDBDataset(Dataset):
def __init__(self, data_root: str, parts_to_load: list[str] = ["base"]):
super().__init__()
self.data_root = data_root
self.subdatasets = {
part: ShardedLMDBDataset(os.path.join(data_root, part))
for part in parts_to_load
}
self.len = len(next(iter(self.subdatasets.values())))
assert all(
len(subdataset) == self.len for subdataset in self.subdatasets.values()
)
def __len__(self):
return self.len
def __getitem__(self, index: int):
ret = {}
for part, subdataset in self.subdatasets.items():
ret.update(subdataset[index])
return ret
class SCFBenchDataset(Dataset):
"""
Unit assumption:
atomic coordinates: angstrom
multipole moments: atomic unit
auxdensity: atomic unit
dm: atomic unit
fock: atomic unit
"""
def __init__(
self,
data_root,
r_max=5.0,
type_names=["H", "C", "N", "O", "F", "P", "S"],
remove_self_loops=True,
parts_to_load=["base", "dm", "fock", "auxdensity.denfit"],
aobasis="def2-svp",
auxbasis="def2-universal-jfit",
use_denfit_ovlp=False,
):
super().__init__()
self.data_root = data_root
self.parts_to_load = parts_to_load
self.dataset = MultipartLMDBDataset(
self.data_root, parts_to_load=self.parts_to_load
)
self.type_names = type_names
self.atom_numbers = [element_to_atomic_number[e] for e in self.type_names]
self.atom_number_to_index = {z: i for i, z in enumerate(self.atom_numbers)}
self.data_r_max = r_max
self.remove_self_loops = remove_self_loops
assert sum(["auxdensity" in p for p in parts_to_load]) <= 1, (
"Only one kind of auxdensity can be loaded."
)
if any(p.startswith("auxdensity") for p in parts_to_load):
self.auxbasis = GTOBasis.from_basis_name(auxbasis, elements=type_names)
self.use_denfit_ovlp = use_denfit_ovlp
if "dm" in parts_to_load or "fock" in parts_to_load or "mo" in parts_to_load:
self.aobasis = GTOBasis.from_basis_name(aobasis, elements=type_names)
self.ao_prod_basis = GTOProductBasisHelper(self.aobasis)
def __len__(self):
return len(self.dataset)
@lru_cache(maxsize=16)
def __getitem__(self, idx):
d = self.dataset[idx].copy()
d["atom_coords"] = d["atom_coords"]
d["edge_index"] = compute_edge_index(
d["atom_coords"], self.data_r_max, self.remove_self_loops
)
ret = {
"z": torch.LongTensor(
[self.atom_number_to_index[n] for n in d["atom_number"]]
),
"pos": torch.FloatTensor(d["atom_coords"]),
"net_charge": torch.LongTensor([int(d["net_charge"])]),
"spin": torch.LongTensor([int(d["spin"])]),
"edge_index": torch.LongTensor(d["edge_index"]),
}
if any(p.startswith("auxdensity") for p in self.parts_to_load):
if self.use_denfit_ovlp:
auxdensity_key = "aux_density_denfit_ovlp"
else:
auxdensity_key = (
"aux_density_jfit"
if "auxdensity.jfit" in self.parts_to_load
else "aux_density_denfit"
)
gtoaux = GTOAuxDensityHelper(d["atom_number"], self.auxbasis)
auxdensity_by_element = gtoaux.split_ao_by_elements(
gtoaux.transform_from_pyscf_to_std(d[auxdensity_key])
)
ret.update(
{
"auxdensity": {
k: torch.FloatTensor(t)
for k, t in auxdensity_by_element.items()
},
"species_indices": {
k: torch.IntTensor(t)
for k, t in gtoaux.atom_indices_by_element.items()
},
}
)
if "dm" in self.parts_to_load:
(
dm_diag_blocks,
dm_diag_masks,
dm_tril_blocks,
dm_tril_masks,
dm_tril_edge_index,
) = self.ao_prod_basis.split_matrix_to_padded_blocks(
d["atom_number"],
self.ao_prod_basis.transform_from_pyscf_to_std(
d["atom_number"], d["density_matrix"]
),
)
ret.update(
{
"dm_diag_blocks": torch.FloatTensor(dm_diag_blocks),
"dm_diag_masks": torch.BoolTensor(dm_diag_masks),
"dm_tril_blocks": torch.FloatTensor(dm_tril_blocks),
"dm_tril_masks": torch.BoolTensor(dm_tril_masks),
"dm_tril_edge_index": torch.IntTensor(dm_tril_edge_index),
}
)
if "fock" in self.parts_to_load:
(
fock_diag_blocks,
fock_diag_masks,
fock_tril_blocks,
fock_tril_masks,
fock_tril_edge_index,
) = self.ao_prod_basis.split_matrix_to_padded_blocks(
d["atom_number"],
self.ao_prod_basis.transform_from_pyscf_to_std(
d["atom_number"], d["fock"]
),
)
ret.update(
{
"fock_diag_blocks": torch.FloatTensor(fock_diag_blocks),
"fock_diag_masks": torch.BoolTensor(fock_diag_masks),
"fock_tril_blocks": torch.FloatTensor(fock_tril_blocks),
"fock_tril_masks": torch.BoolTensor(fock_tril_masks),
"fock_tril_edge_index": torch.IntTensor(fock_tril_edge_index),
}
)
return ret
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