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from __future__ import annotations
from pathlib import Path
from typing import Literal
import torch
from omegaconf import OmegaConf
from tqdm import tqdm
class AffineStatsParallelAlgorithm(torch.nn.Module):
def __init__(
self,
shape: tuple | int,
stable: bool = True,
epsilon: float = 1e-10,
):
"""Accumulate mean and variance online using the "parallel algorithm" algorithm from [1].
Args:
shape: shape of mean, variance, and std array. do not include batch dimension!
stable: compute using the stable version of the algorithm [1]
epsilon: added to the computation of the standard deviation for numerical stability.
References:
[1] https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
"""
super().__init__()
self.register_buffer("n", torch.tensor(0, dtype=torch.long))
self.register_buffer("_mean", torch.zeros(shape))
self.register_buffer("_M2", torch.zeros(shape))
self.register_buffer("epsilon", torch.tensor(epsilon))
if isinstance(shape, int):
self.shape = (shape,)
else:
self.shape = shape
self.stable = stable
def _parallel_algorithm(
self, x: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
assert x.shape[-len(self.shape) :] == self.shape
na = self.n.clone()
nb = x.shape[0]
nab = na + nb
xa = self._mean.clone()
xb = x.mean(dim=0)
delta = xb - xa
if self.stable:
xab = (na * xa + nb * xb) / nab
else:
xab = xa + delta * nb / nab
m2a = self._M2.clone()
m2b = (
x.var(dim=(0,), unbiased=False) * nb
) # do not use bessel's correction then multiply by total number of items in batch.
m2ab = m2a + m2b + delta**2 * na * nb / nab
return nab, xab, m2ab
def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
self.n, self._mean, self._M2 = self._parallel_algorithm(x)
return self.mean, self.std
@property
def mean(self) -> torch.Tensor:
return self._mean
@property
def var(self) -> torch.Tensor:
if self.n > 1:
return self._M2 / (self.n - 1)
else:
return torch.zeros_like(self._M2)
@property
def std(self) -> torch.Tensor:
return torch.sqrt(self.var + self.epsilon)
collect_stats_on_options = Literal["coord", "lattice"]
def compute_affine_stats(
dataset: dataset_options,
collect_stats_on: collect_stats_on_options,
atom_type_manifold: atom_type_manifold_types,
coord_manifold: coord_manifold_types,
lattice_manifold: lattice_manifold_types,
epochs: int = 5,
analog_bits_scale: float = 1.0,
):
from flowmm.cfg_utils import init_loaders
from flowmm.rfm.manifold_getter import ManifoldGetter
train_loader, *_ = init_loaders(dataset=dataset, batch_size=8192)
manifold_getter = ManifoldGetter(
atom_type_manifold=atom_type_manifold,
coord_manifold=coord_manifold,
lattice_manifold=lattice_manifold,
analog_bits_scale=analog_bits_scale,
dataset=dataset,
)
inited = False
for _ in tqdm(range(epochs)):
for batch in tqdm(train_loader):
(
x1,
manifold,
a_manifold,
f_manifold,
l_manifold,
dims,
mask_a_or_f,
) = manifold_getter(
batch.batch,
batch.atom_types,
batch.frac_coords,
batch.lengths,
batch.angles,
split_manifold=True,
)
x0 = manifold.random(*x1.shape, dtype=x1.dtype, device=x1.device)
N = x1.shape[0]
t = torch.rand(N, dtype=x1.dtype, device=x1.device).reshape(-1, 1)
x1 = manifold.projx(x1)
x_t, u_t = manifold.cond_u(x0, x1, t)
x_t = x_t.reshape(N, x0.shape[-1])
u_t = u_t.reshape(N, x0.shape[-1])
# this projects out the mean from the tangent vectors
# our model cannot predict it, so keeping it in inflates the loss
u_t = manifold.proju(x_t, u_t)
atom_types, frac_coords, lattices = manifold_getter.flatrep_to_georep(
x_t,
dims=dims,
mask_a_or_f=mask_a_or_f,
)
vals_x_t = {
"atom": atom_types,
"coord": frac_coords,
"lattice": lattices,
}
u_atom_types, u_frac_coords, u_lattices = manifold_getter.flatrep_to_georep(
u_t,
dims=dims,
mask_a_or_f=mask_a_or_f,
)
vals_u_t = {
"atom": u_atom_types,
"coord": u_frac_coords,
"lattice": u_lattices,
}
if not inited:
aspa_x_t = AffineStatsParallelAlgorithm(
vals_x_t[collect_stats_on].shape[-1]
)
aspa_u_t = AffineStatsParallelAlgorithm(
vals_u_t[collect_stats_on].shape[-1]
)
inited = True
aspa_x_t(vals_x_t[collect_stats_on])
aspa_u_t(vals_u_t[collect_stats_on])
print(
f"{aspa_x_t.mean=}, {aspa_x_t.std=}; {aspa_u_t.mean=}, {aspa_u_t.std=}"
)
return {
"x_t_mean": aspa_x_t.mean,
"x_t_std": aspa_x_t.std,
"u_t_mean": aspa_u_t.mean,
"u_t_std": aspa_u_t.std,
}
def get_affine_stats_filename(
dataset: dataset_options,
manifold_type: atom_type_manifold_types
| coord_manifold_types
| lattice_manifold_types,
) -> str:
return f"stats_{dataset}_{manifold_type}.yaml"
def get_affine_stats(
dataset: dataset_options,
manifold_type: atom_type_manifold_types
| coord_manifold_types
| lattice_manifold_types,
path: str | Path | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
if path is None:
file = Path(__file__).parent / get_affine_stats_filename(dataset, manifold_type)
file = file.resolve()
else:
file = Path(path)
file = file.resolve()
if file.exists():
stats = OmegaConf.load(str(file))
else:
raise FileNotFoundError(
f"{file=} does not exist, have you computed the stats with `compute_affine_stats` already?"
)
return {k: torch.tensor(v) for k, v in stats.items()}
if __name__ == "__main__":
import yaml
from flowmm.cfg_utils import dataset_options
from flowmm.rfm.manifold_getter import (
atom_type_manifold_types,
coord_manifold_types,
lattice_manifold_types,
)
file = Path(__file__).parent / "spd_stats.yaml"
file = file.resolve()
# calculate the stats of each dataset
for dataset in tqdm(list(dataset_options.__args__)):
for collect_stats_on in collect_stats_on_options.__args__:
# if collect_stats_on == "atom":
# pass # categorical are not standardized
if collect_stats_on == "coord":
# flat_torus_01
stats = compute_affine_stats(
dataset=dataset,
collect_stats_on=collect_stats_on,
atom_type_manifold="null_manifold",
coord_manifold="flat_torus_01",
lattice_manifold="non_symmetric",
)
# since translation is removed from the geodesic
stats["u_t_mean"] = torch.zeros_like(stats["u_t_mean"])
# we do not standardize these since they goes through a sinusoidal embedding
stats["x_t_mean"] = torch.zeros_like(stats["x_t_mean"])
stats["x_t_std"] = torch.ones_like(stats["x_t_std"])
file = Path(__file__).parent / get_affine_stats_filename(
dataset, "flat_torus_01"
)
file = file.resolve()
with open(file, "w") as f:
yaml.dump({k: v.tolist() for k, v in stats.items()}, f)
# flat_torus_01_normal
stats = compute_affine_stats(
dataset=dataset,
collect_stats_on=collect_stats_on,
atom_type_manifold="null_manifold",
coord_manifold="flat_torus_01_normal",
lattice_manifold="non_symmetric",
)
# since translation is removed from the geodesic
stats["u_t_mean"] = torch.zeros_like(stats["u_t_mean"])
# we do not standardize these since they goes through a sinusoidal embedding
stats["x_t_mean"] = torch.zeros_like(stats["x_t_mean"])
stats["x_t_std"] = torch.ones_like(stats["x_t_std"])
file = Path(__file__).parent / get_affine_stats_filename(
dataset, "flat_torus_01_normal"
)
file = file.resolve()
with open(file, "w") as f:
yaml.dump({k: v.tolist() for k, v in stats.items()}, f)
# flat_torus_01_fixfirst
stats = compute_affine_stats(
dataset=dataset,
collect_stats_on=collect_stats_on,
atom_type_manifold="null_manifold",
coord_manifold="flat_torus_01_fixfirst",
lattice_manifold="non_symmetric",
)
# since translation is removed from the geodesic
stats["u_t_mean"] = torch.zeros_like(stats["u_t_mean"])
# we do not standardize these since they goes through a sinusoidal embedding
stats["x_t_mean"] = torch.zeros_like(stats["x_t_mean"])
stats["x_t_std"] = torch.ones_like(stats["x_t_std"])
file = Path(__file__).parent / get_affine_stats_filename(
dataset, "flat_torus_01_fixfirst"
)
file = file.resolve()
# flat_torus_01_fixfirst_normal
stats = compute_affine_stats(
dataset=dataset,
collect_stats_on=collect_stats_on,
atom_type_manifold="null_manifold",
coord_manifold="flat_torus_01_fixfirst_normal",
lattice_manifold="non_symmetric",
)
# since translation is removed from the geodesic
stats["u_t_mean"] = torch.zeros_like(stats["u_t_mean"])
# we do not standardize these since they goes through a sinusoidal embedding
stats["x_t_mean"] = torch.zeros_like(stats["x_t_mean"])
stats["x_t_std"] = torch.ones_like(stats["x_t_std"])
file = Path(__file__).parent / get_affine_stats_filename(
dataset, "flat_torus_01_fixfirst_normal"
)
file = file.resolve()
with open(file, "w") as f:
yaml.dump({k: v.tolist() for k, v in stats.items()}, f)
elif collect_stats_on == "lattice":
# non_symmetric
# this one is difficult because its multiplied by L
# so we just leave it untransformed
stats = {
"x_t_mean": torch.zeros(9),
"x_t_std": torch.ones(9),
"u_t_mean": torch.zeros(9),
"u_t_std": torch.ones(9),
}
file = Path(__file__).parent / get_affine_stats_filename(
dataset, "non_symmetric"
)
file = file.resolve()
with open(file, "w") as f:
yaml.dump({k: v.tolist() for k, v in stats.items()}, f)
# spd_euclidean_geo
stats = compute_affine_stats(
dataset=dataset,
collect_stats_on=collect_stats_on,
atom_type_manifold="null_manifold",
coord_manifold="flat_torus_01_fixfirst",
lattice_manifold="spd_euclidean_geo",
)
file = Path(__file__).parent / get_affine_stats_filename(
dataset, "spd_euclidean_geo"
)
file = file.resolve()
with open(file, "w") as f:
yaml.dump({k: v.tolist() for k, v in stats.items()}, f)
# spd_riemanian_geo
stats = compute_affine_stats(
dataset=dataset,
collect_stats_on=collect_stats_on,
atom_type_manifold="null_manifold",
coord_manifold="flat_torus_01_fixfirst",
lattice_manifold="spd_riemanian_geo",
)
file = Path(__file__).parent / get_affine_stats_filename(
dataset, "spd_riemanian_geo"
)
file = file.resolve()
with open(file, "w") as f:
yaml.dump({k: v.tolist() for k, v in stats.items()}, f)
# lattice_params
stats = compute_affine_stats(
dataset=dataset,
collect_stats_on=collect_stats_on,
atom_type_manifold="null_manifold",
coord_manifold="flat_torus_01_fixfirst",
lattice_manifold="lattice_params",
)
file = Path(__file__).parent / get_affine_stats_filename(
dataset, "lattice_params"
)
file = file.resolve()
with open(file, "w") as f:
yaml.dump({k: v.tolist() for k, v in stats.items()}, f)
# lattice_params_normal_base
stats = compute_affine_stats(
dataset=dataset,
collect_stats_on=collect_stats_on,
atom_type_manifold="null_manifold",
coord_manifold="flat_torus_01_fixfirst",
lattice_manifold="lattice_params_normal_base",
)
file = Path(__file__).parent / get_affine_stats_filename(
dataset, "lattice_params_normal_base"
)
file = file.resolve()
with open(file, "w") as f:
yaml.dump({k: v.tolist() for k, v in stats.items()}, f)
else:
raise ValueError()
|