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import torch
from e3nn.o3._irreps import Irreps
from e3nn.util.jit import compile_mode
from onescience.datapipes.materials.nequip import AtomicDataDict
from ._graph_mixin import GraphModuleMixin
from .model_modifier_utils import model_modifier, replace_submodules
@compile_mode("unsupported")
class PartialForceOutput(GraphModuleMixin, torch.nn.Module):
r"""Generate partial and total forces from an energy model.
Args:
func: the energy model
vectorize: the vectorize option to ``torch.autograd.functional.jacobian``,
false by default since it doesn't work well.
"""
vectorize: bool
def __init__(
self,
func: GraphModuleMixin,
vectorize: bool = False,
vectorize_warnings: bool = False,
):
super().__init__()
self.func = func
self.vectorize = vectorize
if vectorize_warnings:
# See https://pytorch.org/docs/stable/generated/torch.autograd.functional.jacobian.html
torch._C._debug_only_display_vmap_fallback_warnings(True)
# check and init irreps
self._init_irreps(
irreps_in=func.irreps_in,
my_irreps_in={AtomicDataDict.PER_ATOM_ENERGY_KEY: Irreps("0e")},
irreps_out=func.irreps_out,
)
self.irreps_out[AtomicDataDict.PARTIAL_FORCE_KEY] = Irreps("1o")
self.irreps_out[AtomicDataDict.FORCE_KEY] = Irreps("1o")
def forward(self, data: AtomicDataDict.Type) -> AtomicDataDict.Type:
data = data.copy()
out_data = {}
def wrapper(pos: torch.Tensor) -> torch.Tensor:
"""Wrapper from pos to atomic energy"""
nonlocal data, out_data
data[AtomicDataDict.POSITIONS_KEY] = pos
out_data = self.func(data)
return out_data[AtomicDataDict.PER_ATOM_ENERGY_KEY].squeeze(-1)
pos = data[AtomicDataDict.POSITIONS_KEY]
partial_forces = torch.autograd.functional.jacobian(
func=wrapper,
inputs=pos,
create_graph=self.training, # needed to allow gradients of this output during training
vectorize=self.vectorize,
)
partial_forces = partial_forces.negative()
# output is [n_at, n_at, 3]
out_data[AtomicDataDict.PARTIAL_FORCE_KEY] = partial_forces
out_data[AtomicDataDict.FORCE_KEY] = partial_forces.sum(dim=0)
return out_data
@compile_mode("script")
class ForceStressOutput(GraphModuleMixin, torch.nn.Module):
r"""Compute forces (and stress if cell is provided) using autograd of an energy model.
See:
Knuth et. al. Comput. Phys. Commun 190, 33-50, 2015
https://pure.mpg.de/rest/items/item_2085135_9/component/file_2156800/content
Args:
func: the energy model to wrap
"""
do_derivatives: bool
def __init__(self, func: GraphModuleMixin, do_derivatives: bool = True):
super().__init__()
self.func = func
self.do_derivatives = do_derivatives
# check and init irreps
self._init_irreps(
irreps_in=self.func.irreps_in.copy(),
irreps_out=self.func.irreps_out.copy(),
)
self.irreps_out[AtomicDataDict.FORCE_KEY] = "1o"
self.irreps_out[AtomicDataDict.STRESS_KEY] = "1o"
self.irreps_out[AtomicDataDict.VIRIAL_KEY] = "1o"
self.irreps_out[AtomicDataDict.EDGE_FORCE_KEY] = "1o"
# for torchscript compat
self.register_buffer("_empty", torch.Tensor())
def forward(self, data: AtomicDataDict.Type) -> AtomicDataDict.Type:
# short-circuit
if not self.do_derivatives:
return self.func(data)
# === LOGIC BRANCHING NOTES ===
# if edge vectors not present, we assume that positions are present
# and proceed with the usual procedure to compute forces, virials, stress
# else, we compute edge forces
# NOTE: if edge vectors are not present, we assume that it is for non-batched inference with no cell
# at the point of making this change, it is specifically for LAMMPS-MLIAP compatibility
if AtomicDataDict.EDGE_VECTORS_KEY not in data:
if AtomicDataDict.BATCH_KEY in data:
batch = data[AtomicDataDict.BATCH_KEY]
num_batch: int = AtomicDataDict.num_frames(data)
else:
# Special case for efficiency
batch = self._empty
num_batch: int = 1
pos = data[AtomicDataDict.POSITIONS_KEY]
has_cell: bool = AtomicDataDict.CELL_KEY in data
if has_cell:
orig_cell = data[AtomicDataDict.CELL_KEY]
# Make the cell per-batch
cell = orig_cell.view(-1, 3, 3).expand(num_batch, 3, 3)
data[AtomicDataDict.CELL_KEY] = cell
else:
# torchscript
orig_cell = self._empty
cell = self._empty
# Add the displacements
# the GradientOutput will make them require grad
# See SchNetPack code:
# https://github.com/atomistic-machine-learning/schnetpack/blob/master/src/schnetpack/atomistic/model.py#L45
# SchNetPack issue:
# https://github.com/atomistic-machine-learning/schnetpack/issues/165
# Paper they worked from:
# Knuth et. al. Comput. Phys. Commun 190, 33-50, 2015
# https://pure.mpg.de/rest/items/item_2085135_9/component/file_2156800/content
if num_batch > 1:
displacement = torch.zeros(
(num_batch, 3, 3),
dtype=pos.dtype,
device=pos.device,
)
else:
displacement = torch.zeros(
(3, 3),
dtype=pos.dtype,
device=pos.device,
)
displacement.requires_grad_(True)
data["_displacement"] = displacement
# in the above paper, the infinitesimal distortion is *symmetric*
# so we symmetrize the displacement before applying it to
# the positions/cell
# This is not strictly necessary (reasoning thanks to Mario):
# the displacement's asymmetric 1o term corresponds to an
# infinitesimal rotation, which should not affect the final
# output (invariance).
# That said, due to numerical error, this will never be
# exactly true. So, we symmetrize the deformation to
# take advantage of this understanding and not rely on
# the invariance here:
symmetric_displacement = 0.5 * (
displacement + displacement.transpose(-1, -2)
)
did_pos_req_grad: bool = pos.requires_grad
pos.requires_grad_(True)
if num_batch > 1:
# bmm is natom in batch
# batched [natom, 1, 3] @ [natom, 3, 3] -> [natom, 1, 3] -> [natom, 3]
data[AtomicDataDict.POSITIONS_KEY] = pos + torch.bmm(
pos.unsqueeze(-2),
torch.index_select(symmetric_displacement, 0, batch),
).squeeze(-2)
else:
# (num_atoms, 3), (3, 3) -> (num_atoms, 3)
data[AtomicDataDict.POSITIONS_KEY] = pos + torch.sum(
pos.view(-1, 3, 1) * symmetric_displacement, 1
)
# assert torch.equal(pos, data[AtomicDataDict.POSITIONS_KEY])
# we only displace the cell if we have one:
if has_cell:
# bmm is num_batch in batch
# here we apply the distortion to the cell as well
# this is critical also for the correctness
# if we didn't symmetrize the distortion, since without this
# there would then be an infinitesimal rotation of the positions
# but not cell, and it thus wouldn't be global and have
# no effect due to equivariance/invariance.
if num_batch > 1:
# [n_batch, 3, 3] @ [n_batch, 3, 3]
data[AtomicDataDict.CELL_KEY] = cell + torch.bmm(
cell, symmetric_displacement
)
else:
# [3, 3] @ [3, 3] --- enforced to these shapes
data[AtomicDataDict.CELL_KEY] = (
cell.view(3, 3)
+ torch.sum(cell.view(3, 3, 1) * symmetric_displacement, 1)
).view(1, 3, 3)
# Call model and get gradients
data = self.func(data)
grads = torch.autograd.grad(
[data[AtomicDataDict.TOTAL_ENERGY_KEY].sum()],
[pos, data["_displacement"]],
create_graph=self.training, # needed to allow gradients of this output during training
)
# Put negative sign on forces
forces = grads[0]
if forces is None:
# condition needed to unwrap optional for torchscript
assert False, "failed to compute forces autograd"
forces = torch.neg(forces)
data[AtomicDataDict.FORCE_KEY] = forces
# Store virial
virial = grads[1]
if virial is None:
# condition needed to unwrap optional for torchscript
assert False, "failed to compute virial autograd"
virial = virial.view(num_batch, 3, 3)
# we only compute the stress (1/V * virial) if we have a cell whose volume we can compute
if has_cell:
# ^ can only scale by cell volume if we have one...:
# Rescale stress tensor
# See https://github.com/atomistic-machine-learning/schnetpack/blob/master/src/schnetpack/atomistic/output_modules.py#L180
# See also https://en.wikipedia.org/wiki/Triple_product
# See also https://gitlab.com/ase/ase/-/blob/master/ase/cell.py,
# which uses np.abs(np.linalg.det(cell))
# First dim is batch, second is vec, third is xyz
# Note the .abs(), since volume should always be positive
# det is equal to a dot (b cross c)
volume = torch.linalg.det(cell).abs().unsqueeze(-1)
# NOTE: to support batching periodic and non-periodic structures together,
# the data processing stage is responsible for ensuring that:
# 1. non-periodic systems have a finite dummy cell to prevent infs in the division below
# 2. stress labels for non-periodic systems are NaN and handled with `ignore_nan` in loss and metrics
stress = virial / volume.view(num_batch, 1, 1)
data[AtomicDataDict.CELL_KEY] = orig_cell
else:
stress = self._empty # torchscript
data[AtomicDataDict.STRESS_KEY] = stress
# see discussion in https://github.com/libAtoms/QUIP/issues/227 about sign convention
# (and conventions docs page)
# they say the standard convention is virial = -stress x volume
# looking above this means that we need to pick up another negative sign for the virial
# to fit this equation with the stress computed above
virial = torch.neg(virial)
data[AtomicDataDict.VIRIAL_KEY] = virial
# Remove helper
del data["_displacement"]
if not did_pos_req_grad:
# don't give later modules one that does
pos.requires_grad_(False)
else:
# we differentiate wrt EDGE_VECTORS_KEY directly in this branch
# NOTE: we only consider the case of non-batched inference, without a cell
# so no batching, no training considerations, no cell
# make `edge_vectors` requires grad
edge_vectors = data[AtomicDataDict.EDGE_VECTORS_KEY]
edge_vectors.requires_grad_(True)
data[AtomicDataDict.EDGE_VECTORS_KEY] = edge_vectors
# do energy model forward and backward
data = self.func(data)
edge_forces = torch.autograd.grad(
[data[AtomicDataDict.TOTAL_ENERGY_KEY].sum()],
[edge_vectors],
# no training arg because we only consider inference
)[0]
# assert needed for TorchScript
assert edge_forces is not None
# NOTE: there shouldn't be a sign flip to match LAMMPS convention
data[AtomicDataDict.EDGE_FORCE_KEY] = edge_forces
return data
@model_modifier(persistent=True, private=False)
@classmethod
def enable_ForceStressOutput(cls, model):
"""Enable force and stress computation."""
def factory(old):
new = cls(func=old.func, do_derivatives=True)
return new
return replace_submodules(model, cls, factory)
@model_modifier(persistent=True, private=False)
@classmethod
def disable_ForceStressOutput(cls, model):
"""Disable force and stress computation."""
def factory(old):
new = cls(func=old.func, do_derivatives=False)
return new
return replace_submodules(model, cls, factory)
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