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# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""JAX bindings for batched naive O(N^2) dual cutoff neighbor list construction."""
from __future__ import annotations
import jax
import jax.numpy as jnp
import warp as wp
from warp.jax_experimental import jax_kernel
from nvalchemiops.jax.neighbors.neighbor_utils import (
compute_naive_num_shifts,
get_neighbor_list_from_neighbor_matrix,
prepare_batch_idx_ptr,
)
from nvalchemiops.neighbors.batch_naive_dual_cutoff import (
_fill_batch_naive_neighbor_matrix_dual_cutoff_overload,
_fill_batch_naive_neighbor_matrix_dual_cutoff_selective_overload,
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_overload,
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_prewrapped_overload,
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_prewrapped_selective_overload,
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_selective_overload,
)
from nvalchemiops.neighbors.neighbor_utils import (
_wrap_positions_batch_overload,
estimate_max_neighbors,
)
__all__ = ["batch_naive_neighbor_list_dual_cutoff"]
# ==============================================================================
# JAX Kernel Wrappers
# ==============================================================================
# No-PBC batch naive dual cutoff neighbor matrix kernel wrappers
_jax_fill_batch_dual_f32 = jax_kernel(
_fill_batch_naive_neighbor_matrix_dual_cutoff_overload[wp.float32],
num_outputs=4,
in_out_argnames=[
"neighbor_matrix1",
"num_neighbors1",
"neighbor_matrix2",
"num_neighbors2",
],
enable_backward=False,
)
_jax_fill_batch_dual_f64 = jax_kernel(
_fill_batch_naive_neighbor_matrix_dual_cutoff_overload[wp.float64],
num_outputs=4,
in_out_argnames=[
"neighbor_matrix1",
"num_neighbors1",
"neighbor_matrix2",
"num_neighbors2",
],
enable_backward=False,
)
# PBC batch naive dual cutoff neighbor matrix kernel wrappers
_jax_fill_batch_dual_pbc_f32 = jax_kernel(
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_overload[wp.float32],
num_outputs=6,
in_out_argnames=[
"neighbor_matrix1",
"neighbor_matrix2",
"neighbor_matrix_shifts1",
"neighbor_matrix_shifts2",
"num_neighbors1",
"num_neighbors2",
],
enable_backward=False,
)
_jax_fill_batch_dual_pbc_f64 = jax_kernel(
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_overload[wp.float64],
num_outputs=6,
in_out_argnames=[
"neighbor_matrix1",
"neighbor_matrix2",
"neighbor_matrix_shifts1",
"neighbor_matrix_shifts2",
"num_neighbors1",
"num_neighbors2",
],
enable_backward=False,
)
# Selective batch dual cutoff neighbor matrix kernel wrappers
_jax_fill_batch_dual_selective_f32 = jax_kernel(
_fill_batch_naive_neighbor_matrix_dual_cutoff_selective_overload[wp.float32],
num_outputs=4,
in_out_argnames=[
"neighbor_matrix1",
"num_neighbors1",
"neighbor_matrix2",
"num_neighbors2",
],
enable_backward=False,
)
_jax_fill_batch_dual_selective_f64 = jax_kernel(
_fill_batch_naive_neighbor_matrix_dual_cutoff_selective_overload[wp.float64],
num_outputs=4,
in_out_argnames=[
"neighbor_matrix1",
"num_neighbors1",
"neighbor_matrix2",
"num_neighbors2",
],
enable_backward=False,
)
# Selective PBC batch dual cutoff neighbor matrix kernel wrappers
_jax_fill_batch_dual_pbc_selective_f32 = jax_kernel(
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_selective_overload[wp.float32],
num_outputs=6,
in_out_argnames=[
"neighbor_matrix1",
"neighbor_matrix2",
"neighbor_matrix_shifts1",
"neighbor_matrix_shifts2",
"num_neighbors1",
"num_neighbors2",
],
enable_backward=False,
)
_jax_fill_batch_dual_pbc_selective_f64 = jax_kernel(
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_selective_overload[wp.float64],
num_outputs=6,
in_out_argnames=[
"neighbor_matrix1",
"neighbor_matrix2",
"neighbor_matrix_shifts1",
"neighbor_matrix_shifts2",
"num_neighbors1",
"num_neighbors2",
],
enable_backward=False,
)
# Prewrapped PBC batch dual cutoff neighbor matrix kernel wrappers
_jax_fill_batch_dual_pbc_prewrapped_f32 = jax_kernel(
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_prewrapped_overload[wp.float32],
num_outputs=6,
in_out_argnames=[
"neighbor_matrix1",
"neighbor_matrix2",
"neighbor_matrix_shifts1",
"neighbor_matrix_shifts2",
"num_neighbors1",
"num_neighbors2",
],
enable_backward=False,
)
_jax_fill_batch_dual_pbc_prewrapped_f64 = jax_kernel(
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_prewrapped_overload[wp.float64],
num_outputs=6,
in_out_argnames=[
"neighbor_matrix1",
"neighbor_matrix2",
"neighbor_matrix_shifts1",
"neighbor_matrix_shifts2",
"num_neighbors1",
"num_neighbors2",
],
enable_backward=False,
)
_jax_fill_batch_dual_pbc_prewrapped_selective_f32 = jax_kernel(
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_prewrapped_selective_overload[
wp.float32
],
num_outputs=6,
in_out_argnames=[
"neighbor_matrix1",
"neighbor_matrix2",
"neighbor_matrix_shifts1",
"neighbor_matrix_shifts2",
"num_neighbors1",
"num_neighbors2",
],
enable_backward=False,
)
_jax_fill_batch_dual_pbc_prewrapped_selective_f64 = jax_kernel(
_fill_batch_naive_neighbor_matrix_pbc_dual_cutoff_prewrapped_selective_overload[
wp.float64
],
num_outputs=6,
in_out_argnames=[
"neighbor_matrix1",
"neighbor_matrix2",
"neighbor_matrix_shifts1",
"neighbor_matrix_shifts2",
"num_neighbors1",
"num_neighbors2",
],
enable_backward=False,
)
# Wrap positions batch kernel wrappers
_jax_wrap_positions_batch_f32 = jax_kernel(
_wrap_positions_batch_overload[wp.float32],
num_outputs=2,
in_out_argnames=["positions_wrapped", "per_atom_cell_offsets"],
enable_backward=False,
)
_jax_wrap_positions_batch_f64 = jax_kernel(
_wrap_positions_batch_overload[wp.float64],
num_outputs=2,
in_out_argnames=["positions_wrapped", "per_atom_cell_offsets"],
enable_backward=False,
)
[docs]
def batch_naive_neighbor_list_dual_cutoff(
positions: jax.Array,
cutoff1: float,
cutoff2: float,
batch_idx: jax.Array | None = None,
batch_ptr: jax.Array | None = None,
pbc: jax.Array | None = None,
cell: jax.Array | None = None,
max_neighbors1: int | None = None,
max_neighbors2: int | None = None,
half_fill: bool = False,
fill_value: int | None = None,
return_neighbor_list: bool = False,
neighbor_matrix1: jax.Array | None = None,
neighbor_matrix2: jax.Array | None = None,
neighbor_matrix_shifts1: jax.Array | None = None,
neighbor_matrix_shifts2: jax.Array | None = None,
num_neighbors1: jax.Array | None = None,
num_neighbors2: jax.Array | None = None,
shift_range_per_dimension: jax.Array | None = None,
num_shifts_per_system: jax.Array | None = None,
max_shifts_per_system: int | None = None,
max_atoms_per_system: int | None = None,
rebuild_flags: jax.Array | None = None,
wrap_positions: bool = True,
) -> (
tuple[
jax.Array,
jax.Array,
jax.Array,
jax.Array,
jax.Array,
jax.Array,
jax.Array,
jax.Array,
]
| tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]
| tuple[jax.Array, jax.Array, jax.Array, jax.Array]
):
"""Compute batched neighbor lists for two cutoff distances using naive O(N^2) algorithm.
This function builds two neighbor matrices simultaneously for different cutoff
distances in a batched manner, which is more efficient than calling the
single-cutoff function twice.
Parameters
----------
positions : jax.Array, shape (total_atoms, 3), dtype=float32 or float64
Concatenated Cartesian coordinates for all systems.
cutoff1 : float
First cutoff distance (typically smaller).
cutoff2 : float
Second cutoff distance (typically larger).
batch_idx : jax.Array, shape (total_atoms,), dtype=int32, optional
System index for each atom.
batch_ptr : jax.Array, shape (num_systems + 1,), dtype=int32, optional
Cumulative atom counts defining system boundaries.
pbc : jax.Array, shape (num_systems, 3) or (1, 3), dtype=bool, optional
Periodic boundary condition flags for each dimension.
cell : jax.Array, shape (num_systems, 3, 3) or (1, 3, 3), dtype=float32 or float64, optional
Cell matrices defining lattice vectors in Cartesian coordinates.
max_neighbors1 : int, optional
Maximum number of neighbors per atom for cutoff1.
max_neighbors2 : int, optional
Maximum number of neighbors per atom for cutoff2.
half_fill : bool, optional - default = False
If True, only store relationships where i < j to avoid double counting.
fill_value : int, optional
Value to use for padding in neighbor matrices. Default is total_atoms.
return_neighbor_list : bool, optional - default = False
If True, convert neighbor matrices to neighbor list (idx_i, idx_j) format.
neighbor_matrix1 : jax.Array, shape (total_atoms, max_neighbors1), dtype=int32, optional
Pre-allocated first neighbor matrix.
neighbor_matrix2 : jax.Array, shape (total_atoms, max_neighbors2), dtype=int32, optional
Pre-allocated second neighbor matrix.
neighbor_matrix_shifts1 : jax.Array, shape (total_atoms, max_neighbors1, 3), dtype=int32, optional
Pre-allocated first shift matrix for PBC.
neighbor_matrix_shifts2 : jax.Array, shape (total_atoms, max_neighbors2, 3), dtype=int32, optional
Pre-allocated second shift matrix for PBC.
num_neighbors1 : jax.Array, shape (total_atoms,), dtype=int32, optional
Pre-allocated first neighbor count array.
num_neighbors2 : jax.Array, shape (total_atoms,), dtype=int32, optional
Pre-allocated second neighbor count array.
shift_range_per_dimension : jax.Array, shape (num_systems, 3), dtype=int32, optional
Pre-computed shift ranges for PBC.
num_shifts_per_system : jax.Array, shape (num_systems,), dtype=int32, optional
Number of periodic shifts per system.
max_shifts_per_system : int, optional
Maximum per-system shift count (launch dimension).
max_atoms_per_system : int, optional
Maximum number of atoms in any system (for PBC batched dispatch).
wrap_positions : bool, default=True
If True, wrap input positions into the primary cell before
neighbor search. Set to False when positions are already
wrapped (e.g. by a preceding integration step) to save two
GPU kernel launches per call.
Returns
-------
results : tuple of jax.Array
Variable-length tuple depending on input parameters:
- No PBC, matrix format: ``(neighbor_matrix1, num_neighbors1, neighbor_matrix2, num_neighbors2)``
- No PBC, list format: ``(neighbor_list1, neighbor_ptr1, neighbor_list2, neighbor_ptr2)``
- With PBC, matrix format: ``(neighbor_matrix1, num_neighbors1, neighbor_matrix_shifts1, neighbor_matrix2, num_neighbors2, neighbor_matrix_shifts2)``
- With PBC, list format: ``(neighbor_list1, neighbor_ptr1, unit_shifts1, neighbor_list2, neighbor_ptr2, unit_shifts2)``
See Also
--------
nvalchemiops.neighbors.batch_naive_dual_cutoff.batch_naive_neighbor_matrix_dual_cutoff : Core warp launcher (no PBC)
nvalchemiops.neighbors.batch_naive_dual_cutoff.batch_naive_neighbor_matrix_pbc_dual_cutoff : Core warp launcher (with PBC)
batch_naive_neighbor_list : Single cutoff version
"""
if pbc is None and cell is not None:
raise ValueError("If cell is provided, pbc must also be provided")
if pbc is not None and cell is None:
raise ValueError("If pbc is provided, cell must also be provided")
# Prepare batch_idx and batch_ptr
batch_idx, batch_ptr = prepare_batch_idx_ptr(
batch_idx, batch_ptr, positions.shape[0]
)
if cell is not None:
cell = cell if cell.ndim == 3 else cell[jnp.newaxis, :, :]
# Ensure cell dtype matches positions dtype so warp overload dispatch is consistent
if cell.dtype != positions.dtype:
cell = cell.astype(positions.dtype)
if pbc is not None:
pbc = pbc if pbc.ndim == 2 else pbc[jnp.newaxis, :]
# Estimate max_neighbors if not provided - use larger cutoff for estimation
if max_neighbors1 is None and (
neighbor_matrix1 is None
or (neighbor_matrix_shifts1 is None and pbc is not None)
or num_neighbors1 is None
):
max_neighbors1 = estimate_max_neighbors(cutoff2) # Use larger cutoff
if max_neighbors2 is None and (
neighbor_matrix2 is None
or (neighbor_matrix_shifts2 is None and pbc is not None)
or num_neighbors2 is None
):
max_neighbors2 = estimate_max_neighbors(cutoff2) # Use larger cutoff
if fill_value is None:
fill_value = jnp.int32(positions.shape[0])
# Allocate first neighbor matrix
if neighbor_matrix1 is None:
neighbor_matrix1 = jnp.full(
(positions.shape[0], max_neighbors1),
fill_value,
dtype=jnp.int32,
)
elif rebuild_flags is None:
neighbor_matrix1 = neighbor_matrix1.at[:].set(fill_value)
# Allocate second neighbor matrix
if neighbor_matrix2 is None:
neighbor_matrix2 = jnp.full(
(positions.shape[0], max_neighbors2),
fill_value,
dtype=jnp.int32,
)
elif rebuild_flags is None:
neighbor_matrix2 = neighbor_matrix2.at[:].set(fill_value)
# Allocate first num_neighbors
if num_neighbors1 is None:
num_neighbors1 = jnp.zeros(positions.shape[0], dtype=jnp.int32)
elif rebuild_flags is None:
num_neighbors1 = num_neighbors1.at[:].set(jnp.int32(0))
# Allocate second num_neighbors
if num_neighbors2 is None:
num_neighbors2 = jnp.zeros(positions.shape[0], dtype=jnp.int32)
elif rebuild_flags is None:
num_neighbors2 = num_neighbors2.at[:].set(jnp.int32(0))
if pbc is not None:
# Allocate shift matrices
if neighbor_matrix_shifts1 is None:
neighbor_matrix_shifts1 = jnp.zeros(
(positions.shape[0], max_neighbors1, 3),
dtype=jnp.int32,
)
elif rebuild_flags is None:
neighbor_matrix_shifts1 = neighbor_matrix_shifts1.at[:].set(jnp.int32(0))
if neighbor_matrix_shifts2 is None:
neighbor_matrix_shifts2 = jnp.zeros(
(positions.shape[0], max_neighbors2, 3),
dtype=jnp.int32,
)
elif rebuild_flags is None:
neighbor_matrix_shifts2 = neighbor_matrix_shifts2.at[:].set(jnp.int32(0))
if (
max_shifts_per_system is None
or num_shifts_per_system is None
or shift_range_per_dimension is None
):
shift_range_per_dimension, num_shifts_per_system, max_shifts_per_system = (
compute_naive_num_shifts(cell, cutoff2, pbc) # Use larger cutoff
)
# Compute max_atoms_per_system if needed
if max_atoms_per_system is None:
try:
atoms_per_system = batch_ptr[1:] - batch_ptr[:-1]
max_atoms_per_system = int(jnp.max(atoms_per_system))
except (
jax.errors.ConcretizationTypeError,
jax.errors.TracerIntegerConversionError,
):
raise ValueError(
"Cannot infer max_atoms_per_system inside jax.jit. "
"Please provide max_atoms_per_system explicitly when using jax.jit."
) from None
if cutoff1 <= 0 and cutoff2 <= 0:
if return_neighbor_list:
if pbc is not None:
return (
jnp.zeros((2, 0), dtype=jnp.int32),
jnp.zeros((positions.shape[0] + 1,), dtype=jnp.int32),
jnp.zeros((0, 3), dtype=jnp.int32),
jnp.zeros((2, 0), dtype=jnp.int32),
jnp.zeros((positions.shape[0] + 1,), dtype=jnp.int32),
jnp.zeros((0, 3), dtype=jnp.int32),
)
else:
return (
jnp.zeros((2, 0), dtype=jnp.int32),
jnp.zeros((positions.shape[0] + 1,), dtype=jnp.int32),
jnp.zeros((2, 0), dtype=jnp.int32),
jnp.zeros((positions.shape[0] + 1,), dtype=jnp.int32),
)
else:
if pbc is not None:
return (
neighbor_matrix1,
num_neighbors1,
neighbor_matrix_shifts1,
neighbor_matrix2,
num_neighbors2,
neighbor_matrix_shifts2,
)
else:
return (
neighbor_matrix1,
num_neighbors1,
neighbor_matrix2,
num_neighbors2,
)
# Select kernel based on dtype
if positions.dtype == jnp.float64:
_jax_fill = _jax_fill_batch_dual_f64
_jax_fill_pbc = _jax_fill_batch_dual_pbc_f64
_jax_fill_selective = _jax_fill_batch_dual_selective_f64
_jax_fill_pbc_selective = _jax_fill_batch_dual_pbc_selective_f64
_jax_fill_pbc_prewrapped = _jax_fill_batch_dual_pbc_prewrapped_f64
_jax_fill_pbc_prewrapped_selective = (
_jax_fill_batch_dual_pbc_prewrapped_selective_f64
)
_jax_wrap_batch = _jax_wrap_positions_batch_f64
else:
_jax_fill = _jax_fill_batch_dual_f32
_jax_fill_pbc = _jax_fill_batch_dual_pbc_f32
_jax_fill_selective = _jax_fill_batch_dual_selective_f32
_jax_fill_pbc_selective = _jax_fill_batch_dual_pbc_selective_f32
_jax_fill_pbc_prewrapped = _jax_fill_batch_dual_pbc_prewrapped_f32
_jax_fill_pbc_prewrapped_selective = (
_jax_fill_batch_dual_pbc_prewrapped_selective_f32
)
_jax_wrap_batch = _jax_wrap_positions_batch_f32
positions = positions.astype(jnp.float32)
total_atoms = positions.shape[0]
num_systems = batch_ptr.shape[0] - 1
batch_idx_i32 = batch_idx.astype(jnp.int32)
batch_ptr_i32 = batch_ptr.astype(jnp.int32)
if pbc is None:
if rebuild_flags is not None:
rf = rebuild_flags.astype(jnp.bool_)
atom_rebuild = rf[batch_idx_i32]
num_neighbors1 = jnp.where(
atom_rebuild, jnp.zeros_like(num_neighbors1), num_neighbors1
)
num_neighbors2 = jnp.where(
atom_rebuild, jnp.zeros_like(num_neighbors2), num_neighbors2
)
neighbor_matrix1, num_neighbors1, neighbor_matrix2, num_neighbors2 = (
_jax_fill_selective(
positions,
float(cutoff1 * cutoff1),
float(cutoff2 * cutoff2),
batch_idx_i32,
batch_ptr_i32,
neighbor_matrix1,
num_neighbors1,
neighbor_matrix2,
num_neighbors2,
half_fill,
rf,
launch_dims=(total_atoms,),
)
)
else:
neighbor_matrix1, num_neighbors1, neighbor_matrix2, num_neighbors2 = (
_jax_fill(
positions,
float(cutoff1 * cutoff1),
float(cutoff2 * cutoff2),
batch_idx_i32,
batch_ptr_i32,
neighbor_matrix1,
num_neighbors1,
neighbor_matrix2,
num_neighbors2,
half_fill,
launch_dims=(total_atoms,),
)
)
else:
if cell.dtype != positions.dtype:
cell = cell.astype(positions.dtype)
if max_atoms_per_system is None:
try:
atoms_per_system = batch_ptr[1:] - batch_ptr[:-1]
max_atoms_per_system = int(jnp.max(atoms_per_system))
except (
jax.errors.ConcretizationTypeError,
jax.errors.TracerIntegerConversionError,
):
raise ValueError(
"Cannot infer max_atoms_per_system inside jax.jit. "
"Please provide max_atoms_per_system explicitly when using jax.jit."
) from None
if wrap_positions:
inv_cell = jnp.linalg.inv(cell)
positions_wrapped = jnp.zeros_like(positions)
per_atom_cell_offsets = jnp.zeros((total_atoms, 3), dtype=jnp.int32)
positions_wrapped, per_atom_cell_offsets = _jax_wrap_batch(
positions,
cell,
inv_cell,
batch_idx_i32,
positions_wrapped,
per_atom_cell_offsets,
launch_dims=(total_atoms,),
)
if rebuild_flags is not None:
rf = rebuild_flags.astype(jnp.bool_)
atom_rebuild = rf[batch_idx_i32]
num_neighbors1 = jnp.where(
atom_rebuild, jnp.zeros_like(num_neighbors1), num_neighbors1
)
num_neighbors2 = jnp.where(
atom_rebuild, jnp.zeros_like(num_neighbors2), num_neighbors2
)
(
neighbor_matrix1,
neighbor_matrix2,
neighbor_matrix_shifts1,
neighbor_matrix_shifts2,
num_neighbors1,
num_neighbors2,
) = _jax_fill_pbc_selective(
positions_wrapped,
per_atom_cell_offsets,
cell,
float(cutoff1 * cutoff1),
float(cutoff2 * cutoff2),
batch_ptr_i32,
shift_range_per_dimension,
num_shifts_per_system,
neighbor_matrix1,
neighbor_matrix2,
neighbor_matrix_shifts1,
neighbor_matrix_shifts2,
num_neighbors1,
num_neighbors2,
half_fill,
rf,
launch_dims=(
num_systems,
max_shifts_per_system,
max_atoms_per_system,
),
)
else:
(
neighbor_matrix1,
neighbor_matrix2,
neighbor_matrix_shifts1,
neighbor_matrix_shifts2,
num_neighbors1,
num_neighbors2,
) = _jax_fill_pbc(
positions_wrapped,
per_atom_cell_offsets,
cell,
float(cutoff1 * cutoff1),
float(cutoff2 * cutoff2),
batch_ptr_i32,
shift_range_per_dimension,
num_shifts_per_system,
neighbor_matrix1,
neighbor_matrix2,
neighbor_matrix_shifts1,
neighbor_matrix_shifts2,
num_neighbors1,
num_neighbors2,
half_fill,
launch_dims=(
num_systems,
max_shifts_per_system,
max_atoms_per_system,
),
)
else:
if rebuild_flags is not None:
rf = rebuild_flags.astype(jnp.bool_)
atom_rebuild = rf[batch_idx_i32]
num_neighbors1 = jnp.where(
atom_rebuild, jnp.zeros_like(num_neighbors1), num_neighbors1
)
num_neighbors2 = jnp.where(
atom_rebuild, jnp.zeros_like(num_neighbors2), num_neighbors2
)
(
neighbor_matrix1,
neighbor_matrix2,
neighbor_matrix_shifts1,
neighbor_matrix_shifts2,
num_neighbors1,
num_neighbors2,
) = _jax_fill_pbc_prewrapped_selective(
positions,
cell,
float(cutoff1 * cutoff1),
float(cutoff2 * cutoff2),
batch_ptr_i32,
shift_range_per_dimension,
num_shifts_per_system,
neighbor_matrix1,
neighbor_matrix2,
neighbor_matrix_shifts1,
neighbor_matrix_shifts2,
num_neighbors1,
num_neighbors2,
half_fill,
rf,
launch_dims=(
num_systems,
max_shifts_per_system,
max_atoms_per_system,
),
)
else:
(
neighbor_matrix1,
neighbor_matrix2,
neighbor_matrix_shifts1,
neighbor_matrix_shifts2,
num_neighbors1,
num_neighbors2,
) = _jax_fill_pbc_prewrapped(
positions,
cell,
float(cutoff1 * cutoff1),
float(cutoff2 * cutoff2),
batch_ptr_i32,
shift_range_per_dimension,
num_shifts_per_system,
neighbor_matrix1,
neighbor_matrix2,
neighbor_matrix_shifts1,
neighbor_matrix_shifts2,
num_neighbors1,
num_neighbors2,
half_fill,
launch_dims=(
num_systems,
max_shifts_per_system,
max_atoms_per_system,
),
)
if return_neighbor_list:
if pbc is not None:
neighbor_list1, neighbor_ptr1, neighbor_list_shifts1 = (
get_neighbor_list_from_neighbor_matrix(
neighbor_matrix1,
num_neighbors=num_neighbors1,
neighbor_shift_matrix=neighbor_matrix_shifts1,
fill_value=fill_value,
)
)
neighbor_list2, neighbor_ptr2, neighbor_list_shifts2 = (
get_neighbor_list_from_neighbor_matrix(
neighbor_matrix2,
num_neighbors=num_neighbors2,
neighbor_shift_matrix=neighbor_matrix_shifts2,
fill_value=fill_value,
)
)
return (
neighbor_list1,
neighbor_ptr1,
neighbor_list_shifts1,
neighbor_list2,
neighbor_ptr2,
neighbor_list_shifts2,
)
else:
neighbor_list1, neighbor_ptr1 = get_neighbor_list_from_neighbor_matrix(
neighbor_matrix1,
num_neighbors=num_neighbors1,
fill_value=fill_value,
)
neighbor_list2, neighbor_ptr2 = get_neighbor_list_from_neighbor_matrix(
neighbor_matrix2,
num_neighbors=num_neighbors2,
fill_value=fill_value,
)
return neighbor_list1, neighbor_ptr1, neighbor_list2, neighbor_ptr2
else:
if pbc is not None:
return (
neighbor_matrix1,
num_neighbors1,
neighbor_matrix_shifts1,
neighbor_matrix2,
num_neighbors2,
neighbor_matrix_shifts2,
)
else:
return neighbor_matrix1, num_neighbors1, neighbor_matrix2, num_neighbors2
