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Copyright (c) Facebook, Inc. and its affiliates.
Copyright (c) Microsoft Corporation.
Licensed under the MIT License.
Adapted from https://github.com/FAIR-Chem/fairchem/blob/main/src/fairchem/core/models/gemnet/utils.py.
"""
import json
from typing import Any, Dict, Optional, Tuple
import torch
from torch_scatter import segment_csr
def read_json(path: str) -> Dict:
""""""
if not path.endswith(".json"):
raise UserWarning(f"Path {path} is not a json-path.")
with open(path, "r") as f:
content = json.load(f)
return content
def update_json(path: str, data: Dict):
""""""
if not path.endswith(".json"):
raise UserWarning(f"Path {path} is not a json-path.")
content = read_json(path)
content.update(data)
write_json(path, content)
def write_json(path: str, data: Dict):
""""""
if not path.endswith(".json"):
raise UserWarning(f"Path {path} is not a json-path.")
with open(path, "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False, indent=4)
def read_value_json(path: str, key: str) -> Optional[Any]:
""""""
content = read_json(path)
if key in content.keys():
return content[key]
else:
return None
def ragged_range(sizes: torch.Tensor) -> torch.Tensor:
"""Multiple concatenated ranges.
Examples
--------
sizes = [1 4 2 3]
Return: [0 0 1 2 3 0 1 0 1 2]
"""
assert sizes.dim() == 1
if sizes.sum() == 0:
return sizes.new_empty(0)
# Remove 0 sizes
sizes_nonzero = sizes > 0
if not torch.all(sizes_nonzero):
sizes = torch.masked_select(sizes, sizes_nonzero)
# Initialize indexing array with ones as we need to setup incremental indexing
# within each group when cumulatively summed at the final stage.
id_steps = torch.ones(sizes.sum(), dtype=torch.long, device=sizes.device)
id_steps[0] = 0
insert_index = sizes[:-1].cumsum(0)
insert_val = (1 - sizes)[:-1]
# Assign index-offsetting values
id_steps[insert_index] = insert_val
# Finally index into input array for the group repeated o/p
res = id_steps.cumsum(0)
return res
def repeat_blocks(
sizes: torch.Tensor,
repeats: torch.Tensor,
continuous_indexing: bool = True,
start_idx: int = 0,
block_inc: int = 0,
repeat_inc: int = 0,
) -> torch.Tensor:
"""Repeat blocks of indices.
Adapted from https://stackoverflow.com/questions/51154989/numpy-vectorized-function-to-repeat-blocks-of-consecutive-elements
continuous_indexing: Whether to keep increasing the index after each block
start_idx: Starting index
block_inc: Number to increment by after each block,
either global or per block. Shape: len(sizes) - 1
repeat_inc: Number to increment by after each repetition,
either global or per block
Examples
--------
sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = False
Return: [0 0 0 0 1 2 0 1 2 0 1 0 1 0 1]
sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True
Return: [0 0 0 1 2 3 1 2 3 4 5 4 5 4 5]
sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ;
repeat_inc = 4
Return: [0 4 8 1 2 3 5 6 7 4 5 8 9 12 13]
sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ;
start_idx = 5
Return: [5 5 5 6 7 8 6 7 8 9 10 9 10 9 10]
sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ;
block_inc = 1
Return: [0 0 0 2 3 4 2 3 4 6 7 6 7 6 7]
sizes = [0,3,2] ; repeats = [3,2,3] ; continuous_indexing = True
Return: [0 1 2 0 1 2 3 4 3 4 3 4]
sizes = [2,3,2] ; repeats = [2,0,2] ; continuous_indexing = True
Return: [0 1 0 1 5 6 5 6]
"""
assert sizes.dim() == 1
assert all(sizes >= 0)
# Remove 0 sizes
sizes_nonzero = sizes > 0
if not torch.all(sizes_nonzero):
assert block_inc == 0 # Implementing this is not worth the effort
sizes = torch.masked_select(sizes, sizes_nonzero)
if isinstance(repeats, torch.Tensor):
repeats = torch.masked_select(repeats, sizes_nonzero)
if isinstance(repeat_inc, torch.Tensor):
repeat_inc = torch.masked_select(repeat_inc, sizes_nonzero)
if isinstance(repeats, torch.Tensor):
assert all(repeats >= 0)
insert_dummy = repeats[0] == 0
if insert_dummy:
one = sizes.new_ones(1)
zero = sizes.new_zeros(1)
sizes = torch.cat((one, sizes))
repeats = torch.cat((one, repeats))
if isinstance(block_inc, torch.Tensor):
block_inc = torch.cat((zero, block_inc))
if isinstance(repeat_inc, torch.Tensor):
repeat_inc = torch.cat((zero, repeat_inc))
else:
assert repeats >= 0
insert_dummy = False
# Get repeats for each group using group lengths/sizes
r1 = torch.repeat_interleave(torch.arange(len(sizes), device=sizes.device), repeats)
# Get total size of output array, as needed to initialize output indexing array
N = (sizes * repeats).sum()
# Initialize indexing array with ones as we need to setup incremental indexing
# within each group when cumulatively summed at the final stage.
# Two steps here:
# 1. Within each group, we have multiple sequences, so setup the offsetting
# at each sequence lengths by the seq. lengths preceding those.
id_ar = torch.ones(N, dtype=torch.long, device=sizes.device)
id_ar[0] = 0
insert_index = sizes[r1[:-1]].cumsum(0)
insert_val = (1 - sizes)[r1[:-1]]
if isinstance(repeats, torch.Tensor) and torch.any(repeats == 0):
diffs = r1[1:] - r1[:-1]
indptr = torch.cat((sizes.new_zeros(1), diffs.cumsum(0)))
if continuous_indexing:
# If a group was skipped (repeats=0) we need to add its size
insert_val += segment_csr(sizes[: r1[-1]], indptr, reduce="sum")
# Add block increments
if isinstance(block_inc, torch.Tensor):
insert_val += segment_csr(block_inc[: r1[-1]], indptr, reduce="sum")
else:
insert_val += block_inc * (indptr[1:] - indptr[:-1])
if insert_dummy:
insert_val[0] -= block_inc
else:
idx = r1[1:] != r1[:-1]
if continuous_indexing:
# 2. For each group, make sure the indexing starts from the next group's
# first element. So, simply assign 1s there.
insert_val[idx] = 1
# Add block increments
insert_val[idx] += block_inc
# Add repeat_inc within each group
if isinstance(repeat_inc, torch.Tensor):
insert_val += repeat_inc[r1[:-1]]
if isinstance(repeats, torch.Tensor):
repeat_inc_inner = repeat_inc[repeats > 0][:-1]
else:
repeat_inc_inner = repeat_inc[:-1]
else:
insert_val += repeat_inc
repeat_inc_inner = repeat_inc
# Subtract the increments between groups
if isinstance(repeats, torch.Tensor):
repeats_inner = repeats[repeats > 0][:-1]
else:
repeats_inner = repeats
insert_val[r1[1:] != r1[:-1]] -= repeat_inc_inner * repeats_inner
# Assign index-offsetting values
id_ar[insert_index] = insert_val
if insert_dummy:
id_ar = id_ar[1:]
if continuous_indexing:
id_ar[0] -= 1
# Set start index now, in case of insertion due to leading repeats=0
id_ar[0] += start_idx
# Finally index into input array for the group repeated o/p
res = id_ar.cumsum(0)
return res
def calculate_interatomic_vectors(
R: torch.Tensor, id_s: torch.Tensor, id_t: torch.Tensor, offsets_st: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Calculate the vectors connecting the given atom pairs,
considering offsets from periodic boundary conditions (PBC).
Parameters
----------
R: Tensor, shape = (nAtoms, 3)
Atom positions.
id_s: Tensor, shape = (nEdges,)
Indices of the source atom of the edges.
id_t: Tensor, shape = (nEdges,)
Indices of the target atom of the edges.
offsets_st: Tensor, shape = (nEdges,)
PBC offsets of the edges.
Subtract this from the correct direction.
Returns
-------
(D_st, V_st): tuple
D_st: Tensor, shape = (nEdges,)
Distance from atom t to s.
V_st: Tensor, shape = (nEdges,)
Unit direction from atom t to s.
"""
Rs = R[id_s]
Rt = R[id_t]
# ReLU prevents negative numbers in sqrt
if offsets_st is None:
V_st = Rt - Rs # s -> t
else:
V_st = Rt - Rs + offsets_st # s -> t
D_st = torch.sqrt(torch.sum(V_st**2, dim=1))
V_st = V_st / D_st[..., None]
return D_st, V_st
def inner_product_normalized(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
"""
Calculate the inner product between the given normalized vectors,
giving a result between -1 and 1.
"""
return torch.sum(x * y, dim=-1).clamp(min=-1, max=1)
def mask_neighbors(neighbors: torch.Tensor, edge_mask: torch.Tensor) -> torch.Tensor:
neighbors_old_indptr = torch.cat([neighbors.new_zeros(1), neighbors])
neighbors_old_indptr = torch.cumsum(neighbors_old_indptr, dim=0)
neighbors = segment_csr(edge_mask.long(), neighbors_old_indptr)
return neighbors
def get_k_index_product_set(
num_k_x: torch.LongTensor, num_k_y: torch.LongTensor, num_k_z: torch.LongTensor
) -> tuple[torch.FloatTensor, int]:
# Get a box of k-lattice indices around (0,0,0)
k_index_sets = (
torch.arange(-num_k_x, num_k_x + 1, dtype=torch.float),
torch.arange(-num_k_y, num_k_y + 1, dtype=torch.float),
torch.arange(-num_k_z, num_k_z + 1, dtype=torch.float),
)
k_index_product_set = torch.cartesian_prod(*k_index_sets)
# Because our "signal" is real-valued, for the Fourier transform it holds that
# F(omega) = F*(-omega) (where F* is the complex conjugate of F). Thus, we only
# need to consider the positive half of the Fourier space.
k_index_product_set = k_index_product_set[k_index_product_set.shape[0] // 2 + 1 :]
# Amount of k-points
num_k_degrees_of_freedom = k_index_product_set.shape[0]
return k_index_product_set, num_k_degrees_of_freedom
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