1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284
|
# Copyright (C) 2021 Jørgen S. Dokken
#
# This file is part of DOLFINX_MPC
#
# SPDX-License-Identifier: MIT
from typing import Tuple
import cffi
import dolfinx.cpp as _cpp
import dolfinx.fem as _fem
import dolfinx.log as _log
import numpy
import numpy.typing as npt
from dolfinx.common import Timer
from dolfinx_mpc.multipointconstraint import MultiPointConstraint
from petsc4py import PETSc as _PETSc
import numba
from .helpers import _forms, extract_slave_cells, pack_slave_facet_info
from .numba_setup import initialize_petsc
ffi, _ = initialize_petsc()
def assemble_vector(form: _forms, constraint: MultiPointConstraint, b: _PETSc.Vec = None) -> _PETSc.Vec:
"""
Assemble a compiled DOLFINx form into vector b.
Parameters
----------
form
The complied linear form
constraint
The multi point constraint
b
PETSc vector to assemble into (optional)
"""
_log.log(_log.LogLevel.INFO, "Assemble MPC vector")
timer_vector = Timer("~MPC: Assemble vector (numba)")
# Unpack Function space data
V = form.function_spaces[0]
pos = V.mesh.geometry.dofmap.offsets
x_dofs = V.mesh.geometry.dofmap.array
x = V.mesh.geometry.x
dofs = V.dofmap.list().array
block_size = V.dofmap.index_map_bs
# Data from multipointconstraint
coefficients = constraint.coefficients()[0]
masters_adj = constraint.masters
c_to_s_adj = constraint.cell_to_slaves
cell_to_slave = c_to_s_adj.array
c_to_s_off = c_to_s_adj.offsets
is_slave = constraint.is_slave
mpc_data = (masters_adj.array, coefficients, masters_adj.offsets, cell_to_slave, c_to_s_off, is_slave)
slave_cells = extract_slave_cells(c_to_s_off)
# Get index map and ghost info
if b is None:
index_map = constraint.function_space.dofmap.index_map
vector = _cpp.la.petsc.create_vector(index_map, block_size)
else:
vector = b
# Pack constants and coefficients
form_coeffs = _cpp.fem.pack_coefficients(form)
form_consts = _cpp.fem.pack_constants(form)
tdim = V.mesh.topology.dim
num_dofs_per_element = V.dofmap.dof_layout.num_dofs
# Assemble vector with all entries
with vector.localForm() as b_local:
_cpp.fem.assemble_vector(b_local.array_w, form, form_consts, form_coeffs)
# Check if we need facet permutations
# FIXME: access apply_dof_transformations here
e0 = form.function_spaces[0].element
needs_transformation_data = e0.needs_dof_transformations or form.needs_facet_permutations
cell_perms = numpy.array([], dtype=numpy.uint32)
if needs_transformation_data:
V.mesh.topology.create_entity_permutations()
cell_perms = V.mesh.topology.get_cell_permutation_info()
if e0.needs_dof_transformations:
raise NotImplementedError("Dof transformations not implemented")
# Assemble over cells
subdomain_ids = form.integral_ids(_fem.IntegralType.cell)
num_cell_integrals = len(subdomain_ids)
is_complex = numpy.issubdtype(_PETSc.ScalarType, numpy.complexfloating)
nptype = "complex128" if is_complex else "float64"
ufcx_form = form.ufcx_form
if num_cell_integrals > 0:
V.mesh.topology.create_entity_permutations()
for i, id in enumerate(subdomain_ids):
cell_kernel = getattr(ufcx_form.integrals(_fem.IntegralType.cell)[i], f"tabulate_tensor_{nptype}")
active_cells = form.domains(_fem.IntegralType.cell, id)
coeffs_i = form_coeffs[(_fem.IntegralType.cell, id)]
with vector.localForm() as b:
assemble_cells(numpy.asarray(b), cell_kernel, active_cells[numpy.isin(active_cells, slave_cells)],
(pos, x_dofs, x), coeffs_i, form_consts,
cell_perms, dofs, block_size, num_dofs_per_element, mpc_data)
# Assemble exterior facet integrals
subdomain_ids = form.integral_ids(_fem.IntegralType.exterior_facet)
num_exterior_integrals = len(subdomain_ids)
if num_exterior_integrals > 0:
V.mesh.topology.create_entities(tdim - 1)
V.mesh.topology.create_connectivity(tdim - 1, tdim)
# Get facet permutations if required
facet_perms = numpy.array([], dtype=numpy.uint8)
if form.needs_facet_permutations:
facet_perms = V.mesh.topology.get_facet_permutations()
perm = (cell_perms, form.needs_facet_permutations, facet_perms)
for i, id in enumerate(subdomain_ids):
facet_kernel = getattr(ufcx_form.integrals(_fem.IntegralType.exterior_facet)[i],
f"tabulate_tensor_{nptype}")
coeffs_i = form_coeffs[(_fem.IntegralType.exterior_facet, id)]
facets = form.domains(_fem.IntegralType.exterior_facet, id)
facet_info = pack_slave_facet_info(facets, slave_cells)
num_facets_per_cell = len(V.mesh.topology.connectivity(tdim, tdim - 1).links(0))
with vector.localForm() as b:
assemble_exterior_slave_facets(numpy.asarray(b), facet_kernel, facet_info, (pos, x_dofs, x),
coeffs_i, form_consts, perm,
dofs, block_size, num_dofs_per_element, mpc_data, num_facets_per_cell)
timer_vector.stop()
return vector
@numba.njit
def assemble_cells(b: npt.NDArray[_PETSc.ScalarType],
kernel: cffi.FFI, active_cells: npt.NDArray[numpy.int32],
mesh: Tuple[npt.NDArray[numpy.int32], npt.NDArray[numpy.int32],
npt.NDArray[numpy.float64]],
coeffs: npt.NDArray[_PETSc.ScalarType],
constants: npt.NDArray[_PETSc.ScalarType],
permutation_info: npt.NDArray[numpy.uint32],
dofmap: npt.NDArray[numpy.int32],
block_size: int,
num_dofs_per_element: int,
mpc: Tuple[npt.NDArray[numpy.int32], npt.NDArray[_PETSc.ScalarType],
npt.NDArray[numpy.int32], npt.NDArray[numpy.int32],
npt.NDArray[numpy.int32], npt.NDArray[numpy.int32]]):
"""Assemble additional MPC contributions for cell integrals"""
ffi_fb = ffi.from_buffer
# Empty arrays mimicking Nullpointers
facet_index = numpy.zeros(0, dtype=numpy.int32)
facet_perm = numpy.zeros(0, dtype=numpy.uint8)
# Unpack mesh data
pos, x_dofmap, x = mesh
# NOTE: All cells are assumed to be of the same type
geometry = numpy.zeros((pos[1] - pos[0], 3))
b_local = numpy.zeros(block_size * num_dofs_per_element, dtype=_PETSc.ScalarType)
for cell_index in active_cells:
num_vertices = pos[cell_index + 1] - pos[cell_index]
cell = pos[cell_index]
# Compute mesh geometry for cell
geometry[:, :] = x[x_dofmap[cell:cell + num_vertices]]
# Assemble local element vector
b_local.fill(0.0)
kernel(ffi_fb(b_local), ffi_fb(coeffs[cell_index, :]),
ffi_fb(constants), ffi_fb(geometry), ffi_fb(facet_index),
ffi_fb(facet_perm))
# NOTE: Here we need to add the apply_dof_transformation function
# Modify global vector and local cell contributions
b_local_copy = b_local.copy()
modify_mpc_contributions(b, cell_index, b_local, b_local_copy, mpc, dofmap,
block_size, num_dofs_per_element)
for j in range(num_dofs_per_element):
for k in range(block_size):
position = dofmap[num_dofs_per_element * cell_index + j] * block_size + k
b[position] += (b_local[j * block_size + k] - b_local_copy[j * block_size + k])
@numba.njit
def assemble_exterior_slave_facets(b: npt.NDArray[_PETSc.ScalarType],
kernel: cffi.FFI,
facet_info: npt.NDArray[numpy.int32],
mesh: Tuple[npt.NDArray[numpy.int32], npt.NDArray[numpy.int32],
npt.NDArray[numpy.float64]],
coeffs: npt.NDArray[_PETSc.ScalarType],
constants: npt.NDArray[_PETSc.ScalarType],
permutation_info: npt.NDArray[numpy.uint32],
dofmap: npt.NDArray[numpy.int32],
block_size: int,
num_dofs_per_element: int,
mpc: Tuple[npt.NDArray[numpy.int32], npt.NDArray[_PETSc.ScalarType],
npt.NDArray[numpy.int32], npt.NDArray[numpy.int32],
npt.NDArray[numpy.int32], npt.NDArray[numpy.int32]],
num_facets_per_cell: int):
"""Assemble additional MPC contributions for facets"""
ffi_fb = ffi.from_buffer
# Unpack facet permutation info
cell_perms, needs_facet_perm, facet_perms = permutation_info
facet_index = numpy.zeros(1, dtype=numpy.int32)
facet_perm = numpy.zeros(1, dtype=numpy.uint8)
# Unpack mesh data
pos, x_dofmap, x = mesh
geometry = numpy.zeros((pos[1] - pos[0], 3))
b_local = numpy.zeros(block_size * num_dofs_per_element, dtype=_PETSc.ScalarType)
for i in range(facet_info.shape[0]):
# Extract cell index (local to process) and facet index (local to cell) for kernel
cell_index, local_facet = facet_info[i]
facet_index[0] = local_facet
# Extract cell geometry
cell = pos[cell_index]
num_vertices = pos[cell_index + 1] - pos[cell_index]
geometry[:, :] = x[x_dofmap[cell:cell + num_vertices]]
# Compute local facet kernel
b_local.fill(0.0)
if needs_facet_perm:
facet_perm[0] = facet_perms[cell_index * num_facets_per_cell + local_facet]
kernel(ffi_fb(b_local), ffi_fb(coeffs[cell_index, :]), ffi_fb(constants), ffi_fb(geometry),
ffi_fb(facet_index), ffi_fb(facet_perm))
# NOTE: Here we need to add the apply_dof_transformation
# Modify local contributions and add global MPC contributions
b_local_copy = b_local.copy()
modify_mpc_contributions(b, cell_index, b_local, b_local_copy,
mpc, dofmap, block_size, num_dofs_per_element)
for j in range(num_dofs_per_element):
for k in range(block_size):
position = dofmap[num_dofs_per_element * cell_index + j] * block_size + k
b[position] += (b_local[j * block_size + k] - b_local_copy[j * block_size + k])
@numba.njit(cache=True)
def modify_mpc_contributions(b: npt.NDArray[_PETSc.ScalarType], cell_index: int,
b_local: npt.NDArray[_PETSc.ScalarType],
b_copy: npt.NDArray[_PETSc.ScalarType],
mpc: Tuple[npt.NDArray[numpy.int32], npt.NDArray[_PETSc.ScalarType],
npt.NDArray[numpy.int32], npt.NDArray[numpy.int32],
npt.NDArray[numpy.int32], npt.NDArray[numpy.int32]],
dofmap: npt.NDArray[numpy.int32],
block_size: int,
num_dofs_per_element: int):
"""
Modify local entries of b_local with MPC info and add modified
entries to global vector b.
"""
# Unwrap MPC data
masters, coefficients, offsets, cell_to_slave, cell_to_slave_offset, is_slave = mpc
# Determine which slaves are in this cell,
# and which global index they have in 1D arrays
cell_slaves = cell_to_slave[cell_to_slave_offset[cell_index]:
cell_to_slave_offset[cell_index + 1]]
# Get local index of slaves in cell
cell_blocks = dofmap[num_dofs_per_element * cell_index:
num_dofs_per_element * cell_index + num_dofs_per_element]
local_index = numpy.empty(len(cell_slaves), dtype=numpy.int32)
for i in range(num_dofs_per_element):
for j in range(block_size):
dof = cell_blocks[i] * block_size + j
if is_slave[dof]:
location = numpy.flatnonzero(cell_slaves == dof)[0]
local_index[location] = i * block_size + j
# Move contribution from each slave to the corresponding master dof
# and zero out local b
for local, slave in zip(local_index, cell_slaves):
cell_masters = masters[offsets[slave]: offsets[slave + 1]]
cell_coeffs = coefficients[offsets[slave]: offsets[slave + 1]]
for m0, c0 in zip(cell_masters, cell_coeffs):
b[m0] += c0 * b_copy[local]
b_local[local] = 0
|