Coverage for pySDC/implementations/controller_classes/controller

1import itertools

2import copy as cp

3import numpy as np

4import dill

6from pySDC.core.controller import Controller

7from pySDC.core import step as stepclass

8from pySDC.core.errors import ControllerError, CommunicationError

9from pySDC.implementations.convergence_controller_classes.basic_restarting import BasicRestarting

12class controller_nonMPI(Controller):

13 """

15 PFASST controller, running serialized version of PFASST in blocks (MG-style)

17 """

19 def __init__(self, num_procs, controller_params, description):

20 """

21 Initialization routine for PFASST controller

23 Args:

24 num_procs: number of parallel time steps (still serial, though), can be 1

25 controller_params: parameter set for the controller and the steps

26 description: all the parameters to set up the rest (levels, problems, transfer, ...)

27 """

29 if 'predict' in controller_params:

30 raise ControllerError('predict flag is ignored, use predict_type instead')

32 # call parent's initialization routine

33 super().__init__(controller_params, description, useMPI=False)

35 self.MS = [stepclass.Step(description)]

37 # try to initialize via dill.copy (much faster for many time-steps)

38 try:

39 for _ in range(num_procs - 1):

40 self.MS.append(dill.copy(self.MS[0]))

41 # if this fails (e.g. due to un-picklable data in the steps), initialize separately

42 except (dill.PicklingError, TypeError, ValueError) as error:

43 self.logger.warning(f'Need to initialize steps separately due to pickling error: {error}')

44 for _ in range(num_procs - 1):

45 self.MS.append(stepclass.Step(description))

47 self.base_convergence_controllers += [BasicRestarting.get_implementation(useMPI=False)]

48 for convergence_controller in self.base_convergence_controllers:

49 self.add_convergence_controller(convergence_controller, description)

51 if self.params.dump_setup:

52 self.dump_setup(step=self.MS[0], controller_params=controller_params, description=description)

54 if num_procs > 1 and len(self.MS[0].levels) > 1:

55 for S in self.MS:

56 for L in S.levels:

57 if not L.sweep.coll.right_is_node:

58 raise ControllerError("For PFASST to work, we assume uend^k = u_M^k")

60 if all(len(S.levels) == len(self.MS[0].levels) for S in self.MS):

61 self.nlevels = len(self.MS[0].levels)

62 else:

63 raise ControllerError('all steps need to have the same number of levels')

65 if self.nlevels == 0:

66 raise ControllerError('need at least one level')

68 self.nsweeps = []

69 for nl in range(self.nlevels):

70 if all(S.levels[nl].params.nsweeps == self.MS[0].levels[nl].params.nsweeps for S in self.MS):

71 self.nsweeps.append(self.MS[0].levels[nl].params.nsweeps)

73 if self.nlevels > 1 and self.nsweeps[-1] > 1:

74 raise ControllerError('this controller cannot do multiple sweeps on coarsest level')

76 if self.nlevels == 1 and self.params.predict_type is not None:

77 self.logger.warning(

78 'you have specified a predictor type but only a single level.. predictor will be ignored'

79 )

81 for C in [self.convergence_controllers[i] for i in self.convergence_controller_order]:

82 C.reset_buffers_nonMPI(self)

83 C.setup_status_variables(self, MS=self.MS)

85 def run(self, u0, t0, Tend):

86 """

87 Main driver for running the serial version of SDC, MSSDC, MLSDC and PFASST (virtual parallelism)

89 Args:

90 u0: initial values

91 t0: starting time

92 Tend: ending time

94 Returns:

95 end values on the finest level

96 stats object containing statistics for each step, each level and each iteration

97 """

99 # some initializations and reset of statistics

100 uend = None

101 num_procs = len(self.MS)

102 for hook in self.hooks:

103 hook.reset_stats()

104

105 # initial ordering of the steps: 0,1,...,Np-1

106 slots = list(range(num_procs))

107

108 # initialize time variables of each step

109 time = [t0 + sum(self.MS[j].dt for j in range(p)) for p in slots]

110

111 # determine which steps are still active (time < Tend)

112 active = [time[p] < Tend - 10 * np.finfo(float).eps for p in slots]

113

114 if not any(active):

115 raise ControllerError('Nothing to do, check t0, dt and Tend.')

116

117 # compress slots according to active steps, i.e. remove all steps which have times above Tend

118 active_slots = list(itertools.compress(slots, active))

119

120 # initialize block of steps with u0

121 self.restart_block(active_slots, time, u0)

122

123 for hook in self.hooks:

124 hook.post_setup(step=None, level_number=None)

125

126 # call pre-run hook

127 for S in self.MS:

128 for hook in self.hooks:

129 hook.pre_run(step=S, level_number=0)

130

131 # main loop: as long as at least one step is still active (time < Tend), do something

132 while any(active):

133 MS_active = [self.MS[p] for p in active_slots]

134 done = False

135 while not done:

136 done = self.pfasst(MS_active)

137

138 restarts = [S.status.restart for S in MS_active]

139 restart_at = np.where(restarts)[0][0] if True in restarts else len(MS_active)

140 if True in restarts: # restart part of the block

141 # initial condition to next block is initial condition of step that needs restarting

142 uend = self.MS[restart_at].levels[0].u[0]

143 time[active_slots[0]] = time[restart_at]

144 self.logger.info(f'Starting next block with initial conditions from step {restart_at}')

145

146 else: # move on to next block

147 # initial condition for next block is last solution of current block

148 uend = self.MS[active_slots[-1]].levels[0].uend

149 time[active_slots[0]] = time[active_slots[-1]] + self.MS[active_slots[-1]].dt

150

151 for S in MS_active[:restart_at]:

152 for C in [self.convergence_controllers[i] for i in self.convergence_controller_order]:

153 C.post_step_processing(self, S, MS=MS_active)

154

155 for C in [self.convergence_controllers[i] for i in self.convergence_controller_order]:

156 [C.prepare_next_block(self, S, len(active_slots), time, Tend, MS=MS_active) for S in self.MS]

157

158 # setup the times of the steps for the next block

159 for i in range(1, len(active_slots)):

160 time[active_slots[i]] = time[active_slots[i] - 1] + self.MS[active_slots[i] - 1].dt

161

162 # determine new set of active steps and compress slots accordingly

163 active = [time[p] < Tend - 10 * np.finfo(float).eps for p in slots]

164 active_slots = list(itertools.compress(slots, active))

165

166 # restart active steps (reset all values and pass uend to u0)

167 self.restart_block(active_slots, time, uend)

168

169 # call post-run hook

170 for S in self.MS:

171 for hook in self.hooks:

172 hook.post_run(step=S, level_number=0)

173

174 return uend, self.return_stats()

175

176 def restart_block(self, active_slots, time, u0):

177 """

178 Helper routine to reset/restart block of (active) steps

179

180 Args:

181 active_slots: list of active steps

182 time: list of new times

183 u0: initial value to distribute across the steps

184

185 """

186

187 # loop over active slots (not directly, since we need the previous entry as well)

188 for j in range(len(active_slots)):

189 # get slot number

190 p = active_slots[j]

191

192 # store current slot number for diagnostics

193 self.MS[p].status.slot = p

194 # store link to previous step

195 self.MS[p].prev = self.MS[active_slots[j - 1]]

196 # resets step

197 self.MS[p].reset_step()

198 # determine whether I am the first and/or last in line

199 self.MS[p].status.first = active_slots.index(p) == 0

200 self.MS[p].status.last = active_slots.index(p) == len(active_slots) - 1

201 # initialize step with u0

202 self.MS[p].init_step(u0)

203 # reset some values

204 self.MS[p].status.done = False

205 self.MS[p].status.prev_done = False

206 self.MS[p].status.iter = 0

207 self.MS[p].status.stage = 'SPREAD'

208 self.MS[p].status.force_done = False

209 self.MS[p].status.time_size = len(active_slots)

210

211 for l in self.MS[p].levels:

212 l.tag = None

213 l.status.sweep = 1

214

215 for p in active_slots:

216 for lvl in self.MS[p].levels:

217 lvl.status.time = time[p]

218

219 for C in [self.convergence_controllers[i] for i in self.convergence_controller_order]:

220 C.reset_status_variables(self, active_slots=active_slots)

221

222 def send_full(self, S, level=None, add_to_stats=False):

223 """

224 Function to perform the send, including bookkeeping and logging

225

226 Args:

227 S: the current step

228 level: the level number

229 add_to_stats: a flag to end recording data in the hooks (defaults to False)

230 """

231

232 def send(source, tag):

233 """

234 Send function

235

236 Args:

237 source: level which has the new values

238 tag: identifier for this message

239 """

240 # sending here means computing uend ("one-sided communication")

241 source.sweep.compute_end_point()

242 source.tag = cp.deepcopy(tag)

243

244 for hook in self.hooks:

245 hook.pre_comm(step=S, level_number=level)

246 if not S.status.last:

247 self.logger.debug(

248 'Process %2i provides data on level %2i with tag %s' % (S.status.slot, level, S.status.iter)

249 )

250 send(S.levels[level], tag=(level, S.status.iter, S.status.slot))

251

252 for hook in self.hooks:

253 hook.post_comm(step=S, level_number=level, add_to_stats=add_to_stats)

254

255 def recv_full(self, S, level=None, add_to_stats=False):

256 """

257 Function to perform the recv, including bookkeeping and logging

258

259 Args:

260 S: the current step

261 level: the level number

262 add_to_stats: a flag to end recording data in the hooks (defaults to False)

263 """

264

265 def recv(target, source, tag=None):

266 """

267 Receive function

268

269 Args:

270 target: level which will receive the values

271 source: level which initiated the send

272 tag: identifier to check if this message is really for me

273 """

274

275 if tag is not None and source.tag != tag:

276 raise CommunicationError('source and target tag are not the same, got %s and %s' % (source.tag, tag))

277 # simply do a deepcopy of the values uend to become the new u0 at the target

278 target.u[0] = target.prob.dtype_u(source.uend)

279 # re-evaluate f on left interval boundary

280 target.f[0] = target.prob.eval_f(target.u[0], target.time)

281

282 for hook in self.hooks:

283 hook.pre_comm(step=S, level_number=level)

284 if not S.status.prev_done and not S.status.first:

285 self.logger.debug(

286 'Process %2i receives from %2i on level %2i with tag %s'

287 % (S.status.slot, S.prev.status.slot, level, S.status.iter)

288 )

289 recv(S.levels[level], S.prev.levels[level], tag=(level, S.status.iter, S.prev.status.slot))

290 for hook in self.hooks:

291 hook.post_comm(step=S, level_number=level, add_to_stats=add_to_stats)

292

293 def pfasst(self, local_MS_active):

294 """

295 Main function including the stages of SDC, MLSDC and PFASST (the "controller")

296

297 For the workflow of this controller, check out one of our PFASST talks or the pySDC paper

298

299 This method changes self.MS directly by accessing active steps through local_MS_active. Nothing is returned.

300

301 Args:

302 local_MS_active (list): all active steps

303 """

304

305 # if all stages are the same (or DONE), continue, otherwise abort

306 stages = [S.status.stage for S in local_MS_active if S.status.stage != 'DONE']

307 if stages[1:] == stages[:-1]:

308 stage = stages[0]

309 else:

310 raise ControllerError('not all stages are equal')

311

312 self.logger.debug(stage)

313

314 MS_running = [S for S in local_MS_active if S.status.stage != 'DONE']

315

316 switcher = {

317 'SPREAD': self.spread,

318 'PREDICT': self.predict,

319 'IT_CHECK': self.it_check,

320 'IT_FINE': self.it_fine,

321 'IT_DOWN': self.it_down,

322 'IT_COARSE': self.it_coarse,

323 'IT_UP': self.it_up,

324 }

325

326 switcher.get(stage, self.default)(MS_running)

327

328 return all(S.status.done for S in local_MS_active)

329

330 def spread(self, local_MS_running):

331 """

332 Spreading phase

333

334 Args:

335 local_MS_running (list): list of currently running steps

336 """

337

338 for S in local_MS_running:

339 # first stage: spread values

340 for hook in self.hooks:

341 hook.pre_step(step=S, level_number=0)

342

343 # call predictor from sweeper

344 S.levels[0].sweep.predict()

345

346 # update stage

347 if len(S.levels) > 1: # MLSDC or PFASST with predict

348 S.status.stage = 'PREDICT'

349 else:

350 S.status.stage = 'IT_CHECK'

351

352 for C in [self.convergence_controllers[i] for i in self.convergence_controller_order]:

353 C.post_spread_processing(self, S, MS=local_MS_running)

354

355 def predict(self, local_MS_running):

356 """

357 Predictor phase

358

359 Args:

360 local_MS_running (list): list of currently running steps

361 """

362

363 for S in local_MS_running:

364 for hook in self.hooks:

365 hook.pre_predict(step=S, level_number=0)

366

367 if self.params.predict_type is None:

368 pass

369

370 elif self.params.predict_type == 'fine_only':

371 # do a fine sweep only

372 for S in local_MS_running:

373 S.levels[0].sweep.update_nodes()

374

375 # elif self.params.predict_type == 'libpfasst_style':

376 #

377 # # loop over all steps

378 # for S in local_MS_running:

379 #

380 # # restrict to coarsest level

381 # for l in range(1, len(S.levels)):

382 # S.transfer(source=S.levels[l - 1], target=S.levels[l])

383 #

384 # # run in serial on coarse level

385 # for S in local_MS_running:

386 #

387 # self.hooks.pre_comm(step=S, level_number=len(S.levels) - 1)

388 # # receive from previous step (if not first)

389 # if not S.status.first:

390 # self.logger.debug('Process %2i receives from %2i on level %2i with tag %s -- PREDICT' %

391 # (S.status.slot, S.prev.status.slot, len(S.levels) - 1, 0))

392 # self.recv(S.levels[-1], S.prev.levels[-1], tag=(len(S.levels), 0, S.prev.status.slot))

393 # self.hooks.post_comm(step=S, level_number=len(S.levels) - 1)

394 #

395 # # do the coarse sweep

396 # S.levels[-1].sweep.update_nodes()

397 #

398 # self.hooks.pre_comm(step=S, level_number=len(S.levels) - 1)

399 # # send to succ step

400 # if not S.status.last:

401 # self.logger.debug('Process %2i provides data on level %2i with tag %s -- PREDICT'

402 # % (S.status.slot, len(S.levels) - 1, 0))

403 # self.send(S.levels[-1], tag=(len(S.levels), 0, S.status.slot))

404 # self.hooks.post_comm(step=S, level_number=len(S.levels) - 1, add_to_stats=True)

405 #

406 # # go back to fine level, sweeping

407 # for l in range(self.nlevels - 1, 0, -1):

408 #

409 # for S in local_MS_running:

410 # # prolong values

411 # S.transfer(source=S.levels[l], target=S.levels[l - 1])

412 #

413 # if l - 1 > 0:

414 # S.levels[l - 1].sweep.update_nodes()

415 #

416 # # end with a fine sweep

417 # for S in local_MS_running:

418 # S.levels[0].sweep.update_nodes()

419

420 elif self.params.predict_type == 'pfasst_burnin':

421 # loop over all steps

422 for S in local_MS_running:

423 # restrict to coarsest level

424 for l in range(1, len(S.levels)):

425 S.transfer(source=S.levels[l - 1], target=S.levels[l])

426

427 # loop over all steps

428 for q in range(len(local_MS_running)):

429 # loop over last steps: [1,2,3,4], [2,3,4], [3,4], [4]

430 for p in range(q, len(local_MS_running)):

431 S = local_MS_running[p]

432

433 # do the sweep with new values

434 S.levels[-1].sweep.update_nodes()

435

436 # send updated values on coarsest level

437 self.send_full(S, level=len(S.levels) - 1)

438

439 # loop over last steps: [2,3,4], [3,4], [4]

440 for p in range(q + 1, len(local_MS_running)):

441 S = local_MS_running[p]

442 # receive values sent during previous sweep

443 self.recv_full(S, level=len(S.levels) - 1, add_to_stats=(p == len(local_MS_running) - 1))

444

445 # loop over all steps

446 for S in local_MS_running:

447 # interpolate back to finest level

448 for l in range(len(S.levels) - 1, 0, -1):

449 S.transfer(source=S.levels[l], target=S.levels[l - 1])

450

451 # send updated values forward

452 self.send_full(S, level=0)

453 # receive values

454 self.recv_full(S, level=0)

455

456 # end this with a fine sweep

457 for S in local_MS_running:

458 S.levels[0].sweep.update_nodes()

459

460 elif self.params.predict_type == 'fmg':

461 # TODO: implement FMG predictor

462 raise NotImplementedError('FMG predictor is not yet implemented')

463

464 else:

465 raise ControllerError('Wrong predictor type, got %s' % self.params.predict_type)

466

467 for S in local_MS_running:

468 for hook in self.hooks:

469 hook.post_predict(step=S, level_number=0)

470

471 for S in local_MS_running:

472 # update stage

473 S.status.stage = 'IT_CHECK'

474

475 def it_check(self, local_MS_running):

476 """

477 Key routine to check for convergence/termination

478

479 Args:

480 local_MS_running (list): list of currently running steps

481 """

482

483 for S in local_MS_running:

484 # send updated values forward

485 self.send_full(S, level=0)

486 # receive values

487 self.recv_full(S, level=0)

488 # compute current residual

489 S.levels[0].sweep.compute_residual(stage='IT_CHECK')

490

491 for S in local_MS_running:

492 if S.status.iter > 0:

493 for hook in self.hooks:

494 hook.post_iteration(step=S, level_number=0)

495

496 # decide if the step is done, needs to be restarted and other things convergence related

497 for C in [self.convergence_controllers[i] for i in self.convergence_controller_order]:

498 C.post_iteration_processing(self, S, MS=local_MS_running)

499 C.convergence_control(self, S, MS=local_MS_running)

500

501 for S in local_MS_running:

502 if not S.status.first:

503 for hook in self.hooks:

504 hook.pre_comm(step=S, level_number=0)

505 S.status.prev_done = S.prev.status.done # "communicate"

506 for hook in self.hooks:

507 hook.post_comm(step=S, level_number=0, add_to_stats=True)

508 S.status.done = S.status.done and S.status.prev_done

509

510 if self.params.all_to_done:

511 for hook in self.hooks:

512 hook.pre_comm(step=S, level_number=0)

513 S.status.done = all(T.status.done for T in local_MS_running)

514 for hook in self.hooks:

515 hook.post_comm(step=S, level_number=0, add_to_stats=True)

516

517 if not S.status.done:

518 # increment iteration count here (and only here)

519 S.status.iter += 1

520 for hook in self.hooks:

521 hook.pre_iteration(step=S, level_number=0)

522 for C in [self.convergence_controllers[i] for i in self.convergence_controller_order]:

523 C.pre_iteration_processing(self, S, MS=local_MS_running)

524

525 if len(S.levels) > 1: # MLSDC or PFASST

526 S.status.stage = 'IT_DOWN'

527 else: # SDC or MSSDC

528 if len(local_MS_running) == 1 or self.params.mssdc_jac: # SDC or parallel MSSDC (Jacobi-like)

529 S.status.stage = 'IT_FINE'

530 else:

531 S.status.stage = 'IT_COARSE' # serial MSSDC (Gauss-like)

532 else:

533 S.levels[0].sweep.compute_end_point()

534 for hook in self.hooks:

535 hook.post_step(step=S, level_number=0)

536 S.status.stage = 'DONE'

537

538 for C in [self.convergence_controllers[i] for i in self.convergence_controller_order]:

539 C.reset_buffers_nonMPI(self)

540

541 def it_fine(self, local_MS_running):

542 """

543 Fine sweeps

544

545 Args:

546 local_MS_running (list): list of currently running steps

547 """

548

549 for S in local_MS_running:

550 S.levels[0].status.sweep = 0

551

552 for k in range(self.nsweeps[0]):

553 for S in local_MS_running:

554 S.levels[0].status.sweep += 1

555

556 for S in local_MS_running:

557 # send updated values forward

558 self.send_full(S, level=0)

559 # receive values

560 self.recv_full(S, level=0, add_to_stats=(k == self.nsweeps[0] - 1))

561

562 for S in local_MS_running:

563 # standard sweep workflow: update nodes, compute residual, log progress

564 for hook in self.hooks:

565 hook.pre_sweep(step=S, level_number=0)

566 S.levels[0].sweep.update_nodes()

567 S.levels[0].sweep.compute_residual(stage='IT_FINE')

568 for hook in self.hooks:

569 hook.post_sweep(step=S, level_number=0)

570

571 for S in local_MS_running:

572 # update stage

573 S.status.stage = 'IT_CHECK'

574

575 def it_down(self, local_MS_running):

576 """

577 Go down the hierarchy from finest to coarsest level

578

579 Args:

580 local_MS_running (list): list of currently running steps

581 """

582

583 for S in local_MS_running:

584 S.transfer(source=S.levels[0], target=S.levels[1])

585

586 for l in range(1, self.nlevels - 1):

587 # sweep on middle levels (not on finest, not on coarsest, though)

588

589 for _ in range(self.nsweeps[l]):

590 for S in local_MS_running:

591 # send updated values forward

592 self.send_full(S, level=l)

593 # receive values

594 self.recv_full(S, level=l)

595

596 for S in local_MS_running:

597 for hook in self.hooks:

598 hook.pre_sweep(step=S, level_number=l)

599 S.levels[l].sweep.update_nodes()

600 S.levels[l].sweep.compute_residual(stage='IT_DOWN')

601 for hook in self.hooks:

602 hook.post_sweep(step=S, level_number=l)

603

604 for S in local_MS_running:

605 # transfer further down the hierarchy

606 S.transfer(source=S.levels[l], target=S.levels[l + 1])

607

608 for S in local_MS_running:

609 # update stage

610 S.status.stage = 'IT_COARSE'

611

612 def it_coarse(self, local_MS_running):

613 """

614 Coarse sweep

615

616 Args:

617 local_MS_running (list): list of currently running steps

618 """

619

620 for S in local_MS_running:

621 # receive from previous step (if not first)

622 self.recv_full(S, level=len(S.levels) - 1)

623

624 # do the sweep

625 for hook in self.hooks:

626 hook.pre_sweep(step=S, level_number=len(S.levels) - 1)

627 S.levels[-1].sweep.update_nodes()

628 S.levels[-1].sweep.compute_residual(stage='IT_COARSE')

629 for hook in self.hooks:

630 hook.post_sweep(step=S, level_number=len(S.levels) - 1)

631

632 # send to succ step

633 self.send_full(S, level=len(S.levels) - 1, add_to_stats=True)

634

635 # update stage

636 if len(S.levels) > 1: # MLSDC or PFASST

637 S.status.stage = 'IT_UP'

638 else: # MSSDC

639 S.status.stage = 'IT_CHECK'

640

641 def it_up(self, local_MS_running):

642 """

643 Prolong corrections up to finest level (parallel)

644

645 Args:

646 local_MS_running (list): list of currently running steps

647 """

648

649 for l in range(self.nlevels - 1, 0, -1):

650 for S in local_MS_running:

651 # prolong values

652 S.transfer(source=S.levels[l], target=S.levels[l - 1])

653

654 # on middle levels: do communication and sweep as usual

655 if l - 1 > 0:

656 for k in range(self.nsweeps[l - 1]):

657 for S in local_MS_running:

658 # send updated values forward

659 self.send_full(S, level=l - 1)

660 # receive values

661 self.recv_full(S, level=l - 1, add_to_stats=(k == self.nsweeps[l - 1] - 1))

662

663 for S in local_MS_running:

664 for hook in self.hooks:

665 hook.pre_sweep(step=S, level_number=l - 1)

666 S.levels[l - 1].sweep.update_nodes()

667 S.levels[l - 1].sweep.compute_residual(stage='IT_UP')

668 for hook in self.hooks:

669 hook.post_sweep(step=S, level_number=l - 1)

670

671 for S in local_MS_running:

672 # update stage

673 S.status.stage = 'IT_FINE'

674

675 def default(self, local_MS_running):

676 """

677 Default routine to catch wrong status

678

679 Args:

680 local_MS_running (list): list of currently running steps

681 """

682 raise ControllerError('Unknown stage, got %s' % local_MS_running[0].status.stage) # TODO

Coverage for pySDC/implementations/controller_classes/controller_nonMPI.py: 99%

295 statements