# This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . # # Copyright(C) 2013-2021 Max-Planck-Society # Authors: Philipp Frank # # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik. import numpy as np from functools import reduce from .. import random from .. import utilities from ..domain_tuple import DomainTuple from ..linearization import Linearization from ..multi_field import MultiField from ..operators.inversion_enabler import InversionEnabler from ..operators.endomorphic_operator import EndomorphicOperator from ..operators.energy_operators import StandardHamiltonian, GaussianEnergy from ..operators.sandwich_operator import SandwichOperator from ..operators.sampling_enabler import SamplingDtypeSetter from ..operators.scaling_operator import ScalingOperator from .energy_adapter import EnergyAdapter from .quadratic_energy import QuadraticEnergy from ..probing import approximation2endo from ..sugar import makeOp from ..operators.adder import Adder from ..utilities import myassert from .energy import Energy from .descent_minimizers import DescentMinimizer, ConjugateGradient def _get_lo_hi(comm, n_samples): ntask, rank, _ = utilities.get_MPI_params_from_comm(comm) return utilities.shareRange(n_samples, ntask, rank) def _modify_sample_domain(sample, domain): """Takes only keys from sample which are also in domain and inserts zeros for keys which are not in sample.domain.""" from ..multi_domain import MultiDomain from ..field import Field from ..domain_tuple import DomainTuple from ..sugar import makeDomain domain = makeDomain(domain) if isinstance(domain, DomainTuple) and isinstance(sample, Field): if sample.domain is not domain: raise TypeError return sample elif isinstance(domain, MultiDomain) and isinstance(sample, MultiField): if sample.domain is domain: return sample out = {kk: vv for kk, vv in sample.items() if kk in domain.keys()} out = MultiField.from_dict(out, domain) return out raise TypeError def _reduce_by_keys(field, operator, keys): """Partially insert a field into an operator If the domain of the operator is an instance of `DomainTuple` Parameters ---------- field : Field or MultiField Potentially partially constant input field. operator : Operator Operator into which `field` is partially inserted. keys : list List of constant `MultiDomain` entries. Returns ------- list The variable part of the field and the contracted operator. """ from ..sugar import is_fieldlike, is_operator myassert(is_fieldlike(field)) myassert(is_operator(operator)) if isinstance(field, MultiField): cst_field = field.extract_by_keys(keys) var_field = field.extract_by_keys(set(field.keys()) - set(keys)) _, new_ham = operator.simplify_for_constant_input(cst_field) return var_field, new_ham myassert(len(keys) == 0) return field, operator class _SelfAdjointOperatorWrapper(EndomorphicOperator): def __init__(self, domain, func): from ..sugar import makeDomain self._func = func self._capability = self.TIMES | self.ADJOINT_TIMES self._domain = makeDomain(domain) def apply(self, x, mode): self._check_input(x, mode) return self._func(x) class _SampledKLEnergy(Energy): """Base class for Energies representing a sampled Kullback-Leibler divergence for the variational approximation of a distribution with another distribution. Supports the samples to be distributed across MPI tasks.""" def __init__(self, mean, hamiltonian, n_samples, mirror_samples, comm, local_samples, nanisinf): super(_SampledKLEnergy, self).__init__(mean) myassert(mean.domain is hamiltonian.domain) self._hamiltonian = hamiltonian self._n_samples = int(n_samples) self._mirror_samples = bool(mirror_samples) self._comm = comm self._local_samples = local_samples self._nanisinf = bool(nanisinf) lin = Linearization.make_var(mean) v, g = [], [] for s in self._local_samples: s = _modify_sample_domain(s, mean.domain) tmp = hamiltonian(lin+s) tv = tmp.val.val tg = tmp.gradient if mirror_samples: tmp = hamiltonian(lin-s) tv = tv + tmp.val.val tg = tg + tmp.gradient v.append(tv) g.append(tg) self._val = utilities.allreduce_sum(v, self._comm)[()]/self.n_eff_samples if np.isnan(self._val) and self._nanisinf: self._val = np.inf self._grad = utilities.allreduce_sum(g, self._comm)/self.n_eff_samples @property def value(self): return self._val @property def gradient(self): return self._grad def at(self, position): return _SampledKLEnergy( position, self._hamiltonian, self._n_samples, self._mirror_samples, self._comm, self._local_samples, self._nanisinf) def apply_metric(self, x): lin = Linearization.make_var(self.position, want_metric=True) res = [] for s in self._local_samples: s = _modify_sample_domain(s, self._hamiltonian.domain) tmp = self._hamiltonian(lin+s).metric(x) if self._mirror_samples: tmp = tmp + self._hamiltonian(lin-s).metric(x) res.append(tmp) return utilities.allreduce_sum(res, self._comm)/self.n_eff_samples @property def n_eff_samples(self): if self._mirror_samples: return 2*self._n_samples return self._n_samples @property def metric(self): return _SelfAdjointOperatorWrapper(self.position.domain, self.apply_metric) @property def samples(self): ntask, rank, _ = utilities.get_MPI_params_from_comm(self._comm) if ntask == 1: for s in self._local_samples: yield s if self._mirror_samples: yield -s else: rank_lo_hi = [utilities.shareRange(self._n_samples, ntask, i) for i in range(ntask)] lo, _ = _get_lo_hi(self._comm, self._n_samples) for itask, (l, h) in enumerate(rank_lo_hi): for i in range(l, h): data = self._local_samples[i-lo] if rank == itask else None s = self._comm.bcast(data, root=itask) yield s if self._mirror_samples: yield -s class _GeoMetricSampler: def __init__(self, position, H, minimizer, start_from_lin, n_samples, mirror_samples, napprox=0, want_error=False): if not isinstance(H, StandardHamiltonian): raise NotImplementedError # Check domain dtype dts = H._prior._met._dtype if isinstance(H.domain, DomainTuple): real = np.issubdtype(dts, np.float) else: real = all([np.issubdtype(dts[kk], np.float) for kk in dts.keys()]) if not real: raise ValueError("_GeoMetricSampler only supports real valued latent DOFs.") # /Check domain dtype if isinstance(position, MultiField): self._position = position.extract(H.domain) else: self._position = position tr = H._lh.get_transformation() if tr is None: raise ValueError("_GeoMetricSampler only works for likelihoods") dtype, f_lh = tr scale = ScalingOperator(f_lh.target, 1.) if isinstance(dtype, dict): sampling = reduce((lambda a,b: a*b), [dtype[k] is not None for k in dtype.keys()]) else: sampling = dtype is not None scale = SamplingDtypeSetter(scale, dtype) if sampling else scale fl = f_lh(Linearization.make_var(self._position)) self._g = (Adder(-self._position) + fl.jac.adjoint@Adder(-fl.val)@f_lh) self._likelihood = SandwichOperator.make(fl.jac, scale) self._prior = SamplingDtypeSetter(ScalingOperator(fl.domain,1.), np.float64) self._met = self._likelihood + self._prior if napprox >= 1: self._approximation = makeOp(approximation2endo(self._met, napprox)).inverse else: self._approximation = None self._ic = H._ic_samp self._minimizer = minimizer self._start_from_lin = start_from_lin self._want_error = want_error sseq = random.spawn_sseq(n_samples) if mirror_samples: mysseq = [] for seq in sseq: mysseq += [seq, seq] else: mysseq = sseq self._sseq = mysseq self._neg = (False, True)*n_samples if mirror_samples else (False, )*n_samples self._n_samples = n_samples self._mirror_samples = mirror_samples @property def n_eff_samples(self): return 2*self._n_samples if self._mirror_samples else self._n_samples @property def position(self): return self._position def _draw_lin(self, neg): s = self._prior.draw_sample(from_inverse=True) s = -s if neg else s nj = self._likelihood.draw_sample() nj = -nj if neg else nj y = self._prior(s) + nj if self._start_from_lin: energy = QuadraticEnergy(s, self._met, y, _grad=self._likelihood(s) - nj) inverter = ConjugateGradient(self._ic) energy, convergence = inverter(energy, preconditioner=self._approximation) yi = energy.position else: yi = s return y, yi def _draw_nonlin(self, y, yi): en = EnergyAdapter(self._position+yi, GaussianEnergy(mean=y)@self._g, nanisinf=True, want_metric=True) en, _ = self._minimizer(en) sam = en.position - self._position if self._want_error: er = y - self._g(sam) er = er.s_vdot(InversionEnabler(self._met, self._ic).inverse(er)) return sam, er return sam def draw_samples(self, comm): local_samples = [] prev = None for i in range(*_get_lo_hi(comm, self.n_eff_samples)): with random.Context(self._sseq[i]): neg = self._neg[i] if (prev is None) or not self._mirror_samples: y, yi = self._draw_lin(neg) if not neg: prev = (-y, -yi) else: (y, yi) = prev prev = None local_samples.append(self._draw_nonlin(y, yi)) return tuple(local_samples) def MetricGaussianKL(mean, hamiltonian, n_samples, mirror_samples, constants=[], point_estimates=[], napprox=0, comm=None, nanisinf=False): """Provides the sampled Kullback-Leibler divergence between a distribution and a Metric Gaussian. A Metric Gaussian is used to approximate another probability distribution. It is a Gaussian distribution that uses the Fisher information metric of the other distribution at the location of its mean to approximate the variance. In order to infer the mean, a stochastic estimate of the Kullback-Leibler divergence is minimized. This estimate is obtained by sampling the Metric Gaussian at the current mean. During minimization these samples are kept constant; only the mean is updated. Due to the typically nonlinear structure of the true distribution these samples have to be updated eventually by intantiating `MetricGaussianKL` again. For the true probability distribution the standard parametrization is assumed. The samples of this class can be distributed among MPI tasks. Parameters ---------- mean : Field Mean of the Gaussian probability distribution. hamiltonian : StandardHamiltonian Hamiltonian of the approximated probability distribution. n_samples : integer Number of samples used to stochastically estimate the KL. mirror_samples : boolean Whether the negative of the drawn samples are also used, as they are equally legitimate samples. If true, the number of used samples doubles. Mirroring samples stabilizes the KL estimate as extreme sample variation is counterbalanced. Since it improves stability in many cases, it is recommended to set `mirror_samples` to `True`. constants : list List of parameter keys that are kept constant during optimization. Default is no constants. point_estimates : list List of parameter keys for which no samples are drawn, but that are (possibly) optimized for, corresponding to point estimates of these. Default is to draw samples for the complete domain. napprox : int Number of samples for computing preconditioner for sampling. No preconditioning is done by default. comm : MPI communicator or None If not None, samples will be distributed as evenly as possible across this communicator. If `mirror_samples` is set, then a sample and its mirror image will always reside on the same task. nanisinf : bool If true, nan energies which can happen due to overflows in the forward model are interpreted as inf. Thereby, the code does not crash on these occasions but rather the minimizer is told that the position it has tried is not sensible. Notes ----- The two lists `constants` and `point_estimates` are independent from each other. It is possible to sample along domains which are kept constant during minimization and vice versa. DomainTuples should never be created using the constructor, but rather via the factory function :attr:`make`! See also -------- `Metric Gaussian Variational Inference`, Jakob Knollmüller, Torsten A. Enßlin, ``_ """ if not isinstance(hamiltonian, StandardHamiltonian): raise TypeError if hamiltonian.domain is not mean.domain: raise ValueError if not isinstance(n_samples, int): raise TypeError if not isinstance(mirror_samples, bool): raise TypeError if isinstance(mean, MultiField) and set(point_estimates) == set(mean.keys()): raise RuntimeError( 'Point estimates for whole domain. Use EnergyAdapter instead.') n_samples = int(n_samples) mirror_samples = bool(mirror_samples) _, ham_sampling = _reduce_by_keys(mean, hamiltonian, point_estimates) lin = Linearization.make_var(mean.extract(ham_sampling.domain), True) met = ham_sampling(lin).metric if napprox >= 1: met._approximation = makeOp(approximation2endo(met, napprox)) local_samples = [] sseq = random.spawn_sseq(n_samples) for i in range(*_get_lo_hi(comm, n_samples)): with random.Context(sseq[i]): local_samples.append(met.draw_sample(from_inverse=True)) local_samples = tuple(local_samples) mean, hamiltonian = _reduce_by_keys(mean, hamiltonian, constants) return _SampledKLEnergy(mean, hamiltonian, n_samples, mirror_samples, comm, local_samples, nanisinf) def GeoMetricKL(mean, hamiltonian, n_samples, minimizer_samp, mirror_samples, start_from_lin = True, constants=[], point_estimates=[], napprox=0, comm=None, nanisinf=True): """Provides the sampled Kullback-Leibler used in geometric Variational Inference (geoVI). In geoVI a probability distribution is approximated with a standard normal distribution in the canonical coordinate system of the Riemannian manifold associated with the metric of the other distribution. The coordinate transformation is approximated by expanding around a point. In order to infer the expansion point, a stochastic estimate of the Kullback-Leibler divergence is minimized. This estimate is obtained by sampling from the approximation using the current expansion point. During minimization these samples are kept constant; only the expansion point is updated. Due to the typically nonlinear structure of the true distribution these samples have to be updated eventually by instantiating `GeoMetricKL` again. For the true probability distribution the standard parametrization is assumed. The samples of this class can be distributed among MPI tasks. Parameters ---------- mean : Field Expansion point of the coordinate transformation. hamiltonian : StandardHamiltonian Hamiltonian of the approximated probability distribution. n_samples : integer Number of samples used to stochastically estimate the KL. minimizer_samp : DescentMinimizer Minimizer used to draw samples. mirror_samples : boolean Whether the mirrored version of the drawn samples are also used. If true, the number of used samples doubles. Mirroring samples stabilizes the KL estimate as extreme sample variation is counterbalanced. start_from_lin : boolean Whether the non-linear sampling should start using the inverse linearized transformation (i.e. the corresponding MGVI sample). If False, the minimization starts from the prior sample. Default is True. constants : list List of parameter keys that are kept constant during optimization. Default is no constants. point_estimates : list List of parameter keys for which no samples are drawn, but that are (possibly) optimized for, corresponding to point estimates of these. Default is to draw samples for the complete domain. napprox : int Number of samples for computing preconditioner for linear sampling. No preconditioning is done by default. comm : MPI communicator or None If not None, samples will be distributed as evenly as possible across this communicator. If `mirror_samples` is set, then a sample and its mirror image will preferably reside on the same task if necessary. nanisinf : bool If true, nan energies which can happen due to overflows in the forward model are interpreted as inf. Thereby, the code does not crash on these occasions but rather the minimizer is told that the position it has tried is not sensible. Notes ----- The two lists `constants` and `point_estimates` are independent from each other. It is possible to sample along domains which are kept constant during minimization and vice versa. DomainTuples should never be created using the constructor, but rather via the factory function :attr:`make`! Note on MPI and mirror_samples: As in MGVI, mirroring samples can help to stabilize the latent mean as it reduces sampling noise. But unlike MGVI a mirrored sample involves an additional solve of the non-linear transformation. Therefore, when using MPI, the mirrored samples also get distributed if enough tasks are available. If there are more total samples than tasks, the mirrored counterparts try to reside on the same task as their non mirrored partners. This ensures that at least the starting position can be re-used. See also -------- `Geometric Variational Inference`, Philipp Frank, Reimar Leike, Torsten A. Enßlin, ``_ """ if not isinstance(hamiltonian, StandardHamiltonian): raise TypeError if hamiltonian.domain is not mean.domain: raise ValueError if not isinstance(n_samples, int): raise TypeError if not isinstance(mirror_samples, bool): raise TypeError if not isinstance(minimizer_samp, DescentMinimizer): raise TypeError if isinstance(mean, MultiField) and set(point_estimates) == set(mean.keys()): s = 'Point estimates for whole domain. Use EnergyAdapter instead.' raise RuntimeError(s) n_samples = int(n_samples) mirror_samples = bool(mirror_samples) _, ham_sampling = _reduce_by_keys(mean, hamiltonian, point_estimates) sampler = _GeoMetricSampler(mean, ham_sampling, minimizer_samp, start_from_lin, n_samples, mirror_samples) local_samples = sampler.draw_samples(comm) mean, hamiltonian = _reduce_by_keys(mean, hamiltonian, constants) return _SampledKLEnergy(mean, hamiltonian, sampler.n_eff_samples, False, comm, local_samples, nanisinf)