Source code for pyro.infer.autoguide.gaussian

# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0

import itertools
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, defaultdict
from contextlib import ExitStack
from types import SimpleNamespace
from typing import Callable, Dict, Optional, Set, Tuple, Union

import torch
from torch.distributions import biject_to

import pyro
import pyro.distributions as dist
import pyro.poutine as poutine
from pyro.distributions import constraints
from pyro.infer.inspect import get_dependencies, is_sample_site
from pyro.nn.module import PyroModule, PyroParam
from pyro.ops.linalg import ignore_torch_deprecation_warnings
from pyro.poutine.runtime import am_i_wrapped, get_plates
from pyro.poutine.util import site_is_subsample

from .guides import AutoGuide
from .initialization import InitMessenger, init_to_feasible
from .utils import deep_getattr, deep_setattr, helpful_support_errors

# Helper to dispatch to concrete subclasses of AutoGaussian, e.g.
#   AutoGaussian(model, backend="dense")
# is converted to
#   AutoGaussianDense(model)
# The intent is to avoid proliferation of subclasses and docstrings,
# and provide a single interface AutoGaussian(...).
class AutoGaussianMeta(type(AutoGuide), ABCMeta):
    backends = {}
    default_backend = "dense"

    def __init__(cls, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert cls.__name__.startswith("AutoGaussian")
        key = cls.__name__.replace("AutoGaussian", "").lower()
        cls.backends[key] = cls

    def __call__(cls, *args, **kwargs):
        if cls is AutoGaussian:
            backend = kwargs.pop("backend", cls.default_backend)
            cls = cls.backends[backend]
        return super(AutoGaussianMeta, cls).__call__(*args, **kwargs)

[docs]class AutoGaussian(AutoGuide, metaclass=AutoGaussianMeta): """ Gaussian guide with optimal conditional independence structure. This is equivalent to a full rank :class:`AutoMultivariateNormal` guide, but with a sparse precision matrix determined by dependencies and plates in the model [1]. Depending on model structure, this can have asymptotically better statistical efficiency than :class:`AutoMultivariateNormal` . This guide implements multiple backends for computation. All backends use the same statistically optimal parametrization. The default "dense" backend has computational complexity similar to :class:`AutoMultivariateNormal` . The experimental "funsor" backend can be asymptotically cheaper in terms of time and space (using Gaussian tensor variable elimination [2,3]), but incurs large constant overhead. The "funsor" backend requires `funsor <>`_ which can be installed via ``pip install pyro-ppl[funsor]``. The guide currently does not depend on the model's ``*args, **kwargs``. Example:: guide = AutoGaussian(model) svi = SVI(model, guide, ...) Example using experimental funsor backend:: !pip install pyro-ppl[funsor] guide = AutoGaussian(model, backend="funsor") svi = SVI(model, guide, ...) **References** [1] S.Webb, A.GoliƄski, R.Zinkov, N.Siddharth, T.Rainforth, Y.W.Teh, F.Wood (2018) "Faithful inversion of generative models for effective amortized inference" [2] F.Obermeyer, E.Bingham, M.Jankowiak, J.Chiu, N.Pradhan, A.M.Rush, N.Goodman (2019) "Tensor Variable Elimination for Plated Factor Graphs" [3] F. Obermeyer, E. Bingham, M. Jankowiak, D. Phan, J. P. Chen (2019) "Functional Tensors for Probabilistic Programming" :param callable model: A Pyro model. :param callable init_loc_fn: A per-site initialization function. See :ref:`autoguide-initialization` section for available functions. :param float init_scale: Initial scale for the standard deviation of each (unconstrained transformed) latent variable. :param str backend: Back end for performing Gaussian tensor variable elimination. Defaults to "dense"; other options include "funsor". """ scale_constraint = constraints.softplus_positive def __init__( self, model: Callable, *, init_loc_fn: Callable = init_to_feasible, init_scale: float = 0.1, backend: Optional[str] = None, # used only by metaclass ): if not isinstance(init_scale, float) or not (init_scale > 0): raise ValueError(f"Expected init_scale > 0. but got {init_scale}") self._init_scale = init_scale self._original_model = (model,) model = InitMessenger(init_loc_fn)(model) super().__init__(model) @staticmethod def _prototype_hide_fn(msg): # In contrast to the AutoGuide base class, this includes observation # sites and excludes deterministic sites. return not is_sample_site(msg) def _setup_prototype(self, *args, **kwargs) -> None: super()._setup_prototype(*args, **kwargs) self.locs = PyroModule() self.scales = PyroModule() self.white_vecs = PyroModule() self.prec_sqrts = PyroModule() self._factors = OrderedDict() self._plates = OrderedDict() self._event_numel = OrderedDict() self._unconstrained_event_shapes = OrderedDict() # Trace model dependencies. model = self._original_model[0] self._original_model = None self.dependencies = poutine.block(get_dependencies)(model, args, kwargs)[ "prior_dependencies" ] # Eliminate observations with no upstream latents. for d, upstreams in list(self.dependencies.items()): if all(self.prototype_trace.nodes[u]["is_observed"] for u in upstreams): del self.dependencies[d] del self.prototype_trace.nodes[d] # Collect factors and plates. for d, site in self.prototype_trace.nodes.items(): # Prune non-essential parts of the trace to save memory. pruned_site, site = site, site.copy() pruned_site.clear() # Collect factors and plates. if site["type"] != "sample" or site_is_subsample(site): continue assert all(f.vectorized for f in site["cond_indep_stack"]) self._factors[d] = self._compress_site(site) plates = frozenset(site["cond_indep_stack"]) if site["fn"].batch_shape != _plates_to_shape(plates): raise ValueError( f"Shape mismatch at site '{d}'. " "Are you missing a pyro.plate() or .to_event()?" ) if site["is_observed"]: # Break irrelevant observation plates. plates &= frozenset().union( *(self._plates[u] for u in self.dependencies[d] if u != d) ) self._plates[d] = plates # Create location-scale parameters, one per latent variable. if site["is_observed"]: # This may slightly overestimate, e.g. for Multinomial. self._event_numel[d] = site["fn"].event_shape.numel() # Account for broken irrelevant observation plates. for f in set(site["cond_indep_stack"]) - plates: self._event_numel[d] *= f.size continue with helpful_support_errors(site): init_loc = biject_to(site["fn"].support).inv(site["value"]).detach() batch_shape = site["fn"].batch_shape event_shape = init_loc.shape[len(batch_shape) :] self._unconstrained_event_shapes[d] = event_shape self._event_numel[d] = event_shape.numel() event_dim = len(event_shape) deep_setattr(self.locs, d, PyroParam(init_loc, event_dim=event_dim)) deep_setattr( self.scales, d, PyroParam( torch.full_like(init_loc, self._init_scale), constraint=self.scale_constraint, event_dim=event_dim, ), ) # Create parameters for dependencies, one per factor. for d, site in self._factors.items(): u_size = 0 for u in self.dependencies[d]: if not self._factors[u]["is_observed"]: broken_shape = _plates_to_shape(self._plates[u] - self._plates[d]) u_size += broken_shape.numel() * self._event_numel[u] d_size = self._event_numel[d] if site["is_observed"]: d_size = min(d_size, u_size) # just an optimization batch_shape = _plates_to_shape(self._plates[d]) # Create parameters of each Gaussian factor. white_vec = init_loc.new_zeros(batch_shape + (d_size,)) # We initialize with noise to avoid singular gradient. prec_sqrt = torch.rand( batch_shape + (u_size, d_size), dtype=init_loc.dtype, device=init_loc.device, ) prec_sqrt.sub_(0.5).mul_(self._init_scale) if not site["is_observed"]: # Initialize the [d,d] block to the identity matrix. prec_sqrt.diagonal(dim1=-2, dim2=-1).fill_(1) deep_setattr(self.white_vecs, d, PyroParam(white_vec, event_dim=1)) deep_setattr(self.prec_sqrts, d, PyroParam(prec_sqrt, event_dim=2)) @staticmethod def _compress_site(site): # Save memory by retaining only necessary parts of the site. return { "name": site["name"], "type": site["type"], "cond_indep_stack": site["cond_indep_stack"], "is_observed": site["is_observed"], "fn": SimpleNamespace( support=site["fn"].support, batch_shape=site["fn"].batch_shape, event_dim=site["fn"].event_dim, ), }
[docs] def forward(self, *args, **kwargs) -> Dict[str, torch.Tensor]: if self.prototype_trace is None: self._setup_prototype(*args, **kwargs) aux_values = self._sample_aux_values(temperature=1.0) values, log_densities = self._transform_values(aux_values) # Replay via Pyro primitives. plates = self._create_plates(*args, **kwargs) for name, site in self._factors.items(): if site["is_observed"]: continue with ExitStack() as stack: for frame in site["cond_indep_stack"]: stack.enter_context(plates[]) values[name] = pyro.sample( name, dist.Delta(values[name], log_densities[name], site["fn"].event_dim), ) return values
[docs] def median(self, *args, **kwargs) -> Dict[str, torch.Tensor]: """ Returns the posterior median value of each latent variable. :return: A dict mapping sample site name to median tensor. :rtype: dict """ with torch.no_grad(), poutine.mask(mask=False): aux_values = self._sample_aux_values(temperature=0.0) values, _ = self._transform_values(aux_values) return values
def _transform_values( self, aux_values: Dict[str, torch.Tensor], ) -> Tuple[Dict[str, torch.Tensor], Union[float, torch.Tensor]]: # Learnably transform auxiliary values to user-facing values. values = {} log_densities = defaultdict(float) compute_density = am_i_wrapped() and poutine.get_mask() is not False for name, site in self._factors.items(): if site["is_observed"]: continue loc = deep_getattr(self.locs, name) scale = deep_getattr(self.scales, name) unconstrained = aux_values[name] * scale + loc # Transform to constrained space. transform = biject_to(site["fn"].support) values[name] = transform(unconstrained) if compute_density: assert transform.codomain.event_dim == site["fn"].event_dim log_densities[name] = transform.inv.log_abs_det_jacobian( values[name], unconstrained ) - scale.log().reshape(site["fn"].batch_shape + (-1,)).sum(-1) return values, log_densities @abstractmethod def _sample_aux_values(self, *, temperature: float) -> Dict[str, torch.Tensor]: raise NotImplementedError
class AutoGaussianDense(AutoGaussian): """ Dense implementation of :class:`AutoGaussian` . The following are equivalent:: guide = AutoGaussian(model, backend="dense") guide = AutoGaussianDense(model) """ def _setup_prototype(self, *args, **kwargs): super()._setup_prototype(*args, **kwargs) # Collect global shapes and per-axis indices. self._dense_shapes = {} global_indices = {} pos = 0 for d, event_shape in self._unconstrained_event_shapes.items(): batch_shape = self._factors[d]["fn"].batch_shape self._dense_shapes[d] = batch_shape, event_shape end = pos + (batch_shape + event_shape).numel() global_indices[d] = torch.arange(pos, end).reshape(batch_shape + (-1,)) pos = end self._dense_size = pos # Create sparse -> dense precision scatter indices. self._dense_scatter = {} for d, site in self._factors.items(): prec_sqrt_shape = deep_getattr(self.prec_sqrts, d).shape info_vec_shape = prec_sqrt_shape[:-1] precision_shape = prec_sqrt_shape[:-1] + prec_sqrt_shape[-2:-1] index1 = torch.zeros(info_vec_shape, dtype=torch.long) index2 = torch.zeros(precision_shape, dtype=torch.long) # Collect local offsets and create index1 for info_vec blockwise. upstreams = [ u for u in self.dependencies[d] if not self._factors[u]["is_observed"] ] local_offsets = {} pos = 0 for u in upstreams: local_offsets[u] = pos broken_plates = self._plates[u] - self._plates[d] pos += self._event_numel[u] * _plates_to_shape(broken_plates).numel() u_index = global_indices[u] # Permute broken plates to the right of preserved plates. u_index = _break_plates(u_index, self._plates[u], self._plates[d]) # Scatter global indices into the [u] block. u_start = local_offsets[u] u_stop = u_start + u_index.size(-1) index1[..., u_start:u_stop] = u_index # Create index2 for precision blockwise. for u, v in itertools.product(upstreams, upstreams): u_index = global_indices[u] v_index = global_indices[v] # Permute broken plates to the right of preserved plates. u_index = _break_plates(u_index, self._plates[u], self._plates[d]) v_index = _break_plates(v_index, self._plates[v], self._plates[d]) # Scatter global indices into the [u,v] block. u_start = local_offsets[u] u_stop = u_start + u_index.size(-1) v_start = local_offsets[v] v_stop = v_start + v_index.size(-1) index2[ ..., u_start:u_stop, v_start:v_stop ] = self._dense_size * u_index.unsqueeze(-1) + v_index.unsqueeze(-2) self._dense_scatter[d] = index1.reshape(-1), index2.reshape(-1) def _sample_aux_values(self, *, temperature: float) -> Dict[str, torch.Tensor]: mvn = self._dense_get_mvn() if temperature == 0: # Simply return the mode. flat_samples = mvn.mean elif temperature == 1: # Sample from a dense joint Gaussian over flattened variables. flat_samples = pyro.sample( f"_{self._pyro_name}_latent", mvn, infer={"is_auxiliary": True} ) else: raise NotImplementedError(f"Invalid temperature: {temperature}") samples = self._dense_unflatten(flat_samples) return samples def _dense_unflatten(self, flat_samples: torch.Tensor) -> Dict[str, torch.Tensor]: # Convert a single flattened sample to a dict of shaped samples. sample_shape = flat_samples.shape[:-1] samples = {} pos = 0 for d, (batch_shape, event_shape) in self._dense_shapes.items(): end = pos + (batch_shape + event_shape).numel() flat_sample = flat_samples[..., pos:end] pos = end # Assumes sample shapes are left of batch shapes. samples[d] = flat_sample.reshape( torch.broadcast_shapes(sample_shape, batch_shape) + event_shape ) return samples def _dense_flatten(self, samples: Dict[str, torch.Tensor]) -> torch.Tensor: # Convert a dict of shaped samples single flattened sample. flat_samples = [] for d, (batch_shape, event_shape) in self._dense_shapes.items(): shape = samples[d].shape sample_shape = shape[: len(shape) - len(batch_shape) - len(event_shape)] flat_samples.append(samples[d].reshape(sample_shape + (-1,))) return, dim=-1) def _dense_get_mvn(self): # Create a dense joint Gaussian over flattened variables. flat_info_vec = torch.zeros(self._dense_size) flat_precision = torch.zeros(self._dense_size**2) for d, (index1, index2) in self._dense_scatter.items(): white_vec = deep_getattr(self.white_vecs, d) prec_sqrt = deep_getattr(self.prec_sqrts, d) info_vec = (prec_sqrt @ white_vec[..., None])[..., 0] precision = prec_sqrt @ prec_sqrt.transpose(-1, -2) flat_info_vec.scatter_add_(0, index1, info_vec.reshape(-1)) flat_precision.scatter_add_(0, index2, precision.reshape(-1)) info_vec = flat_info_vec precision = flat_precision.reshape(self._dense_size, self._dense_size) scale_tril = _precision_to_scale_tril(precision) loc = ( scale_tril @ (scale_tril.transpose(-1, -2) @ info_vec.unsqueeze(-1)) ).squeeze(-1) return dist.MultivariateNormal(loc, scale_tril=scale_tril) class AutoGaussianFunsor(AutoGaussian): """ Funsor implementation of :class:`AutoGaussian` . The following are equivalent:: guide = AutoGaussian(model, backend="funsor") guide = AutoGaussianFunsor(model) """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) _import_funsor() def _setup_prototype(self, *args, **kwargs): super()._setup_prototype(*args, **kwargs) funsor = _import_funsor() # Check TVE condition 1: plate nesting is monotone. for d in self._factors: pd = { for p in self._plates[d]} for u in self.dependencies[d]: pu = { for p in self._plates[u]} if pu <= pd: continue # ok raise NotImplementedError( "Expected monotone plate nesting, but found dependency " f"{repr(u)} -> {repr(d)} leaves plates {pu - pd}. " "Consider splitting into multiple guides via AutoGuideList, " "or replacing the plate in the model by .to_event()." ) # Determine TVE problem shape. factor_inputs: Dict[str, OrderedDict[str, funsor.Domain]] = {} eliminate: Set[str] = set() plate_to_dim: Dict[str, int] = {} for d, site in self._factors.items(): inputs = OrderedDict() for f in sorted(self._plates[d], key=lambda f: f.dim): plate_to_dim[] = f.dim inputs[] = funsor.Bint[f.size] eliminate.add( for u in self.dependencies[d]: if self._factors[u]["is_observed"]: continue inputs[u] = funsor.Reals[self._unconstrained_event_shapes[u]] eliminate.add(u) factor_inputs[d] = inputs self._funsor_factor_inputs = factor_inputs self._funsor_eliminate = frozenset(eliminate) self._funsor_plate_to_dim = plate_to_dim self._funsor_plates = frozenset(plate_to_dim) def _sample_aux_values(self, *, temperature: float) -> Dict[str, torch.Tensor]: funsor = _import_funsor() # Convert torch to funsor. particle_plates = frozenset(get_plates()) plate_to_dim = self._funsor_plate_to_dim.copy() plate_to_dim.update({ f.dim for f in particle_plates}) factors = {} for d, inputs in self._funsor_factor_inputs.items(): batch_shape = torch.Size( p.size for p in sorted(self._plates[d], key=lambda p: p.dim) ) white_vec = deep_getattr(self.white_vecs, d) prec_sqrt = deep_getattr(self.prec_sqrts, d) factors[d] = funsor.gaussian.Gaussian( white_vec=white_vec.reshape(batch_shape + white_vec.shape[-1:]), prec_sqrt=prec_sqrt.reshape(batch_shape + prec_sqrt.shape[-2:]), inputs=inputs, ) # Perform Gaussian tensor variable elimination. if temperature == 1: samples, log_prob = _try_possibly_intractable(, factors=factors, eliminate=self._funsor_eliminate, plates=frozenset(plate_to_dim), sample_inputs={ funsor.Bint[f.size] for f in particle_plates}, ) else: samples, log_prob = _try_possibly_intractable(, factors=factors, eliminate=self._funsor_eliminate, plates=frozenset(plate_to_dim), ) # Substitute noise. sample_shape = torch.Size(f.size for f in particle_plates) noise = torch.randn(sample_shape + log_prob.inputs["aux"].shape) noise.mul_(temperature) aux = funsor.Tensor(noise)[tuple( for f in particle_plates)] with funsor.interpretations.memoize(): samples = {k: v(aux=aux) for k, v in samples.items()} log_prob = log_prob(aux=aux) # Convert funsor to torch. if am_i_wrapped() and poutine.get_mask() is not False: log_prob = funsor.to_data(log_prob, name_to_dim=plate_to_dim) pyro.factor(f"_{self._pyro_name}_latent", log_prob, has_rsample=True) samples = { k: funsor.to_data(v, name_to_dim=plate_to_dim) for k, v in samples.items() } return samples def _precision_to_scale_tril(P): # Ref: Lf = torch.linalg.cholesky(torch.flip(P, (-2, -1))) L_inv = torch.transpose(torch.flip(Lf, (-2, -1)), -2, -1) L = torch.linalg.solve_triangular( L_inv, torch.eye(P.shape[-1], dtype=P.dtype, device=P.device), upper=False ) return L @ignore_torch_deprecation_warnings() def _try_possibly_intractable(fn, *args, **kwargs): # Convert ValueError into NotImplementedError. try: return fn(*args, **kwargs) except ValueError as e: if str(e) != "intractable!": raise e from None raise NotImplementedError( "Funsor backend found intractable plate nesting. " 'Consider using AutoGaussian(..., backend="dense"), ' "splitting into multiple guides via AutoGuideList, or " "replacing some plates in the model by .to_event()." ) from e def _plates_to_shape(plates): shape = [1] * max([0] + [-f.dim for f in plates]) for f in plates: shape[f.dim] = f.size return torch.Size(shape) def _break_plates(x, all_plates, kept_plates): """ Reshapes and permutes a tensor ``x`` with event_dim=1 and batch shape given by ``all_plates`` by breaking all plates not in ``kept_plates``. Each broken plate is moved into the event shape, and finally the event shape is flattend back to a single dimension. """ assert x.shape[:-1] == _plates_to_shape(all_plates) # event_dim == 1 kept_plates = kept_plates & all_plates broken_plates = all_plates - kept_plates if not broken_plates: return x if not kept_plates: # Empty batch shape. return x.reshape(-1) batch_shape = _plates_to_shape(kept_plates) if max(p.dim for p in kept_plates) < min(p.dim for p in broken_plates): # No permutation is necessary. return x.reshape(batch_shape + (-1,)) # We need to permute broken plates left past kept plates. event_dims = {-1} | {p.dim - 1 for p in broken_plates} perm = sorted(range(-x.dim(), 0), key=lambda d: (d in event_dims, d)) return x.permute(perm).reshape(batch_shape + (-1,)) def _import_funsor(): try: import funsor except ImportError as e: raise ImportError( 'AutoGaussian(..., backend="funsor") requires funsor. ' "Try installing via: pip install pyro-ppl[funsor]" ) from e funsor.set_backend("torch") return funsor __all__ = [ "AutoGaussian", ]