Source code for pyro.infer.trace_mean_field_elbo

# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0

import warnings
import weakref

import torch
from torch.distributions import kl_divergence

import pyro.ops.jit
from pyro.distributions.util import scale_and_mask
from pyro.infer.trace_elbo import Trace_ELBO
from pyro.infer.util import (
from pyro.util import warn_if_nan

def _check_mean_field_requirement(model_trace, guide_trace):
    Checks that the guide and model sample sites are ordered identically.
    This is sufficient but not necessary for correctness.
    model_sites = [
        for name, site in model_trace.nodes.items()
        if site["type"] == "sample" and name in guide_trace.nodes
    guide_sites = [
        for name, site in guide_trace.nodes.items()
        if site["type"] == "sample" and name in model_trace.nodes
    assert set(model_sites) == set(guide_sites)
    if model_sites != guide_sites:
            "Failed to verify mean field restriction on the guide. "
            "To eliminate this warning, ensure model and guide sites "
            "occur in the same order.\n"
            + "Model sites:\n  "
            + "\n  ".join(model_sites)
            + "Guide sites:\n  "
            + "\n  ".join(guide_sites)

[docs]class TraceMeanField_ELBO(Trace_ELBO): """ A trace implementation of ELBO-based SVI. This is currently the only ELBO estimator in Pyro that uses analytic KL divergences when those are available. In contrast to, e.g., :class:`~pyro.infer.tracegraph_elbo.TraceGraph_ELBO` and :class:`~pyro.infer.tracegraph_elbo.Trace_ELBO` this estimator places restrictions on the dependency structure of the model and guide. In particular it assumes that the guide has a mean-field structure, i.e. that it factorizes across the different latent variables present in the guide. It also assumes that all of the latent variables in the guide are reparameterized. This latter condition is satisfied for, e.g., the Normal distribution but is not satisfied for, e.g., the Categorical distribution. .. warning:: This estimator may give incorrect results if the mean-field condition is not satisfied. Note for advanced users: The mean field condition is a sufficient but not necessary condition for this estimator to be correct. The precise condition is that for every latent variable `z` in the guide, its parents in the model must not include any latent variables that are descendants of `z` in the guide. Here 'parents in the model' and 'descendants in the guide' is with respect to the corresponding (statistical) dependency structure. For example, this condition is always satisfied if the model and guide have identical dependency structures. """ def _get_trace(self, model, guide, args, kwargs): model_trace, guide_trace = super()._get_trace(model, guide, args, kwargs) if is_validation_enabled(): _check_mean_field_requirement(model_trace, guide_trace) return model_trace, guide_trace
[docs] def loss(self, model, guide, *args, **kwargs): """ :returns: returns an estimate of the ELBO :rtype: float Evaluates the ELBO with an estimator that uses num_particles many samples/particles. """ loss = 0.0 for model_trace, guide_trace in self._get_traces(model, guide, args, kwargs): loss_particle, _ = self._differentiable_loss_particle( model_trace, guide_trace ) loss = loss + loss_particle / self.num_particles warn_if_nan(loss, "loss") return loss
def _differentiable_loss_particle(self, model_trace, guide_trace): elbo_particle = 0 for name, model_site in model_trace.nodes.items(): if model_site["type"] == "sample": if model_site["is_observed"]: elbo_particle = elbo_particle + model_site["log_prob_sum"] else: guide_site = guide_trace.nodes[name] if is_validation_enabled(): check_fully_reparametrized(guide_site) # use kl divergence if available, else fall back on sampling try: kl_qp = kl_divergence(guide_site["fn"], model_site["fn"]) kl_qp = scale_and_mask( kl_qp, scale=guide_site["scale"], mask=guide_site["mask"] ) if torch.is_tensor(kl_qp): assert kl_qp.shape == guide_site["fn"].batch_shape kl_qp_sum = kl_qp.sum() else: kl_qp_sum = ( kl_qp * torch.Size(guide_site["fn"].batch_shape).numel() ) elbo_particle = elbo_particle - kl_qp_sum except NotImplementedError: entropy_term = guide_site["score_parts"].entropy_term elbo_particle = ( elbo_particle + model_site["log_prob_sum"] - entropy_term.sum() ) # handle auxiliary sites in the guide for name, guide_site in guide_trace.nodes.items(): if guide_site["type"] == "sample" and name not in model_trace.nodes: assert guide_site["infer"].get("is_auxiliary") if is_validation_enabled(): check_fully_reparametrized(guide_site) entropy_term = guide_site["score_parts"].entropy_term elbo_particle = elbo_particle - entropy_term.sum() loss = -( elbo_particle.detach() if torch._C._get_tracing_state() else torch_item(elbo_particle) ) surrogate_loss = -elbo_particle return loss, surrogate_loss
[docs]class JitTraceMeanField_ELBO(TraceMeanField_ELBO): """ Like :class:`TraceMeanField_ELBO` but uses :func:`pyro.ops.jit.trace` to compile :meth:`loss_and_grads`. This works only for a limited set of models: - Models must have static structure. - Models must not depend on any global data (except the param store). - All model inputs that are tensors must be passed in via ``*args``. - All model inputs that are *not* tensors must be passed in via ``**kwargs``, and compilation will be triggered once per unique ``**kwargs``. """
[docs] def differentiable_loss(self, model, guide, *args, **kwargs): kwargs["_pyro_model_id"] = id(model) kwargs["_pyro_guide_id"] = id(guide) if getattr(self, "_loss_and_surrogate_loss", None) is None: # build a closure for loss_and_surrogate_loss weakself = weakref.ref(self) @pyro.ops.jit.trace( ignore_warnings=self.ignore_jit_warnings, jit_options=self.jit_options ) def differentiable_loss(*args, **kwargs): kwargs.pop("_pyro_model_id") kwargs.pop("_pyro_guide_id") self = weakself() loss = 0.0 for model_trace, guide_trace in self._get_traces( model, guide, args, kwargs ): _, loss_particle = self._differentiable_loss_particle( model_trace, guide_trace ) loss = loss + loss_particle / self.num_particles return loss self._differentiable_loss = differentiable_loss return self._differentiable_loss(*args, **kwargs)
[docs] def loss_and_grads(self, model, guide, *args, **kwargs): loss = self.differentiable_loss(model, guide, *args, **kwargs) loss.backward() loss = torch_item(loss) warn_if_nan(loss, "loss") return loss