Stochastic Variational Inference (SVI)

class SVI(model, guide, optim, loss, **static_kwargs)[source]

Bases: object

Stochastic Variational Inference given an ELBO loss objective.

References

  1. SVI Part I: An Introduction to Stochastic Variational Inference in Pyro, (http://pyro.ai/examples/svi_part_i.html)

Example:

>>> from jax import random
>>> import jax.numpy as jnp
>>> import numpyro
>>> import numpyro.distributions as dist
>>> from numpyro.distributions import constraints
>>> from numpyro.infer import SVI, Trace_ELBO

>>> def model(data):
...     f = numpyro.sample("latent_fairness", dist.Beta(10, 10))
...     with numpyro.plate("N", data.shape[0]):
...         numpyro.sample("obs", dist.Bernoulli(f), obs=data)

>>> def guide(data):
...     alpha_q = numpyro.param("alpha_q", 15., constraint=constraints.positive)
...     beta_q = numpyro.param("beta_q", lambda rng_key: random.exponential(rng_key),
...                            constraint=constraints.positive)
...     numpyro.sample("latent_fairness", dist.Beta(alpha_q, beta_q))

>>> data = jnp.concatenate([jnp.ones(6), jnp.zeros(4)])
>>> optimizer = numpyro.optim.Adam(step_size=0.0005)
>>> svi = SVI(model, guide, optimizer, loss=Trace_ELBO())
>>> svi_result = svi.run(random.PRNGKey(0), 2000, data)
>>> params = svi_result.params
>>> inferred_mean = params["alpha_q"] / (params["alpha_q"] + params["beta_q"])
Parameters:
  • model – Python callable with Pyro primitives for the model.
  • guide – Python callable with Pyro primitives for the guide (recognition network).
  • optim

    An instance of _NumpyroOptim, a jax.experimental.optimizers.Optimizer or an Optax GradientTransformation. If you pass an Optax optimizer it will automatically be wrapped using numpyro.contrib.optim.optax_to_numpyro().

    >>> from optax import adam, chain, clip
    >>> svi = SVI(model, guide, chain(clip(10.0), adam(1e-3)), loss=Trace_ELBO())
    
  • loss – ELBO loss, i.e. negative Evidence Lower Bound, to minimize.
  • static_kwargs – static arguments for the model / guide, i.e. arguments that remain constant during fitting.
Returns:

tuple of (init_fn, update_fn, evaluate).

init(rng_key, *args, **kwargs)[source]

Gets the initial SVI state.

Parameters:
  • rng_key (jax.random.PRNGKey) – random number generator seed.
  • args – arguments to the model / guide (these can possibly vary during the course of fitting).
  • kwargs – keyword arguments to the model / guide (these can possibly vary during the course of fitting).
Returns:

the initial SVIState

get_params(svi_state)[source]

Gets values at param sites of the model and guide.

Parameters:svi_state – current state of SVI.
Returns:the corresponding parameters
update(svi_state, *args, **kwargs)[source]

Take a single step of SVI (possibly on a batch / minibatch of data), using the optimizer.

Parameters:
  • svi_state – current state of SVI.
  • args – arguments to the model / guide (these can possibly vary during the course of fitting).
  • kwargs – keyword arguments to the model / guide (these can possibly vary during the course of fitting).
Returns:

tuple of (svi_state, loss).

stable_update(svi_state, *args, **kwargs)[source]

Similar to update() but returns the current state if the the loss or the new state contains invalid values.

Parameters:
  • svi_state – current state of SVI.
  • args – arguments to the model / guide (these can possibly vary during the course of fitting).
  • kwargs – keyword arguments to the model / guide (these can possibly vary during the course of fitting).
Returns:

tuple of (svi_state, loss).

run(rng_key, num_steps, *args, progress_bar=True, stable_update=False, **kwargs)[source]

(EXPERIMENTAL INTERFACE) Run SVI with num_steps iterations, then return the optimized parameters and the stacked losses at every step. If num_steps is large, setting progress_bar=False can make the run faster.

Note

For a complex training process (e.g. the one requires early stopping, epoch training, varying args/kwargs,…), we recommend to use the more flexible methods init(), update(), evaluate() to customize your training procedure.

Parameters:
  • rng_key (jax.random.PRNGKey) – random number generator seed.
  • num_steps (int) – the number of optimization steps.
  • args – arguments to the model / guide
  • progress_bar (bool) – Whether to enable progress bar updates. Defaults to True.
  • stable_update (bool) – whether to use stable_update() to update the state. Defaults to False.
  • kwargs – keyword arguments to the model / guide
Returns:

a namedtuple with fields params and losses where params holds the optimized values at numpyro.param sites, and losses is the collected loss during the process.

Return type:

SVIRunResult

evaluate(svi_state, *args, **kwargs)[source]

Take a single step of SVI (possibly on a batch / minibatch of data).

Parameters:
  • svi_state – current state of SVI.
  • args – arguments to the model / guide (these can possibly vary during the course of fitting).
  • kwargs – keyword arguments to the model / guide.
Returns:

evaluate ELBO loss given the current parameter values (held within svi_state.optim_state).

ELBO

class ELBO(num_particles=1)[source]

Bases: numpyro.infer.elbo.Trace_ELBO

Trace_ELBO

class Trace_ELBO(num_particles=1)[source]

Bases: object

A trace implementation of ELBO-based SVI. The estimator is constructed along the lines of references [1] and [2]. There are no restrictions on the dependency structure of the model or the guide.

This is the most basic implementation of the Evidence Lower Bound, which is the fundamental objective in Variational Inference. This implementation has various limitations (for example it only supports random variables with reparameterized samplers) but can be used as a template to build more sophisticated loss objectives.

For more details, refer to http://pyro.ai/examples/svi_part_i.html.

References:

  1. Automated Variational Inference in Probabilistic Programming, David Wingate, Theo Weber
  2. Black Box Variational Inference, Rajesh Ranganath, Sean Gerrish, David M. Blei
Parameters:num_particles – The number of particles/samples used to form the ELBO (gradient) estimators.
loss(rng_key, param_map, model, guide, *args, **kwargs)[source]

Evaluates the ELBO with an estimator that uses num_particles many samples/particles.

Parameters:
  • rng_key (jax.random.PRNGKey) – random number generator seed.
  • param_map (dict) – dictionary of current parameter values keyed by site name.
  • model – Python callable with NumPyro primitives for the model.
  • guide – Python callable with NumPyro primitives for the guide.
  • args – arguments to the model / guide (these can possibly vary during the course of fitting).
  • kwargs – keyword arguments to the model / guide (these can possibly vary during the course of fitting).
Returns:

negative of the Evidence Lower Bound (ELBO) to be minimized.

TraceMeanField_ELBO

class TraceMeanField_ELBO(num_particles=1)[source]

Bases: numpyro.infer.elbo.Trace_ELBO

A trace implementation of ELBO-based SVI. This is currently the only ELBO estimator in NumPyro that uses analytic KL divergences when those are available.

Warning

This estimator may give incorrect results if the mean-field condition is not satisfied. 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.

loss(rng_key, param_map, model, guide, *args, **kwargs)[source]

Evaluates the ELBO with an estimator that uses num_particles many samples/particles.

Parameters:
  • rng_key (jax.random.PRNGKey) – random number generator seed.
  • param_map (dict) – dictionary of current parameter values keyed by site name.
  • model – Python callable with NumPyro primitives for the model.
  • guide – Python callable with NumPyro primitives for the guide.
  • args – arguments to the model / guide (these can possibly vary during the course of fitting).
  • kwargs – keyword arguments to the model / guide (these can possibly vary during the course of fitting).
Returns:

negative of the Evidence Lower Bound (ELBO) to be minimized.

RenyiELBO

class RenyiELBO(alpha=0, num_particles=2)[source]

Bases: numpyro.infer.elbo.Trace_ELBO

An implementation of Renyi’s \(\alpha\)-divergence variational inference following reference [1]. In order for the objective to be a strict lower bound, we require \(\alpha \ge 0\). Note, however, that according to reference [1], depending on the dataset \(\alpha < 0\) might give better results. In the special case \(\alpha = 0\), the objective function is that of the important weighted autoencoder derived in reference [2].

Note

Setting \(\alpha < 1\) gives a better bound than the usual ELBO.

Parameters:
  • alpha (float) – The order of \(\alpha\)-divergence. Here \(\alpha \neq 1\). Default is 0.
  • num_particles – The number of particles/samples used to form the objective (gradient) estimator. Default is 2.

References:

  1. Renyi Divergence Variational Inference, Yingzhen Li, Richard E. Turner
  2. Importance Weighted Autoencoders, Yuri Burda, Roger Grosse, Ruslan Salakhutdinov
loss(rng_key, param_map, model, guide, *args, **kwargs)[source]

Evaluates the Renyi ELBO with an estimator that uses num_particles many samples/particles.

Parameters:
  • rng_key (jax.random.PRNGKey) – random number generator seed.
  • param_map (dict) – dictionary of current parameter values keyed by site name.
  • model – Python callable with NumPyro primitives for the model.
  • guide – Python callable with NumPyro primitives for the guide.
  • args – arguments to the model / guide (these can possibly vary during the course of fitting).
  • kwargs – keyword arguments to the model / guide (these can possibly vary during the course of fitting).
Returns:

negative of the Renyi Evidence Lower Bound (ELBO) to be minimized.