# Runtime Utilities¶

## enable_validation¶

enable_validation(is_validate=True)[source]

Enable or disable validation checks in NumPyro. Validation checks provide useful warnings and errors, e.g. NaN checks, validating distribution arguments and support values, etc. which is useful for debugging.

Note

This utility does not take effect under JAX’s JIT compilation or vectorized transformation jax.vmap().

Parameters: is_validate (bool) – whether to enable validation checks.

## validation_enabled¶

validation_enabled(is_validate=True)[source]

Context manager that is useful when temporarily enabling/disabling validation checks.

Parameters: is_validate (bool) – whether to enable validation checks.

## enable_x64¶

enable_x64(use_x64=True)[source]

Changes the default array type to use 64 bit precision as in NumPy.

Parameters: use_x64 (bool) – when True, JAX arrays will use 64 bits by default; else 32 bits.

## set_platform¶

set_platform(platform=None)[source]

Changes platform to CPU, GPU, or TPU. This utility only takes effect at the beginning of your program.

Parameters: platform (str) – either ‘cpu’, ‘gpu’, or ‘tpu’.

## set_host_device_count¶

set_host_device_count(n)[source]

By default, XLA considers all CPU cores as one device. This utility tells XLA that there are n host (CPU) devices available to use. As a consequence, this allows parallel mapping in JAX jax.pmap() to work in CPU platform.

Note

This utility only takes effect at the beginning of your program. Under the hood, this sets the environment variable XLA_FLAGS=–xla_force_host_platform_device_count=[num_devices], where [num_device] is the desired number of CPU devices n.

Warning

Our understanding of the side effects of using the xla_force_host_platform_device_count flag in XLA is incomplete. If you observe some strange phenomenon when using this utility, please let us know through our issue or forum page. More information is available in this JAX issue.

Parameters: n (int) – number of CPU devices to use.

# Inference Utilities¶

## Predictive¶

class Predictive(model, posterior_samples=None, guide=None, params=None, num_samples=None, return_sites=None, parallel=False, batch_ndims=1)[source]

Bases: object

This class is used to construct predictive distribution. The predictive distribution is obtained by running model conditioned on latent samples from posterior_samples.

Warning

The interface for the Predictive class is experimental, and might change in the future.

Parameters: model – Python callable containing Pyro primitives. posterior_samples (dict) – dictionary of samples from the posterior. guide (callable) – optional guide to get posterior samples of sites not present in posterior_samples. params (dict) – dictionary of values for param sites of model/guide. num_samples (int) – number of samples return_sites (list) – sites to return; by default only sample sites not present in posterior_samples are returned. parallel (bool) – whether to predict in parallel using JAX vectorized map jax.vmap(). Defaults to False. batch_ndims – the number of batch dimensions in posterior samples. Some usages: set batch_ndims=0 to get prediction for 1 single sample set batch_ndims=1 to get prediction for posterior_samples with shapes (num_samples x …) set batch_ndims=2 to get prediction for posterior_samples with shapes (num_chains x N x …). Note that if num_samples argument is not None, its value should be equal to num_chains x N. dict of samples from the predictive distribution.

## log_density¶

log_density(model, model_args, model_kwargs, params)[source]

(EXPERIMENTAL INTERFACE) Computes log of joint density for the model given latent values params.

Parameters: model – Python callable containing NumPyro primitives. model_args (tuple) – args provided to the model. model_kwargs (dict) – kwargs provided to the model. params (dict) – dictionary of current parameter values keyed by site name. log of joint density and a corresponding model trace

## transform_fn¶

transform_fn(transforms, params, invert=False)[source]

(EXPERIMENTAL INTERFACE) Callable that applies a transformation from the transforms dict to values in the params dict and returns the transformed values keyed on the same names.

Parameters: transforms – Dictionary of transforms keyed by names. Names in transforms and params should align. params – Dictionary of arrays keyed by names. invert – Whether to apply the inverse of the transforms. dict of transformed params.

## constrain_fn¶

constrain_fn(model, model_args, model_kwargs, params, return_deterministic=False)[source]

(EXPERIMENTAL INTERFACE) Gets value at each latent site in model given unconstrained parameters params. The transforms is used to transform these unconstrained parameters to base values of the corresponding priors in model. If a prior is a transformed distribution, the corresponding base value lies in the support of base distribution. Otherwise, the base value lies in the support of the distribution.

Parameters: model – a callable containing NumPyro primitives. model_args (tuple) – args provided to the model. model_kwargs (dict) – kwargs provided to the model. params (dict) – dictionary of unconstrained values keyed by site names. return_deterministic (bool) – whether to return the value of deterministic sites from the model. Defaults to False. dict of transformed params.

## potential_energy¶

potential_energy(model, model_args, model_kwargs, params, enum=False)[source]

(EXPERIMENTAL INTERFACE) Computes potential energy of a model given unconstrained params. The inv_transforms is used to transform these unconstrained parameters to base values of the corresponding priors in model. If a prior is a transformed distribution, the corresponding base value lies in the support of base distribution. Otherwise, the base value lies in the support of the distribution.

Parameters: model – a callable containing NumPyro primitives. model_args (tuple) – args provided to the model. model_kwargs (dict) – kwargs provided to the model. params (dict) – unconstrained parameters of model. enum (bool) – whether to enumerate over discrete latent sites. potential energy given unconstrained parameters.

## log_likelihood¶

log_likelihood(model, posterior_samples, *args, parallel=False, batch_ndims=1, **kwargs)[source]

(EXPERIMENTAL INTERFACE) Returns log likelihood at observation nodes of model, given samples of all latent variables.

Parameters: model – Python callable containing Pyro primitives. posterior_samples (dict) – dictionary of samples from the posterior. args – model arguments. batch_ndims – the number of batch dimensions in posterior samples. Some usages: set batch_ndims=0 to get prediction for 1 single sample set batch_ndims=1 to get prediction for posterior_samples with shapes (num_samples x …) set batch_ndims=2 to get prediction for posterior_samples with shapes (num_chains x N x …) kwargs – model kwargs. dict of log likelihoods at observation sites.

## find_valid_initial_params¶

find_valid_initial_params(rng_key, model, init_strategy=<function init_to_uniform>, enum=False, model_args=(), model_kwargs=None, prototype_params=None)[source]

(EXPERIMENTAL INTERFACE) Given a model with Pyro primitives, returns an initial valid unconstrained value for all the parameters. This function also returns the corresponding potential energy, the gradients, and an is_valid flag to say whether the initial parameters are valid. Parameter values are considered valid if the values and the gradients for the log density have finite values.

Parameters: rng_key (jax.random.PRNGKey) – random number generator seed to sample from the prior. The returned init_params will have the batch shape rng_key.shape[:-1]. model – Python callable containing Pyro primitives. init_strategy (callable) – a per-site initialization function. enum (bool) – whether to enumerate over discrete latent sites. model_args (tuple) – args provided to the model. model_kwargs (dict) – kwargs provided to the model. prototype_params (dict) – an optional prototype parameters, which is used to define the shape for initial parameters. tuple of init_params_info and is_valid, where init_params_info is the tuple containing the initial params, their potential energy, and their gradients.

## Initialization Strategies¶

### init_to_feasible¶

init_to_feasible(site=None)[source]

Initialize to an arbitrary feasible point, ignoring distribution parameters.

### init_to_median¶

init_to_median(site=None, num_samples=15)[source]

Initialize to the prior median. For priors with no .sample method implemented, we defer to the init_to_uniform() strategy.

Parameters: num_samples (int) – number of prior points to calculate median.

### init_to_sample¶

init_to_sample(site=None)[source]

Initialize to a prior sample. For priors with no .sample method implemented, we defer to the init_to_uniform() strategy.

### init_to_uniform¶

init_to_uniform(site=None, radius=2)[source]

Initialize to a random point in the area (-radius, radius) of unconstrained domain.

Parameters: radius (float) – specifies the range to draw an initial point in the unconstrained domain.

### init_to_value¶

init_to_value(site=None, values={})[source]

Initialize to the value specified in values. We defer to init_to_uniform() strategy for sites which do not appear in values.

Parameters: values (dict) – dictionary of initial values keyed by site name.

## Tensor Indexing¶

vindex(tensor, args)[source]

See also the convenience wrapper Vindex.

This is useful for writing indexing code that is compatible with batching and enumeration, especially for selecting mixture components with discrete random variables.

For example suppose x is a parameter with len(x.shape) == 3 and we wish to generalize the expression x[i, :, j] from integer i,j to tensors i,j with batch dims and enum dims (but no event dims). Then we can write the generalize version using Vindex

xij = Vindex(x)[i, :, j]

event_shape = (x.size(1),)
assert xij.shape == batch_shape + event_shape


To handle the case when x may also contain batch dimensions (e.g. if x was sampled in a plated context as when using vectorized particles), vindex() uses the special convention that Ellipsis denotes batch dimensions (hence ... can appear only on the left, never in the middle or in the right). Suppose x has event dim 3. Then we can write:

old_batch_shape = x.shape[:-3]
old_event_shape = x.shape[-3:]

xij = Vindex(x)[..., i, :, j]   # The ... denotes unknown batch shape.

new_event_shape = (x.size(1),)
assert xij.shape = new_batch_shape + new_event_shape


Note that this special handling of Ellipsis differs from the NEP [1].

Formally, this function assumes:

1. Each arg is either Ellipsis, slice(None), an integer, or a batched integer tensor (i.e. with empty event shape). This function does not support Nontrivial slices or boolean tensor masks. Ellipsis can only appear on the left as args[0].
2. If args[0] is not Ellipsis then tensor is not batched, and its event dim is equal to len(args).
3. If args[0] is Ellipsis then tensor is batched and its event dim is equal to len(args[1:]). Dims of tensor to the left of the event dims are considered batch dims and will be broadcasted with dims of tensor args.

Note that if none of the args is a tensor with len(shape) > 0, then this function behaves like standard indexing:

if not any(isinstance(a, jnp.ndarray) and len(a.shape) > 0 for a in args):
assert Vindex(x)[args] == x[args]


References

introduces vindex as a helper for vectorized indexing. This implementation is similar to the proposed notation x.vindex[] except for slightly different handling of Ellipsis.
Parameters: tensor (jnp.ndarray) – A tensor to be indexed. args (tuple) – An index, as args to __getitem__. A nonstandard interpetation of tensor[args]. jnp.ndarray
class Vindex(tensor)[source]

Bases: object

Convenience wrapper around vindex().

The following are equivalent:

Vindex(x)[..., i, j, :]
vindex(x, (Ellipsis, i, j, slice(None)))

Parameters: tensor (jnp.ndarray) – A tensor to be indexed. An object with a special __getitem__() method.