Effect Handlers¶
This provides a small set of effect handlers in NumPyro that are modeled after Pyro’s poutine module. For a tutorial on effect handlers more generally, readers are encouraged to read Poutine: A Guide to Programming with Effect Handlers in Pyro. These simple effect handlers can be composed together or new ones added to enable implementation of custom inference utilities and algorithms.
Example
As an example, we are using seed
, trace
and substitute
handlers to define the log_likelihood function below.
We first create a logistic regression model and sample from the posterior distribution over
the regression parameters using MCMC()
. The log_likelihood function
uses effect handlers to run the model by substituting sample sites with values from the posterior
distribution and computes the log density for a single data point. The log_predictive_density
function computes the log likelihood for each draw from the joint posterior and aggregates the
results for all the data points, but does so by using JAX’s auto-vectorize transform called
vmap so that we do not need to loop over all the data points.
>>> import jax.numpy as jnp
>>> from jax import random, vmap
>>> from jax.scipy.special import logsumexp
>>> import numpyro
>>> import numpyro.distributions as dist
>>> from numpyro import handlers
>>> from numpyro.infer import MCMC, NUTS
>>> N, D = 3000, 3
>>> def logistic_regression(data, labels):
... coefs = numpyro.sample('coefs', dist.Normal(jnp.zeros(D), jnp.ones(D)))
... intercept = numpyro.sample('intercept', dist.Normal(0., 10.))
... logits = jnp.sum(coefs * data + intercept, axis=-1)
... return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels)
>>> data = random.normal(random.PRNGKey(0), (N, D))
>>> true_coefs = jnp.arange(1., D + 1.)
>>> logits = jnp.sum(true_coefs * data, axis=-1)
>>> labels = dist.Bernoulli(logits=logits).sample(random.PRNGKey(1))
>>> num_warmup, num_samples = 1000, 1000
>>> mcmc = MCMC(NUTS(model=logistic_regression), num_warmup=num_warmup, num_samples=num_samples)
>>> mcmc.run(random.PRNGKey(2), data, labels)
sample: 100%|██████████| 1000/1000 [00:00<00:00, 1252.39it/s, 1 steps of size 5.83e-01. acc. prob=0.85]
>>> mcmc.print_summary()
mean sd 5.5% 94.5% n_eff Rhat
coefs[0] 0.96 0.07 0.85 1.07 455.35 1.01
coefs[1] 2.05 0.09 1.91 2.20 332.00 1.01
coefs[2] 3.18 0.13 2.96 3.37 320.27 1.00
intercept -0.03 0.02 -0.06 0.00 402.53 1.00
>>> def log_likelihood(rng_key, params, model, *args, **kwargs):
... model = handlers.substitute(handlers.seed(model, rng_key), params)
... model_trace = handlers.trace(model).get_trace(*args, **kwargs)
... obs_node = model_trace['obs']
... return obs_node['fn'].log_prob(obs_node['value'])
>>> def log_predictive_density(rng_key, params, model, *args, **kwargs):
... n = list(params.values())[0].shape[0]
... log_lk_fn = vmap(lambda rng_key, params: log_likelihood(rng_key, params, model, *args, **kwargs))
... log_lk_vals = log_lk_fn(random.split(rng_key, n), params)
... return jnp.sum(logsumexp(log_lk_vals, 0) - jnp.log(n))
>>> print(log_predictive_density(random.PRNGKey(2), mcmc.get_samples(),
... logistic_regression, data, labels))
-874.89813
block¶
- class block(fn=None, hide_fn=None, hide=None, expose_types=None, expose=None)[source]¶
Bases:
Messenger
Given a callable fn, return another callable that selectively hides primitive sites from other effect handlers on the stack. In the absence of parameters, all primitive sites are blocked. hide_fn takes precedence over hide, which has higher priority than expose_types followed by expose. Only the parameter with the precedence is considered.
- Parameters:
fn (callable) – Python callable with NumPyro primitives.
hide_fn (callable) – function which when given a dictionary containing site-level metadata returns whether it should be blocked.
hide (list) – list of site names to hide.
expose_types (list) – list of site types to expose, e.g. [‘param’].
expose (list) – list of site names to expose.
- Returns:
Python callable with NumPyro primitives.
Example:
>>> from jax import random >>> import numpyro >>> from numpyro.handlers import block, seed, trace >>> import numpyro.distributions as dist >>> def model(): ... a = numpyro.sample('a', dist.Normal(0., 1.)) ... return numpyro.sample('b', dist.Normal(a, 1.)) >>> model = seed(model, random.PRNGKey(0)) >>> block_all = block(model) >>> block_a = block(model, lambda site: site['name'] == 'a') >>> trace_block_all = trace(block_all).get_trace() >>> assert not {'a', 'b'}.intersection(trace_block_all.keys()) >>> trace_block_a = trace(block_a).get_trace() >>> assert 'a' not in trace_block_a >>> assert 'b' in trace_block_a
collapse¶
- class collapse(*args, **kwargs)[source]¶
Bases:
trace
EXPERIMENTAL Collapses all sites in the context by lazily sampling and attempting to use conjugacy relations. If no conjugacy is known this will fail. Code using the results of sample sites must be written to accept Funsors rather than Tensors. This requires
funsor
to be installed.
condition¶
- class condition(fn=None, data=None, condition_fn=None)[source]¶
Bases:
Messenger
Conditions unobserved sample sites to values from data or condition_fn. Similar to
substitute
except that it only affects sample sites and changes the is_observed property to True.- Parameters:
fn – Python callable with NumPyro primitives.
data (dict) – dictionary of numpy.ndarray values keyed by site names.
condition_fn – callable that takes in a site dict and returns a numpy array or None (in which case the handler has no side effect).
Example:
>>> from jax import random >>> import numpyro >>> from numpyro.handlers import condition, seed, substitute, trace >>> import numpyro.distributions as dist >>> def model(): ... numpyro.sample('a', dist.Normal(0., 1.)) >>> model = seed(model, random.PRNGKey(0)) >>> exec_trace = trace(condition(model, {'a': -1})).get_trace() >>> assert exec_trace['a']['value'] == -1 >>> assert exec_trace['a']['is_observed']
do¶
- class do(fn=None, data=None)[source]¶
Bases:
Messenger
Given a stochastic function with some sample statements and a dictionary of values at names, set the return values of those sites equal to the values as if they were hard-coded to those values and introduce fresh sample sites with the same names whose values do not propagate.
Composes freely with
condition()
to represent counterfactual distributions over potential outcomes. See Single World Intervention Graphs [1] for additional details and theory.This is equivalent to replacing z = numpyro.sample(“z”, …) with z = 1. and introducing a fresh sample site numpyro.sample(“z”, …) whose value is not used elsewhere.
References:
Single World Intervention Graphs: A Primer, Thomas Richardson, James Robins
- Parameters:
fn – a stochastic function (callable containing Pyro primitive calls)
data – a
dict
mapping sample site names to interventions
Example:
>>> import jax.numpy as jnp >>> import numpyro >>> from numpyro.handlers import do, trace, seed >>> import numpyro.distributions as dist >>> def model(x): ... s = numpyro.sample("s", dist.LogNormal()) ... z = numpyro.sample("z", dist.Normal(x, s)) ... return z ** 2 >>> intervened_model = handlers.do(model, data={"z": 1.}) >>> with trace() as exec_trace: ... z_square = seed(intervened_model, 0)(1) >>> assert exec_trace['z']['value'] != 1. >>> assert not exec_trace['z']['is_observed'] >>> assert not exec_trace['z'].get('stop', None) >>> assert z_square == 1
infer_config¶
- class infer_config(fn=None, config_fn=None)[source]¶
Bases:
Messenger
Given a callable fn that contains NumPyro primitive calls and a callable config_fn taking a trace site and returning a dictionary, updates the value of the infer kwarg at a sample site to config_fn(site).
- Parameters:
fn – a stochastic function (callable containing NumPyro primitive calls)
config_fn – a callable taking a site and returning an infer dict
lift¶
- class lift(fn=None, prior=None)[source]¶
Bases:
Messenger
Given a stochastic function with
param
calls and a prior distribution, create a stochastic function where all param calls are replaced by sampling from prior. Prior should be a distribution or a dict of names to distributions.Consider the following NumPyro program:
>>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro.handlers import lift >>> >>> def model(x): ... s = numpyro.param("s", 0.5) ... z = numpyro.sample("z", dist.Normal(x, s)) ... return z ** 2 >>> lifted_model = lift(model, prior={"s": dist.Exponential(0.3)})
lift
makesparam
statements behave likesample
statements using the distributions inprior
. In this example, site s will now behave as if it was replaced withs = numpyro.sample("s", dist.Exponential(0.3))
.- Parameters:
fn – function whose parameters will be lifted to random values
prior – prior function in the form of a Distribution or a dict of Distributions
mask¶
reparam¶
- class reparam(fn=None, config=None)[source]¶
Bases:
Messenger
Reparametrizes each affected sample site into one or more auxiliary sample sites followed by a deterministic transformation [1].
To specify reparameterizers, pass a
config
dict or callable to the constructor. See thenumpyro.infer.reparam
module for available reparameterizers.Note some reparameterizers can examine the
*args,**kwargs
inputs of functions they affect; these reparameterizers require usinghandlers.reparam
as a decorator rather than as a context manager.- [1] Maria I. Gorinova, Dave Moore, Matthew D. Hoffman (2019)
“Automatic Reparameterisation of Probabilistic Programs” https://arxiv.org/pdf/1906.03028.pdf
replay¶
- class replay(fn=None, trace=None)[source]¶
Bases:
Messenger
Given a callable fn and an execution trace trace, return a callable which substitutes sample calls in fn with values from the corresponding site names in trace.
- Parameters:
fn – Python callable with NumPyro primitives.
trace – an OrderedDict containing execution metadata.
Example:
>>> from jax import random >>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro.handlers import replay, seed, trace >>> def model(): ... numpyro.sample('a', dist.Normal(0., 1.)) >>> exec_trace = trace(seed(model, random.PRNGKey(0))).get_trace() >>> print(exec_trace['a']['value']) -0.20584235 >>> replayed_trace = trace(replay(model, exec_trace)).get_trace() >>> print(exec_trace['a']['value']) -0.20584235 >>> assert replayed_trace['a']['value'] == exec_trace['a']['value']
scale¶
- class scale(fn=None, scale=1.0)[source]¶
Bases:
Messenger
This messenger rescales the log probability score.
This is typically used for data subsampling or for stratified sampling of data (e.g. in fraud detection where negatives vastly outnumber positives).
- Parameters:
scale (float or numpy.ndarray) – a positive scaling factor that is broadcastable to the shape of log probability.
scope¶
- class scope(fn=None, prefix='', divider='/', *, hide_types=None)[source]¶
Bases:
Messenger
This handler prepend a prefix followed by a divider to the name of sample sites.
Example:
>>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro.handlers import scope, seed, trace >>> def model(): ... with scope(prefix="a"): ... with scope(prefix="b", divider="."): ... return numpyro.sample("x", dist.Bernoulli(0.5)) ... >>> assert "a/b.x" in trace(seed(model, 0)).get_trace()
- Parameters:
seed¶
- class seed(fn=None, rng_seed=None, hide_types=None)[source]¶
Bases:
Messenger
JAX uses a functional pseudo random number generator that requires passing in a seed
PRNGKey()
to every stochastic function. The seed handler allows us to initially seed a stochastic function with aPRNGKey()
. Every call to thesample()
primitive inside the function results in a splitting of this initial seed so that we use a fresh seed for each subsequent call without having to explicitly pass in a PRNGKey to each sample call.- Parameters:
Note
Unlike in Pyro, numpyro.sample primitive cannot be used without wrapping it in seed handler since there is no global random state. As such, users need to use seed as a contextmanager to generate samples from distributions or as a decorator for their model callable (See below).
Note
The seed handler has a mutable attribute rng_key which keeps changing after each sample call. Hence an instance of this class (e.g. seed(model, rng_seed=0)) might create tracer-leaks when jitted. A solution is to close the instance in a function, e.g., seeded_model = lambda *args: seed(model, rng_seed=0)(*args). This seeded_model can be jitted.
Example:
>>> from jax import random >>> import numpyro >>> import numpyro.handlers >>> import numpyro.distributions as dist >>> # as context manager >>> with handlers.seed(rng_seed=1): ... x = numpyro.sample('x', dist.Normal(0., 1.)) >>> def model(): ... return numpyro.sample('y', dist.Normal(0., 1.)) >>> # as function decorator (/modifier) >>> y = handlers.seed(model, rng_seed=1)() >>> assert x == y
substitute¶
- class substitute(fn=None, data=None, substitute_fn=None)[source]¶
Bases:
Messenger
Given a callable fn and a dict data keyed by site names (alternatively, a callable substitute_fn), return a callable which substitutes all primitive calls in fn with values from data whose key matches the site name. If the site name is not present in data, there is no side effect.
If a substitute_fn is provided, then the value at the site is replaced by the value returned from the call to substitute_fn for the given site.
Note
This handler is mainly used for internal algorithms. For conditioning a generative model on observed data, please use the
condition
handler.- Parameters:
fn – Python callable with NumPyro primitives.
data (dict) – dictionary of numpy.ndarray values keyed by site names.
substitute_fn – callable that takes in a site dict and returns a numpy array or None (in which case the handler has no side effect).
Example:
>>> from jax import random >>> import numpyro >>> from numpyro.handlers import seed, substitute, trace >>> import numpyro.distributions as dist >>> def model(): ... numpyro.sample('a', dist.Normal(0., 1.)) >>> model = seed(model, random.PRNGKey(0)) >>> exec_trace = trace(substitute(model, {'a': -1})).get_trace() >>> assert exec_trace['a']['value'] == -1
trace¶
- class trace(fn=None)[source]¶
Bases:
Messenger
Returns a handler that records the inputs and outputs at primitive calls inside fn.
Example:
>>> from jax import random >>> import numpyro >>> import numpyro.distributions as dist >>> from numpyro.handlers import seed, trace >>> import pprint as pp >>> def model(): ... numpyro.sample('a', dist.Normal(0., 1.)) >>> exec_trace = trace(seed(model, random.PRNGKey(0))).get_trace() >>> pp.pprint(exec_trace) OrderedDict([('a', {'args': (), 'fn': <numpyro.distributions.continuous.Normal object at 0x7f9e689b1eb8>, 'is_observed': False, 'kwargs': {'rng_key': Array([0, 0], dtype=uint32)}, 'name': 'a', 'type': 'sample', 'value': Array(-0.20584235, dtype=float32)})])