Support for mutable params

Makefile

@@ -17,7 +17,7 @@ install: FORCE
 	pip install -e .[dev,doc,test,examples]
 
 doctest: FORCE
-	$(MAKE) -C docs doctest
+	JAX_PLATFORM_NAME=cpu $(MAKE) -C docs doctest
 
 test: lint FORCE
 	pytest -v test

examples/covtype.py

@@ -174,7 +174,8 @@ def benchmark_hmc(args, features, labels):
         subsample_size = 1000
         guide = AutoBNAFNormal(model, num_flows=1, hidden_factors=[8])
         svi = SVI(model, guide, numpyro.optim.Adam(0.01), Trace_ELBO())
-        params, losses = svi.run(random.PRNGKey(2), 2000, features, labels)
+        svi_result = svi.run(random.PRNGKey(2), 2000, features, labels)
+        params, losses = svi_result.params, svi_result.losses
         plt.plot(losses)
         plt.show()
 

examples/hmcecs.py

@@ -50,9 +50,8 @@ def run_hmcecs(hmcecs_key, args, data, obs, inner_kernel):
     optimizer = numpyro.optim.Adam(step_size=1e-3)
     guide = autoguide.AutoDelta(model)
     svi = SVI(model, guide, optimizer, loss=Trace_ELBO())
-    params, losses = svi.run(
-        svi_key, args.num_svi_steps, data, obs, args.subsample_size
-    )
+    svi_result = svi.run(svi_key, args.num_svi_steps, data, obs, args.subsample_size)
+    params, losses = svi_result.params, svi_result.losses
     ref_params = {"theta": params["theta_auto_loc"]}
 
     # taylor proxy estimates log likelihood (ll) by

numpyro/contrib/control_flow/cond.py

@@ -111,7 +111,7 @@ def cond(pred, true_fun, false_fun, operand):
        ...     return cond(cluster > 0, true_fun, false_fun, None)
        >>>
        >>> svi = SVI(model, guide, numpyro.optim.Adam(1e-2), Trace_ELBO(num_particles=100))
-       >>> params, losses = svi.run(random.PRNGKey(0), num_steps=2500)
+       >>> svi_result = svi.run(random.PRNGKey(0), num_steps=2500)
 
     .. warning:: This is an experimental utility function that allows users to use
         JAX control flow with NumPyro's effect handlers. Currently, `sample` and

numpyro/handlers.py

@@ -600,7 +600,7 @@ class scope(Messenger):
     """
     This handler prepend a prefix followed by a divider to the name of sample sites.
 
-    Example::
+    **Example**
 
     .. doctest::
 
@@ -745,7 +745,7 @@ def __init__(self, fn=None, data=None, substitute_fn=None):
         super(substitute, self).__init__(fn)
 
     def process_message(self, msg):
-        if (msg["type"] not in ("sample", "param", "plate")) or msg.get(
+        if (msg["type"] not in ("sample", "param", "mutable", "plate")) or msg.get(
             "_control_flow_done", False
         ):
             if msg["type"] == "control_flow":

numpyro/infer/elbo.py

@@ -15,7 +15,60 @@
 from numpyro.infer.util import get_importance_trace, log_density
 
 
-class Trace_ELBO:
+class ELBO:
+    """
+    Base class for all ELBO objectives.
+
+    Subclasses should implement either :meth:`loss` or :meth:`loss_with_mutable_state`.
+
+    :param num_particles: The number of particles/samples used to form the ELBO
+        (gradient) estimators.
+    """
+
+    def __init__(self, num_particles=1):
+        self.num_particles = num_particles
+
+    def loss(self, rng_key, param_map, model, guide, *args, **kwargs):
+        """
+        Evaluates the ELBO with an estimator that uses num_particles many samples/particles.
+
+        :param jax.random.PRNGKey rng_key: random number generator seed.
+        :param dict param_map: dictionary of current parameter values keyed by site
+            name.
+        :param model: Python callable with NumPyro primitives for the model.
+        :param guide: Python callable with NumPyro primitives for the guide.
+        :param args: arguments to the model / guide (these can possibly vary during
+            the course of fitting).
+        :param kwargs: keyword arguments to the model / guide (these can possibly vary
+            during the course of fitting).
+        :return: negative of the Evidence Lower Bound (ELBO) to be minimized.
+        """
+        return self.loss_with_mutable_state(
+            rng_key, param_map, model, guide, *args, **kwargs
+        )["loss"]
+
+    def loss_with_mutable_state(
+        self, rng_key, param_map, model, guide, *args, **kwargs
+    ):
+        """
+        Likes :meth:`loss` but also update and return the mutable state, which stores the
+        values at :func:`~numpyro.mutable` sites.
+
+        :param jax.random.PRNGKey rng_key: random number generator seed.
+        :param dict param_map: dictionary of current parameter values keyed by site
+            name.
+        :param model: Python callable with NumPyro primitives for the model.
+        :param guide: Python callable with NumPyro primitives for the guide.
+        :param args: arguments to the model / guide (these can possibly vary during
+            the course of fitting).
+        :param kwargs: keyword arguments to the model / guide (these can possibly vary
+            during the course of fitting).
+        :return: a tuple of ELBO loss and the mutable state
+        """
+        raise NotImplementedError("This ELBO objective does not support mutable state.")
+
+
+class Trace_ELBO(ELBO):
     """
     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
@@ -43,52 +96,56 @@ class Trace_ELBO:
     def __init__(self, num_particles=1):
         self.num_particles = num_particles
 
-    def loss(self, rng_key, param_map, model, guide, *args, **kwargs):
-        """
-        Evaluates the ELBO with an estimator that uses num_particles many samples/particles.
-
-        :param jax.random.PRNGKey rng_key: random number generator seed.
-        :param dict param_map: dictionary of current parameter values keyed by site
-            name.
-        :param model: Python callable with NumPyro primitives for the model.
-        :param guide: Python callable with NumPyro primitives for the guide.
-        :param args: arguments to the model / guide (these can possibly vary during
-            the course of fitting).
-        :param kwargs: keyword arguments to the model / guide (these can possibly vary
-            during the course of fitting).
-        :return: negative of the Evidence Lower Bound (ELBO) to be minimized.
-        """
-
+    def loss_with_mutable_state(
+        self, rng_key, param_map, model, guide, *args, **kwargs
+    ):
         def single_particle_elbo(rng_key):
+            params = param_map.copy()
             model_seed, guide_seed = random.split(rng_key)
             seeded_model = seed(model, model_seed)
             seeded_guide = seed(guide, guide_seed)
             guide_log_density, guide_trace = log_density(
                 seeded_guide, args, kwargs, param_map
             )
+            mutable_params = {
+                name: site["value"]
+                for name, site in guide_trace.items()
+                if site["type"] == "mutable"
+            }
+            params.update(mutable_params)
             seeded_model = replay(seeded_model, guide_trace)
-            model_log_density, _ = log_density(seeded_model, args, kwargs, param_map)
+            model_log_density, model_trace = log_density(
+                seeded_model, args, kwargs, params
+            )
+            mutable_params.update(
+                {
+                    name: site["value"]
+                    for name, site in model_trace.items()
+                    if site["type"] == "mutable"
+                }
+            )
 
             # log p(z) - log q(z)
-            elbo = model_log_density - guide_log_density
-            return elbo
+            elbo_particle = model_log_density - guide_log_density
+            if mutable_params:
+                if self.num_particles == 1:
+                    return elbo_particle, mutable_params
+                else:
+                    raise ValueError(
+                        "Currently, we only support mutable states with num_particles=1."
+                    )
+            else:
+                return elbo_particle, None
 
         # Return (-elbo) since by convention we do gradient descent on a loss and
         # the ELBO is a lower bound that needs to be maximized.
         if self.num_particles == 1:
-            return -single_particle_elbo(rng_key)
+            elbo, mutable_state = single_particle_elbo(rng_key)
+            return {"loss": -elbo, "mutable_state": mutable_state}
         else:
             rng_keys = random.split(rng_key, self.num_particles)
-            return -jnp.mean(vmap(single_particle_elbo)(rng_keys))
-
-
-class ELBO(Trace_ELBO):
-    def __init__(self, num_particles=1):
-        warnings.warn(
-            "Using ELBO directly in SVI is deprecated. Please use Trace_ELBO class instead.",
-            FutureWarning,
-        )
-        super().__init__(num_particles=num_particles)
+            elbos, mutable_state = vmap(single_particle_elbo)(rng_keys)
+            return {"loss": -jnp.mean(elbos), "mutable_state": mutable_state}
 
 
 def _get_log_prob_sum(site):
@@ -128,7 +185,7 @@ def _check_mean_field_requirement(model_trace, guide_trace):
         )
 
 
-class TraceMeanField_ELBO(Trace_ELBO):
+class TraceMeanField_ELBO(ELBO):
     """
     A trace implementation of ELBO-based SVI. This is currently the only
     ELBO estimator in NumPyro that uses analytic KL divergences when those
@@ -146,30 +203,31 @@ class TraceMeanField_ELBO(Trace_ELBO):
         dependency structures.
     """
 
-    def loss(self, rng_key, param_map, model, guide, *args, **kwargs):
-        """
-        Evaluates the ELBO with an estimator that uses num_particles many samples/particles.
-
-        :param jax.random.PRNGKey rng_key: random number generator seed.
-        :param dict param_map: dictionary of current parameter values keyed by site
-            name.
-        :param model: Python callable with NumPyro primitives for the model.
-        :param guide: Python callable with NumPyro primitives for the guide.
-        :param args: arguments to the model / guide (these can possibly vary during
-            the course of fitting).
-        :param kwargs: keyword arguments to the model / guide (these can possibly vary
-            during the course of fitting).
-        :return: negative of the Evidence Lower Bound (ELBO) to be minimized.
-        """
-
+    def loss_with_mutable_state(
+        self, rng_key, param_map, model, guide, *args, **kwargs
+    ):
         def single_particle_elbo(rng_key):
+            params = param_map.copy()
             model_seed, guide_seed = random.split(rng_key)
             seeded_model = seed(model, model_seed)
             seeded_guide = seed(guide, guide_seed)
             subs_guide = substitute(seeded_guide, data=param_map)
             guide_trace = trace(subs_guide).get_trace(*args, **kwargs)
-            subs_model = substitute(replay(seeded_model, guide_trace), data=param_map)
+            mutable_params = {
+                name: site["value"]
+                for name, site in guide_trace.items()
+                if site["type"] == "mutable"
+            }
+            params.update(mutable_params)
+            subs_model = substitute(replay(seeded_model, guide_trace), data=params)
             model_trace = trace(subs_model).get_trace(*args, **kwargs)
+            mutable_params.update(
+                {
+                    name: site["value"]
+                    for name, site in model_trace.items()
+                    if site["type"] == "mutable"
+                }
+            )
             _check_mean_field_requirement(model_trace, guide_trace)
 
             elbo_particle = 0
@@ -196,16 +254,26 @@ def single_particle_elbo(rng_key):
                     assert site["infer"].get("is_auxiliary")
                     elbo_particle = elbo_particle - _get_log_prob_sum(site)
 
-            return elbo_particle
+            if mutable_params:
+                if self.num_particles == 1:
+                    return elbo_particle, mutable_params
+                else:
+                    raise ValueError(
+                        "Currently, we only support mutable states with num_particles=1."
+                    )
+            else:
+                return elbo_particle, None
 
         if self.num_particles == 1:
-            return -single_particle_elbo(rng_key)
+            elbo, mutable_state = single_particle_elbo(rng_key)
+            return {"loss": -elbo, "mutable_state": mutable_state}
         else:
             rng_keys = random.split(rng_key, self.num_particles)
-            return -jnp.mean(vmap(single_particle_elbo)(rng_keys))
+            elbos, mutable_state = vmap(single_particle_elbo)(rng_keys)
+            return {"loss": -jnp.mean(elbos), "mutable_state": mutable_state}
 
 
-class RenyiELBO(Trace_ELBO):
+class RenyiELBO(ELBO):
     r"""
     An implementation of Renyi's :math:`\alpha`-divergence
     variational inference following reference [1].
@@ -235,24 +303,9 @@ def __init__(self, alpha=0, num_particles=2):
                 "for the case alpha = 1."
             )
         self.alpha = alpha
-        super(RenyiELBO, self).__init__(num_particles=num_particles)
+        super().__init__(num_particles=num_particles)
 
     def loss(self, rng_key, param_map, model, guide, *args, **kwargs):
-        """
-        Evaluates the Renyi ELBO with an estimator that uses num_particles many samples/particles.
-
-        :param jax.random.PRNGKey rng_key: random number generator seed.
-        :param dict param_map: dictionary of current parameter values keyed by site
-            name.
-        :param model: Python callable with NumPyro primitives for the model.
-        :param guide: Python callable with NumPyro primitives for the guide.
-        :param args: arguments to the model / guide (these can possibly vary during
-            the course of fitting).
-        :param 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.
-        """
-
         def single_particle_elbo(rng_key):
             model_seed, guide_seed = random.split(rng_key)
             seeded_model = seed(model, model_seed)
@@ -458,7 +511,7 @@ def _compute_downstream_costs(model_trace, guide_trace, non_reparam_nodes):
     return downstream_costs, downstream_guide_cost_nodes
 
 
-class TraceGraph_ELBO:
+class TraceGraph_ELBO(ELBO):
     """
     A TraceGraph implementation of ELBO-based SVI. The gradient estimator
     is constructed along the lines of reference [1] specialized to the case
@@ -479,7 +532,7 @@ class TraceGraph_ELBO:
     """
 
     def __init__(self, num_particles=1):
-        self.num_particles = num_particles
+        super().__init__(num_particles=num_particles)
 
     def loss(self, rng_key, param_map, model, guide, *args, **kwargs):
         """