Log sampled basic_RVs sample_*_predictive functions

pymc/sampling.py

@@ -1622,7 +1622,7 @@ def compile_forward_sampling_function(
     basic_rvs: Optional[List[Variable]] = None,
     givens_dict: Optional[Dict[Variable, Any]] = None,
     **kwargs,
-) -> Callable[..., Union[np.ndarray, List[np.ndarray]]]:
+) -> Tuple[Callable[..., Union[np.ndarray, List[np.ndarray]]], Set[Variable]]:
     """Compile a function to draw samples, conditioned on the values of some variables.
 
     The goal of this function is to walk the aesara computational graph from the list
@@ -1635,13 +1635,10 @@ def compile_forward_sampling_function(
 
     - Variables in the outputs list
     - ``SharedVariable`` instances that are not ``RandomStateSharedVariable`` or ``RandomGeneratorSharedVariable``
-    - Basic RVs that are not in the ``vars_in_trace`` list
+    - Variables that are in the `basic_rvs` list but not in the ``vars_in_trace`` list
     - Variables that are keys in the ``givens_dict``
     - Variables that have volatile inputs
 
-    Where by basic RVs we mean ``Variable`` instances produced by a ``RandomVariable`` ``Op``
-    that are in the ``basic_rvs`` list.
-
     Concretely, this function can be used to compile a function to sample from the
     posterior predictive distribution of a model that has variables that are conditioned
     on ``MutableData`` instances. The variables that depend on the mutable data will be
@@ -1670,12 +1667,19 @@ def compile_forward_sampling_function(
         output of ``model.basic_RVs``) should have a reference to the variables that should
         be considered as random variable instances. This includes variables that have
         a ``RandomVariable`` owner op, but also unpure random variables like Mixtures, or
-        Censored distributions. If ``None``, only pure random variables will be considered
-        as potential random variables.
+        Censored distributions.
     givens_dict : Optional[Dict[aesara.graph.basic.Variable, Any]]
         A dictionary that maps tensor variables to the values that should be used to replace them
         in the compiled function. The types of the key and value should match or an error will be
         raised during compilation.
+
+    Returns
+    -------
+    function: Callable
+        Compiled forward sampling Aesara function
+    volatile_basic_rvs: Set of Variable
+        Set of all basic_rvs that were considered volatile and will be resampled when
+        the function is evaluated
     """
     if givens_dict is None:
         givens_dict = {}
@@ -1741,7 +1745,10 @@ def expand(node):
         for node, value in givens_dict.items()
     ]
 
-    return compile_pymc(inputs, fg.outputs, givens=givens, on_unused_input="ignore", **kwargs)
+    return (
+        compile_pymc(inputs, fg.outputs, givens=givens, on_unused_input="ignore", **kwargs),
+        set(basic_rvs) & (volatile_nodes - set(givens_dict)),  # Basic RVs that will be resampled
+    )
 
 
 def sample_posterior_predictive(
@@ -1900,7 +1907,6 @@ def sample_posterior_predictive(
         vars_ = model.observed_RVs + model.auto_deterministics
 
     indices = np.arange(samples)
-
     if progressbar:
         indices = progress_bar(indices, total=samples, display=progressbar)
 
@@ -1923,17 +1929,17 @@ def sample_posterior_predictive(
     compile_kwargs.setdefault("allow_input_downcast", True)
     compile_kwargs.setdefault("accept_inplace", True)
 
-    sampler_fn = point_wrapper(
-        compile_forward_sampling_function(
-            outputs=vars_to_sample,
-            vars_in_trace=vars_in_trace,
-            basic_rvs=model.basic_RVs,
-            givens_dict=None,
-            random_seed=random_seed,
-            **compile_kwargs,
-        )
+    _sampler_fn, volatile_basic_rvs = compile_forward_sampling_function(
+        outputs=vars_to_sample,
+        vars_in_trace=vars_in_trace,
+        basic_rvs=model.basic_RVs,
+        givens_dict=None,
+        random_seed=random_seed,
+        **compile_kwargs,
     )
-
+    sampler_fn = point_wrapper(_sampler_fn)
+    # All model variables have a name, but mypy does not know this
+    _log.info(f"Sampling: {list(sorted(volatile_basic_rvs, key=lambda var: var.name))}")  # type: ignore
     ppc_trace_t = _DefaultTrace(samples)
     try:
         if isinstance(_trace, MultiTrace):
@@ -2242,7 +2248,7 @@ def sample_prior_predictive(
     compile_kwargs.setdefault("allow_input_downcast", True)
     compile_kwargs.setdefault("accept_inplace", True)
 
-    sampler_fn = compile_forward_sampling_function(
+    sampler_fn, volatile_basic_rvs = compile_forward_sampling_function(
         vars_to_sample,
         vars_in_trace=[],
         basic_rvs=model.basic_RVs,
@@ -2251,6 +2257,8 @@ def sample_prior_predictive(
         **compile_kwargs,
     )
 
+    # All model variables have a name, but mypy does not know this
+    _log.info(f"Sampling: {list(sorted(volatile_basic_rvs, key=lambda var: var.name))}")  # type: ignore
     values = zip(*(sampler_fn() for i in range(samples)))
 
     data = {k: np.stack(v) for k, v in zip(names, values)}