Allow Truncation of CustomDists

pymc/distributions/truncated.py

@@ -17,18 +17,20 @@
 import pytensor
 import pytensor.tensor as pt
 
-from pytensor import scan
+from pytensor import config, graph_replace, scan
 from pytensor.graph import Op
 from pytensor.graph.basic import Node
 from pytensor.raise_op import CheckAndRaise
 from pytensor.scan import until
 from pytensor.tensor import TensorConstant, TensorVariable
 from pytensor.tensor.random.basic import NormalRV
 from pytensor.tensor.random.op import RandomVariable
+from pytensor.tensor.random.type import RandomType
 
 from pymc.distributions.continuous import TruncatedNormal, bounded_cont_transform
 from pymc.distributions.dist_math import check_parameters
 from pymc.distributions.distribution import (
+    CustomSymbolicDistRV,
     Distribution,
     SymbolicRandomVariable,
     _support_point,
@@ -38,8 +40,9 @@
 from pymc.distributions.transforms import _default_transform
 from pymc.exceptions import TruncationError
 from pymc.logprob.abstract import _logcdf, _logprob
-from pymc.logprob.basic import icdf, logcdf
+from pymc.logprob.basic import icdf, logcdf, logp
 from pymc.math import logdiffexp
+from pymc.pytensorf import collect_default_updates
 from pymc.util import check_dist_not_registered
 
 
@@ -49,11 +52,17 @@ class TruncatedRV(SymbolicRandomVariable):
     that represents a truncated univariate random variable.
     """
 
-    default_output = 1
-    base_rv_op = None
-    max_n_steps = None
-
-    def __init__(self, *args, base_rv_op: Op, max_n_steps: int, **kwargs):
+    default_output: int = 0
+    base_rv_op: Op
+    max_n_steps: int
+
+    def __init__(
+        self,
+        *args,
+        base_rv_op: Op,
+        max_n_steps: int,
+        **kwargs,
+    ):
         self.base_rv_op = base_rv_op
         self.max_n_steps = max_n_steps
         self._print_name = (
@@ -63,8 +72,13 @@ def __init__(self, *args, base_rv_op: Op, max_n_steps: int, **kwargs):
         super().__init__(*args, **kwargs)
 
     def update(self, node: Node):
-        """Return the update mapping for the internal RNG."""
-        return {node.inputs[-1]: node.outputs[0]}
+        """Return the update mapping for the internal RNGs.
+
+        TruncatedRVs are created in a way that the rng updats follow the same order as the input RNGs.
+        """
+        rngs = [inp for inp in node.inputs if isinstance(inp.type, RandomType)]
+        next_rngs = [out for out in node.outputs if isinstance(out.type, RandomType)]
+        return dict(zip(rngs, next_rngs))
 
 
 @singledispatch
@@ -141,10 +155,14 @@ class Truncated(Distribution):
 
     @classmethod
     def dist(cls, dist, lower=None, upper=None, max_n_steps: int = 10_000, **kwargs):
-        if not (isinstance(dist, TensorVariable) and isinstance(dist.owner.op, RandomVariable)):
+        if not (
+            isinstance(dist, TensorVariable)
+            and isinstance(dist.owner.op, (RandomVariable, CustomSymbolicDistRV))
+        ):
             if isinstance(dist.owner.op, SymbolicRandomVariable):
                 raise NotImplementedError(
-                    f"Truncation not implemented for SymbolicRandomVariable {dist.owner.op}"
+                    f"Truncation not implemented for SymbolicRandomVariable {dist.owner.op}.\n"
+                    f"You can try wrapping the distribution inside a CustomDist instead."
                 )
             raise ValueError(
                 f"Truncation dist must be a distribution created via the `.dist()` API, got {type(dist)}"
@@ -174,46 +192,54 @@ def rv_op(cls, dist, lower, upper, max_n_steps, size=None):
         if size is None:
             size = pt.broadcast_shape(dist, lower, upper)
         dist = change_dist_size(dist, new_size=size)
+        rv_inputs = [
+            inp
+            if not isinstance(inp.type, RandomType)
+            else pytensor.shared(np.random.default_rng())
+            for inp in dist.owner.inputs
+        ]
+        graph_inputs = [*rv_inputs, lower, upper]
 
         # Variables with `_` suffix identify dummy inputs for the OpFromGraph
-        graph_inputs = [*dist.owner.inputs[1:], lower, upper]
-        graph_inputs_ = [inp.type() for inp in graph_inputs]
+        graph_inputs_ = [
+            inp.type() if not isinstance(inp.type, RandomType) else inp for inp in graph_inputs
+        ]
         *rv_inputs_, lower_, upper_ = graph_inputs_
 
-        # We will use a Shared RNG variable because Scan demands it, even though it
-        # would not be necessary for the OpFromGraph inverse cdf.
-        rng = pytensor.shared(np.random.default_rng())
-        rv_ = dist.owner.op.make_node(rng, *rv_inputs_).default_output()
+        rv_ = dist.owner.op.make_node(*rv_inputs_).default_output()
 
         # Try to use inverted cdf sampling
+        # truncated_rv = icdf(rv, draw(uniform(cdf(lower), cdf(upper))))
         try:
-            # For left truncated discrete RVs, we need to include the whole lower bound.
-            # This may result in draws below the truncation range, if any uniform == 0
-            lower_value = lower_ - 1 if dist.owner.op.dtype.startswith("int") else lower_
-            cdf_lower_ = pt.exp(logcdf(rv_, lower_value))
-            cdf_upper_ = pt.exp(logcdf(rv_, upper_))
-            # It's okay to reuse the same rng here, because the rng in rv_ will not be
-            # used by either the logcdf of icdf functions
+            logcdf_lower_, logcdf_upper_ = Truncated._create_logcdf_exprs(rv_, rv_, lower_, upper_)
+            # We use the first RNG from the base RV, so we don't have to introduce a new one
+            # This is not problematic because the RNG won't be used in the RV logcdf graph
+            uniform_rng_ = next(inp_ for inp_ in rv_inputs_ if isinstance(inp_.type, RandomType))
             uniform_next_rng_, uniform_ = pt.random.uniform(
-                cdf_lower_,
-                cdf_upper_,
-                rng=rng,
-                size=rv_inputs_[0],
+                pt.exp(logcdf_lower_),
+                pt.exp(logcdf_upper_),
+                rng=uniform_rng_,
+                size=rv_.shape,
             ).owner.outputs
-            truncated_rv_ = icdf(rv_, uniform_)
+            truncated_rv_ = icdf(rv_, uniform_, warn_rvs=False)
             return TruncatedRV(
                 base_rv_op=dist.owner.op,
-                inputs=[*graph_inputs_, rng],
-                outputs=[uniform_next_rng_, truncated_rv_],
+                inputs=graph_inputs_,
+                outputs=[truncated_rv_, uniform_next_rng_],
                 ndim_supp=0,
                 max_n_steps=max_n_steps,
-            )(*graph_inputs, rng)
+            )(*graph_inputs)
         except NotImplementedError:
             pass
 
         # Fallback to rejection sampling
-        def loop_fn(truncated_rv, reject_draws, lower, upper, rng, *rv_inputs):
-            next_rng, new_truncated_rv = dist.owner.op.make_node(rng, *rv_inputs).outputs
+        # truncated_rv = zeros(rv.shape)
+        # reject_draws = ones(rv.shape, dtype=bool)
+        # while any(reject_draws):
+        #    truncated_rv[reject_draws] = draw(rv)[reject_draws]
+        #    reject_draws = (truncated_rv < lower) | (truncated_rv > upper)
+        def loop_fn(truncated_rv, reject_draws, lower, upper, *rv_inputs):
+            new_truncated_rv = dist.owner.op.make_node(*rv_inputs_).default_output()
             # Avoid scalar boolean indexing
             if truncated_rv.type.ndim == 0:
                 truncated_rv = new_truncated_rv
@@ -226,7 +252,7 @@ def loop_fn(truncated_rv, reject_draws, lower, upper, rng, *rv_inputs):
 
             return (
                 (truncated_rv, reject_draws),
-                [(rng, next_rng)],
+                collect_default_updates(new_truncated_rv),
                 until(~pt.any(reject_draws)),
             )
 
@@ -236,7 +262,7 @@ def loop_fn(truncated_rv, reject_draws, lower, upper, rng, *rv_inputs):
                 pt.zeros_like(rv_),
                 pt.ones_like(rv_, dtype=bool),
             ],
-            non_sequences=[lower_, upper_, rng, *rv_inputs_],
+            non_sequences=[lower_, upper_, *rv_inputs_],
             n_steps=max_n_steps,
             strict=True,
         )
@@ -246,24 +272,49 @@ def loop_fn(truncated_rv, reject_draws, lower, upper, rng, *rv_inputs):
         truncated_rv_ = TruncationCheck(f"Truncation did not converge in {max_n_steps} steps")(
             truncated_rv_, convergence_
         )
+        # Sort updates of each RNG so that they show in the same order as the input RNGs
+
+        def sort_updates(update):
+            rng, next_rng = update
+            return graph_inputs.index(rng)
+
+        next_rngs = [next_rng for rng, next_rng in sorted(updates.items(), key=sort_updates)]
 
-        [next_rng] = updates.values()
         return TruncatedRV(
             base_rv_op=dist.owner.op,
-            inputs=[*graph_inputs_, rng],
-            outputs=[next_rng, truncated_rv_],
+            inputs=graph_inputs_,
+            outputs=[truncated_rv_, *next_rngs],
             ndim_supp=0,
             max_n_steps=max_n_steps,
-        )(*graph_inputs, rng)
+        )(*graph_inputs)
+
+    @staticmethod
+    def _create_logcdf_exprs(
+        base_rv: TensorVariable,
+        value: TensorVariable,
+        lower: TensorVariable,
+        upper: TensorVariable,
+    ) -> tuple[TensorVariable, TensorVariable]:
+        """Create lower and upper logcdf expressions for base_rv.
+
+        Uses `value` as a template for broadcasting.
+        """
+        # For left truncated discrete RVs, we need to include the whole lower bound.
+        lower_value = lower - 1 if base_rv.type.dtype.startswith("int") else lower
+        lower_value = pt.full_like(value, lower_value, dtype=config.floatX)
+        upper_value = pt.full_like(value, upper, dtype=config.floatX)
+        lower_logcdf = logcdf(base_rv, lower_value, warn_rvs=False)
+        upper_logcdf = graph_replace(lower_logcdf, {lower_value: upper_value})
+        return lower_logcdf, upper_logcdf
 
 
 @_change_dist_size.register(TruncatedRV)
-def change_truncated_size(op, dist, new_size, expand):
-    *rv_inputs, lower, upper, rng = dist.owner.inputs
-    # Recreate the original untruncated RV
-    untruncated_rv = op.base_rv_op.make_node(rng, *rv_inputs).default_output()
+def change_truncated_size(op: TruncatedRV, truncated_rv, new_size, expand):
+    *rv_inputs, lower, upper = truncated_rv.owner.inputs
+    untruncated_rv = op.base_rv_op.make_node(*rv_inputs).default_output()
+
     if expand:
-        new_size = to_tuple(new_size) + tuple(dist.shape)
+        new_size = to_tuple(new_size) + tuple(truncated_rv.shape)
 
     return Truncated.rv_op(
         untruncated_rv,
@@ -275,11 +326,11 @@ def change_truncated_size(op, dist, new_size, expand):
 
 
 @_support_point.register(TruncatedRV)
-def truncated_support_point(op, rv, *inputs):
-    *rv_inputs, lower, upper, rng = inputs
+def truncated_support_point(op: TruncatedRV, truncated_rv, *inputs):
+    *rv_inputs, lower, upper = inputs
 
     # recreate untruncated rv and respective support_point
-    untruncated_rv = op.base_rv_op.make_node(rng, *rv_inputs).default_output()
+    untruncated_rv = op.base_rv_op.make_node(*rv_inputs).default_output()
     untruncated_support_point = support_point(untruncated_rv)
 
     fallback_support_point = pt.switch(
@@ -300,31 +351,25 @@ def truncated_support_point(op, rv, *inputs):
 
 
 @_default_transform.register(TruncatedRV)
-def truncated_default_transform(op, rv):
+def truncated_default_transform(op, truncated_rv):
     # Don't transform discrete truncated distributions
-    if op.base_rv_op.dtype.startswith("int"):
+    if truncated_rv.type.dtype.startswith("int"):
         return None
-    # Lower and Upper are the arguments -3 and -2
-    return bounded_cont_transform(op, rv, bound_args_indices=(-3, -2))
+    # Lower and Upper are the arguments -2 and -1
+    return bounded_cont_transform(op, truncated_rv, bound_args_indices=(-2, -1))
 
 
 @_logprob.register(TruncatedRV)
 def truncated_logprob(op, values, *inputs, **kwargs):
     (value,) = values
-
-    *rv_inputs, lower, upper, rng = inputs
-    rv_inputs = [rng, *rv_inputs]
+    *rv_inputs, lower, upper = inputs
 
     base_rv_op = op.base_rv_op
-    logp = _logprob(base_rv_op, (value,), *rv_inputs, **kwargs)
-    # For left truncated RVs, we don't want to include the lower bound in the
-    # normalization term
-    lower_value = lower - 1 if base_rv_op.dtype.startswith("int") else lower
-    lower_logcdf = _logcdf(base_rv_op, lower_value, *rv_inputs, **kwargs)
-    upper_logcdf = _logcdf(base_rv_op, upper, *rv_inputs, **kwargs)
-
+    base_rv = base_rv_op.make_node(*rv_inputs).default_output()
+    base_logp = logp(base_rv, value)
+    lower_logcdf, upper_logcdf = Truncated._create_logcdf_exprs(base_rv, value, lower, upper)
     if base_rv_op.name:
-        logp.name = f"{base_rv_op}_logprob"
+        base_logp.name = f"{base_rv_op}_logprob"
         lower_logcdf.name = f"{base_rv_op}_lower_logcdf"
         upper_logcdf.name = f"{base_rv_op}_upper_logcdf"
 
@@ -339,37 +384,31 @@ def truncated_logprob(op, values, *inputs, **kwargs):
     elif is_upper_bounded:
         lognorm = upper_logcdf
 
-    logp = logp - lognorm
+    truncated_logp = base_logp - lognorm
 
     if is_lower_bounded:
-        logp = pt.switch(value < lower, -np.inf, logp)
+        truncated_logp = pt.switch(value < lower, -np.inf, truncated_logp)
 
     if is_upper_bounded:
-        logp = pt.switch(value <= upper, logp, -np.inf)
+        truncated_logp = pt.switch(value <= upper, truncated_logp, -np.inf)
 
     if is_lower_bounded and is_upper_bounded:
-        logp = check_parameters(
-            logp,
+        truncated_logp = check_parameters(
+            truncated_logp,
             pt.le(lower, upper),
             msg="lower_bound <= upper_bound",
         )
 
-    return logp
+    return truncated_logp
 
 
 @_logcdf.register(TruncatedRV)
-def truncated_logcdf(op, value, *inputs, **kwargs):
-    *rv_inputs, lower, upper, rng = inputs
-    rv_inputs = [rng, *rv_inputs]
-
-    base_rv_op = op.base_rv_op
-    logcdf = _logcdf(base_rv_op, value, *rv_inputs, **kwargs)
+def truncated_logcdf(op: TruncatedRV, value, *inputs, **kwargs):
+    *rv_inputs, lower, upper = inputs
 
-    # For left truncated discrete RVs, we don't want to include the lower bound in the
-    # normalization term
-    lower_value = lower - 1 if base_rv_op.dtype.startswith("int") else lower
-    lower_logcdf = _logcdf(base_rv_op, lower_value, *rv_inputs, **kwargs)
-    upper_logcdf = _logcdf(base_rv_op, upper, *rv_inputs, **kwargs)
+    base_rv = op.base_rv_op.make_node(*rv_inputs).default_output()
+    base_logcdf = logcdf(base_rv, value)
+    lower_logcdf, upper_logcdf = Truncated._create_logcdf_exprs(base_rv, value, lower, upper)
 
     is_lower_bounded = not (isinstance(lower, TensorConstant) and np.all(np.isneginf(lower.value)))
     is_upper_bounded = not (isinstance(upper, TensorConstant) and np.all(np.isinf(upper.value)))
@@ -382,7 +421,7 @@ def truncated_logcdf(op, value, *inputs, **kwargs):
     elif is_upper_bounded:
         lognorm = upper_logcdf
 
-    logcdf_numerator = logdiffexp(logcdf, lower_logcdf) if is_lower_bounded else logcdf
+    logcdf_numerator = logdiffexp(base_logcdf, lower_logcdf) if is_lower_bounded else base_logcdf
     logcdf_trunc = logcdf_numerator - lognorm
 
     if is_lower_bounded:

tests/distributions/test_mixture.py

@@ -1588,8 +1588,8 @@ def test_hurdle_negativebinomial_graph(self):
         _, nonzero_dist = self.check_hurdle_mixture_graph(dist)
 
         assert isinstance(nonzero_dist.owner.op.base_rv_op, NegativeBinomial)
-        assert nonzero_dist.owner.inputs[2].data == n
-        assert nonzero_dist.owner.inputs[3].data == p
+        assert nonzero_dist.owner.inputs[-4].data == n
+        assert nonzero_dist.owner.inputs[-3].data == p
 
     def test_hurdle_gamma_graph(self):
         psi, alpha, beta = 0.25, 3, 4
@@ -1599,17 +1599,17 @@ def test_hurdle_gamma_graph(self):
         # Under the hood it uses the shape-scale parametrization of the Gamma distribution.
         # So the second value is the reciprocal of the rate (i.e. 1 / beta)
         assert isinstance(nonzero_dist.owner.op.base_rv_op, Gamma)
-        assert nonzero_dist.owner.inputs[2].data == alpha
-        assert nonzero_dist.owner.inputs[3].eval() == 1 / beta
+        assert nonzero_dist.owner.inputs[-4].data == alpha
+        assert nonzero_dist.owner.inputs[-3].eval() == 1 / beta
 
     def test_hurdle_lognormal_graph(self):
         psi, mu, sigma = 0.1, 2, 2.5
         dist = HurdleLogNormal.dist(psi=psi, mu=mu, sigma=sigma)
         _, nonzero_dist = self.check_hurdle_mixture_graph(dist)
 
         assert isinstance(nonzero_dist.owner.op.base_rv_op, LogNormal)
-        assert nonzero_dist.owner.inputs[2].data == mu
-        assert nonzero_dist.owner.inputs[3].data == sigma
+        assert nonzero_dist.owner.inputs[-4].data == mu
+        assert nonzero_dist.owner.inputs[-3].data == sigma
 
     @pytest.mark.parametrize(
         "dist, psi, non_psi_args",