Avoid spurious deprecation warning signature/extended_signature in Cu…

…stomDist

Also allow multivariate CustomDist to be created when signature suffices to infer core shape.

pymc/distributions/custom.py

@@ -27,7 +27,7 @@
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.random.type import RandomGeneratorType, RandomType
 from pytensor.tensor.random.utils import normalize_size_param
-from pytensor.tensor.utils import _parse_gufunc_signature, safe_signature
+from pytensor.tensor.utils import safe_signature
 
 from pymc.distributions.distribution import (
     Distribution,
@@ -108,19 +108,9 @@ def dist(
         class_name: str = "CustomDist",
         **kwargs,
     ):
-        if ndim_supp is None or ndims_params is None:
-            if signature is None:
-                ndim_supp = 0
-                ndims_params = [0] * len(dist_params)
-            else:
-                inputs, outputs = _parse_gufunc_signature(signature)
-                ndim_supp = max(len(out) for out in outputs)
-                ndims_params = [len(inp) for inp in inputs]
-
-        if ndim_supp > 0:
-            raise NotImplementedError(
-                "CustomDist with ndim_supp > 0 and without a `dist` function are not supported."
-            )
+        if ndim_supp is None and signature is None:
+            # Assume a scalar distribution
+            signature = safe_signature([0] * len(dist_params), [0])
 
         dist_params = [as_tensor_variable(param) for param in dist_params]
 
@@ -148,6 +138,7 @@ def dist(
             support_point=support_point,
             ndim_supp=ndim_supp,
             ndims_params=ndims_params,
+            signature=signature,
             dtype=dtype,
             class_name=class_name,
             **kwargs,
@@ -161,8 +152,9 @@ def rv_op(
         logcdf: Callable | None,
         random: Callable | None,
         support_point: Callable | None,
-        ndim_supp: int,
-        ndims_params: Sequence[int],
+        signature: str | None,
+        ndim_supp: int | None,
+        ndims_params: Sequence[int] | None,
         dtype: str,
         class_name: str,
         **kwargs,
@@ -175,6 +167,7 @@ def rv_op(
                 inplace=False,
                 ndim_supp=ndim_supp,
                 ndims_params=ndims_params,
+                signature=signature,
                 dtype=dtype,
                 _print_name=(class_name, f"\\operatorname{{{class_name}}}"),
                 # Specific to CustomDist
@@ -344,7 +337,7 @@ def change_custom_dist_size(op, rv, new_size, expand):
             new_rv_op = rv_type(
                 inputs=[*dummy_params, *rngs],
                 outputs=[dummy_rv, *rngs_updates],
-                signature=signature,
+                extended_signature=extended_signature,
             )
             new_rv = new_rv_op(new_size, *dist_params, *rngs)
 
@@ -357,13 +350,13 @@ def change_custom_dist_size(op, rv, new_size, expand):
 
         inputs = [*dummy_params, *rngs]
         outputs = [dummy_rv, *rngs_updates]
-        signature = cls._infer_final_signature(
+        extended_signature = cls._infer_final_signature(
             signature, n_inputs=len(inputs), n_outputs=len(outputs), n_rngs=len(rngs)
         )
         rv_op = rv_type(
             inputs=inputs,
             outputs=outputs,
-            signature=signature,
+            extended_signature=extended_signature,
         )
         return rv_op(size, *dist_params, *rngs)
 

tests/distributions/test_custom.py

@@ -53,6 +53,9 @@
 from pymc.step_methods import Metropolis
 from pymc.testing import assert_support_point_is_expected
 
+# Raise for any warnings in this file
+pytestmark = pytest.mark.filterwarnings("error")
+
 
 class TestCustomDist:
     @pytest.mark.parametrize("size", [(), (3,), (3, 2)], ids=str)
@@ -105,24 +108,24 @@ def test_custom_dist_without_random(self):
         with pytest.raises(NotImplementedError):
             sample_posterior_predictive(idata, model=model)
 
-    @pytest.mark.xfail(
-        NotImplementedError,
-        reason="Support shape of multivariate CustomDist cannot be inferred. See https://github.com/pymc-devs/pytensor/pull/388",
-    )
     @pytest.mark.parametrize("size", [(), (3,), (3, 2)], ids=str)
     def test_custom_dist_with_random_multivariate(self, size):
+        def random(mu, rng, size):
+            return rng.multivariate_normal(
+                mean=mu.ravel(),
+                cov=np.eye(mu.shape[-1]),
+                size=size,
+            )
+
         supp_shape = 5
         with Model() as model:
             mu = Normal("mu", 0, 1, size=supp_shape)
             obs = CustomDist(
                 "custom_dist",
                 mu,
-                random=lambda mu, rng=None, size=None: rng.multivariate_normal(
-                    mean=mu, cov=np.eye(len(mu)), size=size
-                ),
+                random=random,
                 observed=np.random.randn(100, *size, supp_shape),
-                ndims_params=[1],
-                ndim_supp=1,
+                signature="(n)->(n)",
             )
 
         assert isinstance(obs.owner.op, CustomDistRV)
@@ -156,20 +159,16 @@ def test_custom_dist_old_api_error(self):
             ):
                 CustomDist("a", lambda x: x)
 
-    @pytest.mark.xfail(
-        NotImplementedError,
-        reason="Support shape of multivariate CustomDist cannot be inferred. See https://github.com/pymc-devs/pytensor/pull/388",
-    )
     @pytest.mark.parametrize("size", [None, (), (2,)], ids=str)
     def test_custom_dist_multivariate_logp(self, size):
         supp_shape = 5
         with Model() as model:
 
             def logp(value, mu):
-                return MvNormal.logp(value, mu, pt.eye(mu.shape[0]))
+                return MvNormal.logp(value, mu, pt.eye(mu.shape[-1]))
 
             mu = Normal("mu", size=supp_shape)
-            a = CustomDist("a", mu, logp=logp, ndims_params=[1], ndim_supp=1, size=size)
+            a = CustomDist("a", mu, logp=logp, signature="(n)->(n)", size=size)
 
         assert isinstance(a.owner.op, CustomDistRV)
         mu_test_value = npr.normal(loc=0, scale=1, size=supp_shape).astype(pytensor.config.floatX)
@@ -219,10 +218,6 @@ def density_support_point(rv, size, mu):
         assert evaled_support_point.shape == to_tuple(size)
         assert np.all(evaled_support_point == mu_val)
 
-    @pytest.mark.xfail(
-        NotImplementedError,
-        reason="Support shape of multivariate CustomDist cannot be inferred. See https://github.com/pymc-devs/pytensor/pull/388",
-    )
     @pytest.mark.parametrize("size", [(), (2,), (3, 2)], ids=str)
     def test_custom_dist_custom_support_point_multivariate(self, size):
         def density_support_point(rv, size, mu):
@@ -235,19 +230,14 @@ def density_support_point(rv, size, mu):
                 "a",
                 mu,
                 support_point=density_support_point,
-                ndims_params=[1],
-                ndim_supp=1,
+                signature="(n)->(n)",
                 size=size,
             )
         assert isinstance(a.owner.op, CustomDistRV)
         evaled_support_point = support_point(a).eval({mu: mu_val})
         assert evaled_support_point.shape == (*to_tuple(size), 5)
         assert np.all(evaled_support_point == mu_val)
 
-    @pytest.mark.xfail(
-        NotImplementedError,
-        reason="Support shape of multivariate CustomDist cannot be inferred. See https://github.com/pymc-devs/pytensor/pull/388",
-    )
     @pytest.mark.parametrize(
         "with_random, size",
         [
@@ -267,21 +257,14 @@ def _random(mu, rng=None, size=None):
         else:
             random = None
 
-        mu_val = np.random.normal(loc=2, scale=1, size=5).astype(pytensor.config.floatX)
         with Model():
             mu = Normal("mu", size=5)
-            a = CustomDist("a", mu, random=random, ndims_params=[1], ndim_supp=1, size=size)
+            a = CustomDist("a", mu, random=random, signature="(n)->(n)", size=size)
         assert isinstance(a.owner.op, CustomDistRV)
         if with_random:
-            evaled_support_point = support_point(a).eval({mu: mu_val})
+            evaled_support_point = support_point(a).eval()
             assert evaled_support_point.shape == (*to_tuple(size), 5)
             assert np.all(evaled_support_point == 0)
-        else:
-            with pytest.raises(
-                TypeError,
-                match="Cannot safely infer the size of a multivariate random variable's support_point.",
-            ):
-                evaled_support_point = support_point(a).eval({mu: mu_val})
 
     def test_dist(self):
         mu = 1
@@ -300,6 +283,12 @@ def test_dist(self):
         x_logp = logp(x, test_value)
         assert np.allclose(x_logp.eval(), st.norm(1).logpdf(test_value))
 
+    def test_multivariate_insufficient_signature(self):
+        with pytest.raises(
+            NotImplementedError, match="signature is not sufficient to infer the support shape"
+        ):
+            CustomDist.dist(signature="(n)->(m)")
+
 
 class TestCustomSymbolicDist:
     def test_basic(self):