Require independent dimensions in multivariate size arguments to Rand…

…omVariable

aesara/tensor/random/basic.py

@@ -321,14 +321,21 @@ def rng_fn(cls, rng, mean, cov, size):
 
         if mean.ndim > 1 or cov.ndim > 2:
             # Neither SciPy nor NumPy implement parameter broadcasting for
-            # multivariate normals (or many other multivariate distributions),
-            # so we have implement a quick and dirty one here
+            # multivariate normals (or any other multivariate distributions),
+            # so we need to implement that here
             mean, cov = broadcast_params([mean, cov], cls.ndims_params)
             size = tuple(size or ())
 
             if size:
-                mean = np.broadcast_to(mean, size + mean.shape)
-                cov = np.broadcast_to(cov, size + cov.shape)
+                if (
+                    0 < mean.ndim - 1 <= len(size)
+                    and size[-mean.ndim + 1 :] != mean.shape[:-1]
+                ):
+                    raise ValueError(
+                        "shape mismatch: objects cannot be broadcast to a single shape"
+                    )
+                mean = np.broadcast_to(mean, size + mean.shape[-1:])
+                cov = np.broadcast_to(cov, size + cov.shape[-2:])
 
             res = np.empty(mean.shape)
             for idx in np.ndindex(mean.shape[:-1]):
@@ -352,16 +359,33 @@ class DirichletRV(RandomVariable):
 
     @classmethod
     def rng_fn(cls, rng, alphas, size):
-        if size is None:
-            size = ()
-        samples_shape = tuple(np.atleast_1d(size)) + alphas.shape
-        samples = np.empty(samples_shape)
-        alphas_bcast = np.broadcast_to(alphas, samples_shape)
+        if alphas.ndim > 1:
+            if size is None:
+                size = ()
 
-        for index in np.ndindex(*samples_shape[:-1]):
-            samples[index] = rng.dirichlet(alphas_bcast[index])
+            size = tuple(np.atleast_1d(size))
 
-        return samples
+            if size:
+                if (
+                    0 < alphas.ndim - 1 <= len(size)
+                    and size[-alphas.ndim + 1 :] != alphas.shape[:-1]
+                ):
+                    raise ValueError(
+                        "shape mismatch: objects cannot be broadcast to a single shape"
+                    )
+                samples_shape = size + alphas.shape[-1:]
+            else:
+                samples_shape = alphas.shape
+
+            samples = np.empty(samples_shape)
+            alphas_bcast = np.broadcast_to(alphas, samples_shape)
+
+            for index in np.ndindex(*samples_shape[:-1]):
+                samples[index] = rng.dirichlet(alphas_bcast[index])
+
+            return samples
+        else:
+            return rng.dirichlet(alphas, size=size)
 
 
 dirichlet = DirichletRV()
@@ -579,8 +603,12 @@ def rng_fn(cls, rng, n, p, size):
             size = tuple(size or ())
 
             if size:
-                n = np.broadcast_to(n, size + n.shape)
-                p = np.broadcast_to(p, size + p.shape)
+                if 0 < p.ndim - 1 <= len(size) and size[-p.ndim + 1 :] != p.shape[:-1]:
+                    raise ValueError(
+                        "shape mismatch: objects cannot be broadcast to a single shape"
+                    )
+                n = np.broadcast_to(n, size)
+                p = np.broadcast_to(p, size + p.shape[-1:])
 
             res = np.empty(p.shape, dtype=cls.dtype)
             for idx in np.ndindex(p.shape[:-1]):

aesara/tensor/random/op.py

@@ -190,15 +190,14 @@ def _infer_shape(
 
         size_len = get_vector_length(size)
 
-        if self.ndim_supp == 0 and size_len > 0:
-            # In this case, we have a univariate distribution with a non-empty
-            # `size` parameter, which means that the `size` parameter
-            # completely determines the shape of the random variable.  More
-            # importantly, the `size` parameter may be the only correct source
-            # of information for the output shape, in that we would be misled
-            # by the `dist_params` if we tried to infer the relevant parts of
-            # the output shape from those.
-            return size
+        if size_len > 0:
+            if self.ndim_supp == 0:
+                return size
+            else:
+                supp_shape = self._shape_from_params(
+                    dist_params, param_shapes=param_shapes
+                )
+                return tuple(size) + tuple(supp_shape)
 
         # Broadcast the parameters
         param_shapes = params_broadcast_shapes(
@@ -307,19 +306,19 @@ def make_node(self, rng, size, dtype, *dist_params):
             Existing Aesara `Generator` or `RandomState` object to be used.  Creates a
             new one, if `None`.
         size: int or Sequence
-            Numpy-like size of the output (i.e. replications).
+            NumPy-like size parameter.
         dtype: str
             The dtype of the sampled output.  If the value ``"floatX"`` is
-            given, then ``dtype`` is set to ``aesara.config.floatX``.  This
-            value is only used when `self.dtype` isn't set.
+            given, then `dtype` is set to ``aesara.config.floatX``.  This value is
+            only used when ``self.dtype`` isn't set.
         dist_params: list
             Distribution parameters.
 
         Results
         -------
-        out: `Apply`
-            A node with inputs `(rng, size, dtype) + dist_args` and outputs
-            `(rng_var, out_var)`.
+        out: Apply
+            A node with inputs ``(rng, size, dtype) + dist_args`` and outputs
+            ``(rng_var, out_var)``.
 
         """
         size = normalize_size_param(size)

aesara/tensor/random/opt.py

@@ -83,9 +83,19 @@ def local_rv_size_lift(fgraph, node):
     if get_vector_length(size) > 0:
         dist_params = [
             broadcast_to(
-                p, (tuple(size) + tuple(p.shape)) if node.op.ndim_supp > 0 else size
+                p,
+                (
+                    tuple(size)
+                    + (
+                        tuple(p.shape)[-node.op.ndims_params[i] :]
+                        if node.op.ndims_params[i] > 0
+                        else ()
+                    )
+                )
+                if node.op.ndim_supp > 0
+                else size,
             )
-            for p in dist_params
+            for i, p in enumerate(dist_params)
         ]
     else:
         return

tests/tensor/random/test_basic.py

@@ -534,7 +534,7 @@ def mvnormal_test_fn(mean=None, cov=None, size=None, random_state=None):
                     np.eye(3, dtype=config.floatX) * 10.0,
                 ]
             ),
-            [2, 3],
+            [2, 3, 2],
         ),
         (
             np.array([[0, 1, 2], [4, 5, 6]], dtype=config.floatX),
@@ -551,12 +551,12 @@ def mvnormal_test_fn(mean=None, cov=None, size=None, random_state=None):
                     np.eye(3, dtype=config.floatX) * 10.0,
                 ]
             ),
-            [2, 3],
+            [2, 3, 2, 2],
         ),
         (
             np.array([[0], [10], [100]], dtype=config.floatX),
             np.eye(1, dtype=config.floatX) * 1e-6,
-            [2, 3],
+            [2, 3, 3],
         ),
     ],
 )
@@ -567,6 +567,11 @@ def test_mvnormal_samples(mu, cov, size):
 def test_mvnormal_default_args():
     rv_numpy_tester(multivariate_normal, test_fn=mvnormal_test_fn)
 
+    with pytest.raises(ValueError, match="shape mismatch.*"):
+        multivariate_normal.rng_fn(
+            None, np.zeros((1, 2)), np.ones((1, 2, 2)), size=(4,)
+        )
+
 
 @config.change_flags(compute_test_value="raise")
 def test_mvnormal_ShapeFeature():
@@ -596,11 +601,10 @@ def test_mvnormal_ShapeFeature():
     cov = at.as_tensor(test_covar).type()
     cov.tag.test_value = test_covar
 
-    d_rv = multivariate_normal(mean, cov, size=[2, 3])
+    d_rv = multivariate_normal(mean, cov, size=[2, 3, 2])
 
     fg = FunctionGraph(
-        [i for i in graph_inputs([d_rv]) if not isinstance(i, Constant)],
-        [d_rv],
+        outputs=[d_rv],
         clone=False,
         features=[ShapeFeature()],
     )
@@ -617,10 +621,13 @@ def test_mvnormal_ShapeFeature():
     "alphas, size",
     [
         (np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX), None),
-        (np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX), 10),
         (
             np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX),
-            (10, 2),
+            (10, 3),
+        ),
+        (
+            np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX),
+            (10, 2, 3),
         ),
     ],
 )
@@ -633,6 +640,15 @@ def dirichlet_test_fn(mean=None, cov=None, size=None, random_state=None):
     rv_numpy_tester(dirichlet, alphas, size=size, test_fn=dirichlet_test_fn)
 
 
+def test_dirichlet_rng():
+    alphas = np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX)
+
+    with pytest.raises(ValueError, match="shape mismatch.*"):
+        # The independent dimension's shape is missing from size (i.e. should
+        # be `(10, 2, 3)`)
+        dirichlet.rng_fn(None, alphas, size=(10, 2))
+
+
 M_at = iscalar("M")
 M_at.tag.test_value = 3
 
@@ -644,8 +660,8 @@ def dirichlet_test_fn(mean=None, cov=None, size=None, random_state=None):
         (at.ones((M_at,)), (M_at + 1,)),
         (at.ones((M_at,)), (2, M_at)),
         (at.ones((M_at, M_at + 1)), ()),
-        (at.ones((M_at, M_at + 1)), (M_at + 2,)),
-        (at.ones((M_at, M_at + 1)), (2, M_at + 2, M_at + 3)),
+        (at.ones((M_at, M_at + 1)), (M_at + 2, M_at)),
+        (at.ones((M_at, M_at + 1)), (2, M_at + 2, M_at + 3, M_at)),
     ],
 )
 def test_dirichlet_infer_shape(M, size):
@@ -684,8 +700,7 @@ def test_dirichlet_ShapeFeature():
     d_rv = dirichlet(at.ones((M_at, N_at)), name="Gamma")
 
     fg = FunctionGraph(
-        [i for i in graph_inputs([d_rv]) if not isinstance(i, Constant)],
-        [d_rv],
+        outputs=[d_rv],
         clone=False,
         features=[ShapeFeature()],
     )
@@ -1092,7 +1107,7 @@ def test_betabinom_samples(M, a, p, size):
         (
             np.array([10, 20], dtype=np.int64),
             np.array([[0.999, 0.001], [0.001, 0.999]], dtype=config.floatX),
-            (3,),
+            (3, 2),
             lambda *args, **kwargs: np.stack([np.array([[10, 0], [0, 20]])] * 3),
         ),
     ],
@@ -1109,6 +1124,16 @@ def test_multinomial_samples(M, p, size, test_fn):
     )
 
 
+def test_multinomial_rng():
+    test_M = np.array([10, 20], dtype=np.int64)
+    test_p = np.array([[0.999, 0.001], [0.001, 0.999]], dtype=config.floatX)
+
+    with pytest.raises(ValueError, match="shape mismatch.*"):
+        # The independent dimension's shape is missing from size (i.e. should
+        # be `(1, 2)`)
+        multinomial.rng_fn(None, test_M, test_p, size=(1,))
+
+
 @pytest.mark.parametrize(
     "p, size, test_fn",
     [

tests/tensor/random/test_opt.py

@@ -12,6 +12,7 @@
 from aesara.tensor.elemwise import DimShuffle
 from aesara.tensor.random.basic import (
     dirichlet,
+    multinomial,
     multivariate_normal,
     normal,
     poisson,
@@ -120,12 +121,20 @@ def test_inplace_optimization():
                 np.array([[0], [10], [100]], dtype=config.floatX),
                 np.diag(np.array([1e-6], dtype=config.floatX)),
             ],
-            [2, 3],
+            [2, 3, 3],
         ),
         (
             dirichlet,
             [np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX)],
-            [2, 3],
+            [2, 3, 3],
+        ),
+        (
+            multinomial,
+            [
+                np.array([10, 20], dtype="int64"),
+                np.array([[0.999, 0.001], [0.001, 0.999]], dtype=config.floatX),
+            ],
+            [3, 2],
         ),
     ],
 )
@@ -288,7 +297,7 @@ def test_local_rv_size_lift(dist_op, dist_params, size):
                 np.array([[-1, 20], [300, -4000]], dtype=config.floatX),
                 np.eye(2).astype(config.floatX) * 1e-6,
             ),
-            (3,),
+            (3, 2),
             1e-3,
         ),
     ],