Generalize broadcastables inference and fix Alloc broadcastables case

aesara/gpuarray/basic_ops.py

@@ -19,7 +19,7 @@ from aesara.graph.utils import MethodNotDefined
 from aesara.link.c.interface import HideC
 from aesara.scalar import bool as bool_t
 from aesara.scalar import int32 as int32_t
-from aesara.tensor.basic import Alloc, AllocEmpty, Join, Split, alloc_validate_shape
+from aesara.tensor.basic import Alloc, AllocEmpty, Join, Split, infer_broadcastable
 from aesara.tensor.shape import Reshape
 from aesara.tensor.type import TensorType, values_eq_approx_always_true
 
@@ -909,7 +909,7 @@ class GpuAlloc(HideC, Alloc):
 
     def make_node(self, value, *shape):
         value = as_gpuarray_variable(value, context_name=self.context_name)
-        sh, bcast = alloc_validate_shape(shape)
+        sh, bcast = infer_broadcastable(shape)
         if value.ndim > len(sh):
             TypeError(
                 "The GpuAlloc value to use has more dimensions "
@@ -1071,7 +1071,7 @@ class GpuAllocEmpty(HideC, AllocEmpty):
         )
 
     def make_node(self, *shape):
-        sh, bcast = alloc_validate_shape(shape)
+        sh, bcast = infer_broadcastable(shape)
         output = GpuArrayType(
             dtype=self.dtype, broadcastable=bcast, context_name=self.context_name
         )()

aesara/tensor/basic.py

@@ -22,7 +22,9 @@ from aesara import compile, config, printing
 from aesara import scalar as aes
 from aesara.gradient import DisconnectedType, grad_not_implemented, grad_undefined
 from aesara.graph.basic import Apply, Constant, Variable
+from aesara.graph.fg import FunctionGraph
 from aesara.graph.op import COp, Op
+from aesara.graph.opt_utils import optimize_graph
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import Type
 from aesara.misc.safe_asarray import _asarray
@@ -1324,43 +1326,44 @@ def identity_like(x):
     return eye(x.shape[0], x.shape[1], k=0, dtype=x.dtype)
 
 
-def alloc_validate_shape(shape):
-    sh = [as_tensor_variable(s) for s in shape]
-    bcast = []
-    for i, s in enumerate(sh):
+def infer_broadcastable(shape):
+    """Infer the broadcastable dimensions for `shape`.
 
-        def err_str():
-            if config.exception_verbosity == "high":
-                return "\n" + min_informative_str(s)
-            else:
-                return str(s)
+    `shape` will be validated and constant folded in order to determine
+    which dimensions are broadcastable (i.e. equal to ``1``).
+    """
+    from aesara.tensor.basic_opt import ShapeFeature, topo_constant_folding
 
-        if s.type.dtype not in integer_dtypes:
-            s_as_str = err_str()
-            raise TypeError(
-                "Shape arguments to Alloc must be integers, "
-                f"but argument {i} is not for apply node: {s_as_str}"
-            )
-        if s.ndim != 0:
-            s_as_str = err_str()
-            raise TypeError(
-                "Each shape dimension to Alloc must be a scalar, ",
-                f"but dimension {i} have {int(s.ndim)} dimensions for apply node: {s_as_str}",
-            )
+    def check_type(s):
+        if s.type.dtype in integer_dtypes:
+            return s
+
+        if config.exception_verbosity == "high":
+            s_as_str = "\n" + min_informative_str(s)
+        else:
+            s_as_str = str(s)
+
+        raise TypeError(f"Shapes must be scalar integers; got {s_as_str}")
 
-        # if s is constant 1, then we're broadcastable in that dim
-        try:
-            const_shp = get_scalar_constant_value(s)
-        except NotScalarConstantError:
-            const_shp = None
-        bcast.append(1 == const_shp)
+    sh = [check_type(as_tensor_variable(s, ndim=0)) for s in shape]
+
+    shape_fg = FunctionGraph(
+        outputs=sh,
+        features=[ShapeFeature()],
+        clone=True,
+    )
+    folded_shape = optimize_graph(shape_fg, custom_opt=topo_constant_folding).outputs
+
+    bcast = tuple(getattr(s, "data", s) == 1 for s in folded_shape)
     return sh, bcast
 
 
 class Alloc(COp):
     """Create a `TensorVariable` from an initial value and a desired shape.
 
-    alloc(value, shape0, shape1, ..., shapeN)
+    Usage:
+
+        alloc(value, shape0, shape1, ..., shapeN)
 
     Returns an N-dimensional tensor initialized by a value, using something
     equivalent to
@@ -1380,12 +1383,9 @@ class Alloc(COp):
     _f16_ok = True
     __props__ = ()
 
-    def validate_shape(self, shape):
-        return alloc_validate_shape(shape)
-
     def make_node(self, value, *shape):
         v = as_tensor_variable(value)
-        sh, bcast = alloc_validate_shape(shape)
+        sh, bcast = infer_broadcastable(shape)
         if v.ndim > len(sh):
             raise TypeError(
                 "The Alloc value to use has more dimensions"
@@ -4102,7 +4102,7 @@ class AllocEmpty(COp):
         return np.dtype(self.dtype).num
 
     def make_node(self, *_shape):
-        _shape, bcast = alloc_validate_shape(_shape)
+        _shape, bcast = infer_broadcastable(_shape)
         otype = TensorType(dtype=self.dtype, broadcastable=bcast)
         output = otype()
 
@@ -4363,7 +4363,6 @@ __all__ = [
     "tensor_copy",
     "transfer",
     "alloc",
-    "alloc_validate_shape",
     "identity_like",
     "eye",
     "triu",

aesara/tensor/extra_ops.py

@@ -1585,7 +1585,7 @@ class BroadcastTo(Op):
         a = aet.as_tensor_variable(a)
         shape = aet.as_tensor_variable(shape, ndim=1)
 
-        shape, bcast = aet.alloc_validate_shape(shape)
+        shape, bcast = aet.infer_broadcastable(shape)
 
         out = type(a.type)(dtype=a.type.dtype, broadcastable=bcast)()
 
@@ -1609,7 +1609,7 @@ class BroadcastTo(Op):
         d_wrt_a = broadcast_to(dout, shape).sum(axis=new_dims)
 
         # Determine the dimensions that were broadcast
-        _, shape_bcast = aet.alloc_validate_shape(shape)
+        _, shape_bcast = aet.infer_broadcastable(shape)
         bcast_sums = [
             i
             for i, (a_b, s_b) in enumerate(zip(a.broadcastable, shape_bcast[-a.ndim :]))

aesara/tensor/random/op.py

@@ -7,9 +7,7 @@ import numpy as np
 import aesara
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply, Variable
-from aesara.graph.fg import FunctionGraph
 from aesara.graph.op import Op
-from aesara.graph.opt_utils import optimize_graph
 from aesara.misc.safe_asarray import _asarray
 from aesara.scalar import ScalarVariable
 from aesara.tensor.basic import (
@@ -17,8 +15,8 @@ from aesara.tensor.basic import (
     constant,
     get_scalar_constant_value,
     get_vector_length,
+    infer_broadcastable,
 )
-from aesara.tensor.basic_opt import ShapeFeature, topo_constant_folding
 from aesara.tensor.random.type import RandomType
 from aesara.tensor.random.utils import normalize_size_param, params_broadcast_shapes
 from aesara.tensor.shape import shape_tuple
@@ -276,31 +274,6 @@ class RandomVariable(Op):
 
         return shape
 
-    @config.change_flags(compute_test_value="off")
-    def compute_bcast(self, dist_params, size):
-        """Compute the broadcast array for this distribution's `TensorType`.
-
-        Parameters
-        ----------
-        dist_params: list
-            Distribution parameters.
-        size: int or Sequence (optional)
-            Numpy-like size of the output (i.e. replications).
-
-        """
-        shape = self._infer_shape(size, dist_params)
-
-        shape_fg = FunctionGraph(
-            outputs=[as_tensor_variable(s, ndim=0) for s in shape],
-            features=[ShapeFeature()],
-            clone=True,
-        )
-        folded_shape = optimize_graph(
-            shape_fg, custom_opt=topo_constant_folding
-        ).outputs
-
-        return [getattr(s, "data", s) == 1 for s in folded_shape]
-
     def infer_shape(self, fgraph, node, input_shapes):
         _, size, _, *dist_params = node.inputs
         _, size_shape, _, *param_shapes = input_shapes
@@ -362,7 +335,8 @@ class RandomVariable(Op):
                 "The type of rng should be an instance of either RandomGeneratorType or RandomStateType"
             )
 
-        bcast = self.compute_bcast(dist_params, size)
+        shape = self._infer_shape(size, dist_params)
+        _, bcast = infer_broadcastable(shape)
         dtype = self.dtype or dtype
 
         if dtype == "floatX":

tests/tensor/random/test_op.py

@@ -129,12 +129,12 @@ def test_RandomVariable_bcast():
     s3.tag.test_value = 3
     s3 = Assert("testing")(s3, eq(s1, 1))
 
-    res = rv.compute_bcast([mu, sd], (s1, s2, s3))
-    assert res == [False] * 3
+    res = rv(mu, sd, size=(s1, s2, s3))
+    assert res.broadcastable == (False,) * 3
 
     size = aet.as_tensor((1, 2, 3), dtype=np.int32).astype(np.int64)
-    res = rv.compute_bcast([mu, sd], size)
-    assert res == [True, False, False]
+    res = rv(mu, sd, size=size)
+    assert res.broadcastable == (True, False, False)
 
     res = rv(0, 1, size=aet.as_tensor(1, dtype=np.int64))
     assert res.broadcastable == (True,)

tests/tensor/test_basic.py

@@ -59,6 +59,7 @@ from aesara.tensor.basic import (
     get_scalar_constant_value,
     get_vector_length,
     horizontal_stack,
+    infer_broadcastable,
     inverse_permutation,
     join,
     make_vector,
@@ -90,7 +91,7 @@ from aesara.tensor.elemwise import DimShuffle
 from aesara.tensor.exceptions import NotScalarConstantError
 from aesara.tensor.math import dense_dot, eq
 from aesara.tensor.math import sum as aet_sum
-from aesara.tensor.shape import Reshape, Shape, Shape_i, shape_padright
+from aesara.tensor.shape import Reshape, Shape, Shape_i, shape_padright, specify_shape
 from aesara.tensor.type import (
     TensorType,
     bvector,
@@ -658,6 +659,24 @@ class TestAlloc:
         assert np.array_equal(res, np.full((2, 3), 3, dtype="int64"))
 
 
+def test_infer_broadcastable():
+    with pytest.raises(TypeError, match="^Shapes must be scalar integers.*"):
+        infer_broadcastable([constant(1.0)])
+
+    with config.change_flags(exception_verbosity="high"), pytest.raises(
+        TypeError, match=r"A\. x"
+    ):
+        infer_broadcastable([dscalar("x")])
+
+    with pytest.raises(ValueError, match=".*could not be cast to have 0 dimensions"):
+        infer_broadcastable((as_tensor_variable([[1, 2]]),))
+
+    constant_size = constant([1])
+    specify_size = specify_shape(constant_size, [1])
+    sh, bcast = infer_broadcastable(specify_size)
+    assert bcast == (True,)
+
+
 # This is slow for the ('int8', 3) version.
 def test_eye():
     def check(dtype, N, M_=None, k=0):