Use static-only broadcasting rules to compute shape of broadcasting (#345)

pytensor/tensor/extra_ops.py

 from collections.abc import Collection
-from functools import reduce
 from typing import Iterable, Set, Tuple, Union
 
 import numpy as np
-import numpy.core.numeric
 from numpy.core.multiarray import normalize_axis_index
 
 import pytensor
@@ -14,7 +12,7 @@ from pytensor.gradient import (
     disconnected_type,
     grad_undefined,
 )
-from pytensor.graph.basic import Apply, Constant, Variable, equal_computations
+from pytensor.graph.basic import Apply, Constant, Variable
 from pytensor.graph.op import Op
 from pytensor.link.c.op import COp
 from pytensor.link.c.params_type import ParamsType
@@ -23,12 +21,12 @@ from pytensor.misc.safe_asarray import _asarray
 from pytensor.raise_op import Assert
 from pytensor.scalar import int32 as int_t
 from pytensor.scalar import upcast
-from pytensor.scalar.basic import Composite
 from pytensor.tensor import basic as at
 from pytensor.tensor import get_vector_length
 from pytensor.tensor.exceptions import NotScalarConstantError
 from pytensor.tensor.math import abs as at_abs
-from pytensor.tensor.math import all as at_all
+from pytensor.tensor.math import all as pt_all
+from pytensor.tensor.math import eq as pt_eq
 from pytensor.tensor.math import ge, lt, maximum, minimum, prod
 from pytensor.tensor.math import sum as at_sum
 from pytensor.tensor.subtensor import advanced_inc_subtensor1, set_subtensor
@@ -536,7 +534,7 @@ def bincount(x, weights=None, minlength=None, assert_nonneg=False):
 
     if assert_nonneg:
         assert_op = Assert("Input to bincount has negative values!")
-        x = assert_op(x, at_all(x >= 0))
+        x = assert_op(x, pt_all(x >= 0))
 
     max_value = at.cast(x.max() + 1, "int64")
 
@@ -1436,6 +1434,13 @@ def ravel_multi_index(multi_index, dims, mode="raise", order="C"):
     return RavelMultiIndex(mode=mode, order=order)(*args)
 
 
+_broadcast_assert = Assert(
+    "Could not broadcast dimensions. Broadcasting is only allowed along "
+    "axes that have a statically known length 1. Use `specify_shape` to "
+    "inform PyTensor of a known shape."
+)
+
+
 def broadcast_shape(*arrays, **kwargs) -> Tuple[aes.ScalarVariable, ...]:
     """Compute the shape resulting from broadcasting arrays.
 
@@ -1510,119 +1515,45 @@ def broadcast_shape_iter(
     result_dims = []
 
     for dim_shapes in zip(*array_shapes):
-        # Get the shapes in this dimension that are not definitively
-        # broadcastable (i.e. not symbolically known to be broadcastable)
-        maybe_non_bcast_shapes = [shape for shape in dim_shapes if shape != one_at]
+        # Get the shapes in this dimension that are not broadcastable
+        # (i.e. not symbolically known to be broadcastable)
+        non_bcast_shapes = [shape for shape in dim_shapes if shape != one_at]
 
-        if len(maybe_non_bcast_shapes) == 0:
+        if len(non_bcast_shapes) == 0:
             # Every shape was broadcastable in this dimension
             result_dims.append(one_at)
-        elif len(maybe_non_bcast_shapes) == 1:
+        elif len(non_bcast_shapes) == 1:
             # Only one shape might not be broadcastable in this dimension
-            result_dims.extend(maybe_non_bcast_shapes)
+            result_dims.extend(non_bcast_shapes)
         else:
             # More than one shape might not be broadcastable in this dimension
-
             nonconst_nb_shapes: Set[int] = set()
             const_nb_shapes: Set[Variable] = set()
-            for shape in maybe_non_bcast_shapes:
+            for shape in non_bcast_shapes:
                 if isinstance(shape, Constant):
                     const_nb_shapes.add(shape.value.item())
                 else:
                     nonconst_nb_shapes.add(shape)
 
             if len(const_nb_shapes) > 1:
-                raise ValueError("Could not broadcast dimensions")
-            elif len(const_nb_shapes) == 1:
-                (const_nb_shape,) = const_nb_shapes
-
-                assert const_nb_shape != 1
-
-                const_nt_shape_var = pytensor.scalar.ScalarConstant(
-                    pytensor.scalar.int64, const_nb_shape
+                raise ValueError(
+                    f"Could not broadcast dimensions. Incompatible shapes were {array_shapes}."
                 )
 
-                if len(nonconst_nb_shapes) > 0:
-                    # All the potential non-broadcast shapes need to either
-                    # be broadcastable or equal to the one non-broadcastable
-                    # constant `const_nt_shape_var`.
-                    assert_dim = Assert("Could not broadcast dimensions")
-
-                    scalar_nonconst_nb_shapes = [
-                        at.scalar_from_tensor(s)
-                        if isinstance(s.type, TensorType)
-                        else s
-                        for s in nonconst_nb_shapes
-                    ]
-
-                    dummy_nonconst_nb_shapes = [
-                        aes.get_scalar_type(dtype=v.dtype)()
-                        for v in scalar_nonconst_nb_shapes
-                    ]
-                    assert_cond = reduce(
-                        aes.and_,
-                        (
-                            aes.or_(
-                                aes.eq(nbv, one_at), aes.eq(nbv, const_nt_shape_var)
-                            )
-                            for nbv in dummy_nonconst_nb_shapes
-                        ),
-                    )
-                    assert_cond_op = Composite(dummy_nonconst_nb_shapes, [assert_cond])
-
-                    bcast_dim = assert_dim(
-                        const_nt_shape_var, assert_cond_op(*scalar_nonconst_nb_shapes)
-                    )
-                else:
-                    bcast_dim = const_nt_shape_var
+            if len(const_nb_shapes) == 1:
+                (first_length,) = const_nb_shapes
+                other_lengths = nonconst_nb_shapes
+                first_length = aes.as_scalar(first_length)
             else:
-                # There are no constant, non-broadcastable shapes in this
-                # dimension.
-
-                all_dims_equal = all(
-                    # TODO FIXME: This is a largely deficient, and expensive, means
-                    # of comparing graphs (and especially shapes)
-                    equal_computations([maybe_non_bcast_shapes[0]], [dim])
-                    for dim in maybe_non_bcast_shapes[1:]
-                )
+                first_length, *other_lengths = nonconst_nb_shapes
 
-                if all_dims_equal:
-                    result_dims.append(maybe_non_bcast_shapes[0])
-                    continue
-
-                scalar_maybe_non_bcast_shapes = [
-                    at.scalar_from_tensor(s) if isinstance(s.type, TensorType) else s
-                    for s in maybe_non_bcast_shapes
-                ]
-                dummy_maybe_non_bcast_shapes = [
-                    aes.get_scalar_type(dtype=v.dtype)()
-                    for v in scalar_maybe_non_bcast_shapes
-                ]
-                non_bcast_vec = [
-                    aes.switch(aes.eq(nbv, 1), -one_at, nbv)
-                    for nbv in dummy_maybe_non_bcast_shapes
-                ]
-                dim_max = aes.abs(reduce(aes.scalar_maximum, non_bcast_vec))
-                dim_max_op = Composite(dummy_maybe_non_bcast_shapes, [dim_max])
-
-                dummy_dim_max = dim_max_op(*dummy_maybe_non_bcast_shapes)
-
-                assert_dim = Assert("Could not broadcast dimensions")
-                assert_cond = reduce(
-                    aes.and_,
-                    (
-                        aes.or_(aes.eq(nbv, -one_at), aes.eq(nbv, dummy_dim_max))
-                        for nbv in non_bcast_vec
-                    ),
-                )
-                assert_cond_op = Composite(dummy_maybe_non_bcast_shapes, [assert_cond])
-
-                bcast_dim = assert_dim(
-                    dim_max_op(*scalar_maybe_non_bcast_shapes),
-                    assert_cond_op(*scalar_maybe_non_bcast_shapes),
-                )
+            if len(other_lengths) == 0:
+                result_dims.append(first_length)
+                continue
 
-            result_dims.append(bcast_dim)
+            # Add assert that all remaining shapes are equal
+            condition = pt_all([pt_eq(first_length, other) for other in other_lengths])
+            result_dims.append(_broadcast_assert(first_length, condition))
 
     return tuple(result_dims)
 

tests/tensor/rewriting/test_basic.py

@@ -1703,8 +1703,12 @@ class TestLocalElemwiseAlloc:
         ],
     )
     def test_basic(self, expr, x_shape, y_shape):
-        x = at.tensor(dtype="int64", shape=(None,) * len(x_shape), name="x")
-        y = at.tensor(dtype="int64", shape=(None,) * len(y_shape), name="y")
+        x = at.tensor(
+            dtype="int64", shape=(1 if val == 1 else None for val in x_shape), name="x"
+        )
+        y = at.tensor(
+            dtype="int64", shape=(1 if val == 1 else None for val in y_shape), name="y"
+        )
         z = expr(x, y)
 
         z_opt = pytensor.function(
@@ -1878,7 +1882,8 @@ class TestLocalElemwiseAlloc:
             mode=self.fast_run_mode,
         )
         self.verify_op_count(func, 0, Alloc)
-        self.verify_op_count(func, 1, Assert)
+        # The second assert is from the shape check...
+        self.verify_op_count(func, 2, Assert)
 
     def test_misc(self):
         x = row(dtype=self.dtype)

tests/tensor/rewriting/test_math.py

@@ -608,9 +608,10 @@ class TestAlgebraicCanonizer:
                 ((dv / dy) / dv, [dv, dy], [dvv, dyv], 1, "float64"),
                 ((fv / fy) / fv, [fv, fy], [fvv, fyv], 1, "float32"),
                 # must broadcast as their is a dimshuffle in the computation
-                ((dx / dv) / dx, [dx, dv], [dxv, dvv], 1, "float64"),
+                # The broadcast leads to an extra elemwise to check compatibility
+                ((dx / dv) / dx, [dx, dv], [dxv, dvv], 2, "float64"),
                 # topo: [Shape_i, Shape_i, Elemwise{reciprocal,no_inplace}(<TensorType(float64, row)>), Alloc]
-                ((fx / fv) / fx, [fx, fv], [fxv, fvv], 1, "float32"),
+                ((fx / fv) / fx, [fx, fv], [fxv, fvv], 2, "float32"),
                 # topo: [Shape_i, Shape_i, Elemwise{reciprocal,no_inplace}(<TensorType(float32, row)>), Alloc]
             ]
         ):
@@ -621,9 +622,12 @@ class TestAlgebraicCanonizer:
             elem = [t for t in topo if isinstance(t.op, Elemwise)]
             assert len(elem) == nb_elemwise
             assert isinstance(elem[0].op, (Elemwise,))
-            assert isinstance(
-                elem[0].op.scalar_op,
-                (aes.basic.Reciprocal, aes.basic.TrueDiv),
+            assert any(
+                isinstance(
+                    el.op.scalar_op,
+                    (aes.basic.Reciprocal, aes.basic.TrueDiv),
+                )
+                for el in elem
             )
             assert out_dtype == out.dtype
 

tests/tensor/test_extra_ops.py

@@ -1086,7 +1086,9 @@ def test_broadcast_shape_basic():
     assert any(
         isinstance(node.op, Assert) for node in applys_between([x_at, y_at], b_at)
     )
-    assert np.array_equal([z.eval() for z in b_at], b.shape)
+    # This should fail because it would need dynamic broadcasting
+    with pytest.raises(AssertionError):
+        assert np.array_equal([z.eval() for z in b_at], b.shape)
     b_at = broadcast_shape(shape_tuple(x_at), shape_tuple(y_at), arrays_are_shapes=True)
     assert np.array_equal([z.eval() for z in b_at], b.shape)
 
@@ -1183,8 +1185,8 @@ def test_broadcast_shape_constants():
 @pytest.mark.parametrize(
     ("s1_vals", "s2_vals", "exp_res"),
     [
-        ((2, 2), (1, 2), (2, 2)),
-        ((0, 2), (1, 2), (0, 2)),
+        ((2, 2), (1, 2), AssertionError),
+        ((0, 2), (1, 2), AssertionError),
         ((1, 2, 1), (2, 1, 2, 1), (2, 1, 2, 1)),
     ],
 )
@@ -1203,7 +1205,11 @@ def test_broadcast_shape_symbolic(s1_vals, s2_vals, exp_res):
     res = broadcast_shape(s1s, s2s, arrays_are_shapes=True)
     res = at.as_tensor(res)
 
-    assert tuple(res.eval(eval_point)) == exp_res
+    if exp_res is AssertionError:
+        with pytest.raises(AssertionError):
+            res.eval(eval_point)
+    else:
+        assert tuple(res.eval(eval_point)) == exp_res
 
 
 def test_broadcast_shape_symbolic_one_symbolic():
@@ -1395,7 +1401,7 @@ class TestBroadcastTo(utt.InferShapeTester):
 
 
 def test_broadcast_arrays():
-    x, y = at.dvector(), at.dmatrix()
+    x, y = at.tensor(shape=(1,), dtype="float64"), at.dmatrix()
     x_bcast, y_bcast = broadcast_arrays(x, y)
 
     py_mode = Mode("py", None)

tests/unittest_tools.py

@@ -255,7 +255,7 @@ class InferShapeTester:
         # Check that the Op is removed from the compiled function.
         if check_topo:
             topo_shape = shapes_function.maker.fgraph.toposort()
-            assert not any(isinstance(t.op, cls) for t in topo_shape)
+            assert not any(t in outputs for t in topo_shape)
         topo_out = outputs_function.maker.fgraph.toposort()
         assert any(isinstance(t.op, cls) for t in topo_out)
         # Check that the shape produced agrees with the actual shape.