Remove Unbroadcast Op

doc/library/tensor/basic.rst

@@ -619,9 +619,8 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
 
 .. function:: shape_padleft(x, n_ones=1)
 
-    Reshape `x` by left padding the shape with `n_ones` 1s. Note that all
-    this new dimension will be broadcastable. To make them non-broadcastable
-    see the :func:`unbroadcast`.
+    Reshape `x` by left padding the shape with `n_ones` 1s.
+    All new dimensions will be broadcastable.
 
     :param x: variable to be reshaped
     :type x: any `TensorVariable` (or compatible)
@@ -633,9 +632,8 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
 
 .. function:: shape_padright(x, n_ones=1)
 
-    Reshape `x` by right padding the shape with `n_ones` ones. Note that all
-    this new dimension will be broadcastable. To make them non-broadcastable
-    see the :func:`unbroadcast`.
+    Reshape `x` by right padding the shape with `n_ones` ones.
+    All new dimensions will be broadcastable.
 
     :param x: variable to be reshaped
     :type x: any TensorVariable (or compatible)
@@ -646,9 +644,8 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
 
 .. function:: shape_padaxis(t, axis)
 
-    Reshape `t` by inserting ``1`` at the dimension `axis`. Note that this new
-    dimension will be broadcastable. To make it non-broadcastable
-    see the :func:`unbroadcast`.
+    Reshape `t` by inserting ``1`` at the dimension `axis`.
+    All new dimensions will be broadcastable.
 
     :type x: any `TensorVariable` (or compatible)
     :param x: variable to be reshaped

pytensor/compile/function/pfunc.py

@@ -292,14 +292,8 @@ def rebuild_collect_shared(
                 f" shared_var.type={store_into.type},"
                 f" update_val={update_val}, update_val.type={getattr(update_val, 'type', None)})."
             )
-            err_sug = (
-                "If the difference is related to the broadcast pattern,"
-                " you can call the"
-                " tensor.shape.unbroadcast(var, axis_to_unbroadcast[, ...])"
-                " function to mask broadcastable dimensions."
-            )
 
-            raise TypeError(err_msg, err_sug)
+            raise TypeError(err_msg)
         assert store_into.type.is_super(update_val.type)
 
         update_d[store_into] = update_val

pytensor/ifelse.py

@@ -26,7 +26,7 @@ from pytensor.graph.op import _NoPythonOp
 from pytensor.graph.replace import clone_replace
 from pytensor.graph.rewriting.basic import GraphRewriter, in2out, node_rewriter
 from pytensor.graph.type import HasDataType, HasShape
-from pytensor.tensor.shape import Reshape, Shape, SpecifyShape, Unbroadcast
+from pytensor.tensor.shape import Reshape, Shape, SpecifyShape
 
 
 if TYPE_CHECKING:
@@ -481,7 +481,6 @@ acceptable_ops = (
     Shape,
     SpecifyShape,
     Reshape,
-    Unbroadcast,
     pt.math.Dot,
     pt.math.Max,
     pt.math.Argmax,

pytensor/link/jax/dispatch/shape.py

@@ -4,7 +4,7 @@ from pytensor.graph import Constant
 from pytensor.graph.basic import Apply
 from pytensor.graph.op import Op
 from pytensor.link.jax.dispatch.basic import jax_funcify
-from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape, Unbroadcast
+from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
 from pytensor.tensor.type import TensorType
 
 
@@ -104,11 +104,3 @@ def jax_funcify_SpecifyShape(op, node, **kwargs):
         return x
 
     return specifyshape
-
-
-@jax_funcify.register(Unbroadcast)
-def jax_funcify_Unbroadcast(op, **kwargs):
-    def unbroadcast(x):
-        return x
-
-    return unbroadcast

pytensor/link/numba/dispatch/tensor_basic.py

@@ -17,7 +17,6 @@ from pytensor.tensor.basic import (
     Split,
     TensorFromScalar,
 )
-from pytensor.tensor.shape import Unbroadcast
 
 
 @numba_funcify.register(AllocEmpty)
@@ -232,15 +231,6 @@ def makevector({", ".join(input_names)}):
     return numba_basic.numba_njit(makevector_fn)
 
 
-@numba_funcify.register(Unbroadcast)
-def numba_funcify_Unbroadcast(op, **kwargs):
-    @numba_basic.numba_njit
-    def unbroadcast(x):
-        return x
-
-    return unbroadcast
-
-
 @numba_funcify.register(TensorFromScalar)
 def numba_funcify_TensorFromScalar(op, **kwargs):
     @numba_basic.numba_njit(inline="always")

pytensor/link/pytorch/dispatch/shape.py

@@ -2,7 +2,7 @@ import torch
 
 from pytensor.graph.basic import Constant
 from pytensor.link.pytorch.dispatch.basic import pytorch_funcify
-from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape, Unbroadcast
+from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
 
 
 @pytorch_funcify.register(Reshape)
@@ -56,11 +56,3 @@ def pytorch_funcify_SpecifyShape(op, node, **kwargs):
         return x
 
     return specifyshape
-
-
-@pytorch_funcify.register(Unbroadcast)
-def pytorch_funcify_Unbroadcast(op, **kwargs):
-    def unbroadcast(x):
-        return x
-
-    return unbroadcast

pytensor/scan/basic.py

@@ -15,7 +15,7 @@ from pytensor.scan.utils import expand_empty, safe_new, until
 from pytensor.tensor.basic import get_underlying_scalar_constant_value
 from pytensor.tensor.exceptions import NotScalarConstantError
 from pytensor.tensor.math import minimum
-from pytensor.tensor.shape import shape_padleft, unbroadcast
+from pytensor.tensor.shape import shape_padleft
 from pytensor.tensor.type import TensorType, integer_dtypes
 from pytensor.updates import OrderedUpdates
 
@@ -748,7 +748,7 @@ def scan(
             # defined in scan utils
             sit_sot_scan_inputs.append(
                 expand_empty(
-                    unbroadcast(shape_padleft(actual_arg), 0),
+                    shape_padleft(actual_arg),
                     actual_n_steps,
                 )
             )
@@ -865,13 +865,13 @@ def scan(
     if n_fixed_steps in (1, -1):
         for pos, inner_out in enumerate(outputs):
             # we need to see if we need to pad our sequences with an
-            # unbroadcastable dimension; case example : we return an
+            # extra dimension; case example : we return an
             # output for which we want all intermediate. If n_steps is 1
             # then, if we return the output as given by the innner function
             # this will represent only a slice and it will have one
             # dimension less.
             if isinstance(inner_out.type, TensorType) and return_steps.get(pos, 0) != 1:
-                outputs[pos] = unbroadcast(shape_padleft(inner_out), 0)
+                outputs[pos] = shape_padleft(inner_out)
 
         if not return_list and len(outputs) == 1:
             outputs = outputs[0]
@@ -1002,7 +1002,7 @@ def scan(
                 sit_sot_inner_inputs.append(new_var)
                 sit_sot_scan_inputs.append(
                     expand_empty(
-                        unbroadcast(shape_padleft(input.variable), 0),
+                        shape_padleft(input.variable),
                         actual_n_steps,
                     )
                 )

pytensor/scan/op.py

@@ -166,8 +166,7 @@ def check_broadcast(v1, v2):
         "axis %d in `output_info`. This can happen if one of the "
         "dimension is fixed to 1 in the input, while it is still "
         "variable in the output, or vice-verca. You have to make "
-        "them consistent, e.g. using pytensor.tensor."
-        "{unbroadcast, specify_broadcastable}."
+        "them consistent, e.g. using pytensor.tensor.specify_broadcastable."
     )
     size = min(v1.type.ndim, v2.type.ndim)
     for n, (b1, b2) in enumerate(

pytensor/tensor/basic.py

@@ -53,7 +53,6 @@ from pytensor.tensor.exceptions import NotScalarConstantError
 from pytensor.tensor.shape import (
     Shape,
     Shape_i,
-    Unbroadcast,
     shape,
     shape_padaxis,
     shape_padleft,
@@ -334,9 +333,7 @@ def _get_underlying_scalar_constant_value(
         if not only_process_constants and getattr(v, "owner", None) and max_recur > 0:
             op = v.owner.op
             max_recur -= 1
-            if isinstance(
-                op, Alloc | DimShuffle | Unbroadcast | OutputGuard | DeepCopyOp
-            ):
+            if isinstance(op, Alloc | DimShuffle | OutputGuard | DeepCopyOp):
                 # OutputGuard is only used in debugmode but we
                 # keep it here to avoid problems with old pickles
                 v = v.owner.inputs[0]
@@ -498,14 +495,6 @@ def _get_underlying_scalar_constant_value(
                     grandparent = leftmost_parent.owner.inputs[0]
                     gp_shape = grandparent.type.shape
                     ndim = grandparent.type.ndim
-                    if grandparent.owner and isinstance(
-                        grandparent.owner.op, Unbroadcast
-                    ):
-                        ggp_shape = grandparent.owner.inputs[0].type.shape
-                        l = [
-                            _get_underlying_scalar_constant_value(s) for s in ggp_shape
-                        ]
-                        gp_shape = tuple(l)
 
                     if not (idx < ndim):
                         msg = (

pytensor/tensor/rewriting/shape.py

@@ -42,9 +42,7 @@ from pytensor.tensor.shape import (
     Shape,
     Shape_i,
     SpecifyShape,
-    Unbroadcast,
     specify_shape,
-    unbroadcast,
 )
 from pytensor.tensor.subtensor import Subtensor, get_idx_list
 from pytensor.tensor.type import TensorType, discrete_dtypes, integer_dtypes
@@ -1296,78 +1294,3 @@ def local_track_shape_i(fgraph, node):
     # structure.
     replacement = shape_feature.scheduled[node]
     return [shape_feature.shape_of[replacement][node.op.i]]
-
-
-@register_useless
-@register_canonicalize
-@register_specialize
-@node_rewriter([Unbroadcast])
-def local_useless_unbroadcast(fgraph, node):
-    """Remove `Unbroadcast` if it does not actually change the broadcasting pattern."""
-    if isinstance(node.op, Unbroadcast):
-        x = node.inputs[0]
-        if x.type.ndim == node.outputs[0].type.ndim and all(
-            s1 == s2
-            for s1, s2 in zip(x.type.shape, node.outputs[0].type.shape, strict=True)
-            if s1 == 1 or s2 == 1
-        ):
-            # No broadcastable flag was modified
-            # No need to copy over stack trace,
-            # because x should already have a stack trace.
-            return [x]
-        else:
-            # Keep the flags that modify something
-            new_axes = tuple(ax for ax in node.op.axes if x.type.shape[ax] == 1)
-            if new_axes == node.op.axes:
-                # All flags are useful
-                return None
-            else:
-                r = unbroadcast(x, *new_axes)
-                # Copy over stacktrace from previous output
-                copy_stack_trace(node.outputs, r)
-                return [r]
-
-
-@register_canonicalize
-@register_specialize
-@node_rewriter([Unbroadcast])
-def local_unbroadcast_lift(fgraph, node):
-    """
-    Lifts `Unbroadcast` through unary Elemwise operations,
-    and merges consecutive `Unbroadcast`s.
-
-    Unbroadcast(Elemwise(x)) => Elemwise(Unbroadcast(x))
-    Unbroadcast(Unbroadcast(x)) => Unbroadcast(x)
-
-    TODO: Implement equivalent Elemwise lift for SpecifyShape
-    """
-    op = node.op
-    if not isinstance(op, Unbroadcast):
-        return False
-
-    inp = node.inputs[0]
-    inode = inp.owner
-    if inode and isinstance(inode.op, Elemwise) and len(inode.inputs) == 1:
-        if len(fgraph.clients.get(inp, ())) == 1:
-            unbroadcasted = unbroadcast(inode.inputs[0], *op.axes)
-            copy_stack_trace(node.outputs, unbroadcasted)
-
-            rval = inode.op.make_node(unbroadcasted).outputs
-
-            # Copy over stacktrace from previous output (after unbroadcasting)
-            # and input (after elemwise operation) to new output, because an
-            # error in the new graph could have been caused by either of the
-            # two ops.
-            copy_stack_trace(node.outputs + node.inputs, rval)
-            return rval
-
-    if inode and isinstance(inode.op, Unbroadcast):
-        # Merge axis of each unbroadcast
-        axis = tuple(set(inode.op.axes).union(set(op.axes)))
-        iinput = inode.inputs[0]
-        rval = [unbroadcast(iinput, *axis)]
-        # Copy over stacktrace from previous output (after second unbroadcasting)
-        # and from previous input (after first unbroadcasting) because an error in
-        # the new graph could have been caused by either of the two Unbroadcast ops.
-        copy_stack_trace(node.outputs + node.inputs, rval)
-        return rval

pytensor/tensor/rewriting/subtensor.py

@@ -59,11 +59,9 @@ from pytensor.tensor.rewriting.basic import (
 from pytensor.tensor.shape import (
     Shape,
     SpecifyShape,
-    Unbroadcast,
     shape_padleft,
     shape_tuple,
     specify_shape,
-    unbroadcast,
 )
 from pytensor.tensor.sharedvar import TensorSharedVariable
 from pytensor.tensor.subtensor import (
@@ -429,7 +427,6 @@ def local_subtensor_lift(fgraph, node):
     Handles the following unary ops:
     elemwise(x,...)[idx] -> elemwise(x[idx],...)
       when x,... are broadcasted scalar or not broadcasted at all
-    Unbroadcast(x)[idx] => Unbroadcast(x[idx])
 
     """
     if isinstance(node.op, Subtensor):
@@ -488,40 +485,6 @@ def local_subtensor_lift(fgraph, node):
                 copy_stack_trace([node.outputs[0], node.inputs[0]], ret)
                 return [ret]
 
-        if isinstance(u.owner.op, Unbroadcast):
-            # Subtensor might reduce dim., adapt broadcast pattern accordingly
-            old_axes = u.owner.op.axes
-            new_axes = []
-
-            # loop through indices being subtensor-ed
-            # i indexes broadcastable pattern before subtensor
-            # j indexes broadcastable pattern after subtensor
-            j = 0
-            for i, x in enumerate(node.op.idx_list):
-                # if it is not a slice, it will reduce the dimension, should
-                # not appear in the broascastable dimensions
-                if isinstance(x, slice):
-                    if i in old_axes:
-                        new_axes.append(j)
-                    j += 1
-            # now keep the broadcastable pattern of all
-            # items not appearing in subtensor list
-            for i in range(len(node.op.idx_list), len(u.broadcastable)):
-                if i in old_axes:
-                    new_axes.append(j)
-                j += 1
-
-            subt_x = node.op(u.owner.inputs[0], *node.inputs[1:])
-            # Copy over previous output stacktrace
-            copy_stack_trace(node.outputs[0], subt_x)
-
-            rbcast_subt_x = unbroadcast(subt_x, *new_axes)
-            # Copy over previous output stacktrace
-            # and stacktrace from previous unary operation
-            copy_stack_trace([node.outputs[0], node.inputs[0]], rbcast_subt_x)
-
-            return [rbcast_subt_x]
-
 
 @register_canonicalize
 @register_specialize

pytensor/tensor/shape.py

@@ -18,7 +18,6 @@ from pytensor.link.c.params_type import ParamsType
 from pytensor.npy_2_compat import normalize_axis_tuple
 from pytensor.tensor import _get_vector_length, as_tensor_variable, get_vector_length
 from pytensor.tensor import basic as ptb
-from pytensor.tensor.elemwise import get_normalized_batch_axes
 from pytensor.tensor.exceptions import NotScalarConstantError
 from pytensor.tensor.type import DenseTensorType, TensorType, int_dtypes, tensor
 from pytensor.tensor.type_other import NoneConst, NoneTypeT
@@ -1008,118 +1007,3 @@ def specify_broadcastable(x, *axes):
     axes = normalize_axis_tuple(axes, x.type.ndim)
     shape_info = [1 if i in axes else s for i, s in enumerate(x.type.shape)]
     return specify_shape(x, shape_info)
-
-
-class Unbroadcast(COp):
-    """
-    Mask static broadcastable dimensions of input as `None`
-
-    See Also
-    --------
-    unbroadcast <pytensor.tensor.shape.unbroadcast>
-
-
-    Examples
-    --------
-    ``Unbroadcast((1,))(x)`` would make `x` second static dimension be `None`
-
-    """
-
-    view_map = {0: [0]}
-    _f16_ok = True
-    # Mapping from Type to C code (and version) to use.
-    # In the C code, the name of the input variable is %(iname)s,
-    # the output variable is %(oname)s.
-    c_code_and_version: dict = {}
-
-    check_input = False
-    __props__ = ("axes",)
-    _f16_ok = True
-
-    def __init__(self, *axis):
-        # Sort them to make sure we merge all possible case.
-        items = tuple(sorted(axis))
-        self.axes = items
-        for axis in self.axes:
-            if not isinstance(axis, np.integer | int):
-                raise TypeError(f"Unbroadcast needs integer axes. Got {axis}")
-
-    def __str__(self):
-        return f"{self.__class__.__name__}{{{','.join(str(i) for i in self.axes)}}}"
-
-    def make_node(self, x):
-        x = as_tensor_variable(x)
-        if x.type.ndim <= max(self.axes):
-            raise ValueError("Trying to unbroadcast of non-existent dimension")
-        shape = [
-            None if (sh == 1 and i in self.axes) else sh
-            for i, sh in enumerate(x.type.shape)
-        ]
-        return Apply(self, [x], [x.type.clone(shape=shape)()])
-
-    def perform(self, node, inp, out_):
-        (x,) = inp
-        (out,) = out_
-        out[0] = x
-
-    def grad(self, inp, grads):
-        (x,) = inp
-        (gz,) = grads
-        # restore the broadcasting pattern of the input
-        return [specify_shape(gz, x.type.shape)]
-
-    def infer_shape(self, fgraph, node, ishapes):
-        assert len(ishapes) == 1
-        return [tuple(ishapes[0])]
-
-    def R_op(self, inputs, eval_points):
-        if eval_points[0] is None:
-            return [None]
-        return self(*eval_points, return_list=True)
-
-    def c_code(self, node, nodename, inp, out, sub):
-        (iname,) = inp
-        (oname,) = out
-
-        return f"""
-        Py_XDECREF({oname});
-        {oname} = {iname};
-        Py_XINCREF({oname});
-        """
-
-    def c_code_cache_version(self):
-        return (3,)
-
-
-def unbroadcast(x, *axes):
-    """
-    Mask static broadcastable dimensions of input as `None`
-
-    Parameters
-    ----------
-    x : tensor_like
-        Input pytensor tensor.
-    axis : an int or an iterable object such as list or tuple of int values
-        The broadcastable dimensions of x that should be unbroadcasted.
-
-    Returns
-    -------
-    tensor
-        A pytensor tensor, with static broadcastable dimensions masked as `None`
-
-    """
-    x = as_tensor_variable(x)
-    unbroadcasted_axes = [axis for axis in axes if x.type.shape[axis] == 1]
-    if not unbroadcasted_axes:
-        return x
-    return Unbroadcast(*unbroadcasted_axes)(x)
-
-
-@_vectorize_node.register(Unbroadcast)
-def _vectorize_unbroadcast(
-    op: Unbroadcast, node: Apply, batch_x: TensorVariable
-) -> Apply:
-    core_ndim = node.inputs[0].type.ndim
-    batch_ndim = batch_x.type.ndim - core_ndim
-    batch_axes = get_normalized_batch_axes(op.axes, core_ndim, batch_ndim)
-    return cast(Apply, unbroadcast(batch_x, *batch_axes).owner)

tests/link/jax/test_shape.py

@@ -4,7 +4,7 @@ import pytest
 import pytensor.tensor as pt
 from pytensor.compile.ops import DeepCopyOp, ViewOp
 from pytensor.configdefaults import config
-from pytensor.tensor.shape import Shape, Shape_i, Unbroadcast, reshape
+from pytensor.tensor.shape import Shape, Shape_i, reshape
 from pytensor.tensor.type import iscalar, vector
 from tests.link.jax.test_basic import compare_jax_and_py
 
@@ -70,10 +70,6 @@ def test_jax_compile_ops():
     compare_jax_and_py([], [x], [])
 
     x_np = np.zeros((20, 1, 1))
-    x = Unbroadcast(0, 2)(pt.as_tensor_variable(x_np))
-
-    compare_jax_and_py([], [x], [])
-
     x = ViewOp()(pt.as_tensor_variable(x_np))
 
     compare_jax_and_py([], [x], [])

tests/link/numba/test_tensor_basic.py

@@ -7,7 +7,6 @@ import pytensor.tensor.basic as ptb
 from pytensor import config, function
 from pytensor.compile import get_mode
 from pytensor.scalar import Add
-from pytensor.tensor.shape import Unbroadcast
 from tests.link.numba.test_basic import (
     compare_numba_and_py,
     compare_shape_dtype,
@@ -75,16 +74,6 @@ def test_ScalarFromTensor():
     )
 
 
-def test_Unbroadcast():
-    v, v_test = pt.row(), np.array([[1.0, 2.0]], dtype=config.floatX)
-    g = Unbroadcast(0)(v)
-    compare_numba_and_py(
-        [v],
-        g,
-        [v_test],
-    )
-
-
 @pytest.mark.parametrize(
     "vals, dtype",
     [

tests/link/pytorch/test_shape.py

@@ -2,7 +2,7 @@ import numpy as np
 
 import pytensor.tensor as pt
 from pytensor.configdefaults import config
-from pytensor.tensor.shape import Shape, Shape_i, Unbroadcast, reshape
+from pytensor.tensor.shape import Shape, Shape_i, reshape
 from pytensor.tensor.type import iscalar, vector
 from tests.link.pytorch.test_basic import compare_pytorch_and_py
 
@@ -50,10 +50,3 @@ def test_pytorch_Reshape_dynamic():
     compare_pytorch_and_py(
         [a, shape_pt], [x], [np.r_[1.0, 2.0, 3.0, 4.0].astype(config.floatX), 2]
     )
-
-
-def test_pytorch_unbroadcast():
-    x_np = np.zeros((20, 1, 1))
-    x = Unbroadcast(0, 2)(pt.as_tensor_variable(x_np))
-
-    compare_pytorch_and_py([], [x], [])

tests/scan/test_rewriting.py

@@ -1621,7 +1621,7 @@ class TestSaveMem:
         np.testing.assert_allclose(f(x0=0, seq=test_seq, n_steps=200), 100)
         np.testing.assert_allclose(f(x0=1, seq=test_seq, n_steps=20), 21)
         np.testing.assert_allclose(f(x0=np.e, seq=test_seq, n_steps=1), np.e + 1)
-        with pytest.raises(AssertionError, match="n_steps > 0"):
+        with pytest.raises((AssertionError, IndexError)):
             f(x0=0, seq=test_seq, n_steps=0)
 
         # Evaluate the shape of ys_trace and len_zs to confirm the rewrite worked correctly.

tests/tensor/rewriting/test_basic.py

@@ -77,9 +77,7 @@ from pytensor.tensor.shape import (
     Reshape,
     Shape_i,
     SpecifyShape,
-    Unbroadcast,
     specify_shape,
-    unbroadcast,
 )
 from pytensor.tensor.subtensor import (
     AdvancedIncSubtensor1,
@@ -558,48 +556,6 @@ class TestTile:
                 f(data)
 
 
-class TestUnbroadcast:
-    def setup_method(self):
-        self.mode = get_default_mode().including("canonicalize")
-
-    def test_local_useless_unbroadcast(self):
-        x1 = tensor(dtype="float64", shape=(1, 2))
-        x2 = tensor(dtype="float64", shape=(2, 1))
-        unbroadcast_op = Unbroadcast(0)
-
-        f = function([x1], unbroadcast_op(x1), mode=self.mode)
-        assert (
-            sum(isinstance(node.op, Unbroadcast) for node in f.maker.fgraph.toposort())
-            == 1
-        )
-
-        f = function([x2], unbroadcast_op(x2), mode=self.mode)
-        assert (
-            sum(isinstance(node.op, Unbroadcast) for node in f.maker.fgraph.toposort())
-            == 0
-        )
-
-    def test_local_unbroadcast_lift(self):
-        x = tensor(dtype="float64", shape=(1, 1))
-        y = unbroadcast(pt.exp(unbroadcast(x, 0)), 1)
-
-        assert (
-            sum(
-                isinstance(node.op, Unbroadcast)
-                for node in FunctionGraph([x], [y], copy_inputs=False).toposort()
-            )
-            == 2
-        )
-
-        f = function([x], y, mode=self.mode)
-        assert (
-            sum(isinstance(node.op, Unbroadcast) for node in f.maker.fgraph.toposort())
-            == 1
-        )
-
-        np.testing.assert_almost_equal(f([[1]]), np.exp([[1]]))
-
-
 class TestUselessElemwise:
     def setup_method(self):
         self.mode = get_default_mode().including("canonicalize", "local_fill_to_alloc")

tests/tensor/rewriting/test_subtensor.py

@@ -28,7 +28,6 @@ from pytensor.tensor.rewriting.subtensor import (
 )
 from pytensor.tensor.shape import (
     SpecifyShape,
-    Unbroadcast,
     _shape,
     shape,
     specify_shape,
@@ -55,7 +54,6 @@ from pytensor.tensor.type import (
     lscalar,
     lscalars,
     matrix,
-    row,
     scalar,
     tensor,
     tensor3,
@@ -921,64 +919,6 @@ class TestLocalSubtensorLift:
         assert len(prog) == 2
         f([1, 2, 3], 4)  # let debugmode test something
 
-    def test_basic_8(self):
-        # Test that Subtensor(Unbroadcast(x)) gets optimized into
-        # Unbroadcast(Subtensor(x)).
-
-        # test basic case
-        x = row("x")
-        xval = np.random.random((1, 10)).astype(config.floatX)
-        assert x.broadcastable == (True, False)
-        newx = Unbroadcast(0)(x)
-        assert newx.broadcastable == (False, False)
-
-        f1 = function([x], newx[:2, :5], mode=mode_opt)
-        # Check stacktrace was copied over correctly after opt was applied
-        assert check_stack_trace(f1, ops_to_check=[Subtensor, Unbroadcast])
-        prog = f1.maker.fgraph.toposort()
-        assert isinstance(prog[0].op, Subtensor)
-        assert isinstance(prog[1].op, Unbroadcast)
-        assert (f1(xval) == xval[:2, :5]).all()
-
-        # corner case 1: Unbroadcast changes dims which are dropped through subtensor
-        y = tensor(dtype="float64", shape=(1, 10, 1, 3), name="x")
-        yval = np.random.random((1, 10, 1, 3)).astype(config.floatX)
-        assert y.broadcastable == (True, False, True, False)
-        newy = Unbroadcast(0, 2)(y)
-        assert newy.broadcastable == (False, False, False, False)
-
-        f2 = function([y], newy[:, 3, 0, :], mode=mode_opt)
-        # Check stacktrace was copied over correctly after opt was applied
-        assert check_stack_trace(f2, ops_to_check=[Subtensor, Unbroadcast])
-        prog = f2.maker.fgraph.toposort()
-        assert isinstance(prog[0].op, Subtensor)
-        assert isinstance(prog[1].op, Unbroadcast)
-        assert (f2(yval) == yval[:, 3, 0, :]).all()
-
-        # corner case 2: subtensor idx_list is shorter than resulting broadcast pattern
-        f3 = function([y], newy[:, 3, 0], mode=mode_opt)
-        # Check stacktrace was copied over correctly after opt was applied
-        assert check_stack_trace(f3, ops_to_check=[Subtensor, Unbroadcast])
-        prog = f3.maker.fgraph.toposort()
-        assert isinstance(prog[0].op, Subtensor)
-        assert isinstance(prog[1].op, Unbroadcast)
-        assert (f3(yval) == yval[:, 3, 0]).all()
-
-        # corner case 3: subtensor idx_list is shorter than Unbroadcast.axis
-        z = tensor(dtype="float64", shape=(4, 10, 3, 1), name="x")
-        zval = np.random.random((4, 10, 3, 1)).astype(config.floatX)
-        assert z.broadcastable == (False, False, False, True)
-        newz = Unbroadcast(3)(z)
-        assert newz.broadcastable == (False, False, False, False)
-
-        f4 = function([z], newz[:, 3, 0], mode=mode_opt)
-        # Check stacktrace was copied over correctly after opt was applied
-        assert check_stack_trace(f4, ops_to_check=[Subtensor, Unbroadcast])
-        prog = f4.maker.fgraph.toposort()
-        assert isinstance(prog[0].op, Subtensor)
-        assert isinstance(prog[1].op, Unbroadcast)
-        assert (f4(zval) == zval[:, 3, 0]).all()
-
 
 class TestLocalSubtensorMerge:
     def setup_method(self):

tests/tensor/test_basic.py

@@ -287,7 +287,7 @@ TestAlloc13GradBroadcast = makeBroadcastTester(
     ),
 )
 
-# unbroadcast a row to a matrix
+# broadcast a row to a matrix
 TestAllocb1GradBroadcast = makeBroadcastTester(
     name="Allocb1GradTester",
     op=lambda x: alloc(x, s1, s2),
@@ -299,7 +299,7 @@ TestAllocb1GradBroadcast = makeBroadcastTester(
     ),
 )
 
-# unbroadcast a row to a tensor3
+# broadcast a row to a tensor3
 TestAllocb2GradBroadcast = makeBroadcastTester(
     name="Allocb2GradTester",
     op=lambda x: alloc(x, s1, s2, s3),
@@ -311,7 +311,7 @@ TestAllocb2GradBroadcast = makeBroadcastTester(
     ),
 )
 
-# unbroadcast a col to a matrix
+# broadcast a col to a matrix
 TestAllocb3GradBroadcast = makeBroadcastTester(
     name="Allocb3GradTester",
     op=lambda x: alloc(x, s1, s2),
@@ -323,7 +323,7 @@ TestAllocb3GradBroadcast = makeBroadcastTester(
     ),
 )
 
-# unbroadcast a col to a tensor3
+# broadcast a col to a tensor3
 TestAllocb4GradBroadcast = makeBroadcastTester(
     name="Allocb4GradTester",
     op=lambda x: alloc(x, s1, s2, s3),
@@ -336,7 +336,7 @@ TestAllocb4GradBroadcast = makeBroadcastTester(
 )
 
 
-# Partial unbroadcast of a dimshuffled input
+# Partial broadcast of a dimshuffled input
 TestAllocDimshuffleGradBroadcast = makeBroadcastTester(
     name="Allocb4GradTester",
     op=lambda x: alloc(x.dimshuffle("x", "x", 0), 1, s2, s3),

tests/tensor/test_shape.py

@@ -19,14 +19,12 @@ from pytensor.tensor.shape import (
     Shape,
     Shape_i,
     SpecifyShape,
-    Unbroadcast,
     _specify_shape,
     reshape,
     shape,
     shape_tuple,
     specify_broadcastable,
     specify_shape,
-    unbroadcast,
 )
 from pytensor.tensor.subtensor import Subtensor
 from pytensor.tensor.type import (
@@ -696,66 +694,6 @@ def test_get_vector_length():
     assert get_vector_length(x) == 10
 
 
-class TestUnbroadcast:
-    def test_basic(self):
-        x = matrix()
-        assert unbroadcast(x, 0) is x
-        assert unbroadcast(x, 1) is x
-        assert unbroadcast(x, 1, 0) is x
-        assert unbroadcast(x, 0, 1) is x
-
-        x = row()
-        assert unbroadcast(x, 0) is not x
-        assert unbroadcast(x, 1) is x
-        assert unbroadcast(x, 1, 0) is not x
-        assert unbroadcast(x, 0, 1) is not x
-
-        assert unbroadcast(unbroadcast(x, 0), 0).owner.inputs[0] is x
-
-    def test_infer_shape(self):
-        x = matrix()
-        y = unbroadcast(x, 0)
-        f = pytensor.function([x], y.shape)
-        assert (f(np.zeros((2, 5), dtype=config.floatX)) == [2, 5]).all()
-        topo = f.maker.fgraph.toposort()
-        if config.mode != "FAST_COMPILE":
-            assert len(topo) == 3
-            assert isinstance(topo[0].op, Shape_i)
-            assert isinstance(topo[1].op, Shape_i)
-            assert isinstance(topo[2].op, MakeVector)
-
-        x = row()
-        y = unbroadcast(x, 0)
-        f = pytensor.function([x], y.shape)
-        assert (f(np.zeros((1, 5), dtype=config.floatX)) == [1, 5]).all()
-        topo = f.maker.fgraph.toposort()
-        if config.mode != "FAST_COMPILE":
-            assert len(topo) == 2
-            assert isinstance(topo[0].op, Shape_i)
-            assert isinstance(topo[1].op, MakeVector)
-
-    def test_error_checks(self):
-        with pytest.raises(TypeError, match="needs integer axes"):
-            Unbroadcast(0.0)
-
-        with pytest.raises(ValueError, match="^Trying to unbroadcast"):
-            Unbroadcast(1)(vector())
-
-
-class TestUnbroadcastInferShape(utt.InferShapeTester):
-    def test_basic(self):
-        rng = np.random.default_rng(3453)
-        adtens4 = tensor(dtype="float64", shape=(1, 1, 1, None))
-        adtens4_val = rng.random((1, 1, 1, 3)).astype(config.floatX)
-        self._compile_and_check(
-            [adtens4],
-            [Unbroadcast(0, 2)(adtens4)],
-            [adtens4_val],
-            Unbroadcast,
-            warn=False,
-        )
-
-
 def test_shape_tuple():
     x = Variable(MyType2(), None, None)
     assert shape_tuple(x) == ()
@@ -882,16 +820,3 @@ class TestVectorize:
             match="Invalid number of shape arguments passed into vectorize node of SpecifyShape",
         ):
             vectorize_node(node, tns, *(5, 3, 2, x))
-
-    def test_unbroadcast(self):
-        mat = tensor(
-            shape=(
-                1,
-                1,
-            )
-        )
-        tns = tensor(shape=(4, 1, 1, 1))
-
-        node = unbroadcast(mat, 0).owner
-        vect_node = vectorize_node(node, tns)
-        assert equal_computations(vect_node.outputs, [unbroadcast(tns, 2)])

tests/test_rop.py

@@ -28,7 +28,6 @@ from pytensor.graph.basic import Apply
 from pytensor.graph.op import Op
 from pytensor.tensor.math import argmax, dot
 from pytensor.tensor.math import max as pt_max
-from pytensor.tensor.shape import unbroadcast
 from pytensor.tensor.type import matrix, vector
 from tests import unittest_tools as utt
 
@@ -252,13 +251,6 @@ class TestRopLop(RopLopChecker):
         # vector
         self.check_rop_lop(self.x[:4].dimshuffle("x", 0).sum(axis=0), (4,))
 
-    def test_unbroadcast(self):
-        # I need the sum, because the setup expects the output to be a
-        # vector
-        self.check_rop_lop(
-            unbroadcast(self.x[:4].dimshuffle("x", 0), 0).sum(axis=1), (1,)
-        )
-
     def test_join(self):
         tv = np.asarray(self.rng.uniform(size=(10,)), pytensor.config.floatX)
         t = pytensor.shared(tv)