Only require input_ndim and not input_broadcastable in DimShuffle

pytensor/sparse/sandbox/sp.py

@@ -19,7 +19,6 @@
 from pytensor.tensor.math import dot
 from pytensor.tensor.math import max as pt_max
 from pytensor.tensor.shape import reshape
-from pytensor.tensor.subtensor import DimShuffle
 
 
 def register_specialize(lopt, *tags, **kwargs):
@@ -375,7 +374,7 @@ def convolve(
         [images.shape[0], pt.as_tensor(np.prod(outshp)), pt.as_tensor(nkern)]
     )
     tensout = reshape(output, newshp, ndim=3)
-    output = DimShuffle((False,) * tensout.ndim, (0, 2, 1))(tensout)
+    output = tensout.transpose(0, 2, 1)
     if flatten:
         output = pt.flatten(output, 2)
 
@@ -443,6 +442,6 @@ def max_pool(images, imgshp, maxpoolshp):
     )
     out2 = reshape(out1, pshape, ndim=3)
 
-    out3 = DimShuffle(out2.broadcastable, (0, 2, 1))(out2)
+    out3 = out2.transpose(0, 2, 1)
 
     return pt.flatten(out3, 2), outshp

pytensor/tensor/basic.py

@@ -2042,7 +2042,7 @@ def transpose(x, axes=None):
         # No-op
         return _x
 
-    ret = DimShuffle(tuple(s == 1 for s in _x.type.shape), axes)(_x)
+    ret = _x.dimshuffle(axes)
 
     if _x.name and axes == tuple(range((_x.type.ndim - 1), -1, -1)):
         ret.name = _x.name + ".T"
@@ -3518,7 +3518,7 @@ def grad(self, inp, grads):
                 newdims.append(i)
                 i += 1
 
-        gx = DimShuffle(tuple(s == 1 for s in gx.type.shape), newdims)(gx)
+        gx = gx.dimshuffle(newdims)
         assert gx.type.ndim == x.type.ndim
         assert all(
             s1 == s2

pytensor/tensor/elemwise.py

@@ -1,5 +1,7 @@
+from collections.abc import Sequence
 from copy import copy
 from textwrap import dedent
+from typing import Literal
 
 import numpy as np
 from numpy.core.numeric import normalize_axis_tuple
@@ -54,15 +56,14 @@ class DimShuffle(ExternalCOp):
 
     Parameters
     ----------
-    input_broadcastable
-        The expected broadcastable pattern of the input
+    input_ndim
+        The expected number of dimension of the input
     new_order
         A list representing the relationship between the input's
         dimensions and the output's dimensions. Each element of the
         list can either be an index or 'x'. Indices must be encoded
         as python integers, not pytensor symbolic integers.
-    inplace : bool, optional
-        If True (default), the output will be a view of the input.
+        Missing indexes correspond to drop dimensions.
 
     Notes
     -----
@@ -77,50 +78,47 @@ class DimShuffle(ExternalCOp):
 
     .. code-block:: python
 
-        DimShuffle((False, False, False), ["x", 2, "x", 0, 1])
+        DimShuffle(input_ndim=3, new_order=["x", 2, "x", 0, 1])
 
-    This `Op` will only work on 3d tensors with no broadcastable
-    dimensions.  The first dimension will be broadcastable,
+    This `Op` will only work on 3d tensors.
+    The first dimension of the output will be broadcastable,
     then we will have the third dimension of the input tensor as
     the second of the resulting tensor, etc. If the tensor has
     shape (20, 30, 40), the resulting tensor will have dimensions
     (1, 40, 1, 20, 30). (AxBxC tensor is mapped to 1xCx1xAxB tensor)
 
     .. code-block:: python
 
-        DimShuffle((True, False), [1])
+        DimShuffle(input_ndim=2, new_order=[1])
 
-    This `Op` will only work on 2d tensors with the first dimension
-    broadcastable.
-    The second dimension of the input tensor will be the first dimension of
-    the resulting tensor.
-    If the tensor has shape (1, 20), the resulting tensor will have shape
-    (20, ).
+    This `Op` will only work on 2d tensors with the first dimension broadcastable.
+    The second dimension of the input tensor will be the first dimension of the resulting tensor.
+    If the tensor has shape (1, 20), the resulting tensor will have shape (20, ).
 
     Examples
     --------
     .. code-block:: python
 
-        DimShuffle((), ["x"])  # make a 0d (scalar) into a 1d vector
-        DimShuffle((False, False), [0, 1])  # identity
-        DimShuffle((False, False), [1, 0])  # inverts the 1st and 2nd dimensions
-        DimShuffle((False,), ["x", 0])  # make a row out of a 1d vector
-        # (N to 1xN)
-        DimShuffle((False,), [0, "x"])  # make a column out of a 1d vector
-        # (N to Nx1)
-        DimShuffle((False, False, False), [2, 0, 1])  # AxBxC to CxAxB
-        DimShuffle((False, False), [0, "x", 1])  # AxB to Ax1xB
-        DimShuffle((False, False), [1, "x", 0])  # AxB to Bx1xA
-
-    The reordering of the dimensions can be done with the numpy.transpose
-    function.
-    Adding, subtracting dimensions can be done with reshape.
+        DimShuffle(input_ndim=0, new_order=["x"])  # make a 0d (scalar) into a 1d vector
+        DimShuffle(input_ndim=2, new_order=[0, 1])  # identity
+        DimShuffle(input_ndim=2, new_order=[1, 0])  # transposition
+        # Make a row out of a 1d vector (N to 1xN)
+        DimShuffle(input_ndim=1, new_order=["x", 0])
+        # Make a colum out of a 1d vector (N to Nx1)
+        DimShuffle(input_ndim=1, new_order=[0, "x"])
+        DimShuffle(input_ndim=3, new_order=[2, 0, 1])  # AxBxC to CxAxB
+        DimShuffle(input_ndim=2, new_order=[0, "x", 1])  # AxB to Ax1xB
+        DimShuffle(input_ndim=2, new_order=[1, "x", 0])  # AxB to Bx1xA
 
+    Notes
+    -----
+    The python implementation of this Op combines numpy.transpose for reordering of the dimensions
+    and numpy.reshape for subtracting and adding broadcastable dimensions.
     """
 
     _f16_ok = True
     check_input = False
-    __props__ = ("input_broadcastable", "new_order", "inplace")
+    __props__ = ("input_ndim", "new_order", "inplace")
     c_func_file = "c_code/dimshuffle.c"
     c_func_name = "APPLY_SPECIFIC(cpu_dimshuffle)"
 
@@ -133,16 +131,14 @@ def params_type(self):
             inplace=scalar_bool,
         )
 
-    def __init__(self, input_broadcastable, new_order):
+    def __init__(self, *, input_ndim: int, new_order: Sequence[int | Literal["x"]]):
         super().__init__([self.c_func_file], self.c_func_name)
 
-        self.input_broadcastable = tuple(input_broadcastable)
-        if not all(isinstance(bs, bool | np.bool_) for bs in self.input_broadcastable):
-            raise ValueError(
-                f"input_broadcastable must be boolean, {self.input_broadcastable}"
-            )
-        self.new_order = tuple(new_order)
+        if not isinstance(input_ndim, int):
+            raise TypeError(f"input_ndim must be an integer, got {type(int)}")
 
+        self.input_ndim = input_ndim
+        self.new_order = tuple(new_order)
         self.inplace = True
 
         for i, j in enumerate(new_order):
@@ -152,10 +148,10 @@ def __init__(self, input_broadcastable, new_order):
                         "DimShuffle indices must be Python ints; got "
                         f"{j} of type {type(j)}."
                     )
-                if j >= len(input_broadcastable):
+                if j >= input_ndim:
                     raise ValueError(
                         f"new_order[{i}] is {j}, but the input only has "
-                        f"{len(input_broadcastable)} axes."
+                        f"{input_ndim} axes."
                     )
                 if j in new_order[(i + 1) :]:
                     raise ValueError(
@@ -164,19 +160,7 @@ def __init__(self, input_broadcastable, new_order):
                     )
 
         # List of input dimensions to drop
-        drop = []
-        for i, b in enumerate(input_broadcastable):
-            if i not in new_order:
-                # We want to drop this dimension because it's not a value in
-                # `new_order`
-                if b == 1:
-                    drop.append(i)
-                else:
-                    # We cannot drop non-broadcastable dimensions
-                    raise ValueError(
-                        "Cannot drop a non-broadcastable dimension: "
-                        f"{input_broadcastable}, {new_order}"
-                    )
+        drop = [i for i in range(input_ndim) if i not in new_order]
 
         # This is the list of the original dimensions that we keep
         self.shuffle = [x for x in new_order if x != "x"]
@@ -186,7 +170,6 @@ def __init__(self, input_broadcastable, new_order):
         self.augment = sorted(i for i, x in enumerate(new_order) if x == "x")
         self.drop = drop
 
-        input_ndim = len(input_broadcastable)
         self.is_left_expand_dims = self.augment and (
             input_ndim == 0 or new_order[-input_ndim:] == list(range(input_ndim))
         )
@@ -204,30 +187,29 @@ def __setstate__(self, state):
             # Let's just build the ExternalCOp.
             super().__init__([self.c_func_file], self.c_func_name)
 
-    def make_node(self, _input):
-        input = as_tensor_variable(_input)
-        ib = tuple(s == 1 for s in input.type.shape)
-        if ib != self.input_broadcastable:
-            if len(ib) != len(self.input_broadcastable):
+    def make_node(self, inp):
+        input = as_tensor_variable(inp)
+        if input.type.ndim != self.input_ndim:
+            raise TypeError(
+                "The number of dimensions of the input is incorrect for this op. "
+                f"Expected {self.input_ndim}, got {input.type.ndim}."
+            )
+
+        input_static_shape = input.type.shape
+
+        # Runtime check for invalid drop
+        for d in self.drop:
+            if input_static_shape[d] not in (1, None):
                 raise TypeError(
-                    "The number of dimensions of the "
-                    f"input is incorrect for this op. Expected {self.input_broadcastable}, got {ib}."
+                    f"Input dropped dimension {d} must have length 1 but has {input_static_shape[d]}"
                 )
-            for expected, b in zip(self.input_broadcastable, ib):
-                if expected and not b:
-                    raise TypeError(
-                        "The broadcastable pattern of the "
-                        f"input is incorrect for this op. Expected {self.input_broadcastable}, got {ib}."
-                    )
-                # else, expected == b or not expected and b
-                # Both case are good.
 
         out_static_shape = []
         for dim_idx in self.new_order:
             if dim_idx == "x":
                 out_static_shape.append(1)
             else:
-                out_static_shape.append(input.type.shape[dim_idx])
+                out_static_shape.append(input_static_shape[dim_idx])
 
         output = TensorType(dtype=input.type.dtype, shape=out_static_shape)()
 
@@ -254,12 +236,14 @@ def perform(self, node, inp, out):
         if not isinstance(res, np.ndarray | np.memmap):
             raise TypeError(res)
 
+        # Put dropped axis at end
         res = res.transpose(self.transposition)
 
-        shape = list(res.shape[: len(self.shuffle)])
+        # Define new shape without dropped axis and including new ones
+        new_shape = list(res.shape[: len(self.shuffle)])
         for augm in self.augment:
-            shape.insert(augm, 1)
-        res = res.reshape(shape)
+            new_shape.insert(augm, 1)
+        res = res.reshape(new_shape)
 
         if not self.inplace:
             res = np.copy(res)
@@ -284,22 +268,15 @@ def R_op(self, inputs, eval_points):
     def grad(self, inp, grads):
         (x,) = inp
         (gz,) = grads
-        gz = as_tensor_variable(gz)
         grad_order = ["x"] * x.type.ndim
         for i, v in enumerate(self.new_order):
             if v != "x":
                 grad_order[v] = i
-        # Do not make the DimShuffle inplace as an optimization at the
-        # canonicalization optimization phase will remove the inplace.
-        # The inplace will be reintroduced automatically later in the graph.
-        if inp[0].dtype in discrete_dtypes:
-            return [inp[0].zeros_like(dtype=config.floatX)]
+
+        if x.type.dtype in discrete_dtypes:
+            return [x.zeros_like(dtype=config.floatX)]
         else:
-            return [
-                DimShuffle(tuple(s == 1 for s in gz.type.shape), grad_order)(
-                    Elemwise(scalar_identity)(gz)
-                )
-            ]
+            return [gz.dimshuffle(grad_order)]
 
 
 class DimShufflePrinter(Printer):
@@ -409,7 +386,7 @@ def __setstate__(self, d):
         self.nfunc = None
         self.inplace_pattern = frozendict(self.inplace_pattern)
 
-    def get_output_info(self, dim_shuffle, *inputs):
+    def get_output_info(self, *inputs):
         """Return the outputs dtype and broadcastable pattern and the
         dimshuffled inputs.
 
@@ -427,12 +404,7 @@ def get_output_info(self, dim_shuffle, *inputs):
             if not difference:
                 args.append(input)
             else:
-                args.append(
-                    dim_shuffle(
-                        input.type.broadcastable,
-                        ["x"] * difference + list(range(length)),
-                    )(input)
-                )
+                args.append(input.dimshuffle(["x"] * difference + list(range(length))))
         inputs = args
 
         # HERE: all the broadcast dims have the same length now
@@ -489,7 +461,7 @@ def make_node(self, *inputs):
         using DimShuffle.
         """
         inputs = [as_tensor_variable(i) for i in inputs]
-        out_dtypes, out_shapes, inputs = self.get_output_info(DimShuffle, *inputs)
+        out_dtypes, out_shapes, inputs = self.get_output_info(*inputs)
         outputs = [
             TensorType(dtype=dtype, shape=shape)()
             for dtype, shape in zip(out_dtypes, out_shapes)
@@ -634,7 +606,7 @@ def transform(r):
                 res = pytensor.tensor.basic.constant(
                     np.asarray(r.data), dtype=r.type.dtype
                 )
-                return DimShuffle((), ["x"] * nd)(res)
+                return res.dimshuffle(["x"] * nd)
 
             new_r = Elemwise(node.op, {})(*[transform(ipt) for ipt in node.inputs])
             if isinstance(new_r, list | tuple):
@@ -1707,13 +1679,12 @@ def vectorize_dimshuffle(op: DimShuffle, node: Apply, x: TensorVariable) -> Appl
     batched_ndims = x.type.ndim - node.inputs[0].type.ndim
     if not batched_ndims:
         return node.op.make_node(x)
-    input_broadcastable = x.type.broadcastable[:batched_ndims] + op.input_broadcastable
-    # e.g., ds(matrix, order=(1, "x", 0)) -> ds(tensor4, order=(0, 1, 3, "x", 2))
-    # e.g., ds(row, order=(1, "x")) -> ds(tensor4, order=(0, 1, 3, "x"))
+    # e.g., ds(input_ndim=2, order=(1, "x", 0)) -> ds(input_ndim=4, order=(0, 1, 3, "x", 2))
+    # e.g., ds(input_ndim=2, order=(1, "x")) -> ds(input_ndim=4, order=(0, 1, 3, "x"))
     new_order = list(range(batched_ndims)) + [
         "x" if (o == "x") else (o + batched_ndims) for o in op.new_order
     ]
-    return DimShuffle(input_broadcastable, new_order).make_node(x)
+    return x.dimshuffle(new_order).owner
 
 
 def get_normalized_batch_axes(

pytensor/tensor/extra_ops.py

@@ -41,7 +41,7 @@
 )
 from pytensor.tensor.math import max as pt_max
 from pytensor.tensor.math import sum as pt_sum
-from pytensor.tensor.shape import Shape_i, specify_broadcastable
+from pytensor.tensor.shape import Shape_i
 from pytensor.tensor.subtensor import advanced_inc_subtensor1, set_subtensor
 from pytensor.tensor.type import TensorType, dvector, int_dtypes, integer_dtypes, vector
 from pytensor.tensor.variable import TensorVariable
@@ -609,11 +609,6 @@ def squeeze(x, axis=None):
         # Nothing could be squeezed
         return _x
 
-    # `Dimshuffle` raises when we try to drop an axis that is not statically broadcastable.
-    # We add a `specify_broadcastable` instead of raising.
-    non_broadcastable_axis = [i for i in axis if not _x.broadcastable[i]]
-    _x = specify_broadcastable(_x, *non_broadcastable_axis)
-
     return _x.dimshuffle([i for i in range(_x.ndim) if i not in axis])
 
 

pytensor/tensor/inplace.py

@@ -1,6 +1,6 @@
 from pytensor import printing
 from pytensor.printing import pprint
-from pytensor.tensor.elemwise import DimShuffle, scalar_elemwise
+from pytensor.tensor.elemwise import scalar_elemwise
 
 
 @scalar_elemwise
@@ -429,4 +429,4 @@ def hyp2f1_inplace(a, b, c, z):
 def transpose_inplace(x, **kwargs):
     "Perform a transpose on a tensor without copying the underlying storage"
     dims = list(range(x.ndim - 1, -1, -1))
-    return DimShuffle(x.broadcastable, dims)(x)
+    return x.dimshuffle(dims)