Vectorize shape operations

pytensor/graph/replace.py

@@ -204,7 +204,7 @@ def graph_replace(
 
 
 @singledispatch
-def _vectorize_node(op: Op, node: Apply, *bached_inputs) -> Apply:
+def _vectorize_node(op: Op, node: Apply, *batched_inputs) -> Apply:
     # Default implementation is provided in pytensor.tensor.blockwise
     raise NotImplementedError
 
@@ -215,6 +215,10 @@ def vectorize_node(node: Apply, *batched_inputs) -> Apply:
     return _vectorize_node(op, node, *batched_inputs)
 
 
+def _vectorize_not_needed(op, node, *batched_inputs):
+    return op.make_node(*batched_inputs)
+
+
 @overload
 def vectorize_graph(
     outputs: Variable,

pytensor/tensor/blockwise.py

@@ -8,7 +8,11 @@ from pytensor.gradient import DisconnectedType
 from pytensor.graph.basic import Apply, Constant, Variable
 from pytensor.graph.null_type import NullType
 from pytensor.graph.op import Op
-from pytensor.graph.replace import _vectorize_node, vectorize_graph
+from pytensor.graph.replace import (
+    _vectorize_node,
+    _vectorize_not_needed,
+    vectorize_graph,
+)
 from pytensor.tensor import as_tensor_variable
 from pytensor.tensor.shape import shape_padleft
 from pytensor.tensor.type import continuous_dtypes, discrete_dtypes, tensor
@@ -37,17 +41,6 @@ def safe_signature(
     return f"{inputs_sig}->{outputs_sig}"
 
 
-@_vectorize_node.register(Op)
-def vectorize_node_fallback(op: Op, node: Apply, *bached_inputs) -> Apply:
-    if hasattr(op, "gufunc_signature"):
-        signature = op.gufunc_signature
-    else:
-        # TODO: This is pretty bad for shape inference and merge optimization!
-        #  Should get better as we add signatures to our Ops
-        signature = safe_signature(node.inputs, node.outputs)
-    return cast(Apply, Blockwise(op, signature=signature).make_node(*bached_inputs))
-
-
 class Blockwise(Op):
     """Generalizes a core `Op` to work with batched dimensions.
 
@@ -361,6 +354,15 @@ class Blockwise(Op):
             return self.name
 
 
-@_vectorize_node.register(Blockwise)
-def vectorize_not_needed(op, node, *batch_inputs):
-    return op.make_node(*batch_inputs)
+@_vectorize_node.register(Op)
+def vectorize_node_fallback(op: Op, node: Apply, *bached_inputs) -> Apply:
+    if hasattr(op, "gufunc_signature"):
+        signature = op.gufunc_signature
+    else:
+        # TODO: This is pretty bad for shape inference and merge optimization!
+        #  Should get better as we add signatures to our Ops
+        signature = safe_signature(node.inputs, node.outputs)
+    return cast(Apply, Blockwise(op, signature=signature).make_node(*bached_inputs))
+
+
+_vectorize_node.register(Blockwise, _vectorize_not_needed)

pytensor/tensor/elemwise.py

@@ -7,7 +7,7 @@ from pytensor.configdefaults import config
 from pytensor.gradient import DisconnectedType
 from pytensor.graph.basic import Apply
 from pytensor.graph.null_type import NullType
-from pytensor.graph.replace import _vectorize_node
+from pytensor.graph.replace import _vectorize_node, _vectorize_not_needed
 from pytensor.graph.utils import MethodNotDefined
 from pytensor.link.c.basic import failure_code
 from pytensor.link.c.op import COp, ExternalCOp, OpenMPOp
@@ -22,7 +22,6 @@ from pytensor.scalar.basic import transfer_type, upcast
 from pytensor.tensor import elemwise_cgen as cgen
 from pytensor.tensor import get_vector_length
 from pytensor.tensor.basic import _get_vector_length, as_tensor_variable
-from pytensor.tensor.blockwise import vectorize_not_needed
 from pytensor.tensor.type import (
     TensorType,
     continuous_dtypes,
@@ -1741,7 +1740,7 @@ def _get_vector_length_Elemwise(op, var):
     raise ValueError(f"Length of {var} cannot be determined")
 
 
-_vectorize_node.register(Elemwise, vectorize_not_needed)
+_vectorize_node.register(Elemwise, _vectorize_not_needed)
 
 
 @_vectorize_node.register(DimShuffle)

pytensor/tensor/shape.py

@@ -8,6 +8,7 @@ import numpy as np
 import pytensor
 from pytensor.gradient import DisconnectedType
 from pytensor.graph.basic import Apply, Variable
+from pytensor.graph.replace import _vectorize_node, _vectorize_not_needed
 from pytensor.graph.type import HasShape
 from pytensor.link.c.op import COp
 from pytensor.link.c.params_type import ParamsType
@@ -154,6 +155,9 @@ def _get_vector_length_Shape(op, var):
     return var.owner.inputs[0].type.ndim
 
 
+_vectorize_node.register(Shape, _vectorize_not_needed)
+
+
 def shape_tuple(x: TensorVariable) -> tuple[Variable, ...]:
     r"""Get a tuple of symbolic shape values.
 
@@ -580,6 +584,32 @@ def _get_vector_length_SpecifyShape(op, var):
         raise ValueError(f"Length of {var} cannot be determined")
 
 
+@_vectorize_node.register(SpecifyShape)
+def _vectorize_specify_shape(op, node, x, *shape):
+    old_x, *old_shape = node.inputs
+    batched_ndims = x.type.ndim - old_x.type.ndim
+
+    if any(
+        as_tensor_variable(dim).type.ndim != 0
+        for dim in shape
+        if not (NoneConst.equals(dim) or dim is None)
+    ):
+        raise NotImplementedError(
+            "It is not possible to vectorize the shape argument of SpecifyShape"
+        )
+
+    if len(shape) == len(old_shape):
+        new_shape = tuple([None] * batched_ndims) + shape
+    elif len(shape) == (len(old_shape) + batched_ndims):
+        new_shape = shape
+    else:
+        raise ValueError(
+            "Invalid number of shape arguments passed into vectorize node of SpecifyShape"
+        )
+
+    return specify_shape(x, new_shape).owner
+
+
 class Reshape(COp):
     """Perform a reshape operation of the input x to the new shape shp.
     The number of dimensions to which to reshape to (ndim) must be
@@ -638,7 +668,7 @@ class Reshape(COp):
 
         return Apply(self, [x, shp], [tensor(dtype=x.type.dtype, shape=out_shape)])
 
-    def perform(self, node, inp, out_, params):
+    def perform(self, node, inp, out_, params=None):
         x, shp = inp
         (out,) = out_
         if len(shp) != self.ndim:
@@ -770,6 +800,26 @@ class Reshape(COp):
         """
 
 
+@_vectorize_node.register(Reshape)
+def _vectorize_reshape(op, node, x, shape):
+    old_x, old_shape = node.inputs
+    batched_ndims = x.type.ndim - old_x.type.ndim
+
+    if as_tensor_variable(shape).type.ndim != 1:
+        raise NotImplementedError(
+            "It is not possible to vectorize the shape argument of Reshape"
+        )
+
+    if len(tuple(old_shape)) == len(tuple(shape)):
+        new_shape = [*x.shape[:batched_ndims], *shape]
+    elif len(tuple(old_shape)) == (len(tuple(shape)) - batched_ndims):
+        new_shape = shape
+    else:
+        raise ValueError("Invalid shape length passed into vectorize node of Reshape")
+
+    return reshape(x, new_shape, ndim=len(new_shape)).owner
+
+
 def reshape(x, newshape, ndim=None):
     if ndim is None:
         newshape = at.as_tensor_variable(newshape)
@@ -1034,3 +1084,11 @@ def unbroadcast(x, *axes):
     if not unbroadcasted_axes:
         return x
     return Unbroadcast(*unbroadcasted_axes)(x)
+
+
+@_vectorize_node.register(Unbroadcast)
+def _vectorize_unbroadcast(op: Unbroadcast, node: Apply, x: TensorVariable) -> Apply:
+    batched_ndims = x.type.ndim - node.inputs[0].type.ndim
+    old_axes = op.axes
+    new_axes = (old_axis + batched_ndims for old_axis in old_axes)
+    return unbroadcast(x, *new_axes).owner

tests/tensor/test_shape.py

@@ -5,14 +5,14 @@ import pytensor
 from pytensor import Mode, function, grad
 from pytensor.compile.ops import DeepCopyOp
 from pytensor.configdefaults import config
-from pytensor.graph.basic import Variable
+from pytensor.graph.basic import Variable, equal_computations
 from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.replace import clone_replace
+from pytensor.graph.replace import clone_replace, vectorize_node
 from pytensor.graph.type import Type
 from pytensor.misc.safe_asarray import _asarray
 from pytensor.scalar.basic import ScalarConstant
-from pytensor.tensor import as_tensor_variable, get_vector_length, row
-from pytensor.tensor.basic import MakeVector, constant
+from pytensor.tensor import as_tensor_variable, broadcast_to, get_vector_length, row
+from pytensor.tensor.basic import MakeVector, as_tensor, constant
 from pytensor.tensor.elemwise import DimShuffle, Elemwise
 from pytensor.tensor.rewriting.shape import ShapeFeature
 from pytensor.tensor.shape import (
@@ -706,3 +706,88 @@ def test_shape_tuple():
     assert isinstance(res[1], ScalarConstant)
     assert res[1].data == 2
     assert not isinstance(res[2], ScalarConstant)
+
+
+class TestVectorize:
+    def test_shape(self):
+        vec = tensor(shape=(None,))
+        mat = tensor(shape=(None, None))
+
+        node = shape(vec).owner
+        vect_node = vectorize_node(node, mat)
+        assert equal_computations(vect_node.outputs, [shape(mat)])
+
+    def test_reshape(self):
+        x = scalar("x", dtype=int)
+        vec = tensor(shape=(None,))
+        mat = tensor(shape=(None, None))
+
+        shape = (2, x)
+        node = reshape(vec, shape).owner
+        vect_node = vectorize_node(node, mat, shape)
+        assert equal_computations(
+            vect_node.outputs, [reshape(mat, (*mat.shape[:1], 2, x))]
+        )
+
+        new_shape = (5, 2, x)
+        vect_node = vectorize_node(node, mat, new_shape)
+        assert equal_computations(vect_node.outputs, [reshape(mat, new_shape)])
+
+        with pytest.raises(NotImplementedError):
+            vectorize_node(node, vec, broadcast_to(as_tensor([5, 2, x]), (2, 3)))
+
+        with pytest.raises(
+            ValueError,
+            match="Invalid shape length passed into vectorize node of Reshape",
+        ):
+            vectorize_node(node, vec, (5, 2, x))
+
+        with pytest.raises(
+            ValueError,
+            match="Invalid shape length passed into vectorize node of Reshape",
+        ):
+            vectorize_node(node, mat, (5, 3, 2, x))
+
+    def test_specify_shape(self):
+        x = scalar("x", dtype=int)
+        mat = tensor(shape=(None, None))
+        tns = tensor(shape=(None, None, None))
+
+        shape = (x, None)
+        node = specify_shape(mat, shape).owner
+        vect_node = vectorize_node(node, tns, *shape)
+        assert equal_computations(
+            vect_node.outputs, [specify_shape(tns, (None, x, None))]
+        )
+
+        new_shape = (5, 2, x)
+        vect_node = vectorize_node(node, tns, *new_shape)
+        assert equal_computations(vect_node.outputs, [specify_shape(tns, (5, 2, x))])
+
+        with pytest.raises(NotImplementedError):
+            vectorize_node(node, mat, *([x, x], None))
+
+        with pytest.raises(
+            ValueError,
+            match="Invalid number of shape arguments passed into vectorize node of SpecifyShape",
+        ):
+            vectorize_node(node, mat, *(5, 2, x))
+
+        with pytest.raises(
+            ValueError,
+            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)])