Split aesara.tensor.rewriting.basic rewrites by their aesara.tensor modules

aesara/compile/builders.py

@@ -26,7 +26,7 @@ from aesara.graph.null_type import NullType
 from aesara.graph.op import HasInnerGraph, Op
 from aesara.graph.rewriting.basic import in2out, node_rewriter
 from aesara.graph.utils import MissingInputError
-from aesara.tensor.rewriting.basic import ShapeFeature
+from aesara.tensor.rewriting.shape import ShapeFeature
 
 
 def infer_shape(outs, inputs, input_shapes):

aesara/scan/rewriting.py

@@ -45,8 +45,9 @@ from aesara.tensor.basic import Alloc, AllocEmpty, get_scalar_constant_value
 from aesara.tensor.elemwise import DimShuffle, Elemwise
 from aesara.tensor.exceptions import NotScalarConstantError
 from aesara.tensor.math import Dot, dot, maximum, minimum
-from aesara.tensor.rewriting import basic as basic_opt
-from aesara.tensor.rewriting import math as math_opt
+from aesara.tensor.rewriting.basic import constant_folding, local_useless_switch
+from aesara.tensor.rewriting.elemwise import local_upcast_elemwise_constant_inputs
+from aesara.tensor.rewriting.math import local_abs_merge, local_mul_switch_sink
 from aesara.tensor.shape import shape
 from aesara.tensor.subtensor import (
     IncSubtensor,
@@ -60,11 +61,11 @@ from aesara.tensor.var import TensorConstant, get_unique_value
 
 
 list_opt_slice = [
-    math_opt.local_abs_merge,
-    math_opt.local_mul_switch_sink,
-    basic_opt.local_upcast_elemwise_constant_inputs,
-    basic_opt.local_useless_switch,
-    basic_opt.constant_folding,
+    local_abs_merge,
+    local_mul_switch_sink,
+    local_upcast_elemwise_constant_inputs,
+    local_useless_switch,
+    constant_folding,
 ]
 
 
@@ -2432,7 +2433,7 @@ scan_seqopt1.register(
 
 scan_eqopt2.register(
     "constant_folding_for_scan2",
-    in2out(basic_opt.constant_folding, ignore_newtrees=True),
+    in2out(constant_folding, ignore_newtrees=True),
     "fast_run",
     "scan",
 )

aesara/tensor/basic.py

@@ -29,7 +29,7 @@ from aesara.graph.type import Type
 from aesara.link.c.op import COp
 from aesara.link.c.params_type import ParamsType
 from aesara.misc.safe_asarray import _asarray
-from aesara.printing import min_informative_str, pprint
+from aesara.printing import Printer, min_informative_str, pprint, set_precedence
 from aesara.raise_op import CheckAndRaise, assert_op
 from aesara.scalar import int32
 from aesara.scalar.basic import ScalarConstant, ScalarVariable
@@ -1335,7 +1335,8 @@ def infer_broadcastable(shape):
     `shape` will be validated and constant folded in order to determine
     which dimensions are broadcastable (i.e. equal to ``1``).
     """
-    from aesara.tensor.rewriting.basic import ShapeFeature, topo_constant_folding
+    from aesara.tensor.rewriting.basic import topo_constant_folding
+    from aesara.tensor.rewriting.shape import ShapeFeature
 
     def check_type(s):
         if s.type.dtype in integer_dtypes:
@@ -1709,6 +1710,21 @@ class MakeVector(COp):
 make_vector = MakeVector()
 
 
+class MakeVectorPrinter(Printer):
+    def process(self, r, pstate):
+        if r.owner is None:
+            raise TypeError("Can only print make_vector.")
+        elif isinstance(r.owner.op, MakeVector):
+            with set_precedence(pstate):
+                s = [pstate.pprinter.process(inp) for inp in r.owner.inputs]
+            return f"[{', '.join(s)}]"
+        else:
+            raise TypeError("Can only print make_vector.")
+
+
+pprint.assign(MakeVector, MakeVectorPrinter())
+
+
 @_get_vector_length.register(MakeVector)
 def _get_vector_length_MakeVector(op, var):
     return len(var.owner.inputs)

aesara/tensor/basic_opt.py

@@ -8,3 +8,6 @@ warnings.warn(
 )
 
 from aesara.tensor.rewriting.basic import *  # noqa: F401 E402 F403
+from aesara.tensor.rewriting.elemwise import *  # noqa: F401 E402 F403
+from aesara.tensor.rewriting.extra_ops import *  # noqa: F401 E402 F403
+from aesara.tensor.rewriting.shape import *  # noqa: F401 E402 F403

aesara/tensor/blas.py

@@ -163,7 +163,7 @@ from aesara.tensor.blas_headers import blas_header_text, blas_header_version
 from aesara.tensor.elemwise import DimShuffle, Elemwise
 from aesara.tensor.exceptions import NotScalarConstantError
 from aesara.tensor.math import Dot, add, mul, neg, sub
-from aesara.tensor.rewriting.basic import local_dimshuffle_lift
+from aesara.tensor.rewriting.elemwise import local_dimshuffle_lift
 from aesara.tensor.shape import specify_broadcastable
 from aesara.tensor.type import (
     DenseTensorType,

aesara/tensor/rewriting/__init__.py

 import aesara.tensor.rewriting.basic
+import aesara.tensor.rewriting.elemwise
+import aesara.tensor.rewriting.extra_ops
 import aesara.tensor.rewriting.math
+import aesara.tensor.rewriting.shape
 import aesara.tensor.rewriting.subtensor
 import aesara.tensor.rewriting.uncanonicalize

aesara/tensor/rewriting/extra_ops.py

+import aesara.scalar.basic as aes
+from aesara.graph.rewriting.basic import node_rewriter
+from aesara.tensor.basic import Alloc, as_tensor_variable
+from aesara.tensor.elemwise import Elemwise
+from aesara.tensor.extra_ops import BroadcastTo, Repeat, Unique
+from aesara.tensor.rewriting.basic import register_canonicalize, register_useless
+
+
+@register_useless
+@register_canonicalize
+@node_rewriter([Unique])
+def local_Unique_scalar(fgraph, node):
+    """Convert ``unique(x)`` to ``x`` when ``x`` is a scalar."""
+    if not isinstance(node.op, Unique):
+        return False
+
+    if node.op.return_index or node.op.return_inverse or node.op.return_counts:
+        return False
+
+    uniqued_var = node.inputs[0]
+
+    if uniqued_var.ndim != 0:
+        return False
+
+    old_out = node.outputs[0]
+    res = as_tensor_variable(uniqued_var, ndim=old_out.ndim, dtype=old_out.dtype)
+    return [res]
+
+
+@register_useless
+@register_canonicalize
+@node_rewriter([Unique])
+def local_Unique_Alloc_lift(fgraph, node):
+    """Convert ``unique(alloc(x, ...), axis=None)`` to ``unique(x, axis=None)``.
+
+    This isn't really so much a lift as a "reduction/consumption".
+    """
+    if not isinstance(node.op, Unique):
+        return False
+
+    if (
+        node.op.return_index
+        or node.op.return_inverse
+        or node.op.return_counts
+        or node.op.axis is not None
+    ):
+        return False
+
+    alloc_var = node.inputs[0]
+
+    if not (alloc_var.owner and isinstance(alloc_var.owner.op, Alloc)):
+        return False
+
+    alloced_var, *alloc_shape = alloc_var.owner.inputs
+
+    new_unique, *_ = node.op.make_node(alloced_var).outputs
+
+    old_out = node.outputs[0]
+    new_x = as_tensor_variable(new_unique, ndim=old_out.ndim, dtype=old_out.dtype)
+    return [new_x]
+
+
+@register_useless
+@register_canonicalize
+@node_rewriter([Unique])
+def local_Unique_BroadcastTo_lift(fgraph, node):
+    """Convert ``unique(broadcast_to(x, ...), axis=None)`` to ``unique(x, axis=None)``.
+
+    This isn't really so much a lift as a "reduction/consumption".
+    """
+    if not isinstance(node.op, Unique):
+        return False
+
+    if (
+        node.op.return_index
+        or node.op.return_inverse
+        or node.op.return_counts
+        or node.op.axis is not None
+    ):
+        return False
+
+    bcast_var = node.inputs[0]
+
+    if not (bcast_var.owner and isinstance(bcast_var.owner.op, BroadcastTo)):
+        return False
+
+    bcasted_var, *bcast_shape = bcast_var.owner.inputs
+
+    new_unique, *_ = node.op.make_node(bcasted_var).outputs
+
+    old_out = node.outputs[0]
+    new_x = as_tensor_variable(new_unique, ndim=old_out.ndim, dtype=old_out.dtype)
+    return [new_x]
+
+
+@register_useless
+@register_canonicalize
+@node_rewriter([Unique])
+def local_Unique_Repeat_lift(fgraph, node):
+    """Convert ``unique(repeat(x, ...), axis=None)`` to ``unique(x, axis=None)``.
+
+    This isn't really so much a lift as a "reduction/consumption".
+    """
+    if not isinstance(node.op, Unique):
+        return False
+
+    if (
+        node.op.return_index
+        or node.op.return_inverse
+        or node.op.return_counts
+        or node.op.axis is not None
+    ):
+        return False
+
+    repeat_var = node.inputs[0]
+
+    if not (repeat_var.owner and isinstance(repeat_var.owner.op, Repeat)):
+        return False
+
+    repeated_var, *repeat_shape = repeat_var.owner.inputs
+
+    new_unique, *_ = node.op.make_node(repeated_var).outputs
+
+    old_out = node.outputs[0]
+    new_x = as_tensor_variable(new_unique, ndim=old_out.ndim, dtype=old_out.dtype)
+    return [new_x]
+
+
+@register_useless
+@register_canonicalize
+@node_rewriter([Unique])
+def local_Unique_second(fgraph, node):
+    """Convert ``unique(second(x, ...), axis=None)`` to ``second(x, axis=None)``.
+
+    This isn't really so much a lift as a "reduction/consumption".
+    """
+    if not isinstance(node.op, Unique):
+        return False
+
+    if (
+        node.op.return_index
+        or node.op.return_inverse
+        or node.op.return_counts
+        or node.op.axis is not None
+    ):
+        return False
+
+    second_var = node.inputs[0]
+
+    if not (
+        second_var.owner
+        and isinstance(second_var.owner.op, Elemwise)
+        and isinstance(second_var.owner.op.scalar_op, aes.Second)
+    ):
+        return False
+
+    shape_var, seconded_var = second_var.owner.inputs
+
+    new_unique, *_ = node.op.make_node(seconded_var).outputs
+
+    old_out = node.outputs[0]
+    new_x = as_tensor_variable(new_unique, ndim=old_out.ndim, dtype=old_out.dtype)
+    return [new_x]
+
+
+@register_useless
+@register_canonicalize
+@node_rewriter([BroadcastTo])
+def local_remove_scalar_BroadcastTo(fgraph, node):
+
+    bcast_shape = node.inputs[1:]
+
+    if not bcast_shape:
+        bcasted_var = node.inputs[0]
+        # If this isn't true, the graph is invalid
+        assert bcasted_var.ndim == 0
+        return [bcasted_var]

aesara/tensor/rewriting/math.py

@@ -72,10 +72,8 @@ from aesara.tensor.math import prod, reciprocal, sgn, sigmoid, softplus, sqr, sq
 from aesara.tensor.math import sum as at_sum
 from aesara.tensor.math import true_div
 from aesara.tensor.rewriting.basic import (
-    FusionOptimizer,
     broadcast_like,
     encompasses_broadcastable,
-    fuse_seqopt,
     local_fill_sink,
     register_canonicalize,
     register_specialize,
@@ -84,6 +82,7 @@ from aesara.tensor.rewriting.basic import (
     register_uncanonicalize,
     register_useless,
 )
+from aesara.tensor.rewriting.elemwise import FusionOptimizer, fuse_seqopt
 from aesara.tensor.shape import Shape, Shape_i
 from aesara.tensor.subtensor import Subtensor
 from aesara.tensor.type import (

aesara/tensor/utils.py

@@ -63,7 +63,9 @@ def shape_of_variables(fgraph, input_shapes):
     """
 
     if not hasattr(fgraph, "shape_feature"):
-        fgraph.attach_feature(aesara.tensor.rewriting.basic.ShapeFeature())
+        from aesara.tensor.rewriting.shape import ShapeFeature
+
+        fgraph.attach_feature(ShapeFeature())
 
     input_dims = [
         dimension

tests/compile/test_builders.py

@@ -21,7 +21,7 @@ from aesara.tensor.math import round as at_round
 from aesara.tensor.math import sigmoid
 from aesara.tensor.math import sum as at_sum
 from aesara.tensor.random.utils import RandomStream
-from aesara.tensor.rewriting.basic import ShapeOptimizer
+from aesara.tensor.rewriting.shape import ShapeOptimizer
 from aesara.tensor.shape import specify_shape
 from aesara.tensor.type import TensorType, matrices, matrix, scalar, vector, vectors
 from tests import unittest_tools

tests/tensor/random/test_basic.py

@@ -55,7 +55,7 @@ from aesara.tensor.random.basic import (
     wald,
     weibull,
 )
-from aesara.tensor.rewriting.basic import ShapeFeature
+from aesara.tensor.rewriting.shape import ShapeFeature
 from aesara.tensor.type import iscalar, scalar, tensor
 from tests.unittest_tools import create_aesara_param