Remove InRange scalar Op (#1699)

pytensor/compile/profiling.py

@@ -1464,7 +1464,6 @@ class ProfileStats:
             ps.GE,
             ps.EQ,
             ps.NEQ,
-            ps.InRange,
             ps.Switch,
             ps.OR,
             ps.XOR,

pytensor/scalar/basic.py

@@ -1650,56 +1650,6 @@ class IsInf(FixedLogicalComparison):
 isinf = IsInf()
 
 
-class InRange(LogicalComparison):
-    nin = 3
-
-    def __init__(self, openlow, openhi):
-        self.openlow = openlow
-        self.openhi = openhi
-
-    def impl(self, x, low, hi):
-        if self.openlow and x <= low:
-            return False
-        elif not self.openlow and x < low:
-            return False
-        if self.openhi and x >= hi:
-            return False
-        elif not self.openhi and x > hi:
-            return False
-        return True
-
-    def c_code(self, node, name, inputs, outputs, sub):
-        (x, low, hi) = inputs
-        (z,) = outputs
-
-        cmp1 = ">" if self.openlow else ">="
-        cmp2 = "<" if self.openhi else "<="
-
-        return f"{z} = {x} {cmp1} {low} && {x} {cmp2} {hi};"
-
-    def get_grad(self, elem):
-        if elem.type in complex_types:
-            msg = (
-                "No gradient implemented for complex numbers in "
-                "class scalar.basic.InRange"
-            )
-            raise NotImplementedError(msg)
-        elif elem.type in discrete_types:
-            return elem.zeros_like(dtype=config.floatX)
-        else:
-            return elem.zeros_like()
-
-    def L_op(self, inputs, outputs, gout):
-        (x, low, hi) = inputs
-        (_gz,) = gout
-        grads = [self.get_grad(elem) for elem in [x, low, hi]]
-        return grads
-
-
-inopenrange = InRange(True, True)
-inclosedrange = InRange(False, False)
-
-
 class Switch(ScalarOp):
     nin = 3
     nfunc_spec = ("where", 3, 1)

tests/scalar/test_basic.py

@@ -3,7 +3,6 @@ import pytest
 
 import pytensor
 import pytensor.tensor as pt
-import tests.unittest_tools as utt
 from pytensor.compile.mode import Mode
 from pytensor.graph.fg import FunctionGraph
 from pytensor.link.c.basic import DualLinker
@@ -11,7 +10,6 @@ from pytensor.scalar.basic import (
     EQ,
     ComplexError,
     Composite,
-    InRange,
     ScalarType,
     add,
     and_,
@@ -475,32 +473,6 @@ def test_grad_identity():
     pytensor.gradient.grad(l, x)
 
 
-def test_grad_inrange():
-    for bound_definition in [(True, True), (False, False)]:
-        # Instantiate op, and then take the gradient
-        op = InRange(*bound_definition)
-        x = fscalar("x")
-        low = fscalar("low")
-        high = fscalar("high")
-        out = op(x, low, high)
-        gx, glow, ghigh = pytensor.gradient.grad(out, [x, low, high])
-
-        # We look if the gradient are equal to zero
-        # if x is lower than the lower bound,
-        # equal to the lower bound, between lower and higher bound,
-        # equal to the higher bound and higher than the higher
-        # bound.
-        # Mathematically we should have an infinite gradient when
-        # x is equal to the lower or higher bound but in that case
-        # PyTensor defines the gradient to be zero for stability.
-        f = pytensor.function([x, low, high], [gx, glow, ghigh])
-        utt.assert_allclose(f(0, 1, 5), [0, 0, 0])
-        utt.assert_allclose(f(1, 1, 5), [0, 0, 0])
-        utt.assert_allclose(f(2, 1, 5), [0, 0, 0])
-        utt.assert_allclose(f(5, 1, 5), [0, 0, 0])
-        utt.assert_allclose(f(7, 1, 5), [0, 0, 0])
-
-
 def test_grad_abs():
     a = fscalar("a")
     b = 0.5 * (a + pytensor.tensor.abs(a))