remove ConsiderConstant

pytensor/gradient.py

@@ -2108,44 +2108,6 @@ def _is_zero(x):
     return "yes"
 
 
-class ConsiderConstant(ViewOp):
-    def grad(self, args, g_outs):
-        return [g_out.zeros_like(g_out) for g_out in g_outs]
-
-
-consider_constant_ = ConsiderConstant()
-
-
-def consider_constant(x):
-    """Consider an expression constant when computing gradients.
-
-    DEPRECATED: use `zero_grad` or `disconnected_grad` instead.
-
-    The expression itself is unaffected, but when its gradient is
-    computed, or the gradient of another expression that this
-    expression is a subexpression of, it will not be backpropagated
-    through. In other words, the gradient of the expression is
-    truncated to 0.
-
-    :param x: A PyTensor expression whose gradient should be truncated.
-
-    :return: The expression is returned unmodified, but its gradient
-        is now truncated to 0.
-
-    .. versionadded:: 0.7
-    """
-    warnings.warn(
-        (
-            "`ConsiderConstant` is deprecated; use `zero_grad` or "
-            "`disconnected_grad` instead."
-        ),
-        category=DeprecationWarning,
-        stacklevel=3,
-    )
-
-    return ConsiderConstant()(x)
-
-
 class ZeroGrad(ViewOp):
     def grad(self, args, g_outs):
         return [g_out.zeros_like(g_out) for g_out in g_outs]
@@ -2352,28 +2314,3 @@ def grad_scale(x, multiplier):
     0.416...
     """
     return GradScale(multiplier)(x)
-
-
-DEPRECATED_NAMES = [
-    (
-        "consider_constant_",
-        "`consider_constant_` is deprecated; use `zero_grad` or `disconnected_grad` instead.",
-        ConsiderConstant(),
-    ),
-]
-
-
-def __getattr__(name):
-    """Intercept module-level attribute access of deprecated symbols.
-
-    Adapted from https://stackoverflow.com/a/55139609/3006474.
-
-    """
-    from warnings import warn
-
-    for old_name, msg, old_object in DEPRECATED_NAMES:
-        if name == old_name:
-            warn(msg, DeprecationWarning, stacklevel=2)
-            return old_object
-
-    raise AttributeError(f"module {__name__} has no attribute {name}")

pytensor/tensor/__init__.py

@@ -99,7 +99,7 @@ def _get_vector_length_Constant(op: Union[Op, Variable], var: Constant) -> int:
 
 
 import pytensor.tensor.exceptions  # noqa
-from pytensor.gradient import consider_constant, grad, hessian, jacobian  # noqa
+from pytensor.gradient import grad, hessian, jacobian  # noqa
 
 # adds shared-variable constructors
 from pytensor.tensor import sharedvar  # noqa

tests/test_gradient.py

@@ -766,48 +766,6 @@ def test_subgraph_grad():
         assert np.sum(np.abs(true_grad - pgrad)) < 0.00001
 
 
-class TestConsiderConstant:
-    def test_op_removed(self):
-        from pytensor.gradient import ConsiderConstant, consider_constant
-
-        x = matrix("x")
-
-        with pytest.deprecated_call():
-            y = x * consider_constant(x)
-
-        f = pytensor.function([x], y)
-
-        assert ConsiderConstant not in [
-            type(node.op) for node in f.maker.fgraph.toposort()
-        ]
-
-    def test_grad(self):
-        from pytensor.gradient import consider_constant
-
-        rng = np.random.default_rng(seed=utt.fetch_seed())
-
-        a = np.asarray(rng.standard_normal((5, 5)), dtype=config.floatX)
-
-        x = matrix("x")
-
-        with pytest.deprecated_call():
-            expressions_gradients = [
-                (x * consider_constant(x), x),
-                (x * consider_constant(exp(x)), exp(x)),
-                (consider_constant(x), at.constant(0.0)),
-                (x**2 * consider_constant(x), 2 * x**2),
-            ]
-
-            for expr, expr_grad in expressions_gradients:
-                g = grad(expr.sum(), x)
-                # gradient according to pytensor
-                f = pytensor.function([x], g, on_unused_input="ignore")
-                # desired gradient
-                f2 = pytensor.function([x], expr_grad, on_unused_input="ignore")
-
-                assert np.allclose(f(a), f2(a))
-
-
 class TestZeroGrad:
     def setup_method(self):
         self.rng = np.random.default_rng(seed=utt.fetch_seed())