remove unused code

Author: jessegrabowski

pytensor/tensor/slinalg.py

@@ -12,7 +12,7 @@
 import pytensor.tensor as pt
 from pytensor.compile.builders import OpFromGraph
 from pytensor.gradient import DisconnectedType
-from pytensor.graph.basic import Apply
+from pytensor.graph.basic import Apply, Variable
 from pytensor.graph.op import Op
 from pytensor.tensor import TensorLike, as_tensor_variable
 from pytensor.tensor import basic as ptb
@@ -741,7 +741,8 @@ class LUSolve(OpFromGraph):
 
     def __init__(
         self,
-        *args,
+        inputs: list[Variable],
+        outputs: list[Variable],
         trans: bool = False,
         b_ndim: int | None = None,
         check_finite: bool = False,
@@ -753,7 +754,7 @@ def __init__(
         self.check_finite = check_finite
         self.overwrite_b = overwrite_b
 
-        super().__init__(*args, **kwargs)
+        super().__init__(inputs=inputs, outputs=outputs, **kwargs)
 
 
 def lu_solve(

tests/tensor/test_slinalg.py

@@ -15,7 +15,6 @@
 from pytensor.tensor.slinalg import (
     Cholesky,
     CholeskySolve,
-    LUSolve,
     Solve,
     SolveBase,
     SolveTriangular,
@@ -1033,33 +1032,3 @@ def test_block_diagonal_blockwise():
     B = np.random.normal(size=(1, batch_size, 4, 4)).astype(config.floatX)
     result = block_diag(A, B).eval()
     assert result.shape == (10, batch_size, 6, 6)
-
-
-def lu_solve_1(A, b):
-    lu, pivots = pt.linalg.lu_factor(A)
-    return pt.linalg.lu_solve((lu, pivots), b)
-
-
-def lu_solve_2(A, b, b_ndim=1, trans=0, check_finite=False):
-    lu, pivots = pt.linalg.lu_factor(A)
-    return LUSolve(b_ndim=1, trans=0, check_finite=False)(lu, pivots, b)
-
-
-@pytest.mark.parametrize(
-    "op", [lu_solve_1, lu_solve_2, pt.linalg.solve], ids=["lu_1", "lu_2", "solve"]
-)
-@pytest.mark.parametrize("n", [500])
-def test_solve_methods(op, n, benchmark):
-    A = pt.tensor("A", shape=(n, n))
-    b = pt.tensor("b", shape=(n,))
-
-    x = op(A, b)
-    gx = pt.grad(x.sum(), [A, b])
-    f = pytensor.function([A, b], [x, *gx])
-
-    A_val = np.random.normal(size=(n, n)).astype(config.floatX)
-    b_val = np.random.normal(size=(n,)).astype(config.floatX)
-
-    # Trigger compilation if we're a jit mode
-    f(A_val, b_val)
-    benchmark(f, A_val, b_val)