Skip rewrite tests when mode=FAST_COMPILE

Author: jessegrabowski

pytensor/link/numba/dispatch/basic.py

@@ -812,7 +812,6 @@ def softplus(x):
 @numba_funcify.register(Cholesky)
 def numba_funcify_Cholesky(op, node, **kwargs):
     lower = op.lower
-
     out_dtype = node.outputs[0].type.numpy_dtype
 
     if lower:

pytensor/tensor/slinalg.py

@@ -42,6 +42,9 @@ class Cholesky(Op):
         `scipy.linalg.LinAlgError` if the matrix is not positive definite.
         If on_error is set to 'nan', it will return a matrix containing
         nans instead.
+    overwrite_a: bool, ignored
+        Whether to use the same memory for the output as `a`. This argument is ignored, and
+        included only for consistency with scipy.linalg.cholesky.
     """
 
     # TODO: for specific dtypes
@@ -52,11 +55,13 @@ class Cholesky(Op):
 
     def __init__(self, *, lower=True, on_error="raise", overwrite_a=False):
         self.lower = lower
-        self.overwrite_a = overwrite_a
+
         if on_error not in ("raise", "nan"):
             raise ValueError('on_error must be one of "raise" or ""nan"')
         self.on_error = on_error
-        if overwrite_a:
+
+        self.overwrite_a = overwrite_a
+        if self.overwrite_a:
             self.destroy_map = {0: [0]}
 
     def infer_shape(self, fgraph, node, shapes):
@@ -87,7 +92,7 @@ def perform(self, node, inputs, outputs):
             if self.on_error == "raise":
                 raise
             else:
-                x = (np.zeros(x.shape) * np.nan).astype(x.dtype)
+                x = np.full_like(x, np.nan)
         z[0] = x
 
     def L_op(self, inputs, outputs, gradients):

tests/tensor/rewriting/test_linalg.py

@@ -308,6 +308,10 @@ def test_invalid_batched_a(self):
         )
 
 
+@pytest.mark.skipif(
+    config.mode == "FAST_COMPILE",
+    reason="inplace rewrites disabled when mode is FAST_COMPILE",
+)
 def test_local_inplace_cholesky():
     X = matrix("X")
     L = cholesky(X, overwrite_a=False, lower=True)