Changed copy to deepcopy for rng

This was done for the python linker and numba linker.

deepcopy seems to be the recommended method for
copying a numpy Generator.

After this numpy PR:
numpy/numpy@44ba7ca
`copy` didn't seem to actually make an independent copy of
the `np.random.Generator` objects spawned by `RandomStream`.

This was causing the "test values" computed by e.g.
`RandomStream.uniform` to increment the RNG state, which
was causing tests that rely on `RandomStream` to fail.

Here is some related discussion:
numpy/numpy#24086

I didn't see any official documentation about
a change in numpy that would make copy stop
working.

Author: brendan-m-murphy

pytensor/link/numba/dispatch/random.py

@@ -1,5 +1,5 @@
 from collections.abc import Callable
-from copy import copy
+from copy import copy, deepcopy
 from functools import singledispatch
 from textwrap import dedent
 
@@ -34,7 +34,7 @@ def copy_NumPyRandomGenerator(rng):
     def impl(rng):
         # TODO: Open issue on Numba?
         with numba.objmode(new_rng=types.npy_rng):
-            new_rng = copy(rng)
+            new_rng = deepcopy(rng)
 
         return new_rng
 

pytensor/tensor/random/op.py

@@ -1,6 +1,6 @@
 import warnings
 from collections.abc import Sequence
-from copy import copy
+from copy import deepcopy
 from typing import Any, cast
 
 import numpy as np
@@ -395,7 +395,7 @@ def perform(self, node, inputs, outputs):
 
         # Draw from `rng` if `self.inplace` is `True`, and from a copy of `rng` otherwise.
         if not self.inplace:
-            rng = copy(rng)
+            rng = deepcopy(rng)
 
         outputs[0][0] = rng
         outputs[1][0] = np.asarray(

tests/tensor/random/test_basic.py

@@ -1,6 +1,6 @@
 import pickle
 import re
-from copy import copy
+from copy import deepcopy
 
 import numpy as np
 import pytest
@@ -114,7 +114,9 @@ def test_fn(*args, random_state=None, **kwargs):
 
     pt_rng = shared(rng, borrow=True)
 
-    numpy_res = np.asarray(test_fn(*param_vals, random_state=copy(rng), **kwargs_vals))
+    numpy_res = np.asarray(
+        test_fn(*param_vals, random_state=deepcopy(rng), **kwargs_vals)
+    )
 
     pytensor_res = rv(*params, rng=pt_rng, **kwargs)