Faster RNG deepcopy

pytensor/link/numba/dispatch/random.py

 from collections.abc import Callable
-from copy import deepcopy
 from functools import singledispatch
 from hashlib import sha256
 from textwrap import dedent
@@ -32,6 +31,7 @@ from pytensor.link.utils import (
 )
 from pytensor.tensor import get_vector_length
 from pytensor.tensor.random.op import RandomVariable, RandomVariableWithCoreShape
+from pytensor.tensor.random.utils import custom_rng_deepcopy
 from pytensor.tensor.type_other import NoneTypeT
 from pytensor.tensor.utils import _parse_gufunc_signature
 
@@ -42,7 +42,7 @@ def numba_deepcopy_random_generator(x):
 
         def random_generator_deepcopy(x):
             with numba.objmode(new_rng=types.npy_rng):
-                new_rng = deepcopy(x)
+                new_rng = custom_rng_deepcopy(x)
             return new_rng
 
         return random_generator_deepcopy

pytensor/tensor/random/op.py

 import abc
 import warnings
 from collections.abc import Sequence
-from copy import deepcopy
 from typing import Any, cast
 
 import numpy as np
@@ -23,6 +22,7 @@ from pytensor.tensor.blockwise import OpWithCoreShape
 from pytensor.tensor.random.type import RandomGeneratorType, RandomType
 from pytensor.tensor.random.utils import (
     compute_batch_shape,
+    custom_rng_deepcopy,
     explicit_expand_dims,
     normalize_size_param,
 )
@@ -423,7 +423,7 @@ class RandomVariable(RNGConsumerOp):
 
         # Draw from `rng` if `self.inplace` is `True`, and from a copy of `rng` otherwise.
         if not self.inplace:
-            rng = deepcopy(rng)
+            rng = custom_rng_deepcopy(rng)
 
         outputs[0][0] = rng
         outputs[1][0] = np.asarray(

pytensor/tensor/random/utils.py

 from collections.abc import Callable, Sequence
+from copy import deepcopy
 from functools import wraps
 from itertools import zip_longest
 from types import ModuleType
 from typing import TYPE_CHECKING
 
 import numpy as np
+from numpy.random import Generator
 
 from pytensor.compile.sharedvalue import shared
 from pytensor.graph.basic import Variable
@@ -204,6 +206,16 @@ def normalize_size_param(
     return shape
 
 
+def custom_rng_deepcopy(rng):
+    # This helper exists because copying numpy.random.Generator via deepcopy is slow.
+    # NumPy may implement a faster clone/copy API in the future:
+    # https://github.com/numpy/numpy/issues/24086
+    old_bitgen = rng.bit_generator
+    new_bitgen = type(old_bitgen)(deepcopy(old_bitgen._seed_seq))
+    new_bitgen.state = old_bitgen.state
+    return Generator(new_bitgen)
+
+
 class RandomStream:
     """Module component with similar interface to `numpy.random.Generator`.
 

tests/tensor/random/test_utils.py

+from copy import deepcopy
+
 import numpy as np
 import pytest
 
@@ -7,6 +9,7 @@ from pytensor.graph.rewriting.db import RewriteDatabaseQuery
 from pytensor.tensor.random.utils import (
     RandomStream,
     broadcast_params,
+    custom_rng_deepcopy,
     normalize_size_param,
     supp_shape_from_ref_param_shape,
 )
@@ -348,3 +351,28 @@ def test_normalize_size_param():
 
     sym_tensor_size = tensor(shape=(3,), dtype="int64")
     assert normalize_size_param(sym_tensor_size) is sym_tensor_size
+
+
+def test_custom_rng_deepcopy_matches_deepcopy():
+    rng = np.random.default_rng(123)
+
+    dp = deepcopy(rng).bit_generator
+    fc = custom_rng_deepcopy(rng).bit_generator
+
+    # Same state
+    assert dp.state == fc.state
+    # Same seed sequence
+    assert dp.seed_seq.state == fc.seed_seq.state
+
+
+def test_custom_rng_deepcopy_output_identical():
+    rng = np.random.default_rng(123)
+
+    rng1 = deepcopy(rng)
+    rng2 = custom_rng_deepcopy(rng)
+
+    # Generate numbers from each
+    x1 = rng1.normal(size=10)
+    x2 = rng2.normal(size=10)
+
+    assert np.allclose(x1, x2)