Remove unnecessary handling of no longer supported RandomState

doc/extending/extending_pytensor_solution_1.py

@@ -118,7 +118,7 @@ def setup_method(self):
         self.op_class = SumDiffOp
 
     def test_perform(self):
-        rng = np.random.RandomState(43)
+        rng = np.random.default_rng(43)
         x = matrix()
         y = matrix()
         f = pytensor.function([x, y], self.op_class()(x, y))
@@ -128,7 +128,7 @@ def test_perform(self):
         assert np.allclose([x_val + y_val, x_val - y_val], out)
 
     def test_gradient(self):
-        rng = np.random.RandomState(43)
+        rng = np.random.default_rng(43)
 
         def output_0(x, y):
             return self.op_class()(x, y)[0]
@@ -150,7 +150,7 @@ def output_1(x, y):
         )
 
     def test_infer_shape(self):
-        rng = np.random.RandomState(43)
+        rng = np.random.default_rng(43)
 
         x = dmatrix()
         y = dmatrix()

doc/library/d3viz/index.ipynb

@@ -95,7 +95,7 @@
     "noutputs = 10\n",
     "nhiddens = 50\n",
     "\n",
-    "rng = np.random.RandomState(0)\n",
+    "rng = np.random.default_rng(0)\n",
     "x = pt.dmatrix('x')\n",
     "wh = pytensor.shared(rng.normal(0, 1, (nfeatures, nhiddens)), borrow=True)\n",
     "bh = pytensor.shared(np.zeros(nhiddens), borrow=True)\n",

doc/library/d3viz/index.rst

@@ -58,7 +58,7 @@ hidden layer and a softmax output layer.
     noutputs = 10
     nhiddens = 50
 
-    rng = np.random.RandomState(0)
+    rng = np.random.default_rng(0)
     x = pt.dmatrix('x')
     wh = pytensor.shared(rng.normal(0, 1, (nfeatures, nhiddens)), borrow=True)
     bh = pytensor.shared(np.zeros(nhiddens), borrow=True)

doc/optimizations.rst

@@ -239,7 +239,7 @@ Optimization                                              o4             o3  o2
         See :func:`insert_inplace_optimizer`
 
     inplace_random
-        Typically when a graph uses random numbers, the RandomState is stored
+        Typically when a graph uses random numbers, the random Generator is stored
         in a shared variable, used once per call and, updated after each function
         call.  In this common case, it makes sense to update the random number generator in-place.
 

pytensor/compile/monitormode.py

@@ -104,10 +104,7 @@ def detect_nan(fgraph, i, node, fn):
     from pytensor.printing import debugprint
 
     for output in fn.outputs:
-        if (
-            not isinstance(output[0], np.random.RandomState | np.random.Generator)
-            and np.isnan(output[0]).any()
-        ):
+        if not isinstance(output[0], np.random.Generator) and np.isnan(output[0]).any():
             print("*** NaN detected ***")  # noqa: T201
             debugprint(node)
             print(f"Inputs : {[input[0] for input in fn.inputs]}")  # noqa: T201

pytensor/compile/nanguardmode.py

@@ -34,7 +34,7 @@ def _is_numeric_value(arr, var):
 
     if isinstance(arr, _cdata_type):
         return False
-    elif isinstance(arr, np.random.mtrand.RandomState | np.random.Generator):
+    elif isinstance(arr, np.random.Generator):
         return False
     elif var is not None and isinstance(var.type, RandomType):
         return False

pytensor/link/jax/linker.py

@@ -1,6 +1,6 @@
 import warnings
 
-from numpy.random import Generator, RandomState
+from numpy.random import Generator
 
 from pytensor.compile.sharedvalue import SharedVariable, shared
 from pytensor.link.basic import JITLinker
@@ -21,7 +21,7 @@ def fgraph_convert(self, fgraph, input_storage, storage_map, **kwargs):
 
         # Replace any shared RNG inputs so that their values can be updated in place
         # without affecting the original RNG container. This is necessary because
-        # JAX does not accept RandomState/Generators as inputs, and they will have to
+        # JAX does not accept Generators as inputs, and they will have to
         # be tipyfied
         if shared_rng_inputs:
             warnings.warn(
@@ -79,7 +79,7 @@ def create_thunk_inputs(self, storage_map):
         thunk_inputs = []
         for n in self.fgraph.inputs:
             sinput = storage_map[n]
-            if isinstance(sinput[0], RandomState | Generator):
+            if isinstance(sinput[0], Generator):
                 new_value = jax_typify(
                     sinput[0], dtype=getattr(sinput[0], "dtype", None)
                 )

pytensor/link/numba/linker.py

@@ -16,22 +16,4 @@ def jit_compile(self, fn):
         return jitted_fn
 
     def create_thunk_inputs(self, storage_map):
-        from numpy.random import RandomState
-
-        from pytensor.link.numba.dispatch import numba_typify
-
-        thunk_inputs = []
-        for n in self.fgraph.inputs:
-            sinput = storage_map[n]
-            if isinstance(sinput[0], RandomState):
-                new_value = numba_typify(
-                    sinput[0], dtype=getattr(sinput[0], "dtype", None)
-                )
-                # We need to remove the reference-based connection to the
-                # original `RandomState`/shared variable's storage, because
-                # subsequent attempts to use the same shared variable within
-                # other non-Numba-fied graphs will have problems.
-                sinput = [new_value]
-            thunk_inputs.append(sinput)
-
-        return thunk_inputs
+        return [storage_map[n] for n in self.fgraph.inputs]

pytensor/tensor/random/type.py

@@ -1,12 +1,13 @@
 from typing import TypeVar
 
 import numpy as np
+from numpy.random import Generator
 
 import pytensor
 from pytensor.graph.type import Type
 
 
-T = TypeVar("T", np.random.RandomState, np.random.Generator)
+T = TypeVar("T")
 
 
 gen_states_keys = {
@@ -24,14 +25,10 @@
 
 
 class RandomType(Type[T]):
-    r"""A Type wrapper for `numpy.random.Generator` and `numpy.random.RandomState`."""
-
-    @staticmethod
-    def may_share_memory(a: T, b: T):
-        return a._bit_generator is b._bit_generator  # type: ignore[attr-defined]
+    r"""A Type wrapper for `numpy.random.Generator."""
 
 
-class RandomGeneratorType(RandomType[np.random.Generator]):
+class RandomGeneratorType(RandomType[Generator]):
     r"""A Type wrapper for `numpy.random.Generator`.
 
     The reason this exists (and `Generic` doesn't suffice) is that
@@ -47,6 +44,10 @@ class RandomGeneratorType(RandomType[np.random.Generator]):
     def __repr__(self):
         return "RandomGeneratorType"
 
+    @staticmethod
+    def may_share_memory(a: Generator, b: Generator):
+        return a._bit_generator is b._bit_generator  # type: ignore[attr-defined]
+
     def filter(self, data, strict=False, allow_downcast=None):
         """
         XXX: This doesn't convert `data` to the same type of underlying RNG type
@@ -58,7 +59,7 @@ def filter(self, data, strict=False, allow_downcast=None):
         `Type.filter`, we need to have it here to avoid surprising circular
         dependencies in sub-classes.
         """
-        if isinstance(data, np.random.Generator):
+        if isinstance(data, Generator):
             return data
 
         if not strict and isinstance(data, dict):

tests/unittest_tools.py

@@ -27,8 +27,7 @@ def fetch_seed(pseed=None):
     If config.unittest.rseed is set to "random", it will seed the rng with
     None, which is equivalent to seeding with a random seed.
 
-    Useful for seeding RandomState or Generator objects.
-    >>> rng = np.random.RandomState(fetch_seed())
+    Useful for seeding Generator objects.
     >>> rng = np.random.default_rng(fetch_seed())
     """