keras.utils.set_random_seed clear the global SeedGenerator.

keras/src/backend/jax/distribution_lib.py

@@ -160,13 +160,13 @@ def initialize_rng():
     # Check if the global seed generator is set and ensure it has an initialized
     # seed.  Otherwise, reset the seed to the global seed.
     global_seed_generator = global_state.get_global_attribute(
-        "global_seed_generator"
+        seed_generator.GLOBAL_SEED_GENERATOR
     )
     if global_seed_generator is not None:
         seed = global_seed_generator.get_config()["seed"]
         if seed is None:
             global_state.set_global_attribute(
-                "global_seed_generator",
+                seed_generator.GLOBAL_SEED_GENERATOR,
                 seed_generator.SeedGenerator(
                     seed=global_seed,
                     name=global_seed_generator.name,

keras/src/random/seed_generator.py

@@ -8,6 +8,8 @@
 from keras.src.utils import jax_utils
 from keras.src.utils.naming import auto_name
 
+GLOBAL_SEED_GENERATOR = "global_seed_generator"
+
 
 @keras_export("keras.random.SeedGenerator")
 class SeedGenerator:
@@ -133,10 +135,10 @@ def global_seed_generator():
             "out = keras.random.normal(shape=(1,), seed=self.seed_generator)\n"
             "```"
         )
-    gen = global_state.get_global_attribute("global_seed_generator")
+    gen = global_state.get_global_attribute(GLOBAL_SEED_GENERATOR)
     if gen is None:
         gen = SeedGenerator()
-        global_state.set_global_attribute("global_seed_generator", gen)
+        global_state.set_global_attribute(GLOBAL_SEED_GENERATOR, gen)
     return gen
 
 

keras/src/utils/rng_utils.py

@@ -5,6 +5,7 @@
 from keras.src import backend
 from keras.src.api_export import keras_export
 from keras.src.backend.common import global_state
+from keras.src.random import seed_generator
 from keras.src.utils.module_utils import tensorflow as tf
 
 GLOBAL_RANDOM_SEED = "global_random_seed"
@@ -20,7 +21,7 @@ def set_random_seed(seed):
     sources of randomness, or when certain non-deterministic cuDNN ops are
     involved.
 
-    Calling this utility is equivalent to the following:
+    Calling this utility does the following:
 
     ```python
     import random
@@ -36,6 +37,9 @@ def set_random_seed(seed):
     torch.manual_seed(seed)
     ```
 
+    Additionally, it resets the global Keras `SeedGenerator`, which is used by
+    `keras.random` functions when the `seed` is not provided.
+
     Note that the TensorFlow seed is set even if you're not using TensorFlow
     as your backend framework, since many workflows leverage `tf.data`
     pipelines (which feature random shuffling). Likewise many workflows
@@ -52,6 +56,10 @@ def set_random_seed(seed):
 
     # Store seed in global state so we can query it if set.
     global_state.set_global_attribute(GLOBAL_RANDOM_SEED, seed)
+    # Remove global SeedGenerator, it will be recreated from the seed.
+    global_state.set_global_attribute(
+        seed_generator.GLOBAL_SEED_GENERATOR, None
+    )
     random.seed(seed)
     np.random.seed(seed)
     if tf.available:

keras/src/utils/rng_utils_test.py

@@ -1,6 +1,4 @@
 import numpy as np
-import pytest
-import tensorflow as tf
 
 import keras
 from keras.src import backend
@@ -9,11 +7,7 @@
 
 
 class TestRandomSeedSetting(test_case.TestCase):
-    @pytest.mark.skipif(
-        backend.backend() == "numpy",
-        reason="Numpy backend does not support random seed setting.",
-    )
-    def test_set_random_seed(self):
+    def test_set_random_seed_with_seed_generator(self):
         def get_model_output():
             model = keras.Sequential(
                 [
@@ -23,11 +17,39 @@ def get_model_output():
                 ]
             )
             x = np.random.random((32, 10)).astype("float32")
-            ds = tf.data.Dataset.from_tensor_slices(x).shuffle(32).batch(16)
-            return model.predict(ds)
+            return model.predict(x, batch_size=16)
 
         rng_utils.set_random_seed(42)
         y1 = get_model_output()
-        rng_utils.set_random_seed(42)
+
+        # Second call should produce different results.
         y2 = get_model_output()
-        self.assertAllClose(y1, y2)
+        self.assertNotAllClose(y1, y2)
+
+        # Re-seeding should produce the same results as the first time.
+        rng_utils.set_random_seed(42)
+        y3 = get_model_output()
+        self.assertAllClose(y1, y3)
+
+        # Re-seeding with a different seed should produce different results.
+        rng_utils.set_random_seed(1337)
+        y4 = get_model_output()
+        self.assertNotAllClose(y1, y4)
+
+    def test_set_random_seed_with_global_seed_generator(self):
+        rng_utils.set_random_seed(42)
+        y1 = backend.random.randint((32, 10), minval=0, maxval=1000)
+
+        # Second call should produce different results.
+        y2 = backend.random.randint((32, 10), minval=0, maxval=1000)
+        self.assertNotAllClose(y1, y2)
+
+        # Re-seeding should produce the same results as the first time.
+        rng_utils.set_random_seed(42)
+        y3 = backend.random.randint((32, 10), minval=0, maxval=1000)
+        self.assertAllClose(y1, y3)
+
+        # Re-seeding with a different seed should produce different results.
+        rng_utils.set_random_seed(1337)
+        y4 = backend.random.randint((32, 10), minval=0, maxval=1000)
+        self.assertNotAllClose(y1, y4)