Disable XLA with TensorFlow determinisim

keras/src/trainers/trainer.py

@@ -1122,5 +1122,14 @@ def model_supports_jit(model):
                 return False
     # XLA not supported by some layers
     if all(x.supports_jit for x in model._flatten_layers()):
+        if backend.backend() == "tensorflow":
+            from tensorflow.python.framework.config import (
+                is_op_determinism_enabled,
+            )
+
+            if is_op_determinism_enabled():
+                # disable XLA with determinism enabled since not all ops are
+                # supported by XLA with determinism enabled.
+                return False
         return True
     return False

keras/src/trainers/trainer_test.py

@@ -1718,6 +1718,27 @@ def call(self, x, training=None):
         for v in model._compile_loss.variables:
             self.assertAllClose(v, 0.0)
 
+    @pytest.mark.skipif(
+        backend.backend() != "tensorflow",
+        reason="This test is only applicable to TensorFlow.",
+    )
+    @pytest.mark.requires_trainable_backend
+    def test_jit_compile_with_tf_determinism(self):
+        from tensorflow.python.framework.config import disable_op_determinism
+        from tensorflow.python.framework.config import enable_op_determinism
+
+        enable_op_determinism()
+
+        model = ExampleModel(units=3)
+        model.compile(
+            optimizer=optimizers.SGD(),
+            loss=losses.MeanSquaredError(),
+            metrics=[metrics.MeanSquaredError()],
+        )
+
+        self.assertFalse(model.jit_compile)
+        disable_op_determinism()
+
 
 class TrainerDistributeTest(testing.TestCase):
     @pytest.mark.skipif(