diff --git a/test/expect/HasDecompTest.test_has_decomposition.expect b/test/expect/HasDecompTest.test_has_decomposition.expect
index 883399f855cc..201fd6d989bc 100644
--- a/test/expect/HasDecompTest.test_has_decomposition.expect
+++ b/test/expect/HasDecompTest.test_has_decomposition.expect
@@ -704,6 +704,7 @@ aten::bernoulli.Tensor
 aten::bernoulli.Tensor_out
 aten::bernoulli.float_out
 aten::bernoulli.out
+aten::bernoulli.p
 aten::bernoulli_.Tensor
 aten::bernoulli_.float
 aten::bincount
diff --git a/test/inductor/test_cpu_repro.py b/test/inductor/test_cpu_repro.py
index 5f8e75bde483..67159a010305 100644
--- a/test/inductor/test_cpu_repro.py
+++ b/test/inductor/test_cpu_repro.py
@@ -4943,6 +4943,27 @@ class CPUReproTests(TestCase):
             torch.compile(converted_model)(*example_batch)
             check_metrics_vec_kernel_count(3)
 
+    def test_dropout(self):
+        class Model(nn.Module):
+            def __init__(self, dim):
+                super().__init__()
+                self.dropout = eval(f"nn.Dropout{dim}d(p=0.5)")
+
+            def forward(self, x):
+                torch.manual_seed(0)
+                x = self.dropout(x)
+                return x
+
+        for dim in [1, 2, 3]:
+            model = Model(dim)
+            torch.manual_seed(0)
+            shape = [1, 3] + [256] * dim
+            x = torch.randn(*shape)
+            output = model(x)
+            c_model = torch.compile(model)
+            c_output = c_model(x)
+            self.assertTrue(torch.allclose(output, c_output))
+
 
 if __name__ == "__main__":
     from torch._inductor.test_case import run_tests
diff --git a/test/test_decomp.py b/test/test_decomp.py
index 0177c50ca7d8..c0a0b66300ca 100644
--- a/test/test_decomp.py
+++ b/test/test_decomp.py
@@ -608,17 +608,6 @@ class TestDecomp(TestCase):
         res = torch._decomp.decompositions.uniform(x, low=low, high=high)
         self.assertEqual(ref, res)
 
-    def test_bernoulli_p(self, device):
-        p = 0.3
-        input_t = torch.rand(100, 100)
-        torch.manual_seed(123)
-        ref = torch.ops.aten.bernoulli.p(input_t, p)
-        torch.manual_seed(123)
-        res = torch._decomp.decompositions.bernoulli_p(input_t, p)
-        ref_p = ref.sum() / torch.prod(torch.tensor(ref.size()))
-        res_p = res.sum() / torch.prod(torch.tensor(res.size()))
-        self.assertEqual(ref_p, res_p, atol=0.06 * p, rtol=0.06)
-
     def test_bernoulli_default(self, device):
         p = 0.3
         p_t = p * torch.ones(5, 5)
diff --git a/torch/_decomp/decompositions.py b/torch/_decomp/decompositions.py
index bd6b1cf88bb0..e38281afe00f 100644
--- a/torch/_decomp/decompositions.py
+++ b/torch/_decomp/decompositions.py
@@ -5117,21 +5117,6 @@ def bernoulli(
     return p
 
 
-@register_decomposition(aten.bernoulli.p)
-def bernoulli_p(self, p, *, generator: Optional[torch.Generator] = None):
-    if generator is None:
-        raw_p = torch.rand(self.size(), dtype=torch.float32, device=self.device)
-    else:
-        raw_p = torch.rand(
-            self.size(),
-            generator=generator,
-            dtype=self.float32,
-            device=self.device,
-        )
-    p = (raw_p < p).to(self.dtype)
-    return p
-
-
 def isin_default(elements, test_elements, *, invert=False):
     if elements.numel() == 0:
         return torch.empty_like(elements, dtype=torch.bool)