diff --git a/extra/torch_backend/backend.py b/extra/torch_backend/backend.py
index bd39568b50..e811221ba7 100644
--- a/extra/torch_backend/backend.py
+++ b/extra/torch_backend/backend.py
@@ -526,8 +526,8 @@ tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
     self.assign(Tensor.randint(*self.shape, low=dtypes.min(self.dtype), high=dtypes.max(self.dtype), device=self.device, dtype=self.dtype))),
   "aten.random_.from": inplace_fn("self")(lambda self, from_, to:
     self.assign(Tensor.randint(*self.shape, low=from_, high=to, device=self.device, dtype=self.dtype))),
-  "aten.uniform_": inplace_fn("self")(lambda self, low=0, high=1: self.assign(Tensor.uniform(*self.shape, low=low, high=high))),
-  "aten.normal_": inplace_fn("self")(lambda self, mean=0, std=1: self.assign(Tensor.normal(*self.shape, mean=mean, std=std))),
+  "aten.uniform_": inplace_fn("self")(lambda self, low=0, high=1: self.assign(Tensor.uniform(*self.shape, low=low, high=high, dtype=self.dtype))),
+  "aten.normal_": inplace_fn("self")(lambda self, mean=0, std=1: self.assign(Tensor.normal(*self.shape, mean=mean, std=std, dtype=self.dtype))),
   # these don't work in out form, they have size 0
   "aten.abs": Tensor.abs,
   "aten.logical_not": Tensor.logical_not,
diff --git a/extra/torch_backend/test.py b/extra/torch_backend/test.py
index 99ecb242b8..30117c3dd1 100644
--- a/extra/torch_backend/test.py
+++ b/extra/torch_backend/test.py
@@ -153,6 +153,16 @@ class TestTorchBackend(unittest.TestCase):
         res = torch.ops.aten.isin.Tensor_Tensor_out(a, b, invert=invert, assume_unique=assume_unique, out=out)
         np.testing.assert_equal(out.cpu().numpy(), expected.cpu().numpy())
 
+  def test_uniform(self):
+    for torch_dtype in [torch.float32, torch.float16]:
+      a = torch.rand(10, 10, device=device, dtype=torch_dtype)
+      self.assertEqual(a.dtype, torch_dtype)
+
+  def test_normal(self):
+    for torch_dtype in [torch.float32, torch.float16]:
+      a = torch.randn(10, 10, device=device, dtype=torch_dtype)
+      self.assertEqual(a.dtype, torch_dtype)
+
   @unittest.skip("meh")
   def test_str(self):
     a = torch.ones(4, device=device)