[scan] Support different output dtypes

test/scan/test_scan.py

@@ -371,6 +371,32 @@ def count_number_of_sines(partition_fn):
         count_number_of_sines(min_cut_rematerialization_partition), 10)
     self.assertEqual(count_number_of_sines(default_partition), 0)
 
+  def test_scan_different_dtypes(self):
+    """Test that the combine function can output different dtypes."""
+
+    def fn(carry, x):
+      bf16_value, f32_value = x
+      y = (torch.sin(bf16_value), torch.sin(f32_value))
+      return torch.sin(carry), y
+
+    init = torch.tensor([0.0, 0.0],
+                        requires_grad=True,
+                        device=self.device,
+                        dtype=torch.float16)
+    bf16_xs = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],
+                           requires_grad=True,
+                           device=self.device,
+                           dtype=torch.bfloat16)
+    f32_xs = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],
+                          requires_grad=True,
+                          device=self.device,
+                          dtype=torch.float32)
+    final_carry, ys = self.run_test(fn, init, (bf16_xs, f32_xs))
+    bf16_ys, f32_ys = ys
+    self.assertEqual(final_carry.dtype, torch.float16)
+    self.assertEqual(bf16_ys.dtype, torch.bfloat16)
+    self.assertEqual(f32_ys.dtype, torch.float32)
+
 
 class PyTreeTest(TestBase):
 

torch_xla/experimental/scan.py

@@ -487,16 +487,15 @@ def make_fake_tensor(v: torch.Tensor) -> torch.Tensor:
   fn_carry_out, fn_y_out = split(fn_outputs, carry_len)
   assert carry_len + y_len == len(fn_outputs)
   fn_carry_shapes = [v.shape for v in fn_carry_out]
-  fn_y_shapes = [v.shape for v in fn_y_out]
   for fn_carry_shape, init_leaf in zip(fn_carry_shapes, init):
     assert fn_carry_shape == init_leaf.shape, f"`fn` must keep the `carry` shape unchanged. \
       Got {fn_carry_shape} but expected {init_leaf.shape}"
 
   builder = Builder('scan')
   num_iters = next(iter(tree_iter(xs))).size(0)
   ys = [
-      torch.zeros((num_iters, *fn_y_shape), device=device)
-      for fn_y_shape in fn_y_shapes
+      torch.zeros((num_iters, *v.shape), device=device, dtype=v.dtype)
+      for v in fn_y_out
   ]
   # Start the `curr_iter` loop variable at zero.
   zero = torch.tensor(0, device=device)