[API Compatibility] resolve type assertion for nn.Module.to

python/paddle/nn/layer/layers.py

@@ -3060,7 +3060,7 @@ def to(self, *args, **kwargs) -> Self:
             dtype=dtype,
             blocking=blocking,
             include_sublayers=True,
-            floating_only=False,
+            floating_only=True,
         )
 
     def _apply(
@@ -3214,8 +3214,14 @@ def _to_impl(
             )
 
         def transform(t, device, dtype, blocking):
-            if floating_only and (not paddle.is_floating_point(t)):
-                return t
+            if floating_only and not (
+                paddle.is_floating_point(t) or paddle.is_complex(t)
+            ):
+                # Match PyTorch nn.Module.to semantics: skip dtype casting for
+                # non-floating/complex tensors, but still apply device change.
+                if device is None:
+                    return t
+                return self._transform(t, device, None, blocking)
             return self._transform(t, device, dtype, blocking)
 
         with warnings.catch_warnings():

test/legacy_test/test_api_compatibility_part3.py

@@ -1337,30 +1337,30 @@ def test_layer_no_args(self):
         self.assertIs(ret, linear)
         self.assertEqual(linear.weight.dtype, original_dtype)
 
-    # ---- Layer.to: all-dtype casting ----
+    # ---- Layer.to: floating-only dtype casting (PyTorch-aligned) ----
 
-    def test_layer_cast_all_with_positional_dtype(self):
-        """Layer.to(dtype) casts ALL params and buffers, including int buf."""
+    def test_layer_cast_floating_only_with_positional_dtype(self):
+        """Layer.to(dtype) casts only floating/complex params and buffers."""
         model = self._make_model()
         self.assertEqual(model.int_buf.dtype, paddle.int32)
         model.to(paddle.float64)
         self.assertEqual(model.linear.weight.dtype, paddle.float64)
-        self.assertEqual(model.int_buf.dtype, paddle.float64)
+        self.assertEqual(model.int_buf.dtype, paddle.int32)
 
-    def test_layer_cast_all_with_keyword_dtype(self):
-        """Layer.to(dtype='float64') casts ALL params and buffers."""
+    def test_layer_cast_floating_only_with_keyword_dtype(self):
+        """Layer.to(dtype='float64') casts only floating/complex tensors."""
         model = self._make_model()
         model.to(dtype='float64')
         self.assertEqual(model.linear.weight.dtype, paddle.float64)
-        self.assertEqual(model.int_buf.dtype, paddle.float64)
+        self.assertEqual(model.int_buf.dtype, paddle.int32)
 
-    def test_layer_cast_all_with_tensor(self):
-        """Layer.to(tensor) casts ALL params and buffers."""
+    def test_layer_cast_floating_only_with_tensor(self):
+        """Layer.to(tensor) casts only floating/complex tensors to tensor.dtype."""
         model = self._make_model()
         ref = paddle.to_tensor([1.0], dtype='float64')
         model.to(ref)
         self.assertEqual(model.linear.weight.dtype, paddle.float64)
-        self.assertEqual(model.int_buf.dtype, paddle.float64)
+        self.assertEqual(model.int_buf.dtype, paddle.int32)
 
     # ---- Layer.to: sublayers and chaining ----