address PR review feedback

Fix adjacent latent same-class bug: replace_div_tensor_mode with rounding_mode=None used the generic integer-stays-integer promotion rule instead of promoting to the node output float type before dividing.

Add torch.true_divide(int, int) test to directly cover the true_divide.Tensor resolver rewiring.

Add torch.div(int, int, rounding_mode=None) test to cover the div_tensor_mode fix.

Author: gokulkrishna98

coreai_torch/_aten_to_core.py

@@ -874,13 +874,20 @@ def replace_div_tensor_mode(
         else (node.args[2] if len(node.args) > 2 else None)
     )
 
+    if rounding_mode is None:
+        # True divide: integer operands must promote to the node's float
+        # output type before dividing, not the generic same-kind-stays-integer
+        # promotion rule used by "floor"/"trunc" below.
+        result_type = get_output_element_type_from_node(node)
+        return coreai.broadcasting_divide(
+            coreai.cast(x, result_type), coreai.cast(y, result_type)
+        )
+
     promoted_type = get_promoted_type(x.type, y.type)
     casted_x = coreai.cast(x, promoted_type)
     casted_y = coreai.cast(y, promoted_type)
 
-    if rounding_mode is None:
-        return coreai.broadcasting_divide(casted_x, casted_y)
-    elif rounding_mode == "floor":
+    if rounding_mode == "floor":
         return coreai.broadcasting_floor_divide(casted_x, casted_y)
     elif rounding_mode == "trunc":
         # Integer division already truncates toward zero, so a plain divide

tests/ops/test_ops.py

@@ -1576,6 +1576,31 @@ def forward(self, x: Tensor) -> Tensor:
     await validate_numerical_output(model=DivScalarModel().eval(), x=x)
 
 
+async def test_true_divide_integer_promotes_to_float() -> None:
+    """aten.true_divide.Tensor on integer operands must promote to float before dividing."""
+    x = torch.tensor([7, -7, 3, 1], dtype=torch.int32)
+    y = torch.tensor([2, 2, 2, 4], dtype=torch.int32)
+
+    class TrueDivideModel(nn.Module):
+        def forward(self, x: Tensor, y: Tensor) -> Tensor:
+            return torch.true_divide(x, y)
+
+    await validate_numerical_output(model=TrueDivideModel().eval(), x=x, y=y)
+
+
+async def test_div_tensor_mode_none_integer_promotes_to_float() -> None:
+    """aten.div.Tensor_mode with rounding_mode=None on integer operands must
+    promote to float before dividing, matching aten.div.Tensor semantics."""
+    x = torch.tensor([7, -7, 3, 1], dtype=torch.int32)
+    y = torch.tensor([2, 2, 2, 4], dtype=torch.int32)
+
+    class DivTensorModeModel(nn.Module):
+        def forward(self, x: Tensor, y: Tensor) -> Tensor:
+            return torch.div(x, y, rounding_mode=None)
+
+    await validate_numerical_output(model=DivTensorModeModel().eval(), x=x, y=y)
+
+
 @pytest.mark.parametrize(
     "x,y",
     [