Fix those

malfet · malfet · commit 4a9a6b571fa5 · 2025-07-20T10:28:29.000-07:00
diff --git a/aten/src/ATen/native/TensorShape.cpp b/aten/src/ATen/native/TensorShape.cpp
@@ -274,7 +274,7 @@ TORCH_PRECOMPUTE_META_FUNC(cat)(const ITensorListRef& tensors, int64_t dim) {
   // when computing the actual output dtype and the flags.
   if (is_out_defined) {
     // Check for type promotion, if the output tensor is defined.
-    TORCH_CHECK(
+    TORCH_CHECK_TYPE(
         canCast(out_dtype, result.scalar_type()),
         "torch.cat(): input types can't be cast to the desired output type ",
         result.scalar_type());
diff --git a/test/test_ops.py b/test/test_ops.py
@@ -1109,7 +1109,22 @@ def _case_four_transform(t):
                 if op.is_factory_function and sample.kwargs.get("dtype", None) is None:
                     op_out(out=out)
                 else:
-                    with self.assertRaises(RuntimeError, msg=msg_fail):
+                    # TODO: Remove me when all ops will raise type error on mismatched types
+                    exc_type = (
+                        TypeError
+                        if op.name
+                        in [
+                            "_chunk_cat",
+                            "cat",
+                            "column_stack",
+                            "dstack",
+                            "hstack",
+                            "vstack",
+                            "stack",
+                        ]
+                        else RuntimeError
+                    )
+                    with self.assertRaises(exc_type, msg=msg_fail):
                         op_out(out=out)
 
     @ops(
diff --git a/test/test_type_promotion.py b/test/test_type_promotion.py
@@ -1046,7 +1046,7 @@ def test_cat_out_different_dtypes(self, device):
                     and not (out_dtype.is_floating_point or out_dtype.is_complex))
                     or ((x_dtype.is_complex or y_dtype.is_complex) and not out_dtype.is_complex)):
                 # This combinations do not support type conversion to a different class out type
-                with self.assertRaises(ValueError):
+                with self.assertRaises(TypeError):
                     torch.cat([x, y], out=out)
             else:
                 torch.cat([x, y], out=out)
