asarray: take the default device into consideration.

Fix: #106773

This PR makes it so `asarray` takes the default device into consideration when called with
a Python sequence as the data.

ghstack-source-id: 92adeacccc0cfadb8268996a2e8a784c254debed
Pull Request resolved: #106779

test/test_tensor_creation_ops.py

@@ -3966,6 +3966,11 @@ def test_numpy_scalars(self, device):
         self.assertEqual(tensor.item(), zerodim_arr.item())
         self.assertEqual(tensor.dtype, torch.int32)
 
+    def test_default_device(self, device):
+        with torch.device(device):
+            tensor = torch.asarray(3)
+            self.assertEqual(tensor.device.type, device)
+
 
 instantiate_device_type_tests(TestTensorCreation, globals())
 instantiate_device_type_tests(TestRandomTensorCreation, globals())

torch/_torch_docs.py

@@ -1257,7 +1257,7 @@ def merge_dicts(*dicts):
 
 When :attr:`obj` is none of the above but a scalar, or a sequence of scalars then the
 returned tensor will, by default, infer its datatype from the scalar values, be on the
-CPU device, and not share its memory.
+current default device, and not share its memory.
 
 .. seealso::
 
@@ -1282,7 +1282,8 @@ def merge_dicts(*dicts):
            If ``False`` then the returned tensor shares its memory with :attr:`obj` and an
            error is thrown if it cannot.
     device (:class:`torch.device`, optional): the device of the returned tensor.
-           Default: ``None``, which causes the device of :attr:`obj` to be used.
+           Default: ``None``, which causes the device of :attr:`obj` to be used. Or, if
+           :attr:`obj` is a Python sequence, the current default device will be used.
     requires_grad (bool, optional): whether the returned tensor requires grad.
            Default: ``False``, which causes the returned tensor not to require a gradient.
            If ``True``, then the returned tensor will require a gradient, and if :attr:`obj`

torch/csrc/utils/tensor_new.cpp

@@ -1633,8 +1633,14 @@ Tensor asarray(
   bool force_alias = !copy.value_or(true);
   bool should_warn_numpy_not_writable = false;
 
+  // Used when:
+  // 1. 'obj' implements the buffer protocol and no type is given.
+  // 2. creating a new tensor from a Python sequence.
   auto dtype_unwrapped =
       dtype.value_or(torch::tensors::get_default_scalar_type());
+  // Used when creating a new tensor from a Python sequence.
+  auto device_unwrapped =
+      device.value_or(torch::tensors::get_default_device());
 
   // Check whether 'obj' is a 'Tensor'
   if (THPVariable_Check(obj)) {
@@ -1753,7 +1759,7 @@ Tensor asarray(
     tensor = internal_new_from_data(
         TensorOptions(),
         dtype_unwrapped,
-        device,
+        device_unwrapped,
         obj,
         /* copy_variables = */ false,
         /* copy_numpy = */ false,