Revert "[Nested Tensor] detach (pytorch#84078)"

This reverts commit 092fe71.

Reverted pytorch#84078 on behalf of https://github.com/facebook-github-bot due to Diff reverted internally

Author: pytorchmergebot

aten/src/ATen/native/native_functions.yaml

@@ -4667,7 +4667,6 @@
   variants: function, method
   dispatch:
     CompositeExplicitAutograd: detach
-    NestedTensorCPU, NestedTensorCUDA: detach
 
 # Like `detach()`, but modifies this `Variable` in-place. This method may
 # only be called on non-view `Variable`s. You can use `is_view()` to check

test/test_nestedtensor.py

@@ -10,7 +10,6 @@
     skipMeta,
     onlyCPU
 )
-from torch.testing._internal.common_dtype import floating_types_and_half
 from torch.testing._internal.common_utils import TestCase, IS_FBCODE, run_tests, freeze_rng_state, parametrize, gradcheck
 from torch import nested_tensor
 
@@ -93,6 +92,12 @@ def test_unbind_1(self):
             torch.tensor([1]), torch.tensor([7]),
         )
 
+    # @torch.inference_mode()
+    # def test_unbind_2(self):
+    #     self._test_unbind_case(
+    #         torch.tensor(1), torch.tensor(7),
+    #     )
+
     @torch.inference_mode()
     def test_unbind_3(self):
         self._test_unbind_case(
@@ -297,36 +302,6 @@ def random_nt_pair(self, device, dtype, num_tensors, max_dims):
         return (torch.nested_tensor(ts1, device=device, dtype=dtype),
                 torch.nested_tensor(ts2, device=device, dtype=dtype))
 
-    @dtypes(*floating_types_and_half())
-    @dtypesIfCUDA(torch.float64)
-    def test_detach(self, device, dtype):
-        a = torch.randn(2, 4, device=device, dtype=dtype, requires_grad=False)
-        b = torch.randn(5, 4, device=device, dtype=dtype, requires_grad=False)
-        x = torch.nested_tensor([a, b]).requires_grad_()
-
-        x_detach = x.detach()
-
-        z = x_detach * 4
-        self.assertFalse(x_detach.requires_grad)
-        self.assertFalse(z.requires_grad)
-
-        a = torch.randn(2, 4, device=device, dtype=dtype, requires_grad=True)
-        b = torch.randn(5, 4, device=device, dtype=dtype, requires_grad=True)
-        x = torch.nested_tensor([a, b])
-
-        y = x * 2
-        y = y.detach()
-        self.assertFalse(y.requires_grad)
-        self.assertIsNone(y.grad_fn)
-
-        z = x + y
-        z.to_padded_tensor(0).sum().backward()
-        # This is an incorrect gradient, but we assume that's what the user
-        # wanted. detach() is an advanced option.
-        self.assertEqual(a.grad, torch.ones(2, 4, device=device, dtype=dtype))
-        self.assertEqual(b.grad, torch.ones(5, 4, device=device, dtype=dtype))
-
-
     @dtypes(torch.float, torch.float16, torch.double)
     def test_unbind_noncontiguous(self, device, dtype):
         nt_contiguous, nt_noncontiguous = random_nt_noncontiguous_pair((2, 3, 6, 7), device, dtype)