[fix] don't expose unique_dim in torch (#63080)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/62793

This is mostly a quick fix. I think the more correct fix could be updating `unique_dim` to `_unique_dim` which could be BC-breaking for C++ users (� maybe). Maybe something else I am missing.

~~Not sure how to add a test for it.~~ Have tested it locally.

We can add a test like following. Tested this locally, it fails currently but passes with the fix.
```python
        def test_wildcard_import(self):
            exec('from torch import *')

```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/63080

Reviewed By: gchanan

Differential Revision: D30738711

Pulled By: zou3519

fbshipit-source-id: b86d0190e45ba0b49fd2cffdcfd2e3a75cc2a35e

diff --git a/test/test_torch.py b/test/test_torch.py
index d166f96..3c47fd8 100644 (file)
--- a/test/test_torch.py
+++ b/test/test_torch.py
@@ -121,6 +121,9 @@ class AbstractTestCases:
         def test_dir(self):
             dir(torch)
 
+        def test_wildcard_import(self):
+            exec('from torch import *')
+
         @wrapDeterministicFlagAPITest
         def test_deterministic_flag(self):
             for deterministic in [True, False]:

diff --git a/torch/__init__.py b/torch/__init__.py
index a910882..5740b7a 100644 (file)
--- a/torch/__init__.py
+++ b/torch/__init__.py
@@ -624,8 +624,14 @@ if TYPE_CHECKING:
     # PR #43339 for details.
     from torch._C._VariableFunctions import *  # type: ignore[misc] # noqa: F403
 
+# Ops not to be exposed in `torch` namespace,
+# mostly helper ops.
+PRIVATE_OPS = (
+    'unique_dim',
+)
+
 for name in dir(_C._VariableFunctions):
-    if name.startswith('__'):
+    if name.startswith('__') or name in PRIVATE_OPS:
         continue
     globals()[name] = getattr(_C._VariableFunctions, name)
     __all__.append(name)

diff --git a/torch/functional.py b/torch/functional.py
index 63470cf..a773333 100644 (file)
--- a/torch/functional.py
+++ b/torch/functional.py
@@ -629,9 +629,6 @@ def istft(input: Tensor, n_fft: int, hop_length: Optional[int] = None,
                      normalized, onesided, length, return_complex)
 
 
-del torch.unique_dim
-
-
 if TYPE_CHECKING:
     # These _impl functions return a variable number of tensors as output with
     # __torch_function__; tuple unpacking is done already rather than being