Fixed a memory leak when calling from_numpy on a numpy array with an … (#121156)

…unsupported dtype.

Fixes #121138.

The lambda function that DECREFs the object is not called when the dtype conversion functions throws. This PR moves the conversion before the INCREF, which prevents the memory leak.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/121156
Approved by: https://github.com/soulitzer, https://github.com/albanD

test/test_numpy_interop.py

@@ -6,6 +6,7 @@ import torch
 import numpy as np
 
 from itertools import product
+import sys
 
 from torch.testing._internal.common_utils import \
     (skipIfTorchDynamo, TestCase, run_tests)
@@ -257,6 +258,18 @@ class TestNumPyInterop(TestCase):
         x.strides = (3,)
         self.assertRaises(ValueError, lambda: torch.from_numpy(x))
 
+    @skipIfTorchDynamo("No need to test invalid dtypes that should fail by design.")
+    def test_from_numpy_no_leak_on_invalid_dtype(self):
+        # This used to leak memory as the `from_numpy` call raised an exception and didn't decref the temporary
+        # object. See https://github.com/pytorch/pytorch/issues/121138
+        x = np.array("value".encode('ascii'))
+        for _ in range(1000):
+            try:
+                torch.from_numpy(x)
+            except TypeError:
+                pass
+        self.assertTrue(sys.getrefcount(x) == 2)
+
     @skipMeta
     def test_from_list_of_ndarray_warning(self, device):
         warning_msg = r"Creating a tensor from a list of numpy.ndarrays is extremely slow"