use try/finally to reset set_per_process_memory_fraction

This follows a similar pattern elsewhere in test_cuda.py.

Author: jeffdaily

test/test_cuda.py

@@ -5456,67 +5456,68 @@ def test_mempool_limited_memory_with_allocator(self):
         nelem_1mb = 1024 * 1024 // 4
 
         self._setup_mempool_limited_memory_test(80)
-        # remaining free mem: 80 mb
-        # mempool_use [] 0 mb
-        # mempool_do_not_use [] 0 mb
-        # default pool [] 0 mb
-        with torch.cuda.use_mem_pool(pool_do_not_use):
-            a = torch.randn(40 * nelem_1mb, device="cuda")
-        with torch.cuda.use_mem_pool(pool_use):
-            b = torch.randn(40 * nelem_1mb, device="cuda")
-        a_dataptr = a.data_ptr()
-        b_dataptr = b.data_ptr()
-        # remaining free mem: 0 mb
-        # mempool_do_not_use [aaaa] 40 mb
-        # mempool_use [bbbb] 40 mb
-        # default pool [] 0 mb
-        with self.assertRaises(torch.OutOfMemoryError):
-            # out of memory
-            c = torch.randn(40 * nelem_1mb, device="cuda")
-
-        del a, b
-        # remaining free mem: 0 mb
-        # mempool_do_not_use [____] 40 mb
-        # mempool_use [____] 40 mb
-        # default pool [] 0 mb
-
-        # c should not oom and instead can use mempool_use as fallback
-        c = torch.randn(30 * nelem_1mb, device="cuda")
-        c_dataptr = c.data_ptr()
-        # remaining free mem: 0 mb
-        # mempool_do_not_use [____] 40 mb
-        # mempool_use [ccc_] 40 mb
-        # default pool [] 0 mb
-        with self.assertRaises(torch.OutOfMemoryError):
-            # out of memory since can't use mempool_do_not_use
-            d = torch.randn(30 * nelem_1mb, device="cuda")
-
-        del c
-        # remaining free mem: 0 mb
-        # mempool_do_not_use [____] 40 mb
-        # mempool_use [____] 40 mb
-        # default pool [] 0 mb
-
-        # expect that we used same memory address for both a and c
-        self.assertEqual(b_dataptr, c_dataptr)
-
-        # make sure we can still use mempool_use as intended after c is deleted
-        with torch.cuda.use_mem_pool(pool_use):
-            e = torch.randn(20 * nelem_1mb, device="cuda")
-        # remaining free mem: 0 mb
-        # mempool_do_not_use [____] 40 mb
-        # mempool_use [ee__] 40 mb
-        # default pool [] 0 mb
-
-        e_dataptr = e.data_ptr()
-        del e
-
-        self.assertEqual(e_dataptr, c_dataptr)
+        try:
+            # remaining free mem: 80 mb
+            # mempool_use [] 0 mb
+            # mempool_do_not_use [] 0 mb
+            # default pool [] 0 mb
+            with torch.cuda.use_mem_pool(pool_do_not_use):
+                a = torch.randn(40 * nelem_1mb, device="cuda")
+            with torch.cuda.use_mem_pool(pool_use):
+                b = torch.randn(40 * nelem_1mb, device="cuda")
+            a_dataptr = a.data_ptr()
+            b_dataptr = b.data_ptr()
+            # remaining free mem: 0 mb
+            # mempool_do_not_use [aaaa] 40 mb
+            # mempool_use [bbbb] 40 mb
+            # default pool [] 0 mb
+            with self.assertRaises(torch.OutOfMemoryError):
+                # out of memory
+                c = torch.randn(40 * nelem_1mb, device="cuda")
 
-        # pool's destructor calls emptyCache()
-        del pool_use, pool_do_not_use
+            del a, b
+            # remaining free mem: 0 mb
+            # mempool_do_not_use [____] 40 mb
+            # mempool_use [____] 40 mb
+            # default pool [] 0 mb
+
+            # c should not oom and instead can use mempool_use as fallback
+            c = torch.randn(30 * nelem_1mb, device="cuda")
+            c_dataptr = c.data_ptr()
+            # remaining free mem: 0 mb
+            # mempool_do_not_use [____] 40 mb
+            # mempool_use [ccc_] 40 mb
+            # default pool [] 0 mb
+            with self.assertRaises(torch.OutOfMemoryError):
+                # out of memory since can't use mempool_do_not_use
+                d = torch.randn(30 * nelem_1mb, device="cuda")
+
+            del c
+            # remaining free mem: 0 mb
+            # mempool_do_not_use [____] 40 mb
+            # mempool_use [____] 40 mb
+            # default pool [] 0 mb
 
-        self._teardown_mempool_limited_memory_test()
+            # expect that we used same memory address for both a and c
+            self.assertEqual(b_dataptr, c_dataptr)
+
+            # make sure we can still use mempool_use as intended after c is deleted
+            with torch.cuda.use_mem_pool(pool_use):
+                e = torch.randn(20 * nelem_1mb, device="cuda")
+            # remaining free mem: 0 mb
+            # mempool_do_not_use [____] 40 mb
+            # mempool_use [ee__] 40 mb
+            # default pool [] 0 mb
+
+            e_dataptr = e.data_ptr()
+            del e
+
+            self.assertEqual(e_dataptr, c_dataptr)
+
+            # pool's destructor calls emptyCache()
+            del pool_use, pool_do_not_use
+        finally:
+            self._teardown_mempool_limited_memory_test()
 
     def test_mempool_multithread(self):
         pool_ids = []