Revert "[ROCm] Enabling several UTs (#161715)"

This reverts commit b9ba612f7a968f7b27e121ca8f4d0a4d954f5354.

Reverted https://github.com/pytorch/pytorch/pull/161715 on behalf of https://github.com/jeanschmidt due to Need to revert in order to revert https://github.com/pytorch/pytorch/pull/159473, feel free to merge it back once conflicts are cleared ([comment](https://github.com/pytorch/pytorch/pull/161715#issuecomment-3264040604))

test/test_dataloader.py

@@ -32,11 +32,13 @@ from torch.testing._internal.common_utils import (
     parametrize,
     run_tests,
     skipIfNoDill,
+    skipIfRocm,
     skipIfXpu,
     slowTest,
     TEST_CUDA,
     TEST_NUMPY,
     TEST_WITH_ASAN,
+    TEST_WITH_ROCM,
     TEST_WITH_TSAN,
     TestCase,
     xfailIfLinux,
@@ -94,7 +96,7 @@ TEST_CUDA_IPC = (
     and sys.platform != "darwin"
     and sys.platform != "win32"
     and not IS_JETSON
-    #    and not TEST_WITH_ROCM
+    and not TEST_WITH_ROCM
 )  # https://github.com/pytorch/pytorch/issues/90940
 
 TEST_MULTIGPU = TEST_CUDA_IPC and torch.cuda.device_count() > 1
@@ -1863,6 +1865,7 @@ except RuntimeError as e:
             list(iter(ChainDataset([dataset1, self.dataset])))
 
     @unittest.skipIf(not TEST_CUDA_IPC, "CUDA IPC not available")
+    @skipIfRocm  # https://github.com/pytorch/pytorch/issues/90940
     def test_multiprocessing_contexts(self):
         reference = [
             torch.arange(3),
@@ -2487,6 +2490,7 @@ except RuntimeError as e:
                 self.assertFalse(pin_memory_thread.is_alive())
 
     # Takes 2.5min to finish, see https://github.com/pytorch/pytorch/issues/46065
+    @skipIfRocm
     @unittest.skipIf(not HAS_PSUTIL, "psutil not found")
     @slowTest
     def test_proper_exit(self):
@@ -3130,6 +3134,7 @@ class TestDictDataLoader(TestCase):
             self.assertTrue(sample["another_dict"]["a_number"].is_pinned())
 
     @skipIfXpu
+    @skipIfRocm
     @unittest.skipIf(TEST_CUDA, "Test for when CUDA is not available")
     def test_pin_memory_no_cuda(self):
         loader = DataLoader(self.dataset, batch_size=2, pin_memory=True)