Revert "Stop parsing command line arguments every time common_utils is imported. (#156703)"

This reverts commit ac7b4e7fe4d233dcd7f6343d42b4fa3d64bce548.

Reverted https://github.com/pytorch/pytorch/pull/156703 on behalf of https://github.com/clee2000 due to failing internally D80206253, see above comment for details ([comment](https://github.com/pytorch/pytorch/pull/156703#issuecomment-3362156908))

test/conftest.py

@@ -21,16 +21,6 @@ from _pytest.terminal import _get_raw_skip_reason
 from pytest_shard_custom import pytest_addoptions as shard_addoptions, PytestShardPlugin
 
 
-try:
-    from torch.testing._internal.common_utils import parse_cmd_line_args
-except ImportError:
-    # Temporary workaround needed until parse_cmd_line_args makes it into a nightlye because
-    # main / PR's tests are sometimes run against the previous day's nightly which won't
-    # have this function.
-    def parse_cmd_line_args():
-        pass
-
-
 if TYPE_CHECKING:
     from _pytest._code.code import ReprFileLocation
 
@@ -93,7 +83,6 @@ def pytest_addoption(parser: Parser) -> None:
 
 
 def pytest_configure(config: Config) -> None:
-    parse_cmd_line_args()
     xmlpath = config.option.xmlpath_reruns
     # Prevent opening xmllog on worker nodes (xdist).
     if xmlpath and not hasattr(config, "workerinput"):

test/jit/test_autodiff_subgraph_slicing.py

@@ -27,9 +27,6 @@ from torch.testing._internal.jit_utils import (
 )
 
 
-assert GRAPH_EXECUTOR is not None
-
-
 @unittest.skipIf(
     GRAPH_EXECUTOR == ProfilingMode.SIMPLE, "Simple Executor doesn't support gradients"
 )

test/test_jit.py

@@ -3,13 +3,6 @@
 
 import torch
 
-if __name__ == '__main__':
-    from torch.testing._internal.common_utils import parse_cmd_line_args
-
-    # The value of GRAPH_EXECUTOR depends on command line arguments so make sure they're parsed
-    # before instantiating tests.
-    parse_cmd_line_args()
-
 # This is how we include tests located in test/jit/...
 # They are included here so that they are invoked when you call `test_jit.py`,
 # do not run these test files directly.
@@ -104,7 +97,7 @@ import torch.nn.functional as F
 from torch.testing._internal import jit_utils
 from torch.testing._internal.common_jit import check_against_reference
 from torch.testing._internal.common_utils import run_tests, IS_WINDOWS, \
-    GRAPH_EXECUTOR, suppress_warnings, IS_SANDCASTLE, ProfilingMode, \
+    suppress_warnings, IS_SANDCASTLE, GRAPH_EXECUTOR, ProfilingMode, \
     TestCase, freeze_rng_state, slowTest, TemporaryFileName, \
     enable_profiling_mode_for_profiling_tests, TEST_MKL, set_default_dtype, num_profiled_runs, \
     skipIfCrossRef, skipIfTorchDynamo
@@ -165,7 +158,6 @@ def doAutodiffCheck(testname):
     if "test_t_" in testname or testname == "test_t":
         return False
 
-    assert GRAPH_EXECUTOR
     if GRAPH_EXECUTOR == ProfilingMode.SIMPLE:
         return False
 
@@ -209,7 +201,6 @@ def doAutodiffCheck(testname):
     return testname not in test_exceptions
 
 
-assert GRAPH_EXECUTOR
 # TODO: enable TE in PE when all tests are fixed
 torch._C._jit_set_texpr_fuser_enabled(GRAPH_EXECUTOR == ProfilingMode.PROFILING)
 torch._C._jit_set_profiling_executor(GRAPH_EXECUTOR != ProfilingMode.LEGACY)

test/test_jit_autocast.py

@@ -5,17 +5,12 @@ from torch.cuda.amp import autocast
 from typing import Optional
 
 import unittest
+from test_jit import JitTestCase
 from torch.testing._internal.common_cuda import TEST_CUDA
-from torch.testing._internal.common_utils import parse_cmd_line_args, run_tests, skipIfTorchDynamo
+from torch.testing._internal.common_utils import run_tests, skipIfTorchDynamo
 from torch.testing import FileCheck
 from jit.test_models import MnistNet
 
-if __name__ == '__main__':
-    # The value of GRAPH_EXECUTOR depends on command line arguments so make sure they're parsed
-    # before instantiating tests.
-    parse_cmd_line_args()
-
-from test_jit import JitTestCase
 TEST_BFLOAT16 = TEST_CUDA and torch.cuda.is_bf16_supported()
 
 @skipIfTorchDynamo("Not a TorchDynamo suitable test")

test/test_jit_fuser.py

@@ -9,13 +9,6 @@ import torch.nn.functional as F
 from torch.testing import FileCheck
 from unittest import skipIf
 
-if __name__ == "__main__":
-    from torch.testing._internal.common_utils import parse_cmd_line_args
-
-    # The value of GRAPH_EXECUTOR depends on command line arguments so make sure they're parsed
-    # before instantiating tests.
-    parse_cmd_line_args()
-
 from torch.testing._internal.common_utils import run_tests, IS_SANDCASTLE, ProfilingMode, GRAPH_EXECUTOR, \
     enable_profiling_mode_for_profiling_tests, IS_WINDOWS, TemporaryDirectoryName, shell
 from torch.testing._internal.jit_utils import JitTestCase, enable_cpu_fuser, _inline_everything, \

test/test_jit_fuser_legacy.py

@@ -2,14 +2,6 @@
 
 import sys
 sys.argv.append("--jit-executor=legacy")
-
-if __name__ == "__main__":
-    from torch.testing._internal.common_utils import parse_cmd_line_args
-
-    # The value of GRAPH_EXECUTOR depends on command line arguments so make sure they're parsed
-    # before instantiating tests.
-    parse_cmd_line_args()
-
 from test_jit_fuser import *  # noqa: F403
 
 if __name__ == '__main__':

test/test_jit_fuser_te.py

@@ -22,13 +22,6 @@ from torch.testing import FileCheck
 torch._C._jit_set_profiling_executor(True)
 torch._C._get_graph_executor_optimize(True)
 
-if __name__ == "__main__":
-    from torch.testing._internal.common_utils import parse_cmd_line_args
-
-    # The value of GRAPH_EXECUTOR depends on command line arguments so make sure they're parsed
-    # before instantiating tests.
-    parse_cmd_line_args()
-
 from itertools import combinations, permutations, product
 from textwrap import dedent
 

test/test_jit_legacy.py

@@ -2,14 +2,7 @@
 
 import sys
 sys.argv.append("--jit-executor=legacy")
-from torch.testing._internal.common_utils import parse_cmd_line_args, run_tests
+from test_jit import *  # noqa: F403
-
-if __name__ == '__main__':
-    # The value of GRAPH_EXECUTOR depends on command line arguments so make sure they're parsed
-    # before instantiating tests.
-    parse_cmd_line_args()
-
-from test_jit import *  # noqa: F403, F401
 
 if __name__ == '__main__':
     run_tests()

torch/testing/_internal/common_distributed.py

@@ -33,7 +33,6 @@ import torch.nn as nn
 from torch._C._autograd import DeviceType
 from torch._C._distributed_c10d import _SymmetricMemory
 from torch._logging._internal import trace_log
-from torch.testing._internal import common_utils
 from torch.testing._internal.common_utils import (
     FILE_SCHEMA,
     find_free_port,
@@ -773,12 +772,7 @@ class MultiProcessTestCase(TestCase):
             process = proc(
                 target=self.__class__._run,
                 name="process " + str(rank),
-                args=(
+                args=(rank, self._current_test_name(), self.file_name, child_conn),
-                    rank,
-                    self._current_test_name(),
-                    self.file_name,
-                    child_conn,
-                ),
                 kwargs={
                     "fake_pg": getattr(self, "fake_pg", False),
                 },
@@ -855,7 +849,6 @@ class MultiProcessTestCase(TestCase):
             torch._C._set_print_stack_traces_on_fatal_signal(True)
         # Show full C++ stacktraces when a Python error originating from C++ is raised.
         os.environ["TORCH_SHOW_CPP_STACKTRACES"] = "1"
-        common_utils.set_rng_seed()
 
         # self.id() == e.g. '__main__.TestDistributed.test_get_rank'
         # We're retrieving a corresponding test and executing it.
@@ -1677,10 +1670,6 @@ class MultiProcContinuousTest(TestCase):
         self.rank = cls.rank
         self.world_size = cls.world_size
         test_fn = getattr(self, test_name)
-
-        # Ensure all the ranks use the same seed.
-        common_utils.set_rng_seed()
-
         # Run the test function
         test_fn(**kwargs)
 

torch/testing/_internal/common_fsdp.py

@@ -58,7 +58,6 @@ from torch.testing._internal.common_distributed import (
 from torch.testing._internal.common_utils import (
     FILE_SCHEMA,
     get_cycles_per_ms,
-    set_rng_seed,
     TEST_CUDA,
     TEST_HPU,
     TEST_XPU,
@@ -1229,7 +1228,6 @@ class FSDPTest(MultiProcessTestCase):
         dist.barrier(device_ids=device_ids)
 
         torch._dynamo.reset()
-        set_rng_seed()
         self.run_test(test_name, pipe)
         torch._dynamo.reset()
 

torch/testing/_internal/common_nn.py

@@ -15,7 +15,6 @@ import torch.cuda
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn import _reduction as _Reduction
-from torch.testing._internal import common_utils
 from torch.testing._internal.common_utils import TestCase, to_gpu, freeze_rng_state, is_iterable, \
     gradcheck, gradgradcheck, set_default_dtype, skipIfTorchDynamo, TEST_WITH_ROCM
 from torch.testing._internal.common_cuda import TEST_CUDA, SM90OrLater
@@ -1079,7 +1078,6 @@ def single_batch_reference_fn(input, parameters, module):
 
 
 def get_new_module_tests():
-    common_utils.set_rng_seed()
     new_module_tests = [
         poissonnllloss_no_reduce_test(),
         bceloss_no_reduce_test(),

torch/testing/_internal/common_utils.py

@@ -101,35 +101,9 @@ except ImportError:
     has_pytest = False
 
 
-SEED = 1234
 MI300_ARCH = ("gfx942",)
 MI200_ARCH = ("gfx90a")
 
-class ProfilingMode(Enum):
-    LEGACY = 1
-    SIMPLE = 2
-    PROFILING = 3
-
-# Set by parse_cmd_line_args() if called
-CI_FUNCTORCH_ROOT = ""
-CI_PT_ROOT = ""
-CI_TEST_PREFIX = ""
-DISABLED_TESTS_FILE = ""
-GRAPH_EXECUTOR : Optional[ProfilingMode] = None
-LOG_SUFFIX = ""
-PYTEST_SINGLE_TEST = ""
-REPEAT_COUNT = 0
-RERUN_DISABLED_TESTS = False
-RUN_PARALLEL = 0
-SHOWLOCALS = False
-SLOW_TESTS_FILE = ""
-TEST_BAILOUTS = False
-TEST_DISCOVER = False
-TEST_IN_SUBPROCESS = False
-TEST_SAVE_XML = ""
-UNITTEST_ARGS : list[str] = []
-USE_PYTEST = False
-
 def freeze_rng_state(*args, **kwargs):
     return torch.testing._utils.freeze_rng_state(*args, **kwargs)
 
@@ -864,6 +838,11 @@ class decorateIf(_TestParametrizer):
         yield (test_wrapper, test_name, {}, decorator_fn)
 
 
+class ProfilingMode(Enum):
+    LEGACY = 1
+    SIMPLE = 2
+    PROFILING = 3
+
 def cppProfilingFlagsToProfilingMode():
     old_prof_exec_state = torch._C._jit_set_profiling_executor(True)
     old_prof_mode_state = torch._C._get_graph_executor_optimize(True)
@@ -882,7 +861,6 @@ def cppProfilingFlagsToProfilingMode():
 def enable_profiling_mode_for_profiling_tests():
     old_prof_exec_state = False
     old_prof_mode_state = False
-    assert GRAPH_EXECUTOR
     if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
         old_prof_exec_state = torch._C._jit_set_profiling_executor(True)
         old_prof_mode_state = torch._C._get_graph_executor_optimize(True)
@@ -917,7 +895,6 @@ meth_call = torch._C.ScriptMethod.__call__
 def prof_callable(callable, *args, **kwargs):
     if 'profile_and_replay' in kwargs:
         del kwargs['profile_and_replay']
-        assert GRAPH_EXECUTOR
         if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
             with enable_profiling_mode_for_profiling_tests():
                 callable(*args, **kwargs)
@@ -947,91 +924,72 @@ def _get_test_report_path():
     test_source = override if override is not None else 'python-unittest'
     return os.path.join('test-reports', test_source)
 
-def parse_cmd_line_args():
+is_running_via_run_test = "run_test.py" in getattr(__main__, "__file__", "")
-    global CI_FUNCTORCH_ROOT
+parser = argparse.ArgumentParser(add_help=not is_running_via_run_test, allow_abbrev=False)
-    global CI_PT_ROOT
+parser.add_argument('--subprocess', action='store_true',
-    global CI_TEST_PREFIX
+                    help='whether to run each test in a subprocess')
-    global DISABLED_TESTS_FILE
+parser.add_argument('--seed', type=int, default=1234)
-    global GRAPH_EXECUTOR
+parser.add_argument('--accept', action='store_true')
-    global LOG_SUFFIX
+parser.add_argument('--jit-executor', '--jit_executor', type=str)
-    global PYTEST_SINGLE_TEST
+parser.add_argument('--repeat', type=int, default=1)
-    global REPEAT_COUNT
+parser.add_argument('--test-bailouts', '--test_bailouts', action='store_true')
-    global RERUN_DISABLED_TESTS
+parser.add_argument('--use-pytest', action='store_true')
-    global RUN_PARALLEL
+parser.add_argument('--save-xml', nargs='?', type=str,
-    global SHOWLOCALS
+                    const=_get_test_report_path(),
-    global SLOW_TESTS_FILE
+                    default=_get_test_report_path() if IS_CI else None)
-    global TEST_BAILOUTS
+parser.add_argument('--discover-tests', action='store_true')
-    global TEST_DISCOVER
+parser.add_argument('--log-suffix', type=str, default="")
-    global TEST_IN_SUBPROCESS
+parser.add_argument('--run-parallel', type=int, default=1)
-    global TEST_SAVE_XML
+parser.add_argument('--import-slow-tests', type=str, nargs='?', const=DEFAULT_SLOW_TESTS_FILE)
-    global UNITTEST_ARGS
+parser.add_argument('--import-disabled-tests', type=str, nargs='?', const=DEFAULT_DISABLED_TESTS_FILE)
-    global USE_PYTEST
+parser.add_argument('--rerun-disabled-tests', action='store_true')
-
+parser.add_argument('--pytest-single-test', type=str, nargs=1)
-    is_running_via_run_test = "run_test.py" in getattr(__main__, "__file__", "")
+parser.add_argument('--showlocals', action=argparse.BooleanOptionalAction, default=False)
-    parser = argparse.ArgumentParser(add_help=not is_running_via_run_test, allow_abbrev=False)
-    parser.add_argument('--subprocess', action='store_true',
-                        help='whether to run each test in a subprocess')
-    parser.add_argument('--accept', action='store_true')
-    parser.add_argument('--jit-executor', '--jit_executor', type=str)
-    parser.add_argument('--repeat', type=int, default=1)
-    parser.add_argument('--test-bailouts', '--test_bailouts', action='store_true')
-    parser.add_argument('--use-pytest', action='store_true')
-    parser.add_argument('--save-xml', nargs='?', type=str,
-                        const=_get_test_report_path(),
-                        default=_get_test_report_path() if IS_CI else None)
-    parser.add_argument('--discover-tests', action='store_true')
-    parser.add_argument('--log-suffix', type=str, default="")
-    parser.add_argument('--run-parallel', type=int, default=1)
-    parser.add_argument('--import-slow-tests', type=str, nargs='?', const=DEFAULT_SLOW_TESTS_FILE)
-    parser.add_argument('--import-disabled-tests', type=str, nargs='?', const=DEFAULT_DISABLED_TESTS_FILE)
-    parser.add_argument('--rerun-disabled-tests', action='store_true')
-    parser.add_argument('--pytest-single-test', type=str, nargs=1)
-    parser.add_argument('--showlocals', action=argparse.BooleanOptionalAction, default=False)
 
 # Only run when -h or --help flag is active to display both unittest and parser help messages.
-    def run_unittest_help(argv):
+def run_unittest_help(argv):
-        unittest.main(argv=argv)
+    unittest.main(argv=argv)
 
-    if '-h' in sys.argv or '--help' in sys.argv:
+if '-h' in sys.argv or '--help' in sys.argv:
-        help_thread = threading.Thread(target=run_unittest_help, args=(sys.argv,))
+    help_thread = threading.Thread(target=run_unittest_help, args=(sys.argv,))
-        help_thread.start()
+    help_thread.start()
-        help_thread.join()
+    help_thread.join()
 
-    args, remaining = parser.parse_known_args()
+args, remaining = parser.parse_known_args()
-    if args.jit_executor == 'legacy':
+if args.jit_executor == 'legacy':
-        GRAPH_EXECUTOR = ProfilingMode.LEGACY
+    GRAPH_EXECUTOR = ProfilingMode.LEGACY
-    elif args.jit_executor == 'profiling':
+elif args.jit_executor == 'profiling':
-        GRAPH_EXECUTOR = ProfilingMode.PROFILING
+    GRAPH_EXECUTOR = ProfilingMode.PROFILING
-    elif args.jit_executor == 'simple':
+elif args.jit_executor == 'simple':
-        GRAPH_EXECUTOR = ProfilingMode.SIMPLE
+    GRAPH_EXECUTOR = ProfilingMode.SIMPLE
-    else:
+else:
-        # infer flags based on the default settings
+    # infer flags based on the default settings
-        GRAPH_EXECUTOR = cppProfilingFlagsToProfilingMode()
+    GRAPH_EXECUTOR = cppProfilingFlagsToProfilingMode()
 
-    RERUN_DISABLED_TESTS = args.rerun_disabled_tests
+RERUN_DISABLED_TESTS = args.rerun_disabled_tests
 
-    SLOW_TESTS_FILE = args.import_slow_tests
+SLOW_TESTS_FILE = args.import_slow_tests
-    DISABLED_TESTS_FILE = args.import_disabled_tests
+DISABLED_TESTS_FILE = args.import_disabled_tests
-    LOG_SUFFIX = args.log_suffix
+LOG_SUFFIX = args.log_suffix
-    RUN_PARALLEL = args.run_parallel
+RUN_PARALLEL = args.run_parallel
-    TEST_BAILOUTS = args.test_bailouts
+TEST_BAILOUTS = args.test_bailouts
-    USE_PYTEST = args.use_pytest
+USE_PYTEST = args.use_pytest
-    PYTEST_SINGLE_TEST = args.pytest_single_test
+PYTEST_SINGLE_TEST = args.pytest_single_test
-    TEST_DISCOVER = args.discover_tests
+TEST_DISCOVER = args.discover_tests
-    TEST_IN_SUBPROCESS = args.subprocess
+TEST_IN_SUBPROCESS = args.subprocess
-    TEST_SAVE_XML = args.save_xml
+TEST_SAVE_XML = args.save_xml
-    REPEAT_COUNT = args.repeat
+REPEAT_COUNT = args.repeat
-    SHOWLOCALS = args.showlocals
+SEED = args.seed
-    if not getattr(expecttest, "ACCEPT", False):
+SHOWLOCALS = args.showlocals
-        expecttest.ACCEPT = args.accept
+if not getattr(expecttest, "ACCEPT", False):
-    UNITTEST_ARGS = [sys.argv[0]] + remaining
+    expecttest.ACCEPT = args.accept
-
+UNITTEST_ARGS = [sys.argv[0]] + remaining
-    set_rng_seed()
+torch.manual_seed(SEED)
 
 # CI Prefix path used only on CI environment
-    CI_TEST_PREFIX = str(Path(os.getcwd()))
+CI_TEST_PREFIX = str(Path(os.getcwd()))
-    CI_PT_ROOT = str(Path(os.getcwd()).parent)
+CI_PT_ROOT = str(Path(os.getcwd()).parent)
-    CI_FUNCTORCH_ROOT = str(os.path.join(Path(os.getcwd()).parent, "functorch"))
+CI_FUNCTORCH_ROOT = str(os.path.join(Path(os.getcwd()).parent, "functorch"))
 
 def wait_for_process(p, timeout=None):
     try:
@@ -1180,9 +1138,7 @@ def lint_test_case_extension(suite):
     return succeed
 
 
-def get_report_path(argv=None, pytest=False):
+def get_report_path(argv=UNITTEST_ARGS, pytest=False):
-    if argv is None:
-        argv = UNITTEST_ARGS
     test_filename = sanitize_test_filename(argv[0])
     test_report_path = TEST_SAVE_XML + LOG_SUFFIX
     test_report_path = os.path.join(test_report_path, test_filename)
@@ -1233,11 +1189,7 @@ def get_pytest_test_cases(argv: list[str]) -> list[str]:
     return test_collector_plugin.tests
 
 
-def run_tests(argv=None):
+def run_tests(argv=UNITTEST_ARGS):
-    parse_cmd_line_args()
-    if argv is None:
-        argv = UNITTEST_ARGS
-
     # import test files.
     if SLOW_TESTS_FILE:
         if os.path.exists(SLOW_TESTS_FILE):
@@ -1807,7 +1759,6 @@ def skipIfLegacyJitExecutor(msg="test doesn't currently work with legacy JIT exe
         if not isinstance(fn, type):
             @wraps(fn)
             def wrapper(*args, **kwargs):
-                assert GRAPH_EXECUTOR
                 if GRAPH_EXECUTOR == ProfilingMode.LEGACY:
                     raise unittest.SkipTest(msg)
                 else:
@@ -2428,9 +2379,7 @@ def get_function_arglist(func):
     return inspect.getfullargspec(func).args
 
 
-def set_rng_seed(seed=None):
+def set_rng_seed(seed):
-    if seed is None:
-        seed = SEED
     torch.manual_seed(seed)
     random.seed(seed)
     if TEST_NUMPY:
@@ -3453,7 +3402,7 @@ class TestCase(expecttest.TestCase):
 
     def setUp(self):
         check_if_enable(self)
-        set_rng_seed()
+        set_rng_seed(SEED)
 
         # Save global check sparse tensor invariants state that can be
         # restored from tearDown:

torch/testing/_internal/distributed/distributed_test.py

@@ -137,18 +137,16 @@ class Foo:
 f = Foo(10)
 f.bar = 1
 
+foo_cpu_tensor = Foo(torch.randn(3, 3))
 
-# Defer instantiation until the seed is set so that randn() returns the same
-# values in all processes.
-def create_collectives_object_test_list():
-    return [
-        {"key1": 3, "key2": 4, "key3": {"nested": True}},
-        f,
-        Foo(torch.randn(3, 3)),
-        "foo",
-        [1, 2, True, "string", [4, 5, "nested"]],
-    ]
 
+COLLECTIVES_OBJECT_TEST_LIST = [
+    {"key1": 3, "key2": 4, "key3": {"nested": True}},
+    f,
+    foo_cpu_tensor,
+    "foo",
+    [1, 2, True, "string", [4, 5, "nested"]],
+]
 
 # Allowlist of distributed backends where profiling collectives is supported.
 PROFILING_SUPPORTED_BACKENDS = [
@@ -398,6 +396,12 @@ class ControlFlowToyModel(nn.Module):
             return F.relu(self.lin1(x))
 
 
+DDP_NET = Net()
+BN_NET = BatchNormNet()
+BN_NET_NO_AFFINE = BatchNormNet(affine=False)
+ONLY_SBN_NET = nn.SyncBatchNorm(2, momentum=0.99)
+
+
 def get_timeout(test_id):
     test_name = test_id.split(".")[-1]
     if test_name in CUSTOMIZED_TIMEOUT:
@@ -4289,7 +4293,7 @@ class DistributedTest:
             # as baseline
 
             # cpu training setup
-            model = Net()
+            model = DDP_NET
 
             # single gpu training setup
             model_gpu = copy.deepcopy(model)
@@ -4344,7 +4348,7 @@ class DistributedTest:
             _group, _group_id, rank = self._init_global_test()
 
             # cpu training setup
-            model_base = Net()
+            model_base = DDP_NET
 
             # DDP-CPU training setup
             model_DDP = copy.deepcopy(model_base)
@@ -5493,7 +5497,7 @@ class DistributedTest:
         def _test_DistributedDataParallel_with_amp(self, grad_is_view=False):
             torch.manual_seed(31415)
             # Creates model and optimizer in default precision
-            model = Net().cuda()
+            model = copy.deepcopy(DDP_NET).cuda()
             optimizer = torch.optim.SGD(model.parameters(), lr=0.03)
 
             # Creates a GradScaler once at the beginning of training.
@@ -5578,7 +5582,7 @@ class DistributedTest:
             # as baseline
 
             # cpu training setup
-            model = BatchNormNet() if affine else BatchNormNet(affine=False)
+            model = BN_NET if affine else BN_NET_NO_AFFINE
 
             # single gpu training setup
             model_gpu = copy.deepcopy(model)
@@ -5628,7 +5632,6 @@ class DistributedTest:
         def _test_post_localSGD_optimizer_parity(self, create_averager, grad_is_view):
             learning_rate = 0.03
 
-            DDP_NET = Net()
             net = torch.nn.parallel.DistributedDataParallel(
                 copy.deepcopy(DDP_NET).cuda(),
                 device_ids=[self.rank],
@@ -5695,7 +5698,7 @@ class DistributedTest:
             learning_rate = 0.03
 
             net_using_post_localSGD_opt = torch.nn.parallel.DistributedDataParallel(
-                Net().cuda(), device_ids=[self.rank]
+                copy.deepcopy(DDP_NET).cuda(), device_ids=[self.rank]
             )
 
             averager = create_averager()
@@ -5845,7 +5848,7 @@ class DistributedTest:
             bs_offset = int(rank * 2)
             global_bs = int(num_processes * 2)
 
-            model = nn.SyncBatchNorm(2, momentum=0.99)
+            model = ONLY_SBN_NET
             model_gpu = copy.deepcopy(model).cuda(rank)
             model_DDP = nn.parallel.DistributedDataParallel(
                 model_gpu, device_ids=[rank]
@@ -6055,7 +6058,6 @@ class DistributedTest:
         def test_DistributedDataParallel_SyncBatchNorm_Diff_Input_Sizes_Running_Value(
             self,
         ):
-            ONLY_SBN_NET = nn.SyncBatchNorm(2, momentum=0.99)
             _group, _group_id, rank = self._init_global_test()
             model = nn.parallel.DistributedDataParallel(
                 ONLY_SBN_NET.cuda(rank), device_ids=[rank]
@@ -6123,7 +6125,7 @@ class DistributedTest:
         def test_DistributedDataParallel_SyncBatchNorm_half(self):
             _group, _group_id, rank = self._init_global_test()
 
-            model = BatchNormNet()
+            model = copy.deepcopy(BN_NET)
             model = model.half()
             model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
             model = nn.parallel.DistributedDataParallel(
@@ -6139,7 +6141,7 @@ class DistributedTest:
 
         def _test_ddp_logging_data(self, is_gpu):
             rank = dist.get_rank()
-            model_DDP = Net()
+            model_DDP = copy.deepcopy(DDP_NET)
             if is_gpu:
                 model_DDP = nn.parallel.DistributedDataParallel(
                     model_DDP.cuda(rank), device_ids=[rank]
@@ -6415,7 +6417,7 @@ class DistributedTest:
             BACKEND == "nccl", "nccl does not support DDP on CPU models"
         )
         def test_static_graph_api_cpu(self):
-            model_DDP = nn.parallel.DistributedDataParallel(Net())
+            model_DDP = nn.parallel.DistributedDataParallel(DDP_NET)
             expected_err = "should be called before training loop starts"
             with self.assertRaisesRegex(RuntimeError, expected_err):
                 local_bs = 2
@@ -6648,7 +6650,7 @@ class DistributedTest:
         def _test_allgather_object(self, subgroup=None):
             # Only set device for NCCL backend since it must use GPUs.
 
-            gather_objects = create_collectives_object_test_list()
+            gather_objects = COLLECTIVES_OBJECT_TEST_LIST.copy()
 
             backend = os.environ["BACKEND"]
             if backend == "nccl":
@@ -6692,7 +6694,7 @@ class DistributedTest:
 
         def _test_gather_object(self, pg=None):
             # Ensure stateful objects can be gathered
-            gather_objects = create_collectives_object_test_list()
+            gather_objects = COLLECTIVES_OBJECT_TEST_LIST.copy()
             my_rank = dist.get_rank(pg)
 
             backend = os.environ["BACKEND"]
@@ -7262,7 +7264,7 @@ class DistributedTest:
                     return x
 
             torch.cuda.set_device(self.rank)
-            model_bn = BatchNormNet()
+            model_bn = BN_NET
             model_bn = nn.SyncBatchNorm.convert_sync_batchnorm(
                 copy.deepcopy(model_bn)
             ).cuda(self.rank)
@@ -7558,7 +7560,7 @@ class DistributedTest:
                     loss.backward()
 
         def _test_broadcast_object_list(self, group=None):
-            gather_objects = create_collectives_object_test_list()
+            gather_objects = COLLECTIVES_OBJECT_TEST_LIST.copy()
 
             # Only set device for NCCL backend since it must use GPUs.
             # Case where rank != GPU device.
@@ -8282,11 +8284,10 @@ class DistributedTest:
         @require_backend_is_available({"gloo"})
         def test_scatter_object_list(self):
             src_rank = 0
-            collectives_object_test_list = create_collectives_object_test_list()
             scatter_list = (
-                collectives_object_test_list
+                COLLECTIVES_OBJECT_TEST_LIST
                 if self.rank == src_rank
-                else [None for _ in collectives_object_test_list]
+                else [None for _ in COLLECTIVES_OBJECT_TEST_LIST]
             )
             world_size = dist.get_world_size()
             scatter_list = scatter_list[:world_size]
@@ -8299,8 +8300,8 @@ class DistributedTest:
             dist.scatter_object_list(output_obj_list, scatter_list, src=src_rank)
             self.assertEqual(
                 output_obj_list[0],
-                collectives_object_test_list[
+                COLLECTIVES_OBJECT_TEST_LIST[
-                    self.rank % len(collectives_object_test_list)
+                    self.rank % len(COLLECTIVES_OBJECT_TEST_LIST)
                 ],
             )
             # Ensure errors are raised upon incorrect arguments.
@@ -9986,7 +9987,7 @@ class DistributedTest:
             "Only Nccl & Gloo backend support DistributedDataParallel",
         )
         def test_sync_bn_logged(self):
-            model = BatchNormNet()
+            model = BN_NET
             rank = self.rank
             # single gpu training setup
             model_gpu = model.cuda(rank)