run_test.py

#!/usr/bin/env python3

import argparse
import copy
from datetime import datetime
from distutils.version import LooseVersion
import functools
import os
import pathlib
import shutil
import signal
import subprocess
import sys
import tempfile
import json
import glob
from typing import Dict, Optional, List, cast, Any

import torch
from torch.utils import cpp_extension
from torch.testing._internal.common_utils import (
    IS_CI,
    FILE_SCHEMA,
    TEST_WITH_ROCM,
    shell,
    set_cwd,
    parser as common_parser,
    is_slow_gradcheck_env,
)
import torch.distributed as dist
from torch.multiprocessing import current_process, get_context

REPO_ROOT = pathlib.Path(__file__).resolve().parent.parent

try:
    # using tools/ to optimize test run.
    sys.path.append(str(REPO_ROOT))
    from tools.stats.export_test_times import TEST_TIMES_FILE
    from tools.testing.test_selections import (
        get_reordered_tests,
        get_test_case_configs,
        calculate_shards,
        NUM_PROCS
    )
    HAVE_TEST_SELECTION_TOOLS = True
except ImportError:
    HAVE_TEST_SELECTION_TOOLS = False
    print(
        "Unable to import test_selections from tools/testing. Running without test selection stats..."
    )


# Note [ROCm parallel CI testing]
# https://github.com/pytorch/pytorch/pull/85770 added file-granularity parallel testing.
# In .ci/pytorch/test.sh, TEST_CONFIG == "default", CUDA and HIP_VISIBLE_DEVICES is set to 0.
# This results in multiple test files sharing the same GPU.
# This should be a supported use case for ROCm, but it exposed issues in the kernel driver resulting in hangs.
# See https://github.com/pytorch/pytorch/issues/90940.
#
# Further, ROCm self-hosted runners have up to 4 GPUs.
# Device visibility was set to 0 to match CUDA test behavior, but this was wasting available GPU resources.
# Assigning each Pool worker their own dedicated GPU avoids the ROCm oversubscription issues.
# This should also result in better overall wall clock time since all GPUs can be utilized.
def maybe_set_hip_visible_devies():
    # Special handling of ROCm GHA runners for parallel (file granularity) tests.
    if torch.version.hip:
        p = current_process()
        if p.name != 'MainProcess':
            # this is a Process from a parallel Pool, not the MainProcess
            os.environ['HIP_VISIBLE_DEVICES'] = str(p._identity[0] % NUM_PROCS)


def strtobool(s):
    if s.lower() in ["", "0", "false", "off"]:
        return False
    return True


def discover_tests(
        base_dir: Optional[pathlib.Path] = None,
        blocklisted_patterns: Optional[List[str]] = None,
        blocklisted_tests: Optional[List[str]] = None,
        extra_tests: Optional[List[str]] = None) -> List[str]:
    """
    Searches for all python files starting with test_ excluding one specified by patterns
    """
    def skip_test_p(name: str) -> bool:
        rc = False
        if blocklisted_patterns is not None:
            rc |= any(name.startswith(pattern) for pattern in blocklisted_patterns)
        if blocklisted_tests is not None:
            rc |= name in blocklisted_tests
        return rc
    cwd = pathlib.Path(__file__).resolve().parent if base_dir is None else base_dir
    # This supports symlinks, so we can link domain library tests to PyTorch test directory
    all_py_files = [pathlib.Path(p) for p in glob.glob(f"{cwd}/**/test_*.py", recursive=True)]
    rc = [str(fname.relative_to(cwd))[:-3] for fname in all_py_files]
    # Invert slashes on Windows
    if sys.platform == "win32":
        rc = [name.replace('\\', '/') for name in rc]
    rc = [test for test in rc if not skip_test_p(test)]
    if extra_tests is not None:
        rc += extra_tests
    return sorted(rc)


TESTS = discover_tests(
    blocklisted_patterns=[
        'ao',
        'bottleneck_test',
        'custom_backend',
        'custom_operator',
        'fx',        # executed by test_fx.py
        'jit',      # executed by test_jit.py
        'mobile',
        'onnx',
        'package',  # executed by test_package.py
        'quantization',  # executed by test_quantization.py
        'autograd',  # executed by test_autograd.py
    ],
    blocklisted_tests=[
        'test_bundled_images',
        'test_cpp_extensions_aot',
        'test_determination',
        'test_jit_fuser',
        'test_jit_simple',
        'test_jit_string',
        'test_kernel_launch_checks',
        'test_nnapi',
        'test_segment_reductions',
        'test_static_runtime',
        'test_throughput_benchmark',
        'test_typing',
        "distributed/bin/test_script",
        "distributed/elastic/multiprocessing/bin/test_script",
        "distributed/launcher/bin/test_script",
        "distributed/launcher/bin/test_script_init_method",
        "distributed/launcher/bin/test_script_is_torchelastic_launched",
        "distributed/launcher/bin/test_script_local_rank",
        "distributed/test_c10d_spawn",
        'distributions/test_transforms',
        'distributions/test_utils',
    ],
    extra_tests=[
        "test_cpp_extensions_aot_ninja",
        "test_cpp_extensions_aot_no_ninja",
        "distributed/elastic/timer/api_test",
        "distributed/elastic/timer/local_timer_example",
        "distributed/elastic/timer/local_timer_test",
        "distributed/elastic/events/lib_test",
        "distributed/elastic/metrics/api_test",
        "distributed/elastic/utils/logging_test",
        "distributed/elastic/utils/util_test",
        "distributed/elastic/utils/distributed_test",
        "distributed/elastic/multiprocessing/api_test",
    ]
)

# The doctests are a special case that don't correspond to a file that discover
# tests can enable.
TESTS = TESTS + ['doctests']

FSDP_TEST = [test for test in TESTS if test.startswith("distributed/fsdp")]

WINDOWS_BLOCKLIST = [
    "distributed/nn/jit/test_instantiator",
    "distributed/rpc/test_faulty_agent",
    "distributed/rpc/test_tensorpipe_agent",
    "distributed/rpc/test_share_memory",
    "distributed/rpc/cuda/test_tensorpipe_agent",
    "distributed/pipeline/sync/skip/test_api",
    "distributed/pipeline/sync/skip/test_gpipe",
    "distributed/pipeline/sync/skip/test_inspect_skip_layout",
    "distributed/pipeline/sync/skip/test_leak",
    "distributed/pipeline/sync/skip/test_portal",
    "distributed/pipeline/sync/skip/test_stash_pop",
    "distributed/pipeline/sync/skip/test_tracker",
    "distributed/pipeline/sync/skip/test_verify_skippables",
    "distributed/pipeline/sync/test_balance",
    "distributed/pipeline/sync/test_bugs",
    "distributed/pipeline/sync/test_checkpoint",
    "distributed/pipeline/sync/test_copy",
    "distributed/pipeline/sync/test_deferred_batch_norm",
    "distributed/pipeline/sync/test_dependency",
    "distributed/pipeline/sync/test_inplace",
    "distributed/pipeline/sync/test_microbatch",
    "distributed/pipeline/sync/test_phony",
    "distributed/pipeline/sync/test_pipe",
    "distributed/pipeline/sync/test_pipeline",
    "distributed/pipeline/sync/test_stream",
    "distributed/pipeline/sync/test_transparency",
    "distributed/pipeline/sync/test_worker",
    "distributed/elastic/agent/server/test/api_test",
    "distributed/elastic/multiprocessing/api_test",
    "distributed/_shard/checkpoint/test_checkpoint"
    "distributed/_shard/checkpoint/test_file_system_checkpoint"
    "distributed/_shard/sharding_spec/test_sharding_spec",
    "distributed/_shard/sharding_plan/test_sharding_plan",
    "distributed/_shard/sharded_tensor/test_sharded_tensor",
    "distributed/_shard/sharded_tensor/test_sharded_tensor_reshard",
    "distributed/_shard/sharded_tensor/ops/test_chunk",
    "distributed/_shard/sharded_tensor/ops/test_elementwise_ops",
    "distributed/_shard/sharded_tensor/ops/test_embedding",
    "distributed/_shard/sharded_tensor/ops/test_embedding_bag",
    "distributed/_shard/sharded_tensor/ops/test_binary_cmp",
    "distributed/_shard/sharded_tensor/ops/test_init",
    "distributed/_shard/sharded_tensor/ops/test_math_ops",
    "distributed/_shard/sharded_tensor/ops/test_matrix_ops",
    "distributed/_shard/sharded_tensor/ops/test_softmax",
    "distributed/_shard/sharded_optim/test_sharded_optim",
] + FSDP_TEST

ROCM_BLOCKLIST = [
    "distributed/rpc/test_faulty_agent",
    "distributed/rpc/test_tensorpipe_agent",
    "distributed/rpc/test_share_memory",
    "distributed/rpc/cuda/test_tensorpipe_agent",
    "distributed/_shard/checkpoint/test_checkpoint"
    "distributed/_shard/checkpoint/test_file_system_checkpoint"
    "distributed/_shard/sharding_spec/test_sharding_spec",
    "distributed/_shard/sharding_plan/test_sharding_plan",
    "distributed/_shard/sharded_tensor/test_sharded_tensor",
    "distributed/_shard/sharded_tensor/test_sharded_tensor_reshard",
    "distributed/_shard/sharded_tensor/ops/test_chunk",
    "distributed/_shard/sharded_tensor/ops/test_elementwise_ops",
    "distributed/_shard/sharded_tensor/ops/test_embedding",
    "distributed/_shard/sharded_tensor/ops/test_embedding_bag",
    "distributed/_shard/sharded_tensor/ops/test_binary_cmp",
    "distributed/_shard/sharded_tensor/ops/test_init",
    "distributed/_shard/sharded_tensor/ops/test_math_ops",
    "distributed/_shard/sharded_tensor/ops/test_matrix_ops",
    "distributed/_shard/sharded_tensor/ops/test_softmax",
    "distributed/_shard/sharded_optim/test_sharded_optim",
    "test_determination",
    "test_jit_legacy",
    "test_cuda_nvml_based_avail",
]

RUN_PARALLEL_BLOCKLIST = [
    "test_cpp_extensions_jit",
    "test_cpp_extensions_open_device_registration",
    "test_jit_disabled",
    "test_mobile_optimizer",
    "test_multiprocessing",
    "test_multiprocessing_spawn",
    "test_namedtuple_return_api",
    "test_overrides",
    "test_show_pickle",
    "test_tensorexpr",
    "test_cuda_primary_ctx",
    "test_cuda_trace",
    "test_cuda_nvml_based_avail",
] + FSDP_TEST

CI_SERIAL_LIST = [
    'test_nn',
    'test_fake_tensor',
    'test_cpp_api_parity',
    'test_reductions',
    'test_cuda',
    'test_jit_cuda_fuser',  # OOM on test_issue_1785, also profiling?
    'test_indexing',
    'test_fx_backends',
    'test_linalg',
    'test_cpp_extensions_jit',
    'test_torch',
    'test_tensor_creation_ops',
    'test_sparse_csr',
    'test_dispatch',
    'test_spectral_ops',    # Cause CUDA illegal memory access https://github.com/pytorch/pytorch/issues/88916
    'nn/test_pooling',
    'nn/test_convolution',  # Doesn't respect set_per_process_memory_fraction, results in OOM for other tests in slow gradcheck
    'distributions/test_distributions',
    'test_autograd',  # slow gradcheck runs a test that checks the cuda memory allocator
    'test_prims',  # slow gradcheck runs a test that checks the cuda memory allocator
    'test_modules',  # failed test due to mismatched elements
    'functorch/test_vmap',  # OOM
    'test_fx',  # gets SIGKILL
    'test_dataloader',  # frequently hangs for ROCm
    'test_serialization',   # test_serialization_2gb_file allocates a tensor of 2GB, and could cause OOM
    '_nvfuser/test_torchscript',  # OOM on test_issue_1785
    'test_schema_check',  # Cause CUDA illegal memory access https://github.com/pytorch/pytorch/issues/95749
    'functorch/test_memory_efficient_fusion',   # Cause CUDA OOM on ROCm
]

# A subset of our TEST list that validates PyTorch's ops, modules, and autograd function as expected
CORE_TEST_LIST = [
    "test_autograd",
    "test_modules",
    "test_nn",
    "test_ops",
    "test_ops_gradients",
    "test_ops_fwd_gradients",
    "test_ops_jit",
    "test_torch"
]

# A list of distributed tests that run on multiple backends, i.e. gloo, nccl. These backends are spread out
# among all available shards to speed up the test. The list of backends are copied from the tests themselves
DISTRIBUTED_TESTS_WITH_MULTIPLE_BACKENDS = {
    "distributed/test_distributed_spawn": [
        "gloo",
        "nccl",
        "ucc",
    ],
    "distributed/algorithms/quantization/test_quantization": [
        "gloo",
        "nccl",
    ],
}

# if a test file takes longer than 5 min, we add it to TARGET_DET_LIST
SLOW_TEST_THRESHOLD = 300

DISTRIBUTED_TESTS_CONFIG = {}


if dist.is_available():
    DISTRIBUTED_TESTS_CONFIG["test"] = {"WORLD_SIZE": "1"}
    if not TEST_WITH_ROCM and dist.is_mpi_available():
        DISTRIBUTED_TESTS_CONFIG["mpi"] = {
            "WORLD_SIZE": "3",
            "TEST_REPORT_SOURCE_OVERRIDE": "dist-mpi",
        }
    if dist.is_nccl_available():
        DISTRIBUTED_TESTS_CONFIG["nccl"] = {
            "WORLD_SIZE": "2" if torch.cuda.device_count() == 2 else "3",
            "TEST_REPORT_SOURCE_OVERRIDE": "dist-nccl",
        }
    if dist.is_gloo_available():
        DISTRIBUTED_TESTS_CONFIG["gloo"] = {
            "WORLD_SIZE": "2" if torch.cuda.device_count() == 2 else "3",
            "TEST_REPORT_SOURCE_OVERRIDE": "dist-gloo",
        }
    if dist.is_ucc_available():
        DISTRIBUTED_TESTS_CONFIG["ucc"] = {
            "WORLD_SIZE": "2" if torch.cuda.device_count() == 2 else "3",
            "TEST_REPORT_SOURCE_OVERRIDE": "dist-ucc",
            "UCX_TLS": "tcp",
            "UCC_TLS": "nccl,ucp",
            "UCC_TL_UCP_TUNE": "cuda:0",  # don't use UCP TL on CUDA as it is not well supported
        }

# https://stackoverflow.com/questions/2549939/get-signal-names-from-numbers-in-python
SIGNALS_TO_NAMES_DICT = {
    getattr(signal, n): n for n in dir(signal) if n.startswith("SIG") and "_" not in n
}

CPP_EXTENSIONS_ERROR = """
Ninja (https://ninja-build.org) is required for some of the C++ extensions
tests, but it could not be found. Install ninja with `pip install ninja`
or `conda install ninja`. Alternatively, disable said tests with
`run_test.py --exclude test_cpp_extensions_aot_ninja test_cpp_extensions_jit`.
"""

PYTORCH_COLLECT_COVERAGE = bool(os.environ.get("PYTORCH_COLLECT_COVERAGE"))

JIT_EXECUTOR_TESTS = [
    "test_jit_profiling",
    "test_jit_legacy",
    "test_jit_fuser_legacy",
]

DISTRIBUTED_TESTS = [test for test in TESTS if test.startswith("distributed")]
FUNCTORCH_TESTS = [test for test in TESTS if test.startswith("functorch")]

TESTS_REQUIRING_LAPACK = [
    "distributions/test_constraints",
    "distributions/test_distributions",
]

# These are just the slowest ones, this isn't an exhaustive list.
TESTS_NOT_USING_GRADCHECK = [
    # Note that you should use skipIfSlowGradcheckEnv if you do not wish to
    # skip all the tests in that file, e.g. test_mps
    "doctests",
    "test_meta",
    "test_hub",
    "test_fx",
    "test_decomp",
    "test_cpp_extensions_jit",
    "test_jit",
    "test_ops",
    "test_ops_jit",
    "dynamo/test_recompile_ux",
    "inductor/test_smoke",
    "test_quantization",
]

def print_to_stderr(message):
    print(message, file=sys.stderr)


def get_executable_command(options, disable_coverage=False):
    if options.coverage and not disable_coverage:
        executable = ["coverage", "run", "--parallel-mode", "--source=torch"]
    else:
        executable = [sys.executable, "-bb"]
    return executable


def run_test(
    test_module,
    test_directory,
    options,
    launcher_cmd=None,
    extra_unittest_args=None,
    env=None,
) -> int:
    maybe_set_hip_visible_devies()
    unittest_args = options.additional_unittest_args.copy()
    if options.verbose:
        unittest_args.append(f'-{"v"*options.verbose}')  # in case of pytest
    if test_module in RUN_PARALLEL_BLOCKLIST:
        unittest_args = [
            arg for arg in unittest_args if not arg.startswith("--run-parallel")
        ]
    if extra_unittest_args:
        assert isinstance(extra_unittest_args, list)
        unittest_args.extend(extra_unittest_args)

    # If using pytest, replace -f with equivalent -x
    if options.pytest:
        unittest_args.extend(get_pytest_args(options))
        unittest_args = [arg if arg != "-f" else "-x" for arg in unittest_args]
    if IS_CI:
        ci_args = ["--import-slow-tests", "--import-disabled-tests"]
        if os.getenv("PYTORCH_TEST_RERUN_DISABLED_TESTS", "0") == "1":
            ci_args.append("--rerun-disabled-tests")
        # use the downloaded test cases configuration, not supported in pytest
        unittest_args.extend(ci_args)
    if test_module in PYTEST_SKIP_RETRIES:
        if not options.pytest:
            raise RuntimeError("A test running without pytest cannot skip retries using "
                               "the PYTEST_SKIP_RETRIES set.")
        unittest_args = [arg for arg in unittest_args if "--reruns" not in arg]

    # Extra arguments are not supported with pytest
    executable = get_executable_command(options)

    # Can't call `python -m unittest test_*` here because it doesn't run code
    # in `if __name__ == '__main__': `. So call `python test_*.py` instead.
    argv = [test_module + ".py"] + unittest_args

    os.makedirs(REPO_ROOT / "test" / "test-reports", exist_ok=True)
    log_fd, log_path = tempfile.mkstemp(dir=REPO_ROOT / "test" / "test-reports",
                                        prefix="{}_".format(test_module.replace("\\", "-").replace("/", "-")),
                                        suffix=".log")
    os.close(log_fd)
    command = (launcher_cmd or []) + executable + argv
    print_to_stderr("Executing {} ... [{}]".format(command, datetime.now()))
    with open(log_path, "w") as f:
        ret_code = shell(command, test_directory, stdout=f, stderr=f, env=env)
    print_log_file(test_module, log_path, failed=(ret_code != 0))
    os.remove(log_path)
    return ret_code


def test_cuda_primary_ctx(test_module, test_directory, options):
    return run_test(
        test_module, test_directory, options, extra_unittest_args=["--subprocess"]
    )


run_test_with_subprocess = functools.partial(run_test, extra_unittest_args=["--subprocess"])


def get_run_test_with_subprocess_fn():
    return lambda test_module, test_directory, options: run_test_with_subprocess(test_module, test_directory, options)


def _test_cpp_extensions_aot(test_directory, options, use_ninja):
    if use_ninja:
        try:
            cpp_extension.verify_ninja_availability()
        except RuntimeError:
            print(CPP_EXTENSIONS_ERROR)
            return 1

    # Wipe the build folder, if it exists already
    cpp_extensions_test_dir = os.path.join(test_directory, "cpp_extensions")
    cpp_extensions_test_build_dir = os.path.join(cpp_extensions_test_dir, "build")
    if os.path.exists(cpp_extensions_test_build_dir):
        shutil.rmtree(cpp_extensions_test_build_dir)

    # Build the test cpp extensions modules
    shell_env = os.environ.copy()
    shell_env["USE_NINJA"] = str(1 if use_ninja else 0)
    cmd = [sys.executable, "setup.py", "install", "--root", "./install"]
    return_code = shell(cmd, cwd=cpp_extensions_test_dir, env=shell_env)
    if return_code != 0:
        return return_code
    if sys.platform != "win32":
        return_code = shell(
            cmd,
            cwd=os.path.join(cpp_extensions_test_dir, "no_python_abi_suffix_test"),
            env=shell_env,
        )
        if return_code != 0:
            return return_code

    # "install" the test modules and run tests
    python_path = os.environ.get("PYTHONPATH", "")
    from shutil import copyfile

    os.environ['USE_NINJA'] = shell_env['USE_NINJA']
    test_module = "test_cpp_extensions_aot" + ("_ninja" if use_ninja else "_no_ninja")
    copyfile(
        test_directory + "/test_cpp_extensions_aot.py",
        test_directory + "/" + test_module + ".py",
    )
    try:
        cpp_extensions = os.path.join(test_directory, "cpp_extensions")
        install_directory = ""
        # install directory is the one that is named site-packages
        for root, directories, _ in os.walk(os.path.join(cpp_extensions, "install")):
            for directory in directories:
                if "-packages" in directory:
                    install_directory = os.path.join(root, directory)

        assert install_directory, "install_directory must not be empty"
        os.environ["PYTHONPATH"] = os.pathsep.join([install_directory, python_path])
        return run_test(test_module, test_directory, options)
    finally:
        os.environ["PYTHONPATH"] = python_path
        if os.path.exists(test_directory + "/" + test_module + ".py"):
            os.remove(test_directory + "/" + test_module + ".py")
        os.environ.pop('USE_NINJA')


def test_cpp_extensions_aot_ninja(test_module, test_directory, options):
    return _test_cpp_extensions_aot(test_directory, options, use_ninja=True)


def test_cpp_extensions_aot_no_ninja(test_module, test_directory, options):
    return _test_cpp_extensions_aot(test_directory, options, use_ninja=False)


def test_distributed(test_module, test_directory, options):
    # MPI tests are broken with Python-3.9
    mpi_available = subprocess.call(
        "command -v mpiexec", shell=True
    ) == 0 and sys.version_info < (3, 9)
    if options.verbose and not mpi_available:
        print_to_stderr("MPI not available -- MPI backend tests will be skipped")

    if options.shard:
        which_shard, num_shards = options.shard
    else:
        which_shard = num_shards = 1
    # Round-robin all backends to different shards
    backend_to_shard = {backend: i % num_shards + 1
                        for i, backend in enumerate(DISTRIBUTED_TESTS_WITH_MULTIPLE_BACKENDS[test_module])}
    print_to_stderr(f"Map different backends to different shards for {test_module}: {backend_to_shard}")

    config = DISTRIBUTED_TESTS_CONFIG
    for backend, env_vars in config.items():
        if sys.platform == "win32" and backend != "gloo":
            continue
        if backend == "mpi" and not mpi_available:
            continue
        # Default to the first shard if seeing an unrecognized backend
        if which_shard != backend_to_shard.get(backend, 1):
            print_to_stderr(f"Shard {which_shard}: {backend} should be run in {backend_to_shard.get(backend, 1)}")
            continue
        for with_init_file in {True, False}:
            if sys.platform == "win32" and not with_init_file:
                continue
            tmp_dir = tempfile.mkdtemp()
            if options.verbose:
                init_str = "with {} init_method"
                with_init = init_str.format("file" if with_init_file else "env")
                print_to_stderr(
                    "Running distributed tests for the {} backend {} in shard {} of {}".format(
                        backend, with_init, which_shard, num_shards
                    )
                )
            old_environ = dict(os.environ)
            os.environ["TEMP_DIR"] = tmp_dir
            os.environ["BACKEND"] = backend
            os.environ["INIT_METHOD"] = "env://"
            os.environ.update(env_vars)
            if with_init_file:
                if test_module == "test_distributed_spawn":
                    init_method = f"{FILE_SCHEMA}{tmp_dir}/"
                else:
                    init_method = f"{FILE_SCHEMA}{tmp_dir}/shared_init_file"
                os.environ["INIT_METHOD"] = init_method
            try:
                os.mkdir(os.path.join(tmp_dir, "barrier"))
                os.mkdir(os.path.join(tmp_dir, "test_dir"))
                if backend == "mpi":
                    # test mpiexec for --noprefix option
                    with open(os.devnull, "w") as devnull:
                        allowrunasroot_opt = (
                            "--allow-run-as-root"
                            if subprocess.call(
                                'mpiexec --allow-run-as-root -n 1 bash -c ""',
                                shell=True,
                                stdout=devnull,
                                stderr=subprocess.STDOUT,
                            )
                            == 0
                            else ""
                        )
                        noprefix_opt = (
                            "--noprefix"
                            if subprocess.call(
                                f'mpiexec {allowrunasroot_opt} -n 1 --noprefix bash -c ""',
                                shell=True,
                                stdout=devnull,
                                stderr=subprocess.STDOUT,
                            )
                            == 0
                            else ""
                        )

                    mpiexec = ["mpiexec", "-n", "3", noprefix_opt, allowrunasroot_opt]

                    return_code = run_test(
                        test_module, test_directory, options, launcher_cmd=mpiexec
                    )
                else:
                    return_code = run_test(test_module, test_directory, options, extra_unittest_args=["--subprocess"])
                if return_code != 0:
                    return return_code
            finally:
                shutil.rmtree(tmp_dir)
                os.environ.clear()
                os.environ.update(old_environ)
    return 0


def run_doctests(test_module, test_directory, options):
    """
    Assumes the incoming test module is called doctest, and simply executes the
    xdoctest runner on the torch library itself.
    """
    import xdoctest
    import pathlib
    pkgpath = pathlib.Path(torch.__file__).parent

    exclude_module_list = []
    enabled = {
        # TODO: expose these options to the user
        # For now disable all feature-conditional tests
        # 'lapack': 'auto',
        # 'cuda': 'auto',
        # 'cuda1': 'auto',
        # 'qengine': 'auto',
        'lapack': 0,
        'cuda': 0,
        'cuda1': 0,
        'qengine': 0,
        'autograd_profiler': 0,
        'cpp_ext': 0,
        'monitor': 0,
        "onnx": "auto",
    }

    # Resolve "auto" based on a test to determine if the feature is available.
    if enabled['cuda'] == 'auto' and torch.cuda.is_available():
        enabled['cuda'] = True

    if enabled['cuda1'] == 'auto' and torch.cuda.is_available() and torch.cuda.device_count() > 1:
        enabled['cuda1'] = True

    if enabled['lapack'] == 'auto' and torch._C.has_lapack:
        enabled['lapack'] = True

    if enabled['qengine'] == 'auto':
        try:
            # Is there a better check if quantization is enabled?
            import torch.ao.nn.quantized as nnq  # NOQA
            torch.backends.quantized.engine = 'qnnpack'
            torch.backends.quantized.engine = 'fbgemm'
        except (ImportError, RuntimeError):
            ...
        else:
            enabled['qengine'] = True

    if enabled["onnx"] == "auto":
        try:
            import onnx  # NOQA
            import onnxscript  # NOQA
            import onnxruntime  # NOQA
        except ImportError:
            exclude_module_list.append("torch.onnx._internal.fx.*")
            enabled["onnx"] = False
        else:
            enabled["onnx"] = True

    # Set doctest environment variables
    if enabled['cuda']:
        os.environ['TORCH_DOCTEST_CUDA'] = '1'

    if enabled['cuda1']:
        os.environ['TORCH_DOCTEST_CUDA1'] = '1'

    if enabled['lapack']:
        os.environ['TORCH_DOCTEST_LAPACK'] = '1'

    if enabled['qengine']:
        os.environ['TORCH_DOCTEST_QENGINE'] = '1'

    if enabled['autograd_profiler']:
        os.environ['TORCH_DOCTEST_AUTOGRAD_PROFILER'] = '1'

    if enabled['cpp_ext']:
        os.environ['TORCH_DOCTEST_CPP_EXT'] = '1'

    if enabled['monitor']:
        os.environ['TORCH_DOCTEST_MONITOR'] = '1'

    if enabled["onnx"]:
        os.environ['TORCH_DOCTEST_ONNX'] = '1'

    if 0:
        # TODO: could try to enable some of these
        os.environ['TORCH_DOCTEST_QUANTIZED_DYNAMIC'] = '1'
        os.environ['TORCH_DOCTEST_ANOMOLY'] = '1'
        os.environ['TORCH_DOCTEST_AUTOGRAD'] = '1'
        os.environ['TORCH_DOCTEST_HUB'] = '1'
        os.environ['TORCH_DOCTEST_DATALOADER'] = '1'
        os.environ['TORCH_DOCTEST_FUTURES'] = '1'

    pkgpath = os.path.dirname(torch.__file__)

    xdoctest_config = {
        'global_exec': r'\n'.join([
            'from torch import nn',
            'import torch.nn.functional as F',
            'import torch',
        ]),
        'analysis': 'static',  # set to "auto" to test doctests in compiled modules
        'style': 'google',
        'options': '+IGNORE_WHITESPACE',
    }
    xdoctest_verbose = max(1, options.verbose)
    run_summary = xdoctest.runner.doctest_module(
        os.fspath(pkgpath), config=xdoctest_config, verbose=xdoctest_verbose,
        command=options.xdoctest_command, argv=[],
        exclude=exclude_module_list)
    result = 1 if run_summary.get('n_failed', 0) else 0
    return result


def print_log_file(test: str, file_path: str, failed: bool) -> None:
    num_lines = sum(1 for _ in open(file_path, 'rb'))
    n = 100
    with open(file_path, "r") as f:
        print_to_stderr("")
        if failed:
            if n < num_lines:
                print_to_stderr(f"Expand the folded group to see the beginning of the log file of {test}")
                print_to_stderr(f"##[group]PRINTING BEGINNING OF LOG FILE of {test} ({file_path})")
                for _ in range(num_lines - n):
                    print_to_stderr(next(f).rstrip())
                print_to_stderr("##[endgroup]")
            for _ in range(min(n, num_lines)):
                print_to_stderr(next(f).rstrip())
            print_to_stderr(f"FINISHED PRINTING LOG FILE of {test} ({file_path})")
        else:
            print_to_stderr(f"Expand the folded group to see the log file of {test}")
            print_to_stderr(f"##[group]PRINTING LOG FILE of {test} ({file_path})")
            print_to_stderr(f.read())
            print_to_stderr("##[endgroup]")
            print_to_stderr(f"FINISHED PRINTING LOG FILE of {test} ({file_path})")
        print_to_stderr("")


def get_pytest_args(options):
    if os.getenv("PYTORCH_TEST_RERUN_DISABLED_TESTS", "0") == "1":
        # When under rerun-disabled-tests mode, run the same tests multiple times to determine their
        # flakiness status. Default to 50 re-runs
        rerun_options = ["--flake-finder", "--flake-runs=50"]
    elif options.continue_through_error:
        # If continue through error, don't stop on first failure
        rerun_options = ["--reruns=2"]
    else:
        # When under the normal mode, retry a failed test 2 more times. -x means stop at the first
        # failure
        rerun_options = ["-x", "--reruns=2"]

    pytest_args = [
        "--use-pytest",
        "-vv",
        "-rfEX",
        "-p", "no:xdist",
    ]
    pytest_args.extend(rerun_options)
    return pytest_args

def run_test_ops(test_module, test_directory, options):
    default_unittest_args = get_pytest_args(options)

    return_codes = []
    os.environ["NUM_PARALLEL_PROCS"] = str(NUM_PROCS)
    pool = get_context("spawn").Pool(NUM_PROCS)
    for i in range(NUM_PROCS):
        extra_unittest_args = default_unittest_args.copy()
        extra_unittest_args.extend([
            f"--shard-id={i}",
            f"--num-shards={NUM_PROCS}",
            "-k=not _linalg_cholesky_",
        ])

        return_code = pool.apply_async(
            run_test,
            args=(test_module, test_directory, copy.deepcopy(options)),
            kwds={
                "extra_unittest_args": extra_unittest_args,
            },
        )
        return_codes.append(return_code)

    pool.close()
    pool.join()
    del os.environ["NUM_PARALLEL_PROCS"]

    for return_code in return_codes:
        if return_code.get() != 0:
            return return_code.get()

    extra_unittest_args = default_unittest_args.copy()
    extra_unittest_args.extend([
        "-k=_linalg_cholesky_",
    ])

    return_code = run_test(
        test_module,
        test_directory,
        copy.deepcopy(options),
        extra_unittest_args=extra_unittest_args,
    )
    return return_code


CUSTOM_HANDLERS = {
    "test_cuda_primary_ctx": test_cuda_primary_ctx,
    "test_cuda_nvml_based_avail": get_run_test_with_subprocess_fn(),
    "test_cuda_trace": get_run_test_with_subprocess_fn(),
    "test_cpp_extensions_aot_no_ninja": test_cpp_extensions_aot_no_ninja,
    "test_cpp_extensions_aot_ninja": test_cpp_extensions_aot_ninja,
    "distributed/test_distributed_spawn": test_distributed,
    "distributed/algorithms/quantization/test_quantization": test_distributed,
    "distributed/test_c10d_nccl": get_run_test_with_subprocess_fn(),
    "distributed/test_c10d_gloo": get_run_test_with_subprocess_fn(),
    "distributed/test_c10d_common": get_run_test_with_subprocess_fn(),
    "distributed/test_c10d_spawn_gloo": get_run_test_with_subprocess_fn(),
    "distributed/test_c10d_spawn_nccl": get_run_test_with_subprocess_fn(),
    "distributed/test_c10d_spawn_ucc": get_run_test_with_subprocess_fn(),
    "distributed/test_store": get_run_test_with_subprocess_fn(),
    "distributed/test_pg_wrapper": get_run_test_with_subprocess_fn(),
    "distributed/rpc/test_faulty_agent": get_run_test_with_subprocess_fn(),
    "distributed/rpc/test_tensorpipe_agent": get_run_test_with_subprocess_fn(),
    "distributed/rpc/test_share_memory": get_run_test_with_subprocess_fn(),
    "distributed/rpc/cuda/test_tensorpipe_agent": get_run_test_with_subprocess_fn(),
    "doctests": run_doctests,
    "inductor/test_torchinductor_opinfo": run_test_ops,
    "test_ops": run_test_ops,
    "test_ops_gradients": run_test_ops,
    "test_ops_fwd_gradients": run_test_ops,
    "test_ops_jit": run_test_ops,
    "functorch/test_ops": run_test_ops,
    # not a test_ops file, but takes 2 hrs on some architectures and
    # run_test_ops is good at parallelizing things
    "test_decomp": run_test_ops,
}


PYTEST_SKIP_RETRIES = {
    'test_public_bindings'
}


def parse_test_module(test):
    return test.split(".")[0]


class TestChoices(list):
    def __init__(self, *args, **kwargs):
        super().__init__(args[0])

    def __contains__(self, item):
        return list.__contains__(self, parse_test_module(item))


def parse_args():
    parser = argparse.ArgumentParser(
        description="Run the PyTorch unit test suite",
        epilog="where TESTS is any of: {}".format(", ".join(TESTS)),
        formatter_class=argparse.RawTextHelpFormatter,
        parents=[common_parser]
    )
    parser.add_argument(
        "-v",
        "--verbose",
        action="count",
        default=0,
        help="print verbose information and test-by-test results",
    )
    parser.add_argument("--jit", "--jit", action="store_true", help="run all jit tests")
    parser.add_argument(
        "--distributed-tests",
        "--distributed-tests",
        action="store_true",
        help="run all distributed tests",
    )
    parser.add_argument(
        "--functorch",
        "--functorch",
        action="store_true",
        help=(
            "If this flag is present, we will only run functorch tests. "
            "If this flag is not present, we will run all tests "
            "(including functorch tests)."
        )
    )
    parser.add_argument(
        "--mps",
        "--mps",
        action="store_true",
        help=(
            "If this flag is present, we will only run test_mps and test_metal"
        )
    )
    parser.add_argument(
        "-core",
        "--core",
        action="store_true",
        help="Only run core tests, or tests that validate PyTorch's ops, modules,"
        "and autograd. They are defined by CORE_TEST_LIST."
    )
    parser.add_argument(
        "-pt",
        "--pytest",
        action="store_true",
        help="If true, use `pytest` to execute the tests. E.g., this runs "
        "TestTorch with pytest in verbose and coverage mode: "
        "python run_test.py -vci torch -pt",
    )
    parser.add_argument(
        "-c",
        "--coverage",
        action="store_true",
        help="enable coverage",
        default=PYTORCH_COLLECT_COVERAGE,
    )
    parser.add_argument(
        "-i",
        "--include",
        nargs="+",
        choices=TestChoices(TESTS),
        default=TESTS,
        metavar="TESTS",
        help="select a set of tests to include (defaults to ALL tests)."
        " tests must be a part of the TESTS list defined in run_test.py",
    )
    parser.add_argument(
        "-x",
        "--exclude",
        nargs="+",
        choices=TESTS,
        metavar="TESTS",
        default=[],
        help="select a set of tests to exclude",
    )
    parser.add_argument(
        "-f",
        "--first",
        choices=TESTS,
        metavar="TESTS",
        help="select the test to start from (excludes previous tests)",
    )
    parser.add_argument(
        "-l",
        "--last",
        choices=TESTS,
        metavar="TESTS",
        help="select the last test to run (excludes following tests)",
    )
    parser.add_argument(
        "--bring-to-front",
        nargs="+",
        choices=TestChoices(TESTS),
        default=[],
        metavar="TESTS",
        help="select a set of tests to run first. This can be used in situations"
        " where you want to run all tests, but care more about some set, "
        "e.g. after making a change to a specific component",
    )
    parser.add_argument(
        "--ignore-win-blocklist",
        action="store_true",
        help="always run blocklisted windows tests",
    )
    # NS: Disable target determination until it can be made more reliable
    # parser.add_argument(
    #     "--determine-from",
    #     help="File of affected source filenames to determine which tests to run.",
    # )
    parser.add_argument(
        "--continue-through-error",
        "--keep-going",
        action="store_true",
        help="Runs the full test suite despite one of the tests failing",
        default=strtobool(os.environ.get("CONTINUE_THROUGH_ERROR", "False")),
    )
    parser.add_argument(
        "additional_unittest_args",
        nargs="*",
        help="additional arguments passed through to unittest, e.g., "
        "python run_test.py -i sparse -- TestSparse.test_factory_size_check",
    )
    parser.add_argument(
        "--shard",
        nargs=2,
        type=int,
        help="runs a shard of the tests (taking into account other selections), e.g., "
        "--shard 2 3 will break up the selected tests into 3 shards and run the tests "
        "in the 2nd shard (the first number should not exceed the second)",
    )
    parser.add_argument(
        "--exclude-jit-executor",
        action="store_true",
        help="exclude tests that are run for a specific jit config",
    )
    parser.add_argument(
        "--exclude-distributed-tests",
        action="store_true",
        help="exclude distributed tests",
    )
    parser.add_argument(
        "--dry-run",
        action="store_true",
        help="Only list the test that will run.",
    )
    parser.add_argument(
        "--xdoctest-command",
        default='all',
        help=(
            "Control the specific doctest action. "
            "Use 'list' to simply parse doctests and check syntax. "
            "Use 'all' to execute all doctests or specify a specific "
            "doctest to run")
    )

    group = parser.add_mutually_exclusive_group()
    group.add_argument(
        "--dynamo",
        action="store_true",
        help="Run tests with TorchDynamo+EagerBackend turned on",
    )
    group.add_argument(
        "--inductor",
        action="store_true",
        help="Run tests with TorchInductor turned on",
    )

    return parser.parse_args()


def find_test_index(test, selected_tests, find_last_index=False):
    """Find the index of the first or last occurrence of a given test/test module in the list of selected tests.

    This function is used to determine the indices when slicing the list of selected tests when
    ``options.first``(:attr:`find_last_index`=False) and/or ``options.last``(:attr:`find_last_index`=True) are used.

    :attr:`selected_tests` can be a list that contains multiple consequent occurrences of tests
    as part of the same test module, e.g.:

    ```
    selected_tests = ['autograd', 'cuda', **'torch.TestTorch.test_acos',
                     'torch.TestTorch.test_tan', 'torch.TestTorch.test_add'**, 'utils']
    ```

    If :attr:`test`='torch' and :attr:`find_last_index`=False, result should be **2**.
    If :attr:`test`='torch' and :attr:`find_last_index`=True, result should be **4**.

    Args:
        test (str): Name of test to lookup
        selected_tests (list): List of tests
        find_last_index (bool, optional): should we lookup the index of first or last
            occurrence (first is default)

    Returns:
        index of the first or last occurrence of the given test
    """
    idx = 0
    found_idx = -1
    for t in selected_tests:
        if t.startswith(test):
            found_idx = idx
            if not find_last_index:
                break
        idx += 1
    return found_idx


def exclude_tests(exclude_list, selected_tests, exclude_message=None, exact_match=False):
    for exclude_test in exclude_list:
        tests_copy = selected_tests[:]
        for test in tests_copy:
            if (not exact_match and test.startswith(exclude_test)) or test == exclude_test:
                if exclude_message is not None:
                    print_to_stderr("Excluding {} {}".format(test, exclude_message))
                selected_tests.remove(test)
    return selected_tests


def must_serial(file: str) -> bool:
    return (
        os.getenv("PYTORCH_TEST_RUN_EVERYTHING_IN_SERIAL", "0") == "1" or
        "distributed" in os.getenv("TEST_CONFIG", "") or
        "dynamo" in os.getenv("TEST_CONFIG", "") or
        "distributed" in file or
        file in CUSTOM_HANDLERS or
        file in RUN_PARALLEL_BLOCKLIST or
        file in CI_SERIAL_LIST or
        file in JIT_EXECUTOR_TESTS
    )


def can_run_in_pytest(test):
    return os.getenv('PYTORCH_TEST_DO_NOT_USE_PYTEST', '0') == '0'


def get_selected_tests(options):
    selected_tests = options.include

    # filter if there's JIT only and distributed only test options
    if options.jit:
        selected_tests = list(
            filter(lambda test_name: "jit" in test_name, selected_tests)
        )

    if options.distributed_tests:
        selected_tests = list(
            filter(lambda test_name: (test_name in DISTRIBUTED_TESTS and
                                      test_name not in DISTRIBUTED_TESTS_WITH_MULTIPLE_BACKENDS),
                   selected_tests)
        )

    # Filter to only run core tests when --core option is specified
    if options.core:
        selected_tests = list(
            filter(lambda test_name: test_name in CORE_TEST_LIST, selected_tests)
        )

    # Filter to only run functorch tests when --functorch option is specified
    if options.functorch:
        selected_tests = [tname for tname in selected_tests if tname in FUNCTORCH_TESTS]

    if options.mps:
        selected_tests = ['test_mps', 'test_metal']
    else:
        # Exclude all mps tests otherwise
        options.exclude.extend(['test_mps', 'test_metal'])

    # process reordering
    if options.bring_to_front:
        to_front = set(options.bring_to_front)
        selected_tests = options.bring_to_front + list(
            filter(lambda name: name not in to_front, selected_tests)
        )

    if options.first:
        first_index = find_test_index(options.first, selected_tests)
        selected_tests = selected_tests[first_index:]

    if options.last:
        last_index = find_test_index(options.last, selected_tests, find_last_index=True)
        selected_tests = selected_tests[: last_index + 1]

    # process exclusion
    if options.exclude_jit_executor:
        options.exclude.extend(JIT_EXECUTOR_TESTS)

    if options.exclude_distributed_tests:
        options.exclude.extend(DISTRIBUTED_TESTS)

    # these tests failing in CUDA 11.6 temporary disabling. issue https://github.com/pytorch/pytorch/issues/75375
    if torch.version.cuda is not None and LooseVersion(torch.version.cuda) >= "11.6":
        options.exclude.extend(["distributions/test_constraints"])

    selected_tests = exclude_tests(options.exclude, selected_tests)

    if sys.platform == "win32" and not options.ignore_win_blocklist:
        target_arch = os.environ.get("VSCMD_ARG_TGT_ARCH")
        if target_arch != "x64":
            WINDOWS_BLOCKLIST.append("cpp_extensions_aot_no_ninja")
            WINDOWS_BLOCKLIST.append("cpp_extensions_aot_ninja")
            WINDOWS_BLOCKLIST.append("cpp_extensions_jit")
            WINDOWS_BLOCKLIST.append("jit")
            WINDOWS_BLOCKLIST.append("jit_fuser")

        selected_tests = exclude_tests(WINDOWS_BLOCKLIST, selected_tests, "on Windows")

    elif TEST_WITH_ROCM:
        selected_tests = exclude_tests(ROCM_BLOCKLIST, selected_tests, "on ROCm")

    # sharding
    if options.shard:
        assert len(options.shard) == 2, "Unexpected shard format"
        assert min(options.shard) > 0, "Shards must be positive numbers"
        which_shard, num_shards = options.shard
        assert (
            which_shard <= num_shards
        ), "Selected shard must be less than or equal to total number of shards"
        assert num_shards <= len(
            selected_tests
        ), f"Number of shards must be less than {len(selected_tests)}"

        if num_shards == 1:
            return selected_tests

        # Download previous test times to make sharding decisions
        path = os.path.join(str(REPO_ROOT), TEST_TIMES_FILE)
        if os.path.exists(path):
            with open(path, "r") as f:
                test_file_times = cast(Dict[str, Any], json.load(f))
        else:
            test_file_times = {}
        test_config = os.environ.get("TEST_CONFIG")
        if test_config not in test_file_times:
            print(
                "::warning:: Gathered no stats from artifacts. Proceeding with default sharding plan."
            )
            selected_tests = selected_tests[which_shard - 1 :: num_shards]
        else:
            print("Found test time stats from artifacts")
            test_file_times_config = test_file_times[test_config]
            shards = calculate_shards(num_shards, selected_tests, test_file_times_config,
                                      must_serial=must_serial)
            _, tests_from_shard = shards[which_shard - 1]
            selected_tests = tests_from_shard

    # skip all distributed tests if distributed package is not available.
    if not dist.is_available():
        selected_tests = exclude_tests(DISTRIBUTED_TESTS, selected_tests,
                                       "PyTorch is built without distributed support.")

    # skip tests that require LAPACK when it's not available
    if not torch._C.has_lapack:
        selected_tests = exclude_tests(TESTS_REQUIRING_LAPACK, selected_tests,
                                       "PyTorch is built without LAPACK support.")

    if is_slow_gradcheck_env():
        selected_tests = exclude_tests(TESTS_NOT_USING_GRADCHECK, selected_tests,
                                       "Running in slow gradcheck mode, skipping tests "
                                       "that don't use gradcheck.", exact_match=True)

    if options.distributed_tests:
        # Run distributed tests with multiple backends across all shards, one per backend
        selected_tests.extend(DISTRIBUTED_TESTS_WITH_MULTIPLE_BACKENDS.keys())
        selected_tests.reverse()

    return selected_tests


def run_test_module(test: str, test_directory: str, options) -> Optional[str]:
    maybe_set_hip_visible_devies()

    test_module = parse_test_module(test)

    # Printing the date here can help diagnose which tests are slow
    print_to_stderr("Running {} ... [{}]".format(test, datetime.now()))
    handler = CUSTOM_HANDLERS.get(test_module, run_test)
    return_code = handler(test_module, test_directory, options)
    assert isinstance(return_code, int) and not isinstance(
        return_code, bool
    ), f"While running {test} got non integer return code {return_code}"
    if return_code == 0:
        return None

    message = f"{test} failed!"
    if return_code < 0:
        # subprocess.Popen returns the child process' exit signal as
        # return code -N, where N is the signal number.
        signal_name = SIGNALS_TO_NAMES_DICT[-return_code]
        message += f" Received signal: {signal_name}"
    return message


def main():
    options = parse_args()

    test_directory = str(REPO_ROOT / "test")
    selected_tests = get_selected_tests(options)

    if options.verbose:
        print_to_stderr("Selected tests:\n {}".format("\n ".join(selected_tests)))

    if options.dry_run:
        return

    if options.coverage and not PYTORCH_COLLECT_COVERAGE:
        shell(["coverage", "erase"])

    if IS_CI:
        selected_tests = get_reordered_tests(selected_tests)
        # downloading test cases configuration to local environment
        get_test_case_configs(dirpath=test_directory)

    if options.dynamo:
        os.environ["PYTORCH_TEST_WITH_DYNAMO"] = "1"
    elif options.inductor:
        os.environ["PYTORCH_TEST_WITH_INDUCTOR"] = "1"

    failure_messages = []

    # parallel = in parallel with other files
    # serial = this file on it's own.  The file might still be run in parallel with itself (ex test_ops)
    selected_tests_parallel = [x for x in selected_tests if not must_serial(x)]
    selected_tests_serial = [x for x in selected_tests if x not in selected_tests_parallel]
    print_to_stderr("parallel (file granularity) tests:\n {}".format("\n ".join(selected_tests_parallel)))
    print_to_stderr("serial (file granularity) tests:\n {}".format("\n ".join(selected_tests_serial)))

    # See Note [ROCm parallel CI testing]
    pool = get_context("spawn").Pool(NUM_PROCS, maxtasksperchild=None if torch.version.hip else 1)
    os.makedirs(REPO_ROOT / "test" / "test-reports", exist_ok=True)

    def success_callback(err_message):
        if err_message is None:
            return True
        failure_messages.append(err_message)
        print_to_stderr(err_message)
        if not options.continue_through_error:
            pool.terminate()
        return False

    try:
        os.environ['PARALLEL_TESTING'] = '1'
        for test in selected_tests_parallel:
            options_clone = copy.deepcopy(options)
            if can_run_in_pytest(test):
                options_clone.pytest = True
            pool.apply_async(run_test_module, args=(test, test_directory, options_clone), callback=success_callback)
        pool.close()
        pool.join()
        del os.environ['PARALLEL_TESTING']

        if not options.continue_through_error and len(failure_messages) != 0:
            raise RuntimeError(
                "\n".join(failure_messages) +
                "\n\nTip: You can keep running tests even on failure by "
                "passing --keep-going to run_test.py.\n"
                "If running on CI, add the 'keep-going' label to "
                "your PR and rerun your jobs."
            )

        for test in selected_tests_serial:
            options_clone = copy.deepcopy(options)
            if can_run_in_pytest(test):
                options_clone.pytest = True
            err_message = run_test_module(test, test_directory, options_clone)
            if err_message is None:
                continue
            failure_messages.append(err_message)
            if not options_clone.continue_through_error:
                raise RuntimeError(err_message)
            print_to_stderr(err_message)
    finally:
        pool.terminate()
        pool.join()

        if options.coverage:
            from coverage import Coverage

            with set_cwd(test_directory):
                cov = Coverage()
                if PYTORCH_COLLECT_COVERAGE:
                    cov.load()
                cov.combine(strict=False)
                cov.save()
                if not PYTORCH_COLLECT_COVERAGE:
                    cov.html_report()

    if len(failure_messages) != 0:
        for err in failure_messages:
            print_to_stderr(err)
        sys.exit(1)


if __name__ == "__main__":
    main()