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# mypy: allow-untyped-defs
from __future__ import annotations
import atexit
import ctypes
import dataclasses
import functools
import logging
import os
import pickle
import queue
import selectors
import subprocess
import sys
import time
import warnings
from collections.abc import Iterable, Sequence
from concurrent.futures import ThreadPoolExecutor
from ctypes import byref, c_size_t, c_void_p, CDLL
from typing import Any, Callable, IO, Optional, TYPE_CHECKING, Union
import torch
import torch._inductor.async_compile # noqa: F401 required to warm up AsyncCompile pools
from torch._dynamo.device_interface import get_interface_for_device
from torch._dynamo.testing import rand_strided
from torch._inductor import ir
from torch._inductor.codecache import (
CppCodeCache,
CUDACodeCache,
DLLWrapper,
get_hash,
PyCodeCache,
)
from torch._inductor.utils import get_gpu_type, get_ld_library_path, is_gpu
from torch._logging import getArtifactLogger
from torch.utils._ordered_set import OrderedSet
if TYPE_CHECKING:
from types import ModuleType
from torch._inductor.select_algorithm import TritonTemplateCaller
from . import config
from .runtime.benchmarking import benchmarker
from .virtualized import V
CUDA_VISIBLE_DEVICES = "CUDA_VISIBLE_DEVICES"
autotuning_log = getArtifactLogger(__name__, "autotuning")
class NonzeroWorkspaceNotSupportedError(Exception):
pass
class TuningProcess:
"""
Class to launch and interact with a benchmarking subprocess.
"""
@staticmethod
def process_main(read_pipe: IO[bytes], write_pipe: IO[bytes]) -> None:
"""
Entry point for the child process.
"""
autotuning_log.debug(
"Started autotune subprocess %s. Visible devices: %s",
os.getpid(),
os.environ.get(CUDA_VISIBLE_DEVICES),
)
def workloop():
while True:
job = TuningProcess.recv(read_pipe)
if job is None:
# None is a sentinel for the child to shut down
break
try:
result = job()
except Exception as e:
result = e
TuningProcess.send(result, write_pipe)
try:
workloop()
except EOFError:
# The parent closed the pipe
pass
@staticmethod
def send(obj: Any, write_pipe: IO[bytes]) -> None:
pickle.dump(obj, write_pipe)
write_pipe.flush()
@staticmethod
def recv(read_pipe: IO[bytes]) -> Any:
return pickle.load(read_pipe)
def __init__(self, device: Optional[int]):
self.device = device
self.start()
def start(self):
"""
Start the benchmarking subprocess.
"""
entry = os.path.join(os.path.dirname(__file__), "__autotune_main__.py")
subproc_read_fd, write_fd = os.pipe()
read_fd, subproc_write_fd = os.pipe()
self.write_pipe = os.fdopen(write_fd, "wb")
self.read_pipe = os.fdopen(read_fd, "rb")
self.selector = selectors.DefaultSelector()
self.selector.register(self.read_pipe, selectors.EVENT_READ)
cmd = [
sys.executable,
entry,
f"--parent={os.getpid()}",
f"--read-fd={str(subproc_read_fd)}",
f"--write-fd={str(subproc_write_fd)}",
]
extra_env = {
# We need to set the PYTHONPATH so the subprocess can find torch.
"PYTHONPATH": os.environ.get(
"TORCH_CUSTOM_PYTHONPATH", os.pathsep.join(sys.path)
),
# We shouldn't be using the Triton async compile subprocess pool,
# but as a precaution set the env var that disables its creation.
"TORCH_WARM_POOL": "0",
# Some internal usages need a modified LD_LIBRARY_PATH.
"LD_LIBRARY_PATH": get_ld_library_path(),
# This will cause the subprocs to profile using the profiler.
"TORCHINDUCTOR_PROFILE_WITH_DO_BENCH_USING_PROFILING": "1"
if config.profile_bandwidth_with_do_bench_using_profiling
else "0",
}
if self.device is not None:
extra_env[CUDA_VISIBLE_DEVICES] = str(self.device)
self.process = subprocess.Popen(
cmd,
env={**os.environ, **extra_env},
pass_fds=(subproc_read_fd, subproc_write_fd),
)
os.close(subproc_read_fd)
os.close(subproc_write_fd)
self.running = True
def alive(self) -> bool:
"""
True if the subprocess is still running.
"""
return self.running and self.process.poll() is None
def put(self, req: Any) -> None:
"""
Push a work item to the child process.
"""
if not self.alive():
self.start()
TuningProcess.send(req, self.write_pipe)
def get(self, timeout: float = 120.0) -> Any:
"""
Get a response from the child process. Raises TimeoutError on timeout;
raises EOFError if the subprocess crashes.
"""
try:
if not self.selector.select(timeout):
raise TimeoutError(f"Timeout in autotune subprocess {self.process.pid}")
result = TuningProcess.recv(self.read_pipe)
except TimeoutError:
self.kill()
raise
except EOFError:
# The subprocess crashed
self.close()
raise
except Exception:
autotuning_log.exception(
"Unexpected exception in autotune subprocess %s", self.process.pid
)
self.kill()
raise
if isinstance(result, Exception):
raise result
return result
def shutdown(self, wait: bool = True) -> None:
"""
Signal the child process to shut down gracefully.
"""
if self.alive():
TuningProcess.send(None, self.write_pipe)
if wait:
self.wait()
def wait(self) -> None:
"""
Wait for the child process to exit.
"""
if self.alive():
self.process.wait()
self.close()
def close(self) -> None:
"""
Close resources.
"""
self.selector.close()
self.read_pipe.close()
self.write_pipe.close()
self.running = False
def kill(self) -> None:
"""
Send a SIGKILL to the child process.
"""
if self.alive():
autotuning_log.error(
"Sending SIGKILL to autotune subprocess %d",
self.process.pid,
)
self.process.kill()
self.close()
class TuningProcessPool:
"""
Maintains a pool of TuningProcesses to benchmark kernels in parallel
across devices. By default, we create one TuningProcess per device and
set the sub-process environment to make only that device visible.
"""
def __init__(self) -> None:
"""
Start the child processes.
"""
devices = self.get_device_list()
autotuning_log.debug("Sub-process autotune device list: %s", devices)
# Launch the child processes.
self.processes = [TuningProcess(device=device) for device in devices]
self.process_queue: queue.Queue[TuningProcess] = queue.Queue()
for p in self.processes:
self.process_queue.put(p)
# Use a thread pool to manage distributing work to the subprocesses.
# Threads block on an available process, so it makes sense to match
# the number of threads with the number of devices.
self.executor = ThreadPoolExecutor(max_workers=len(devices))
@staticmethod
def get_device_list() -> Sequence[Optional[int]]:
"""
Gather the list of devices to be used in the pool.
"""
if not config.autotune_multi_device:
# Don't use multiple devices
return [None]
gpu_type = get_gpu_type()
device_interface = get_interface_for_device(gpu_type)
count = device_interface.device_count()
# If the user specified the visible devices in the env, use those.
if CUDA_VISIBLE_DEVICES in os.environ:
devices = [int(d) for d in os.environ[CUDA_VISIBLE_DEVICES].split(",")]
assert len(devices) <= count
return devices
return list(range(count))
def shutdown(self) -> None:
"""
Signal all child processes to exit.
"""
self.executor.shutdown()
for p in self.processes:
p.shutdown(wait=False)
for p in self.processes:
p.wait()
def target(self, choice: TritonTemplateCaller) -> float:
"""
Entry point for the thread-pool helper threads: Wait for an open TuningProcess,
remove it from the queue, execute the benchmark in that subprocess, and return
the TuningProcess to the queue.
"""
assert choice.bmreq is not None
process = self.process_queue.get()
process.put(choice.bmreq.benchmark)
try:
return process.get(
config.max_autotune_subproc_result_timeout_seconds,
)
except TimeoutError:
warnings.warn(
f"Timed out benchmarking choice '{choice}'. It will be ignored. "
"Please debug the root cause in case the choice can bring perf gains."
)
# Set to INF so this choice will be ignored
return float("inf")
except Exception:
warnings.warn(
f"Failed to benchmark choice '{choice}'. It will be ignored. "
"Please debug the root cause in case the choice can bring perf gains."
)
# Set to INF so this choice will be ignored
return float("inf")
finally:
self.process_queue.put(process)
def benchmark(
self,
choices: list[TritonTemplateCaller],
) -> dict[TritonTemplateCaller, float]:
"""
Benchmark each choice in a separate process.
"""
# Use a ThreadExecutorPool to spread the work across the subprocesses and
# to grab subprocesses as soon as they're free.
results = dict(zip(choices, self.executor.map(self.target, choices)))
return results
LayoutOrBuffer = Union[ir.Layout, ir.Buffer]
@dataclasses.dataclass
class TensorMeta:
device: torch.device
dtype: torch.dtype
sizes: torch._prims_common.ShapeType
strides: torch._prims_common.StrideType
offset: int
name: Optional[str] = None
@classmethod
def from_irnodes(
cls, irnodes: Union[LayoutOrBuffer, Sequence[LayoutOrBuffer]]
) -> Union[TensorMeta, list[TensorMeta]]:
if isinstance(irnodes, Sequence):
result: list[Any] = [cls.from_irnodes(x) for x in irnodes]
assert all(isinstance(x, TensorMeta) for x in result)
return result
node = irnodes
if isinstance(node, ir.Layout):
node = ir.Buffer(name="fake", layout=node)
dtype = node.get_dtype()
assert dtype is not None
device = node.get_device()
assert device is not None
return TensorMeta(
device=device,
dtype=dtype,
sizes=V.graph.sizevars.size_hints(
node.get_size(),
fallback=config.unbacked_symint_fallback,
),
strides=V.graph.sizevars.size_hints(
node.get_stride(),
fallback=config.unbacked_symint_fallback,
),
offset=V.graph.sizevars.size_hint(
node.get_layout().offset,
fallback=config.unbacked_symint_fallback,
),
name=node.get_name(),
)
def to_tensor(self) -> torch.Tensor:
return rand_strided(
self.sizes,
self.strides,
device=self.device,
dtype=self.dtype,
extra_size=self.offset,
)
@dataclasses.dataclass
class BenchmarkRequest:
"""
Only handle triton template benchmark for now. The extern kernel benchmark
can be done inside the same process since they usually don't cause crash.
Important: Instances of this class and subclasses have to be serializable
across process boundaries. Do not put CUDA Tensors in here!
"""
def __init__(
self,
kernel_name: str,
input_tensor_meta: Union[TensorMeta, list[TensorMeta]],
output_tensor_meta: Union[TensorMeta, list[TensorMeta]],
extra_args: Iterable[Any],
) -> None:
# the kernel name defined in the module
self.kernel_name = kernel_name
if isinstance(input_tensor_meta, TensorMeta):
input_tensor_meta = [input_tensor_meta]
self.input_tensor_meta = input_tensor_meta
if isinstance(output_tensor_meta, (tuple, list)):
if len(output_tensor_meta) > 1:
# Each output with same meta for Grouped GEMM
assert all(
getattr(output_tensor_meta[0], attr) == getattr(x, attr)
for x in output_tensor_meta
for attr in ["device", "dtype", "sizes", "strides", "offset"]
)
output_tensor_meta = output_tensor_meta[0]
self.output_tensor_meta = output_tensor_meta
self.extra_args = extra_args
def make_run_fn(
self, *input_tensors: torch.Tensor, out: torch.Tensor
) -> Callable[[], None]:
raise NotImplementedError
def cleanup_run_fn(self) -> None:
pass
def do_bench(
self,
fn,
*input_tensors: torch.Tensor,
out: Optional[torch.Tensor] = None,
) -> float:
raise NotImplementedError
def benchmark(
self,
*input_tensors: torch.Tensor,
out: Optional[torch.Tensor] = None,
) -> float:
debug = autotuning_log.isEnabledFor(logging.DEBUG)
if debug:
start_ts = time.time()
# create args and out tensor
if out is None:
assert len(input_tensors) == 0
input_tensors = tuple(x.to_tensor() for x in self.input_tensor_meta)
out = self.output_tensor_meta.to_tensor()
if debug:
create_tensor_elapse = time.time() - start_ts # type: ignore[possibly-undefined]
start_ts = time.time()
try:
fn = self.make_run_fn(*input_tensors, out=out)
except NonzeroWorkspaceNotSupportedError:
# Skipping all ops with nonzero workspace requirements
autotuning_log.info("Skipping op due to nonzero workspace requirement")
return float("inf")
if debug:
load_elapse = time.time() - start_ts # type: ignore[possibly-undefined]
start_ts = time.time()
res = self.do_bench(fn, *input_tensors, out)
if debug:
bench_elapse = time.time() - start_ts # type: ignore[possibly-undefined]
autotuning_log.debug(
"InChildProcess %s: load %f, create tensor %f, bench %f",
str(self),
load_elapse, # type: ignore[possibly-undefined]
create_tensor_elapse, # type: ignore[possibly-undefined]
bench_elapse,
)
self.cleanup_run_fn()
return res
class _TestBenchmarkRequest(BenchmarkRequest):
"""
Supports unit testing. Defined in this file instead of the test file so the
TuningProcess sub-process can unpickle these objects.
"""
def __init__(
self,
result: float = 0.0,
device: Optional[int] = None,
sleep: Optional[float] = None,
exc: Optional[Exception] = None,
crash: bool = False,
):
self.result = result
self.device = device
self.sleep = sleep
self.exc = exc
self.crash = crash
def benchmark(
self, *input_tensors: torch.Tensor, out: Optional[torch.Tensor] = None
) -> float:
if self.device is not None:
assert os.environ.get(CUDA_VISIBLE_DEVICES, None) == str(self.device)
if self.sleep:
time.sleep(self.sleep)
if self.exc:
raise self.exc
if self.crash:
sys.exit(1)
return self.result
class GPUDeviceBenchmarkMixin:
def do_bench(
self,
fn,
*input_tensors: torch.Tensor,
out: Optional[torch.Tensor] = None,
) -> float:
device_idx_set = OrderedSet(
tensor.device.index
for tensor in [*input_tensors, out]
if isinstance(tensor, torch.Tensor)
and is_gpu(tensor.device.type)
and tensor.device.index is not None
)
assert len(device_idx_set) <= 1, f"Can not mix devices {device_idx_set}"
device_type = next(
(
tensor.device.type
for tensor in input_tensors
if is_gpu(tensor.device.type)
),
"cuda",
)
device_interface = get_interface_for_device(device_type)
if len(device_idx_set) == 1:
device_idx = next(iter(device_idx_set))
else:
device_idx = device_interface.current_device()
with device_interface.device(device_idx): # type: ignore[attr-defined]
res = benchmarker.benchmark_gpu(fn)
device_interface.synchronize() # shake out any CUDA errors
return res
class CPUDeviceBenchmarkMixin:
def do_bench(
self,
fn,
*input_tensors: torch.Tensor,
out: Optional[torch.Tensor] = None,
) -> float:
return benchmarker.benchmark_cpu(fn)
class TritonBenchmarkRequest(BenchmarkRequest):
# Important: Instances of this class have to be serializable
# across process boundaries. Do not put CUDA Tensors in here!
def __init__(
self,
kernel_name: str,
input_tensor_meta: Union[TensorMeta, list[TensorMeta]],
output_tensor_meta: Union[TensorMeta, list[TensorMeta]],
extra_args: Iterable[Any],
module_path: str, # the path of the module defining the triton kernel
module_cache_key: str,
num_stages: int,
num_warps: int,
num_consumer_groups: int = 0,
num_buffers_warp_spec: int = 0,
matrix_instr_nonkdim: int = 0, # only used for hip to choose the shape of mfma instruction.
waves_per_eu: int = 0, # only used for hip to schedule waves per execution unit
kpack: int = 0, # ROCm specific gemm parameter
) -> None:
super().__init__(kernel_name, input_tensor_meta, output_tensor_meta, extra_args)
self.module_path = module_path
self.module_cache_key = module_cache_key
self.num_stages = num_stages
self.num_warps = num_warps
self.num_consumer_groups = num_consumer_groups
self.num_buffers_warp_spec = num_buffers_warp_spec
self.matrix_instr_nonkdim = matrix_instr_nonkdim
self.waves_per_eu = waves_per_eu
self.kpack = kpack
def make_run_fn(
self, *input_tensors: torch.Tensor, out: torch.Tensor
) -> Callable[[], None]:
mod = PyCodeCache.load_by_key_path(self.module_cache_key, self.module_path)
autotuning_log.debug(
"benchmark module key: %s, path: %s",
self.module_cache_key,
self.module_path,
)
run_method = getattr(mod, self.kernel_name).run
extra_args = list(self.extra_args)
run_method.__self__.with_bandwidth_info = False
# Newer version of triton add warmup argument to JITFunction.run.
# This code handles backward-compatibility.
warmup_arg = {}
import inspect
if "warmup" in inspect.signature(run_method).parameters:
warmup_arg["warmup"] = False
if out.device.type == "cpu":
stream = 0
else:
device_type = out.device.type
device_interface = get_interface_for_device(device_type)
stream = device_interface.get_raw_stream(
self.output_tensor_meta.device.index
)
if isinstance(
getattr(mod, self.kernel_name),
torch._inductor.runtime.triton_heuristics.DebugAutotuner,
):
return functools.partial(
run_method,
*input_tensors,
out,
*extra_args,
**warmup_arg,
stream=stream,
)
else:
return functools.partial(
run_method,
*input_tensors,
out,
*extra_args,
**warmup_arg,
stream=stream,
benchmark_run=True,
)
def precompile(self):
mod = PyCodeCache.load_by_key_path(self.module_cache_key, self.module_path)
getattr(mod, self.kernel_name).precompile()
def __str__(self) -> str:
return f"{self.kernel_name=}, {self.module_path=}, {self.module_cache_key=}"
class TritonGPUBenchmarkRequest(GPUDeviceBenchmarkMixin, TritonBenchmarkRequest):
pass
class TritonCPUBenchmarkRequest(CPUDeviceBenchmarkMixin, TritonBenchmarkRequest):
pass
class CUDABenchmarkRequest(GPUDeviceBenchmarkMixin, BenchmarkRequest):
"""
A class to handle CUDA (CUTLASS) benchmark requests. This class is for
managing the lifecycle of a CUDA kernel benchmark, including compiling
the source code, managing workspace memory, and executing the kernel.
Important: Instances of this class have to be serializable across
process boundaries. Do not put CUDA Tensors in here!
"""
def __init__(
self,
kernel_name: str,
input_tensor_meta: Union[TensorMeta, list[TensorMeta]],
output_tensor_meta: Union[TensorMeta, list[TensorMeta]],
extra_args: Iterable[Any],
source_code: str,
) -> None:
super().__init__(kernel_name, input_tensor_meta, output_tensor_meta, extra_args)
self.source_code = source_code
self.workspace_size: int = 0
self.workspace: Optional[torch.Tensor] = None
self.DLL: Optional[DLLWrapper] = None
self._workspace_size_updated = False
self.hash_key: str = ""
self.source_file: str = ""
self.hash_key, self.source_file = CUDACodeCache.write(self.source_code, "so")
def precompile(self):
"""
Precompile the CUDA source code to populate the CUDACodeCache.
This may happen in a separate thread pool.
"""
autotuning_log.debug("Precompiling %s", self)
CUDACodeCache.compile(self.source_code, "so")
autotuning_log.debug("Done precompiling %s", self)
def make_run_fn(
self, *input_tensors: torch.Tensor, out: torch.Tensor
) -> Callable[[], None]:
"""
Create a function to run the CUDA kernel with the given input and output tensors.
"""
self.ensure_dll_loaded()
self.update_workspace_size()
args = [c_void_p(tensor.data_ptr()) for tensor in list(input_tensors) + [out]]
autotuning_log.debug(
"make_run_fn: self.kernel_name=%s, self.source_file=%s, self.hash_key=%s, self.DLL=%s, args=%s, self.extra_args=%s",
self.kernel_name,
self.source_file,
self.hash_key,
self.DLL,
args,
self.extra_args,
)
stream_ptr = c_void_p(torch.cuda.current_stream().cuda_stream)
run_method = getattr(self.DLL, self.kernel_name)
workspace_ptr = c_void_p(0)
if self.workspace_size > 0:
self.workspace = torch.zeros(
(self.workspace_size + 7) // 8,
dtype=torch.float64,
device=out.device,
)
workspace_ptr = c_void_p(self.workspace.data_ptr())
# Generate partial function.
ret = functools.partial(
run_method,
*args,
*self.extra_args,
None, # null workspace size ptr
workspace_ptr, # set workspace ptr,
stream_ptr,
)
# sanity check to make sure we cleanup run fn properly
try:
ret()
except RuntimeError as e:
err_msg = str(e)
def raise_runtime_error():
raise RuntimeError(err_msg)
self.cleanup_run_fn()
return raise_runtime_error
return ret
def update_workspace_size(self) -> None:
if self._workspace_size_updated:
return
self.ensure_dll_loaded()
unique_input_count = len(
{meta.name for meta in self.input_tensor_meta} # noqa: set_linter
)
args = [c_void_p(None) for _ in range(unique_input_count + 1)]
stream_ptr = c_void_p(torch.cuda.current_stream().cuda_stream)
run_method = getattr(self.DLL, self.kernel_name)
# Retrieve workspace_size and initialize workspace.
c_workspace_size = c_size_t()
run_method(
*args, # input ptrs and output ptrs
*self.extra_args,
byref(
c_workspace_size
), # set workspace size ptr to retrieve workspace size
None, # null workspace ptr
stream_ptr,
)
torch.cuda.synchronize() # shake out any CUDA errors
self.workspace_size = c_workspace_size.value
autotuning_log.debug(
"update_workspace_size called: new workspace size=%d, self.kernel_name=%s, self.source_file=%s, self.hash_key=%s, self.DLL=%s, args=%s, self.extra_args=%s", # noqa: B950
self.workspace_size,
self.kernel_name,
self.source_file,
self.hash_key,
self.DLL,
args,
self.extra_args,
)
self._workspace_size_updated = True
def ensure_dll_loaded(self):
if self.DLL is None:
self.DLL, self.hash_key, self.source_file = CUDACodeCache.load(
self.source_code, "so"
)
def cleanup_run_fn(self) -> None:
if self.DLL is not None:
self.DLL.close()
self.DLL = None
self.workspace = None
def __str__(self) -> str:
return f"{self.kernel_name=}, {self.source_file=}, {self.hash_key=}"
class CppBenchmarkRequest(CPUDeviceBenchmarkMixin, BenchmarkRequest):
# Important: Instances of this class have to be serializable
# across process boundaries. Do not put Tensors in here!
def __init__(
self,
kernel_name: str,
input_tensor_meta: Union[TensorMeta, list[TensorMeta]],
output_tensor_meta: Union[TensorMeta, list[TensorMeta]],
extra_args: Iterable[Any],
source_code: str,
) -> None:
super().__init__(kernel_name, input_tensor_meta, output_tensor_meta, extra_args)
self.source_code = source_code
self.hash_key = get_hash(source_code)
self.DLL: Optional[Union[CDLL, ModuleType]] = None
def precompile(self):
# Prepopulate CppCodeCache
# may happen in separate Threadpool
autotuning_log.debug("Precompiling %s", self)
CppCodeCache.load(self.source_code, device_type="cpu")
autotuning_log.debug("Done precompiling %s", self)
def make_run_fn(
self, *input_tensors: torch.Tensor, out: torch.Tensor
) -> Callable[[], None]:
# TODO(jgong5): use CppPythonBindingsCodeCache for better binding perf
self.DLL = CppCodeCache.load(self.source_code, device_type="cpu")
args = [tensor.data_ptr() for tensor in list(input_tensors) + [out]]
autotuning_log.debug(
"make_run_fn: self.kernel_name=%s, self.DLL=%s, args=%s, self.extra_args=%s",
self.kernel_name,
self.DLL,
args,
self.extra_args,
)
run_method = getattr(self.DLL, self.kernel_name)
# Assume only size with type ctypes.c_ulonglong in extra_args
assert all(isinstance(arg, ctypes.c_ulonglong) for arg in self.extra_args)
run_method.argtypes = [ctypes.c_ulonglong] * (
len(args) + len(list(self.extra_args))
)
# Generate partial function.
return functools.partial(
run_method,
*args,
*self.extra_args,
)
def cleanup_run_fn(self) -> None:
if self.DLL is not None:
"""
Check close attr due to it crash on Windows.
"""
if hasattr(self.DLL, "close"):
self.DLL.close()
def __str__(self) -> str:
return f"{self.kernel_name=}"
@functools.cache
def get_tuning_process_pool() -> TuningProcessPool:
pool = TuningProcessPool()
atexit.register(pool.shutdown)
return pool
def benchmark_in_sub_process(
choices: list[TritonTemplateCaller],
) -> dict[TritonTemplateCaller, float]:
"""
Do benchmarking in a subprocess and return the perf number (latency).
"""
return get_tuning_process_pool().benchmark(choices)
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