Source code for torch.distributed.algorithms.join
import warnings
from abc import ABC, abstractmethod
from types import TracebackType
from typing import Any, List, NamedTuple, Optional, Type
import torch
import torch.distributed as dist
__all__ = ['JoinHook', 'Joinable', 'Join']
[docs]class JoinHook:
r"""
This defines a join hook, which provides two entry points in the join context manager.
Entry points : a main hook, which is called repeatedly while there exists a non-joined
process, and a post-hook, which is called once all processes have joined.
To implement a join hook for the generic join context manager, define a
class that inherits from :class:`JoinHook` and override ``main_hook()`` and
``post_hook()`` as appropriate.
"""
[docs] def main_hook(self) -> None:
r"""Call this hook while there exists a non-joined process to shadow collective communications in a training iteration.
Training iteration i.e., in one forward pass, backward pass, and optimizer step.
"""
...
[docs] def post_hook(self, is_last_joiner: bool) -> None:
r"""
Call hook after all processes have joined.
It is passed an additional ``bool`` argument ``is_last_joiner``, which indicates if the rank is one of the last to join.
Arguments:
is_last_joiner (bool): ``True`` if the rank is one of the last to
join; ``False`` otherwise.
"""
...
[docs]class Joinable(ABC):
r"""
This defines an abstract base class for joinable classes.
A joinable class
(inheriting from :class:`Joinable`) should implement :meth:`join_hook`,
which returns a :class:`JoinHook` instance, in addition to
:meth:`join_device` and :meth:`join_process_group` that return device and
process group information, respectively.
"""
@abstractmethod
def __init__(self):
super().__init__()
self._join_config = _JoinConfig.construct_disabled_join_config()
[docs] @abstractmethod
def join_hook(self, **kwargs) -> JoinHook:
r"""
Return a :class:`JoinHook` instance for the given :class:`Joinable`.
Arguments:
kwargs (dict): a :class:`dict` containing any keyword arguments
to modify the behavior of the join hook at run time; all
:class:`Joinable` instances sharing the same join context
manager are forwarded the same value for ``kwargs``.
"""
...
@property
@abstractmethod
def join_device(self) -> torch.device:
r"""Return the device from which to perform collective communications needed by the join context manager."""
...
@property
@abstractmethod
def join_process_group(self) -> Any:
r"""Returns the process group for the collective communications needed by the join context manager itself."""
...
class _JoinConfig(NamedTuple):
r"""This includes all fields needed from a :class:`Joinable` instance for the join context manager side."""
enable: bool
throw_on_early_termination: bool
is_first_joinable: bool
@staticmethod
def construct_disabled_join_config():
r"""Return a :class:`_JoinConfig` instance indicating that join-related logic should be disabled.
e.g. if the caller is not in a join context manager.
"""
return _JoinConfig(
enable=False,
throw_on_early_termination=False,
is_first_joinable=False
)
[docs]class Join:
r"""
This class defines the generic join context manager, which allows custom hooks to be called after a process joins.
These hooks should shadow the
collective communications of non-joined processes to prevent hanging and
erroring and to ensure algorithmic correctness. Refer to :class:`JoinHook`
for details about the hook definition.
.. warning::
The context manager requires each participating :class:`Joinable` to
call the method :meth:`notify_join_context()` before its own per-
iteration collective communications to ensure correctness.
.. warning::
The context manager requires that all ``process_group`` attributes in
the :class:`JoinHook` objects are the same. If there are multiple
:class:`JoinHook` objects, then the ``device`` of the first is used.
The process group and device information is used for checking for non-
joined processes and for notifying processes to throw an exception if
``throw_on_early_termination`` is enabled, both of which using an all-
reduce.
Arguments:
joinables (List[Joinable]): a list of the participating
:class:`Joinable` s; their hooks are iterated over in the given
order.
enable (bool): a flag enabling uneven input detection; setting to
``False`` disables the context manager's functionality and should
only be set when the user knows the inputs will not be uneven
(default: ``True``).
throw_on_early_termination (bool): a flag controlling whether to throw an
exception upon detecting uneven inputs (default: ``False``).
Example::
>>> import os
>>> import torch
>>> import torch.distributed as dist
>>> import torch.multiprocessing as mp
>>> # xdoctest: +SKIP
>>> import torch.nn.parallel.DistributedDataParallel as DDP
>>> import torch.distributed.optim.ZeroRedundancyOptimizer as ZeRO
>>> from torch.distributed.algorithms.join import Join
>>>
>>> # On each spawned worker
>>> def worker(rank):
>>> dist.init_process_group("nccl", rank=rank, world_size=2)
>>> model = DDP(torch.nn.Linear(1, 1).to(rank), device_ids=[rank])
>>> optim = ZeRO(model.parameters(), torch.optim.Adam, lr=0.01)
>>> # Rank 1 gets one more input than rank 0
>>> inputs = [torch.tensor([1.]).to(rank) for _ in range(10 + rank)]
>>> with Join([model, optim]):
>>> for input in inputs:
>>> loss = model(input).sum()
>>> loss.backward()
>>> optim.step()
>>> # All ranks reach here without hanging/erroring
"""
def __init__(
self,
joinables: List[Joinable],
enable: bool = True,
throw_on_early_termination: bool = False,
**kwargs,
):
if len(joinables) == 0:
raise ValueError("The join context manager requires at least one joinable")
self._joinables = joinables
self._join_hooks = [joinable.join_hook(**kwargs) for joinable in self._joinables]
self._enable = enable
self._throw_on_early_termination = throw_on_early_termination
self._set_joinable_configs()
self._extract_dist_info()
def _set_joinable_configs(self) -> None:
r"""Set the :class:`_JoinConfig` of each participating :class:`Joinable`."""
assert len(self._joinables) > 0
is_first_joinable = True
for joinable in self._joinables:
joinable._join_config = _JoinConfig(
enable=self._enable,
throw_on_early_termination=self._throw_on_early_termination,
is_first_joinable=is_first_joinable
)
is_first_joinable = False
def _extract_dist_info(self) -> None:
r"""
Extract the process group and device information from the joinables.
If there are multiple joinables, then the context manager uses the
first specified device.
Preconditions:
``self._joinables`` is not ``None`` and is non-empty.
Raises:
ValueError
If there are multiple conflicting ``process_group`` attributes
among the ``Joinable`` objects.
"""
process_group = None
device = None
for joinable in self._joinables:
if process_group is None:
process_group = joinable.join_process_group
elif process_group != joinable.join_process_group:
raise ValueError("Using join context manager with multiple process groups")
if device is None:
device = joinable.join_device
self._process_group = process_group
self._rank = dist.get_rank(self._process_group)
self._device = device
def __enter__(self):
...
def __exit__(
self,
type: Optional[Type[BaseException]],
value: Optional[BaseException],
traceback: Optional[TracebackType]
):
r"""
Repeatedly runs the main hooks until all processes join; then, runs the post-hooks.
Raises:
RuntimeError
If ``throw_on_early_termination=True``.
"""
if not self._enable or type:
return # propagate the exception directly if one was raised
all_procs_joined = False
is_last_joiner = True
i = 0
WARN_THRESHOLD = 1000
warnings.simplefilter("once")
while not all_procs_joined:
if i > WARN_THRESHOLD:
warnings.warn(
"Detected uneven input skew of greater than "
f"{WARN_THRESHOLD}. This means that rank "
f"{self._rank} has at least {WARN_THRESHOLD} "
f"fewer inputs than other currently-active ranks. "
"This level of skew could lead to performance "
"degradation during training."
)
# Shadow the all-reduce in non-joined processes
num_nonjoined_procs = self._get_num_nonjoined_procs()
if num_nonjoined_procs == 0:
all_procs_joined = True
else:
if self._throw_on_early_termination:
self._notify_procs_to_terminate()
# Run main hooks
for join_hook in self._join_hooks:
join_hook.main_hook()
is_last_joiner = False
i += 1
# Run post-hooks
for join_hook in self._join_hooks:
join_hook.post_hook(is_last_joiner)
def _get_num_nonjoined_procs(self):
r"""Return the number of non-joined processes by shadowing an all-reduce in the non-joined processes."""
num_nonjoined_procs = torch.zeros(1, device=self._device)
dist.all_reduce(num_nonjoined_procs, group=self._process_group)
return num_nonjoined_procs.item()
def _notify_procs_to_terminate(self):
r"""Schedule an all-reduce to notify non-joined processes to terminate.
Also raise a ``RuntimeError`` indicating that the current process has exhausted its inputs.
"""
ones = torch.ones(1, device=self._device)
dist.all_reduce(ones, group=self._process_group)
raise RuntimeError(f"Rank {self._rank} exhausted all inputs.")
[docs] @staticmethod
def notify_join_context(joinable: Joinable):
r"""
Notifies the join context manager that the calling process has not yet joined.
Then, if ``throw_on_early_termination=True``, checks if uneven inputs have been detected
(i.e. if one process has already joined) and throws an exception if so.
This method should be called from a :class:`Joinable` object before
its per-iteration collective communications. For example, this should
be called at the beginning of the forward pass in
:class:`DistributedDataParallel`.
Only the first :class:`Joinable` object passed into the context
manager performs the collective communications in this method, and
for the others, this method is vacuous.
Arguments:
joinable (Joinable): the :class:`Joinable` object calling this
method.
Returns:
An async work handle for the all-reduce meant to notify the context
manager that the process has not yet joined if ``joinable`` is the
first one passed into the context manager; ``None`` otherwise.
"""
assert hasattr(joinable, "_join_config"), \
f"Check that the {type(joinable)} constructor calls the " \
"``Joinable`` constructor"
join_config = joinable._join_config
# First joinable is responsible for the collective communications
if not join_config.is_first_joinable or not join_config.enable:
return None
device = joinable.join_device
process_group = joinable.join_process_group
# Schedule an all-reduce to indicate that the caller has not yet joined
ones = torch.ones(1, device=device)
work = dist.all_reduce(ones, group=process_group, async_op=True)
if join_config.throw_on_early_termination:
# Check if uneven inputs have been detected
zeros = torch.zeros(1, device=device)
dist.all_reduce(zeros, group=process_group)
should_throw = zeros.item()
if should_throw:
raise RuntimeError(
"Detected at least one rank that exhausted inputs. "
"Throwing across all ranks."
)
return work
