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# Generic Join Context Manager

The generic join context manager facilitates distributed training on uneven inputs. This page outlines the API of the relevant classes: `Join`, `Joinable`, and `JoinHook`. For a tutorial, see Distributed Training with Uneven Inputs Using the Join Context Manager.

class torch.distributed.algorithms.Join(joinables, enable=True, throw_on_early_termination=False, **kwargs)

  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 `JoinHook` for details about the hook definition.

  Warning

  The context manager requires each participating `Joinable` to call the method `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 `JoinHook` objects are the same. If there are multiple `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.

  Parameters:

    - joinables (List[Joinable]) – a list of the participating `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
      >>> 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

  static notify_join_context(joinable)

    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 `Joinable` object before its per-iteration collective communications. For example, this should be called at the beginning of the forward pass in `DistributedDataParallel`.

    Only the first `Joinable` object passed into the context manager performs the collective communications in this method, and for the others, this method is vacuous.

    Parameters:

      joinable (Joinable) – the `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.

class torch.distributed.algorithms.Joinable

  This defines an abstract base class for joinable classes. A joinable class (inheriting from `Joinable`) should implement `join_hook()`, which returns a `JoinHook` instance, in addition to `join_device()` and `join_process_group()` that return device and process group information, respectively.

  abstract property join_device: device

    Returns the device from which to perform collective communications needed by the join context manager implementation itself.

  abstract join_hook(**kwargs)

    Returns a `JoinHook` instance for the given `Joinable`.

    Parameters:

      kwargs (dict) – a `dict` containing any keyword arguments to modify the behavior of the join hook at run time; all `Joinable` instances sharing the same join context manager are forwarded the same value for `kwargs`.

    Return type:

      JoinHook

  abstract property join_process_group: Any

    Returns the process group for the collective communications needed by the join context manager itself.

class torch.distributed.algorithms.JoinHook

  This defines a join hook, which provides two entry points in the join context manager: 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 `JoinHook` and override `main_hook()` and `post_hook()` as appropriate.

  main_hook()

    This hook is called repeatedly while there exists a non-joined process to shadow collective communications in one training iteration (i.e. in one forward pass, backward pass, and optimizer step).

  post_hook(is_last_joiner)

    This hook is called 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.

    Parameters:

      is_last_joiner (bool) – `True` if the rank is one of the last to join; `False` otherwise.

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