| import functools |
| |
| |
| def async_execution(fn): |
| r""" |
| A decorator for a function indicating that the return value of the function |
| is guaranteed to be a :class:`~torch.futures.Future` object and this |
| function can run asynchronously on the RPC callee. More specifically, the |
| callee extracts the :class:`~torch.futures.Future` returned by the wrapped |
| function and installs subsequent processing steps as a callback to that |
| :class:`~torch.futures.Future`. The installed callback will read the value |
| from the :class:`~torch.futures.Future` when completed and send the |
| value back as the RPC response. That also means the returned |
| :class:`~torch.futures.Future` only exists on the callee side and is never |
| sent through RPC. This decorator is useful when the wrapped function's |
| (``fn``) execution needs to pause and resume due to, e.g., containing |
| :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. |
| |
| .. note:: To enable asynchronous execution, applications must pass the |
| function object returned by this decorator to RPC APIs. If RPC detected |
| attributes installed by this decorator, it knows that this function |
| returns a ``Future`` object and will handle that accordingly. |
| However, this does not mean this decorator has to be outmost one when |
| defining a function. For example, when combined with ``@staticmethod`` |
| or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the |
| inner decorator to allow the target function be recognized as a static |
| or class function. This target function can still execute asynchronously |
| because, when accessed, the static or class method preserves attributes |
| installed by ``@rpc.functions.async_execution``. |
| |
| |
| Example:: |
| The returned :class:`~torch.futures.Future` object can come from |
| :meth:`~torch.distributed.rpc.rpc_async`, |
| :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` |
| constructor. The example below shows directly using the |
| :class:`~torch.futures.Future` returned by |
| :meth:`~torch.futures.Future.then`. |
| |
| >>> from torch.distributed import rpc |
| >>> |
| >>> # omitting setup and shutdown RPC |
| >>> |
| >>> # On all workers |
| >>> @rpc.functions.async_execution |
| >>> def async_add_chained(to, x, y, z): |
| >>> # This function runs on "worker1" and returns immediately when |
| >>> # the callback is installed through the `then(cb)` API. In the |
| >>> # mean time, the `rpc_async` to "worker2" can run concurrently. |
| >>> # When the return value of that `rpc_async` arrives at |
| >>> # "worker1", "worker1" will run the lambda function accordingly |
| >>> # and set the value for the previously returned `Future`, which |
| >>> # will then trigger RPC to send the result back to "worker0". |
| >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( |
| >>> lambda fut: fut.wait() + z |
| >>> ) |
| >>> |
| >>> # On worker0 |
| >>> # xdoctest: +SKIP |
| >>> ret = rpc.rpc_sync( |
| >>> "worker1", |
| >>> async_add_chained, |
| >>> args=("worker2", torch.ones(2), 1, 1) |
| >>> ) |
| >>> print(ret) # prints tensor([3., 3.]) |
| |
| When combined with TorchScript decorators, this decorator must be the |
| outmost one. |
| |
| >>> from torch import Tensor |
| >>> from torch.futures import Future |
| >>> from torch.distributed import rpc |
| >>> |
| >>> # omitting setup and shutdown RPC |
| >>> |
| >>> # On all workers |
| >>> @torch.jit.script |
| >>> def script_add(x: Tensor, y: Tensor) -> Tensor: |
| >>> return x + y |
| >>> |
| >>> @rpc.functions.async_execution |
| >>> @torch.jit.script |
| >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: |
| >>> return rpc.rpc_async(to, script_add, (x, y)) |
| >>> |
| >>> # On worker0 |
| >>> ret = rpc.rpc_sync( |
| >>> "worker1", |
| >>> async_add, |
| >>> args=("worker2", torch.ones(2), 1) |
| >>> ) |
| >>> print(ret) # prints tensor([2., 2.]) |
| |
| When combined with static or class method, this decorator must be the |
| inner one. |
| |
| >>> from torch.distributed import rpc |
| >>> |
| >>> # omitting setup and shutdown RPC |
| >>> |
| >>> # On all workers |
| >>> class AsyncExecutionClass: |
| >>> |
| >>> @staticmethod |
| >>> @rpc.functions.async_execution |
| >>> def static_async_add(to, x, y, z): |
| >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( |
| >>> lambda fut: fut.wait() + z |
| >>> ) |
| >>> |
| >>> @classmethod |
| >>> @rpc.functions.async_execution |
| >>> def class_async_add(cls, to, x, y, z): |
| >>> ret_fut = torch.futures.Future() |
| >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( |
| >>> lambda fut: ret_fut.set_result(fut.wait() + z) |
| >>> ) |
| >>> return ret_fut |
| >>> |
| >>> @rpc.functions.async_execution |
| >>> def bound_async_add(self, to, x, y, z): |
| >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( |
| >>> lambda fut: fut.wait() + z |
| >>> ) |
| >>> |
| >>> # On worker0 |
| >>> ret = rpc.rpc_sync( |
| >>> "worker1", |
| >>> AsyncExecutionClass.static_async_add, |
| >>> args=("worker2", torch.ones(2), 1, 2) |
| >>> ) |
| >>> print(ret) # prints tensor([4., 4.]) |
| >>> |
| >>> ret = rpc.rpc_sync( |
| >>> "worker1", |
| >>> AsyncExecutionClass.class_async_add, |
| >>> args=("worker2", torch.ones(2), 1, 2) |
| >>> ) |
| >>> print(ret) # prints tensor([4., 4.]) |
| |
| This decorator also works with RRef helpers, i.e., . |
| :meth:`torch.distributed.rpc.RRef.rpc_sync`, |
| :meth:`torch.distributed.rpc.RRef.rpc_async`, and |
| :meth:`torch.distributed.rpc.RRef.remote`. |
| |
| >>> from torch.distributed import rpc |
| >>> |
| >>> # reuse the AsyncExecutionClass class above |
| >>> rref = rpc.remote("worker1", AsyncExecutionClass) |
| >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) |
| >>> print(ret) # prints tensor([4., 4.]) |
| >>> |
| >>> rref = rpc.remote("worker1", AsyncExecutionClass) |
| >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() |
| >>> print(ret) # prints tensor([4., 4.]) |
| >>> |
| >>> rref = rpc.remote("worker1", AsyncExecutionClass) |
| >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() |
| >>> print(ret) # prints tensor([4., 4.]) |
| """ |
| @functools.wraps(fn) |
| def wrapper(*args, **kwargs): |
| return fn(*args, **kwargs) |
| # Can't declare and use attributes of function objects (mypy#2087) |
| wrapper._wrapped_async_rpc_function = fn # type: ignore[attr-defined] |
| return wrapper |
