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