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<h1>Source code for torch.distributed.rpc.functions</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">functools</span>
<div class="viewcode-block" id="async_execution"><a class="viewcode-back" href="../../../../rpc.html#torch.distributed.rpc.functions.async_execution">[docs]</a><span class="k">def</span> <span class="nf">async_execution</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> A decorator for a function indicating that the return value of the function</span>
<span class="sd"> is guaranteed to be a :class:`~torch.futures.Future` object and this</span>
<span class="sd"> function can run asynchronously on the RPC callee. More specifically, the</span>
<span class="sd"> callee extracts the :class:`~torch.futures.Future` returned by the wrapped</span>
<span class="sd"> function and installs subsequent processing steps as a callback to that</span>
<span class="sd"> :class:`~torch.futures.Future`. The installed callback will read the value</span>
<span class="sd"> from the :class:`~torch.futures.Future` when completed and send the</span>
<span class="sd"> value back as the RPC response. That also means the returned</span>
<span class="sd"> :class:`~torch.futures.Future` only exists on the callee side and is never</span>
<span class="sd"> sent through RPC. This decorator is useful when the wrapped function's</span>
<span class="sd"> (``fn``) execution needs to pause and resume due to, e.g., containing</span>
<span class="sd"> :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.</span>
<span class="sd"> .. note:: To enable asynchronous execution, applications must pass the</span>
<span class="sd"> function object returned by this decorator to RPC APIs. If RPC detected</span>
<span class="sd"> attributes installed by this decorator, it knows that this function</span>
<span class="sd"> returns a ``Future`` object and will handle that accordingly.</span>
<span class="sd"> However, this does not mean this decorator has to be outmost one when</span>
<span class="sd"> defining a function. For example, when combined with ``@staticmethod``</span>
<span class="sd"> or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the</span>
<span class="sd"> inner decorator to allow the target function be recognized as a static</span>
<span class="sd"> or class function. This target function can still execute asynchronously</span>
<span class="sd"> because, when accessed, the static or class method preserves attributes</span>
<span class="sd"> installed by ``@rpc.functions.async_execution``.</span>
<span class="sd"> Example::</span>
<span class="sd"> The returned :class:`~torch.futures.Future` object can come from</span>
<span class="sd"> :meth:`~torch.distributed.rpc.rpc_async`,</span>
<span class="sd"> :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`</span>
<span class="sd"> constructor. The example below shows directly using the</span>
<span class="sd"> :class:`~torch.futures.Future` returned by</span>
<span class="sd"> :meth:`~torch.futures.Future.then`.</span>
<span class="sd"> >>> from torch.distributed import rpc</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # omitting setup and shutdown RPC</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # On all workers</span>
<span class="sd"> >>> @rpc.functions.async_execution</span>
<span class="sd"> >>> def async_add_chained(to, x, y, z):</span>
<span class="sd"> >>> # This function runs on "worker1" and returns immediately when</span>
<span class="sd"> >>> # the callback is installed through the `then(cb)` API. In the</span>
<span class="sd"> >>> # mean time, the `rpc_async` to "worker2" can run concurrently.</span>
<span class="sd"> >>> # When the return value of that `rpc_async` arrives at</span>
<span class="sd"> >>> # "worker1", "worker1" will run the lambda function accordingly</span>
<span class="sd"> >>> # and set the value for the previously returned `Future`, which</span>
<span class="sd"> >>> # will then trigger RPC to send the result back to "worker0".</span>
<span class="sd"> >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then(</span>
<span class="sd"> >>> lambda fut: fut.wait() + z</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # On worker0</span>
<span class="sd"> >>> # xdoctest: +SKIP</span>
<span class="sd"> >>> ret = rpc.rpc_sync(</span>
<span class="sd"> >>> "worker1",</span>
<span class="sd"> >>> async_add_chained,</span>
<span class="sd"> >>> args=("worker2", torch.ones(2), 1, 1)</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>> print(ret) # prints tensor([3., 3.])</span>
<span class="sd"> When combined with TorchScript decorators, this decorator must be the</span>
<span class="sd"> outmost one.</span>
<span class="sd"> >>> from torch import Tensor</span>
<span class="sd"> >>> from torch.futures import Future</span>
<span class="sd"> >>> from torch.distributed import rpc</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # omitting setup and shutdown RPC</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # On all workers</span>
<span class="sd"> >>> @torch.jit.script</span>
<span class="sd"> >>> def script_add(x: Tensor, y: Tensor) -> Tensor:</span>
<span class="sd"> >>> return x + y</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @rpc.functions.async_execution</span>
<span class="sd"> >>> @torch.jit.script</span>
<span class="sd"> >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:</span>
<span class="sd"> >>> return rpc.rpc_async(to, script_add, (x, y))</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # On worker0</span>
<span class="sd"> >>> ret = rpc.rpc_sync(</span>
<span class="sd"> >>> "worker1",</span>
<span class="sd"> >>> async_add,</span>
<span class="sd"> >>> args=("worker2", torch.ones(2), 1)</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>> print(ret) # prints tensor([2., 2.])</span>
<span class="sd"> When combined with static or class method, this decorator must be the</span>
<span class="sd"> inner one.</span>
<span class="sd"> >>> from torch.distributed import rpc</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # omitting setup and shutdown RPC</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # On all workers</span>
<span class="sd"> >>> class AsyncExecutionClass:</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> @rpc.functions.async_execution</span>
<span class="sd"> >>> def static_async_add(to, x, y, z):</span>
<span class="sd"> >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then(</span>
<span class="sd"> >>> lambda fut: fut.wait() + z</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @classmethod</span>
<span class="sd"> >>> @rpc.functions.async_execution</span>
<span class="sd"> >>> def class_async_add(cls, to, x, y, z):</span>
<span class="sd"> >>> ret_fut = torch.futures.Future()</span>
<span class="sd"> >>> rpc.rpc_async(to, torch.add, args=(x, y)).then(</span>
<span class="sd"> >>> lambda fut: ret_fut.set_result(fut.wait() + z)</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>> return ret_fut</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @rpc.functions.async_execution</span>
<span class="sd"> >>> def bound_async_add(self, to, x, y, z):</span>
<span class="sd"> >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then(</span>
<span class="sd"> >>> lambda fut: fut.wait() + z</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # On worker0</span>
<span class="sd"> >>> ret = rpc.rpc_sync(</span>
<span class="sd"> >>> "worker1",</span>
<span class="sd"> >>> AsyncExecutionClass.static_async_add,</span>
<span class="sd"> >>> args=("worker2", torch.ones(2), 1, 2)</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>> print(ret) # prints tensor([4., 4.])</span>
<span class="sd"> >>></span>
<span class="sd"> >>> ret = rpc.rpc_sync(</span>
<span class="sd"> >>> "worker1",</span>
<span class="sd"> >>> AsyncExecutionClass.class_async_add,</span>
<span class="sd"> >>> args=("worker2", torch.ones(2), 1, 2)</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>> print(ret) # prints tensor([4., 4.])</span>
<span class="sd"> This decorator also works with RRef helpers, i.e., .</span>
<span class="sd"> :meth:`torch.distributed.rpc.RRef.rpc_sync`,</span>
<span class="sd"> :meth:`torch.distributed.rpc.RRef.rpc_async`, and</span>
<span class="sd"> :meth:`torch.distributed.rpc.RRef.remote`.</span>
<span class="sd"> >>> from torch.distributed import rpc</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # reuse the AsyncExecutionClass class above</span>
<span class="sd"> >>> rref = rpc.remote("worker1", AsyncExecutionClass)</span>
<span class="sd"> >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)</span>
<span class="sd"> >>> print(ret) # prints tensor([4., 4.])</span>
<span class="sd"> >>></span>
<span class="sd"> >>> rref = rpc.remote("worker1", AsyncExecutionClass)</span>
<span class="sd"> >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()</span>
<span class="sd"> >>> print(ret) # prints tensor([4., 4.])</span>
<span class="sd"> >>></span>
<span class="sd"> >>> rref = rpc.remote("worker1", AsyncExecutionClass)</span>
<span class="sd"> >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()</span>
<span class="sd"> >>> print(ret) # prints tensor([4., 4.])</span>
<span class="sd"> """</span>
<span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">fn</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">wrapper</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">return</span> <span class="n">fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="c1"># Can't declare and use attributes of function objects (mypy#2087)</span>
<span class="n">wrapper</span><span class="o">.</span><span class="n">_wrapped_async_rpc_function</span> <span class="o">=</span> <span class="n">fn</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="k">return</span> <span class="n">wrapper</span></div>
</pre></div>
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