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"""Async API. |
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This module contains the API for parallelism in TorchScript, notably: |
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* torch.jit.fork |
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* torch.jit.wait |
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This is not intended to be imported directly; please use the exposed |
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functionalities in `torch.jit`. |
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""" |
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import torch |
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from torch._jit_internal import Future |
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from torch.jit._builtins import _register_builtin |
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from torch.utils import set_module |
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set_module(Future, "torch.jit") |
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def fork(func, *args, **kwargs): |
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r""" |
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Create an asynchronous task executing `func` and a reference to the value of the result of this execution. |
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`fork` will return immediately, so the return value of `func` may not have been computed yet. To force completion |
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of the task and access the return value invoke `torch.jit.wait` on the Future. `fork` invoked |
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with a `func` which returns `T` is typed as `torch.jit.Future[T]`. `fork` calls can be arbitrarily |
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nested, and may be invoked with positional and keyword arguments. |
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Asynchronous execution will only occur when run in TorchScript. If run in pure python, |
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`fork` will not execute in parallel. `fork` will also not execute in parallel when invoked |
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while tracing, however the `fork` and `wait` calls will be captured in the exported IR Graph. |
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.. warning:: |
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`fork` tasks will execute non-deterministically. We recommend only spawning |
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parallel fork tasks for pure functions that do not modify their inputs, |
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module attributes, or global state. |
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Args: |
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func (callable or torch.nn.Module): A Python function or `torch.nn.Module` |
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that will be invoked. If executed in TorchScript, it will execute asynchronously, |
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otherwise it will not. Traced invocations of fork will be captured in the IR. |
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``*args``, ``**kwargs``: arguments to invoke `func` with. |
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Returns: |
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`torch.jit.Future[T]`: a reference to the execution of `func`. The value `T` |
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can only be accessed by forcing completion of `func` through `torch.jit.wait`. |
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Example (fork a free function): |
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.. code-block:: python |
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import torch |
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from torch import Tensor |
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def foo(a: Tensor, b: int) -> Tensor: |
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return a + b |
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def bar(a): |
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fut: torch.jit.Future[Tensor] = torch.jit.fork(foo, a, b=2) |
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return torch.jit.wait(fut) |
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script_bar = torch.jit.script(bar) |
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input = torch.tensor(2) |
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# only the scripted version executes asynchronously |
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assert script_bar(input) == bar(input) |
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# trace is not run asynchronously, but fork is captured in IR |
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graph = torch.jit.trace(bar, (input,)).graph |
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assert "fork" in str(graph) |
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Example (fork a module method): |
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.. code-block:: python |
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import torch |
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from torch import Tensor |
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class AddMod(torch.nn.Module): |
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def forward(self, a: Tensor, b: int): |
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return a + b |
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class Mod(torch.nn.Module): |
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def __init__(self) -> None: |
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super(self).__init__() |
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self.mod = AddMod() |
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def forward(self, input): |
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fut = torch.jit.fork(self.mod, a, b=2) |
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return torch.jit.wait(fut) |
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input = torch.tensor(2) |
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mod = Mod() |
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assert mod(input) == torch.jit.script(mod).forward(input) |
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""" |
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return torch._C.fork(func, *args, **kwargs) |
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def wait(future): |
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r""" |
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Force completion of a `torch.jit.Future[T]` asynchronous task, returning the result of the task. |
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See :func:`~fork` for docs and examples. |
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Args: |
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future (torch.jit.Future[T]): an asynchronous task reference, created through `torch.jit.fork` |
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Returns: |
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`T`: the return value of the completed task |
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""" |
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return torch._C.wait(future) |
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_register_builtin(wait, "aten::wait") |
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