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import sys
import torch
def is_available():
return hasattr(torch._C, "_dist_autograd_init")
if is_available() and not torch._C._dist_autograd_init():
raise RuntimeError("Failed to initialize torch.distributed.autograd")
if is_available():
from torch._C._distributed_autograd import (
get_gradients,
backward,
_init,
_new_context,
_release_context,
_get_max_id,
_is_valid_context,
_retrieve_context,
_current_context,
_get_debug_info,
DistAutogradContext,
)
class context(object):
'''
Context object to wrap forward and backward passes when using
distributed autograd. The ``context_id`` generated in the ``with``
statement is required to uniquely identify a distributed backward pass
on all workers. Each worker stores metadata associated with this
``context_id``, which is required to correctly execute a distributed
autograd pass.
Example::
>>> import torch.distributed.autograd as dist_autograd
>>> # xdoctest: +SKIP
>>> with dist_autograd.context() as context_id:
>>> t1 = torch.rand((3, 3), requires_grad=True)
>>> t2 = torch.rand((3, 3), requires_grad=True)
>>> loss = rpc.rpc_sync("worker1", torch.add, args=(t1, t2)).sum()
>>> dist_autograd.backward(context_id, [loss])
'''
def __enter__(self):
self.autograd_context = _new_context()
return self.autograd_context._context_id()
def __exit__(self, type, value, traceback):
_release_context(self.autograd_context._context_id())
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