Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53670
This puts deploy into the torch::deploy namespace. It also renames some
objects to better match their behavior:
PythonObject -> Obj, in the future it will refer to either a python object or a handle to a script obj, so rename it torch::deploy::Obj to be generic
MovableObject -> ReplicatedObj, to prevent confusion with "std::move" which is unrelated, and to note that we are replicating this object across interpreters.
Test Plan: Imported from OSS
Reviewed By: wconstab
Differential Revision: D26932131
Pulled By: zdevito
fbshipit-source-id: 8041d6c5b2041a7c3192c1a17d2edb38112a89f3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51754
This API allows you to manage multiple python interpreters in a single
process to deploy PyTorch models packaged with torch.package.
torch/csrc/deploy/deploy.h contains the API definition
torch/csrc/deploy/test_deploy.cpp has some examples.
Notes:
* mutex is added to PyTorchStreamReader to make it safe to use from multiple threads at once.
* USE_DEPLOY is only true for the special libtorch_deployinterpreter.so library, when enabled
we use a hash table to maintain PyObject <> at::Tensor mappping rather than the internal pointer
in Tensor since >1 interpreter may have a reference to the tensor.
* serialization.py has some additional functions for creating pickle objects
but keeping storages in memory for use transfering tensors between interpreters
Test Plan: Imported from OSS
Reviewed By: wconstab
Differential Revision: D26329468
Pulled By: zdevito
fbshipit-source-id: d75f4ebb9a27f1d911179d9996041bcb3ca04a07
