Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65861
First in a series. This PR changes the code in deploy.h/cpp and
interpreter_impl.h/cpp to be camel case instead of snake case. Starting
with this as it has the most impact on downstream users.
Test Plan: Imported from OSS
Reviewed By: shannonzhu
Differential Revision: D31291183
Pulled By: suo
fbshipit-source-id: ba6f74042947c9a08fb9cb3ad7276d8dbb5b2934
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63380
Crosses off some more of #62011, see the test in the stacked PR #63381
Test Plan: Imported from OSS
Reviewed By: malfet, seemethere
Differential Revision: D30455843
Pulled By: driazati
fbshipit-source-id: d473545d05ffa0b2476968f0b1c55f3a16a2c755
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
This PR suppresses clang-tidy warnings in the codebase (for now) so that we can re-enable clang-tidy checks on master.
I ran this script to add the `NOLINTNEXTLINE` comments (on a devserver):
```bash
python3 setup.py develop
# Uses same script that's run on CI and adds the -j (parallel), -s (add comments), -k (continue if diagnostic errors are found) options
python3 tools/clang_tidy.py \
-j \
-s \
-k \
-v \
--paths torch/csrc/ \
-g"-torch/csrc/jit/passes/onnx/helper.cpp" \
-g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \
-g"-torch/csrc/jit/serialization/onnx.cpp" \
-g"-torch/csrc/jit/serialization/export.cpp" \
-g"-torch/csrc/jit/serialization/import.cpp" \
-g"-torch/csrc/jit/serialization/import_legacy.cpp" \
-g"-torch/csrc/onnx/init.cpp" \
-g"-torch/csrc/cuda/nccl.*" \
-g"-torch/csrc/cuda/python_nccl.cpp" \
-g"-torch/csrc/autograd/FunctionsManual.cpp" \
-g"-torch/csrc/generic/*.cpp" \
-g"-torch/csrc/jit/codegen/cuda/runtime/*" \
-g"-torch/csrc/deploy/interpreter/interpreter.cpp" \
-g"-torch/csrc/deploy/interpreter/interpreter.h" \
-g"-torch/csrc/deploy/interpreter/interpreter_impl.h" \
-g"-torch/csrc/deploy/interpreter/test_main.cpp"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60649
Test Plan: Verified changes by re-running the script (without the `-s` option) and seeing no warnings/errors.
Reviewed By: walterddr, janeyx99
Differential Revision: D29504258
Pulled By: 1ntEgr8
fbshipit-source-id: 78310b30ee8213b73ddb4771ad874665323e7a4e
Summary:
Switches most of the simple for loops outside of `jit` directories to use `c10::irange`.
Generated with D28874212.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59481
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D28909681
fbshipit-source-id: ec9ab1bd602933238d9d0f73d4d8d027b75d9d85
Summary:
In my last PR I've missed CUDA and distributed folders, fixing this now
This change is autogenerated by `python tool/clang_tidy.py -s`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57235
Reviewed By: janeyx99
Differential Revision: D28084444
Pulled By: malfet
fbshipit-source-id: bf222f69ee90c7872c3cb0931e8cdb84f0cb3cda
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
