Commit Graph

5 Commits

Author SHA1 Message Date
Oguz Ulgen
221350e3a4 Add None return type to init -- tests (#132352)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132352
Approved by: https://github.com/ezyang
ghstack dependencies: #132335, #132351
2024-08-01 15:44:51 +00:00
Xuehai Pan
046e88a291 [BE] [3/3] Rewrite super() calls in test (#94592)
Rewrite Python built-in class `super()` calls. Only non-semantic changes should be applied.

- #94587
- #94588
- #94592

Also, methods with only a `super()` call are removed:

```diff
class MyModule(nn.Module):
-   def __init__(self):
-       super().__init__()
-
    def forward(self, ...):
        ...
```

Some cases that change the semantics should be kept unchanged. E.g.:

f152a79be9/caffe2/python/net_printer.py (L184-L190)

f152a79be9/test/test_jit_fuser_te.py (L2628-L2635)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94592
Approved by: https://github.com/ezyang, https://github.com/seemethere
2023-02-12 22:20:53 +00:00
Larry Liu
e345138591 [retake2][mobile] Fix lightweight dispatch OOM error by introducing selective build (#80791)
To fix #78540 I committed #78983 which is reverted due to internal CI failure. Then I comitted #79215 which was only fixing the failure but didn't have the full feature of #78983. This PR is another try.

This PR adds script to dump all operators from test models and automatically write into `lightweight_dispatch_ops.yaml`. This way we don't have to manually update the yaml file.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80791
Approved by: https://github.com/raziel
2022-07-15 18:04:25 +00:00
Priya Ramani
600a01a082 Add test with multiple ops (#73888)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/73888

Test Plan: Imported from OSS

Reviewed By: larryliu0820

Differential Revision: D34702376

Pulled By: priyaramani

fbshipit-source-id: 500791fa0e708adda73a41db5c54c32c2629d78a
(cherry picked from commit 568872a84e92b2223f422bebb4a80b7d4957f94d)
2022-03-08 22:17:33 +00:00
Mengwei Liu
9ce9803abe [PyTorch] Add codegen unboxing ability (#69881)
Summary:
RFC: https://github.com/pytorch/rfcs/pull/40

This PR (re)introduces python codegen for unboxing wrappers. Given an entry of `native_functions.yaml` the codegen should be able to generate the corresponding C++ code to convert ivalues from the stack to their proper types. To trigger the codegen, run
```
tools/jit/gen_unboxing.py -d cg/torch/share/ATen
```

Merged changes on CI test. In https://github.com/pytorch/pytorch/issues/71782 I added an e2e test for static dispatch + codegen unboxing. The test exports a mobile model of mobilenetv2, load and run it on a new binary for lite interpreter: `test/mobile/custom_build/lite_predictor.cpp`.

## Lite predictor build specifics

1. Codegen: `gen.py` generates `RegisterCPU.cpp` and `RegisterSchema.cpp`. Now with this PR, once `static_dispatch` mode is enabled, `gen.py` will not generate `TORCH_LIBRARY` API calls in those cpp files, hence avoids interaction with the dispatcher. Once `USE_LIGHTWEIGHT_DISPATCH` is turned on, `cmake/Codegen.cmake` calls `gen_unboxing.py` which generates `UnboxingFunctions.h`, `UnboxingFunctions_[0-4].cpp` and `RegisterCodegenUnboxedKernels_[0-4].cpp`.
2. Build: `USE_LIGHTWEIGHT_DISPATCH` adds generated sources into `all_cpu_cpp` in `aten/src/ATen/CMakeLists.txt`. All other files remain unchanged. In reality all the `Operators_[0-4].cpp` are not necessary but we can rely on linker to strip them off.

## Current CI job test coverage update

Created a new CI job `linux-xenial-py3-clang5-mobile-lightweight-dispatch-build` that enables the following build options:
* `USE_LIGHTWEIGHT_DISPATCH=1`
* `BUILD_LITE_INTERPRETER=1`
* `STATIC_DISPATCH_BACKEND=CPU`

This job triggers `test/mobile/lightweight_dispatch/build.sh` and builds `libtorch`. Then the script runs C++ tests written in `test_lightweight_dispatch.cpp` and `test_codegen_unboxing.cpp`. Recent commits added tests to cover as many C++ argument type as possible: in `build.sh` we installed PyTorch Python API so that we can export test models in `tests_setup.py`. Then we run C++ test binary to run these models on lightweight dispatch enabled runtime.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/69881

Reviewed By: iseeyuan

Differential Revision: D33692299

Pulled By: larryliu0820

fbshipit-source-id: 211e59f2364100703359b4a3d2ab48ca5155a023
(cherry picked from commit 58e1c9a25e3d1b5b656282cf3ac2f548d98d530b)
2022-03-01 23:28:13 +00:00