Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57879
_save_data() and _load_data() were designed as a protocol of data serialization of trainer client. As confirmed with kwanmacher and dreiss , they are not used. In addition, there's no plan to use them in Federated Learning flow. Remove them for now.
Test Plan: Imported from OSS
Reviewed By: kwanmacher
Differential Revision: D28306682
Pulled By: iseeyuan
fbshipit-source-id: 1b993ce4d78e372ae9b83bcbe496a196f9269d47
First, create a new test file. Test files should have be placed in this
directory, with a name that starts with test_, like test_foo.cpp.
In general a single test suite
Add your test file to the JIT_TEST_SRCS list in test/cpp/jit/CMakeLists.txt.
A test file may look like:
#include<gtest/gtest.h>usingnamespace::torch::jitTEST(FooTest,BarBaz){// ...
}// Append '_CUDA' to the test case name will automatically filter it out if CUDA
// is not compiled.
TEST(FooTest,NeedsAGpu_CUDA){// ...
}// Similarly, if only one GPU is detected, tests with `_MultiCUDA` at the end
// will not be run.
TEST(FooTest,NeedsMultipleGpus_MultiCUDA){// ...
}
Building and running the tests
The following commands assume you are in PyTorch root.
# ... Build PyTorch from source, e.g.
python setup.py develop
# (re)build just the binary
ninja -C build bin/test_jit
# run tests
build/bin/test_jit --gtest_filter='glob_style_filter*'