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Pavithran Ramachandran f984e50f39 Extend jit::load to work on flatbuffer file; Take 2 (#75256 ) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/75256 ghstack-source-id: 153138970 Test Plan: CI Reviewed By: iseeyuan Differential Revision: D35399581 fbshipit-source-id: dafe9d301009d3f70986ed92bfe06d160ab90ba0 (cherry picked from commit ccc860fd07946de5aae12bc179a0b8bbba83b997)		2022-04-06 17:54:01 +00:00
..
any.cpp
autograd.cpp
CMakeLists.txt
dataloader.cpp	Fix sign-compare violations in cpp tests	2022-04-04 23:05:31 +00:00
dispatch.cpp
enum.cpp
expanding-array.cpp
fft.cpp
functional.cpp
grad_mode.cpp
imethod.cpp
inference_mode.cpp
init.cpp	Fix sign-compare violations in cpp tests	2022-04-04 23:05:31 +00:00
init_baseline.h
init_baseline.py
integration.cpp
jit.cpp
memory.cpp
meta_tensor.cpp
misc.cpp
module.cpp
moduledict.cpp
modulelist.cpp
modules.cpp
namespace.cpp
nn_utils.cpp	Fix sign-compare violations in cpp tests	2022-04-04 23:05:31 +00:00
operations.cpp
optim.cpp
optim_baseline.h
optim_baseline.py
ordered_dict.cpp
parallel.cpp
parallel_benchmark.cpp
parameterdict.cpp	Fix sign-compare violations in cpp tests	2022-04-04 23:05:31 +00:00
parameterlist.cpp
README.md
rnn.cpp
sequential.cpp
serialize.cpp	Extend jit::load to work on flatbuffer file; Take 2 (#75256 )	2022-04-06 17:54:01 +00:00
special.cpp
static.cpp
support.cpp
support.h
tensor.cpp
tensor_cuda.cpp
tensor_flatten.cpp
tensor_indexing.cpp
tensor_options.cpp
tensor_options_cuda.cpp
torch_include.cpp
transformer.cpp

README.md

C++ Frontend Tests

In this folder live the tests for PyTorch's C++ Frontend. They use the GoogleTest test framework.

CUDA Tests

To make a test runnable only on platforms with CUDA, you should suffix your test with _CUDA, e.g.

TEST(MyTestSuite, MyTestCase_CUDA) { }

To make it runnable only on platforms with at least two CUDA machines, suffix it with _MultiCUDA instead of _CUDA, e.g.

TEST(MyTestSuite, MyTestCase_MultiCUDA) { }

There is logic in main.cpp that detects the availability and number of CUDA devices and supplies the appropriate negative filters to GoogleTest.

Integration Tests

Integration tests use the MNIST dataset. You must download it by running the following command from the PyTorch root folder:

$ python tools/download_mnist.py -d test/cpp/api/mnist

The required paths will be referenced as test/cpp/api/mnist/... in the test code, so you must run the integration tests from the PyTorch root folder.