Files
pytorch/test/cpp/tensorexpr/README.md
Mikhail Zolotukhin 1a4f997178 [TensorExpr] Add a class for representing data type. (#33217)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33217

Test Plan: Imported from OSS

Differential Revision: D19848380

Pulled By: ZolotukhinM

fbshipit-source-id: d8683f8fc4555d2456cd2a7c827d8e8231915b49
2020-02-21 13:10:12 -08:00

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# TensorExpr C++ Tests
## How to add a new test
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`.
Here is an example test file you can copy-paste.
```cpp
#include <test/cpp/tensorexpr/test_base.h>
// Tests go in torch::jit
namespace torch {
namespace jit {
// 1. Test cases are void() functions.
// 2. They start with the prefix `test`
void testCaseOne() {
// ...
}
void testCaseTwo() {
// ...
}
}
}
```
Then, register your test in `tests.h`:
```cpp
// Add to TH_FORALL_TESTS_CUDA instead for CUDA-requiring tests
#define TH_FORALL_TESTS(_) \
_(ADFormulas) \
_(Attributes) \
...
_(CaseOne) // note that the `test` prefix is omitted.
_(CaseTwo)
```
We glob all the test files together in `CMakeLists.txt` so that you don't
have to edit it every time you add a test. Unfortunately, this means that in
order to get the build to pick up your new test file, you need to re-run
cmake:
```
python setup.py build --cmake
```
## How do I run the tests?
The following commands assume you are in PyTorch root.
```bash
# (re)build the test binary
ninja build/bin/test_tensorexpr
# run
build/bin/test_tensorexpr --gtest_filter='glob_style_filter*'
```