dccd0f2de6
Summary:
Linting `torch/csrc/` (non-recursive) and `torch/csrc/autograd` (non-recursive).
Fixed things like:
- `typedef` vs `using`
- Use `.empty()` instead of comparing with empty string/using `.size() == 0`
- Use range for loops instead of old style loops (`modernize-`)
- Remove some `virtual` + `override`
- Replace `stdint.h` with `cstdint`
- Replace `return Type(x, y)` with `return {x, y}`
- Use boolean values (`true`/`false`) instead of numbers (1/0)
- More ...
ezyang apaszke cpuhrsch
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11050
Differential Revision: D9597505
Pulled By: goldsborough
fbshipit-source-id: cb0fb4793ade885a8dbf4b10484487b84c64c7f2
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|---|---|---|
| .. | ||
| any.cpp | ||
| cursor.cpp | ||
| integration.cpp | ||
| main.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modules.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| optim.cpp | ||
| parallel.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialization.cpp | ||
| static.cpp | ||
| tensor_cuda.cpp | ||
| tensor_options_cuda.cpp | ||
| tensor_options.cpp | ||
| tensor.cpp | ||
| util.h | ||
C++ API Tests
In this folder live the tests for PyTorch's C++ API (formerly known as autogradpp). They use the Catch2 test framework.
CUDA Tests
The way we handle CUDA tests is by separating them into a separate TEST_CASE
(e.g. we have optim and optim_cuda test cases in optim.cpp), and giving
them the [cuda] tag. Then, inside main.cpp we detect at runtime whether
CUDA is available. If not, we disable these CUDA tests by appending ~[cuda]
to the test specifications. The ~ disables the tag.
One annoying aspect is that Catch only allows filtering on test cases and not
sections. Ideally, one could have a section like LSTM inside the RNN test
case, and give this section a [cuda] tag to only run it when CUDA is
available. Instead, we have to create a whole separate RNN_cuda test case and
put all these CUDA sections in there.
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