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Summary:
This PR brings to changes to the recently landed C++ Frontend dataloader:
1. Removes the `size()` method from `BatchDataset`. This makes it cleaner to implement unsized ("infinite stream") datasets. The method was not used much beyond initial configuration.
2. Makes the index type of a dataset a template parameter of `BatchDataset` and `Sampler`. This essentially allows custom index types instead of only `vector<size_t>`. This greatly improves flexibility.
See the `InfiniteStreamDataset` and `TestIndex` datasets in the tests for what this enables.
Some additional minor updates and code movements too.
apaszke SsnL
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12960
Differential Revision: D12893342
Pulled By: goldsborough
fbshipit-source-id: ef03ea0f11a93319e81fba7d52a0ef1a125d3108
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| .. | ||
| any.cpp | ||
| CMakeLists.txt | ||
| cursor.cpp | ||
| dataloader.cpp | ||
| expanding-array.cpp | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modules.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| optim.cpp | ||
| ordered-dict.cpp | ||
| parallel.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialize.cpp | ||
| static.cpp | ||
| support.h | ||
| tensor_cuda.cpp | ||
| tensor_options_cuda.cpp | ||
| tensor_options.cpp | ||
| tensor.cpp | ||
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.