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d7e4a379a0
Summary:
Follow-ups after this PR:
* Remove `LossClosureOptimizer`, and merge `Optimizer` into `OptimizerBase` (and rename the merged class to Optimizer)
* Merge the LBFGS-specific serialize test function and the generic `test_serialize_optimizer` function, possibly by passing a bool `has_only_global_state` flag into the `test_serialize_optimizer` function to denote whether `size()` should be equal to 1 or 2?
* https://github.com/pytorch/pytorch/pull/34564#discussion_r393780303
* It seems that we don't have the equivalent `XORConvergence_LBFGS` test like the other optimizers, and it would be good to add one
* Remove mentions of `parameters_` in optimizer.cpp, de-virtualize all functions, and remove the `OptimizerBase(std::vector<Tensor> parameters)` constructor from `OptimizerBase`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34564
Test Plan: Imported from GitHub, without a `Test Plan:` line.
Differential Revision: D20495701
Pulled By: anjali411
fbshipit-source-id: 6d35286d2decb6f7dff93d9d3e57515770666622
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| .. | ||
| any.cpp | ||
| autograd.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| dispatch.cpp | ||
| enum.cpp | ||
| expanding-array.cpp | ||
| functional.cpp | ||
| init.cpp | ||
| init_baseline.h | ||
| init_baseline.py | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modulelist.cpp | ||
| modules.cpp | ||
| namespace.cpp | ||
| nn_utils.cpp | ||
| optim.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| ordered_dict.cpp | ||
| parallel.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialize.cpp | ||
| static.cpp | ||
| support.cpp | ||
| support.h | ||
| tensor.cpp | ||
| tensor_cuda.cpp | ||
| tensor_indexing.cpp | ||
| tensor_options.cpp | ||
| tensor_options_cuda.cpp | ||
| torch_include.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.