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e23a9dc140
Summary:
This PR refactors RNN / GRU / LSTM layers in C++ API to exactly match the implementation in Python API.
**BC-breaking changes:**
- Instead of returning `RNNOutput`, RNN / GRU forward method now returns `std::tuple<Tensor, Tensor>`, and LSTM forward method now returns `std::tuple<Tensor, std::tuple<Tensor, Tensor>>`, matching Python API.
- RNN / LSTM / GRU forward method now accepts the same inputs (input tensor and optionally hidden state), matching Python API.
- RNN / LSTM / GRU now has `forward_with_packed_input` method which accepts `PackedSequence` as input and optionally hidden state, matching the `forward(PackedSequence, ...)` variant in Python API.
- In `RNNOptions`
- `tanh()` / `relu()` / `activation` are removed. Instead, `nonlinearity` is added which takes either `torch::kTanh` or `torch::kReLU`
- `layers` -> `num_layers`
- `with_bias` -> `bias`
- In `LSTMOptions`
- `layers` -> `num_layers`
- `with_bias` -> `bias`
- In `GRUOptions`
- `layers` -> `num_layers`
- `with_bias` -> `bias`
The majority of the changes in this PR focused on refactoring the implementations in `torch/csrc/api/src/nn/modules/rnn.cpp` to match the Python API. RNN tests are then changed to reflected the revised API design.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34322
Differential Revision: D20311699
Pulled By: yf225
fbshipit-source-id: e2b60fc7bac64367a8434647d74c08568a7b28f7
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|---|---|---|
| .. | ||
| any.cpp | ||
| autograd.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| dispatch.cpp | ||
| enum.cpp | ||
| expanding-array.cpp | ||
| functional.cpp | ||
| init_baseline.h | ||
| init_baseline.py | ||
| init.cpp | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modulelist.cpp | ||
| modules.cpp | ||
| namespace.cpp | ||
| nn_utils.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.cpp | ||
| support.h | ||
| tensor_cuda.cpp | ||
| tensor_indexing.cpp | ||
| tensor_options_cuda.cpp | ||
| tensor_options.cpp | ||
| tensor.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.