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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63878 See https://github.com/pytorch/pytorch/issues/64407, https://github.com/pytorch/pytorch/issues/62032 for context: In this PR: - Add boxed kernel by replicating `gen_inplace_or_view`'s logic that is ONLY for use with the Autograd not-implemented kernel - Unlike `gen_inplace_or_view` we always pass a view_func to as_view in order to ensure that an "derivative is not implemented" error is raised even if an in-place update is performed on the view. Without the `view_func`, the CopySlice + AsStridedBackward nodes would replace the NotImplemented node. - This limitation makes it impossible to use this node for general use - view relationship must be between first input (must be tensor) and first output (may be tensor or vec of tensor) - do not support non-differentiable views (_values, _indices, view.dtype) - view relationship is always fw and bw differentiable - Adds the macro `#define REGISTER_AUTOGRAD_NOT_IMPLEMENTED_FALLBACK(ns, op)` to be the interface for this feature: - static initialization can be slowed down(? not measured) if there are many registrations, because each line translates to 2 library calls but the workaround is just to manually use the two functions `AutogradNotImplementedFallback` and `ADInplaceOrViewFallback` and call `m.impl`. - Adds testing: - for views: view relationship created - performing in-place operation on the view, raises properly - trying to create two view relationships is not allowed, - single view relationship but not first input/first output should error - view relation created properly for tensor vector output - for in-place: - version count bump - triggers rebase_history - multiple mutations is okay and also updates version counter - TODO (follow up): Update tutorials for adding third-party operators (and document the above limitations) - TODO (follow up): Look at torch-audio/torch-vision and identify places where this can simplify existing code EDIT: Made it more clear what is introduced in this PR and moved some more contextual stuff into the issue itself Test Plan: Imported from OSS Reviewed By: albanD Differential Revision: D30901714 Pulled By: soulitzer fbshipit-source-id: 48de14c28be023ff4bd31b7ea5e7cba88aeee04c |
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| .. | ||
| any.cpp | ||
| autograd.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| dispatch.cpp | ||
| enum.cpp | ||
| expanding-array.cpp | ||
| fft.cpp | ||
| functional.cpp | ||
| grad_mode.cpp | ||
| imethod.cpp | ||
| inference_mode.cpp | ||
| init.cpp | ||
| init_baseline.h | ||
| init_baseline.py | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| meta_tensor.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| moduledict.cpp | ||
| modulelist.cpp | ||
| modules.cpp | ||
| namespace.cpp | ||
| nn_utils.cpp | ||
| operations.cpp | ||
| optim.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| ordered_dict.cpp | ||
| parallel.cpp | ||
| parallel_benchmark.cpp | ||
| parameterdict.cpp | ||
| parameterlist.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialize.cpp | ||
| special.cpp | ||
| static.cpp | ||
| support.cpp | ||
| support.h | ||
| tensor.cpp | ||
| tensor_cuda.cpp | ||
| tensor_flatten.cpp | ||
| tensor_indexing.cpp | ||
| tensor_options.cpp | ||
| tensor_options_cuda.cpp | ||
| torch_include.cpp | ||
| transformer.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.