Summary:
Hi yf225 , here is the C++ frontend API MultiMarginLoss implementation and tests https://github.com/pytorch/pytorch/issues/27198. Could you review it and tell me if it is okay?
I am not entirely sure I used `c10::optional` correctly, but `options.weight()` resulted in a compilation error, so I went with `options.weight().value()` instead of `value_or()` to follow the logic in `torch.nn._WeightedLoss.register_buffer` (where one can pass a `None` value).
Oh, and are the tests supposed to be skipped or did I do something wrong? I ran `pytest test/test_cpp_api_parity.py -k Loss -v` , and the `L1Loss` test passed but the others were skipped...
Thank you for the review in any case!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27424
Differential Revision: D17839963
Pulled By: yf225
fbshipit-source-id: f4b6012590cf22d56d42751c214df80cce717cb8
Summary:
added more variables to EmbeddingOptions and updated EmbeddingImpl reset, forward functions. Also added EmbeddingBag.
-----
This PR is BC-breaking in the following way:
Previously, `EmbeddingOptions` supports `count` and `dimension` as options arguments. After this PR, they are renamed to `num_embeddings` and `embedding_dim` respectively.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26358
Differential Revision: D17714337
Pulled By: yf225
fbshipit-source-id: f9f969c68e4bece106b92f8e2e02ac39c8455fb7
Summary:
This PR makes the following improvements:
1. Add `forward_with_indices` method to all C++ MaxPool modules, to return the max indices along with the outputs. (We can't make two `forward` methods that return different types based on input, because that will break the type deduction of `torch::detail::return_type_of_forward_t`)
2. Add `max_poolNd_with_indices` to `torch::nn::functional`, to be used when indices of the max values are needed. (We can't merge this with `torch::nn::functional::max_poolNd` because the return type of `max_poolNd` has to be defined statically).
3. Improve `pretty_print` of C++ MaxPoolNd and AvgPoolNd modules to match the Python `extra_repr`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26521
Differential Revision: D17507358
Pulled By: yf225
fbshipit-source-id: b6c0e2b27b38378cdc0c75f4bfc797b3c6b17cd9
Summary:
C++ `nn::Distance` tests can take advantage of the newly released multi-dimensional tensor constructor https://github.com/pytorch/pytorch/pull/26210 to simplify the tensor constructions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26539
Differential Revision: D17501041
Pulled By: yf225
fbshipit-source-id: 21d5f95ab3ec02227115c823c581218cee2ce458
Summary:
This PR adds Average Pool module to C++ front-end.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25800
Differential Revision: D17318094
Pulled By: yf225
fbshipit-source-id: c914c0e802bbe5f1d1f0a21a669c28bc956899db
Summary:
yf225 This is L1Loss module. I don't think that ```_Loss``` and ```_WeightedLoss``` as base Python classes do anything. First one sets reduction type and also takes in ```reduce``` parameter which is deprecated. The second one only registers ```weight``` parameter. I don't think that we should keep this structure. What do you think?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25902
Differential Revision: D17307045
Pulled By: yf225
fbshipit-source-id: ad3eda2ee8dcf4465054b376c1be89b39d11532f
Summary:
This PR simplifies header inclusion in `test/cpp/api/modules.cpp`, so that when we add a new `torch::nn` module and add the test in `modules.cpp`, we can check that the new module's header is included in `torch/torch.h`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25921
Differential Revision: D17303220
Pulled By: yf225
fbshipit-source-id: 327db0ff2f075d52e7b594b3dffc5a59441e0931
Summary:
Currently there is a mismatch in naming between Python BatchNorm `running_var` and C++ BatchNorm `running_variance`, which causes JIT model parameters loading to fail (https://github.com/pytorch/vision/pull/728#issuecomment-466067138):
```
terminate called after throwing an instance of 'c10::Error'
what(): No such serialized tensor 'running_variance' (read at /home/shahriar/Build/pytorch/torch/csrc/api/src/serialize/input-archive.cpp:27)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x85 (0x7f2d92d32f95 in /usr/local/lib/libc10.so)
frame #1: torch::serialize::InputArchive::read(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, at::Tensor&, bool) + 0xdeb (0x7f2d938551ab in /usr/local/lib/libtorch.so.1)
frame #2: torch::nn::Module::load(torch::serialize::InputArchive&) + 0x98 (0x7f2d9381cd08 in /usr/local/lib/libtorch.so.1)
frame #3: torch::nn::Module::load(torch::serialize::InputArchive&) + 0xf9 (0x7f2d9381cd69 in /usr/local/lib/libtorch.so.1)
frame #4: torch::nn::Module::load(torch::serialize::InputArchive&) + 0xf9 (0x7f2d9381cd69 in /usr/local/lib/libtorch.so.1)
frame #5: torch::nn::operator>>(torch::serialize::InputArchive&, std::shared_ptr<torch::nn::Module> const&) + 0x32 (0x7f2d9381c7b2 in /usr/local/lib/libtorch.so.1)
frame #6: <unknown function> + 0x2b16c (0x5645f4d1916c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
frame #7: <unknown function> + 0x27a3c (0x5645f4d15a3c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
frame #8: <unknown function> + 0x2165c (0x5645f4d0f65c in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
frame #9: <unknown function> + 0x1540b (0x5645f4d0340b in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
frame #10: __libc_start_main + 0xf3 (0x7f2d051dd223 in /usr/lib/libc.so.6)
frame #11: <unknown function> + 0x1381e (0x5645f4d0181e in /home/shahriar/Projects/CXX/build-TorchVisionTest-Desktop_Qt_5_12_1_GCC_64bit-Debug/TorchVisionTest)
```
Renaming C++ BatchNorm `running_variance` to `running_var` should fix this problem.
This is a BC-breaking change, but it should be easy for end user to rename `running_variance` to `running_var` in their call sites.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17371
Reviewed By: goldsborough
Differential Revision: D14172775
Pulled By: yf225
fbshipit-source-id: b9d3729ec79272a8084269756f28a8f7c4dd16b6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16751
This was made more complicated by the fact that ivalue::IntList
is a thing. So I had to fix all of the sites where we referring
to IValue post facto.
The following codemods were run, in this order:
```
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntList IntArrayRef
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntArrayRef::create IntList::create
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in ivalue::IntArrayRef ivalue::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in Tag::IntArrayRef Tag::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in isIntArrayRef isIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in toIntArrayRef toIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'Shared<IntArrayRef>' 'Shared<IntList>'
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'intrusive_ptr<IntArrayRef>' 'intrusive_ptr<IntList>'
```
Some manual fixups were done afterwards; they can be reviewed separately
at https://github.com/pytorch/pytorch/pull/16752
Reviewed By: dzhulgakov
Differential Revision: D13954363
fbshipit-source-id: b5c40aacba042402155a2f5a229fa6db7992ac64
Summary:
In Python, you can use the call operator to invoke the `forward()` method of a module. In C++ this was currently not possible, because I couldn't figure out how to deduce the return type of a module's `forward()` method under the constraint that `forward()` may not exist at all (since the base module class in C++ does not mandate a `forward()` method). I now figured it out, so the call operator can be used.
ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15831
Differential Revision: D13652676
Pulled By: goldsborough
fbshipit-source-id: ccab45a15215dda56460e560f0038781b539135f
Summary:
This is a pre-cursor diff to Python <-> C++ frontend integration -- I have a follow-up PR coming for that. This PR changes the C++ frontend module interface to replace the custom "cursor"s I introduced some time ago with `OrderedDict`. I introduced cursors at the time as a convenient way of applying functions and query operations on a modules' parameters, buffers and modules, allowing things like `module.parameters().map(my_func)`. However, I noticed that (1) this functionality is easily implement-able on top of a regular data structure and (2) more importantly, using OrderedDicts is much, much easier for Python integration. This is especially true given that ScriptModule today also uses OrderedDict. Since C++ frontend modules and ScriptModules will soon too share as many implementation details as possible, it is overall the best move to ditch the custom cursor datastructure and pervasively use OrderedDict everywhere.
For this I did:
1. Changed the C++ frontend module interface to more closely match the Python one by providing `parameters()`, `named_parameters()` and other methods Python provides. This is very important for the following diff which binds these into Python for inter-op with Python modules.
2. In lieu of the `Cursor::apply()` method I added `nn::Module::apply`. This again is one more unifying step between Python and C++, since Python modules have an apply function too.
3. Deleted all uses of Cursor.
4. Tidied and beefed up the `OrderedDict` class. In particular, I made `OrderedDict::Item` store an `std::pair` under the hood, because that is trivial to bind into Python and saved me a lot of headaches. `key` and `value` become methods instead of fields, which they should have been from the very start anyway because it allows exactly these kinds of changes, as per usual good software engineering principle of encapsulation.
5. Added many tests for the OrderedDict use in `nn::Module`.
ebetica ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13427
Differential Revision: D12894092
Pulled By: goldsborough
fbshipit-source-id: 715770c95a9643753a1db26d7f9da9a78619a15d
Summary:
In TorchScript and C++ extensions we currently advocate a mix of `torch::` and `at::` namespace usage. In the C++ frontend I had instead exported all symbols from `at::` and some from `c10::` into the `torch::` namespace. This is far, far easier for users to understand, and also avoid bugs around creating tensors vs. variables. The same should from now on be true for the TorchScript C++ API (for running and loading models) and all C++ extensions.
Note that since we're just talking about typedefs, this change does not break any existing code.
Once this lands I will update stuff in `pytorch/tutorials` too.
zdevito ezyang gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13523
Differential Revision: D12942787
Pulled By: goldsborough
fbshipit-source-id: 76058936bd8707b33d9e5bbc2d0705fc3d820763
