Summary:
In accordance with https://github.com/pytorch/pytorch/issues/25883, I added the `MultiLabelSoftMarginLoss` module and `multilabel_soft_margin_loss` functional.
It looks like there isn't a C++ ATen implementation of `multilabel_soft_margin_loss`, so I translated the python version, which does not rely on a C/C++ backend either.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27669
Differential Revision: D17907608
Pulled By: yf225
fbshipit-source-id: ccb02951e009973c2adbe604593ce929f10c39eb
Summary:
Hi yf225 , I had to create a new branch to tackle merge conflict since I am using cloud due to some limitations on my PC. Therefore, I don't have enough command there.
Also, I have incorporated the changes you have put before here
https://github.com/pytorch/pytorch/pull/27613
Also, it would be great if you could recommend me some resources to work smmothly on GCP..:-D
Thank you
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27713
Differential Revision: D17899695
Pulled By: yf225
fbshipit-source-id: eb6643223148774a5cbbd093bdcc5623872e5bba
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
