Summary:
`at::ArrayRef` / `torch::IntArrayRef` should be discouraged in user code, because users might not be aware of the fact that it doesn't own the underlying data, which already leads to memory access bugs when they try to write the following:
```cpp
auto expected_sizes = torch::IntArrayRef({2, 16, 6}); // The memory that represents `{2, 16, 6}` is released after this line
ASSERT_EQ(output.sizes(), expected_sizes); // `expected_sizes` is pointing to invalid memory region
```
This PR changes all usage of `at::ArrayRef` and `torch::IntArrayRef` to the corresponding `std::vector` version, so that users won't pick up the habit of using `ArrayRef` by looking at the test code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27884
Differential Revision: D17921646
Pulled By: yf225
fbshipit-source-id: 461e79fc22b598aac230d36cc028085ce6cbe937
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:
Addresses https://github.com/pytorch/pytorch/issues/27048
PR Summary:
Files Added:
_torch/csrc/api/include/torch/nn/options/normalization.h
torch/csrc/api/include/torch/nn/functional/normalization.h_
Files Modified:
_test/cpp/api/functional.cpp
torch/csrc/api/include/torch/nn/functional.h_
---
yf225 : I couldn't find a C++ equivalent of gradcheck(), is there such a function or is it sufficient to call .backward() in the test body? I don't think any solutions are checked for the Python tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27280
Differential Revision: D17902109
Pulled By: yf225
fbshipit-source-id: 1bce1a88103d0f1848633fec90fde95ea8f3d1ed
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27177
Add support for F::one_hot C++ function.
Test Plan:
Added 3 new tests to verify API is working
Imported from OSS
Differential Revision: D17697934
fbshipit-source-id: a8127fb87c00daa119bb92a5702bc4bbba48290d
Summary:
This ensures that `F::cosine_similarity` and `F::pairwise_distance` can be used simply by including `torch/torch.h` and set `namespace F = torch::nn::functional`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26559
Differential Revision: D17507421
Pulled By: yf225
fbshipit-source-id: f895dde3634d5c8ca66ee036903e327e5cdab6b1
Summary:
This PR aims to re-organize C++ API `torch::nn` folder structure in the following way:
- Every module in `torch/csrc/api/include/torch/nn/modules/` (except `any.h`, `named_any.h`, `modulelist.h`, `sequential.h`, `embedding.h`) has a strictly equivalent Python file in `torch/nn/modules/`. For example:
`torch/csrc/api/include/torch/nn/modules/pooling.h` -> `torch/nn/modules/pooling.py`
`torch/csrc/api/include/torch/nn/modules/conv.h` -> `torch/nn/modules/conv.py`
`torch/csrc/api/include/torch/nn/modules/batchnorm.h` -> `torch/nn/modules/batchnorm.py`
`torch/csrc/api/include/torch/nn/modules/sparse.h` -> `torch/nn/modules/sparse.py`
- Containers such as `any.h`, `named_any.h`, `modulelist.h`, `sequential.h` are moved into `torch/csrc/api/include/torch/nn/modules/container/`, because their implementations are too long to be combined into one file (like `torch/nn/modules/container.py` in Python API)
- `embedding.h` is not renamed to `sparse.h` yet, because we have another work stream that works on API parity for Embedding and EmbeddingBag, and renaming the file would cause conflict. After the embedding API parity work is done, we will rename `embedding.h` to `sparse.h` to match the Python file name, and move the embedding options out to options/ folder.
- `torch/csrc/api/include/torch/nn/functional/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/functional/pooling.h` contains the functions for pooling, which are then used by the pooling modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`.
- `torch/csrc/api/include/torch/nn/options/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/options/pooling.h` contains MaxPoolOptions, which is used by both MaxPool modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`, and max_pool functions in `torch/csrc/api/include/torch/nn/functional/pooling.h`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26262
Differential Revision: D17422426
Pulled By: yf225
fbshipit-source-id: c413d2a374ba716dac81db31516619bbd879db7f
