Summary:
Add torch::nn::BatchNorm1d function/module support for the C++ API.
torch::nn::BatchNorm{2,3}d will be added after this PR is merged.
Related Issue: https://github.com/pytorch/pytorch/issues/25883
Reviewer: yf225
I would like to discuss about below items.
* Necessity of `num_batches_tracked` in `BatchNormImplBase`
* `num_batches_tracked` is needed to calculate `momentum` when we do not feed `momentum` argument in Python API. But in C++ API, `momentum` argument has a default value.
* `num_batches_tracked` is only used for counting up `BatchNorm1d::foward()` call. I think it is no necessary for user anymore.
* The design of `BatchNorm{1,2,3}dOptions`
* We have already `BatchNormOptions` used for deprecated `BatchNorm` module. However, it is hard to use it for `BatchNorm{1,2,3}dOptions` because of the arguments disagreement of each modules.
* In this PR, I introduce `BatchNormOptionsv2` template class for the `BatchNorm{1,2,3}dOptions`. But I'm not sure this design is good or not.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28176
Differential Revision: D18196843
Pulled By: yf225
fbshipit-source-id: 667e2b5de4150d5776c41b9088c9e6c2ead24cd4
Summary:
https://github.com/pytorch/pytorch/issues/25883
I put grid_sample in vision.h with affine grid.
I have a question in string argument(interpolation mode, padding mode)
I reuse torch::native::detail::GridSamplerInterpolation in GridSampler.h instead of using string.
It follows the way that uses reduction enum in loss functions.
I am not sure this is right.
yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28354
Differential Revision: D18109333
Pulled By: yf225
fbshipit-source-id: 1bf972b671b107464f73b937bbe0de76fb259fbf
Summary:
This PR adds ```MSELoss```, ```KLDivLoss``` and ```BCELoss```. The tests for ```BCELoss``` fail with the following error:
```
unknown file: Failure
C++ exception with description "autograd_meta() INTERNAL ASSERT FAILED at /home/shahriar/Contrib/pytorch/c10/core/TensorImpl.h:533, please report a bug to PyTorch. set_requires_grad is not implemented for Tensor (set_requires_grad at /home/shahriar/Contrib/pytorch/c10/core/TensorImpl.h:533)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27156
Differential Revision: D17960323
Pulled By: yf225
fbshipit-source-id: 84b8431064f2f573679c03a8d7994e3e2f81a4d1
Summary:
Adds support for the Bilinear layer to the C++ frontend
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26082
Differential Revision: D17954148
Pulled By: yf225
fbshipit-source-id: 5e746bdea29b00e25969cd7a22044b8059b53687
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
