Summary:
* Deletes all weak script decorators / associated data structures / methods
* In order to keep supporting the standard library in script, this enables recursive script on any function defined in `torch.nn`
* Most changes in `torch/nn` are the result of `ag -Q "weak" torch/nn/ -l | xargs sed -i '/weak/d'`, only `rnn.py` needed manual editing to use the `ignore` and `export` to continue supporting the overloaded `forward` methods
* `Sequential`/`ModuleList` no longer need to be added to constants since they are compiled on demand
This should also fix https://github.com/pytorch/pytorch/issues/22212
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22212
Differential Revision: D15988346
Pulled By: driazati
fbshipit-source-id: af223e3ad0580be895377312949997a70e988e4f
Summary:
The changes include:
1. Allow key/value to have different number of features with query. It supports the case when key and value have different feature dimensions.
2. Support three separate proj_weight, in addition to a single in_proj_weight. The proj_weight of key and value may have different dimension with that of query so three separate proj_weights are necessary. In case that key and value have same dimension as query, it is preferred to use a single large proj_weight for performance reason. However, it should be noted that using a single large weight or three separate weights is a size-dependent decision.
3. Give an option to use static k and v in the multihead_attn operator (see saved_k and saved_v). Those static key/value tensors can now be re-used when training the model.
4. Add more test cases to cover the arguments.
Note: current users should not be affected by the changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21288
Differential Revision: D15738808
Pulled By: zhangguanheng66
fbshipit-source-id: 288b995787ad55fba374184b3d15b5c6fe9abb5c
Summary:
A bunch of modules were missing entries for `__constants__` which was making their `__repr__`s not work. Others had `__constants__` that were not necessary since it was provided by some parent class instead.
Fixes#20978
](https://our.intern.facebook.com/intern/diff/15539518/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21071
Pulled By: driazati
Differential Revision: D15539518
fbshipit-source-id: 24bdd1ef41ef636eefd5d2bad4ab2d79646ed4f0
Summary:
Remove the internal functions in multi_head_attention_forward. Those internal functions cause 10-15% performance regression and there is possibly a JIT issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20653
Differential Revision: D15398888
Pulled By: cpuhrsch
fbshipit-source-id: 0a3f053a4ade5009e73d3974fa6733c2bff9d929
Summary:
Attention mask should be of shape `(L, S)` since it is added to `attn_output_weights`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20850
Differential Revision: D15495587
Pulled By: ezyang
fbshipit-source-id: 61d6801da5291df960daab273e874df28aedbf6e
Summary:
Fix a typo in the doc.
Add an AssertError check back to MultiheadAttention module
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20492
Differential Revision: D15349008
Pulled By: cpuhrsch
fbshipit-source-id: 2d898345f03787c713e537673613a748ad826b34
Summary:
Moving functions from torch/nn/modules/activation.py to torch/nn/functional.py. For functions not implemented (_get_input_buffer and _set_input_buffer), a TODO is added.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20415
Differential Revision: D15318078
Pulled By: jamarshon
fbshipit-source-id: 5ca698e2913821442cf8609cc61ac8190496a3c6
Summary:
To fully support incremental_state function, it requires several additional utils available in fairseq. However, we lack a problem for the unit test. Therefore, the incremental_state function will be disable for now. If it is needed in the future, a feature request could be created. Fixed#20132
Add some unit tests to cover the arguments of MultiheadAttention module, including bias, add_bias_kv, add_zero_attn, key_padding_mask, need_weights, attn_mask.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20177
Differential Revision: D15304575
Pulled By: cpuhrsch
fbshipit-source-id: ebd8cc0f11a4da0c0998bf0c7e4e341585e5685a
Summary:
See Issue #20301
Specifying dim in docstring example to prevent UserWarning.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20310
Differential Revision: D15277734
Pulled By: ezyang
fbshipit-source-id: 2e8b748dbe743675a5a538ccbe97713aad02e8ac
Summary:
Import MultiheadAttention into the core pytorch framework.
Users now can import MultiheadAttention directly from torch.nn.
See "Attention Is All You Need" for more details related to MultiheadAttention function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18334
Differential Revision: D14577966
Pulled By: zhangguanheng66
fbshipit-source-id: 756c0deff623f3780651d9f9a70ce84516c806d3
Summary:
The documentation for LogSigmoid says:
> Applies the element-wise function:
> \<blank\>
Now the documentation properly displays the math string.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16900
Differential Revision: D14020097
Pulled By: ezyang
fbshipit-source-id: 41e229d0fcc6b9bb53367be548bf85286dc13546
Summary:
PR to update the shape notation for all of the torch.nn modules to take a unified form. The goal is to make these definitions machine-readable and those checkable by unifying the style across all of the different modules.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15741
Differential Revision: D13709601
Pulled By: ezyang
fbshipit-source-id: fb89a03903fdf0cd0dcf76f3e469b8582b2f3634
Summary:
This PR:
1. add tests for batchnorm/dropout for train/eval parameter mutatino
2. remove training constants from all our standard library
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14780
Differential Revision: D13331578
Pulled By: wanchaol
fbshipit-source-id: d92ca3ce38cc2888688d50fe015e3e22539a20a5
Summary:
This PR adds weak modules for all activation modules and uses `test_nn` module tests to test weak modules that have been annotated with `weak_module` and therefore are in `torch._jit_internal._weak_types`
Also depends on #14379
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14238
Differential Revision: D13252887
Pulled By: driazati
fbshipit-source-id: e9638cf74089884a32b8f0f38396cf432c02c988
Summary:
This PR adds weak modules for all activation modules and uses `test_nn` module tests to test weak modules that have been annotated with `weak_module` and therefore are in `torch._jit_internal._weak_types`
Also depends on #14379
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14238
Differential Revision: D13192230
Pulled By: driazati
fbshipit-source-id: 36488960b6c91448b38c0fa65422539a93af8c5e
Summary:
- fixes https://github.com/pytorch/pytorch/issues/10723
- migrate PReLU to ATen and deprecate legacy PReLU
- performance:
CPU with weight.numel() = 1
```
>>> m = nn.PReLU()
>>> x = torch.randn(100, 100, 100, requires_grad=True)
>>> %timeit -r 100 y = m(x)
100 loops, best of 100: 9.43 ms per loop
>>> y = m(x).sum()
>>> %timeit -r 100 y.backward(retain_graph=True)
10 loops, best of 100: 24.4 ms per loop
>>> m = nn.PReLU()
>>> x = torch.randn(100, 100, 100, requires_grad=True)
>>> %timeit -r 100 y = m(x)
1000 loops, best of 100: 695 µs per loop
>>> y = m(x).sum()
>>> %timeit -r 100 y.backward(retain_graph=True)
100 loops, best of 100: 2.47 ms per loop
```
CPU with weight.numel() = channels
```
>>> m = nn.PReLU(100)
>>> x = torch.randn(100, 100, 100, requires_grad=True)
>>> %timeit -r 100 y = m(x)
1000 loops, best of 100: 603 µs per loop
>>> y = m(x).sum()
>>> %timeit -r 100 y.backward(retain_graph=True)
100 loops, best of 100: 13.3 ms per loop
>>> m = nn.PReLU(100)
>>> x = torch.randn(100, 100, 100, requires_grad=True)
>>> %timeit -r 100 y = m(x)
1000 loops, best of 100: 655 µs per loop
>>> y = m(x).sum()
>>> %timeit -r 100 y.backward(retain_graph=True)
100 loops, best of 100: 2.45 ms per loop
```
CUDA with weight.numel() = 1
```
>>> m = nn.PReLU().cuda()
>>> x = torch.randn(100, 100, 100, requires_grad=True).cuda()
>>> %timeit -r 100 torch.cuda.synchronize(); y = m(x); torch.cuda.synchronize();
10000 loops, best of 100: 187 µs per loop
>>> y = m(x).sum()
>>> %timeit -r 100 torch.cuda.synchronize(); y.backward(retain_graph=True); torch.cuda.synchronize();
100 loops, best of 100: 2.01 ms per loop
>>> m = nn.PReLU().cuda()
>>> x = torch.randn(100, 100, 100, requires_grad=True).cuda()
>>> %timeit -r 100 torch.cuda.synchronize(); y = m(x); torch.cuda.synchronize();
1000 loops, best of 100: 195 µs per loop
>>> y = m(x).sum()
>>> %timeit -r 100 torch.cuda.synchronize(); y.backward(retain_graph=True); torch.cuda.synchronize();
100 loops, best of 100: 2.28 ms per loop
```
CUDA with weight.numel() = channel
```
>>> m = nn.PReLU(100).cuda()
>>> x = torch.randn(100, 100, 100, requires_grad=True).cuda()
>>> %timeit -r 100 torch.cuda.synchronize(); y = m(x); torch.cuda.synchronize();
1000 loops, best of 100: 174 µs per loop
>>> y = m(x).sum()
>>> %timeit -r 100 torch.cuda.synchronize(); y.backward(retain_graph=True); torch.cuda.synchronize();
100 loops, best of 100: 2.27 ms per loop
>>> m = nn.PReLU(100).cuda()
>>> x = torch.randn(100, 100, 100, requires_grad=True).cuda()
>>> %timeit -r 100 torch.cuda.synchronize(); y = m(x); torch.cuda.synchronize();
10000 loops, best of 100: 181 µs per loop
>>> y = m(x).sum()
>>> %timeit -r 100 torch.cuda.synchronize(); y.backward(retain_graph=True); torch.cuda.synchronize();
100 loops, best of 100: 2.26 ms per loop
```
The huge performance regression in CPU when weight.numel() = 1 is addressed by replacing at::CPU_tensor_apply* with parallelized kernels.
ezyang SsnL zou3519 soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11758
Differential Revision: D9995799
Pulled By: weiyangfb
fbshipit-source-id: d289937c78075f46a54dafbde92fab0cc4b5b86e
This PR enables users to print extra information of their subclassed nn.Module.
Now I simply insert the user-defined string at the ending of module name, which should be discussed in this PR.
Before this PR, users should redefine the __repr__ and copy&paste the source code from Module.
* Add support for extra information on Module
* Rewrite the repr method of Module
* Fix flake8
* Change the __repr__ to get_extra_repr in Linear
* Fix extra new-line for empty line
* Add test for __repr__ method
* Fix bug of block string indent
* Add indent for multi-line repr test.
* Address review comments
* Update tutorial for creating nn.Module
* Fix flake8, add extra_repr of bilinear
* Refactor DropoutNd
* Change to extra_repr in some Modules
* Fix flake8
* Refactor padding modules
* Refactor pooling module
* Fix typo
* Change to extra_repr
* Fix bug for GroupNorm
* Fix bug for LayerNorm
* Fix some minor errors in existing docs.
* Fix Convolution and Pooling docs in torch.nn.functional
* Cleaned up torch.nn.functional docs
* Address @SsnL 's comments
* Add multiplication sign missing in docs
* Fix more typos, and clear some warnings
* Change infinity symbol in LPPool2d
* Revert some changes in torch.nn.functional
* Few more minor changes
* Improvize documentation
1. Add formula for erf, erfinv
2. Make exp, expm1 similar to log, log1p
3. Symbol change in ge, le, ne, isnan
* Fix minor nit in the docstring
* More doc improvements
1. Added some formulae
2. Complete scanning till "Other Operations" in Tensor docs
* Add more changes
1. Modify all torch.Tensor wherever required
* Fix Conv docs
1. Fix minor nits in the references for LAPACK routines
* Improve Pooling docs
1. Fix lint error
* Improve docs for RNN, Normalization and Padding
1. Fix flake8 error for pooling
* Final fixes for torch.nn.* docs.
1. Improve Loss Function documentation
2. Improve Vision Layers documentation
* Fix lint error
* Improve docstrings in torch.nn.init
* Fix lint error
* Fix minor error in torch.nn.init.sparse
* Fix Activation and Utils Docs
1. Fix Math Errors
2. Add explicit clean to Makefile in docs to prevent running graph generation script
while cleaning
3. Fix utils docs
* Make PYCMD a Makefile argument, clear up prints in the build_activation_images.py
* Fix batch norm doc error
The nn.* counterpart of #5443 . Mostly removed Variable wrapper. Also added doc for nn.RReLU.
Notice that torch.randn(*, requires_grad=True) isn't documented until #5462 is done.
- Cleaned up THNN and THCUNN code and kernels
- Improved THCUNN kernel performance 5x, making it match cuDNN performance
- Added support for computing softmax over arbitrary dims
NOTE: The default dim for 3D inputs is now 1 (used to be 0)
- Both functions now accept inputs with arbitrarily many dimensions
- Autograd functions no longer save the input (it's unnecessary)
- Added cuDNN bindings for softmax, but they are unused as THCUNN
matches or even exceeds cuDNN performance
