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:
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:
* Constructs a new type at runtime so that `isinstance` checks work for
weak modules assigned to `ScriptModule`s
* Fix some extraneous names in `__constants__`
* Add `in_features` and `out_features` to `nn.Linear` `__constants__`
Fixes#19363
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20190
Pulled By: driazati
Differential Revision: D15302350
fbshipit-source-id: 1d4d21ed44ab9578a4bc2a72396a82e9bbcd387c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
Sparse Linear in TH(CU)NN implements sparse linear layers without
using sparse matrices.
It is currently not documented in PyTorch and there is no functional or
module interface. This means it is unused from a PyTorch point of view.
The reason for removing it is twofold:
- The module uses sort, which I would like to move to ATen.
- When we implement a SparseLinear layer, we would want to do it
using sparse tensors, so it's not all that useful, anyway.
I checked this on slack with soumith, I hope the above is an accurate
representation. All bad ideas are my own.
This is part of the ongoing work to move
sort/topk/mode/median/kthvalue to ATen.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17610
Differential Revision: D14280663
Pulled By: gchanan
fbshipit-source-id: 289231d2c20626855ce2ceecd4f204b460c32378
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:
The `EmbeddingBag` module does not include a `from_pretrained` method like the `Embedding` module. I added it for consistency between the two modules.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15273
Differential Revision: D13547842
Pulled By: soumith
fbshipit-source-id: 8ffde51ff0c1e8fc8310263b6f375da88089ff7d
Summary:
Resubmitting PR #14415
The tests added for Embedding + EmbeddingBag had random numbers as input, which affected the random number generator & caused the flakey test to break.
Everything but the last two commits have already been accepted
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14509
Differential Revision: D13247917
Pulled By: eellison
fbshipit-source-id: ea6963c47f666c07687787e2fa82020cddc6aa15
Summary:
Add support for Embedding and EmbeddingBag in script. Both functions require with torch.no_grad(), which we don't have any plans to support in the near future. To work around this, I added a embedding_renorm function without derivatives.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14415
Reviewed By: wanchaol
Differential Revision: D13219647
Pulled By: eellison
fbshipit-source-id: c90706aa6fbd48686eb10f3efdb65844be7b8717
Summary:
Made the previous description for max_norm more precise, avoiding 'this' and describing what actually happens in the code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13310
Differential Revision: D12840813
Pulled By: SsnL
fbshipit-source-id: 98090c884267a62ce93cd85da84252d46926dfa5
* Add max mode support to EmbeddingBag
* Lint fix
* Fix compilation issue on other platforms
* Rebase + don't waste memory when not in max mode
* Oops, missed a spot
* Fix whitespace from merge
* less precision
* Lower precision to avoid spurious failures
* Minor typo
* Switch to size()
* Codemod to update our codebase to 0.4 standard
* Update some of the test scri[ts
* remove Variable in test_clip_grad_value
* fix _symbolic_override_wrapper_maker
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
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.
Implements nn.Embedding (lookup table) in ATen.
Breaking change: new optional argument padding_idx in F.embedding to
match nn.Embedding.
Note that there are a few bugs in Embedding that are inherited from the
previous code:
- CUDA renorm has race conditions if index contains duplicate entries
- sparse gradient doesn't work with scale_grad_by_freq
* Fix docs for nn.Embedding and F.embedding.
- add description of 'sparse' argument (#3104)
- fix F.embedding example (resulted in RuntimeError)
* Make EmbeddingBag a New Style Function.
* Add a functional interface for EmbeddingBag
* Fix failing tests: add max_norm and norm_type to context,
and fix typo in backend call.
* Docfix: remove torch.manual_seed from example code.
* Add a note about using sparse keyword in Embedding function.
Here's the command I used to invoke autopep8 (in parallel!):
git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i
Several rules are ignored in setup.cfg. The goal is to let autopep8
handle everything which it can handle safely, and to disable any rules
which are tricky or controversial to address. We may want to come back
and re-enable some of these rules later, but I'm trying to make this
patch as safe as possible.
Also configures flake8 to match pep8's behavior.
Also configures TravisCI to check the whole project for lint.
Uses the assignment syntax to get deterministic ordering of parameters.
The ordering of parameters using the constructor syntax is
non-deterministic because kwargs use dict() in Python 3.5 and earlier.
