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.
