Commit Graph

15 Commits

Author SHA1 Message Date
Thiago Crepaldi
89e79f844d Add list of supported ATen ops by ONNX converter into torch.onnx page
This PR introduces a new documentation page with a list of supported ATen operators by the ONNX converter.

When `make html` (or similar) are called, a python script will generate a temporary CSV file inside the doc build folder with a list of operators/opsets currently supported by the PyTorch ONNX exporter. That CSV is used by Sphinx to build a HTML table using the same theme as the rest of the documentation.

That page is linked to the existing `onnx.rst`, including its table of contents.

@BowenBao @shubhambhokare1 Feel free to add more details on how the script cross reference onnx symbolics and aten operators list from torch jit api`

Below is the workflow for the changed pages:

The initial torch.onnx page was modified to add a link to the list of supported aten operators
![image](https://user-images.githubusercontent.com/5469809/159046387-c459bffc-c9b2-4fcb-8468-8181fdddf911.png)

The screen below highlights the text structure changes to the `ATen operartors` section
![image](https://user-images.githubusercontent.com/5469809/159046730-ccd1e594-c8e6-4b8d-a9ec-8bf6ad58a435.png)

Finally the new page with the list of supported operators is shown below
![image](https://user-images.githubusercontent.com/5469809/159046872-0d99b769-8b95-4c2b-99a9-a8cfdd0b6ecf.png)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/74397
Approved by: https://github.com/garymm, https://github.com/malfet
2022-04-07 00:05:44 +00:00
Vasiliy Kuznetsov
74b23b2066 quantization: autogenerate quantization backend configs for documentation (#75126)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75126

Quantization has a high volume of configurations of how to quantize an
op for a reference model representation which is useful for a lowering
step for a backend.  An example of this is

```
 {'dtype_configs': [{'input_dtype': torch.quint8,
										 'output_dtype': torch.quint8}],
	'observation_type': <ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT: 0>,
	'pattern': <class 'torch.nn.modules.conv.ConvTranspose1d'>},
```

These configs are checked into master, and they are created with Python functions.
Therefore, there is no easy way for the user to see what the configs actually
are without running some Python code.

This PR is one approach to document these configs. Here is what this is doing:
1. during documentation build, write a text file of the configs
2. render that text file on a quantization page, with some additional context

In the future, this could be extended to autogenerate better looking tables
such as: op support per backend and dtype, op support per valid quantization settings per backend,
etc.

Test Plan:
```
cd docs
make html
cd html
python -m http.server 8000
// render http://[::]:8000/quantization-backend-configuration.html
// it renders correctly
```

Reviewed By: ejguan

Differential Revision: D35365461

Pulled By: vkuzo

fbshipit-source-id: d60f776ccb57da9db3d09550e4b27bd5e725635a
(cherry picked from commit 14865c0e23bc080120342c8f9278f0fae8eb8fbd)
2022-04-04 22:22:30 +00:00
Rodrigo Berriel
11ca641491 [docs] Add images to some activation functions (#65415)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/65368. See discussion in the issue.

cc mruberry SsnL jbschlosser soulitzer

Pull Request resolved: https://github.com/pytorch/pytorch/pull/65415

Reviewed By: soulitzer

Differential Revision: D31093303

Pulled By: albanD

fbshipit-source-id: 621c74c7a2aceee95e3d3b708c7f1a1d59e59b93
2021-09-22 11:05:29 -07:00
Rodrigo Berriel
f0ada4bd54 [docs] Remove .data from some docs (#65358)
Summary:
Related to https://github.com/pytorch/pytorch/issues/30987. Fix the following task:

- [ ] Remove the use of `.data` in all our internal code:
  - [ ] ...
  - [x] `docs/source/scripts/build_activation_images.py` and `docs/source/notes/extending.rst`

In `docs/source/scripts/build_activation_images.py`, I used `nn.init` because the snippet already assumes `nn` is available (the class inherits from `nn.Module`).

cc albanD

Pull Request resolved: https://github.com/pytorch/pytorch/pull/65358

Reviewed By: malfet

Differential Revision: D31061790

Pulled By: albanD

fbshipit-source-id: be936c2035f0bdd49986351026fe3e932a5b4032
2021-09-21 06:32:31 -07:00
Shen Li
1022443168 Revert D30279364: [codemod][lint][fbcode/c*] Enable BLACK by default
Test Plan: revert-hammer

Differential Revision:
D30279364 (b004307252)

Original commit changeset: c1ed77dfe43a

fbshipit-source-id: eab50857675c51e0088391af06ec0ecb14e2347e
2021-08-12 11:45:01 -07:00
Zsolt Dollenstein
b004307252 [codemod][lint][fbcode/c*] Enable BLACK by default
Test Plan: manual inspection & sandcastle

Reviewed By: zertosh

Differential Revision: D30279364

fbshipit-source-id: c1ed77dfe43a3bde358f92737cd5535ae5d13c9a
2021-08-12 10:58:35 -07:00
Adnios
09a8f22bf9 Add mish activation function (#58648)
Summary:
See issus: https://github.com/pytorch/pytorch/issues/58375

Pull Request resolved: https://github.com/pytorch/pytorch/pull/58648

Reviewed By: gchanan

Differential Revision: D28625390

Pulled By: jbschlosser

fbshipit-source-id: 23ea2eb7d5b3dc89c6809ff6581b90ee742149f4
2021-05-25 10:36:21 -07:00
Xiaomeng Yang
4ae832e106 Optimize SiLU (Swish) op in PyTorch (#42976)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42976

Optimize SiLU (Swish) op in PyTorch.

Some benchmark result

input = torch.rand(1024, 32768, dtype=torch.float, device="cpu")
forward: 221ms -> 133ms
backward: 600ms -> 170ms

input = torch.rand(1024, 32768, dtype=torch.double, device="cpu")
forward: 479ms -> 297ms
backward: 1438ms -> 387ms

input = torch.rand(8192, 32768, dtype=torch.float, device="cuda")
forward: 24.34ms -> 9.83ms
backward: 97.05ms -> 29.03ms

input = torch.rand(4096, 32768, dtype=torch.double, device="cuda")
forward: 44.24ms -> 30.15ms
backward: 126.21ms -> 49.68ms

Test Plan: buck test mode/dev-nosan //caffe2/test:nn -- "SiLU"

Reviewed By: houseroad

Differential Revision: D23093593

fbshipit-source-id: 1ba7b95d5926c4527216ed211a5ff1cefa3d3bfd
2020-08-16 13:21:57 -07:00
Xiaomeng Yang
2460dced8f Add torch.nn.GELU for GELU activation (#28944)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28944

Add torch.nn.GELU for GELU activation

Test Plan: buck test mode/dev-nosan //caffe2/test:nn -- "GELU"

Reviewed By: hl475, houseroad

Differential Revision: D18240946

fbshipit-source-id: 6284b30def9bd4c12bf7fb2ed08b1b2f0310bb78
2019-11-03 21:55:05 -08:00
Xiang Gao
6fc75eadf0 Add CELU activation to pytorch (#8551)
Summary:
Also fuse input scale multiplication into ELU

Paper:
https://arxiv.org/pdf/1704.07483.pdf
Pull Request resolved: https://github.com/pytorch/pytorch/pull/8551

Differential Revision: D9088477

Pulled By: SsnL

fbshipit-source-id: 877771bee251b27154058f2b67d747c9812c696b
2018-08-01 07:54:44 -07:00
vishwakftw
49f88ac956 Add grid lines for activation images, fixes #9130 (#9134)
Summary:
1. Add dashed light blue line for asymptotes.
2. RReLU was missing the activation image.
3. make clean in docs will remove the activation images too.

Sample image:
![image](https://user-images.githubusercontent.com/23639302/42224142-5d66bd0a-7ea7-11e8-8b0a-26918df12f7c.png)
Closes https://github.com/pytorch/pytorch/pull/9134

Differential Revision: D8726880

Pulled By: ezyang

fbshipit-source-id: 35f00ee08a34864ec15ffd6228097a9efbc8dd62
2018-07-03 19:10:00 -07:00
Tongzhou Wang
e0f3e5dc77 fix activation images not showing up on official website (#6367) 2018-04-07 11:06:24 -04:00
Vishwak Srinivasan
32b3841553 [ready] General documentation improvements (#5450)
* 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
2018-03-08 13:21:12 -05:00
Adam Paszke
b1dec4a74f
Fix doc-push (#5494) 2018-03-01 17:37:30 +01:00
Piotr Mitros
7b33ef4cff Documentation cleanup for activation functions (#5457) 2018-03-01 14:53:11 +01:00