Summary:
A tiny PR to update the links in the lefthand navbar under Libraries. The canonical link for vision and text is `https://pytorch.org/vision/stable` and `https://pytorch.org/text/stable` respectively. The link without the `/stable` works via a redirect, this is cleaner.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51103
Reviewed By: izdeby
Differential Revision: D26079760
Pulled By: heitorschueroff
fbshipit-source-id: df1fa64d7895831f4e6242445bae02c1faa5e4dc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50791
Add a dedicated pipeline parallelism doc page explaining the APIs and
the overall value of the module.
ghstack-source-id: 120257168
Test Plan:
1) View locally
2) waitforbuildbot
Reviewed By: rohan-varma
Differential Revision: D25967981
fbshipit-source-id: b607b788703173a5fa4e3526471140506171632b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48280
Adding new API for the kineto profiler that supports enable predicate
function
Test Plan: unit test
Reviewed By: ngimel
Differential Revision: D25142220
Pulled By: ilia-cher
fbshipit-source-id: c57fa42855895075328733d7379eaf3dc1743d14
Summary:
xref gh-46927 to the 1.7 release branch
This backports a fix to the script to push docs to pytorch/pytorch.github.io. Specifically, it pushes to the correct directory when a tag is created here. This issue became apparent in the 1.7 release cycle and should be backported to here.
Along the way, fix the canonical link to the pytorch/audio documentation now that they use subdirectories for the versions, xref pytorch/audio#992. This saves a redirect.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47349
Reviewed By: zhangguanheng66
Differential Revision: D25073752
Pulled By: seemethere
fbshipit-source-id: c778c94a05f1c3e916217bb184f69107e7d2c098
Summary:
CC: gchanan jspisak seemethere
I previewed the docs and they look reasonable. Let me know if I missed anything.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46880
Reviewed By: seemethere, izdeby
Differential Revision: D24551503
Pulled By: robieta
fbshipit-source-id: 627f73d3dd4d8f089777bca8653702735632b9fc
Summary:
This PR adds the `torch.linalg` namespace as part of our continued effort to be more compatible with NumPy. The namespace is tested by adding a single function, `torch.linalg.outer`, and testing it in a new test suite, test_linalg.py. It follows the same pattern that https://github.com/pytorch/pytorch/pull/41911, which added the `torch.fft` namespace, did.
Future PRs will likely:
- add more functions to torch.linalg
- expand the testing done in test_linalg.py, including legacy functions, like torch.ger
- deprecate existing linalg functions outside of `torch.linalg` in preference to the new namespace
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42664
Reviewed By: ngimel
Differential Revision: D22991019
Pulled By: mruberry
fbshipit-source-id: 39258d9b116a916817b3588f160b141f956e5d0b
Summary:
This PR creates a new namespace, torch.fft (torch::fft) and puts a single function, fft, in it. This function is analogous to is a simplified version of NumPy's [numpy.fft.fft](https://numpy.org/doc/1.18/reference/generated/numpy.fft.fft.html?highlight=fft#numpy.fft.fft) that accepts no optional arguments. It is intended to demonstrate how to add and document functions in the namespace, and is not intended to deprecate the existing torch.fft function.
Adding this namespace was complicated by the existence of the torch.fft function in Python. Creating a torch.fft Python module makes this name ambiguous: does it refer to a function or module? If the JIT didn't exist, a solution to this problem would have been to make torch.fft refer to a callable class that mimicked both the function and module. The JIT, however, cannot understand this pattern. As a workaround it's required to explicitly `import torch.fft` to access the torch.fft.fft function in Python:
```
import torch.fft
t = torch.randn(128, dtype=torch.cdouble)
torch.fft.fft(t)
```
See https://github.com/pytorch/pytorch/issues/42175 for future work. Another possible future PR is to get the JIT to understand torch.fft as a callable class so it need not be imported explicitly to be used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41911
Reviewed By: glaringlee
Differential Revision: D22941894
Pulled By: mruberry
fbshipit-source-id: c8e0b44cbe90d21e998ca3832cf3a533f28dbe8d
Summary:
xref gh-38011.
Fixes two warnings when building documentation by
- using the external link to torchvision
- install tensorboard before building documentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41334
Reviewed By: ngimel
Differential Revision: D22753083
Pulled By: mruberry
fbshipit-source-id: 876377e9bd09750437fbfab0378664b85701f827
Summary:
Fixes gh-40046
PR gh-37419 refactored the content of `docs/source/rpc/index.rst` into `docs/source/rpc.rst` but did not link to the latter from `doc/source/index.rst` so the top-level RPC documentation is missing from https://pytorch.org/docs/master/.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40077
Differential Revision: D22068128
Pulled By: mrshenli
fbshipit-source-id: 394433f98f86509e0c9cb6d910a86fb8a2932683
Summary:
xref gh-32838, gh-34032
This is a major refactor of parts of the documentation to split it up using sphinx's `autosummary` feature which will build out `autofuction` and `autoclass` stub files and link to them. The end result is that the top module pages like torch.nn.rst and torch.rst are now more like table-of-contents to the actual single-class or single-function documentations pages.
Along the way, I modified many of the docstrings to eliminate sphinx warnings when building. I think the only thing I changed from a non-documentation perspective is to add names to `__all__` when adding them to `globals()` in `torch.__init__.py`
I do not know the CI system: are the documentation build artifacts available after the build, so reviewers can preview before merging?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37419
Differential Revision: D21337640
Pulled By: ezyang
fbshipit-source-id: d4ad198780c3ae7a96a9f22651e00ff2d31a0c0f
Summary:
This PR comes from discussion with albanD in https://fb.quip.com/npBHAXaPfnbu. Main goal is to clarify view ops with general outplace/inplace ops and remind users about the difference.
For reference this information is only available in code which is internal and hard to find. Also changes to this list actually affect users so we think it's better to expose it as public information. It's also helpful for new backend like XLA when implementing PyTorch ops. 19bbb4fccb/tools/autograd/gen_autograd.py (L32-L68)
Please feel free to comment!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32560
Differential Revision: D20161069
Pulled By: ailzhang
fbshipit-source-id: b5f1fd4353fe7594a427784db288aeb5a37dc521
Summary:
Also, windows memory failures responsible for the earlier reversion have been fixed.
This PR (initially) contains 2 commits:
* a revert of the revert
* all changes to implement the original Apex scale update heuristic, squashed into a single commit for easier diff review
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33366
Differential Revision: D20099026
Pulled By: ngimel
fbshipit-source-id: 339b9b6bd5134bf055057492cd1eedb7e4461529
Summary:
This PR implements the gradient scaling API that mruberry, jjsjann123, ngimel, zdevito, gchanan and I have been discussing. Relevant issue/RFC: https://github.com/pytorch/pytorch/issues/25081.
Volume-wise, this PR is mostly documentation and tests. The Python API (found entirely in `torch/cuda/amp/amp_scaler.py`) is lightweight . The exposed functions are intended to make the implementation and control flow of gradient scaling convenient, intuitive, and performant.
The API is probably easiest to digest by looking at the documentation and examples. `docs/source/amp.rst` is the homepage for the Automatic Mixed Precision package. `docs/source/notes/amp_examples.rst` includes several examples demonstrating common but not-immediately-obvious use cases. Examples are backed by tests in `test_cuda.py` (and thankfully the tests pass :P).
Two small utility kernels have been added in `native/cuda/AmpKernels.cu` to improve performance and avoid host-device synchronizations wherever possible.
Existing optimizers, both in the wild and in Pytorch core, do not need to change to use the scaling API.
However, the API was also designed to establish a contract between user scripts and optimizers such that writers of _new_ custom optimizers have the control points they need to implement fast, optionally sync-free updates. User scripts that obey the scaling API can drop such custom optimizers in and reap performance benefits without having to change anything aside from the optimizer constructor itself. [I know what the contract with custom optimizers should be](35829f24ef/torch/cuda/amp/amp_scaler.py (L179-L184)), but I'm waiting for review on the rest of the API before I go about documenting it (it will be given a dedicated section in `docs/source/notes/amp_examples.rst`.
Currently, the gradient scaling examples do not include the auto-casting API as discussed in https://github.com/pytorch/pytorch/issues/25081. The gradient scaling API is intended to be orthogonal/modular relative to autocasting. Without auto-casting the gradient scaling API is fully use-_able_, but not terribly use-_ful_, so it's up to you guys whether you want to wait until auto-casting is ready before merging the scaling API as well.
### Todo
- [ ] How do I get c10 registered status for my two custom kernels? They're very simple.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26512
Differential Revision: D19859905
Pulled By: mruberry
fbshipit-source-id: bb8ae6966214718dfee11345db824389e4286923
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27782
Warnings show up when running `make html` to build documentation. All of
the warnings are very reasonable and point to bugs in our docs. This PR
attempts to fix most of those warnings.
In the future we will add something to the CI that asserts that there
are no warnings in our docs.
Test Plan: - build and view changes locally
Differential Revision: D17887067
Pulled By: zou3519
fbshipit-source-id: 6bf4d08764759133b20983d6cd7f5d27e5ee3166
Summary:
resolves issues:
https://github.com/pytorch/pytorch/issues/27703
Updates to index for v1.3.0
* add javasphinx to the required sphinx plugins
* Update "Package Reference" to "Python API"
* Add in torchaudio and torchtext reference links so they show up across all docs not just the main page
* Add "Other Languages" section, add in C++ docs, add in Javadocs
* Add link to XLA docs under Notes: http://pytorch.org/xla/
this includes changes to:
docs/source/conf.py
docs/source/index.rst
docs/source/nn.rst
docs/requirements.txt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27721
Differential Revision: D17881973
Pulled By: jlin27
fbshipit-source-id: ccc1e9e4da17837ad99d25df997772613f76aea8
Summary:
- Update torch.rst to remove certain autofunction calls
- Add reference to Quantization Functions section in nn.rst
- Update javadocs for v1.3.0
- Update index.rst:
- Update "Package Reference" to "Python API"
- Add in torchaudio and torchtext reference links so they show up across all docs not just the main page
- Add "Other Languages" section, add in C++ docs, add in Javadocs
- Add link to XLA docs under Notes: http://pytorch.org/xla/
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27676
Differential Revision: D17850696
Pulled By: brianjo
fbshipit-source-id: 3de146f065222d1acd9a33aae3b543927a63532a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27173
`docs/source/named_tensor.rst` is the entry point; most users will land
either here or the named tensor tutorial when looking to use named
tensors. We should strive to make this as readable, concise, and understandable
as possible.
`docs/source/name_inference.rst` lists all of the name inference rules.
It should be clear but it's hard to make it concise.
Please let me know if anything doesn't make sense and please propose
alternative wordings and/or restructuring to improve the documentation.
This should ultimately get cherry-picked into the 1.3 branch as one
monolithic commit so it would be good to get all necessary changes made
in this PR and not have any follow ups.
Test Plan: - built and reviewed locally with `cd docs/ && make html`.
Differential Revision: D17763046
Pulled By: zou3519
fbshipit-source-id: c7872184fc4b189d405b18dad77cad6899ae1522
Summary:
Adds links to torchaudio and torchtext to docs index. We should eventually evolve this to bring the audio and text docs builds in like torchvision.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24245
Differential Revision: D17163539
Pulled By: soumith
fbshipit-source-id: 5754bdf7579208e291e53970b40f73ef119b758f
Summary:
This was previously buggy and not being displayed on master. This fixes
the issues with the script to generate the builtin function schemas and
moves it to its own page (it's 6000+ lines of schemas)
Sphinx looks like it will just keep going if it hits errors when importing modules, we should find out how to turn that off and put it in the CI
This also includes some other small fixes:
* removing internal only args from `script()` and `trace()` docs, this also requires manually keeping these argument lists up to date but I think the cleanliness is worth it
* removes outdated note about early returns
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24056
Pulled By: driazati
Differential Revision: D16742406
fbshipit-source-id: 9102ba14215995ffef5aaafcb66a6441113fad59
Summary:
This cleans up the `torch.utils.tensorboard` API to remove all kwargs usage (which isn't clear to the user) and removes the "experimental" warning in prep for our 1.2 release.
We also don't need the additional PyTorch version checks now that we are in the codebase itself.
cc ezyang lanpa natalialunova
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21786
Reviewed By: natalialunova
Differential Revision: D15854892
Pulled By: orionr
fbshipit-source-id: 06b8498826946e578824d4b15c910edb3c2c20c6
Summary:
This PR adds TensorBoard logging support natively within PyTorch. It is based on the tensorboardX code developed by lanpa and relies on changes inside the tensorflow/tensorboard repo landing at https://github.com/tensorflow/tensorboard/pull/2065.
With these changes users can simply `pip install tensorboard; pip install torch` and then log PyTorch data directly to the TensorBoard protobuf format using
```
import torch
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
s1 = torch.rand(1)
writer.add_scalar('data/scalar1', s1[0], 0)
writer.close()
```
Design:
- `EventFileWriter` and `RecordWriter` from tensorboardX now live in tensorflow/tensorboard
- `SummaryWriter` and PyTorch-specific conversion from tensors, nn modules, etc. now live in pytorch/pytorch. We also support Caffe2 blobs and nets.
Action items:
- [x] `from torch.utils.tensorboard import SummaryWriter`
- [x] rename functions
- [x] unittests
- [x] move actual writing function to tensorflow/tensorboard in https://github.com/tensorflow/tensorboard/pull/2065
Review:
- Please review for PyTorch standard formatting, code usage, etc.
- Please verify unittest usage is correct and executing in CI
Any significant changes made here will likely be synced back to github.com/lanpa/tensorboardX/ in the future.
cc orionr, ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16196
Differential Revision: D15062901
Pulled By: orionr
fbshipit-source-id: 3812eb6aa07a2811979c5c7b70810261f9ea169e
Summary: Adding new documents to the PyTorch website to describe how PyTorch is governed, how to contribute to the project, and lists persons of interest.
Reviewed By: orionr
Differential Revision: D14394573
fbshipit-source-id: ad98b807850c51de0b741e3acbbc3c699e97b27f
Summary:
[Edit: after applied colesbury 's suggestions]
* Hub module enable users to share code + pretrained weights through github repos.
Example usage:
```
hub_model = hub.load(
'ailzhang/vision:hub', # repo_owner/repo_name:branch
'wrapper1', # entrypoint
1234, # args for callable [not applicable to resnet18]
pretrained=True) # kwargs for callable
```
* Protocol on repo owner side: example https://github.com/ailzhang/vision/tree/hub
* The "published" models should be at least in a branch/tag. It can't be a random commit.
* Repo owner should have the following field defined in `hubconf.py`
* function/entrypoint with function signature `def wrapper1(pretrained=False, *args, **kwargs):`
* `pretrained` allows users to load pretrained weights from repo owner.
* `args` and `kwargs` are passed to the callable `resnet18`, repo owner should clearly specify their help message in the docstring
```
def wrapper1(pretrained=False, *args, **kwargs):
"""
pretrained (bool): a recommended kwargs for all entrypoints
args & kwargs are arguments for the function
"""
from torchvision.models.resnet import resnet18
model = resnet18(*args, **kwargs)
checkpoint = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
if pretrained:
model.load_state_dict(model_zoo.load_url(checkpoint, progress=False))
return model
```
* Hub_dir
* `hub_dir` specifies where the intermediate files/folders will be saved. By default this is `~/.torch/hub`.
* Users can change it by either setting the environment variable `TORCH_HUB_DIR` or calling `hub.set_dir(PATH_TO_HUB_DIR)`.
* By default, we don't cleanup files after loading so that users can use cache next time.
* Cache logic :
* We used the cache by default if it exists in `hub_dir`.
* Users can force a fresh reload by calling `hub.load(..., force_reload=True)`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12228
Differential Revision: D10511470
Pulled By: ailzhang
fbshipit-source-id: 12ac27f01d33653f06b2483655546492f82cce38
Summary:
dlpacks deserve documentation. :)
I wonder whether it might make sense to merge the various small torch.utils pages (and include a link for the larger ones, e.g. data) to enhance the structure in the docs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9343
Differential Revision: D8801227
Pulled By: soumith
fbshipit-source-id: 2980d271971743b86f052bec5a2cb4d146a90d9b
* Add device docs; match constructor parameter names with attribute names.
* Use double quotes for strings.
* Update printing.
* Separate device ordinal-only construction into a separate note.
* Use current device.
* Implement torch.util.bottleneck
This is a tool that is intended to be used as initial exploratory
debugging of bottlenecks in user scripts. Run it with
python -m torch.utils.bottleneck /path/to/source/script.py
* Refactor and address comments
* Fix tests
* Allow passing of args to the profiled script
* Replace Variable
This removes the StochasticFunctions for bernoulli, multinomial, and
normal and replaces them with classes in the torch.distributions
package. Each distribution supports the differentiable log_prob function
that returns the log of the pdf/pmf of the samples.
The current StochasticFunction implementation has a few problems: it can
be painful to use when there are multiple stochastic outputs which need
to be back-propagated through. It also requires that we store grad_fns
on Variables that have requires_grad=False in order to find stochastic
nodes.
