Summary:
There is a very common error when writing docs: One forgets to write a matching `` ` ``, and something like ``:attr:`x`` is rendered in the docs. This PR fixes most (all?) of these errors (and a few others).
I found these running ``grep -r ">[^#<][^<]*\`"`` on the `docs/build/html/generated` folder. The regex finds an HTML tag that does not start with `#` (as python comments in example code may contain backticks) and that contains a backtick in the rendered HTML.
This regex has not given any false positive in the current codebase, so I am inclined to suggest that we should add this check to the CI. Would this be possible / reasonable / easy to do malfet ?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60474
Reviewed By: mrshenli
Differential Revision: D29309633
Pulled By: albanD
fbshipit-source-id: 9621e0e9f87590cea060dd084fa367442b6bd046
Summary:
Adds a note explaining the difference between several often conflated mechanisms in the autograd note
Also adds a link to this note from the docs in `grad_mode` and `nn.module`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58513
Reviewed By: gchanan
Differential Revision: D28651129
Pulled By: soulitzer
fbshipit-source-id: af9eb1749b641fc1b632815634eea36bf7979156
Summary:
This adds the methods `Tensor.cfloat()` and `Tensor.cdouble()`.
I was not able to find the tests for `.float()` functions. I'd be happy to add similar tests for these functions once someone points me to them.
Fixes https://github.com/pytorch/pytorch/issues/56014
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58137
Reviewed By: ejguan
Differential Revision: D28412288
Pulled By: anjali411
fbshipit-source-id: ff3653cb3516bcb3d26a97b9ec3d314f1f42f83d
Summary:
Fixes https://github.com/pytorch/pytorch/issues/56243 by adding a note to mutating functions not following the trailing `_` convention in `torch/nn/modules/module.py`
I can also raise separate PRs for other files, if needed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56877
Reviewed By: ezyang
Differential Revision: D28008856
Pulled By: jbschlosser
fbshipit-source-id: 63bfca0df05e49fceadd3167b1427dcb5542206a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54812
Needed for quantization since different attribute might refer to the same module instance
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D27408376
fbshipit-source-id: cada85c4a1772d3dd9502c3f6f9a56d690d527e7
Summary:
This PR adds the functionality to use channals_last_3d, aka, NDHWC, in Conv3d. It's only enabled when cuDNN version is greater than or equal to 8.0.5.
Todo:
- [x] add memory_format test
- [x] add random shapes functionality test
Close https://github.com/pytorch/pytorch/pull/52547
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48430
Reviewed By: mrshenli
Differential Revision: D27641452
Pulled By: ezyang
fbshipit-source-id: 0e98957cf30c50c3390903d307dd43bdafd28880
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
See the discussion here: https://github.com/pytorch/pytorch/pull/50431
~~Not completely done yet - need to figure out the backwards compatibility stuff as well as `RemovableHandle`.~~
~~Also, this concretely breaks Torchscript (which tries to script the properties), and more generally, probably requires modifying Torchscript hook support: https://github.com/pytorch/pytorch/issues/34329~~
Just kidding, I think all problems are solved :)
Another thing I could do in this PR is to simply replace all the `len(x) > 0` checks with the faster checks. That's about 1.5-2k more Python instructions and .4 - .5 microseconds slower.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52576
Reviewed By: ailzhang
Differential Revision: D26650352
Pulled By: Chillee
fbshipit-source-id: 0fd73e916354b9e306701a8a396c5dc051e69f0d
Summary:
Add a new device type 'XPU' ('xpu' for lower case) to PyTorch. Changes are needed for code related to device model and kernel dispatch, e.g. DeviceType, Backend and DispatchKey etc.
https://github.com/pytorch/pytorch/issues/48246
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49786
Reviewed By: mrshenli
Differential Revision: D25893962
Pulled By: ezyang
fbshipit-source-id: 7ff0a316ee34cf0ed6fc7ead08ecdeb7df4b0052
Summary:
Fixes https://github.com/pytorch/pytorch/issues/49726
Just cleaned up the unnecessary `ModuleAttributeError`
BC-breaking note:
`ModuleAttributeError` was added in the previous unsuccessful [PR](https://github.com/pytorch/pytorch/pull/49879) and removed here. If a user catches `ModuleAttributeError` specifically, this will no longer work. They should catch `AttributeError` instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50298
Reviewed By: mrshenli
Differential Revision: D25907620
Pulled By: jbschlosser
fbshipit-source-id: cdfa6b1ea76ff080cd243287c10a9d749a3f3d0a
Summary:
These unused variables were identified by [pyflakes](https://pypi.org/project/pyflakes/). They can be safely removed to simplify the code and possibly improve performance.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50100
Reviewed By: ezyang
Differential Revision: D25797764
Pulled By: smessmer
fbshipit-source-id: ced341aee692f429d2dcc3a4ef5c46c8ee99cabb
Summary:
Fixes https://github.com/pytorch/pytorch/issues/598
This is BC-breaking as we now explicitly don't call the hook when there are not Tensors at the top level of the output.
This feature was not working anyways as the returned grad_input/grad_output were wrong (not respecting the output structure and wrong inputs for multi-Node Module).
This is also BC-breaking as we now report the correct gradients for `nn.Module`s that contain multiple autograd `Node`s while we use to return bad results before.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46163
Reviewed By: ailzhang, mruberry
Differential Revision: D24894180
Pulled By: albanD
fbshipit-source-id: e1b5d193d2818eb2f51e2a2722c7405c8bd13c2b
Summary:
Added a convenience function that allows users to load models without DP/DDP from a DP/DDP state dict.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45643
Reviewed By: rohan-varma
Differential Revision: D24574649
fbshipit-source-id: 17d29ab16ae24a30890168fa84da6c63650e61e9
Summary:
This PR makes it possible to cast the parameters of nn.Module to complex dtypes.
The following code works with the proposed changes.
```python
In [1]: import torch
In [2]: lin = torch.nn.Linear(5, 1).to(torch.complex64)
In [3]: lin(torch.zeros(3, 5, dtype=torch.complex64))
Out[3]:
tensor([[-0.1739+0.j],
[-0.1739+0.j],
[-0.1739+0.j]], grad_fn=<AddmmBackward>)
```
Fixes https://github.com/pytorch/pytorch/issues/43477.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44788
Reviewed By: zou3519
Differential Revision: D24307225
Pulled By: anjali411
fbshipit-source-id: dacc4f5c8c9a99303f74d1f5d807cd657b3b69b5
Summary:
Retake on https://github.com/pytorch/pytorch/issues/40493 after all the feedback from albanD
This PR implements the generic Lazy mechanism and a sample `LazyLinear` layer with the `UninitializedParameter`.
The main differences with the previous PR are two;
Now `torch.nn.Module` remains untouched.
We don't require an explicit initialization or a dummy forward pass before starting the training or inference of the actual module. Making this much simpler to use from the user side.
As we discussed offline, there was the suggestion of not using a mixin, but changing the `__class__` attribute of `LazyLinear` to become `Linear` once it's completely initialized. While this can be useful, by the time being we need `LazyLinear` to be a `torch.nn.Module` subclass since there are many checks that rely on the modules being instances of `torch.nn.Module`.
This can cause problems when we create complex modules such as
```
class MyNetwork(torch.nn.Module):
def __init__(self):
super(MyNetwork, self).__init__()
self.conv = torch.nn.Conv2d(20, 4, 2)
self.linear = torch.nn.LazyLinear(10)
def forward(self, x):
y = self.conv(x).clamp(min=0)
return self.linear(y)
```
Here, when the __setattr__ function is called at the time LazyLinear is registered, it won't be added to the child modules of `MyNetwork`, so we have to manually do it later, but currently there is no way to do such thing as we can't access the parent module from LazyLinear once it becomes the Linear module. (We can add a workaround to this if needed).
TODO:
Add convolutions once the design is OK
Fix docstrings
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44538
Reviewed By: ngimel
Differential Revision: D24162854
Pulled By: albanD
fbshipit-source-id: 6d58dfe5d43bfb05b6ee506e266db3cf4b885f0c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45766
As per subj, making KeyError message more verbose.
Test Plan:
Verified that breakage can be successfully investigated with verbose error message
unit tests
Reviewed By: esqu1
Differential Revision: D24080362
fbshipit-source-id: f4e22a78809e5cff65a69780d5cbbc1e8b11b2e5
Summary:
A small change that adds a docstring that can be found with
`getattr(nn.Module, nn.Module.forward.__name__, None).__doc__`
Fixes https://github.com/pytorch/pytorch/issues/43057
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43063
Reviewed By: mrshenli
Differential Revision: D23161782
Pulled By: ezyang
fbshipit-source-id: 95456f858e2b6a0e41ae551ea4ec2e78dd35ee3f
Summary:
Make function from method
Since _forward_unimplemented is defined within the nn.Module class,
pylint (correctly) complains about not implementing this method in subclasses.
Fixes https://github.com/pytorch/pytorch/issues/42305
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42356
Reviewed By: mruberry
Differential Revision: D22867255
Pulled By: ezyang
fbshipit-source-id: ccf3e45e359d927e010791fadf70b2ef231ddb0b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41283
in optimizer.zero_grad(), detach_ is useful to avoid memory leak only when grad has grad_fn, so add check to call grad.detach_ only when the grad has grad_fn in zero_grad() function
ghstack-source-id: 108702289
Test Plan: unit test
Reviewed By: mrshenli
Differential Revision: D22487315
fbshipit-source-id: 861909b15c8497f1da57f092d8963d4920c85e38
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40807
We pack a lot of logic into `jit/__init__.py`, making it unclear to
developers and users which parts of our API are public vs. internal. This
is one in a series of PRs intended to pull implementation out into
separate files, and leave `__init__.py` as a place to register the
public API.
This PR moves all the tracing-related stuff out, and fixes other spots up
as necessary. Followups will move other core APIs out.
The desired end-state is that we conform to the relevant rules in [PEP 8](https://www.python.org/dev/peps/pep-0008/#public-and-internal-interfaces). In particular:
- Internal implementation goes in modules prefixed by `_`.
- `__init__.py` exposes a public API from these private modules, and nothing more.
- We set `__all__` appropriately to declare our public API.
- All use of JIT-internal functionality outside the JIT are removed (in particular, ONNX is relying on a number internal APIs). Since they will need to be imported explicitly, it will be easier to catch new uses of internal APIs in review.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D22320645
Pulled By: suo
fbshipit-source-id: 0720ea9976240e09837d76695207e89afcc58270
Summary:
Apologize if this seems trivial, but i'd like to fix them on my way of reading some of the source code. Thanks!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40692
Differential Revision: D22284651
Pulled By: mrshenli
fbshipit-source-id: 4259d1808aa4d15a02cfd486cfb44dd75fdc58f8
Summary:
This allows registering hooks that will be executed for every module.
This idea arose in a discussion with tkerola and niboshi kindly proposed this approach.
The use case for this is to avoid boilerplate code when registering the same hook for all the modules in a complex model, the internal use-case was to allow every model to accept a NumPy array in the forward pass in a simpler way. Other use cases involve general mechanisms for plotting or tracing & debugging.
Currently, this is shared for all the modules but this can be worked out to have the hooks shared only per type of module.
If this functionality is not needed feel free to close the PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38972
Differential Revision: D22091364
Pulled By: albanD
fbshipit-source-id: 204ff5f9e119eff5bdd9140c64cb5dc467bb23a2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38211
Just because the annotations are inline doesn't mean the files type
check; most of the newly annotated files have type errors and I
added exclusions for them in mypy.ini. The payoff of moving
all of these modules inline is I can delete the relevant code
generation logic for the pyi files (which was added ignore
annotations that weren't actually relevant anymore.)
For the most part the translation was completely mechanical, but there
were two hairy issues. First, I needed to work around a Python 3.6 and
earlier bug where Generic has a nontrivial metaclass. This fix is in
torch/jit/__init__.py. Second, module.py, we need to apply the same
fix for avoiding contravariance checks that the pyi file used to have;
this is done by declaring forward as a variable (rather than a
function), which appears to be sufficient enough to get mypy to not
contravariantly check input arguments.
Because we aren't actually typechecking these modules in most
cases, it is inevitable that some of these type annotations are wrong.
I slavishly copied the old annotations from the pyi files unless there
was an obvious correction I could make. These annotations will probably
need fixing up later.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D21497397
Pulled By: ezyang
fbshipit-source-id: 2b08bacc152c48f074e7edc4ee5dce1b77d83702
Summary:
jit.ScriptModule deletes all the actual attributes but still uses the nn.Module implementation.
Since I don't know how to add this new set() to the ScriptModule, it is simpler to just raise a nice error for now.
I also reverted the logic so that an empty set() (which is always the case in a ScriptModule) means that everything is persistent.
cc zdevito should we open an issue to add this to the ScriptModule?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38131
Differential Revision: D21502183
Pulled By: albanD
fbshipit-source-id: 96f83098d9a2a9156e8af5bf5bd3526dd0fefc98
Summary:
Fixes https://github.com/pytorch/pytorch/issues/37182
The `zero_grad` wrapper from `_replicate_for_data_parallel` can't be pickled. So instead, I set an attribute `_is_replica = True` and check for this in `Module.zero_grad`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37307
Differential Revision: D21246119
Pulled By: mrshenli
fbshipit-source-id: 4755786d48a20bc247570ba672de9dd526914ce1
Summary:
As described in https://github.com/pytorch/pytorch/issues/33934, the current attribute error in `nn.Module`'s properties are wrong.
```python
from torch import nn
class MyModule(nn.Module):
property
def something(self):
hey = self.unknown_function()
return hey
model = MyModule()
print(model.something)
```
This raises `AttributeError: 'MyModule' object has no attribute 'something'` when what we want is `AttributeError: MyModule instance has no attribute 'unknown_function'`.
This fixes this issue and will make properties much easier to debug !
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34324
Differential Revision: D20645563
Pulled By: ezyang
fbshipit-source-id: 130f861851bdbef43803569a5ce9e24d2b942179
