Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/23417
Test Plan:
cd docs; make html
Imported from OSS
Differential Revision: D16523781
Pulled By: ilia-cher
fbshipit-source-id: d6c09e8a85d39e6185bbdc4b312fea44fcdfff06
Summary:
Covering fleet-wide profiling, api logging, etc.
It's my first time writing rst, so suggestions are definitely welcomed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23010
Differential Revision: D16456721
Pulled By: dzhulgakov
fbshipit-source-id: 3d3018f41499d04db0dca865bb3a9652d8cdf90a
Summary:
This is a modified version of https://github.com/pytorch/pytorch/pull/14705 since commit structure for that PR is quite messy.
1. Add `IterableDataset`.
3. So we have 2 data loader mods: `Iterable` and `Map`.
1. `Iterable` if the `dataset` is an instance of `IterableDataset`
2. `Map` o.w.
3. Add better support for non-batch loading (i.e., `batch_size=None` and `batch_sampler=None`). This is useful in doing things like bulk loading.
3. Refactor `DataLoaderIter` into two classes, `_SingleProcessDataLoaderIter` and `_MultiProcessingDataLoaderIter`. Rename some methods to be more generic, e.g., `get_batch` -> `get_data`.
4. Add `torch.utils.data.get_worker_info` which returns worker information in a worker proc (e.g., worker id, dataset obj copy, etc.) and can be used in `IterableDataset.__iter__` and `worker_init_fn` to do per-worker configuration.
5. Add `ChainDataset`, which is the analog of `ConcatDataset` for `IterableDataset`.
7. Import torch.utils.data in `torch/__init__.py`
9. data loader examples and documentations
10. Use `get_worker_info` to detect whether we are in a worker process in `default_collate`
Closes https://github.com/pytorch/pytorch/issues/17909, https://github.com/pytorch/pytorch/issues/18096, https://github.com/pytorch/pytorch/issues/19946, and some of https://github.com/pytorch/pytorch/issues/13023
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19228
Reviewed By: bddppq
Differential Revision: D15058152
fbshipit-source-id: 9e081a901a071d7e4502b88054a34b450ab5ddde
Summary:
include atomicAdd commentary as this is less well known
There is some discussion in #12207
Unfortunately, I cannot seem to get the ..include working in `_tensor_docs.py` and `_torch_docs.py`. I could use a hint for that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12217
Differential Revision: D10419739
Pulled By: SsnL
fbshipit-source-id: eecd04fb7486bd9c6ee64cd34859d61a0a97ec4e
Summary:
goldsborough Modify the docs to match the changes made in #4999
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12158
Differential Revision: D10103964
Pulled By: SsnL
fbshipit-source-id: 1b8692da86aca1a52e8d2e6cea76a5ad1f71e058
Summary:
This adds a Note on making experiments reproducible.
It also adds Instructions for building the Documentation to `README.md`. Please ping if I missed any requirements.
I'm not sure what to do about the submodule changes. Please advise.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11329
Differential Revision: D9784939
Pulled By: ezyang
fbshipit-source-id: 5c5acbe343d1fffb15bdcb84c6d8d925c2ffcc5e
Summary:
Commits:
1. In extension doc, get rid of all references of `Variable` s (Closes#6947 )
+ also add minor improvements
+ also added a section with links to cpp extension :) goldsborough
+ removed mentions of `autograd.Function.requires_grad` as it's not used anywhere and hardcoded to `return_Py_True`.
2. Fix several sphinx warnings
3. Change `*` in equations in `module/conv.py` to `\times`
4. Fix docs for `Fold` and `Unfold`.
+ Added better shape check for `Fold` (it previously may give bogus result when there are not enough blocks). Added test for the checks.
5. Fix doc saying `trtrs` not available for CUDA (#9247 )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9239
Reviewed By: soumith
Differential Revision: D8762492
Pulled By: SsnL
fbshipit-source-id: 13cd91128981a94493d5efdf250c40465f84346a
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
* Deprecate ctx.saved_variables via python warning.
Advises replacing saved_variables with saved_tensors.
Also replaces all instances of ctx.saved_variables with ctx.saved_tensors in the
codebase.
Test by running:
```
import torch
from torch.autograd import Function
class MyFunction(Function):
@staticmethod
def forward(ctx, tensor1, tensor2):
ctx.save_for_backward(tensor1, tensor2)
return tensor1 + tensor2
@staticmethod
def backward(ctx, grad_output):
var1, var2 = ctx.saved_variables
return (grad_output, grad_output)
x = torch.randn((3, 3), requires_grad=True)
y = torch.randn((3, 3), requires_grad=True)
model = MyFunction()
model.apply(x, y).sum().backward()
```
and assert the warning shows up.
* Address comments
* Add deprecation test for saved_variables
* Add a FAQ, for now just 'out of memory' advice.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Updates based on comments.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* minor copyedit
