Summary:
You can find the latest rendered version in the `python_doc_build` CI job below, in the artifact tab of that build on circle CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55966
Reviewed By: H-Huang
Differential Revision: D28032446
Pulled By: albanD
fbshipit-source-id: 227ad37b03d39894d736c19cae3195b4d56fc62f
Summary:
This PR tries to make the docs of `torch.linalg` have/be:
- More uniform notation and structure for every function.
- More uniform use of back-quotes and the `:attr:` directive
- More readable for a non-specialised audience through explanations of the form that factorisations take and when would it be beneficial to use what arguments in some solvers.
- More connected among the different functions through the use of the `.. seealso::` directive.
- More information on when do gradients explode / when is a function silently returning a wrong result / when things do not work in general
I tried to follow the structure of "one short description and then the rest" to be able to format the docs like those of `torch.` or `torch.nn`. I did not do that yet, as I am waiting for the green light on this idea:
https://github.com/pytorch/pytorch/issues/54878#issuecomment-816636171
What this PR does not do:
- Clean the documentation of other functions that are not in the `linalg` module (although I started doing this for `torch.svd`, but then I realised that this PR would touch way too many functions).
Fixes https://github.com/pytorch/pytorch/issues/54878
cc mruberry IvanYashchuk
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56265
Reviewed By: H-Huang
Differential Revision: D27993986
Pulled By: mruberry
fbshipit-source-id: adde7b7383387e1213cc0a6644331f0632b7392d
Summary:
No oustanding issue, can create it if needed.
Was looking for that resource and it was moved without fixing the documentation.
Cheers
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56776
Reviewed By: heitorschueroff
Differential Revision: D27967020
Pulled By: ezyang
fbshipit-source-id: a5cd7d554da43a9c9e44966ccd0b0ad9eef2948c
Summary:
In the optimizer documentation, many of the learning rate schedulers [examples](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) are provided according to a generic template. In this PR we provide a precise simple use case example to show how to use learning rate schedulers. Moreover, in a followup example we show an example how to chain two schedulers next to each other.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56705
Reviewed By: ezyang
Differential Revision: D27966704
Pulled By: iramazanli
fbshipit-source-id: f32b2d70d5cad7132335a9b13a2afa3ac3315a13
Summary:
The pre-amble here is misformatted at least and is hard to make sense of: https://pytorch.org/docs/master/quantization.html#prototype-fx-graph-mode-quantization
This PR is trying to make things easier to understand.
As I'm new to this please verify that my modifications remain in line with what may have been meant originally.
Thanks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52192
Reviewed By: ailzhang
Differential Revision: D27941730
Pulled By: vkuzo
fbshipit-source-id: 6c4bbf7c87d8fb87ab5d588b690a72045752e47a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56528
Tried to search across internal and external usage of DataLoader. People haven't started to use `generator` for `DataLoader`.
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D27908487
Pulled By: ejguan
fbshipit-source-id: 14c83ed40d4ba4dc988b121968a78c2732d8eb93
Summary:
Fixes https://github.com/pytorch/pytorch/issues/53964. cc albanD almson
## Major changes:
- Overhauled the actual loss calculation so that the shapes are now correct (in functional.py)
- added the missing doc in nn.functional.rst
## Minor changes (in functional.py):
- I removed the previous check on whether input and target were the same shape. This is to allow for broadcasting, say when you have 10 predictions that all have the same target.
- I added some comments to explain each shape check in detail. Let me know if these should be shortened/cut.
Screenshots of updated docs attached.
Let me know what you think, thanks!
## Edit: Description of change of behaviour (affecting BC):
The backwards-compatibility is only affected for the `reduction='none'` mode. This was the source of the bug. For tensors with size (N, D), the old returned loss had size (N), as incorrect summation was happening. It will now have size (N, D) as expected.
### Example
Define input tensors, all with size (2, 3).
`input = torch.tensor([[0., 1., 3.], [2., 4., 0.]], requires_grad=True)`
`target = torch.tensor([[1., 4., 2.], [-1., 2., 3.]])`
`var = 2*torch.ones(size=(2, 3), requires_grad=True)`
Initialise loss with reduction mode 'none'. We expect the returned loss to have the same size as the input tensors, (2, 3).
`loss = torch.nn.GaussianNLLLoss(reduction='none')`
Old behaviour:
`print(loss(input, target, var)) `
`# Gives tensor([3.7897, 6.5397], grad_fn=<MulBackward0>. This has size (2).`
New behaviour:
`print(loss(input, target, var)) `
`# Gives tensor([[0.5966, 2.5966, 0.5966], [2.5966, 1.3466, 2.5966]], grad_fn=<MulBackward0>)`
`# This has the expected size, (2, 3).`
To recover the old behaviour, sum along all dimensions except for the 0th:
`print(loss(input, target, var).sum(dim=1))`
`# Gives tensor([3.7897, 6.5397], grad_fn=<SumBackward1>.`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56469
Reviewed By: jbschlosser, agolynski
Differential Revision: D27894170
Pulled By: albanD
fbshipit-source-id: 197890189c97c22109491c47f469336b5b03a23f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56547
**Summary**
This commit tweaks the docstrings of `PackageExporter` so that they look
nicer on the docs website.
**Test Plan**
Continuous integration.
Test Plan: Imported from OSS
Reviewed By: ailzhang
Differential Revision: D27912965
Pulled By: SplitInfinity
fbshipit-source-id: 38c0a715365b8cfb9eecdd1b38ba525fa226a453
Summary:
This PR fixes the formatting issues in the new language reference
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56042
Reviewed By: gmagogsfm
Differential Revision: D27830179
Pulled By: nikithamalgifb
fbshipit-source-id: bce3397d4de3f1536a1a8f0a16f10a703e7d4406
Summary:
Reference: https://github.com/pytorch/pytorch/issues/50345
Changes:
* Add `i0e`
* Move some kernels from `UnaryOpsKernel.cu` to `UnarySpecialOpsKernel.cu` to decrease compilation time per file.
Time taken by i0e_vs_scipy tests: around 6.33.s
<details>
<summary>Test Run Log</summary>
```
(pytorch-cuda-dev) kshiteej@qgpu1:~/Pytorch/pytorch_module_special$ pytest test/test_unary_ufuncs.py -k _i0e_vs
======================================================================= test session starts ========================================================================
platform linux -- Python 3.8.6, pytest-6.1.2, py-1.9.0, pluggy-0.13.1
rootdir: /home/kshiteej/Pytorch/pytorch_module_special, configfile: pytest.ini
plugins: hypothesis-5.38.1
collected 8843 items / 8833 deselected / 10 selected
test/test_unary_ufuncs.py ...sss.... [100%]
========================================================================= warnings summary =========================================================================
../../.conda/envs/pytorch-cuda-dev/lib/python3.8/site-packages/torch/backends/cudnn/__init__.py:73
test/test_unary_ufuncs.py::TestUnaryUfuncsCUDA::test_special_i0e_vs_scipy_cuda_bfloat16
/home/kshiteej/.conda/envs/pytorch-cuda-dev/lib/python3.8/site-packages/torch/backends/cudnn/__init__.py:73: UserWarning: PyTorch was compiled without cuDNN/MIOpen support. To use cuDNN/MIOpen, rebuild PyTorch making sure the library is visible to the build system.
warnings.warn(
-- Docs: https://docs.pytest.org/en/stable/warnings.html
===================================================================== short test summary info ======================================================================
SKIPPED [3] test/test_unary_ufuncs.py:1182: not implemented: Could not run 'aten::_copy_from' with arguments from the 'Meta' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_copy_from' is only available for these backends: [BackendSelect, Named, InplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, UNKNOWN_TENSOR_TYPE_ID, AutogradMLC, AutogradNestedTensor, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].
BackendSelect: fallthrough registered at ../aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Named: registered at ../aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
InplaceOrView: fallthrough registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:56 [backend fallback]
AutogradOther: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
AutogradCPU: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
AutogradCUDA: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
AutogradXLA: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
UNKNOWN_TENSOR_TYPE_ID: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
AutogradMLC: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
AutogradNestedTensor: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
AutogradPrivateUse1: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
AutogradPrivateUse2: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
AutogradPrivateUse3: registered at ../torch/csrc/autograd/generated/VariableType_4.cpp:8761 [autograd kernel]
Tracer: registered at ../torch/csrc/autograd/generated/TraceType_4.cpp:9348 [kernel]
Autocast: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:250 [backend fallback]
Batched: registered at ../aten/src/ATen/BatchingRegistrations.cpp:1016 [backend fallback]
VmapMode: fallthrough registered at ../aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
==================================================== 7 passed, 3 skipped, 8833 deselected, 2 warnings in 6.33s =====================================================
```
</details>
TODO:
* [x] Check rendered docs (https://11743402-65600975-gh.circle-artifacts.com/0/docs/special.html)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54409
Reviewed By: jbschlosser
Differential Revision: D27760472
Pulled By: mruberry
fbshipit-source-id: bdfbcaa798b00c51dc9513c34626246c8fc10548
Summary:
Related to https://github.com/pytorch/pytorch/issues/52256
Use autosummary instead of autofunction to create subpages for optim and cuda functions/classes.
Also fix some minor formatting issues in optim.LBFGS and cuda.stream docstings
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55673
Reviewed By: jbschlosser
Differential Revision: D27747741
Pulled By: zou3519
fbshipit-source-id: 070681f840cdf4433a44af75be3483f16e5acf7d
Summary:
Related to https://github.com/pytorch/pytorch/issues/52256
Use autosummary instead of autofunction to create subpages for autograd functions. I left the autoclass parts intact but manually laid out their members.
Also the Latex formatting of the spcecial page emitted a warning (solved by adding `\begin{align}...\end{align}`) and fixed alignment of equations (by using `&=` instead of `=`).
zou3519
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55672
Reviewed By: jbschlosser
Differential Revision: D27736855
Pulled By: zou3519
fbshipit-source-id: addb56f4f81c82d8537884e0ff243c1e34969a6e
Summary:
Fixes https://github.com/pytorch/pytorch/issues/35901
This change is designed to prevent fragmentation in the Caching Allocator. Permissive block splitting in the allocator allows very large blocks to be split into many pieces. Once split too finely it is unlikely all pieces will be 'free' at that same time so the original allocation can never be returned. Anecdotally, we've seen a model run out of memory failing to alloc a 50 MB block on a 32 GB card while the caching allocator is holding 13 GB of 'split free blocks'
Approach:
- Large blocks above a certain size are designated "oversize". This limit is currently set 1 decade above large, 200 MB
- Oversize blocks can not be split
- Oversize blocks must closely match the requested size (e.g. a 200 MB request will match an existing 205 MB block, but not a 300 MB block)
- In lieu of splitting oversize blocks there is a mechanism to quickly free a single oversize block (to the system allocator) to allow an appropriate size block to be allocated. This will be activated under memory pressure and will prevent _release_cached_blocks()_ from triggering
Initial performance tests show this is similar or quicker than the original strategy. Additional tests are ongoing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44742
Reviewed By: ngimel
Differential Revision: D23752058
Pulled By: ezyang
fbshipit-source-id: ccb7c13e3cf8ef2707706726ac9aaac3a5e3d5c8
Summary:
Related to https://github.com/pytorch/pytorch/issues/52256
Use autosummary instead of autofunction to create subpages for `torch.fft` and `torch.linalg` functions.
zou3519
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55748
Reviewed By: jbschlosser
Differential Revision: D27739282
Pulled By: heitorschueroff
fbshipit-source-id: 37aa06cb8959721894ffadc15ae8c3b83481a319
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55817
**Summary**
This commit makes minor edits to the docstrings of `PackageExporter` so
that they render properly in the `torch.package` API reference.
**Test Plan**
Continuous integration (especially the docs tests).
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D27726817
Pulled By: SplitInfinity
fbshipit-source-id: b81276d7278f586fceded83d23cb4d0532f7c629
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55812
**Summary**
This commit creates a barebones API reference doc for `torch.package`.
The content is sourced from the docstrings in the source for the
`torch.package`.
**Test Plan**
Continuous integration (specifically the docs tests).
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D27726816
Pulled By: SplitInfinity
fbshipit-source-id: 5e9194536f80507e337b81c5ec3b5635d7121818
Summary:
Reference: https://github.com/pytorch/pytorch/issues/50345
Chages:
* Alias for sigmoid and logit
* Adds out variant for C++ API
* Updates docs to link back to `special` documentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54759
Reviewed By: mrshenli
Differential Revision: D27615208
Pulled By: mruberry
fbshipit-source-id: 8bba908d1bea246e4aa9dbadb6951339af353556
Summary:
Related to https://github.com/pytorch/pytorch/issues/52256
Splits torch.nn.functional into a table-of-contents page and many sub-pages, one for each function
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55038
Reviewed By: gchanan
Differential Revision: D27502677
Pulled By: zou3519
fbshipit-source-id: 38e450a0fee41c901eb56f94aee8a32f4eefc807
Summary:
This PR adds `torch.linalg.eig`, and `torch.linalg.eigvals` for NumPy compatibility.
MAGMA uses a hybrid CPU-GPU algorithm and doesn't have a GPU interface for the non-symmetric eigendecomposition. It means that it forces us to transfer inputs living in GPU memory to CPU first before calling MAGMA, and then transfer results from MAGMA to CPU. That is rather slow for smaller matrices and MAGMA is faster than CPU path only for matrices larger than 3000x3000.
Unfortunately, there is no cuSOLVER function for this operation.
Autograd support for `torch.linalg.eig` will be added in a follow-up PR.
Ref https://github.com/pytorch/pytorch/issues/42666
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52491
Reviewed By: anjali411
Differential Revision: D27563616
Pulled By: mruberry
fbshipit-source-id: b42bb98afcd2ed7625d30bdd71cfc74a7ea57bb5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55253
Previously DDP communication hooks takes a tensor list as the input. Now only takes a single tensor, as the preparation of retiring SPMD and only providing a single model replica for DDP communication hooks.
The next step is limiting only 1 model replica in Reducer.
ghstack-source-id: 125677637
Test Plan: waitforbuildbot
Reviewed By: zhaojuanmao
Differential Revision: D27533898
fbshipit-source-id: 5db92549c440f33662cf4edf8e0a0fd024101eae
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55031
It turns out that PowerSGD hooks can work on PyTorch native AMP package, but not Apex AMP package, which can somehow mutate gradients during the execution of communication hooks.
{F561544045}
ghstack-source-id: 125268206
Test Plan:
Used native amp backend for the same pytext model and worked:
f261564342
f261561664
Reviewed By: rohan-varma
Differential Revision: D27436484
fbshipit-source-id: 2b63eb683ce373f9da06d4d224ccc5f0a3016c88
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52859
This reverts commit 92a4ee1cf6.
Added support for bfloat16 for CUDA 11 and removed fast-path for empty input tensors that was affecting autograd graph.
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision: D27402390
Pulled By: heitorschueroff
fbshipit-source-id: 73c5ccf54f3da3d29eb63c9ed3601e2fe6951034
