Summary:
See NumPy's division documentation here: https://numpy.org/doc/1.18/reference/generated/numpy.divide.html#numpy.divide.
True division is the same as PyTorch's default division except when both inputs are integer or bool tensors. In the latter case the inputs are (conceptually) cast to the default floating type before the division is performed.
The function is implemented for dense and sparse tensors and supports exporting to ONNX from PyTorch's eager mode or JIT traces. The function is inherently incompatible with exporting to ONNX via JIT script, and is another datapoint suggesting we should deprecate exporting scripted graphs to ONNX.
Tests are added for the type promotion, named tensor, and ONNX export behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34236
Reviewed By: houseroad
Differential Revision: D20334087
Pulled By: mruberry
fbshipit-source-id: 83d00d886f46f713215d7d9e02ffd043164c57f1
Summary:
Improves explanation of non-determinism when running on GPUs. Adds info about `torch.nn.BCELoss` operating non-deterministically on GPUs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33795
Differential Revision: D20284880
Pulled By: ngimel
fbshipit-source-id: d543959636d261a80c234150304344b19a37ba5d
Summary:
When docs are built, conf.py points to a _templates-stable/layout.html that does not exist.
Adding this file here so future stable docs will build with Google Analytics tags and without the unstable able that is in _templates/layout.html
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33770
Differential Revision: D20164895
Pulled By: jlin27
fbshipit-source-id: 5fca9f9b825b1484dab52e2b2d91f92ae6372371
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34081
Before this commit, applications have to do the following to configure
number of threads in ProcessGroup RPC backend:
```
op = ProcessGroupRpcBackendOptions()
op.rpc_timeout = rpc_timeout
op.init_method = init_method
op.num_send_recv_threads = 32
init_rpc(...., rpc_backend_options=op)
```
After this commit, it can be simplified to:
```
init_rpc(...., rpc_backend_options=ProcessGroupRpcBackendOptions(num_send_recv_threads=32))
```
Fixes#34075
Test Plan: Imported from OSS
Differential Revision: D20227344
Pulled By: mrshenli
fbshipit-source-id: def4318e987179b8c8ecca44d7ff935702c8a6e7
Summary:
Fixes https://github.com/pytorch/pytorch/issues/33182
This adds private API functions that developers of types that implement `__torch_function__` can use to ensure full coverage of the subset of the PyTorch API that can be overrided.
I've refactored some of the code in the tests into a new `torch._overrides.get_overridable_functions` function. I've also changed `TENSOR_LIKE_TORCH_OVERRIDES` into `torch._overrides.get_testing_overrides` and `IGNORED_TORCH_FUNCTIONS` into `torch._overrides.get_ignored_functions`. Making these two static global variables in the tests into functions should allow rewriting their implementation to construct their return values instead of just statically defining the return value as is done here. Currently that is blocked on not being able to inspect function signatures of compiled kernels in PyTorch (see https://github.com/pytorch/pytorch/issues/28233). See the docs I've added for usage examples of these new functions. I also refactored the existing override tests to make use of these new functions, which should be a good forcing function to make sure they're kept up-to-date.
Finally, while working on this I discovered that `TestTorchFunctionOverrides.test_mean` and `TestTorchFunctionOverrides.test_mm` weren't ever being run because they were getting clobbered by the other dynamically generated override tests. I fixed that by renaming the tests and then fixing the actual test code. I've verified that all the subclassing semantics is correct and that the updated test answers are correct. I'm happy to put the fixes to the existing tests in as a separate pull request if that would be easier to review.
ping cpuhrsch since the feature request originally came from them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33791
Differential Revision: D20195053
Pulled By: cpuhrsch
fbshipit-source-id: 1585f4e405f5223932b410eae03a288dc8eb627e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33705
The fact that there were two overloads appears to be a historical
artifact that dates back to when goldsborough originally added these
bindings in the first place. If TensorOptions is made optional,
then you only need one overload, not two, as they are exactly redundant
with each other. When MemoryFormat was added, it was made a little
harder to do this, as the C++ syntax at::empty_like(t, memory_format) would
not work if you collapsed the overload; but now it works because TensorOptions
supports MemoryFormat.
The upshot is, I can get rid of all the overloads and just have one overload.
Amazingly, this change is backwards compatible, as the test attests. While
I was at it, I also deleted the overload name from the functions entirely.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D20073355
Pulled By: bhosmer
fbshipit-source-id: c6a8908213b32ccf6737ea864d135e2cce34f56b
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33711Fixed#33480
This makes `dist_autograd.backward` and `dist_optimizer.step` functional by making the user explicitly pass in the `context_id` as opposed to relying on the confusing thread_local context_id.
This diff incorporates these API changes and all places where these functions are called.
More concretely, this code:
```
with dist_autograd.context():
# Forward pass.
dist_autograd.backward([loss.sum()])
dist_optim.step()
```
should now be written as follows:
```
with dist_autograd.context() as context_id:
# Forward pass.
dist_autograd.backward(context_id, [loss.sum()])
dist_optim.step(context_id)
```
Test Plan: Ensuring all existing dist_autograd and dist_optimizer tests pass with the new API. Also added a new test case for input checking.
Differential Revision: D20011710
fbshipit-source-id: 216e12207934a2a79c7223332b97c558d89d4d65
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:
Addressing issue https://github.com/pytorch/pytorch/issues/18125
This implements a mixture distributions, where all components are from the same distribution family. Right now the implementation supports the ```mean, variance, sample, log_prob``` methods.
cc: fritzo and neerajprad
- [x] add import and `__all__` string in `torch/distributions/__init__.py`
- [x] register docs in docs/source/distributions.rst
### Tests
(all tests live in tests/distributions.py)
- [x] add an `Example(MixtureSameFamily, [...])` to the `EXAMPLES` list,
populating `[...]` with three examples:
one with `Normal`, one with `Categorical`, and one with `MultivariateNormal`
(to exercise, `FloatTensor`, `LongTensor`, and nontrivial `event_dim`)
- [x] add a `test_mixture_same_family_shape()` to `TestDistributions`. It would be good to test this with both `Normal` and `MultivariateNormal`
- [x] add a `test_mixture_same_family_log_prob()` to `TestDistributions`.
- [x] add a `test_mixture_same_family_sample()` to `TestDistributions`.
- [x] add a `test_mixture_same_family_shape()` to `TestDistributionShapes`
### Triaged for follup-up PR?
- support batch shape
- implement `.expand()`
- implement `kl_divergence()` in torch/distributions/kl.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22742
Differential Revision: D19899726
Pulled By: ezyang
fbshipit-source-id: 9c816e83a2ef104fe3ea3117c95680b51c7a2fa4
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/33083
Added more recommendations, some notes and warning
Test Plan: cd docs ; make html
Differential Revision: D19829133
Pulled By: ilia-cher
fbshipit-source-id: b9fbd89f5875b3ce35cc42ba75a3b44bb132c506
Summary:
* New ops supported for exporting.
* Updates on support for tensor indexing and dynamic list of tensors.
* lara-hdr, spandantiwari Should we also include updates on torchvision support in this page?
cc houseroad, neginraoof Please review if I have missed anything.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32805
Reviewed By: hl475
Differential Revision: D19635699
Pulled By: houseroad
fbshipit-source-id: b6be4fce641f852dcbceed20b4433f4037d8024a
Summary:
"in_features" and "out_features" are not defined. Possibly a typo. They should be "input_features" and "output_features" instead
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31682
Differential Revision: D19251685
Pulled By: zou3519
fbshipit-source-id: ac9e524e792a1853a16e8876d76b908495d8f35e
Summary:
Add a section for unsupported ops, and modules. Automatically generate the properties and attributes that aren't bound, and for ops that have semantic mismatches set up tests so the docs stay up to date.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31329
Differential Revision: D19164472
Pulled By: eellison
fbshipit-source-id: 46290bb8a64d9de928cfb1eda5ff4558c3799c88
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31069
Just to clarify that they are still experimental.
Test Plan: Imported from OSS
Differential Revision: D18920496
Pulled By: suo
fbshipit-source-id: d2f3014592a01a21f7fc60a4ce46dd0bfe5e19e9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31068
Let's get it out of the early parts now that the recursive API has been
around for a while
Test Plan: Imported from OSS
Differential Revision: D18920498
Pulled By: suo
fbshipit-source-id: 6f4389739dd9e7e5f3014811b452249cc21d88e7
Summary:
Adds `torch.floor_divide` following the numpy's `floor_divide` api. I only implemented the out-of-place version, I can add the inplace version if requested.
Also fixes https://github.com/pytorch/pytorch/issues/27512
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30493
Differential Revision: D18896211
Pulled By: eellison
fbshipit-source-id: ee401c96ab23a62fc114ed3bb9791b8ec150ecbd
Summary:
This is a re-do of https://github.com/pytorch/pytorch/issues/27064, which was reverted (b8792c0438). This was landed at the same time as other work that added new operators to the `torch` namespace so the check for whether the `torch` namespace is exhaustively checked for overridability was triggering test failures.
I've temporarily disabled that check and added an explanatory comment that the check will be re-enabled in a future PR that will be merged during a time when the commit velocity on PyTorch is lower.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30730
Differential Revision: D18813270
Pulled By: ezyang
fbshipit-source-id: 70477c4656dca8fea6e7bc59259555041fcfbf68
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30467
Introduce function jit.export_opnames(module), which returns a list of all operator names used in the module and its submodules. One usage is to have mobile custom build to link only operators in the returned list to save the mobile size.
Example:
import torch
m = torch.jit.load("example.pt")
print(torch.jit.export_opnames(m))
The outputs are in alphabetical order:
['aten::_convolution', 'aten::add.Tensor', 'aten::add_.Tensor', 'aten::addmm', 'aten::append.Tensor', 'aten::cat', 'aten::dropout', 'aten::embedding', 'aten::matmul', 'aten::max.dim', 'aten::mul.Tensor', 'aten::permute', 'aten::relu', 'aten::t', 'aten::tanh', 'prim::ListConstruct', 'prim::TupleConstruct', 'prim::TupleUnpack']
Test Plan: Imported from OSS
Differential Revision: D18801619
Pulled By: iseeyuan
fbshipit-source-id: f9b198d3e82b095daf704ee595d8026ad889bb13
Summary:
With the CI failure caused in 8bbafa0b32 fixed (incorrect return type of the lambdas in CUDA kernels)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30521
Differential Revision: D18770151
Pulled By: ailzhang
fbshipit-source-id: 02f0fe1d5718c34d24da6dbb5884ee8b247ce39a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30491
Our RPC API docs presents the APIs well but misses a general
introduction to the APIs. Readers might be a little lost the first
time landing this page. This commits reorganizes the APIs into
four components from user's perspective, RPC, RRef, dist autograd,
and dist optimizer. It also adds an intro to each and briefly
discribes why we provide those.
Test Plan: Imported from OSS
Differential Revision: D18723294
Pulled By: mrshenli
fbshipit-source-id: 4aced4ab537b070aa780aaaf9724659fd47cb3cb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30330
This is now possible due to previous changes made in `gloo` and `ProcessGroupGloo`. We `abort` the listener thread that is waiting for a message, and join all other threads. The API is changed so that the previous `wait_all_workers` does not destroy the agent, and this is now done in a new `shutdown` method. All callsites are updated appropriately.
ghstack-source-id: 94673884
ghstack-source-id: 94673884
Test Plan: Unit tests pass.
Reviewed By: mrshenli
Differential Revision: D18661775
fbshipit-source-id: 5aaa7c14603e18253394224994f6cd43234301c2
Summary:
This adds a listing of the parts of the `typing` module that are unsupported
This is also a first pass decisions on features are 'unlikely to be implemented' vs 'not implemented' so they're open to discussion
](https://our.intern.facebook.com/intern/diff/18665628/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30344
Pulled By: driazati
Differential Revision: D18665628
fbshipit-source-id: 22b8ebbde23df03839306cdb4344ca18a44f2c29
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30208
Adds default arg for init_method so users don't have to pass this in,
and moves it to `RpcBackendOptions` struct. Removes `init_method` arg from rpc.init_rpc. Also fixes some docs.
ghstack-source-id: 94500475
Test Plan: Unit tests pass.
Reviewed By: mrshenli
Differential Revision: D18630074
fbshipit-source-id: 04b7dd7ec96f4c4da311b71d250233f1f262135a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30020
This is now possible due to previous changes made in `gloo` and `ProcessGroupGloo`. We `abort` the listener thread that is waiting for a message, and join all other threads. The destructor calls this same `localShutdown` method, but we ensure this is not called multiple times.
ghstack-source-id: 94415336
Test Plan: Unit tests pass.
Differential Revision: D5578006
fbshipit-source-id: 6258879fb44c9fca97fdfad64468c1488c16ac02
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30240
Get rid of the following warning when build docs:
```
/Users/shenli/Project/pytorch/docs/source/notes/rref.rst:184: WARNING: Error in "code" directive:
maximum 1 argument(s) allowed, 6 supplied.
.. code::
import torch
import torch.distributed.rpc as rpc
# on worker A
rref = rpc.remote('B', torch.add, args=(torch.ones(2), 1))
# say the rref has RRefId 100 and ForkId 1
rref.to_here()
```
Test Plan: Imported from OSS
Differential Revision: D18640016
Pulled By: mrshenli
fbshipit-source-id: d527827f01183411d4b4c73e0a976bdd7fccbf49
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30052
Some of the examples provided in `rpc/api.py` were not updated along
with the code changes, this PR updates them. Also removes the
`dist.ProcessGroup` information since `init_rpc` now initializes a default
process group.
ghstack-source-id: 94273004
Test Plan: Unit tests pass
Differential Revision: D18582596
fbshipit-source-id: a637683f0221f9600f7e50b74e9f7e5a1d331d8f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30066
This commit adds design reasoning and walks through four scenarios
for RRef.
Test Plan: Imported from OSS
Reviewed By: rohan-varma
Differential Revision: D18595094
Pulled By: mrshenli
fbshipit-source-id: 134102901ce515a44a2e7cd013b62143a6158120
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30050
Renames this API to wait_all_workers as discussed.
ghstack-source-id: 94273005
Test Plan: Unit tests pass
Differential Revision: D18581466
fbshipit-source-id: 4ff5d5fb2d528f17252d5b5f30c3047d2efb92bf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30160
The path torch.distributed.rpc.api is an implementation detail, which
should not be used by applications to import RPC APIs. Instead, all
RPC APIs are exposed directly as torch.distributed.rpc.*. This
commit makes the API doc consistent with the above expectation.
Test Plan: Imported from OSS
Differential Revision: D18616359
Pulled By: mrshenli
fbshipit-source-id: 8207f7d36c24cf55af737c03a27fd1896c231641
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29762
Rename this API as discussed, since it's use cases extend beyond only
model parallelism.
ghstack-source-id: 94020627
Test Plan: Unit tests pass
Differential Revision: D18491743
fbshipit-source-id: d07676bb14f072c64da0ce99ee818bcc582efc57
Summary:
Small fixes to rpc docs:
- mark as experimental and subject to change
- Reference the distributed autograd design document in pytorch notes page.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29857
Differential Revision: D18526252
Pulled By: rohan-varma
fbshipit-source-id: e09757fa60a9f8fe9c76a868a418a1cd1c300eae
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29927
With the docs page now up, we can update the links in the design doc
to point to the docs page.
ghstack-source-id: 94055423
Test Plan: waitforbuildbot
Differential Revision: D18541878
fbshipit-source-id: f44702d9a8296ccc0a5d58d56c3b6dc8a822b520
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29175
Updates our docs to include a design doc for distributed autograd.
Currently, this doc only covers the FAST mode algorithm. The Smart mode
algorithm section just refers to the original RFC.
There is a section for Distributed Optimizer that we can complete once we've
finalized the API for the same.
ghstack-source-id: 93701129
Test Plan: look at docs.
Differential Revision: D18318949
fbshipit-source-id: 670ea1b6bb84692f07facee26946bbc6ce8c650c
Summary:
cumsum/cumprod perform their own respective operations over a desired dimension, but there is no reduction in dimensions in the process, i.e. they are not reduction operations and hence just keep the input names of the tensor on which the operation is performed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29453
Differential Revision: D18455683
Pulled By: anjali411
fbshipit-source-id: 9e250d3077ff3d8f3405d20331f4b6ff05151a28
Summary:
Fixes https://github.com/pytorch/pytorch/issues/28658
I have added the link to the docs for `flatten_parameters`.
RNNBase is a superclass of RNN, LSTM and GRM classes. Should I add a link to `flatten_parameters()` in those sections as well ?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29196
Differential Revision: D18326815
Pulled By: ezyang
fbshipit-source-id: 4239019112e77753a0820aea95c981a2c868f5b0
Summary:
At the encouragement of Pyro developers and https://github.com/pytorch/pytorch/issues/13811, I have opened this PR to move the (2D) von Mises distribution upstream.
CC: fritzo neerajprad
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17168
Differential Revision: D18249048
Pulled By: ezyang
fbshipit-source-id: 3e6df9006c7b85da7c4f55307c5bfd54c2e254e6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27254
`MultiplicativeLR` consumes a function providing the multiplicative factor at each epoch. It mimics `LambdaLR` in its syntax.
Test Plan: Imported from OSS
Differential Revision: D17728088
Pulled By: vincentqb
fbshipit-source-id: 1c4a8e19a4f24c87b5efccda01630c8a970dc5c9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27927
This fixes
`WARNING: html_static_path entry '_images' does not exist`
by removing '_images' from conf.py. As far as I can tell, '_images' in
`html_static_path` is only necessary if images already exist in the
`_images` folder; otherwise, sphinx is able to auto-generate _images
into the build directory and populate it correctly.
Test Plan: - build and view the docs locally.
Differential Revision: D17915109
Pulled By: zou3519
fbshipit-source-id: ebcc1f331475f52c0ceadd3e97c3a4a0d606e14b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27850
Many of these are real problems in the documentation (i.e., link or
bullet point doesn't display correctly).
Test Plan: - built and viewed the documentation for each change locally.
Differential Revision: D17908123
Pulled By: zou3519
fbshipit-source-id: 65c92a352c89b90fb6b508c388b0874233a3817a
Summary:
People get confused with partial support otherwise: https://github.com/pytorch/pytorch/issues/27811#27729
Suggestions on where else put warnings are welcomed (probably in tutorials - cc SethHWeidman )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27829
Differential Revision: D17910931
Pulled By: dzhulgakov
fbshipit-source-id: 37a169a4bef01b94be59fe62a8f641c3ec5e9b7c
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:
This was written by Raghu, Jessica, Dmytro and myself.
This PR will accumulate additional changes (there are a few more things we need to add to this actual rst file). I'll probably add the related image files to this PR as well.
I'm breaking draft PR https://github.com/pytorch/pytorch/pull/27553 into more easily digestible pieces.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27559
Differential Revision: D17843414
Pulled By: gottbrath
fbshipit-source-id: 434689f255ac1449884acf81f10e0148d0d8d302
Summary:
Added Complex support with AVX to unary ops and binary ops.
I need to add nan propagation to minimum() and maximum() in the future.
In-tree changes to pytorch to support complex numbers are being submitted here.
Out-of-tree support for complex numbers is here: pytorch-cpu-strided-complex extension
Preliminary Benchmarks are here.
I tried rrii and riri and found that riri is better in most situations.
Divide is very slow because you can't reduce 1/(x+y)
Sqrt is also very slow.
Reciprocal could be sped up after I add conj()
Everything else is typically within 20% of the real number performance.
Questions:
Why does macOS not support mil? #if AT_MKL_ENABLED() && !defined(__APPLE__) in vml.h. MKL does support some complex operations like Abs, so I was curious about trying it.
Is MKL just calling AVX?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26500
Differential Revision: D17835431
Pulled By: ezyang
fbshipit-source-id: 6746209168fbeb567af340c22bf34af28286bd54
Summary:
According to https://github.com/pytorch/pytorch/issues/27285 , seems we do not intend to use shebang as an indication of Python version, thus
we enable EXE001 flake8 check.
For violations, we either remove shebang from non-executable Python scripts or grant them executable permission.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27560
Differential Revision: D17831782
Pulled By: ezyang
fbshipit-source-id: 6282fd3617b25676a6d959af0d318faf05c09b26
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 comprehensive memory instrumentation to the CUDA caching memory allocator.
# Counters
Added comprehensive instrumentation for the following stats:
- Allocation requests (`allocation`)
- Allocated memory (`allocated_bytes`)
- Reserved segments from cudaMalloc (`segment`)
- Reserved memory (`reserved_bytes`)
- Active memory blocks (`active`)
- Active memory (`active_bytes`)
- Inactive, non-releasable blocks (`inactive_split`)
- Inactive, non-releasable memory (`inactive_split_bytes`)
- Number of failed cudaMalloc calls that result in a cache flush and retry (`cuda_malloc_retries`)
- Number of OOMs (`num_ooms`)
Except for the last two, these stats are segmented between all memory, large blocks, and small blocks. Along with the current value of each stat, historical counts of allocs/frees as well as peak usage are tracked by the allocator.
# Snapshots
Added the capability to get a "memory snapshot" – that is, to generate a complete dump of the allocator block/segment state.
# Implementation: major changes
- Added `torch.cuda.memory_stats()` (and associated C++ changes) which returns all instrumented stats as a dictionary.
- Added `torch.cuda.snapshot()` (and associated C++ changes) which returns a complete dump of the allocator block/segment state as a list of segments.
- Added memory summary generator in `torch.cuda.memory_summary()` for ease of client access to the instrumentation stats. Potentially useful to dump when catching OOMs. Sample output here: https://pastebin.com/uKZjtupq
# Implementation: minor changes
- Add error-checking helper functions for Python dicts and lists in `torch/csrc/utils/`.
- Existing memory management functions in `torch.cuda` moved from `__init__.py` to `memory.py` and star-imported to the main CUDA module.
- Add various helper functions to `torch.cuda` to return individual items from `torch.cuda.memory_stats()`.
- `torch.cuda.reset_max_memory_cached()` and `torch.cuda.reset_max_memory_allocated()` are deprecated in favor of `reset_peak_stats`. It's a bit difficult to think of a case where only one of those stats should be reset, and IMO this makes the peak stats collectively more consistent.
- `torch.cuda.memory_cached()` and `torch.cuda.max_memory_cached()` are deprecated in favor of `*memory_reserved()`.
- Style (add access modifiers in the allocator class, random nit fixes, etc.)
# Testing
- Added consistency check for stats in `test_cuda.py`. This verifies that the data from `memory_stats()` is faithful to the data from `snapshot()`.
- Ran on various basic workflows (toy example, CIFAR)
# Performance
Running the following speed benchmark: https://pastebin.com/UNndQg50
- Before this PR: 45.98 microseconds per tensor creation
- After this PR: 46.65 microseconds per tensor creation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27361
Differential Revision: D17758747
Pulled By: jma127
fbshipit-source-id: 5a84e82d696c40c505646b9a1b4e0c3bba38aeb6
Summary:
10 lines of error context (on both sides) is overkill, especially now
that we have line numbers. With a compilation stack of a couple
functions, it becomes a pain to scroll to the top of the stack to see
the real error every time.
This also fixes class names in the compilation stack to a format of
`ClassName.method_name` instead of the the full qualified name
Old output
```
clip_boxes_to_image(Tensor boxes, (int, int) size) -> (Tensor):
Expected a value of type 'Tuple[int, int]' for argument 'size' but instead found type 'Tuple[int, int, int]'.
:
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:365:20
top_n_idx = self._get_top_n_idx(objectness, num_anchors_per_level)
batch_idx = torch.arange(num_images, device=device)[:, None]
objectness = objectness[batch_idx, top_n_idx]
levels = levels[batch_idx, top_n_idx]
proposals = proposals[batch_idx, top_n_idx]
final_boxes = []
final_scores = []
for boxes, scores, lvl, img_shape in zip(proposals, objectness, levels, image_shapes):
boxes = box_ops.clip_boxes_to_image(boxes, img_shape)
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
keep = box_ops.remove_small_boxes(boxes, self.min_size)
boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep]
# non-maximum suppression, independently done per level
keep = box_ops.batched_nms(boxes, scores, lvl, self.nms_thresh)
# keep only topk scoring predictions
keep = keep[:self.post_nms_top_n]
boxes, scores = boxes[keep], scores[keep]
final_boxes.append(boxes)
final_scores.append(scores)
'RegionProposalNetwork.filter_proposals' is being compiled since it was called from 'RegionProposalNetwork.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:446:8
num_images = len(anchors)
num_anchors_per_level = [o[0].numel() for o in objectness]
objectness, pred_bbox_deltas = \
concat_box_prediction_layers(objectness, pred_bbox_deltas)
# apply pred_bbox_deltas to anchors to obtain the decoded proposals
# note that we detach the deltas because Faster R-CNN do not backprop through
# the proposals
proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors)
proposals = proposals.view(num_images, -1, 4)
boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
losses = {}
if self.training:
assert targets is not None
labels, matched_gt_boxes = self.assign_targets_to_anchors(anchors, targets)
regression_targets = self.box_coder.encode(matched_gt_boxes, anchors)
loss_objectness, loss_rpn_box_reg = self.compute_loss(
objectness, pred_bbox_deltas, labels, regression_targets)
losses = {
'RegionProposalNetwork.forward' is being compiled since it was called from 'MaskRCNN.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/generalized_rcnn.py:53:8
"""
if self.training and targets is None:
raise ValueError("In training mode, targets should be passed")
original_image_sizes = [(img.shape[-2], img.shape[-3]) for img in images]
images, targets = self.transform(images, targets)
features = self.backbone(images.tensors)
if isinstance(features, torch.Tensor):
features = OrderedDict([(0, features)])
proposals, proposal_losses = self.rpn(images, features, targets)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)
losses = {}
losses.update(detector_losses)
losses.update(proposal_losses)
# TODO: multiple return types??
# if self.training:
```
New output
```
RuntimeError:
clip_boxes_to_image(Tensor boxes, (int, int) size) -> (Tensor):
Expected a value of type 'Tuple[int, int]' for argument 'size' but instead found type 'Tuple[int, int, int]'.
:
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:365:20
final_scores = []
for boxes, scores, lvl, img_shape in zip(proposals, objectness, levels, image_shapes):
boxes = box_ops.clip_boxes_to_image(boxes, img_shape)
~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
keep = box_ops.remove_small_boxes(boxes, self.min_size)
boxes, scores, lvl = boxes[keep], scores[keep], lvl[keep]
'RegionProposalNetwork.filter_proposals' is being compiled since it was called from 'RegionProposalNetwork.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/rpn.py:446:8
proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors)
proposals = proposals.view(num_images, -1, 4)
boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
losses = {}
'RegionProposalNetwork.forward' is being compiled since it was called from 'MaskRCNN.forward'
at /home/davidriazati/dev/vision/torchvision/models/detection/generalized_rcnn.py:53:8
if isinstance(features, torch.Tensor):
features = OrderedDict([(0, features)])
proposals, proposal_losses = self.rpn(images, features, targets)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
detections = self.transform.postprocess
```
](https://our.intern.facebook.com/intern/diff/17560963/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26765
Pulled By: driazati
Differential Revision: D17560963
fbshipit-source-id: e463548744b505ca17f0158079b80e08fda47d49
Summary:
Adds the method `add_hparams` to `torch.utils.tensorboard` API docs. Will want to have this in PyTorch 1.3 release.
cc sanekmelnikov lanpa natalialunova
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27344
Differential Revision: D17753689
Pulled By: orionr
fbshipit-source-id: cc8636e0bdcf3f434444cd29471c62105491039d
Summary:
Resubmit of https://github.com/pytorch/pytorch/pull/25980.
Our old serialization was in tar (like `resnet18-5c106cde.pth` was in this format) so let's only support automatically unzip if checkpoints are zipfiles.
We can still manage to get it work with tarfile, but let's delay it when there's an ask.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26723
Differential Revision: D17551795
Pulled By: ailzhang
fbshipit-source-id: 00b4e7621f1e753ca9aa07b1fe356278c6693a1e
Summary:
This PR does a few small improvements to hub:
- add support `verbose` option in `torch.load`. Note that this mutes hitting cache message but keeps the message of first download as suggested. fixes https://github.com/pytorch/pytorch/issues/24791
- add support loading state dict from tar file or zip file in `torch.hub.load_state_dict_from_url`.
- add `torch.hub.download_url_to_file` as public API, and add BC bit for `_download_url_to_file`.
- makes hash check in filename optional through `check_hash`, many users don't have control over the naming, relaxing this constraint could potentially avoid duplicating download code on user end.
- move pytorch CI off `pytorch/vision` and use `ailzhang/torchhub_example` as a dedicated test repo. fixes https://github.com/pytorch/pytorch/issues/25865
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25980
Differential Revision: D17495679
Pulled By: ailzhang
fbshipit-source-id: 695df3e803ad5f9ca33cfbcf62f1a4f8cde0dbbe
Summary:
Changelog:
- Remove `torch.gels` which was deprecated in v1.2.0
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26480
Test Plan: - No tests were changed and all callsites for `torch.gels` where modified to `torch.lstsq` when `torch.lstsq` was introduced
Differential Revision: D17527207
Pulled By: zou3519
fbshipit-source-id: 28e2fa3a3bf30eb6b9029bb5aab198c4d570a950
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26240
In particular adds support for empty/empty_like which is needed for memory layouts to work.
Test Plan: Imported from OSS
Differential Revision: D17443220
Pulled By: dzhulgakov
fbshipit-source-id: 9c9e25981999c0edaf40be104a5741e9c62a1333
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25263
This adds an api to return true in script and false in eager, which together with ignore allows guarding of not yet supported JIT features. Bikeshedding requested please.
cc zou3519
```
def foo():
if not torch.jit.is_scripting():
return torch.linear(...)
else:
return addmm(...)
```
Test Plan: Imported from OSS
Differential Revision: D17272443
Pulled By: eellison
fbshipit-source-id: de0f769c7eaae91de0007b98969183df93a91f42
Summary:
Improve handling of mixed-type tensor operations.
This PR affects the arithmetic (add, sub, mul, and div) operators implemented via TensorIterator (so dense but not sparse tensor ops).
For these operators, we will now promote to reasonable types where possible, following the rules defined in https://github.com/pytorch/pytorch/issues/9515, and error in cases where the cast would require floating point -> integral or non-boolean to boolean downcasts.
The details of the promotion rules are described here:
https://github.com/nairbv/pytorch/blob/promote_types_strict/docs/source/tensor_attributes.rst
Some specific backwards incompatible examples:
* now `int_tensor * float` will result in a float tensor, whereas previously the floating point operand was first cast to an int. Previously `torch.tensor(10) * 1.9` => `tensor(10)` because the 1.9 was downcast to `1`. Now the result will be the more intuitive `tensor(19)`
* Now `int_tensor *= float` will error, since the floating point result of this operation can't be cast into the in-place integral type result.
See more examples/detail in the original issue (https://github.com/pytorch/pytorch/issues/9515), in the above linked tensor_attributes.rst doc, or in the test_type_promotion.py tests added in this PR:
https://github.com/nairbv/pytorch/blob/promote_types_strict/test/test_type_promotion.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22273
Reviewed By: gchanan
Differential Revision: D16582230
Pulled By: nairbv
fbshipit-source-id: 4029cca891908cdbf4253e4513c617bba7306cb3
Summary:
All of the code examples should now run as unit tests, save for those
that require interaction (i.e. show `pdb` usage) and those that use
CUDA.
`save` had to be moved before `load` in `jit/__init__.py` so `load`
could use the file generated by `save`
](https://our.intern.facebook.com/intern/diff/17192417/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25668
Pulled By: driazati
Differential Revision: D17192417
fbshipit-source-id: 931b310ae0c3d2cc6affeabccae5296f53fe42bc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25262
Preserve the type of ignore'd functions on serialization. Currently we first compile an ignore'd function with it's annotated type when first compiling, but do not preserve it. This is important for being able to compile models with not-yet-supported features in JIT.
```
torch.jit.ignore
def unsupported(x):
return x
def foo():
if not torch.jit._is_scripting():
return torch.linear(...)
else:
return unsupported(...)
```
Test Plan: Imported from OSS
Reviewed By: driazati
Differential Revision: D17199043
Pulled By: eellison
fbshipit-source-id: 1196fd94c207b9fbee1087e4b2ef7d4656a6647f
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:
I think...
I'm having issues building the site, but it appears to get rid of the error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25544
Differential Revision: D17157327
Pulled By: ezyang
fbshipit-source-id: 170235c52008ca78ff0d8740b2d7f5b67397b614
Summary:
I presume this is what was intended.
cc t-vi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25011
Differential Revision: D16980939
Pulled By: soumith
fbshipit-source-id: c55b22e119f3894bd124eb1dce4f92a719ac047a
Summary:
Another pass over the docs, this covers most of the remaining stuff
* content updates for new API
* adds links to functions instead of just names
* removes some useless indentations
* some more code examples + `testcode`s
](https://our.intern.facebook.com/intern/diff/16847964/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24445
Pulled By: driazati
Differential Revision: D16847964
fbshipit-source-id: cd0b403fe4a89802ce79289f7cf54ee0cea45073
Summary:
Stacked PRs
* #24445 - [jit] Misc doc updates #2
* **#24435 - [jit] Add docs to CI**
This integrates the [doctest](http://www.sphinx-doc.org/en/master/usage/extensions/doctest.html) module into `jit.rst` so that we can run our code examples as unit tests. They're added to `test_jit.py` under the `TestDocs` class (which takes about 30s to run). This should help prevent things like #24429 from happening in the future. They can be run manually by doing `cd docs && make doctest`.
* The test setup requires a hack since `doctest` defines everything in the `builtins` module which upsets `inspect`
* There are several places where the code wasn't testable (i.e. it threw an exception on purpose). This may be resolvable, but I'd prefer to leave that for a follow up. For now there are `TODO` comments littered around.
](https://our.intern.facebook.com/intern/diff/16840882/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24435
Pulled By: driazati
Differential Revision: D16840882
fbshipit-source-id: c4b26e7c374cd224a5a4a2d523163d7b997280ed
Summary:
This patch writes documentation for `Tensor.record_stream()`, which is not a documented API currently. I've discussed publishing it with colesbury in https://github.com/pytorch/pytorch/issues/23729.
The documentation is based on [the introduction at `CUDACachingAllocator.cpp`](25d1496d58/c10/cuda/CUDACachingAllocator.cpp (L47-L50)). ~~I didn't explain full details of the life cycle of memory blocks or stream awareness of the allocator for the consistent level of details with other documentations.~~ I explained about the stream awareness in a note block.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24078
Differential Revision: D16743526
Pulled By: zou3519
fbshipit-source-id: 05819c3cc96733e2ba93c0a7c0ca06933acb22f3
Summary:
This is a bunch of changes to the docs for stylistic changes,
correctness, and updates to the new script API / recent TorchScript
changes (i.e. namedtuple)
For reviewers, ping me to see a link of the rendered output.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24371
Pulled By: driazati
Differential Revision: D16832417
fbshipit-source-id: a28e748cf1b590964ca0ae2dfb5d8259c766a203
Summary:
Stacked PRs
* #24258 - [jit] Add `trace_module` to docs
* **#24208 - [jit] Cleanup documentation around `script` and `trace`**
Examples / info was duplicated between `ScriptModule`, `script`, and
`trace`, so this PR consolidates it and moves some things around to make
the docs more clear.
For reviewers, if you want to see the rendered output, ping me for a
link
](https://our.intern.facebook.com/intern/diff/16746236/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24208
Pulled By: driazati
Differential Revision: D16746236
fbshipit-source-id: fac3c6e762a31c897b132b8421baa8d4d61f694c
Summary:
**Patch Description**:
Update the docs to reflect one no longer needs to install tensorboard nightly, as Tensorboard 1.14.0 was [released last week](https://github.com/tensorflow/tensorboard/releases/tag/1.14.0).
**Testing**:
Haven't actually tested pytorch with tensorboard 1.14 yet. I'll update this PR once I have.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22026
Differential Revision: D16772136
Pulled By: orionr
fbshipit-source-id: 2e1e17300f304f50026837abbbc6ffb25704aac0
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:
Adds new people and reorders sections to make more sense
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23693
Differential Revision: D16618230
Pulled By: dzhulgakov
fbshipit-source-id: 74191b50c6603309a9e6d14960b7c666eec6abdd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23376
This uses master version of sphinxcontrib-katex as it only
recently got prerender support.
Fixes#20984
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D16582064
Pulled By: ezyang
fbshipit-source-id: 9ef24c5788c19572515ded2db2e8ebfb7a5ed44d
Summary:
Changelog:
- Rename `gels` to `lstsq`
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lstsq` under the name `gels` and add a deprecation warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23460
Test Plan: - All tests should pass to confirm that the patch is correct
Differential Revision: D16547834
Pulled By: colesbury
fbshipit-source-id: b3bdb8f4c5d14c7716c3d9528e40324cc544e496
Summary:
Use the recursive script API in the existing docs
TODO:
* Migration guide for 1.1 -> 1.2
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21612
Pulled By: driazati
Differential Revision: D16553734
fbshipit-source-id: fb6be81a950224390bd5d19b9b3de2d97b3dc515
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:
Thanks adefazio for the feedback, adding a note to the Contribution guide so that folks don't start working on code without checking with the maintainers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23513
Differential Revision: D16546685
Pulled By: soumith
fbshipit-source-id: 1ee8ade963703c88374aedecb8c9e5ed39d7722d
Summary:
This is still work in progress.
There are several more items to add to complete this doc, including
- [x] LHS indexing, index assignments.
- [x] Tensor List.
- [x] ~Shape/Type propagation.~
- [x] FAQs
Please review and share your thoughts, feel free to add anything that you think should be included as well. houseroad spandantiwari lara-hdr neginraoof
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23185
Differential Revision: D16459647
Pulled By: houseroad
fbshipit-source-id: b401c005f848d957541ba3b00e00c93ac2f4609b
