Summary:
Changelog:
- Renames `btrifact` and `btrifact_with_info` to `lu`to remain consistent with other factorization methods (`qr` and `svd`).
- Now, we will only have one function and methods named `lu`, which performs `lu` decomposition. This function takes a get_infos kwarg, which when set to True includes a infos tensor in the tuple.
- Rename all tests, fix callsites
- Create a tentative alias for `lu` under the name `btrifact` and `btrifact_with_info`, and add a deprecation warning to not promote usage.
- Add the single batch version for `lu` so that users don't have to unsqueeze and squeeze for a single square matrix (see changes in determinant computation in `LinearAlgebra.cpp`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18435
Differential Revision: D14680352
Pulled By: soumith
fbshipit-source-id: af58dfc11fa53d9e8e0318c720beaf5502978cd8
Summary:
Deleting batch tensor since we are no longer maintaining the project and keeping it functional is blocking other improvements.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18575
Differential Revision: D14671126
Pulled By: eellison
fbshipit-source-id: b42d5b699c4d12171ed95e6d3a977532167f0d2c
Summary:
This will allow pathlib.Path object to the torch.load as an input argument.
Fixes#16607
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18562
Differential Revision: D14668255
Pulled By: soumith
fbshipit-source-id: 0ae4f7c210918582912f2d1ef2a98f1ab288c540
Summary:
Addind the same warning message already present in the mse_loss function to the L1 losses when input and target sizes are different.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18565
Differential Revision: D14671415
Pulled By: soumith
fbshipit-source-id: 01f5e1fb1ea119dbb2aecf1d94d0cb462f284982
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18226
ghimport-source-id: b9ec8651212875b30971cc6859d2ddec6559ae3a
If modules become first-class IValues, then the slots will no longer be raw pointers but (IValue, index) pairs. This commit inserts the Slot abstraction so that this change can be made in later patches.
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18226 Add Slot type to abstract the raw pointers being used for slots.**
Differential Revision: D14542022
fbshipit-source-id: b81d7f4334c983d663e7551bda82df43680d7c5f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18512
Ceil and Floor have been supported since version 6 of ONNX: export them using the native onnx ops instead of an Aten op.
Similarly, support for the Where op has been added in version 9, so we don't need to wrap these op in an Aten op.
Reviewed By: houseroad
Differential Revision: D14635130
fbshipit-source-id: d54a2b6e295074a6214b5939b21051a6735c9958
Summary:
While benchmarking a kernel with broadcasted inputs, I noticed
that is was much slower than a hand-coded kernel for the smae task.
The kernel in question computed a * b + c for a of shape
32 x 32 x 10240 and b and c of shape 1 x 32 x 1.
This patch accellerates said kernel from 450us to 250us on my GTX1080Ti.
I didn't change half because there doesn't seem to be __ldg for
half.
An alternative could be to sprinkle const and restrict.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18540
Differential Revision: D14657840
Pulled By: soumith
fbshipit-source-id: 408847346ec12d1d1d9b119ac50bbc70f0d9ed33
Summary:
This implements a cyclical learning rate (CLR) schedule with an optional inverse cyclical momentum. More info about CLR: https://github.com/bckenstler/CLR
This is finishing what #2016 started. Resolves#1909.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18001
Differential Revision: D14451845
Pulled By: sampepose
fbshipit-source-id: 8f682e0c3dee3a73bd2b14cc93fcf5f0e836b8c9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18538
ghimport-source-id: 665b09f158d1c5dd94686d4212792504b55b7f73
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18538 Completely synchronize behavior of Facebook flake8 and public flake8.**
Previously, developers at Facebook had the very funny experience
wherein /usr/local/bin/flake8 behaved differently than a freshly
installed flake8 from pip. In this commit, I add enough ignores to
.flake8 and install enough plugins to make the Facebook flake8
and public flake8 line up exactly. These means you don't have
to care which flake8 you use; they all will report accurate information
on your Python files.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14652336
fbshipit-source-id: ba7776eaa139cf2e3df2e65349da6fd7c99acca4
Summary:
This allows you to embed checks in IR, making the test more readable.
E.g.
```
graph_str = 'graph(%0 : Double(5, 5)):
# CHECK: aten::relu
%1 : Double(5, 5) = aten::relu(%0)
return (%1)'
FileCheck().run(graph_str, parseIR(graph_str))
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18304
Differential Revision: D14652372
Pulled By: eellison
fbshipit-source-id: 7430b9d1dc2b7584704375aac02d7392ecec76a0
Summary:
Previously we were moving nodes with writers into differentiable subgraphs, without necessarily preserving whether or not they were written to. This can lead to bugs with CSE, which needs that context.
I'm not completely sure if there's anything else we can do to be more aggresive here - inline these nodes and not run CSE and just run constant pooling, or possibly something else, but I think we should land this correctness condition first and then possibly think further.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18491
Differential Revision: D14648562
Pulled By: eellison
fbshipit-source-id: bc1e444774ccdb708e22f0e06a477a221a231f9e
Summary:
Is Tensor has been brought up as misleading a couple times, rename it isCompleteTensor for clarity.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18437
Differential Revision: D14605223
Pulled By: eellison
fbshipit-source-id: 189f67f12cbecd76516a04e67d8145c260c79036
Summary:
Enable unit tests working with ROCm 2.3. In particular, these are unit tests where we skipped for double data types previously and some tests for multi-GPU setups.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18537
Differential Revision: D14651822
Pulled By: ezyang
fbshipit-source-id: 7dd575504ebe235a91489866c91000e9754b1235
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18494
Today we have some C2 end2end test run requiring reading model data from external filesystem (for example, Gluster and AWS). This could be a source for flaky test when the external filesystems are not reachable during the tests.
In this diff, we add try/catch logic around where we download models and open model files from external system. In case such attempts fails, we will catch the excption and let the unittest skip the current test instead of failure.
I also refactor the code a little bit by removing some duplicated logic on downloading and build the c2 model data. It has been duplicated in two classes and a few functions...
Reviewed By: yinghai
Differential Revision: D14442241
fbshipit-source-id: da8bf56c8d096efa34ca2070de5cd10a18aad70c
Summary:
We are about to merge onnxifi quantization support soon. Before that, I would like to merge this diff seperately to make sure it doesnt break anything.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18486
Reviewed By: bddppq, houseroad
Differential Revision: D14626419
Pulled By: yinghai
fbshipit-source-id: 504c1eae60be1e629203267b59defb8b69d82c0a
Summary:
There are a number of pages in the docs that serve insecure content. AFAICT this is the sole source of that.
I wasn't sure if docs get regenerated for old versions as part of the automation, or if those would need to be manually done.
cf. https://github.com/pytorch/pytorch.github.io/pull/177
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18508
Differential Revision: D14645665
Pulled By: zpao
fbshipit-source-id: 003563b06048485d4f539feb1675fc80bab47c1b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18507
ghimport-source-id: 1c3642befad2da78a7e5f39d6d58732b85c76267
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18507 Upgrade flake8-bugbear to master, fix the new lints.**
It turns out Facebobok is internally using the unreleased master
flake8-bugbear, so upgrading it grabs a few more lints that Phabricator
was complaining about but we didn't get in open source.
A few of the getattr sites that I fixed look very suspicious (they're
written as if Python were a lazy language), but I didn't look more
closely into the matter.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14633682
fbshipit-source-id: fc3f97c87dca40bbda943a1d1061953490dbacf8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18385
By moving the weight offload into the backend initialization function, we can instantiate the backend once by creating the OnnxifiOp once and then clean up the parameter workspace. And we need to keep hold of that instantiated net (OnnxifiOp) without cleaning it. Subsequent ctor of OnnxifiOp of the same model will hit the cached backend and they will not look into weight offloading, which is safe as the weight is already gone.
Reviewed By: ipiszy
Differential Revision: D14590379
fbshipit-source-id: f7f34016e09777ad3df0af487885cd14658e1044
Summary:
Added full instructions for how to use the `ccache` package. Thanks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18495
Differential Revision: D14635351
Pulled By: ezyang
fbshipit-source-id: 158e1052bae580e95f73644252fdbddcc0213128
Summary:
This depend on https://github.com/pytorch/pytorch/pull/16039
This prevent people (reviewer, PR author) from forgetting adding things to `tensors.rst`.
When something new is added to `_tensor_doc.py` or `tensor.py` but intentionally not in `tensors.rst`, people should manually whitelist it in `test_docs_coverage.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16057
Differential Revision: D14619550
Pulled By: ezyang
fbshipit-source-id: e1c6dd6761142e2e48ec499e118df399e3949fcc
Summary:
arguments order is okay to be different
ajyu
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18466
Differential Revision: D14627258
Pulled By: bddppq
fbshipit-source-id: 430e1fb1bea2c5639a547ae7c1652368788c86b9
