Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52786
Previously, NNC did not sanitize input names. I ran into this in the next PR when making subgraph creation preserve debug names caused a number of NNC cuda failures. I also previously ran into this with some masked_fill failures internally, which led me to disable the operator.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D26696699
Pulled By: eellison
fbshipit-source-id: 7c3af4d559d58762fb8332666784a4d5cd6a4167
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51484
This PR moves the linear weights of a frozen model to MKLDNN. When the weights are already in MKLDNN, just computing a single linear by converting the input and output from/to mkldnn provides large speedups. I benchmark'd the results of the top 200 shapes in predictor [here](https://www.internalfb.com/phabricator/paste/view/P171537854) (taken from aten::matmul), as well as verified that it sped up popular models. .
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D26696698
Pulled By: eellison
fbshipit-source-id: 53d03b9e6956e11b700ee58214e2266e2aa4106a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51483
This PR moves the conv weights of a frozen model to MKLDNN, and AOT reorders the weights. When the weights are already in MKLDNN, just computing a single conv by converting the input and output from/to mkldnn provides large speedups. I benchmark'd the results of the top 200 shapes in predictor [here](https://www.internalfb.com/phabricator/paste/view/P171537938), as well as verified that it sped up popular models in torchvision.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D26696703
Pulled By: eellison
fbshipit-source-id: 0b4441bee4f6e0890a4540fbca3bb5e58b8c5adf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53063
The problem was that a derived class was marked with "py::nodelete",
while the base class wasn't. Now they both are marked correctly.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D26737877
Pulled By: ZolotukhinM
fbshipit-source-id: 17d9d430651c8f695fc7b6bf6784e7719e20a4d2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52901
This PR implements IR Verifier and adds a call to it in `LoopNest`
constructors. Checks that were in expr/stmt constructors before are now
moved to the corresponding `::make` functions or to the verifier. They
didn't really help from the constructors anyway since an exception
thrown from there led to a segfault due to the fact our memory
management works (object was not fully created but was registered in the
kernel arena for destruction anyway).
Fixes#52778.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D26682928
Pulled By: ZolotukhinM
fbshipit-source-id: c56524015cdffb1ed8bce4394509961a4071dcfa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53050
As title. We would like to use alternative pickler/unpickler
implementations without changing the entire RPCPickler, to make it
possible to send objects over the wire that are coming from a
torch.package
Test Plan: Imported from OSS
Reviewed By: Lilyjjo
Differential Revision: D26734592
Pulled By: suo
fbshipit-source-id: d9d9fa62ee15bfcb00e09192030541b61df8c682
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53049
This makes our API symmetric--now we have an `Importer` aware Pickler
and Unpickler implementation that have similar interfaces.
Test Plan: Imported from OSS
Reviewed By: Lilyjjo
Differential Revision: D26734593
Pulled By: suo
fbshipit-source-id: 3479437cf6b98e0d6a8aa4907c75f0c61d5495d4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53048
I am planning the custom pickler and unpicklers that we use as
semi-public interfaces for `torch.rpc` to consume. Some prefatory
movements here.
Test Plan: Imported from OSS
Reviewed By: Lilyjjo
Differential Revision: D26734594
Pulled By: suo
fbshipit-source-id: 105ae1161d90f24efc7070a8d80c6ac3d2111bea
Summary:
Do not build PyTorch if `setup.py` is called with 'sdist' option
Regenerate bundled license while sdist package is being built
Refactor `check_submodules` out of `build_deps` and check that submodules project are present during source package build stage.
Test that sdist package is configurable during `asan-build` step
Fixes https://github.com/pytorch/pytorch/issues/52843
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52908
Reviewed By: walterddr
Differential Revision: D26685176
Pulled By: malfet
fbshipit-source-id: 972a40ae36e194c0b4e0fc31c5e1af1e7a815185
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52870
Add the missing parts to support to_backend modules by lite interpreter.
1. Add ISINSTANCE instruction support, which is used in to_backend for output type check.
2. Bypass lite interpreter's type parser by checking the qualified name. If it starts with "torch.jit", use the same type resolver as nn module (starting with "__torch__").
Tests
Mobile module is serialized and loaded in ```BackendTest.TestCompiler```. The results are compared to those from original torchscript module.
Test Plan: Imported from OSS
Reviewed By: raziel
Differential Revision: D26715351
Pulled By: iseeyuan
fbshipit-source-id: ad9d74ee81c6aa692ab9e5dd7a9003bae5d4f01f
Summary:
The previous code allowed these tests to run every four hours on certain ci-all branches...which is really bad and resource intensive. This code removes that, but then disallows the 11.2 and 9.2 tests to be run on ci-all branches.
To debug CUDA 11.2 or 9.2 tests, one must now manually change the config to allow for them. (Look at https://github.com/pytorch/pytorch/issues/51888 and https://github.com/pytorch/pytorch/issues/51598 for examples of how to do that.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53069
Reviewed By: H-Huang
Differential Revision: D26739738
Pulled By: janeyx99
fbshipit-source-id: 7577b9b2e876bac0e4e868ce2a1f3ffdb6aca597
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53028
TORCH_CHECK (and variants) wrap the condition in C10_UNLIKELY, so this code is both prettier and better.
ghstack-source-id: 122755165
Test Plan: CI
Reviewed By: malfet
Differential Revision: D26522821
fbshipit-source-id: 70aa11f1859f979657a1f376f7039b5015c69321
Summary:
Adds a script so that we can take wheels directly from
download.pytorch.org and publish them to pypi
This is currently mainly used to prep windows binaries for publication to PyPI
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53056
Reviewed By: H-Huang
Differential Revision: D26738642
Pulled By: seemethere
fbshipit-source-id: 96777ed6c3f3454bddb4bc13121f727074312816
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53016
We just checked in the generated files directly.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D26724876
Pulled By: ezyang
fbshipit-source-id: 887d781cac47b7cf16ba2cd6079c63b8f186fe44
Summary:
Updated version following https://github.com/pytorch/pytorch/issues/52764 (including comments from Shen), but this one I expect to be able to land.
ZeroRedundancyOptimizer:
- bucket as tensor views, optional
- make a lot of attributes private
- minor unit test refactor
- adding coverage in the unit test for with and without bucket views
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52987
Reviewed By: mrshenli
Differential Revision: D26728851
Pulled By: blefaudeux
fbshipit-source-id: f8c745966719c9076c20a554ef56198fb838856c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52141
Remove BufferShuffleDataSet, as it's not being used anywhere within PyTorch (no usage on Github based on a search) and it's not included in the release of PyTorch 1.7.1.
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision: D26710940
Pulled By: ejguan
fbshipit-source-id: 90023b4bfb105d6aa392753082100f9181ecebd0
Summary:
Enabling four test cases in test_cuda.py for ROCm because they are passing.
Signed-off-by: Kyle Chen <kylechen@amd.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52739
Reviewed By: H-Huang
Differential Revision: D26706321
Pulled By: ngimel
fbshipit-source-id: 6907c548c4ac4e387f0eb7c646e8a01f0d036c8a
Summary:
This is an automated pull request to update the first-party submodule for [pytorch/FBGEMM](https://github.com/pytorch/FBGEMM).
New submodule commit: a431ee37cb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52992
Test Plan: Ensure that CI jobs succeed on GitHub before landing.
Reviewed By: dskhudia
Differential Revision: D26718007
fbshipit-source-id: 7b35ab2012b8b6300a6e78c8425f9e08864a9f68
Summary:
This tests a simple failure mode for a TypeCheck when a shape changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52933
Reviewed By: H-Huang
Differential Revision: D26727583
Pulled By: Krovatkin
fbshipit-source-id: b277218af9572cd6f89f2ece044f7d84d4c10283
Summary:
In `__iter__` of the `RandomSampler`, when `self.replacement` is `False` in the original code, `self.generator` is always used in the `torch.randperm` instead of the generator we set.
Fixes https://github.com/pytorch/pytorch/issues/52568
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52956
Reviewed By: mruberry
Differential Revision: D26724303
Pulled By: H-Huang
fbshipit-source-id: 86f2795c76f3548e31181fb077af046078a173cb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52928
Changes the user facing API of `prepare_single_model_output` to
require a list of nodes instead of a list of subgraphs. This ensures
that how we define a subgraph is an implementation detail and is
not exposed to the user, keeping the eng cost of updating this
implementation later low.
Test Plan:
```
python test/test_quantization.py TestFXNumericSuiteCoreAPIs
```
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D26693471
fbshipit-source-id: 67c2feb844556225e36f8d6d4023246939bcb445
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52927
Refactor to use an existing util instead of duplicating code, no logic
change.
Test Plan:
CI
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D26693474
fbshipit-source-id: 06b7047eb9a762557b7f679347e424c0dd009aad
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52926
Model name is already stored in the Loggers in the prepare call.
Removing the need to specify it again in the extract activations
functions, to simplify things.
Test Plan:
```
python test/test_quantization.py TestFXNumericSuiteCoreAPIs
```
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D26693473
fbshipit-source-id: 52511cacc16f79fa09c78ccde78e7f439f4b315c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52925
Cleans up some incorrect comments and docblocks in
`numeric_suite_core_apis.py`.
Test Plan:
CI
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D26693472
fbshipit-source-id: 17f3ff464c6ea01374bcc6ac5899da7034627152
