Summary:
- Target Sha1: ae108ef49aa5623b896fc93d4298c49d1750d9ba
- Make USE_XNNPACK a dependent option on cmake minimum version 3.12
- Print USE_XNNPACK under cmake options summary, and print the
availability from collet_env.py
- Skip XNNPACK based tests when XNNPACK is not available
- Add SkipIfNoXNNPACK wrapper to skip tests
- Update cmake version for xenial-py3.7-gcc5.4 image to 3.12.4
- This is required for the backwards compatibility test.
The PyTorch op schema is XNNPACK dependent. See,
aten/src/ATen/native/xnnpack/RegisterOpContextClass.cpp for
example. The nightly version is assumed to have USE_XNNPACK=ON,
so with this change we ensure that the test build can also
have XNNPACK.
- HACK: skipping test_xnnpack_integration tests on ROCM
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72642
Reviewed By: kimishpatel
Differential Revision: D34456794
Pulled By: digantdesai
fbshipit-source-id: 85dbfe0211de7846d8a84321b14fdb061cd6c037
(cherry picked from commit 6cf48e7b64d6979962d701b5d493998262cc8bfa)
Summary:
Invoking `pip` or `pip3` yields list of packages invoked for `pip` alias on the path, rather than for the one currently being executed. Changed `get_pip_packages` to use `sys.executable + '-mpip'`
Also, add mypy to the list of packages of interest
Discovered while looking at https://github.com/pytorch/pytorch/issues/63279
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63321
Reviewed By: walterddr
Differential Revision: D30342099
Pulled By: malfet
fbshipit-source-id: fc8d17cf2ddcf18236cfde5c1b9edb4e72804ee0
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: zou3519
Differential Revision: D29186394
Pulled By: ezyang
fbshipit-source-id: c88918836db3f51df59de6d1b3e03602ebe306a9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55647
This adds [breakpad](https://github.com/google/breakpad) which comes with out-of-the-box utilities to register a signal handler that writes out a minidump on an unhandled exception. Right now this is gated behind a flag in `torch.utils`, but in the future it could be on by default. Sizewise this adds aboute 500k to `libtorch_cpu.so` (187275968 B to 187810016 B).
```bash
$ cat <<EOF > test.py
import torch
torch.utils.enable_minidump_collection()
# temporary util that just segfaults
torch._C._crash()
EOF
$ python test.py
Wrote minidump to /tmp/pytorch_crashes/6a829041-50e9-4247-ea992f99-a74cf47a.dmp
fish: “python test.py” terminated by signal SIGSEGV (Address boundary error)
$ minidump-2-core /tmp/pytorch_crashes/6a829041-50e9-4247-ea992f99-a74cf47a.dmp -o core.dmp
$ gdb python core.dmp
... commence debugging ...
```
Right now all exceptions that get passed up to Python don't trigger the signal handler (which by default only
handles [these](https://github.com/google/breakpad/blob/main/src/client/linux/handler/exception_handler.cc#L115)). It would be possible for PyTorch exceptions to explicitly write a minidump when passed up to Python (maybe only when the exception is unhandled or something).
Test Plan: Imported from OSS
Reviewed By: ailzhang
Differential Revision: D27679767
Pulled By: driazati
fbshipit-source-id: 1ab3b5160b6dc405f5097eb25acc644d533358d7
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:
Inspired by https://github.com/pytorch/pytorch/issues/47993, this fixes the import error in `collect_env.py` with older version of PyTorch when `torch.version` does not have `hip` property.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48076
Reviewed By: seemethere, xuzhao9
Differential Revision: D25024352
Pulled By: samestep
fbshipit-source-id: 7dff9d2ab80b0bd25f9ca035d8660f38419cdeca
Summary:
Moved all torch specific checks under `if TORCH_AVAILABLE` block
Embed gpu_info dict back into SystemEnv constructor creation and deduplicate some code between HIP and CUDA cases
Fixes https://github.com/pytorch/pytorch/issues/47397
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47398
Reviewed By: walterddr
Differential Revision: D24740421
Pulled By: malfet
fbshipit-source-id: d0a1fe5b428617cb1a9d027324d24d7371c68d64
Summary:
This adds HIP version info to the `collect_env.py` output.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44106
Reviewed By: VitalyFedyunin
Differential Revision: D23652341
Pulled By: zou3519
fbshipit-source-id: a1f5bce8da7ad27a1277a95885934293d0fd43c5
Summary:
No type annotations can be added to the script, as it still have to be Python-2 compliant.
Make changes to avoid variable type redefinition.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43062
Reviewed By: zou3519
Differential Revision: D23132991
Pulled By: malfet
fbshipit-source-id: 360c02e564398f555273e5889a99f834a5467059
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35615
Python 2 has reached end-of-life and is no longer supported by PyTorch.
Now we can clean up a lot of cruft that we put in place to support it.
These changes were all done manually, and I skipped anything that seemed
like it would take more than a few seconds, so I think it makes sense to
review it manually as well (though using side-by-side view and ignoring
whitespace change might be helpful).
Test Plan: CI
Differential Revision: D20842886
Pulled By: dreiss
fbshipit-source-id: 8cad4e87c45895e7ce3938a88e61157a79504aed
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
Fix issue #12174 for Mac OSX.
PS: This is a duplicate of PR #16968 that got messed up. Sorry for the confusion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16999
Differential Revision: D14050669
Pulled By: zou3519
fbshipit-source-id: a4594c03ae8e0ca91a4836408b6c588720162c9f
Summary:
This PR implements:
1. a fix to issue #12174 - determine the location of cudnn library using `ldconfig`
2. a fix to determine the installed conda packages (in recent versions of conda, the command `conda` is a Bash function that cannot be called within a python script, so using CONDA_EXE environment variable instead)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16859
Differential Revision: D14000399
Pulled By: soumith
fbshipit-source-id: 905658ecacb0ca0587a162fade436de9582d32ab
Summary:
Since pip 18.0 (2018-07-22), `legacy` is no longer a valid choice for `pip list --format` as can be seen in the [Release Notes](https://pip.pypa.io/en/stable/news/#id62). Therefore, the options now are: `columns`, `freeze` and `json`. With `legacy`, this is how it looked like:
```
[...]
Versions of relevant libraries:
[pip3] numpy (1.16.1)
[pip3] torch (1.0.1)
[pip3] torchvision (0.2.1)
[...]
```
Changing to `freeze`, this is how it looks like:
```
[...]
Versions of relevant libraries:
[pip3] numpy==1.16.1
[pip3] torch==1.0.1
[pip3] torchvision==0.2.1
[...]
```
Currently, this is what happens:
```
[...]
Versions of relevant libraries:
[pip] Could not collect
[...]
```
The `freeze` option is also available in old pip, so this change is backwards compatible. Also, if we would like to keep the old style, which I think it is not necessary, I could easily change that.
---
In case anyone wants to know how `columns` looks like (I prefer `freeze`):
```
[...]
Versions of relevant libraries:
[pip3] numpy 1.16.1
[pip3] torch 1.0.1
[pip3] torchvision 0.2.1
[...]
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16798
Differential Revision: D13971793
Pulled By: soumith
fbshipit-source-id: 3721d9079a2afa245e1185f725598901185ea4cd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13141
This is an example diff to show what lint rules are being applied.
Reviewed By: mingzhe09088
Differential Revision: D10858478
fbshipit-source-id: cbeb013f10f755b0095478adf79366e7cf7836ff
* Add environment collection script
Fixes#6111. This should make it easier for users to report bugs by giving
them a script to collect system environment information.
Changes include:
- Refactor out the environment collecting code from utils.bottleneck
- Add script (collect_env.py)
- Cleaned up the issues template so that it suggests using the script
and is more readable.
Testing: added expect tests to go with 4 CI configurations. Whenever one
of these configurations gets updated, the test will fail until the test
also gets updated.
* Expect tests
* Update issue template
* Fix random space
* Minor improvement to issue template; fix expect test
* Skip expect test if BUILD_ENVIRONMENT not found; test fix; split off smoke/expect test
