d8c8ba2440
Fix unused Python variables in test/[e-z]* ( #136964 )
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Pull Request resolved: https://github.com/pytorch/pytorch/pull/136964
Approved by: https://github.com/justinchuby , https://github.com/albanD
2024-12-18 23:02:30 +00:00
ba48cf6535
[BE][Easy][6/19] enforce style for empty lines in import segments in test/ ( #129757 )
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See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501 . Most changes are auto-generated by linter.
You can review these PRs via:
```bash
git diff --ignore-all-space --ignore-blank-lines HEAD~1
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129757
Approved by: https://github.com/ezyang
2024-07-17 06:42:37 +00:00
d5182bb75b
Enable UFMT on test/test_cuda*.py ( #124352 )
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Part of: #123062
Ran lintrunner on:
- test/test_cuda.py
- test/test_cuda_expandable_segments.py
- test/test_cuda_multigpu.py
- test/test_cuda_nvml_based_avail.py
- test/test_cuda_primary_ctx.py
- test/test_cuda_sanitizer.py
- test/test_cuda_trace.py
Detail:
```bash
$ lintrunner -a --take UFMT --all-files
ok No lint issues.
Successfully applied all patches.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124352
Approved by: https://github.com/ezyang
2024-04-25 18:31:08 +00:00
eb7adc3ae0
Refactor gpu trace to be device-agnostic ( #121794 )
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# Motivation
Refactor gpu trace to be device-agnostic. gpu trace is usually used in runtime components, including Device, Stream, Event, Guard, and Allocator. It should be device-agnostic and can be shared among each device backend.
# Solution
move `_cuda_trace.py` to `_gpu_trace.py`, which makes each device backend owns their callback, respectively.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121794
Approved by: https://github.com/jgong5 , https://github.com/albanD , https://github.com/EikanWang , https://github.com/gujinghui
2024-03-30 13:04:38 +00:00
968c4c4154
Revert "Refactor gpu trace to be device-agnostic ( #121794 )"
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This reverts commit 74deacbf31d032a2659dc1633dc3e5248921d466.
Reverted https://github.com/pytorch/pytorch/pull/121794 on behalf of https://github.com/huydhn due to Sorry for reverting your change but it breaks ROCm jobs in trunk 74deacbf31https://github.com/pytorch/pytorch/pull/121794#issuecomment-2013674083 ))
2024-03-21 20:33:17 +00:00
74deacbf31
Refactor gpu trace to be device-agnostic ( #121794 )
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# Motivation
Refactor gpu trace to be device-agnostic. gpu trace is usually used in runtime components, including Device, Stream, Event, Guard, and Allocator. It should be device-agnostic and can be shared among each device backend.
# Solution
move `_cuda_trace.py` to `_gpu_trace.py`, which makes each device backend owns their callback, respectively.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121794
Approved by: https://github.com/jgong5 , https://github.com/albanD , https://github.com/EikanWang , https://github.com/gujinghui
2024-03-21 01:52:58 +00:00
f9ed1c432d
Revert "Refactor gpu trace to be device-agnostic ( #121794 )"
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This reverts commit 0ff1109e2688b8c841c9dd0eeecfba16f027b049.
Reverted https://github.com/pytorch/pytorch/pull/121794 on behalf of https://github.com/jeanschmidt due to Reverting to see if rocm trunk errors are related ([comment](https://github.com/pytorch/pytorch/pull/121794#issuecomment-2007519408 ))
2024-03-19 15:40:26 +00:00
0ff1109e26
Refactor gpu trace to be device-agnostic ( #121794 )
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# Motivation
Refactor gpu trace to be device-agnostic. gpu trace is usually used in runtime components, including Device, Stream, Event, Guard, and Allocator. It should be device-agnostic and can be shared among each device backend.
# Solution
move `_cuda_trace.py` to `_gpu_trace.py`, which makes each device backend owns their callback, respectively.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121794
Approved by: https://github.com/jgong5 , https://github.com/albanD , https://github.com/EikanWang , https://github.com/gujinghui
2024-03-19 06:02:28 +00:00
d3839b624b
[ROCm] HIP Lazy Streams ( #119996 )
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For ROCm/HIP, each stream is lazily initialized rather than creating all streams when the first stream is requested. HIP streams are not as lightweight as CUDA streams; the pooling strategy can affect performance.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119996
Approved by: https://github.com/ezyang
2024-02-20 16:24:04 +00:00
8ddca5aeae
markDynamoStrictTest some more tests ( #115857 )
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Pull Request resolved: https://github.com/pytorch/pytorch/pull/115857
Approved by: https://github.com/voznesenskym
ghstack dependencies: #115845 , #115855 , #115856
2023-12-15 01:22:38 +00:00
bb2fcc7659
unify TEST_CUDA ( #106685 )
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Fixes #ISSUE_NUMBER
as title, unify TEST_CUDA
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106685
Approved by: https://github.com/zou3519
2023-08-10 09:01:36 +00:00
eea0733045
Reduce pytest blocklist ( #96016 )
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`TestCase = object` or variations of it get switched to `TestCase = NoTest`.
unittest collects test based on subclassing unittest.TestCase, so setting TestCase = object removes it from unittest test collection. pytest collects based on name (https://docs.pytest.org/en/7.1.x/reference/reference.html#confval-python_classes ) but can be told to ignore a class (bottom of https://docs.pytest.org/en/7.1.x/example/pythoncollection.html#changing-naming-conventions )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96016
Approved by: https://github.com/ZainRizvi , https://github.com/huydhn
2023-03-07 18:30:27 +00:00
67d6f7160c
Add synchronize hooks ( #84427 )
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Pull Request resolved: https://github.com/pytorch/pytorch/pull/84427
Approved by: https://github.com/ngimel , https://github.com/lw
2022-09-09 13:56:59 +00:00
916def84d4
CUDA trace Python hooks ( #82824 )
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### Description
This adds Python hooks into PyTorch that allow the user to register their own callbacks for events such as tensor allocation, stream allocation, event record / wait etc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82824
Approved by: https://github.com/lw , https://github.com/ezyang , https://github.com/malfet
2022-08-11 10:21:40 +00:00
