8390843eba
Invalidate StorageImpl instances when tensor is overwritten with cudagraphs ( #125264 )
...
Fixes #104435
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125264
Approved by: https://github.com/ezyang
2024-07-16 14:29:29 +00:00
78799e82b0
Revert "Invalidate StorageImpl instances when tensor is overwritten with cudagraphs ( #125264 )"
...
This reverts commit 1bc390c5f5ac065c156f55f4eceed267ecc67b41.
Reverted https://github.com/pytorch/pytorch/pull/125264 on behalf of https://github.com/jithunnair-amd due to test test/inductor/test_cudagraph_trees.py::CudaGraphTreeTests::test_fallback_to_eager_if_recompiling_too_many_times is failing https://github.com/pytorch/pytorch/actions/runs/9933628108/job/27477785946 1bc390c5f5fa5f572748https://github.com/pytorch/pytorch/pull/125264#issuecomment-2229508737 ))
2024-07-15 21:59:46 +00:00
1bc390c5f5
Invalidate StorageImpl instances when tensor is overwritten with cudagraphs ( #125264 )
...
Fixes #104435
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125264
Approved by: https://github.com/ezyang
2024-07-15 04:16:17 +00:00
f6a0be5023
Add warpSize to Device properties ( #128449 )
...
Adding warp_size to CudaDeviceProperties.
>>> import torch
>>> prop = torch.cuda.get_device_properties(torch.cuda.current_device())
>>> prop.warp_size
64
>>>
@jeffdaily @pruthvistony @jithunnair-amd @ROCmSupport
Co-authored-by: Jithun Nair <37884920+jithunnair-amd@users.noreply.github.com >
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128449
Approved by: https://github.com/eqy , https://github.com/jataylo , https://github.com/jithunnair-amd , https://github.com/malfet
2024-07-01 09:13:32 +00:00
169b4ca07e
add uuid in cudaDeviceProperties ( #125083 )
...
Replaces #99967 .
Fixes #99903 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125083
Approved by: https://github.com/pruthvistony , https://github.com/albanD , https://github.com/eqy , https://github.com/malfet
2024-06-27 23:53:13 +00:00
f42d5b6dca
[Memory Snapshot] Make recordAnnotations callback initialize lazily ( #129242 )
...
Summary: Make the recordAnnotations' Record function callback lazily initialize when record memory history starts. This will help reduce the impact on Time To First Batch metric.
Test Plan: CI and ran locally.
Differential Revision: D58875576
Pulled By: aaronenyeshi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129242
Approved by: https://github.com/zdevito
2024-06-22 04:05:55 +00:00
b5d541609d
[Memory Snapshot] Add recordAnnotations to capture record_function annotations ( #129072 )
...
Summary:
Add new traceEvents into Memory Snapshot for record_function annotations. These will capture both the profiler's step annotation as well as user annotations.
Test Plan:
CI
Pulled By:
aaronenyeshi
Differential Revision: D55941362
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129072
Approved by: https://github.com/zdevito
2024-06-19 18:05:41 +00:00
0e7bd7fedd
[ROCm] TunableOp improvements ( #124362 )
...
- use less memory; smaller default hipblaslt workspace size
- options to avoid cache effects
- icache flush option
- rotating buffers during tuning
- python APIs
- unit tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124362
Approved by: https://github.com/xw285cornell
2024-06-03 22:30:11 +00:00
718bb9016f
Revert "[Memory Snapshot] Add recordAnnotations to capture record_function annotations ( #124179 )"
...
This reverts commit 187aeaeabf612824c2d0e9be72f80ce6612760d4.
Reverted https://github.com/pytorch/pytorch/pull/124179 on behalf of https://github.com/clee2000 due to test_tensorexpr.py::TestTensorExprFuser::test_simple_add is causing a segfault https://github.com/pytorch/pytorch/actions/runs/9097383783/job/25007155440 187aeaeabfhttps://github.com/pytorch/pytorch/pull/124179#issuecomment-2112948246 ))
2024-05-15 16:11:47 +00:00
187aeaeabf
[Memory Snapshot] Add recordAnnotations to capture record_function annotations ( #124179 )
...
Summary: Add new traceEvents into Memory Snapshot for record_function annotations. These will capture both the profiler's step annotation as well as user annotations.
Test Plan:
CI
New Snapshot Generated:
devvm2184.cco0.facebook.com.Apr_19_13_27_14.3072800.snapshot.pickle
Snippet of Snapshot device_traces show `ProfilerStep#0`, and `## forward ##` annotations:
```
[[{'action': 'user_defined',
'addr': 0,
'size': 0,
'stream': 0,
'time_us': 1713558427168556,
'frames': [{'name': 'START', 'filename': 'ProfilerStep#0', 'line': 0}]},
{'action': 'user_defined',
'addr': 0,
'size': 0,
'stream': 0,
'time_us': 1713558427168738,
'frames': [{'name': 'END', 'filename': 'ProfilerStep#0', 'line': 0}]},
{'action': 'user_defined',
'addr': 0,
'size': 0,
'stream': 0,
'time_us': 1713558427168865,
'frames': [{'name': 'START', 'filename': 'ProfilerStep#1', 'line': 0}]},
{'action': 'user_defined',
'addr': 0,
'size': 0,
'stream': 0,
'time_us': 1713558427168920,
'frames': [{'name': 'START', 'filename': '## forward ##', 'line': 0}]},
{'action': 'alloc',
'addr': 140166073581568,
'size': 3211264,
'stream': 0,
'time_us': 1713558427172978,
'frames': [{'name': '_conv_forward',
'filename': '/mnt/xarfuse/uid-416185/235d4caf-seed-nspid4026531836_cgpid32884718-ns-4026531840/torch/nn/modules/conv
```
Differential Revision: D55941362
Pulled By: aaronenyeshi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124179
Approved by: https://github.com/zdevito
2024-05-15 14:19:40 +00:00
ed327876f5
[codemod] c10:optional -> std::optional ( #126135 )
...
Generated by running the following from PyTorch root:
```
find . -regex ".*\.\(cpp\|h\|cu\|hpp\|cc\|cxx\)$" | grep -v "build/" | xargs -n 50 -P 4 perl -pi -e 's/c10::optional/std::optional/'
```
`c10::optional` is just an alias for `std::optional`. This removes usages of that alias in preparation for eliminating it entirely.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126135
Approved by: https://github.com/Skylion007 , https://github.com/malfet , https://github.com/albanD , https://github.com/aaronenyeshi
2024-05-14 19:35:51 +00:00
6fd745255e
Revert "add uuid in cudaDeviceProperties ( #125083 )"
...
This reverts commit 3f36145db298f7305b3b4df6c82c9101025a049a.
Reverted https://github.com/pytorch/pytorch/pull/125083 on behalf of https://github.com/izaitsevfb due to Fails internal builds with: no member named 'uuid' in 'hipDeviceProp_t' ([comment](https://github.com/pytorch/pytorch/pull/125083#issuecomment-2103315320 ))
2024-05-09 19:52:45 +00:00
3f36145db2
add uuid in cudaDeviceProperties ( #125083 )
...
Replaces #99967 .
Fixes #99903 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125083
Approved by: https://github.com/pruthvistony , https://github.com/albanD , https://github.com/eqy
2024-05-08 19:15:55 +00:00
5fd0b6e5f7
Revert "add uuid in cudaDeviceProperties ( #125083 )"
...
This reverts commit f35fe4eaf1e9fa2e631f6bf1a3eb6e5fbf14183b.
Reverted https://github.com/pytorch/pytorch/pull/125083 on behalf of https://github.com/clee2000 due to test_uuid is flaky. ex https://github.com/pytorch/pytorch/actions/runs/8988855916/job/24692369523 https://hud.pytorch.org/flakytest?name=test_uuid&suite=TestCuda&file=%25&limit=300 ([comment](https://github.com/pytorch/pytorch/pull/125083#issuecomment-2099029993 ))
2024-05-07 18:16:27 +00:00
f35fe4eaf1
add uuid in cudaDeviceProperties ( #125083 )
...
Replaces #99967 .
Fixes #99903 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125083
Approved by: https://github.com/pruthvistony , https://github.com/albanD , https://github.com/eqy
2024-05-07 01:26:01 +00:00
c20cf97366
Move some cudagraphs checks into C++ ( #122251 )
...
Based off of https://github.com/pytorch/pytorch/pull/111094
This + cpp guards improves TIMM geomean optimizer performance by about 20%
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122251
Approved by: https://github.com/eellison
2024-03-21 01:02:23 +00:00
b6139b1e57
[PyTorch][CUDA Caching Allocator] Export sync-stream-and-free-HBM counter in memory_stats for performance debugging ( #120050 )
...
Differential Revision: D53734057
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120050
Approved by: https://github.com/xw285cornell
2024-02-27 04:34:53 +00:00
97918e8c37
[Clang-tidy header][18/N] Enable clang-tidy on headers in torch/csrc/cuda ( #118504 )
...
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118504
Approved by: https://github.com/albanD
2024-02-23 16:47:33 +00:00
3cd6a21e8f
[DeviceIndex][6/N] Use DeviceIndex in more places ( #120133 )
...
This PR follows the series of patches beginning with #119142 and fixes various XPU and python related methods to use DeviceIndex.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120133
Approved by: https://github.com/Skylion007
2024-02-21 06:24:23 +00:00
7973ac586d
[Memory Snapshot] Add CUDAAllocatorConfig details into snapshot metadata ( #119404 )
...
Summary:
Include the CUDAAllocatorConfig at the time of snapshot into the snapshot file. These include adding variables:
```
double garbage_collection_threshold;
size_t max_split_size;
size_t pinned_num_register_threads;
bool expandable_segments;
bool release_lock_on_cudamalloc;
bool pinned_use_cuda_host_register;
std::string last_allocator_settings;
std::vector<size_t> roundup_power2_divisions;
```
Test Plan:
`PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True ` produces
```
{'PYTORCH_CUDA_ALLOC_CONF': 'expandable_segments:True',
'max_split_size': -1,
'garbage_collection_threshold': 0.0,
'expandable_segments': True,
'pinned_num_register_threads': 1,
'release_lock_on_cudamalloc': False,
'pinned_use_cuda_host_register': False,
'roundup_power2_divisions': {'1': 0,
'2': 0,
'4': 0,
'8': 0,
'16': 0,
'32': 0,
'64': 0,
'128': 0,
'256': 0,
'512': 0,
'1024': 0,
'2048': 0,
'4096': 0,
'8192': 0,
'16384': 0,
'32768': 0}}
```
`PYTORCH_CUDA_ALLOC_CONF="max_split_size_mb:2000,roundup_power2_divisions:[256:1,512:2,1024:4,>:8]"` produces
```
{'PYTORCH_CUDA_ALLOC_CONF': 'max_split_size_mb:2000,roundup_power2_divisions:[256:1,512:2,1024:4,>:8]',
'max_split_size': 2097152000,
'garbage_collection_threshold': 0.0,
'expandable_segments': False,
'pinned_num_register_threads': 1,
'release_lock_on_cudamalloc': False,
'pinned_use_cuda_host_register': False,
'roundup_power2_divisions': {'1': 1, '2': 1, '4': 1, '8': 1, '16': 1, '32': 1, '64': 1, '128': 1, '256': 1, '512': 2, '1024': 8, '2048': 8, '4096': 8, '8192': 8, '16384': 8, '32768': 8}
}
```
Differential Revision: D53536199
Pulled By: aaronenyeshi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119404
Approved by: https://github.com/zdevito
2024-02-17 01:16:37 +00:00
d4882e438a
[DeviceIndex][5/N] Use DeviceIndex in more places ( #119866 )
...
This PR follows the series of patches beginning with #119142 and fixes various CUDA related methods to use DeviceIndex.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119866
Approved by: https://github.com/Skylion007
2024-02-15 07:01:43 +00:00
10f3abc6b8
[DeviceIndex][3/N] Use DeviceIndex in more places ( #119635 )
...
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119635
Approved by: https://github.com/ezyang
2024-02-12 21:31:27 +00:00
5c46600f84
[RELAND] refactor lazy init to device-agnostic ( #119248 )
...
# Motivation
This PR intends to extend `cuda_lazy_init` to `device_lazy_init` which is a device-agnostic API that can support any backend. And change `maybe_initialize_cuda` to `maybe_initialize_device` to support lazy initialization for CUDA while maintaining scalability.
# Design
We maintain a flag for each backend to manage the lazy initialization state separately.
# Additional Context
No need more UTs.
This is a reland PR, the original PR is [refactor lazy init to device-agnostic](https://github.com/pytorch/pytorch/pull/118846 ).
This is a common PR, and does not trigger xpu ciflow.
Differential Revision: [D53478332](https://our.internmc.facebook.com/intern/diff/D53478332 )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119248
Approved by: https://github.com/EikanWang , https://github.com/gujinghui , https://github.com/jgong5 , https://github.com/atalman
2024-02-07 15:58:51 +00:00
ab613a4019
Revert "refactor lazy init to device-agnostic ( #118846 )"
...
This reverts commit 520771d7b35034c96c5b4604ecf8960e6aab856f.
Reverted https://github.com/pytorch/pytorch/pull/118846 on behalf of https://github.com/atalman due to Failing, tests https://github.com/pytorch/torchdistx/blob/main/src/python/torchdistx/_C/fake.cc#L11 ([comment](https://github.com/pytorch/pytorch/pull/118846#issuecomment-1927651305 ))
2024-02-05 18:06:30 +00:00
520771d7b3
refactor lazy init to device-agnostic ( #118846 )
...
# Motivation
This PR intends to extend `cuda_lazy_init` to `device_lazy_init` which is a device-agnostic API that can support any backend. And change `maybe_initialize_cuda` to `maybe_initialize_device` to support lazy initialization for CUDA while maintaining scalability.
# Design
We maintain a flag for each backend to manage the lazy initialization state separately.
# Additional Context
No need more UTs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118846
Approved by: https://github.com/malfet
2024-02-02 12:10:39 +00:00
6da0e7f84b
[Clang-tidy header][17/N] Apply clang-tidy on headers in torch/csrc/cuda ( #117829 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117829
Approved by: https://github.com/albanD
2024-01-26 13:33:24 +00:00
91bbcf8c71
[1/N] replace THPUtils_assert with TORCH_CHECK ( #116675 )
...
This PR replaces THPUtils_assert with TORCH_CHECK.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116675
Approved by: https://github.com/albanD
2024-01-04 11:15:33 +00:00
ba06951c66
[BE] [cuDNN] Always build assuming cuDNN >= 8.1 ( #95722 )
...
<!--
copilot:summary
-->
### <samp>🤖 Generated by Copilot at 27084ed</samp>
This pull request simplifies and cleans up the code that uses the cuDNN library for convolution, batch normalization, CTC loss, and quantized operations. It removes the unnecessary checks and conditions for older cuDNN versions and the experimental cuDNN v8 API, and ~~replaces them with the stable `cudnn_frontend` API that requires cuDNN v8 or higher. It also adds the dependency and configuration for the `cudnn_frontend` library in the cmake and bazel files.~~ Correction: The v7 API will still be available with this PR, and can still be used, without any changes to the defaults. This change simply always _builds_ the v8 API, and removes the case where _only_ the v7 API is built.
This is a re-land of https://github.com/pytorch/pytorch/pull/91527
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95722
Approved by: https://github.com/malfet , https://github.com/atalman
2024-01-03 15:41:28 +00:00
0aa185f394
[BE] Make torch.cuda.has_magma a build time check ( #116299 )
...
Perhaps originally one needed to query about GPU capability, but right now it's a simple check for a build time flag: 52f0457d7d/aten/src/ATen/cuda/detail/CUDAHooks.cpp (L165-L171)0978482afa/torch/csrc/Module.cpp (L1793-L1794)https://github.com/pytorch/pytorch/pull/116299
Approved by: https://github.com/seemethere , https://github.com/albanD
2023-12-26 23:37:23 +00:00
4afe2687d5
Reland "Serve multistream graph captures from correct pool ( #114647 )" ( #116199 )
...
Fixes a variable shadowing problem that broke internal builds.
This reverts commit fe156456194ed64bdf8b086d469b3643515a2baf.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116199
Approved by: https://github.com/eellison
2023-12-20 21:22:34 +00:00
fe15645619
Revert "Serve multistream graph captures from correct pool ( #114647 )"
...
This reverts commit 8a445f7bd5bef43b30b61b20483d606c6e42e606.
Reverted https://github.com/pytorch/pytorch/pull/114647 on behalf of https://github.com/jeanschmidt due to breaking multiple internal build jobs, please check internal diff in order to obtain more details ([comment](https://github.com/pytorch/pytorch/pull/114647#issuecomment-1864840724 ))
2023-12-20 17:11:42 +00:00
8a445f7bd5
Serve multistream graph captures from correct pool ( #114647 )
...
This fixes #114320 by placing the logic for determining whether to allocate
to a pool inside a callback that is controlled by CUDAGraph.cpp or by the
python bound api to allocate a stream directly to a pool.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114647
Approved by: https://github.com/ngimel , https://github.com/eellison
2023-12-18 18:24:15 +00:00
5989e1222d
[BE] Set torch.cuda.has_half to True ( #115884 )
...
This check was introduced by https://github.com/pytorch/pytorch/pull/5417 and then turned into a tautology by https://github.com/pytorch/pytorch/pull/10147
So I guess it's time to let go of all that dynamic initialization (and may be just delete it in 2.3?)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115884
Approved by: https://github.com/kit1980
2023-12-15 02:30:55 +00:00
01ec71e466
[NFC][Autotune] Use device_prop.regsPerMultiprocessor instead of hardcoded reg number. ( #115094 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115094
Approved by: https://github.com/jansel
2023-12-05 23:49:46 +00:00
f9bf104c64
[2/N] Fixes clang-tidy warnings in header files ( #113727 )
...
This PR fixes more clang-tidy warnings in common headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113727
Approved by: https://github.com/Skylion007
2023-11-16 13:21:15 +00:00
3b80577212
[Memory Snapshot] Add timestamps to memory events collected in snapshots ( #112266 )
...
Summary: Use the same clock as the profiler to collect the timestamps on when memory events occurred. Save these to the snapshot dicts as well, so that they can be saved with the raw memory events.
Test Plan:
CI
Observed that trace_entry will now have time_us field, and it is ascending. For example:
```
trace entry: {'action': 'free_requested', 'addr': 140366476918784, 'size': 8192, 'stream': 0, 'time_us': 1698326576864190}
trace entry: {'action': 'free_completed', 'addr': 140366476918784, 'size': 8192, 'stream': 0, 'time_us': 1698326576864190}
trace entry: {'action': 'free_requested', 'addr': 140366476936192, 'size': 8192, 'stream': 0, 'time_us': 1698326576864194}
trace entry: {'action': 'free_completed', 'addr': 140366476936192, 'size': 8192, 'stream': 0, 'time_us': 1698326576864194}
trace entry: {'action': 'free_requested', 'addr': 140366641430528, 'size': 8192000, 'stream': 0, 'time_us': 1698326576864205}
trace entry: {'action': 'free_completed', 'addr': 140366641430528, 'size': 8192000, 'stream': 0, 'time_us': 1698326576864205}
trace entry: {'action': 'free_requested', 'addr': 140366403571712, 'size': 4000, 'stream': 0, 'time_us': 1698326576864209}
trace entry: {'action': 'free_completed', 'addr': 140366403571712, 'size': 4000, 'stream': 0, 'time_us': 1698326576864209}
```
Differential Revision: D50602011
Pulled By: aaronenyeshi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112266
Approved by: https://github.com/zdevito
2023-11-14 18:48:59 +00:00
3c9a59cb8d
Revert "[BE] [cuDNN] Always build assuming cuDNN >= 8.0 ( #95722 )"
...
This reverts commit df4f0b3829f8e8b623f4e94a8536cfa58ccfb9af.
Reverted https://github.com/pytorch/pytorch/pull/95722 on behalf of https://github.com/PaliC due to is breaking a bunch of internal pytorch users ([comment](https://github.com/pytorch/pytorch/pull/95722#issuecomment-1806131675 ))
2023-11-10 17:26:36 +00:00
df4f0b3829
[BE] [cuDNN] Always build assuming cuDNN >= 8.0 ( #95722 )
...
<!--
copilot:summary
-->
### <samp>🤖 Generated by Copilot at 27084ed</samp>
This pull request simplifies and cleans up the code that uses the cuDNN library for convolution, batch normalization, CTC loss, and quantized operations. It removes the unnecessary checks and conditions for older cuDNN versions and the experimental cuDNN v8 API, and ~~replaces them with the stable `cudnn_frontend` API that requires cuDNN v8 or higher. It also adds the dependency and configuration for the `cudnn_frontend` library in the cmake and bazel files.~~ Correction: The v7 API will still be available with this PR, and can still be used, without any changes to the defaults. This change simply always _builds_ the v8 API, and removes the case where _only_ the v7 API is built.
This is a re-land of https://github.com/pytorch/pytorch/pull/91527
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95722
Approved by: https://github.com/malfet
2023-11-08 07:53:23 +00:00
9d765d28ca
[pytorch] Add binding to get nccl version suffix ( #112884 )
...
Summary: Adds a Python to C binding to get the NCCL_SUFFIX value for more accurate NCCL version information and add that to the NCCL version tuple.
Differential Revision: D50978181
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112884
Approved by: https://github.com/kwen2501
2023-11-08 02:51:22 +00:00
333d5821ee
[ROCm] Add gcnArchName to collect_env and torch.cuda.get_device_properties ( #107477 )
...
Printing just the device name is not helpful when investigating PyTorch issues filed for specific AMD GPUs, as the support/issue might depend on the gfx arch, which is part of the gcnArchName property.
`torch.cuda.get_device_properties(0).gcnArchName` will print the value of the `gcnArchName` property: eg.
```
>>> torch.cuda.get_device_properties(0).gcnArchName
'gfx906:sramecc+:xnack-'
```
```
root@6f064e3c19fb:/data/pytorch/test# python ../torch/utils/collect_env.py
...
GPU models and configuration: AMD Radeon Graphics(gfx906:sramecc+:xnack-)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107477
Approved by: https://github.com/albanD
2023-10-31 23:05:36 +00:00
9c7391ea36
Revert " [1/N] Apply clang-tidy to c10 cuda files ( #111137 )"
...
This reverts commit 43b023694eea4348fa28e8028fa7445d6375860c.
Reverted https://github.com/pytorch/pytorch/pull/111137 on behalf of https://github.com/malfet due to Was reverted internally due to the failures in torch.cuda.memory_stats(device=0) (presumably) ([comment](https://github.com/pytorch/pytorch/pull/111137#issuecomment-1769274103 ))
2023-10-18 20:32:53 +00:00
43b023694e
[1/N] Apply clang-tidy to c10 cuda files ( #111137 )
...
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111137
Approved by: https://github.com/zou3519 , https://github.com/Skylion007
2023-10-17 04:52:50 +00:00
a6b452dfdc
[2/N] Enable Wunused-result, Wunused-variable and Wmissing-braces in torch targets ( #110836 )
...
This PR enables Wunused-result, Wunused-variable and Wmissing-braces because our code base is clean.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110836
Approved by: https://github.com/Skylion007
2023-10-11 23:49:15 +00:00
3ec33957eb
[1/N] Enable Wunused-result and Wunused-variable in torch targets ( #110722 )
...
They are useful for checking results of function calls.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110722
Approved by: https://github.com/Skylion007
2023-10-08 23:43:45 +00:00
30c4c6ff9b
[PyTorch CCA] Refactor caching allocator config code ( #110123 )
...
Summary: This diff refactors the code by moving CUDAAllocatorConfig into the header file. This config refactoring is done so that we can use the same config code for CUDA pinned memory as well.
Test Plan: sandcastle
Differential Revision: D49653265
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110123
Approved by: https://github.com/zdevito
2023-10-04 14:58:23 +00:00
5565a29568
Release GIL in torch.cuda ops wherever possible. ( #109159 )
...
Most `torch.cuda` ops (ex: `torch.cuda.synchronize`) do not release GIL in C++ land. This has the potential of causing deadlocks and freeze the python process. For example, `torch.cuda.synchronize` could hold GIL and get blocked on some operation. However, that operation might never complete in python land since GIL is held by `torch.cuda.synchronize`.
In this PR, I've tried to release GIL as much as possible in `torch.cuda` ops.
See https://github.com/pytorch/pytorch/issues/109074 for an example of how holding GIL causes a deadlock.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109159
Approved by: https://github.com/ezyang
2023-09-25 14:35:31 +00:00
01fc6466d1
[Reland] [1/N] fix clang-tidy warnings in torch/csrc ( #108114 )
...
Reland of PR #107648 with auto replaced with Py_ssize_t in eval_frame.c. This PR applies fixes to some found issues by clang-tidy in torch/csrc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108114
Approved by: https://github.com/Skylion007
2023-08-30 17:11:16 +00:00
8cbf77585d
Revert "[1/N] fix clang-tidy warnings in torch/csrc ( #107648 )"
...
This reverts commit 49eeca00d1e76dd0158758f2c29da6b1d06bf54a.
Reverted https://github.com/pytorch/pytorch/pull/107648 on behalf of https://github.com/osalpekar due to This causes breakages due to underspecified type ([comment](https://github.com/pytorch/pytorch/pull/107648#issuecomment-1696372588 ))
2023-08-28 20:35:12 +00:00
49eeca00d1
[1/N] fix clang-tidy warnings in torch/csrc ( #107648 )
...
Apply fixes to some found issues by clang-tidy in torch/csrc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107648
Approved by: https://github.com/Skylion007
2023-08-25 00:30:09 +00:00
c9b5e9d7a8
[allocator] register oom observers on every device ( #107399 )
...
This change is to match the behavior of _record_memory_history which was
recently changed to enable history recording on all devices rather than
the current one. It prevents confusing situations where the observer
was registered before the device was set for the training run.
It also ensures the allocators have been initialized in the python binding just in case this is the first call to the CUDA API.
Fixes #107330
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107399
Approved by: https://github.com/eellison
ghstack dependencies: #107171
2023-08-23 18:57:24 +00:00
