id int64 2.74B 3.05B | title stringlengths 1 255 | user stringlengths 2 26 | state stringclasses 2
values | labels listlengths 0 24 | comments int64 0 206 | author_association stringclasses 4
values | body stringlengths 7 62.5k ⌀ | is_title bool 1
class |
|---|---|---|---|---|---|---|---|---|
2,961,473,536 | [PP] Add schedule visualizer | H-Huang | closed | [
"oncall: distributed",
"release notes: distributed (pipeline)",
"module: pipelining"
] | 1 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150333
Added a new private file (`_schedule_visualizer.py`) with some helper methods that can be used to visualize the operations of a schedule and plot with matplotlib.
Interleaved1F1B (pp_group=4, microbatches=8):
![image](h... | true |
2,961,471,730 | Stash tensors for reduce_scatter_v and all_gather_v | kwen2501 | closed | [
"oncall: distributed",
"release notes: distributed (c10d)"
] | 1 | CONTRIBUTOR | Mirror of #149753 for 2.7 release.
Fix 1 of 3 for https://github.com/pytorch/pytorch/pull/148590
https://github.com/pytorch/pytorch/pull/148590 removed record_stream. Since previous AVOID_RECORD flag does not cover reduce_scatter_v and all_gather_v which are in coalescing form, these two ops were missed. Causing ... | true |
2,961,460,155 | [FlexAttention] Don't load invalid values from mask mod | drisspg | open | [
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150331
## Summary
See https://github.com/pytorch/pytorch/issues/150321 for more details
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenya... | true |
2,961,460,009 | [FlexAttention] Allow dispatch to SAC for flex | drisspg | closed | [] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150331
* __->__ #150330
| true |
2,961,456,398 | Testing binary builds | malfet | closed | [
"topic: not user facing",
"ciflow/binaries_wheel"
] | 1 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
| true |
2,961,449,189 | ☂️ Update submodule dependencies to supported version of Cmake | malfet | open | [
"module: build",
"triaged"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Recent cmake-4.0.0 uncovered that number of PyTorch projects depend on pretty old (and sometimes archived) repositories which are no longer compatible and should be cleaned up
Below is the list of the submodules that needs to be udpated/removed (in no particular order):
- [ ] protobuf
- [x] ... | true |
2,961,429,184 | [cherry-pick] [CI] Disable some tests that are failing in periodic #150059 | atalman | closed | [
"topic: not user facing",
"ciflow/periodic",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Disabling some tests to restore periodic
nogpu avx512 timeout:
https://hud.pytorch.org/pytorch/pytorch/commit/59f14d19aea4091c65cca2417c509e3dbf60c0ed#38492953496-box
profiler failure: https://hud.pytorch.org/pytorch/pytorch/commit/7ae0ce6360b6e4f944906502d20da24c04debee5#38461255009-box
test_accelerator fail... | true |
2,961,424,430 | Add reverse engineered code to iOS build | JohnDaWalka | open | [
"triaged",
"open source",
"topic: not user facing"
] | 2 | NONE | Add reverse engineered code to the repository and update relevant scripts and documentation.
* **Build Process**
- Include reverse engineered code in the build process in `.circleci/scripts/binary_ios_build.sh` and `scripts/build_ios.sh`.
- Update `scripts/xcode_build.rb` to include reverse engineered code in the ... | true |
2,961,305,050 | Avoid circular imports in tracing_state_functions | justinchuby | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 12 | COLLABORATOR | tracing_state_functions references some torch functions from submodules like `torch.onnx.is_in_onnx_export` that could trigger module initialization & circular imports. I turned the mapping into a function so that the dictionary is not initialized at torch import.
(discovered in https://github.com/pytorch/pytorch/pu... | true |
2,961,299,108 | [ROCm] cmake 4 workaround for hiprtc | amdfaa | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | CONTRIBUTOR | cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,961,227,011 | [dynamo] add error message for unsupported LOAD_BUILD_CLASS | williamwen42 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"module: compile ux"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150341
* __->__ #150323
Improved error message for https://github.com/pytorch/pytorch/issues/128942
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kade... | true |
2,961,217,925 | [logging] Add pgo remote get/put timings to dynamo_compile | masnesral | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150322
Test Plan: https://fburl.com/scuba/dynamo_compile/sandbox/xf950tw8
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,961,191,886 | Masking out Loads from mask_modded out regions | drisspg | open | [
"triaged",
"module: flex attention"
] | 0 | CONTRIBUTOR | # Summary
There is a very common gotcha/footgun that users run into when composing score_mods w/ blockmasks.
```Py
import torch
from torch.nn.attention.flex_attention import create_block_mask, flex_attention
B, H, SEQ_LEN, HEAD_DIM = 1, 1, 128, 16
MAX_LEN = 127 # 1 less then possible values
buffer = torch.arange(MA... | true |
2,961,145,000 | Update gloo submodule | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | That updates its CMake minimum version(via https://github.com/facebookincubator/gloo/pull/424 ) and removes cmake-4.0.0 workarounds for gloo
| true |
2,961,142,030 | [dynamo] Bad error message when trying to compile async functions due to bad bytecode jump targets | williamwen42 | open | [
"triaged",
"oncall: pt2",
"module: dynamo",
"module: compile ux"
] | 0 | MEMBER | Encountered this when trying to compile unsupported async bytecodes.
Repro:
```python
import torch
async def fn():
return 1
torch.compile(fn, backend="eager", fullgraph=True)()
```
Output:
```
(/data/users/williamwen/py312-env) [williamwen@devgpu020.odn1 /data/users/williamwen/pytorch (84684e93)]$ python play... | true |
2,961,135,303 | [cuDNN][SDPA][WIP] cuDNN >= 9.8.0 should support seqlen 1 | eqy | closed | [
"module: cudnn",
"module: cuda",
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: sdpa"
] | 2 | COLLABORATOR | Still testing edge cases
cc @csarofeen @ptrblck @xwang233 @msaroufim | true |
2,961,130,405 | [WIP] standalone torch.inductor.compile API | zou3519 | closed | [
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150317
[no-ci]
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,961,064,782 | assert on all_reduce_event only if it's not CPU device. | Ritesh1905 | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | Summary: For CPU based runs, `all_reduce_event` would be None since this is the result of the `all_reduce_stream.record_event()`, which does not do much other than returning None when device type is CPU.
Test Plan: CI
Differential Revision: D72176406
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wc... | true |
2,961,019,783 | Update periodic.yml to test NVIDIA GPU hosted runner | zhe-thoughts | closed | [
"open source",
"topic: not user facing"
] | 2 | NONE | Update periodic.yml to test NVIDIA GPU hosted runner
Fixes #ISSUE_NUMBER
| true |
2,960,998,471 | [PT2][cutlass backend] No suitable Cutlass GEMM configs for max-autotune mode | efsotr | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 2 | NONE | ### 🐛 Describe the bug
```python
import os
os.environ["TORCHINDUCTOR_MAX_AUTOTUNE_GEMM_BACKENDS"] = "ATEN,TRITON,CPP,CUTLASS"
# os.environ["TORCH_LOGS"] = "+dynamo"
import logging
import torch
import torch._inductor.config as config
import torch._inductor.codegen.cuda.gemm_template as gemm_template
gemm_template.lo... | true |
2,960,978,614 | Lint rule for always using std::optional? | clee2000 | closed | [
"module: ci",
"module: lint",
"triaged"
] | 2 | CONTRIBUTOR | There were a couple of PRs to use std::optional instead of c10::optional and a few other similar functions. Is there a lint rule we can make to prevent people from continuing to use c10::optional?
Some internal failures might have been caught if this lint rule did exist: https://github.com/pytorch/pytorch/pull/150129... | true |
2,960,946,019 | [WIP] Refactor CUDAAllocatorConfig to reuse AllocatorConfig | guangyey | open | [
"open source",
"release notes: cpp",
"topic: not user facing"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151298
* #138222
* __->__ #150312
* #149601
| true |
2,960,933,781 | [Inductor] Synchronize type annotations between torch and triton | penguin-wwy | open | [
"triaged",
"open source",
"topic: not user facing",
"module: inductor"
] | 13 | CONTRIBUTOR | cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,960,923,800 | cd: Fix naming for windows arm64 libtorch builds | seemethere | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150310
Apparently the magical incantation to name these correctly lies in the
build_variant variable otherwise it silently does nothing.
Signed-off-by: Eli Uriegas <eliuriegas@meta.com> | true |
2,960,874,595 | DISABLED test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_int16 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 7 | NONE | Platforms: linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_int16&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/3968788... | true |
2,960,856,869 | Update ExecuTorch pin to latest viable/strict 3/28/2025 | mergennachin | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | From latest viable/strict: https://hud.pytorch.org/hud/pytorch/executorch/viable%2Fstrict/1?per_page=50
Fixes https://github.com/pytorch/pytorch/issues/144480
This commit has important CI stability fixes, such as https://github.com/pytorch/executorch/pull/9561 and https://github.com/pytorch/executorch/pull/9634 | true |
2,960,834,655 | test dynamo | Sunnie912 | open | [
"fb-exported",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Summary: Testing failures in the github
Differential Revision: D72172374
| true |
2,960,808,257 | [Hierarchical Compile] Apply deduplication after output node creation | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150306
* #150305
* #150304
* #150303
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,960,808,085 | [Hierarchical Compile] Add cycle detection to graph region expansion | mlazos | closed | [
"Merged",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150306
* __->__ #150305
* #150304
* #150303
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,960,807,909 | [Hierarchical Compile] Add cycle detection function for debug | mlazos | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150306
* #150305
* __->__ #150304
* #150303
Remove print
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,960,807,748 | [Hierarchical Compile] Remove spammy debug log | mlazos | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150306
* #150305
* #150304
* __->__ #150303
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,960,768,915 | allow collectives to be DCEd during collective optimizations, fix bad partitioner save decision | bdhirsh | open | [
"oncall: distributed",
"module: inductor",
"ciflow/inductor",
"release notes: AO frontend"
] | 1 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/146693. There are more details in the issue discussion.
I tested the repro and confirmed that we no longer save the allgather'd tensor for backward. Going to move a (smaller version of) that repro into a test and update soon.
Here's a tlparse pair of the generated... | true |
2,960,590,390 | [Build] Fix XPU builds inside venv | pytorchbot | closed | [
"open source",
"release notes: build",
"topic: bug fixes",
"topic: not user facing",
"ciflow/xpu"
] | 2 | COLLABORATOR | Update the torch-xpu-ops commit to [3ee2bd2f13e1ed17a685986ff667a58bed5f2aa5](https://github.com/intel/torch-xpu-ops/commit/3ee2bd2f13e1ed17a685986ff667a58bed5f2aa5)
- Fix the build error if users build torch xpu through python virtual environment. It was due to that torch-xpu-ops uses `${PYTHON_EXECUTABLE}` to get... | true |
2,960,456,392 | Update torch-xpu-ops commit pin to 3ee2bd2 | chuanqi129 | closed | [
"open source",
"Merged",
"topic: not user facing"
] | 10 | COLLABORATOR | Update the torch-xpu-ops commit to [3ee2bd2f13e1ed17a685986ff667a58bed5f2aa5](https://github.com/intel/torch-xpu-ops/commit/3ee2bd2f13e1ed17a685986ff667a58bed5f2aa5) | true |
2,960,416,808 | DISABLED AotInductorTest.FreeInactiveConstantBufferRuntimeConstantFoldingCuda (build.bin.test_aoti_inference) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"oncall: export",
"module: aotinductor"
] | 28 | NONE | Platforms: inductor
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=AotInductorTest.FreeInactiveConstantBufferRuntimeConstantFoldingCuda&suite=build.bin.test_aoti_inference&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/p... | true |
2,960,416,709 | DISABLED test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_float64 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 7 | NONE | Platforms: linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_float64&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/396... | true |
2,960,410,195 | Disable cache and utilization stats uploading steps on s390x | AlekseiNikiforovIBM | open | [
"triaged",
"open source",
"ciflow/trunk",
"topic: not user facing",
"ciflow/s390"
] | 10 | COLLABORATOR | There are no AWS credentials available on s390x runners. These steps are failing anyway due to that. | true |
2,960,400,404 | [RFC] zentorch Integration | naveenthangudu | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 8 | NONE | # zentorch Integration
## Table of Contents
- [1. Authors](#1-authors)
- [2. Summary](#2-summary)
- [3. Highlights](#3-highlights)
- [4. Motivation](#4-motivation)
- [4.1. Benchmarking Configuration](#41-benchmarking-configuration)
- [4.2. Single Core Performance Summary](#42-single-core-performance-summary)
- [... | true |
2,960,393,544 | Add overload for __getitem__ of Sequentail to fix type hint | FFFrog | closed | [
"open source",
"topic: not user facing"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150295
As the title stated.
Realted issue:
https://github.com/pytorch/pytorch/issues/150257 | true |
2,960,251,472 | Make PyTorch buildable by CMake-4.x on s390x | AlekseiNikiforovIBM | closed | [
"open source",
"Merged",
"topic: not user facing",
"ciflow/binaries_wheel"
] | 7 | COLLABORATOR | This is a continuation of
https://github.com/pytorch/pytorch/pull/150203
that fixes nightly build on s390x. | true |
2,960,144,601 | Unify on dynamo_compile as the overall wait counter | ppanchalia | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Summary:
dynamo_compile for the most part has been accounting for compile time except autotuning.
all_compilation_types had earlier been injected on fx_codegen_and_compile, which was incorrect.
Add autotuining to dynamo and deprcate all_compilation_types counter.
Differential Revision: D72145447
cc ... | true |
2,960,004,959 | [clang-tidy] Get rid-off dangerouse clang-tidy option | ivanmurashko | open | [
"fb-exported",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Summary: WarningsAsErrors is a special option that should be applied carefully. At our case clang-tidy checks have a lot of false positives and as result WarningsAsErrors will prevent the corresponding diffs from landing
Test Plan: No special test required here
Differential Revision: D72159625
| true |
2,959,971,510 | Add differentiable ops hint message in Module docs | zeshengzong | open | [
"triaged",
"open source",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Fixes #101934
## Test Result
### Before
### After
| true |
2,959,951,442 | Remove torch functions that do not support device arguments from _device_constructor | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 11 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150290
As the title stated
In Addition:
- I have checked all the functions in _device_constructor and found ``torch.vander`` also don`t support device arguments
- Remove the duplicated function such as torch.ones and torch.asarray
... | true |
2,959,932,100 | [Dynamo][Misc] Apply typing hints for `codegen` | shink | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 12 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,959,845,593 | [Intel GPU] Allow XPU backend in Quantize operators | yucai-intel | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: quantization"
] | 14 | CONTRIBUTOR | This modification is to support torch.quantize_per_channel() on XPU, otherwise it will cause a segmentation fault. | true |
2,959,817,103 | [Inductor XPU] Support mkldnn fusion for XPU. | etaf | open | [
"open source",
"module: inductor",
"ciflow/inductor"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150287
* #150286
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,959,816,979 | [Inductor XPU] Refine `test_mkldnn_pattern_matcher.py` to be reusable for XPU. | etaf | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150287
* __->__ #150286
This PR extracts some test cases from TestPatternMatcher into a newly created TestPatternMatcherGeneric, and uses instantiate_device_type_tests to make them reusable across multiple devices.
cc @voznesenskym @... | true |
2,959,786,360 | CuSparse doesn't work on sparse tensor | qwerty10086 | open | [
"module: sparse",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
I need to compute very large sparse matrix multiplications in my project, and I encountered the same issue as [#103820](https://github.com/pytorch/pytorch/issues/103820). It's mentioned that CUDA 12 provide two new algorithms with less memory occupation. I tested them on my matrices and they wo... | true |
2,959,759,778 | torch.fill bug | 0x45f | open | [
"triaged",
"module: python frontend"
] | 2 | NONE | ### 🐛 Describe the bug
```
import torch
import numpy as np
torch.set_default_device("cuda")
x = torch.randn(4, 5)
# y = torch.randn(4, 5)
out = torch.fill(x, 1)
print(out)
```
raise error:
```
Traceback (most recent call last):
File "/home/wangzhen/gems-ops/FlagGems/build/run.py", line 11, in <module>
ou... | true |
2,959,706,801 | Update slow tests | pytorchupdatebot | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/slow",
"ci-no-td"
] | 6 | COLLABORATOR | This PR is auto-generated weekly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/weekly.yml).
Update the list of slow tests. | true |
2,959,603,294 | Add `label_smoothing` param in `nn.BCELoss` and `nn.BCEWithLogitsLoss` | zeshengzong | open | [
"open source",
"release notes: nn"
] | 4 | CONTRIBUTOR | Fixes #91545
## Changes
- Add `label_smoothing` param and docs
- Add test case for `label_smoothing`
- Remove duplicate description in `nn.BCELoss` and `nn.BCEWithLogitsLoss`
## Test Result
```bash
pytest -s test/test_nn.py -k test_bce
```
![image](https://github.com/user-attachments/assets/30c0b7... | true |
2,959,418,248 | rename_privateuse1_backend arise CUPTI_ERROR_NOT_INITIALIZED error. | kstreee-furiosa | closed | [
"oncall: profiler",
"module: PrivateUse1"
] | 2 | NONE | ### 🐛 Describe the bug
Following code arise cuda profiling warnings and an error. In case I install torch==2.5.1+cpu, it works well without warnings and an error.
```python
import torch
# If comment out `torch.utils.rename_privateuse1_backend("test")`, works fine.
torch.utils.rename_privateuse1_backend("test")
wit... | true |
2,959,392,506 | Discrepancy between eager and torch.compile (mode='max-autotune-no-cudagraphs') outputs under strict tolerance | tinywisdom | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 1 | NONE | ### 🐛 Describe the bug
I'm observing **output inconsistencies** between Eager mode and `torch.compile` when using `mode="max-autotune-no-cudagraphs"`, and this behavior persists in both stable and nightly builds(2.6.0.dev20241112+cu121).
In particular, the outputs differ beyond acceptable tolerances for some models,... | true |
2,959,392,351 | [MPS] Add support for hermite_polynomial_h. | dcci | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 4 | MEMBER | null | true |
2,959,381,652 | softmax: add device check for xpu with half_to_float | weishi-deng | open | [
"open source",
"release notes: xpu",
"module: xpu"
] | 11 | CONTRIBUTOR | To support "half_to_float" functionality on xpu devices, we add the device checks for xpu devices here.
cc @gujinghui @EikanWang @fengyuan14 @guangyey | true |
2,959,332,725 | Sparse tensor indexing not implemented, but partially supported by using index_select | zbh2047 | open | [
"module: sparse",
"triaged"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
I want to index a sparse tensor with a dense index tensor `idx`. This setting is quite useful in extracting sub-matrices or obtaining samples from a sparse tensor dataset. However, I found this operation is not implemented, but partially supported in an inconsistent sense.
Fir... | true |
2,959,238,895 | [AOTInductor] Add User Managed buffer for AOTI constant buffer. | muchulee8 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150276
Summary:
We add the functionality to allow users to directly pass in a at::Tensor
into AOTInductor, that would be used as the constant.
This user managed buffer skips the copying step in AOTInductor, and let
users to directly ... | true |
2,959,238,814 | [AOTInductor] Introduce MaybeOwningAtenTensorHandle for ConstantMap | muchulee8 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150275
Summary:
We used RAIIAtenTensorHandle for ConstantMap, where RAIIAtenTensorHandle
is a unique_ptr, indicating that all memory handling is by the
AOTInductor internally.
In this PR, we introduce ConstantAtenTensorHandle which ... | true |
2,959,237,630 | [AOTInductor] Free tensors in test | muchulee8 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150274
Summary:
This PR frees tensor that were new-ed within the test itself to prevent
memory leak.
Test Plan:
Fixing tests itself.
Reviewers:
Subscribers:
Tasks:
Tags: | true |
2,959,233,392 | DISABLED test_tensor_with_grad_to_scalar_warning (__main__.TestTorch) | pytorch-bot[bot] | open | [
"module: autograd",
"triaged",
"module: flaky-tests",
"skipped",
"module: python frontend"
] | 1 | NONE | Platforms: asan, linux, mac, macos, rocm, win, windows
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_tensor_with_grad_to_scalar_warning&suite=TestTorch&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39... | true |
2,959,184,428 | Update Doc for Intel XPU Profiling | pytorchbot | closed | [
"open source"
] | 1 | COLLABORATOR | Updated below two pages for Intel XPU
https://pytorch.org/docs/stable/torch.compiler_profiling_torch_compile.html
https://pytorch.org/docs/stable/profiler.html
cc @svekars @brycebortree @sekyondaMeta @AlannaBurke @tstatler | true |
2,959,182,157 | [fbcode]Removing `@NoIntBaseDeprecated` annotation in `evaluation.thrift` file | Sunnie912 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Summary: #buildall
Test Plan:
```
buck test 'fbcode//mode/opt' fbcode//caffe2/torch/fb/training_toolkit/applications/bulk_eval/tests:evaluator_test -- --exact 'caffe2/torch/fb/training_toolkit/applications/bulk_eval/tests:evaluator_test - test_setup_evaluation_utils (caffe2.torch.fb.training_toolkit.applications.bulk_... | true |
2,959,169,648 | Add error check for out variant of tensordot function with requries_grad tensor | cz2h | closed | [
"module: bc-breaking",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: autograd",
"release notes: linalg_frontend",
"topic: bc breaking"
] | 13 | CONTRIBUTOR | Fixes #147846. Previously there is no error out under out variant of`tensordot` while `requires_grad=True`. This can cause potential issue when out tensor is part of a computation graph.
Enforces the out variant of tensordot to run without setting `requries_grad=True`. Change same to #117067
cc @ezyang @gchanan | true |
2,959,132,004 | [AOTInductor] Modify test for Memory tracking for memory-related | muchulee8 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 14 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150269
operations
Summary:
Fix the test for memory tracking. This PR does:
(1) Add tracking before and after for all memory-related operations.
Make sure the operation do indeed captures memory both in CUDA and
torch's CUDACachAlloc... | true |
2,959,031,738 | Add a test for checking that the CUDA stubs directory is not in libcaffe2_nvrts.so's RPATH or RUNPATH | Flamefire | open | [
"triaged",
"open source",
"topic: not user facing"
] | 1 | COLLABORATOR | The CUDA stub directory must not appear in the rpath or RUNPATH of any library as that would make it unusable at runtime. This should no longer happen (it did before, see the previous PR) but we better check that it stays like that. See the referenced issue https://github.com/pytorch/pytorch/issues/35418
The test ve... | true |
2,958,962,575 | Gradient update with `differentiable=True` is slightly different from the default | dilithjay | closed | [
"module: autograd",
"module: optimizer",
"triaged"
] | 3 | NONE | ### 🐛 Describe the bug
I observe a slight difference in the weights produced when `differentiable=True` as opposed to the default, specifically for a higher number of updates.
The following is the code to reproduce:
```python
import torch
from learn2learn import clone_module
lr = 0.01
n_updates = 100
# -----------... | true |
2,958,912,075 | Pytorch nightly Cuda 12.8 - 'too many resources requested for launch' with multiple layers of LayerNorm after strided Conv1d | Pyr-000 | closed | [
"high priority",
"module: cuda",
"triaged",
"Blackwell"
] | 19 | NONE | ### 🐛 Describe the bug
The current pytorch nightly build for CUDA 12.8: `2.8.0.dev20250327+cu128` yields the following error:
`RuntimeError: CUDA error: too many resources requested for launch`
When running a backward pass through a model with multiple strided Conv1d modules followed by LayerNorm modules.
The error ... | true |
2,958,748,550 | Graph break on Tensor._make_subclass | KareemMusleh | closed | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 12 | NONE | ### 🐛 Describe the bug
I am having the following problem
```python
from torch import nn
import torch
torch_compile_options = {
"epilogue_fusion" : True,
"max_autotune" : True,
"shape_padding" : True,
"trace.enabled" : True,
"triton.cudagraphs" : False,
}
class a(nn.Linear):
def __... | true |
2,958,742,336 | Refresh expected results. | laithsakka | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150264
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,958,689,615 | [submodule] Bump ITTAPI to 3.25.5 | cyyever | closed | [
"triaged",
"open source",
"oncall: profiler",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"topic: build"
] | 18 | COLLABORATOR | It hasn't been updated for 3 years. And also to remove CMake 4 workaround.
cc @robieta @chaekit @guotuofeng @guyang3532 @dzhulgakov @davidberard98 @briancoutinho @sraikund16 @sanrise | true |
2,958,668,229 | Fake mode mismatch when doing nested compile + tensor subclass | gau-nernst | open | [
"triaged",
"oncall: pt2",
"module: fakeTensor",
"module: pt2-dispatcher"
] | 2 | NONE | ### 🐛 Describe the bug
```python
import torch
import torch.nn.functional as F
from torch import Tensor, nn
class MyEmbedding(nn.Module):
def __init__(self, num_embeds, embed_dim):
super().__init__()
self.weight = nn.Parameter(torch.randn(num_embeds, embed_dim))
def forward(self, x):
... | true |
2,958,636,790 | UNSTABLE pull / linux-jammy-py3-clang12-executorch / build | clee2000 | closed | [
"module: ci",
"unstable"
] | 2 | CONTRIBUTOR | > Please provide a brief reason on why you need to mark this job as unstable.
executorch is not yet compatible with cmake 4.0.0 but doesn't pin cmake, similar to https://github.com/pytorch/pytorch/pull/150158.
Marking this as unstable until we executorch updates?
cc @seemethere @malfet @pytorch/pytorch-dev-infra | true |
2,958,593,616 | [XPU] `torch.xpu.is_available()` fails on Intel Arc A770 on latest nightly. | simonlui | closed | [
"needs reproduction",
"triaged",
"module: regression",
"module: xpu"
] | 3 | NONE | ### 🐛 Describe the bug
Using the latest nightly 2.8.0.dev20250327+xpu build with my Intel Arc A770, I get the following:
```
Python 3.12.5 | Intel Corporation | (main, Sep 9 2024, 23:35:37) [GCC 14.1.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>>torch.xpu.is_a... | true |
2,958,509,083 | [inductor][comms] fix node_summary for composite scheduler nodes | xmfan | closed | [
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150074
* __->__ #150258
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,958,154,573 | Type hint bug report relating to Sequential | erlebach | open | [
"module: typing",
"triaged"
] | 0 | NONE | ### 🐛 Describe the bug
Consider the following code in Cursor (also in VSCode):
```python
from torch import nn
from torch.nn import Module, Sequential
dim = 10
heads = 10
num_kv_per_token = 10
to_adaptive_step = Sequential(
nn.Linear(dim, heads * num_kv_per_token),
nn.Linear(dim, heads * num_kv_per_token),
... | true |
2,958,084,238 | [inductor] Fix inductor windows linker error | jansel | closed | [
"module: build",
"module: windows",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 13 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150256
Fixes #149889
cc @malfet @seemethere @peterjc123 @mszhanyi @skyline75489 @nbcsm @iremyux @Blackhex @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @c... | true |
2,957,945,267 | [MPS] grad scaler | Isalia20 | closed | [
"triaged",
"open source",
"module: amp (automated mixed precision)",
"Merged",
"ciflow/trunk",
"module: mps",
"release notes: mps"
] | 14 | COLLABORATOR | Fixes #142397
Basic implementation is done. What's left:
- [x] Different dtype/device tensors in the TensorList
- [x] fast path for grouping the foreach kernel
- [x] Tests
Regarding tests, I found some tests in `test/test_torch.py` for GradScaler but I couldn't figure out what is the best way to enable the tes... | true |
2,957,864,225 | RNN training is very slow on Intel xpu | JimmysAIPG | open | [
"triaged",
"module: xpu"
] | 7 | NONE | ### 🐛 Describe the bug
Hello, I am a newbie and I just switched from the cuda environment to the xpu environment for learning. I found that when I use GRU or LSTM to train the model, the speed is very slow in the xpu environment. Is there a problem?
Please refer to the attachment for the code
[imdb.txt](https://git... | true |
2,957,821,440 | pytorch for NVIDIA-5090 | Reginald-L | open | [
"needs reproduction",
"module: binaries",
"module: cuda",
"triaged"
] | 4 | NONE | I am installing the pytorch gpu version on an RTX5090 device, but I am getting an error:
here is my torch version:
Name: torch
Version: 2.8.0.dev20250327+cu128
Summary: Tensors and Dynamic neural networks in Python with strong G... | true |
2,957,777,398 | RuntimeError: Subtracting Reconstructed Jagged Nested Tensor Fails with Shape Mismatch | xsgxlz | open | [
"module: docs",
"triaged",
"module: nestedtensor"
] | 2 | NONE | ### 🐛 Describe the bug
When a `torch.nested.nested_tensor` with `layout=torch.jagged` is converted to a padded tensor using `to_padded_tensor()` and then reconstructed back into a jagged `nested_tensor` using its offsets, performing a binary operation (like subtraction) between the original and the reconstructed nest... | true |
2,957,731,885 | [Release/2.7] Update torch-xpu-ops commit pin (For CI test) | xytintel | closed | [
"open source",
"topic: not user facing",
"ciflow/xpu",
"release notes: xpu"
] | 6 | CONTRIBUTOR | Update the torch-xpu-ops commit to [b18528c455d0297b89b255e93b86ff668069459f](https://github.com/intel/torch-xpu-ops/commit/b18528c455d0297b89b255e93b86ff668069459f), include
- Bugfix of performance issue relating to GRF configuration.
| true |
2,957,723,095 | ROCm: Add trailing comma for consistency in gfx architecture list | jagadish-amd | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Adding trailing comma for consistency.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,957,720,209 | [ROCm] change preferred blas lib defaults | pytorchbot | closed | [
"module: rocm",
"open source",
"ciflow/rocm"
] | 1 | COLLABORATOR | Fixes #148883
Fixes #150155
Also adds at::BlasBackend:Default. Instinct cards prefer hipBLASLt, everything else prefers rocBLAS.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,957,712,319 | dist.all_reduce should check if all tensors are same data type when using nccl | hyleepp | closed | [
"oncall: distributed"
] | 2 | NONE | ### 🐛 Describe the bug
Hello, I find that when I use nccl as backend, if the data type of a tensor on different devices do not match, then all reduce will give a very strange value (looks like overflow).
```python
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import os
def setup(... | true |
2,957,707,161 | [MPSInductor] Specify `max_total_threads_per_threadgroup` | malfet | closed | [
"Merged",
"topic: bug fixes",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150247
When generating reduction kernel, otherwise compiler can unroll loops too much that kernel could not be launched for the intended threadgroup size
Extend `c10::metal::max` to accept different dtypes
Together this fixes ... | true |
2,957,707,117 | [BE] Fix signed/unsigned comparison warning | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150247
* __->__ #150246
One will see them only if compilation fails, but still | true |
2,957,640,076 | Add cmake variable USE_ROCM_CK | trixirt | open | [
"module: rocm",
"open source"
] | 6 | NONE | To control the use of ROCm Composable Kernel usage.
CK is not compatible with all rocBLAS gpu's, so the user must explicitly choose to use CK.
Fixes #150187
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,957,575,223 | Update type of `create_block_mask` to more accurately reflect things | Chillee | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"suppress-api-compatibility-check",
"suppress-bc-linter"
] | 4 | COLLABORATOR | Fixes some mypy issues | true |
2,957,569,926 | if blaslt fails, fall back to blas | jeffdaily | closed | [
"open source"
] | 2 | COLLABORATOR | Fixes #150016.
This is implemented for both cublaslt and hipblaslt. gemm_and_bias on failure will fall back to unfused path. lt gemm on failure falls back to gemm even if gemm preference is set to lt.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150147
Approved by: https://github.com/malfet | true |
2,957,503,285 | Feature Request: Enhance Nested Tensor Operations for Direct RoPE Application | xsgxlz | open | [
"triaged",
"module: nestedtensor"
] | 2 | NONE | ### 🚀 The feature, motivation and pitch
**Feature Proposal:**
We propose enhancing the operations available for `torch.NestedTensor` to facilitate the direct and efficient application of position-dependent transformations, specifically Rotary Positional Embeddings (RoPE), without extra unnecessary memory operations
... | true |
2,957,500,581 | [decomps] Add decomposition for linalg_vector_norm | SS-JIA | open | [
"ciflow/inductor"
] | 8 | CONTRIBUTOR |
Summary:
## Context
`linalg_vector_norm` is required to run Vision Transformer models in ExecuTorch. Currently, model export + inference fails because ExecuTorch doesn't have a kernel for `linalg_vector_norm`.
However, there is a decomposition for the operator. Add the decomposition to the core decomp table to unblo... | true |
2,957,457,596 | Fix NVTX functions compatibility with torch.compile(fullgraph=True) | zsnoob | open | [
"triaged",
"open source",
"release notes: cuda",
"module: dynamo",
"release notes: dynamo"
] | 4 | NONE | ## Problem Solved
This PR resolves the incompatibility between NVTX functions and torch._dynamo. When attempting to use NVTX profiling tools within code compiled with `torch.compile(fullgraph=True)`, the following error occurs:
```
torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor int call_function <... | true |
2,957,455,316 | [state dict] add strict check when there are more keys in global than local state | mori360 | open | [
"oncall: distributed",
"release notes: distributed (checkpoint)"
] | 4 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/149516
In broadcast_from_rank0, currently there's no strict check in loading global state dict to local.
If there are any keys that in global but not in local, they will be added into local state_dict no matter strict is True or False
Here are the logic after this ... | true |
2,957,426,301 | remove guard _size_oblivious from expand and make it more resilient to data dependent errors. | laithsakka | closed | [
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150238
* #148809
When we do not know that requested_length == x, we do not have to fail we can make it runtime assert with sym_or.
address #150235 https://github.com/pytorch/pytorch/issues/128645
cc @voznesenskym @penguinwu @Ei... | true |
2,957,408,362 | [MPS] Fix dot/mm for conj_tensors | pytorchbot | closed | [
"open source",
"release notes: mps",
"ciflow/mps"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150157
- Distinguish between conjugated/non_conjugated inputs by appending conjugation to the operator key
- For matmul or dot, add `conjugateWithTensor:name:` calls before running the op
- Enable testing for conjugated ops by p... | true |
2,957,404,986 | [dynamic shapes] rewrite expand with guard_or_false | pianpwk | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: export"
] | 4 | CONTRIBUTOR | Rewrites the expand decomposition to avoid unbacked errors, assuming the general path where `input shape == output shape or input shape == 1`.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,957,383,626 | Fix _Waitcounter decorator and dd backward pass wait counter | ppanchalia | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 13 | CONTRIBUTOR | Summary:
This will log a wait counter with for backward compile and fixes weirdness with nested context managers.
Since the old wait counters added through dynamo_timed were never created with the nesting issue. I am also changing the key nomenclature from `pytorch.dynamo_timed` to `pytorch.wait_counter`. We want t... | true |
2,957,377,947 | [CI] Fix log artifact not containing test logs attempt 2 | clee2000 | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
Take two of https://github.com/pytorch/pytorch/pull/149577 since it didn't work | true |
2,957,373,430 | [aten] 8 bytes aligned vector loads for bf16 and fp16 dtypes in torch.cat | zhaozhul | closed | [
"Merged",
"ciflow/trunk",
"release notes: cuda",
"topic: performance"
] | 8 | CONTRIBUTOR |
Enable aligned vector loading for 2 bytes datatypes in torch.cat. Specifically:
1. reduce the vector length to 8 bytes for 2-byte types (fp16, bf16 etc)
2. enable through a conditional template
The reason why 8-byte vector loading was chosen for fp16 and bf16:
16-byte load results in heavier register overhead... | true |
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