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 |
|---|---|---|---|---|---|---|---|---|
3,025,476,125 | [conda] Remove conda usage from TD llm retriever job | clee2000 | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Remove conda usage from TD llm retriever job
python3 in the base is python3.9 right now. I'm not sure what the best way to deal with a potentially different python version would be, dnf install? | true |
3,025,439,538 | Unusually slow draft_export time | tugsbayasgalan | open | [
"triage review",
"oncall: pt2",
"oncall: export"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
To repro:
1. Clone https://github.com/SWivid/F5-TTS
2. Apply: https://gist.github.com/tugsbayasgalan/1adddb5517e1648c91c94bc2bd1ae098
3. Install with torch-nightly.
4. Run:
```
f5-tts_infer-cli --model F5TTS_v1_Base -c src/f5_tts/infer/examples/basic/basic.toml --gen_text "pytorch is th... | true |
3,025,428,573 | [Security] Advise against loading untrusted TorchScripts | malfet | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | As torchscripted model is a Turing complete program | true |
3,025,407,592 | pin_memory crashes for big tensors and leaks page locked memory | c-rizz | open | [
"module: memory usage",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
Allocating pinning large tensors (>2 GB on my machine) crashes with "CUDA error: invalid arguement". Also, it seems to allocate additional memory compared to the actual tensor size, maybe due to [150517](https://github.com/pytorch/pytorch/issues/150517). Memory that is not freed after the crash... | true |
3,025,287,243 | [AOTI] Fix a memory leak in model_package_loader | desertfire | closed | [
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: inductor (aoti)"
] | 6 | CONTRIBUTOR | Summary: There was a char array allocated but never freed. It was found by valgrind and verified fixed with this PR, although it's not easy to write a unit test for it.
| true |
3,025,261,386 | Remove cuda dependencies from non cuda buids | atalman | closed | [
"Merged",
"ciflow/binaries",
"topic: not user facing"
] | 5 | CONTRIBUTOR | These dependancies added to fix poetry issue on pypi. However inclusion of these dependencies creates issue with poetry on download.pytorch.org due to poetry reading first available wheel on index for METADATA requirements. Hence all metadata requirements for CPU wheels can't list any cuda dependencies.
Injecting th... | true |
3,025,234,483 | [dynamo] Guard serialization for NAME_MATCH | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152332
* #152331
* #152330
* #152329
* #152328
* #152327
* #152326
* #152325
Differential Revision: [D73780430](https://our.internmc.facebook.com/intern/diff/D73780430/)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @... | true |
3,025,234,340 | [dynamo] Guard serialization for DISPATCH_KEY_SET_MATCH | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152332
* __->__ #152331
* #152330
* #152329
* #152328
* #152327
* #152326
* #152325
Differential Revision: [D73780433](https://our.internmc.facebook.com/intern/diff/D73780433/)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @... | true |
3,025,233,909 | [dynamo] Guard serialization for ID_MATCH | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152332
* #152331
* __->__ #152330
* #152329
* #152328
* #152327
* #152326
* #152325
Differential Revision: [D73780431](https://our.internmc.facebook.com/intern/diff/D73780431/)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @... | true |
3,025,233,776 | [dynamo] Guard serialization for NONE_MATCH. | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152332
* #152331
* #152330
* __->__ #152329
* #152328
* #152327
* #152326
* #152325
Differential Revision: [D73780435](https://our.internmc.facebook.com/intern/diff/D73780435/)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @... | true |
3,025,233,632 | [dynamo] Guard serialization for BOOL_MATCH. | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152332
* #152331
* #152330
* #152329
* __->__ #152328
* #152327
* #152326
* #152325
Differential Revision: [D73780434](https://our.internmc.facebook.com/intern/diff/D73780434/)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @... | true |
3,025,233,510 | [dynamo] Guard serialization for DICT_CONTAINS | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152332
* #152331
* #152330
* #152329
* #152328
* __->__ #152327
* #152326
* #152325
Adding serialization for DICT_CONTAINS
Differential Revision: [D73780432](https://our.internmc.facebook.com/intern/diff/D73780432/)
cc @voznesenskym @peng... | true |
3,025,233,374 | [dynamo] Guard serialization for DICT_VERSION | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152332
* #152331
* #152330
* #152329
* #152328
* #152327
* __->__ #152326
* #152325
I think we shouldn't support DICT_VERSION for 2 reasons:
1. dict version is not well defined across processes
2. they are pretty rare (only with pytree call... | true |
3,025,233,226 | [dynamo] Guard serialization for TYPE_MATCH | zhxchen17 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152332
* #152331
* #152330
* #152329
* #152328
* #152327
* #152326
* __->__ #152325
Adding guard serialization for TYPE_MATCH
Differential Revision: [D73780438](https://our.internmc.facebook.com/intern/diff/D73780438/)
cc @voznesenskym @p... | true |
3,025,148,900 | [benchmarking] Inc aarch64 bench shards to 15 | malfet | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152324
As it frequently timing out with 12, but also it feels like shards are somewhat unbalanced
I.e. if one to look at https://github.com/pytorch/pytorch/actions/runs/14696840776/job/41239776679
Shard 12 takes 3.6 hours, while shar... | true |
3,025,146,877 | compile generates inefficient code when mutating small slice of a graph input | bdhirsh | open | [
"triaged",
"module: functionalization",
"oncall: pt2",
"module: inductor",
"module: pt2-dispatcher"
] | 0 | CONTRIBUTOR | See this repro:
```python
import torch
def plus_one(x):
x[0].add_(1.0)
return x
x_og = torch.randn(32 * 1024, 1024, device="cuda", dtype=torch.float32)
x = x_og.clone()
plus_one(x)
plus_one_compiled = torch.compile(plus_one)
x = x_og.clone()
plus_one_compiled(x)
```
if you run with `TORCH_LOGS="output_code"`... | true |
3,024,957,450 | Skip test requiring MKL | Flamefire | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | COLLABORATOR | `test_reproduce_121253_issue_addmm_fusion_check` checks for "mkl._mkl_linear" being found in the generated source which cannot be there when MKL isn't available.
Add skip marker similar to other tests in this file.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @... | true |
3,024,900,747 | [torch-xpu-ops] Update torch-xpu-ops commit pin. | etaf | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"keep-going",
"ciflow/xpu",
"ci-no-td"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152321
Update the torch-xpu-ops commit to [655fa9bc7f88ab5bd3766b5f2fd5b43989c2caca](https://github.com/intel/torch-xpu-ops/commit/655fa9bc7f88ab5bd3766b5f2fd5b43989c2caca), including:
- Fixes batch_norm numeric error by adding... | true |
3,024,523,730 | [dynamo] Use getattr when accessing self.value.__module__ in SkipFunctionVariable | wdziurdz | closed | [
"open source",
"topic: not user facing",
"module: dynamo"
] | 7 | CONTRIBUTOR | Fixes #152316
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
3,024,507,273 | Fix common_distributed.py to NOT set root logger | wizzniu | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | CONTRIBUTOR | Using `logging.basicConfig` to set root logger's level is not a good behavior. Fix common_distributed.py to set level for current logger only, because it affects downstream's 3rd-party testing plugins.
cc @ezyang @albanD
| true |
3,024,461,295 | DISABLED test_comprehensive_pca_lowrank_cuda_float64 (__main__.TestInductorOpInfoCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 4 | NONE | Platforms: inductor
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_comprehensive_pca_lowrank_cuda_float64&suite=TestInductorOpInfoCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/41249343003).
Over ... | true |
3,024,442,315 | Correct torch.xpu.is_bf16_supported return False if no XPU detected | guangyey | closed | [
"open source",
"Merged",
"ciflow/trunk",
"keep-going",
"ciflow/xpu",
"release notes: xpu",
"module: xpu"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152317
# Motivation
Fix https://github.com/pytorch/pytorch/issues/152301
When XPU is not available, calling `torch.xpu.is_bf16_supported()` still returns `True`, which is inconsistent with the expected behavior (should be False).
... | true |
3,024,385,848 | [dynamo] torch._dynamo crashes on `self.value.__module__` inside SkipFunctionVariable.call_function() (PyTorch 2.7, works 2.6) | wdziurdz | open | [
"high priority",
"needs reproduction",
"triaged",
"module: regression",
"oncall: pt2",
"module: dynamo"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
Start cacth after upgrade from 2.6 to 2.7. crash in dynamo . The crash happens because the PyTorch doesn’t check whether the object has a `__module__` attribute:
```python
[rank1]: File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1754, in _wrapped_call_impl
[ra... | true |
3,024,358,898 | Fixed RELEASE.md typo | Ariouz | open | [
"triaged",
"open source",
"topic: not user facing"
] | 2 | NONE | Fixed two short typo errors in RELEASE.md | true |
3,024,145,651 | [ATen][CUDA][SDPA] Enable SDPA on sm_121 | Aidyn-A | closed | [
"module: cuda",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: cuda",
"topic: not user facing",
"module: core aten",
"module: sdpa"
] | 5 | COLLABORATOR | This PR adds support for `sm_121` of the DGX Spark. The `sm_121` is binary compatible with `sm_120` (just like `sm_89` and `sm_86`), therefore a compilation targeting `sm_121` is not required.
cc @ptrblck @msaroufim @eqy @jerryzh168 @manuelcandales @SherlockNoMad @angelayi | true |
3,024,102,892 | setuptools.build_meta:__legacy__ backend is deprecated | atupone | open | [
"triaged",
"open source",
"topic: not user facing"
] | 1 | CONTRIBUTOR | setuptools.build_meta:__legacy__ backend is deprecated
see https://projects.gentoo.org/python/guide/qawarn.html#deprecated-pep-517-backends
| true |
3,023,937,623 | [cp] dispatch flex_attention_backward to CP impl in TorchDispatchMode | XilunWu | open | [
"oncall: distributed",
"ciflow/inductor",
"module: context parallel",
"release notes: context parallel"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152311
* #151497
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
3,023,894,833 | [DCP] failure case of save method | XuezheMax | open | [
"oncall: distributed checkpointing"
] | 0 | NONE | ### 🐛 Describe the bug
[#147675](https://github.com/pytorch/pytorch/pull/147675) fixed the issue of dcp `gather_object`. However, in `broadcast_object`, the similar bug is still there. https://github.com/pytorch/pytorch/blob/13966d0bf55f858f7512c8f4258900a9289ed01b/torch/distributed/checkpoint/utils.py#L122
I manual... | true |
3,023,887,057 | Softmax Decomp Causes Incorrect Gradients when Using `torch.compile` with `F.multi_head_attention_forward` | defaultd661 | open | [
"high priority",
"triaged",
"module: correctness (silent)",
"oncall: pt2",
"module: decompositions",
"module: aotdispatch",
"module: sdpa",
"ubn"
] | 5 | NONE | ### 🐛 Describe the bug
When using `torch.compile` to compile a model that internally calls `torch.nn.functional.multi_head_attention_forward`, the computed gradients differ significantly from the ones obtained via eager mode.
### To Reproduce
```
import torch
import torch.nn as nn
import torch.nn.functional as F
c... | true |
3,023,870,078 | bizarre behavior with torch module's Attribute Error | ZiyaoLi | open | [
"module: nn",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
when executing the following code:
```python
import torch
class A(torch.nn.Module):
def __init__(self):
super().__init__()
@property
def foo(self):
return self.bar # attr error
a = A()
print(a.foo)
```
I obtain
```bash
Traceback (most recent call last):
File... | true |
3,023,831,198 | Recompile issue after fp8 conversion | shiyang-weng | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
We are enabling fp8 on mlp. To support fp8 we need to write some conversions.
After conversions find following recompile issue.
torch._dynamo.exc.RecompileError: Recompiling function forward in test_fp8.py:38
triggered by the following guard failure(s):
- 2/0: tensor 'input' size mismat... | true |
3,023,771,137 | [Cutlass] Fix int check in example tensor creation | mlazos | closed | [
"Merged",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150910
* #152390
* #150909
* #150908
* #150907
* #151406
* #150906
* #151713
* #151405
* #150905
* __->__ #152306
* #152305
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayi... | true |
3,023,771,067 | [Cutlass] Remove unused dtype conversion map | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150910
* #152390
* #150909
* #150908
* #150907
* #151406
* #150906
* #151713
* #151405
* #150905
* #152306
* __->__ #152305
Previously merged:
* #150904
* #150903
* #150346
* #150345
* #150344
cc @voznesenskym @p... | true |
3,023,758,310 | Fix StringCoordView::substr after D73379178 / #151810 | swolchok | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152304
Received complaint that we broke something. After a bunch of debugging, landed on this test + fix.
Differential Revision: [D73754877](https://our.internmc.facebook.com/intern/diff/D73754877/)
**NOTE FOR REVIEWERS**: This PR ... | true |
3,023,748,330 | Fix redistribute new_local_tensor be None case | wanchaol | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: distributed (dtensor)"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152303
as titled, we can just set new_local_tensor to be the local tensor and
remove the None check, as there would be cases where there's no
transformation needed (i.e. src_placements and dst_placements are the same,
and we st... | true |
3,023,698,392 | NCCL out of memory error after updating to PyTorch 2.7 | BaconGabe | open | [
"oncall: distributed",
"triaged",
"module: nccl",
"module: regression"
] | 14 | NONE | ### 🐛 Describe the bug
After updating to PyTorch 2.7, using init process group with nccl and calling `DDP(model, device_ids=[rank])` results in a out of memory error. This makes absolutely no sense because it happens even when I am using extremely small amounts of memory, and DDP with nccl worked perfectly fine befor... | true |
3,023,620,150 | Unexpected result from `torch.xpu.is_bf16_supported()` when XPU is unavailable | defaultd661 | closed | [
"triaged",
"module: xpu"
] | 1 | NONE | ### 🐛 Describe the bug
When `torch.xpu.is_available()` returns `False`, calling `torch.xpu.is_bf16_supported()` still returns `True`, which is inconsistent with the expected behavior (should be `False`).
### To Reproduce
```
import torch
def test_bug():
print('torch.xpu.is_available() =', torch.xpu.is_available(... | true |
3,023,614,912 | Unexpected behavior when using dist.all_reduce(x, op=dist.ReduceOp.SUM) | fhk357869050 | open | [
"oncall: distributed",
"triaged",
"module: c10d"
] | 1 | NONE | ### 🐛 Describe the bug
```
import torch
import torch.distributed as dist
from torch.multiprocessing import Process
import numpy as np
def exec_op(rank):
dist.init_process_group(backend='gloo', rank=rank, world_size=2, init_method=f'tcp://127.0.0.1:40001')
np.random.seed(1024 + rank)
x = np.random.unifor... | true |
3,023,609,155 | `torch.compile()` produces incorrect results for `asinh_()` operation on large/small values | defaultd661 | open | [
"high priority",
"triaged",
"module: correctness (silent)",
"module: edge cases",
"oncall: pt2",
"module: inductor",
"oncall: cpu inductor"
] | 2 | NONE | ### 🐛 Describe the bug
### To Reproduce
```
import torch
import numpy as np
def test_bug():
x = torch.tensor([[-1e+30, 1e+30, -5e+28, 5e+28, -7.5e+29, 7.5e+29,
-2e+30, 2e+30, 0.0]], dtype=torch.float32).repeat(3, 1)
eager_tensor = x.clone()
eager_tensor.asinh_()
eager_np = eager_tensor.numpy... | true |
3,023,598,455 | Enable the AMP precision with freezing for CPU nightly test | LifengWang | open | [
"triaged",
"open source",
"release notes: releng"
] | 1 | CONTRIBUTOR | Hi, @desertfire. Since we recommend users to use AMP precision and run with `--freezing` for CPU x86 Inductor inference, we suggest adding the AMP freezing test to the CPU nightly tests.
cc @chuanqi129 @zxd1997066 | true |
3,023,598,223 | Flex attention: batch-index-dependent block mask causes error with changing batch size | zhihanyang2022 | closed | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 1 | NONE | ### 🐛 Describe the bug
I'm trying to do attention with a *custom* attention mask that *depends on the batch index*.
My square attention mask has the following structure:
- First `n` rows is causal
- Afterwards everything is bidirectional
`n` is different for each batch index, and is specified through the tensor of... | true |
3,023,579,086 | [Break XPU] chunk_cat accuracy failed on XPU Inductor UT. | etaf | closed | [
"triaged",
"module: xpu"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
Since the PR #151263 landed, the Inductor UTs that related to `chunk_cat` got accuracy failures.
The root cause is #151263 start to support contiguous inputs which break the old assumption that all the inputs is contiguous. The implementation in torch-xpu-ops still use the old assumption and g... | true |
3,023,578,067 | `vmap` not working on `torch.arange`, `torch.scalar_tensor`, and `torch.ones` | defaultd661 | open | [
"triaged",
"module: vmap",
"module: functorch"
] | 0 | NONE | ### 🐛 Describe the bug
# torch.arange
### To Reproduce
```
import torch
from functools import partial
def test_bug():
batched_arange = torch.vmap(partial(torch.arange, step=1))
start = torch.tensor([1, 2, 3], dtype=torch.int64)
end = torch.tensor([25, 26, 27], dtype=torch.int64)
batched_arange(start... | true |
3,023,563,083 | Unexpected overflow behavior when using `torch.addcmul` | defaultd661 | open | [
"module: cpu",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
This issue is similar to the one reported in [#98691](https://github.com/pytorch/pytorch/issues/98691), where operations on mixed precision tensors lead to unexpected overflow behaviors.
### To Reproduce
```
def test_bug():
import torch
input_tensor = torch.zeros([1], dtype=torch.float... | true |
3,023,536,059 | `torch.sparse.log_softmax` output mismatch between CPU and CUDA | defaultd661 | open | [
"module: sparse",
"triaged",
"topic: bug fixes"
] | 1 | NONE | ### 🐛 Describe the bug
When applying `torch.sparse.log_softmax` on a sparse tensor, the outputs on CPU and CUDA are inconsistent.
### To Reproduce
```
import torch
from torch.sparse import log_softmax as sparse_log_softmax
def test_bug():
a = torch.rand(4, 3)
b = a - 10000000.0
b_sparse = b.to_sparse()... | true |
3,023,534,525 | `torch==2.6` broke `nn.Module.dtype` typing | jamesbraza | open | [
"module: typing",
"triaged",
"module: regression"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
With the below Python 3.12 code, `torch==2.5.1`, and `mypy==1.15.0` there are no type errors:
```python
import torch
from torch import nn
module: nn.Module
with torch.autocast(device_type=module.device.type, dtype=module.dtype):
...
```
Then with Python 3.13, `torch==2.6.0` or `torch==2.... | true |
3,023,511,854 | Windows CUDA Build Failure: Ambiguous std in cuda_vectorized_test.cu (CUDA 12.6/MSVC 2019) | jifferyfeng | closed | [
"oncall: pt2"
] | 0 | NONE | ### 🐛 Describe the bug
When building PyTorch from source on Windows, the compilation fails with the following error in cuda_vectorized_test.cu:
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\include\xtree(1394): error C2872: “std”: ambiguous symbol
Error Context:
The issue... | true |
3,023,487,281 | [Intel GPU][PT2.8]scaled_dot_product_attention returns wrong output | LuFinch | open | [
"triaged",
"module: xpu"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
Using nightly build PT2.8, this sample code will return wrong output:
```
import torch
from datasets import load_dataset
from transformers import pipeline, Wav2Vec2Processor
model_id = "facebook/hubert-large-ls960-ft"
device = "xpu"
torch_dtype = torch.float16
generator = pipeline(
"automa... | true |
3,023,478,343 | [inductor] Skip isinf check for FP8 E4M3 dtype | sarckk | open | [
"module: inductor",
"ciflow/inductor",
"release notes: inductor",
"release notes: inductor (aoti)"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152289
Both Float8E4M3FN and Float8E4M3FNUZ [do not support representing infinity](https://github.com/openxla/stablehlo/blob/main/rfcs/20230321-fp8_fnuz.md), so skip `isinf()` check in inductor.
Fixes #149002. New UT passes wit... | true |
3,023,477,390 | [1/N] Use std::filesystem | cyyever | closed | [
"oncall: distributed",
"oncall: jit",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 7 | COLLABORATOR | Maybe it is time to use std::filesystem because CXX11 ABI is now the default. The changes are for jit and distributed code.
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
3,023,453,811 | [cudagraphs] Fix issue in collecting static_input_idxs | anijain2305 | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 14 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152287
related to https://github.com/pytorch/pytorch/issues/152275
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amj... | true |
3,023,396,775 | [DTensor] enable SimpleFSDP's composability with Tensor Parallel | ruisizhang123 | open | [
"oncall: distributed",
"triaged",
"open source",
"module: dynamo"
] | 5 | CONTRIBUTOR | This PR adds support for SimpleFSDP's composability with Tensor Parallel. This is done by enabling a DTensor redistribution from the FSDP submesh toward TP submesh in `distribute_tensor` API.
1. **Correctness**: The end-to-end SimpleFSDP TP integration has been proved to work in the PR from this fork: tianyu-l/pytor... | true |
3,023,390,323 | Error after successful build: No module named 'torch._C._distributed_c10d' | henrydwright | open | [
"oncall: distributed",
"module: build",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
Built wheel from source on Windows (arm64) with USE_DISTRIBUTED=0 and USE_CUDA=0 by running `python setup.py bdist_wheel -v`. No errors during build or install.
Aside from unrelated warning from cpuinfo, below works fine
```python
import torch
x = torch.rand(5,3)
print(x)
```
When attempting... | true |
3,023,361,580 | [inductor] set correct precompile start time | sarckk | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152284
Fixes #148777
With num_worker set to 1, ran script in #148777
before:
```
Precompiling benchmark choice TritonTemplateCaller took 0.19s
Precompiling benchmark choice TritonTemplateCaller took 0.38s
Precompiling benc... | true |
3,023,280,301 | Forward compatibility in torch.export | lminer | open | [
"oncall: pt2",
"oncall: export"
] | 2 | NONE | ### 🚀 The feature, motivation and pitch
Are there any plans to guarantee forward compatibility in torch.export once it leaves beta? I have models that need to be converted to coreml and to litert, both of which are pinned to specific and conflicting versions of pytorch. It is useful to be able to export in the traini... | true |
3,023,272,806 | [MPS] col2im kernel implementation | Isalia20 | closed | [
"open source",
"Merged",
"module: mps",
"release notes: mps",
"ciflow/mps"
] | 4 | COLLABORATOR | Fixes #151820
Also requested in #141287
Mainly based on the cuda kernel implementations
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen | true |
3,023,223,796 | [CI] Add xpu inductor test into periodic workflow | chuanqi129 | open | [
"triaged",
"open source",
"release notes: releng",
"ciflow/periodic",
"keep-going"
] | 7 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
3,023,158,314 | Update `torch/nn/modules/conv.py` to use Literal for support padding modes | Skylion007 | open | [
"good first issue",
"module: typing",
"triaged",
"actionable"
] | 7 | COLLABORATOR | ### 🚀 The feature, motivation and pitch
It would be great to update `torch/nn/modules/conv.py` to use typing.Literal instead of just `str` to denote with padding modes are actually supported by various operations.
for example instead of
`padding_mode : str`
do
`padding_mode: Literal["valid", "same"]` etc to the ty... | true |
3,023,037,578 | Make scaler.step() return if step was skipped or not | pyphan1 | closed | [
"module: optimizer",
"triaged"
] | 5 | NONE | ### 🚀 The feature, motivation and pitch
Make calling scaler.step(optimizer) return if the step was skipped or not instead of always returning None, or make it print when it skips a step
for example we can use:
stepped = scaler.step(optimizer)
if not stepped:
print('Step was skipped because of an underflow or ove... | true |
3,023,028,552 | MPS: Conv1d fails with NotImplementedError for output_channels > 65536 | ehartford | open | [
"module: convolution",
"triaged",
"module: mps"
] | 4 | NONE | ### 🐛 Describe the bug
Running torch.nn.functional.conv1d (or torch.nn.Conv1d) on the MPS backend results in the following error when the number of output channels exceeds 65536:
`NotImplementedError: Output channels > 65536 not supported at the MPS device.`
This limitation prevents certain common model architectur... | true |
3,022,983,557 | Fix initGdsBindings declaration | cyyever | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | COLLABORATOR | Move initGdsBindings into the correct namespace. | true |
3,022,953,307 | `setup.py develop` command is disappearing soon from `setuptools` | rgommers | open | [
"high priority",
"module: build",
"oncall: releng",
"triaged",
"topic: devs"
] | 12 | COLLABORATOR | PyTorch still uses the `python setup.py develop` command to build PyTorch and work with it during development and in CI, in multiple places (see [this code search query](https://github.com/search?q=repo%3Apytorch%2Fpytorch%20%22setup.py%20develop%22&type=code) and the main development instructions at https://github.com... | true |
3,022,891,143 | [cudagraphs][HF][torch 2.7] Excessive cudagraph re-recording for HF LLM models | anijain2305 | open | [
"high priority",
"triaged",
"oncall: pt2"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
`transformers` repo has temporarily pinned the torch version to be <2.7 (HF [PR](https://github.com/huggingface/transformers/pull/37760) to block 2.7)
I find that there is cudagraph recording on each invocation. The issue is present on the `main` branch as well. Here is the [tlparse](https://m... | true |
3,022,841,048 | [Dynamo] Replace `unimplemented` with `unimplemented_v2` in `torch/_dynamo/variables/misc.py` [1/2] | shink | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 7 | CONTRIBUTOR | Part of #147913
Replace `unimplemented` with`unimplemented_v2` in `torch/_dynamo/variables/misc.py`
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
3,022,795,301 | Fix constant folding cloning constants | muchulee8 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152273
Summary:
Bug fix for #135060
Simple review:
https://github.com/pytorch/pytorch/pull/135060/files#diff-f23386709ff7e1235b15e18f835a48e5124e0ddd596aeb33c201daad1abbedd7R357
We mistakenly typed get_attr into getattr.
This causes... | true |
3,022,682,201 | [AOTInductor] Propagate ConstantType for main graph. | muchulee8 | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor (aoti)"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152272
Summary:
We need to make sure all named_parameters and named_buffers be
propagated if we use runtime constant folding.
Test Plan:
python test/inductor/test_aot_inductor.py -k test_constant_type_propagation
Reviewer... | true |
3,022,597,820 | Fix clang-tidy suppression in torch/csrc/jit | cyyever | closed | [
"oncall: jit",
"open source",
"Merged",
"NNC",
"ciflow/trunk",
"release notes: jit"
] | 4 | COLLABORATOR | Remove some clang-tidy suppression in torch/csrc/jit by applying fixes or refactoring.
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
3,022,584,271 | Question about that support of torch.compile for a custom CUDA operator? | HiIcy | open | [
"module: docs",
"triaged",
"module: custom-operators",
"oncall: pt2",
"module: pt2-dispatcher"
] | 7 | NONE | I have a model that uses custom CUDA operators. Now I want to modify it to support torch.compile. However, when I refer to this link https://pytorch.org/tutorials/advanced/cpp_custom_ops.html#conclusion, after modification, although it appears to support compile from the profiler, there is no change in performance. I w... | true |
3,022,560,480 | Arbitrary Code Execution Risk in `torch.distributed.utils.overload` When Misused in Type Annotations | vwrewsge | open | [
"oncall: distributed",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
## Summary
The `overload` decorator imported from `torch.distributed.utils` can be misused to execute arbitrary system commands via malicious type annotations. This may create a security vulnerability: **arbitrary code execution upon parsing the file**, even if the malicious function is neve... | true |
3,022,359,486 | [MPSInductor] Fix masked_fill decomp | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152268
* #152266
By adding `mps` to the list of accelerators that can work with CPU scalars
Fixes `GPUTests.test_masked_fill_promotion_mps`
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blz... | true |
3,022,341,850 | [ROCm] Maxpool backward NHWC Perf Improvement targeting Resnet scenarios | amd-hhashemi | open | [
"module: rocm",
"open source",
"release notes: cuda"
] | 4 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
3,022,068,035 | [MPSInductor][BE] Only include headers when needed | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #152268
* __->__ #152266
Store headers used by shader in `MetalKernel.headers`
Add headers when function depending on it gets invoked
Generate majority of a special ops from template
Delete two unused functors: `entr` and `xlog1py` as th... | true |
3,022,025,574 | [BE]: Cleanup traceutils with fmtlib | Skylion007 | closed | [
"oncall: distributed",
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 12 | COLLABORATOR | Simplify code and make it faster.
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
3,022,022,369 | Add private config to broadcast rank0 decision from the partitioner to all ranks | fmassa | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 14 | MEMBER | Summary: This PR adds a private configuration to the partitioner that ensures that the decision taken is the same across all ranks. This is a temporary workaround, as when size_hints are also taken into account in compiler collectives this workaround will not be needed anymore.
Test Plan:
This has been tested on some ... | true |
3,022,011,880 | FSDP OOM during initialization | fingertap | closed | [
"oncall: distributed",
"module: memory usage",
"triaged"
] | 7 | NONE | ### 🐛 Describe the bug
When trying to train Llama 4 with FSDP, I found that the peak memory explodes during the initialization of FSDP. The following mini-repro exposes this bug.
```python
from functools import partial
import torch
import torch.distributed as dist
from torch.distributed.fsdp import FullyShardedData... | true |
3,021,999,506 | `iter()` and `reversed()` do not raise `StopIteration` when exhausted in torch.compile | guilhermeleobas | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
The expected behavior is to raise `StopIteration` after the iterator is exhausted, but inside Dynamo, the iterator is not being properly exhausted ~when `(force_)unpack_var_sequence(...)` is called~.
Reproducer:
```python
import torch
@torch.compile(backend="eager", fullgraph=True)
def foo_i... | true |
3,021,982,230 | Context Parallel -- unsharded output doesn't match output without CP. | sen-ppl | open | [
"oncall: distributed",
"triaged"
] | 26 | NONE | ### 🐛 Describe the bug
Hello, I wrapped my model CP context manager and see my attention module's un-sharded outputs are different from the outputs when CP size = 1. The un-sharded inputs (Q,K,V) are the same as (Q,K,V) when CP is off.
```
# entry point file
cp_buffers = [x, y] + [m.self_attn.rotary_emb.cos_cached fo... | true |
3,021,980,672 | Configurable logging for cpp_extensions.py | msaroufim | closed | [
"Merged",
"ciflow/trunk",
"topic: bc breaking",
"topic: not user facing"
] | 13 | MEMBER | Today `cpp_extensions` makes heavy use of printing to stderr, this makes our life harder in KernelBot where we typically rely on stderr to only surface real errors but instead today cpp_extensions leverages stderr for updates that could be qualified as INFO, WARNING, ERROR
Now instead we'll recommend users of our cp... | true |
3,021,974,374 | [BE] Remove dangling # in contributing.md | msaroufim | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | MEMBER | I frequently come to CONTRIBUTING.md to copy paste the below snippet to rebuild pytorch which in zsh gives this error because zsh interprets # as a command. These comments add nothing so just removing
```
error: pathspec 'sync' did not match any file(s) known to git
error: pathspec 'the' did not match any file(s) ... | true |
3,021,969,136 | Do not r edirect warnings to stderr in cpp_extension.py | msaroufim | closed | [
"module: cpu"
] | 3 | MEMBER | - **divup op**
- **update**
- **update**
- **update**
- **cu**
- **update**
- **update**
- **simply templates**
- **update**
- **update**
- **update**
- **old**
- **le ci est vert**
- **Trigger build**
- **Do not redirect warnings to stderr in cpp_extension.py**
Fixes #ISSUE_NUMBER
cc @jgong5 @mingfeima @XiaobingSup... | true |
3,021,946,302 | [FR] Support BSHM-layout scaled_dot_product_attention without transpose. | ghostplant | open | [
"triaged",
"module: sdpa"
] | 15 | NONE | What's the plan to support direct computation given (Batch, Seq, Head, Model_dim) Q/K/V tensors, without additional expensive back-and-forth?
```sh
q = torch.randn([b, s, h, m])
k = torch.randn([b, s, h, m])
v = torch.randn([b, s, h, m])
scores = torch.nn.functional.scaled_dot_product_attention(q, k, v, no_transpose=... | true |
3,021,853,799 | Move code out of individual token linters | rec | open | [
"module: bc-breaking",
"open source",
"topic: not user facing",
"suppress-bc-linter"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152256
* #148959
* #151906
cc @ezyang @gchanan | true |
3,021,789,824 | Pytorch 2.7.0 with XPU (silently) crashing | blaz-r | closed | [] | 1 | NONE | ### 🐛 Describe the bug
I've installed the latest version of pytorch 2.7.0 with xpu support on a Windows 11 Intel NUC. When I try to use the xpu in pytorch the program just silently fails.
If I run `torch.xpu._is_compiled()` I get True, but just running `torch.xpu.is_available()` fails.
I also tried running the code ... | true |
3,021,772,579 | Fix typos in multiple files | co63oc | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"release notes: linalg_frontend",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Fix typos in multiple files
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @jerryzh168 | true |
3,021,631,345 | Aborted (core dumped) in torch.flipud | cx104906 | closed | [] | 1 | NONE | ### 🐛 Describe the bug
Reproduce
```
curl -L -o 002-args "https://github.com/cx104906/poc/raw/main/pytorch/id%3A000002-args"
curl -L -o 002-kwargs "https://github.com/cx104906/poc/raw/main/pytorch/id%3A000002-kwargs"
python run.py
```
run.py:
```
import torch
import pickle
print(torch.__version__)
mylist = torch.l... | true |
3,021,628,861 | [Inductor] weird reordering behavior with `wait_tensor` | YouJiacheng | closed | [] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
Case 1: return the average
`wait` is NOT pushed to the end
```python
@torch.compile
def foo(x: Tensor, y: Tensor):
x_avg = fcol.all_reduce(x, "avg", "0")
y_sq = y * y
return x_avg, y_sq
```
```python
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(a... | true |
3,021,557,990 | Windows inductor genarated code without function declaration, and compile failed on MSVC. | xuhancn | open | [
"module: windows",
"oncall: pt2",
"module: inductor",
"oncall: cpu inductor"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
Reproducer:
```cmd
pytest -v test\inductor\test_cpu_cpp_wrapper.py -k test_add_complex4_cpu_cpp_wrapper -s
```
### Error logs
Error message:
```cmd
_____________________________________________________________________________________________________________ TestCppWrapper.test_add_complex4_cp... | true |
3,021,460,946 | Reapply "Rewrite the guts of torch::jit::Lexer to speed it up (#151850)" | swolchok | closed | [
"oncall: jit",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: jit",
"ci-no-td",
"ciflow/s390"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #152250
Almost-exact reapply of #151850 (adding minor reviewer nits) . AFAICT it was reverted unnecessarily.
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel | true |
3,021,431,771 | [DTensor] [distributed]: Operator aten.masked_fill_.Scalar does not have a sharding strategy registered | dest1n1s | open | [
"oncall: distributed",
"triaged"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
Hi, currently the operator `aten.masked_fill_.Scalar` does not have a sharding strategy registered. It's a rather common operator which will be called in the backward of `torch.norm`. As a workaround, I need to do:
```python
def norm(x: torch.Tensor, device_mesh: DeviceMesh | ... | true |
3,021,407,813 | [refactor] refactor dense implementation of auto_functionalized_v2 for better clarity | ydwu4 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151067
* __->__ #152248
* #152247
* #152246
* #152245
* #152244
* #152073
* #152072
Abstracts away two helper functions (get_mutable_args_from_schema and _generate_new_op_kwargs_from_bases) to make the code better organized and more re-usa... | true |
3,021,407,720 | [hop] make materialize_as_graph's include and exclude dispatch key set optional | ydwu4 | closed | [
"Merged",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151067
* #152248
* __->__ #152247
* #152246
* #152245
* #152244
* #152073
* #152072
| true |
3,021,407,680 | [hop][schema] allow adding kw_only info to schema argument | ydwu4 | closed | [
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151067
* #152248
* #152247
* __->__ #152246
* #152245
* #152244
* #152073
* #152072
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amja... | true |
3,021,407,650 | [hop][be] make check_input_alias_and_mutation_return_ouputs create new fake mode | ydwu4 | closed | [
"Merged",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151067
* #152248
* #152247
* #152246
* __->__ #152245
* #152244
* #152073
* #152072
| true |
3,021,407,573 | [HOP][be] make supports_input_mutation and aliasisng a class field | ydwu4 | 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):
* #151067
* #152248
* #152247
* #152246
* #152245
* __->__ #152244
* #152073
* #152072
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amja... | true |
3,021,346,429 | py_limited_api=True in PyTorch2.7 will break the build of extensions | airMeng | closed | [
"module: cpp-extensions",
"triaged",
"module: regression"
] | 10 | NONE | ### 🐛 Describe the bug
When we compile the torch extension like SGLang does, we need to set ```py_limited_api=True``` to maintain the compatibility between different python version, for example what the SGLang does https://github.com/sgl-project/sglang/blob/main/sgl-kernel/setup_cpu.py#L75C1-L85C2
```python
ext_modul... | true |
3,021,300,247 | Enable 8byte vector loading for fp16/bf16 | jeetkanjani7 | open | [
"fb-exported",
"release notes: cuda"
] | 6 | CONTRIBUTOR | Test Plan:
Tested via local benchmarks and e2e runs. The bandwidth improves by 2.5x on A100 80GB gpu for 2 byte data types (fp16, bf16). Also significant improvement in e2e qps.
{F1977455759}
Differential Revision: D73225699
| true |
3,021,290,251 | [export] Preserve custom metadata for tensor constants | yiming0416 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 9 | CONTRIBUTOR | Summary:
Fixes https://github.com/pytorch/pytorch/issues/151476
The `custom_meta` collected from `mod` has keys that follow name of nodes in `mod`, which are inconsistent with the node names after the naming pass. For example a constant `b` will become `c_b`.
Test Plan: buck2 run caffe2/test:test_export -- -r test_run... | true |
3,021,289,026 | Updates to build on Noble (Ubuntu24.04) and py3.12 | jithunnair-amd | open | [
"module: rocm",
"open source",
"topic: not user facing"
] | 1 | COLLABORATOR | TODO:
- [ ] Add a build job for Ubuntu24.04 + py3.12
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
3,021,274,866 | Fix an incorrect link markup | koyuki7w | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (ddp)"
] | 3 | CONTRIBUTOR | Remove extra whitespace so the link works correctly.
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k | true |
3,021,269,788 | [executorch hash update] update the pinned executorch hash | pytorchupdatebot | open | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 40 | COLLABORATOR | This PR is auto-generated nightly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/nightly.yml).
Update the pinned executorch hash. | true |
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