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 |
|---|---|---|---|---|---|---|---|---|
2,964,096,425 | Raise error/warning when calling collectives with tensors of different dtypes. | gkroiz | closed | [
"oncall: distributed"
] | 3 | NONE | ### 🚀 The feature, motivation and pitch
I've noticed that collectives with tensors of different data types will cause hangs. This behavior makes sense and I think this issue should only happen when there is a logical error in user code. However, it could be helpful if some warning/error was raise for when this happe... | true |
2,964,095,892 | pytorch pip install instructions: always include the cuda index | stas00 | open | [
"module: docs",
"oncall: releng",
"triaged",
"needs design"
] | 4 | CONTRIBUTOR | I'd like to propose that the default CUDA install command should include the explicit CUDA version target? i.e. instead of:
pip3 install torch torchvision torchaudio
this:
pip3 install torch torchvision torchaudio --index-url <https://download.pytorch.org/whl/cu124>
![Image](https://github.com/user-atta... | true |
2,964,083,110 | [WIP][dynamic shapes] guard_or_false rewrite for fake_impls.py:infer_size, compute_contiguous | pianpwk | open | [] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
| true |
2,964,068,197 | UNSTABLE pull / linux-jammy-xpu-2025.0-py3.9 / build | malfet | closed | [
"module: ci",
"triaged",
"unstable"
] | 2 | CONTRIBUTOR | It started to fail after a PR that did not touch anything related to XPUs were landed see https://hud.pytorch.org/hud/pytorch/pytorch/783f045c4f26cb9d11789d80f68a86854dfad9f9/1?per_page=50&name_filter=xpu&mergeLF=true
cc @seemethere @pytorch/pytorch-dev-infra | true |
2,964,029,625 | [dynamo] Lazily import fsdp-related modules | anijain2305 | open | [
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150689
* __->__ #150429
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,964,020,921 | [export] Strict-export fails with list of modules | angelayi | open | [
"oncall: pt2",
"oncall: export"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
([x-post from internal](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1732340610969558/))
```python
def test_list_model(self):
class A(torch.nn.Module):
def __init__(self):
super().__init__()
self.a = torch.nn.Parameter(tor... | true |
2,964,013,459 | [BE] Move all lint runner to 24.04 | malfet | closed | [
"Merged",
"topic: not user facing"
] | 5 | CONTRIBUTOR | As Ubuntu-20 reached EOL on Apr 1st, see https://github.com/actions/runner-images/issues/11101
This forces older python version to be 3.8
Delete all linux-20.04 runners from the lintrunner.yml | true |
2,963,987,119 | clang-format aten/src/ATen/cpu/vec/*.h | swolchok | closed | [
"module: cpu",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150568
* #150380
* __->__ #150426
I got a complaint about indentation on #150380. Make the machines fix it for us.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,963,985,001 | clang-format aten/src/ATen/cpu/vec/*.h | swolchok | closed | [
"module: cpu"
] | 4 | CONTRIBUTOR | I got a complaint about indentation on #150380. Make the machines fix it for us.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,963,983,966 | [ez] Remove dead lite interpreter CI code | clee2000 | closed | [
"Merged",
"topic: not user facing"
] | 4 | CONTRIBUTOR | There are no lite-interpreter build environments in CI
I assume every mac build is arm64 | true |
2,963,970,643 | Make CompileEventLogger more defensive w.r.t to AOTAutogradCache and FXGraphCache | jamesjwu | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150423
This PR makes it so that we don't crash due to logging if we invoke AOTAutogradCache/FXGraphCache without using dynamo. This is preparation for supporting certain VLLM use cases where they store graph modules and have special... | true |
2,963,957,515 | Test self hosted GPU runner | zhe-thoughts | open | [
"triaged",
"open source",
"topic: not user facing",
"ciflow/periodic"
] | 2 | NONE | This is for experimenting with hosting github runners on nvidia managed hardware | true |
2,963,903,833 | Support tuning of _scaled_grouped_mm | bertmaher | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 21 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150421
This includes the default aten implementation, as well as a Triton
implementation imported from FBGEMM
(https://github.com/pytorch/FBGEMM/blob/main/fbgemm_gpu/experimental/gemm/triton_gemm/grouped_gemm.py)
cc @voznesenskym @p... | true |
2,963,881,484 | UNSTABLE inductor-unittest / linux-jammy-cpu-py3.12-gcc11-inductor-halide / build | seemethere | closed | [
"module: ci",
"triaged",
"oncall: pt2",
"module: inductor",
"unstable",
"topic: inductor halide backend"
] | 3 | MEMBER | This is actually a 2 part failure:
Part 1
* The actual job `linux-jammy-cpu-py3.12-gcc11-inductor-halide` is failing because the docker image is attempting to be rebuilt on a `c5.2xlarge` ([link](https://github.com/pytorch/pytorch/actions/runs/14191050396/job/39755627529))
* This is causing a timeout because the `c5... | true |
2,963,856,466 | Add stride + dtype to autotune results | PaulZhang12 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150419
Add stride/dtype info to autotune gemm results. New output header:
`AUTOTUNE mm(1024x1024, 1024x7680)`
`strides: [1, 1024], [7680, 1]`
`dtypes: torch.bfloat16, torch.bfloat16`
cc @voznesenskym @penguinwu @EikanWan... | true |
2,963,853,936 | ci: Use cache / progress when local docker build | seemethere | closed | [
"topic: not user facing"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150418
It's a bit annoying to try and work on these locally when the cache /
progress isn't being used so let's just set it so that those flags are
only valid when in CI directly.
`${CI}` is a default environment variable that's def... | true |
2,963,849,380 | DISABLED test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_uint8 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 5 | 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_uint8&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/3976085... | true |
2,963,787,976 | `torch.backends.mkldnn.flags()` CM should not warn | pytorchbot | closed | [
"module: cpu",
"module: mkldnn",
"open source",
"ciflow/linux-aarch64"
] | 1 | COLLABORATOR | By returning `None` rather than `False` from `THPModule_allowTF32OneDNN` when USE_XPU is not defined
Added regression test
Fixes https://github.com/pytorch/pytorch/issues/149829
| true |
2,963,779,297 | [Inductor] Fix scaled_mm template migration missing endif block | PaulZhang12 | open | [
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150415
* #150045
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov
Differential Revision: [D... | true |
2,963,762,482 | Fix for CVE-2024-7804 needed | MateiLipan | closed | [] | 1 | NONE | Cross posting from https://github.com/pytorch/pytorch/issues/149044#issuecomment-2757233290
@ZainRizvi let me know if this is the right place
Hi team,
Didn't know where it is best to write this, but I would suggest scheduling [CVE-2024-7804](https://www.cve.org/CVERecord?id=CVE-2024-7804) for a future release . Even t... | true |
2,963,681,991 | Disable -Werror for s390x test module compilation | AlekseiNikiforovIBM | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/s390"
] | 3 | COLLABORATOR | This change should make nightly testsuite green again for s390x. | true |
2,963,606,447 | [MPSInductor] Fix neg for unsigned types | 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):
* __->__ #150412
* #150386
By more-or-less copy-n-pasting the fix from https://github.com/pytorch/pytorch/pull/94035
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipisz... | true |
2,963,563,561 | Test layout_opt_default set to 0 | atalman | open | [
"ciflow/periodic",
"module: inductor",
"ciflow/inductor",
"keep-going",
"ci-no-td",
"ciflow/inductor-periodic"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,963,381,544 | Add torch._scaled_mm for CPU | yanbing-j | closed | [
"module: cpu",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/slow",
"ciflow/rocm",
"ci-no-td",
"ciflow/inductor-rocm",
"ciflow/rocm-mi300"
] | 15 | COLLABORATOR | This PR is the duplicated one for https://github.com/pytorch/pytorch/pull/139975.
This PR is to add torch._scaled_mm for CPU backend.
_scaled_mm_out_cpu and _scaled_mm_cpu are new added and included in torch._scaled_mm CPU dispatch. We also add _scaled_mm_out_cpu_emulated as a fallback function if the current pla... | true |
2,963,319,009 | Update torch-xpu-ops commit pin | xytintel | closed | [
"open source",
"topic: not user facing",
"ciflow/xpu"
] | 9 | CONTRIBUTOR | Update the torch-xpu-ops commit to [11320f39484d1887870f24172c4803392491a76c](https://github.com/intel/torch-xpu-ops/commit/11320f39484d1887870f24172c4803392491a76c), include
- Move torch-xpu-ops commit pin from release/2.7 to main branch.
- ~~Bugfix of building error relating to XCCL. (Since https://github.com/pytor... | true |
2,962,954,897 | tensor with same shape, all contiguous, but have different stride | Xiang-cd | open | [
"triaged"
] | 7 | NONE | ### 🐛 Describe the bug
```python
base = './'
q = torch.load(f'{base}/problemq2.pt')
k = torch.load(f'{base}/problemk2.pt')
v = torch.load(f'{base}/problemv2.pt')
o = torch.load(f'{base}/problemo2.pt')
print(q.stride(), k.stride(),v.stride(),o.stride())
print(q.shape, k.shape, v.shape, o.shape)
print(q.is_contiguous(... | true |
2,962,896,411 | DISABLED test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_int8 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 7 | NONE | Platforms: linux
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_int8&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39748509020).
... | true |
2,962,896,405 | DISABLED test_matrix_rank_basic_cuda_float32 (__main__.TestLinalgCUDA) | pytorch-bot[bot] | open | [
"module: rocm",
"triaged",
"module: flaky-tests",
"module: linear algebra",
"skipped"
] | 7 | NONE | Platforms: rocm
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_matrix_rank_basic_cuda_float32&suite=TestLinalgCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39743051664).
Over the past 3 hours, it... | true |
2,962,872,771 | Generalize compile collective to avoid cuda-bias | Chao1Han | closed | [
"open source",
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo",
"ciflow/xpu"
] | 15 | CONTRIBUTOR | Fixes https://github.com/intel/torch-xpu-ops/issues/1527
Let the combination of `compile` and `collective` to support more devices.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames @zhangxiaoli73 @kwen2501 @... | true |
2,962,861,927 | Running `LazyModuleMixin` example throw errors | zeshengzong | closed | [
"module: nn",
"triaged"
] | 0 | CONTRIBUTOR | ### 📚 The doc issue
Running example in doc [LazyModuleMixin](https://pytorch.org/docs/stable/generated/torch.nn.modules.lazy.LazyModuleMixin.html) got errors like this:
```python
class LazyMLP(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.fc1 = torch.nn.LazyLinear(10)
... | true |
2,962,787,847 | [Attention] Always pad in preprocess_mask to avoid recompilations | ChuanqiXu9 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 10 | CONTRIBUTOR | Motivation: for the following script:
```
// demo.py
import torch
import json
from transformers import BertModel, BertConfig
CONFIG = """
{
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropou... | true |
2,962,782,805 | Feature Request: Add minLSTM and minGRU Modules | constatza | open | [
"module: nn",
"module: rnn",
"triaged",
"module: python frontend"
] | 0 | NONE | ### Feature, motivation, pitch
**Summary:**
Implement minimal versions of LSTM and GRU (minLSTM and minGRU) in PyTorch. These modules simplify traditional RNNs by removing hidden state dependencies and non-linearities, enabling parallel training via a parallel scan algorithm.
**Motivation:**
- **Speed:** Achieves... | true |
2,962,770,497 | AssertionError: found no DeviceMesh from dtensor args for c10d.broadcast_.default! | KylinC | closed | [
"oncall: distributed",
"module: dtensor"
] | 9 | NONE | ### 🐛 Describe the bug
my bash script:
```
CUDA_VISIBLE_DEVICES=3,4,5,6 nohup accelerate launch --config_file /archive/share/cql/LLM-FoR-ALL/mini_vlm/accelerate_config.yaml /archive/share/cql/LLM-FoR-ALL/mini_vlm/qwen25vl_sft.py > /archive/share/cql/LLM-FoR-ALL/mini_vlm/logs/output_sft.log 2>&1 &
```
accelerate_conf... | true |
2,962,760,362 | RuntimeError when exporting large model to ONNX due to 2GiB protobuf limit | byrcoder | closed | [] | 5 | NONE | ### 🐛 Describe the bug
When exporting videoseal model (https://github.com/facebookresearch/videoseal/) to ONNX format, I encountered the following error:
`RuntimeError: The serialized model is larger than the 2GiB limit imposed by the protobuf library...`
** The sample code
video_model = videoseal.load("videoseal... | true |
2,962,648,889 | torch wheels are unusable if CUDA RPMs are installed on the system (was Import error in nvidia/cuda:12.6.3-cudnn-devel-rockylinux9) | hzhangxyz | open | [
"module: binaries",
"module: cuda",
"triaged",
"module: third_party",
"has workaround"
] | 8 | NONE | ### 🐛 Describe the bug
```python
import torch
```
### Versions
Collecting environment information...
PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A
OS: Rocky Linux 9.5 (Blue Onyx) (x86_64)
GCC version: (GCC) 11.5.0 20240719 (Red Hat 11.5.0-5)
Clang version:... | true |
2,962,474,171 | [Reland] Launch kernel on current stream & remove `record_stream` entirely | kwen2501 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)",
"ci-no-td"
] | 13 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150398
Relanding #148590 due to merge conflict.
This PR has multiple changes to `ProcessGroupNCCL` (which unfortunately are related):
1. When async_op=False, we directly launch the collective on "current" stream, instead of a ... | true |
2,962,391,038 | update get start xpu document for v2.7 | ZhaoqiongZ | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: docs",
"release notes: xpu"
] | 14 | CONTRIBUTOR | update get start xpu document for v2.7 | true |
2,962,366,887 | Compare device name of profiler dynamically | elpis-furiosa | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: PrivateUse1"
] | 6 | CONTRIBUTOR | Compare self.use_device of torch.autograd.profiler.profiler with _get_privateuse1_backend_name(), since privateuse1 backend can be renamed.
cc @NmomoN @mengpenghui @fwenguang @cdzhan @1274085042 @PHLens @albanD | true |
2,962,339,582 | [Doc] Update CMAKE_PREFIX_PATH for XPU windows README | pytorchbot | closed | [
"open source",
"topic: not user facing"
] | 1 | COLLABORATOR | We found that the `pip install cmake` and `conda install cmake` has different behavior.
The reason is that the pip installed one doesn't find the corresponding libs under conda env. So we need to set the `CMAKE_PREFIX_PATH` for alignment.
cc @svekars @sekyondaMeta @AlannaBurke @gujinghui @EikanWang @fengyuan14 @guan... | true |
2,962,305,674 | Bugfix in backward of linalg.eigh | podgorskiy | open | [
"fb-exported",
"topic: not user facing"
] | 12 | NONE | Summary:
Bugfix in backward of linalg.eigh
The expression `VhgV = VhgV - at::matmul(V.mH(), V * at::real(diag_VhgV).unsqueeze(-2))` was simplified to `VhgV = 0.5 *(vhgv - vhgv.T)` for hermitian matrices, which does not seem to be correct.
I do not understand where `VhgV = 0.5 *(vhgv - vhgv.T)` came from, but the g... | true |
2,962,234,702 | [DTensor] Fix compute_local_shape_and_global_offset for uneven sharding | wconstab | closed | [
"oncall: distributed",
"release notes: distributed (fsdp)",
"ciflow/inductor",
"release notes: distributed (checkpoint)"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150393
* #150146
* #148894
This fix is needed for cases where distributed checkpointing (DCP) is used to
save a local state dict. That's becuase DCP relies on the local-shape / global-offset
for each rank being correct to save files... | true |
2,962,151,208 | DISABLED test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_int64 (__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_int64&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/3973712... | true |
2,962,141,813 | Add new dependences for gen_pyi.py | FFFrog | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150391
As the title stated.
When we update some functions in _torch_docs.py or _tensor_docs.py, and execute some commands (like ``python setup.py evolve``) to install the latest version, the description about the function we just ch... | true |
2,962,137,551 | On SM89, Triton is not supported as Inductor GEMM backend? | henrylhtsang | closed | [
"triaged",
"oncall: pt2",
"module: inductor",
"upstream triton"
] | 7 | CONTRIBUTOR | ### 🐛 Describe the bug
repro 1:
1. Get a SM89 machine
2. run
```
TORCHINDUCTOR_AUTOTUNE_FALLBACK_TO_ATEN=0 pytest -v test/inductor/test_benchmark_fusion.py -k test_avoid_register_spilling_cuda
TORCHINDUCTOR_AUTOTUNE_FALLBACK_TO_ATEN=0 pytest -v test/inductor/test_torchinductor.py -k test_linear_dynamic_maxautotune_cu... | true |
2,962,118,211 | Enabling xpu in OffsetBasedRNGTracker . | pytorchbot | closed | [
"oncall: distributed",
"open source",
"ciflow/inductor"
] | 1 | COLLABORATOR | Else torch.distributed breaks on xpu devices.
Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,962,113,991 | double free or corruption (out) in torch.as_strided_scatter | qiqicliff | open | [
"module: crash",
"module: error checking",
"triaged",
"topic: fuzzer"
] | 3 | NONE | ### 🐛 Describe the bug
Fuzzing of the edge cases in api torch.as_strided_scatter as below:
```
import torch
input = torch.randn(4, 4)
src = torch.full((2, 2), 10.0)
size = 2, 2
stride = 2, 1
storage_offset = 9223372036854775807
torch.as_strided_scatter(input, src, size, stride, storage_offset)
```
## output
crushed ... | true |
2,962,112,065 | Inconsistent results with PyTorch DeepLabV3 model even after fixing random seeds | wwwwwly | closed | [] | 2 | NONE | I encountered non-deterministic behavior when using PyTorch's DeepLabV3 model with pretrained weights. Despite fixing all random seeds, repeated executions still produce different results.
Code for fixing random seeds and model implementation are as follows.
```python
import torch
import torch.nn as nn
import numpy as ... | true |
2,962,109,718 | [MPSInductor] torch.complex128 is unsupported on MPS | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150412
* __->__ #150386
Same as torch.float64
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,962,055,151 | Fix device description of torch.asarray to avoid ambiguity | FFFrog | closed | [
"open source",
"release notes: python_frontend"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150391
* __->__ #150385
As the title stated.
Related Issue:
https://github.com/pytorch/pytorch/issues/150199 | true |
2,962,047,677 | Add `mse_loss_backward_out` type promotion | zeshengzong | open | [
"triaged",
"open source",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Fixes #94086, following #94621
## Test Result
```bash
pytest test/test_nn.py -k test_mse_loss_backward_promotion
```
| true |
2,962,047,222 | bound sympy accuracy | avikchaudhuri | open | [
"module: cpu",
"fb-exported",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 13 | CONTRIBUTOR | Differential Revision: D72215735
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,962,030,412 | [MPS] Test bf16 perf of few unary and binary ops | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150386
* __->__ #150382
| true |
2,962,030,077 | "RuntimeError: makeDeviceForHostname(): unsupported gloo device" with nightly torch 2.8 | AznamirWoW | open | [
"high priority",
"triage review",
"oncall: distributed",
"triaged",
"module: regression"
] | 10 | NONE | ### 🐛 Describe the bug
Nightly 2.8 torch results in an error during attempt to init a distributed training
```python
import sys
import os
import torch.distributed as dist
from random import randint
import torch
os.environ["USE_LIBUV"] = "0" if sys.platform == "win32" else "1"
os.environ["MASTER_ADDR"] = "localhost... | true |
2,961,997,310 | Make at::vec::Vectorized ops work with scalars | swolchok | closed | [
"module: cpu",
"Merged",
"release notes: cpp"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150568
* __->__ #150380
I noticed that I couldn't use `vec::Vectorized` operations with scalars, even though there is an implicit conversion from `T` to `vec::Vectorized<T>`, so I made it work.
Test Plan: Added tests. Reverted vec_base.h, ... | true |
2,961,982,550 | aten_mm_info counters not being logged properly in `_compile_fx_inner` | exclamaforte | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
This line fails:
https://github.com/pytorch/pytorch/blob/main/torch/_inductor/compile_fx.py#L879
when with `aten._int_mm_{m}_{n}_{k}` and others:
https://github.com/pytorch/pytorch/blob/main/torch/_inductor/kernel/mm.py#L717
From this PR:
https://github.com/pytorch/pytorch/pull/148800?fbclid=I... | true |
2,961,961,345 | Not generating runtime checks when the number of inputs is large | yushangdi | closed | [
"fb-exported",
"module: inductor",
"ciflow/inductor",
"release notes: export"
] | 3 | CONTRIBUTOR | Summary: if the number of inputs/outputs is large, we don’t generate the check_inputs_outputs unless the aot_inductor.compile_wrapper_with_O0 flag is set, and if the environment variable AOTI_RUNTIME_CHECK_INPUTS is set when the check inputs are not generated, we just error out and say you have to compile again with t... | true |
2,961,947,382 | [CI] Skip test_copy_large_tensor on M2-15 runners | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps"
] | 3 | CONTRIBUTOR | They have more than 12Gb memory, but may be running this test causes OOM in CI | true |
2,961,889,701 | [dynamic shapes] stop writing Max(*, 1) for strides | pianpwk | open | [
"release notes: export"
] | 1 | CONTRIBUTOR | When handling strides, avoiding generating Max(u0, 1) expressions if we can, since it'll be hard to deal with these once we move away from guard_size_oblivious.
Looking at what the code was before sym_max was introduced (https://github.com/pytorch/pytorch/pull/94400 in `_prims_common/__init__.py`), this change seems... | true |
2,961,888,927 | [re_build] Get output from stdout and sterr in local and remote execution and better error msg for too big to optimize | yushangdi | closed | [
"fb-exported",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | Summary:
In aoti, we make a better error message for the `too big to optimize` error by suggesting the `aot_inductor.compile_wrapper_opt_level = 'O0'` flag.
Test Plan:
result example:
```
error: Function _ZN5torch12aot_inductorL22__check_inputs_outputsEPP16AtenTensorOpaqueS3_ is too big to optimize [-Werror,-... | true |
2,961,871,649 | bf16 grouped gemm | ngimel | closed | [
"module: cuda",
"Merged",
"topic: not user facing"
] | 4 | COLLABORATOR | Enabled bf16 grouped gemm with an API similar to _scaled_group_gemm, except without scale and fast accum arguments. All transpose variants are enabled, unlike scaled gemm. Ideally we'd factor out a lot more code from scaled gemm, currently there's a lot of repetition between scaled and non-scaled versions. I factored o... | true |
2,961,870,345 | [AMD] [TRITON] [INDUCTOR] Add tl.assume to enable bufferops on AMD | njriasan | closed | [
"module: rocm",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | Summary: Update the GEMM template to include the necessary `tl.assume` annotations to enable bufferops with AMD.
Test Plan: Tested manually with a simple matmul run with torch.complie(f, mode="max-autotune") the environment variables TRITON_ALWAYS_COMPILE=1 AMDGCN_ENABLE_DUMP=1 AMDGCN_USE_BUFFER_OPS=1.
Inspecting t... | true |
2,961,856,469 | [dtensor][tp] add a ParallelStyle PrepareModuleInputOutput | tianyu-l | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: distributed (dtensor)"
] | 6 | CONTRIBUTOR | Needed this class for because `parallelize_module` takes a dict, which doesn't allow `PrepareModuleInput` and `PrepareModuleOutput` to be applied at the same time.
The `PrepareModuleInputOutput` in this PR initializes two variables `prepare_module_input` and `prepare_module_output` and uses them to process module / ... | true |
2,961,828,501 | Dynamic shapes doesn't work with kwargs | tugsbayasgalan | open | [
"oncall: pt2",
"oncall: export"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
```
def test_dynamic_shapes_kwargs(self):
class Foo(torch.nn.Module):
def forward(self, *, x, y, **kwargs):
z = kwargs["z"]
return x.sum() + y.sum() + z.sum()
inputs = {"x": torch.randn(4, 4), "y": torch.randn(4, 4), "z":... | true |
2,961,828,485 | [Profiler] Fix Empty C Call Queue | sraikund16 | closed | [
"fb-exported",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: profiler",
"topic: bug fixes",
"ci-no-td"
] | 20 | CONTRIBUTOR | Summary:
My commandeer of https://github.com/pytorch/pytorch/pull/150102
Based on description of PR it seems that we need to add C calls for each starting python event with a callable such that when the tracing exits we will have a matching enter for any given exit. It adds some unnecessary events at worst but prev... | true |
2,961,821,620 | [hop schema] add gen_schema support for invoke_subgraph | ydwu4 | closed | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150369
* #149688
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,961,808,651 | Support `copy` kwarg in `torch.reshape()` following Python array API standard | leofang | open | [
"triaged",
"module: python array api"
] | 1 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
As title. Currently (as of PyTorch 2.6.0), this is not yet supported.
- `torch.reshape`: https://pytorch.org/docs/2.6/generated/torch.reshape.html#torch-reshape
- `array_api.reshape`: https://data-apis.org/array-api/2024.12/API_specification/generated/array_api.reshape.html#res... | true |
2,961,797,801 | [ONNX] decomp does not preserve custom CompositeImplicitAutograd ops | borisfom | closed | [
"module: onnx",
"triaged"
] | 39 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
I recently was forced to implement some composite custom ops with CompositeImplicitAutograd on top of another, more general custom op, for ONNX/TRT export purposes(the original op was used for both forward and backward and therefore had sequence output type that neither onnxrun... | true |
2,961,766,903 | `torch.vdot()` returns zero when input tensors have complex data type | canturk | closed | [
"needs reproduction",
"triaged",
"module: macos",
"module: linear algebra",
"module: intel"
] | 4 | NONE | ### 🐛 Describe the bug
`torch.vdot()` returns zero when the input tensors are complex data type:
```
>>> torch.vdot(torch.tensor([2, 3]), torch.tensor([2, 1])) # input arguments are real tensors
tensor(7)
>>>
>>> a = torch.tensor([1 +2j, 3 - 1j])
>>> b = torch.tensor([2 +1j, 4 - 0j])
>>> torch.vdot(a, b) # input... | true |
2,961,746,493 | Build MacOS CI with MKLDNN | malfet | open | [
"ciflow/trunk",
"release notes: build",
"topic: improvements",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150365
To reduce divergence between aarch64 and MacOS builds
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @cha... | true |
2,961,745,422 | AOTI doesn't error if dynamic shape gets specialized during lowering | angelayi | open | [
"triaged",
"oncall: pt2",
"export-triaged",
"oncall: export",
"module: aotinductor"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
In some passes inductor is able to further shape specialize a dynamic shape. However AOTI does not error on this (during lowering or during runtime). Example:
```python
def test_aoti_specialization(self):
from torch._inductor.pattern_matcher import (
fwd_only,
... | true |
2,961,744,407 | Fix typo | malfet | closed | [
"oncall: distributed",
"Merged",
"release notes: distributed (c10d)"
] | 3 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/150339
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,961,742,870 | [BE] Get rid of cross-compile and x86 build options for Mac | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150365
* __->__ #150362
As both cross-compilation and x86 builds has been removed a while back
Remove stale TODO about building with OpenMP support | true |
2,961,732,263 | [ROCm] cmake 4 workaround for hiprtc | pytorchbot | closed | [
"module: rocm",
"open source",
"ciflow/rocm"
] | 1 | COLLABORATOR | cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,961,730,249 | CUTLASS backend updates: Instantiation level, long compilation and long autotuning time | henrylhtsang | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 8 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
This document is intended to be a status update for the CUTLASS backend, including a brief summary of its prospects and outstanding issues. The focus is on H100.
# The good
As it is known, CUTLASS can outperform Aten and Triton meaningfully (5% - 10%+) on many shapes with exha... | true |
2,961,711,084 | [PP] Update 1f1b cooldown None steps | H-Huang | open | [
"oncall: distributed",
"release notes: distributed (pipeline)"
] | 1 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151249
* #151248
* __->__ #150359
* #150347
This shouldn't make a difference to the schedule runtime since `None` ops are skipped, but helps for readability and visualization. We previously added `None` after each step in 1f1b during coold... | true |
2,961,699,718 | `torch.backends.mkldnn.flags()` CM should not warn | malfet | closed | [
"module: cpu",
"module: mkldnn",
"Merged",
"ciflow/trunk",
"release notes: python_frontend",
"topic: bug fixes",
"ciflow/linux-aarch64"
] | 7 | CONTRIBUTOR | By returning `None` rather than `False` from `THPModule_allowTF32OneDNN` when USE_XPU is not defined
Added regression test
Fixes https://github.com/pytorch/pytorch/issues/149829
Fixes #ISSUE_NUMBER
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @gujinghui @PenghuiCheng @jianyuh @min-jean-cho... | true |
2,961,686,142 | [dtensor] add op support for select_backward and slice_backward | tianyu-l | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"release notes: distributed (dtensor)"
] | 6 | CONTRIBUTOR | Inheriting and rebasing @awgu 's PR https://github.com/pytorch/pytorch/pull/149071
- fixed an issue for `select_backward` and an issue for `slice_backward`
- removed `_experimental_ops.py` as it becomes empty
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,961,684,263 | [fr] Add logger config for flight record in PGNCCL | fduwjj | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 6 | CONTRIBUTOR | Summary: We want to move from a scuba based direct logging to a logger config based logging. Mostly changes are internal but we need to change the exception to exception_msg.
Test Plan: Following https://www.internalfb.com/wiki/Server_Logging/Getting_Started_with_Logging/Onboarding_Existing_Scribe-Based_Logging_(Alpha... | true |
2,961,677,747 | support nested compile when inner compile is inside of __torch_dispatch__ | bdhirsh | open | [
"module: dynamo",
"ciflow/inductor",
"release notes: AO frontend"
] | 1 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/150262
At a high level, the idea is that anywhere we do fake tensor prop inside of compile, there is a risk that this fake prop can recursively invoke compilation. This can happens if we are doing fake prop on a tensor subclass, and it's __torch_dispatch__ uses torch.c... | true |
2,961,676,303 | [DTensor][tp] fix errors in FSDP+TP checkpointing test | XilunWu | closed | [
"oncall: distributed",
"better-engineering",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/periodic",
"release notes: distributed (checkpoint)"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150354
## Summary
remove the `tp_parallelize_plan` assignment that accidentally rewrites the previous assignments in `test_fsdp_dsd.py`.
## Test
`pytest test/distributed/checkpoint/fsdp/test_fsdp_dsd.py`
cc @H-Huang @awg... | true |
2,961,669,791 | Memory leak base tests for compile | IvanKobzarev | open | [
"topic: not user facing"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150353
| true |
2,961,645,371 | Revert "[PGNCCL] Launch kernel on current stream & remove `record_stream` entirely (#148590) | atalman | closed | [
"oncall: distributed",
"release notes: distributed (c10d)"
] | 1 | CONTRIBUTOR | This reverts commit ef6296e7f20d744a0cfed81cab573d60204e7626.
Reverting this since its reverted on trunk
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,961,644,693 | test enummeta | Sunnie912 | open | [
"fb-exported",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Differential Revision: D72196352
| true |
2,961,643,098 | DISABLED test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_int32 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 8 | 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_int32&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39713... | true |
2,961,639,251 | [DO NOT REVIEW] Update _fsdp_param_group.py | Ritesh1905 | open | [
"oncall: distributed",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | asset `all_reduce_event` is None only if it's not a cpu based device.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,961,627,788 | [ROCm] update test buffer fudge factor for hipblaslt | ethanwee1 | closed | [
"oncall: distributed",
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/periodic",
"ciflow/rocm"
] | 6 | CONTRIBUTOR | The default workspace for hipblaslt is larger than for cublas/cublaslt which requires a slight increase to the buffer needed.
Forward-fix for #150227 that broke ROCm distributed tests but wasn't part of initial CI signal.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @jeffd... | true |
2,961,619,309 | [PP] Add schedule visualizer | H-Huang | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (pipeline)",
"module: pipelining"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #151249
* #151248
* #150359
* __->__ #150347
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.
InterleavedZeroBubble(pp_grou... | true |
2,961,598,859 | [Cutlass] Integrate EVT codegen into 3x gemm template | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150910
* #150909
* #150908
* #150907
* #150906
* #150905
* #150904
* #150903
* __->__ #150346
* #150345
* #150344
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @ji... | true |
2,961,598,751 | [Cutlass] Codegen for EVT Epilogue | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150910
* #150909
* #150908
* #150907
* #150906
* #150905
* #150904
* #150903
* #150346
* __->__ #150345
* #150344
Previously merged:
* #150344
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhu... | true |
2,961,598,673 | [Cutlass] Import cutlass python API for EVT | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 7 | CONTRIBUTOR | This imports the pieces of the cutlass python API that are needed for python EVT tracing. It builds on existing importing for cutlass_library. Once EVT tracing has been added to cutlass_library (should be later this year) this can be removed.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom)... | true |
2,961,584,322 | [ez][inductor][tests] Skip triton backend only for CPU tests | henrylhtsang | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150343
Motivation: to unblock https://github.com/pytorch/pytorch/pull/148622
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @mu... | true |
2,961,538,008 | Torch trace doesn't respect @torch.jit.ignore on torch.nn.Module forward method | bpottersta | open | [
"oncall: jit"
] | 0 | NONE | ### 🐛 Describe the bug
Tracing a class derived from torch.nn.Module doesn't respect the @torch.jit.ignore decorator on the forward method.
There is good reason to want a class to derive from torch.nn.Module but NOT trace the forward method, see example below.
The forward method is valid and useful in usages with pyt... | true |
2,961,536,810 | [dynamo] add reason field to torch.compiler.disable | williamwen42 | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo",
"module: compile ux"
] | 6 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150440
* __->__ #150341
Implements https://github.com/pytorch/pytorch/issues/146445
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @am... | true |
2,961,511,691 | [AOTI] Skip test_buffer_mutation_and_force_mmap_weights for fbcode | desertfire | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Summary: Skip due to an older ideep version
Differential Revision: D72190746
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,961,505,496 | [typo] pecify -> specify | bobcao3 | closed | [
"oncall: distributed",
"module: docs",
"triaged",
"actionable"
] | 0 | NONE | https://github.com/pytorch/pytorch/blob/80b7f6b70426ae329b1c99a7efb863835d1de0cb/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp#L4753
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @svekars @sekyondaMeta @AlannaBurke | true |
2,961,503,950 | Copy native runtime code to OSS. | zhxchen17 | open | [
"ciflow/trunk",
"topic: not user facing"
] | 14 | CONTRIBUTOR | Summary:
# High level context
Torch native runtime (codename: Sigmoid) is a new feature we're upstreaming to libtorch. Native runtime will execute a graph based on torch.export() format directly and the entire runtime is written in C++ so there's no Python dependency for running this.
The open sourcing part of... | true |
2,961,497,028 | [cuDNN][SDPA] Loosen constraints for GQA for cuDNN Attention | eqy | closed | [
"module: cudnn",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: sdpa"
] | 9 | COLLABORATOR | cuDNN attention doesn't require key and value tensors to have the same number of heads
cc @csarofeen @ptrblck @xwang233 | true |
2,961,484,530 | [parallelize_module] uneven sharding + use_local_output breaks | wconstab | open | [
"oncall: distributed",
"triaged",
"module: dtensor"
] | 4 | CONTRIBUTOR | Rowwise/ColwiseParallel strategies both default to 'use_local_output=True'.
If a linear layer has an uneven size (e.g. 5) on the sharded dimension, bad things happen.
1) first linear (input projection) will produce an output of global size (5,), with shards of (3, ) and (2, ) on 2 ranks.
2) second linear (output pro... | true |
2,961,477,097 | [PGNCCL][BE] Merge mutex into TensorShelf for encapsulation | kwen2501 | closed | [
"oncall: distributed",
"release notes: distributed (c10d)"
] | 2 | CONTRIBUTOR | ghstack-source-id: e4b48e5473af4c7fbc227e63948633a33b1c7a59
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150130
(cherry picked from commit 783c3c823ef261cf00d33568966357cd97909cd6)
Fix 3 of 3 for https://github.com/pytorch/pytorch/pull/148590
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337... | true |
2,961,475,116 | [c10d] Move unstashing from watchdog to main thread | kwen2501 | closed | [
"oncall: distributed",
"release notes: distributed (c10d)"
] | 2 | CONTRIBUTOR | ghstack-source-id: 2a00866ec975f1beac417b4c9e7829baebabe843
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150079
(cherry picked from commit 27b79263d78594e466d578fa88b570be2dd626ae)
Fix 2 of 3 for #148590
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
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