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 |
|---|---|---|---|---|---|---|---|---|
2,758,536,451 | triton.codegen_upcast_to_fp32 breaks bitcast/bitwise ops | Xynonners | open | [
"triaged",
"module: type promotion",
"oncall: pt2",
"module: inductor"
] | 1 | NONE | ### 🐛 Describe the bug
It seems, that after using a .view(int_dtype) on a float tensor,
triton.codegen_upcast_to_fp32 (enabled by default) attempts to recast that bitcast int back to a fp32 float.
ablation: inductor
short reproducer:
```python
import torch
@torch.compile(options={"triton.codegen_upcast... | true |
2,758,532,367 | [Inductor][CPP] Enable Bias add for Group GEMM Template | leslie-fang-intel | closed | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143850
* __->__ #143820
* #143796
**Summary**
In this PR, we move the `store_output` and `store_pointwise_nodes` to standalone functions for Group GEMM epilogue fusion to prepare for following Epilogue fusion PR. And we support Bias add a... | true |
2,758,506,184 | Update torch-xpu-ops commit pin | xytintel | closed | [
"open source",
"topic: not user facing",
"module: inductor",
"ciflow/xpu"
] | 4 | CONTRIBUTOR | Update the torch-xpu-ops commit to [0f48ac](https://github.com/intel/torch-xpu-ops/commit/0f48ac07e42ce30d2d07447f4b49bb4ab23f8e64), includes:
- Fix building issue for transformer related operators
- Improve XPU operator coverage
- Performance optimization for several SYCL kernels
cc @voznesenskym @penguinwu @E... | true |
2,758,467,374 | [inductor] Move GPUTarget backwards compat to triton_compat.py | jansel | closed | [
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/inductor-rocm"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143835
* __->__ #143818
* #143817
* #143815
* #143814
* #143813
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPep... | true |
2,758,467,327 | [inductor] Drop support for pre-ASTSource Triton | jansel | closed | [
"Merged",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143835
* #143818
* __->__ #143817
* #143815
* #143814
* #143813
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPep... | true |
2,758,419,961 | pytorch v2.4.1 build for nvidia jetson orin nano 8GB | lida2003 | closed | [
"module: build",
"triaged",
"module: jetson"
] | 2 | NONE | ### 🐛 Describe the bug
pytorch v2.4.1 build for nvidia jetson orin 8GB
Previous discussion here FYI: https://forums.developer.nvidia.com/t/request-build-script-for-pytorch-or-up-to-date-pytorh-binary-release-supporting-jetson-boards-running-l4t35-6-ubuntu20-04/316972/12
```
Software part of jetson-stats 4.2.... | true |
2,758,414,759 | [inductor] Minor refactor of hip compile_meta | jansel | closed | [
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143835
* #143818
* #143817
* __->__ #143815
* #143814
* #143813
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPep... | true |
2,758,402,028 | [inductor] Refactor conditional triton imports into triton_compat.py | jansel | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143835
* #143818
* #143817
* #143815
* __->__ #143814
* #143813
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPep... | true |
2,758,381,276 | [inductor] Reorder imports in codecache.py | jansel | closed | [
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143835
* #143818
* #143817
* #143815
* #143814
* __->__ #143813
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPep... | true |
2,758,375,582 | [inductor] Used fixed configs for contiguous reductions | jansel | open | [
"Stale",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"no-stale",
"release notes: inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143812
* #142295
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aak... | true |
2,758,367,285 | [Functorch] Refactor vmapify autograd function: remove cell mutation | yanboliang | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143811
| true |
2,758,320,265 | "Access denied" error at PyTorch ROCm 6.2+ wheel repo | runtimeHorror | closed | [
"needs reproduction",
"module: binaries",
"module: rocm",
"triaged"
] | 3 | NONE | ### 🐛 Describe the bug
Cannot access the directory or download anything from the repo.
Running
```
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4
Looking in indexes: https://download.pytorch.org/whl/rocm6.2.4
```
gives
```
ERROR: Could not find a versio... | true |
2,758,230,733 | Inductor with dynamic shapes fails for randint with >INT_MAX maximum value | ngimel | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 0 | COLLABORATOR | The generated annotation for max value (`ks1`) is `i32`
```
@triton_heuristics.pointwise(
size_hints={'x': 1048576},
filename=__file__,
triton_meta={'signature': {'in_ptr0': '*i64', 'out_ptr0': '*i64', 'load_seed_offset': 'i32', 'ks1': 'i32', 'xnumel': 'i32'}, 'device': DeviceProperties(type='cuda', in... | true |
2,758,222,876 | Inductor cache: Revamp how we handle frozen params | masnesral | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 13 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143808
Summary: In https://github.com/pytorch/pytorch/pull/143563 we have a report of a problem with the treatment of frozen params in the inductor cache implementation. There seems to be a path where new constants are added in the ... | true |
2,758,125,478 | XPU ConvTranspose2d Causes DataLoader Memory Leak | ekaakurniawan | closed | [
"triaged",
"module: xpu"
] | 4 | NONE | ### 🐛 Describe the bug
I run the following notebook on XPU (device_type = "xpu") failed with "Too many open files" error. It seems the DataLoader does not close the files. The memory increases slowly from 2 GiB to 8 GiB within 3 epochs. Running on CPU (device_type = "cpu") is fine.
[Convolutional Autoencoder Notebook... | true |
2,758,125,260 | Enable clang-tidy on torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp | cyyever | closed | [
"oncall: distributed",
"oncall: jit",
"triaged",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: distributed (c10d)",
"ciflow/periodic",
"ci-no-td",
"ciflow/inductor-cu126"
] | 14 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire ... | true |
2,758,123,547 | Remove remove_non_owning_ref_types | cyyever | closed | [
"open source",
"Stale",
"topic: not user facing"
] | 9 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,758,108,965 | [17/N] Fix extra warnings brought by clang-tidy-17 | cyyever | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,757,878,225 | XPU Manywheel builds linux and windows are failing since Dec 23, 2024 | atalman | closed | [
"module: binaries",
"triaged",
"module: xpu"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
I see following Failures on XPU builds since Dec 23, 2024:
Linux XPU:
https://github.com/pytorch/pytorch/actions/runs/12474101389/job/34819441679
Windows XPU:
https://github.com/pytorch/pytorch/actions/runs/12478637812/job/34826509154
```
[linux-binary-manywheel / manywheel-py3_9-... | true |
2,757,877,917 | [inductor] fix the `adaptive_avg_pool` on processing int64 | shaoyuyoung | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 7 | CONTRIBUTOR | Fixes #143801
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov | true |
2,757,876,873 | [inductor] `AdaptiveAvgPool` behaves differently on eager and inductor when meeting internal int64 dtypes | shaoyuyoung | closed | [
"oncall: pt2"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
related to #143752.
#143762 fixes #143752.
However, I found that after #143762 landed, `AdaptiveAvgPool` still has the same issue.
```
import torch
import torch.nn as nn
import torch.nn.functional as F
torch.manual_seed(0)
from torch._inductor import config
config.fallback_random =... | true |
2,757,694,262 | The tensor-based computation of exponentiation and logarithmic operations is much slower than using NumPy | yxma2015 | open | [
"needs reproduction",
"module: performance",
"module: cpu",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
Hi there, hope this message finds you well.
I have encountered a significant performance issue when using PyTorch tensors for exponentiation (torch.exp()) and logarithmic operations (torch.log()) compared to NumPy. Specifically, these tensor operations are much slower than their NumPy counte... | true |
2,757,545,033 | Add get_stream_from_external API for CUDA backend | guangyey | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: python_frontend"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143849
* __->__ #143799
* #141123
* #141119
* #142347
| true |
2,757,456,027 | FlightRecorderEventTest::test_all_events is flaky | lw | closed | [
"oncall: distributed",
"module: flaky-tests"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
The test test/distributed/flight_recorder/test_fr_analysis.py::FlightRecorderEventTest::test_all_events is flaky.
You can see here a sample failure: https://github.com/pytorch/pytorch/actions/runs/12470584195/job/34807434998?pr=143747.
This flakiness was introduced in https://github.com... | true |
2,757,330,788 | Propagate callable parameter types using ParamSpec (#142306) | kaspell | closed | [
"oncall: distributed",
"module: cpu",
"module: typing",
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"module: dynamo",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | The codebase has a few locations where callable parameter type information is lost when the unpackings *args and **kwargs are typed as Any. Refactor these instances to retain type information using typing_extensions.ParamSpec.
Also, in these functions, enforce return type with TypeVar.
Addresses #142306
cc @H-Hu... | true |
2,757,327,237 | [Inductor][CPP] Enable Grouped GEMM Template | leslie-fang-intel | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143897
* __->__ #143796
**Summary**
Enable the CPP Grouped GEMM Fusion, lowering and Grouped GEMM Template following the RFC: https://github.com/pytorch/pytorch/issues/144012
- Support flexible number of GEMMs
- Share activation acros... | true |
2,757,293,256 | PyTorch source code build failed on some Windows 11 environment caused by C++ protocol buffer compiler | chuanqi129 | open | [
"module: build",
"module: windows",
"triaged"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
The pytorch source code build crashed on Windows 11 caused by **C++ protocol buffer compiler**
```
>python setup.py bdist_wheel
Building wheel torch-2.6.0a0+git0189052
-- Building version 2.6.0a0+git0189052
cmake --build . --target install --config Release
[1/2444] Running C++ protoco... | true |
2,757,268,473 | [DONT MERGE]xpu env build cpu whl | chuanqi129 | closed | [
"open source",
"Stale",
"ciflow/binaries",
"topic: not user facing"
] | 2 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,757,254,912 | Fix empty matrix handling of addmv in inductor | maybeLee | closed | [
"triaged",
"open source",
"Merged",
"Stale",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 16 | CONTRIBUTOR | This is a resubmission of my previous PR that I accidentally deleted, apology in advance if any inconvenience caused. Below are details of this PR.
Fix an issue when torch.addmv behaves inconsistent between torch.compile mode and eager mode. Here is the code to reproduce:
```
import torch
import numpy as np
... | true |
2,757,253,087 | [don't merge] use vs2022 build windows cpu wheel. | xuhancn | closed | [
"open source",
"ciflow/binaries",
"topic: not user facing"
] | 10 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,757,249,041 | [inducotr] [cuda] `frexp` output different result when meeting `inf` | shaoyuyoung | open | [
"triaged",
"oncall: pt2",
"module: inductor",
"upstream triton"
] | 8 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom**: When input tensor is `inf`, the second tensor returned by `frexp` is `-2147483648`. Eager output is zero (CPU inductor is also zero)
**device**: only cuda
**exposed area**: only input tensor is `inf` (`nan` wouldn't trigger inconsistency)
**code**
```python
import torc... | true |
2,757,200,944 | Flex attention with nested tensors, bug in `create_nested_block_mask` | VivekPanyam | closed | [
"triaged",
"module: nestedtensor",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 2 | CONTRIBUTOR | The following code is from `_nested_mod_func_adapter` which is a helper function used by `create_nested_block_mask`. Conceptually, it wraps a mod function that operates on individual batch items of a nested tensor and transforms the inputs so it works on a single packed item. However, the below code doesn't appear to u... | true |
2,757,170,812 | fix randint distribution for large max | ngimel | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: cpp",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 15 | COLLABORATOR | Fixes #ISSUE_NUMBER
Similar to #143682, for large maximum values we were sampling integers via % and it doesn't provide uniform distribution. Here we limit the max skew to approx 1% (random32 is used for max values `<= 2**32 / 128`)
This comes with significant perf penalty, especially for cuda, but it's a pretty bad ... | true |
2,757,161,174 | @custom_op extensions could not be export.export()ed via AOT and run from C++ | borisfom | closed | [
"module: docs",
"module: error checking",
"triaged",
"module: custom-operators",
"oncall: pt2",
"oncall: export",
"module: pt2-dispatcher"
] | 18 | CONTRIBUTOR | ### 🐛 Describe the bug
Here is the repro. I am adding a @custom_op to a working example that saves ExportedProgram via AOT and runs it from C++. When I add custom operation, it stops working :
Error: Could not find schema for mylib::custom_add.
```
import torch
def custom_add_direct(a: torch.Tensor, b: torch... | true |
2,757,106,794 | UNSTABLE periodic / linux-focal-rocm6.2-py3.10 / test (distributed) | jithunnair-amd | closed | [
"module: rocm",
"module: ci",
"unstable"
] | 2 | COLLABORATOR | We are working on updating labels and `.env` files on the ROCm runners
cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @seemethere @malfet @pytorch/pytorch-dev-infra | true |
2,757,101,184 | [Intel GPU] Avoid copy when the input of Matmul is broadcasted | jianyizh | closed | [
"module: cpu",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 20 | CONTRIBUTOR | Avoid copy when the input of Matmul is 3D and broadcasted on batch dim. oneDNN support implicit broadcast semantics i.e., src can be broadcasted into weight if the corresponding dimension in src is 1 (and vice versa). On Max 1100, timm resmlp_12_224 amp_fp16 inference with bs=128 can improve from 42ms to 13.7 ms on to... | true |
2,757,100,952 | Generalize pin memory logic for accelerator when non blocking copy happened | guangyey | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/mps",
"ciflow/xpu",
"module: accelerator"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143783
* #144959
# Motivation
fix https://github.com/pytorch/pytorch/issues/143641
Generalize pin memory logic for accelerator when non-blocking copy happened. Each accelerator has its implementation on `empty_strided`. The accele... | true |
2,757,064,887 | [micro_pipeline_tp] don't pass return_A to fused_all_gather_scaled_matmul | yifuwang | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143782
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenya... | true |
2,757,026,080 | torch/accelerator: fix device type comparison (#143541) | guangyey | closed | [
"open source"
] | 2 | COLLABORATOR | This was failing without the fix:
```
python -c 'import torch; d=torch.device("xpu:0"); torch.accelerator.current_stream(d)'
```
with:
```
ValueError: xpu doesn't match the current accelerator xpu.
```
CC: @guangyey, @EikanWang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143541
Approved... | true |
2,757,024,078 | Looking for valid compiling option for extension based on torch-2.1.0+cpu.cxx11.abi | dilililiwhy | open | [
"high priority",
"needs reproduction",
"module: crash",
"module: cpp-extensions",
"triaged",
"has workaround"
] | 9 | CONTRIBUTOR | ### 🐛 Describe the bug
Try to compile extension based on [torch-2.1.0+cpu.cxx11.abi](https://download.pytorch.org/whl/cpu-cxx11-abi/torch-2.1.0%2Bcpu.cxx11.abi-cp39-cp39-linux_x86_64.whl#sha256=f100b87d0e307dcac6321dd8f4895f14f6fa6974a921e9e7369bd9c7be4f0d5d) and set D_GLIBCXX_USE_CXX11_ABI=1.
env info:
```
Ar... | true |
2,757,014,985 | [inductor] [dtype] `ReplicationPad` raise dtype error on eager but pass the check on indcutor | shaoyuyoung | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom**: when using a normal input to this model, `signbit` output a `bool` value. `replication_pad` rejects bool on eager but pass the check on inductor. I'm not sure which one should be taken.
**device**: both on cpu and cuda
**exposed area**: ReplicationPad1d, ReplicationPad2d, Re... | true |
2,756,932,200 | Sort requirements.txt | Raymo111 | closed | [
"better-engineering",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | MEMBER | null | true |
2,756,924,519 | [CUDA][CUDA graphs][RNG] Skip replay prologue if `wholegraph_increment` is 0 | eqy | closed | [
"module: cuda",
"module: random",
"open source",
"Merged",
"module: cuda graphs",
"ciflow/trunk",
"topic: not user facing"
] | 3 | COLLABORATOR | #143572
cc @ptrblck @msaroufim @pbelevich @mcarilli @ezyang @eellison @penguinwu | true |
2,756,813,851 | Remove builder repo from workflows and scripts | atalman | closed | [
"Merged",
"ciflow/binaries",
"ciflow/trunk",
"release notes: releng"
] | 6 | CONTRIBUTOR | Part of https://github.com/pytorch/builder/issues/2054
Builder is repo is no longer used. Hence remove any references to builder repo
| true |
2,756,793,993 | [pytorch/et] Allow ET to save additional resources for completing a trace like generated kernels and index tensor data | sanrise | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 48 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143775
The resources directory lets ET observer dump any additional data like Triton kernels while capturing the ET.
This allows us to use the ET trace to replay PT2 workloads and get visibility into data like generated kernels and ... | true |
2,756,762,298 | CUDA error when compiling loss function | tianyu-l | open | [
"module: activation checkpointing",
"triaged",
"oncall: pt2"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
In torchtitan, we recently turned on torch.compile on the loss function. It runs well until a recent pytorch nightly. As it broke CI, we have to turn it off in https://github.com/pytorch/torchtitan/pull/755. Please help resolve so that we can re-enable it.
### Error logs
There are various err... | true |
2,756,744,467 | "Unknown builtin op" error during jit.load() of TorchScript module with @custom_op | borisfom | closed | [
"oncall: jit",
"triaged",
"module: custom-operators",
"oncall: pt2",
"module: pt2-dispatcher"
] | 23 | CONTRIBUTOR | ### 🐛 Describe the bug
Here is a simple repro:
1. Run the file below to produce "custom_module.pt"
2. Run: python -c 'import torch; torch.jit.load("custom_module.pt")'
```
import torch
@torch.library.custom_op("mylib::custom_add", mutates_args=())
def custom_add(a: torch.Tensor, b: torch.Tensor) -> torc... | true |
2,756,743,693 | cpp_wrapper: minimize pybind11 dependency | benjaminglass1 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143909
* #143421
* #143223
* #141371
* __->__ #143772
Only include the parts of `pybind11` that handle GIL management within `cpp_wrapper`. This dramatically improves compilation times by reducing the number of headers we compile. Improvem... | true |
2,756,715,187 | [TGIF][Easy] Slightly improve the logging for tgif split pass | faran928 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx"
] | 17 | CONTRIBUTOR | Summary:
1. Added more details for some of the assert statements.
2. Moved assert statements to use tgif_assert
Test Plan: all unit tests should pass
Reviewed By: jingsh
Differential Revision: D67608251
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,756,710,810 | [inductor][cpu] Accuracy failure on bmm max_autotune for offset input weights | frost-intel | open | [
"oncall: pt2",
"oncall: cpu inductor"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
Accuracy error is occurring for BMM max_autotune code when input weights have an offset. Issue is not reproducible on main due to #143102 but after #143141 lands, this issue shows up. Found testing torchbench `sam` model with `--amp`.
Here is a sample test to reproduce (could be added to `te... | true |
2,756,694,457 | [ROCm] Use `linux.rocm.gpu.2` for 2-GPU and `linux.rocm.gpu.4` for 4-GPU runners | jithunnair-amd | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/periodic",
"ciflow/rocm"
] | 5 | COLLABORATOR | * Will enable us to target `periodic`/distributed CI jobs to 4-GPU runners using a different label `linux.rocm.gpu.4`
* Use 2-GPU runners for `trunk`, `pull` and `slow` (in addition to `inductor-rocm`) as well (although this currently will not change anything, since all our MI2xx runners have both `linux.rocm.gpu` and... | true |
2,756,679,872 | Update tag_regex in filter_test_configs.py for workflows such as `inductor-rocm` | jithunnair-amd | closed | [
"module: rocm",
"open source",
"Merged",
"topic: not user facing",
"test-config/default",
"ciflow/rocm"
] | 3 | COLLABORATOR | This helps to make `continue-through-error`/`keep-going` work as expected on `inductor-rocm` workflow jobs.
Without this, the code here doesn't enter the `if` condition: https://github.com/pytorch/pytorch/blob/6ccb8ed1868984d9d2ea4e48a085508d1027cd9b/.github/scripts/filter_test_configs.py#L577
Tested via [this PR... | true |
2,756,625,241 | Revert "Exclude py 31.3t triton package from PyTorch 3.13t wheel" | atalman | closed | [
"topic: not user facing"
] | 1 | CONTRIBUTOR | Reverts pytorch/pytorch#143244 | true |
2,756,601,513 | [inductor] Fix for extract_target with dots | jansel | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143766
Fixes #143650
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @... | true |
2,756,584,605 | [inductor] Improve error message for assert_size_stride | jansel | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143765
```
>>> torch._C._dynamo.guards.assert_size_stride(torch.randn(10), (10,), (2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AssertionError: expected size 10==10, stride 1==2 at dim=0
This error mo... | true |
2,756,562,153 | [dynamo] Add test for #143697 | jansel | 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):
* __->__ #143764
The issue from #143697 seems to already be fixed.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,756,551,140 | [BE] Only print MKL version on x86 platforms | malfet | closed | [
"Merged",
"ciflow/trunk",
"release notes: python_frontend",
"topic: docs"
] | 4 | CONTRIBUTOR | As it will obviously be missing on ARM/S390, etc
Test plan: run `python3 -c "import torch;print(torch.__config__.parallel_info())"` on both x86 and non-x86 system | true |
2,756,546,032 | [inductor] Make adaptive_max_pool2d error on int64 | jansel | closed | [
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143762
Fixes #143752
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @... | true |
2,756,442,904 | [BE]: Properly forward raise pickle exception with from | Skylion007 | closed | [
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"release notes: package/deploy",
"topic: not user facing"
] | 3 | COLLABORATOR | Properly raises the pickle exception with from. Provides a more informative stack trace and forwards information about the exception that led to the current exception. | true |
2,756,380,932 | [DTensor] Add strategy for _scaled_mm | lw | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (dtensor)"
] | 13 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143760
This is done by copying the one for a regular mm, and enforcing that the scales have the same sharding scheme as their respective operands. This works because scales are 2-d tensors that must "broadcast" to the operands. This ... | true |
2,756,340,120 | compiled autograd tests should use expecttest | zou3519 | open | [
"module: tests",
"triaged",
"enhancement",
"oncall: pt2",
"module: compiled autograd"
] | 0 | CONTRIBUTOR | The current expected_logs mechanism make it difficult to see what is going on. If there's an error then it looks like the following:
The nice thing about expecttest is that it tells me what the expected output lines look lik... | true |
2,756,272,487 | Inconsistent results between F.linear and manual computation | eliahuhorwitz | closed | [
"module: numerical-stability",
"module: nn"
] | 1 | NONE | ### 🐛 Describe the bug
I am observing an inconsistency between the results of F.linear and the manual computation of xW^T+b.
Below is a snipped that reproduces this (I ran it on a CPU, and on float16, float32, and float64):
```python
import torch
from torch import nn
from torch.nn import functional as F
... | true |
2,756,145,240 | [don't merge] disable xpu env installation. | xuhancn | closed | [
"open source",
"ciflow/binaries",
"topic: not user facing"
] | 1 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,756,139,055 | `self.__dict__[...] = ...` produces a graph break | akihironitta | closed | [
"triaged",
"oncall: pt2",
"module: dynamo",
"dynamo-triage-jan2025"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
In https://github.com/pyg-team/pytorch_geometric/issues/9879, the issue author tries to create a `torch_geometric.data.Data` ([docs](https://pytorch-geometric.readthedocs.io/en/stable/generated/torch_geometric.data.Data.html)) in a region, however, it leads to a graph break on nightly.
Here'... | true |
2,756,112,478 | [CompiledAutograd] No implicit dtype cast as expected | mieshkiwrk | closed | [
"triaged",
"oncall: pt2",
"module: compiled autograd"
] | 2 | NONE | ### 🐛 Describe the bug
Unfortunately I don't have simple reproducer for now, trying to get one but without success so far (also pytorch minifier ends up with error).
Problem observed is that given model when run with eager performs implicit dtype cast from fp32 to bf16
```
THPEngine_run_backward
-> PythonEn... | true |
2,756,071,215 | nn.LayerNorm is slower than naive implementation when dimension is low | qwertyforce | open | [
"module: performance",
"module: nn",
"triaged",
"module: norms and normalization"
] | 0 | NONE | ### 🐛 Describe the bug
```python
import torch
import torch.nn as nn
import time
import matplotlib.pyplot as plt
from tqdm import tqdm
class ElementwiseLayerNorm(nn.Module):
def __init__(self, dim, eps=1e-5, elementwise_affine=True):
super(ElementwiseLayerNorm, self).__init__()
self.ep... | true |
2,756,025,579 | [BE][CI] bump `ruff` to 0.8.4 | XuehaiPan | closed | [
"oncall: distributed",
"module: cpu",
"module: lint",
"module: mkldnn",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (ddp)",
"topic: not user facing",
"fx",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"ciflow/linux-aarch64"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143753
Changes:
1. Bump `ruff` from 0.7.4 to 0.8.4
2. Change `%`-formatted strings to f-string
3. Change arguments with the `__`-prefix to positional-only arguments with the `/` separator in function signature.
cc @H-Huang @... | true |
2,756,012,663 | [eager] [inductor] `AdaptiveMaxPool1d` (`AdaptiveMaxPool2d`) behave differently on eager and inductor when meeting internal int64 dtypes | shaoyuyoung | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
I think it's a problem with eager's internal processing mechanism. inductor works well for **implicit type conversion**, but unfortunately, eager will raise an error (although the external input looks fine because I used fp32 as the external input).
However, to be honest, I am not sure wha... | true |
2,755,869,396 | [ONNX] exported model for Phi-2 is wrong before optimization and correct after | xadupre | closed | [
"module: onnx",
"triaged"
] | 2 | COLLABORATOR | ### 🐛 Describe the bug
If ``ep.optimize()`` is not run, the exporter model for Phi 2 is wrong.
```
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Load model from dump_bash_bench/Phi2LM_2Layer-onnx_dynamo-cpu-float16-d1rt1/model_Phi2LM_2Layer-onnx_dynamo-d1rt1.onnx failed:Node (n... | true |
2,755,814,475 | Update TorchDynamo-based ONNX Exporter example code. | fatcat-z | closed | [
"oncall: distributed",
"module: onnx",
"module: cpu",
"triaged",
"module: mkldnn",
"open source",
"ciflow/trunk",
"release notes: onnx",
"release notes: quantization",
"topic: docs",
"ciflow/mps",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"ciflow/linux-aarch64"
] | 10 | COLLABORATOR | Address comments earlier.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @gujinghui @PenghuiCheng @jianyuh @min-jean-cho @yanbing-j @Guobing-Chen @Xia-Weiwen @snadampal @voznesenskym @penguinwu @EikanWang @zhuhaoz... | true |
2,755,789,505 | Update module.py as per #142306 | grussdorian | closed | [
"triaged",
"open source",
"Stale",
"release notes: fx"
] | 6 | NONE | release notes: fx Issue #142306
Minor work in Improve typing of args and kwargs with ParamSpec
**register_forward_hook**
Key changes:
1. Replace Any with Input/Output type vars for inputs/outputs
2. Ensure the output type of the hook matches its input type
3. Keep the Dict[s... | true |
2,755,740,754 | [DTensor]`Linear` fails on 3D DTensor with `batch size > 1` and `Replicate` input redistributed from `Shard` | FindDefinition | open | [
"oncall: distributed",
"module: dtensor"
] | 1 | NONE | ### 🐛 Describe the bug
`Linear` fails on 3D DTensor with `batch size > 1` and Replicate input from shard (not divisible by TP size).
* Error Message
```
[rank3]: Traceback (most recent call last):
[rank3]: File "/path/to/pytorch_bug/linear_bug.py", line 34, in <module>
[rank3]: mod(x_dt)
[rank3]: ... | true |
2,755,560,485 | [DTensor] Add aten.amin/amax to linear_reduction_strategy | lw | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (dtensor)"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143747
In the same vein as https://github.com/pytorch/pytorch/pull/134206, these two ops still seemed missing.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,755,552,013 | [Inductor][CPP] Fix Data Type issue of frexp | leslie-fang-intel | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143746
**Summary**
Fix issue: https://github.com/pytorch/pytorch/issues/143729. `frexp` has 1 input but 2 output tensor with different data type, current `deduce_dtype_for_cpp_cse_variable` can't deduce the data type for each outpu... | true |
2,755,426,592 | Update slow tests | pytorchupdatebot | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/slow",
"ci-no-td"
] | 6 | COLLABORATOR | This PR is auto-generated weekly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/weekly.yml).
Update the list of slow tests. | true |
2,755,409,160 | [don't merge] use vs2019 build xpu | xuhancn | closed | [
"open source",
"ciflow/binaries",
"topic: not user facing"
] | 1 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,755,382,921 | Enable onednn in pytorch for ppc64le architecture | Tiwari-Avanish | closed | [
"module: cpu",
"triaged",
"open source",
"Merged",
"Reverted",
"release notes: quantization",
"release notes: build",
"topic: improvements",
"ci-no-td"
] | 39 | CONTRIBUTOR | This PR will enable onednn for powerpc Architecture which will help to do quantization of the model via onednn for powerpc.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,755,322,641 | [Export] fake mode mismatch error inside `export_for_training` with multiple kwargs | Xia-Weiwen | closed | [
"oncall: pt2",
"oncall: export"
] | 3 | COLLABORATOR | ### 🐛 Describe the bug
Repro:
```python
import torch
from torch.export import export_for_training
from transformers import AlbertTokenizer, AlbertModel
print("[info] load model")
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
model = AlbertModel.from_pretrained("albert-base-v1")
model = model... | true |
2,755,308,374 | Enable SVE ACLE implementation for tanH Aten op for FP32 dType. | maajidkhann | closed | [
"module: cpu",
"triaged",
"open source",
"module: arm",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/linux-aarch64"
] | 40 | CONTRIBUTOR | In deep learning models, the tanh (hyperbolic tangent) function is a widely used activation function, primarily in feedforward networks, recurrent neural networks (RNNs), and various other architectures.
Also, the tanh (hyperbolic tangent) function is commonly used in **Physics-Informed Neural Networks (PINNs).** PI... | true |
2,755,290,274 | Enable fx_quantization for arm | choudhary-devang | closed | [
"module: cpu",
"triaged",
"open source",
"module: arm",
"release notes: quantization",
"topic: not user facing"
] | 9 | NONE | FX Graph Mode Quantization (https://pytorch.org/docs/stable/quantization.html) is an automated quantization workflow in PyTorch and It improves upon Eager Mode Quantization by adding support for functionals and automating the quantization process.
Currently, this flow is enabled for CPU's only on x86 platforms.
*... | true |
2,755,271,047 | Modify the tolerance level in TIMM benchmark for XPU PreCI | xytintel | open | [
"triaged",
"open source",
"Stale",
"module: dynamo"
] | 5 | CONTRIBUTOR | cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,755,193,049 | [inductor] [cpu] [silent] `avg_pool2d` incorrectly process int64 | shaoyuyoung | closed | [
"triaged",
"oncall: pt2",
"oncall: cpu inductor"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
I think this is related to #143729 but the symptom is different.
in #143729, CPU inductor raises `compileError` but this time, avg_pool2d outputs a silent incorrectness.
Should this be a **hig-pri**?
BTW, cuda would reject the Long dtype.
exposed area: `avg_pool1d`, `avg_pool2d` and `... | true |
2,755,149,145 | Enable FSDP2 on XPU device | zhangxiaoli73 | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)"
] | 7 | CONTRIBUTOR | **Motivation:** Enabling FSDP2 on XPU device.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @gujinghui @jgong5 @guangyey | true |
2,755,143,999 | Add torch.topk indices vary description | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: python_frontend"
] | 3 | CONTRIBUTOR | Fixes #133542
**Test Result**
**Before**
**After**
| true |
2,755,133,648 | Enable coalescing path on XPU and dispatch to XPU tensor barrier if XCCL backend is specified. | zhangxiaoli73 | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 10 | CONTRIBUTOR | **Motivation:**
- Enable coalescing path on XPU for `batch_isend_irecv`.
- If XCCL backend is specified, then construct a XPU tensor to ensure `barrier` dispatch to XCCL backend.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @gujinghui @jgong5 @guangyey | true |
2,755,119,487 | [ROCm] [APU] Incorrect call of HIP mem outage | KISSEsWHISPERsFEEtBACKHUGs | open | [
"module: rocm",
"triaged"
] | 3 | NONE | ```
HSA_OVERRIDE_GFX_VERSION=9.0.0 \
CL_DEVICE_GLOBAL_FREE_MEMORY_AMD=24396768 \
CL_DEVICE_GLOBAL_MEM_SIZE=25189056512 \
CL_DEVICE_MAX_MEM_ALLOC_SIZE=21410698032 \
PYTORCH_HIP_MEM_ALLOC=strict PYTORCH_NO_HIP_MEMORY_CACHING=1 AMD_SERIALIZE_KERNEL=3 TORCH_USE_HIP_DSA=1 \
HSA_... | true |
2,755,094,414 | [CI] enable operator benchmark on CPU | LifengWang | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: releng",
"skip-pr-sanity-checks",
"ciflow/op-benchmark"
] | 25 | CONTRIBUTOR | This is to enable operator benchmark for CPU to track op level performance. This PR is motivated by PR: https://github.com/pytorch/pytorch/issues/120982 and investigate feasibility in https://github.com/pytorch/pytorch/pull/127216
cc @albanD | true |
2,755,068,410 | [export]`torch.export(strict=False)` produce wrong program when provide kwargs with arbitrary order | FindDefinition | closed | [
"oncall: pt2",
"oncall: export"
] | 2 | NONE | ### 🐛 Describe the bug
torch.export produce wrong program when we use kwargs that have different order with `forward` signature and `strict=False`.
* Reproduce Code
```Python
import torch
class TestKwMod(torch.nn.Module):
def __init__(self):
super().__init__()
self.layer1 = torch.nn.Li... | true |
2,755,054,593 | [Easy] Add torch.range, torch.arange params optional description | zeshengzong | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: python_frontend"
] | 12 | CONTRIBUTOR | Fixes #129333
**Test Result**
**Before**
**After**
![image](https://github.com/user-attachments/assets/34e2c41f-8b54-417... | true |
2,755,046,035 | Apply clang-format for ATen/core/op_registration headers | zeshengzong | closed | [
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Code change via add path config in `.lintrunner.toml` file and running
```bash
$ lintrunner -a --take CLANGFORMAT --all-files
```
cc @malfet | true |
2,755,023,386 | [inductor] [cpu] [CppCompileError] inductor can't pass the check for multiplication of different dtypes of tensor | shaoyuyoung | closed | [
"triaged",
"oncall: pt2",
"oncall: cpu inductor"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
In this situation, the first return value for `torch.frexp` is **int32** and the second is **float32**.
When these two elements are multiplied, the inductor raises the **CppCompileError** while eager passes the check and outputs the correct result.
Interestingly, The CPU backend will reject... | true |
2,754,863,156 | [ROCm] PyTorch multiprocess fails to create memory with IPC | GZGavinZhao | closed | [
"needs reproduction",
"module: multiprocessing",
"module: rocm",
"triaged"
] | 12 | NONE | ### 🐛 Describe the bug
Run the `mnist_hogwild` example from pytorch/examples@1bef748fab064e2fc3beddcbda60fd51cb9612d2 (current HEAD) using the command `python3 main.py --cuda`, I get the following error:
```
Traceback (most recent call last):
File "/home/gavinzhao/CS/ML/examples/mnist_hogwild/main.py", line 96... | true |
2,754,831,511 | [Codemod][AddExplicitStrictExportArg] Update export test harness | gmagogsfm | closed | [
"fb-exported",
"Stale",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Differential Revision: D67580336
| true |
2,754,810,418 | Adding support for differentiable lr, weight_decay, and betas in Adam/AdamW | EmmettBicker | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"release notes: optim"
] | 19 | CONTRIBUTOR | Third PR in a series of PRs to broaden differentiable optimizer support w/ @janeyx99 (sorry for pinging over the holidays! I just wanted to put this one out but I am definitely not asking for review or anything like that rn)
This is also going to probably be my last PR before the holidays!
Note: This is a branch ... | true |
2,754,807,064 | Better fix for f-strings in set_linter for py3.12 | jansel | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143725
#143628 didn't handle a few cases right for example:
```py
$ python3 tools/linter/adapters/set_linter.py torch/_inductor/scheduler.py
torch/_inductor/scheduler.py:261:24: Builtin `set` is deprecated
259 | ... | true |
2,754,782,528 | nn.MultiheadAttention string representation | jake-yukich | closed | [
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 6 | NONE | Fixes #143669
| true |
2,754,759,191 | Inductor Cutlass backend: Eliminate unused code. | kadeng | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Summary: Eliminates an unused file and some smaller unused code fragments from the inductor cutlass codebase.
Test Plan: CI
Differential Revision: D67579837
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @muchulee8 ... | true |
2,754,725,140 | [dynamo] Remove DICT_SUBCLASS_GUARD_MANAGER and use dict.keys | anijain2305 | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143698
* #143699
* __->__ #143722
In hinsight, we never needed a DICT_SUBCLASS_GUARD_MANAGER, because Dynamo would inline through the overridden keys method. In this PR, we ensure that while creating guards and constructing variable tracke... | true |
2,754,690,800 | add "enabled=True" to DistributedDataParallel.no_sync() | avihu111 | open | [
"oncall: distributed",
"module: ddp"
] | 4 | NONE | ### 🚀 The feature, motivation and pitch
Training a model with DDP and gradient accumulation is quite common.
To avoid unnecessary sync, the no_sync() operation is used.
Providing an `enabled=True` argument is already done in pytorch, and is very useful in pytorch in `torch.amp.autocast` and `torch.amp.GradScaler`... | true |
2,754,621,034 | [inductor][gpu] torch.nn.functional.avg_pool1d outputs incorrect result when input.numel() is 1 | maybeLee | closed | [
"module: nn",
"triaged",
"oncall: pt2",
"module: inductor"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
This issue is similar to my previous one (https://github.com/pytorch/pytorch/issues/143719).
When the `input` argument contains only one element, torch.nn.functional.avg_pool1d will output incorrect result.
Here is the code to reproduce:
```
import torch
@torch.compile
def avg_pool1... | true |
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