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 |
|---|---|---|---|---|---|---|---|---|
2,891,606,244 | DISABLED test_mark_unbacked_strict (__main__.MiscTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamic shapes"
] | 4 | NONE | Platforms: asan, linux, mac, macos, rocm, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_mark_unbacked_strict&suite=MiscTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/38092982283).
Over the ... | true |
2,891,606,243 | DISABLED test_export_defaults_ok_dynamic_shapes (__main__.DynamicShapesExportTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 5 | NONE | Platforms: asan, linux, rocm, slow, mac, macos
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_export_defaults_ok_dynamic_shapes&suite=DynamicShapesExportTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch... | true |
2,891,605,755 | DISABLED test_sys_modules_dynamic_shapes (__main__.DynamicShapesMiscTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 4 | NONE | Platforms: mac, macos, linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_sys_modules_dynamic_shapes&suite=DynamicShapesMiscTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/38097103660).
... | true |
2,891,553,598 | [AOTI][torchbench] microbench_unbacked_tolist_sum fails | desertfire | open | [
"triaged",
"oncall: pt2",
"export-triaged",
"oncall: export",
"module: aotinductor"
] | 0 | CONTRIBUTOR | Repro:
```
python benchmarks/dynamo/torchbench.py --accuracy --inference --bfloat16 --export-a
ot-inductor --disable-cudagraphs --device cuda --only microbench_unbacked_tolist_sum
```
Error:
```
RuntimeError: Failed to run autotuning code block: An exception occurred in a subprocess:
...
RecursionError: maximum recursi... | true |
2,891,440,632 | [pytree] simplify public API exposition with `__module__` | XuehaiPan | open | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: pytree",
"module: dynamo",
"ciflow/inductor",
"ci-test-showlocals"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148328
* #148180
* #137400
* #152624
Before this PR, the following statements are already available:
```python
import torch
import torch.utils.pytree.python
from torch.utils.pytree import python
from torch.utils.pytree impo... | true |
2,891,417,016 | [ROCm] Unskip flex attention UTs after triton 3.3 bump | AmdSampsa | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm",
"ciflow/inductor-rocm"
] | 11 | COLLABORATOR | Enable `test_flex_attention.py::TestLearnableBiases` unit tests.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashok... | true |
2,891,378,626 | fx graph fails to recognize tensor.T as a 'call_method' node | XinyiYuan | closed | [
"triaged",
"module: fx",
"oncall: pt2"
] | 3 | NONE | ### 🐛 Describe the bug
I was trying to understand the mechanisms of fx graphs when i encountered this problem:
```python
import torch
class MyLinear(torch.nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = torch.nn.Parameter(torch.randn(out_features, in_f... | true |
2,891,127,941 | Create unique test report files for distributed tests | Flamefire | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | COLLABORATOR | The distributed tests are executed once for each backend and for each init method.
`$TEST_REPORT_SOURCE_OVERRIDE` is used such that test results from different backends are stored in different files.
The same needs to be done for the init method.
Move the setting of the variable into `test_distributed` and incorpo... | true |
2,891,100,890 | `torch.linalg` routines break for inputs of more than 2**32 elements | cybersupersoap | open | [
"triaged",
"module: linear algebra",
"topic: fuzzer"
] | 4 | NONE | ### 🐛 Describe the bug
I have packed six `INTERNAL ASSERT FAILED`, two `Segmentation fault`, and two `Floating point exception` into this issue. I am reporting these because the error message says 'please report a bug to PyTorch', or because they caused crashes.
A `INTERNAL ASSERT FAILED` at "/pytorch/aten/s... | true |
2,890,919,312 | [Windows][Inductor][XPU] Unload triton pyd files to be able to remove them on Windows. | etaf | 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):
* __->__ #148323
* #147727
* #148538
* #148534
In `fresh_inductor_cache` remove pyd files will raise permission error
on Windows because they are still used by the process.
So we clear the references to the loaded pyd libray obj and unload the... | true |
2,890,886,854 | Checking for cuda version to see if bf16 is natively supported or emulated | KennyStryker | open | [
"module: cuda",
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 5 | NONE | BF16 is only natively supported on Ampere architecture or higher with CUDA.
Previously, using `torch.cuda.is_bf16_supported()` would return `True` even on hardware such as the Nvidia RTX 2000 series, Nvidia Tesla T4, and other devices below capability 8.0 (Ampere). This was misleading because, starting from CUDA 10:... | true |
2,890,799,310 | Huge numerical precision error when `torch.tensor(3811, dtype=torch.float16)` | wangzhen0518 | closed | [
"module: bfloat16",
"module: half"
] | 1 | NONE | ### 🐛 Describe the bug
There is huge numerical precision error when initializing tensors with some value in torch.float16 and torch.bfloat16.
```python
import torch
torch.tensor(3811, dtype=torch.float16) # tensor(3812., dtype=torch.float16)
torch.tensor(3811, dtype=torch.bfloat16) # tensor(3808., dtype=torch.bfloat1... | true |
2,890,786,262 | [ATen][CUDA] Optimize 128 bit vectorization | Aidyn-A | closed | [
"module: cuda",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: cuda",
"ciflow/periodic",
"ciflow/binaries_wheel",
"module: core aten"
] | 11 | COLLABORATOR | Fixes #147376.
As per request: https://github.com/pytorch/pytorch/pull/145746#pullrequestreview-2642118301
This PR omits sm80 or older of using vec8 kernels due to long compilation and large binary size.
cc @ptrblck @msaroufim @eqy @jerryzh168 @manuelcandales @SherlockNoMad @angelayi | true |
2,890,768,223 | [CD] Upgrade xpu runtime pypi packages version and enable windows kineto again | chuanqi129 | closed | [
"open source",
"Merged",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 8 | COLLABORATOR | Fixes https://github.com/pytorch/pytorch/issues/145155
| true |
2,890,674,871 | [TEST][SPARSE] Simplify branching in test_cusparselt_backend | Aidyn-A | closed | [
"module: tests",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | COLLABORATOR | Due to introduction of CUDA versions, the branching becomes more complicated. This PR is proposed to simplify branching in `test_cusparselt_backend` in order to avoid checking each and every CUDA version.
cc @mruberry @ZainRizvi | true |
2,890,648,304 | compile SageAttention faing error C2872: “std” for latest torch nightly | NOFOX | open | [
"module: windows",
"triaged",
"module: regression",
"oncall: pt2"
] | 10 | NONE | ENV: Win11 ,VS2022, Torch:
print(torch.version)
2.7.0.dev20250302+cu128
print(torchvision.version)
0.22.0.dev20250302+cu128
print(torch.cuda.is_available())
True
print(torch.cuda.get_device_name(0))
NVIDIA GeForce RTX 5080
print(torch.cuda.get_device_capability(0))
(12, 0)
compile SageAttention faing error C2872: “std... | true |
2,890,533,255 | do not run `test_ck_blas_library` on cpu | oraluben | closed | [
"triaged",
"open source",
"Merged",
"topic: not user facing"
] | 10 | CONTRIBUTOR | Fix on non-rocm:
```
root@e01-tw-ue5g2g3sap6:~/pytorch/test# python test_linalg.py TestLinalgCPU.test_ck_blas_library_cpu
E
======================================================================
ERROR: test_ck_blas_library_cpu (__main__.TestLinalgCPU)
------------------------------------------------------------... | true |
2,890,423,982 | [Doc] [Win] libuv installation doc is not correct. | Stonepia | open | [
"module: windows",
"module: docs",
"triaged",
"topic: docs"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
The Windows build recipe requires the following steps when building PyTorch:
```Bash
conda install -c conda-forge libuv=1.39
```
This would throw the following error:
```Bash
>conda install -c conda-forge libuv=1.39 -y
Solving environment: failed
PackagesNotFoundError: The following packages... | true |
2,890,387,684 | The recorded step number in profiler is wrong | Qizhi697 | open | [
"oncall: profiler"
] | 0 | NONE | ### 🐛 Describe the bug
When wait=1, the profiler recorded fn[dispatch/conbine] 30/10=3 steps.
```python
schedule = torch.profiler.schedule(wait=1, warmup=1, active=2, repeat=0)
with torch.profiler.profile(activities=[torch.profiler.ProfilerActivity.CPU,torch.profiler.ProfilerActivity.CUDA], schedule=schedule) as pro... | true |
2,890,337,857 | [CD] Upgrade Windows xpu support package to 2025.0.1 for binary compression | chuanqi129 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/binaries_wheel",
"ciflow/xpu"
] | 6 | COLLABORATOR | The binary compression feature can reduce the size of the Torch XPU Windows wheel packages
| true |
2,890,327,920 | DISABLED test_int_shape_inplace_binops (__main__.MiscTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2"
] | 3 | NONE | Platforms: asan, linux, mac, macos, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_int_shape_inplace_binops&suite=MiscTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/38071508310).
Over the pa... | true |
2,890,325,688 | DISABLED test_empty_graph_nested_calls_fullgraph_True_dynamic_shapes (__main__.DynamicShapesReproTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamic shapes"
] | 5 | NONE | Platforms: asan, linux, slow, mac, macos
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_empty_graph_nested_calls_fullgraph_True_dynamic_shapes&suite=DynamicShapesReproTests&limit=100) and the most recent trunk [workflow logs](https://github.com/p... | true |
2,890,298,153 | ci: Switch manywheel build.sh to just use dev | seemethere | closed | [
"Merged",
"topic: not user facing"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143672
* #148419
* #148406
* __->__ #148310
To avoid annoying error message like:
> fatal: no tag exactly matches 'a6520c85bd85875b09f2c68e51622699d7d07595'
These were popping up when GITHUB_REF is not set so let's just assume
that if som... | true |
2,890,263,614 | DISABLED test_cond_autograd_zeros_unused_branch_complex_compile_mode_compile (__main__.TestControlFlow) | ankurneog | closed | [
"skipped"
] | 1 | CONTRIBUTOR | Platforms: rocm
This test was disabled because it is failing on main branch ([recent examples](https://torch-ci.com/failure?failureCaptures=%5B%22functorch%2Ftest_control_flow.py%3A%3ATestControlFlow%3A%3Atest_cond_autograd_zeros_unused_branch_complex_compile_mode_compile%22%5D)). | true |
2,890,263,065 | DISABLED test_cond_autograd_zeros_unused_branch_complex_compile_mode_compile (__main__.TestControlFlow) | ankurneog | closed | [
"skipped"
] | 1 | CONTRIBUTOR | Platforms: rocm
This test was disabled because it is failing on main branch ([recent examples](https://torch-ci.com/failure?failureCaptures=%5B%22functorch%2Ftest_control_flow.py%3A%3ATestControlFlow%3A%3Atest_cond_autograd_zeros_unused_branch_complex_compile_mode_compile%22%5D)). | true |
2,890,262,366 | batching rule for `aten::scatter_add_` | ZhongkuiMa | open | [
"triaged",
"enhancement",
"module: vmap",
"module: functorch"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
Hi Guys,
I'm a PhD student and working on a Pytorch project. Currently, I encountered the following warning.
> UserWarning: There is a performance drop because we have not yet implemented the batching rule for aten::scatter_add_. Please file us an issue on GitHub so that we c... | true |
2,890,181,352 | torch.vmap incompatibility with DLPack functions | BillHuang2001 | open | [
"triaged",
"module: vmap",
"module: dlpack",
"module: functorch"
] | 8 | NONE | ### 🐛 Describe the bug
Currently, torch.vmap does not work with DLPack functions, although it is expected to. When attempting to use torch.vmap with DLPack interop (e.g., between PyTorch and Numpy / JAX), the operation fails.
I tested this behavior with Numpy on CPU and with JAX on both CPU and GPU and none of the c... | true |
2,890,140,846 | Reland: [inductor] Simplify grid handling | jansel | closed | [
"module: rocm",
"fb-exported",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ciflow/mps",
"skip-pr-sanity-checks",
"module: inductor",
"ciflow/inductor",
"ciflow/xpu",
"ci-no-td",
"ciflow/inductor-rocm"
] | 29 | CONTRIBUTOR | Summary:
Relands D69965761 / https://github.com/pytorch/pytorch/pull/147583
Before this PR, calling a triton kernel would look like:
```py
kernel.run(a, b, xnumel, grid=grid(xnumel), stream=stream0)
```
where the `grid=` was passed as a callable (function closure) arg. This PR removes the grid arg:
```py
ker... | true |
2,890,094,891 | Optimize param `prepend` class reference `torch.nn.Module` | zeshengzong | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 13 | CONTRIBUTOR | Fixes #147696
## Changes
Change `prepend` description `torch.nn.modules.Module` to `torch.nn.Module`
## Test Result
### Before
### After
![image](https://github.com/user-attachments/assets/1d2a5708-62d1-428... | true |
2,890,028,288 | Better log message to update pr_time_benchmarks/expected_results.csv | jansel | closed | [
"Merged",
"Reverted",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148292
* #148288
* #148261
* #148260
* #148243
* __->__ #148303
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,890,011,725 | broadcast_object_list not release GPU | psc0606 | open | [
"oncall: distributed",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
This is a very simple code snippet. The master process broadcasts parameters to worker processes through a manual Gradio click callback. After the worker processes have received the parameters, the GPU utilization occupied by workers doesn't get released (showing 100% utilization constantly). W... | true |
2,890,009,812 | ```torch.as_strided``` negative stride SIGSEV fix when using ```torch.compile``` | AmalDevHaridevan | open | [
"triaged",
"open source",
"Stale",
"topic: not user facing"
] | 4 | NONE | Fixes #147100
```meta``` tensors (and by extension FakeTensors) are immune to ```TORCH_CHECK```. This was found by tracing the execution of ```_dispatch_impl ``` method of ``` FakeTensorMode ``` context manager.
This PR introduces special handling (just) for empty tensors, so that ```Dynamo``` can compile modules ... | true |
2,889,968,404 | `torch.Tensor.pinverse` can cause an `INTERNAL ASSERT FAILED` | cybersupersoap | closed | [] | 1 | NONE | ### 🐛 Describe the bug
A `INTERNAL ASSERT FAILED` will be raised when using `torch.Tensor.pinverse`
```python
import torch
_input_tensor = torch.rand(2**31, 3)
_output_tensor = torch.Tensor.pinverse(_input_tensor)
print('Input tensor: ', _input_tensor)
print('Output tensor: ', _output_tensor)
```
Error message:
```
R... | true |
2,889,965,919 | `torch.linalg.cond` can cause an `INTERNAL ASSERT FAILED` | cybersupersoap | closed | [] | 1 | NONE | ### 🐛 Describe the bug
A `INTERNAL ASSERT FAILED` will be raised when using `torch.linalg.cond`
```python
import torch
A = torch.rand(2**31, 3)
cond_A = torch.linalg.cond(A)
print('cond_A = ', cond_A)
```
Error message:
```
RuntimeError: false INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/native/BatchLinearAlgebr... | true |
2,889,963,878 | `torch.linalg.pinv` can cause an `INTERNAL ASSERT FAILED` | cybersupersoap | closed | [] | 1 | NONE | ### 🐛 Describe the bug
A `INTERNAL ASSERT FAILED` will be raised when using torch.linalg.pinv
```python
import torch
import torch
A = torch.randn((2**31, 3))
A_inv = torch.linalg.pinv(A)
```
Error message:
```
RuntimeError: false INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp":1604,... | true |
2,889,961,531 | `torch.nansum` can cause a `Segmentation fault (core dumped)` | cybersupersoap | closed | [] | 1 | NONE | ### 🐛 Describe the bug
A `Segmentation fault` will be raised when using torch.nansum
```python
import torch
import numpy as np
arg_1_tensor = torch.rand([2, 2], dtype=torch.float32)
arg_1 = arg_1_tensor.clone()
arg_2 = np.array(0)
res = torch.nansum(arg_1, dim=arg_2)
```
Error message:
```
Segmentation fault (core d... | true |
2,889,919,103 | DISABLED test_int_shape_binops (__main__.MiscTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 4 | NONE | Platforms: asan, linux, mac, macos, rocm
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_int_shape_binops&suite=MiscTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/38064942957).
Over the past 3 hou... | true |
2,889,918,544 | DISABLED test_dont_aggressively_write_assert_dynamic_shapes (__main__.DynamicShapesReproTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 5 | NONE | Platforms: asan, linux, rocm, mac, macos
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_dont_aggressively_write_assert_dynamic_shapes&suite=DynamicShapesReproTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/py... | true |
2,889,917,934 | Treat CUDA warnings as errors | cyyever | open | [
"oncall: distributed",
"open source",
"Stale",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | COLLABORATOR | Fixes #ISSUE_NUMBER
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 @chenyang78 @kadeng @chauhang @amjames | true |
2,889,910,605 | Upgrade submodule oneDNN to v3.7.1 | yanbing-j | closed | [
"module: mkldnn",
"open source",
"module: arm",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"intel",
"module: inductor",
"ciflow/inductor",
"ciflow/xpu",
"ci-no-td",
"ciflow/linux-aarch64"
] | 8 | COLLABORATOR | This PR is to upgrade submodule oneDNN to v3.7.1.
## Improvements
- Improved performance of convolution and matmul primitives on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids).
- Improved performance of int8 and fp32 forward convolution primitive on pro... | true |
2,889,875,266 | [fx] Optimize TracerBase.create_arg and Graph._gen_python_code | jansel | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148292
* #148288
* #148261
* #148260
* #148243
Before: 19502951 function calls (18702776 primitive calls) in 8.533 seconds
After: 16402551 function calls (15602452 primitive calls) in 7.701 seconds
cc @ezyang @SherlockNoMad @E... | true |
2,889,870,254 | Fix extra semicolon warning | cyyever | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 4 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,889,866,526 | [PoC] Add RECORD_FUNCTION calls for aoti shim layer wrappers | sanchitintel | closed | [
"open source",
"ciflow/trunk",
"ciflow/inductor"
] | 3 | COLLABORATOR | Ports @chunyuan-w's [work](https://github.com/pytorch/pytorch/commit/734f940f527a53bde1334b8a8819062c78029f2f#diff-b60511be1e7fafc2c45e7c0cb3e769ad48b2a1060a69759f58979ffc33b38a79) to main branch to ensure some ops would appear in Inductor-CPU profiling results.
SDPA op doesn't appear in profiling results with this ... | true |
2,889,840,486 | Triton Kernel Rejects NamedTupleVariable Arguments | cora-codes | open | [
"triaged",
"module: fx",
"oncall: pt2",
"module: dynamo",
"module: user triton"
] | 9 | NONE | ### 🚀 The feature, motivation and pitch
PyTorch's TorchDynamo fails when passing NamedTupleVariable to Triton kernels, raising "Unexpected argument type for a Triton kernel". It would be nice to support named tuple arguments since it makes writing Triton kernels far cleaner.
```python
import torch
import typing
impo... | true |
2,889,840,116 | [fx] Optimizations for node name generation | jansel | closed | [
"Merged",
"Reverted",
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148292
* __->__ #148288
* #148261
* #148260
* #148243
Before:
After:
![image](https://github.com/user-attachments/assets/44c6e578-c63e-4a43-b3e0... | true |
2,889,833,358 | [MPS] add slogdet and logdet implementations to mps | Isalia20 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: improvements",
"module: mps",
"release notes: mps",
"ciflow/mps"
] | 15 | COLLABORATOR | Low hanging fruits, all ops for these are implemented so just adding them to native functions adds the functionality on mps. Probably next op I should add should be lu solve seeing as how many ops need it for the grad calculation
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen | true |
2,889,814,194 | [BE][MPS] Use `copysign` for imaginary part of sqrt | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148286
* #148285
Also it's tempting trying to replace `a*a + b*b` with `dot(input[index])` but for some reason it results in a slightly different output | true |
2,889,800,275 | [MPS] Fix sqrt and other for `torch.chalf` | malfet | closed | [
"Merged",
"topic: bug fixes",
"release notes: mps",
"ciflow/mps"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148286
* __->__ #148285
Those kernels, instead of being instantiated for half2 (which corresponds to ComplexHalf) were instnatiated for short2, which resuled in the following test
```
% python3 -c "import torch; print(torch.rand(6, device='... | true |
2,889,677,479 | [BE] Fix extra semicolon warning | malfet | closed | [
"module: cpu",
"better-engineering",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Introduced by https://github.com/pytorch/pytorch/pull/146596
I.e. while building locally my log was littered with
```
In file included from /Users/malfet/git/pytorch/pytorch/aten/src/ATen/native/LossNLL2d.cpp:5:
In file included from /Users/malfet/git/pytorch/pytorch/aten/src/ATen/native/cpu/utils.h:5:
In file... | true |
2,889,469,310 | RuntimeError: use_libuv was requested but PyTorch was build without libuv support | jiangxinufo | open | [
"oncall: distributed",
"triaged"
] | 3 | NONE | ### 🐛 Describe the bug
RuntimeError: use_libuv was requested but PyTorch was build without libuv support
(llama_factory) PS F:\jx\LLaMA-Factory> llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
[INFO|2025-03-02 18:38:41] llamafactory.cli:157 >> Initializing distributed tasks at: 127... | true |
2,889,439,101 | [fx] reimplement `fx.map_aggregate` with pytree | XuehaiPan | closed | [
"open source",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148282
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,889,409,121 | [torch] Fix unsafe concurrent access to autocast_enabled | t-ivan-gr | closed | [
"oncall: jit",
"fb-exported",
"module: amp (automated mixed precision)",
"Merged",
"ciflow/trunk",
"release notes: jit"
] | 16 | CONTRIBUTOR | Summary: Making autocast_enabled atomic, as it can be accessed from multiple threads
Differential Revision: D70456813
cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @mcarilli @ptrblck @leslie-fang-intel | true |
2,889,384,326 | nn.Matmul return different ret within Parameter and Tensor | zhaozheng09 | closed | [] | 2 | NONE | ### 🐛 Describe the bug
```
import ast
import time
import torch
import re
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
os.environ['NVIDIA_TF32_OVERRIDE'] = '0'
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
os.environ["CUBLAS_WO... | true |
2,889,297,443 | [AOTI] Fix aot_inductor_package test errors | desertfire | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Summary: Fix fbcode test failures introduced by https://github.com/pytorch/pytorch/pull/147975. Make sure script.ld is copied to the build-time directory.
Differential Revision: D70454149
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy ... | true |
2,889,263,474 | INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/NamedTensorUtils.cpp":163, please report a bug to PyTorch | cybersupersoap | closed | [] | 1 | NONE | ### 🐛 Describe the bug
### 🐛 Describe the bug
A `INTERNAL ASSERT FAILED` will be raised when using `torch.tensor`
```python
import torch
tensor_names = []
x = torch.tensor([[1, 2, 3, 4], [4, 3, 2, 1]], dtype=torch.float32, names=tensor_names)
```
Error messages:
```
RuntimeError: !names.empty() INTERNAL ASSERT FA... | true |
2,889,263,463 | [MPS] Speedup interpolation | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148277
First of all, perf claims made in https://github.com/pytorch/pytorch/pull/145581 and https://github.com/pytorch/pytorch/pull/148154 are too good to be true (due to the bug in the script that did not call `torch.mps.synchron... | true |
2,889,261,995 | `torch.sparse.sum` can cause a `Segmentation fault (core dumped)` | cybersupersoap | closed | [] | 1 | NONE | ### 🐛 Describe the bug
### 🐛 Describe the bug
A `Floating point exception` will be raised when using `torch.sparse.sum`
```python
import torch
input = torch.sparse_coo_tensor(torch.tensor([[0, 1, -1], [2, 0, 2]]), torch.tensor([1, 2, 3]), torch.Size([3, 3]))
torch.sparse.sum(input, dim=-1)
```
Error messages:
```
... | true |
2,889,258,121 | `torch.nn.LazyConvTranspose1d` can cause a `Floating point exception (core dumped)` | cybersupersoap | closed | [] | 1 | NONE | ### 🐛 Describe the bug
A `Floating point exception` will be raised when using `torch.nn.LazyConvTranspose1d`
```python
try:
import torch
import numpy as np
input_data = torch.randn(3, 5, 7)
conv1d_transpose = torch.nn.LazyConvTranspose1d(3, 2, stride=2**31, padding=1) # Setting stride to an out-of-bo... | true |
2,889,208,308 | Updates to build rowwise scaled mm kernel on SM10.0a | danielvegamyhre | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"topic: build",
"module: inductor",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | ## Summary
Update cmake files and RowwiseScaledMM.cu to build on SM10.0a arch.
**NOTE**: performance optimization will be done in separate follow up PRs
## Steps to verify build
1. Access devgpu/machine with B200 GPUs, verify B200s are visible w/ `nvidia-smi`
2. Install CUDA tookit 12.8
- e.g. see [Nvidia... | true |
2,889,173,850 | SIGSEGV due to insufficient return value checking for PyFrame_GetLocals | thomasdullien | open | [
"needs reproduction",
"module: crash",
"triaged",
"module: python frontend"
] | 4 | NONE | ### 🐛 Describe the bug
I'm getting a SIGSEGV when running some Torch code locally. It appears to be a null pointer dereference caused by insufficient return value checking of PyFrame_GetLocals (which, starting from more recent Python versions, can in theory return NULL -- but all the code calling it blindly assumes i... | true |
2,889,166,293 | [MPS] metal unary kernel for sqrt | Isalia20 | closed | [
"open source",
"Merged",
"topic: performance",
"module: mps",
"release notes: mps",
"ciflow/mps"
] | 10 | COLLABORATOR | Issue #148219 highlighted the high dispatch times of ops which ran with MPS Graph on smaller tensors. This PR rewrites the sqrt with metal kernel to mitigate that issue
## Speedups:
Matrix size means NxN matrix here.
![speedup_sqrt](https://github.com/user-attachments/assets/db0a705b-1a0e-42b4-bd42-4e7960415c81... | true |
2,889,162,744 | Fix bug when Inductor include path contains spaces | vladkvit | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 14 | CONTRIBUTOR | This PR fixes a bug with how include directories with spaces are handled on Windows. I ran into an edge case with torch.compile() - it will error out with an exception on Windows. In particular, it will try to execute the following: `cl /I C:/Program Files/Python311/Include ...`, where `C:/Program` will be treated as s... | true |
2,889,141,383 | [Inductor] Hot fix after #148011 | anmyachev | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 5 | COLLABORATOR | cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @davidberard98
| true |
2,889,097,171 | Should DTensor support `Shard()` placement without dim requirement? | kwen2501 | open | [
"oncall: distributed",
"triaged",
"module: dtensor"
] | 21 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
`ShardStorage` is name for a placement which shards a tensor element-wise, based on the elements' storage order, without describing which dimension to shard the tensor on.
(Edit: I realized that `Shard()` without dim specification can be used to denote the same meaning.)
It m... | true |
2,889,093,221 | ONNX Export Produces main_graph Instead of torch_jit and Fails on aten::format in PyTorch 2.x | antoninononooono | closed | [
"module: onnx",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
Description:
I am trying to export a Transformer-based speech model to ONNX using PyTorch 2.x. However, I encountered two major issues:
Exporting results in main_graph instead of a proper JIT script model, making the exported model unusable.
The aten::format operator is not supported in ONNX o... | true |
2,889,093,188 | Build rowwise scaled mm CUDA kernel on SM10.0a (B200) | danielvegamyhre | closed | [
"oncall: distributed",
"topic: not user facing",
"topic: build"
] | 1 | CONTRIBUTOR | WIP - a bunch of formatting changes are getting included in this PR automatically, need to exclude those changes somehow.
## Summary
Update cmake files and RowwiseScaledMM.cu to build on SM10.0a arch.
## Steps to verify build
1. Access devgpu/machine with B200 GPUs, verify B200s are visible w/ `nvidia-smi`
2. ... | true |
2,889,086,715 | Fix dist.init_process_group on windows | H-Huang | closed | [
"oncall: distributed",
"module: windows",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148266
Fix https://github.com/pytorch/pytorch/issues/139990
We don't build libuv on windows so anything that creates `TCPStore` which includes `init_process_group()` will fail, which is a bad experience. We should just default ... | true |
2,889,055,307 | Fix macro for bit_cast in c10/util/bit_cast.h - one line change | wschin | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | COLLABORATOR | Fixes #148263.
| true |
2,889,052,828 | [BE][Ez]: Update fmt submodule to 11.1.4 | Skylion007 | closed | [
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | COLLABORATOR | This minor release is mostly bugfixes, ABI fixes, and compiler support fixes. | true |
2,889,046,820 | Wrong macro used when building c10/util/bit_cast.h with std::bit_cast | wschin | closed | [
"module: build",
"triaged",
"bug"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
When building PyTorch with `clang++-17 -std=gnu++20 -x c++`, the macro https://github.com/pytorch/pytorch/blob/d23051f29ba01d0b5a1da03ed1f023bfe643b640/c10/util/bit_cast.h#L6 decides to use `bit_cast` from standard C++ library. However, that version of `clang++-17` does NOT implement it, so a ... | true |
2,889,044,080 | Typo Errors fixed in multiple files | ENUMERA8OR | closed | [
"oncall: distributed",
"triaged",
"open source",
"Merged",
"release notes: mobile",
"module: dynamo",
"release notes: distributed (checkpoint)",
"module: compiled autograd",
"oncall: distributed checkpointing"
] | 6 | CONTRIBUTOR | # Fix typo errors across PyTorch codebase
This PR fixes various spelling errors throughout the PyTorch codebase to improve documentation quality and code readability.
## Changes Made
### Documentation Fixes
- Changed "seperate" to "separate" in multiple files:
- `setup.py`: Build system documentation
- ... | true |
2,889,042,542 | [fx] Move Node._prepend/Node._remove_from_list to C++ | jansel | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148292
* #148288
* __->__ #148261
* #148260
* #148243
Microbenchmarking `fx.symbolic_trace(lambda x: functools.reduce(operator.add, [x, *range(100000)]))`, before:
```
24303536 function calls (23503339 primitive calls) in 10.726 seconds
... | true |
2,889,042,516 | [fx] Move Node._update_args_kwargs to C++ | jansel | closed | [
"Merged",
"Reverted",
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148292
* #148288
* #148261
* __->__ #148260
* #148243
Microbenchmarking `fx.symbolic_trace(lambda x: functools.reduce(operator.add, [x, *range(100000)]))`, before:
```
25203549 function calls (24403352 primitive calls) in 12.090 seconds
... | true |
2,889,020,952 | Raise a warning when `torch.nn.utils.clip_grad_norm_` receives an exhausted generator | Orimalca | open | [
"module: nn",
"module: error checking",
"triaged",
"actionable"
] | 2 | NONE | The current clip_grad_norm_ and clip_grad_value_ functions accept an Iterable[Tensor] as input. However, if that iterable is a generator that has already been consumed, then it’s effectively empty when passed in, and no gradient clipping occurs—silently. This can cause subtle bugs when the user thinks they are clipping... | true |
2,888,998,456 | [BE]: No include left behind - recursive glob setuptools support | Skylion007 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: build",
"topic: improvements",
"topic: not user facing"
] | 9 | COLLABORATOR | Fixes #148256
TestPlan check the printout from the setup.py build and verify the files are still included. | true |
2,888,991,921 | [FSDP2] HSDP with globally sharded fp32 weights and optimizer states | ChrisLiu6 | open | [
"oncall: distributed",
"triaged",
"module: fsdp"
] | 5 | NONE | ### 🚀 The feature, motivation and pitch
First, I hope to show respect to the FSDP/FSDP2 team. I have been using FSDP for a long time and am recently working on migrating to FSDP2. I feel the new APIs are now more user-friendly and logically sound. Thanks so much for your efforts!
I am writing to inquiry if the follo... | true |
2,888,987,598 | Simplify package_data handling in setup.py | Skylion007 | closed | [
"module: build",
"triaged",
"enhancement"
] | 1 | COLLABORATOR | ### 🚀 The feature, motivation and pitch
As of setuptools v62.3.0, package_data field in setup.py now finally supports recursive glob. https://github.com/pypa/setuptools/blob/v62.3.0/CHANGES.rst This means we can include all the header files in our setup.py and won't run into issues when forgetting to include them or ... | true |
2,888,921,729 | Set requires grad in TensorMaker::make_tensor() | irshadcc | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: cpp"
] | 33 | CONTRIBUTOR | Fixes #146419
| true |
2,888,877,317 | [Inductor][CPP] Fix the vec codegen for tanh | 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):
* __->__ #148254
**Summary**
Fix https://github.com/pytorch/pytorch/issues/148241, The previous vectorized code generation for `tanh` used a decomposed implementation, leading to numerical differences that were further amplified by `atan2`. ... | true |
2,888,830,734 | Improve Notation for Score Function in Documentation | songyuc | open | [
"module: distributions",
"module: docs",
"triaged",
"actionable"
] | 1 | NONE | ### 📚 The doc issue
**Description:**
I noticed that the current PyTorch documentation on the distributions page (https://pytorch.org/docs/stable/distributions.html) presents the score function using the following formula:
$$
\Delta \theta = \alpha r\frac{\partial \log p\left(a|\pi^\theta(s)\right)}{\partial \theta}
... | true |
2,888,824,937 | Can't pass `strict=False` when loading a distributed checkpoint. Succeeds without warnings for "unexpected" keys, fails for "missing" keys. | baldassarreFe | closed | [
"triaged",
"oncall: distributed checkpointing"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
Goal: save a model with distributed checkpointer, then load it into a smaller model (drop the extra parameters) or into a bigger model (don't change the missing parameters).
With the default pytorch functions `state_dict()` and `load_state_dict()`, it's easy to pass `strict=False` and get a su... | true |
2,888,778,960 | Errors: train a model of sparsity with tensorrt-model-optimization and FSDP. | Vieeo | closed | [
"oncall: distributed",
"module: fsdp"
] | 3 | NONE | ### 🐛 Describe the bug
I’m training flux-dev model of sparsity with accelerate and FSDP.
This is FSDP config with accelerator.
distributed_type: FSDP
fsdp_config:
fsdp_auto_wrap_policy: SIZE_BASED_WRAP
fsdp_backward_prefetch: BACKWARD_PRE
fsdp_forward_prefetch: true
fsdp_min_num_params: 1000000
fsdp_offload_params: ... | true |
2,888,757,244 | BrokenPipeError: [Errno 32] Broken pipe when lacking Numpy package | Cookiee235 | open | [
"needs reproduction",
"triaged",
"oncall: pt2",
"module: dynamo"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
When the `Numpy` package is uninstalled, the torch will throw a warning message, but it crashes in the inference stage for the compiled model.
I do some experiments to isolate this bug:
* install the Numpy --> run well
* remove the `torch.compile` statement --> run well
```python
import tor... | true |
2,888,732,369 | Typo errors fixed in various files | ENUMERA8OR | closed | [
"oncall: distributed",
"open source",
"release notes: mobile",
"module: dynamo",
"module: compiled autograd"
] | 2 | CONTRIBUTOR | 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 @chenyang78 @kadeng @chauhang @amjames @LucasLLC @MeetVadakkanchery @mhorowitz @pradeepfn @ekr0 @xmfan | true |
2,888,727,774 | Typo Errors fixed | ENUMERA8OR | closed | [] | 2 | CONTRIBUTOR | null | true |
2,888,666,631 | `torch.multinomial` outputs inconsistency on ARM and x86 | Leo-Imperial | open | [
"module: distributions",
"triaged",
"module: random"
] | 7 | NONE | ### 🐛 Describe the bug
**Description:**
All tensors and generators are set up on the CPU, independent of specific devices. When experiments were conducted, running the code on NPU (ARM) and GPU (X86) produced differing results, and even within NPU (ARM) and NPU (X86), the outputs varied. However, running the same cod... | true |
2,888,642,417 | Remove unneeded Clang-tidy suppression | cyyever | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 7 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,888,598,000 | [PT2E x86 & Intel GPU] Collapse dim in qlinear_pointwise_binary fusion | ZhiweiYan-96 | closed | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/xpu"
] | 1 | COLLABORATOR | # Motivation
Currently, most of `qlinear+add` path would hit fusion `qlinear_pointwise_binary` with `sum` as post op. But it has not collapse the input dim when `dim>2`. This PR intends to trigger dimension collapse in qlinear_bianry for 3D linear cases.
Stack from [ghstack](https://github.com/ezyang/ghstack) (old... | true |
2,888,597,034 | [inductor] `nn.Upsample-torch.linalg.lu_factor` outputs inconsistent results with eager | shaoyuyoung | closed | [
"high priority",
"triaged",
"module: correctness (silent)",
"oncall: pt2",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom description**: `nn.Upsample-torch.linalg.lu_factor` outputs inconsistent results with eager. Note that trigger condition is `scale_factor>=2`.
**device backend**: both CPP and triton
**repro**
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch._ind... | true |
2,888,593,972 | [fx] Move map_aggregate to C++ | jansel | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148292
* #148288
* #148261
* #148260
* __->__ #148243
Microbenchmarking `fx.symbolic_trace(lambda x: functools.reduce(operator.add, [x, *range(100000)]))`, before:
```
30603618 function calls (29403419 primitive calls) in 13.744 seconds
... | true |
2,888,592,165 | [FSDP2] Unclear behavior of `ignored_params` in `fully_shard` | leonardo0lyj | closed | [
"oncall: distributed",
"module: docs",
"module: fsdp"
] | 2 | NONE | Hey Andrew @awgu, as a big fan of FSDP2, it is great to see [`ignored_params`](https://github.com/pytorch/pytorch/blob/6eff6b28e4d09cbf632f79502a8e317bf5b53c34/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L179) supported now 👍:
```python
"""
ignored_params: Optional(Set[nn.Parameter]): The set of pa... | true |
2,888,587,037 | [inductor] [cpu] `nn.Tanhshrink-atan2` output inconsistent results with eager | shaoyuyoung | closed | [
"oncall: pt2",
"oncall: cpu inductor"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
**symptom description**: when using `nn.Tanhshrink-atan2` together, output is inconsistent with eager.
**device backend**: only CPP
**repro**
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch._inductor import config
import os
config.fallback_random = True
... | true |
2,888,470,940 | handle jk for emulation runs | BoyueZheng | open | [
"fb-exported",
"Stale",
"module: inductor"
] | 8 | NONE | Summary:
seeing jk error for a platform which has no service network, https://www.internalfb.com/sandcastle/workflow/2260807012946333388/artifact/actionlog.2260807013059769273.stdout.1?selectedLines=1979-1980-1-1
so just fallback if JK is disabled
Test Plan: ez
Reviewed By: openrichardfb
Differential Revision: D70... | true |
2,888,448,720 | [MPS] Fix SDPA crash | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148239
If operation is invoked with mask twice it will crash, as mask expansion logic was implemented inside cache creation block, which is executed only once for all shapes
Fixes https://github.com/pytorch/pytorch/issues/148194 whi... | true |
2,888,411,504 | [ROCm][TunableOp] Add support for rowwise scaling on scaled GEMM. | naromero77amd | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: linalg_frontend",
"topic: not user facing",
"ciflow/rocm",
"ciflow/rocm-mi300"
] | 4 | COLLABORATOR | This PR adds support for rowwise scaling versus tensorwise scaling on scaled GEMM.
There are few other items included in this PR as well:
- Fixes for offline tuning of scaled GEMM
- Simplification of existing offline UT
- Update existing online UT to also test rowwise versus tensorwise scaled GEMM
- New UT for o... | true |
2,888,398,710 | Enable XPU for Inductor MM Triton Kernel Benchmark | EikanWang | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"keep-going",
"ciflow/xpu"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148237
#147620 enabled `force_shape_pad` for triton kernel benchmark. Intel GPU supports this scenario. Hence, we need to enable the case in this PR. Otherwise, there would be a test case regression for Intel GPU as #147620 has been... | true |
2,888,364,304 | [cutlass backend] try reenable subproc add mm test | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148236
* #148234
* #148233
* #148229
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,888,362,022 | Make require_contiguous require exact strides instead of stride order | eellison | open | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148235
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,888,346,646 | [cutlass backend] Expand addmm test to AOTI and dynamic shape | henrylhtsang | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148234
Fixed one problem with symint, enabled addmm for dynamic shape.
Not every case of addmm is supported yet. The case where bias has shape (N) is not supported yet, for some reason. My hunch is something wrong about stride.... | true |
2,888,334,274 | [cutlass backend] fix assertion that prevent self multiplication | henrylhtsang | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 13 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #148233
# Problem:
In a matmul, sometimes some of the nodes are the same. Say `A @ A`. In that case, when writing the stride of node B, we have to figure out if we want lda or ldb, which points to the same node, and we have no way t... | true |
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