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 |
|---|---|---|---|---|---|---|---|---|
2,954,343,638 | [Release-only] Pin intel-oneapi-dnnl to 2025.0.1-6 | chuanqi129 | closed | [
"open source",
"topic: not user facing",
"ciflow/xpu"
] | 1 | COLLABORATOR | To fix CI builds. Addresses https://github.com/pytorch/pytorch/issues/149995 for release/2.7 branch | true |
2,954,226,997 | [Easy/Profiler] Set Duration to -1 for unfinished CPU events | sraikund16 | closed | [
"enhancement",
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: profiler"
] | 4 | CONTRIBUTOR | Summary: Some OSS Kineto users were requesting that we allow for 0 duration events in Kineto even though they won't be seen on the trace. To allow this we changed the handling of said events in D71510383. However this causes unfinished events in collection to never be post processed; this diff fixes said issue.
Test P... | true |
2,954,223,980 | [PGNCCL][BE] Merge mutex into TensorShelf for encapsulation | kwen2501 | closed | [
"oncall: distributed",
"release notes: distributed (c10d)"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150130
* #150079
* #148590
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o
Differential Revision: [](https://our.internmc.facebook.com/intern/diff/) | true |
2,954,125,396 | Add one_shot_all_reduce_copy to allow non-symm-mem allocated tensors to be reduced | ngimel | closed | [
"oncall: distributed",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: distributed (c10d)",
"ci-no-td"
] | 12 | COLLABORATOR | Per title, we want to be able to use it even if inputs are not registered. Separate copy would add latency, and one-shot is all about the lowest possible latency.
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,954,098,470 | Revert "Parallelize sort" | ZainRizvi | closed | [] | 1 | CONTRIBUTOR | Reverts pytorch/pytorch#149765
Reverting because it breaks inductor tests. Details in https://github.com/pytorch/pytorch/pull/149505#issuecomment-2759082390 | true |
2,953,970,030 | [dynamic shapes] guard_or_false for _reshape_view_helper, utils._infer_size for wildcard dims | pianpwk | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: export",
"ci-no-td"
] | 32 | CONTRIBUTOR | For reshape/view: removes fast paths for 0 elements, checking dimensions to skip. Modifies the loop accumulating input elements, to raise a UserError if we run out of dimensions, graph breaking for compile and erroring out for export.
For infer_size: assumes if user passes us an unbacked, it's probably not -1
Will ... | true |
2,953,947,765 | update release 2.7 xla pin | zpcore | closed | [
"open source",
"release notes: releng"
] | 1 | CONTRIBUTOR | Fix the CI failure with outdated XLA pin. This mirrors the fix in https://github.com/pytorch/pytorch/pull/149381.
| true |
2,953,784,780 | [DO NOT MERGE] Tests runners enqueued forever | jeanschmidt | closed | [
"topic: not user facing"
] | 1 | CONTRIBUTOR | Test ci jobs that request a label that should keep enqueued for a very long time | true |
2,953,780,491 | torch.unique undocumented behaviour | zurkin1 | open | [
"triaged",
"module: python frontend"
] | 6 | NONE | ### 📚 The doc issue
https://pytorch.org/docs/stable/generated/torch.unique.html
The documentation page is wrong and gives unclear explanation for torch.unique (which affects torch.unique_consecutive as well). Using the example from this page:
a = torch.tensor([[[1, 1, 0, 0],[1, 1, 0, 0],[0, 0, 1, 1],],[[0, 0, 1, 1]... | true |
2,953,754,611 | Add flag for source hash symbol allocation | bobrenjc93 | closed | [
"release notes: fx",
"fx",
"module: dynamo",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150123
https://github.com/pytorch/pytorch/pull/149665 is quite difficult to land so let's do it step by step. Let's land the flag first. | true |
2,953,743,321 | [pytorch][triton] Warp specialization support in TritonTemplate for torchinductor (#148503) | mandroid6 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 11 | CONTRIBUTOR | Summary:
Currently only `num_warps` and `num_stages` are supported as one of the kernel options for inductor auto-tuning using `TritonTemplate`.
In order to allow warp-specialization kernel options should allow specifying `num_consumer_groups` and `num_buffers_warp_spec` as well.
NOTE: Currently gating changes... | true |
2,953,535,553 | torch.compile on MPS progress tracker | malfet | open | [
"triaged",
"module: mps"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
`torch.compile` support for MPS device is an early prototype and attempt to use it to accelerate end-to-end network is likely to fail. This issue is used to highlight known problems and track progress towards tentative beta status for 2.8.0 release
- [x] multi-stage well ford reductions are no... | true |
2,953,504,191 | DISABLED test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_bool (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 8 | NONE | Platforms: linux, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_bool&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/39505122... | true |
2,953,504,057 | DISABLED test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_bfloat16 (__main__.TestForeachCUDA) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"module: mta"
] | 9 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_foreach_copy_with_multi_dtypes__foreach_copy_cuda_bfloat16&suite=TestForeachCUDA&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/3951257419... | true |
2,953,404,418 | PyTorch 2.6 License Issues | AWilcke | open | [
"oncall: releng",
"triaged",
"module: third_party"
] | 4 | NONE | Our scanner detected these licenses in the torch-2.6.0.dist-info/LICENSE file:
third_party/kineto/libkineto/third_party/dynolog/third_party/cpr/test/LICENSE - under GPL-3.0
Bison implementation for Yacc-like parsers in C - under LGPL-3.0 (with a linking exception)
an NVIDIA license and a GPL-3.0 license ... | true |
2,953,136,233 | Fix typo | hotdog123what321 | closed | [
"open source",
"topic: not user facing"
] | 6 | NONE | Fixes #ISSUE_NUMBER
| true |
2,953,103,956 | S390x: update more tests | AlekseiNikiforovIBM | open | [
"module: cpu",
"triaged",
"open source",
"ciflow/trunk",
"release notes: quantization",
"release notes: releng",
"fx",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"ciflow/s390"
] | 4 | COLLABORATOR | Enable more tests on s390x.
Fix a couple of s390x-specific issues.
Mark more tests as failing or skipped on s390x.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @jerryzh168 @ezyang @SherlockNoMad @EikanWang @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @zhuhaozhe @blzheng @jiayisunx @i... | true |
2,953,054,732 | [BE] Suppress user_warnings while running opinfo tests | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150115
Some of the samples are constructed in a way that are expected to trigger those, but what's the point displaying them | true |
2,953,028,619 | [Miscompilation] inductor produce inconsistent inference results with the eager mode | Cookiee235 | closed | [
"high priority",
"triaged",
"oncall: pt2",
"module: inductor",
"oncall: cpu inductor"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
Eager produced `37269600` while inductor produced `inf` (> 3.4e38). They have a significant difference.
```python
import torch
class SimpleModel(torch.nn.Module):
def forward(self, x):
x = torch.arctan(x)
x = torch.linalg.cond(x)
return x
model = SimpleModel()
in... | true |
2,952,988,954 | [inductor] Significant difference produced when compile the model resnet18 | Cookiee235 | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
```python
import torch
inputs = torch.randn(1, 3, 224, 224, device='cuda')
model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
model = model.cuda()
model.eval()
with torch.no_grad():
res = model(inputs)
compiled_model = torch.compile(model, backend='inductor')
wi... | true |
2,952,949,345 | [Dynamo] Fix `dict.items()` return type | shink | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 22 | CONTRIBUTOR | Fixes #150110
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,952,944,439 | Use constants.onnx default opset for export compat | novikov-alexander | open | [
"triaged",
"open source",
"release notes: onnx"
] | 6 | NONE | The current rules for opsets are confusing, and the comments associated with them are outdated. This is particularly problematic for dynamo export, where the opset is hardcoded to version 18. To improve clarity and maintainability, it would be beneficial to use global constants wherever possible. | true |
2,952,925,974 | [Dynamo] `dict.items()` returns a tuple instead of `dict_items` obj | shink | closed | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Repro:
```python
def repro():
def fn():
d = dict({"a": 1, "b": "2", "c": torch.tensor(3)})
return d.items()
opt_fn = torch.compile(fn, backend="eager", fullgraph=True)
ref = fn()
res = opt_fn()
print(f"Eager: {ref}")
print(f"Dynamo: {res}")
```
Will g... | true |
2,952,898,235 | Feature universe -# 添加宇宙同构优化模块 / Add Cosmic Isomorphism Optimization Module | loning | closed | [
"open source"
] | 4 | NONE | # 添加宇宙同构优化模块 / Add Cosmic Isomorphism Optimization Module
## 概述 / Overview
本PR基于量子经典二元论和宇宙同构原理实现了一组PyTorch优化器和模型优化组件，将理论物理概念应用于深度学习优化。
This PR implements a set of PyTorch optimizers and model optimization components based on quantum-classical dualism and cosmic isomorphism principles, applying theoretical phys... | true |
2,952,715,250 | More revert | jamesjwu | closed | [
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"keep-going"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150108
* #150107
* #149054
* #149657
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,952,696,923 | [not for commit] Revert some parts of previous diff | jamesjwu | closed | [
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"keep-going"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150108
* __->__ #150107
* #149054
* #149657
I'm going crazy debugging a test timeout, testing if reverting parts of my stack cause the timeout to disappear
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @z... | true |
2,952,601,466 | Add option to define OpenBLAS version for manylinux Dockerfile_2_28_aarch64 | davsva01 | open | [
"triaged",
"open source",
"topic: not user facing"
] | 1 | NONE | Adds optional variable OPENBLAS_VERSION to `.ci/docker/common/install_openblas.sh` used to define which version of OpenBLAS to install. Adds argument to `Dockerfile_2_28_aarch64` image.
| true |
2,952,357,794 | [cherry-pick] [Submodule] [cpuinfo] cpuinfo update (#149305) | ozanMSFT | open | [
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 1 | COLLABORATOR | (cherry picked from commit ce54c430c0e9d5e6e9ee0b1d85bddd04fbcbca4e)
(PR: #149305 )
---
Updating `cpuinfo` module.
Relevant:
https://github.com/pytorch/cpuinfo/issues/270
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149305 Approved by: https://github.com/malfet
| true |
2,952,277,524 | multidimensional slicing | avikchaudhuri | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 9 | CONTRIBUTOR | Differential Revision: D71962884
Fixes #150057
| true |
2,952,272,936 | fix range constraints for expr | avikchaudhuri | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 5 | CONTRIBUTOR | During tracing it is possible for a `s1: VR[2, inf]` to be replaced by a `s0: VR[3, inf]` (note smaller range) by the shape env. But after export, unfortunately we'd previously record `range_constraints[s0] = VR[2, inf]` (note larger range), which is incorrect.
This is because we'd map `s1.node.expr` (`s0`) to the ... | true |
2,952,267,011 | Adding call to RecordCCall such that the PyCCall Events are inserted into the queue. This ensures that the profiling doesn't break with 'with_stack' flag set. | arjun-choudhry | closed | [
"triaged",
"open source",
"ciflow/trunk",
"release notes: profiler",
"topic: bug fixes"
] | 9 | NONE | Created in leiu of #148958.
Closes #136817 , #101632
| true |
2,952,149,923 | [RLEASE ONLY CHANGES] Apply release only chnages to release 2.7 (#149056) | etaf | closed | [
"module: rocm",
"open source",
"release notes: releng",
"ciflow/inductor"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* (to be filled)
* [RLEASE ONLY CHANGES] Apply release only chnages to release 2.7
* fix_lint_workflow
* docker_release
* fix_check_binary
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayan... | true |
2,952,148,746 | [RLEASE ONLY CHANGES] Apply release only chnages to release 2.7 (#149056) | etaf | closed | [
"module: rocm",
"release notes: releng",
"ciflow/inductor"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* (to be filled)
* [RLEASE ONLY CHANGES] Apply release only chnages to release 2.7
* fix_lint_workflow
* docker_release
* fix_check_binary
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayan... | true |
2,952,125,630 | DISABLED test_comprehensive_fft_irfftn_cuda_float16 (__main__.TestInductorOpInfoCUDA) | IvanKobzarev | closed | [
"triaged",
"skipped",
"oncall: pt2"
] | 2 | CONTRIBUTOR | Platforms: <fill this in or delete. Valid labels are: asan, linux, mac, macos, rocm, win, windows.>
This test was disabled because it is failing on main branch ([recent examples](https://torch-ci.com/failure?failureCaptures=%5B%22inductor%2Ftest_torchinductor_opinfo.py%3A%3ATestInductorOpInfoCUDA%3A%3Atest_comprehensi... | true |
2,951,986,576 | Aborted (core dumped) | Cookiee235 | open | [
"module: crash",
"module: cuda",
"module: error checking",
"triaged"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
```python
import torch
cuda_tensor = torch.tensor([1.0, 2.0, 3.0], device='cuda')
mem_ptr = cuda_tensor.data_ptr()
torch.cuda.caching_allocator_delete(mem_ptr)
```
Aborted (core dumped)
### Versions
PyTorch version: 2.7.0.dev20250308+cu126
Is debug build: False
CUDA used to build PyTorch... | true |
2,951,985,930 | Fix `L1Loss`, `MSELoss`, `HuberLoss` missing `weight` param | zeshengzong | open | [
"triaged",
"open source",
"release notes: nn"
] | 5 | CONTRIBUTOR | Fixes #149841
## Changes
- Add missing `weight` param for `L1Loss`, `MSELoss`, `HuberLoss`
- Add doc description
- Add weight test case
## Test Result
![image](https://github.com/user-attachments/assets/4b4b375e... | true |
2,951,965,711 | FSDP OOM during `sync_params_and_buffers` | KimmiShi | open | [
"oncall: distributed",
"triaged",
"module: fsdp"
] | 7 | NONE | ### 🐛 Describe the bug
I am trying to use FSDP to infer a 72B model, and to avoid CPU OOM I only load weights on rank-0, and then FSDP broadcast param to other ranks. This works well with a 7B param model, however when I try this script on a 72B model (on 16 A100s) I got GPU OOM.
```python
import torch
import torch.d... | true |
2,951,952,614 | Remove torch XPU ABI=0 build logic for old compiler | guangyey | open | [
"module: mkldnn",
"open source",
"ciflow/trunk",
"topic: build",
"ciflow/xpu",
"release notes: xpu",
"ciflow/linux-aarch64"
] | 5 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150095
# Motivation
Follow https://github.com/pytorch/pytorch/pull/149888, this PR intends to remove ABI=0 build logic for PyTorch XPU build with old compiler.
# Additional Context
This PR depends on XPU CI pass, which will be ... | true |
2,951,923,764 | [CPU]detectron2_fcos_r_50_fpn multiple thread float32 static shape default wrapper eager_two_runs_differ accuracy failure in 2025-03-24 nightly release | zxd1997066 | closed | [
"module: cpu",
"triaged"
] | 5 | CONTRIBUTOR | ### 🐛 Describe the bug
detectron2_fcos_r_50_fpn multiple thread float32 static shape default wrapper accuracy failure
the bad commit: 842d51500be144d53f4d046d31169e8f46c063f6
```
/workspace/pytorch# bash inductor_single_run.sh multiple inference accuracy torchbench detectron2_fcos_r_50_fpn float32
Testing with induct... | true |
2,951,879,564 | [export] Generate meta kernel | angelayi | closed | [
"ciflow/inductor",
"release notes: export"
] | 1 | CONTRIBUTOR | After draft-export tracing, we accumulate an "operator profile" of all the calls to this operator. The operator profile includes a list of the input tensor metadata and output tensor metadata, where the tensor metadata contains the rank, dtype, device, and layout. We can then use this to generate and register a meta ke... | true |
2,951,875,267 | Add `_foreach_fill_` ops | zeshengzong | open | [
"open source",
"release notes: foreach_frontend"
] | 2 | CONTRIBUTOR | Fixes #108445
| true |
2,951,866,376 | 'torch.mps' has no attribute 'current_device' | morestart | closed | [] | 5 | NONE | ### 🐛 Describe the bug
'torch.mps' has no attribute 'current_device'
### Versions
2.6.0 | true |
2,951,857,786 | [inductor] No type promotion for slice_scatter | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150082
* __->__ #150090
* #148953
* #150036
* #149667
* #149087
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauha... | true |
2,951,823,534 | Compilation failed for the frozen model | Cookiee235 | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
For the frozen model, the `torch.compile` will fail and throw "AttributeError: 'RecursiveScriptModule' object has no attribute 'training'".
A workaround to this bug is to add "model.eval" or "model.trainning =False" for the frozen model.
All in all, I still hope this bug can be fixed in the Pyt... | true |
2,951,812,709 | RuntimeError: (*bias): last dimension must be contiguous | pass-lin | closed | [] | 0 | NONE | When I implemented the model using keras, I reported this error to the gpu in the torch backend.
this error report in [here](https://btx.cloud.google.com/invocations/a4bd9556-5747-4656-8df5-1c2a92206b57/targets/keras_hub%2Fgithub%2Fubuntu%2Fgpu%2Ftorch%2Fpresubmit/log),and the issue in [here](https://github.com/keras-... | true |
2,951,804,139 | DISABLED test_triton_kernel_to_post_grad_tracing_cuda (__main__.TestProvenanceTracingArtifact) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 5 | NONE | Platforms: linux, rocm, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_triton_kernel_to_post_grad_tracing_cuda&suite=TestProvenanceTracingArtifact&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/3... | true |
2,951,606,948 | fix ambiguous error message | Cookiee235 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 25 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,951,555,096 | backward cleanup for #148430 | laithsakka | closed | [
"ciflow/trunk",
"release notes: fx",
"fx",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150085
* #148430
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,951,484,817 | [CD] Fix the libgomp twice load issue | chuanqi129 | open | [
"triaged",
"open source",
"topic: not user facing"
] | 8 | COLLABORATOR | Fixes #149422
| true |
2,951,429,475 | [Inductor] RuntimeError: Sparse CSR tensors do not have strides | Cookiee235 | open | [
"triaged",
"oncall: pt2",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
```python
import torch
class TestModel(torch.nn.Module):
def forward(self, x):
x_sparse = x.to_sparse_csr()
mat1 = torch.ones(3, 2)
mat2 = torch.ones(2, 3)
mm_res = torch.sparse.sampled_addmm(x_sparse, mat1, mat2)
dense_res = mm_res.to_dense()
... | true |
2,951,428,840 | [invoke_subgraph] Support None in the fwd output | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150486
* #150450
* __->__ #150082
| true |
2,951,333,414 | strange error when distributed training | Pydataman | open | [
"needs reproduction",
"oncall: distributed",
"triaged",
"oncall: pt2"
] | 2 | NONE | in torch2.2.1+cuda121, there is no problem with small datasets after distributed training, but this problem occurs during training with hundreds of millions of large datasets.
TypeError: _broadcast_coalesced(): incompatible function arguments. The following argument types are supported:
1. (process_group: torch._C._... | true |
2,951,274,145 | [FlexAttention] Allow dispatch to SAC for flex | drisspg | open | [
"module: activation checkpointing",
"release notes: nn",
"module: inductor",
"ciflow/inductor",
"module: higher order operators",
"module: flex attention"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150080
cc @soulitzer @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @zou3519 @ydwu4 @Chillee ... | true |
2,951,144,392 | [c10d] Move unstashing from watchdog to main thread | kwen2501 | closed | [
"oncall: distributed",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150130
* __->__ #150079
* #148590
This is a fix to the PR below.
### Context
If a work has completed but user didn't call `work.wait()`, we have responsibility to unstash the tensors (to allow memory recycle). Previously we perform th... | true |
2,951,140,664 | [RFC] Remove periodic/unstable jobs that has been continuously broken for more than 30 days | malfet | open | [
"module: ci",
"triaged"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
Right now we have a number of non-merge blocking jobs which still running and/if when they started to fail nobody is looking to fix those
I propose to start removing jobs that has been continuously broken for more than 30 days
### Versions
CI
cc @seemethere @pytorch/pytorch-dev-infra | true |
2,951,108,402 | Enable -Wunused on torch targets | cyyever | closed | [
"module: cpu",
"triaged",
"open source",
"Merged",
"Reverted",
"ciflow/binaries",
"ciflow/trunk",
"release notes: quantization",
"topic: not user facing",
"ciflow/periodic",
"ci-no-td",
"skip-url-lint"
] | 19 | COLLABORATOR | For GCC, ``-Wunused`` contains:
```
-Wunused-function
Warn whenever a static function is declared but not defined or a non\-inline static function is unused.
-Wunused-label
Warn whenever a label is declared but not used.
To suppress this warning use the unused attribute.
-Wunused-parameter
Warn whenever a f... | true |
2,951,088,706 | [WIP] Fix XPU build. | etaf | closed | [
"open source",
"topic: not user facing",
"ciflow/xpu"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149862
* __->__ #150076
| true |
2,951,065,160 | [BE] do not retain/release tensor | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 10 | CONTRIBUTOR | `Tensor::as_strided__symint` is inplace op that returns self, no need to retain it
| true |
2,951,057,540 | [inductor][comms] skip reorder_for_locality for wait nodes | xmfan | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 10 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150074
* #150258
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muc... | true |
2,951,057,488 | [ca][ddp] loud error with c++ reducer | xmfan | closed | [
"oncall: distributed",
"release notes: distributed (c10d)"
] | 1 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150258
* __->__ #150073
* #150074
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,951,047,161 | [StaticRuntime] Fuse SigridHash | csteegz | open | [
"oncall: jit",
"fb-exported",
"release notes: jit"
] | 3 | NONE | Summary: Previously, SigridHash could be fused by static runtime. That got broken when a new parameter was added to SigridHash - This diff brings back that fusion to try to drive performance benefits.
Test Plan: Unit tests and internal preproc perf tests
Differential Revision: D69498170
cc @EikanWang @jgong5 @wen... | true |
2,951,039,389 | Revert "[BE][Attention] Use `isneginf` (#139763)" | jeffhataws | closed | [
"triaged",
"open source",
"better-engineering",
"topic: not user facing"
] | 10 | NONE | This reverts commit 157c18a180398eddef52da559fe1649e35ce61f1.
Fixes https://github.com/pytorch/xla/issues/8746 and https://github.com/pytorch/xla/issues/8423
| true |
2,951,004,628 | [cachinghostallocator] remove the check on cudaHostRegister path | 842974287 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | CONTRIBUTOR | Summary:
In the cudaHostAlloc path, the flag we used is `cudaHostAllocDefault` [0] which don't really have this strict enforcement (devicePtr retrieved from ` cudaHostGetDevicePointer(()` point to the same addr as the hostPtr) according to the guide [1]. This diff removes the check so that the host register path works ... | true |
2,950,997,818 | [export][schema_upgrader][refactor] create a folder that holds different major version schemas | ydwu4 | open | [
"fb-exported",
"ciflow/trunk",
"ciflow/inductor",
"release notes: export"
] | 5 | CONTRIBUTOR | Summary:
A lot of places are directly referencing the the dataclasses in schema.py. Given we need to keep the original data class to maintain BC after a major version bump, we'll create multiple schemas of different major versions.
Note that this also implies that we need an upgrader if we remove a dataclass (or re... | true |
2,950,988,021 | Fix #149806 : Fix path lookup in _preload_cuda_deps | pytorchbot | closed | [
"open source"
] | 1 | COLLABORATOR | @pytorchbot label "bug"
Fixes #149806
| true |
2,950,987,066 | [MPS] fix attention enable_gqa crash on mps | pytorchbot | closed | [
"open source",
"release notes: mps",
"ciflow/mps"
] | 1 | COLLABORATOR | Fixes #149132
cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen | true |
2,950,977,557 | Delete linux-focal-cuda12_6-py3_10-gcc11-bazel-test | malfet | closed | [
"Merged",
"topic: not user facing"
] | 3 | CONTRIBUTOR | It's been broken for a while even when this jobs were still called ` linux-focal-cuda12.4-py3.10-gcc9-bazel-test`
Last time it run successfully on Feb 21st
| true |
2,950,970,918 | rework test_mem_get_info for single gpu case | Fuzzkatt | open | [
"triaged",
"open source",
"topic: not user facing"
] | 1 | COLLABORATOR | Increase block size from 8 mb to 512 mb since jetson has unified cpu / gpu mem and the blocksizes seem to need to be much larger. Also add sleeps after synchronizes since they are needed to consistently pass on nvidia internal CI. Ideally would not be a long term solution, will follow up with debugging why torch.cuda.m... | true |
2,950,953,846 | No stacktrace found for torch.check deferred runtime asserts | laithsakka | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 1 | CONTRIBUTOR | ```
@torch.compile(
backend="inductor",
fullgraph=True,
dynamic=True,
)
def f(a, b):
torch._check(
a.size()[0] > 0,
"linalg.vector_norm cannot compute the {ord} norm on an empty tensor "
"because the operation does not have an identity",
)
# _check_vector... | true |
2,950,952,062 | Torch.check does not preserve original error message in the deferred runtime assert | laithsakka | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 1 | CONTRIBUTOR | ```
@torch.compile(
backend="inductor",
fullgraph=True,
dynamic=True,
)
def f(a, b):
torch._check(
a.size()[0] > 0,
"linalg.vector_norm cannot compute the {ord} norm on an empty tensor "
"because the operation does not have an identity",
)
# _check_vector... | true |
2,950,943,166 | Support HOPs in fx_graph_runnable | xmfan | open | [
"triaged",
"actionable",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher"
] | 1 | MEMBER | ### 🚀 The feature, motivation and pitch
The fx_graph_runnable file is a standalone script that can be repro the run. Useful for fast debugging and allows trying out things directly in the graph, but it isn't runnable when HOPs are in the graph. As a workaround, i'm stiching dummy data into the graph.
https://manifol... | true |
2,950,937,400 | [Export] [Core ATen] [Decomposition] `linalg_vector_norm` not decomposed | YifanShenSZ | open | [
"triaged",
"oncall: pt2",
"export-triaged",
"oncall: export"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
As defined in [native functions](https://github.com/pytorch/pytorch/blob/v2.6.0/aten/src/ATen/native/native_functions.yaml#L14189-L14199), operator `linalg_vector_norm` is not tagged as `core`, which means it doesn't belong to core aten. In another word, when we run
```
import torch
class Mod... | true |
2,950,928,340 | [MPS] Add `chebyshev_polynomial_[uvw]` | malfet | closed | [
"Merged",
"topic: not user facing",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150060
For both eager and inductor
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,950,908,164 | [CI] Disable some tests that are failing in periodic | clee2000 | closed | [
"Merged",
"topic: not user facing",
"ciflow/periodic",
"module: inductor",
"ciflow/inductor",
"keep-going"
] | 5 | CONTRIBUTOR | Disabling some tests to restore periodic
nogpu avx512 timeout:
https://hud.pytorch.org/pytorch/pytorch/commit/59f14d19aea4091c65cca2417c509e3dbf60c0ed#38492953496-box
profiler failure: https://hud.pytorch.org/pytorch/pytorch/commit/7ae0ce6360b6e4f944906502d20da24c04debee5#38461255009-box
test_accelerator fail... | true |
2,950,908,001 | Fix bug in _load_state_dict_from_keys method | ankitageorge | closed | [
"oncall: distributed",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"release notes: distributed (checkpoint)"
] | 5 | CONTRIBUTOR | Summary:
The _load_state_dict_from_keys method specifies that `Loads any key specified in this set. If no keys are specified, the entire checkpoint is loaded.`
But this isn't happening right now, because an empty keys arg is passed in as a set() to `_load_state_dict` and keys is expected to be None for it to actually b... | true |
2,950,899,088 | [export] Export fails with multiple dimension indexing | angelayi | closed | [
"oncall: pt2",
"module: dynamic shapes",
"export-triaged",
"oncall: export"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
```python
def test_slicing(self):
class M(torch.nn.Module):
def forward(self, x, y):
b = x.item()
torch._check_is_size(b)
torch._check(b < y.shape[0])
return y[0, b]
print(torch.export.expor... | true |
2,950,889,692 | FlexAttention inductor tensor has no attribute `get_dtype` | tsengalb99 | closed | [
"needs reproduction",
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 3 | NONE | ### 🐛 Describe the bug
I am getting the following bug when compiling flex attention
``` torch._inductor.exc.InductorError: LoweringException: AttributeError: 'Tensor' object has no attribute 'get_dtype'```
This error does not happen without torch compile.
### Versions
Collecting environment information...
PyTorch... | true |
2,950,885,166 | Custom attributes for ONNX operations ? | borisfom | closed | [
"module: onnx",
"triaged"
] | 1 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
For custom ONNX operations, it would be nice to be able to specify custom attributes of the node.
For example, if I define custom ONNX op “mydomain::mycustomop”, which I plan to implement as TensorRT plugin, ‘mydomain’ currently ends up in ‘domain’ attribute of the node.
Ten... | true |
2,950,854,114 | `scaled_dot_product_attention` backwards: illegal memory access with large inputs | jatentaki | open | [
"module: crash",
"module: cuda",
"triaged",
"module: sdpa"
] | 3 | NONE | ### 🐛 Describe the bug
With a large enough input, `scaled_dot_product_attention` crashes with illegal CUDA memory access in backwards pass. It appears important to provide an attention mask.
## Repro script
```python
import torch
device = torch.device("cuda")
dtype = torch.bfloat16 # doesn't seem to matter, also fa... | true |
2,950,841,426 | [Dynamo] Add debug linting option for graph dedupe | mlazos | closed | [
"Merged",
"ciflow/trunk",
"module: dynamo",
"ciflow/inductor",
"release notes: dynamo"
] | 6 | CONTRIBUTOR | As title
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,950,801,002 | Profiler doesn't seem to work on AMD CPUs | RedTachyon | open | [
"module: rocm",
"triaged",
"oncall: profiler"
] | 5 | CONTRIBUTOR | ### 🐛 Describe the bug
Initially spotted in https://github.com/pytorch/torchtune/pull/2522
A minimal version of the code that crashes is something like:
```python
import torch
import torch.profiler
def minimal_crash():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using dev... | true |
2,950,774,435 | [MPS] `chebyshev_polynomial_t` returns garbage if 2nd arg is scalar | malfet | closed | [
"triaged",
"module: correctness (silent)",
"module: mps"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
I.e. running something like `torch.special.chebyshev_polynomial_t(torch.rand(4, 4, device='mps'), 2)` will return tensor filled with 1s on M1 machine, but will work fine on everything newer
```
% python3 -c "import torch;x=torch.rand(4, 4, device='mps');print(torch.special.chebyshev_polynomial_... | true |
2,950,771,333 | `torch.view_as_complex()` does not work on memory layout produced by `torch.contiguous()` after transpose | alisterburt | open | [
"triaged",
"module: complex"
] | 0 | NONE | ### 🐛 Describe the bug
```python
import torch
def print_strides(x):
print(x.stride(0), x.stride(1), x.stride(2))
x = torch.rand(336, 1, 2)
print_strides(x) # 2 2 1
torch.view_as_complex(x) # success!
x = torch.rand(336, 2, 1)
x = x.transpose(1, 2).contiguous()
print_strides(x) # 2 1 1
torch.view_as_complex(x)... | true |
2,950,714,492 | 2.7 test docker image has NCCL with older CUDA | andreasst | open | [
"oncall: distributed",
"oncall: releng",
"triaged",
"module: docker"
] | 2 | NONE | ### 🐛 Describe the bug
The `ghcr.io/pytorch/pytorch-test:2.7.0-cuda12.8-cudnn9-devel` nightly image (`sha256:f89440bd12a73cec62f03099885089d9d7f0084ea8fc08fa4967a63151dfa6f2`) has a NCCL version compiled against an older CUDA 12.2 version from pip package `nvidia-nccl-cu12`
```
$ strings /opt/conda/lib/python3.11/si... | true |
2,950,698,249 | cuda memory error thrown by torch. | Corey4005 | open | [
"module: windows",
"triaged",
"module: wsl"
] | 1 | NONE | ### 🐛 Describe the bug
Hello, I am receiving a Error 2: out of memory error after installing torch on WSL2:
```
Python 3.12.3 (main, Feb 4 2025, 14:48:35) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.is_available()
/home/user/another/li... | true |
2,950,679,605 | XPU build failure with DLE 2025.1.0 | pbchekin | open | [
"module: build",
"oncall: profiler",
"module: xpu"
] | 9 | NONE | Deep Learning Essentials 2025.1.0 has been [released](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html?packages=dl-essentials&dl-essentials-os=linux&dl-lin=offline). Building PyTorch XPU with this release fails with the following errors:
```
pytorch/third_party/kineto/libkineto... | true |
2,950,637,882 | ROCM Nightly Build failure | yangw-dev | closed | [
"module: rocm",
"module: ci",
"triaged"
] | 2 | CONTRIBUTOR | # Description
See HUD: [pytorch nightly](https://hud.pytorch.org/hud/pytorch/pytorch/nightly/1?per_page=50)
the Pytorch nightly rocm keeps failing due to time out in upload-artifact step
see 2025-03-26 nightly release
[linux-binary-libtorch / libtorch-rocm6_2_4-shared-with-deps-release-build / build](https://github.... | true |
2,950,637,331 | Merge Triton ScaledMM as epilogue to MM template | PaulZhang12 | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 16 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #150415
* __->__ #150045
Previously, scaled_mm's (FP8 matmul) Triton lowering for inductor was in a separate template. This PR consolidates that lowering into the mm template, with an added epilogue to deal with multiplying the scales. This... | true |
2,950,628,531 | [draft][FSDP2] Reorder FSDP2 pre_forward | mori360 | open | [
"oncall: distributed",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/148831
When using checkpoint() of FSDP2, pre_forward will be called twice
Then second time to be call will have the training state as pre_backward,
If mp_policy is set, args would not be casted in the seconds time, raise error `torch.utils.checkpoint: Recomputed value... | true |
2,950,611,791 | [programming model] make stacktraces for data-dependent errors more friendly | bdhirsh | open | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: compile ux"
] | 1 | CONTRIBUTOR | @HDCharles recently ran into a particularly huge one (~380 lines):
```
tlp python generate.py --checkpoint_path ../checkpoints/$MODEL_REPO/model.pth --compile
Using device=cuda
Loading model ...
Time to load model: 12.12 seconds
/home/cdhernandez/.conda/envs/pytorch-3.12/lib/python3.12/contextlib.py:105: FutureWarning:... | true |
2,950,603,265 | [MPSInductor] Run chebyshev_polynomial_t tests | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Test name should start with `test_`
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,950,579,726 | Need a more descriptive error when running ROCm tests on a non-ROCm machine | ahmadsharif1 | closed | [
"module: rocm",
"module: error checking",
"triaged",
"rocm"
] | 3 | CONTRIBUTOR | Hi,
I was trying to reproduce this error:
https://github.com/pytorch/pytorch/actions/runs/14086862973/job/39455645090
```
PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=1 PYTORCH_TEST_WITH_ROCM=1 python test/test_ops.py TestCommonCUDA.test_noncontiguous_samples_native_layer_norm_cuda_float32
Traceback (most recent call last):
... | true |
2,950,540,697 | [c10d] Test multiple CUDA Graph captures | kwen2501 | open | [
"oncall: distributed",
"ciflow/trunk",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150040
1. Do multiple captures
2. Perform multiple collectives in one capture
3. Multiple replays (existing)
cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,950,516,964 | [CI] VS2022 jobs seems to be running VS2019 still | malfet | open | [
"module: windows",
"module: ci",
"triaged",
"module: regression"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
See https://hud.pytorch.org/hud/pytorch/pytorch/039ebdc19287bba56fdb6d6bb9e693b3c88de927/1?per_page=50&name_filter=vs2022&mergeLF=true
But if one to look at any of the build logs VS2019 is still used
```
2025-03-26T17:38:23.1336363Z -- The CXX compiler identification is MSVC 19.29.30158.0
2025-... | true |
2,950,489,776 | [ONNX] Annotate None inputs in symbolic ops | justinchuby | closed | [
"module: onnx",
"open source",
"Merged",
"ciflow/trunk",
"release notes: onnx",
"topic: improvements"
] | 3 | COLLABORATOR | Add `None` to type annotations of `torch.onnx.ops.symbolic*` ops and improve tests to test support for optional inputs. Previously it was omitted mistakenly even though the implementation supports it. | true |
2,950,394,057 | [MPSInductor] Move threadfence at the right location | pytorchbot | closed | [
"open source"
] | 1 | COLLABORATOR | Not sure how it worked in the past, but fence should be before first read from the shared memory, not after it.
This bug was exposed by https://github.com/pytorch/pytorch/pull/148969 which removed unnecessary barrier before calling `threadgroup_reduce` functions
Test plan:
```
% python3 generate.py --checkpoint_pat... | true |
2,950,357,360 | [easy] Use config patch to toggle capture_scalar_output | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148953
* __->__ #150036
* #149667
* #149087
| true |
2,950,342,522 | [aotd] Config to guess_tangents_stride | IvanKobzarev | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"module: dynamo",
"ciflow/inductor"
] | 10 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150035
Differential Revision: [D71907684](https://our.internmc.facebook.com/intern/diff/D71907684)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @cheny... | true |
2,950,292,909 | Compile earlier PyTorch versions on Blackwell | asiron | closed | [
"module: build",
"module: cuda",
"triaged"
] | 1 | NONE | It seems that if you have a 5000 series GPU (Blackwell) with Compute Capability 12.0, you are forced to use CUDA 12.8. Is it supposed to be possible to compile older PyTorch versions (specifically 1.13 or 2.0) using CUDA 12.8 ?
I tried by pulling and checking out `v1.13.1` then I exported the following variables:
```... | true |
2,950,289,612 | [nn.utils] scale_grad_ with for_each | IvanKobzarev | open | [
"release notes: nn",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #150033
Distributed workloads like torch tune often have to do scaling gradients before loss computation.
https://github.com/pytorch/torchtune/blob/main/torchtune/training/_grad_scaler.py#L11
Adding `scale_grad_` that allows to u... | true |
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