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 |
|---|---|---|---|---|---|---|---|---|
2,766,846,545 | cpp_wrapper AOTI: Precompile device-specific header files | benjaminglass1 | closed | [
"open source",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144124
* #144123
* #144002
* #143909
* #143421
* #143223
* #141371
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8... | true |
2,766,846,515 | cpp_wrapper AOTI: Move #includes to per-device header files | benjaminglass1 | closed | [
"open source",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 2 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144124
* __->__ #144123
* #144002
* #143909
* #143421
* #143223
* #141371
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8... | true |
2,766,841,755 | [MPSInductor][EZ] Fix logical_[or|end] ops | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143966
* #144084
* #144083
* #144050
* #144105
* __->__ #144122
* #144051
* #144055
For boolean operands it does not really matter whether `&` or `&&` is
used, but if one ever to rely on operator precedence, then bitwise ops
should have hig... | true |
2,766,835,218 | [mps/inductor] Add support for atanh(). | dcci | closed | [
"Merged",
"ciflow/trunk",
"module: mps",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 6 | MEMBER | cc @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov | true |
2,766,829,107 | [Submodule] Turning flash-attention integration into 3rd party submod | drisspg | closed | [
"ciflow/trunk",
"topic: not user facing",
"skip-pr-sanity-checks",
"ciflow/inductor",
"suppress-bc-linter",
"ciflow/rocm",
"ci-no-td",
"module: sdpa"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144120
# Summary
### Sticky points
Cuda-graph rng handling has changed / deviated from original implementation. We will be left with a dangling 'offset' val and confusing naming due to BC
## Dependencies
- Flash PR: http... | true |
2,766,829,071 | working | drisspg | closed | [
"topic: not user facing"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144120
* __->__ #144119
| true |
2,766,820,801 | Migrate the rest of CUDA 12.1 jobs to 12.4 | huydhn | closed | [
"Merged",
"topic: not user facing",
"ciflow/periodic",
"ciflow/inductor-periodic"
] | 4 | CONTRIBUTOR | CUDA 12.4 is the default now and we don't build nightly 12.1 anymore, so it's time to move the rest of CI jobs to 12.4. I also clean up some redundant CI jobs on periodic and inductor-periodic. | true |
2,766,819,823 | Multihead Attention with mask producing float32 spontaneously, somehow compile cache related | IlanCosman | closed | [
"oncall: pt2"
] | 1 | NONE | ### 🐛 Describe the bug
Here is a minimal reproducer. It compiles but produces a warning about float32 usage.
```
UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
```
```pyt... | true |
2,766,819,123 | RNN batch_first argument only works on the input not h_0 when if should work on both | jsyoo61 | open | [
"module: nn",
"module: rnn",
"triaged"
] | 4 | NONE | ### 🚀 The feature, motivation and pitch
Hi,
the RNN batch_first argument only works on the input not h_0 when if should work on both. This applies to all 3 RNN implementations ([RNN](https://pytorch.org/docs/stable/generated/torch.nn.RNN.html), [GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html#torc... | true |
2,766,810,271 | [compiled autograd] support Tensor Subclasses in AOTBackward | zou3519 | closed | [
"oncall: distributed",
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: composability",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd",
"ci-no-td"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144115
* #143417
* #143405
* #143387
* #143304
* #143296
Compiled autograd's initial trace traces through the AOTBackward
epilogue. The Tensor Subclass code is not traceable. This PR changes it
so that when we see Tensor Subclass con... | true |
2,766,810,238 | [ca] add test_dtensor_compile.py to compiled autograd tests | zou3519 | closed | [
"oncall: distributed",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144115
* __->__ #144114
* #143417
* #143405
* #143387
* #143304
* #143296
This is just #144107, I put it here because ghstack with multiple users
is weird. | true |
2,766,800,403 | [cpu/sorting] Throw an error when trying to sort complex numbers. | dcci | closed | [
"module: sorting and selection",
"Merged",
"ciflow/trunk",
"release notes: linalg_frontend"
] | 5 | MEMBER | It doesn't really make sense to sort complex numbers as they are not comparable.
Fixes #129296
| true |
2,766,798,835 | Use the build environment as sccache prefix instead of workflow name | huydhn | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | This is an attempt to improve cache usage for jobs in non-pull workflows like periodic, slow, or inductor as we are seeing build timeout there from time to time, for example https://github.com/pytorch/pytorch/actions/runs/12553928804. The build timeout never happens in pull or trunk AFAICT because they are more up to ... | true |
2,766,796,036 | Use c10 version of half/bfloat16 in executorch | swolchok | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: build",
"topic: not user facing"
] | 9 | CONTRIBUTOR | Summary:
X-link: https://github.com/pytorch/executorch/pull/7040
Accomplished by importing relevant files from c10 into
executorch/runtime/core/portable_type/c10, and then using `using` in
the top-level ExecuTorch headers. This approach should keep the
ExecuTorch build hermetic for embedded use cases. In the future, w... | true |
2,766,780,923 | [typing] Add type hints to `@property` and `@lazy_property` in `torch.distributions`. | randolf-scholz | closed | [
"module: distributions",
"module: typing",
"open source",
"Merged",
"ciflow/trunk",
"release notes: python_frontend",
"suppress-bc-linter"
] | 8 | CONTRIBUTOR | Fixes #76772, #144196
Extends #144106
- added type annotations to `lazy_property`.
- added type annotation to all `@property` and `@lazy_property` inside `torch.distributions` module.
- added simply type-check unit test to ensure type inference is working.
- replaced deprecated annotations like `typing.List` wit... | true |
2,766,774,566 | Uneven Sharding in DTensor Leads to unexpected tensor resolution with `full_tensor` | coreyjadams | open | [
"oncall: distributed",
"triaged",
"actionable",
"module: dtensor"
] | 4 | NONE | ### 🐛 Describe the bug
Appears related at least to #143372. tl;dr: DTensor `full_tensor` operations are incorrect if sharding is not even AND if sharding isn't implicitly matching the uneven sharding that DTensor expects.
I've recently hit this issue - uneven sharding of `DTensor` leads to unexpected results. ... | true |
2,766,740,777 | [inductor] Add type annotations to _inductor/utils.py | rec | closed | [
"module: typing",
"open source",
"better-engineering",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 17 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144108
cc @ezyang @malfet @xuzhao9 @gramster @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @... | true |
2,766,714,007 | [ca] add test_dtensor_compile.py to compiled autograd tests | xmfan | closed | [
"oncall: distributed",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 11 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144107
more than half the tests use autograd, pass rate 19/26
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaoz... | true |
2,766,704,282 | added type hints to `lazy_property` | randolf-scholz | closed | [
"module: distributions",
"module: typing",
"open source"
] | 4 | CONTRIBUTOR | Partial fix for #76772, it remains to add type hints to all the properties of the predefined distribution objects.
EDIT: #144110 builds on top of this PR and provides these type hints.
cc @fritzo @neerajprad @alicanb @nikitaved @ezyang @malfet @xuzhao9 @gramster | true |
2,766,647,259 | [MPSInductor] Add signbit op support | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143966
* #144084
* #144083
* #144050
* #144156
* __->__ #144105
* #144122
* #144051
* #144055
By mapping it to `metal::signbit`
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @ji... | true |
2,766,629,233 | MPS returns 0 for `BCEWithLogitsLoss` on empty tensors while CPU and CUDA return nan | dylwil3 | closed | [
"triaged",
"actionable",
"module: mps",
"module: empty tensor"
] | 6 | NONE | ### 🐛 Describe the bug
```python
import torch
import torch.nn.functional as F
x = torch.tensor([])
y = torch.tensor([])
loss = F.binary_cross_entropy_with_logits
print(loss(x.to("cpu"),y.to("cpu"))) # tensor(nan)
if torch.cuda.is_available():
print(loss(x.to("cuda"),y.to("cuda"))) # tensor(nan, de... | true |
2,766,627,302 | Update TorchInductor to support removed AttrsDescriptor in upstream Triton | jansel | closed | [
"high priority",
"triaged",
"oncall: pt2",
"module: inductor"
] | 6 | CONTRIBUTOR | https://github.com/triton-lang/triton/pull/5512 removed `AttrsDescriptor` which TorchInductor generates in its output code.
To support Triton versions after that PR we will need to update the code we generate.
cc @ezyang @gchanan @zou3519 @kadeng @msaroufim @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @G... | true |
2,766,598,517 | allow_in_graph footgun: nested user functions | zou3519 | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 0 | CONTRIBUTOR | https://github.com/pytorch/pytorch/blob/bb5e439f2d8a46172b8b7d2fdb7609822b9a97b1/torch/_dynamo/decorators.py#L138-L153
allow_in_graph recognizes functions by their Python id. A nested user function might get deallocated and the id reused. This may lead to nondeterministic behavior. These dicts should be weakkeydicti... | true |
2,766,571,397 | Clarify what we mean by decoupled weight decay in the *AdamWs | janeyx99 | closed | [
"Merged",
"ciflow/trunk",
"topic: docs",
"release notes: optim"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144101
| true |
2,766,556,815 | [dtensor] expose the __create_chunk_list__ in the doc | wanchaol | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (dtensor)"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144100
* #144099
as titled, this PR expose this dunder method as a public API in the doc,
so that different checkpoint implementations can leverage this protocol,
instead of exposing a separate API
cc @H-Huang @awgu @kwen2501 @fegin... | true |
2,766,556,763 | [dtensor] improve doc of the DTensor class | wanchaol | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (dtensor)"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144100
* __->__ #144099
as titled: explicitly list all public members to make sure the public
API stays consistent, also use groupwise as the member order to make doc
look better
cc @H-Huang @awgu @kwen2501 @fegin @fduwjj @wz337 @wconstab ... | true |
2,766,517,937 | [ROCm][Windows] Fix export macros | m-gallus | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm"
] | 4 | CONTRIBUTOR | For correct import and export of functions when the dynamic linkage is used for HIP libraries on windows, the appropriate export/import macros need to be put in place. This Pull Request utilizes existing CUDA import/export macros by converting them to corresponding HIP macros during the hipification process.
cc @jeffd... | true |
2,766,509,490 | partitioner: when materializing unbacked tensor intermediates, apply hint to symbol, not expr | bdhirsh | closed | [
"Merged",
"ciflow/trunk",
"release notes: composability",
"module: dynamo",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/144095
open to suggestions: the `hint_int(..., fallback=...)` API feels like a bit of a footgun, because:
(1) we use the same guess for every unbacked symint (both symbols, and compound expressions)
(2) the user may have established some relationship between some u... | true |
2,766,507,672 | [Release/2.6][MPS] Fix crash on CPU scalars | malfet | closed | [
"release notes: mps",
"ciflow/mps"
] | 1 | CONTRIBUTOR | This cherry-picks following PR into 2.6 branch that fixes crash when fmin/fmax, bucketize or Metal kernels are invoked with CPU tensors
- **[MPS] Fix fmin/fmax for scalar argument (#143934)**
- **[MPS] Handle implicit cpu-scalar-to-gpu transfer (#144055)**
| true |
2,766,501,141 | activation memory budget partitioner can fail with unbacked symints | bdhirsh | closed | [
"high priority",
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 2 | CONTRIBUTOR | internal xref: https://fb.workplace.com/groups/1075192433118967/posts/1567692087202330/?comment_id=1572673046704234&reply_comment_id=1577244289580443
Stacktrace below. Still working on a minimal repro, but a few things that become apparent from looking at the [tlparse](https://manifold.edge.x2p.facebook.net/v0/read/... | true |
2,766,484,562 | [profiler][python 3.13] profiler with_stack=True failing on python 3.13 | davidberard98 | open | [
"oncall: profiler"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
Repro:
```python
import torch
class ModuleA(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(4, 4)
def forward(self, x):
return self.linear(x)
class ModuleB(torch.nn.Module):
def __init__(self):
... | true |
2,766,475,422 | remove allow-untyped-defs from _export/db/logging.py | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"release notes: export"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144093
| true |
2,766,475,341 | remove allow-untyped-defs from torch/mps/event.py | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/mps"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144093
* __->__ #144092
| true |
2,766,475,264 | remove allow-untyped-defs from ao/quantization/experimental/fake_quantize.py | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"release notes: quantization",
"topic: not user facing",
"release notes: AO frontend"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144093
* #144092
* __->__ #144091
| true |
2,766,475,160 | remove allow-untyped-defs from distributed/elastic/utils/data/cycling_iterator.py | bobrenjc93 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"release notes: distributed (torchelastic)"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144090
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,766,475,065 | remove allow-untyped-defs from utils/_import_utils.py | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144089
| true |
2,766,474,986 | remove allow-untyped-defs from utils/data/datapipes/iter/streamreader.py | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"release notes: dataloader",
"topic: not user facing"
] | 12 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144088
| true |
2,766,440,615 | [ROCm][NFC] Fix condition for small tensor tuning | doru1004 | closed | [
"module: rocm",
"open source",
"Merged",
"ciflow/trunk",
"release notes: cuda",
"ciflow/rocm"
] | 4 | CONTRIBUTOR | Fix condition for small tensor tuning to not impact non-ROCm compilation.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,766,375,407 | Fix nan propagation for minimum() and maximum() in MPS | jhavukainen | closed | [
"open source",
"Merged",
"module: mps",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 5 | COLLABORATOR | Fixes #143976
- Moves minimum and maximum operations to use the NaN propagating call into MPSGraph instead of the default one.
- Adds test for the NaN propagating case to `test_mps.py`.
- Adjusts the inductor metal backend implementation for minimum and maximum to also respect the nan propagation.
Additions b... | true |
2,766,315,965 | Added 'Use tensor in PyTorch' section to README | guan0612 | closed | [
"open source",
"topic: not user facing"
] | 3 | NONE | Add 'Use tensor in PyTorch'
| true |
2,766,307,631 | [MPSInductor] Add `masked` implementation | malfet | closed | [
"Merged",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143966
* #144170
* __->__ #144084
* #144083
* #144162
* #144167
More or less borrowed from
https://github.com/pytorch/pytorch/blob/22580f160e9ff6f5a54bc5abd03ba3eb75519e10/torch/_inductor/codegen/halide.py#L549-L563
`pytest test/induc... | true |
2,766,307,455 | [MPSInductor] Add `floor_div` and `index_expr` implementation | malfet | closed | [
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143966
* #144170
* #144084
* __->__ #144083
* #144162
* #144167
Simply copy-n-pasted from CPPInductor
`pytest test/inductor/test_torchinductor.py -k _mps` score is 418 failed, 337 passed, 32 skipped
cc @voznesenskym @penguinwu... | true |
2,766,267,081 | Added a usage example to the README | nash0220 | closed | [
"triaged",
"open source",
"topic: not user facing"
] | 3 | NONE | This PR adds a simple example of using PyTorch to build a neural network. | true |
2,766,238,216 | [AOTI] Remove more AOTI_TORCH_EXPORT | desertfire | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Summary: Similar to https://github.com/pytorch/pytorch/pull/142500, remove redundant AOTI_TORCH_EXPORT from several cpp files, to solve a windows build issue.
Differential Revision: D67762069
| true |
2,766,231,810 | Inconsistent `padding_value` rounding decision when using `torch.nn.utils.rnn.pad_sequence` under torch.compile and eager | meetmul | open | [
"module: nn",
"triaged",
"module: type promotion",
"oncall: pt2",
"module: inductor"
] | 0 | NONE | ### 🐛 Describe the bug
I think this is caused by the inconsistent type casting between torch.compile and eager.
When `sequences` is a mixed of complex and integer tensors, pad_sequence under torch.compile mode will directly round `padding_value` to 0 but eager mode will keep `padding_value` as -0.7. See below code ... | true |
2,766,092,921 | Test s390x docker image build | AlekseiNikiforovIBM | closed | [
"open source",
"topic: not user facing"
] | 1 | COLLABORATOR | Test s390x docker image build | true |
2,766,026,893 | Fix PythonMod printing | isuruf | closed | [
"module: cpu",
"module: regression",
"open source",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: dynamo"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144078
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @voznesenskym @penguinwu @EikanWang @Guobing-Chen @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPepp... | true |
2,766,026,015 | broken link at https://pytorch.org/docs/stable/_modules/torch/_tensor.html#Tensor.backward | nadeeer | closed | [] | 1 | NONE | ### 📚 The doc issue
I am trying to check the source code for Tensor.backward() and tried to follow the link in the documentation page with no luck.
### Suggest a potential alternative/fix
_No response_ | true |
2,766,003,518 | [reland][AMD] Turn on TF32 for aten::mm (#143549) | jeanschmidt | closed | [
"fb-exported",
"module: dynamo",
"ciflow/inductor",
"ci-no-td"
] | 124 | CONTRIBUTOR | Summary:
hipblaslt supports TF32, so adding the support.
Original PR https://github.com/pytorch/pytorch/pull/139869
Test Plan: CI
Reviewed By: leitian
Differential Revision: D67431681
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyan... | true |
2,765,944,123 | [regression] Incorrect symbolic output shape and guards for arange, avg pool and conv ops | BartlomiejStemborowski | closed | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: dynamo"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
When using latest PyTorch 2.6 RC, it looks like the output shape metadata in the compile dynamic flow is incorrect for the arange OP.
I received the following graph, where an output shape is calculated as: (s0 + 1//2) where in PT 2.5 it is: ((s0 + 1)//2).
PT 2.6 graph:
```
TRACED GRAPH... | true |
2,765,896,004 | [Feat]: Add Multithreading support for kleidiai groupwise GEMM kernels | nikhil-arm | closed | [
"module: cpu",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: linalg_frontend",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"ciflow/linux-aarch64"
] | 3 | COLLABORATOR | KleidiAI Groupwise GEMM Kernel was not 2D Blocked. This change adds supports for 2D blocking of GEMM kernel to efficiently split workload & speedup GEMM kernel over multiple threads.
Performance improvements:
7B model Pre-fill speedup from 145 t/s to 175 t/s
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel ... | true |
2,765,848,497 | Avoid overflow in vector_norm for scalar input | isuruf | closed | [
"open source",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 16 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144073
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov
... | true |
2,765,672,368 | Compile error for custom op with optional mutable tensor list argument | jerrychenhf | closed | [
"triaged",
"module: custom-operators",
"module: functionalization",
"oncall: pt2",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
It showed that the Torch auto functionalization doesn't support custom op with optional mutable tensor list argument.
The following code shows this problem. "Tensor(a!)[]? out_list" argument of the op is not supported for auto functionalization:
```
import torch
@torch.library.custom_... | true |
2,765,649,409 | torch-nightly doesn't support tesla v100 | Serenagirl | open | [
"needs reproduction",
"module: binaries",
"module: cuda",
"triaged"
] | 6 | NONE | ### 🐛 Describe the bug
env:TeslaV100,driver 560.35.03 cuda 12.4
use pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124
python:
import torch
print(torch.randn(5, 5).to(device)+torch.randn(5, 5).to(device))
but cuda12.4 supports v100,i can't find which torc... | true |
2,765,646,098 | Fix torch.normal ignores default_device | zeshengzong | closed | [
"module: distributions",
"triaged",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 12 | CONTRIBUTOR | Fixes #122886
1. Enable `torch.normal` working with `DeviceContext` to get default device which set via `set_default_device`.
2. Add hint in `set_default_device` doc, suggest use `torch.Tensor.to` method move to desired device explicitly.
**Test Result**
1. **Doc Preview**
![image](https://github.com/user-atta... | true |
2,765,632,914 | [AMD] [ROCm] Numerical difference between Pytorch 2.6.0.dev of ROCm 6.2 and ROCm 6.3 | tjtanaa | closed | [
"high priority",
"module: rocm",
"triaged"
] | 5 | NONE | ### 🐛 Describe the bug
# Description
I am getting different numerical output results between Pytorch 2.6.0.dev of ROCm 6.2 and ROCm 6.3.
All the tests in the https://github.com/linkedin/Liger-Kernel/pull/506 pass with PyTorch 2.6.0.dev of ROCm 6.2
However, one of the tests fail in the environment with PyTorch ... | true |
2,765,631,970 | redundant recompilation caused by duplicated Sym() | MetaBlues | open | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: dynamo",
"recompilations"
] | 4 | NONE | ### 🐛 Describe the bug
Hello
I've been trying to reduce the number of recompiles during Megatron training recently and noticed that strange recompiles happenned on RMSNorm.
```
@torch.compile(dynamic=True)
def rmsnorm_without_weight(hidden_states, eps=1e-6, dtype=torch.bfloat16):
variance = hidden_states.to... | true |
2,765,627,121 | [dynamo][BE] move `zip_longest` polyfill to submodule `polyfills.itertools` | XuehaiPan | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 7 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144067
* #144066
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,765,626,616 | [dynamo][BE] move `dropwhile` polyfill to submodule `polyfills.itertools` | XuehaiPan | closed | [
"open source",
"Merged",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144067
* __->__ #144066
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,765,615,681 | [cpu][vec] support reduce ops for add and max | Valentine233 | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | COLLABORATOR | ### Description
During the support of INT8 SDPA https://github.com/pytorch/ao/pull/1372, we find that `at::vec::vec_reduce_all<int32_t>` would go into slow scalar path when doing sum and max. So here, we support the two reduce-related ops `reduce_add` and `reduce_max` for `vec512` and `vec256`, using the Sequence i... | true |
2,765,615,306 | Support nanj in inductor | isuruf | closed | [
"open source",
"Merged",
"ciflow/trunk",
"module: inductor",
"ciflow/inductor",
"release notes: inductor"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144064
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov
... | true |
2,765,559,063 | When I use the optimizer, there is no gradient due to the use of unit8, but I have to use unit8 | wang1528186571 | closed | [] | 1 | NONE | ### 🐛 Describe the bug
def apply_relighting_tensor(tensor, alpha, beta):
tensor = tensor * 255.0
new_tensor = tensor.to(torch.uint8)
new_tensor = new_tensor * alpha + beta / 255.0
new_tensor = torch.abs(new_tensor)
new_tensor = new_tensor.to(torch.float32)
new_tensor = new_tensor / ... | true |
2,765,470,431 | [dynamo][dicts] Remove special casing for SUPPORTED_NODES and sys.modules | anijain2305 | closed | [
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144062
* #144061
* #143997
* #144160
* #144158
* #144141
* #144130
* #144129
After https://github.com/pytorch/pytorch/pull/143997, the special casing is not required.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @... | true |
2,765,470,366 | [dynamo][refactor] Collect dict like variable building in one place | anijain2305 | closed | [
"module: dynamo",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144062
* __->__ #144061
* #143997
* #144160
* #144158
* #144141
* #144130
* #144129
| true |
2,765,465,035 | call dist.nn.all_reduce then compute loss with torch.logdet().sum() raise grad Tensors must be contiguous error | ultranity | closed | [
"oncall: distributed",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
BG: error when verifying #58005, where batch computations like torch.logdet and torch.sum will raise Error: grad Tensors must be contiguous error
repoduce code:
```
import torch
import torch.distributed as dist
import torch.distributed.nn
from functools import partial
def worker(gpu, U... | true |
2,765,450,146 | [Inductor][CPP] Fix Inductor integer avg pool | DDEle | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 5 | CONTRIBUTOR | Fixes #143738. Currently the scaler for averaging is rounded to 0 if dtype is an integer, resulting to all-zero output. This fix uses `truediv` instead for integer cases.
## Test
```bash
pytest -vs ./test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool1d_cpu_... | true |
2,765,404,286 | [Inductor] Fix `torch.polygamma()` when n == 0 | shink | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 7 | CONTRIBUTOR | Fixes #143648
aten:
https://github.com/pytorch/pytorch/blob/dec1a6d0f05f838dcec10492ef6091501258f816/aten/src/ATen/native/cpu/UnaryOpsKernel.cpp#L436-L447
compiled kernel code:
```
cpp_fused_polygamma_0 = async_compile.cpp_pybinding(['const float*', 'float*'], '''
#include "/tmp/torchinductor_devuser/tmpi... | true |
2,765,378,690 | [Inductor UT] Generalize device-bias code in test_torchinductor.py introduced by #143884. | etaf | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/xpu"
] | 3 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144057
Fix #144056
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauh... | true |
2,765,377,255 | [Break XPU] Hard code “cuda” in GPU test case introduced by #143884 cause failure on XPU. | etaf | closed | [
"triaged",
"module: xpu"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
The PR #143884 introduced a new test case torch/_inductor/test_torchinductor.py:test_donated_buffer_inplace_gpt which is not specified requires_cuda but hard code device type cuda, cause it fails on XPU.
https://github.com/pytorch/pytorch/blob/dec1a6d0f05f838dcec10492ef6091501258f816/test/ind... | true |
2,765,359,612 | [MPS] Handle implicit cpu-scalar-to-gpu transfer | malfet | closed | [
"Merged",
"release notes: mps",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143966
* #144084
* #144083
* #144051
* #144050
* __->__ #144055
Followup after https://github.com/pytorch/pytorch/pull/143934, this check is no longer necessary and fixes a subset of inductor tests
Before `pytest test/inductor/test_torc... | true |
2,765,301,857 | item() on DTensor only grabs the local tensor | ad8e | closed | [
"oncall: distributed",
"triaged",
"module: dtensor"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
An example of a tensor for which the local tensor is insufficient is a norm, which is sharded across many GPUs.
I have not run this testcase because I don't have a convenient 2-GPU system, but the correct print would be `8` (norm of the whole tensor), and I expect this to print `5.65 = 4sq... | true |
2,765,235,593 | cuDNN version is not detected correctly in PyTorch | celestinoxp | closed | [
"module: cudnn",
"module: cuda",
"triaged"
] | 4 | NONE | ### 🐛 Describe the bug
I am experiencing issues with PyTorch not detecting the correct version of cuDNN. Here’s the setup:
I installed Nightly PyTorch 2.6 using the following command:
```python
pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu118
```
I installed also the lates... | true |
2,765,234,427 | Fix dangling autogenerated sphinx source code links | Impaler343 | open | [
"triaged",
"open source",
"topic: docs",
"module: python frontend"
] | 8 | NONE | Fixes #143910
Broken source links can be fixed by adding return types for the functions.
Seems like almost all of the functions in ```_tensor.py``` have this problem and I've tried to address a few of them.
Few of the return types are not constant in type or number for which I have no solution
cc @albanD | true |
2,765,225,848 | [MPSInductor] Preserve dtype during load | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143966
* #144122
* #144084
* #144083
* #144050
* #144105
* __->__ #144051
* #144055
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng ... | true |
2,765,225,825 | [MPSInductor] Fix multi rangevar kernel invocation | malfet | closed | [
"Merged",
"topic: improvements",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143966
* #144084
* #144083
* __->__ #144050
* #144156
* #144105
* #144122
* #144051
* #144055
By changing `thread_position_in_grid` type to uint{n} and passing
dimentions during the kernel call
`pytest test/inductor/test_torchinductor.... | true |
2,765,205,342 | Add CUDA aarch64 triton wheel build | Skylion007 | closed | [
"open source",
"Stale",
"topic: not user facing",
"ciflow/binaries_wheel"
] | 2 | COLLABORATOR | Create aarch64 triton wheel build | true |
2,765,185,622 | Dynamo is not supported on Python 3.13+ | Vectorrent | closed | [
"oncall: pt2"
] | 1 | NONE | ### 🐛 Describe the bug
I recently updated my system (Arch Linux), and with that came an upgrade to Python v3.13.1.
Since then, I have had trouble with code that used to work, in older versions of Python. For example, the error below comes from `torch.compile` being used with FlexAttention, in the [bytelatent](http... | true |
2,765,179,040 | Propagate callable parameter types using ParamSpec (#142306) | yijun-lee | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 17 | CONTRIBUTOR | Fixes #142306
This PR includes typing improvements and refactoring for the following files:
- __init__.py
- decorators.py
- _ops.py
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,764,980,567 | switch Windows XPU build to vs2019. | xuhancn | closed | [
"module: windows",
"open source",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu",
"module: xpu"
] | 5 | COLLABORATOR | Fixes #ISSUE_NUMBER
cc @peterjc123 @mszhanyi @skyline75489 @nbcsm @iremyux @Blackhex @gujinghui @EikanWang @fengyuan14 @guangyey | true |
2,764,944,198 | With FSDP2, a small tensor on a 1-GPU world size has grad=0 | ad8e | open | [
"oncall: distributed",
"triaged",
"module: fsdp"
] | 9 | CONTRIBUTOR | ### 🐛 Describe the bug
I train a model normally, and one of the parameters remains at 0 throughout the run. Its grad is always zero, but it should be a large value.
Ablations:
If I use world_size 8, I don't see this. The parameter moves and the grad is 30000 rather than 0.
If I change the parameter from shape ... | true |
2,764,774,912 | [inductor] Refactor CachingAutotuner so that it can pickle | jansel | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144288
* __->__ #144044
These are refactors needed for #144288
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @Colin... | true |
2,764,736,403 | Fixed doc where more than one device specified since only one device is used (#17553) | Stacie-Herda | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 8 | CONTRIBUTOR | Fixes #17553
| true |
2,764,717,067 | [ScaledMM] Fix NaNs in test for garbage input data | drisspg | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #144042
| true |
2,764,692,066 | [Inductor] Generalize tiling algorithm to handle fused reductions | blaine-rister | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | # Issue
This PR cleans up an edge case that wasn't handled by https://github.com/pytorch/pytorch/pull/137243. The existing tiling code assumes that `node.get_ranges()` is a reliable source of pointwise and reduction numels. This is true for pointwise kernels, but the situation is more complicated with reductions. Si... | true |
2,764,685,152 | Torch.sparse.mm failing gradient computation at half precision. | tanayarora09 | open | [
"module: sparse",
"triaged",
"module: half"
] | 0 | NONE | ### 🐛 Describe the bug
When using torch.autocast, torch.sparse.mm(sparse_csr_tensor, dense_tensor) fails on the gradient computation with an unhelpful error. Half precision matrix multiplication with csr tensors was completed here https://github.com/pytorch/pytorch/issues/41069.
Simple reproduction:
```
weig... | true |
2,764,663,831 | [Mac/M1] torch.compile() -- expm1 returns an inaccurate result compared to the interpreted version | dcci | open | [
"oncall: pt2",
"oncall: cpu inductor"
] | 2 | MEMBER | ### 🐛 Describe the bug
Input:
```
davidino@davidino-mbp pytorch % cat /tmp/repro.py
import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = torch.floor(x)
x = torch.angle(x)
x = torch.sin(x)
s = tor... | true |
2,764,614,180 | [ROCm] Print amdgpu info on bare metal for CI runners | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | COLLABORATOR | cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,764,601,195 | cpp_extension.py expects an integer on CUDA_ARCH, failing with Grace Hopper. | surak | open | [
"module: cpp-extensions",
"module: cuda",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
Grace hopper reports as 9.0a, not 9.0, and cpp_extension.py will bark when it expects an integer as the second part of it on autodetect.
The current workaround is to set `TORCH_CUDA_ARCH_LIST="9.0a"` while building it.
```
torch/utils/cpp_extension.py",
line 1972, in _get_cuda_arc... | true |
2,764,575,796 | [ONNX] Documentation describe the metadata stored in exported models | justinchuby | open | [
"module: onnx",
"triaged"
] | 2 | COLLABORATOR | null | true |
2,764,557,563 | CheckpointError with torch.compile + checkpointing + DDP | TidalPaladin | closed | [
"oncall: distributed",
"module: activation checkpointing",
"triaged",
"oncall: pt2"
] | 1 | NONE | ### 🐛 Describe the bug
In instances where torch.compile is combined with DDP and checkpointing, the following error is raised:
```
torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint: A different number of tensors was saved during the original forward and recomputation.
```
I have only been able to r... | true |
2,764,440,663 | [Intel XPU] enable kineto for XPU Windows. | xuhancn | closed | [
"module: windows",
"triaged",
"open source",
"Merged",
"ciflow/binaries",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu",
"module: xpu"
] | 7 | COLLABORATOR | This PR will turn on `kineto` on Windowx XPU wheel build.
For `kineto` on Windows XPU, the build time dependencies list:
1. Intel PTI, it contained by oneAPI 2025+.
2. Level zero SDK: https://github.com/oneapi-src/level-zero/releases/download/v1.14.0/level-zero-sdk_1.14.0.zip
**Note:**
We need to manual setu... | true |
2,764,425,084 | Training fails with Torch 2.1.0 on Nvidia Jetpack 5.1.2 | mfatih7 | open | [
"triaged",
"module: jetson"
] | 0 | NONE | ### 🐛 Describe the bug
Hello
We are trying to run a training on Nvidia Jetson devices with compute capabilities 7.2 and 8.7.
The system properties are as follows:
```
Python 3.8
Torch 2.1.0
Torchvision 0.16.2
CUDA 11.4
Nvidia Jetpack 5.1.2
Ubuntu 20.04
```
At the begining of a simple MNIST training, ... | true |
2,764,390,427 | if pytorch wheel package support avx512? | risemeup1 | closed | [] | 1 | NONE | ### 🐛 Describe the bug
“My CPU system supports AVX512, and I want to use a PyTorch package that supports AVX512. Which one should I choose, or do I have to build from source?”
### Versions
.... | true |
2,764,386,614 | Is the page 'PyTorch ONNX Exporter Code Reviews and Duty Rotation' of wiki still in use? | dune0310421 | closed | [
"module: onnx",
"triaged"
] | 2 | NONE | Hello everyone, I'm a PhD student who is interested at the governance mechanism of PyTorch. I noticed that there is a page 'PyTorch ONNX Exporter Code Reviews and Duty Rotation' in PyTorch wiki, which hasn't been modified for three years. Could you please let me know if this page is still in use? Additionally, I'm wond... | true |
2,764,386,549 | Enable mkldnn pattern matcher tests for BF16 on AArch64 | Mousius | closed | [
"open source",
"module: arm",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/linux-aarch64"
] | 11 | CONTRIBUTOR | Fixes #143146
cc @malfet @snadampal @milpuz01 @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov | true |
2,764,271,329 | logaddexp fails on complex tensors in torch.compile | maybeLee | closed | [
"triaged",
"module: complex",
"oncall: pt2",
"module: inductor"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
When using logaddexp to perform computation on complex tensors, this API works fine under eager mode but it fails under torch.compile with the following error message:
```
NameError: name 'nanj' is not defined. Did you mean: 'nan'?
```
Here is the code to reproduce:
```
import torch... | true |
2,764,255,902 | [ROCm] Add miopen_batch_norm to meta_registrations to fix AOTI issue | pytorchbot | closed | [
"module: rocm",
"open source",
"ciflow/rocm"
] | 3 | COLLABORATOR | Currently the upstream example for AOTI usage breaks on ROCm (https://pytorch.org/tutorials/recipes/torch_export_aoti_python.html)
```
File "/root/upstream/torch/_dynamo/exc.py", line 317, in unimplemented
raise Unsupported(msg, case_name=case_name)
torch._dynamo.exc.Unsupported: unsupported operator: aten.mi... | true |
2,764,255,489 | [ROCm] Guard triton backend call around cuda.is_available | pytorchbot | closed | [
"module: rocm",
"open source",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm"
] | 1 | COLLABORATOR | To resolve: https://github.com/pytorch/test-infra/issues/6082
Calling into Triton's get_backend_options will initialise CUDA and break CPU-only environments that may have hip installed.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @voznesensky... | true |
2,764,253,966 | Respect ROCR_VISIBLE_DEVICES on AMD GPU device discovery | pytorchbot | closed | [
"open source"
] | 1 | COLLABORATOR | Reland of #140320 after failing test on trunk. Fixes potential environment clobbering in test, makes ROCr+HIP devices (if specified together) more robust to index errors.
Fixes #140318
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,764,139,679 | torch.cuda.empty_cache() causes extra memory usage on 'cuda:0' | JimmyTauH | open | [
"module: cuda",
"triaged",
"module: CUDACachingAllocator"
] | 2 | NONE | ### 🐛 Describe the bug
# Issue Description:
When utilizing PyTorch with a specific CUDA device (in this case, 'cuda:8'), calling `torch.cuda.empty_cache()` unexpectedly results in additional memory allocation on 'cuda:0', approximately 255MB. This behavior is contrary to expectations, as the operation should ideally... | true |
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