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 |
|---|---|---|---|---|---|---|---|---|
2,761,200,623 | [EZ] Update jinja2 to 3.1.5 | malfet | closed | [
"better-engineering",
"Merged",
"topic: not user facing"
] | 6 | CONTRIBUTOR | To make Dependabot happy about https://cwe.mitre.org/data/definitions/150.html
| true |
2,761,195,654 | Update scheduler.py | malfet | closed | [
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143922
* #143921
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aak... | true |
2,761,195,618 | Add mps to GPU_TYPES | malfet | closed | [
"module: inductor",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143922
* __->__ #143921
Because it is a GPU
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desert... | true |
2,761,174,598 | [ReduceOps] Add dimension checking for cummin()/cummax(). | dcci | closed | [
"Merged",
"module: reductions",
"ciflow/trunk",
"release notes: linalg_frontend"
] | 6 | MEMBER | Summary: cum{min,max} didn't guard against 0-d vector and allowed an arbitrary dimension to be passed.
Test Plan: torch_test.py
Reviewers:
Subscribers:
Tasks:
Tags:
Fixes #71477
| true |
2,761,154,042 | remove allow-untyped-defs from ao/nn/qat/dynamic/modules/linear.py | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"release notes: AO frontend"
] | 12 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143919
| true |
2,761,154,002 | remove allow-untyped-defs from utils/tensorboard/_convert_np.py | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143918
| true |
2,761,153,957 | remove allow-untyped-defs from distributed/elastic/multiprocessing/subprocess_handler/handlers.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):
* __->__ #143917
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,761,153,865 | remove allow-untyped-defs from _inductor/codegen/aoti_hipify_utils.py | bobrenjc93 | 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):
* __->__ #143916
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov | true |
2,761,153,809 | remove allow-untyped-defs from distributed/pipelining/_unflatten.py | bobrenjc93 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143916
* __->__ #143915
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,761,136,144 | RuntimeError: could not create an engine | xyang2013 | closed | [
"module: windows",
"triaged",
"module: xpu"
] | 25 | NONE | ### 🐛 Describe the bug
Hi, I experienced the following error (the message before the exception):
File c:\Users\xiaoy\anaconda3\envs\llm2\Lib\site-packages\torch\nn\modules\linear.py:125, in Linear.forward(self, input)
124 def forward(self, input: Tensor) -> Tensor:
--> 125 return F.linear(input, self.weight, sel... | true |
2,761,126,573 | Set up Mac builds with clang >= 17 even though Xcode only has at most clang 16 | swolchok | open | [
"module: binaries",
"module: ci",
"triaged",
"enhancement"
] | 4 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
This would enable a couple disparate improvements:
1) Our binary releases should include the latest compiler features and optimizations. The concrete motivating example is that the compiler used for Mac wheels apparently doesn't pass [`COMPILER_SUPPORTS_BF16_TARGET`](https://g... | true |
2,761,088,011 | Fix always true scaled_mm test | dnikolaev-amd | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: float8",
"ciflow/rocm"
] | 17 | CONTRIBUTOR | Looks like `out_fp8` should use matmul without scales and `out_fp8_s` with
Scales were optional arguments before PR https://github.com/pytorch/pytorch/pull/128683
Then test_float8_scale started comparing two identical results and lost its meaning
Reason of making scales required https://github.com/pytorch/pytorch/pu... | true |
2,761,034,531 | Add `_benchmark_func` convenience method | malfet | closed | [
"Stale",
"release notes: benchmark",
"topic: improvements",
"ciflow/mps"
] | 3 | CONTRIBUTOR | Which could be used to benchmark simple ops with just one line of code, for example:
```shell
% python -c "import torch;print(torch.testing._benchmark_func(torch.add, (1024, 1024), device='mps', dtype=torch.int32))"
<torch.utils.benchmark.utils.common.Measurement object at 0x1081dee40>
f(*args);torch.mps.synchroni... | true |
2,761,029,625 | The link for the source in page torch.Tensor.backward is broken. | qqwqqw689 | closed | [
"module: docs",
"module: autograd",
"triaged",
"needs design"
] | 3 | NONE | ### 📚 The doc issue
The link for the source in page torch.Tensor.backward is broken.[link](https://pytorch.org/docs/stable/generated/torch.Tensor.backward.html)
### Suggest a potential alternative/fix
_No response_
cc @svekars @brycebortree @sekyondaMeta @AlannaBurke @ezyang @albanD @gqchen @pearu @nikitaved @soul... | true |
2,761,016,695 | cpp_wrapper: Move #includes to per-device header files | benjaminglass1 | closed | [
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/xpu",
"ci-no-td"
] | 17 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144349
* #144293
* #144002
* __->__ #143909
This prepares us for the next PR in the stack, where we introduce pre-compiled per-device header files to save compilation time.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @Xiao... | true |
2,760,977,421 | [EZ] Update sympy to 1.13.3 | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 9 | CONTRIBUTOR | And remove python>=3.9 check as it currently covers all supported python versions
Fixes https://github.com/pytorch/pytorch/issues/143907
| true |
2,760,882,282 | update `sympy` version in `requirement.txt` | evan0greenup | closed | [
"triage review",
"module: build",
"module: ci"
] | 1 | NONE | ### 🐛 Describe the bug
Now, sympy version is `1.13.3`, but `torch` is hard required `sympy` version to be `1.13.1`, it will cause inconvenient in an environment which require `sympy` to be latest.
### Versions
<https://github.com/pytorch/pytorch/blob/a20765a9c1e578beb5e53f9a3ef0c13ea6839768/requirements.txt#L19>
... | true |
2,760,761,266 | How to correctly asynchronously copy a GPU tensor to a CPU tensor in another process without introducing blocking? | zhanghb55 | open | [
"needs reproduction",
"oncall: distributed",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
I am developing a distributed PyTorch application designed to asynchronously transfer data from a GPU process to a CPU process, ensuring that GPU computations remain non-blocking. In my current implementation, I utilize the non-blocking copy_ method to transfer data from a GPU tensor to a CPU... | true |
2,760,730,501 | The special size tensor containing batches has a difference of a few tens of thousands in calculation results between CPU and GPU | fine2copyV | open | [
"needs reproduction",
"module: cuda",
"triaged"
] | 4 | NONE | ### 🐛 Describe the bug
You can modify the comments to switch and run to view the changes in the results！
```
import torch.nn as nn
import torch.nn.functional as F
import torch
BN_MOMENTUM = 0.1
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=... | true |
2,760,710,227 | [Inductor][CPP] Remove redundant Buffers after Grouped GEMM Fusion | leslie-fang-intel | closed | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143904
* #143897
* #143796
**Summary**
In this PR, we remove the extra kernel arguments and the extra buffers allocation when any `MultiOutput Buffer` is consumed by an out-template epilogue. If any `MultiOutput Buffer` is consumed... | true |
2,760,678,480 | [Quant][Inductor][X86] Separate unary post op fusion and lowering for qlinear | Xia-Weiwen | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"intel",
"module: inductor",
"ciflow/inductor"
] | 8 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144318
* #144312
* #144224
* __->__ #143903
**Summary**
The current implementation fuses quantized ops and their post ops and lowers the fused the op to cpp backend in the same pass. It is better to separate post op fusion and lowering be... | true |
2,760,655,983 | [Easy] add quotes to shell activation commands | XuehaiPan | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143262
* __->__ #143902
| true |
2,760,622,674 | gpu, matmul, shape is bad, the debug quits and I got no way to hold it. | YagaoDirac | closed | [
"needs reproduction",
"module: cuda",
"triaged"
] | 2 | NONE | ### 🐛 Describe the bug
python312 pytorch2.5.1+cu124
win11, vs code.
gtx1660
inside a customized autograd.function.
very small model.
I messed with the shape, and the matmul throwed. I started to check everything as usual, but the process quits like 20seconds after it throwed.
Then I move the entire task to cpu... | true |
2,760,618,934 | FSDP mixed precision ignores buffer_dtype | GLivshits | closed | [
"oncall: distributed",
"module: fsdp"
] | 1 | NONE | ### 🐛 Describe the bug
Hello. I found out that buffers in FSDP are not casted to requested dtype, and code breaks. User is forced to cast buffers each time in forward.
Piece of error:
```
File "/home/user/regbuf_compile_debug.py", line 44, in forward
return nn.functional.conv2d(x, self.kernel, groups=self... | true |
2,760,616,632 | Fix boundary conditions for hardswish backward | CaoE | closed | [
"module: cpu",
"open source",
"Merged",
"ciflow/trunk",
"release notes: nn",
"topic: not user facing"
] | 7 | COLLABORATOR | Fixes #136345.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 | true |
2,760,613,728 | pytorch2.5.1的版本支持这个算子了吗：aclnnFusedInferAttentionScoreV2 | ZWQ2-A11Y | closed | [
"triage review",
"module: PrivateUse1"
] | 3 | NONE | pytorch2.5.1的版本支持这个算子了吗：aclnnFusedInferAttentionScoreV2
cc @NmomoN @mengpenghui @fwenguang @cdzhan @1274085042 @PHLens | true |
2,760,568,736 | [Inductor][CPP] Enable Epilogue Fusion for Grouped GEMM Template | 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):
* __->__ #143897
* #143796
**Summary**
In this PR, we enable the epilogues fusion and code generation for Grouped GEMM. Here are the high-level description of how we implement it.
**Fusion**
- The Grouped GEMM Template produces a `Temp... | true |
2,760,465,533 | Using acc_t for log_softmax | yanbing-j | open | [
"module: cpu",
"open source",
"ciflow/trunk",
"topic: not user facing"
] | 11 | COLLABORATOR | This PR is to fix https://github.com/pytorch/pytorch/issues/140222. Using high precision as the accumulate type for log_softmax forward. Reproducer in the issue can pass now.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143896
cc @jgong5 @mingfeima @XiaobingSuper @sanch... | true |
2,760,441,591 | When using torch.compile to compile the function _kernel_make_viewless_tensor, an error occurs：AssertionError: wrong number of dimensions | FY-Summer | closed | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 3 | NONE | ### 🐛 Describe the bug
test device： NVidia L20
software version：
torch 2.5.1
torchaudio 2.5.1
torchvision 0.20.1
triton 3.1.0
The test codes are as follows.
I’m sure it’s related to the parameter “requires_grad” of the function ”_kernel_make... | true |
2,760,435,965 | Fix fft jit ops cpu | ZhaoqiongZ | closed | [
"triaged",
"open source",
"Stale",
"ciflow/trunk",
"topic: not user facing"
] | 12 | CONTRIBUTOR | Fixes #142484 | true |
2,760,354,401 | [Inductor] Implement primitive Metal compiler | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: improvements",
"release notes: mps",
"ciflow/mps",
"module: inductor",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143893
* #143892
Still work in progress, only works for element wise operations. Current implementation could be used to turn something like
```python
def f(x):
return x[:,::2].sin() + x[:, 1::2].cos()
```
into the followi... | true |
2,760,354,375 | [Inductor] Add MPS device op overrides | malfet | 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):
* #143893
* __->__ #143892
Mostly dummy interface as MPS backend currently limited to a single device
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @cheny... | true |
2,760,354,344 | [Dynamo] Add MPSDevice interface | malfet | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143893
* #143892
* __->__ #143891
That simply checks if device is available and whether or not it supports bf16
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyan... | true |
2,760,342,091 | TORCH_NCCL_ENABLE_TIMING break nccl/matmul overlapping | cos120 | closed | [
"oncall: distributed",
"module: nccl"
] | 22 | NONE | ### 🐛 Describe the bug
I am using Megatron-LM for training, I found that if I set `TORCH_NCCL_ENABLE_TIMING=1`, all overlaping kernels in Megatron-LM will not overlapped, including dw/dx backward in layer norm and zero1 reduce scatter/allgather not overlapping with matmul.
I have submit a issue to `TransformerEn... | true |
2,760,310,498 | The in-place version of unsqueezed is not supported by TorchDynamo when used in a specific way | meetmul | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 0 | NONE | ### 🐛 Describe the bug
If I directly call `torch.Tensor.unsqueeze_(x,y)` in my function, torch.compile fails with InternalTorchDynamoError. However, if I change the code to `x.unsqueeze_(y) format, torch.compile works.
code:
```python
import torch
@torch.compile
def f1(x, y):
return x.unsqueeze(y)
... | true |
2,760,287,213 | [dynamo] Trace through overridden __getattribute__ method | anijain2305 | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"keep-going"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143698
* __->__ #143888
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,760,268,096 | `torch.accelerator` cross-device utilities and properties | stas00 | open | [
"triaged",
"module: accelerator"
] | 2 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
as suggested by @albanD [here](https://pytorch.slack.com/archives/C3PDTEV8E/p1735120754479929?thread_ts=1735017298.875249&cid=C3PDTEV8E) opening an issue to discuss which cross-device utilities and device property fields should pytorch support.
1. properties report at the ... | true |
2,760,254,098 | [RFC] Identifying dynamic int8 symmetric vs asymmetric quantization of activation/input in Inductor-CPU | sanchitintel | open | [
"oncall: pt2",
"oncall: cpu inductor"
] | 0 | COLLABORATOR | ### 🚀 The feature, motivation and pitch
## Problem statement
If int8 asymmetric quantization is used, at Inductor compile time, the input used while invoking `torch.compile` might be such that the zero-points of activation for some quantized linear may _coincidentally_ be zero (per-tensor quantization) or all ze... | true |
2,760,246,016 | restore 'unused' variable to fix test_cuda_device_memory_allocated | dnikolaev-amd | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 5 | CONTRIBUTOR | This PR fix `test_cuda_multigpu.py::TestCudaMultiGPU::test_cuda_device_memory_allocated`
by restoring a deleted 'unused' variable from commit https://github.com/pytorch/pytorch/commit/d8c8ba24404ef892d4d948eb095b69d90b9ba7e6
cc @jithunnair-amd @jeffdaily @pruthvistony | true |
2,760,218,546 | [Inductor] Relax size constraints for re-inplacing | BoyuanFeng | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | Current reinplacing requires input buffer and output buffer has exactly the same storage size. However, matmul padding may increase the tensor size slightly for better performance, which prevents reinplacing.
This PR changes the size constraints to be:
- input and output buffer have exactly the same symbolic expres... | true |
2,760,201,298 | [dtensor] add src_data_rank to distribute_tensor API | wanchaol | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (dtensor)"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #144005
* __->__ #143883
As titled, this PR add a kwarg src_data_rank to the distribute_tensor
API, to allow user specify a specific rank as the full tensor source
data. Previously we by default specify group_rank=0 as the source of
truth fo... | true |
2,760,194,003 | Add support for list, tuple and dict in numeric debugger | jerryzh168 | closed | [
"Merged",
"ciflow/trunk",
"release notes: quantization"
] | 6 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143882
Summary:
Previously numeric debugger only supports torch.Tensor, this PR adds support for list, tuple and dict as well
Test Plan:
python test/test_quantization.py -k test_extract_results_from_loggers_list_output
Reviewers:
... | true |
2,760,172,691 | remove allow-untyped-defs from _inductor/codegen/cpu_device_op_overrides.py | bobrenjc93 | 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):
* __->__ #143881
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov | true |
2,760,141,925 | Add option to serialization config to reduce random reads from get_record_offset when loading with mmap=True | mikaylagawarecki | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"release notes: python_frontend",
"topic: improvements",
"ciflow/inductor",
"ci-no-td"
] | 13 | CONTRIBUTOR | ## Background
This PR adds `torch.utils.serialization.config.load.calculate_storage_offsets`. This option relies on the previous PR in this stack, where storage order was changed to non lexicographical. A `.format_version` entry was added to the zipfile and `calculate_storage_offsets` will only work on checkpoints ... | true |
2,760,141,879 | Remove lexicographical sorting of storage keys in torch.save | mikaylagawarecki | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"ci-no-td"
] | 27 | CONTRIBUTOR | Currently the order lexicographical (i.e. 0, 10, 11, ...19, 2, ....) instead of 0, 1, 2, 3, 4, 5 (the order that storage metadata is actually pickled in), since PyTorch will never be used with Python < 3.7 we can be assured that the keys will be read in the order of insertion (numerically sorted)
This makes it such ... | true |
2,760,111,010 | [fr][c10d] fix flaky test | c-p-i-o | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143878
* #143865
Summary:
Test erroneously assumed that input/output sizes are same and that all
states are matchable.
Fixes issue #143798
Test Plan:
Test passes
Reviewers
Test passes
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin ... | true |
2,760,071,733 | dont assign a size to _assert_scalar in partitioner | bdhirsh | closed | [
"Merged",
"ciflow/trunk",
"release notes: composability",
"ciflow/inductor"
] | 8 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/143876
Open to other suggestions - we have an invariant that all nodes in our ATen graphs should have a `meta['val']` field, but I don't think this is actually true in all cases, so I just hardcoded the invariant to ignore `_assert_scalar()` (which is a "special" op us... | true |
2,760,059,559 | `aten._assert_scalar` can hard error the partitioner | bdhirsh | closed | [
"triaged",
"oncall: pt2",
"module: dynamic shapes",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 0 | CONTRIBUTOR | internal xref: https://fb.workplace.com/groups/1075192433118967/permalink/1567692087202330/
(second xref: https://fb.workplace.com/groups/1075192433118967/posts/1574136133224592/?comment_id=1575214129783459&reply_comment_id=1577334836238055)
I haven't been able to run the internal repro properly, but I did make a... | true |
2,760,054,588 | Use random64 in Fischer-Yates algorithm for large N (#143682) | ngimel | closed | [
"release notes: dataloader"
] | 1 | COLLABORATOR | Fixes bug in randperm https://nbsanity.com/static/a4774194938414dedcec7d6e99727d31/Shuffling_20in_20torch_20vs_20numpy-public.html
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143682
Approved by: https://github.com/eqy, https://github.com/albanD
Fixes #ISSUE_NUMBER
| true |
2,760,047,872 | [Performance] Simple arithemtic operations are slower using MPS than Metal | malfet | closed | [
"module: performance",
"triaged",
"module: mps"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
Reported by @swolchok and could be confirmed by running something like the following
```python
import torch
from timeit import default_timer
from torch.utils.benchmark import Measurement, Timer
def bench_binary(
n,
binary_func,
dtype=torch.float32,
) -> Measurement:
... | true |
2,760,031,393 | use statically known true over guards in tensor view ops | bobrenjc93 | closed | [
"ciflow/trunk",
"topic: not user facing"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143873
internal xref: https://fb.workplace.com/groups/1075192433118967/posts/1570866680218204/
Differential Revision: [D67651945](https://our.internmc.facebook.com/intern/diff/D67651945) | true |
2,760,030,184 | [FlexAttention] make bm creation cuda-graphable | drisspg | closed | [
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"module: flex attention"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143872
# Summary
Addresses: https://github.com/pytorch/pytorch/issues/143840
Current dynamic failing test: test/inductor/test_flex_attention.py::TestBlockMask::test_compiling_create_block_mask_no_recompile - torch._dynamo.exc.... | true |
2,760,011,236 | remove allow-untyped-defs from torch/distributed/pipelining/_debug.py | bobrenjc93 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143871
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,760,011,176 | remove allow-untyped-defs from _inductor/codegen/rocm/rocm_template_buffer.py | bobrenjc93 | closed | [
"module: rocm",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/rocm"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143870
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @j... | true |
2,760,011,123 | remove allow-untyped-defs from distributed/elastic/multiprocessing/errors/handlers.py | bobrenjc93 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"release notes: distributed (torchelastic)"
] | 12 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143869
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,760,011,088 | remove allow-untyped-defs from fx/experimental/refinement_types.py | bobrenjc93 | closed | [
"Merged",
"ciflow/trunk",
"release notes: fx",
"topic: not user facing",
"fx"
] | 10 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143868
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,760,011,032 | remove allow-untyped-defs from torch/ao/quantization/experimental/APoT_tensor.py | bobrenjc93 | closed | [
"release notes: quantization",
"release notes: AO frontend"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143873
* #143871
* #143870
* #143869
* #143868
* __->__ #143867
| true |
2,759,989,549 | Fix batch-specific attention mod for NJT + Flex | jbschlosser | closed | [
"Merged",
"ciflow/trunk",
"topic: bug fixes",
"release notes: nested tensor"
] | 9 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143866
Fixes #143788 | true |
2,759,977,242 | [fr][c10d] log trace capture enabled or not in flight recorder | c-p-i-o | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (c10d)"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #143878
* __->__ #143865
Summary:
Refactor logging for flight recorder so we can log if the capture was
with or without stack trace capture enabled.
We introduce a new column ('trace_enabled') in the logger.
Test Plan:
Tested on local job a... | true |
2,759,949,743 | Adaptive pool MPS | sebassaras02 | open | [
"triaged",
"enhancement",
"module: pooling",
"module: mps"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
Hello, I've been trying to train a VGG architecture over a M3 chip.
I have this mistake:
RuntimeError: Adaptive pool MPS: output sizes must be divisible by input sizes. Non-divisible input sizes are not implemented on MPS device yet. For now, you can manually transfer ten... | true |
2,759,820,850 | Composite RoPE gives ridiculous profiling trace | Mmmofan | closed | [
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
As I described in https://discuss.pytorch.org/t/composite-rope-backward-gives-a-large-tocopybackward0-in-profiling-trace/214668 , this code outputs a ridiculous trace json:
```python
#!/usr/bin/env python
# encoding: utf-8
import torch
from torch.nn import functional as F
import time
... | true |
2,759,745,960 | pytorch v2.2.2 build for nvidia jetson orin nano 8GB | lida2003 | closed | [
"module: build",
"triaged",
"module: jetson"
] | 2 | NONE | ### 🐛 Describe the bug
pytorch v2.2.2 build for nvidia jetson orin 8GB
Previous discussion here FYI: https://forums.developer.nvidia.com/t/request-build-script-for-pytorch-or-up-to-date-pytorh-binary-release-supporting-jetson-boards-running-l4t35-6-ubuntu20-04/316972/12
```
commit 39901f229520a5256505ec24782f7... | true |
2,759,696,192 | _transform_bias_rescale_qkv cpu op get error on debug build | garfield1997 | open | [
"module: nn",
"triaged"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
The following code will produce the following error
code
```
import torch
qkv = torch.randn([4, 16, 576])
qkv_bias = torch.randn([576])
num_heads=4
torch._transform_bias_rescale_qkv(
qkv, qkv_bias, num_heads
)
```
output
```
Traceback (most recent call last):
File ... | true |
2,759,665,288 | MPSNDArray 限制了单个 NDArray 的内存大小上限为 4GB | OutisLi | open | [
"needs reproduction",
"module: crash",
"triaged",
"module: 64-bit",
"module: mps"
] | 2 | NONE | ### 🐛 Describe the bug
/AppleInternal/Library/BuildRoots/b11baf73-9ee0-11ef-b7b4-7aebe1f78c73/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MPSCore/Types/MPSNDArray.mm:850: failed assertion `[MPSNDArray initWithDevice:descriptor:isTextureBacked:] Error: total bytes of NDArray > 2**32'
[1] 13512 abo... | true |
2,759,644,273 | Make init_method deprecated to fix TCP connection refused error | taozhiwei | open | [
"oncall: distributed",
"triaged",
"open source",
"topic: not user facing",
"module: inductor"
] | 11 | CONTRIBUTOR | ```
import os
os.environ["TORCH_CPP_LOG_LEVEL"] = "INFO"
os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
import torch
import torch.distributed as dist
def main():
rank = int(os.environ["RANK"]) if "RANK" in os.environ else 0
world_size = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.e... | true |
2,759,631,923 | `@torch.jit.script` causes `pytest-cov` to miss function body | anvdn | open | [
"oncall: jit",
"feature"
] | 1 | NONE | ### 🐛 Describe the bug
When decorating a function with `@torch.jit.script`, its body's code coverage is ignored by `pytest-cov`. Even with exhaustive testing, the coverage report always considered the function code as uncovered.
### Instructions to reproduce
```
root/
│
├── ml_framework/
│ └── module.py
... | true |
2,759,624,458 | pytorch v2.3.1 build for nvidia jetson orin nano 8GB | lida2003 | closed | [
"module: build",
"module: jetson"
] | 1 | NONE | ### 🐛 Describe the bug
pytorch v2.3.1 build for nvidia jetson orin 8GB
Previous discussion here FYI: https://forums.developer.nvidia.com/t/request-build-script-for-pytorch-or-up-to-date-pytorh-binary-release-supporting-jetson-boards-running-l4t35-6-ubuntu20-04/316972/12
```
$ git log -n 1
commit 63d5e9221bedd... | true |
2,759,594,987 | Fix _create_c10d_store error | taozhiwei | closed | [
"oncall: distributed",
"module: rocm",
"module: cpu",
"release notes: releng",
"fx",
"module: inductor",
"module: dynamo"
] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
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 @ashokei @jingxu10 @ezyang @SherlockNoMad @EikanW... | true |
2,759,484,185 | Can't script a tensorrt model | He1pa | open | [
"oncall: jit"
] | 2 | NONE | ### 🐛 Describe the bug
I am a newbie for pytorch. I try to use tensorrt to optimize the model and save it as trt engine(*.plan). I tried the following:
torch -> trt model -> torch script -> trt engine
try to script a tensorrt model
```
class Model(nn.Module):
def __init__(self):
super(Model, self)... | true |
2,759,467,427 | Update torch-xpu-ops commit pin | xytintel | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 4 | CONTRIBUTOR | Update the torch-xpu-ops commit to [214f33](https://github.com/intel/torch-xpu-ops/commit/214f33b9d969930a18656a82b5c5d8da53cdcb8e), includes:
- Fix building issue for transformer related operators
- Improve XPU operator coverage
| true |
2,759,457,585 | [CPU][Operator] one channel_shuffle test of the operator benchmark has a Performance fluctuation issue | LifengWang | open | [
"needs reproduction",
"module: performance",
"module: nn",
"triaged"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
I conducted the operator benchmark and found one channel_shuffle test of the operator benchmark has a performance fluctuation issue. The test is benchmarkchannel_shuffle_batch_size4_channels_per_group64_height64_width64_groups4_channel_lastTrue.
Set up the test environment according to the [... | true |
2,759,446,630 | [CI] Disable sccache for xpu test | chuanqi129 | closed | [
"open source",
"Merged",
"topic: not user facing",
"ciflow/xpu"
] | 3 | COLLABORATOR | WA for https://github.com/pytorch/pytorch/issues/143585
| true |
2,759,412,541 | [WIP] [Inductor][CPP] Support Group GEMM Epilogue Fusion | leslie-fang-intel | closed | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143850
* #143820
* #143796
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @cha... | true |
2,759,384,359 | Refine CUDA Stream priority | guangyey | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: cuda",
"topic: improvements"
] | 4 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143849
* #143799
* #141123
* #141119
* #142347
# Motivation
As mentioned in https://github.com/pytorch/pytorch/pull/141119#discussion_r1897480515, we properly handle the priority value if it is outside of the priority range.
# A... | true |
2,759,286,265 | [Inductor][CPU] Fix C++ compile error of torch.max on bool type | blzheng | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143848
Fix https://github.com/pytorch/pytorch/issues/143568
Before:
After:
![image](https://github.com/user-attachments/assets/91f72920-64... | true |
2,759,239,903 | memory_format=torch.preserve_format doesn't apply to tensors with strides of zero | EmmettBicker | closed | [
"triage review",
"module: python frontend"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
The memory_format=torch.preserve_format seems to ignore tensors with a 0 stride somewhere, like in this following example. I don't know if this is intentional or not, but I wanted to bring it up in case it wasn't!
```py
import torch
arg = torch.randn([2,1]).expand(2,2)
print(arg.stride(... | true |
2,759,233,264 | Check F2C BLAS for OpenBLAS and other vendors | isuruf | open | [
"open source",
"release notes: build"
] | 6 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143846
This issue came from https://github.com/conda-forge/pytorch-cpu-feedstock/issues/180. MKL follows the F2C convention for returning single precision floats as doubles and uses the G77 convention for returning complex valued ... | true |
2,759,200,450 | [Inductor][lowering] support out_dtype for dequant lowering | Valentine233 | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 3 | COLLABORATOR | In lowering, support the parameter `out_dtype` for `dequant_per_tensor` and `dequant_per_channel`.
Fix the following runtime error issue found in https://github.com/pytorch/ao/pull/1372:
```
File "/home/liaoxuan/pytorch_ao/torch/_inductor/lowering.py", line 452, in wrapped
out = decomp_fn(*args, **kwargs)
... | true |
2,759,148,084 | Bump jinja2 from 3.1.4 to 3.1.5 in /.ci/docker | dependabot[bot] | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"dependency issue",
"python"
] | 4 | CONTRIBUTOR | Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.4 to 3.1.5.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/pallets/jinja/releases">jinja2's releases</a>.</em></p>
<blockquote>
<h2>3.1.5</h2>
<p>This is the Jinja 3.1.5 security fix release, which fixes security issue... | true |
2,759,146,299 | [Submodule] Bump libfmt to 11.1.0 | cyyever | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,759,145,849 | subgraph rewriter supports matched pattern with no users | YangQun1 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: fx",
"fx"
] | 8 | CONTRIBUTOR | Fixes #143841
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv | true |
2,759,143,372 | Subgraph rewriter failed when the matched pattern has no users | YangQun1 | closed | [
"oncall: pt2",
"oncall: export"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
The subgraph rewriter will throw an error "The returning_nodes should have at least one user node", when the matched pattern has no users in the original graph.
Can reproduce with below example
```python
class M(torch.nn.Module):
def forward(self, x, y, cache):
m = torch.mul(... | true |
2,759,123,651 | FlexAttention `create_block_mask` contains a CUDA sync | moinnadeem | closed | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 1 | NONE | ### 🐛 Describe the bug
I am trying to capture our model forward pass into a CUDA graph, but Flex Attention's `create_block_mask` contains a graph break.
I'm honestly not sure if this is a "bug" or a "feature request".
I have tested `create_block_mask` both with and without `_compile=True` and it happens in both... | true |
2,759,055,014 | [CD] Remove redundant triton dependency for xpu wheels | chuanqi129 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/binaries_wheel"
] | 8 | COLLABORATOR | Due to XPU CD wheels enabled pypi dependencies by https://github.com/pytorch/pytorch/pull/141135, so the PYTORCH_EXTRA_INSTALL_REQUIREMENTS has value for XPU CD wheel build.
Works for https://github.com/pytorch/pytorch/issues/139722 and https://github.com/pytorch/pytorch/issues/114850
Fixes #143838
| true |
2,759,054,047 | PyTorch XPU 2.6 RC wheel has multiple triton dependencies | chuanqi129 | closed | [
"triaged",
"module: xpu"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
Currently, the xpu CD linux wheels have multiple triton pypi packages dependencies, which depends on `triton` and `pytorch-triton-xpu`, refer
```
$ pip install torch==2.6 --index-url https://download.pytorch.org/whl/test/xpu
Looking in indexes: https://download.pytorch.org/whl/test/xpu
C... | true |
2,758,895,058 | [BE]: Update mypy to 1.14.0 | Skylion007 | closed | [
"open source",
"Stale",
"topic: not user facing"
] | 3 | COLLABORATOR | Updates mypy to the latest and greatest | true |
2,758,891,270 | Integration of AdamCPR Optimizer into PyTorch | ZiadHelal | open | [
"module: optimizer",
"triaged"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
# Proposal: Integration of AdamCPR Optimizer into PyTorch
**Authors:**
- @ZiadHelal
## **Summary**
We propose the integration of AdamCPR, a novel deep learning optimizer developed at the University of Freiburg, into PyTorch's core optimizer library. AdamCPR builds upo... | true |
2,758,860,352 | [inductor] Simplify get_launch_args_* handling | jansel | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/inductor-rocm"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #143835
* #143818
* #143817
* #143815
* #143814
* #143813
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPep... | true |
2,758,847,815 | Copy trans fixl2 miss | coderfeli | closed | [
"oncall: distributed",
"module: rocm",
"release notes: releng",
"module: inductor"
] | 2 | NONE | Fixes #ISSUE_NUMBER
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 @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wen... | true |
2,758,846,948 | [1/N]Add Intel GPU Support to Torch Test Cases | daisyden | closed | [
"triaged",
"open source",
"Stale",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/xpu",
"ci-no-td"
] | 7 | NONE | As the first step to https://github.com/pytorch/pytorch/issues/142029:
- Define device checkers in common_utils.py to facilitate test generalization, for example GPU_TYPE for current available gpu device.
- Define dtypesIfGPU and backward_dtypesIfGPU in OpInfo
- Use GPU_TYPE, dtypesIfGPU and backward_dtypesIfGPU t... | true |
2,758,831,533 | flex_attention: OutOfResources | rmmr | closed | [
"triaged",
"oncall: pt2",
"module: higher order operators",
"module: pt2-dispatcher",
"module: flex attention"
] | 1 | NONE | ### 🐛 Describe the bug
Not sure if my expectations are wrong, but this should just work?
```
import torch
from torch.nn.attention.flex_attention import flex_attention
torch.compiler.reset()
flex_attention = torch.compile(flex_attention)
torch.manual_seed(1)
x = torch.rand(1, 1, 32, 256).to(device="cuda")
... | true |
2,758,781,540 | After pth is converted into ptl, the prediction result is very different from pth | lizhiwen19900709 | open | [
"oncall: jit"
] | 1 | NONE | ### 🐛 Describe the bug
# 加载原始配置和模型
checkpoint = torch.load(checkpoint_path, map_location='cuda')
args = checkpoint['args']
args.num_classes = 250
# 构建模型
model, _, _ = build_model(args)
model.load_state_dict(checkpoint['model'])
model.eval()
wrapped_model = DETRWrapper(model... | true |
2,758,751,093 | [Doc] Add `weight` and `bias` attributes to RMSNorm and GroupNorm | gau-nernst | closed | [
"triaged",
"open source",
"Stale"
] | 3 | NONE | I noticed RMSNorm doc doesn't mention about `weight` and `bias` attributes like LayerNorm does, so I adds it here. While adding that, I saw GroupNorm also didn't have such attribute doc, so I added it too.
New rendered text
Class | Doc
------|------
RMSNorm | <img width="656" alt="image" src="https://github.com... | true |
2,758,717,385 | [DCP]Distributed checkpoint `set_optimizer_state_dict` cause optimizer step error when optimizer contains empty param group | FindDefinition | closed | [
"oncall: distributed",
"module: optimizer",
"triaged"
] | 9 | NONE | ### 🐛 Describe the bug
DCP `set_optimizer_state_dict` introduce wrong param group and cause `optim.step` raise error when original state dict contains param group that doesn't have any parameters.
* Error Message
```
[rank1]: Traceback (most recent call last):
[rank1]: File "/path/to/pytorch_bug/dcp_bug.p... | true |
2,758,695,846 | XPU PyTorch 2.6 WARNING: hwloc library not found in /tcm/latest/lib | ekaakurniawan | closed | [
"triaged",
"module: xpu"
] | 3 | NONE | ### 🐛 Describe the bug
When setting up UMF environment variables, I get the following warning. It is due to ONEAPI_ROOT is never set.
```
$ source /opt/intel/oneapi/umf/0.9/env/vars.sh
WARNING: hwloc library not found in /tcm/latest/lib
```
I need to run oneAPI setup variables to clear the warning. Please help... | true |
2,758,685,432 | [don't merge] build cpu via vs2022 (test diff) | xuhancn | closed | [
"open source",
"ciflow/binaries",
"topic: not user facing"
] | 1 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,758,684,671 | Tensor.item() blocks cudaLaunchKernel on other threads. | li-yi-dong | closed | [] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
Tensor.item() using `cudaMemcpyAsync` which triggers a Memcpy DtoH (Device -> Pageable). It seems that this kind of `cudaMemcpyAsync` would block any other `cudaLaunchKernel`, even on other thread.
I'm... | true |
2,758,643,409 | [inductor][cpu] AMP/FP32 single thread performance regression in 2024-12-23 nightly release | zxd1997066 | open | [
"needs reproduction",
"triaged",
"oncall: pt2",
"module: dynamo"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
<p>AMP static shape default wrapper</p><table border="1" class="dataframe table">
<thead>
<tr style="text-align: right;">
<th>suite</th>
<th>name</th>
<th>thread</th>
<th>batch_size_new</th>
<th>speed_up_new</th>
<th>inductor_new</th>
<th... | true |
2,758,605,243 | Possible race condition found in TailLogTest.test_tail | cdzhan | open | [
"oncall: distributed",
"module: tests",
"module: elastic"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
### Error message
```bash
Time: 12/20/2024 10:05:37, Level: 40000, Log: Traceback (most recent call last):
File "/opt/py3.10/lib/python3.10/unittest/case.py", line 59, in testPartExecutor
yield
File "/opt/py3.10/lib/python3.10/unittest/case.py", line 591, in run
self._callT... | true |
2,758,557,847 | [Inductor][CPP][CPU] Fix floating point exception error during division/mod | maybeLee | closed | [
"triaged",
"open source",
"Stale",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 4 | CONTRIBUTOR | Fixes #143649
This PR fixes the floating point exception in four operators: torch.floor_divide, torch.remainder, torch.fmod, torch.divide.
Before this PR, when both `a` and `b` are integer tensors and `b=0`:
| API | Eager Mode | torch.compile mode |
| -------- | ------- | ------- |
| torch.floor_divid... | true |
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