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 |
|---|---|---|---|---|---|---|---|---|
2,917,834,772 | scaled_dot_product_attention crashes on apple silicon | jjh42 | closed | [
"module: crash",
"triaged",
"module: mps",
"module: sdpa"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
This following python code fails and ends the process on macos 15.3.1 (M1 Pro).
```python
import torch
import torch.nn.functional as F
print(torch.__version__)
device = torch.device('mps')
B=2
T=3
n_kv_head = 2
n_q_head = 4
dim = 8
attn_mask = torch.ones((T, T)).to(device)
q = torch.rand... | true |
2,917,805,444 | nn.GaussianNLLLoss and F.gaussian_nll_loss do not work with scalar `var` | connor-krill | closed | [
"module: loss",
"triaged",
"module: python frontend"
] | 3 | NONE | ### 🐛 Describe the bug
The documentation for [nn.GaussianNLLLoss](https://pytorch.org/docs/stable/generated/torch.nn.GaussianNLLLoss.html) states that the `var` input can be a scalar value, but an error occurs if a float is used. Similarly, the documentation for the functional version [nn.functional.gaussian_nll_loss... | true |
2,917,735,045 | Deterministic support for adaptive_avg_pool2d_backward_cuda | gill179 | open | [
"module: cuda",
"triaged",
"module: determinism",
"module: python frontend"
] | 0 | NONE | ### 🚀 The feature, motivation and pitch
UserWarning: adaptive_avg_pool2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True, warn_only=True)'. You can file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic sup... | true |
2,917,647,320 | [cherry-pick] [CI] Don't clean workspace when fetching repo (#147994) | atalman | closed | [
"topic: not user facing"
] | 2 | CONTRIBUTOR | Cherry-Pick the revert: [CI] Don't clean workspace when fetching repo (#147994) | true |
2,917,605,740 | "asinh" operator is supported in ONNX, but conversion to ONNX fails? | yuecheng-ma | closed | [
"module: onnx",
"triaged",
"onnx-triaged"
] | 3 | NONE | ### 🐛 Describe the bug
According to the ONNX operator docs, this operator has been supported since version 9. But when exporting my PyTorch model to ONNX with opset version explicitly set to 20, I still get an 'unsupported operator' error. What could be the reason?
![Image](https://github.com/user-attachments/assets/... | true |
2,917,519,768 | added fake tensor support for foreach_copy | pralay-das | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: inductor"
] | 22 | CONTRIBUTOR | Fixes #149111
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,917,509,315 | SubsetRandomSampler - changed iteration over tensor to iteration over list | arn4 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: dataloader"
] | 7 | CONTRIBUTOR | Digging further the problem at https://github.com/UKPLab/sentence-transformers/pull/3261, it boils down to this expensive loop over a torch tensor. Looping over a list, like in RandomSampler, solves the issue.
| true |
2,917,495,887 | Remove runtime dependency on packaging | pytorchbot | closed | [
"open source",
"module: inductor",
"ciflow/inductor"
] | 1 | COLLABORATOR | Looks like after https://github.com/pytorch/pytorch/pull/148924
We are seeing this error in nightly test:
https://github.com/pytorch/pytorch/actions/runs/13806023728/job/38616861623
```
File "/Users/runner/work/_temp/anaconda/envs/test_conda_env/lib/python3.13/site-packages/torch/_inductor/pattern_matcher.py", ... | true |
2,917,350,105 | `torch.multinomial` fails under multi-worker DataLoader with a CUDA error: `Assertion cumdist[size - 1] > 0` failed | yewentao256 | closed | [
"triaged",
"module: data",
"module: python frontend"
] | 2 | CONTRIBUTOR | ### 🐛 Describe the bug
When using `torch.multinomial` in a Dataset/IterableDataset within a `DataLoader` that has multiple workers (num_workers > 0), an assertion error is thrown from a CUDA kernel:
```bash
pytorch\aten\src\ATen\native\cuda\MultinomialKernel.cu:112: block: [0,0,0], thread: [0,0,0] Assertion `cumdist... | true |
2,917,337,808 | [MPS] Add `torch.special.bessel_[jy][01]` implementations | malfet | closed | [
"Merged",
"topic: improvements",
"release notes: mps",
"ciflow/mps"
] | 5 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149123
By copy-n-pasting functions from
https://github.com/pytorch/pytorch/blob/f59064f2b700860a16db1930c30a4691ab663401/aten/src/ATen/native/cuda/Math.cuh#L1463
With an ugly workaround for `bessel_y[01]` to avoid internal c... | true |
2,917,155,986 | Update the heuristic for AArch64 bmm/baddbmm | michalowski-arm | open | [
"module: cpu",
"triaged",
"open source",
"module: arm",
"release notes: linalg_frontend"
] | 6 | CONTRIBUTOR | Updates heuristic for bmm/baddbmm and consolidates all heuristic logic in a single location
- The goal of the consolidation is to improve maintainability and readability of the heuristic logic. Instead of different parts scattered across two files, this patch centralizes everything inside `Matmul.cpp`, where there ... | true |
2,917,058,303 | DISABLED test_compile_body_aliasing_contents_backend_aot_eager (__main__.TestCompileTorchbind) | pytorch-bot[bot] | closed | [
"module: flaky-tests",
"skipped",
"oncall: pt2",
"oncall: export"
] | 10 | NONE | Platforms: asan, linux, rocm, win, windows, slow
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_compile_body_aliasing_contents_backend_aot_eager&suite=TestCompileTorchbind&limit=100) and the most recent trunk [workflow logs](https://github.com/py... | true |
2,916,941,531 | Add `keepdim` parameter for `torch.nn.functional.cosine_similarity` | ringohoffman | open | [
"module: nn",
"triaged",
"actionable"
] | 2 | CONTRIBUTOR | ### 🚀 The feature, motivation and pitch
A lot of aggregation functions computed over specific dimensions have a `keepdim` parameter so that you don't have to unsqueeze the output back into its original dimensionality. I think it would be nice if `cosine_similarity` did too.
https://github.com/pytorch/pytorch/blob/bd... | true |
2,916,821,226 | The device_id parameter of distributed.init_process_group will cause each process to occupy video memory on the first accessible GPU | Staten-Wang | closed | [
"oncall: distributed",
"triaged",
"bug"
] | 1 | NONE | ### 🐛 Describe the bug
The device_id parameter of distributed.init_process_group will cause each process to occupy video memory on the first accessible GPU.
For example, I set the environment variable to "CUDA_VISIBLE_DEVICES": "0,1" . After init_process_group is executed, rank 1 will also occupy some video memory o... | true |
2,916,821,224 | Add dim parameter to torch.bucketize | Aure20 | open | [
"triaged",
"needs design",
"module: python frontend"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
Currently I need to modify a 2D tensor but I want to use different boundaries for different rows, what I am doing is to use list comprehension and bucketizing each row one by one and then stack the list again (see example). It would be convenient to have a dim parameter and the... | true |
2,916,593,394 | Seeking minimal example to use `register_replacement` to inject kernels for both training and inference | mayank31398 | closed | [
"module: docs",
"module: autograd",
"triaged",
"oncall: pt2",
"module: inductor"
] | 3 | CONTRIBUTOR | ### 📚 The doc issue
Hi, it would be awesome if we can add a minimal example for this.
Lets say I want to replace:
```python
def forward(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
x = x * 3
z = x * F.silu(y)
return z
```
with a custom autograd function:
```python
class MyFunc(torch.autograd.Functi... | true |
2,916,516,388 | [inductor][cpu]performance regression in 2025-03-10 nightly release | zxd1997066 | open | [
"oncall: pt2",
"oncall: cpu inductor"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
<p>fp32 static shape cpp 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>eager_new<... | true |
2,916,498,055 | [Profiler][HPU] Fix incorrect availabilities for HPU | pytorchbot | closed | [
"open source"
] | 1 | COLLABORATOR | Fixes #148661
| true |
2,916,392,783 | [Intel GPU] Allow XPU backend in Depthwise_conv2d&3d operators | yucai-intel | open | [
"module: cpu",
"open source",
"Merged",
"Reverted",
"ciflow/trunk",
"ciflow/xpu",
"release notes: xpu",
"module: xpu",
"ci-no-td"
] | 19 | CONTRIBUTOR | This modification is to support XPU kernels for depthwise_conv2d and depthwise_conv3d.
Currently, when running depthwise_conv on XPU devices, it is calculated with Mkldnn via the ConvBackend::Overrideable path.
After this modification, depthwise_conv will be calculated directly using XpuDepthwise3d when the Mkldnn ba... | true |
2,916,245,741 | [CI] Increase shards number for XPU ci UT tests | chuanqi129 | open | [
"triaged",
"open source",
"ciflow/trunk",
"topic: not user facing",
"ciflow/xpu"
] | 1 | COLLABORATOR | To reduce the ci time cost | true |
2,916,119,766 | Failed to install PyTorch 2.7 based on python 3.13t(free-threaded) on Windows OS | jameszhouyi | closed | [] | 0 | NONE | ### 🐛 Describe the bug
**Reproduce steps:**
conda create -n nogil2 --override-channels -c conda-forge python-freethreading
conda activate nogil2
pip install torch torchvision torchaudio --pre --index-url https://download.pytorch.org/whl/nightly/cu128
ERROR: Cannot install torchvision==0.22.0.dev20250226+cu128, torch... | true |
2,916,084,683 | _foreach_copy_ doesn't support copy data between different devices (like cpu-cuda) in compile mode | pralay-das | closed | [
"triaged",
"module: mta",
"oncall: pt2"
] | 1 | CONTRIBUTOR | ### 🐛 Describe the bug
Currently for _foreach_copy ops if our self and src tensorList have different device type (and vice versa), we are getting error in compile mode whereas in eager it is working fine.
<img width="899" alt="Image" src="https://github.com/user-attachments/assets/2cd89f95-4650-427c-aeb6-3567ec4e908... | true |
2,916,071,553 | [XPU] Enable Windows CI/CD test for XPU | chuanqi129 | open | [
"module: ci",
"triaged",
"enhancement",
"module: xpu"
] | 3 | COLLABORATOR | According https://github.com/pytorch/pytorch/issues/114850, the XPU linux CI/CD build and tests has been setup. Currently, the XPU Windows CI/CD only focus on torch build and some basic smoke tests, there is no real xpu test cases covered in CI/CD due to lack XPU Windows GHA runners. We're working on the runner solutio... | true |
2,916,024,532 | Super tiny fix typo | fzyzcjy | closed | [
"open source",
"Merged",
"topic: not user facing",
"module: dynamo"
] | 6 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,915,985,341 | Support return values in generators | fzyzcjy | open | [
"triaged",
"open source",
"module: dynamo",
"release notes: dynamo"
] | 4 | CONTRIBUTOR | Fixes #149037
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,915,925,616 | Update the baseline for max_autotune ci workflow | LifengWang | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: releng",
"module: dynamo"
] | 27 | CONTRIBUTOR | Since the issue https://github.com/pytorch/pytorch/issues/148535 is fixed in PR https://github.com/pytorch/pytorch/pull/148923, update the baseline for max_autotune ci workflow.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @c... | true |
2,915,853,340 | Migrate aten.split.Tensor from using Sharding Rule to Sharding Strategy | mrmiywj | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"module: dtensor"
] | 6 | CONTRIBUTOR | Summary:
Use Sharding Strategy for aten.split.Tensor instead of sharding rule
Test Plan:
pytest test/distributed/tensor/test_dtensor_ops.py -s -k split
Reviewers:
xilunwu
Subscribers:
Tasks:
Tags:
Fixes #ISSUE_NUMBER
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c... | true |
2,915,822,009 | [inductor] post grad graph with scatter_upon_const_tensor lowering is not runnable | xmfan | closed | [
"triaged",
"oncall: pt2",
"module: aotdispatch",
"module: pt2-dispatcher"
] | 2 | MEMBER | ### 🐛 Describe the bug
tlparse: https://fburl.com/fxa5v5rk
See this post_grad_graph: https://fburl.com/k6zd56mh
If we directly execute this graph, it will error here:
```python
# post-grad graph
where_2: "i64[512*s0, 1][1, 1]cuda:0" = torch.ops.aten.where.self(ne_257, unsqueeze_3, full_default_1); unsqueeze_3 = ful... | true |
2,915,820,743 | ci: Fix check_binary gcc abi check | seemethere | closed | [
"Merged",
"topic: not user facing"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149102
* __->__ #149104
All of our binaries should be built with the cxx11-abi now so lets fix
this check to reflect reality.
I also noticed that this particular script is not used widely since this
issue should've been caught in nightlies... | true |
2,915,803,241 | [FSDP2] Add set_reshard_after_forward | mori360 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 12 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/149029
Add `set_reshard_after_forward` to set `post_forward_mesh_info` so as to decide `_reshard_after_forward`
Add unit test similar to `test_fully_shard_communication_count`, the FSDPModule would perform as `._reshard_after_forward=True` after `.set_reshard_after_... | true |
2,915,782,037 | ci: Update linux_job references to v2 | seemethere | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 3 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149102
* #149104
This is probably a bit overdue but trying to update these so we can
finally get rid of all the remnants that rely on non-manylinux2_28 stuff
and conda stuff
Signed-off-by: Eli Uriegas <github@terriblecode.com> | true |
2,915,747,530 | DISABLED test_donated_buffer1_dynamic_shapes (__main__.DynamicShapesAotAutogradFallbackTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 5 | NONE | Platforms: linux, mac, macos
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_donated_buffer1_dynamic_shapes&suite=DynamicShapesAotAutogradFallbackTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/38... | true |
2,915,737,456 | Memory leak when using get_model_state_dict with FSDP-sharded models | mertyg | open | [
"oncall: distributed",
"triaged",
"module: fsdp"
] | 13 | NONE | ### 🐛 Describe the bug
I'm attempting to use the FSDP2 API to shard a model, extract its state dictionary (for potential future use), and then completely remove the model from memory. Extracting the state dict somehow causes there to remain references to the underlying model around, and there ends up being a memory l... | true |
2,915,721,044 | [CI] Move ASAN jobs to clang-18 | cyyever | open | [
"open source",
"topic: not user facing"
] | 4 | COLLABORATOR | Use clang-18 for ASAN jobs.
FBGEMM has to be disabled because the following error
```
AddressSanitizer:DEADLYSIGNAL
#0 0x7f2c21dadef6 in fbgemm::EmbeddingSpMDMKernelSignature<float, long, long, float>::Type fbgemm::GenerateEmbeddingSpMDMWithStrides<float, long, long, float, false>(long, bool, bool, int, bool, b... | true |
2,915,718,445 | Add meta function for out variants of ones,zeros,empty | cz2h | closed | [
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo"
] | 9 | CONTRIBUTOR | Open another PR to fix merge conflicts. Fixes https://github.com/pytorch/pytorch/issues/135832
For aten.ones, aten.zeros, followed this [link](https://docs.google.com/document/d/1GgvOe7C8_NVOMLOCwDaYV1mXXyHMXY7ExoewHqooxrs/edit?tab=t.0#heading=h.64r4npvq0w0) to register meta functions.
For aten.empty.out, followe... | true |
2,915,702,722 | Aten arange behavior when dtype is int64 and step size is greater than range | satheeshhab | open | [
"triaged",
"actionable",
"module: python frontend"
] | 3 | CONTRIBUTOR | ### 🐛 Describe the bug
While testing corner cases on torch.arange, i see the following behavior when dtype is int64 and step size is greater than range.
On CPU, i get the following behavior for arange.
>> a = torch.arange(0, 0.5, 1, dtype=torch.int64)
>> a
tensor([], dtype=torch.int64)
>> a = torch.arange(0, 0.5, 1, ... | true |
2,915,649,453 | How to determine which part of torch.compile undergoes recompiling after caching | janak2 | open | [
"triaged",
"oncall: pt2"
] | 2 | NONE | ### 🐛 Describe the bug
Thanks for the helpful blog: https://dev-discuss.pytorch.org/t/how-to-bring-compile-time-down-to-zero-our-plans-and-direction-may-14th-edition/2089
I am currently caching all 3 stages of the compiler but only seeing ~50% reduction in compile time.
How do I determine which part of the compilat... | true |
2,915,621,727 | Unrestrict some onlyCPU tests | cyyever | open | [
"open source",
"topic: not user facing"
] | 4 | COLLABORATOR | Test these on all devices to avoid diverse behaviour. | true |
2,915,618,684 | How to skip backward specific steps in torch.compile | janak2 | open | [
"triaged",
"oncall: pt2"
] | 3 | NONE | ### 🐛 Describe the bug
I couldn't find much documentation around how we can skip backward specific-steps in torch.compile/AOT autograd.
Some info would be helpful.
### Error logs
_No response_
### Versions
NA
cc @chauhang @penguinwu | true |
2,915,600,607 | [Distributed] Treat third-party devices with `set_rng_state()` and `get_rng_state` as CUDA-like devices when calling `manual_seed()` | shink | closed | [
"oncall: distributed",
"triaged",
"open source",
"topic: not user facing",
"module: dtensor",
"module: accelerator"
] | 25 | CONTRIBUTOR | Fixes #148858
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @tianyu-l @XilunWu @albanD @guangyey @EikanWang | true |
2,915,596,294 | Remove runtime dependency on packaging | atalman | closed | [
"Merged",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 6 | CONTRIBUTOR | Looks like after https://github.com/pytorch/pytorch/pull/148924
We are seeing this error in nightly test:
https://github.com/pytorch/pytorch/actions/runs/13806023728/job/38616861623
```
File "/Users/runner/work/_temp/anaconda/envs/test_conda_env/lib/python3.13/site-packages/torch/_inductor/pattern_matcher.py", ... | true |
2,915,569,982 | Ignore missing-field-initializers warnings of Gemm::Arguments constructors | cyyever | closed | [
"open source",
"release notes: cuda",
"ciflow/periodic"
] | 2 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,915,569,518 | Better warning once for `cuDNN/MIOpen` not enabled | zeshengzong | open | [
"module: cudnn",
"module: tests",
"triaged"
] | 0 | CONTRIBUTOR | ### 🐛 Describe the bug
While running some tests, seems repeating error messages about `cuDNN/MIOpen` not enabled, maybe better to warn only once for users
```bash
pytest test/test_dataloader.py
```
### Versions
Collecting ... | true |
2,915,502,100 | Update Kineto Submodule | sraikund16 | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 4 | CONTRIBUTOR | Summary: We have made a lot of changes in Kineto this month. It is a good idea to update the submodule in now especially since the roctracer-sdk change will be very large
Test Plan: CI
Differential Revision: D71082829
| true |
2,915,429,277 | [ROCm][TunableOp] More TF32 support. | naromero77amd | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/rocm",
"ciflow/rocm-mi300"
] | 5 | COLLABORATOR | This PR includes additional enhancements to TF32 support in TunableOp.
- OpSignature now differentiates between float32 and tf32 data types.
- Offline tuning now supports TF32.
- Unit tests for online and offline tuning of TF32.
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @j... | true |
2,915,429,092 | [invoke_subgraph] Fake tensor prop caching | anijain2305 | closed | [
"Merged",
"topic: not user facing",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148953
* #150036
* #149667
* __->__ #149087
Redoing https://github.com/pytorch/pytorch/pull/137808 | true |
2,915,419,295 | Fix B018 Useless Expressions in Multiple Files (#106571) | rocordemu | closed | [
"oncall: distributed",
"triaged",
"open source",
"module: inductor",
"module: dynamo",
"release notes: distributed (checkpoint)"
] | 5 | NONE | ### Description
This PR addresses `flake8-bugbear` `B018` warnings ("Found useless expression") by removing unused tuple and constant expressions in three files. These fixes clean up the codebase, reducing potential confusion and aligning with the linting goals of #106571. As a first-time contributor (coming from Node... | true |
2,915,389,040 | [AOTI] Re-enable AOTI cpp unit test | desertfire | closed | [
"Merged",
"topic: not user facing",
"ciflow/inductor"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149085
Summary: test_inductor_aoti was removed by accident previously. Add it back. | true |
2,915,388,660 | [WIP][dynamic shapes] use statically_known_true for _reshape_view_helper | pianpwk | closed | [
"fb-exported",
"release notes: fx",
"ciflow/inductor"
] | 2 | CONTRIBUTOR | Differential Revision: D71081192
| true |
2,915,263,113 | Support missing bitwise onnx ops (__rshift__, __lshift__) | nlgranger | closed | [
"module: onnx",
"triaged"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
Some bitwise operations are not supported by onnx export (with or without dynamo).
So far I identified `__rshift__` and `__lshift__` -> [BitShift](https://github.com/onnx/onnx/blob/main/docs/Operators.md#BitShift)
Here is an mro of the failed export:
```py
import math
impo... | true |
2,915,238,748 | BC fix for AOTIModelPackageLoader() constructor defaults | jbschlosser | closed | [
"Merged",
"ciflow/trunk",
"topic: bug fixes",
"ciflow/inductor",
"release notes: inductor",
"module: aotinductor"
] | 11 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149082
The default value for `run_single_threaded` was wrongly specified in the .cpp file instead of the header, breaking C++-side instantiation of `AOTIModelPackageLoader` with no arguments. This PR fixes this and adds a test for t... | true |
2,915,235,946 | DISABLED test_destruct_before_terminate_pg (__main__.ProcessGroupNCCLGroupTest) | pytorch-bot[bot] | closed | [
"oncall: distributed",
"module: flaky-tests",
"skipped"
] | 2 | NONE | Platforms: linux
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_destruct_before_terminate_pg&suite=ProcessGroupNCCLGroupTest&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/38650096870).
Over the past 3... | true |
2,915,235,934 | DISABLED test_aoti_debug_printer_codegen_cuda (__main__.AOTInductorTestABICompatibleGpu) | pytorch-bot[bot] | open | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: inductor"
] | 3 | NONE | Platforms: inductor, rocm
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_aoti_debug_printer_codegen_cuda&suite=AOTInductorTestABICompatibleGpu&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/3866075014... | true |
2,915,235,826 | DISABLED test_wrap_pytree_kwargs_dynamic_shapes (__main__.DynamicShapesHigherOrderOpTests) | pytorch-bot[bot] | closed | [
"triaged",
"module: flaky-tests",
"skipped",
"oncall: pt2",
"module: dynamo"
] | 4 | NONE | Platforms: linux, slow, mac, macos
This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_wrap_pytree_kwargs_dynamic_shapes&suite=DynamicShapesHigherOrderOpTests&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs... | true |
2,915,208,884 | Remove torch.export.export_for_inference | gmagogsfm | closed | [
"module: bc-breaking",
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: bc breaking",
"release notes: export"
] | 9 | CONTRIBUTOR | Summary: Remove torch.export.export_for_inference, it is redundant and can always be replaced with torch.export.export_for_training() + run_decompositions()
Test Plan: unit tests
Differential Revision: D71069057
cc @ezyang @gchanan | true |
2,915,205,359 | Fix outdated docstring of torch.export.export regarding strict flag | gmagogsfm | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"release notes: export"
] | 6 | CONTRIBUTOR | Summary: Fix outdated docstring of torch.export.export regarding strict flag
Test Plan: None, doc only change
Differential Revision: D71068215
| true |
2,915,153,450 | [ROCm] Improve softmax performance | doru1004 | closed | [
"module: rocm",
"triaged",
"open source",
"Merged",
"ciflow/trunk",
"release notes: cuda",
"ciflow/rocm"
] | 6 | CONTRIBUTOR | This patch improves the performance of softmax for 2D tensors by:
using a softmax calculation which eliminates the increase of shared memory usage with the size of the tensor and relies on global memory accesses for the tensor data accesses while still using shared memory for the actual reduction step (the shared me... | true |
2,915,135,434 | xpu: target torch::xpurt not found linking with libtorch installed from XPU wheels | dvrogozh | closed | [
"module: cpp",
"triaged",
"module: xpu"
] | 6 | CONTRIBUTOR | Consider that Pytorch XPU is installed on the newly configure system with:
```
# pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/xpu
# pip3 list | grep torch
torch 2.7.0.dev20250312+xpu
```
Further, consider the use case when someone works on C++ library/executable and wants t... | true |
2,915,114,371 | [FSDP2] Update ignored_params docstring and add unit test | mori360 | closed | [
"oncall: distributed",
"Merged",
"ciflow/trunk",
"release notes: distributed (fsdp)",
"ciflow/inductor"
] | 4 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/148242
ignored_params won't be moved to devices in full_shard(), update docstring.
Add unit test `test_move_states_to_device_ignored_param_device` to show that ignored_params won't be moved during full_shard(), but would be after `model.cuda()`
cc @H-Huang @awgu @k... | true |
2,915,108,119 | [AOTI][refactor] Split MiniArrayRef into a separate header | desertfire | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor"
] | 7 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149073
Summary: MiniArrayRef is a common utility and will be used by the libtorch-free AOTI.
Differential Revision: [D71064657](https://our.internmc.facebook.com/intern/diff/D71064657) | true |
2,915,102,098 | [compile] Switch off inference_mode for fake prop while compiling | anijain2305 | closed | [
"oncall: distributed",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"keep-going"
] | 3 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #148953
* __->__ #149072
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 ... | true |
2,915,092,170 | [DTensor] Fix`slice_backward` strategy, add `select_int`, `select_backward` strategies | awgu | closed | [
"oncall: distributed",
"open source",
"release notes: distributed (dtensor)"
] | 2 | COLLABORATOR | For `slice_backward`:
1. `slice_backward` was missing `schema_info` leading to a caching bug
2. We do not need to redistribute to replicate if a shard dim differs from the slice `dim`
For `select_int` and `select_backward`, we add strategies.
For `select_backward` and `slice_backward`, we need to specify that t... | true |
2,915,088,508 | [DTensor] Fix `local_map` with multi-threading | awgu | closed | [
"oncall: distributed",
"open source",
"Merged",
"ciflow/trunk",
"release notes: distributed (dtensor)"
] | 6 | COLLABORATOR | Using `nonlocal device_mesh` is not safe with multi-threading
cc @H-Huang @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o | true |
2,915,025,635 | [FlexAttention] Allow caching of backwards func | drisspg | open | [
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"module: flex attention"
] | 4 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149069
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @Chillee @yanboliang @BoyuanFeng | true |
2,915,015,838 | [do-not-land] add tests | xmfan | open | [
"topic: not user facing",
"module: dynamo",
"ciflow/inductor"
] | 1 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149068
* #149067
* #149066
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,915,015,718 | [do-not-land] test decorator changes | xmfan | open | [
"module: dynamo",
"ciflow/inductor"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* (to be filled)
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames | true |
2,915,015,595 | [do-not-land] test eval_frame changes | xmfan | open | [
"module: dynamo",
"ciflow/inductor"
] | 2 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149068
* #149067
* __->__ #149066
| true |
2,914,898,833 | [ONNX Export] dynamic_shapes ignored during model export. | spkgyk | closed | [
"module: onnx",
"triaged"
] | 9 | NONE | ### 🐛 Describe the bug
```python
from torch.export import Dim
from pathlib import Path
import onnx
import onnxruntime
import torch
model = model
model.load_state_dict(checkpoint.get("state_dict"), strict=True)
model.eval()
with torch.no_grad():
data = torch.randn(1, 3, 256, 256)
torch_outputs = model(data)... | true |
2,914,889,687 | [ca] clean up aot node deduping | xmfan | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing",
"module: dynamo",
"ciflow/inductor",
"module: compiled autograd"
] | 5 | MEMBER | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* #149229
* #149014
* __->__ #149064
rename the AOT nodes as we copy paste them into the CA graph
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @c... | true |
2,914,774,055 | Consolidate torchbind fake class registration | yushangdi | closed | [
"fb-exported",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 7 | CONTRIBUTOR | Summary: Remove duplicated fake class registration
Test Plan: CI
Differential Revision: D71052419
| true |
2,914,730,532 | Reserve customized modules in torch.compile's dynamo tracer | trajepl | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 2 | NONE | ### 🚀 The feature, motivation and pitch
## Description
Hi PyTorch team,
I’ve encountered an issue with torch.compile when working with customized modules. Specifically, torch.compile tends to step into customized modules and decompose them into built-in functions and modules. This leads to the loss of the original m... | true |
2,914,704,783 | Best way to disable "fx graph cache hit for key"? | henrylhtsang | closed | [
"triaged",
"module: fx.passes",
"module: inductor"
] | 1 | CONTRIBUTOR | I have a possibly niche use case:
* I might rerun the same run a few times
* So I will run into "fx graph cache hit for key"
* I want to see precompilation and autotuning in the logs
* So I want to bypass fx graph cache
* Want to avoid having to C++ compile the kernel again (codecache does that), since C++ compile... | true |
2,914,675,068 | aot autograd cache causes TORCH_LOGS=aot to not print out the aot_graphs | zou3519 | open | [
"module: logging",
"triaged",
"oncall: pt2",
"module: aotdispatch",
"compile-cache"
] | 4 | CONTRIBUTOR | I think the main problem is that I don't know how to disable the aotautograd cache, but we should have some sort of recommended workflow for seeing the aot graphs in this case
cc @oulgen @jamesjwu @masnesral @chauhang @penguinwu @bdhirsh | true |
2,914,659,921 | [inductor] Fix profiler tests with latest Triton | pytorchbot | closed | [
"open source",
"topic: not user facing",
"module: inductor",
"ciflow/inductor"
] | 1 | COLLABORATOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149025
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov | true |
2,914,655,899 | [pytorch] Fix duplicated Malloc/Free insertation when using IRBuilderBase::CreateMalloc/CreateFree in LLVM 18+ | HighW4y2H3ll | closed | [
"oncall: jit",
"fb-exported",
"Merged",
"NNC",
"ciflow/trunk",
"release notes: jit"
] | 4 | CONTRIBUTOR | Summary:
Pytorch unitest hangs when jitting the Tensor kernel. The problem exists for LLVM version >= 18 due to this upstream change: https://github.com/llvm/llvm-project/commit/45bb45f2ae89df6c0e54ead2258764ec91f5f5f5
`IRBuilderBase::CreateCall` will insert the instruction into the BasicBlock by default. And we don't... | true |
2,914,631,285 | Symintify transpose_ | angelayi | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 7 | CONTRIBUTOR | Fixes https://github.com/pytorch/pytorch/issues/148702 | true |
2,914,558,296 | [RLEASE ONLY CHANGES] Apply release only chnages to release 2.7 | atalman | closed | [
"module: rocm",
"release notes: releng",
"ciflow/inductor"
] | 1 | CONTRIBUTOR | Same as: https://github.com/pytorch/pytorch/pull/143085
Generated by: ``scripts/release/apply-release-changes.sh``
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd | true |
2,914,546,559 | fix untyped decorator lints | aorenste | open | [
"oncall: distributed",
"oncall: jit",
"release notes: quantization",
"fx",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"release notes: distributed (checkpoint)",
"release notes: export"
] | 1 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149055
cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @ezyang @SherlockNoMad @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzh... | true |
2,914,469,598 | Store statically launchable CachingAutotuners inside CompiledFXGraph.triton_bundle | jamesjwu | closed | [
"Merged",
"Reverted",
"ciflow/trunk",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ci-no-td"
] | 24 | CONTRIBUTOR | Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #149054
This PR adds CachingAutotuners that are statically launchable to FXGraphCache's cache entry.
Regular CachingAutotuners, with triton kernels attached to them, are not very good to cache: they are very large, and take huge ... | true |
2,914,455,680 | [CI] Fix xpu linux test permission issue and add ci docker image pull | chuanqi129 | closed | [
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 9 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,914,444,493 | Use schema as source of truth + support ones_like/empty_like | janeyx99 | closed | [
"Merged",
"release notes: cpp",
"ciflow/inductor"
] | 5 | CONTRIBUTOR | This change does 2 important things:
(a) Instead of relying on IValue type as source of truth, we use the schema as the source of truth, which is important as IValue types are overloaded and can ambiguously convert incorrectly. For example, a MemoryFormat will look like an int + get converted to an int64_t vs a Memory... | true |
2,914,405,440 | Fix checkout on xpu? | clee2000 | closed | [] | 2 | CONTRIBUTOR | Fixes #ISSUE_NUMBER
| true |
2,914,292,708 | DISABLED test_cond_autograd_zeros_unused_branch_complex_compile_mode_compile (__main__.TestControlFlow) | jithunnair-amd | closed | [
"module: rocm",
"triaged",
"skipped"
] | 2 | COLLABORATOR | Re-attempting skip from https://github.com/pytorch/pytorch/issues/148308
Platforms: rocm
This test was disabled because it is failing on main branch ([recent examples](https://hud.pytorch.org/failure?name=rocm-mi300%20%2F%20linux-focal-rocm6.3-py3.10%20%2F%20test%20(default%2C%206%2C%206%2C%20linux.rocm.gpu.mi300.2)&... | true |
2,914,289,934 | `pytroch.distribute` should support "meta" device tensors | slitvinov | open | [
"oncall: distributed",
"triaged"
] | 0 | NONE | This example fails. It would be a great debugging tool for checking the metadata of tensors, especially considering the difficulty of debugging distributed programs.
https://pytorch.org/docs/stable/meta.html
```
$ cat meta.py
import torch
import torch.distributed as dist
import torch.distributed.elastic.multiprocessin... | true |
2,914,280,814 | multinomial does not preserve dynamic dimension | xadupre | closed | [
"oncall: pt2",
"oncall: export"
] | 0 | COLLABORATOR | ### 🐛 Describe the bug
multinomial expects a fixed dimension for the number of samples. It should be dynamic.
```python
import torch
class Model(torch.nn.Module):
def forward(self, x, y):
return torch.multinomial(x, y.shape[0])
model = Model()
inputs = (
torch.tensor([[4, 5],[6,7]], dtype=torch.flo... | true |
2,914,273,279 | Unsupported: call_method NNModuleVariable() register_forward_hook [NestedUserFunctionVariable()] {} | bhack | open | [
"triaged",
"oncall: pt2",
"module: dynamo"
] | 4 | CONTRIBUTOR | ### 🐛 Describe the bug
AOTI compiling and exporting https://github.com/cvlab-kaist/Chrono I had this issue in the log related to `register_forward_hook`
### Error logs
[error.log](https://github.com/user-attachments/files/19212384/error.log)
### Versions
2.6.0 and nightly
cc @chauhang @penguinwu @voznesenskym @E... | true |
2,914,246,224 | Enable modernize-use-default-member-init | cyyever | closed | [
"module: cpu",
"triaged",
"module: mkldnn",
"open source",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"topic: not user facing",
"module: inductor",
"ciflow/inductor",
"ciflow/linux-aarch64"
] | 10 | COLLABORATOR | ``modernize-use-default-member-init`` prefers initialisation in class members, that make more ``= default`` constructors possible. Some violations or modernize rules have been fixed.
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @gujinghui @PenghuiCheng @jianyuh @min-jean-cho @yanbing-j @Guob... | true |
2,914,234,459 | NotImplementedError: aten::_log_softmax_backward_data with SparseCUDA backend | rangehow | open | [
"module: sparse",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
```python
class NDPTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
input_ids = inputs.pop("input_ids")
attention_mask = inputs.pop("attention_mask")
cnt_list = inputs.pop(
"cnt_list"
)
... | true |
2,914,220,977 | [v.2.7.0] Release Tracker | atalman | closed | [
"module: ci",
"triaged",
"release tracker"
] | 93 | CONTRIBUTOR | We cut a [release branch](https://github.com/pytorch/pytorch/tree/release/2.7) for the 2.7.0 release.
Our plan from this point from this point is roughly:
* Phase 1 (until 3/31/25): work on finalizing the release branch
* Phase 2 (after 3/31/25): perform extended integration/stability/performance testing based on Rel... | true |
2,914,161,788 | [FIX] remove the duplicate key in DEFAULT_STATIC_QUANT_MODULE_MAPPINGS | hackty | closed | [
"open source",
"Merged",
"ciflow/trunk",
"release notes: quantization",
"release notes: AO frontend"
] | 8 | CONTRIBUTOR | nn.Dropout appeared at line 81 | true |
2,914,085,000 | Github Actions API is unstable - High queue times for GHA | jeanschmidt | closed | [
"ci: sev",
"ci: sev-infra.thirdparty"
] | 1 | CONTRIBUTOR | ## Current Status
Mitigated on github side - recovering queue of jobs
## Error looks like
Queued jobs, failing to pick up runners
## Incident timeline (all times pacific)
* 04:00 Starded
* 06:56 Identified
* 07:12 GH API seems to be start recovering
## User impact
* queued jobs
* increased TTS on CI
## Root cause
*... | true |
2,914,081,606 | [DO NOT MERGE] [TRITON] Test enablement of buffer ops in AMD triton | jataylo | open | [
"open source",
"ciflow/trunk",
"topic: not user facing",
"ciflow/inductor",
"keep-going",
"ciflow/rocm",
"ciflow/inductor-rocm"
] | 4 | COLLABORATOR | Fixes #ISSUE_NUMBER
| true |
2,914,068,317 | Only 2D, 3D, 4D, 5D padding with non-constant padding are supported for now | fallen-leaves-web | open | [
"module: nn",
"triaged"
] | 1 | NONE | ### 🐛 Describe the bug
Hello, thanks for sharing the work.
I encountered an issue while running my ESPnet-based TTS script on Windows. Here is the error message I got:
G:\code> & g:/University_documents_over_four_years/AI语音/.conda/python.exe g:/code/tts1.py
Failed to import Flash Attention, using ESPnet default: No ... | true |
2,913,783,273 | [ROCm] [Testing] enable NHWC convolutions by default on CDNA arch | jataylo | open | [
"module: rocm",
"open source",
"release notes: rocm",
"module: inductor",
"module: dynamo",
"ciflow/inductor",
"ciflow/rocm",
"ciflow/inductor-perf-test-nightly-rocm"
] | 4 | COLLABORATOR | Also enabled layout optimisation by default on ROCm so Inductor models will see the benefit
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @hongxiayang @naromero77amd @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ip... | true |
2,913,778,503 | Update nightly PyTorch version to 2.8.0 | atalman | closed | [
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 6 | CONTRIBUTOR | Branch for 2.7: https://github.com/pytorch/pytorch/tree/release/2.7
Same as https://github.com/pytorch/pytorch/pull/135916
| true |
2,913,773,552 | (Will PR if ok) Support generator returning values | fzyzcjy | open | [
"triaged",
"oncall: pt2"
] | 6 | CONTRIBUTOR | ### 🐛 Describe the bug
Hi thanks for the library! It would be great if generators returning values could be supported. I will make a PR if this feature looks OK.
For example:
```python
import torch
def exhaust_generator(g):
ans = []
while True:
try:
ans.append(next(g))
except St... | true |
2,913,757,823 | [AOTInductor] support specify outputs which should be captured | zzq96 | open | [
"triaged",
"module: aotinductor"
] | 1 | NONE | ### 🚀 The feature, motivation and pitch
In train, model forward may return more outputs for computing loss, like `return {"logits":logits, "rpr":rpr}`
but in inference, we only need some of them, like `return {"logits":logits}`, so torch can simplify graph and ignore some nodes related to `rpr`.
### Alternatives
... | true |
2,913,271,530 | Force build to conform C++ standard on windows by adding /permissive- flag | Stonepia | closed | [
"module: windows",
"open source",
"Merged",
"ciflow/trunk",
"release notes: jit",
"topic: improvements",
"module: xpu"
] | 7 | CONTRIBUTOR | Fixes #147366
1. Add `/permissive-` to the `torch_compile_options` for the build to conform to the C++ standard.
2. Fix the error when trying to assign a string literal to a non-const ptr.
The `/permissive-` flag can be found at https://learn.microsoft.com/en-us/cpp/build/reference/permissive-standards-conforma... | true |
2,913,164,681 | Avoid regenerating template_kernels each time tuned_mm is called with the tensors of the same shape. | laithsakka | open | [
"triaged",
"oncall: pt2",
"module: inductor"
] | 0 | CONTRIBUTOR | When tuned_mm is called with tensors of the same shape, I expect the selection of auto tuning to always be the same. However in one of the models i am working on we noticed that the call to
```
mm_template.maybe_append_choice(
choices,
input_nodes=(mat1, mat2),
layo... | true |
2,913,031,558 | [regression] Fix pin_memory() when it is called before device lazy initialization. | BartlomiejStemborowski | closed | [
"module: regression",
"open source",
"Merged",
"ciflow/trunk",
"topic: not user facing"
] | 19 | CONTRIBUTOR | PR #145752 has added a check in the isPinnedPtr to check if a device is initialized before checking if the tensor is pinned. Also that PR has added a lazy initialization trigger when an at::empty is called with a pinned param set to true. However, when the tensor is firstly created and it is pinned in a separate call b... | true |
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