sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: storybook
Issue: [Bug]: Backgrounds toolbar addon causes multiple rerenders and errors when switching backgrounds on a docs page with over 4 stories
Labels: bug,needs triage

### Describe the bug

When switching to another background via the backgrounds toolbar addon on a docs page with over 4 stories, the docs page rerenders and reloads multiple times and the ff errors are thrown  as shown on the console:
![image](https://github.com/user-attachments/assets/a3dca972-8638-49e3-b666-d895253bdf69)

```
manager  received storyRenderPhaseChanged but was unable to determine the source of the event
manager  received storyRendered but was unable to determine the source of the event
manager  received storybook/docs/snippet-rendered but was unable to determine the source of the event
```

### Reproduction link

https://github.com/merri-dei/storybook-nextjs-issue/tree/main

### Reproduction steps

1. Go to the above link.
2. Clone the repository.
3. Open the repository in an IDE.
4. Install all dependencies via `pnpm install`.
5. Run `npm run storybook`.
6. Visit `http://localhost:6006`.
7. Open the Button docs page.
8. Change the background via the Backgrounds toolbar addon. You might need to do this multiple times to see the errors on the console.

Expected:
The docs page rerenders once to update the background of the stoires and doesn't reload. No error is logged onto the console.

Actual:
The docs page rerenders multiple times and reloads. Errors are logged onto the console:

https://github.com/user-attachments/assets/13ce6e61-c1e5-42dd-b977-15b64518c42f

Note that this doesn't happen on docs pages with 4 stories or less:

https://github.com/user-attachments/assets/3fb03824-55db-4bf3-8f71-eb601c9c9100



### System

```bash
Storybook Environment Info:

  System:
    OS: macOS 14.6.1
    CPU: (8) arm64 Apple M1
    Shell: 5.9 - /bin/zsh
  Binaries:
    Node: 18.19.0 - ~/.nvm/versions/node/v18.19.0/bin/node
    Yarn: 1.22.21 - ~/.nvm/versions/node/v18.19.0/bin/yarn
    npm: 10.2.3 - ~/.nvm/versions/node/v18.19.0/bin/npm
    pnpm: 8.15.4 - ~/.nvm/versions/node/v18.19.0/bin/pnpm <----- active
  Browsers:
    Chrome: 128.0.6613.113
    Edge: 128.0.2739.42
    Safari: 17.6
  npmPackages:
    @storybook/addon-console: ^3.0.0 => 3.0.0 
    @storybook/addon-essentials: ^8.2.9 => 8.2.9 
    @storybook/addon-interactions: ^8.2.9 => 8.2.9 
    @storybook/addon-links: ^8.2.9 => 8.2.9 
    @storybook/addon-onboarding: ^8.2.9 => 8.2.9 
    @storybook/blocks: ^8.2.9 => 8.2.9 
    @storybook/nextjs: ^8.2.9 => 8.2.9 
    @storybook/react: ^8.2.9 => 8.2.9 
    @storybook/test: ^8.2.9 => 8.2.9 
    eslint-plugin-storybook: ^0.8.0 => 0.8.0 
    storybook: ^8.2.9 => 8.2.9
```


### Additional context

_No response_
Repository: godot
Issue: Android splash screen followed by gray wave transition into Godot splash screen
Labels: bug,platform:android,topic:porting

### Tested versions

4.3.stable.official on Windows; Godot app 4.3 on Android
Only one Android phone tested, though.

### System information

Windows 10 - Godot 4.3; Android Pixel 7a, all launchers

### Issue description

Somewhat related to https://github.com/godotengine/godot/pull/92965, when a Godot-built APK launches, the android splash screen always plays a swipe cut transition from the top of the screen into the Godot game splash screen. The swipe cut resembles a gray wave. On my app, I discovered this because I wanted to launch into a pure white screen that matches my game's start screen. In my case:

1. I updated AndroidManifest.xml to make the windowSplashScreenBackground white
2. I set my game to start at a white screen
3. I set Godot's splash screen to just the color white

So I expected the app to launch all white, and then the first time I'd see any change was after my game had started. However, there's a very ugly gray, cut-off transition after the white splash screen from Android's launcher, which makes my cool app feel crappy and lame.

You can see this in the Godot 4.3 app as well, when the app launches, which should show that it probably affects all Godot-built Android apps. 

### Steps to reproduce

1. Open the official Godot 4.3 app
2. Observe the brief swooshing wave color
3. Close the app from Recent Apps to force a cold relaunch

### Minimal reproduction project (MRP)

I don't have an MRP. Look at the official Godot app.
Repository: PowerToys
Issue: Improve powertoys run vscode workspaces launcher
Labels: Needs-Triage

### Description of the new feature / enhancement

Currently to open up vscode workspace it spawns .cmd file. In Enterprise environment .cmd extension is tied to notepad, therefore workspaces doesnt open up

### Scenario when this would be used?

In enterprise environments where security hardening is done.

### Supporting information

_No response_
Repository: pytorch
Issue: DISABLED test_transformer_runtime (__main__.TestRuntimeEstimator)
Labels: module: rocm,triaged,skipped

Platforms: rocm

Failure introduced by https://github.com/pytorch/pytorch/pull/134243

This test was disabled because it is failing on main branch ([recent examples](https://torch-ci.com/failure?failureCaptures=%5B%22distributed%2F_tools%2Ftest_runtime_estimator.py%3A%3ATestRuntimeEstimator%3A%3Atest_transformer_runtime%22%5D)).

cc @jeffdaily @sunway513 @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang
Repository: ant-design
Issue: bug[Table]: error border-radius when column scroll
Labels: 🐛 Bug,help wanted,Inactive

### Reproduction link

[https://ant.design/components/table-cn](https://ant.design/components/table-cn)

### Steps to reproduce

https://github.com/user-attachments/assets/8c5afa4e-1a56-49d5-994a-641a48bd4e9e

### What is expected?

scroll 列时圆角样式正常

### What is actually happening?

scroll 列时圆角样式不正常

| Environment | Info |
| --- | --- |
| antd | 5.20.3 |
| React | 18.3.1 |
| System | Windows 11 |
| Browser | Chrome999 |
Repository: godot
Issue: Type Variation doesn't show new type when create it in the theme
Labels: enhancement,topic:editor,usability,documentation,topic:gui

### Tested versions

v4.3.stable.steam [77dcf97d8]

### System information

windows 10

### Issue description

![image](https://github.com/user-attachments/assets/db42ea3d-5f97-4c2c-a041-eaf5d1746a9c)
When I create a new Type in the theme, it doesn't show up in the Type Variation but I can use the created type manually
![image](https://github.com/user-attachments/assets/4464bd78-4db7-4596-a363-9d4792c9a13a)


### Steps to reproduce

"First, create a new theme. Then, within the theme, create a new type under the types category.

Next, select the theme in the PanelContainer node.

After that, look for the newly created type variation under Type Variation.

If it cannot be found, manually entering the new variation name works to apply it."

### Minimal reproduction project (MRP)

[bug.zip](https://github.com/user-attachments/files/16811793/bug.zip)
Repository: next.js
Issue: Inconsistent handling of notFound() from server action
Labels: bug,Runtime

### Link to the code that reproduces this issue

https://github.com/VitaliyPotapov/next-reproduction-app

### To Reproduce

1. `npm run dev` and navigate to http://localhost:3000
2. click `Form with server action` -> `not-found.tsx` is correctly shown
3. click `Button with server action` -> there is uncaught error `NEXT_NOT_FOUND`

### Current vs. Expected behavior

I except that click on `Button with server action` would show `not-found.tsx`.
So that handling of notFound() from server action to be consistent, no matter how I invoke that server action: via form or via click handler.

### Provide environment information

```bash
Operating System:
  Platform: darwin
  Arch: arm64
  Version: Darwin Kernel Version 23.5.0: Wed May  1 20:12:58 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6000
  Available memory (MB): 16384
  Available CPU cores: 10
Binaries:
  Node: 18.20.1
  npm: 10.5.0
  Yarn: N/A
  pnpm: N/A
Relevant Packages:
  next: 15.0.0-canary.135 // Latest available version is detected (15.0.0-canary.135).
  eslint-config-next: N/A
  react: 19.0.0-rc-7771d3a7-20240827
  react-dom: 19.0.0-rc-7771d3a7-20240827
  typescript: 5.3.3
Next.js Config:
  output: N/A
```


### Which area(s) are affected? (Select all that apply)

Runtime

### Which stage(s) are affected? (Select all that apply)

next dev (local)

### Additional context

_No response_
Repository: pytorch
Issue: AOTInductor doesn't support mutable custom operators
Labels: triaged,module: custom-operators,oncall: pt2,oncall: export,module: pt2-dispatcher,module: aotinductor

### 🐛 Describe the bug

I am trying compile a torchrec model which use fbgemm IntNBitTableBatchedEmbeddingBagsCodegen, which in turn use bounds_check_indices kernel,  and part of the fx graph is:
```
    ...
    %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%permute_2d_sparse_data, 0), kwargs = {})
    %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%permute_2d_sparse_data, 1), kwargs = {})
    %view_2 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%getitem, [-1]), kwargs = {})
    %asynchronous_complete_cumsum : [num_users=1] = call_function[target=torch.ops.fbgemm.asynchronous_complete_cumsum.default](args = (%view_2,), kwargs = {})
    %auto_functionalized : [num_users=3] = call_function[target=torch._higher_order_ops.auto_functionalize.auto_functionalized](args = (fbgemm.bounds_check_indices.default,), kwargs = {rows_per_table: %b__tensor_constant0, indices: %getitem_1, offsets: %asynchronous_complete_cumsum, bounds_check_mode: 1, warning: %b__tensor_constant1, weights: None, B_offsets: None, max_B: -1})
    %getitem_4 : [num_users=1] = call_function[target=operator.getitem](args = (%auto_functionalized, 1), kwargs = {})
    %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%auto_functionalized, 2), kwargs = {})
    %getitem_6 : [num_users=1] = call_function[target=operator.getitem](args = (%auto_functionalized, 3), kwargs = {})
    ...
```
the kernel is not supported because it is mutable, what is the general solution to wrap a custom op which is mutable in inductor?

### Versions

torch=2.4.0
torchrec=0.8.0+cu121
fbgemm_gpu=0.8.0+cu121
```[tasklist]
### Tasks
```


cc @ezyang @chauhang @penguinwu @avikchaudhuri @gmagogsfm @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4 @zou3519 @bdhirsh @desertfire @chenyang78 @mcarilli @eellison @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @kadeng @muchulee8 @ColinPeppler @amjames
Repository: ollama
Issue: add Qwen2-VL
Labels: model request

SOTA light weight vision model
[https://github.com/QwenLM/Qwen2-VL](https://github.com/QwenLM/Qwen2-VL)

llama.cpp issue [#9246](https://github.com/ggerganov/llama.cpp/issues/9246)
Repository: pytorch
Issue: DISABLED test_max_split_expandable (__main__.TestCudaMallocAsync)
Labels: module: cuda,triaged,module: flaky-tests,skipped

Platforms: linux, slow

This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_max_split_expandable&suite=TestCudaMallocAsync&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/29456799947).

Over the past 3 hours, it has been determined flaky in 16 workflow(s) with 48 failures and 16 successes.

**Debugging instructions (after clicking on the recent samples link):**
DO NOT ASSUME THINGS ARE OKAY IF THE CI IS GREEN. We now shield flaky tests from developers so CI will thus be green but it will be harder to parse the logs.
To find relevant log snippets:
1. Click on the workflow logs linked above
2. Click on the Test step of the job so that it is expanded. Otherwise, the grepping will not work.
3. Grep for `test_max_split_expandable`
4. There should be several instances run (as flaky tests are rerun in CI) from which you can study the logs.



<details><summary>Sample error message</summary>

```
Traceback (most recent call last):
  File "/var/lib/jenkins/workspace/test/test_cuda.py", line 4116, in test_max_split_expandable
    a = alloc(40)
  File "/var/lib/jenkins/workspace/test/test_cuda.py", line 4111, in alloc
    return torch.ones(n * mb, dtype=torch.int8, device="cuda")
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 40.00 MiB. GPU 0 has a total capacity of 7.43 GiB of which 6.92 GiB is free. Process 102870 has 68.75 MiB memory in use. Process 102895 has 80.75 MiB memory in use. Process 104347 has 68.75 MiB memory in use. Process 104367 has 291.75 MiB memory in use. 120.00 MiB allowed; Of the allocated memory 50.00 MiB is allocated by PyTorch, with 14.00 MiB allocated in private pools (e.g., CUDA Graphs), and 90.00 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

To execute this test, run the following from the base repo dir:
    python test/test_cuda.py TestCudaMallocAsync.test_max_split_expandable

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
```

</details>


Test file path: `test_cuda.py`

cc @ptrblck @msaroufim @clee2000
Repository: rust
Issue: Too long compile time with implementing a trait function
Labels: A-LLVM,I-compiletime,T-compiler,C-bug

<!--
Thank you for filing a bug report! 🐛 Please provide a short summary of the bug,
along with any information you feel relevant to replicating the bug.
-->

I found that implementing a specific trait function causes too long compile time.

If there is `expression` function implementation at https://github.com/veryl-lang/veryl/blob/3516e3c73a62a9d74301336a4cf7d11013445e57/crates/emitter/src/emitter.rs#L984 , compilation time is 1600s.
And if the implmentation is commented out, compilation time is 5s.

If `codegen-units = 1` is specified, there is no problem.

The reproduce way is here:

```sh
git clone https://github.com/veryl-lang/veryl -b v0.12.0
cd veryl
git submodule update --init
sed -i 's/codegen/#codegen/' Cargo.toml
rm ./crates/parser/build.rs

# remove `expression` function implementation
sed -i '984,992d' ./crates/emitter/src/emitter.rs

# compile dependencies at first
cargo build --release --manifest-path crates/emitter/Cargo.toml

# There is not `expression` function implementation
# about 5s
touch  ./crates/emitter/src/emitter.rs
time cargo build --release --manifest-path crates/emitter/Cargo.toml

# There is `expression` function implementation
# about 1600s
git checkout ./crates/emitter/src/emitter.rs
time cargo build --release --manifest-path crates/emitter/Cargo.toml
```

### Meta
<!--
If you're using the stable version of the compiler, you should also check if the
bug also exists in the beta or nightly versions.
-->

`rustc --version --verbose`:
```
rustc 1.80.1 (3f5fd8dd4 2024-08-06)
binary: rustc
commit-hash: 3f5fd8dd41153bc5fdca9427e9e05be2c767ba23
commit-date: 2024-08-06
host: x86_64-unknown-linux-gnu
release: 1.80.1
LLVM version: 18.1.7
```

<!--
Include a backtrace in the code block by setting `RUST_BACKTRACE=1` in your
environment. E.g. `RUST_BACKTRACE=1 cargo build`.
-->
<details><summary>Backtrace</summary>
<p>

```
<backtrace>
```

</p>
</details>


<!-- TRIAGEBOT_START -->

<!-- TRIAGEBOT_ASSIGN_START -->

<!-- TRIAGEBOT_ASSIGN_DATA_START$${"user":"DianQK"}$$TRIAGEBOT_ASSIGN_DATA_END -->

<!-- TRIAGEBOT_ASSIGN_END -->
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Repository: pytorch
Issue: The documentation lacks an explanation of the constraints between larger padding and padding mode in convolutional layers
Labels: module: docs,module: convolution,triaged,actionable,module: padding

### 📚 The doc issue

When using `conv2d`, I set a large padding and chose the circular padding mode. Because the edge data of the input tensor is insufficient for circular padding, this results in an error during execution. However, this error is not mentioned in the documentation. 
```python
import torch

x = torch.randn(1, 3, 224, 224)
p = torch.nn.Conv2d(in_channels=3, out_channels=3, kernel_size=1, padding=(1234, 1234), padding_mode="circular")
print(p(x))
```
```
Traceback (most recent call last):
    return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
RuntimeError: Padding value causes wrapping around more than once.
```

### Suggest a potential alternative/fix

I believe the documentation should not only provide a reference for the API but also guide users on how to use these APIs correctly. If the documentation could specify that certain padding modes may not be suitable in certain situations (such as when the padding is too large), it would help users design more robust models.

cc @svekars @brycebortree @tstatler
Repository: pytorch
Issue: LPPool2d lacks a check for the validity of norm_type
Labels: module: error checking,triaged,actionable,module: norms and normalization

### 🐛 Describe the bug

```python
import torch

x = torch.randn(1, 3, 224, 224)
p = torch.nn.LPPool2d(norm_type=0.0, kernel_size=2)
print(p(x))
```
When the norm_type of torch.nn.LPPool2d is set to 0, a division by zero error occurs during the computation. Because the norm_type parameter is used to specify the value of p in the Lp norm, when p=0, the calculation formula will include a division by zero, causing the program to crash.
In my opinion, the following improvements can be made:

1. **Parameter Check**: Add a check for `norm_type` before creating the `LPPool2d` layer to ensure it is not set to 0.

2. **Improve Documentation and Error Messages**: Update the `LPPool2d` documentation to clearly state that `norm_type` cannot be 0, or add a check in the code implementation to throw a more meaningful error message earlier.

### Versions

torch 2.0.0

cc @malfet
Repository: svelte
Issue: `state_reference_locally` warning message is unclear
Labels: documentation

### Describe the bug

https://github.com/sveltejs/svelte/blob/0203eb319b5d86138236158e3ae6ecf29e26864c/packages/svelte/src/compiler/warnings.js#L642-L648

"How to use a closure?" was asked several times in discord. In addition, closure only refers to functions where classes also serve the same purpose.

I propose an update to the message
"State referenced in its own scope will never update. Did you mean to reference it inside either a function or a class?"

related discord posts
https://discord.com/channels/457912077277855764/1235970955612786719
https://discord.com/channels/457912077277855764/1278658234810634282
https://discord.com/channels/457912077277855764/1275188058089586688

### Reproduction

.

### Logs

_No response_

### System Info

```shell
.
```


### Severity

annoyance
Repository: transformers
Issue: The model's address is https://huggingface.co/Xenova/nllb-200-distilled-600M/tree/main/onnx。I don't know how to load encode.onnx and decoder.onnx, and successfully translate a sentence into another language. Can you help me write an inference code to achieve the translation effect through the encoder and decoder? thank you
Labels: Feature request

### Feature request

hello，The model's address is [https://huggingface.co/Xenova/nllb-200-distilled-600M/tree/main/onnx。I](https://huggingface.co/Xenova/nllb-200-distilled-600M/tree/main/onnx%E3%80%82I) don't know how to load encode.onnx and decoder.onnx, and successfully translate a sentence into another language. Can you help me write an inference code to achieve the translation effect through the encoder and decoder? thank you

### Motivation

hello，The model's address is [https://huggingface.co/Xenova/nllb-200-distilled-600M/tree/main/onnx。I](https://huggingface.co/Xenova/nllb-200-distilled-600M/tree/main/onnx%E3%80%82I) don't know how to load encode.onnx and decoder.onnx, and successfully translate a sentence into another language. Can you help me write an inference code to achieve the translation effect through the encoder and decoder? thank you

### Your contribution

hello，The model's address is [https://huggingface.co/Xenova/nllb-200-distilled-600M/tree/main/onnx。I](https://huggingface.co/Xenova/nllb-200-distilled-600M/tree/main/onnx%E3%80%82I) don't know how to load encode.onnx and decoder.onnx, and successfully translate a sentence into another language. Can you help me write an inference code to achieve the translation effect through the encoder and decoder? thank you
Repository: pytorch
Issue: torch::jit::load invalid_path error
Labels: oncall: jit

### 🐛 Describe the bug

I'm trying to load a .pt model by using jit::load but for some reason i get "open file failed because of errno 22 on fopen: Invalid argument, file path: ". I'm able to open the file by using fopen in this way:

```
  FILE* file2 = fopen("models/model.pt", "r");
```

But when I try 

`torch::jit::load("models/model.pt");`

I get the error mentioned before. Any ideas? Thanks!

### Versions

Collecting environment information...
PyTorch version: 2.3.1+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Microsoft Windows 11 Home
GCC version: Could not collect
Clang version: Could not collect
CMake version: version 3.30.0-rc4
Libc version: N/A

Python version: 3.12.2 | packaged by Anaconda, Inc. | (main, Feb 27 2024, 17:28:07) [MSC v.1916 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-11-10.0.22631-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture=9
CurrentClockSpeed=2100
DeviceID=CPU0
Family=107
L2CacheSize=2048
L2CacheSpeed=
Manufacturer=AuthenticAMD
MaxClockSpeed=2100
Name=AMD Ryzen 5 3500U with Radeon Vega Mobile Gfx
ProcessorType=3
Revision=6145

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] onnxruntime==1.17.1
[pip3] torch==2.3.1
[conda] blas                      1.0                         mkl
[conda] mkl                       2021.4.0                 pypi_0    pypi
[conda] mkl-service               2.4.0           py312h2bbff1b_1
[conda] mkl_fft                   1.3.8           py312h2bbff1b_0
[conda] mkl_random                1.2.4           py312h59b6b97_0
[conda] numpy                     1.26.4          py312hfd52020_0
[conda] numpy-base                1.26.4          py312h4dde369_0
[conda] torch                     2.3.1                    pypi_0    pypi

cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel