sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: godot
Issue: PopupMenu horizontal margins in the editor theme became too low since 4.3.dev3
Labels: bug,topic:editor,regression

### Tested versions

- Reproducible in: 4.3.dev3, 4.3.dev4, 4.3.dev fe01776f05b1787b28b4a270d53037a3c25f4ca2
- Not reproducible in: 4.2.1.stable, 4.3.dev2

### System information

Godot v4.3.dev (fe01776f05b1787b28b4a270d53037a3c25f4ca2) - Fedora Linux 39 (KDE Plasma) - Wayland - GLES3 (Compatibility) - NVIDIA GeForce RTX 4090 (nvidia; 545.29.06) - 13th Gen Intel(R) Core(TM) i9-13900K (32 Threads)

### Issue description

PopupMenu horizontal margins in the editor theme became too low since 4.3.dev3. This makes popups feel less consistent with the rest of the editor theme (and most other applications).

Look at the space between the edge of the popup and the **New Scene** text:

4.3.dev1 | 4.3.dev4
-|-
![editor_popupmenu_4 3 dev1](https://github.com/godotengine/godot/assets/180032/f241d416-37d5-4d30-b0c5-f94166c52ccd) | ![editor_popupmenu_4 3 dev4](https://github.com/godotengine/godot/assets/180032/7c57c0ea-5738-42fb-8d86-a95be47982aa)

Back-and-forth animation to compare:

![comparison webp](https://github.com/godotengine/godot/assets/180032/d5f055a9-a30b-4ab3-9c71-59e1564f26c5)

I bisected the regression to dc3b07e5515f71ee4a0c55217b12e98e098ea3e9 (https://github.com/godotengine/godot/pull/87085). 

While we're at it, we may want to further increase this horizontal margin to make it closer to most other applications (example with a Qt dropdown on KDE Breeze theme):

![qt_dropdown](https://github.com/godotengine/godot/assets/180032/1177cb8f-27a4-4599-9102-8df1dc7faa75)



### Steps to reproduce

- Open any popup or dropdown in the editor.

### Minimal reproduction project (MRP)

N/A
Repository: pytorch
Issue: Proposal to make `torch.nn.modules.utils._pair` public
Labels: module: nn,triaged

Non-public functions `torch.nn.modules.utils._pair`,  `torch.nn.modules.utils._ntuple`, etc. are widely used outside of PyTorch itself.

Usage of non-public APIs outside of PyTorch is problematic for documentation and backward compatibility reasons.

How about we make the APIs public by first creating public aliases, for example `torch.nn.modules.utils.pair` (or even `torch.utils.pair`) for `torch.nn.modules.utils._pair`, then updating existing Meta-internal and popular open source user code to use the public aliases (using TorchFix https://github.com/pytorch-labs/torchfix), and after couple of releases we can delete the non-public names?

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki @malfet
Repository: godot
Issue: Pathfinding doesn't produce shortest path on vertical surfaces
Labels: topic:navigation

### Tested versions

Tested in 4.2.1

### System information

Godot v4.2.1.stable - Linux Mint 21.3 (Virginia) - X11 - Vulkan (Forward+) - dedicated NVIDIA GeForce GTX 1070 (nvidia; 545.29.06) - AMD Ryzen 5 3600 6-Core Processor (12 Threads)

### Issue description

3D pathfinding doesn't seem to produce the shortest path when you have vertical surfaces, often snapping to corners instead of going directly to the destination.
![path1](https://github.com/godotengine/godot/assets/2514990/3923d562-30d5-410e-9adc-e8a89743a15c)
![path2](https://github.com/godotengine/godot/assets/2514990/9eef06de-3fc1-4d4f-8965-27138b96b468)
This is using NavigationServer3D.map_get_path with optimized=true (but results are similar with optimized=false). The navigation mesh was made in blender since it doesn't seem to be possible to bake a navigation mesh with vertical surfaces.

### Steps to reproduce

1. Make a navigation mesh with some vertical surfaces.
2. Call NavigationServer3D.map_get_path between two points on walls.
3. Observe results.

### Minimal reproduction project (MRP)

[aaru-minimal.zip](https://github.com/godotengine/godot/files/14658151/aaru-minimal.zip)
Repository: PowerToys
Issue: "keyboard manager" function add "short-cut key"
Labels: Idea-Enhancement,Product-Keyboard Shortcut Manager,Needs-Triage

### Description of the new feature / enhancement

I want to function key

"keyboard manager" function add "short-cut key"  on-off activation key



### Scenario when this would be used?

I want to function key

"keyboard manager" function add "short-cut key"  on-off activation key


### Supporting information

_No response_
Repository: ant-design
Issue: 1.mentions组件删除at的人可以整体删除而不是删除字
Labels: 💡 Feature Request,Inactive

### What problem does this feature solve?

产品设计 @人名  的为整体性 删除时应整体删除

### What does the proposed API look like?

无

<!-- generated by ant-design-issue-helper. DO NOT REMOVE -->
Repository: ant-design
Issue: Notification 收起时，文字没有跟着收起，只是面板收起了
Labels: 🐛 Bug,Inactive

### Reproduction link

[![Edit on StackBlitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/edit/react-v5juzs?file=demo.tsx)

### Steps to reproduce

点击打开，观察收起动画
![image](https://github.com/ant-design/ant-design/assets/47104575/12eaa3e3-25a4-468e-aa43-c4b75fd74682)

### What is expected?

文字一起收起

### What is actually happening?

文字没有收起

| Environment | Info |
| --- | --- |
| antd | 5.15.3 |
| React | latest |
| System | mac |
| Browser | chrome |

<!-- generated by ant-design-issue-helper. DO NOT REMOVE -->
Repository: pytorch
Issue: CUDA: Performance issues with `torch.unique`
Labels: module: performance,module: cuda,triaged

### 🐛 Describe the bug

I've noticed some performance problems with `torch.unique`. I tested the following:
```py
import timeit

import torch

number = 10

t = torch.randint(10, (1_000_000, ), device="cpu")
t_cuda = t.to("cuda")

print(timeit.timeit("t.unique(dim=0)", globals=globals(), number=number))
print(timeit.timeit("t_cuda.unique(dim=0)", globals=globals(), number=number))

t = torch.randint(10, (1_000, 1_000), device="cpu")
t_cuda = t.to("cuda")

print(timeit.timeit("t.unique(dim=0)", globals=globals(), number=number))
print(timeit.timeit("t_cuda.unique(dim=0)", globals=globals(), number=number))

t = torch.randint(10, (10, 100, 1_000), device="cpu")
t_cuda = t.to("cuda")

print(timeit.timeit("t.unique(dim=0)", globals=globals(), number=number))
print(timeit.timeit("t_cuda.unique(dim=0)", globals=globals(), number=number))
```
Output:
```py
27.928257999941707
0.323013799963519
0.03842799994163215
0.05541190004441887
0.004202899988740683
5.427228400018066
```

It makes sense that the tests get better since it compares a million values in the first test, a thousand in the second, and only ten different tensors in the third. The unexpected behavior is that the cuda implementation somehow gets worse on the last test.

### Versions

PyTorch version: 2.2.0+cu121                                                                                     
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Microsoft Windows 10 Pro
GCC version: (x86_64-posix-seh-rev2, Built by MinGW-W64 project) 12.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: N/A

Python version: 3.11.1 (tags/v3.11.1:a7a450f, Dec  6 2022, 19:58:39) [MSC v.1934 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.19045-SP0
Is CUDA available: True
CUDA runtime version: 12.1.66
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080
Nvidia driver version: 546.65
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture=9
CurrentClockSpeed=3701
DeviceID=CPU0
Family=107
L2CacheSize=6144
L2CacheSpeed=
Manufacturer=AuthenticAMD
MaxClockSpeed=3701
Name=AMD Ryzen 9 5900X 12-Core Processor
ProcessorType=3
Revision=8450

Versions of relevant libraries:
[pip3] numpy==1.26.3
[pip3] torch==2.2.0+cu121
[pip3] torchvision==0.17.0+cu121
[conda] Could not collect


cc @ptrblck
Repository: pytorch
Issue: DISABLED test_fake_tensor_mode_huggingface_gpt2 (__main__.TORCH_NN_MODULE)
Labels: module: onnx,triaged,module: flaky-tests,skipped

Platforms: linux

This test was disabled because it is failing in CI. See [recent examples](https://hud.pytorch.org/flakytest?name=test_fake_tensor_mode_huggingface_gpt2&suite=TORCH_NN_MODULE&limit=100) and the most recent trunk [workflow logs](https://github.com/pytorch/pytorch/runs/22858564562).

Over the past 3 hours, it has been determined flaky in 2 workflow(s) with 2 failures and 2 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_fake_tensor_mode_huggingface_gpt2`
4. There should be several instances run (as flaky tests are rerun in CI) from which you can study the logs.


Test file path: `onnx/test_fx_to_onnx_with_onnxruntime/TestFxToOnnxFakeTensorWithOnnxRuntime_op_level_debug_True_dynamic_shapes_True_load_checkpoint_during_init_False_export_within_fake_mode_True_model_type_TorchModelType.py` or `onnx/test_fx_to_onnx_with_onnxruntime`

cc @clee2000
Repository: ant-design
Issue: The `width` property of Table.Column supports responsive declaration
Labels: 💡 Feature Request,Inactive

### What problem does this feature solve?

- Considering responsive design needs, the table width should adjust dynamically based on varying screen resolutions.
- One approach to achieve this manually is as follows:
```
columns = [{
    ...,
    width: isWideScreen() ? 120 : 80
}]
```

However, it would be more efficient if the Table.Column component supported responsive declaration of width, such as:
```
columns = [{
    ...,
    width: { xs: 80, md: 120, lg: 'auto' }
}]

```

### What does the proposed API look like?

width: { xs: 80, md: 120, lg: 'auto' }

<!-- generated by ant-design-issue-helper. DO NOT REMOVE -->
Repository: godot
Issue: Depth buffer resolution is unexpectedly small when sampled in spatial shader
Labels: topic:rendering,needs testing,topic:3d

### Tested versions

Noticed in: v4.2.1.stable (when porting a working project from 3.x)
Also found when testing: v4.0.0, v4.2.2-rc2, v4.3-dev1, v4.3-dev2
**Partially fixed in: v4.3-dev3 and later** (Forward+ renderer is fixed, Mobile renderer still has same issue)

### System information

Godot v4.2.1.stable - macOS 14.3.1 - Vulkan (Forward+) - dedicated AMD Radeon Pro 555X - Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (12 Threads)

### Issue description

On my system (macOS, Intel CPU, AMD GPU), 3D shaders which sample the depth buffer have visual artifacts that suggest that the depth buffer being rendered at a much lower resolution than the main camera.

Here's a scene with a plane above a torus. The plane has a simple shader attached (included in sample project zip, see below) which samples the depth buffer and uses it to set the albedo. The result appears chunky, with depth buffer resolution appearing ~8x less in both dimensions than the main camera's output.

https://github.com/godotengine/godot/assets/5893857/4cc0dcb1-3e74-4bfb-8130-5493855c088a

A similar effect can be seen when using the Proximity Fade property on the standard material.

https://github.com/godotengine/godot/assets/5893857/39e35225-7a84-4780-963e-948844142557

I encountered this when porting a shader that uses the depth buffer from Godot 3.4 to Godot 4.2.1. That shader worked as expected on the same computer under that version of Godot (and the GLES 2 backend) - i.e. the depth texture was the same resolution as the scene.




### Steps to reproduce

I can repro this on my machine with either the Forward+ or Mobile rendering method. I guess depth buffer sampling isn't supported in Compatibility mode so this bug isn't relevant there.

### Minimal reproduction project (MRP)

This test project contains the scene shown above. It's configured with the standard material with the proximity fade property enabled. The shader code shown above is also included.

[DepthBufferBugRepro.zip](https://github.com/godotengine/godot/files/14662273/DepthBufferBugRepro.zip)
Repository: go
Issue: cmd/compile: greedy basic block layout
Labels: Performance,NeedsInvestigation,compiler/runtime

### Proposal Details

Fine-grained performance metrics (such as L1-iTLB, IPC, branch-load, branch-load-miss, etc.) and optimization experiments(#63670) have repeatedly shown that block layout can noticably impact code execution efficiency. 

In contrast to the current case-by-case tuning approach, I propose adopting the traditional and time-tested Pettis-Hansen (PH) basic block layout algorithm. Its core concept is to place hot block as closely together as possible, allowing basic blocks to fall through whenever feasible, thereby reducing jump instructions. This principle is considered the golden rule in the field of block layout, and state-of-the-art algorithms like extTSP and almost all variants are based on this idea, incorporating advanced heuristic techniques.

The PH algorithm relies on a weighted chain graph, where the weights represent the frequency of edges. In the absence of PGO information, we can only resort to branch prediction results from `likelyadjust` pass. In the future, we can incorporate PGO data as weights to make the algorithm even more effective.

### Experiment Results
![image](https://github.com/golang/go/assets/5010047/e7bf3ff4-9e1b-454b-a058-2ffad3225356)

The x-axis represents testcase id, the y-axis indicates the performance change in percentage points, and negative values denote performance improvement.
Repository: ant-design
Issue: Popover 的 zIndex 比 Modal 的大？
Labels: Inactive,unconfirmed

### Reproduction link

[![Edit on StackBlitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/edit/react-hmtytu?file=demo.tsx)

### Steps to reproduce

先打开 Popover，再打开 Modal

### What is expected?

Modal 在 Popover 上面

### What is actually happening?

没在上面

| Environment | Info |
| --- | --- |
| antd | 5.15.3 |
| React | latest |
| System | mac |
| Browser | chrome |

<!-- generated by ant-design-issue-helper. DO NOT REMOVE -->
Repository: pytorch
Issue: In a simple model, exporting onnx loses model operations
Labels: module: onnx,triaged

### 🐛 Describe the bug

torchscript tracing works
torchscript based onnx exporting not works
dynamo based onnx exporting works

```python
import torch
import torch.nn as nn

class Model(nn.Module):
    def forward(self, x):
        y = x.clone()
        yyy = y[1,...]
        yyy += 2
        return y

net = Model()
net.eval()

x = torch.ones(2, 3, 4)

print(x)
# tensor([[[1., 1., 1., 1.],
#          [1., 1., 1., 1.],
#          [1., 1., 1., 1.]],
#
#         [[1., 1., 1., 1.],
#          [1., 1., 1., 1.],
#          [1., 1., 1., 1.]]])

a = net(x)

print(a)
# tensor([[[1., 1., 1., 1.],
#          [1., 1., 1., 1.],
#          [1., 1., 1., 1.]],
#
#         [[3., 3., 3., 3.],
#          [3., 3., 3., 3.],
#          [3., 3., 3., 3.]]])

# export torchscript
mod = torch.jit.trace(net, x)
mod.save("bug.pt")

net2 = torch.jit.load("bug.pt")
b = net2(x)

print(b)
# tensor([[[1., 1., 1., 1.],
#          [1., 1., 1., 1.],
#          [1., 1., 1., 1.]],
#
#         [[3., 3., 3., 3.],
#          [3., 3., 3., 3.],
#          [3., 3., 3., 3.]]])

# export onnx
torch.onnx.export(net, x, "bug.onnx", input_names = ['input'], output_names = ['output'])

import onnxruntime as ort
ort_sess = ort.InferenceSession("bug.onnx")
output = ort_sess.run(None, {'input': x.numpy()})

print(output)
# [array([[[1., 1., 1., 1.],
#         [1., 1., 1., 1.],
#         [1., 1., 1., 1.]],
#
#        [[1., 1., 1., 1.],
#         [1., 1., 1., 1.],
#         [1., 1., 1., 1.]]], dtype=float32)]

# export onnx with dynamo
mod2 = torch.onnx.dynamo_export(net, x)
mod2.save("bug.dynamo.onnx")

ort_sess = ort.InferenceSession("bug.dynamo.onnx")
output2 = ort_sess.run(None, {'l_x_': x.numpy()})

print(output2)
# [array([[[1., 1., 1., 1.],
#         [1., 1., 1., 1.],
#         [1., 1., 1., 1.]],
#
#        [[3., 3., 3., 3.],
#         [3., 3., 3., 3.],
#         [3., 3., 3., 3.]]], dtype=float32)]

```

### Versions

python 3.12
pytorch v2.2.1
Repository: ui
Issue: [Bug]: Eslint complaint of unused props passed to Calendar icons
Labels: bug

### Describe the bug

Think a screenshot is enough

![Screenshot 2024-03-20 at 10 58 30 AM](https://github.com/shadcn-ui/ui/assets/9624361/f16fe220-f2a6-4690-be1a-b59ebcf57d01)


### Affected component/components

Calendar

### How to reproduce

Just check the source code for the calendar

### Codesandbox/StackBlitz link

_No response_

### Logs

_No response_

### System Info

```bash
No need to provide this
```


### Before submitting

- [X] I've made research efforts and searched the documentation
- [X] I've searched for existing issues
Repository: transformers
Issue: [Feature request] FastV: Plug-and-play inference acceleration  for  large vision language models
Labels: Feature request,Vision,optimization

### Feature request

FastV is a plug-and-play inference acceleration method for large vision language models relying on visual tokens. It could reach 45% theoretical FLOPs reduction without harming the performance through pruning redundant visual tokens in deep layers. It is originally implemented in https://github.com/pkunlp-icler/FastV  , with [paper](https://arxiv.org/abs/2403.06764) and [demo](https://www.fastv.work/)

![fastv_tradeoff](https://github.com/huggingface/transformers/assets/43977888/97490036-8942-440f-b46b-5629e68d817a)

*aokvqa results*
| Model                         | Score | latency / first output token (A100 80G) | GPU Memory |
| ----------------------------- | ----- | --------------------------------------- | ---------- |
| 7B Vanilla Decoding           | 76.8  | 0.138s                                  | 18G        |
| 13B Vanilla Decoding          | 81.9  | 0.203s                                  | 33G        |
| \- 13B FastV (K=2 R=25%)      | 81.8  | 0.181s                                  | 29G        |
| \- 13B FastV (K=2 R=50%)      | 81.3  | 0.155s                                  | 28G        |
| \- 13B FastV (K=2 R=75%)      | 80.9  | **0.124s**                                  | 27G        |
| 13B Vanilla Decoding 4Bit     | 81.5  | 0.308s                                  | 12G        |
| \- 13B FastV 4Bit (K=2 R=25%) | 81.7  | 0.277s                                  | 11G        |
| \- 13B FastV 4Bit (K=2 R=50%) | 81.1  | 0.275s                                  | 10G        |
| \- 13B FastV 4Bit (K=2 R=75%) | 80.3  | 0.245s                                  | **9G**         |

### Motivation

I want to merge the FastV feature to speed up the inference of HF's multimodal models (Llava is the first one).  Since Llava is based on Llama, I need to add features to both the Llama and Llava model. 

I would add a fastv_config parameters to the modeling_llava.py from llava and add a new forward function named fastv_forward from llama. I want to know if it is ok to do that or I should create a new model.

### Your contribution

I would submit a PR.
Repository: PowerToys
Issue: Always on top: make child windows automatically on-top
Labels: Idea-Enhancement,Product-Always On Top,Needs-Triage

### Description of the new feature / enhancement

It would be helpful if child windows were also set to on-top when their opener is on-top. Perhaps as an option in case not everybody wants it (though I've not thought of a reason not to). Perhaps also a further option to make the child "follow" future changes to its parent's on-top state, but that's not so important fo me.

### Scenario when this would be used?

I have a small Gmail window set to always-on-top but if I use its "In new Window" button to see a thread in a separate window, it opens behind the parent window where I can't easily access it.

### Supporting information

_No response_
Repository: ollama
Issue: Does ollama also plan to support the sound models?
Labels: feature request

### What are you trying to do?

Currently, ollama supports most llms and I know that it also supports vision model. I thought it would be nice if the sound model could also be used through ollama. I wonder if there are any plans like this.

### How should we solve this?

I know that there are currently sound models released on huggingface. There are several TTS and STT models released as open source. I think these models could also support model serving and API like llm.

### What is the impact of not solving this?

_No response_

### Anything else?

_No response_
Repository: next.js
Issue: revalidate interval clears cache of all root and page components
Labels: bug

### Link to the code that reproduces this issue

https://github.com/timuric/nextjs-minimal-issue/tree/revalidate-time

### To Reproduce

1. Clear .next cache (optional)
2. `pnpm run build && pnpm start`
3. Open http intercepter to observe requests made by next
4. Refresh page several times to observe requests


### Current vs. Expected behavior

#### Current
All requests are invalidated. Furthermore, each second page load creates double the requests. Here is an example of a single page load with six requests instead of one. 
<img width="1295" alt="image" src="https://github.com/vercel/next.js/assets/4006792/c1c030bc-3dc1-4297-b9d4-a46ef95d440c">

#### Expected
Only Todos requests should be made between page loads
 

### Provide environment information

```bash
Operating System:
  Platform: darwin
  Arch: arm64
  Version: Darwin Kernel Version 23.4.0: Wed Feb 21 21:44:43 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T6000
Binaries:
  Node: 20.5.0
  npm: 9.8.0
  Yarn: 1.22.21
  pnpm: 8.10.5
Relevant Packages:
  next: 14.1.3
  eslint-config-next: 14.1.3
  react: 18.2.0
  react-dom: 18.2.0
  typescript: 5.4.2
Next.js Config:
  output: N/A
```


### Which area(s) are affected? (Select all that apply)

App Router

### Which stage(s) are affected? (Select all that apply)

next dev (local), next start (local)

### Additional context

Could be related to https://github.com/vercel/next.js/issues/63509
Repository: storybook
Issue: [Bug]: A sandbox created with a version specifier will have a caret for all versions
Labels: bug,needs triage

### Describe the bug

Running `npx storybook@<VERSION> sandbox` will create a sandbox that depends on eg. `"storybook": "^<VERSION>"` in `package.json`, which will mean it won't get the actual version specified, but the latest that supports the range.

This might only be a problem for canary versions, eg. `npx storybook@0.0.0-pr-26557-sha-7290a2c2 sandbox` which is how I discovered it.

### To Reproduce

1. `npx storybook@0.0.0-pr-26557-sha-7290a2c2 sandbox`
2. cd into sandbox
3. `yarn install`
4. `yarn why storybook`
5. See that the version it reports doesn't match what was attempted to being installed, because there is a caret in `package.json`

### System

```bash
Storybook Environment Info:

  System:
    OS: macOS 13.1
    CPU: (10) arm64 Apple M1 Pro
    Shell: 5.8.1 - /bin/zsh
  Binaries:
    Node: 18.19.0 - ~/Library/Caches/fnm_multishells/23231_1710319496014/bin/node
    Yarn: 4.1.1 - ~/Library/Caches/fnm_multishells/23231_1710319496014/bin/yarn <----- active
    npm: 10.2.3 - ~/Library/Caches/fnm_multishells/23231_1710319496014/bin/npm
  Browsers:
    Chrome: 122.0.6261.129
    Edge: 122.0.2365.92
    Safari: 16.2
```


### Additional context

_No response_