sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: pytorch
Issue: [ONNX] pad_sequence() is not exportable, with neither legacy onnx.export nor with dynamo_export
Labels: module: onnx,triaged,oncall: pt2,module: dynamic shapes,oncall: export

### 🐛 Describe the bug

Here, a heavily used pattern lifted from NVIDIA NeMo / Riva TTS use case. Not exportable with neither method: 
```python
import torch

def batch_from_ragged(text: torch.Tensor, batch_lengths: torch.Tensor):
    lens = batch_lengths[1:] - batch_lengths[:-1]
    texts = torch.nn.utils.rnn.pad_sequence(
        torch.tensor_split(text, batch_lengths),
        batch_first=True,
        padding_value=-1
    )
    return texts, lens

class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
    def forward(self, text, batch_lengths):
        return batch_from_ragged(text, batch_lengths)

text = torch.randint(1024,(1024,), dtype=torch.int64)
batch_lengths=torch.tensor([2,6,7,11,28,39], dtype=torch.int64)
model = Model()
expected = model(text,batch_lengths)
# onnx.export() fails with:                                                                                                                                               
# torch.onnx.errors.UnsupportedOperatorError: Exporting the operator 'aten::pad_sequence' to ONNX opset version 17 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues.                                                                                    
torch.onnx.export(model,(text, batch_lengths), "ragged_legacy.onnx")                                                                                                    

# dynamo_export() fails with :                                                                                                                                            
# RuntimeError: Failed running call_function <built-in method tensor_split of type object at 0x7efdfd04eee0>(*(FakeTensor(..., size=(1024,), dtype=torch.int64), FakeTensor(..., size=(6,), dtype=torch.int64)), **{}):                                                                                                                            
# Could not guard on data-dependent expression u0 < 0 (unhinted: u0 < 0).  (Size-like symbols: none)                                                                      
onnx_program = torch.onnx.dynamo_export(model, text, batch_lengths)
onnx_program.save('ragged.onnx')

```

### Versions

Pytorch nightly 05/24

cc @ezyang @chauhang @penguinwu @avikchaudhuri @gmagogsfm @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4
Repository: pytorch
Issue: [dynamo] Dynamic slicing on data-dependent value is not supported (regression from legacy ONNX)
Labels: module: onnx,triaged,oncall: pt2,module: dynamo,oncall: export

### 🐛 Describe the bug

Here is a repro case lifted from NVIDIA NeMo, used by TTS nets. This scripted method is essentially a reimplementation of pad_sequence(tensor_split(...)) sequence - workaround for ONNX export issue #127153. onnx.export() does work with that scripted function, while dynamo_export() fails : 

```python
import torch

@torch.jit.script
def batch_from_ragged(text: torch.Tensor, batch_lengths: torch.Tensor):
    batch_lengths = batch_lengths.to(dtype=torch.int64)
    lens = batch_lengths[1:] - batch_lengths[:-1]
    max_len = torch.max(lens)
    index = 1
    num_batches = batch_lengths.shape[0] - 1
    texts = torch.zeros(num_batches, max_len, dtype=torch.int64, device=text.device)
    last_index = index - 1
    while index < batch_lengths.shape[0]:
        seq_start = batch_lengths[last_index]
        seq_end = batch_lengths[index]
        cur_seq_len = seq_end - seq_start
        texts[last_index, :cur_seq_len] = text[seq_start:seq_end]
        last_index = index
        index += 1
    return texts, lens

class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
    def forward(self, text, batch_lengths):
        return batch_from_ragged(text, batch_lengths)

text = torch.randint(1024,(1024,), dtype=torch.int64)
batch_lengths=torch.tensor([2,6,7,11,28,39], dtype=torch.int64)
model = Model()
expected = model(text,batch_lengths)
# This works fine                                                                                                                                                         
torch.onnx.export(model,(text, batch_lengths), "ragged_legacy.onnx",)
# This fails with :                                                                                                                                                       
# torch._dynamo.exc.Unsupported: Dynamic slicing on data-dependent value is not supported                                                                                 
#  from user code:                                                                                                                                                        
#         texts[last_index, :cur_seq_len] = text[seq_start:seq_end]                                                                                                       

onnx_program = torch.onnx.dynamo_export(model, text, batch_lengths)
onnx_program.save('ragged.onnx')
 

### Versions

Pytorch nightly 05/24.

cc @ezyang @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @amjames @rec @avikchaudhuri @gmagogsfm @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4 @msaroufim @bdhirsh @anijain2305
Repository: react
Issue: [DevTools Bug]: version 5.2.0  icon opien in editor cannot click , color is gray
Labels: Type: Bug,Status: Unconfirmed,Component: Developer Tools

### Website or app

https://github.com/YingchenWang999/react-demo

### Repro steps

cd  react-demo   
npm run dev 
 open in chrome 
open reactdevtools 
set open in editor url  is vscode 
choose seletor component 

see 

<img width="1509" alt="image" src="https://github.com/facebook/react/assets/56105589/07538d58-2fb1-4745-a4a6-969fbd22b7b4">



### How often does this bug happen?

Every time

### DevTools package (automated)

_No response_

### DevTools version (automated)

_No response_

### Error message (automated)

_No response_

### Error call stack (automated)

_No response_

### Error component stack (automated)

_No response_

### GitHub query string (automated)

_No response_
Repository: vscode
Issue: [Bug] Emoji/Full-width chars are not measured against font
Labels: bug,font-rendering,confirmation-pending

Reproduction:
1. Use a font that monospaces certain emojis / fullwidth chars
2. move up/down between columns that have differing amounts of these characters (ex. "a❗bc" and "aabc" between the 'b' and 'c'

Problem: The caret jumps columns when it shouldn't

Solution: For fonts that override these characters to be a different width, please respect the width. For clicking into lines it actually works fine, just traversing up and down between columns is broken

[Isolated example](https://microsoft.github.io/monaco-editor/playground.html?source=v0.49.0-rc#XQAAAAIRAgAAAAAAAABBqQkHQ5NjdMeMm-jY7SIQ9S7DNlzs5W-mwj0fe1ZCDRFc9ws9XQE0SJE1jc2VKxhaLFIw9vEWdz-byd2eRaK1ZYMCRnq3Ff3Lbp5gdiVBEZKHDYiqZ0Ow52PTT2rYDF84Ae4zx6yWbLZTQtK4keoVPOjsMzkD6C1nDfou8kAmgnBIearpiyGgcccXPD8uEPXbI894PBa6toQuY96KcwTe7WXtF9c5AD9wUGxMhrzTWHWPOZUWi7v0PkDip2iv_r5cGfqXrw4H1eHokMAv2oHp2xQxF08OlTGADMZNAuZJbBvp7yrEa9vPht9wTvPbz4GapnC9Tx-XoFkRJbvx86zbiYSBnGCuiP2XuroM1s_tGHp3JO1sooNp_c8H_pp_hzDv9OhP_Q-m_OszIzirqUJ1GfVgOzetBNhJKTNrdyah8-mtOfpqbYaYhZDERZMV0RTP34vo_hEBHO5g0Cjdxb2mik6ja40SEf_0q6P-)

Additional information:
(Suspected) Sources of problem behavior:
![image](https://github.com/microsoft/monaco-editor/assets/34655998/9b101769-f0ec-4e69-a8bd-af337c3004fb)
![image](https://github.com/microsoft/monaco-editor/assets/34655998/260044f3-147a-4768-b1be-afb0dd4a4f2e)
Example of characters that would fail even with typical common font: "⟦⟧"
![image](https://github.com/microsoft/monaco-editor/assets/34655998/e084766b-4a25-48ca-b0fc-7b36d96f6d0b)
Repository: godot
Issue: CharacterBody3D with capsule collider stuck on edge of ramp with move_and_slide()
Labels: bug,topic:physics,topic:3d

### Tested versions

- Reproducable in: 4.2.2 Stable

### System information

Linux, Windows 11 Vulkan(Forward+)

### Issue description

When moving a CharacterBody3D with a CapsuleShape3D with move_and_slide(), it gets stuck on the edges/top of ramps.
This is likely a repeat of #79542 and the project file provided is the same.

### Steps to reproduce

Move CharacterBody3D with a CapsuleShape3D up a ramp. 
Will either: 
1. Act as intended, running off the edge (rarely)
2. Get stuck at the top edge, treating it as a wall (often), unintended.
3. Get stuck or snap character to the top edge of the ramp (often), unintended.


### Minimal reproduction project (MRP)

[capsule_collision.zip](https://github.com/godotengine/godot/files/15441613/capsule_collision.zip)
Repository: neovim
Issue: `vim.o.mouse = nil` no longer works as of nvim 0.10.0
Labels: needs:decision,api,compatibility,needs:discussion,has:bisected,options

### Problem

In my Neovim config (`init.lua`), I have the following line:
```lua
vim.o.mouse = nil
```
When I was using nvim 0.9.5, this would properly change the value of `mouse` (verifiable with `:set mouse?` printing `mouse=`).

However, in nvim 0.10, this no longer works. `mouse` is set to `nvi` despite this line remaining in my config (verifiable with `:set mouse?` printing `mouse=nvi`). I can run `:set mouse=` in every new Neovim instance, but this seems like a regression. For what it's worth, running `:lua vim.o.mouse = nil` doesn't change anything even in a Neovim instance, although `:lua print(vim.o.mouse)` does reflect the latest change made by `:set mouse`.

### Steps to reproduce

1. Create `minimal.lua` as:
    ```lua
    vim.o.mouse = nil
    ```
2. Run `nvim --clean -u minimal.lua`
3. In Neovim, run `:set mouse?`
4. Observe that `mouse=nvi`

The same steps with Neovim 0.9.5 have `mouse=`

### Expected behavior

Neovim 0.10.0 should continue to allow you to set `mouse` using `vim.o.mouse`, or, if not, there should be a clear migration path to continue setting this configuration option in Lua

### Neovim version (nvim -v)

nvim v0.10.0

### Vim (not Nvim) behaves the same?

N/A

### Operating system/version

macOS 14.5

### Terminal name/version

alacritty 0.13.2

### $TERM environment variable

xterm-256color

### Installation

Nixpkgs
Repository: godot
Issue: PROPERTY_HINT_RANGE doesn't work for properties of a custom ArrayMesh class if the setter on that property re-computes the mesh.
Labels: discussion,topic:editor,topic:plugin

### Tested versions

Reproducable in 4.2.2.stable.official.15073afe3

### System information

Godot v4.2.2.stable - Ubuntu 22.04.4 LTS 22.04 - Wayland - Vulkan (Forward+) - dedicated AMD Radeon RX 580 Series (RADV POLARIS10) () - AMD Ryzen 9 3900X 12-Core Processor (24 Threads)

### Issue description

I've created a GDExtension class that extends `ArrayMesh` and have added a `PROPERTY_HINT_RANGE` to a property that has a setter defined that will regenerate the mesh whenever the property is changed. I was expecting to be able to use the slider within the editor and see changes to the generated mesh in real time, however any slider operation immediately loses focus and prevents the slider from being useful (it only drags one `set` at a time). 

I suspect that setting the mesh, is triggering a `Resource` `changed` signal and this may be re-rendering the property list, such that the range loses state. I can see that Godot is requesting property information whenever I drag on the slider.

### Steps to reproduce

Create a class that extends `ArrayMesh` and add a `PROPERTY_HINT_RANGE` to a `float` property that has a setter defined that will regenerate the mesh whenever the property is changed.

### Minimal reproduction project (MRP)

I can attach a GDExtension project on request (the shared library objects are not so small), the mesh generation code is not trivial and I don't have a GDScript version of it.
Repository: ollama
Issue: aya model : error when using the generate endpoint
Labels: bug

### What is the issue?

I am running aya model locally. When i just start the model with `ollama run aya` and interact in the terminal, it works fine. But when I try using it via POSTMAN on Windows 10 at (http://127.0.0.1:11434/api/generate) with the following data:

```
{
  "model": "aya",
  "prompt": "Are there multilingual open source large language models available? Respond using JSON",
  "format": "json",
  "stream": false
}
```
i get the following error:

`{
    "error": "error reading llm response: read tcp 127.0.0.1:60589->127.0.0.1:60580: wsarecv: An existing connection was forcibly closed by the remote host."
}`

For other models (e.g. phi3), it works correctly with the same setup.
e.g:
```
{
  "model": "phi3:mini",
  "prompt": "Are there multilingual open source large language models available? Respond using JSON",
  "format": "json",
  "stream": false
}'
```
works.


I can see the following logging for aya (the last line shows only the error but not any other details.):

> llm_load_tensors: ggml ctx size =    0.27 MiB
> llm_load_tensors: offloading 32 repeating layers to GPU
> llm_load_tensors: offloading non-repeating layers to GPU
> llm_load_tensors: offloaded 33/33 layers to GPU
> llm_load_tensors:        CPU buffer size =   820.31 MiB
> llm_load_tensors:      CUDA0 buffer size =  4564.83 MiB
> ........................................................................
> llama_new_context_with_model: n_ctx      = 2048
> llama_new_context_with_model: n_batch    = 512
> llama_new_context_with_model: n_ubatch   = 512
> llama_new_context_with_model: freq_base  = 10000.0
> llama_new_context_with_model: freq_scale = 1
> llama_kv_cache_init:      CUDA0 KV buffer size =   256.00 MiB
> llama_new_context_with_model: KV self size  =  256.00 MiB, K (f16):  128.00 MiB, V (f16):  128.00 MiB
> llama_new_context_with_model:  CUDA_Host  output buffer size =     0.99 MiB
> llama_new_context_with_model:      CUDA0 compute buffer size =   508.00 MiB
> llama_new_context_with_model:  CUDA_Host compute buffer size =    12.01 MiB
> llama_new_context_with_model: graph nodes  = 968
> llama_new_context_with_model: graph splits = 2
> INFO [wmain] model loaded | tid="9016" timestamp=1716611056
> time=2024-05-25T06:24:16.906+02:00 level=INFO source=server.go:545 msg="llama runner started in 8.06 seconds"
> [GIN] 2024/05/25 - 06:24:19 | 500 |   14.8679199s |       127.0.0.1 | POST     "/api/generate"


### OS

Windows

### GPU

Nvidia

### CPU

Intel

### Ollama version

_No response_
Repository: stable-diffusion-webui
Issue: Update Linux Install Instructions
Labels: enhancement

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What would your feature do ?

After filing #15384 and getting some feedback from other users having this same problem and finding a common solution, I put together a guide for installing on more modern Linux distros. Incorporating this into the wiki/readme would be extremely valuable for bringing on new users.

### Proposed workflow

My installation guide: https://github.com/OttCS/OttCS/blob/main/blog/2024/5/linux-automatic1111-sd.md

### Additional information

I couldn't get feedback in the developer discord for how to contribute to the wiki, if someone could explain how to do that I'd be glad to do it myself.
Repository: TypeScript
Issue: [API] Ability to check if  a parameter is being passed to const type parameter
Labels: Suggestion,Awaiting More Feedback

### 🔍 Search Terms

const type parameter

### ✅ Viability Checklist

- [X] This wouldn't be a breaking change in existing TypeScript/JavaScript code
- [X] This wouldn't change the runtime behavior of existing JavaScript code
- [X] This could be implemented without emitting different JS based on the types of the expressions
- [X] This isn't a runtime feature (e.g. library functionality, non-ECMAScript syntax with JavaScript output, new syntax sugar for JS, etc.)
- [X] This isn't a request to add a new utility type: https://github.com/microsoft/TypeScript/wiki/No-New-Utility-Types
- [X] This feature would agree with the rest of our Design Goals: https://github.com/Microsoft/TypeScript/wiki/TypeScript-Design-Goals

### ⭐ Suggestion

Hi from [ts-api-utils](https://github.com/JoshuaKGoldberg/ts-api-utils).

We'd like to be able to check if an expression, passed as an argument of a call expression, is in a "const context". Checking for `as const` is fairly trivial as we can just search up the syntax tree, but checking for `const type parameters` seems impossible **without** using TypeScript internals.

#### With code examples:

We can currently check if `foo` is in a "const context" here fairly trivially:

```ts
const test1 = { foo: 1 } as const;
```

But can't do the same for `foo` in this example:

```ts
declare const fn: <const A>(param: A) => unknown;
const test2 = fn({ foo: 2 });
```

#### Proposal

Ideally, we would like an officially supported way to make such a check.

We currently have a [PR](https://github.com/JoshuaKGoldberg/ts-api-utils/pull/519) in progress that works using ts internals, but we'd prefer not to rely on ts internals.

### 📃 Motivating Example

N/A

### 💻 Use Cases

1. What do you want to use this for?
determining whether a type is readonly
https://github.com/eslint-functional/eslint-plugin-functional/issues/827

3. What shortcomings exist with current approaches?
There doesn't seem to be anyway official way to do this

4. What workarounds are you using in the meantime?
Use ts internals
Repository: pytorch
Issue: The dynamic compilation does not handle composite shapes with multiple dimensions very effectively
Labels: triaged,oncall: pt2,module: dynamic shapes

### 🐛 Describe the bug

Hello, I have encountered a performance issue and would greatly appreciate any assistance. Thank you.
After compiling the unet model with `unet = torch.compile(unet, dynamic=True)`, it is capable of generating videos with multiple shapes. However, this process significantly slows down compared to testing with only one shape.
There are a total of 9 shapes that I use for testing purposes(The input has NCDHW 5 dims but only changing D, H, W dim) .During the warmup phase, the model generates the largest shape. Once all 9 shapes have been generated, the model's performance slows down when returning to the largest shape, as compared to its performance during the eager phase.
Is the performance related to the cache size? Torch dynamo settings lies below
```Python
    torch._dynamo.config.suppress_errors = True
    torch._dynamo.config.cache_size_limit = 64
```

### Error logs

_No response_

### Minified repro

_No response_

### Versions

PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 11 (bullseye) (x86_64)
GCC version: (Debian 10.2.1-6) 10.2.1 20210110
Clang version: Could not collect
CMake version: version 3.18.4
Libc version: glibc-2.31

Python version: 3.9.2 (default, Feb 28 2021, 17:03:44)  [GCC 10.2.1 20210110] (64-bit runtime)
Python platform: Linux-5.15.120.bsk.2-amd64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.2.140

cc @ezyang @msaroufim @bdhirsh @anijain2305 @chauhang
Repository: pytorch
Issue: BUG: `torch.pow(-0.0)` wrongly returns `-0.0` (should be +0.0)
Labels: module: cpu,triaged,actionable,module: python frontend

### 🐛 Describe the bug

The following code reproduces the bug:

```python
import torch
x = torch.tensor([0.0, -0.0])
print(torch.pow(x, 0.5), torch.pow(x, 0.6))
```

prints

```
tensor([0., -0.]) tensor([0., 0.])
```

According to IEEE spec as well as Array API spec, `pow(-0.0, 0.5)` should return `+0.0`. But the above code returns `-0.0`.

### Versions

PyTorch version: 2.3.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 14.4.1 (arm64)
GCC version: Could not collect
Clang version: 15.0.0 (clang-1500.3.9.4)
CMake version: version 3.25.2
Libc version: N/A

Python version: 3.11.9 (main, Apr  2 2024, 08:25:04) [Clang 15.0.0 (clang-1500.3.9.4)] (64-bit runtime)
Python platform: macOS-14.4.1-arm64-arm-64bit
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:
Apple M1

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.3.0
[conda] Could not collect


cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @albanD @malfet
Repository: flutter
Issue: Provide an empty status placeholder for DataTable
Labels: c: new feature,framework,f: material design,c: proposal,P3,team-design,triaged-design

### Use case

```dart
DataTable（
  columns:[],
  rows:[],
  noData: Text('no Data')
）
```
### Proposal

 I hope to be able to achieve this effect, if there is no data when I can display the table header and a temporary no data

![image](https://github.com/flutter/flutter/assets/122137647/c15a5d92-20b7-4c5b-aa70-2e1b3a868217)

![image](https://github.com/flutter/flutter/assets/122137647/58e7c209-0cde-4e60-80f9-76131d84171a)
 When there is no data, I want to display the table header content and a widget with no data for now
Repository: youtube-dl
Issue: [Twitch] Insufficient access. OAuth may be required.
Labels: question

is it possible to donwload vods of a person you have subbed to on twitch the error that comes up is ERROR: Insufficient access. OAuth may be required.
 when i try doing this/
Repository: ollama
Issue: Repeating answers in an instance.
Labels: bug

### What is the issue?

Hi, i'm using llama2 models, and when i asked the Ai to explain something, it does responds and explain it, but when it reached the end, it instead print out certain line, 
```
[INST: <</INST]
```  
then repeats another answer regarding the current questions.

### OS

Windows

### GPU

Nvidia

### CPU

Intel

### Ollama version

ollama 0.1.38
Repository: rust
Issue: compiletest should note when test output is normalized
Labels: A-testsuite,A-diagnostics,T-bootstrap,D-confusing,A-contributor-roadblock,A-compiletest

compiletest has two basic kinds of normalization:
1. normalization it does itself, either built-in or through a `normalize-stderr` pattern. this is what the UI suite uses.
2. normalization it doesn't know about, e.g. in a run-make test

normalizing outputs can lead to some extremely confusing errors, such as https://github.com/rust-lang/rust/pull/121571#issuecomment-2129928216. to avoid misleading contributors, it should note when the output has been normalized.

the first part is not too hard; it can save list of lines that have been modified in each test and print something like "note: 32/32 lines that did not match were normalized before comparison; see build/test/ui/foo.out.orig for unnormalized version"
(note that the unnormalized file already exists today but almost nobody knows about it).

the second part is trickier; run-make tests often run sed before comparing, and this is opaque to compiletest. perhaps fixing this can be part of the ongoing work to switch run-make to rust? and, in the meantime, introduce a new SED script, based off cut-and-grep.sh, to prevent the problem from getting worse? cc @jieyouxu

@rustbot label A-contributor-roadblock A-diagnostics D-confusing A-testsuite