sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: pytorch
Issue: `torch.onnx.export` doesn't correctly constfold constants and DequantLinear
Labels: module: onnx,triaged

### 🐛 Describe the bug

This is my model

```
class ConvNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(7*7*32, num_classes)
        
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out
```

And this is how I apply QAT and explicitly quantize both activation and weight in int8 mode.

```
torch.backends.quantized.engine = "qnnpack"
my_config = torch.ao.quantization.qconfig.QConfig(activation=torch.ao.quantization.observer.MinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_tensor_symmetric), 
                                                  weight=torch.ao.quantization.observer.default_observer.with_args(dtype=torch.qint8, qscheme=torch.per_tensor_symmetric))
model.qconfig = my_config

model_fused = torch.ao.quantization.fuse_modules(model,
    [["1.layer1.0", "1.layer1.1"], ["1.layer2.0", "1.layer2.1"]])
model = torch.quantization.prepare_qat(model_fused.train())

device = "cpu"
model.to(device)
torch.quantization.convert(model, inplace=True)
```
 However, when I export the model to onnx

```
import pytorch_quantization
from pytorch_quantization import nn as quant_nn
from pytorch_quantization import quant_modules

dummy_input = torch.randn(100, 1, 28, 28)  # Batch size = 100, 1 channel, 28x28 image

# Export the quantized model to ONNX
torch.onnx.export(model, dummy_input, "cnn_quantization.onnx", verbose=True, opset_version=13)
```

In the exported ONNX model, the conv biases are int32 and there is a DequantLinear on top of bias constants. I wonder why `torch.onnx.export` doesn't correctly constfold bias constants and DequantLinear?  This later fails the onnx2trt converter, because trt doesn't support int32 input into dequant linear. So this bug is a blocker for us.

### Versions

2.2.2
Repository: godot
Issue: Pinned property updates in Inspector when default value changes
Labels: bug,topic:editor

### Tested versions

4.2.1.stable & master[9d6bdbc56e0ac99a6cc3aaed1c114a6528cb87fc]

### System information

Arch Linux

### Issue description

For a pinned property, if it's pinned to the current default value, then the value shown by the inspector is updated when the default value changes.

Reloading this scene fixes the problem.

See steps to reproduce for details.

### Steps to reproduce

Use the MRP.

1. Open three scenes: `base`, `left_handed`, and `right_handed`.
2. Check the state of `Handedness` in the inspector in these three open scenes.
    * Base scene shows `Left` as it's the current default value.
    * Left handed scene shows `Left`, and it's not revertable.
    * Right handed scene shows `Right`, and it's revertable.
4. Switch to Base scene.
5. Change the default value for `handedness` in `base.gd` to `Right`, and save the script.
6. Check the state of `Handedness` in the inspector in these three open scenes.
    * Base scene shows `Right` as it's the current default value.
    * Left handed scene shows `Right`, and it's not revertable.
        * Expected: shows `Left` and it's revertable.
        * Reloading this scene fixes the problem.
    * Right handed scene shows `Right`, and it's not revertable.

### Minimal reproduction project (MRP)

[pin.zip](https://github.com/godotengine/godot/files/14914298/pin.zip)
Repository: flutter
Issue: Composing characters (e.g. Umlauts) using physical keyboard on iOS generates two characters
Labels: a: text input,platform-ios,a: internationalization,has reproducible steps,P2,found in release: 3.19,team-text-input,triaged-text-input,found in release: 3.22

### Steps to reproduce

1. Create any flutter project with a TextField in it
2. Start on any iOS device with a **physical keyboard** and US layout
3. Try composing the characters "ü", "ä", "ö", "ñ" (case doesn't matter) using: ALT-u + u, ALT-u + a, ALT-u + o, ALT-n + n

### Expected results

The result should be `ü`, `ä`, `ö`, `ñ` (respectively)

### Actual results

Actual result: `¨ü`, `¨ä`, `¨ö`, `˜ñ`

### Code sample

<details open><summary>Code sample</summary>

Not needed, it is 100% reproducible with any code with a textfield in it using physical keyboard.

</details>


### Screenshots or Video

<details open>
<summary>Screenshots </summary>

![Screenshot 2024-04-09 at 08 55 32](https://github.com/flutter/flutter/assets/10852644/ef0a220e-0950-4d73-8a9a-93b71ede42cd)

</details>


### Logs

<details open><summary>Logs</summary>

```console
[Paste your logs here]
```

</details>


### Flutter Doctor output

<details open><summary>Doctor output</summary>

```console
❯ flutter doctor
Doctor summary (to see all details, run flutter doctor -v):
[✓] Flutter (Channel stable, 3.19.5, on macOS 14.4.1 23E224 darwin-arm64, locale en-US)
[✓] Android toolchain - develop for Android devices (Android SDK version 34.0.0)
[✓] Xcode - develop for iOS and macOS (Xcode 15.3)
[✓] Chrome - develop for the web
[✓] Android Studio (version 2022.3)
[✓] Android Studio (version 2021.3)
[✓] VS Code (version 1.86.2)
[✓] Connected device (8 available)
    ! Error: Browsing on the local area network for isoWatch. Ensure the device is unlocked and discoverable via Bluetooth. (code -27)
    ! Error: iPhone X has recently restarted. Xcode will continue when iPhone X is unlocked. (code -14)
[✓] Network resources

• No issues found!
```

</details>
Repository: flutter
Issue: camera add automaticallyEnablesLowLightBoost 
Labels: c: new feature,platform-ios,p: camera,package,P2,team-ios,triaged-ios

### Use case

In low-light conditions, camera preview stream need to take a clear picture

### Proposal

Add support for Flutter camera plugin: https://developer.apple.com/documentation/avfoundation/avcapturedevice/1624610-automaticallyenableslowlightboos
Repository: godot
Issue: Load a scene that references an image in thread may cause memory leaks when running in headless mode
Labels: bug,topic:core,needs testing

### Tested versions

Godot v4.3.dev5.mono

### System information

Godot v4.3.dev5.mono - macOS 12.7.4 - Vulkan (Forward+) - dedicated AMD Radeon Pro 460 - Intel(R) Core(TM) i7-6920HQ CPU @ 2.90GHz (8 Threads)

### Issue description

Load a scene that references an image in thread may cause memory leaks when running in headless mode

```
Task.Run(() =>
{
    GD.Load<PackedScene>(scenePath);
});
```

### Steps to reproduce

1、--headless mode
2、Task.Run or other thread 
3、Load a scene that references an image (sprite2d)
4、quit
5、Leaked instance: Image:9223372066013578630


### Minimal reproduction project (MRP)

[MRP.zip](https://github.com/godotengine/godot/files/14916646/MRP.zip)
Repository: godot
Issue: Camera3D.get_camera_projection().get_aspect() returns unexpected results for PROJECTION_FRUSTUM
Labels: bug,topic:3d

### Tested versions

v4.2.1.stable.official [b09f793f5]

### System information

Godot v4.2.1.stable - Ubuntu 22.04.4 LTS 22.04 - X11 - Vulkan (Mobile) - dedicated NVIDIA GeForce GTX 1060 6GB (nvidia; 470.239.06) - AMD Ryzen Threadripper 1900X 8-Core Processor (16 Threads)

### Issue description

Camera3D.get_camera_projection().get_aspect() returns a value that it not the aspect ratio of the viewport if PROJECTION_FRUSTUM is used with a frustum_offset.  The distorted aspect ratio is not actually used for rendering.


### Steps to reproduce

Create a 3D scene.  Put a Camera3D node in the scene.  Set its projection type to frustum.  Set its frustum offset to (0, 1).  Attach a script to the camera that prints get_camera_projection().get_aspect().  Run the scene.

### Minimal reproduction project (MRP)

[test01.zip](https://github.com/godotengine/godot/files/14916430/test01.zip)
Repository: go
Issue: x/playground: playground times out when executing simple snippet
Labels: NeedsInvestigation

### Go version

go1.21

### Output of `go env` in your module/workspace:

```shell
N/A
```


### What did you do?

I've tried to run the code on Go Playground.

https://go.dev/play/p/k82r6VIVJO1?v=goprev

```go
package main

import "fmt"

func main() {
	fmt.Println("a")
}
```

### What did you see happen?

The program is failed to build due to timeout.

<img width="2527" alt="スクリーンショット 2024-04-09 18 10 52" src="https://github.com/golang/go/assets/55221074/05f34bc1-645b-418f-a31a-63ac37fe6a6d">


### What did you expect to see?

It succeed to run the program with using Go 1.21 on Go Playground.
Repository: PowerToys
Issue: Option for FancyZones layouts to set active window
Labels: Product-FancyZones,Needs-Triage

### Description of the new feature / enhancement

Each layout has the option to set one zone as "focus" / "make active on layout switch".
When hotkey-switching to a layout with a "focus" zone, the window in that zone becomes the active window.

This makes the window in the "focus" zone immediately interactable & appear on top of any overlapping zones.

### Scenario when this would be used?

A set of layouts with multiple small zones & one large zone, with windows in each.
Hotkeys are used to switch which between these layouts to select which zone becomes the large one.

Switching between these layouts is only ever done to interact with the newly expanded window.
This feature would remove the additional step of clicking on the window or using `alt+tab` after every layout switch.

### Supporting information

_No response_
Repository: rust
Issue: Figure out the interaction of GVN and function/vtable pointers
Labels: T-compiler,C-bug,A-mir-opt

GVN has special treatment for function/vtable pointers. I haven't fully understood why.

Function pointers have unstable comparison. We set the `unnamed_addr` flag in LLVM. I am not entirely sure how far that attribute goes, but I think it means that `==` involving such functions is basically non-deterministic? @nikic @bjorn3 maybe you can help here -- can the result of comparing function pointers depend on whether or not optimizations are applied? Or how exactly `unnamed_addr` affect the generated code?

@cjgillot could you give an example for the code you are concerned about, where GVN would go wrong if it did not special-case function pointers? I think you gave an example a while ago but I can't find it right now.
Repository: stable-diffusion-webui
Issue: [Bug]: Codeformer "Weight" option does not have any effect
Labels: bug-report

### Checklist

- [X] The issue exists after disabling all extensions
- [X] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused by a bug in the webui
- [X] The issue exists in the current version of the webui
- [X] The issue has not been reported before recently
- [ ] The issue has been reported before but has not been fixed yet

### What happened?

Codeformer "Weight" option does not have any effect.

### Steps to reproduce the problem

Extras -> Codeformer ->
  Set the Weight 0 -> Generate
  Set the Weight 1 -> Generate

Compare faces of generated images. They exactly are the same.


### What should have happened?

Faces should look different depending on the Weight option!

### What browsers do you use to access the UI ?

Mozilla Firefox

### Sysinfo

[sysinfo-2024-04-09-09-57.json](https://github.com/AUTOMATIC1111/stable-diffusion-webui/files/14917016/sysinfo-2024-04-09-09-57.json)


### Console logs

```Shell
Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug  1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]
Version: v1.8.0
Commit hash: bef51aed032c0aaa5cfd80445bc4cf0d85b408b5
Launching Web UI with arguments:
no module 'xformers'. Processing without...
no module 'xformers'. Processing without...
No module 'xformers'. Proceeding without it.
Loading weights [6ce0161689] from C:\BBs\sd.webui\webui\models\Stable-diffusion\v1-5-pruned-emaonly.safetensors
Creating model from config: C:\BBs\sd.webui\webui\configs\v1-inference.yaml
Running on local URL:  http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.
Startup time: 8.6s (prepare environment: 1.9s, import torch: 3.0s, import gradio: 0.7s, setup paths: 0.8s, initialize shared: 0.2s, other imports: 0.5s, load scripts: 0.8s, create ui: 0.3s, gradio launch: 0.3s).
Applying attention optimization: Doggettx... done.
Model loaded in 2.7s (create model: 0.7s, apply weights to model: 1.6s, calculate empty prompt: 0.1s).
```


### Additional information

_No response_
Repository: ollama
Issue: Potential problems with the `llm/ext_server/server.cpp` not accepting `--ubatch-size ` option
Labels: bug

### What is the issue?

Not sure what to list this issue under (it's a potential bug I think).

Recently `llama.cpp` has added an option called `--ubatch-size ` and appears to have changed the default value (and possibly meaning of) the old  `--batch-size ` option:

https://github.com/ggerganov/llama.cpp/pull/6017

The PR says:

> By default `n_batch` is 4096, `n_ubatch` is 512

but I think it is actually 2048 and 512 now.

This better explains the difference between the options:

https://github.com/ggerganov/llama.cpp/discussions/6328

There are several potential problems and implications of this change for Ollama:

- It looks like `llm/ext_server/server.cpp` is using some old code that handles the `n_batch` in the old way, but this in turn seems to be importing headers, etc from a newer version of `lamma.cpp` (b2581), who's `examples/server.cpp` code does appear to have the new `n_batch` / `n_ubatch` logic in it.
- It looks like `api/types.go` is setting the `n_batch`  value back down to 512, when the new default is supposed to be 2048.
- Since Ollama is using the `--embedding` option there are some potential problems mentioned here: https://github.com/ggerganov/llama.cpp/issues/6263
- Not being able to set the `n_ubatch` value is causing regressions for people using multiple GPUs (https://github.com/ggerganov/llama.cpp/issues/6075 & https://github.com/ggerganov/llama.cpp/issues/6476).

---

https://github.com/ollama/ollama/blob/main/llm/ext_server/server.cpp:
```
    printf("  --pooling {none,mean,cls}\n");
    printf("                        pooling type for embeddings, use model default if unspecified\n");
    printf("  -b N, --batch-size N      batch size for prompt processing (default: %d)\n", params.n_batch);
    printf("  --memory-f32              use f32 instead of f16 for memory key+value (default: disabled)\n");
```

vs:

https://github.com/ggerganov/llama.cpp/blob/37e7854c104301c5b5323ccc40e07699f3a62c3e/examples/server/server.cpp:
```
    printf("  --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
    printf("  -dt N, --defrag-thold N\n");
    printf("                            KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
    printf("  -b N, --batch-size N      logical maximum batch size (default: %d)\n", params.n_batch);
    printf("  -ub N, --ubatch-size N    physical maximum batch size (default: %d)\n", params.n_ubatch);
    printf("  --memory-f32              use f32 instead of f16 for memory key+value (default: disabled)\n");
```

---

```
- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`
- `-ub N`, `--ubatch-size N`: Physical maximum batch size. Default: `512`
```
Repository: TypeScript
Issue: Incorrect Chinese Translation for 'initializer' and Incorrect Use of Chinese Quotation Marks
Labels: Bug,Fix Available

### 🔎 Search Terms

initializer, i18n, Chinese

### 🕗 Version & Regression Information

- This changed in commit (https://github.com/microsoft/TypeScript/commit/15c9cc8a324e6197179be298d58bab2805992aa3)



### ⏯ Playground Link

_No response_

### 💻 Code

```ts
// Your code here
```


### 🙁 Actual behavior

The current Chinese translation for 'initializer' is incorrect, as it has been translated as '初始化表杰式', which is not grammatically correct in Chinese. Additionally, the translation for accessing the property ['0'] incorrectly uses Chinese quotation marks [“0”], which may cause confusion for Chinese users. These issues should be addressed to improve the clarity and accuracy of the Chinese translations.

### 🙂 Expected behavior

I expected the Chinese translation for 'initializer' to be corrected to a grammatically correct form. Additionally, I expected the use of Chinese quotation marks for accessing the property ['0'] to be corrected to standard English quotation marks. These corrections would improve the accuracy and readability of the Chinese translations.

### Additional information about the issue

_No response_
Repository: flutter
Issue: two_dimensional_scrollables package enhancement: TableView / ListView widget with column sorting/resizing and row selection
Labels: framework,would be a good package,package,c: proposal,P3,team-framework,triaged-framework,p: two_dimensional_scrollables

### Use case

The current `TableView` widget in the [`two_dimensional_scrollables`](https://pub.dev/packages/two_dimensional_scrollables) package lacks some essential features for creating sophisticated and interactive data tables / lists. As a developer, I want to build a list view similar to the Explorer list view in Windows Explorer or the list view in the macOS Finder. Specifically, I need the following functionalities:

1. **Resizable Columns**: Users should be able to adjust column widths according to their preferences.
2. **Sortable Columns**: Clickable column headers that allow sorting the list by the selected column.
3. **Row Selection**: Users should be able to select individual rows using mouse, touch, or keyboard input.
4. **Multiple Row Selection**: Support for selecting multiple rows simultaneously.

### Proposal

I propose extending the existing `TableView` widget or introducing a new widget specifically designed for these advanced features. Here's what I envision:

- The existing `TableView` widget with extended functionality, or a new `ListView`-type widget
- Includes resizable columns, sortable headers, and row selection capabilities.
- Provides an intuitive API for developers to configure and customize behavior.
- Ideally, this widget should be part of the `two_dimensional_scrollables` package.

Sample screenshot of a macOS Finder files list view:

<img width="1032" alt="image" src="https://github.com/flutter/flutter/assets/108275944/449231e4-0f7f-4fe9-92e1-f86c8bc575cc">
Repository: vscode
Issue: Adopt the onEnter rules inside of the `reindent lines` command
Labels: polish,editor-autoindent

This was noticed in the following comment https://github.com/microsoft/vscode/issues/209863#issuecomment-2044630746. When applying the `Reindent Lines` operations, the onEnter rules are not referred to. As a result, the indentation after applying the `Reindent Lines` command does not match the indentation when typing the same code from top to bottom. 

Since we are using the `Reindent Lines` code in order to detect cases of failing indentation, if we adopt the onEnter rules now, this could eclipse the indentation issues in our regexes and the code. Hence the onEnter rules should be adopted towards the end of the improvement cycle.
Repository: tensorflow
Issue: [DOCS] Missing complex input for Round op
Labels: type:docs-bug,awaiting review,type:bug,comp:ops,TF 2.16

### Issue type

Documentation Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

binary

### TensorFlow version

2.15.0

### Custom code

No

### OS platform and distribution

MacOS Sonoma

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

While testing the `Round` operation, I noticed an inconsistency with the [documentation](https://www.tensorflow.org/api_docs/python/tf/raw_ops/Round). On the official page, it's described that a complex tensor can be passed as input. However, when I try to replicate this in my environment, it doesn't work as expected. I have to apply the `Round` operation separately to the real and imaginary parts of the tensor to achieve the desired result.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
x = tf.constant([1+2j, 3+4j])
rounded = tf.raw_ops.Round(x=x)
```


### Relevant log output

```shell
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/lucatam/Documents/OpenSourceProject/openvino/opvenv/lib/python3.9/site-packages/tensorflow/python/util/tf_export.py", line 403, in wrapper
    return f(**kwargs)
  File "/Users/lucatam/Documents/OpenSourceProject/openvino/opvenv/lib/python3.9/site-packages/tensorflow/python/ops/gen_math_ops.py", line 8770, in round
    _ops.raise_from_not_ok_status(e, name)
  File "/Users/lucatam/Documents/OpenSourceProject/openvino/opvenv/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 5883, in raise_from_not_ok_status
    raise core._status_to_exception(e) from None  # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.NotFoundError: Could not find device for node: {{node Round}} = Round[T=DT_COMPLEX128]
All kernels registered for op Round:
  device='XLA_CPU_JIT'; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BFLOAT16, DT_COMPLEX128, DT_HALF]
  device='CPU'; T in [DT_HALF]
  device='CPU'; T in [DT_FLOAT]
  device='CPU'; T in [DT_DOUBLE]
  device='CPU'; T in [DT_BFLOAT16]
  device='CPU'; T in [DT_INT32]
  device='CPU'; T in [DT_INT64]
 [Op:Round] name:
```
Repository: vscode
Issue: Add option to trim trailing whitespaces only at the end of lines that contain code
Labels: feature-request,editor-autoindent

This feature request is inspired from the following comment https://github.com/microsoft/vscode/issues/40115#issuecomment-2044780745

It is currently possible to trim the trailing whitespaces with the following setting `editor.trimAutoWhitespace`. When this setting is disabled, the editor never trims trailing white spaces.

This could be undesirable if for example a user wants to keep whitespaces on a line that only contains whitespaces and trim trailing whitespaces on a line that contains symbols other than whitespaces. Consider the following code (where the dots represent whitespaces):

```
function f() {
....const hi = {
.......a:'hi'...
....}
....
```

In this case we may want to keep the whitespaces on the last line, but delete the whitespaces at the end of the third line. 

A new setting could be proposed to control this.