sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: PowerToys
Issue: Fully customize taskbar
Labels: Idea-New PowerToy

This feature will allow PowerToys users to customize their taskbar to their liking

**What is the expected behavior of the proposed feature?**

When enabled, an option for a taskbar editor would appear. 

The taskbar editor would have an app drawer to allow the user to pick and choose apps pinned on the taskbar.

Other tools in the editor will be an aligning tool which would allow apps to be pinned in any order to the user would like. For example, a user could pin 4 apps directly in the middle of the taskbar.

A removal tool for removing certain elements in the taskbar. 

A shortcut tool for pinning certain shortcuts in apps. For example, a shortcut which opens a certain page in the settings.

**A widget drawer for taskbar widgets**

Widgets such as an expanded calendar or a mini file explorer could be added. Other widgets can be added later.

**What is the scenario this would be used?**

Taskbar customization will let PowerToys users to customize their taskbar to their liking. Advanced actions can be performed on the taskbar. This will add a lot of functionality and use to PowerToys

-A taskbar enthusiast
---

If you'd like to see this feature implemented, add a 👍 reaction to this post.
Repository: flutter
Issue: Migrate the rest of macrobenchmarks performance test to the host independent solution
Labels: team

This is the issue to track the migration. These issues are breakdowns of #62171. 

- [x] `animated_placeholder_perf` #64478 
- [x] `backdrop_filter_perf` #64482
- [x] `color_filter_and_fade_perf` #64484
- [x] `cubic_bezier_perf` #64487
- [ ] `fading_child_animation_perf` #64490
- [ ] `imagefiltered_transform_animation_perf` #64495
- [x] `textfield_perf` #64503
Repository: pytorch
Issue: Make busy waiting time in DDP (kSynchronizeBusyWaitMillis) a configurable environment variable
Labels: oncall: distributed,triaged

## 🚀 Feature

Make busy waiting time in DDP (kSynchronizeBusyWaitMillis) a configurable environment variable, so users can customize the checking interval via environment variable.

https://github.com/pytorch/pytorch/blob/6e1127ea3f64034329062ca9ad6c60a553809566/torch/lib/c10d/ProcessGroupNCCL.cpp#L232

## Motivation

We spot a potential performance issue in the case of NCCL_BLOCKING_WAIT being turned on and model with many SyncBatchNorm operators. When NCCL_BLOCKING_WAIT is turned on, GPU is kept idle after NCCL kernel finishes the execution since SyncBatchNorm checks the completion of NCCL every 10 milli-seconds (the default value of kSynchronizeBusyWaitMillis). Making it configurable allows user to easily tune the checking interval for performance improvement in user's scenarios.


## Pitch

1. Create a environment variable for the checking interval, Say "NCCL_POLLING_INTERVAL_MICROSECONDS", and let DDP load the checking interval from the environment variable;
2. If NCCL_POLLING_INTERVAL_MICROSECONDS is not set by user, we will use 10000, which is the default value of kSynchronizeBusyWaitMillis currently.

## Alternatives

<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->

## Additional context

<!-- Add any other context or screenshots about the feature request here. -->


cc @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @xush6528 @osalpekar @jiayisuse @agolynski
Repository: go
Issue: runtime: TestPreemptionAfterSyscall is flaky on Plan 9
Labels: Testing,OS-Plan9,NeedsInvestigation

[2020-08-24T16:53:02-fb5c3ea/plan9-386-0intro](https://build.golang.org/log/38c700bea05aa70fab749566da192d6d5999138d)
[2020-08-20T15:14:12-c57c021/plan9-386-0intro](https://build.golang.org/log/2d4995df2af8e3284eb55cdd7d809f8a11c7063c)
[2020-08-15T05:58:24-c0dded0/plan9-386-0intro](https://build.golang.org/log/4604fe2ede42598bb947b5ffa9bd67f7607d0812)

Also https://storage.googleapis.com/go-build-log/8d475a42/windows-386-2008_02320057.log; see previously #34901.
Repository: pytorch
Issue: [jit] cannot use functools.partial with TorchScript
Labels: oncall: jit,months

## 🐛 Bug

TorchScript wants to derive a name from a function using `__name__`. `functools.partial` does not have `__name__` but has `name`. 

## To Reproduce

```py
from functools import partial

import torch


def sum_fun(x: torch.Tensor, dim: int = 0) -> torch.Tensor:
    return torch.sum(x, dim=dim)


sum_script = torch.jit.script(sum_fun) # works
sum_partial_script = torch.jit.script(partial(sum_fun, dim=-1)) # fails
```

```python-traceback
~/miniforge3/envs/panama/lib/python3.8/site-packages/torch/jit/__init__.py in script(obj, optimize, _frames_up, _rcb)
   1261         return torch.jit._recursive.create_script_module(obj, torch.jit._recursive.infer_methods_to_compile)
   1262 
-> 1263     qualified_name = _qualified_name(obj)
   1264     if inspect.isclass(obj):
   1265         # If this type is a `nn.Module` subclass, they probably meant to pass

~/miniforge3/envs/panama/lib/python3.8/site-packages/torch/_jit_internal.py in _qualified_name(obj)
    758         return obj.qualified_name
    759 
--> 760     name = obj.__name__
    761     if name == '<lambda>':
    762         name = '_lambda'  # make name a valid identifier

AttributeError: 'functools.partial' object has no attribute '__name__'

```


## Expected behavior

Both work :)

## Environment

<details>
PyTorch version: 1.5.1
Is debug build: False
CUDA used to build PyTorch: 10.1

OS: CentOS Linux release 7.4.1708 (Core)  (x86_64)
GCC version: (GCC) 4.8.5 20150623 (Red Hat 4.8.5-16)
Clang version: Could not collect
CMake version: version 2.8.12.2

Python version: 3.8 (64-bit runtime)
Is CUDA available: False
CUDA runtime version: No CUDA
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA

Versions of relevant libraries:
[pip3] numpy==1.19.1
[pip3] torch==1.5.1
[pip3] torchvision==0.6.0a0+35d732a
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               10.1.243             h6bb024c_0  
[conda] hamiltorch                0.3.1.dev3               pypi_0    pypi
[conda] mkl                       2020.2                      256    conda-forge
[conda] mkl-service               2.3.0            py38he904b0f_0  
[conda] mkl_fft                   1.1.0            py38h23d657b_0  
[conda] mkl_random                1.1.1            py38hcb8c335_0    conda-forge
[conda] numpy                     1.19.1           py38hbc911f0_0  
[conda] numpy-base                1.19.1           py38hfa32c7d_0  
[conda] pytorch                   1.5.1           py3.8_cuda10.1.243_cudnn7.6.3_0    pytorch
[conda] torchvision               0.6.1                py38_cu101    pytorch
</details>


cc @gmagogsfm
Repository: TypeScript
Issue: Treatment of recursive tuples that utilize type spread.
Labels: Needs Investigation

**TypeScript Version:**  4.0.2

**Search Terms:** spread tuple in recursive tuple type

**Code**

I'm trying to model a type that could evaluate to any of the following:

```ts
["list", "of", "Post"];
["list", "of", "list", "of", "Post"];
["list", "of", "list", "of", "list", "of", "Post"];
// ... could go on
```

The following approach produces a circularity error, even though tuple types are circularity-capable since 3.7 :/

```ts
type Nested = ["list", "of", ...(["Post"] | Nested)];
```

**Expected behavior:**

To be able to type-check the following (for example):

```ts
const nested: Nested = ["list", "of", "list", "of", "Post"];
```

**Actual behavior:**

Circularity error.

Any help would be greatly appreciated! Thank you!
Repository: flutter
Issue: FlutterErrorDetails context should be renamed to reason
Labels: framework,c: API break,c: proposal,P3,team-framework,triaged-framework

`FlutterErrorDetails` has a parameter named `context`, this is misleading because `context` in Flutter usually means a `BuildContext`, whereas here's diagnostic data. 

I suggest this be renamed to `reason` as the `DiagnosticsNode` is supposed to be converted to English for an explanation of the error.
Repository: rust
Issue: compiletest: Way to bless pretty testsuite
Labels: A-testsuite,C-enhancement,T-bootstrap,E-medium,A-compiletest

Currently, compiletest have no way to bless tests in `src/test/pretty`.
I use
```
rustc +stage1 -Z unpretty=hir,typed src/test/pretty/issue-4264.rs > src/test/pretty/issue-4264.pp
```

to manually bless a test.

@rustbot modify labels: T-compiler
Repository: youtube-dl
Issue: maybe a new quick config switch option like -c preconf1 to read from preconf1.conf or a abbreviate of --ignore-config like -ic
Labels: request

- [X] I'm reporting a feature request
- [X] I've verified that I'm running youtube-dl version **2020.07.28**
- [X] I've searched the bugtracker for similar feature requests including closed ones


## Description

`--proxy` and `--write-sub` and `--sub-lang eng` and etc. is used by many users every time using youtube-dl. some sites may need `--proxy` and specific output template` and others not. so many users may have more than one or two configs.

`--ignore-config` can solve some of this issue but it seems too long to type it every time.

maybe a new quick config switch option like -c preconf1 to read from preconf1.conf or an abbreviate of --ignore-config like -ic?
Repository: kubernetes
Issue: late binding volume binding + multiple volumes
Labels: kind/bug,sig/storage,lifecycle/frozen

**What happened**:

The `WaitForFirstConsumer` (= late binding) volume binding mode is supposed to ensure that volumes get created in a topology that is accessible by the node on which a pod is about to start. This can fail when there are multiple volumes involved:
- first volume gets created
- second volume creation fails, for example because there is not enough space in the accessible topology segments of the node
- pod gets rescheduled, but has to land in the same location again because of first volume
- second volume creation fails as before

**What you expected to happen**:

One possible solution is to delete the first volume and try again from scratch elsewhere. Tentative KEP is here: https://github.com/kubernetes/enhancements/pull/1703

/sig storage
Repository: flutter
Issue: Support adding a Flutter add-to-app module to a Flutter-created app
Labels: c: new feature,tool,engine,c: proposal,P3,team-engine,triaged-engine

## Use case

I have been working on a SDK project which is written with flutter. We implement our common business logic with flutter, and then provide Android/iOS native interfaces which just forward API request from native to flutter to work.
Then we build and assemble all the things together(generated libapp.so, flutter asserts and else), deliver as SDK in the form of AAR and Framework to developers.

To integrate the SDK to the projects which do not use flutter , it is simple and just works great.

But for those project which also use flutter, it does not work. As our SDK already has its own libapp.so, and the target project will also generates its own libapp.so too, but flutter engine does not support load two or more aot library simultaneously. 

For now or future, our sdk just cannot be open source, so we cannot provide a flutter package to use. So we have to figure out another way to work out this situation.

## Proposal

- Flutter engine support load two or more aot library simultaneously
- Flutter build tools support merge aot library to a whole one. Or support merge some kind of intermediate artifacts of aot library.
- Any other better ways?
Repository: godot
Issue: Cannot reimport atlas texture on Mac OS 10.15
Labels: topic:import

<!-- Please search existing issues for potential duplicates before filing yours:
https://github.com/godotengine/godot/issues?q=is%3Aissue
-->

**Godot version:**
<!-- Specify commit hash if using non-official build. -->
Godot Engine v3.2.2.stable.official


**OS/device including version:**
<!-- Specify GPU model, drivers, and the backend (GLES2, GLES3, Vulkan) if graphics-related. -->
OpenGL ES 3.0 Renderer: AMD Radeon Pro 560X OpenGL Engine


**Issue description:**
<!-- What happened, and what was expected. -->
When I re-import 17 images, errors shown in the console:

 scene/resources/bit_map.cpp:38 - Condition "p_size.height < 1" is true.
 Can't find file 'res://assets/atlas2.png'.
 No loader found for resource: res://assets/atlas2.png.
 No loader found for resource: res://assets/atlas2.png.
 core/io/resource_format_binary.cpp:360 - Couldn't load resource: res://assets/atlas2.png
 No loader found for resource: res://assets/atlas2.png.
 No loader found for resource: res://assets/atlas2.png.
 core/io/resource_format_binary.cpp:360 - Couldn't load resource: res://assets/atlas2.png

**Steps to reproduce:**
import atlas texture as in the official document.

**Minimal reproduction project:**
<!-- A small Godot project which reproduces the issue. Drag and drop a zip archive to upload it. -->
Repository: PowerToys
Issue: [PowerRename] Add settings option to choose a default date field
Labels: Idea-Enhancement,Help Wanted,Product-PowerRename,Priority-2,Cost-Medium

Reference https://github.com/microsoft/PowerToys/issues/909 for this feature improvement 
Currently, the `File Created` date is used and there is no option to use a different date field (`Date modified`, `Date taken` etc.).

Proposal: add an option in the PowerRename Settings to select the default date field.
When switching to WinUI 3.0 we will reconsider an probably move the date field selector to the PowerRename dialog.
Repository: rust
Issue: Naked functions don't produce an active frame for asm! .seh_* directives on Windows
Labels: A-naked,C-bug,requires-nightly,F-asm

I'm trying to port some Unix `asm!` code, using the `.cfi_*` directives inside naked functions to provide information to the unwinder/debugger about  call frames, to Windows.

The following code compiles on MacOs and Linux under ` rustc 1.47.0-nightly (f44c6e4e2 2020-08-24)`:

```rust
#[naked]
unsafe extern "C" fn a() {
    asm!(".cfi_def_cfa rbp, 16")
}
````

If I try to do something similar on Windows:

```rust
#[naked]
unsafe extern "C" fn a() {
    asm!(".seh_setframe rbp, 16")
}
```

I get:  `error: .seh_ directive must appear within an active frame`. Only if I add `.seh_proc a`, `.seh_endproc` and declare the function `#[no_mangle]` the code compiles:

```rust
#[naked]
#[no_mangle]
unsafe extern "C" fn a() {
    asm!(
        ".seh_proc a",
        ".seh_setframe rbp, 16",
        ".seh_endproc"
    )
}
```
I don't think this is correct though, as the return statement in the naked function happens after `.seh_endproc` and is excluded from the frame information.

I expect the `#[naked]` function to behave the same way on Windows as on Unix platforms, where a `cfi_startproc` is implicitly added to `#[naked]` functions.
Repository: pytorch
Issue: NCCL WARN Your program may be hanging, this may be caused by a collective mismatch around rank 2. Please check your collective calls at and around this rank. You can use NCCL_DEBUG=INFO and NCCL_DEBUG_SUBSYS=COLL to see the collective logs
Labels: oncall: distributed,module: cuda,triaged,module: nccl

## 🐛 Bug

Training hangs at .backward() call. I am using a multi-stream network in which I have two backbones and concatenate their outputs. I face the problem that NCCL hangs, when I use a single stream network with one backbone, it works. The problem only arises on multiple-gpu training, with single gpu, it works fine. 

## To Reproduce

Steps to reproduce the behavior:

```
#!/usr/bin/env bash

CONFIG=$1
GPUS=$2
PORT=${PORT:-29500}

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
    $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3}
```


<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->

Full nccl debug logs: https://drive.google.com/file/d/1pncwFIzI9MJ2iy6xDyjycX92MeDsGFBF/view?usp=sharing 

<!-- A clear and concise description of what you expected to happen. -->

## Environment
```
PyTorch version: 1.5.1
Is debug build: No
CUDA used to build PyTorch: 10.2

OS: Ubuntu 18.04.4 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.13.3

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.0.130
GPU models and configuration: 
GPU 0: Tesla V100-SXM2-16GB
GPU 1: Tesla V100-SXM2-16GB
GPU 2: Tesla V100-SXM2-16GB
GPU 3: Tesla V100-SXM2-16GB

Nvidia driver version: 440.33.01
cuDNN version: Probably one of the following:
/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7.6.5
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn.so.7.6.5

Versions of relevant libraries:
[pip3] numpy==1.15.4
[conda] blas                      1.0                         mkl  
[conda] mkl                       2020.0                      166  
[conda] mkl-service               2.3.0            py37he904b0f_0  
[conda] mkl_fft                   1.0.15           py37ha843d7b_0  
[conda] mkl_random                1.1.0            py37hd6b4f25_0  
[conda] torch                     1.5.1                    pypi_0    pypi
[conda] torchvision               0.6.1                    pypi_0    pypi
```
## Additional context

I am using the MMDetection framework for detection. Single backbone MaskRCNN works, however, when I try to have two backbones and concatenate their output before passing on to the RPN and further network, nccl hangs at backward during multi-gpu training and thus, only the first batch is loaded and it gets stuck there. 

cc @ngimel @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @xush6528 @osalpekar @jiayisuse @agolynski
Repository: TypeScript
Issue: Folding function calls folds closing parenthesis
Labels: Suggestion,Experience Enhancement

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**TypeScript Version:**  4.0.1-rc, 4.0.2, 4.1.0-dev.20200825

<!-- Search terms you tried before logging this (so others can find this issue more easily) -->
**Search Terms:**
fold call function parenthesis bracket

**Code**

```ts
declare const f: (_: unknown) => void
f({
  a: 3
})
f(
  3
)
```

**Expected behavior:**

The function calls should fold like this:

```ts
f({ // ...
})
f( // ...
)
```

This was the behaviour for the first function call in 4.0.0 because of the object literal:
![screenshot of folding behaviour in TS 4.0.0](https://user-images.githubusercontent.com/31467609/91164184-03f46f80-e712-11ea-93b5-158823a6822e.png)

**Actual behavior:**

The function calls fold like this, including the last line with the closing parenthesis:

```ts
f({ // ...
f( // ...
```

![screenshot of folding behaviour in TS 4.0.1 onwards](https://user-images.githubusercontent.com/31467609/91164422-53d33680-e712-11ea-9168-4431ed41cf3e.png)


**Playground Link:** I couldn't reproduce this [in the playground](https://www.typescriptlang.org/play?ts=4.0.2#code/CYUwxgNghgTiAEYD2A7AzgF3gMwFzwAoB9fAVxQGsUkB3FASngF4A+eANyQEtgAobAgG9e8eFHwBmXgF96-AiPhS5QA), so I'm not sure if this is an issue with VSCode.

**Related Issues:** may have been caused by #39109 which fixes #38597 (it was the only relevant issue I found that was fixed for 4.0.1 and this bug doesn't occur in 4.0.0)
Repository: pytorch
Issue: JIT tracing incorrectly records some slice bounds
Labels: high priority,triage review,oncall: jit

```python
import torch

class TestModule(torch.nn.Module):
    def forward(self, features, features_len):
        # assumes batch_size == 1
        eos_mask = torch.arange(features.shape[1]).eq(features_len).float()
        features[:,:,2] = eos_mask.unsqueeze(0)
        return features

features = torch.tensor(
    [[[1.0, 1.0, 0.0],
     [0.0, 0.0, 0.0]]]
)
print('features.shape=', features.shape)
features_len = torch.tensor([1])

module = TestModule()
traced_module = torch.jit.trace_module(module, dict(forward=(features, features_len,)))
print('Traced module code:')
print(traced_module.code)

print('Original module output:')
print(module(features.clone(), features_len))
print('Traced module output:')
print(module(features.clone(), features_len))

new_features = torch.tensor(
    [[[1.0, 1.0, 0.0],
     [1.0, 1.0, 0.0],
     [0.0, 0.0, 0.0]]]
)
print('new_features.shape=', new_features.shape)
new_features_len = torch.tensor([2])

print('Original module output:')
print(module(new_features.clone(), new_features_len))
print('Traced module output:')
print(traced_module(new_features.clone(), new_features_len))
```

```
features.shape= torch.Size([1, 2, 3])
Traced module code:
def forward(self,
    features: Tensor,
    features_len: Tensor) -> Tensor:
  _0 = ops.prim.NumToTensor(torch.size(features, 1))
  _1 = torch.arange(annotate(number, _0), dtype=None, layout=0, device=torch.device("cpu"), pin_memory=False)
  eos_mask = torch.to(torch.eq(_1, features_len), 6, False, False, None)
  _2 = torch.unsqueeze(eos_mask, 0)
  _3 = torch.slice(features, 0, 0, 9223372036854775807, 1)
  _4 = torch.slice(_3, 1, 0, 9223372036854775807, 1)
  _5 = torch.select(_4, 2, 2)
  _6 = torch.expand(torch.view(_2, [2]), [1, 2], implicit=True)
  _7 = torch.copy_(_5, _6, False)
  return features

Original module output:
tensor([[[1., 1., 0.],
         [0., 0., 1.]]])
Traced module output:
tensor([[[1., 1., 0.],
         [0., 0., 1.]]])
new_features.shape= torch.Size([1, 3, 3])
Original module output:
tensor([[[1., 1., 0.],
         [1., 1., 0.],
         [0., 0., 1.]]])
Traced module output:
Traceback (most recent call last):
  File "foo.py", line 38, in <module>
    print(traced_module(new_features.clone(), new_features_len))
  File "/miniconda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript (most recent call last):
foo.py(7): forward
/miniconda/lib/python3.8/site-packages/torch/nn/modules/module.py(704): _slow_forward
/miniconda/lib/python3.8/site-packages/torch/nn/modules/module.py(720): _call_impl
/miniconda/lib/python3.8/site-packages/torch/jit/__init__.py(1109): trace_module
foo.py(18): <module>
RuntimeError: shape '[2]' is invalid for input of size 3
```

cc @ezyang @gchanan @zou3519 @gmagogsfm