sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: vscode
Issue: vscode diff to allow regexp exclusions
Labels: feature-request,diff-editor

Issue Type: <b>Feature Request</b>

This is purely a diff visualization feature request and would not be applicable to patch creation.

I would like to add a configureable regexp exclusion from the differential.
I have output that changes by shifting indexes making the differential really hard to discern the substantive changes.

In my example, cool was added, and an external application out of my control reindexes all of the lines.
In this case I would like to prevent the diff from catching "^line\d+:".

My apologies if this was asked elsewhere, I couldn't find it from any searches.

Thanks

VS Code version: Code 1.66.0 (e18005f0f1b33c29e81d732535d8c0e47cafb0b5, 2022-03-30T05:50:04.419Z)
OS version: Darwin x64 21.4.0
Restricted Mode: No


![image-20220425-215433](https://user-images.githubusercontent.com/104513208/165549702-508156b3-dae4-464d-8a4f-1e4e763cd677.png)
Repository: material-ui
Issue: [Snackbar] Support API to create Alert with Snackbar globally
Labels: new feature,component: snackbar,component: alert

### Duplicates

- [X] I have searched the existing issues

### Latest version

- [X] I have tested the latest version

### Summary 💡

I think it cacn be used like this [example](https://element-plus.org/en-US/component/message.html#basic-usage)

We can provide an API to create it, it can reduce user's work

### Examples 🌈

I have a idea to realize it,

```jsx
export function TipMessage({ message, duration = 3000, type = 'info', anchorOrigin }) {
  const container = document.createElement('div')
  ReactDOM.render(
    <TipAlert
      message={message}
      duration={duration}
      type={type}
      anchorOrigin={anchorOrigin}
    />,
    container,
  )
  document.querySelector('body').append(container)
}

function TipAlert(props) {
  const [open, setOpen] = useState(true)

  return <>
    <Snackbar open={open} onClose={() => setOpen(false)} anchorOrigin={props.anchorOrigin} autoHideDuration={props.duration}>
      <Alert severity={props.type}>{props.message}</Alert>
    </Snackbar>
  </>
}
```

### Motivation 🔦

It can help developers when they want to give a globl tip on the page.
Repository: pytorch
Issue: [checkpoint] Switch away from pickle-base serialization
Labels: triaged,sharded_tensor

### 🚀 The feature, motivation and pitch

We currently use pickle for serializing everything.
Beyond efficiency issues, it makes backwards compatibility quite hard.

While have the default implementation pickle based is a reasonable default (it has worked for torch.save) we should make the interface plugable to other implementations.

### Alternatives

_No response_

### Additional context

_No response_
Repository: rust
Issue: Build failure in x86 container on M1 chips
Labels: O-macos,O-Arm,C-bug,S-needs-repro,S-needs-info

When creating a release build of https://github.com/rust-lang/regex in an x86 Linux container on M1, I received the following error:
```
qemu: uncaught target signal 11 (Segmentation fault) - core dumped
error: could not compile `regex-syntax`

Caused by:
  process didn't exit successfully: `rustc --crate-name regex_syntax --edition=2018 regex-syntax/src/lib.rs --error-format=json --json=diagnostic-rendered-ansi,artifacts,future-incompat --crate-type lib --emit=dep-info,metadata,link -C opt-level=3 -C embed-bitcode=no -C debuginfo=2 --cfg 'feature="default"' --cfg 'feature="unicode"' --cfg 'feature="unicode-age"' --cfg 'feature="unicode-bool"' --cfg 'feature="unicode-case"' --cfg 'feature="unicode-gencat"' --cfg 'feature="unicode-perl"' --cfg 'feature="unicode-script"' --cfg 'feature="unicode-segment"' -C metadata=a79db002df5a8cc9 -C extra-filename=-a79db002df5a8cc9 --out-dir /root/regex/target/release/deps -L dependency=/root/regex/target/release/deps` (signal: 11, SIGSEGV: invalid memory reference)
```

Toolchain version: 1.60.0.

It worked fine with native MacOS environment on M1. The build failure only happen in x86 container:
```
docker run -t --platform linux/amd64 -i ubuntu:18.04 /bin/bash
```

The same issue has been[ raised in the regex repo](https://github.com/rust-lang/regex/issues/851), but Andrew thought this is a compiler compatibility issue in qemu and I agree with him.
Repository: flutter
Issue: Support discovering and connecting to Android/ADB devices over WiFi
Labels: platform-android,tool,P3,team-android,triaged-android

This was raised against the VS Code extension at https://github.com/Dart-Code/Dart-Code/issues/3939, but after discussing with @jacob314 I think it would make more sense implemented in flutter_tools (fyi @christopherfujino).

The request is to be able to discover and connect to Android devices using ADB over WiFi. Today you have to either manually use `adb connect` for the device to show up in `flutter devices` (and therefore the IDE), or use something like this VS Code extension that locates the devices and connects them for you:

https://marketplace.visualstudio.com/items?itemName=AakashP.adb-qr

I'm not familiar with the mechanism it uses to find the devices (I can see references to mDNS/Bonjour in the code, but I don't have a lot of knowledge of them).

Exposing this to IDEs through the device daemon similar to emulators might be nice. For example IDEs can ask for a list of emulators, and then request to launch one. This request would be to search for devices on the network, and then request to connect one (such that it shows up as a device once connected). The user would have to provide a pairing code that would be sent to the daemon with the connection request.

I don't know how common it is to pair devices over WiFi in this way though, so I don't know what the demand is for this.
Repository: pytorch
Issue: Enhance _verify_param_shape_across_processes
Labels: high priority,triage review,oncall: distributed,better-engineering,pt_distributed_rampup

### 🚀 The feature, motivation and pitch

This check in reducer.cpp serves the purpose of checking the parameter shape (and # of params) across processes, although, when there is an issue, the issue is only reported on non-0 ranks and rank 0 must be taken down through either async error handling or nccl_blocking_wait.

We should enhance this to make rank 0 fail more quickly with an appropriate error message as well. This will enhance debugability for FSDP and DDP. 

### Alternatives

_No response_

### Additional context

_No response_

cc @ezyang @gchanan @zou3519 @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @SciPioneer @H-Huang @kwen2501
Repository: pytorch
Issue: [checkpoint] Extension hooks to support logging and telemetry
Labels: triaged,sharded_tensor

### 🚀 The feature, motivation and pitch

The current load/saving code, in particular the FileSystem one, doesn't have any hooks to enable telemetry or logging of its activity.

Given how fragile and time consuming checkpointing is, this is an important feature to support production usage of this API.

### Alternatives

_No response_

### Additional context

_No response_
Repository: flutter
Issue: [packages] Adopt `code-excerpt` across all READMEs
Labels: team,d: api docs,package,team-ecosystem,P2,triaged-ecosystem

https://github.com/flutter/plugins/pull/5339 will add the ability to manage our README code snippets with `code-excerpt` so that they stop getting out of sync with changes in APIs, Dart, or lints, as well as sometimes just having typos that make them incorrect from the start. Once that lands we should start proactively updating all of our READMEs to use it, rather than waiting for bugs to be found, since it's a pretty easy change and will almost certainly find and fix latent issues in our READMEs given the steady stream of PRs we get about them.

That will also allow us to turn on enforcement in the repo to ensure that new code samples are written that way, so we don't create new documentation tech debt by accident.

Here's the list of exceptions we need to eliminate:
- [x] camera_web
- [x] cross_file
- [x] css_colors
- [ ] espresso
- [x] extension_google_sign_in_as_googleapis_auth
- [x] flutter_image
- [x] flutter_markdown
- [x] go_router
- [x] go_router_builder
- [x] google_maps_flutter/google_maps_flutter
- [x] google_sign_in/google_sign_in
- [x] google_sign_in_web
- [x] image_picker_for_web
- [ ] in_app_purchase/in_app_purchase
- [x] ios_platform_images
- [ ] flutter_svg
- [ ] flutter_svg_test
- [x] multicast_dns
- [x] palette_generator
- [x] pigeon
- [x] plugin_platform_interface
- [ ] pointer_interceptor
- [ ] quick_actions/quick_actions
- [x] rfw
- [x] webview_flutter_android
- [x] webview_flutter_web
Repository: pytorch
Issue: Pyre type checking fails 
Labels: module: typing,triaged

### 🐛 Describe the bug

Pyre type checker fails on PyTorch when running any of the following:

```
import torch

torch.device('cpu')
torch.distributed.all_gather(...)
# other distributed methods
```

Running pyre outputs something like the following:

```
utilities/device.py:39:17 Undefined attribute [16]: Module `torch` has no attribute `device`.
utilities/distributed.py:24:4 Undefined attribute [16]: Module `torch.distributed` has no attribute `all_gather`.
utilities/distributed.py:45:51 Undefined attribute [16]: Module `torch.distributed` has no attribute `is_initialized`.
utilities/distributed.py:50:16 Undefined attribute [16]: Module `torch.distributed` has no attribute `group`.
utilities/distributed.py:55:17 Undefined attribute [16]: Module `torch.distributed` has no attribute `get_world_size`.
utilities/distributed.py:56:4 Undefined attribute [16]: Module `torch.distributed` has no attribute `barrier`.
utilities/distributed.py:65:4 Undefined attribute [16]: Module `torch.distributed` has no attribute `all_gather`.
utilities/distributed.py:81:4 Undefined attribute [16]: Module `torch.distributed` has no attribute `all_gather`.
```


### Versions

Collecting environment information...
PyTorch version: 1.11.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 12.3.1 (arm64)
GCC version: Could not collect
Clang version: 13.1.6 (clang-1316.0.21.2.3)
CMake version: version 3.22.4
Libc version: N/A

Python version: 3.8.11 (default, Jul 29 2021, 14:57:32)  [Clang 12.0.0 ] (64-bit runtime)
Python platform: macOS-12.3.1-arm64-arm-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
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

Versions of relevant libraries:
[pip3] mypy==0.812
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.22.3
[pip3] torch==1.11.0
[pip3] torchmetrics==0.8.0
[pip3] torchrec==0.1.0
[pip3] torchx-nightly==2022.4.26
[conda] mypy                      0.812                    pypi_0    pypi
[conda] mypy-extensions           0.4.3                    pypi_0    pypi
[conda] numpy                     1.22.3                   pypi_0    pypi
[conda] torch                     1.11.0                   pypi_0    pypi
[conda] torchmetrics              0.8.0                    pypi_0    pypi
[conda] torchrec                  0.1.0                    pypi_0    pypi
[conda] torchx-nightly            2022.4.26                pypi_0    pypi

cc @ezyang @malfet @rgommers @xuzhao9 @gramster
Repository: TypeScript
Issue: Split the ES5 library definitions into their own files
Labels: Suggestion,Awaiting More Feedback

#### TL;DR

Along the same lines as the suggestion put forward in #39255, I'd like to float the idea that the definitions held in `lib.es5.d.ts` be split up into their own individual files. I'd like to suggest this change to enable granular overrides (thanks to #45771) to the core library definitions, without having to copy the entire ES5 library in the process.

## :thinking: Problem

I'm working on a project at the moment that would benefit from some aggressive tweaks to the `Promise` definition(s) in `lib.es5.d.ts` and `lib.es2015.promise.d.ts`. For clarity, these changes involve typing the rejection path of all promises, and enforcing `.catch` in some instances. I'm aware that a change similar to this has been thoroughly discussed and declined for good reasons in the past.

As mentioned above, thanks to #45771 we're now able to override the core library definitions as and when required. However, to heavily augment, or otherwise redefine the very lowest definitions of any type in the core library, one must (in the case of `lib.es5.d.ts`) redefine the entire ES5 library, opening up a small can of worms in the process (licensing of the types, and tracking changes in the "source" copies of the types, to name a few).

## :bulb: Solution

The simplest solution I can see is to split up `lib.es5.d.ts` into individual files that can then be later overridden by upstream developers as they see fit. `lib.es5.d.ts` would become a barrel file referencing the these "new" definitions, thereby avoiding any nasty braking changes.

In terms of approach, I would imagine declaring each primitive (or otherwise major feature of ES5) in its own definition file would make the most sense. This would offer a very high level of customization for the end developer, for an overall low maintenance cost at both ends.

In terms of format, the work has been done already with the more modern `lib.es20xx.<feature>.d.ts` naming pattern. Following this would result in a structure similar to the below for our good old friend, ES2009:

```
lib/
├─ lib.es5.d.ts
├─ lib.es2009.object.d.ts
├─ lib.es2009.global.d.ts
├─ lib.es2009.string.d.ts
├─ lib.es2009.number.d.ts
└─ ... ect ...
```

> I'll follow this ticket up with a PR containing an initial implementation of the above :+1:
Repository: pytorch
Issue: Expand pow and float_pow sampling function for more coverage
Labels: module: tests,triaged

### 🐛 Describe the bug

This issue is linked to: https://github.com/pytorch/pytorch/issues/74279
with matching PR: #76825

After looking into gradient computations for the pow and float pow the nans arise when lhs or base tensor has values that are negative.  Since the exponent or rhs tensor has fractional values for the opinfo test the power function can and does map floats -> complex  values in the analytical case. An example of nans in the function.
```
import torch
def rand_lh(shape, low=0, high=1):
    rand_val = torch.rand(shape)
    result = high * rand_val + low * (1 - rand_val)
    return result

a = rand_lh((1,2,3,4), low = -1 )
power = torch.rand((1,2,3,4))

print(torch.pow(a, power))
```

<img width="430" alt="Screen Shot 2022-04-27 at 1 51 35 PM" src="https://user-images.githubusercontent.com/32754868/165628746-15e8aa4f-663b-4db2-ac2a-92f0e0c855ca.png">

In order to unskip TestGradients the lhs low value can be set to 0 so that this tensor doesn't include any negative values.   Follow up work would be maybe adding documentation that includes this note. Or updating pow to promote the output tensor to complex type. 

### Versions

Collecting environment information...
PyTorch version: 1.12.0a0+git2469525
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 12.3.1 (arm64)
GCC version: Could not collect
Clang version: 13.1.6 (clang-1316.0.21.2.3)
CMake version: version 3.22.1
Libc version: N/A

Python version: 3.10.4 (main, Mar 31 2022, 03:37:37) [Clang 12.0.0 ] (64-bit runtime)
Python platform: macOS-12.3.1-arm64-arm-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
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

Versions of relevant libraries:
[pip3] mypy==0.942
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.2
[pip3] torch==1.12.0a0+git2469525
[conda] mypy                      0.942                    pypi_0    pypi
[conda] mypy_extensions           0.4.3           py310hca03da5_1
[conda] nomkl                     3.0                           0
[conda] numpy                     1.21.2          py310hb38b75b_0
[conda] numpy-base                1.21.2          py310h6269429_0
[conda] torch                     1.12.0a0+git2469525           dev_0    <develop>

cc @mruberry
Repository: pytorch
Issue: `F.interpolate` uses incorrect size when `align_corners=True`
Labels: triaged,module: interpolation

### 🐛 Describe the bug

Currently for given `scale_factor`  `F.interpolate` uses the same formula to compute output size for both modes of `align_corners`.
For mode `align_corners=False` it's fine, but for `align_corners=True` it's incorrect. as it's makes actual scale factor (defined as scale of image features) dependant from input image size.

Current formula has [off-by-one error](https://en.wikipedia.org/wiki/Off-by-one_error).
For example, for image size of 4 there are 3 (4-1) sections in between pixels. With `scale_factor=2` each section will be populated with 1 new pixel. So correct new size will be 7.
Or, in general, `new_size = (size - 1) * scale_factor + 1`.

Given [this](http://entropymine.com/imageworsener/matching/) `align_corners=False` matches the "match edges" model, and `align_corners=True` - the "match centers" model, but currently scale factor controls scale of image dimensions, not of image features.

This is current behavior:
```
>>> x = torch.rand(1, 1, 4, 4)
>>> y = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
>>> y.shape
(1, 1, 8, 8)  # incorrect
>>> torch.allclose(x, y[:, :, ::2, ::2])
False
```
Example of correct behavior:
```
>>> y = F.interpolate(x, (7, 7), mode='bilinear', align_corners=True)
>>> torch.allclose(x, y[:, :, ::2, ::2])
True
```

### Versions

Any currently available pytorch version.
Repository: pytorch
Issue: [NVFuser] Automated generation of microbenchmarks
Labels: triaged,module: nvfuser

### 🚀 The feature, motivation and pitch

The NVFuser microbenchmarks in torchbench (https://github.com/pytorch/benchmark/pull/793) provide a good way to monitor nvfuser performance. They are generated by collecting the fusion groups from a torchbench run - which gives a closer look at nvfuser performance for each of the fusion groups. However, they are not updated when nvfuser or torchbench change.

Things that would change the microbenchmarks:
* changes to classify_graphs.py
* nvfuser changes that modify the fusion groups that get captured
* torchbench updates

Things that need to be automated:
* [ ] codify the torchbench command (& any modifications, if necessary) that is used to generate the microbenchmarks. The original run was done with lazy tensor (which should generate larger fusion groups than JIT) & nvfuser.
* [ ] write a tool that merges the existing microbenchmarks in torchbench with new IR graphs generated by a more recent run of torchbench (i.e. we don't want to lose old microbenchmarks or microbenchmarks that were given more descriptive names, especially if any of them were manually generated)
* [ ] codify the pipeline of (run torchbench with flags) -> (log_extract) -> (classify_graphs.py) -> (merge with existing graphs) -> (update torchbench IR)
* [ ] update classify_graphs.py to support more types of ops. It maps (op) -> (general type of op), e.g. pointwise, reduction, view. "would be trivial to expand this with unary ops at least, potential reduction via the classifications in shape analysis" -- @eellison 
* [ ] (optional) pipeline for automatically running the updates, so that a human doesn't need to remember to run the script in order to 

### Alternatives

_No response_

### Additional context

_No response_
Repository: pytorch
Issue: [JIT] make IRAttributeError extend jit::Error
Labels: oncall: jit

### 🚀 The feature, motivation and pitch

This would give IRAttributeError backtraces, and make it easier to debug via logs.

### Alternatives

_No response_

### Additional context

_No response_
Repository: PowerToys
Issue: [FancyZones] "Window to last zone" / "Window to active screen" priority
Labels: Idea-Enhancement,Product-FancyZones,Needs-Triage

### Description of the new feature / enhancement

Hey there! I currently use both the "newly created windows to last known zone" and "Newly created windows to active screen" features for FancyZones, but I wish there was a way to have more granular control over the features' behavior when both are active.

Ideally for me, I would like newly created windows to go to their last known zones upon system reboot/login, but after that have new windows go to the screen my mouse is on. It seems that currently, "last known zone" overrides the "active screen" function, which is kind of annoying honestly when newly created windows (ie. those from frequently closed and opened apps like windows explorer) keep spawning on my active screen for a split second then teleport up to my other screen's zone.

My current workaround is to disable "windows to last known zone" completely, but when logging in or rebooting it obviously requires me to take a minute to rearrange my startup programs into their respective zones/screens (I have 4 monitors and some of my zone layouts consist of 5-6 zones per screen, so I'm probably an edge-case here 😝)

Perhaps if there were a way to let FancyZones know that apps are opening during startup (and put those in their last known zone), or even just a timer that gives "windows to active monitor" priority over "last known zone" after a set # of seconds after startup...



### Scenario when this would be used?

I would use this every day for general usage across all applications

### Supporting information

_No response_
Repository: kubernetes
Issue: [E2E] Review FilterNonRestartablePods
Labels: area/test,priority/backlog,sig/node,area/node-e2e,help wanted,needs-triage

`FilterNonRestartablePods` (and the corresponding `isNotRestartAlwaysMirrorPod`) are confusing functions. Either these are buggy, or they need to be renamed with much more descriptive comments.

1. Should non-mirror pods with a `RestartPolicy=Never` be considered non-restartable?
2. What does this mean: `pods that will never get recreated if deleted after termination`? Deleted pods will only be recreated if the controller decides to recreate them (in the case of mirror pods, the kubelet is the controller).

https://github.com/kubernetes/kubernetes/blob/a814b2af1c632afb9e2e231ce76379fa3c27bb25/test/e2e/framework/pod/resource.go#L479-L502

I'm missing too much context on the related tests to know what the right way of fixing this is.
/help

/sig node
/area test node-e2e
Repository: pytorch
Issue: Docs source link broken for all methods defined on Tensor in _tensor.py, e.g. `Tensor.register_hook()` 
Labels: module: docs,triaged

### 📚 The doc issue

https://pytorch.org/docs/stable/generated/torch.Tensor.register_hook.html
Clicking the "source" link redirects to https://pytorch.org/docs/stable/_modules/torch/_tensor.html#Tensor.register_hook, which does not exist.

This is true in the master docs as well: https://pytorch.org/docs/master/generated/torch.Tensor.register_hook.html

### Suggest a potential alternative/fix

Fix the link.

cc @svekars @brycebortree @ezyang @albanD @zou3519 @gqchen @pearu @nikitaved @soulitzer @Lezcano @Varal7 @brianjo @mruberry