sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: pytorch
Issue: Silent incorrect running with zero padding for Conv1d
Labels: module: error checking,module: convolution,triaged,module: padding

## 🐛 Bug
According to https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html, the input tensor for `torch.nn.Conv1d` should be in the shape of `(N, C, L)` and the `kernel_size` (int or tuple) indicates the size of the convolving kernel.

However, feeding a 4-dim tensor with a tuple of two integers as its `kernel_size` magically makes the API run without throwing any exception.

It's also confusing to me that during the execution for padding modes like `'reflect', 'replicate', 'circular'`, the exception is raised, while padding mode of `'zeros'` runs silently.

## To Reproduce
```
import torch

padding_modes = ['zeros', 'reflect', 'replicate', 'circular']

def run():
    image = torch.rand(2, 3, 10, 11).cuda()
    for mode in padding_modes:
        try:
            net = torch.nn.Conv1d(3, 4, kernel_size=[2,2], padding_mode=mode).cuda()
            out = net(image)
            print(out.size())
        except Exception as e:
            print(e)
        
run()
```
The output is
```
zeros passed:  torch.Size([2, 4, 9, 10])
reflect failed:  4D tensors expect 4 values for padding
replicate failed:  4D tensors expect 4 values for padding
circular failed:  4D tensors expect 4 values for padding
```

By the way, the stack trace for Exception is:
```
  File ".../python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File ".../python3.6/site-packages/torch/nn/modules/conv.py", line 263, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File ".../python3.6/site-packages/torch/nn/modules/conv.py", line 256, in _conv_forward
    return F.conv1d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
  File ".../python3.6/site-packages/torch/nn/functional.py", line 4015, in _pad
    assert len(pad) == 4, "4D tensors expect 4 values for padding"
AssertionError: 4D tensors expect 4 values for padding
```

## Expected behavior
(1) If 4D-tensor input is allowed in `Conv1d`, please make it clear in the documentation, and it should also be allowed in other padding modes.
(2) If 4D-tensor input is disallowed in `Conv1d`, please raise the Exception for `"zeros"` mode instead of running silently. Also, there might be better error message instead of `4D tensors expect 4 values for padding`.

## Environment
PyTorch Version (e.g., 1.0): 1.8
OS (e.g., Linux): Linux
How you installed PyTorch (conda, pip, source): pip
Python version: 3.6.6
CUDA/cuDNN version: cuDNN 11.1
GPU models and configuration: A100

## Additional context

<!-- Add any other context about the problem here. -->
Repository: nvm
Issue: nvm v0.38.0: node url not found
Labels: installing node,OS: Mac OS,needs followup

![image](https://user-images.githubusercontent.com/20673060/115009662-1a3c7f00-9edf-11eb-9c87-3fdeff45f657.png)
![image](https://user-images.githubusercontent.com/20673060/115009767-33453000-9edf-11eb-9568-e3cc8689374e.png)

how to deal with?
Repository: tauri
Issue: Feature: Generate Typescript functions for Rust commands
Labels: type: feature request,priority: 3 low

Goal: Atomatically generate Typescript functions for Rust commands in Typescript environment

Why: Use Typescript with type checking for Rust commands rather than `invoke('some string', somedata)`

How:


Assume these Rust commands are available

```rust
#[tauri::command]
async fn my_first_custom_command(message: String) -> Result<String, String> {
  Ok("simple type command".into())
}

#[derive(serde::Serialize)]
struct CustomResponse {
  message: String,
  other_val: usize,
}

#[derive(serde::Deserialize)]
struct CustomRequest {
  message: String,
  request: usize,
}

#[tauri::command]
async fn my_second_custom_command(req: CustomRequest, hello: String) -> Result<CustomResponse, String> {
  Ok(CustomResponse {
    message: "custom response".into(),
    other_val: 42,
  })
}
```

Using Typescript, we can create functions that conform to the Rust commands:

```typescript
import tauriapi from "@tauri-apps/api";

const { invoke } = tauriapi.tauri;

function my_first_custom_command(message: String): Promise<String> {
  return invoke('my_first_custom_command', {
    message
  })
}

type CustomRequest = {
  message: String;
  request: Number;
}

type CustomResponse = {
  message: String;
  other_val: Number;
}

function my_second_custom_command(req: CustomRequest, hello: String): Promise<CustomResponse>{
  return invoke('my_second_custom_command', {req, hello})
}

export {
  my_first_custom_command,
  my_second_custom_command
}
```

Now we can call `my_first_custom_command` and `my_second_custom_command` with type checking in Typescript.

```typescript
my_first_custom_command("this must be string")
.then((res) => {
  // res is String
  console.log(res);
});

my_second_custom_command({
  message: "this must be CustomRequest",
  request: 123,
}, "this must be string")
.then((res) => {
  // res is CustomResponse
  console.log(res.message, res.other_val);
});
```

The steps can be:
- Read the `tauri::commands` function in Rust
- Note the input parameters and output type
- Generate the corresponding type and function in typescript

Problems that may arises:
- Rust commands are written into different files
- Rust commands use Struct defined in other files
- The Typescript generated file(s) must be re-generated on each rebuild

Simple solution to the above solutions:
- Rust commands are placed into a Rust module (src-tauri/src/tauri_commands/*.rs)
- Typescript generated files are placed into a seperated modules (tauri_commands/*.tsx)
- `yarn tauri dev` generates Typescript files everytime a file in `src-tauri/src/tauri_commands` is modified

In discord chat, Denjell suggest that a WASM function can be generated from these Rust commands.

Originated from:
https://discord.com/channels/616186924390023171/731495047962558564/832483720728412192
Repository: PowerToys
Issue: [FancyZones] - Extending  multiple zones window snapping
Labels: Idea-Enhancement,Product-FancyZones

With the key-combination <kbd>Win</kbd> + <kbd>Control</kbd> + <kbd>Alt</kbd> you can expand a window to multiple zones.
Eg. I'm using a 3 x 2 grid as my zone-layout.
If I have a window snapped to zone 1 and  4 and I use <kbd>Win</kbd> + <kbd>Control</kbd> + <kbd>Alt</kbd> my window will be snapped to zone 1, 2, 4 and 5.
Another <kbd>Win</kbd> + <kbd>Control</kbd> + <kbd>Alt</kbd> will snap my window to zone 1, 2, 3, 4, 5 and 6.

If I'm now pressing again the key-combination nothing happens anymore.
What I prefer to have is that in this case my window will be snapped to zone 2, 3, 5 and 6. Pressing again would snap to zone 3 and 6.

![image](https://user-images.githubusercontent.com/25179893/115010048-3c1d0e00-9ead-11eb-935a-ba8a901dff07.png)

In the case there are more vertical columns, let's say a grid of 4 x 2, the snapping with this key-combination would be as follow :

- window initially snapped to zone 1 and 5
- snapped to zone 1, 2, 5, 6
- snapped to zone 1, 2, 3, 5, 6, 7
- snapped to zone 1, 2, 3, 4, 5, 6, 7, 8
- snapped to zone 2, 3, 4, 6. 7, 8
- snapped to zone 3, 4, 7, 8
- snapped to zone 7, 8

And maybe apply this functionality only to the monitor where the window is positioned. 

![image](https://user-images.githubusercontent.com/25179893/115011692-37f1f000-9eaf-11eb-9e2b-79fc8d2ec6ca.png)


If you'd like to see this feature implemented, add a 👍 reaction to this post.
Repository: transformers
Issue: Running gpt-neo 2.7B with less than 13GB of system memory like Colab
Labels: Feature request

# 🚀 Feature request

A way to conserve regular system memory while loading large models. On systems without much system memory, the process crashes because it tries to load both the weight checkpoint and the model into system memory. In the case of gpt-neo 2.7B, this is even worse because the checkpoint is offered only in float32 format, taking up twice as much space.

## Motivation

Free Colab often offers GPUs with 16GB of VRAM but only about 13GB of RAM. This increases the barrier of entry for people to play with these kinds of models. I have found a way to load models in this kind of situation, but it is not general or well integrated. By posting it here, I hope that a more general implementation can be built at some point.

This would also help #11271.

## Your contribution

It is possible to work around this by loading the checkpoint directly into VRAM, casting it to float16, instantiating the model in VRAM and only then applying the weights from the checkpoint.

To do this, first a patch has to be applied to src/transformers/models/gpt_neo/modeling_gpt_neo.py. This is based on the 4.5.1 release.

    703c703
    <         self.h = nn.ModuleList([GPTNeoBlock(config, layer_id=i) for i in range(config.num_layers)])
    ---
    >         self.h = nn.ModuleList([GPTNeoBlock(config, layer_id=i).half().cuda() for i in range(config.num_layers)])
    890,891c890,891
    <         self.transformer = GPTNeoModel(config)
    <         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
    ---
    >         self.transformer = GPTNeoModel(config).half().cuda()
    >         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False).half().cuda()

This causes space for the biggest part of the model to be allocated directly on the GPU, which has more space in the free Colab scenario. It also moves the other parts of the model to GPU. Now the model can be instantiated like this:

    from transformers.file_utils import cached_path, WEIGHTS_NAME, hf_bucket_url
    model_name = "EleutherAI/gpt-neo-2.7B"
    archive_file = hf_bucket_url(model_name, filename=WEIGHTS_NAME)
    resolved_archive_file = cached_path(archive_file)
    checkpoint = torch.load(resolved_archive_file, map_location="cuda:0")
    for k in checkpoint.keys():
      checkpoint[k] = checkpoint[k].half()
    model = GPTNeoForCausalLM.from_pretrained(model_name, state_dict=checkpoint).half().to("cuda")
    for k in list(checkpoint.keys()):
      del checkpoint[k]
Repository: pytorch
Issue: torch.nn.quantized.functional.conv_transpose1d/2d/3d support
Labels: module: docs,oncall: quantization,feature,low priority,triaged

## 🚀 Feature
Add support for 8bit conv_transpose  inference operators in the form of torch.nn.quantized.functional.conv_transpose1d/2d/3d.

## Motivation

I haven't seen this mentioned elsewhere so i figured opening an issue to track it wouldn't hurt.
The main use case I see at the moment is in quantized neural vocoders for speech synthesis, which is the typical task that can be done at the edge and would benefit from quantization. I've worked on quantizing a MelGAN variant in the past and 8 bits can get you pretty far if it's trained right. I'm sure there are other use cases in generative computer vision models or similar too.

## Pitch

Support torch.nn.quantized.functional.conv_transpose1d/2d/3d.

## Alternatives

The alternative is to not perform quantization and keep the layer in some type of floating point.

## Additional context

The main issue is obviously having a backend that implements this, and I'm not aware of any open source arm/x86/cuda/hip library that does it. Pointers are welcome.


cc @svekars @carljparker @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel @brianjo @mruberry
Repository: pytorch
Issue: Reflect padding_mode should be supported for Conv3d
Labels: module: docs,module: convolution,triaged

## 🐛 Bug
According to https://pytorch.org/docs/stable/generated/torch.nn.Conv3d.html, `padding_mode` has 4 choices including  'zeros', 'reflect', 'replicate' or 'circular'. However, currently `reflect` mode is not supported.

## To Reproduce
```
import torch
padding_modes = ['zeros', 'reflect', 'replicate', 'circular']

image = torch.rand(2, 3, 3, 3, 3).cuda()
net = torch.nn.Conv3d(3, 4, kernel_size=3, padding_mode="reflect").cuda()
out = net(image)
```
It throws the following exception:
```
 File ".../nightly/lib64/python3.6/site-packages/torch/nn/modules/module.py", line 1015, in _call_impl
    return forward_call(*input, **kwargs)
  File ".../nightly/lib64/python3.6/site-packages/torch/nn/modules/conv.py", line 574, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File ".../nightly/lib64/python3.6/site-packages/torch/nn/modules/conv.py", line 560, in _conv_forward
    input, self._reversed_padding_repeated_twice, mode=self.padding_mode
  File ".../nightly/lib64/python3.6/site-packages/torch/nn/functional.py", line 4142, in _pad
    raise NotImplementedError
NotImplementedError
```

## Expected behavior
It would be better if the `reflect` mode for padding can be supported.
Otherwise, this mode should be eliminated from the documentation.

## Environment
PyTorch Version (e.g., 1.0): Nightly build
OS (e.g., Linux): Linux
How you installed PyTorch (conda, pip, source): pip
Python version: 3.6.6
CUDA/cuDNN version: cuDNN 11.1
GPU models and configuration: A100

## Additional context

<!-- Add any other context about the problem here. -->


cc @brianjo @mruberry
Repository: animate.css
Issue: Make backout exits and entrances use a variable
Labels: feature request,v5

**Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

Currently the backout exits use hardcoded pixel translations. This has 2 drawbacks:
- It won't work for very large resolutions
- It creates a time gap between the `animationend` event and the UX of when the animation ends for smaller elements.

Consider I have an element of fixed width `500px`, inside it I slide an element to the left, it will be translated to `-2000px`. The first 20% of the animation time is spent on scaling, after that the remaining 80% is spent on moving the item. This means that after 0.2 seconds of movement the item is no longer visible (overflow hidden).
Suppose when the animation ends I want to start another animation, like something sliding in from the right, this will start very late because half of both animations are not visible


**Describe the solution you'd like** A clear and concise description of what you want to happen.
1. Configure the pixel values using variables so that they can be overridden.
2. Default to using viewport relative units so that it'll work on large resolutions. (this is optional and not strictly related, just something I noticed when reviewing the source code)


**Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered.
Current alternative is to use `setInterval` to trigger the next animation early and remove the previous element mid animation.

**Additional context** Add any other context or screenshots about the feature request here.

I'm happy to make a PR.
Repository: rust
Issue: rustdoc: [src] view is broken in lynx
Labels: T-rustdoc,C-bug,A-rustdoc-ui

I tried this: Opening https://rust-for-linux.github.io/docs/src/kernel/sync/condvar.rs.html#19-23 in `lynx`.

I expected to see this happen: Either the source code is shown side-by-side with the line numbers, or the line numbers aren't shown at all.

Instead, this happened: ![image](https://user-images.githubusercontent.com/23638587/115027786-1fbfa800-9e92-11eb-853a-b0cf0a2405a6.png)

I think there's a very old issue about this that was fixed for newer browsers, but I can't seem to find it. This has definitely happened on ancient docs in docs.rs before. cc @ojeda - is this something you care about? I saw someone mentioned it in https://lkml.org/lkml/2021/4/16/49 but I don't know if that was actually serious.

### Meta

Not sure what rust-for-linux is using, but I can replicate the issue with rustdoc 1.53.0-nightly (d0695c908 2021-04-12).
Repository: storybook
Issue: Panel isn't visible when its size is exceeding the viewport height
Labels: question / support,ui

**Describe the bug**
We are using the controls add-on. When we have a large number of controls rendered, the panel isn't shown anymore as soon as it runs out of vertical space. We only see the preview and the panel is hidden behind it but not visible due to a low (non-existing) z-index. https://github.com/storybookjs/storybook/blob/1e97c2690fe080012ddcaee177d27327cb50d51e/lib/ui/src/components/layout/container.tsx#L139

**To Reproduce**
I found only one consistent way to reproduce the bug. 

1. Increase the Panels height by dragging it to the top
2. Reduce the browsers window and make the Panel "overflow" the Preview
3. Reload the browser
4. Wonder where the Panel is gone
5. Increase the height of the browser window
6. See your panel again


![Kapture 2021-04-16 at 15 45 15](https://user-images.githubusercontent.com/1778130/115033836-2b7b9080-9ecb-11eb-8220-252180bea1cc.gif)


**Expected behavior**
Have the panel always visible.

**Screenshots**
If applicable, add screenshots to help explain your problem.


**Code snippets**
If applicable, add code samples to help explain your problem.


**Additional Information**

Why is this a problem? I guess the height of the panel is stored somewhere in local storage. Since we are embedding storybook into a styleguide, the available viewport there is much smaller than the browsers window. If someone is visiting our storybook application and dragging the panel (height stored in local storage) and is then visiting the styleguide where the same story is loading in an iframe, the panel's height is too big and therefore not rendered in the styleguide.

At least that's what I imagine what's happening.
Repository: flutter
Issue: [google_maps_flutter_web] Markers anchor and rotation have no effect
Labels: p: maps,platform-web,package,has reproducible steps,P2,found in release: 2.10,found in release: 2.13,team-web,triaged-web

Building Marker with custom anchor offset does not change default, bottom based anchor. 
Rotation have no effect on built Marker.
Repository: ant-design
Issue: Table Doesn't Re-render When an Item is Changed in IE-11
Labels: Internet Explorer,Inactive,IE | Firefox | Safari

- [ ] I have searched the [issues](https://github.com/ant-design/ant-design/issues) of this repository and believe that this is not a duplicate.

### Reproduction link
[![Edit on CodeSandbox](https://codesandbox.io/static/img/play-codesandbox.svg)](https://codesandbox.io/s/hbyeb)

### Steps to reproduce
Delete a row on the table.

### What is expected?
The table re-renders so that the deleted row has been removed.

### What is actually happening?
The table doesn't re-render so the table appears to still contain the record (even though it doesn't).

| Environment | Info |
|---|---|
| antd | 4.15.1 |
| React | 16.13.1 |
| System | 10.0.18363 |
| Browser | 1909 |

---
This is easiest to see when viewing the Editable Cells demo on the ant-design website through IE11.


<!-- generated by ant-design-issue-helper. DO NOT REMOVE -->
Repository: godot
Issue: TextureRect renders as black when Stretch Mode is set to Tile on GLES2
Labels: bug,platform:ios,topic:rendering,topic:gui,topic:2d

<!-- 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. -->
3.2.3 and 3.3 RC9

**OS/device including version:**
<!-- Specify GPU model, drivers, and the backend (GLES2, GLES3, Vulkan) if graphics-related. -->
iPhone SE (2nd version) running iOS 14.4.2. This bug was discovered using GLES2. When I switched rendering to GLES3 I did not encounter the bug.

**Issue description:**
<!-- What happened, and what was expected. -->
A TextureRect when put into tile mode will only render as black. And not simply the texture, but the whole area of the texture rect will render as black.
![IMG_0151](https://user-images.githubusercontent.com/6944363/115037020-0885b800-9e9c-11eb-9a8a-4b65e8abc756.PNG)


**Steps to reproduce:**
Make a scene with a texture rect, switch its stretch mode to tile. Make sure that the render mode is on GLES2. Export an iPhone project. Run in Xcode.

**Minimal reproduction project:**
<!-- A small Godot project which reproduces the issue. Drag and drop a zip archive to upload it. -->
I have created a sample project and removed my Team ID and Bundle ID (I hope)
[Sample Project.zip](https://github.com/godotengine/godot/files/6325642/Sample.Project.zip)
Repository: terminal
Issue: "Open in Windows Terminal" of WT Preview disappears if WT is not installed
Labels: Issue-Bug,Product-Terminal,Priority-3,Area-ShellExtension

### Windows Terminal version (or Windows build number)

Windows 10.0.21354.0

Windows Terminal Preview v1.8.1032.0

### Other Software

_No response_

### Steps to reproduce

Uninstall Windows Terminal

### Expected Behavior

Only "Open in Windows Terminal" of WT disappears

### Actual Behavior

"Open in Windows Terminal" of both WT and WT Preview disappear
Repository: neovim
Issue: Calling :q while there is a scheduled function that calls 'vsplit | q' causes a core dump
Labels: io,bug-crash,event-loop

<!-- Before reporting: search existing issues and check the FAQ. -->

- `nvim --version`:
```
NVIM v0.5.0-dev+1260-ga6504ec33
Build type: RelWithDebInfo
LuaJIT 2.0.5
Compilation: /usr/bin/cc -D_FORTIFY_SOURCE=2 -march=x86-64 -mtune=generic -O2 -pipe -fno-plt -U_FORTIFY_SOURCE -D_FORTIFY_SOURCE=1 -O2 -g -Og -g -Wall -Wextra -pedantic -Wno-unused-parameter -Wstrict-prototypes -std=gnu99 -Wshadow -Wconversion -Wmissing-prototypes -Wimplicit-fallthrough -Wvla -fstack-protector-strong -fno-common -fdiagnostics-color=auto -DINCLUDE_GENERATED_DECLARATIONS -D_GNU_SOURCE -DNVIM_MSGPACK_HAS_FLOAT32 -DNVIM_UNIBI_HAS_VAR_FROM -DMIN_LOG_LEVEL=3 -I/home/apple/.cache/yay/neovim-git/src/build/config -I/home/apple/.cache/yay/neovim-git/src/neovim-git/src -I/usr/include -I/home/apple/.cache/yay/neovim-git/src/build/src/nvim/auto -I/home/apple/.cache/yay/neovim-git/src/build/include
Compiled by apple

Features: +acl +iconv +tui
See ":help feature-compile"

   system vimrc file: "$VIM/sysinit.vim"
  fall-back for $VIM: "/usr/share/nvim"
```
- `vim -u DEFAULTS` (version: ) behaves differently? N/A afaik
- Operating system/version:
Arch Linux x86_64   5.11.13-arch1-1
glibc 2.33-4
- Terminal name/version:
zsh 5.8.1
- `$TERM`:
alacritty

### Steps to reproduce using `nvim -u NORC`

```vim
nvim -u NORC --cmd "lua vim.schedule(function() vim.cmd('vsplit | q') end)" +q
nvim -u NORC --cmd "lua vim.schedule(function() vim.cmd('vsplit | q') end) vim.cmd('q')" also works
```

### Actual behaviour
Neovim coredumps somewhere in libuv.
### Expected behaviour
Neovim quits normally.

There isn't usually a reason to be calling `:q` right after startup but in my case I'm using the `vsplit | q` to force certain plugins to load early and I'm using `+q` to benchmark my config startup with hyperfine. (As an aside, is there a way to quit w/o finishing scheduled commands first? I don't care too much about how long the commands take, but I am interested in the time it takes to schedule them)