sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: pytorch
Issue: TORCH_COMPILE_CPROFILE does not work for python 3.12
Labels: needs reproduction,triaged,oncall: pt2,vllm-compile

### 🐛 Describe the bug

this simple code:

```python
import torch
def fn(x):
   a = torch.cos(x)
   b = torch.sin(a)
   return b
new_fn = torch.compile(fn, backend="inductor")
input_tensor = torch.randn(10000).to(device="cuda:0")
a = new_fn(input_tensor)
```

does not work for python 3.12: 

`torch._dynamo.exc.InternalTorchDynamoError: Another profiling tool is already active`

might be related: https://github.com/python/cpython/issues/110770

changing python versions to less than 3.12 works.

### Versions

python 3.12 + pytorch 2.4

cc @ezyang @chauhang @penguinwu @zou3519
Repository: transformers
Issue: Request more specific info from bug reporters when opening deepspeed issues
Labels: Feature request

### Feature request

Hi!

I would like the bug reporters to be prompted (or have section to fill in the reports template) to provide `ds_report` info and `zero3` config when opening a bug report related to deepspeed integration (maybe it could be more general). Anything to make sure these bits of info are more likely to included upfront would make some of these issues much more actionable.

### Motivation

I've been looking at some deepspeed integration bugs lately (#28808,#29348,#31867), I noticed that often more deepspeed info has to be requested. I was wondering if some specific (and maybe **BOLDED**)  guidelines about what info to provide would go a long way when opening bug reports. I think a reminder to include `zero configs` and `ds_report` might be helpful. I believe this is particularily a pitfall for stuff that is often parsed in (configs, etc).  
Something like:

### Reproduction
Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
If you have code snippets, error messages, stack traces please provide them here as well.
Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
*If you are opening an issue related to one of the following please ensure the this info is included in your reproduction script:
Deepspeed - zero3 config, ds_report output,
Trainer - your trainer config file,
etc.*

@ArthurZucker @amyeroberts
Repository: pytorch
Issue: TORCH_LOGS=cache only reports on remote cache, but it should report local cache too
Labels: module: logging,triaged,oncall: pt2

### 🐛 Describe the bug

You can tell because it's 

```
register_log(
    "cache", ("torch._inductor.remote_cache", "torch._inductor.fb.remote_cache")
)   
```

@masnesral is this easy to fix for you?

cc @chauhang @penguinwu @oulgen 

### Versions

main
Repository: pytorch
Issue: TORCH_LOGS=+inductor spams too much information about cache
Labels: triaged,oncall: pt2,compile-cache

### 🐛 Describe the bug

The cache logs are extremely long and involve things like

```
V1013 19:33:50.459000 2627268 torch/_inductor/codecache.py:821] [8/0] [4ti6faxqet4l7zbjc2ovkfelze6usgng2xzzy4disiquolmsoyx] example_inputs[154]
: TensorMetadata(dtype=torch.float32, shape=torch.Size([384]), stride=(1,), device=device(type='cuda', index=0), layout=torch.strided, memory_f
ormat=torch.contiguous_format, storage_offset=0, storage_bytes=None, requires_grad=False, is_quantized=False, is_conj=False, is_neg=False, is_i
nference=False, is_sparse=False, is_coalesced=None, dense_dim=None, sparse_dim=None)                                                           
V1013 19:33:50.459000 2627268 torch/_inductor/codecache.py:821] [8/0] [4ti6faxqet4l7zbjc2ovkfelze6usgng2xzzy4disiquolmsoyx] example_inputs[155]
: TensorMetadata(dtype=torch.float32, shape=torch.Size([384]), stride=(1,), device=device(type='cuda', index=0), layout=torch.strided, memory_f
ormat=torch.contiguous_format, storage_offset=0, storage_bytes=None, requires_grad=False, is_quantized=False, is_conj=False, is_neg=False, is_i
nference=False, is_sparse=False, is_coalesced=None, dense_dim=None, sparse_dim=None)
```

over and over again. These would be more appropriately done as dedicated artifacts that are not included in the DEBUG log.

cc @chauhang @penguinwu @masnesral @oulgen 

### Versions

main
Repository: transformers
Issue: Differential Attention implementation for BERT.
Labels: Feature request

### Feature request

The paper "Differential Transformers" implements a differential attention mechanism which calculates the attention scores as the difference between two separate softmax attention maps leading to better long-context modeling and key information retrieval.

LINK : [Paper](https://arxiv.org/pdf/2410.05258)

### Motivation

Although the paper focuses on decoder only models, I think this differential attention mechanism might be helpful with encoder only models as well.

### Your contribution

I can work on this feature, if the HuggingFace team considers it as a valuable addition.
Repository: kubernetes
Issue: Scheduling Problems Caused by Definition of Persistent Volumes and Ephemeral Volumes
Labels: sig/scheduling,sig/storage,kind/feature,lifecycle/stale,needs-triage

### What would you like to be added?

k8s version is 1.28.1.
In the same sts， a 10 GB persistent volume and a 10 MB ephemeral volume are defined.

ephemeral volume：
```
  - ephemeral:
      volumeClaimTemplate:
        metadata:
          creationTimestamp: null
        spec:
          accessModes:
          - ReadWriteOnce
          resources:
            requests:
              storage: 10Mi
          storageClassName: localcsi
          volumeMode: Filesystem
    name: share
```

persistent volume ：
```
  volumes:
  - name: search-pod-pvc
    persistentVolumeClaim:
      claimName: search-pod-pvc-pod-0
```

In normal cases, the two volumes can be created and scheduled.
```
ns         pod-0-share                                                        Bound    pvc-2cb4b639-9d8a-4f44-af7a-16692ff4e7dd   10Mi       RWO            localcsi       6h42m
ns         search-pod-pvc-pod-0                                      Bound    pvc-32195605-0fa6-4ee2-b1fb-a90a8a7a6b1e   10Gi       RWO            localcsi       6h42m

```

However, in some small resource scenarios, if the ephemeral volume is scheduled before the persistent volume, the persistent volume may fail to be scheduled due to insufficient resource.
```
ns         pod-0-share                                                        Bound     pvc-39836b73-3f85-4660-a890-2eaa75cee4c0   10Mi       RWO            localcsi       3d1h
ns         search-pod-pvc-pod-0                                      Pending                                                                        localcsi       3d1h

```



### Why is this needed?

Is there any optimization solution for this scheduling problem in the community?
Repository: excalidraw
Issue: AppState type inconsistency: "`collaborators.forEach` is not a function"
Labels: bug

The API documentation, built in types, and inspection of the react state all say that the type of `initialData.appState.collaborators` should be an object. However, the code `collaborators.forEach` is also being used, causing a crash when passing in a saved `appState` object to the `initialData` prop on the `<Excalidraw />` element.

Steps to reproduce:

1. Save the app state using the imperative API and see that the collaborators entry is "{}":
  ```tsx
  const appState = excalidrawApi.getAppState();
  console.log(JSON.stringify(appState));
  ```

2. Parse and use this app state in the `initialData` prop:
  ```tsx
  <Excalidraw
    initialData={{
      appState: JSON.parse(appState),
    }}
  />
  ```

This will cause a crash because `collaborators.forEach` is not a function on an object.
Repository: flutter
Issue: When connecting to device, `StateError: Bad sate: Stream has already been listened to`
Labels: c: crash,P2,team-tool,triaged-tool

Since 3.24.0

On 3.24.3, affects 1% of all clients that have reported a crash (current overall crash rate is ~1%)

Occurs exclusively on `flutter attach`

```
StateError: Bad state: Stream has already been listened to.
at _StreamController._subscribe(stream_controller.dart:686)
at _ControllerStream._createSubscription(stream_controller.dart:837)
at _StreamImpl.listen(stream_impl.dart:497)
at new _ForwardingStreamSubscription(stream_pipe.dart:114)
at _ForwardingStream._createSubscription(stream_pipe.dart:86)
at _ForwardingStream.listen(stream_pipe.dart:81)
at FlutterDevice.connect(resident_runner.dart:267)
at ResidentRunner.connectToServiceProtocol(resident_runner.dart:1404)
at HotRunner.attach(run_hot.dart:236)
at AttachCommand._attachToDevice(attach.dart:403)
at <asynchronous gap>(async)
at AttachCommand.runCommand(attach.dart:265)
at <asynchronous gap>(async)
at FlutterCommand.run.<anonymous closure>(flutter_command.dart:1408)
at <asynchronous gap>(async)
at AppContext.run.<anonymous closure>(context.dart:153)
at <asynchronous gap>(async)
at CommandRunner.runCommand(command_runner.dart:212)
at <asynchronous gap>(async)
at FlutterCommandRunner.runCommand.<anonymous closure>(flutter_command_runner.dart:420)
at <asynchronous gap>(async)
at AppContext.run.<anonymous closure>(context.dart:153)
at <asynchronous gap>(async)
at FlutterCommandRunner.runCommand(flutter_command_runner.dart:364)
at <asynchronous gap>(async)
at run.<anonymous closure>.<anonymous closure>(runner.dart:130)
at <asynchronous gap>(async)
at AppContext.run.<anonymous closure>(context.dart:153)
at <asynchronous gap>(async)
at main(executable.dart:93)
at <asynchronous gap>(async)
```
Repository: deno
Issue: deno info: support resolving npm specifiers when nodeModules: manual
Labels: feat,2.0 feedback,deno info

Version: Deno 2.0.0

I think it's quite common need to check what concrete version of a dependency is installed. When using node.js/npm I would normally use `npm ls <package>` which I think has deno equivalent in `deno info`, but unfortunatelly it doesn't work without explicitly specifying `-node-modules-dir=auto` which could be confusing for newcommers.
Repository: PowerToys
Issue: Workspaces: Skype App captured but not launched
Labels: Issue-Bug,Needs-Triage,Needs-Team-Response,Product-Workspaces

### Microsoft PowerToys version

0.85.0

### Installation method

PowerToys auto-update

### Running as admin

None

### Area(s) with issue?

Workspaces

### Steps to reproduce

I've created a Workspace in which i have captured Skype (the App version, not the Desktop one). When relaunching the workspace, Skype does not start.

### ✔️ Expected Behavior

Start Skype App when launching a workspace which contains it.

### ❌ Actual Behavior

Everything else is launched except Skype App.

### Other Software

_No response_
Repository: angular
Issue: Selectors are not available in documentation
Labels: area: docs-infra

### Describe the problem that you experienced

Selectors for built in directives and components, and the name of pipes are not described in the new documentation.

### Enter the URL of the topic with the problem

https://angular.dev/api/common/DecimalPipe

### Describe what you were looking for in the documentation

I'd like to see these information in the new documentation, just like in the old one. 

### Describe the actions that led you to experience the problem

_No response_

### Describe what you want to experience that would fix the problem

_No response_

### Add a screenshot if that helps illustrate the problem

https://v17.angular.io/api/common/DecimalPipe
![Image](https://github.com/user-attachments/assets/1ec020ab-3ec7-4db7-8e4b-d81908106f90)
https://angular.dev/api/common/DecimalPipe
![Image](https://github.com/user-attachments/assets/3a853d31-1bca-4e8d-ae87-a1ae233cebf5)


### If this problem caused an exception or error, please paste it here

_No response_

### If the problem is browser-specific, please specify the device, OS, browser, and version

_No response_

### Provide any additional information here in as much as detail as you can

These information are really useful, like the `DecimalPipe` class has a different name, so people need to dig into the tutorial to see how to use them.
Repository: react
Issue: [DevTools Bug]: React Devtools do not show source position for host component
Labels: Type: Bug,Status: Unconfirmed,Component: Developer Tools

### Website or app

none

### Repro steps

<img width="1715" alt="image" src="https://github.com/user-attachments/assets/86400e7a-d653-4ee5-a9c7-f7287931a224">
As you see, it should show source filed

### How often does this bug happen?

Every time

### DevTools package (automated)

_No response_

### DevTools version (automated)

_No response_

### Error message (automated)

_No response_

### Error call stack (automated)

_No response_

### Error component stack (automated)

_No response_

### GitHub query string (automated)

_No response_
Repository: PowerToys
Issue: Fancy zones - application on full screen show in selected zone only
Labels: Needs-Triage

### Description of the new feature / enhancement

In case application is switched to full screen (e.g. vide from web page) it is shoved on real full screen. Would it be possible to single fancy zones windows be used to show this full screen application and block complete monitor ?

### Scenario when this would be used?

click on full screen button inside video on web page

### Supporting information

_No response_
Repository: rust
Issue: Closure breaks function with higher-ranked trait bound
Labels: A-closures,T-compiler,C-bug,T-types,A-higher-ranked

I tried this code:

```rust
pub fn f<T>()
where
    for<'a> &'a T:,
{
    || {};
}
```

I expected this to compile, perhaps with warnings. Instead, I get this error:

```rust
error: `T` does not live long enough
 --> src\lib.rs:5:5
  |
5 |     || {};
  |     ^^^^^
```

Rust version: rustc 1.84.0-nightly (27861c429 2024-10-13)

As best as I can tell, this happens when a generic is involved in a higher-ranked trait bound, and a closure is defined inside the function. It seems like since the closure is somewhat generic over `T`, the closure needs to satisfy something about the bound. Things that also exhibit the issue:

- Adding more bounds
- Using the HRTB with a GAT, e.g. `T: for<'a> Trait<Item<'a> = &'a ()>`
- Adding arguments or a return to the closure
- Using or calling the closure (calling the closure adds another error on the call expression)
- Replacing the closure with an async block

Things that don't exhibit the issue:
- A HRTB with a trait generic over a lifetime: `T: for<'a> Trait<'a>` or `for<'a> T: Trait<'a>`
- A HRTB on a concrete type: `for<'a> &'a ():`
- Defining the closure inside a function inside `f`

This is probably the same as #102540 but that issue misidentified the problem and is old. I did some quick checking of old versions and this compiles in 1.20.0 and 1.30.1, throws ICE in 1.40.0, and gives an error in 1.50.0 and 1.60.0. The 1.60.0 error has a little more info.

```rust
error: higher-ranked lifetime error
 --> src\lib.rs:5:5
  |
5 |     || {};
  |     ^^^^^
  |
  = note: could not prove for<'a, 'r> &'a T: 'r
```

The ICE from 1.40.0 looks similar to #59311 (weirdly, predates 1.30) and #71546 and looks like this:

```rust
error: internal compiler error: broken MIR in DefId(0:12 ~ rusttest[7b76]::f[0]) (NoSolution): could not prove Binder(OutlivesPredicate(&'a T, ReEmpty))
 --> src\lib.rs:5:5
  |
5 |     || {};
  |     ^^^^^
```
Repository: ollama
Issue: Model Push Successful but Ignored by Ollama Registry - Cannot Pull Model After Push
Labels: bug,ollama.com

### What is the issue?

After successfully pushing a model to the Ollama registry using `ollama push`, the model seems to be ignored by the Ollama service. I cannot pull the model from the registry, and the service reports that "**No models have been pushed**" when accessing the registry URL.

This issue persists even though the push operation completes successfully. When attempting to pull the model afterward, the following error occurs:

```
Error: pull model manifest: file does not exist
```

### Steps to Reproduce:

1. **Pushing a Model:**
    I pushed the model using the following command:
    ```bash
    ollama push bona/bge_m3_korean:latest
    ```
    The push operation completed successfully:
    ```
    retrieving manifest
    pushing 61bb0c982884... 100% ▕█████████████████████████████████████████████████████████████████████▏ 1.2 GB
    pushing 578a2e81f706... 100% ▕█████████████████████████████████████████████████████████████████████▏   95 B
    ```

2. **Listing Models Locally:**
    After the push, I confirmed the model exists locally using `ollama list`:
    ```bash
    ollama list
    ```
    Output:
    ```
    NAME                                  ID              SIZE      MODIFIED
    bona/bge_m3_korean:latest             949236422f50    1.2 GB    2 minutes ago
    ```

3. **Pulling the Model from the Registry:**
    When trying to pull the model after pushing it:
    ```bash
    ollama pull bona/bge_m3_korean:latest
    ```
    I get the following error:
    ```
    Error: pull model manifest: 
    file does not exist
    ```

4. **Checking the Model on the Ollama Registry:**
    When I visit the registry URL (https://ollama.com/bona/bge-m3-korean), it shows:
    ```
    No models have been pushed.
    ```

### Environment:

- **Ollama version:** 0.3.12
  
HOW CAN I SOLVE IT?

### OS

WSL2

### GPU

AMD, Other

### CPU

Intel

### Ollama version

0.3.12
Repository: transformers
Issue: TimeSeriesTransformerModel: add no time lags option
Labels: Feature request

### Feature request

`TimeSeriesTransformerModel` has no option to insert no lags in the model. Setting `lags_sequence = []` in the `config` will return an error. An alternative to circumvent the problem, i.e. `lags_sequence = [0]` will result in having two identical copies of the temporal input (`lagged_sequence` and `time_feat`) to be concatenated in the `temporal_input`. 

### Motivation

Would make the model more user-friendly in cases where no additional lag is needed.

### Your contribution

A simple implementation would entail adding an `if len(lags_sequence) > 0` clause prior to adding the subsequences to the input tensor [here](https://github.com/huggingface/transformers/blob/7434c0ed21a154136b0145b0245ae9058005abac/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py#L1292).
Repository: react
Issue: Bug: eslint-plugin-react-hooks treats a non-react `use` function the same way as `React.use`
Labels: Component: ESLint Rules

React version: 18.3.1
eslint-plugin-react-hooks: 5.0.0

## Steps To Reproduce

1. install eslint-plugin-react-hooks 5.0.0
2. write this code
  ```
  function smth(use: () => void) {
    use();
  }
  ```
3. get an error
`React Hook "use" is called in function "smth" that is neither a React function component nor a custom React Hook function. React component names must start with an uppercase letter. React Hook names must start with the word "use".eslint[react-hooks/rules-of-hooks](https://reactjs.org/docs/hooks-rules.html)`

We got this error with `use` function in the callback of playwright's `test.extend()`

```
const test = base.extend<{ track: E2EGeneratedTrackFragment }>({
  track: async ({}, use) => {
    // --------------^ local `use` function
    const track = await generateTestTrack('e2e');
    await addTrackToArcade(track.id);
    await use(track);
    // -----^ the error is here
    await deleteTestTrack(track.id);
  },
});
```

Link to code example: -

## The current behavior

any custom `use` function is treated like `React.use`


## The expected behavior

only `use()` function from react is linted by those rules

## Additional

There is no error with eslint-plugin-react-hooks v4.6.2
Repository: pytorch
Issue: Take device of input tensor into consideration for torch::data::transforms::Normalize and possibly other transformations
Labels: module: dataloader,triaged

### 🚀 The feature, motivation and pitch

Currently if used on a tensor that is on a device different from `CPU` `torch::data::transforms::Normalize` results in an exception

> Normalization of input tensor failed: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
>
> Exception raised from compute_types at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\TensorIterator.cpp:503 (most recent call first): ...

Looking at the implementation (found in `include\torch\csrc\api\include\torch\data\transforms\tensor.h`)

```cpp
template <typename Target = Tensor>
struct Normalize : public TensorTransform<Target> {
  /// Constructs a `Normalize` transform. The mean and standard deviation can be
  /// anything that is broadcastable over the input tensors (like single
  /// scalars).
  Normalize(ArrayRef<double> mean, ArrayRef<double> stddev)
      : mean(torch::tensor(mean, torch::kFloat32)
                 .unsqueeze(/*dim=*/1)
                 .unsqueeze(/*dim=*/2)),
        stddev(torch::tensor(stddev, torch::kFloat32)
                   .unsqueeze(/*dim=*/1)
                   .unsqueeze(/*dim=*/2)) {}

  torch::Tensor operator()(Tensor input) override {
    return input.sub(mean).div(stddev);
  }

  torch::Tensor mean, stddev;
};
```

the transformation clearly ignores the device that `input` is on and assumes that it is on CPU since both `mean` and `stddev` are created by default on CPU.

**Proposal:** any auxiliary tensor that is created in order to apply a given transformation (not only `Normalize`) should be moved to the device where the input tensor(s) are located.



### Alternatives

-  Move the input tensor to CPU, apply normalization and then move back to original device
   **Drawbacks:** possibly additional memory transfers, decrease in performance if other device faster for given operation(s)
- Custom implementation of the specific transformation
  **Drawbacks:** does not really help improve the code base that is shipped with a libtorch setup.

### Additional context

_No response_

cc @andrewkho @divyanshk @SsnL @VitalyFedyunin @dzhulgakov