* in. com.
* upd. ackn.
* update __init__
* nits
* nits + format
* used mx.maximum(x, 0) instead of calling the function and moves relu6 under relu2 to make it nicer
* same with _make_activation_module
* Update python/mlx/nn/layers/activations.py
upd
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* update funct.rst
* upd. layers.rst
---------
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Added scalar to stubs to fix Unkown Type Hint
### Proposed changes
Issue #2478 reports that several type annotations in the MLX stubs degrade to Unknown/Any in editors like VS Code with Pylance, due to missing imports (Union, Optional, Tuple) and an undefined scalar type alias.
This PR updates the stub generation patterns to:
• Add missing typing imports in mlx.core.__prefix__ so that Union, Optional, Tuple, etc. are always available.
• Define and export scalar: TypeAlias = Union[int, float, bool] in mlx.core.__suffix__ so that functions typed with Union[scalar, array] resolve correctly instead of falling back to Any.
• Update submodule stub prefixes (distributed, fast, linalg, metal, random) to import scalar alongside array, Device, and Stream, ensuring type checkers resolve the union consistently across modules.
With these changes, functions like mlx.add now display rich type signatures such as:
```
def add(
a: scalar | array,
b: scalar | array,
stream: Stream | Device | None = None
) -> array
```
instead of degrading to Any.
### Checklist
• I have read the CONTRIBUTING document
• I have run pre-commit run --all-files to format my code / installed pre-commit prior to committing changes
• I have added tests that prove my fix is effective or that my feature works (n/a — stub generation only)
• I have updated the necessary documentation (if needed)
* add bool to patterns
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* Link with cuDNN
* Initial implementation
* Remove backend apis
* Fix recording cudnn conv
* More unused backend apis
* Fix C++ conv tests
* include cudnn as python dep
* Install libcudnn9-dev-cuda-12 in CI
* cudnn only accepts contiguous inputs
* Switch to backend apis
* Plan needs to be kept alive
* Turn off tf32
* Add cache
* Test the native cuda graph api
* Set cudnn stream before execution
* Make LRUCache more like a normal container
* Do error check for cublas handle
* Zero-initilizing array
* Use tf32 for conv
* Skip TestConv.test_torch_conv_2D test
---------
Co-authored-by: Awni Hannun <awni@apple.com>
