* Add the const-set op.
* Cuda implementation.
* Bugfix.
* Metal cleanup.
* Add the metal kernels.
* Add some testing.
* Finish the metal implementation.
* Bump the version.
* Start updating to cudarc 0.14.
* Adapt a couple more things.
* And a couple more fixes.
* More tweaks.
* And a couple more fixes.
* Bump the major version number.
* Proper module system for the cuda kernels.
* Proper ptx loading.
* Launch the sort kernel.
* Custom op.
* Start using the builder pattern.
* More builder.
* More builder.
* Get candle-core to compile.
* Get the tests to pass.
* Get candle-nn to work too.
* Support for custom cuda functions.
* cudnn fixes.
* Get flash attn to run.
* Switch the crate versions to be alpha.
* Bump the ug dependency.
* update to cudarc to v0.13.5 to support cuda 12.8
* Bump the crate version.
---------
Co-authored-by: Michael McCulloch <michael.james.mcculloch@fastmail.com>
* Add some group parameter to convolutions.
* Avoid some unnecessary groups checks.
* Move the tensor convolution bits.
* Properh handling of groups.
* Bump the crate version.
* And add a changelog.
* Again set a few extra params.
* Use the appropriate kernel sizes.
* Add all the kernel sizes.
* Parallel compiling.
* Reduce the amount of parallelism.
* Add the missing kernel.
* Fix a typo.
* Remove bf16 support for now.
* Add some flash-attn kernel, import the code for flash-attn v2 from Dao-AILab.
* More flash attn.
* Set up the flash attn parameters.
* Get things to compile locally.
* Move the flash attention files in a different directory.
* Build the static C library with nvcc.
* Add more flash attention.
* Update the build part.
* Better caching.
* Exclude flash attention from the default workspace.
* Put flash-attn behind a feature gate.
* Get the flash attn kernel to run.
* Move the flags to a more appropriate place.
* Enable flash attention in llama.
* Use flash attention in llama.
