* Boilerplate for the quantized cuda support.
* More basic cuda support.
* More cuda quantization (quantize on cpu for now).
* Add the dequantization bit.
* Start adding some dedicated cuda kernels from llama.cpp.
* Move the kernel code.
* Start interfacing with the kernel.
* Tweak the kernel launch params.
* Bugfix for quantized metal.
* Fix some clippy lints.
* Tweak the launch parameters.
* Tweak cuda basics to perform a quantized matmul.
* Perform the dequantization on the cpu + use cublas for matmul.
* Add the dequantization kernel.
* Test the qmatmul.
* More kernels.
* Matmul-vec kernel.
* Add a couple kernels.
* More dequantization kernels.
* im2col implementation for conv2d.
* Fix for the im2col implementation to match the current conv2d.
* Small optimization.
* Add a cuda kernel.
* Handle arbitrary layouts.
* Im2Col cuda code.
* Add the dilation parameter.
* Restore the basic optimizer example.
* Dilation support in cudnn.
* Use the dilation parameter in the cpu backend.
* More dilation support.
* No support for dilation in transposed convolutions.
* Add dilation to a test.
* Remove a print.
* Helper function.
* Add to the cuda example a reproduction of the issue.
* Tweak.
* Add a test using non-square matrixes.
* Fix the conv2d kernel.
* Display the error.
* And tweak the comment.
* Add a couple functions required for yolo.
* Add the yolo-v3 example.
* Add minimum and maximum.
* Use the newly introduced maximum.
* Cuda support for min/max + add some testing.
* Allow for more tests to work with accelerate.
* Fix a typo.
* Add a cuda kernel for avg-pool2d.
* Avoid running out of bounds.
* Finish wiring the avg pool kernel + add some testing.
* Support for max-pool + testing.
* Add a naive conv2d cuda kernel.
* Proper conv2d support on the rust side.
* Conv1d testing on gpu.
* Also use the test on gpus.
* Fix the clean-ptx target.
* Cuda support for the mnist training.
* min/max fix + testing.
* Add the argmin/argmax tests.
* More cuda support for argmin/argmax.
* Cuda kernels for argmin and argmax.
* Skeleton methods for IndexAdd/ScatterAdd.
* Add a Map2InPlace trait.
* Add the glue code for the index-add/scatter-add kernels.
* Tweak the file name: embeddings -> indexing.
* Add the cuda kernel for indexadd.
* And add the scatter-add kernels.
* Cleanup some todos.
* Fix more todo.
* Optimize for the contiguous case.
* Add the IntDType trait.
* Handle the intdtype trait for more ops.
* Remove a todo.
* Remove a todo.
