* Add a cudnn feature to be used for conv2d.
* Allocate the proper workspace.
* Only create a single cudnn handle per cuda device.
* Proper cudnn usage.
* Bugfix.
* Track the conv2d operations in stable-diffusion.
* Add more tracing to stable-diffusion.
* Also trace the resnet bits.
* Trace the attention blocks.
* Also trace the attention inner part.
* Small tweak.
* Add more tracing to the whisper example.
* Support accelerate in more examples.
* Use accelerate for pointwise functions.
* Use accelerate for binary operations too.
* Bugfix for binary operation: use the rhs before the lhs.
* Start adding a stable-diffusion example.
* Proper computation of the causal mask.
* Add the chunk operation.
* Work in progress: port the attention module.
* Add some dummy modules for conv2d and group-norm, get the attention module to compile.
* Re-enable the 2d convolution.
* Add the embeddings module.
* Add the resnet module.
* Add the unet blocks.
* Add the unet.
* And add the variational auto-encoder.
* Use the pad function from utils.
* Move the vision datasets to a separate crate.
* Move the batcher bits.
* Update the readme.
* Move the tiny-stories bits.
---------
Co-authored-by: Jane Doe <jane.doe@example.org>
- Loading with memmap
- Loading a sharded tensor
- Moved some snippets to `candle-examples/src/lib.rs` This is because
managing book specific dependencies is a pain https://github.com/rust-lang/mdBook/issues/706
- This causes a non aligned inclusion https://github.com/rust-lang/mdBook/pull/1856 which we have
to ignore fmt to remove.
mdbook might need some more love :)
* Rework the var-builder to handle initializations.
* Add some helper functions for layer creation.
* Improve the layer initializations.
* Get initialized variables.
* Precompute the rot embeddings when training lamas.
* Rework the commands and run inference by default.
* Add the training module and load the training dataset.
* Random dataset iterator.
* Proper valid-loss computation.
* Compute the evaluation loss.
* Add more substance to the training loop.
