v0.8.0

Breaking changes

• Serialization is now much faster because we avoid base64 encoding the serialized tensors. As a result, files serialized with newer versions of torch can't be opened with older versions of torch. Set options(torch.serialization_version = 1) if you want your file to be readable by older versions. (#803)
• Deprecated support for CUDA 10.2 on Windows. (#835)
• linalg_matrix_rank and linalg_pinv gained atol and rtol arguments while deprecating tol and rcond. (#835)

New features

• Improved auto-detection of CUDA version on Windows. (#798, @SvenVw)
• Improved parallel dataloaders performance by using a socket conection to transfer data between workers and the main process. (#803)
• keep_graph now defaults to the value of create_graph when calling $backward(). We also renamed it to retain_graph to match PyTorch. (#811)
• Optimizers created with optimizer now carry the classname in the generator and in instances. Optimizer generators now have the class torch_optimizer_generator. The class of torch optimizers has been renamed from torch_Optimizer to torch_optimizer. (#814)
• New utility function nn_prune_head() to prune top layer(s) of a network (#819 @cregouby)
• torch_kron() is now exported (#818).
• Added nn_embedding_bag. (#827, @egillax)
• nn_multihead_attention now supports the batch_first option. (#828, @jonthegeek)
• It's now possible to modify the gradient of a tensor using the syntax x$grad <- new_grad. (#832)
• sampler() is now exported allowing to create custom samplers that can be passed to dataloader(). (#833)
• Creating nn_modules without a initialize method is now supported. (#834)
• Added lr_reduce_on_plateau learning rate scheduler. (#836, @egillax)
• torch_tensor(NULL) no longer fails. It now returns a tensor with no dimensions and no data. (#839)
• Improved complex numbers handling, including better printing and support for casting from and to R. (#844)

Bug fixes

• Fixed bug in weight decay handling in the Adam optimizer. (#824, @egillax)
• Fixed bug in nn_l1_loss. (#825, @sebffischer)

Documentation

• Nice error message when embed_dim is not divisible by num_heads in nn_multihead_attention. (#828)

Internal

• Updated to LibTorch v1.11.0. (#835)
• Moved error message translations into R, this makes easier to add new ones and update the existing. (#841)