v0.3.0

Breaking changes

• torch_nonzero and tensor$nonzero() now return 1-based indexes. (#432)
• Breaking change: torch_arange returns in the closed interval [start, end] instead of the half open [start, end). This makes it behave similar to R's seq. (#506)

New features

• torch_split now accepts a list of sizes as well as a fixed size. (#429)
• Added nn_layer_norm. (#435)
• Allow timeout=360 as install_torch() parameter for large file download (@cregouby #438)
• Added install_torch_from_file() and get_install_libs_url()for setup cases where direct download is not possible (@cregouby #439)
• Added mean.torch_tensor (#448)
• New arguments worker_globals and worker_packages allowing to easily pass objects to workers in parallel dataloaders (#449).
• We now call R garbage collector when there's no memory available on GPU, this can help in a few cases when the laziness of the garbage collector allows too many tensors to be on memory even though they are no longer referenced in R. (#456)
• Implemented nn_group_norm and fixed a bug in nnf_group_norm (#474)
• Added backend functions allowing us to query which optimizations LibTorch was compiled with (#476)
• Added normal distribution (#462)
• Added bernoulli distribution (#484)
• as.list for nn_modules (#492)
• Enumerate support in Bernoulli distribution (#490)
• Added Poisson Distriibution (#495)
• Allow optional .getbatch in datasets/dataloaders (#498)
• nn_lstm, nn_gru and nn_gru can now use cudnn accelerations when available (#503).
• Added Gamma distribution (#489)
• We now respect the TORCH_HOME env var to automatically install torch. (#522)
• Implement comparison operator != for torch dtypes. (#524)
• Added Chi-square distribution. (#518)
• Added optimizer function allowing to easily implement custom optimizers. (#527)

Bug fixes

• Fixed bug in optim_lbfgs that would make model objects exponentially big. (#431)
• Correctly handle NaNs in L-BFGS optimizer (#433)
• The default collate function now respects the data type when converting to a tensor (if the dataset returns an R object) (#434)
• Fixed torch_normal. (#450)
• Fixed backward compatibility issue when loading models saved in older versions of torch. This bug was introduced in #452 and is now fixed and we also added a regression test. (#458)
• Fixed bug when using RNN's on the GPU (#460)
• Found and fixed some memory leaks, specially when creating datatypes from strings and when saving models with torch_save. (#454)
• Fixed bug in nnf_pad when using mode='circular'. (#471)
• Bugfixes in nn_multihead_attention (#496)
• Fixed bug when using packed sequences with nn_lstm (#500)
• Fixed bug in the to method of nn_module that would reset the requires_grad attribute of parameters. (#501)
• Added strong_wolfe option to optim_lbfgs. (#517)
• Fixed default argument of nn_init_trunc_normal_ initializer function. (#535)

Documentation

• Added vignette on reading models from Python (#469)

Internal changes

• Removed the PerformanceReporter from tests to get easier to read stack traces. (#449)
• Internal change in the R7 classes so R7 objects are simple external pointer instead of environments. This might cause breaking change if you relied on saving any kind of state in the Tensor object. (#452)
• Internal refactoring making Rcpp aware of some XPtrTorch* types so making it simpler to return them from Rcpp code. This might cause a breaking change if you are relying on torch_dtype() being an R6 class. (#451)
• Internal changes to auto unwrap arguments from SEXP's in Rcpp. This will make easier to move the dispatcher system to C++ in the future, but already allows us to gain ~30% speedups in small operations. (#454)
• Added a Windows GPU CI workflow (#508).
• Update to LibTorch v1.8 (#513)
• Moved some parts of the dispatcher to C++ to make it faster. (#520)