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$finalize_deep_clone() method for nn_module which allows to run some code after cloning a module.compare_proxy method for the torch_tensor type was added it allows to compare torch tensors using testthat::expect_equal().nnf_multi_head_attention_forward (#1154 @cregouby)$detach() on a tensor now preserves attributes (#1136)create_nn_module_callable()grad_fn now appends a new line at the end.local_autocast(). (#1142)torch_arange() causing it to return 1:(n-1) values when specific request dtype = torch_int64() (#1160)torch_save serialization format. It’s ~10x faster and since it’s based on safetensors, files can be read with any safetensors implementation. (#1071)torch_load no longer supports device=NULL to load weights in the same device they were saved. (#1085)as.array now moves tensors to the cpu before copying data into R. (#1080)dtype with a NULL. (#1090)torch_cfloat64. (#1091)out_features attribute in the nn_linear module. (#1097)nn_modules with empty state dicts. (#1108)distr_multivariate_normal now correctly handles precision matrix’s. (#1110)length.torch_tensor implementation to R7 to avoid problems when a torch dataset has the torch_tensor class. (#1111)nn_module. (#1123)load_state_dict() for optimizers now default to cloning the tensors in the state dict, so they don’t keep references to objects in the dict. (#1041)nn_utils_weight_norm (#1025)jit_ops allowing to access JIT operators. (#1023)with_device and local_device to allow temporarily modify the default device tensors get initialized. (#1034)nnf_gelu() and nn_gelu() gained the approximate argument. (#1043)!= for torch devices. (#1042)nn_module_dict(). (#1046)load_state_dict(), also using less memory. It’ possible to use the legacy implementation if required, see PR. (#1051)as.numeric(). (#1056)torch_tensor_from_buffer() and buffer_from_torch_tensor() that allow low level creation of torch tensors. (#1061, #1062)install_torch(). (#1055)state_dict() even if they should not. (#1036)optim_adamw class name.nn_cross_entropy_loss class name. (#1043)RemoteSha is a package version. (#1058)CXXSTD to Makevars to enable C+11 compilation options..refer_to_state_dict to the load_state_dict() nn_module() method. Allows loading the state dict into the model keeping parmaters as references to that state dict. (#1036)nn_silu() and nnf_silu(). (#985)nn_modules. (#986)local_no_grad() and local_enable_grad() as alternatives for the with_ functions. (#990)optim_adamw optimizer. (#991)batch_size=1 is used. (#994)torch_where now returns 1-based indices when it’s called with the condition argument only. (#951, @skeydan)torch_fft_fftfreq() function is now exported. (#950, @skeydan)distr_normal$sample() not being able to generate reproducible results after setting seeds. (#938)torch_cat error message now correctly reflects 1-based indexing. (#952, @skeydan)sprintf. (#959, @shikokuchuo)glue (#960)torch_bincount is now 1-based indexed. (#896)torch_movedim() and $movedim() are now both 1-based indexed. (#905)cuda_synchronize() to allow synchronization of CUDA operations. (#887)cuda_empty_cache() to allow freeing memory from the caching allocator to the system. (#903)$is_sparse() method to check wether a Tensor is sparse or not. (#903)dataset_subset now adds a class to the modified dataset that is the same as the original dataset classes postfixed with _subset. (#904)torch_serialize() to allow creating a raw vector from torch objects. (#908)torch_arange that was causing the end value not getting included in the result. (#885, @skeydan)install_torch(reinstall = TRUE). (#883)dims argument in torch_tile() is no longer modified, as it’s not meant to be the a 1-based dimension. (#905)nn_module$state_diict() now detaches output tensors by default. (#916)$ method for R7 classes in C/C++ to improve speed when calling methods. (#873)backward() call. This improves speed because we no longer need to call GC everytime backward is called. (#873)nnf_ functions have been updated to use a single torch_ kernel instead of the custom implementation. (#896)torch_bernoulli() with device="gpu". (#906)PRECXX11ABI=1. This can be useful in environments with older versions of GLIBC. (#870)options(torch.serialization_version = 1) if you want your file to be readable by older versions. (#803)linalg_matrix_rank and linalg_pinv gained atol and rtol arguments while deprecating tol and rcond. (#835)keep_graph now defaults to the value of create_graph when calling $backward(). We also renamed it to retain_graph to match PyTorch. (#811)optimizer now carry the classname in the generator and in instances. Optimizer generators now have the class torch_optimizer_generator. The class of torch optimizers has been renamed from torch_Optimizer to torch_optimizer. (#814)nn_prune_head() to prune top layer(s) of a network (#819 @cregouby)torch_kron() is now exported (#818).nn_embedding_bag. (#827, @egillax)nn_multihead_attention now supports the batch_first option. (#828, @jonthegeek)x$grad <- new_grad. (#832)sampler() is now exported allowing to create custom samplers that can be passed to dataloader(). (#833)nn_modules without a initialize method is now supported. (#834)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)nn_l1_loss. (#825, @sebffischer)embed_dim is not divisible by num_heads in nn_multihead_attention. (#828)cuda_runtime_version() to query the CUDA Tolkit version that torch is using. (#790)torch_sort and Tensor$sort now return 1-indexed results. (#709, @mohamed-180)dim from 1 to 2 in nnf_cosine_similarity. (#769)jit_save_for_mobile allows to save a traced model in bytecode form, to be loaded by a LiteModuleLoader. (#713)is_torch_tensor to check wether an object is a tensor or not. (#730, @rdinnager)cuda_get_device_properties(device) that allows one to query device capability and other properties. (#734, @rdinnager)call_torch_function() to allow calling potentially unexported torch core functions. (#743, @rdinnager)inst directory. This will allow for packages extending torch to bind directly to its C++ library. (#718)dataset_subset will use the .getbatch method of the wrapped dataset if one is available. (#742, @egillax)nn_flatten and nn_unflatten modules. (#773)cuda_memory_stats() and cuda_memory_summary() to verify the amount of memory torch is using from the GPU. (#774)backends_cudnn_version() to query the CuDNN version found by torch. (#774).validate_sample for the Distribution class that would incorrectly check for tensors. (#739, @hsbadr)autograd_functions. (#750)autograd_grad to deadlock when used with custom autograd functions. (#771)torch_max and torch_min that would fail with length=2 Tensors. (#772)torch_tensor is now a C++ only function slighly increasing performance in a few situations. (#784)nn_sequential is now a bare nn_module, allowing to easily inherit from it. This is a breaking change if you used the name argument. The name behavior can be achieved by subclassing; see the tests in the PR. (#699)nn_sequential modules using [. (#678, @mohamed-180)padding='same' and padding='valid' when using convolutions. (#679)nnf_cross_entropy now uses the ATen cross_entropy operation directly instead of doing logsoftmax + NLLLoss. (#680)nn_sequential and still want that the specific S3 methods still work. (#701)torch_manual_seed() now matches PyTorch’s behavior so we can more easily compare implementations. Since this is a breaking change we added the torch.old_seed_behavior=TRUE option so users can stick to the old behavior. (#639)torch_index or torch_index_put. (#649)nn_modules. (#593)jit_compile function that allows compiling arbitrary TorchScript code into script function that can be serialized and executed. (#601)jit_trace support for nn_module created from R. (#604)contrib_sort_vertices to efficiently sort vertices on CUDA. (#619)with_detect_anomaly to debug autograd errors. (#628)traced_module$graph_for() to allow inspecting the optimized jit graph. (#643)slc to allow dynamically creating slices when indexing tensors. (#648).getbatch method that didn’t return a torch_tensor. (#615)%/% caused by a call to deprecated torch_floor_divide (#616)gc when CUDA memory is exhausted we now call R_RunPendingFinalizers. This should improve memory usage, because we will now delete tensors earlier. (#654)torch_multinomial now returns 1-based indexes to comply with 1-based indexing across torch. (#588)jit_trace functions with more than 1 argument. (#544)torch_diff function and added docs for it. (#565)device argument to torch_load() allowing one to select to which device parameters should be loaded. (#578)distr_categorical() (#576)distr_mixture_same_family() (#576)load_state_dict() and state_dict() for optimizers. (#585)lists containing torch_tensors using torch_save. This allows us to save the state of optimizers and modules using torch_save(). (#586)nn_multihead_attention when q,k,v inputs not all the same. (@jonathanbratt #540)$copy_ so it correctly respects the src requires_grad() when reloading saved models with torch_load(). (#545)nn_init_xavier_normal_() and nn_init_xavier_uniform_() standard deviation calculation. (#557)torch_tensordot() when called when infering dimensions. (#563).getbatch now takes an integer vector as input instead of a list(). (#572)tensor$size() when indexing with negative numbers. (#570)log_prob of distr_bernoulli() (#581)XPtrTorchOptionalTensor class. (#565)torch_nonzero and tensor$nonzero() now return 1-based indexes. (#432)torch_arange returns in the closed interval [start, end] instead of the half open [start, end). This makes it behave similar to R’s seq. (#506)torch_split now accepts a list of sizes as well as a fixed size. (#429)nn_layer_norm. (#435)timeout=360 as install_torch() parameter for large file download (@cregouby #438)install_torch_from_file() and get_install_libs_url()for setup cases where direct download is not possible (@cregouby #439)mean.torch_tensor (#448)worker_globals and worker_packages allowing to easily pass objects to workers in parallel dataloaders (#449).nn_group_norm and fixed a bug in nnf_group_norm (#474)as.list for nn_modules (#492)nn_lstm, nn_gru and nn_gru can now use cudnn accelerations when available (#503).!= for torch dtypes. (#524)optimizer function allowing to easily implement custom optimizers. (#527)optim_lbfgs that would make model objects exponentially big. (#431)NaNs in L-BFGS optimizer (#433)torch_normal. (#450)torch_save. (#454)nnf_pad when using mode='circular'. (#471)nn_multihead_attention (#496)nn_lstm (#500)to method of nn_module that would reset the requires_grad attribute of parameters. (#501)strong_wolfe option to optim_lbfgs. (#517)nn_init_trunc_normal_ initializer function. (#535)torch_dtype() being an R6 class. (#451)torch_one_hot and nnf_one_hot use 1-based indexing. (#410)nn_module$eval() and nn_module$train() now return a callable nn_module instead of a nn_Module. (#425)gc when torch might need more memory (#402)nn_module (#417)requires_grad attribute using the $<- operator (#419)length method for the nn_sequential container. (#423)nnf_normalize and x$norm() as they were not able to be called (#409)nn_module documentation (#399).coro::exhausted intead of raising stop_iteration_error when the dataloader exceeds. (#366)torch_argsort and x$argsort to return 1-based indexes (#342)torch_argmax, torch_argmin, x$argmax() and x$argmin() return 1-based indexes. (#389)$element_size() method (@dirkschumacher #322)$bool() method (@dirkschumacher #323)torch__addr and torch__addr_ have been removed as they are no longer available in LibTorch 1.7.long tensors to R and there’s a chance of an integer overflow. (#347)private and active methods in nn_module’s and dataset’s. (#349)nn_batch_norm3d (@mattwarkentin #354)nn_lstm and nn_gru modules. (#362)nn_module UI by improving autocomplete support and adding a print method (#391)grad_fn of a Tensor that doesn’t have one. See (#321)torch_norm so it can be called with a dim argument. (#345)torch_hann_window with an invalid NULL window_length. (#351)torch_stft calls for LibTorch 1.7 (added the return_complex argument) (#355)nvcc --version instead of nvidia-smi to find the CUDA version as nvidia-smi reports the latest supported version and not the installed one. (#363)RandomSampler(replacement = TRUE) to never take the last element in the dataset. (84861fa)torch_topk and x$topk so the returned indexes are 1-based (#280)1 - torch_tensor(1, device = "cuda") to fail because 1 was created in the CPU. (#279)dataloader output (#286)torch_narrow, Tensor$narrow() and Tensor$narrow_copy are now indexed starting at 1. (#294)Tensor$is_leaf is now an active method. (#295)torch_einsum. (#296)nn_module_list() to correctly name added modules, otherwise they are not returned when doing state_dict() on it. (#300)nn_batchnorm* so it returns the same results as PyTorch (#302)nn_module$parameter when there were shared parameters between layers. (#306)$max and $min to return 1-based indexes. (#315)utils_data_default_collate to support converting R objects to torch tensors when needed. (#269)as.matrix method for torch Tensors. (#282)print(totrch_tensor(1:40)) if it spans for more than 30 lines. This is useful for not spamming the console or taking very long to print when you print a very large tensor. (#283)nnf_contrib_sparsemax and nn_contrib_sparsemax. (#309)$<- and [[<- operators for the nn_module class. (#253)nn_parameter, nn_buffer, and is_* auxiliary functions.NEWS.md file to track changes to the package.These binaries (installable software) and packages are in development.
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