High-Performance Open-Source Archive
torch_triu_indices() and
torch_tril_indices() now return 1-based indexes
(#1382).$indices() now returns 1-based indexes (#1382).torch_sitrep() for torch installation situation
report (#1415, @cregouby).%*% operator for torch tensors (#1379, @caio-hamamura).head() and tail() methods for torch
tensors (#1388).torch_scaled_dot_product_attention()
(#1404).torch_sparse_sampled_addmm() (#1427).torch_ldexp() (#1407).cudatoolkit R package for CUDA library
loading (#1422).as_array() support for sparse tensors
(#1387).to_sparse() crash,
and as_array() detection (#1431).dataset()/nn_module() crash when
initialize parameter is named d (#1424, @Chandraveer-Singh).bfloat16 not being recognized as a floating point
dtype (#1408).lantern.h include causing macro
redefinition warning (#1430).Rf_findVarInFrame with
R_getVarEx for R 4.6 compatibility (#1421).nn_aum_loss() (#1310 @cregouby)Bug fixes:
RTLD_DEEPBIND (#1268)optim_ignite_<name>()) are available: Adam, AdamW,
Adagrad, RMSprop,SGD. These can be used as drop-in replacements for
optim_<name> but are considerably faster as they wrap
the LibTorch implementation of the optimizer. The biggest speed
differences can be observed for complex optimizers such as
AdamW.torch_iinfo() now support all integer dtypes (#1190
@cregouby)nnf_multi_head_attention_forward() (#1205)jit_trace (#1246)$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.
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