Package 'mafR'

Utility to manage torch tensors

Description

(Currently not used nor exported) utility converting an R object to a torch tensor.

Usage

.r_to_torch(x, py_handle, device)

Arguments

x	An R object suitable for use in reticulate::r_to_py(x) (this being as indefinite as the r_to_py documentation in this respect.)
py_handle	The return value of get_py_MAF_handle, or possibly more generally an environment with (at least) elements torch and device defined as in such a return value.
device	Character: "cpu"; or a GPU backend, either "cuda" (or "cuda:0", etc.) or "mps" depending on system capabilities.

Value

r_to_torch returns a 32-bit floating-point torch tensor allocated on the given device.

Examples

# See help("get_py_MAF_handle") for session setup, providing 'py_handle' argument in particular.

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Python controls

Description

Interface to control variables in a Python environment possibly used by Infusion. Currently the only implemented control is that of the torch random seed.

Usage

control_py_env(py_handle, seed = NULL)

Arguments

py_handle	An R environment that provides access to a Python evaluation environment, as produced by get_py_MAF_handle
seed	Numeric: passed (as integer value) to torch.random.manual_seed.

Value

Returns NULL invisibly.

See Also

get_py_MAF_handle

Examples

# See help("get_py_MAF_handle") for basic usage

# The following test of the first argument may be useful in broader contexts:
## Not run: 
if (inherits(py_handle,"environment"))  control_py_env(py_handle, seed=0L)

## End(Not run)

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Utilities to manage Python environment and torch tensors

Description

Utility initializing a Python environment for running zuko.flows.MAF and retrieving it.

Usage

get_py_MAF_handle(envir, reset=FALSE, torch_device="cpu", GPU_mem=NULL,
                    verbose = TRUE)

Arguments

envir	An environment (in the R sense) initialized as shown in the Examples.
reset	Boolean: Whether to reinitialize the Python session or not.
torch_device	Character: "cpu"; or a GPU backend, either "cuda" (or "cuda:0", etc.) or "mps" depending on system capabilities.
GPU_mem	For development purposes (effect is complicated). An amount of (dedicated) GPU memory, in bytes.
verbose	Boolean. Whether to print some messages or not.

Value

If successful, get_py_MAF_handle returns the modified input environment. If sourcing the Python code provided by mafR failed (presumably from trying to use an improperly set-up Python environment), the error condition message is returned.

See Also

init_py_env, control_py_env

Examples

## Not run:  # too slow for CRAN checks
# Initialization of python virtual environment:
init_py_env(test_cuda=FALSE)

# Initialization of python session (defines functions etc. 
#   in python session and provide access to them from R):    
#
my_env <- list2env(list(is_set=FALSE),parent = emptyenv())
py_handle <- get_py_MAF_handle(my_env, reset=FALSE, torch_device="cpu")

# => get_py_MAF_handle() has added objects in myenv:
my_env$torch # Imported Python package (result of reticulate::import("torch"))
my_env$device # the torch_device
# and functions for MAF training, such as myenv$MAF_density_estimation.

# The returned 'py_handle' can be used by oher functions, e.g.:
control_py_env(py_handle, seed=0L)
# alters the seed in the python session.

## End(Not run)

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Recipe for installation using py_require

Description

Initialize the required Python environment with the required modules, using procedures introduced in reticulate 1.42.0.

Usage

init_py_env(
    cuda=FALSE, test_cuda=cuda, test_dynamo=TRUE, force=FALSE,
    test_torch=test_cuda || test_dynamo,
    verbose=interactive())

Arguments

cuda	Boolean: Whether to install cuda device (only needed once).
test_cuda	Boolean: whether to test that cuda device can be used.
test_dynamo	Boolean: whether to test that "torch._dynamo" module can be imported. In particular, this test whether libstdc++ is accessible where this module expects it. See Details if the test fails
test_torch	Boolean: whether to test that "torch" module can be imported.
force	Boolean: Whether to force re-installation of miniconda.
verbose	Boolean: Whether to reprot progress of the different steps of the procedure.

Details

The the attempt to import the "torch._dynamo" module may fail on linux with message ImportError: cannot import name 'NP_SUPPORTED_MODULES' from 'torch._dynamo.utils'. If so, seek the libstdc++ library in the r-reticulate architecture (something like /home/<MY_USER_NAME>/.local/share/r-miniconda/lib/libstdc++.so.6.0.29), and either create a symbolic link to it as libstdc++.so.6 in (likely path) /usr/lib/x86_64-linux-gnu/, or change the PATH variable: export LD_LIBRARY_PATH=/home/<MY_USER_NAME>/.local/share/r-miniconda/lib/:$LD_LIBRARY_PATH. On the WSL, I found that the symbolic link is overwritten each time the WSL is restarted.

Value

Returns NULL invisibly.

See Also

get_py_MAF_handle

Examples

# See help("get_py_MAF_handle") for basic usage

# The following test may be useful in broader contexts:
## Not run:  # too slow for CRAN checks
if ( ! reticulate::py_available(initialize = FALSE)) init_py_env(test_cuda=FALSE)

## End(Not run)

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Interface for masked autoregressive flows

Description

This wraps Python procedures to train Masked Autoregressive Flows (MAFs, Paramakarios et al. 2017) using the Python package zuko. It has been tested with version 1.1.0, 1.2.0 and 1.4.0 of that package. Note that objects created by its version 1.2.0 cannot be read with its version 1.1.0 (i.e., when saved in and read from pickle files).

The simplest portable way to get mafR working may be to install it in a conda environment. Below is a complete installation recipe. Alternative installation procedures (notably, using reticulate functions) may be found in files not necessarily included in this package but available on its Git repository, https://github.com/f-rousset/mafR.

mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh

~/miniconda3/bin/conda init bash
conda create --name maf-conda python==3.10
conda activate maf-conda

pip install zuko

conda install R                                       
conda install conda-forge::r-gmp
conda install conda-forge::gsl

and, in an R session within the maf-conda environment:

install.packages("reticulate") 
library(reticulate)
use_condaenv(condaenv="maf-conda", conda="~/miniconda3/bin/conda")
install.packages("mafR")

# 'mafR' was first designed for use with 'Infusion':
install.packages("Infusion")
install.packages("Rmixmod") # only a Suggested dependency of Infusion, but needed.

References

Papamakarios, G., D. Sterratt, and I. Murray. 2019. Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:837-848, 2019. https://doi.org/10.48550/arXiv.1705.07057 ; https://proceedings.mlr.press/v89/papamakarios19a.html

Rozet, F., Divo, F., Schnake, S (2023) Zuko: Normalizing flows in PyTorch. https://doi.org/10.5281/zenodo.7625672

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