call_torch_function: Call a (Potentially Unexported) Torch Function

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Call a (Potentially Unexported) Torch Function

Description

This function allows calling a function prefixed with torch_, including unexported functions which could have potentially valuable uses but which do not yet have a user-friendly R wrapper function. Therefore, this function should be used with extreme caution. Make sure you understand what the function expects as input. It may be helpful to read the torch source code for help with this, as well as the documentation for the corresponding function in the Pytorch C++ API. Generally for development and advanced use only.

Usage

call_torch_function(name, ..., quiet = FALSE)

Arguments

name	Name of the function to call as a string. Should start with "torch_"
...	A list of arguments to pass to the function. Argument splicing with ⁠!!!⁠ is supported.
quiet	If TRUE, suppress warnings with valuable information about the dangers of this function.

Value

The return value from calling the function name with arguments ...

Examples

if (torch_is_installed()) {
## many unexported functions do 'backward' calculations (e.g. derivatives)
## These could be used as a part of custom autograd functions for example.
x <- torch_randn(10, requires_grad = TRUE)
y <- torch_tanh(x)
## calculate backwards gradient using standard torch method
y$backward(torch_ones_like(x))
x$grad
## we can get the same result by calling the unexported `torch_tanh_backward()`
## function. The first argument is 1 to setup the Jacobian-vector product.
## see https://pytorch.org/blog/overview-of-pytorch-autograd-engine/ for details.
call_torch_function("torch_tanh_backward", 1, y)
all.equal(call_torch_function("torch_tanh_backward", 1, y, quiet = TRUE), x$grad)

}

torch documentation built on June 7, 2023, 6:19 p.m.