fidlej/torchffi

FFI bindings for Torch7. Allows LuaJIT-speed access to Tensors and Storages.

TorchFFI

This package exposes Torch's Tensor and Storage data structures, through LuaJIT's FFI. This allows extremely fast access to Tensors and Storages, all from Lua.

The original neural network from Torch7, 'nn', contains stable and widely used modules. 'nnx' contains more experimental, unproven modules, and optimizations. Eventually, modules that become stable enough will make their way into 'nn' (some already have).

Disclaimer: DONT USE THIS PACKAGE WITHOUT FIRST CHECKING ITS MODULES !!!

Installation

• Install Torch7 (refer to its own documentation). It is necessary to build it with LuaJIT, to enable FFI:

$ cmake . -D WITH_LUA_JIT=1
$ make install

• Install torchffi:

$ torch-pkg -local install torchffi

Usage

Simply require torchffi, and you'll have access to a new function in torch, called torch.data(obj):

> t = torch.randn(3,2)
> print(t)
 0.8008 -0.6103
 0.6473 -0.1870
-0.0023 -0.4902
[torch.DoubleTensor of dimension 3x2]

> t_data = torch.data(t)
> for i = 0,t:nElement()-1 do t_data[i] = 0 end
> print(t)
0 0
0 0
0 0
[torch.DoubleTensor of dimension 3x2]

Accessing the raw data of a Tensor like this is extremely efficient, in fact, it's almost as fast as C in lots of cases.

WARNING: bear in mind that accessing the raw data like this is dangerous, and should only be done on contiguous tensors (if a tensor is not contiguous, then you have to use it size and stride information). Making sure a tensor is contiguous is easy:

> t = torch.randn(3,2)
> t_noncontiguous = t:transpose(1,2)

-- it would be unsafe to work with torch.data(t_noncontiguous)

> t_transposed_and_contiguous = t:noncontiguous:contiguous()

-- it is now safe to work with the raw pointer

> data = torch.data(t_contiguous)