Best conversion between LuaJIT ffi cdata and torch Tensor

Question

What's the best way to perform the conversion between LuaJIT ffi cdata [1] and Torch Tensor [2].

According to Mike's reply in lua-user mail list [3], if we really want to convert cdata to lua plain table, we have to do a loop to copy each item to a new created one. And actually Torch Tensor do provide some interface for better LuaJIT ffi access [4]. So, my current solution is do a loop first and convert the cdata to a lua plain table and then call the tensor construction function that create a tensor from a table [5].

But actually in my case, I need to do similar conversion between LuaJIT ffi cdata and Torch Tensor very frequently, is there any better approach rather than loop copy?

• [1] http://luajit.org/ext_ffi_api.html (Creating cdata Objects Section)
• [2] https://github.com/torch/torch7/blob/master/doc/tensor.md
• [3] http://lua-users.org/lists/lua-l/2011-03/msg00584.html
• [4] https://github.com/torch/torch7/blob/master/doc/tensor.md#luajit-ffi-access
• [5] https://github.com/torch/torch7/blob/master/doc/tensor.md#torchtensortable

Answer 1

If your cdata represents a contiguous array of data then you can use ffi.copy. Here is a toy example:

require 'torch'
ffi = require 'ffi'

-- create a random float array
n = 3
x = torch.rand(n):float()
cdata = x:data()
assert(type(cdata) == 'cdata')

-- copy this cdata into a destination tensor
y = torch.FloatTensor(n)
ffi.copy(y:data(), cdata, n*ffi.sizeof('float'))

assert(x:equal(y))