LuaTorch is eager-execution. The problem with LuaTorch is the GC. You cannot rely on traditional GC for good work, since each tensor is megabytes (at the time), now gigabytes large, you need to collect them aggressively rather than at intervals (Python's reference-counting system solves this issue, and of course, by "collecting", I don't mean free the memory (PyTorch has a simple slab allocator to manage CUDA memory)).
With Lua Torch the model execution was eager, but you still had to construct the model graph beforehand - it wasn't "define by run" like PyTorch.
Back in the day, having completed Andrew Ng's ML coursew, I then built my own C++ NN framework copying this graph-mode Lua Torch API. One of the nice things about explicitly building a graph was that my framework supported having the model generate a GraphViz DOT representation of itself so I could visualize it.
Ah, I get what you mean now. I am mixing up the nn module and the tensor execution bits. (to be fair, the PyTorch nn module carries over many these quirks!).
