Not really. We've been on that road before. Embedding computation graph into the file makes changes to the computation graph harder (you need to make sure it is backward compatible). This is OK in general (as we have onnx already), but then if you have dynamic shape and the fact that different optimizations we implemented are actually tied to the computation graph, this is simply not optimal. (BTW, this is why PyTorch just embed the code into the pth file, much easier and backward compatible than a static computation graph).
wait, why is embedding the graph into the file bad?
it enables really clean separation of the core autodiff library and whatever backend you want to use to accelerate the graph computations, which can simply read the file and be completely independent of the core implementation
but also, if you just store the tensors in some arbitrary order and then store the indices of the order in which they have to read and traversed, you can easily adjust the graph to add stuff like layer fusion or smth similar (i'm not really familiar w/ comp graph optimisations tbh)
