Generate meaningful names with from_list() about nir

[Jegp]

I see how it would make sense to have more meaningful names via the from_list constructor.

I'm not sure I'm a fan of the indexing, though. Doesn't that imply there is an ordering based on each node type? That dense comes before dense_1?

What about retaining the index across node types? Given the graph is constructed from a list, wouldn't it make sense to have a graph Linear -> Linear look like this? Input_0 -> Linear_1 -> Linear_2 -> Output_3?

Concretely, it could look something like this:

return NIRGraph(
    nodes={f"{type(n).__name__}_{str(i)}": n for i, n in enumerate(nodes)},
    edges=[(str(i), str(i + 1)) for i in range(len(nodes) - 1)],
)

[matjobst]

Thank you for your proposal, that is of course another option. That has the nice benefit of not requiring a per-node-type counter.

However, it might also get confusing if you have a graph Dense->LIF->Dense. Then you would have Input_0->Dense_1->LIF_2->Dense_3->Output_4, with Dense_1 and Dense_3, but no Dense_2.

In the Tensorflow-like naming, that would be input->dense->lif->dense_1->output.

I do not see the ordering implication as an issue here. But maybe that is also because I am used to the way Tensorflow does the default naming. Also this default naming is only for the from_list which can only create a sequential graph, giving a deterministic naming.

Addendum: In your proposal the edges also need the changed names :)

return NIRGraph(
    nodes={f"{type(n).__name__}_{str(i)}": n for i, n in enumerate(nodes)},
    edges=[(f"{type(nodes[i]).__name__}_{str(i)}", f"{type(nodes[i+1]).__name__}_{str(i)}") for i in range(len(nodes) - 1)],
)

[Jegp]

I honestly don't have strong opinions here. I can see both systems working. If you're happy with the Tensorflow style, I would be fine to sign off on that :-)

It's definitely better than status quo!