Allow partitioning, quantization and compilation for graphs with multiple subgraphs

[FireFox317]

The current state of the code does not allow for all kinds of operations (partitioning, quantization and compilation) to be performed on a graph with multiple subgraphs. For example the yolov4-tiny model, has split operations which are not supported by the DPU, thus the graph should be split into multiple subgraphs and each of these subgraphs should be quantized and compiled to IR for the DPU.

The problem already starts in partitioning. After partitioning the graph into subgraphs, the code figure out what the biggest subgraph is and then throws away all the other subgraphs, such that only one subgraph is left. This however, completely changes the behavior of the model and is not resulting in the intended behavior. The following code deletes all the other subgraphs:

pyxir/python/pyxir/graph/partitioning/xgraph_partitioner.py

Lines 213 to 225 in 0972aed

	largest_xp, largest_xp_size = "", 0
	for xp in sorted(par_2_names.keys()):
	if len(par_2_names[xp]) > largest_xp_size:
	largest_xp = xp
	largest_xp_size = len(par_2_names[xp])
	for xp in list(par_2_names.keys()):
	if xp != largest_xp:
	del par_2_names[xp]
	for xname, xp_lst in list(name_2_pars.items()):
	if xp_lst[0] != largest_xp:
	del name_2_pars[xname]

By removing this code, is is possible to generate a graph with multiple subgraphs, but then in the next stage (quantization), it doesn't work, since that code assumes that only one subgraph is present. There seems to be some code written that should be able to quantize a graph with multiple subgraphs, but that currently doesn't work either. (See base_subgraph_quantizer.py)

It would be nice if the pyXIR flow would support this use case. I'm currently looking into this and figuring out if I can get this to work.

[ssunrt]

Hi, is there any progress on this issue?