Extractor dfs performance

[kylesayrs]

Description

This change modifies the DFS search used in model extraction to use sets rather than iterables

Motivation and Context

While shape inference is the most significant bottleneck, these changes are a step in the direction of being able to support model extraction for very large graphs.

Test Script

import onnx
from onnx.utils import Extractor

model = onnx.load("obertquant.onnx")
extractor = Extractor(model)
extracted_model = extractor.extract_model(
    input_names=["input_ids", "attention_mask", "token_type_ids"], output_names=["2058"]
)
onnx.save(extracted_model, "truncated.onnx")

Benchmarks were produced using pyinstrument and analyzing the Extractor.extract_model function

Model Name	Num Nodes	Previous	New
obertquant.onnx	1271	0.158s	0.110s
ai-town-3B.onnx	3515	8.002s	3.725s

[kylesayrs]

Related: onnx/onnx#6213