Sometimes, ONNX exporter creates a constant subgraph to get a constant tensor, like this:
Actually, the shape tensor of the four Add nodes matter. If we pre-compute this subgraph, then we only need to keep one shape tensor instead of a constant subgraph. The constant folding of onnx_tool will replace these constant subgraphs to constant tensors.
python API
import onnx_tool
rawonnx='test.onnx'
m = onnx_tool.Model(rawonnx,constant_folding=True)
m.save_model('folded.onnx')cli
python -m onnx_tool -i test.onnx -m constant_folding -o folded.onnx
GraphProto Nodes Count:7689
Contant folding ['/Constant', '/Constant_1', '/Constant_2']... 3343 Nodes
Removed 3343 layers without any compute fusion.
ReproduceCode
GraphProto Nodes Count:1139
Contant folding ['Identity_0', 'Identity_1', 'Identity_2']... 1029 Nodes
Only 110 layers remained. A successful case.
ReproduceCode
Constant folding depends on onnx_tool.Node's value_infer implementation.
You may see this exception if your model is not supported for constant folding:
raise NotImplementedError(f'this Node {self.op_type}-{self.name} has no value_infer')You need to disable the constant folding flag, or add this op's value_infer implementation.