[Bug] Segmentation Fault when Running Relay Transformations Iteratively

[Jupiterghy]

Executing a sequence of Relay transformations iteratively using a custom script results in a segmentation fault (Segmentation fault (core dumped)). This issue seems to be related to the quantity of transformations applied rather than the specific optimizations in the optimizations array.

Actual behavior

Segmentation fault (core dumped)

Environment

• Operating System: Ubuntu 18.04.5
• TVM version: 0.15.dev0
• ONNX: 1.15.0

Steps to reproduce

1. Download the ONNX model
2. Execute the script:

import onnx
import tvm
from tvm import relay

def mr1(mod):
    optimizations = [tvm.relay.transform.CombineParallelBatchMatmul(), tvm.relay.transform.CombineParallelConv2D(), tvm.relay.transform.CombineParallelDense(), tvm.relay.transform.DeadCodeElimination(), tvm.relay.transform.DefuseOps(), tvm.relay.transform.DivToMul(), tvm.relay.transform.DynamicToStatic(), tvm.relay.transform.EliminateCommonSubexpr(), tvm.relay.transform.EtaExpand(), tvm.relay.transform.FakeQuantizationToInteger(), tvm.relay.transform.FastMath(), tvm.relay.transform.FirstOrderGradient(), tvm.relay.transform.FlattenAtrousConv(), tvm.relay.transform.FoldConstant(), tvm.relay.transform.FoldExplicitPadding(), tvm.relay.transform.FoldScaleAxis(), tvm.relay.transform.ForwardFoldScaleAxis(), tvm.relay.transform.InferType(), tvm.relay.transform.Inline(), tvm.relay.transform.LambdaLift(), tvm.relay.transform.LazyGradientInit(), tvm.relay.transform.ManifestLifetimes(), tvm.relay.transform.MergeCompilerRegions(), tvm.relay.transform.PartialEvaluate(), tvm.relay.transform.PartitionGraph(), tvm.relay.transform.RemoveUnusedFunctions(), tvm.relay.transform.SimplifyExpr(), tvm.relay.transform.SimplifyInference()]
    for i in range(len(optimizations)):
        for j in range(i + 1, len(optimizations)):
            opt1 = optimizations[i]
            opt2 = optimizations[j]

            try:
                module = mod
                module = tvm.relay.transform.InferType()(module)
                module_once = opt1(opt2(module))
            except Exception as e:
                continue

if __name__ == "__main__":
    model_file = 'model.onnx'
    onnx_model = onnx.load(model_file)
    shape_dict = {'v10_0': [], 'v9_0': []}
    mod, params = relay.frontend.from_onnx(onnx_model, shape_dict, freeze_params=True)
    mr1(mod)

Triage

• needs-triage