[I] [Bug] Inconsistent Transformation Results in TVM's DivToMul Optimization [tvm]

["Jupiterghy (via GitHub)" <gi...@apache.org>]

Jupiterghy opened a new issue, #16819:
URL: https://github.com/apache/tvm/issues/16819

   I have encountered an issue while using the relay.transform.DivToMul() optimization in TVM. I don’t know why there exists an inconsistency. I think the two methods should yield the same result while actually not.
   
   ### Expected behavior
   
   The modules after transformation should be structural equal.
   
   ### Actual behavior
   
   The two transformed modules (module and module_once) are not structurally equal.
   
   ### Environment
   
   - Operating System: Ubuntu 18.04.5
   - TVM version: 0.15.dev0
   - ONNX: 1.15.0
   
   ### Steps to reproduce
   1. Download the [ONNX model](https://github.com/Jupiterghy/onnx_model/blob/main/1.onnx)
   2. Execute the script:
   ```python
   import onnx
   import tvm
   from tvm import relay
   
   def apply_optimizations(module, opt, num_iterations=1):
       for _ in range(num_iterations):
           module = opt(module)
       return module
   
   
   if __name__ == "__main__":
       onnx_file = "model.onnx"
       onnx_model = onnx.load(onnx_file)
   
       shape_dict = {'v25_0': [1, 1, 1], 'v4_0': [12, 2, 45, 1, 1], 'v19_0': [12, 1, 45, 1, 1], 'v15_0': [12, 1, 45, 1, 1], 'v14_0': [12, 52, 45, 1, 1]}
   
       mod, params = relay.frontend.from_onnx(onnx_model, shape_dict, freeze_params=True)
   
       opt = relay.transform.DivToMul()
       module = opt(mod)
       module_once = apply_optimizations(mod, opt, num_iterations=1)
       assert tvm.ir.structural_equal(module, module_once)
   ```
   ### Triage
   
   * needs-triage
   


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