[GitHub] [tvm] YuhengHuang42 commented on issue #7563: Accuracy drop when use batch and opt_level=3

[GitBox <gi...@apache.org>]

YuhengHuang42 commented on issue #7563:
URL: https://github.com/apache/tvm/issues/7563#issuecomment-811245830


   Hi, I'm interested in this bug and did some experiments. Here are some findings:
   
   1. If you do the FuseOps transform first on the relay graph, i.e.
   
   ```
   seq = tvm.transform.Sequential(
           [
               transform.SimplifyInference(),
               transform.FuseOps()
           ]
       )   
   with tvm.transform.PassContext(opt_level=opt_level):
       mod = seq(mod)
   # build the model
   # ...
   ```
   
   Then the final result is correct.
   
   2. If you use opt_level=4 to build the model, then the final result is also correct:
   
   ```
   opt_level = 4
   with tvm.transform.PassContext(opt_level=opt_level):
       lib = relay.build(mod, target='llvm', params=param)
   ```
   
   This seems pretty weird to me. So I disable some passes here, trying to dig deeper.
   
   ```
   disabled_pass = ["CombineParallelConv2D", "CombineParallelDense", "CombineParallelBatchMatmul", "FastMath"]
   opt_level = 4
   with tvm.transform.PassContext(opt_level=opt_level, disabled_pass=disabled_pass):
       lib = relay.build(mod, target='llvm', params=param)
   ```
   
   As far as I know, these four passes are the only ones that are enabled by default. However, disable these passes doesn't influence the final result: the result is still correct.
   
   My environment:
   
   Build from source at 2988a08e3ff4a8956ac9b23e662374f6d8f7f4d9,
   
   OS: macOS 10.15.7
   
   As I'm new to TVM, I'm stuck here and can't dig deeper at present. Hope these info can help you find the root cause of the bug.


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