[GitHub] [tvm] wzh99 commented on issue #11696: [Bug] `tanh` outputs `1.` for `nan` input at optimization level 4

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wzh99 commented on issue #11696:
URL: https://github.com/apache/tvm/issues/11696#issuecomment-1154691081

   @ganler Thanks for your reply. Your explanation is reasonable. I agree that fast math implementation does not always need to consider all kinds of invalid inputs.
   
   Actually I does not intentionally feed `nan` to `tanh` to discover this inconsistency. Instead, I generated a computation graph and feed it with valid inputs. The `nan`s appear in intermediate results. The test case shown above is a reduced version of what is generated by my fuzzer. I think that the `nan`s in intermediate values are not completely avoidable for arbitrary tensor programs. 
   
   Just now I figure out a quick fix of this inconsistency without introducing overhead. I replace the clamping code in [tvm/include/tvm/topi/elemwise.h](https://github.com/apache/tvm/blob/8341e33d05868b7bb8496c913679b7951836f3b9/include/tvm/topi/elemwise.h) with the following code:
   ```c++
   auto x = maximum(make_const(in->dtype, -9.0), minimum(make_const(in->dtype, 9.0), in));
   ```
   And the result at optimization level 4 is consistent with lower levels:
   ```
   opt_level=0 [nan]
   opt_level=1 [nan]
   opt_level=2 [nan]
   opt_level=3 [nan]
   opt_level=4 [nan]
   ```
   I just exchange the two arguments in `maximum` and `minimum`. The reason why this works is probably that `minimum(x, 9)` is implemented by `x <= 9 ? x : 9`. If `x` is `nan`, its comparison with any floating point is false. In this case `x <= 9 ? x : 9` evaluates to 9, which is a wrong clamp. In contrast, `9 <= x ? 9 : x` evaluates to `nan` and `x` is not clamped to a wrong value. 


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