soiferj edited a comment on issue #4879: [Relay][Pass] Fix bug in re-processing 
call node in MergeComposite pass
URL: https://github.com/apache/incubator-tvm/pull/4879#issuecomment-586527376
 
 
   This is actually a good exercise, as some other tests are actually failing 
now due to the graphs not being the same. For example, `test_branch_merge`. 
@mbarrett97, I'll push the changes, let me know what you think.
   
   Expected:
   ```
   fn (%a: Tensor[(10, 10), float32], %b: Tensor[(10, 10), float32], %c: 
Tensor[(10, 10), float32]) -> Tensor[(10, 10), float32] {
     %2 = fn (%in_1: Tensor[(10, 10), float32], %in_2: Tensor[(10, 10), 
float32], Composite="add_sub_mul", Primitive=1) -> Tensor[(10, 10), float32] {
       %0 = add(%in_1, %in_2) /* ty=Tensor[(10, 10), float32] */;
       %1 = subtract(%in_1, %in_2) /* ty=Tensor[(10, 10), float32] */;
       multiply(%0, %1) /* ty=Tensor[(10, 10), float32] */
     };
     %3 = %2(%a, %b) /* ty=Tensor[(10, 10), float32] */;
     %4 = %2(%c, %3) /* ty=Tensor[(10, 10), float32] */;
     nn.relu(%4) /* ty=Tensor[(10, 10), float32] */
   }
   ```
   
   Result:
   ```
   fn (%a: Tensor[(10, 10), float32], %b: Tensor[(10, 10), float32], %c: 
Tensor[(10, 10), float32]) -> Tensor[(10, 10), float32] {
     %2 = fn (%x: Tensor[(10, 10), float32], %y: Tensor[(10, 10), float32], 
Primitive=1, Composite="add_sub_mul") -> Tensor[(10, 10), float32] {
       %0 = add(%x, %y) /* ty=Tensor[(10, 10), float32] */;
       %1 = subtract(%x, %y) /* ty=Tensor[(10, 10), float32] */;
       multiply(%0, %1) /* ty=Tensor[(10, 10), float32] */
     };
     %3 = %2(%a, %b) /* ty=Tensor[(10, 10), float32] */;
     %6 = fn (%x1: Tensor[(10, 10), float32], %y1: Tensor[(10, 10), float32], 
Primitive=1, Composite="add_sub_mul") -> Tensor[(10, 10), float32] {
       %4 = add(%x1, %y1) /* ty=Tensor[(10, 10), float32] */;
       %5 = subtract(%x1, %y1) /* ty=Tensor[(10, 10), float32] */;
       multiply(%4, %5) /* ty=Tensor[(10, 10), float32] */
     };
     %7 = %6(%c, %3) /* ty=Tensor[(10, 10), float32] */;
     nn.relu(%7) /* ty=Tensor[(10, 10), float32] */
   }
   ```
   
   +1 that we should confirm which order is expected for alpha_equals.

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