[GitHub] [incubator-tvm] DKXXXL commented on issue #4468: [RFC] Data-flow Analysis Functionality on TVM IR

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DKXXXL commented on issue #4468: [RFC] Data-flow Analysis Functionality on TVM 
IR
URL: https://github.com/apache/incubator-tvm/issues/4468#issuecomment-562559678
 
 
   Hi @junrushao1994 ,
       an over-simplified example from the industrial context is the following:
   ```python
   ...
   B0 = tvm.compute((m,n), lambda i,j: A0[i,j] + 2*A1[i,j], name = "B0")
   C0 = tvm.compute((m,n), lambda i,j: A0[i,j] + 2*A1[i,j], name = "C0")
   D0 = tvm.compute((m,n), lambda i,j: B0[i,j] + 3*C0[i,j], name = "D0")
   ...
   ```
   The customized TVM will schedule and use `compute_at` to the extreme, and 
transform into something like 
   ```cpp
   ...
   for (i, 0, m) {
   for (j, 0, n) {
           B0[0] = (A0[((i*stride) + (j*stride))] + (2f*A1[((i*stride) + 
(j*stride))]))
           C0[0] = (A0[((i*stride) + (j*stride))] + (2f*A1[((i*stride) + 
(j*stride))]))
           D0[((i*stride) + (j*stride))] = (B0[0] + (2f*C0[0]))
   }}
   ...
   ```
   
   This gives our 'incomplete' CSE and Copy Propagation a chance to make the C0 
assigned by B0 and replace C0’s appearance in D0 into B0 and make C0 dead (or 
not? dependent on the future).
   ```cpp
      ...
   for (i, 0, m) {
   for (j, 0, n) {
           B0[0] = (A0[((i*stride) + (j*stride))] + (2f*A1[((i*stride) + 
(j*stride))]))
           C0[0] = B0[0]
           D0[((i*stride) + (j*stride))] = (B0[0] + (2f*B0[0]))
   }}
      ...
   ```
   
   
   The above ‘incomplete’ CSE and Copy Propagation pass can do things safely in 
a straight-line code in a small range (without data-flow analysis), but the 
same thing did not happen for dead code elimination – if we don’t know any live 
information out of this for loop, we cannot just eliminate the assignment to 
C0[0].
   
   Generally speaking, dead code can arise after copy propagation and how dead 
code arises in TVM is similar to how they arise in LLVM and traditional 
compiler passes.

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