pytorch/benchmarks/cpp
Mikhail Zolotukhin b01a15d3d3 [TensorExpr] Redesign Rfactor loopnest transformation. (#55324)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55324

With this change `rfactor` only affects the passed loop and its body
never touching anything outside (that was a rootcause of a bug with the
previous implementation). Also, we don't have an `insertion_point`
parameter anymore - its meaning was vague, and the effect of it
should've been achievable with other transformations anyway.

The new `rfactor` semantics is as follows:

```
Requirements:
 * S is the reduction store
 * S is the only statement in the innermost loop
 * There is at least two reduction arguments in S
 * OUTER_REDUCTION_FOR loop corresponds to the outermost reduction variable
 used in the store and all other reduction variables are index variables of
 children loops of OUTER_REDUCTION_FOR
 * OUTER_REDUCTION_FOR is a perfect loop nest, i.e. it has only loops
 corresponding to the other reduction variables and the store, nested into
 each other

What it does:
  * Introduce a new buffer with an extra dimension of a size equal to the
  span of the loop OUTER_REDUCTION_FOR (the new buffer is returned via
  RFAC_BUF_PTR)
  * Insert an initialization store for the new buffer in
  OUTER_REDUCTION_FOR before its nested loop
  * Replace the reduction store to the original buffer with the reduction
  store to the temp buffer, removing the index var of OUTER_REDUCTION_FOR
  from reduction arguments
  * Insert a final reduction store over the extra dimension of the new
  buffer to the original buffer
  * Returns TRUE if the transformation succeeded and FALSE otherwise

Example:
Original IR:
S1: for i        # normal axis
S2:   X[i] = 0
S3:   for j      # reduction axis
S4:     for k    # reduction axis
S5:       X[i] = ReduceOp(X[i] + Y[i,j,k], reduce_axis={j,k})

After RFACTOR(S5, S3)
S1: for i               # normal axis
S2:   X[i] = 0
S3:   for j             # reduction axis for X, normal axis for X_rfac
        X_rfac[i,j] = 0
S4:     for k           # reduction axis
          X_rfac[i,j] = ReduceOp(X_rfac[i,j] + Y[i,j,k], reduce_axis={k})
        X[i] = ReduceOp(X[i] + X_rfac[i,j], reduce_axis={j})
```

Differential Revision: D27694960

Test Plan: Imported from OSS

Reviewed By: navahgar

Pulled By: ZolotukhinM

fbshipit-source-id: 076fa6a1df2c23f5948302aa6b43e82cb222901c
2021-04-13 12:08:48 -07:00
..
tensorexpr [TensorExpr] Redesign Rfactor loopnest transformation. (#55324) 2021-04-13 12:08:48 -07:00