llvm-project/mlir/g3doc/Canonicalization.md

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Operation Canonicalization in MLIR

Canonicalization is an important part of compiler IR design - it makes it easier to implement reliable compiler transformations, be able to reason about what is better or worse in the code, and forces interesting discussions about what the goals of a particular level of IR are interested in. Dan Gohman wrote an article exploring these issues, it is worth reading if you're not familiar with these concepts.

Most compilers have canonicalization passes, and sometimes they have many different ones (e.g. instcombine, dag combine, etc in LLVM). Because MLIR is a multi-level IR, we can provide a single canonicalization infrastructure and reuse it across many different IRs that it represents. This document describes the general approach, global canonicalizations performed, and provides sections to capture IR specific rules for reference.

General Design

MLIR has a single canonicalization pass, which iteratively applies canonicalization transformations in a greedy way until the IR converges. These transformations are defined by the operations themselves, which allows each dialect to define its own set of operations and canonicalizations together.

Some important things to think about w.r.t. canonicalization patterns:

  • Repeated applications of patterns should converge. Unstable or cyclic rewrites will cause infinite loops in the canonicalizer.

  • It is generally better to canonicalize towards operations that have fewer uses of a value when the operands are duplicated, because some patterns only match when a value has a single user. For example, it is generally good to canonicalize "x + x" into "x * 2", because this reduces the number of uses of x by one.

  • It is always good to eliminate operations entirely when possible, e.g. by folding known identities (like "x + 0 = x").

Globally Applied Rules

These transformation are applied to all levels of IR:

  • Elimination of operations that have no side effects and have no uses.

  • Constant folding - e.g. "(addi 1, 2)" to "3". Constand folding hooks are specified by operations.

  • Move constant operands to commutative binary operators to the right side - e.g. "(addi 4, x)" to "(addi x, 4)".

Builtin Ops Canonicalizations

These transformations are applied to builtin ops:

  • constant ops are uniqued and hoisted into the entry block of a function.
  • (TODO) Merge affine.apply operations that directly feed each other.

Standard Ops Canonicalizations

  • Shape folding of alloc operations to turn dynamic dimensions into static ones.
  • Folding memref_cast operations into users where possible.