PDL presents a high level abstraction for the rewrite pattern infrastructure available in MLIR. This abstraction allows for representing patterns transforming MLIR, as MLIR. This allows for applying all of the benefits that the general MLIR infrastructure provides, to the infrastructure itself. This means that pattern matching can be more easily verified for correctness, targeted by frontends, and optimized.
PDL abstracts over various different aspects of patterns and core MLIR data structures. Patterns are specified via a pdl.pattern operation. These operations contain a region body for the "matcher" code, and terminate with a pdl.rewrite that either dispatches to an external rewriter or contains a region for the rewrite specified via pdl. The types of values in pdl are handle types to MLIR C++ types, with !pdl.attribute, !pdl.operation, and !pdl.type directly mapping to mlir::Attribute, mlir::Operation*, and mlir::Value respectively.
An example pattern is shown below:
mlir // pdl.pattern contains metadata similarly to a `RewritePattern`. pdl.pattern : benefit(1) { // External input operand values are specified via `pdl.input` operations. // Result types are constrainted via `pdl.type` operations. %resultType = pdl.type %inputOperand = pdl.input %root, %results = pdl.operation "foo.op"(%inputOperand) -> %resultType pdl.rewrite(%root) { pdl.replace %root with (%inputOperand) } }
This is a culmination of the work originally discussed here: https://groups.google.com/a/tensorflow.org/g/mlir/c/j_bn74ByxlQ
Depends On D84577
The lint check is also the source of the build failure