mlir - Intermediate representation language

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mlir is an intermediate representation language created in 2019 by Chris Lattner.

#255on PLDB

5Years Old

84Repos

Download source code:

git clone https://github.com/tensorflow/mlir

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"Multi-Level Intermediate Representation" Compiler Infrastructure

Tags: intermediate representation language
mlir is developed on GitHub and has 1,728 stars
There are at least 84 mlir repos on GitHub
Early development of mlir happened in https://github.com/tensorflow
mlir is written in Markdown
The Google BigQuery Public Dataset GitHub snapshot shows 27 users using mlir in 30 repos on GitHub
First announcement of mlir
Read more about mlir on the web: 1.
1 PLDB concepts link to mlir: Mojo

Example from the web:

// Syntactically similar to LLVM:
func @testFunction(%arg0: i32) {
  %x = call @thingToCall(%arg0) : (i32) -> i32
  br ^bb1
^bb1:
  %y = addi %x, %x : i32
  return %y : i32
}

Example from Compiler Explorer:

// Example code of an affine reduction.
// MLIR example code may not always work out of the box because the textual MLIR format is not stable.
// The example tries to be compatible with the latest MLIR version, which may not work on previous versions.

func @affine_parallel_with_reductions_i64(%arg0: memref<3x3xi64>, %arg1: memref<3x3xi64>) -> (i64, i64) {
  %0:2 = affine.parallel (%kx, %ky) = (0, 0) to (2, 2) reduce ("addi", "muli") -> (i64, i64) {
            %1 = affine.load %arg0[%kx, %ky] : memref<3x3xi64>
            %2 = affine.load %arg1[%kx, %ky] : memref<3x3xi64>
            %3 = arith.muli %1, %2 : i64
            %4 = arith.addi %1, %2 : i64
            affine.yield %3, %4 : i64, i64
          }
  return %0#0, %0#1 : i64, i64
}

Language features

Feature	Supported	Example	Token
Comments	✓	// A comment
Line Comments	✓	// A comment	//
Semantic Indentation	X