MLIR

Multi-Level IR Compiler Framework

Background: declarative builders API

The main purpose of the declarative builders API is to provide an intuitive way of constructing MLIR programmatically. In the majority of cases, the IR we wish to construct exhibits structured control-flow. Declarative builders provide an API to make MLIR construction and manipulation very idiomatic, for the structured control-flow case, in C++.

ScopedContext

mlir::edsc::ScopedContext provides an implicit thread-local context, supporting a simple declarative API with globally accessible builders. These declarative builders are available within the lifetime of a ScopedContext.

ValueHandle and IndexHandle

mlir::edsc::ValueHandle and mlir::edsc::IndexHandle provide typed abstractions around an mlir::Value. These abstractions are “delayed”, in the sense that they allow separating declaration from definition. They may capture IR snippets, as they are built, for programmatic manipulation. Intuitive operators are provided to allow concise and idiomatic expressions.

ValueHandle zero = std_constant_index(0);
IndexHandle i, j, k;

Intrinsics

mlir::edsc::ValueBuilder is a generic wrapper for the mlir::Builder::create method that operates on ValueHandle objects and return a single ValueHandle. For instructions that return no values or that return multiple values, the mlir::edsc::InstructionBuilder can be used. Named intrinsics are provided as syntactic sugar to further reduce boilerplate.

using load = ValueBuilder<LoadOp>;
using store = InstructionBuilder<StoreOp>;

LoopBuilder and AffineLoopNestBuilder

mlir::edsc::AffineLoopNestBuilder provides an interface to allow writing concise and structured loop nests.

  ScopedContext scope(f.get());
  ValueHandle i(indexType),
              j(indexType),
              lb(f->getArgument(0)),
              ub(f->getArgument(1));
  ValueHandle f7(std_constant_float(llvm::APFloat(7.0f), f32Type)),
              f13(std_constant_float(llvm::APFloat(13.0f), f32Type)),
              i7(constant_int(7, 32)),
              i13(constant_int(13, 32));
  AffineLoopNestBuilder(&i, lb, ub, 3)([&]{
      lb * index_type(3) + ub;
      lb + index_type(3);
      AffineLoopNestBuilder(&j, lb, ub, 2)([&]{
          ceilDiv(index_type(31) * floorDiv(i + j * index_type(3), index_type(32)),
                  index_type(32));
          ((f7 + f13) / f7) % f13 - f7 * f13;
          ((i7 + i13) / i7) % i13 - i7 * i13;
      });
  });

IndexedValue

mlir::edsc::IndexedValue provides an index notation around load and store operations on abstract data types by overloading the C++ assignment and parenthesis operators. The relevant loads and stores are emitted as appropriate.

Putting it all together

With declarative builders, it becomes fairly concise to build rank and type-agnostic custom operations even though MLIR does not yet have generic types. Here is what a definition of a general pointwise add looks in Tablegen with declarative builders.

def AddOp : Op<"x.add">,
    Arguments<(ins Tensor:$A, Tensor:$B)>,
    Results<(outs Tensor: $C)> {
  code referenceImplementation = [{
    auto ivs = makeIndexHandles(view_A.rank());
    auto pivs = makePIndexHandles(ivs);
    IndexedValue A(arg_A), B(arg_B), C(arg_C);
    AffineLoopNestBuilder(pivs, view_A.getLbs(), view_A.getUbs(), view_A.getSteps())(
      [&]{
        C(ivs) = A(ivs) + B(ivs)
      });
  }];
}

Depending on the function signature on which this emitter is called, the generated IR resembles the following, for a 4-D memref of vector<4xi8>:

// CHECK-LABEL: func @t1(%lhs: memref<3x4x5x6xvector<4xi8>>, %rhs: memref<3x4x5x6xvector<4xi8>>, %result: memref<3x4x5x6xvector<4xi8>>) -> () {
//       CHECK: affine.for {{.*}} = 0 to 3 {
//       CHECK:   affine.for {{.*}} = 0 to 4 {
//       CHECK:     affine.for {{.*}} = 0 to 5 {
//       CHECK:       affine.for {{.*}}= 0 to 6 {
//       CHECK:         {{.*}} = load %arg1[{{.*}}] : memref<3x4x5x6xvector<4xi8>>
//       CHECK:         {{.*}} = load %arg0[{{.*}}] : memref<3x4x5x6xvector<4xi8>>
//       CHECK:         {{.*}} = addi {{.*}} : vector<4xi8>
//       CHECK:         store {{.*}}, %arg2[{{.*}}] : memref<3x4x5x6xvector<4xi8>>

or the following, for a 0-D memref<f32>:

// CHECK-LABEL: func @t3(%lhs: memref<f32>, %rhs: memref<f32>, %result: memref<f32>) -> () {
//       CHECK: {{.*}} = load %arg1[] : memref<f32>
//       CHECK: {{.*}} = load %arg0[] : memref<f32>
//       CHECK: {{.*}} = addf {{.*}}, {{.*}} : f32
//       CHECK: store {{.*}}, %arg2[] : memref<f32>

Similar APIs are provided to emit the lower-level loop.for op with LoopNestBuilder. See the builder-api-test.cpp test for more usage examples.

Since the implementation of declarative builders is in C++, it is also available to program the IR with an embedded-DSL flavor directly integrated in MLIR. We make use of these properties in the tutorial.

Spoiler: MLIR also provides Python bindings for these builders, and a full-fledged Python machine learning DSL with automatic differentiation targeting MLIR was built as an early research collaboration.