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. The Declarative builders in the EDSC library (Embedded Domain Specific Constructs) provide an API to make MLIR construction and manipulation very idiomatic, for the structured control-flow case, in C++.


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.


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

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

LoopBuilder and AffineLoopNestBuilder

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

  ScopedContext scope(f.get());
  Value i, j, lb(f->getArgument(0)), ub(f->getArgument(1));
  Value 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)),
          ((f7 + f13) / f7) % f13 - f7 * f13;
          ((i7 + i13) / i7) % i13 - i7 * i13;


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 = [{
    SmallVector<Value, 4> ivs(view_A.rank());
    IndexedValue A(arg_A), B(arg_B), C(arg_C);
      ivs, 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 scf.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.