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>;
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)), 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.
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); AffineLoopNestBuilder( 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.