# RUN: %PYTHON %s | FileCheck %s from mlir.ir import * from mlir.dialects import builtin from mlir.dialects import func from mlir.dialects import linalg from mlir.dialects.linalg.opdsl.lang import * T1 = TV.T1 T2 = TV.T2 @linalg_structured_op def fill_poly(value=ScalarDef(T1), O=TensorDef(U, output=True)): O[None] = TypeFn.cast_signed(U, value) @linalg_structured_op def fill_rank_zero_poly(I=TensorDef(T1), O=TensorDef(U, output=True)): O[None] = TypeFn.cast_signed(U, I[None]) with Context() as ctx, Location.unknown(): module = Module.create() f32 = F32Type.get() with InsertionPoint(module.body): # Fill indexing maps. # CHECK-DAG: #[[$MAP0:.+]] = affine_map<() -> ()> # CHECK-DAG: #[[$MAP1:.+]] = affine_map<(d0, d1) -> ()> # CHECK-DAG: #[[$MAP2:.+]] = affine_map<(d0, d1) -> (d0, d1)> # CHECK-DAG: #[[$MAP3:.+]] = affine_map<(d0, d1, d2) -> ()> # CHECK-DAG: #[[$MAP4:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)> # CHECK-LABEL: @test_fill_0d # CHECK: linalg.generic # CHECK-SAME: indexing_maps = [#[[$MAP0]], #[[$MAP0]] # CHECK-SAME: iterator_types = [] @func.FuncOp.from_py_func(f32, RankedTensorType.get([], f32)) def test_fill_0d(value, init_result): return fill_poly(value, outs=[init_result]) # CHECK-LABEL: @test_fill_2d # CHECK: linalg.generic # CHECK-SAME: indexing_maps = [#[[$MAP1]], #[[$MAP2]]] # CHECK-SAME: iterator_types = ["parallel", "parallel"] @func.FuncOp.from_py_func(f32, RankedTensorType.get([4, 16], f32)) def test_fill_2d(value, init_result): return fill_poly(value, outs=[init_result]) # CHECK-LABEL: @test_fill_rank_zero_3d # CHECK: linalg.generic # CHECK-SAME: indexing_maps = [#[[$MAP3]], #[[$MAP4]]] # CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"] @func.FuncOp.from_py_func( RankedTensorType.get([], f32), RankedTensorType.get([4, 8, 16], f32)) def test_fill_rank_zero_3d(input, init_result): return fill_rank_zero_poly(input, outs=[init_result]) print(module)