mlir/test/python/dialects/linalg/opdsl/emit_fill.py

# 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)