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# 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)
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