torch-mlir/frontends/pytorch/test/graph_import/if.py

36 lines
1.1 KiB
Python

# -*- Python -*-
# This file is licensed under a pytorch-style license
# See frontends/pytorch/LICENSE for license information.
import torch
import torch_mlir
# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
mb = torch_mlir.ModuleBuilder()
# CHECK-LABEL: @f(
# CHECK-SAME: %[[B:.*]]: !basicpy.BoolType,
# CHECK-SAME: %[[I:.*]]: i64) -> i64 {
@mb.import_function
@torch.jit.script
def f(b: bool, i: int):
# CHECK: %[[I1:.*]] = basicpy.bool_cast %[[B]] : !basicpy.BoolType -> i1
# CHECK: %[[RES:.*]] = scf.if %[[I1]] -> (i64) {
# CHECK: %[[ADD:.*]] = torch.kernel_call "aten::add" %[[I]], %[[I]]
# CHECK: scf.yield %[[ADD]] : i64
# CHECK: } else {
# CHECK: %[[MUL:.*]] = torch.kernel_call "aten::mul" %[[I]], %[[I]]
# CHECK: scf.yield %[[MUL]] : i64
# CHECK: }
# CHECK: return %[[RES:.*]] : i64
if b:
return i + i
else:
return i * i
# elif is modeled as a nested if, so no need to specially test it here.
assert isinstance(f, torch.jit.ScriptFunction)
mb.module.operation.print()
print()