Function run_mlir_mode

src/jit/test/codegen.cpp:251–284

template <typename tag, int arity>
void run_mlir_mode(CompNode cn) {
    set_backend(Backend::MLIR);
    auto graph = ComputingGraph::make();
    auto opt = get_mode_opt(tag::mode);
    HostTensorGenerator<dtype::Float32, RandomDistribution::UNIFORM> gen(
            opt.low, opt.high);

    SmallVector<std::shared_ptr<HostTensorND>> hosts;
    VarNodeArray input_vars;
    for (int i = 0; i < arity; i++) {
        hosts.push_back(gen({2323, 4242}, cn));
        input_vars.push_back(opr::Host2DeviceCopy::make(*graph, hosts[i]).node());
    }

    auto y = opr::Elemwise::make(input_vars, tag::mode);

    auto ig_gen = std::make_unique<InternalGraphGenerator>(y.node()->owner_opr());

    for (auto i : get_rev_topo_order(y)) {
        if (!i->template same_type<opr::Host2DeviceCopy>()) {
            ig_gen->add_opr(i);
        }
    }

    auto igraph = ig_gen->generate();
    auto y_jit = JITExecutor::make(igraph, ig_gen->orig_inps());

    HostTensorND host_y, host_y_jit;
    auto func = graph->compile(
            {make_callback_copy(y, host_y), make_callback_copy(y_jit, host_y_jit)});
    func->execute();

    MGB_ASSERT_TENSOR_NEAR(host_y, host_y_jit, opt.maxerr);
}
#endif
}  // anonymous namespace