fusion.cpp 57.5 KB
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/**
 * \file src/jit/test/fusion.cpp
 * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
 *
 * Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 */

#include "./helper.h"

#include "megbrain_build_config.h"

#include "megbrain/gopt/framework.h"
#include "megbrain/gopt/misc.h"
#include "megbrain/graph/cg.h"
#include "megbrain/jit/ast_c.h"
#include "megbrain/jit/executor_opr.h"
#include "megbrain/jit/fusion_pass.h"
#include "megbrain/opr/basic_arith_wrapper.h"
#include "megbrain/opr/blas.h"
#include "megbrain/opr/tensor_manip.h"
#include "megbrain/opr/utility.h"
#include "megbrain/test/autocheck.h"
#include "megbrain/test/helper.h"
#include "megbrain/opr/dnn/convolution.h"

#if MGB_JIT

using namespace mgb;
using namespace jit;

#define FOREACH_CASE(cb)                                                       \
    cb(basic) cb(shape_change) cb(large_num_inps) cb(simple_exp)               \
    cb(complex_exp) cb(exp_pow) cb(cache) cb(all_oprs)                         \
    cb(expand_jit_executor) cb(multi_device) cb(multi_shape)                   \
    cb(non_contig) cb(visit_complexity) cb(imm_scalar)                         \
    cb(jit_grad) cb(concat_input) cb(special_graph_input)

namespace {
#define def_tag(x) \
    struct x {};
FOREACH_CASE(def_tag)
#undef def_tag

#define t(n) n,
using test_types = ::testing::Types<FOREACH_CASE(t) void>;
#undef t

template <typename tag>
void run(Backend backend, CompNode cn);

template <typename T>
size_t find_opr_num(SymbolVar endpoint) {
    size_t opr_num = 0;
    auto cb = [&opr_num](cg::OperatorNodeBase* opr) {
        if (opr->same_type<T>()) {
            opr_num++;
        }
    };
    cg::DepOprIter{cb}.add(endpoint.node()->owner_opr());
    return opr_num;
}

template <typename T>
SmallVector<T*> find_oprs(SymbolVar endpoint) {
    SmallVector<T*> res;
    auto cb = [&res](cg::OperatorNodeBase* opr) {
        if (opr->same_type<T>()) {
            auto ptr = &(opr->cast_final_safe<T>());
            res.push_back(ptr);
        }
    };
    cg::DepOprIter{cb}.add(endpoint.node()->owner_opr());
    return res;
}

template <typename T>
SmallVector<T*> find_oprs(cg::AsyncExecutable& func) {
    SmallVector<T*> res;
    auto cb = [&res](cg::OperatorNodeBase* opr) {
        if (opr->same_type<T>()) {
            auto ptr = &(opr->cast_final_safe<T>());
            res.push_back(ptr);
        }
        return true;
    };
    func.iter_opr_seq(cb);
    return res;
}

//! make a pair of functions with and without JIT optimization
std::pair<std::unique_ptr<cg::AsyncExecutable>,
          std::unique_ptr<cg::AsyncExecutable>>
make_func_pair(HostTensorND& dst0, HostTensorND& dst1,
               thin_function<SymbolVar(ComputingGraph&)> make_dst,
               uint8_t jit_level) {
    auto g0 = ComputingGraph::make();
    g0->options().graph_opt_level = 0;
    auto f0 = g0->compile({make_callback_copy(make_dst(*g0), dst0)});

    auto g1 = ComputingGraph::make();
    g1->options().graph_opt_level = 3;
    g1->options().graph_opt.jit = jit_level;
    auto f1 = g1->compile({make_callback_copy(make_dst(*g1), dst1)});

    EXPECT_FALSE(find_oprs<JITExecutor>(*f1).empty());
    return {std::move(f0), std::move(f1)};
}

template <>
void run<void>(Backend, CompNode) {}

template <>
void run<basic>(Backend backend, CompNode cn) {
    set_backend(backend);

    HostTensorGenerator<> gen;
    auto host_x0 = gen({3, 3}, cn), host_x1 = gen({3, 1}, cn),
         host_x2 = gen({1, 1}, cn), host_x3 = gen({3, 1}, cn);
    auto make_dst = [&](ComputingGraph& graph) {
        auto a = opr::Host2DeviceCopy::make(graph, host_x0),
             b = opr::Host2DeviceCopy::make(graph, host_x1),
             c = opr::Host2DeviceCopy::make(graph, host_x2),
             d = opr::Host2DeviceCopy::make(graph, host_x3);
        return a * b + c * a + d + d + d;
    };
    HostTensorND host_z1, host_z2;
    auto funcs = make_func_pair(host_z1, host_z2, make_dst, 2);
    funcs.first->execute();
    funcs.second->execute();
    MGB_ASSERT_TENSOR_EQ(host_z1, host_z2);
    auto jits = find_oprs<JITExecutor>(*funcs.second);
    ASSERT_EQ(2u, jits.size());
    // only one broadcast is allowed in JIT fusion
    ASSERT_EQ(1u, jits[0]->input().size());
    ASSERT_EQ(4u, jits[1]->input().size());
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    //! check memfwd
    ASSERT_EQ(prev_dev_ptr(jits[0]->input(0)),
              prev_dev_ptr(jits[0]->output(0)));
    ASSERT_EQ(prev_dev_ptr(jits[1]->input(0)),
              prev_dev_ptr(jits[1]->output(0)));
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}

template <>
void run<shape_change>(Backend backend, CompNode cn) {
    set_backend(backend);

    HostTensorGenerator<> gen;
    auto host_x0 = gen({3, 3}, cn), host_x1 = gen({3, 1}, cn),
         host_x2 = gen({1, 1}, cn), host_x3 = gen({1, 3}, cn);

    auto run_gen = [&](size_t n, bool dim = false, bool swap = false) {
        if (dim) {
            host_x0->copy_from(*gen({n, n, 3}, cn));
            host_x1->copy_from(*gen({n, 1, 1}, cn));
            host_x2->copy_from(*gen({1, 1, 3}, cn));
            host_x3->copy_from(*gen({1, n, 1}, cn));
        } else {
            host_x0->copy_from(*gen({n, n}, cn));
            host_x1->copy_from(*gen({n, 1}, cn));
            host_x2->copy_from(*gen({1, 1}, cn));
            host_x3->copy_from(*gen({1, n}, cn));
        }
        if (swap) {
            std::swap(*host_x1, *host_x3);
        }
    };

    using JITOprArr = std::array<JITExecutor*, 2>;
    auto make_func = [&](HostTensorND& out, JITOprArr* jit) {
        auto graph = ComputingGraph::make();
        graph->options().graph_opt_level = 0;
        auto a = opr::Host2DeviceCopy::make(*graph, host_x0),
             b = opr::Host2DeviceCopy::make(*graph, host_x1),
             c = opr::Host2DeviceCopy::make(*graph, host_x2),
             d = opr::Host2DeviceCopy::make(*graph, host_x3);

        auto y = opr::abs(a) * (b + c) * d - (b + c) * c * b;
        if (jit) {
            graph->options().graph_opt_level = 3;
        }
        auto func = graph->compile({make_callback_copy(y, out)});
        if (jit) {
            unpack_vector(find_oprs<JITExecutor>(*func), (*jit)[0], (*jit)[1]);
        }
        return func;
    };
    JITOprArr jits;
    HostTensorND host_y1, host_y2;
    auto func1 = make_func(host_y1, nullptr), func2 = make_func(host_y2, &jits);

    auto run = [&]() -> std::array<Executable*, 2> {
        func1->execute();
        func2->execute();
        auto chk = [&]() { MGB_ASSERT_TENSOR_EQ(host_y1, host_y2); };
        chk();
        return {jits[0]->executable(), jits[1]->executable()};
    };

    auto exe_shp3 = run();

    {
        run_gen(5);
        auto exe_shp5 = run();
        if (backend == Backend::HALIDE) {
            ASSERT_NE(exe_shp3, exe_shp5);
        } else {
            ASSERT_EQ(exe_shp3, exe_shp5);
        }
    }

    // change ndim
    run_gen(3, true);
    ASSERT_NE(exe_shp3, run());

    // change bcast pattern
    {
        run_gen(3, false, true);
        auto exe_chg = run();
        if (backend == Backend::HALIDE) {
            ASSERT_NE(exe_shp3, exe_chg);
        } else {
            ASSERT_EQ(exe_shp3, exe_chg);
        }
    }

    run_gen(3);
    ASSERT_EQ(exe_shp3, run());
}

template <>
void run<large_num_inps>(Backend backend, CompNode cn) {
    set_backend(backend);

    HostTensorGenerator<> gen;
    int inp_nr = 120;
    std::vector<std::shared_ptr<HostTensorND>> host_xs;
    for (int i = 0; i < inp_nr; i++)
        host_xs.push_back(gen({4, 3, 2, 1}, cn));

    auto make_dst = [&](ComputingGraph& graph) {
        std::vector<SymbolVar> dev_xs;
        for (int i = 0; i < inp_nr; i++)
            dev_xs.push_back(opr::Host2DeviceCopy::make(graph, host_xs[i]));

        auto y = dev_xs[0] + dev_xs[1];
        for (int i = 2; i < inp_nr; i++)
            y = y + dev_xs[i];
        return y;
    };
    HostTensorND host_y1, host_y2;
    auto funcs = make_func_pair(host_y1, host_y2, make_dst, 2);
    funcs.first->execute();
    funcs.second->execute();
    MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);

    ASSERT_GT(find_oprs<JITExecutor>(*funcs.second).size(), 1u);
}

template <>
void run<concat_input>(Backend backend, CompNode cn) {
    set_backend(backend);
    FusionChecker checker{
            4,
            [](const SymbolVarArray& inp) -> SymbolVar {
                auto spl = opr::Split::make(
                        inp[0],
                        opr::Split::Options::make_partition(inp[0], 1, {1, 1}));
                return spl[1] * inp[1] + inp[2] * spl[1] + inp[3] + inp[3];
            },
            cn};
    checker.disable_opr_type_check().run({TensorShape{3, 2}, {3, 1}, {3, 1}, {3, 1}});
}

template <>
void run<simple_exp>(Backend backend, CompNode cn) {
    set_backend(backend);

    FusionChecker checker{2,
                          [](const SymbolVarArray& inp) -> SymbolVar {
                              return inp[0] + inp[1];
                          },
                          cn};
    checker.enable_direct_build().run({TensorShape{3, 3}, {3, 3}});
}

template <>
void run<jit_grad>(Backend backend, CompNode cn) {
    set_backend(backend);

    FusionChecker checker{
            1,
            [](const SymbolVarArray& inp) -> SymbolVar { return inp[0] + 1; },
            cn};
    checker.enable_direct_build().run({TensorShape{3, 1}});
}

template <>
void run<exp_pow>(Backend backend, CompNode cn) {
    set_backend(backend);

    FusionChecker checker{
            3,
            [](const SymbolVarArray& inp) -> SymbolVar {
                auto iabs = opr::abs(inp[0]) + .23f;
                return opr::exp(inp[0]) + opr::exp(inp[1]) -
                       opr::exp(inp[2]) * opr::pow(opr::abs(inp[1]) + 0.2f,
                                                   opr::abs(inp[2]) + 0.1f) +
                       opr::powf(inp[0], 2) - opr::powf(inp[0], -3) +
                       opr::powf(iabs, 1.f / 3.f) +
                       opr::PowC::make(iabs, -1.f / 3.f) +
                       opr::PowC::make(iabs, .5f) + opr::PowC::make(iabs, -.5f);
            },
            cn};
    checker.run({TensorShape{2, 3}, {2, 3}, {2, 3}});
}

template <>
void run<complex_exp>(Backend backend, CompNode cn) {
    set_backend(backend);

    FusionChecker checker{4,
                          [](const SymbolVarArray& inp) -> SymbolVar {
                              return opr::abs(inp[0]) * (inp[1] + inp[2]) *
                                             inp[3] -
                                     (inp[1] + inp[2]) * inp[2] / inp[1];
                          },
                          cn};
    checker.run({TensorShape{3, 3}, {1, 3}, {3, 1}, {1, 3}});
}

template <>
void run<cache>(Backend backend, CompNode cn) {
    set_backend(backend);

    auto graph = ComputingGraph::make();
    HostTensorGenerator<> gen;
    auto host_a = gen({1}, cn), host_b = gen({1}, cn), host_c = gen({1}, cn);
    auto a = opr::Host2DeviceCopy::make(*graph, host_a),
         b = opr::Host2DeviceCopy::make(*graph, host_b),
         c = opr::Host2DeviceCopy::make(*graph, host_c), x = opr::sin(a + 1),
         y = opr::cos(b + 1), z = opr::sin(c + 1);

    gopt::GraphOptimizer gopt;
    gopt.add_pass<gopt::JITFusionPass>();
    VarNodeArray vars{x.node(), y.node(), z.node()};
    gopt.apply_inplace(vars);

    ASSERT_NE(vars[0], vars[1]);
    ASSERT_NE(vars[0], vars[2]);
    ASSERT_NE(vars[1], vars[2]);

    auto func = graph->compile({{vars[0], {}}, {vars[1], {}}, {vars[2], {}}});
    func->execute();

    auto get_exe = [](SymbolVar var) {
        return var.node()
                ->owner_opr()
                ->cast_final_safe<JITExecutor>()
                .executable();
    };
    auto ex0 = get_exe(vars[0]), ex1 = get_exe(vars[1]), ex2 = get_exe(vars[2]);
    ASSERT_EQ(ex0, ex2);
    ASSERT_NE(ex0, ex1);
}

template <>
void run<all_oprs>(Backend backend, CompNode cn) {
    // test all supported modes in multiple threads
    set_backend(backend);

    std::vector<std::pair<const char*, thin_function<void()>>> tasks;

    static auto itrans_none = [](SymbolVar* data, size_t size) {};
    static auto itrans_pos = [](SymbolVar* data, size_t size) {
        for (size_t i = 0; i < size; ++i) {
            data[i] = opr::abs(data[i]) + float(0.1f + 0.23f * i);
        }
    };
    static auto itrans_clip1 = [](SymbolVar* data, size_t size) {
        for (size_t i = 0; i < size; ++i) {
            data[i] = opr::max(opr::min(data[i], data[i].make_scalar_dt(0.9f)),
                               data[i].make_scalar_dt(-0.9f));
        }
    };
    static auto itrans_gt0 = [](SymbolVar* data, size_t size) {
        for (size_t i = 0; i < size; ++i) {
            data[i] = opr::max(data[i], data[i].make_scalar_dt(0.1f));
        }
    };
    static auto itrans_ne0 = [](SymbolVar* data, size_t size) {
        for (size_t i = 0; i < size; ++i) {
            auto mask = opr::abs(data[i]) < 0.1f;
            data[i] = data[i] * (1.f - mask) + mask * (data[i] + 1.f);
        }
    };

#define DO_CHK_ELEM(_mode, _arity, _do_grad, _itrans, _shps...)         \
    tasks.emplace_back(#_mode, [cn]() {                                 \
        FusionChecker chk{_arity,                                       \
                          [](SymbolVarArray inp) -> SymbolVar {         \
                              itrans_##_itrans(inp.data(), inp.size()); \
                              return opr::Elemwise::make(               \
                                      inp, opr::Elemwise::Mode::_mode); \
                          },                                            \
                          cn};                                          \
        chk.enable_direct_build();                                      \
        if (!_do_grad) {                                                \
            chk.disable_inp_grad();                                     \
        }                                                               \
        chk.run({_shps});                                               \
    })

#define CHECK_ELEM1(_mode, _do_grad, _itrans) \
    DO_CHK_ELEM(_mode, 1, _do_grad, _itrans, TensorShape{9, 12, 7})
#define CHECK_ELEM2(_mode, _do_grad, _itrans)                       \
    DO_CHK_ELEM(_mode, 2, _do_grad, _itrans, TensorShape{9, 12, 7}, \
                TensorShape{9, 1, 7})
#define CHECK_ELEM3(_mode, _do_grad, _itrans)                       \
    DO_CHK_ELEM(_mode, 3, _do_grad, _itrans, TensorShape{9, 12, 7}, \
                TensorShape{9, 1, 7}, TensorShape{1, 12, 7})
#define CHECK_ELEM4(_mode, _do_grad, _itrans)                       \
    DO_CHK_ELEM(_mode, 4, _do_grad, _itrans, TensorShape{9, 12, 7}, \
                TensorShape{9, 1, 7}, TensorShape{1, 12, 7},        \
                TensorShape{9, 12, 1})

    CHECK_ELEM1(RELU, true, none);
    CHECK_ELEM1(ABS, true, none);
    CHECK_ELEM1(ACOS, true, clip1);
    CHECK_ELEM1(ASIN, true, clip1);
    CHECK_ELEM1(CEIL, false, none);
    CHECK_ELEM1(COS, true, none);
    CHECK_ELEM1(EXP, true, none);
    CHECK_ELEM1(EXPM1, true, none);
    CHECK_ELEM1(FLOOR, false, none);
    CHECK_ELEM1(LOG, true, gt0);
    CHECK_ELEM1(LOG1P, true, gt0);
    CHECK_ELEM1(NEGATE, true, none);
    CHECK_ELEM1(SIGMOID, true, none);
    CHECK_ELEM1(SIN, true, none);
    CHECK_ELEM1(TANH, true, none);
    CHECK_ELEM1(ERF, true, none);
    CHECK_ELEM1(ERFC, true, none);
    CHECK_ELEM1(H_SWISH, true, none);

    CHECK_ELEM2(ABS_GRAD, true, none);
    CHECK_ELEM2(ADD, true, none);
    CHECK_ELEM2(FLOOR_DIV, false, ne0);
    CHECK_ELEM2(MAX, true, none);
    CHECK_ELEM2(MIN, true, none);
    CHECK_ELEM2(MOD, false, ne0);
    CHECK_ELEM2(MUL, true, none);
    CHECK_ELEM2(POW, true, pos);
    CHECK_ELEM2(SIGMOID_GRAD, true, none);
    CHECK_ELEM2(SUB, true, none);
    CHECK_ELEM2(SWITCH_GT0, true, none);
    CHECK_ELEM2(TANH_GRAD, true, none);
    CHECK_ELEM2(TRUE_DIV, true, ne0);
    CHECK_ELEM2(LOG_SUM_EXP, true, none);
    CHECK_ELEM2(H_SWISH_GRAD, false, none);

    CHECK_ELEM2(LT, false, none);
    CHECK_ELEM2(LEQ, false, none);
    CHECK_ELEM2(EQ, false, none);

    CHECK_ELEM2(ATAN2, true, gt0);

    CHECK_ELEM3(COND_LEQ_MOV, false, none);
    CHECK_ELEM3(FUSE_MUL_ADD3, true, none);

    CHECK_ELEM4(FUSE_MUL_ADD4, true, none);

    CHECK_ELEM2(FUSE_ADD_RELU, true, none);
    CHECK_ELEM2(FUSE_ADD_SIGMOID, true, none);
    CHECK_ELEM2(FUSE_ADD_TANH, true, none);
    CHECK_ELEM2(FUSE_ADD_H_SWISH, true, none);

    ASSERT_EQ(ast_c::elem_opr_generator().size(), tasks.size());

    auto type_cvt_test = [&](const char* name, DType src_dtype,
                             DType dst_dtype) {
        tasks.emplace_back(name, [cn, src_dtype, dst_dtype]() {
            FusionChecker checker{
                    1,
                    [dst_dtype](const SymbolVarArray& inp) -> SymbolVar {
                        return opr::TypeCvt::make(inp[0], dst_dtype);
                    },
                    cn};
            checker.enable_direct_build();
            checker.set_dtype(0, src_dtype).run({TensorShape{4, 7, 99, 1}});
        });
    };

    type_cvt_test("f16->f32", dtype::Float16(), dtype::Float32());
    type_cvt_test("f32->f16", dtype::Float32(), dtype::Float16());

#undef CHECK_ELEM1
#undef CHECK_ELEM2
#undef CHECK_ELEM3
#undef CHECK_ELEM4
#undef DO_CHK_ELEM

    std::vector<std::thread> workers;
    std::atomic_size_t finished_tasks{0};
    auto worker = [&tasks, &finished_tasks](int wid) {
        for (;;) {
            size_t id = finished_tasks.fetch_add(1);
            if (id >= tasks.size()) {
                return;
            }
            if (!::testing::Test::HasFailure()) {
                mgb_log("going to run %s on worker %d", tasks[id].first, wid);
                ASSERT_NO_THROW(tasks[id].second())
                        << "failed for " << tasks[id].first;
            }
        }
    };
    int nr_worker;
    if (auto set = MGB_GETENV("MGB_JIT_TEST_WORKER")) {
        nr_worker = std::stoi(set);
    } else {
        nr_worker = CompNode::get_device_count(CompNode::DeviceType::CPU) / 2;
    }

    if (nr_worker == 1) {
        worker(-1);
    } else {
        for (int i = 0; i < nr_worker; ++i) {
            workers.emplace_back(worker, i);
        }
        for (auto&& i : workers) {
            i.join();
        }
    }

    ASSERT_GE(finished_tasks.load(), tasks.size());
}

template <>
void run<expand_jit_executor>(Backend backend, CompNode cn) {
    set_backend(backend);

    auto make_jit = [](SymbolVar target, const SymbolVarArray& inputs) {
        auto y = target.node();
549
        auto ig_gen = std::make_unique<InternalGraphGenerator>(y->owner_opr());
550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838
        auto inputs_vptr = cg::to_var_node_array(inputs);
        for (auto i : get_rev_topo_order(
                     target, {inputs_vptr.begin(), inputs_vptr.end()})) {
            ig_gen->add_opr(i);
        }
        auto igraph = ig_gen->generate();
        return JITExecutor::make(igraph, ig_gen->orig_inps());
    };

    auto graph = ComputingGraph::make();
    graph->options().graph_opt_level = 3;
    HostTensorGenerator<> gen;
    auto host_x = gen({3, 3}, cn);
    auto x = opr::Host2DeviceCopy::make(*graph, host_x);
    auto type_cvt_x = opr::TypeCvt::make(x, dtype::Float16());
    auto relu_x = opr::relu(type_cvt_x);
    auto sin_x = opr::sin(relu_x);

    auto host_y = gen({3, 3}, cn);
    auto y = opr::Host2DeviceCopy::make(*graph, host_y);
    auto type_cvt_y = opr::TypeCvt::make(y, dtype::Float16());
    auto relu_y = opr::relu(type_cvt_y);
    auto sin_y = opr::sin(relu_y);

    auto fusion_x = make_jit(sin_x, {relu_x});
    auto fusion_y = make_jit(sin_y, {type_cvt_y});

    auto z = fusion_x + fusion_y;

    // expanding at endpoint
    auto fusion0_x = make_jit(sin_x, {type_cvt_x});
    auto fusion1_x = make_jit(fusion0_x, {x});
    auto fusion2_x = make_jit(sin_x, {x});
    ASSERT_EQ(fusion1_x, fusion2_x);

    // expand mulitple JITExecutor
    auto fusion_z = make_jit(z, {x, y});
    auto fusion_z_expected = make_jit(sin_x + sin_y, {x, y});
    ASSERT_EQ(fusion_z, fusion_z_expected);
}

SymbolVar jit_stop(SymbolVar x) {
    return opr::Sleep::make(x, 1e-3);
}

template <>
void run<multi_device>(Backend backend, CompNode cn) {
    set_backend(backend);

    auto loc = cn.locator_logical();
    mgb_assert(loc.device >= 0);
    loc.device += 1;
    if (loc.device >= static_cast<int>(CompNode::get_device_count(loc.type))) {
        return;
    }

    HostTensorGenerator<> gen;
    auto cn1 = CompNode::load(loc);
    auto host_x = gen({42, 23}, cn);
    auto make_dst = [&](ComputingGraph& graph) {
        auto x = opr::Host2DeviceCopy::make(graph, host_x),
             a = opr::tanh(x) + opr::sin(x), y = opr::Copy::make(x, cn1),
             b = opr::tanh(y) + opr::sin(y);
        return jit_stop(a) + opr::Copy::make(b, cn);
    };
    HostTensorND host_z1, host_z2;
    auto funcs = make_func_pair(host_z1, host_z2, make_dst, 2);
    for (int i = 0; i < 8; ++i) {
        funcs.first->execute();
        funcs.second->execute();
        if (i == 4) {
            host_x->copy_from(*gen({10, 20, 3}, cn));
        } else {
            host_x->copy_from(*gen(host_x->shape(), cn));
        }
        MGB_ASSERT_TENSOR_EQ(host_z1, host_z2);
    }

    auto jits = find_oprs<JITExecutor>(*funcs.second);
    ASSERT_EQ(2u, jits.size());
    ASSERT_EQ(jits[0]->internal_graph().output(),
              jits[1]->internal_graph().output());
}

template <>
void run<multi_shape>(Backend backend, CompNode cn) {
    // multiple shapes of same computing expr
    set_backend(backend);

    HostTensorGenerator<> gen;
    auto host_x = gen({4, 2, 3}, cn), host_y = gen({4, 2}, cn);
    auto make_dst = [&](ComputingGraph& graph) {
        auto x = opr::Host2DeviceCopy::make(graph, host_x).rename("x"),
             y = opr::Host2DeviceCopy::make(graph, host_y).rename("y"),
             jit0 = jit_stop(opr::sin(x) * x),
             a = opr::AxisAddRemove::make(
                     opr::Reduce::make(jit0,
                                       {opr::Reduce::Param::Mode::SUM, 2}),
                     {opr::AxisAddRemove::AxisDesc::make_remove(2)}),
             jit1 = jit_stop(opr::sin(a) + opr::sin(y)),
             jit2 = opr::sin(jit1) * jit1;
        return jit2;
    };
    HostTensorND host_z1, host_z2;
    auto funcs = make_func_pair(host_z1, host_z2, make_dst, 2);
    auto jits = find_oprs<JITExecutor>(*funcs.second);
    ASSERT_EQ(3u, jits.size());
    ASSERT_EQ(jits[0]->internal_graph().output(),
              jits[2]->internal_graph().output());
    for (int i = 0; i < 8; ++i) {
        funcs.first->execute();
        funcs.second->execute();
        if (i == 4) {
            host_x->copy_from(*gen({3, 7, 5}, cn));
            host_y->copy_from(*gen({3, 7}, cn));
        } else {
            host_x->copy_from(*gen(host_x->shape(), cn));
            host_y->copy_from(*gen(host_y->shape(), cn));
        }
        MGB_ASSERT_TENSOR_EQ(host_z1, host_z2);
    }
}

template <>
void run<non_contig>(Backend backend, CompNode cn) {
    set_backend(backend);

    HostTensorGenerator<> gen;
    auto host_x = gen({2, 3}, cn);
    SmallVector<std::pair<SymbolVar, SymbolVar>> subs;
    auto make_dst = [&](ComputingGraph& graph) {
        auto x = opr::Host2DeviceCopy::make(graph, host_x),
             y = opr::Subtensor::make(
                     x, {opr::Subtensor::AxisIndexer::make_interval(
                                1, x.make_scalar(1), x.make_scalar(3), None)});
        subs.emplace_back(x, y);
        return opr::sin(y) * y;
    };
    HostTensorND y0, y1;
    auto funcs = make_func_pair(y0, y1, make_dst, 2);
    for (size_t s : {4, 7}) {
        *host_x = *gen({3, s});
        funcs.first->execute();
        funcs.second->execute();
        MGB_ASSERT_TENSOR_EQ(y0, y1);
    }

    ASSERT_EQ(2u, subs.size());
    for (int i = 0; i < 2; ++i) {
        auto p0 = static_cast<const float*>(prev_dev_ptr(subs[i].first)) + 1,
             p1 = static_cast<const float*>(prev_dev_ptr(subs[i].second));
        if (backend != Backend::HALIDE || !i) {
            ASSERT_EQ(p0, p1);
        } else {
            ASSERT_NE(p0, p1);
        }
    }
}

template <>
void run<visit_complexity>(Backend backend, CompNode cn) {
    // build a graph that would have exponential complexity if graph visiting is
    // not correctly implemented
    set_backend(backend);

    HostTensorGenerator<dtype::Float32, RandomDistribution::UNIFORM> gen{0.01f,
                                                                         0.02f};
    auto host_x = gen({3, 4}, cn);
    auto make_dst = [&](ComputingGraph& graph) {
        auto x = opr::Host2DeviceCopy::make(graph, host_x);
        auto y = x;
        for (int i = 0; i < 32; ++i) {
            y = y * y + y;
        }
        return y;
    };
    HostTensorND host_y1, host_y2;
    auto funcs = make_func_pair(host_y1, host_y2, make_dst, 2);
    funcs.first->execute();
    funcs.second->execute();
    MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);

    ASSERT_EQ(1u, find_oprs<JITExecutor>(*funcs.second).size());
    ASSERT_TRUE(find_oprs<opr::Elemwise>(*funcs.second).empty());
}

template <>
void run<imm_scalar>(Backend backend, CompNode cn) {
    set_backend(backend);

    HostTensorGenerator<> gen;
    auto host_x = gen({2, 3, 4}, cn);
    auto make_dst = [&](ComputingGraph& graph) {
        auto x = opr::Host2DeviceCopy::make(graph, host_x);
        return (x * x + 1.f) / (opr::sin(x) + 1.2f) * .3f;
    };
    HostTensorND host_y1, host_y2;
    auto funcs = make_func_pair(host_y1, host_y2, make_dst, 2);

    funcs.first->execute();
    funcs.second->execute();
    MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);

    JITExecutor* jit;
    unpack_vector(find_oprs<JITExecutor>(*funcs.second), jit);
    ASSERT_TRUE(find_oprs<opr::Elemwise>(*funcs.second).empty());

    ASSERT_EQ(1u, jit->input().size());
    ASSERT_TRUE(jit->input(0)->owner_opr()->same_type<opr::Host2DeviceCopy>());
}

template <>
void run<special_graph_input>(Backend backend, CompNode cn) {
    set_backend(backend);

    HostTensorGenerator<> gen;
    auto host_x = gen({3, 3}, cn);
    auto host_y = gen({2, 1}, cn);
    auto make_dst = [&](ComputingGraph& graph) {
        auto x = opr::Host2DeviceCopy::make(graph, host_x);
        auto y = opr::Host2DeviceCopy::make(graph, host_y);
        auto spl = opr::Split::make(x,
                        opr::Split::Options::make_partition(x, 1, {1, 2}));
        auto mat = mgb::opr::MatrixMul::make(spl[1], y);
        return (spl[0] * spl[0] + 1.f) / (mat + 1.2f) * .3f;
    };
    HostTensorND host_y1, host_y2;
    auto funcs = make_func_pair(host_y1, host_y2, make_dst, 2);

    funcs.first->execute();
    funcs.second->execute();
    MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);

    JITExecutor* jit;
    unpack_vector(find_oprs<JITExecutor>(*funcs.second), jit);
    ASSERT_TRUE(find_oprs<opr::Elemwise>(*funcs.second).empty());
    ASSERT_EQ(2u, jit->input().size());
}

}  // namespace

#if MGB_JIT_HALIDE
TEST(TestJITFusionHalide, SimpleReduce) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    auto graph = ComputingGraph::make();
    graph->options().graph_opt_level = 3;
    graph->options().graph_opt.jit = 2;
    HostTensorGenerator<> gen;
    auto host_x0 = gen({3, 3}), host_x1 = gen({3, 1});
    auto a = opr::Host2DeviceCopy::make(*graph, host_x0),
         b = opr::Host2DeviceCopy::make(*graph, host_x1),
         y = opr::reduce_sum(a + b, opr::GetVarShape::make(b)),
         z = opr::reduce_sum(a * b, opr::GetVarShape::make(a)) + y;

    SymbolVar z_opt;
    unpack_vector(gopt::GraphOptimizer{}
                          .add_preset_passes(true, nullptr, &(graph->options()))
                          .apply({{z}})
                          .endpoint_vars(),
                  z_opt);
    ASSERT_EQ(2u, find_opr_num<mgb::jit::JITExecutor>(z_opt));
    HostTensorND h;
    graph->compile({make_callback_copy(z_opt, h)})
            ->to_json()
            ->writeto_fpath(
                    output_file("TestJITFusionHalide.SimpleReduce.json"));
}

TEST(TestJITFusionHalide, JITExecutor) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    auto graph = ComputingGraph::make();
    graph->options().graph_opt_level = 3;
    graph->options().graph_opt.jit = 2;
    HostTensorGenerator<> gen;
    auto host_x0 = gen({3, 3}), host_x1 = gen({3, 1}), host_x2 = gen({3, 3}),
         host_x3 = gen({3, 1});
    auto a = opr::Host2DeviceCopy::make(*graph, host_x0),
         b = opr::Host2DeviceCopy::make(*graph, host_x1),
         c = opr::Host2DeviceCopy::make(*graph, host_x2),
         d = opr::Host2DeviceCopy::make(*graph, host_x3),
         shape_of_b = opr::GetVarShape::make(b),
         shape_of_a = opr::GetVarShape::make(a),
         y = opr::reduce_sum(a + b, shape_of_b),
         z = opr::reduce_sum(a * b, shape_of_a);
    auto ig_gen_1 =
839
            std::make_unique<InternalGraphGenerator>(y.node()->owner_opr());
840
    auto ig_gen_2 =
841
            std::make_unique<InternalGraphGenerator>(z.node()->owner_opr());
842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443
    {
        ThinHashSet<VarNode*> nd_set;
        nd_set.insert(a.node());
        nd_set.insert(b.node());
        nd_set.insert(shape_of_b.node());
        auto topo = get_rev_topo_order(y, nd_set);
        for (auto opr : topo) {
            ig_gen_1->add_opr(opr);
        }
    }
    {
        ThinHashSet<VarNode*> nd_set;
        nd_set.insert(a.node());
        nd_set.insert(b.node());
        nd_set.insert(shape_of_a.node());
        auto topo = get_rev_topo_order(z, nd_set);
        for (auto opr : topo) {
            ig_gen_2->add_opr(opr);
        }
    }
    auto ig_1 = ig_gen_1->generate(), ig_2 = ig_gen_2->generate();
    auto jit_1 = JITExecutor::make(ig_1, ig_gen_1->orig_inps());
    auto jit_2 = JITExecutor::make(ig_2, ig_gen_2->orig_inps());
    auto w = opr::reduce_sum(a * b + c * d, opr::GetVarShape::make(a)),
         x = w + jit_1, u = x * jit_2;

    SymbolVar u_opt;
    unpack_vector(gopt::GraphOptimizer{}
                          .add_preset_passes(true, nullptr, &(graph->options()))
                          .apply({{u}})
                          .endpoint_vars(),
                  u_opt);
    ASSERT_EQ(2u, find_opr_num<mgb::jit::JITExecutor>(u_opt));
    ASSERT_GT(1u, find_opr_num<opr::Elemwise>(u_opt));
    HostTensorND h;
    graph->compile({make_callback_copy(u_opt, h)})
            ->to_json()
            ->writeto_fpath(
                    output_file("TestJITFusionHalide.JITExecutor.json"));
}

TEST(TestJITFusionHalide, BatchNormalization) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    auto graph1 = ComputingGraph::make();
    graph1->options().graph_opt_level = 3;
    graph1->options().graph_opt.jit = 2;
    HostTensorGenerator<dtype::Float32, RandomDistribution::UNIFORM> gen{0.1,
                                                                         1};
    size_t n = 32, c = 24, h = 28, w = 28;
    auto host_x0 = gen({n, c, h, w});
    auto host_tshp = std::make_shared<HostTensorND>(host_x0->comp_node(),
                                                    dtype::Int32());
    host_tshp->resize({4});
    host_tshp->ptr<int>()[0] = 1;
    host_tshp->ptr<int>()[1] = c;
    host_tshp->ptr<int>()[2] = 1;
    host_tshp->ptr<int>()[3] = 1;
    auto host_pow = std::make_shared<HostTensorND>(host_x0->comp_node(),
                                                   dtype::Float32());
    host_pow->resize({1});
    host_pow->ptr<float>()[0] = -0.5;
    auto pow = opr::Host2DeviceCopy::make(*graph1, host_pow, {"pow"});
    auto x = opr::Host2DeviceCopy::make(*graph1, host_x0, {"x"}),
         tshp = opr::Host2DeviceCopy::make(*graph1, host_tshp, {"tshp"});
    auto xshp = opr::GetVarShape::make(x);
    auto reduce_size = opr::reduce_prod(xshp, xshp.make_scalar(1)) /
                       opr::reduce_prod(tshp, tshp.make_scalar(1));
    auto xx = opr::Elemwise::make({2 * x}, opr::Elemwise::Param::Mode::RELU);
    auto x1 = opr::reduce_sum(xx, tshp);
    auto x2 = opr::reduce_sum_sqr(xx, tshp);
    auto var = (x2 - x1 * x1 / reduce_size) / (reduce_size - 1),
         regular_var = var + (float)(1e-5);
    auto invsqrt_var = opr::Elemwise::make({regular_var, pow},
                                           opr::Elemwise::Param::Mode::POW);
    auto ovar = (x - x1 / reduce_size) * invsqrt_var;
    HostTensorND h_ovar;

    using Callback = thin_function<void(DeviceTensorND&)>;
    using OutputSpecItem = std::pair<SymbolVar, Callback>;
    using OutputSpec = std::vector<OutputSpecItem>;
    OutputSpec out_spec;
    out_spec.push_back(make_callback_copy(ovar, h_ovar));
    HostTensorND h_grad;
    bool do_grad = true;
    if (do_grad) {
        auto reduce_ovar = opr::reduce_sum(ovar * ovar, ovar.make_scalar(1));
        auto grad = cg::grad(reduce_ovar, x);
        out_spec.push_back(make_callback_copy(grad, h_grad));
    }
    auto func1 = graph1->compile(out_spec);
    func1->to_json()->writeto_fpath(
            output_file("TestJITFusionHalide.BatchNormalization.json"));
    func1->execute();

    auto graph2 = ComputingGraph::make();
    graph2->options().graph_opt_level = 0;
    auto pow_ = opr::Host2DeviceCopy::make(*graph2, host_pow, {"pow"});
    auto x_ = opr::Host2DeviceCopy::make(*graph2, host_x0, {"x"}),
         tshp_ = opr::Host2DeviceCopy::make(*graph2, host_tshp, {"tshp"});
    auto xshp_ = opr::GetVarShape::make(x_);
    auto reduce_size_ = opr::reduce_prod(xshp_, xshp_.make_scalar(1)) /
                        opr::reduce_prod(tshp_, tshp_.make_scalar(1));
    auto xx_ = opr::Elemwise::make({2 * x_}, opr::Elemwise::Param::Mode::RELU);
    auto x1_ = opr::reduce_sum(xx_, tshp_);
    auto x2_ = opr::reduce_sum_sqr(xx_, tshp_);
    auto var_ = (x2_ - x1_ * x1_ / reduce_size_) / (reduce_size_ - 1),
         regular_var_ = var_ + (float)(1e-5);
    auto invsqrt_var_ = opr::Elemwise::make({regular_var_, pow_},
                                            opr::Elemwise::Param::Mode::POW);
    auto ovar_ = (x_ - x1_ / reduce_size_) * invsqrt_var_;
    HostTensorND h_ovar_;

    OutputSpec out_spec_;
    out_spec_.push_back(make_callback_copy(ovar_, h_ovar_));
    HostTensorND h_grad_;
    if (do_grad) {
        auto reduce_ovar = opr::reduce_sum(ovar_ * ovar_, ovar_.make_scalar(1));
        auto grad = cg::grad(reduce_ovar, x_);
        out_spec_.push_back(make_callback_copy(grad, h_grad_));
    }
    auto func2 = graph2->compile(out_spec_);
    func2->execute();

    MGB_ASSERT_TENSOR_NEAR(h_ovar_, h_ovar, 3e-5);
    if (do_grad){
        MGB_ASSERT_TENSOR_NEAR(h_grad_, h_grad, 3e-4);
    }
}

TEST(TestJITFusionHalide, ReduceShapeManip) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);
    auto cn = CompNode::load("gpu0");
    HostTensorGenerator<> gen;

    auto do_chk = [&](bool dyn_shape) {
        auto host_x = gen({7, 8, 9}, cn);
        // TODO: handle opr fusion without shape constraints, and test dynamic
        // shape case where target shape can be inferred
        auto make_dst = [&host_x, dyn_shape](ComputingGraph& cg) {
            auto x = opr::Host2DeviceCopy::make(cg, host_x), xm2 = x * 2,
                 one = x.make_scalar(1),
                 tshp = opr::Concat::make(
                         {one,
                          opr::GetVarShape::make(
                                  dyn_shape ? opr::MarkDynamicVar::make(xm2)
                                            : xm2,
                                  1),
                          one},
                         0),
                 y = opr::reduce_sum(xm2, tshp) + 3;
            return y;
        };

        HostTensorND host_y0, host_y1;
        auto funcs = make_func_pair(host_y0, host_y1, make_dst, 2);
        auto run = [&]() {
            funcs.first->execute();
            funcs.second->execute();
            MGB_ASSERT_TENSOR_NEAR(host_y0, host_y1, 1e-5);
        };
        funcs.second->to_json()->writeto_fpath(output_file(ssprintf(
                "TestJITFusionHalide.ReduceShapeManip%d.json", dyn_shape)));
        run();
        host_x->copy_from(*gen({13, 4, 5}, cn));
        run();

        if (!dyn_shape) {
            JITExecutor* jit;
            unpack_vector(find_oprs<JITExecutor>(*funcs.second), jit);
            ASSERT_TRUE(jit->input(0)
                                ->owner_opr()
                                ->same_type<opr::Host2DeviceCopy>());
            ASSERT_EQ(2u, jit->input().size());
            auto dep_type = jit->node_prop().dep_map().at(jit->input(1));
            ASSERT_EQ(cg::OperatorNodeBase::NodeProp::DepType::HOST_VALUE,
                      dep_type);
            ASSERT_EQ(0u, find_oprs<opr::Elemwise>(*funcs.second).size());
        }
    };
    do_chk(false);
    do_chk(true);
}

TEST(TestJITFusionHalide, ReduceExp) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    FusionChecker checker{
            2,
            [](const SymbolVarArray& inp) -> SymbolVar {
                auto var1 =
                        opr::reduce_sum(inp[0], opr::GetVarShape::make(inp[1]));
                auto var2 = opr::reduce_sum_sqr(inp[0] + inp[1],
                                                opr::GetVarShape::make(inp[1]));
                return var1 + var2;
            },
            CompNode::load("gpu0")};
    checker.run({TensorShape{3, 3}, {3, 1}});
    checker.run({TensorShape{3, 3}, {1}});  // to scalar
}

TEST(TestJITFusionHalide, ReduceO16xC32) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    using DataType = opr::Reduce::Param::DataType;
    FusionChecker checker{
            2,
            [](const SymbolVarArray& inp) -> SymbolVar {
                auto var1 = opr::Reduce::make(
                        inp[0],
                        {opr::Reduce::Mode::SUM, 1, DataType::FLOAT_O16xC32},
                        {});
                auto var2 = opr::Reduce::make(inp[0],
                                              {opr::Reduce::Mode::SUM_SQR, 1,
                                               DataType::FLOAT_O16xC32},
                                              {});
                return var1 + var2;
            },
            CompNode::load("gpu0")};
    checker.disable_inp_grad().run({TensorShape{3, 3}, {3, 1}});
}

TEST(TestJITFusionHalide, ReduceSum) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    FusionChecker checker{2,
                          [](const SymbolVarArray& inp) -> SymbolVar {
                              auto var1 = opr::reduce_sum(
                                      inp[0], opr::GetVarShape::make(inp[1]));
                              return var1 + inp[1];
                          },
                          CompNode::load("gpu0")};
    checker.run({TensorShape{3, 3}, {3, 1}});
    checker.run({TensorShape{3, 3}, {1}});  // test reduce to scalar
}

TEST(TestJITFusionHalide, ReduceSumSqr) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    FusionChecker checker{2,
                          [](const SymbolVarArray& inp) -> SymbolVar {
                              auto var1 = opr::reduce_sum_sqr(
                                      inp[0], opr::GetVarShape::make(inp[1]));
                              return var1 + inp[1];
                          },
                          CompNode::load("gpu0")};
    checker.run({TensorShape{3, 3}, {3, 1}});
    checker.run({TensorShape{3, 3}, {3, 3}});  // test side effect
}

TEST(TestJITFusionHalide, ReduceMax) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    FusionChecker checker{2,
                          [](const SymbolVarArray& inp) -> SymbolVar {
                              auto var1 = opr::reduce_max(
                                      inp[0], opr::GetVarShape::make(inp[1]));
                              return var1 + inp[1];
                          },
                          CompNode::load("gpu0")};
    checker.run({TensorShape{3, 3}, {3, 1}});
}

TEST(TestJITFusionHalide, ReduceMin) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    FusionChecker checker{2,
                          [](const SymbolVarArray& inp) -> SymbolVar {
                              auto var1 = opr::reduce_min(
                                      inp[0], opr::GetVarShape::make(inp[1]));
                              return var1 + inp[1];
                          },
                          CompNode::load("gpu0")};
    checker.run({TensorShape{3, 3}, {3, 1}});
}

TEST(TestJITFusionHalide, ReduceProduct) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    FusionChecker checker{2,
                          [](const SymbolVarArray& inp) -> SymbolVar {
                              auto var1 = opr::reduce_prod(
                                      inp[0], opr::GetVarShape::make(inp[1]));
                              return var1 + inp[1];
                          },
                          CompNode::load("gpu0")};
    checker.run({TensorShape{3, 3}, {3, 1}});
}

TEST(TestJITFusionHalide, ReduceMean) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);

    FusionChecker checker{2,
                          [](const SymbolVarArray& inp) -> SymbolVar {
                              auto var1 = opr::Reduce::make(
                                      inp[0], opr::Reduce::Param::Mode::MEAN,
                                      opr::GetVarShape::make(inp[1]));
                              return var1 + inp[1];
                          },
                          CompNode::load("gpu0")};
    checker.run({TensorShape{3, 3}, {3, 1}});
}

TEST(TestJITFusionHalide, SameGradOpr) {
    REQUIRE_GPU(1);
    set_backend(Backend::HALIDE);
    auto cn = CompNode::load("gpu0");

    auto graph = ComputingGraph::make();
    HostTensorGenerator<> gen;
    auto host_x0 = gen({3, 3}, cn), host_x1 = gen({3, 1}, cn),
         host_x2 = gen({3, 3}, cn);
    auto a = opr::Host2DeviceCopy::make(*graph, host_x0),
         b = opr::Host2DeviceCopy::make(*graph, host_x1),
         c = opr::Host2DeviceCopy::make(*graph, host_x2);

    auto y = (a + b) * c;
    auto reduce_y = opr::reduce_sum(y * y, y.make_scalar(1));
    auto a_grad = opr::VirtualGrad::make(reduce_y.node(), a.node());
    auto b_grad = opr::VirtualGrad::make(reduce_y.node(), b.node());
    auto c_grad = opr::VirtualGrad::make(reduce_y.node(), c.node());

    gopt::GraphOptimizer gopt;
    gopt.add_pass<gopt::JITFusionPass>(true);
    gopt.add_pass<gopt::ExpandVirtualGradPass>();

    VarNodeArray vars{y.node(), a_grad.node(), b_grad.node(), c_grad.node()};
    gopt.apply_inplace(vars);
    ASSERT_EQ(vars[1]->owner_opr()->input(0), vars[2]->owner_opr()->input(0));
    ASSERT_NE(vars[1]->owner_opr()->input(0), vars[3]->owner_opr()->input(0));
}

template <typename tag>
class TestJITHalideFusionCuda : public ::testing::Test {};
TYPED_TEST_CASE(TestJITHalideFusionCuda, test_types);
TYPED_TEST(TestJITHalideFusionCuda, run) {
    set_backend(Backend::NONE);

    REQUIRE_GPU(1);
    run<TypeParam>(Backend::HALIDE, CompNode::load("gpu0"));

    set_backend(Backend::NONE);
}
#endif  // MGB_JIT_HALIDE

template <typename tag>
class TestJITNvrtcFusion : public ::testing::Test {};
TYPED_TEST_CASE(TestJITNvrtcFusion, test_types);
TYPED_TEST(TestJITNvrtcFusion, run) {
    set_backend(Backend::NONE);

    REQUIRE_GPU(1);
    run<TypeParam>(Backend::NVRTC, CompNode::load("gpu0"));

    set_backend(Backend::NONE);
}

TEST(TestJITNvrtcFusion, SourceCache) {
    REQUIRE_GPU(1);
    set_backend(Backend::NVRTC);

    std::string cache_cat;
    std::vector<std::string> sources;
    auto on_cache_get = [&](const std::string& category, const void* key,
                            size_t key_size, const void*, size_t) {
        if (cache_cat.empty()) {
            cache_cat = category;
        } else {
            ASSERT_EQ(cache_cat, category);
        }
        sources.push_back(std::string{static_cast<const char*>(key), key_size});
    };
    PersistentCacheHook cache_hook{on_cache_get};

    auto cn = CompNode::load("gpu0");

    auto run = [cn]() {
        HostTensorGenerator<> gen;
        auto host_x = gen({2, 3}, cn);
        auto make_dst = [&](ComputingGraph& graph) {
            auto x = opr::Host2DeviceCopy::make(graph, host_x),
                 y = jit_stop(x * opr::sin(x)), z = y + opr::tanh(y);
            return z;
        };
        HostTensorND host_y1, host_y2;
        auto funcs = make_func_pair(host_y1, host_y2, make_dst, 2);
        ASSERT_EQ(2u, find_oprs<JITExecutor>(*funcs.second).size());
        funcs.first->execute();
        funcs.second->execute();
        MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);
    };

    for (size_t i = 0; i < 4; ++i) {
        run();
        ASSERT_EQ((i + 1) * 2, sources.size());
        ASSERT_EQ(sources[0], sources[i * 2]);
        ASSERT_EQ(sources[1], sources[i * 2 + 1]);
    }
}

TEST(TestJITNvrtc, DimshuffleFusion) {
    REQUIRE_GPU(1);
    set_backend(Backend::NVRTC);
    auto cn = CompNode::load("gpu0");
    HostTensorGenerator<> gen;
    // single dimshuffle
    {
        auto host_x = gen({2, 3, 8, 8}, cn);
        auto host_w = gen({3, 3, 1, 1}, cn);
        auto make_dst = [&](ComputingGraph& graph) {
            auto data = opr::SharedDeviceTensor::make(graph, *host_x);
            auto w = opr::SharedDeviceTensor::make(graph, *host_w);
            opr::Convolution::Param param;
            auto x = opr::Convolution::make(data, w, param);
            x = opr::relu(x);
            x = opr::Dimshuffle::make(x, {1, 2, 3, 0});
            x = opr::TypeCvt::make(x, dtype::Float16{});
            return x;
        };
        HostTensorND host_y1, host_y2;
        auto funcs = make_func_pair(host_y1, host_y2, make_dst, 1);

        ASSERT_EQ(1u, find_oprs<JITExecutor>(*funcs.second).size());
        ASSERT_EQ(1u, find_oprs<opr::Convolution>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Elemwise>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Dimshuffle>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::TypeCvt>(*funcs.second).size());
        funcs.first->execute();
        funcs.second->execute();
        MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);
    }
    // multi dimshuffle in one branch
    {
        auto host_x = gen({3, 4, 6}, cn);
        auto make_dst = [&](ComputingGraph& graph) {
            auto data = opr::SharedDeviceTensor::make(graph, *host_x);
            auto x = opr::relu(data);
            x = opr::Dimshuffle::make(x, {2, 0, 1});
            x = opr::sigmoid(x);
            x = opr::Dimshuffle::make(x, {1, 0, -1, 2});
            x = opr::TypeCvt::make(x, dtype::Float16{});
            return x;
        };
        HostTensorND host_y1, host_y2;
        auto funcs = make_func_pair(host_y1, host_y2, make_dst, 1);
        ASSERT_EQ(1u, find_oprs<JITExecutor>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Elemwise>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Dimshuffle>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::TypeCvt>(*funcs.second).size());
        funcs.first->execute();
        funcs.second->execute();
        MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);
    }

    // multi dimshuffle in two branch
    {
        auto host_x = gen({3, 4, 6}, cn);
        auto make_dst = [&](ComputingGraph& graph) {
            auto data = opr::SharedDeviceTensor::make(graph, *host_x);
            auto x = opr::relu(data);
            x = opr::Dimshuffle::make(x, {2, 0, 1});
            x = opr::sigmoid(x);
            x = opr::Dimshuffle::make(x, {1, 0, -1, 2});
            x = opr::TypeCvt::make(x, dtype::Float16{});

            auto y = opr::sigmoid(data);
            y = opr::Dimshuffle::make(y, {0, 2, -1, 1});
            y = opr::TypeCvt::make(y, dtype::Float16{});

            auto z = x + y;
            return z;
        };
        HostTensorND host_y1, host_y2;
        auto funcs = make_func_pair(host_y1, host_y2, make_dst, 1);
        ASSERT_EQ(1u, find_oprs<JITExecutor>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Elemwise>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Dimshuffle>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::TypeCvt>(*funcs.second).size());
        funcs.first->execute();
        funcs.second->execute();
        MGB_ASSERT_TENSOR_NEAR(host_y1, host_y2, 1e-3);
    }

    // dimshuffle pattern length > 4
    {
        auto host_x = gen({4, 3, 4, 6}, cn);
        auto make_dst = [&](ComputingGraph& graph) {
            auto data = opr::SharedDeviceTensor::make(graph, *host_x);
            auto x = opr::relu(data);
            x = opr::Dimshuffle::make(x, {2, 1, 0, -1, 3});
            x = opr::TypeCvt::make(x, dtype::Float16{});

            return x;
        };
        HostTensorND host_y1, host_y2;
        auto g0 = ComputingGraph::make();
        g0->options().graph_opt_level = 0;
        auto f0 = g0->compile({make_callback_copy(make_dst(*g0), host_y1)});

        auto g1 = ComputingGraph::make();
        g1->options().graph_opt_level = 3;
        g1->options().graph_opt.jit = 1;
        auto f1 = g1->compile({make_callback_copy(make_dst(*g1), host_y2)});

        EXPECT_TRUE(find_oprs<JITExecutor>(*f1).empty());
        f0->execute();
        f1->execute();
        MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);
    }

    // dimshuffle is endpoint
    {
        auto host_x = gen({4, 3, 4, 6}, cn);
        auto make_dst = [&](ComputingGraph& graph) {
            auto x = opr::TypeCvt::make(
                    opr::Host2DeviceCopy::make(graph, host_x),
                    dtype::Float16{});
            auto y = opr::Dimshuffle::make(x, {3, 0, 1, 2});
            return y;
        };
        HostTensorND host_y;
        auto g1 = ComputingGraph::make();
        g1->options().graph_opt_level = 3;
        g1->options().graph_opt.jit = 1;
        auto f1 = g1->compile({make_callback_copy(make_dst(*g1), host_y)});
        EXPECT_TRUE(find_oprs<JITExecutor>(*f1).empty());
    }
}

TEST(TestJITNvrtc, DimshuffleGrad) {
    REQUIRE_GPU(1);
    set_backend(Backend::NVRTC);
    auto cn = CompNode::load("gpu0");
    HostTensorGenerator<> gen;
    // single dimshuffle
    {
        auto host_x = gen({2, 3, 8, 8}, cn);
        auto host_w = gen({3, 3, 1, 1}, cn);
        auto make_dst = [&](ComputingGraph& graph) {
            auto data = opr::SharedDeviceTensor::make(graph, *host_x);
            auto w = opr::SharedDeviceTensor::make(graph, *host_w);
            opr::Convolution::Param param;
            auto x = opr::Convolution::make(data, w, param);
            x = opr::relu(x);
            x = opr::Dimshuffle::make(x, {1, 2, 3, 0});
            x = opr::TypeCvt::make(x, dtype::Float16{});
            auto loss = opr::reduce_sum(x, x.make_scalar(1));
            auto grad = cg::grad(loss, w);
            return grad;
        };
        HostTensorND host_y1, host_y2;
        auto funcs = make_func_pair(host_y1, host_y2, make_dst, 1);

        ASSERT_EQ(1u, find_oprs<JITExecutor>(*funcs.second).size());
        ASSERT_EQ(1u, find_oprs<opr::Convolution>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Elemwise>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Dimshuffle>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::TypeCvt>(*funcs.second).size());
        funcs.first->execute();
        funcs.second->execute();
        MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);
    }
    // multi dimshuffle in two branch
    {
        auto host_x = gen({3, 4, 6}, cn);
        auto make_dst = [&](ComputingGraph& graph) {
            auto data = opr::SharedDeviceTensor::make(graph, *host_x);
            auto x = opr::relu(data);
            x = opr::Dimshuffle::make(x, {2, 0, 1});
            x = opr::sigmoid(x);
            x = opr::Dimshuffle::make(x, {1, 0, -1, 2});
            x = opr::TypeCvt::make(x, dtype::Float16{});

            auto y = opr::sigmoid(data);
            y = opr::Dimshuffle::make(y, {0, 2, -1, 1});
            y = opr::TypeCvt::make(y, dtype::Float16{});

            auto z = x + y;
            auto loss = opr::reduce_sum(z, z.make_scalar(1));
            auto grad = cg::grad(loss, data);
            return grad;
        };
        HostTensorND host_y1, host_y2;
        auto funcs = make_func_pair(host_y1, host_y2, make_dst, 1);
        ASSERT_EQ(1u, find_oprs<JITExecutor>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Elemwise>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::Dimshuffle>(*funcs.second).size());
        ASSERT_EQ(0u, find_oprs<opr::TypeCvt>(*funcs.second).size());
        funcs.first->execute();
        funcs.second->execute();
        MGB_ASSERT_TENSOR_NEAR(host_y1, host_y2, 1e-3);
    }
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    {
        FusionChecker checker{2,
            [](const SymbolVarArray& inp) -> SymbolVar {
                auto var = opr::Dimshuffle::make(inp[0], {1, 2, 3, 0});
                return inp[1] * var;
            },
            CompNode::load("gpu0")};
        checker.set_jit_level(1)
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               .run({TensorShape{1, 2, 3, 4}, {2, 3, 4, 1}})
               .run({TensorShape{3, 4, 1, 2}, {4, 1, 2, 3}})
               .run({TensorShape{4, 6, 3, 5}, {6, 3, 5, 4}});
    }
}

TEST(TestJITExecutor, GradBehavior) {
    REQUIRE_GPU(1);
    auto cn = CompNode::load("gpu0");
    HostTensorGenerator<> gen;
    {
        set_backend(Backend::NVRTC);
        auto graph = ComputingGraph::make();
        auto host_a = gen({2, 3, 4}, cn);
        auto a = opr::Host2DeviceCopy::make(*graph, host_a),
            x = opr::exp(a + 1);

        gopt::GraphOptimizer gopt;
        gopt.add_pass<gopt::JITFusionPass>();
        VarNodeArray dest_vars{x.node()};
        gopt.apply_inplace(dest_vars);
        x = opr::reduce_sum(dest_vars[0], a.make_scalar_dt(1));
        SmallVector<jit::JITExecutor*> jits;
        auto on_opr = [&jits](cg::OperatorNodeBase* op) {
            if (auto jit = op->try_cast_final<jit::JITExecutor>()) {
                jits.push_back(jit);
            }
        };
        auto grad_a = cg::grad(x, a);
        cg::DepOprIter{on_opr}.add(grad_a);
        ASSERT_EQ(jits.size(), 2);
        // input of forward jit executor: host_a
        ASSERT_EQ(jits[0]->input().size(), 1);
        // input of grad jit executor:
        //      output of forward jit executor, output grad
        ASSERT_EQ(jits[1]->input().size(), 2);
        // internal graph is (input: og, out | output: og * out)
        size_t nr_ph = 0, nr_mul = 0;
        cg::DepOprIter{
            [&nr_ph, &nr_mul](cg::OperatorNodeBase* op) {
                if (op->same_type<jit::JITPlaceholder>()) {
                    ++ nr_ph;
                    return;
                }
                if(auto mul = op->try_cast_final<opr::Elemwise>()) {
                    using Mode = opr::Elemwise::Mode;
                    if (mul->param().mode == Mode::MUL) {
                        ++ nr_mul;
                        return;
                    }
                }
                mgb_throw(MegBrainError, "unexpected op %s", op->cname());
            }}
            .add(jits[1]->internal_graph_ptr()->output());
        ASSERT_EQ(nr_ph, 2);
        ASSERT_EQ(nr_mul, 1);
    }
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#if MGB_JIT_HALIDE
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    {
        set_backend(Backend::HALIDE);
        auto graph = ComputingGraph::make();
        auto host_a = gen({2, 3, 4}, cn);
        auto a = opr::Host2DeviceCopy::make(*graph, host_a),
            x = opr::exp(a + 1);

        gopt::GraphOptimizer gopt;
        gopt.add_pass<gopt::JITFusionPass>();
        VarNodeArray dest_vars{x.node()};
        gopt.apply_inplace(dest_vars);
        x = opr::reduce_sum(dest_vars[0], a.make_scalar_dt(1));
        size_t nr_ops = 0, nr_jits = 0;
        auto on_opr = [&nr_jits, &nr_ops](cg::OperatorNodeBase* op) {
            if (op->same_type<jit::JITExecutor>()) {
                ++ nr_jits;
            }
            ++ nr_ops;
        };
        auto grad_a = cg::grad(x, a);
        cg::DepOprIter{on_opr}.add(grad_a);
        // in Halide backend, grad internal graph would be expanded into
        // original graph, so there was only one JITExecutor
        ASSERT_EQ(nr_jits, 1);
        // the grad of a is broadcast(JITExecutor.output(0), a.shape()),
        // so the oprs depended by grad_a are H2D(a), JITExecutor,
        // GetVarShape(a) and broadcast
        ASSERT_EQ(nr_ops, 4);
    }
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#endif // MGB_JIT_HALIDE
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    {
        set_backend(Backend::NVRTC);
        auto graph = ComputingGraph::make();
        auto host_a = gen({2, 3, 4}, cn);
        auto a = opr::SharedDeviceTensor::make(*graph, *host_a),
            x = a * 2 + 1;

        gopt::GraphOptimizer gopt;
        gopt.add_pass<gopt::JITFusionPass>();
        VarNodeArray dest_vars{x.node()};
        gopt.apply_inplace(dest_vars);
        x = opr::reduce_sum(dest_vars[0], a.make_scalar_dt(1));
        auto grad_a = cg::grad(x, a);
        // all inputs of grad jit executor are const, its internal graph
        // would be expanded into original graph for more optimizations,
        // so no JITExecutor can be found
        cg::DepOprIter{[](cg::OperatorNodeBase* op) {
            ASSERT_FALSE(op->same_type<jit::JITExecutor>());}
        }.add(grad_a);
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    }
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}

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#if MGB_JIT_MLIR

void run_mlir(CompNode cn) {
    set_backend(Backend::MLIR);

    HostTensorGenerator<> gen;
    auto host_x0 = gen({23, 42}, cn), host_x1 = gen({23, 1}, cn),
         host_x2 = gen({1, 42}, cn), host_x3 = gen({23, 42}, cn),
         host_x4 = gen({1, 42}, cn), host_x5 = gen({23, 1}, cn);

    auto make_dst = [&](ComputingGraph& graph) {
        auto a = opr::Host2DeviceCopy::make(graph, host_x0),
         b = opr::Host2DeviceCopy::make(graph, host_x1),
         c = opr::Host2DeviceCopy::make(graph, host_x2),
         d = opr::Host2DeviceCopy::make(graph, host_x3),
         e = opr::Host2DeviceCopy::make(graph, host_x4);
        return a + opr::max(b, c) + opr::max(d, e);
    };
    HostTensorND host_y1, host_y2;
    auto funcs = make_func_pair(host_y1, host_y2, make_dst, 2);

    funcs.first->execute();
    funcs.second->execute();
    MGB_ASSERT_TENSOR_EQ(host_y1, host_y2);

    JITExecutor* jit;
    unpack_vector(find_oprs<JITExecutor>(*funcs.second), jit);
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    ASSERT_EQ(0u, find_oprs<opr::Elemwise>(*funcs.second).size());
    ASSERT_EQ(5u, jit->input().size());
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}

TEST(TestJITExecutor, TestJITMlirFusion) {
    run_mlir(CompNode::load("cpu0"));
}

TEST(TestJITExecutor, TestJITMlirFusionGpu) {
    REQUIRE_GPU(1);
    run_mlir(CompNode::load("gpu0"));
}

#endif // MGB_JIT_MLIR

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#endif  // MGB_JIT

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