text: #include <test/cpp/jit/test_utils.h>
  
  #include <gtest/gtest.h>
  
  #include <c10/core/TensorOptions.h>
  #include <torch/csrc/autograd/generated/variable_factories.h>
  #include <torch/csrc/jit/api/module.h>
  #include <torch/csrc/jit/frontend/resolver.h>
  #include <torch/csrc/jit/mobile/backport.h>
  #include <torch/csrc/jit/mobile/backport_manager.h>
  #include <torch/csrc/jit/mobile/import.h>
  #include <torch/csrc/jit/mobile/model_compatibility.h>
  #include <torch/csrc/jit/mobile/module.h>
  #include <torch/csrc/jit/mobile/parse_bytecode.h>
  #include <torch/csrc/jit/mobile/parse_operators.h>
  #include <torch/csrc/jit/mobile/runtime_compatibility.h>
  #include <torch/csrc/jit/serialization/export.h>
  #include <torch/csrc/jit/serialization/import.h>
  #include <torch/custom_class.h>
  #include <torch/torch.h>
  
  #include <unordered_set>
  
  // Tests go in torch::jit
  namespace torch {
  namespace jit {
  
  TEST(LiteInterpreterTest, UpsampleNearest2d) {
    Module m("m");
    m.define(R"(
      def forward(self, input: Tensor, scale:float):
        return torch.upsample_nearest2d(input, [1, 1], float(scale), float(scale))
    )");
  
    std::vector<IValue> inputs;
    inputs.emplace_back(torch::rand({1, 3, 128, 128}));
    inputs.emplace_back(at::Scalar(2.0));
    auto ref = m.forward(inputs);
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    IValue res;
    res = bc.forward(inputs);
  
    auto resd = res.toTensor();
    auto refd = ref.toTensor();
    ASSERT_TRUE(resd.equal(refd));
  }
  
  TEST(LiteInterpreterTest, CheckAttrAccess) {
    Module m("m");
    m.register_attribute("mobile_optimized", BoolType::get(), true);
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    bool mobile_optimized = bc.attr("mobile_optimized", false).toBool();
  
    AT_ASSERT(mobile_optimized);
    m.setattr("mobile_optimized", false);
    ss = std::stringstream();
    m._save_for_mobile(ss);
    bc = _load_for_mobile(ss);
    mobile_optimized = bc.attr("mobile_optimized", false).toBool();
  
    AT_ASSERT(!mobile_optimized);
  }
  
  TEST(LiteInterpreterTest, MethodInvocation) { // NOLINT (use =delete in gtest)
    const std::vector<std::string> test_programs{
        // test invoking a method with default parameter
        R"(
        def test_func(self, x, b : int = 4):
          return self.foo + x + b
        )",
        // inner method call with default parameter (gets inlined)
        R"(
        def add_with_default_arg(self, x, b : int = 4):
          return self.foo + x + b
        def test_func(self, x):
          return self.add_with_default_arg(x)  # invoke method w/ default arg
        )",
        // simple method call
        R"(
        def test_func(self, x):
          b = 4
          return self.foo + x + b
        )",
    };
    for (const auto& test_program : test_programs) {
      Module m("m");
      m.register_parameter("foo", torch::ones({}), false);
      m.define(test_program);
  
      const int fortyTwo = 42; // (keep linter happy)
      auto minput = fortyTwo * torch::ones({});
      auto ref = m.run_method("test_func", minput);
  
      std::stringstream ss;
      m._save_for_mobile(ss);
      mobile::Module bc = _load_for_mobile(ss);
      const auto& test_func = bc.get_method("test_func");
      IValue res;
      for (int i = 0; i < 3; ++i) {
        res = test_func({minput});
      }
  
      auto resd = res.toTensor().item<float>();
      auto refd = ref.toTensor().item<float>();
      AT_ASSERT(resd == refd);
    }
  }
  
  TEST(LiteInterpreterTest, Conv) {
    auto s = std::getenv("PYTORCH_TEST_WITH_TSAN");
    if (s && strcmp(s, "1") == 0)
      return;
  
    std::vector<torch::jit::IValue> inputs;
  
    Module m("m");
    m.register_parameter("weight", torch::ones({20, 1, 5, 5}), false);
    m.register_parameter("bias", torch::ones({20}), false);
    m.define(R"(
      def forward(self, input):
        return torch._convolution(input, self.weight, self.bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
    )");
  
    // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers,modernize-use-emplace)
    inputs.push_back(torch::ones({1, 1, 28, 28}));
  
    auto outputref = m.forward(inputs).toTensor();
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    IValue res;
    for (int i = 0; i < 3; ++i) {
      res = bc.get_method("forward")(inputs);
    }
    auto output = res.toTensor();
    AT_ASSERT(outputref.dim() == output.dim());
    AT_ASSERT(
        outputref[0][0][0][0].item<int>() == output[0][0][0][0].item<int>());
  }
  
  TEST(LiteInterpreterTest, Inline) {
    Module m("m");
    m.define(R"JIT(
    def foo1(self, x):
        return x + 1
  
    def foo2(self, x):
        return self.foo1(x) + 2
  
    def foo3(self, x):
        return self.foo2(x) + 3
    )JIT");
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    std::vector<torch::jit::IValue> inputs({torch::ones({})});
    auto output = bc.get_method("foo3")(inputs);
    AT_ASSERT(output.toTensor().item<float>() == 7.0);
  }
  
  TEST(LiteInterpreterTest, Tuple) {
    Module m("m");
    m.define(R"JIT(
    def foo(self, x):
        return (1, 2, x + 3)
  
    def forward(self, x):
        tuple = self.foo(x)
        return tuple
    )JIT");
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    std::vector<torch::jit::IValue> inputs({torch::ones({})});
    auto output = bc.get_method("forward")(inputs);
    AT_ASSERT(output.toTuple()->elements()[1].toInt() == 2);
  }
  
  TEST(LiteInterpreterTest, Dict) {
    Module m("m");
    m.define(R"JIT(
    def foo(self, x):
        return {"result": x + 1}
  
    def forward(self, x):
        d = self.foo(x)
        return d
    )JIT");
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    std::vector<torch::jit::IValue> inputs({torch::ones({})});
    auto output = bc.get_method("forward")(inputs);
    AT_ASSERT(output.toGenericDict().at("result").toTensor().item().toInt() == 2);
  }
  
  TEST(LiteInterpreterTest, PrimOverload) {
    /*
    // temporarily disabled
    script::Module m("m");
    m.define(R"JIT(
    def forward(self, x):
        result = [1, 2]
        result.append(3)
        return result
    )JIT");
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    std::vector<torch::jit::IValue> inputs({torch::ones({})});
    auto output = bc.get_method("forward")(inputs);
    AT_ASSERT(output.toIntList()[2] == 3);
    */
  }
  
  TEST(LiteInterpreterTest, Prim) {
    Module m("m");
    m.define(R"JIT(
          def forward(self, x):
              return int(x)
    )JIT");
  
    std::vector<IValue> inputs;
    auto minput = 3.5 * torch::ones({});
    inputs.emplace_back(minput);
    auto ref = m.run_method("forward", minput);
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    IValue res;
    for (int i = 0; i < 3; ++i) {
      // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
      auto bcinputs = inputs;
      res = bc.get_method("forward")(bcinputs);
    }
  
    auto resi = res.toInt();
    auto refi = ref.toInt();
    AT_ASSERT(resi == refi);
  }
  
  TEST(LiteInterpreterTest, PrimScalar) {
    Module m("m");
    m.define(R"JIT(
          def forward(self, x):
              return int(x.item())
    )JIT");
  
    std::vector<IValue> inputs;
    auto minput = 3.5 * torch::ones({});
    inputs.emplace_back(minput);
    auto ref = m.run_method("forward", minput);
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    IValue res;
    for (int i = 0; i < 3; ++i) {
      // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
      auto bcinputs = inputs;
      res = bc.get_method("forward")(bcinputs);
    }
  
    auto resi = res.toInt();
    auto refi = ref.toInt();
    AT_ASSERT(resi == refi);
  }
  
  TEST(LiteInterpreterTest, LoadOrigJit) {
    Module m("m");
    m.register_parameter("foo", torch::ones({}), false);
    m.define(R"(
      def forward(self, x):
        b = 4
        return self.foo + x + b
    )");
    std::stringstream ss;
    m.save(ss);
    ASSERT_THROWS_WITH_MESSAGE(_load_for_mobile(ss), "file not found");
  }
  
  TEST(LiteInterpreterTest, WrongMethodName) {
    Module m("m");
    m.register_parameter("foo", torch::ones({}), false);
    m.define(R"(
      def add(self, x):
        b = 4
        return self.foo + x + b
    )");
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    std::vector<IValue> inputs;
    auto minput = 5 * torch::ones({});
    inputs.emplace_back(minput);
    ASSERT_THROWS_WITH_MESSAGE(
        bc.get_method("forward")(inputs), "is not defined");
  }
  
  TEST(LiteInterpreterTest, SetState) {
    Module m("m");
    m.register_parameter("foo", torch::ones({}), false);
    m.define(R"(
      def __getstate__(self):
        return self.foo + self.foo
      def __setstate__(self, a):
        self.foo = a
      def forward(self, x):
        b = 4
        return self.foo + x + b
    )");
  
    std::vector<IValue> inputs;
    auto minput = 5 * torch::ones({});
    inputs.emplace_back(minput);
  
    std::stringstream ms;
    m.save(ms);
    auto loaded_m = load(ms);
    auto ref = loaded_m.run_method("forward", minput);
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    IValue res;
    for (int i = 0; i < 3; ++i) {
      // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
      auto bcinputs = inputs;
      res = bc.get_method("forward")(bcinputs);
    }
  
    auto resd = res.toTensor().item<float>();
    auto refd = ref.toTensor().item<float>();
    AT_ASSERT(resd == refd);
  }
  
  class TorchBindLiteInterpreterTestStruct
      : public torch::jit::CustomClassHolder {
   public:
    std::string get(at::Tensor t) {
      std::stringstream ss;
      ss << "Hello! Your tensor has ";
      ss << t.numel();
      ss << " elements!";
      return ss.str();
    }
  };
  
  namespace {
  struct ClassNamespaceValue : public SugaredValue {
    explicit ClassNamespaceValue(c10::QualifiedName name)
        : basename_(std::move(name)) {}
  
    std::shared_ptr<SugaredValue> attr(
        const SourceRange& loc,
        Function& m,
        const std::string& name) override {
      const auto fullName = c10::QualifiedName(basename_, name);
  
      // Check to see if it is a custom class.
      if (auto custom_class = getCustomClass(fullName.qualifiedName())) {
        return std::make_shared<ClassValue>(custom_class);
      }
  
      // If it's not a custom class, assume it's another namespace
      // NOLINTNEXTLINE(performance-move-const-arg)
      return std::make_shared<ClassNamespaceValue>(std::move(fullName));
    }
  
    std::string kind() const override {
      return "Class Namespace";
    }
  
   private:
    c10::QualifiedName basename_;
  };
  
  struct TestModuleResolver : public Resolver {
    std::shared_ptr<SugaredValue> resolveValue(
        const std::string& name,
        Function& m,
        const SourceRange& loc) override {
      if (name == "torch") {
        return std::make_shared<BuiltinModule>("aten");
      } else if (name == "__torch__") {
        return std::make_shared<ClassNamespaceValue>(c10::QualifiedName(name));
      }
  
      return nullptr;
    }
  
    TypePtr resolveType(const std::string& name, const SourceRange& loc)
        override {
      return nullptr;
    }
  };
  } // namespace
  
  TEST(LiteInterpreterTest, BuiltinClass) {
    script::Module m("m");
  
    auto cls = getCustomClass(
        "__torch__.torch.classes._TorchScriptTesting._LiteInterpreterTest");
    TORCH_INTERNAL_ASSERT(cls);
    c10::intrusive_ptr<torch::CustomClassHolder> obj_holder;
    m.register_attribute("my_obj", cls, IValue::make_capsule(obj_holder));
  
    m.register_parameter("foo", torch::ones({}), false);
    m.define(
        R"(
      def __getstate__(self):
        return 1
      def __setstate__(self, a):
        self.my_obj = __torch__.torch.classes._TorchScriptTesting._LiteInterpreterTest()
  
      def forward(self, x) -> str:
        return self.my_obj.get(x)
    )",
        std::make_shared<TestModuleResolver>());
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    auto res =
        bc.get_method("forward")(std::vector<IValue>{torch::zeros({3, 4})});
    const auto& str = res.toStringRef();
    std::string expected = "Hello! Your tensor has 12 elements!";
    AT_ASSERT(str == expected);
  }
  
  TEST(LiteInterpreterTest, BuiltinFunction) {
    script::Module m("m");
    auto custom_class_obj =
        make_custom_class<TorchBindLiteInterpreterTestStruct>();
    m.register_attribute("my_obj", custom_class_obj.type(), custom_class_obj);
    m.define(R"(
      def forward(self, x) -> str:
        return self.my_obj.get(x)
    )");
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    auto res =
        bc.get_method("forward")(std::vector<IValue>{torch::zeros({3, 4})});
    // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
    auto str = res.toStringRef();
    std::string expected = "Hello! Your tensor has 12 elements!";
    AT_ASSERT(str == expected);
  }
  
  #if !defined FB_XPLAT_BUILD
  TEST(LiteInterpreterTest, GetRuntimeByteCodeVersion) {
    auto runtime_bytecode_version = _get_runtime_bytecode_version();
    AT_ASSERT(
        runtime_bytecode_version ==
        caffe2::serialize::kMaxSupportedBytecodeVersion);
  }
  
  /**
   * The test below is disarmed for FB internal xplat builds since
   * BUCK requires us to pass in the script_module_v4.ptl file in
   * as a resource dependency of the build rule for this file, and
   * we would need to access it via the C++ Resources API instead
   * of directly reading from disk (which is what the open source
   * build/run does).
   */
  TEST(LiteInterpreterTest, GetByteCodeVersion) {
    std::string filePath(__FILE__);
    auto test_model_file_v4 =
        filePath.substr(0, filePath.find_last_of("/\\") + 1);
    test_model_file_v4.append("script_module_v4.ptl");
  
    auto version_v4 = _get_model_bytecode_version(test_model_file_v4);
    AT_ASSERT(version_v4 == 4);
  }
  
  #endif // !defined(FB_XPLAT_BUILD)
  
  TEST(LiteInterpreterTest, GetContainTypes) {
    Module m("m");
    m.define(R"(
      def forward(self):
        return 3
    )");
  
    std::stringstream ss;
    m._save_for_mobile(ss, {}, true);
  
    auto contained_types = _get_mobile_model_contained_types(ss);
    AT_ASSERT(contained_types.size() >= 0);
  }
  
  namespace {
  
  void compareModelOutput(
      c10::ArrayRef<IValue> actual_result_list,
      const std::vector<Tensor>& expect_result_list) {
    AT_ASSERT(actual_result_list.size() == expect_result_list.size());
    AT_ASSERT(actual_result_list[0].toTensor().equal(expect_result_list[0]));
    AT_ASSERT(
        actual_result_list[1].toTensor().dim() == expect_result_list[1].dim());
    AT_ASSERT(actual_result_list[2].toTensor().equal(expect_result_list[2]));
    AT_ASSERT(actual_result_list[3].toTensor().equal(expect_result_list[3]));
  }
  
  void runAndCheckTorchScriptModel(
      std::stringstream& input_model_stream,
      const std::vector<IValue>& input_data,
      const std::vector<Tensor>& expect_result_list,
      const int64_t expect_version) {
    auto actual_version = _get_model_bytecode_version(input_model_stream);
    AT_ASSERT(actual_version == expect_version);
  
    // Load and run the backport model, then compare the result with expect
    // result
    Module m_mobile = load(input_model_stream);
  
    auto actual_result = m_mobile.forward(input_data);
    const auto& actual_result_list = actual_result.toTuple()->elements();
    compareModelOutput(actual_result_list, expect_result_list);
  }
  
  void runAndCheckBytecodeModel(
      std::stringstream& input_model_stream,
      const std::vector<IValue>& input_data,
      const std::vector<Tensor>& expect_result_list,
      const int64_t expect_version) {
    auto actual_version = _get_model_bytecode_version(input_model_stream);
    AT_ASSERT(actual_version == expect_version);
  
    // Load and run the backport model, then compare the result with expect
    // result
    Module m_mobile = load(input_model_stream);
  
    auto actual_result = m_mobile.forward(input_data);
    const auto& actual_result_list = actual_result.toTuple()->elements();
  
    compareModelOutput(actual_result_list, expect_result_list);
  }
  
  void backportAllVersionCheck(
      std::stringstream& test_model_file_stream,
      std::vector<IValue>& input_data,
      std::vector<Tensor>& expect_result_list,
      const int64_t expect_from_version) {
    auto from_version = _get_model_bytecode_version(test_model_file_stream);
    AT_ASSERT(from_version == expect_from_version);
  
    // Backport script_module_v5.ptl to an older version
    constexpr int64_t minimum_to_version = 4;
    int64_t current_to_version = from_version - 1;
  
    // Verify all candidate to_version work as expected. All backport to version
    // larger than minimum_to_version should success.
    while (current_to_version >= minimum_to_version) {
      // Do not declare std::stringstream oss outside of the while loop as
      // oss.clear() doesn't reset the stream content, only clears out error state
      // flag in stringstream causing a problematic stream. Instead, it's cleaner
      // and safer to just declare a new std::stringstream one and swap them.
      std::stringstream oss;
      bool backPortSuccess =
          _backport_for_mobile(test_model_file_stream, oss, current_to_version);
      AT_ASSERT(backPortSuccess);
  
      // Check backport model version
      auto backport_version = _get_model_bytecode_version(oss);
      AT_ASSERT(backport_version == current_to_version);
  
      // Load and run the backport model, then compare the result with expect
      // result
      runAndCheckBytecodeModel(
          oss, input_data, expect_result_list, current_to_version);
      runAndCheckTorchScriptModel(
          oss, input_data, expect_result_list, current_to_version);
  
      current_to_version--;
    }
    //  backport to minimum version - 1 should fail
    std::stringstream oss;
    bool backPortSuccess =
        _backport_for_mobile(test_model_file_stream, oss, minimum_to_version - 1);
    AT_ASSERT(!backPortSuccess);
  }
  } // namespace
  
  #if !defined FB_XPLAT_BUILD
  TEST(LiteInterpreterTest, BackPortByteCodeModelAllVersions) {
    torch::jit::Module module("m");
    // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
    module.register_parameter("weight", torch::ones({20, 1, 5, 5}), false);
    // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
    module.register_parameter("bias", torch::ones({20}), false);
    module.define(R"(
      def forward(self, input):
        x1 = torch.zeros(2, 2)
        x2 = torch.empty_like(torch.empty(2, 2))
        x3 = torch._convolution(input, self.weight, self.bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
        # Add torch.add operator to cover bytecode version bump from 6 to 7
        # for bytecode version 7, the main change is to support defaults arguments with out arguments
        x = 2 * torch.ones(1)
        h = torch.ones(1)
        torch.add(x, h, out=x)
        return (x1, x2, x3, x)
    )");
  
    torch::jit::Module module_freeze = freeze(module);
  
    std::stringstream input_model_stream;
    module_freeze._save_for_mobile(input_model_stream);
    std::vector<IValue> input_data =
        std::vector<IValue>({torch::ones({1, 1, 28, 28})});
    std::vector<Tensor> expect_result_list;
    expect_result_list.emplace_back(at::ones({2, 2}, ScalarType::Float) * 0);
    expect_result_list.emplace_back(at::ones({2, 2}, ScalarType::Float));
    expect_result_list.emplace_back(
        at::ones({1, 20, 24, 24}, ScalarType::Float) * 26);
    expect_result_list.emplace_back(3 * at::ones({1}));
  
    backportAllVersionCheck(
        input_model_stream,
        input_data,
        expect_result_list,
        caffe2::serialize::kProducedBytecodeVersion);
  }
  #endif // !defined(FB_XPLAT_BUILD)
  
  TEST(LiteInterpreterTest, GetRuntimeOpsAndInfo) {
    auto runtime_ops = _get_runtime_ops_and_info();
    // Ballpark estimate of the minimal number of ops; just used to
    // verify API returns a reasonably large number.
    AT_ASSERT(runtime_ops.size() > 2900);
  }
  
  TEST(LiteInterpreterTest, isCompatibleSuccess) {
    // test trivial success case
    auto runtime_info = RuntimeCompatibilityInfo::get();
    std::unordered_map<std::string, OperatorInfo> model_ops;
    model_ops["aten::add.Scalar"] = OperatorInfo{2};
  
    std::unordered_set<std::string> types = {"List", "int", "NamedTuple"};
    auto model_info = ModelCompatibilityInfo{
        caffe2::serialize::kMaxSupportedBytecodeVersion, model_ops, types};
  
    AT_ASSERT(
        is_compatible(runtime_info, model_info).status ==
        ModelCompatibilityStatus::OK);
  }
  
  TEST(LiteInterpreterTest, isCompatibleFail) {
    // test trivial failure due to ops
    std::unordered_map<std::string, OperatorInfo> model_ops;
    model_ops["aten::add.Scalar"] = OperatorInfo{2};
    auto model_info = ModelCompatibilityInfo{
        caffe2::serialize::kMaxSupportedBytecodeVersion, model_ops};
    std::unordered_map<std::string, OperatorInfo> runtime_ops;
    runtime_ops["aten::add.Int"] = OperatorInfo{2};
    auto runtime_info = RuntimeCompatibilityInfo{
        caffe2::serialize::kMaxSupportedBytecodeVersion,
        runtime_ops,
        _get_mobile_supported_types()};
  
    auto result = is_compatible(runtime_info, model_info);
    AT_ASSERT(result.status = ModelCompatibilityStatus::ERROR);
    AT_ASSERT(
        result.errors[0] ==
        "Operator 'aten::add.Scalar' missing from runtime (not found)");
  
    // test trivial failure due to bytecode
    runtime_ops["aten::add.Scalar"] = OperatorInfo{2};
    runtime_info = RuntimeCompatibilityInfo{
        caffe2::serialize::kMaxSupportedBytecodeVersion,
        runtime_ops,
        _get_mobile_supported_types()};
    model_info.bytecode_version =
        caffe2::serialize::kMaxSupportedBytecodeVersion + 1;
  
    result = is_compatible(runtime_info, model_info);
    AT_ASSERT(result.status = ModelCompatibilityStatus::ERROR);
  
    // test trivial failure due to type
    runtime_info = RuntimeCompatibilityInfo::get();
    std::unordered_set<std::string> types = {"List", "int", "Sequence"};
  
    model_info = ModelCompatibilityInfo{
        caffe2::serialize::kMaxSupportedBytecodeVersion, model_ops, types};
  
    AT_ASSERT(
        is_compatible(runtime_info, model_info).status ==
        ModelCompatibilityStatus::ERROR);
  }
  
  TEST(LiteInterpreterTest, Eval) {
    std::vector<torch::jit::IValue> inputs;
  
    Module m("m");
    m.define(R"(
      def __init__(self, x):
        self.training = True
  
      def forward(self, input):
        return torch.dropout(input, 1.0, self.training)
    )");
  
    // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers,modernize-use-emplace)
    inputs.push_back(torch::ones({1, 1, 28, 28}));
    m.eval();
    auto outputref = m.forward(inputs).toTensor();
  
    // save m in training mode to make sure that mobile eval() will correctly
    // change back to eval mode
    m.train();
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    bc.eval();
    IValue res;
    for (int i = 0; i < 3; ++i) {
      res = bc.get_method("forward")(inputs);
    }
    auto output = res.toTensor();
    AT_ASSERT(outputref.dim() == output.dim());
    AT_ASSERT(
        outputref[0][0][0][0].item<int>() == output[0][0][0][0].item<int>());
  }
  
  TEST(LiteInterpreterTest, FindWrongMethodName) {
    Module m("m");
    m.register_parameter("foo", torch::ones({}), false);
    m.define(R"(
      def add(self, x):
        b = 4
        return self.foo + x + b
    )");
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    ASSERT_TRUE(bc.find_method("forward") == c10::nullopt);
  }
  
  TEST(LiteInterpreterTest, FindAndRunMethod) {
    Module m("m");
    m.register_parameter("foo", torch::ones({}), false);
    m.define(R"(
      def add_it(self, x):
        b = 4
        return self.foo + x + b
    )");
  
    std::vector<IValue> inputs;
    auto minput = 5 * torch::ones({});
    inputs.emplace_back(minput);
    auto ref = m.get_method("add_it")(inputs);
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    IValue res;
    for (int i = 0; i < 3; ++i) {
      auto bcinputs = inputs;
      auto method = bc.find_method("add_it");
      AT_ASSERT(method != c10::nullopt);
      res = (*method)(std::move(bcinputs));
    }
  
    auto resd = res.toTensor().item<float>();
    auto refd = ref.toTensor().item<float>();
    AT_ASSERT(resd == refd);
  }
  
  TEST(LiteInterpreterTest, RunMethodVariadic) {
    Module m("m");
    m.register_parameter("foo", torch::ones({}), false);
    m.define(R"(
      def add_three(self, x, y):
        return self.foo + x + y
    )");
  
    std::vector<IValue> inputs;
    auto inputx = 5 * torch::ones({});
    auto inputy = 4 * torch::ones({});
    auto ref = m.run_method("add_three", inputx, inputy);
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    IValue res = bc.run_method("add_three", inputx, inputy);
  
    auto resd = res.toTensor().item<float>();
    auto refd = ref.toTensor().item<float>();
    AT_ASSERT(resd == refd);
  }
  
  TEST(LiteInterpreterTest, DuplicateSetState) {
    Module m("M");
    m.register_parameter("foo", torch::ones({}), false);
    m.define(R"(
      def __getstate__(self):
        return self.foo + self.foo
      def __setstate__(self, a):
        self.foo = a
      def forward(self, x):
        b = 4
        return self.foo + x + b
    )");
  
    Module b("B");
    b.register_module("M0", m);
    b.register_module("M1", m);
    b.define(R"(
      def forward(self, x):
        return self.M0.forward(x) + self.M1.forward(x)
    )");
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    const auto methods = bc.get_methods();
    const size_t expected_n = 3;
    ASSERT_EQ(methods.size(), expected_n);
  }
  
  TEST(LiteInterpreterTest, ExtraFiles) {
    const auto script = R"JIT(
      def forward(self):
          x = torch.rand(5, 5)
          x = x.mm(x)
          return x
    )JIT";
  
    auto module =
        std::make_shared<Module>("Module", std::make_shared<CompilationUnit>());
    module->define(script);
    std::ostringstream oss;
    std::unordered_map<std::string, std::string> extra_files;
    extra_files["metadata.json"] = "abc";
    extra_files["mobile_info.json"] = "{\"key\": 23}";
    module->_save_for_mobile(oss, extra_files);
  
    std::istringstream iss(oss.str());
    caffe2::serialize::IStreamAdapter adapter{&iss};
    std::unordered_map<std::string, std::string> loaded_extra_files;
    loaded_extra_files["metadata.json"] = "";
    torch::jit::_load_for_mobile(iss, torch::kCPU, loaded_extra_files);
    ASSERT_EQ(loaded_extra_files["metadata.json"], "abc");
  
    loaded_extra_files.clear();
    std::vector<std::string> all_files =
        caffe2::serialize::PyTorchStreamReader(&iss).getAllRecords();
  
    for (auto& file_name : all_files) {
      if (file_name.find("extra/") == 0) {
        loaded_extra_files[file_name.substr(6)] = "";
      }
    }
  
    torch::jit::_load_for_mobile(iss, torch::kCPU, loaded_extra_files);
    ASSERT_EQ(loaded_extra_files["metadata.json"], "abc");
    ASSERT_EQ(loaded_extra_files["mobile_info.json"], "{\"key\": 23}");
  }
  
  TEST(LiteInterpreterTest, OpNameExportFetchRootOperators) {
    torch::jit::Module m("m");
    m.register_parameter("weight", torch::ones({20, 1, 5, 5}), false);
    m.register_parameter("bias", torch::ones({20}), false);
    m.define(R"(
      def forward(self, input):
        x1 = torch.zeros(2, 2)
        x2 = torch.empty_like(torch.empty(2, 2))
        x3 = torch._convolution(input, self.weight, self.bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
        return (x1, x2, x3)
    )");
    m.eval();
  
    std::stringstream ss;
    m._save_for_mobile(ss);
  
    torch::jit::mobile::Module ptl_model = torch::jit::_load_for_mobile(ss);
    std::set<std::string> operator_names =
        torch::jit::mobile::_export_operator_list(ptl_model);
    std::set<std::string> expected_operator_names = {
        "aten::_convolution",
        "aten::empty.memory_format",
        "aten::empty_like",
        "aten::zeros",
    };
    EXPECT_EQ(operator_names, expected_operator_names)
        << "Expected the root operator lists to be the same";
  }
  
  TEST(LiteInterpreterTest, DefaultArgsConv) {
    auto s = std::getenv("PYTORCH_TEST_WITH_TSAN");
    if (s && strcmp(s, "1") == 0)
      return;
  
    std::vector<torch::jit::IValue> inputs;
  
    Module m("m");
    m.register_parameter("weight", torch::ones({20, 1, 5, 5}), false);
    m.register_parameter("bias", torch::ones({20}), false);
    m.define(R"(
      def forward(self, input):
        return torch.conv2d(input, self.weight, self.bias, [1, 1], [0, 0], [1, 1], 1)
    )");
  
    inputs.push_back(torch::ones({1, 1, 28, 28}));
  
    auto outputref = m.forward(inputs).toTensor();
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    IValue res;
    for (int i = 0; i < 1; ++i) {
      res = bc.get_method("forward")(inputs);
    }
    auto output = res.toTensor();
    AT_ASSERT(outputref.dim() == output.dim());
    AT_ASSERT(output.equal(outputref));
  }
  
  TEST(RunTimeTest, ParseBytecode) {
    // A simple example to show a simple bytecode that can be used independent of
    // PyTorch TorchScript serialization (unpickler, etc) and operator library.
    // It has basic control flow (if, else) and basic data orchestration (list
    // construction). The original PyTorch program:
  
    //  class Module(torch.nn.Module):
    //
    //    def __init__(self):
    //      super().__init__()
    //
    //    def forward(self, x: int, h: int, xfirst: bool):
    //      if xfirst:
    //        return [x, h]
    //      else:
    //        return [h, x]
  
    // 1. Prepare for the bytecode. In reality it can be from a customized
    // deserializer.
    std::vector<IValue> instructions{
        to_tuple({"STOREN", 1, 4}),
        to_tuple({"DROPR", 1, 0}),
        to_tuple({"MOVE", 4, 0}),
        to_tuple({"JF", 5, 0}),
        to_tuple({"LOAD", 2, 0}),
        to_tuple({"LOAD", 3, 0}),
        to_tuple({"LIST_CONSTRUCT", 0, 2}),
        to_tuple({"JMP", 4, 0}),
        to_tuple({"LOAD", 3, 0}),
        to_tuple({"LOAD", 2, 0}),
        to_tuple({"LIST_CONSTRUCT", 1, 2}),
        to_tuple({"STORE", 5, 0}),
        to_tuple({"DROPR", 3, 0}),
        to_tuple({"DROPR", 2, 0}),
        to_tuple({"MOVE", 5, 0}),
        to_tuple({"RET", 0, 0}),
    };
    std::vector<IValue> operators; // empty for this example
    std::vector<IValue> constants; // empty for this example
  
    std::vector<IValue> types{"List[int]", "List[int]"};
    // 2. Parse the function
    std::string function_name("test_function");
    auto function = std::unique_ptr<mobile::Function>(
        new mobile::Function(c10::QualifiedName(function_name)));
    c10::ivalue::TupleElements debug_handles_m_tuple;
    parseInstructions(
        function_name,
        std::move(*c10::ivalue::Tuple::create(instructions)).elements(),
        debug_handles_m_tuple,
        function.get());
    parseTypes(c10::ivalue::Tuple::create(types)->elements(), function.get());
    const size_t rsize = 5;
    parseRegisterSize(rsize, function.get());
  
    // 3. Prepare for inputs and run the function
    // Note that the first input is reserved for Module object.
    // Since this is a function test and Module object is not required,
    // a dummy IValue (0) is added here.
    std::vector<IValue> inputs{0, 1, 2, true};
    function->run(inputs);
    auto output = inputs[0].toList();
    ASSERT_EQ(output[0], 1);
    ASSERT_EQ(output[1], 2);
  
    std::vector<IValue> inputs1{0, 1, 2, false};
    function->run(inputs1);
    auto output1 = inputs1[0].toList();
    ASSERT_EQ(output1[0], 2);
    ASSERT_EQ(output1[1], 1);
  }
  
  TEST(RunTimeTest, ParseOperator) {
    // A simple example to show a simple bytecode that can be used independent of
    // PyTorch TorchScript serialization (unpickler, etc) and operator library.
    // It has one operator and we should be able to register it. The original
    // PyTorch program:
  
    // class Add(torch.nn.Module):
    //     def __init__(self):
    //         super(Add, self).__init__()
  
    //     def forward(self, a, b):
    //         return a + b
  
    // 1. Prepare for the bytecode. In reality it can be from a customized
    // deserializer.
    std::vector<IValue> instructions{
        to_tuple({"STOREN", 1, 3}),
        to_tuple({"DROPR", 1, 0}),
        to_tuple({"MOVE", 2, 0}),
        to_tuple({"MOVE", 3, 0}),
        to_tuple({"OP", 0, 0}),
        to_tuple({"RET", 0, 0}),
    };
    std::vector<IValue> operators{
        to_tuple({"aten::add", "Tensor", 2}),
    };
    std::vector<IValue> constants{
        to_tuple({1}),
    };
    int64_t model_version = caffe2::serialize::kProducedBytecodeVersion;
    // 2. Parse the function
    std::string function_name("test_function");
    auto function = std::unique_ptr<mobile::Function>(
        new mobile::Function(c10::QualifiedName(function_name)));
    c10::ivalue::TupleElements debug_handles_m_tuple;
    parseInstructions(
        function_name,
        std::move(*c10::ivalue::Tuple::create(instructions)).elements(),
        debug_handles_m_tuple,
        function.get());
    parseOperators(
        std::move(*c10::ivalue::Tuple::create(operators)).elements(),
        model_version,
        1,
        function.get());
    const size_t rsize = 5;
    parseRegisterSize(rsize, function.get());
  
    // 3. Prepare for inputs and run the function
    // Note that the first input is reserved for Module object.
    // Since this is a function test and Module object is not required,
    // a dummy IValue (0) is added here.
    std::vector<IValue> inputs{0, at::tensor(1), at::tensor(2)};
    function->run(inputs);
    auto output = inputs[0];
    ASSERT_EQ(output, at::tensor(3));
  }
  
  namespace {
  void testLiteModuleCompareResultTensors(
      Module& m,
      const std::vector<torch::jit::IValue>& inputs,
      const std::string& method_name = "forward") {
    auto outputref = m.get_method(method_name)(inputs).toTensor();
  
    std::stringstream ss;
    m._save_for_mobile(ss);
    mobile::Module bc = _load_for_mobile(ss);
    IValue res;
    for (int i = 0; i < 3; ++i) {
      res = bc.get_method(method_name)(inputs);
    }
    auto output = res.toTensor();
    AT_ASSERT(outputref.dim() == output.dim());
    AT_ASSERT(output.equal(outputref));
  }
  
  void testDefaultArgsPinv(int num_args) {
    Module m("m");
    if (num_args == 1) {
      m.define(R"(
        def forward(self, input):
          return torch.linalg_pinv(input)
      )");
    } else if (num_args == 2) {
      m.define(R"(
        def forward(self, input):
          return torch.linalg_pinv(input, 1e-5)
      )");
    } else if (num_args == 3) {
      m.define(R"(
        def forward(self, input):
          return torch.linalg_pinv(input, 1e-5, True)
      )");
    }
  
    std::vector<torch::jit::IValue> inputs;
    const int N = 28;
    auto input = torch::range(1, N * N, 1);
    input[0] = 1; // a more stable matrix
    input = input.view({N, N});
    inputs.push_back(input);
    testLiteModuleCompareResultTensors(m, inputs);
  }
  } // namespace
  
  #if !defined FB_XPLAT_BUILD
  TEST(LiteInterpreterTest, DefaultArgsPinv) {
    // Test with different number of specified arguments.
    // Arguments not specified take default value.
    for (int num_args = 1; num_args <= 3; ++num_args) {
      testDefaultArgsPinv(num_args);
    }
  
    //  bytecode with one specified argument:
    //  (6,
    //      ('__torch__.m.forward',
    //          (('instructions',
    //              (('STOREN', 1, 2),
    //                  ('DROPR', 1, 0),
    //                  ('MOVE', 2, 0),
    //                  ('OP', 0, 0),
    //                  ('RET', 0, 0))),
    //              ('operators', (('aten::linalg_pinv', '', 1),)),
    //              ('constants', (False, 1e-15)), # default constants are not
    //              used
    //              ('types', ()),
    //              ('register_size', 2)),
    //          (('arguments',
    //              ((('name', 'self'), ('type', '__torch__.m'), ('default_value',
    //              None)),
    //                  (('name', 'input'), ('type', 'Tensor'), ('default_value',
    //                  None)))),
    //              ('returns',
    //                  ((('name', ''), ('type', 'Tensor'), ('default_value',
    //                  None)),)))))
  
    //  bytecode with 2 specified argument:
    //  (6,
    //      ('__torch__.m.forward',
    //          (('instructions',
    //              (('STOREN', 1, 2),
    //                  ('DROPR', 1, 0),
    //                  ('MOVE', 2, 0),
    //                  ('LOADC', 1, 0), # added LOADC for specified argument
    //                  ('OP', 0, 0),
    //                  ('RET', 0, 0))),
    //              ('operators', (('aten::linalg_pinv', '', 2),)),
    //              ('constants', (False, 1e-05)), # updated constant table
    //              ('types', ()),
    //              ('register_size', 2)),
    //          (('arguments',
    //              ((('name', 'self'), ('type', '__torch__.m'), ('default_value',
    //              None)),
    //                  (('name', 'input'), ('type', 'Tensor'), ('default_value',
    //                  None)))),
    //              ('returns',
    //                  ((('name', ''), ('type', 'Tensor'), ('default_value',
    //                  None)),)))))
  
    //  bytecode with 3 specified arguments:
    //  (6,
    //      ('__torch__.m.forward',
    //          (('instructions',
    //              (('STOREN', 1, 2),
    //                  ('DROPR', 1, 0),
    //                  ('MOVE', 2, 0),
    //                  ('LOADC', 1, 0),
    //                  ('LOADC', 0, 0),
    //                  ('OP', 0, 0),
    //                  ('RET', 0, 0))),
    //              ('operators', (('aten::linalg_pinv', '', 3),)),
    //              ('constants', (True, 1e-05)),
    //              ('types', ()),
    //              ('register_size', 2)),
    //          (('arguments',
    //              ((('name', 'self'), ('type', '__torch__.m'), ('default_value',
    //              None)),
    //                  (('name', 'input'), ('type', 'Tensor'), ('default_value',
    //                  None)))),
    //              ('returns',
    //                  ((('name', ''), ('type', 'Tensor'), ('default_value',
    //                  None)),)))))
  }
  
  TEST(LiteInterpreterTest, DefaultArgsTensorinvSpecifyDefault) {
    // The second argument is specified, but the value is the same as the default
    // value. It's treated as "not specified" since the value can be fetched from
    // schema.
    Module m("m");
    m.define(R"(
      def forward(self, input):
        return torch.linalg_tensorinv(input, 2)
    )");
    torch::jit::MobileCode code(m.get_method("forward").graph(), "forward");
    auto arg_nums = code.op_to_num_specified_args();
    ASSERT_EQ(arg_nums.size(), 1);
    ASSERT_EQ(arg_nums["aten::linalg_tensorinv"], 1);
    std::vector<torch::jit::IValue> inputs;
    const int N = 4;
    auto input = torch::rand({N, N, N, N});
    inputs.push_back(input);
    testLiteModuleCompareResultTensors(m, inputs);
  }
  
  void testDefaultArgsPinvWithOutArg(int num_args) {
    Module m("m");
    if (num_args == 1) {
      m.define(R"(
        def forward(self, input):
          return torch.linalg_pinv(input, out=input)
      )");
    } else if (num_args == 2) {
      m.define(R"(
        def forward(self, input):
          return torch.linalg_pinv(input, 1e-5, out=input)
      )");
    } else if (num_args == 3) {
      m.define(R"(
        def forward(self, input):
          return torch.linalg_pinv(input, 1e-5, True, out=input)
      )");
    }
  
    const int N = 28;
    auto input = torch::range(1, N * N, 1);
    input[0] = 10000; // a more stable matrix
    input = input.view({N, N});
    auto ref = m.run_method("forward", input);
    TORCH_CHECK(!input.equal(torch::range(1, N * N, 1)));
    TORCH_CHECK(input.equal(ref.toTensor()));
  }
  
  TEST(LiteInterpreterTest, DefaultArgsPinvWithOutArg) {
    // Test with different number of specified arguments + out arg.
    // Arguments not specified take default value.
    for (int num_args = 1; num_args <= 3; ++num_args) {
      testDefaultArgsPinvWithOutArg(num_args);
    }
  }
  
  TEST(LiteInterpreterTest, DefaultArgsWithOutArg) {
    Module m("m");
    m.define(R"(
      def forward(self, x, h):
        torch.add(x, h, out=x)
    )");
  
    std::vector<IValue> inputs;
    auto input_x = 2 * torch::ones({});
    auto input_h = torch::ones({});
    auto ref = m.run_method("forward", input_x, input_h);
  
    std::stringstream ss;
  
    m._save_for_mobile(ss, {}, true);
    mobile::Module bc = _load_for_mobile(ss);
    bc.run_method("forward", input_x, input_h);
    AT_ASSERT(input_x.equal(4 * torch::ones({})));
  
    auto ops = _get_model_ops_and_info(ss);
    auto op = ops.find("aten::add.out");
    TORCH_CHECK(
        op != ops.end() && op->second.num_schema_args.has_value() &&
        op->second.num_schema_args.value() == 3);
  }
  
  TEST(LiteInterpreterTest, TestExceptionStackWithTwoLevelModuleHierarchy) {
    Module a("A");
    a.define(R"(
      def bar(self, x, y):
        return x + y
    )");
    Module b("B");
    b.register_module("A0", a);
    b.define(R"(
      def foo(self, x, y):
        return self.A0.bar(x, y) + 2
    )");
    Module c("C");
    c.register_module("B0", b);
    c.define(R"(
      def forward(self, x, y):
        return self.B0.foo(x, y) + 3
    )");
  
    std::vector<IValue> inputs;
    inputs.emplace_back(torch::rand({2, 4}));
    inputs.emplace_back(torch::rand({13, 9}));
  
    std::stringstream ss;
    c._save_for_mobile(ss, ExtraFilesMap(), true);
    auto lite_m = _load_for_mobile(ss);
    std::string error_pattern = R"(
    Module hierarchy:top(C)::<unknown>.B0(B)::foo.A0(A)::bar.aten::add
  Traceback of TorchScript (most recent call last):
    File "<string>", line 3, in <unknown>
  
      def forward(self, x, y):
        return self.B0.foo(x, y) + 3
               ~~~~~~~~~~~ <--- HERE
  
    File "<string>", line 3, in foo
  
      def foo(self, x, y):
        return self.A0.bar(x, y) + 2
               ~~~~~~~~~~~ <--- HERE
  
    File "<string>", line 3, in bar
  
      def bar(self, x, y):
        return x + y
               ~~~~~ <--- HERE
    )";
    ASSERT_THROWS_WITH_MESSAGE(lite_m.forward(inputs), error_pattern);
  }
  #endif // !defined(FB_XPLAT_BUILD)
  
  namespace {
  static auto reg =
      torch::class_<TorchBindLiteInterpreterTestStruct>(
          "_TorchScriptTesting",
          "_LiteInterpreterTest")
          .def(torch::init<>())
          .def("get", &TorchBindLiteInterpreterTestStruct::get)
          .def_pickle(
              // __getattr__
              [](const c10::intrusive_ptr<TorchBindLiteInterpreterTestStruct>&
                     self) -> int64_t { return 0; },
              // __setattr__
              [](int64_t state) {
                return c10::make_intrusive<TorchBindLiteInterpreterTestStruct>();
              });
  
  } // namespace
  
  TEST(LiteInterpreterTest, OperatorCacheDifferentiatesDefaultArgs) {
    // Create 3 methods:
    //
    // 1. forward() returns a tensor with dtype=torch.int64 (4)
    // 2. forward2() returns a tensor with dtype=torch.float32 (6)
    // 3. forward3() returns a tensor with dtype=torch.float32 but
    //    the dtype is inferred by the input tensor's dtype
    //
    // If caching works correctly, then the result from the full-jit
    // module and the lite module will be the same. Otherwise, it
    // will be different if we don't correctly ignore the cache
    // entry for an operator that has a different number of
    // arguments.
    Module m("m");
    m.define(R"(
      def forward(self):
        ret1 = torch.new_empty(torch.zeros(10), [10], dtype=4)
        return ret1.fill_(25)
    )");
    m.define(R"(
      def forward2(self):
        ret1 = torch.new_empty(torch.zeros(10), [10], dtype=6)
        return ret1.fill_(32.0)
    )");
    m.define(R"(
      def forward3(self):
        ret1 = torch.new_empty(torch.zeros(10), [10])
        return ret1.fill_(12.0)
    )");
  
    std::vector<torch::jit::IValue> inputs;
    testLiteModuleCompareResultTensors(m, inputs, "forward");
    testLiteModuleCompareResultTensors(m, inputs, "forward2");
    testLiteModuleCompareResultTensors(m, inputs, "forward3");
  }
  
  } // namespace jit
  } // namespace torch
  