test_modules.py

# Owner(s): ["module: dynamo"]

from copy import deepcopy
from unittest.mock import patch

import torch

import torch._dynamo.test_case
import torch._dynamo.testing
from torch._dynamo.eval_frame import unsupported
from torch._dynamo.mutation_guard import GenerationTracker
from torch._dynamo.testing import same
from torch.nn import functional as F
from torch.nn.modules.lazy import LazyModuleMixin
from torch.nn.parameter import Parameter, UninitializedParameter

try:
    from . import test_functions
except ImportError:
    import test_functions


class BasicModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = torch.nn.Linear(10, 10)
        self.scale = torch.randn(1, 10)

    def forward(self, x):
        return F.relu(self.linear1(x)) * self.scale


class FnMember(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = torch.nn.Linear(10, 10)
        self.activation = F.relu

    def forward(self, x):
        x = self.linear1(x)
        if self.activation:
            x = self.activation(x)
        return x


class FnMemberCmp(torch.nn.Module):
    def __init__(self, activation):
        super().__init__()
        self.linear1 = torch.nn.Linear(10, 10)
        self.activation = activation

    def forward(self, x):
        x = self.linear1(x)
        if self.activation is not None:
            x = self.activation(x)
        if self.activation is None:
            x = torch.sigmoid(x)
        return x


class SubmoduleExample(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = BasicModule()
        self.layer2 = BasicModule()
        self.scale = torch.randn(1, 10)

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        return x * self.scale


class IsTrainingCheck(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = torch.nn.Linear(10, 10)
        self.linear2 = torch.nn.Linear(10, 10)
        self.train(True)

    def forward(self, x):
        if self.training:
            mod = self.linear1
        else:
            mod = self.linear2
        return F.relu(mod(x))


class IsEvalCheck(IsTrainingCheck):
    def __init__(self):
        super().__init__()
        self.train(False)


class ModuleMethodCall(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = BasicModule()
        self.layer2 = BasicModule()
        self.scale = torch.randn(1, 10)

    def call_and_scale(self, mod, x):
        x = mod(x)
        return x * self.scale

    def forward(self, x):
        x1 = self.call_and_scale(self.layer1, x)
        x2 = self.call_and_scale(self.layer2, x)
        return x1 + x2


class UnsupportedMethodCall(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = BasicModule()
        self.scale = torch.randn(1, 10)

    def call_and_scale(self, mod, x):
        x = mod(x)
        x = x * self.scale
        return unsupported(x, x)

    def forward(self, x):
        x1 = self.call_and_scale(self.layer1, x)
        return x + x1


class UnsupportedModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = BasicModule()
        self.scale = torch.randn(1, 10)

    def forward(self, x):
        x = self.layer1(x) * self.scale
        return unsupported(x, x)


class UnsupportedModuleCall(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.mod = UnsupportedModule()

    def forward(self, x):
        return 1 + self.mod(x * 1.5)


class ModuleStaticMethodCall(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = BasicModule()
        self.layer2 = BasicModule()
        self.scale = torch.randn(1, 10)

    @staticmethod
    def call_and_scale(scale, mod, x):
        x = mod(x)
        return x * scale

    def forward(self, x):
        x1 = self.call_and_scale(self.scale, self.layer1, x)
        x2 = self.call_and_scale(self.scale, self.layer2, x)
        return x1 + x2


class ModuleClassMethodCall(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = BasicModule()
        self.layer2 = BasicModule()
        self.scale = torch.randn(1, 10)

    @classmethod
    def call_and_scale(cls, scale, mod, x):
        x = mod(x)
        return x * scale

    def forward(self, x):
        x1 = self.call_and_scale(self.scale, self.layer1, x)
        x2 = self.call_and_scale(self.scale, self.layer2, x)
        return x1 + x2


class ModuleProperty(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.scale = torch.randn(1, 10)

    @property
    def scale_alias(self):
        return self.scale

    def forward(self, x):
        return x * self.scale_alias


class ConstLoop(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = torch.nn.Linear(10, 10)
        self.count = 3

    def forward(self, x):
        for i in range(self.count):
            x = torch.sigmoid(self.linear1(x))
        return x


class ViaModuleCall(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = torch.nn.Linear(10, 10)

    def forward(self, x):
        return test_functions.constant3(torch.sigmoid(self.linear1(x)), x)


class IsNoneLayer(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = torch.nn.Linear(10, 10)
        self.layer2 = None
        self.train(True)

    def forward(self, x):
        if self.layer1 is not None:
            x = self.layer1(x)
        if self.layer2 is not None:
            x = self.layer2(x)
        return x


class LayerList(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = [
            torch.nn.Linear(10, 10),
            torch.nn.ReLU(),
            torch.nn.Linear(10, 10),
        ]

    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return x


class ModuleList(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = torch.nn.ModuleList(
            [
                torch.nn.Linear(10, 10),
                torch.nn.ReLU(),
                torch.nn.Linear(10, 10),
                torch.nn.ReLU(),
            ]
        )

    def forward(self, x):
        for i in range(len(self.layers)):
            x = self.layers[i](x)

        for layer in self.layers:
            x = layer(x)

        for layer, val in zip(self.layers, (x, x, x, x)):
            x = layer(x) + val

        for layer, val in zip(self.layers, (1, 2, 3, 4)):
            x = layer(x) + val

        for idx, layer in enumerate(self.layers):
            x = layer(x) * idx

        for idx, layer in enumerate(self.layers[::-1]):
            x = layer(x) * idx

        return x


class ModuleDict(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = torch.nn.ModuleDict(
            {
                "0": torch.nn.Linear(10, 10),
            }
        )

    def forward(self, x):
        # TODO(future PR): handle more logic
        x = self.layers["0"](x)
        return x


class TensorList(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = (
            torch.randn((1, 10)),
            torch.randn((10, 1)),
            torch.randn((1, 10)),
            torch.randn((10, 1)),
        )

    def forward(self, x):
        for layer in self.layers:
            x = x * layer
        return x


class Children(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.l1 = torch.nn.Linear(10, 10)
        self.l2 = torch.nn.ReLU()
        self.l3 = torch.nn.Linear(10, 10)
        self.l4 = torch.nn.ReLU()

    def forward(self, x):
        for block in self.children():
            x = block(x)
        return x


class IntArg(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = torch.nn.Linear(10, 10)

    def forward(self, x, offset=1):
        x = F.relu(self.layer1(x)) + offset
        return x


class Seq(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = torch.nn.Sequential(
            torch.nn.Linear(10, 10),
            torch.nn.ReLU(),
            torch.nn.Linear(10, 10),
            torch.nn.ReLU(),
        )

    def forward(self, x):
        return self.layers(x)


class Cfg:
    def __init__(self):
        self.val = 0.5
        self.count = 3


class CfgModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.cfg = Cfg()
        self.layer = torch.nn.Linear(10, 10)

    def forward(self, x):
        for i in range(self.cfg.count):
            x = self.layer(x + self.cfg.val)
        return x


class StringMember(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = torch.nn.Linear(10, 10)
        self.mode = "some_string"

    def forward(self, x):
        if self.mode == "some_string":
            return F.relu(self.linear1(x))


class _Block(torch.nn.Module):
    def forward(self, x):
        return 1.5 * torch.cat(x, 1)


class _DenseBlock(torch.nn.ModuleDict):
    _version = 2

    def __init__(
        self,
        num_layers: int = 3,
    ) -> None:
        super().__init__()
        for i in range(num_layers):
            self.add_module("denselayer%d" % (i + 1), _Block())

    def forward(self, init_features):
        features = [init_features]
        for name, layer in self.items():
            new_features = layer(features)
            features.append(new_features)
        return torch.cat(features, 1)


class DenseNetBlocks(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = _DenseBlock()

    def forward(self, x):
        return self.layers(x)


class MaterializedModule(torch.nn.Module):
    """Once the below lazy module is initialized with its first input,
    it is transformed into this module."""

    param: Parameter

    def __init__(self):
        super().__init__()
        self.register_parameter("param", None)

    def forward(self, x):
        return x


class LazyModule(LazyModuleMixin, MaterializedModule):
    param: UninitializedParameter
    cls_to_become = MaterializedModule

    def __init__(self):
        super().__init__()
        self.param = UninitializedParameter()

    def initialize_parameters(self, x):
        self.param.materialize(x.shape)


def requires_grad1(module: torch.nn.Module, recurse: bool = False) -> bool:
    requires_grad = any([p.requires_grad for p in module.parameters(recurse)])
    return requires_grad


def requires_grad2(module: torch.nn.Module, recurse: bool = False) -> bool:
    requires_grad = any(p.requires_grad for p in module.parameters(recurse))
    return requires_grad


class ParametersModule1(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = torch.nn.Linear(10, 10)
        self.scale = torch.nn.Parameter(torch.randn(1, 10))

    def forward(self, x):
        if not requires_grad1(self):
            return F.relu(self.linear1(x)) * self.scale
        else:
            return x + 1


class ParametersModule2(ParametersModule1):
    def forward(self, x):
        if not requires_grad2(self):
            return F.relu(self.linear1(x)) * self.scale
        else:
            return x + 1


class ParametersModule3(ParametersModule1):
    def forward(self, x):
        ones = torch.ones(10, dtype=next(self.parameters()).dtype)
        return F.relu(self.linear1(x)) * self.scale + ones


class SuperModule(BasicModule):
    def forward(self, x):
        x = super().forward(x)
        return x + 10.0


class ComplicatedSuperParent(torch.nn.Module):
    @classmethod
    def custom_add(cls, x):
        x = x + x
        return x


class SuperChildCallsClassMethod(ComplicatedSuperParent):
    @classmethod
    def child_func(cls, x):
        x = super().custom_add(x)
        return x

    def forward(self, x):
        x = self.child_func(x)
        return x


class HasAttrModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.scale = torch.nn.Parameter(torch.randn(1, 10))

    def forward(self, x):
        x = F.relu(x)
        if hasattr(self, "scale"):
            x *= self.scale
        if hasattr(self, "scale2"):
            x *= self.scale2
        return x


class EnumValues(torch.nn.ModuleDict):
    def __init__(
        self,
        num_layers: int = 3,
    ) -> None:
        super().__init__()
        for i in range(num_layers):
            self.add_module("denselayer%d" % (i + 1), _Block())

    def forward(self, init_features):
        features = [init_features]
        for idx, layer in enumerate(self.values()):
            new_features = layer(features)
            features.append(new_features)
        return torch.cat(features, 1)


class CallForwardDirectly(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = BasicModule()
        self.layer2 = torch.nn.Linear(10, 10)

    def forward(self, x):
        x = self.layer1.forward(x)
        x = self.layer2.forward(x)
        return x


class ModuleNameString(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = torch.nn.Linear(10, 10)

    def forward(self, x):
        if self.__class__.__name__ == "ABC":
            return 10
        if self.linear1.__class__.__name__ == "Linear":
            return F.relu(self.linear1(x) + 10)
        return 11


class SelfMutatingModule(torch.nn.Module):
    def __init__(self, layer):
        super().__init__()
        self.layer = layer
        self.counter = 0

    def forward(self, x):
        result = self.layer(x) + self.counter
        self.counter += 1
        return F.relu(result)


class ModuleAttributePrecedenceBase(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def linear(self, x):
        return x * 2.0


class ModuleAttributePrecedence(ModuleAttributePrecedenceBase):
    def __init__(self):
        super().__init__()
        self.activation = torch.nn.ReLU()
        self.linear = torch.nn.Linear(10, 10)
        self.initializer = torch.ones([10, 10])
        self.scale = 0.5

    def activation(self, x):
        return x * 1.2

    def initializer(self):
        return torch.zeros([10, 10])

    def scale(self):
        return 2.0

    def forward(self, x):
        # object attribute takes precedence unless it's a nn.Module
        return self.activation(self.linear(self.initializer + x)) * self.scale


def make_test(fn, expected_ops=None):
    def test_fn(self):
        return torch._dynamo.testing.standard_test(
            self, fn=fn, nargs=1, expected_ops=expected_ops
        )

    fn.eval()
    return test_fn


class NNModuleTests(torch._dynamo.test_case.TestCase):
    test_seq = make_test(Seq())
    test_basicmodule1 = make_test(BasicModule())
    test_basicmodule2 = make_test(BasicModule())
    test_submodules1 = make_test(SubmoduleExample())
    test_submodules2 = make_test(SubmoduleExample())
    test_modulemethod1 = make_test(ModuleMethodCall())
    test_modulemethod2 = make_test(ModuleMethodCall())
    test_module_static_method = make_test(ModuleStaticMethodCall())
    test_fnmember = make_test(FnMember())
    test_fnmembercmp1 = make_test(FnMemberCmp(F.relu))
    test_fnmembercmp2 = make_test(FnMemberCmp(None))
    test_constloop = make_test(ConstLoop())
    test_istraining1 = make_test(IsTrainingCheck())
    test_istraining2 = make_test(IsTrainingCheck())
    test_iseval1 = make_test(IsEvalCheck())
    test_iseval2 = make_test(IsEvalCheck())
    test_viamodulecall = make_test(ViaModuleCall())
    test_isnonelayer = make_test(IsNoneLayer())
    test_layerlist = make_test(LayerList())
    test_tensorlist = make_test(TensorList())
    test_intarg = make_test(IntArg())
    test_cfgmod = make_test(CfgModule())
    test_stringmember = make_test(StringMember())
    test_modulelist = make_test(ModuleList())
    test_moduledict = make_test(ModuleDict())
    test_super1 = make_test(SuperModule())
    test_super_class_method = make_test(SuperChildCallsClassMethod())
    test_children = make_test(Children())
    test_densenet = make_test(DenseNetBlocks())
    test_parameters1 = make_test(ParametersModule1())
    test_parameters2 = make_test(ParametersModule2())
    test_parameters3 = make_test(ParametersModule3(), expected_ops=5)
    test_hasattr = make_test(HasAttrModule())
    test_enumvalues = make_test(EnumValues())
    test_module_class_method = make_test(ModuleClassMethodCall())
    test_module_property = make_test(ModuleProperty())
    test_forward_directly = make_test(CallForwardDirectly())
    test_module_name_string = make_test(ModuleNameString())
    test_module_attribute_precedence = make_test(ModuleAttributePrecedence())

    def test_unsupportedmethod(self):
        m = UnsupportedMethodCall()
        i = torch.randn(10)
        cnt = torch._dynamo.testing.CompileCounter()
        opt_m = torch._dynamo.optimize(cnt)(m)
        r = opt_m(i)
        self.assertTrue(torch._dynamo.testing.same(r, m(i)))
        self.assertEqual(cnt.op_count, 5)

    def test_unsupportedmodule(self):
        m = UnsupportedModuleCall()
        i = torch.randn(10)
        cnt = torch._dynamo.testing.CompileCounter()
        opt_m = torch._dynamo.optimize(cnt)(m)
        r = opt_m(i)
        self.assertTrue(torch._dynamo.testing.same(r, m(i)))
        self.assertEqual(cnt.op_count, 6)

    def test_self_mutating1(self):
        m1 = torch.nn.Linear(10, 10)
        m2 = SelfMutatingModule(m1)
        m3 = SelfMutatingModule(m1)
        m4 = SelfMutatingModule(m1)
        i = torch.randn(10)
        out2 = [m2(i), m2(i), m2(i)]
        cnt = torch._dynamo.testing.CompileCounter()
        opt_m3 = torch._dynamo.optimize_assert(cnt)(m3)
        opt_m4 = torch._dynamo.optimize_assert(cnt)(m4)
        out3 = [opt_m3(i), opt_m3(i), opt_m3(i)]
        out4 = [opt_m4(i), opt_m4(i), opt_m4(i)]
        self.assertTrue(torch._dynamo.testing.same(out2, out3))
        self.assertTrue(torch._dynamo.testing.same(out2, out4))
        self.assertEqual(cnt.frame_count, 3)

    @patch.object(torch._dynamo.config, "raise_on_ctx_manager_usage", False)
    def test_generation_tag(self):
        cnt = torch._dynamo.testing.CompileCounter()

        # guarantee that we have installed
        # the generation tagging function
        with torch._dynamo.optimize_assert(cnt):
            pass

        m1 = torch.nn.Linear(10, 10)
        prev_generation = GenerationTracker.get_generation_value(m1)
        cur_generation = prev_generation + 1

        with torch._dynamo.optimize_assert(cnt):
            m2 = torch.nn.Linear(10, 10)

        self.assertEqual(GenerationTracker.get_generation_value(m1), prev_generation)
        self.assertEqual(GenerationTracker.get_generation_value(m2), cur_generation)
        # check that newly constructed instances
        # also have the same generation (even if copied from an old instance)
        m3 = deepcopy(m1)
        self.assertEqual(GenerationTracker.get_generation_value(m3), cur_generation)

    def test_simple_torch_function(self):
        def foo(x):
            # function call, twice to test wrapping
            x = F.sigmoid(x)
            x = F.sigmoid(x)
            # method call, twice to test wrapping
            x = x.sigmoid()
            x = x.sigmoid()
            return x

        class TensorProxy(torch.Tensor):
            @classmethod
            def __torch_function__(cls, func, types, args=(), kwargs=None):
                return super().__torch_function__(func, types, args, kwargs)

        torch._dynamo.config.traceable_tensor_subclasses.add(TensorProxy)

        x = torch.randn(1).as_subclass(TensorProxy)
        cnt = torch._dynamo.testing.CompileCounter()
        out1 = foo(x)
        opt_foo = torch._dynamo.optimize(cnt, nopython=True)(foo)
        out2 = opt_foo(x)

        self.assertEqual(cnt.op_count, 4)
        self.assertTrue(torch._dynamo.testing.same(out1, out2))

        torch._dynamo.config.traceable_tensor_subclasses.remove(TensorProxy)

    def test_torch_function_with_closure(self):
        def run():

            counter = 0

            def foo(x):
                # function call, twice to test wrapping
                x = F.sigmoid(x)
                x = F.sigmoid(x)
                # method call, twice to test wrapping
                x = x.sigmoid()
                x = x.sigmoid()
                return x

            class TensorProxy(torch.Tensor):
                @classmethod
                def __torch_function__(cls, func, types, args=(), kwargs=None):
                    nonlocal counter
                    # for now, only support reads from closure cells
                    # TODO(future PR): support writes as well
                    counter + 1
                    return super().__torch_function__(func, types, args, kwargs)

            torch._dynamo.config.traceable_tensor_subclasses.add(TensorProxy)

            x = torch.randn(1).as_subclass(TensorProxy)
            x = torch.randn(1)
            cnt = torch._dynamo.testing.CompileCounter()
            out1 = foo(x)
            opt_foo = torch._dynamo.optimize(cnt, nopython=True)(foo)
            out2 = opt_foo(x)

            self.assertEqual(cnt.op_count, 4)
            self.assertTrue(torch._dynamo.testing.same(out1, out2))

            torch._dynamo.config.traceable_tensor_subclasses.remove(TensorProxy)

        run()

    @patch.object(torch._dynamo.config, "raise_on_ctx_manager_usage", False)
    def test_nn_moduledict_contains(self):
        class M(torch.nn.Module):
            def __init__(self, module_dict):
                super().__init__()
                self.module_dict = module_dict

            def forward(self, x):
                if "foo" in self.module_dict:
                    x = torch.mul(x, 1.0)
                x = torch.add(x, 1.0)
                return x

        module_dict = torch.nn.ModuleDict({"foo": torch.nn.Conv2d(1, 1, 1)})
        m = M(module_dict)
        data = torch.randn(1)
        out1 = m(data)
        cnt = torch._dynamo.testing.CompileCounter()
        opt_m = torch._dynamo.optimize(cnt, nopython=True)(m)
        out2 = opt_m(data)
        self.assertEqual(cnt.op_count, 2)
        self.assertTrue(torch._dynamo.testing.same(out1, out2))

        module_dict = torch.nn.ModuleDict({"bar": torch.nn.Conv2d(1, 1, 1)})
        m = M(module_dict)
        data = torch.randn(1)
        out1 = m(data)
        cnt = torch._dynamo.testing.CompileCounter()
        torch._dynamo.reset()
        opt_m = torch._dynamo.optimize(cnt, nopython=True)(m)
        out2 = opt_m(data)

        self.assertEqual(cnt.op_count, 1)
        self.assertTrue(torch._dynamo.testing.same(out1, out2))

        module_dict = torch.nn.ModuleDict({"cat": torch.nn.Conv2d(1, 1, 1)})
        pre = m(data)
        cnt.clear()

        with torch._dynamo.optimize(cnt, nopython=False):
            opt_pre = m(data)
            m = M(module_dict)
            data = torch.randn(1)
            out1 = m(data)

        out_post = m(data)
        self.assertEqual(cnt.frame_count, 1)
        self.assertEqual(cnt.op_count, 1)
        self.assertTrue(torch._dynamo.testing.same(pre, opt_pre))
        self.assertTrue(torch._dynamo.testing.same(out1, out_post))

    def test_lazy_module(self):
        input_shape = (16, 3, 6, 7, 8)

        cnt = torch._dynamo.testing.CompileCounter()
        module = LazyModule()

        def test_static_module():
            input = torch.ones(*input_shape)
            module(input)

        opt_test_static_module = torch._dynamo.optimize(cnt)(test_static_module)
        opt_test_static_module()

        self.assertTrue(
            isinstance(module, MaterializedModule),
            "Module should be transformed to an instance of MaterializedModule.",
        )
        self.assertEqual(module.param.shape, input_shape)

        # test when mapped to UnspecializedNNModule
        module = LazyModule()

        def test_unspecialized():
            nonlocal module
            module = LazyModule()
            input = torch.ones(*input_shape)
            module(input)

        opt_test_unspecialized = torch._dynamo.optimize(cnt)(test_unspecialized)
        opt_test_unspecialized()

        self.assertTrue(
            isinstance(module, MaterializedModule),
            "Module should be transformed to an instance of MaterializedModule.",
        )
        self.assertEqual(module.param.shape, input_shape)

        # test with a static module in torch.*
        module = torch.nn.modules.LazyBatchNorm3d(
            affine=False, track_running_stats=False
        )

        cnt = torch._dynamo.testing.CompileCounter()

        torch._dynamo.reset()

        def test_torch_static():
            input = torch.ones(*input_shape)
            return module(input)  # fully materialized

        opt_test_torch_static = torch._dynamo.optimize(cnt)(test_torch_static)
        opt_test_torch_static()
        out = opt_test_torch_static()

        self.assertTrue(same(out, module(torch.ones(*input_shape))))

        self.assertTrue(
            isinstance(module, torch.nn.modules.batchnorm.BatchNorm3d),
            "Module should be transformed to an instance of BatchNorm3d.",
        )
        self.assertEqual(cnt.frame_count, 1, "No guards should have triggered.")

    def test_call_fn_with_non_const_inputs_safe(self):
        class ModuleSpecialFwd(torch.nn.Module):
            def __init__(self):
                super(ModuleSpecialFwd, self).__init__()
                self.conv = torch.nn.Conv2d(
                    in_channels=3, out_channels=20, kernel_size=(5, 5)
                )

            def _conv_forward(self, x):
                return self.conv._conv_forward(x, self.conv.weight, self.conv.bias)

            def forward(self, x):
                return self._conv_forward(x)

        mod = ModuleSpecialFwd()
        rx = torch.randn([3, 10, 10])
        real = mod(rx)
        graph, _ = torch._dynamo.export(mod, rx)
        self.assertTrue(torch._dynamo.testing.same(real, graph(rx)))


if __name__ == "__main__":
    from torch._dynamo.test_case import run_tests

    run_tests()