# Owner(s): ["oncall: jit"] import io import os import pathlib import sys import unittest from typing import NamedTuple, Optional import torch from torch import Tensor from torch.testing._internal.common_utils import TemporaryFileName # Make the helper files in test/ importable pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) sys.path.append(pytorch_test_dir) from torch.testing._internal.jit_utils import JitTestCase, clear_class_registry ENABLE_FLATBUFFER = os.environ.get("ENABLE_FLATBUFFER", "0") == "1" if __name__ == "__main__": raise RuntimeError( "This test file is not meant to be run directly, use:\n\n" "\tpython test/test_jit.py TESTNAME\n\n" "instead." ) class TestSaveLoad(JitTestCase): def test_different_modules(self): """ Exercise the situation where we have the same qualified name in two different CompilationUnits on save/load. """ class Foo(torch.nn.Module): def __init__(self): super(Foo, self).__init__() self.foo = torch.nn.Linear(2, 2) self.bar = torch.nn.Linear(2, 2) def forward(self, x): x = self.foo(x) x = self.bar(x) return x first_script_module = torch.jit.script(Foo()) first_saved_module = io.BytesIO() torch.jit.save(first_script_module, first_saved_module) first_saved_module.seek(0) clear_class_registry() class Foo(torch.nn.Module): def __init__(self): super(Foo, self).__init__() self.foo = torch.nn.Linear(2, 2) def forward(self, x): x = self.foo(x) return x second_script_module = torch.jit.script(Foo()) second_saved_module = io.BytesIO() torch.jit.save(torch.jit.script(Foo()), second_saved_module) second_saved_module.seek(0) clear_class_registry() self.assertEqual( first_script_module._c.qualified_name, second_script_module._c.qualified_name, ) class ContainsBoth(torch.nn.Module): def __init__(self): super().__init__() self.add_module("second", torch.jit.load(second_saved_module)) self.add_module("first", torch.jit.load(first_saved_module)) def forward(self, x): x = self.first(x) x = self.second(x) return x sm = torch.jit.script(ContainsBoth()) contains_both = io.BytesIO() torch.jit.save(sm, contains_both) contains_both.seek(0) sm = torch.jit.load(contains_both) def test_different_functions(self): """ Exercise the situation where we have the same qualified name in two different CompilationUnits on save/load. """ def lol(x): return x class Foo(torch.nn.Module): def forward(self, x): return lol(x) first_script_module = torch.jit.script(Foo()) first_saved_module = io.BytesIO() torch.jit.save(first_script_module, first_saved_module) first_saved_module.seek(0) clear_class_registry() def lol(x): # noqa: F811 return "hello" class Foo(torch.nn.Module): def forward(self, x): return lol(x) second_script_module = torch.jit.script(Foo()) second_saved_module = io.BytesIO() torch.jit.save(torch.jit.script(Foo()), second_saved_module) second_saved_module.seek(0) clear_class_registry() self.assertEqual( first_script_module._c.qualified_name, second_script_module._c.qualified_name, ) class ContainsBoth(torch.nn.Module): def __init__(self): super().__init__() self.add_module("second", torch.jit.load(second_saved_module)) self.add_module("first", torch.jit.load(first_saved_module)) def forward(self, x): x = self.first(x) x = self.second(x) return x sm = torch.jit.script(ContainsBoth()) contains_both = io.BytesIO() torch.jit.save(sm, contains_both) contains_both.seek(0) sm = torch.jit.load(contains_both) def test_different_interfaces(self): """ Exercise the situation where we have the same qualified name in two different CompilationUnits on save/load. """ @torch.jit.interface class MyInterface(object): def bar(self, x: Tensor) -> Tensor: pass @torch.jit.script class ImplementInterface(object): def __init__(self): pass def bar(self, x): return x class Foo(torch.nn.Module): __annotations__ = {"interface": MyInterface} def __init__(self): super().__init__() self.interface = ImplementInterface() def forward(self, x): return self.interface.bar(x) first_script_module = torch.jit.script(Foo()) first_saved_module = io.BytesIO() torch.jit.save(first_script_module, first_saved_module) first_saved_module.seek(0) clear_class_registry() @torch.jit.interface class MyInterface(object): def not_bar(self, x: Tensor) -> Tensor: pass @torch.jit.script # noqa: F811 class ImplementInterface(object): # noqa: F811 def __init__(self): pass def not_bar(self, x): return x class Foo(torch.nn.Module): __annotations__ = {"interface": MyInterface} def __init__(self): super().__init__() self.interface = ImplementInterface() def forward(self, x): return self.interface.not_bar(x) second_script_module = torch.jit.script(Foo()) second_saved_module = io.BytesIO() torch.jit.save(torch.jit.script(Foo()), second_saved_module) second_saved_module.seek(0) clear_class_registry() self.assertEqual( first_script_module._c.qualified_name, second_script_module._c.qualified_name, ) class ContainsBoth(torch.nn.Module): def __init__(self): super().__init__() self.add_module("second", torch.jit.load(second_saved_module)) self.add_module("first", torch.jit.load(first_saved_module)) def forward(self, x): x = self.first(x) x = self.second(x) return x sm = torch.jit.script(ContainsBoth()) contains_both = io.BytesIO() torch.jit.save(sm, contains_both) contains_both.seek(0) sm = torch.jit.load(contains_both) def test_many_collisions(self): class MyCoolNamedTuple(NamedTuple): a: int @torch.jit.interface class MyInterface(object): def bar(self, x: Tensor) -> Tensor: pass @torch.jit.script class ImplementInterface(object): def __init__(self): pass def bar(self, x): return x def lol(x): return x class Foo(torch.nn.Module): interface: MyInterface def __init__(self): super().__init__() self.foo = torch.nn.Linear(2, 2) self.bar = torch.nn.Linear(2, 2) self.interface = ImplementInterface() def forward(self, x): x = self.foo(x) x = self.bar(x) x = lol(x) x = self.interface.bar(x) return x, MyCoolNamedTuple(a=5) first_script_module = torch.jit.script(Foo()) first_saved_module = io.BytesIO() torch.jit.save(first_script_module, first_saved_module) first_saved_module.seek(0) clear_class_registry() @torch.jit.interface class MyInterface(object): def not_bar(self, x: Tensor) -> Tensor: pass @torch.jit.script # noqa: F811 class ImplementInterface(object): # noqa: F811 def __init__(self): pass def not_bar(self, x): return x def lol(x): # noqa: F811 return "asdofij" class MyCoolNamedTuple(NamedTuple): # noqa: F811 a: str class Foo(torch.nn.Module): interface: MyInterface def __init__(self): super().__init__() self.foo = torch.nn.Linear(2, 2) self.interface = ImplementInterface() def forward(self, x): x = self.foo(x) self.interface.not_bar(x) x = lol(x) return x, MyCoolNamedTuple(a="hello") second_script_module = torch.jit.script(Foo()) second_saved_module = io.BytesIO() torch.jit.save(second_script_module, second_saved_module) second_saved_module.seek(0) clear_class_registry() self.assertEqual( first_script_module._c.qualified_name, second_script_module._c.qualified_name, ) class ContainsBoth(torch.nn.Module): def __init__(self): super().__init__() self.add_module("second", torch.jit.load(second_saved_module)) self.add_module("first", torch.jit.load(first_saved_module)) def forward(self, x): x, named_tuple_1 = self.first(x) x, named_tuple_2 = self.second(x) return len(x + named_tuple_2.a) + named_tuple_1.a sm = torch.jit.script(ContainsBoth()) contains_both = io.BytesIO() torch.jit.save(sm, contains_both) contains_both.seek(0) sm = torch.jit.load(contains_both) def test_save_load_with_extra_files(self): class MyMod(torch.jit.ScriptModule): @torch.jit.script_method def forward(self, a): return a # specifically test binary data value = b"bar\x00\xffbaz" expected_extra_files = {} expected_extra_files["foo"] = value # verify that str to bytes conversion also works expected_extra_files["foo2"] = "bar" m = MyMod() # Save to file. with TemporaryFileName() as fname: m.save(fname, _extra_files=expected_extra_files) # values don't matter extra_files = {"foo": "", "foo2": None} torch.jit.load(fname, _extra_files=extra_files) self.assertEqual(value, extra_files["foo"]) # results come back always as bytes self.assertEqual(b"bar", extra_files["foo2"]) # Use torch.jit API torch.jit.save(m, fname, _extra_files=expected_extra_files) extra_files["foo"] = "" torch.jit.load(fname, _extra_files=extra_files) self.assertEqual(value, extra_files["foo"]) # Save to buffer. buffer = io.BytesIO(m.save_to_buffer(_extra_files=expected_extra_files)) extra_files = {"foo": ""} torch.jit.load(buffer, _extra_files=extra_files) self.assertEqual(value, extra_files["foo"]) # Use torch.jit API buffer = io.BytesIO() torch.jit.save(m, buffer, _extra_files=expected_extra_files) buffer.seek(0) extra_files = {"foo": ""} torch.jit.load(buffer, _extra_files=extra_files) self.assertEqual(value, extra_files["foo"]) # Non-existent file 'bar' with self.assertRaises(RuntimeError): extra_files["bar"] = "" torch.jit.load(buffer, _extra_files=extra_files) def test_save_load_using_pathlib(self): class MyMod(torch.jit.ScriptModule): @torch.jit.script_method def forward(self, a): return 2 * a m = MyMod() # Save then load. with TemporaryFileName() as fname: path = pathlib.Path(fname) m.save(path) m2 = torch.jit.load(path) x = torch.tensor([1.0, 2.0, 3.0, 4.0]) self.assertTrue(torch.equal(m(x), m2(x))) def test_save_nonexit_file(self): class Foo(torch.nn.Module): def forward(self, x): return 2 * x script_module = torch.jit.script(Foo()) with self.assertRaises(RuntimeError): script_module.save("NonExist/path/test.pt") def test_save_namedtuple_input_only(self): """ Even if a NamedTuple is only used as an input argument, saving and loading should work correctly. """ global FooTuple # see [local resolution in python] class FooTuple(NamedTuple): a: int class MyModule(torch.nn.Module): def forward(self, x: FooTuple) -> torch.Tensor: return torch.tensor(3) m_loaded = self.getExportImportCopy(torch.jit.script(MyModule())) output = m_loaded(FooTuple(a=5)) self.assertEqual(output, torch.tensor(3)) def test_save_namedtuple_output_only(self): """ Even if a NamedTuple is only used as an output argument, saving and loading should work correctly. """ global FooTuple # see [local resolution in python] class FooTuple(NamedTuple): a: int class MyModule(torch.nn.Module): def forward(self) -> Optional[FooTuple]: return None m_loaded = self.getExportImportCopy(torch.jit.script(MyModule())) output = m_loaded() self.assertEqual(output, None) def test_save_load_params_buffers_submodules(self): """ Check that parameters, buffers, and submodules are the same after loading. """ class Submodule(torch.nn.Module): def __init__(self): super().__init__() class TestModule(torch.nn.Module): def __init__(self): super().__init__() self.add_module("submodule_a", Submodule()) self.register_parameter( "parameter_a", torch.nn.Parameter(torch.randn(4)) ) self.register_buffer("buffer", torch.randn(4)) self.t = torch.rand(4) # not buffer self.parameter_b = torch.nn.Parameter(torch.randn(4)) self.submodule_b = Submodule() m = TestModule() m_loaded = self.getExportImportCopy(torch.jit.script(m)) # Check submodules. self.assertEqual( len(list(m.named_modules())), len(list(m_loaded.named_modules())) ) for m_s, loaded_s in zip(m.named_modules(), m_loaded.named_modules()): m_name, _ = m_s loaded_name, _ = loaded_s self.assertEqual(m_name, loaded_name) # Check parameters. self.assertEqual(len(list(m.parameters())), len(list(m_loaded.parameters()))) for m_p, loaded_p in zip(m.parameters(), m_loaded.parameters()): self.assertEqual(m_p, loaded_p) # Check buffers. self.assertEqual( len(list(m.named_buffers())), len(list(m_loaded.named_buffers())) ) for m_b, loaded_b in zip(m.named_buffers(), m_loaded.named_buffers()): m_name, m_buffer = m_b loaded_name, loaded_buffer = loaded_b self.assertEqual(m_name, loaded_name) self.assertEqual(m_buffer, loaded_buffer) def test_save_load_meta_tensors(self): """ Check that parameters, buffers, and submodules are the same after loading for a module with parameters and buffers that are meta tensors """ class Foo(torch.nn.Module): def __init__(self): super(Foo, self).__init__() self.foo = torch.nn.Linear(2, 3, device="meta") self.bar = torch.nn.Linear(3, 4) self.register_buffer("buffer", torch.randn(4, device="meta")) def forward(self, x): x = self.foo(x) x = self.bar(x) return x m = Foo() m_loaded = self.getExportImportCopy(torch.jit.script(m)) # Check submodules. self.assertEqual( len(list(m.named_modules())), len(list(m_loaded.named_modules())) ) self.assertEqual( set(name for name, _ in m.named_modules()), set(name for name, _ in m_loaded.named_modules()), ) # Check parameters. m_params = dict(m.named_parameters()) m_loaded_params = dict(m_loaded.named_parameters()) self.assertEqual(len(m_params), len(m_loaded_params)) self.assertEqual(m_params, m_loaded_params) # Check buffers. m_buffers = dict(m.named_buffers()) m_loaded_buffers = dict(m_loaded.named_buffers()) self.assertEqual(len(m_buffers), len(m_loaded_buffers)) self.assertEqual(m_buffers, m_loaded_buffers) # Check params and buffers that are/are not meta tensors self.assertTrue(m_params["foo.weight"].is_meta) self.assertTrue(m_loaded_params["foo.weight"].is_meta) self.assertTrue(m_params["foo.bias"].is_meta) self.assertTrue(m_loaded_params["foo.bias"].is_meta) self.assertFalse(m_params["bar.weight"].is_meta) self.assertFalse(m_loaded_params["bar.weight"].is_meta) self.assertFalse(m_params["bar.bias"].is_meta) self.assertFalse(m_loaded_params["bar.bias"].is_meta) self.assertTrue(m_buffers["buffer"].is_meta) self.assertTrue(m_loaded_buffers["buffer"].is_meta) def script_module_to_buffer(script_module): module_buffer = io.BytesIO( script_module._save_to_buffer_for_lite_interpreter(_use_flatbuffer=True) ) module_buffer.seek(0) return module_buffer @unittest.skipIf( not ENABLE_FLATBUFFER, "Need to enable flatbuffer to run the below tests" ) class TestSaveLoadFlatbuffer(JitTestCase): def test_different_modules(self): """ Exercise the situation where we have the same qualified name in two different CompilationUnits on save/load. """ class Foo(torch.nn.Module): def __init__(self): super(Foo, self).__init__() self.foo = torch.nn.Linear(2, 2) self.bar = torch.nn.Linear(2, 2) def forward(self, x): x = self.foo(x) x = self.bar(x) return x first_script_module = torch.jit.script(Foo()) first_saved_module = script_module_to_buffer(first_script_module) clear_class_registry() class Foo(torch.nn.Module): def __init__(self): super(Foo, self).__init__() self.foo = torch.nn.Linear(2, 2) def forward(self, x): x = self.foo(x) return x second_script_module = torch.jit.script(Foo()) second_saved_module = script_module_to_buffer(second_script_module) clear_class_registry() self.assertEqual( first_script_module._c.qualified_name, second_script_module._c.qualified_name, ) class ContainsBoth(torch.nn.Module): def __init__(self): super().__init__() self.add_module( "second", torch.jit.load(second_saved_module) ) self.add_module( "first", torch.jit.load(first_saved_module) ) def forward(self, x): x = self.first(x) x = self.second(x) return x sm = torch.jit.script(ContainsBoth()) contains_both = script_module_to_buffer(sm) sm = torch.jit.load(contains_both) def test_different_functions(self): """ Exercise the situation where we have the same qualified name in two different CompilationUnits on save/load. """ def lol(x): return x class Foo(torch.nn.Module): def forward(self, x): return lol(x) first_script_module = torch.jit.script(Foo()) first_saved_module = script_module_to_buffer(first_script_module) clear_class_registry() def lol(x): # noqa: F811 return "hello" class Foo(torch.nn.Module): def forward(self, x): return lol(x) second_script_module = torch.jit.script(Foo()) second_saved_module = script_module_to_buffer(second_script_module) clear_class_registry() self.assertEqual( first_script_module._c.qualified_name, second_script_module._c.qualified_name, ) class ContainsBoth(torch.nn.Module): def __init__(self): super().__init__() self.add_module( "second", torch.jit.load(second_saved_module) ) self.add_module( "first", torch.jit.load(first_saved_module) ) def forward(self, x): x = self.first(x) x = self.second(x) return x sm = torch.jit.script(ContainsBoth()) contains_both = script_module_to_buffer(sm) sm = torch.jit.load(contains_both) def test_different_interfaces(self): """ Exercise the situation where we have the same qualified name in two different CompilationUnits on save/load. """ @torch.jit.interface class MyInterface(object): def bar(self, x: Tensor) -> Tensor: pass @torch.jit.script class ImplementInterface(object): def __init__(self): pass def bar(self, x): return x class Foo(torch.nn.Module): __annotations__ = {"interface": MyInterface} def __init__(self): super().__init__() self.interface = ImplementInterface() def forward(self, x): return self.interface.bar(x) first_script_module = torch.jit.script(Foo()) first_saved_module = script_module_to_buffer(first_script_module) clear_class_registry() @torch.jit.interface class MyInterface(object): def not_bar(self, x: Tensor) -> Tensor: pass @torch.jit.script # noqa: F811 class ImplementInterface(object): # noqa: F811 def __init__(self): pass def not_bar(self, x): return x class Foo(torch.nn.Module): __annotations__ = {"interface": MyInterface} def __init__(self): super().__init__() self.interface = ImplementInterface() def forward(self, x): return self.interface.not_bar(x) second_script_module = torch.jit.script(Foo()) second_saved_module = script_module_to_buffer(second_script_module) clear_class_registry() self.assertEqual( first_script_module._c.qualified_name, second_script_module._c.qualified_name, ) class ContainsBoth(torch.nn.Module): def __init__(self): super().__init__() self.add_module( "second", torch.jit.load(second_saved_module) ) self.add_module( "first", torch.jit.load(first_saved_module) ) def forward(self, x): x = self.first(x) x = self.second(x) return x sm = torch.jit.script(ContainsBoth()) contains_both = script_module_to_buffer(sm) sm = torch.jit.load(contains_both) def test_many_collisions(self): class MyCoolNamedTuple(NamedTuple): a: int @torch.jit.interface class MyInterface(object): def bar(self, x: Tensor) -> Tensor: pass @torch.jit.script class ImplementInterface(object): def __init__(self): pass def bar(self, x): return x def lol(x): return x class Foo(torch.nn.Module): interface: MyInterface def __init__(self): super().__init__() self.foo = torch.nn.Linear(2, 2) self.bar = torch.nn.Linear(2, 2) self.interface = ImplementInterface() def forward(self, x): x = self.foo(x) x = self.bar(x) x = lol(x) x = self.interface.bar(x) return x, MyCoolNamedTuple(a=5) first_script_module = torch.jit.script(Foo()) first_saved_module = script_module_to_buffer(first_script_module) clear_class_registry() @torch.jit.interface class MyInterface(object): def not_bar(self, x: Tensor) -> Tensor: pass @torch.jit.script # noqa: F811 class ImplementInterface(object): # noqa: F811 def __init__(self): pass def not_bar(self, x): return x def lol(x): # noqa: F811 return "asdofij" class MyCoolNamedTuple(NamedTuple): # noqa: F811 a: str class Foo(torch.nn.Module): interface: MyInterface def __init__(self): super().__init__() self.foo = torch.nn.Linear(2, 2) self.interface = ImplementInterface() def forward(self, x): x = self.foo(x) self.interface.not_bar(x) x = lol(x) return x, MyCoolNamedTuple(a="hello") second_script_module = torch.jit.script(Foo()) second_saved_module = script_module_to_buffer(second_script_module) clear_class_registry() self.assertEqual( first_script_module._c.qualified_name, second_script_module._c.qualified_name, ) class ContainsBoth(torch.nn.Module): def __init__(self): super().__init__() self.add_module( "second", torch.jit.load(second_saved_module) ) self.add_module( "first", torch.jit.load(first_saved_module) ) def forward(self, x): x, named_tuple_1 = self.first(x) x, named_tuple_2 = self.second(x) return len(x + named_tuple_2.a) + named_tuple_1.a sm = torch.jit.script(ContainsBoth()) contains_both = script_module_to_buffer(sm) sm = torch.jit.load(contains_both) def test_save_load_using_pathlib(self): class MyMod(torch.jit.ScriptModule): @torch.jit.script_method def forward(self, a): return 2 * a m = MyMod() # Save then load. with TemporaryFileName() as fname: path = pathlib.Path(fname) torch.jit.save_jit_module_to_flatbuffer(m, path) m2 = torch.jit.load(path) x = torch.tensor([1.0, 2.0, 3.0, 4.0]) self.assertTrue(torch.equal(m(x), m2(x))) def test_save_namedtuple_input_only(self): """ Even if a NamedTuple is only used as an input argument, saving and loading should work correctly. """ global FooTuple # see [local resolution in python] class FooTuple(NamedTuple): a: int class MyModule(torch.nn.Module): def forward(self, x: FooTuple) -> torch.Tensor: return torch.tensor(3) m_loaded = self.getExportImportCopy(torch.jit.script(MyModule())) output = m_loaded(FooTuple(a=5)) self.assertEqual(output, torch.tensor(3)) def test_save_namedtuple_output_only(self): """ Even if a NamedTuple is only used as an output argument, saving and loading should work correctly. """ global FooTuple # see [local resolution in python] class FooTuple(NamedTuple): a: int class MyModule(torch.nn.Module): def forward(self) -> Optional[FooTuple]: return None m_loaded = self.getExportImportCopy(torch.jit.script(MyModule())) output = m_loaded() self.assertEqual(output, None) def test_module_info_flatbuffer(self): class Foo(torch.nn.Module): def __init__(self): super(Foo, self).__init__() self.foo = torch.nn.Linear(2, 2) self.bar = torch.nn.Linear(2, 2) def forward(self, x): x = self.foo(x) x = self.bar(x) return x first_script_module = torch.jit.script(Foo()) first_saved_module = io.BytesIO() torch.jit.save_jit_module_to_flatbuffer( first_script_module, first_saved_module) first_saved_module.seek(0) expected = { 'bytecode_version': 4, 'operator_version': 4, 'function_names': {'__torch__.___torch_mangle_0.Foo.forward'}, 'type_names': set(), 'opname_to_num_args': {'aten::linear': 3}} self.assertEqual( torch.jit._serialization.get_flatbuffer_module_info(first_saved_module), expected) def test_save_load_params_buffers_submodules(self): """ Check that parameters, buffers, and submodules are the same after loading. """ class Submodule(torch.nn.Module): def __init__(self): super().__init__() class TestModule(torch.nn.Module): def __init__(self): super().__init__() self.add_module("submodule_a", Submodule()) self.register_parameter( "parameter_a", torch.nn.Parameter(torch.randn(4)) ) self.register_buffer("buffer", torch.randn(4)) self.t = torch.rand(4) # not buffer self.parameter_b = torch.nn.Parameter(torch.randn(4)) self.submodule_b = Submodule() m = TestModule() m_loaded = self.getExportImportCopy(torch.jit.script(m)) # Check submodules. self.assertEqual( len(list(m.named_modules())), len(list(m_loaded.named_modules())) ) for m_s, loaded_s in zip(m.named_modules(), m_loaded.named_modules()): m_name, _ = m_s loaded_name, _ = loaded_s self.assertEqual(m_name, loaded_name) # Check parameters. self.assertEqual(len(list(m.parameters())), len(list(m_loaded.parameters()))) for m_p, loaded_p in zip(m.parameters(), m_loaded.parameters()): self.assertEqual(m_p, loaded_p) # Check buffers. self.assertEqual( len(list(m.named_buffers())), len(list(m_loaded.named_buffers())) ) for m_b, loaded_b in zip(m.named_buffers(), m_loaded.named_buffers()): m_name, m_buffer = m_b loaded_name, loaded_buffer = loaded_b self.assertEqual(m_name, loaded_name) self.assertEqual(m_buffer, loaded_buffer) def test_save_load_with_extra_files(self): """ Check that parameters, buffers, and submodules are the same after loading. """ class Module(torch.nn.Module): def __init__(self): super().__init__() def forward(self, x: Tensor): return x module = Module() script_module = torch.jit.script(module) script_module_io = io.BytesIO() extra_files = {"abc.json": "[1,2,3]"} script_module._save_for_lite_interpreter(script_module_io, _extra_files=extra_files, _use_flatbuffer=True) script_module_io.seek(0) re_extra_files = {} torch._C._get_model_extra_files_from_buffer(script_module_io, _extra_files=re_extra_files) self.assertEqual(extra_files, re_extra_files)