mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
b0fdca8855
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68358 Test Plan: Imported from OSS Reviewed By: albanD Differential Revision: D33433730 Pulled By: tugsbayasgalan fbshipit-source-id: 202c58365bae13195d3545cefcb0da9162b02151
487 lines
15 KiB
Python
487 lines
15 KiB
Python
# Owner(s): ["oncall: jit"]
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from typing import NamedTuple, Optional
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import io
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import os
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import pathlib
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import sys
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from torch import Tensor
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from torch.testing._internal.common_utils import TemporaryFileName
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import torch
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# Make the helper files in test/ importable
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(pytorch_test_dir)
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from torch.testing._internal.jit_utils import (JitTestCase,
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clear_class_registry)
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if __name__ == "__main__":
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raise RuntimeError(
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"This test file is not meant to be run directly, use:\n\n"
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"\tpython test/test_jit.py TESTNAME\n\n"
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"instead."
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)
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class TestSaveLoad(JitTestCase):
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def test_different_modules(self):
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"""
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Exercise the situation where we have the same qualified name
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in two different CompilationUnits on save/load.
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"""
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class Foo(torch.nn.Module):
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def __init__(self):
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super(Foo, self).__init__()
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self.foo = torch.nn.Linear(2, 2)
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self.bar = torch.nn.Linear(2, 2)
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def forward(self, x):
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x = self.foo(x)
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x = self.bar(x)
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return x
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first_script_module = torch.jit.script(Foo())
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first_saved_module = io.BytesIO()
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torch.jit.save(first_script_module, first_saved_module)
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first_saved_module.seek(0)
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clear_class_registry()
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class Foo(torch.nn.Module):
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def __init__(self):
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super(Foo, self).__init__()
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self.foo = torch.nn.Linear(2, 2)
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def forward(self, x):
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x = self.foo(x)
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return x
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second_script_module = torch.jit.script(Foo())
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second_saved_module = io.BytesIO()
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torch.jit.save(torch.jit.script(Foo()), second_saved_module)
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second_saved_module.seek(0)
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clear_class_registry()
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self.assertEqual(
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first_script_module._c.qualified_name, second_script_module._c.qualified_name
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)
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class ContainsBoth(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.add_module("second", torch.jit.load(second_saved_module))
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self.add_module("first", torch.jit.load(first_saved_module))
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def forward(self, x):
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x = self.first(x)
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x = self.second(x)
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return x
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sm = torch.jit.script(ContainsBoth())
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contains_both = io.BytesIO()
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torch.jit.save(sm, contains_both)
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contains_both.seek(0)
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sm = torch.jit.load(contains_both)
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def test_different_functions(self):
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"""
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Exercise the situation where we have the same qualified name
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in two different CompilationUnits on save/load.
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"""
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def lol(x):
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return x
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class Foo(torch.nn.Module):
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def forward(self, x):
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return lol(x)
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first_script_module = torch.jit.script(Foo())
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first_saved_module = io.BytesIO()
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torch.jit.save(first_script_module, first_saved_module)
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first_saved_module.seek(0)
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clear_class_registry()
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def lol(x): # noqa: F811
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return "hello"
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class Foo(torch.nn.Module):
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def forward(self, x):
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return lol(x)
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second_script_module = torch.jit.script(Foo())
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second_saved_module = io.BytesIO()
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torch.jit.save(torch.jit.script(Foo()), second_saved_module)
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second_saved_module.seek(0)
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clear_class_registry()
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self.assertEqual(
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first_script_module._c.qualified_name, second_script_module._c.qualified_name
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)
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class ContainsBoth(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.add_module("second", torch.jit.load(second_saved_module))
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self.add_module("first", torch.jit.load(first_saved_module))
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def forward(self, x):
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x = self.first(x)
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x = self.second(x)
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return x
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sm = torch.jit.script(ContainsBoth())
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contains_both = io.BytesIO()
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torch.jit.save(sm, contains_both)
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contains_both.seek(0)
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sm = torch.jit.load(contains_both)
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def test_different_interfaces(self):
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"""
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Exercise the situation where we have the same qualified name
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in two different CompilationUnits on save/load.
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"""
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@torch.jit.interface
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class MyInterface(object):
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def bar(self, x: Tensor) -> Tensor:
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pass
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@torch.jit.script
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class ImplementInterface(object):
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def __init__(self):
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pass
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def bar(self, x):
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return x
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class Foo(torch.nn.Module):
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__annotations__ = {"interface": MyInterface}
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def __init__(self):
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super().__init__()
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self.interface = ImplementInterface()
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def forward(self, x):
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return self.interface.bar(x)
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first_script_module = torch.jit.script(Foo())
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first_saved_module = io.BytesIO()
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torch.jit.save(first_script_module, first_saved_module)
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first_saved_module.seek(0)
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clear_class_registry()
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@torch.jit.interface
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class MyInterface(object):
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def not_bar(self, x: Tensor) -> Tensor:
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pass
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@torch.jit.script # noqa: F811
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class ImplementInterface(object): # noqa: F811
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def __init__(self):
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pass
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def not_bar(self, x):
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return x
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class Foo(torch.nn.Module):
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__annotations__ = {"interface": MyInterface}
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def __init__(self):
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super().__init__()
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self.interface = ImplementInterface()
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def forward(self, x):
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return self.interface.not_bar(x)
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second_script_module = torch.jit.script(Foo())
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second_saved_module = io.BytesIO()
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torch.jit.save(torch.jit.script(Foo()), second_saved_module)
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second_saved_module.seek(0)
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clear_class_registry()
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self.assertEqual(
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first_script_module._c.qualified_name, second_script_module._c.qualified_name
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)
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class ContainsBoth(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.add_module("second", torch.jit.load(second_saved_module))
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self.add_module("first", torch.jit.load(first_saved_module))
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def forward(self, x):
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x = self.first(x)
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x = self.second(x)
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return x
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sm = torch.jit.script(ContainsBoth())
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contains_both = io.BytesIO()
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torch.jit.save(sm, contains_both)
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contains_both.seek(0)
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sm = torch.jit.load(contains_both)
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def test_many_collisions(self):
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class MyCoolNamedTuple(NamedTuple):
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a: int
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@torch.jit.interface
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class MyInterface(object):
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def bar(self, x: Tensor) -> Tensor:
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pass
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@torch.jit.script
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class ImplementInterface(object):
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def __init__(self):
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pass
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def bar(self, x):
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return x
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def lol(x):
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return x
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class Foo(torch.nn.Module):
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interface: MyInterface
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def __init__(self):
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super().__init__()
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self.foo = torch.nn.Linear(2, 2)
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self.bar = torch.nn.Linear(2, 2)
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self.interface = ImplementInterface()
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def forward(self, x):
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x = self.foo(x)
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x = self.bar(x)
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x = lol(x)
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x = self.interface.bar(x)
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return x, MyCoolNamedTuple(a=5)
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first_script_module = torch.jit.script(Foo())
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first_saved_module = io.BytesIO()
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torch.jit.save(first_script_module, first_saved_module)
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first_saved_module.seek(0)
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clear_class_registry()
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@torch.jit.interface
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class MyInterface(object):
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def not_bar(self, x: Tensor) -> Tensor:
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pass
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@torch.jit.script # noqa: F811
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class ImplementInterface(object): # noqa: F811
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def __init__(self):
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pass
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def not_bar(self, x):
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return x
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def lol(x): # noqa: F811
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return "asdofij"
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class MyCoolNamedTuple(NamedTuple): # noqa: F811
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a: str
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class Foo(torch.nn.Module):
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interface: MyInterface
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def __init__(self):
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super().__init__()
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self.foo = torch.nn.Linear(2, 2)
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self.interface = ImplementInterface()
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def forward(self, x):
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x = self.foo(x)
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self.interface.not_bar(x)
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x = lol(x)
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return x, MyCoolNamedTuple(a="hello")
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second_script_module = torch.jit.script(Foo())
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second_saved_module = io.BytesIO()
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torch.jit.save(second_script_module, second_saved_module)
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second_saved_module.seek(0)
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clear_class_registry()
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self.assertEqual(
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first_script_module._c.qualified_name, second_script_module._c.qualified_name
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)
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class ContainsBoth(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.add_module("second", torch.jit.load(second_saved_module))
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self.add_module("first", torch.jit.load(first_saved_module))
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def forward(self, x):
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x, named_tuple_1 = self.first(x)
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x, named_tuple_2 = self.second(x)
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return len(x + named_tuple_2.a) + named_tuple_1.a
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sm = torch.jit.script(ContainsBoth())
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contains_both = io.BytesIO()
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torch.jit.save(sm, contains_both)
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contains_both.seek(0)
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sm = torch.jit.load(contains_both)
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def test_save_load_with_extra_files(self):
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class MyMod(torch.jit.ScriptModule):
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@torch.jit.script_method
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def forward(self, a):
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return a
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# specifically test binary data
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value = b"bar\x00\xffbaz"
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expected_extra_files = {}
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expected_extra_files['foo'] = value
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# verify that str to bytes conversion also works
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expected_extra_files['foo2'] = "bar"
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m = MyMod()
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# Save to file.
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with TemporaryFileName() as fname:
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m.save(fname, _extra_files=expected_extra_files)
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# values don't matter
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extra_files = {'foo': '', 'foo2': None}
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torch.jit.load(fname, _extra_files=extra_files)
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self.assertEqual(value, extra_files['foo'])
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# results come back always as bytes
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self.assertEqual(b"bar", extra_files['foo2'])
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# Use torch.jit API
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torch.jit.save(m, fname, _extra_files=expected_extra_files)
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extra_files['foo'] = ''
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torch.jit.load(fname, _extra_files=extra_files)
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self.assertEqual(value, extra_files['foo'])
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# Save to buffer.
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buffer = io.BytesIO(m.save_to_buffer(_extra_files=expected_extra_files))
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extra_files = {'foo': ''}
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torch.jit.load(buffer, _extra_files=extra_files)
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self.assertEqual(value, extra_files['foo'])
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# Use torch.jit API
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buffer = io.BytesIO()
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torch.jit.save(m, buffer, _extra_files=expected_extra_files)
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buffer.seek(0)
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extra_files = {'foo': ''}
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torch.jit.load(buffer, _extra_files=extra_files)
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self.assertEqual(value, extra_files['foo'])
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# Non-existent file 'bar'
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with self.assertRaises(RuntimeError):
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extra_files['bar'] = ''
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torch.jit.load(buffer, _extra_files=extra_files)
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def test_save_load_using_pathlib(self):
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class MyMod(torch.jit.ScriptModule):
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@torch.jit.script_method
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def forward(self, a):
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return 2 * a
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m = MyMod()
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# Save then load.
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with TemporaryFileName() as fname:
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path = pathlib.Path(fname)
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m.save(path)
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m2 = torch.jit.load(path)
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x = torch.tensor([1., 2., 3., 4.])
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self.assertTrue(torch.equal(m(x), m2(x)))
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def test_save_nonexit_file(self):
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class Foo(torch.nn.Module):
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def forward(self, x):
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return 2 * x
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script_module = torch.jit.script(Foo())
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with self.assertRaises(RuntimeError):
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script_module.save("NonExist/path/test.pt")
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def test_save_namedtuple_input_only(self):
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"""
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Even if a NamedTuple is only used as an input argument, saving and
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loading should work correctly.
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"""
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global FooTuple # see [local resolution in python]
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class FooTuple(NamedTuple):
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a: int
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class MyModule(torch.nn.Module):
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def forward(self, x: FooTuple) -> torch.Tensor:
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return torch.tensor(3)
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m_loaded = self.getExportImportCopy(torch.jit.script(MyModule()))
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output = m_loaded(FooTuple(a=5))
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self.assertEqual(output, torch.tensor(3))
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def test_save_namedtuple_output_only(self):
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"""
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Even if a NamedTuple is only used as an output argument, saving and
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loading should work correctly.
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"""
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global FooTuple # see [local resolution in python]
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class FooTuple(NamedTuple):
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a: int
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class MyModule(torch.nn.Module):
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def forward(self) -> Optional[FooTuple]:
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return None
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m_loaded = self.getExportImportCopy(torch.jit.script(MyModule()))
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output = m_loaded()
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self.assertEqual(output, None)
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def test_save_load_params_buffers_submodules(self):
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"""
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Check that parameters, buffers, and submodules are the same after loading.
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"""
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class Submodule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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class TestModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.add_module("submodule_a", Submodule())
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self.register_parameter("parameter_a", torch.nn.Parameter(torch.randn(4)))
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self.register_buffer("buffer", torch.randn(4))
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self.t = torch.rand(4) # not buffer
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self.parameter_b = torch.nn.Parameter(torch.randn(4))
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self.submodule_b = Submodule()
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m = TestModule()
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m_loaded = self.getExportImportCopy(torch.jit.script(m))
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# Check submodules.
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self.assertEqual(len(list(m.named_modules())), len(list(m_loaded.named_modules())))
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for m_s, loaded_s in zip(m.named_modules(), m_loaded.named_modules()):
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m_name, _ = m_s
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loaded_name, _ = loaded_s
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self.assertEqual(m_name, loaded_name)
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# Check parameters.
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self.assertEqual(len(list(m.parameters())), len(list(m_loaded.parameters())))
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for m_p, loaded_p in zip(m.parameters(), m_loaded.parameters()):
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self.assertEqual(m_p, loaded_p)
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# Check buffers.
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self.assertEqual(len(list(m.named_buffers())), len(list(m_loaded.named_buffers())))
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for m_b, loaded_b in zip(m.named_buffers(), m_loaded.named_buffers()):
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m_name, m_buffer = m_b
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loaded_name, loaded_buffer = loaded_b
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self.assertEqual(m_name, loaded_name)
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self.assertEqual(m_buffer, loaded_buffer)
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