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# Owner(s): ["oncall: jit"]
import torch
import os
import sys
from torch.testing._internal.jit_utils import JitTestCase
from typing import Dict, Any, List
# 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)
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 TestModuleAPIs(JitTestCase):
def test_default_state_dict_methods(self):
"""Tests that default state dict methods are automatically available"""
class DefaultStateDictModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(6, 16, 5)
self.fc = torch.nn.Linear(16 * 5 * 5, 120)
def forward(self, x):
x = self.conv(x)
x = self.fc(x)
return x
m1 = torch.jit.script(DefaultStateDictModule())
m2 = torch.jit.script(DefaultStateDictModule())
state_dict = m1.state_dict()
m2.load_state_dict(state_dict)
def test_customized_state_dict_methods(self):
"""Tests that customized state dict methods are in effect"""
class CustomStateDictModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(6, 16, 5)
self.fc = torch.nn.Linear(16 * 5 * 5, 120)
self.customized_save_state_dict_called: bool = False
self.customized_load_state_dict_called: bool = False
def forward(self, x):
x = self.conv(x)
x = self.fc(x)
return x
@torch.jit.export
def _save_to_state_dict(self, destination: Dict[str, torch.Tensor],
prefix: str, keep_vars: bool):
self.customized_save_state_dict_called = True
return {"dummy": torch.ones(1)}
@torch.jit.export
def _load_from_state_dict(self,
state_dict: Dict[str, torch.Tensor],
prefix: str, local_metadata: Any,
strict: bool, missing_keys: List[str],
unexpected_keys: List[str],
error_msgs: List[str]):
self.customized_load_state_dict_called = True
return
m1 = torch.jit.script(CustomStateDictModule())
self.assertFalse(m1.customized_save_state_dict_called)
state_dict = m1.state_dict()
self.assertTrue(m1.customized_save_state_dict_called)
m2 = torch.jit.script(CustomStateDictModule())
self.assertFalse(m2.customized_load_state_dict_called)
m2.load_state_dict(state_dict)
self.assertTrue(m2.customized_load_state_dict_called)
def test_submodule_customized_state_dict_methods(self):
"""Tests that customized state dict methods on submodules are in effect"""
class CustomStateDictModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(6, 16, 5)
self.fc = torch.nn.Linear(16 * 5 * 5, 120)
self.customized_save_state_dict_called: bool = False
self.customized_load_state_dict_called: bool = False
def forward(self, x):
x = self.conv(x)
x = self.fc(x)
return x
@torch.jit.export
def _save_to_state_dict(self, destination: Dict[str, torch.Tensor],
prefix: str, keep_vars: bool):
self.customized_save_state_dict_called = True
return {"dummy": torch.ones(1)}
@torch.jit.export
def _load_from_state_dict(self,
state_dict: Dict[str, torch.Tensor],
prefix: str, local_metadata: Any,
strict: bool, missing_keys: List[str],
unexpected_keys: List[str],
error_msgs: List[str]):
self.customized_load_state_dict_called = True
return
class ParentModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.sub = CustomStateDictModule()
def forward(self, x):
return self.sub(x)
m1 = torch.jit.script(ParentModule())
self.assertFalse(m1.sub.customized_save_state_dict_called)
state_dict = m1.state_dict()
self.assertTrue(m1.sub.customized_save_state_dict_called)
m2 = torch.jit.script(ParentModule())
self.assertFalse(m2.sub.customized_load_state_dict_called)
m2.load_state_dict(state_dict)
self.assertTrue(m2.sub.customized_load_state_dict_called)
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