1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
|
# Owner(s): ["oncall: package/deploy"]
from io import BytesIO
from textwrap import dedent
from unittest import skipIf
import torch
from torch.package import PackageExporter, PackageImporter, sys_importer
from torch.testing._internal.common_utils import IS_FBCODE, IS_SANDCASTLE, run_tests
try:
from torchvision.models import resnet18
HAS_TORCHVISION = True
except ImportError:
HAS_TORCHVISION = False
skipIfNoTorchVision = skipIf(not HAS_TORCHVISION, "no torchvision")
try:
from .common import PackageTestCase
except ImportError:
# Support the case where we run this file directly.
from common import PackageTestCase
@skipIf(True, "Does not work with recent torchvision, see https://github.com/pytorch/pytorch/issues/81115")
@skipIfNoTorchVision
class ModelTest(PackageTestCase):
"""End-to-end tests packaging an entire model."""
@skipIf(
IS_FBCODE or IS_SANDCASTLE,
"Tests that use temporary files are disabled in fbcode",
)
def test_resnet(self):
resnet = resnet18()
f1 = self.temp()
# create a package that will save it along with its code
with PackageExporter(f1) as e:
# put the pickled resnet in the package, by default
# this will also save all the code files references by
# the objects in the pickle
e.intern("**")
e.save_pickle("model", "model.pkl", resnet)
# we can now load the saved model
i = PackageImporter(f1)
r2 = i.load_pickle("model", "model.pkl")
# test that it works
input = torch.rand(1, 3, 224, 224)
ref = resnet(input)
self.assertEqual(r2(input), ref)
# functions exist also to get at the private modules in each package
torchvision = i.import_module("torchvision")
f2 = BytesIO()
# if we are doing transfer learning we might want to re-save
# things that were loaded from a package.
# We need to tell the exporter about any modules that
# came from imported packages so that it can resolve
# class names like torchvision.models.resnet.ResNet
# to their source code.
with PackageExporter(f2, importer=(i, sys_importer)) as e:
# e.importers is a list of module importing functions
# that by default contains importlib.import_module.
# it is searched in order until the first success and
# that module is taken to be what torchvision.models.resnet
# should be in this code package. In the case of name collisions,
# such as trying to save a ResNet from two different packages,
# we take the first thing found in the path, so only ResNet objects from
# one importer will work. This avoids a bunch of name mangling in
# the source code. If you need to actually mix ResNet objects,
# we suggest reconstructing the model objects using code from a single package
# using functions like save_state_dict and load_state_dict to transfer state
# to the correct code objects.
e.intern("**")
e.save_pickle("model", "model.pkl", r2)
f2.seek(0)
i2 = PackageImporter(f2)
r3 = i2.load_pickle("model", "model.pkl")
self.assertEqual(r3(input), ref)
@skipIfNoTorchVision
def test_model_save(self):
# This example shows how you might package a model
# so that the creator of the model has flexibility about
# how they want to save it but the 'server' can always
# use the same API to load the package.
# The convension is for each model to provide a
# 'model' package with a 'load' function that actual
# reads the model out of the archive.
# How the load function is implemented is up to the
# the packager.
# get our normal torchvision resnet
resnet = resnet18()
f1 = BytesIO()
# Option 1: save by pickling the whole model
# + single-line, similar to torch.jit.save
# - more difficult to edit the code after the model is created
with PackageExporter(f1) as e:
e.intern("**")
e.save_pickle("model", "pickled", resnet)
# note that this source is the same for all models in this approach
# so it can be made part of an API that just takes the model and
# packages it with this source.
src = dedent(
"""\
import importlib
import torch_package_importer as resources
# server knows to call model.load() to get the model,
# maybe in the future it passes options as arguments by convension
def load():
return resources.load_pickle('model', 'pickled')
"""
)
e.save_source_string("model", src, is_package=True)
f2 = BytesIO()
# Option 2: save with state dict
# - more code to write to save/load the model
# + but this code can be edited later to adjust adapt the model later
with PackageExporter(f2) as e:
e.intern("**")
e.save_pickle("model", "state_dict", resnet.state_dict())
src = dedent(
"""\
import importlib
import torch_package_importer as resources
from torchvision.models.resnet import resnet18
def load():
# if you want, you can later edit how resnet is constructed here
# to edit the model in the package, while still loading the original
# state dict weights
r = resnet18()
state_dict = resources.load_pickle('model', 'state_dict')
r.load_state_dict(state_dict)
return r
"""
)
e.save_source_string("model", src, is_package=True)
# regardless of how we chose to package, we can now use the model in a server in the same way
input = torch.rand(1, 3, 224, 224)
results = []
for m in [f1, f2]:
m.seek(0)
importer = PackageImporter(m)
the_model = importer.import_module("model").load()
r = the_model(input)
results.append(r)
self.assertEqual(*results)
@skipIfNoTorchVision
def test_script_resnet(self):
resnet = resnet18()
f1 = BytesIO()
# Option 1: save by pickling the whole model
# + single-line, similar to torch.jit.save
# - more difficult to edit the code after the model is created
with PackageExporter(f1) as e:
e.intern("**")
e.save_pickle("model", "pickled", resnet)
f1.seek(0)
i = PackageImporter(f1)
loaded = i.load_pickle("model", "pickled")
# Model should script successfully.
scripted = torch.jit.script(loaded)
# Scripted model should save and load successfully.
f2 = BytesIO()
torch.jit.save(scripted, f2)
f2.seek(0)
loaded = torch.jit.load(f2)
input = torch.rand(1, 3, 224, 224)
self.assertEqual(loaded(input), resnet(input))
if __name__ == "__main__":
run_tests()
|
