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
|
#include <gtest/gtest.h>
#include <test/cpp/jit/test_utils.h>
#include <torch/csrc/autograd/generated/variable_factories.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/frontend/resolver.h>
#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/module.h>
#include <unordered_set>
namespace torch {
namespace jit {
namespace mobile {
TEST(RunTimeTest, LoadAndForward) {
// Load check in model: sequence.ptl
std::string filePath(__FILE__);
auto testModelFile = filePath.substr(0, filePath.find_last_of("/\\") + 1);
testModelFile.append("sequence.ptl");
// sequence.ptl source code:
// class A(torch.nn.Module):
// def __init__(self):
// super(A, self).__init__()
//
// def forward(self, x):
// return x + 1
//
// class B(torch.nn.Module):
// def __init__(self):
// super(B, self).__init__()
//
// def forward(self, x):
// return x + 2
//
// class C(torch.nn.Module):
// def __init__(self):
// super(C, self).__init__()
// self.A0 = A()
// self.B0 = B()
//
// def forward(self, x):
// return self.A0.forward(self.B0.forward(x))
Module bc = _load_for_mobile(testModelFile);
auto forward_method = bc.find_method("forward");
std::vector<c10::IValue> input{c10::IValue(at::tensor(1))};
const auto result = bc.forward(input);
const auto expected_result = c10::IValue(at::tensor(4));
ASSERT_EQ(result, expected_result);
}
TEST(RunTimeTest, Delegate) {
std::string filePath(__FILE__);
auto testModelFile = filePath.substr(0, filePath.find_last_of("/\\") + 1);
// "delegate_test.ptl" is generated from test/cpp/jit/test_backend.cpp,
// BackendTest.TestCompiler. This test is on target runtime. It has
// model running capability, but no compilation and serialization.
// The mobile model delegated to the "backend_with_compiler_demo" backend
// The model is from the jit code:
// Module m("m");
// m.define(R"(
// def forward(self, x, h):
// return x + h
// )");
testModelFile.append("delegate_test.ptl");
auto mlm = _load_for_mobile(testModelFile);
std::vector<IValue> inputs;
inputs.emplace_back(2.0 * at::ones({}));
inputs.emplace_back(1.0 * at::ones({}));
auto mres = mlm.forward(inputs);
AT_ASSERT(mres.toTensor().equal(3 * at::ones({})));
}
TEST(RunTimeTest, DelegateException) {
std::string filePath(__FILE__);
auto testModelFile = filePath.substr(0, filePath.find_last_of("/\\") + 1);
/*
* Model: delegated_submodule_with_debug_info.ptl
* Model structure:
* def AA(..):
* def forward(self, x, y):
* return x + y
*
* def A(..):
* def __init__(..):
* self.AA0 = AA()
* def forward(self, x, y):
* return self.AA0.forward(x, y) + 3
*
* def B(..):
* def forward(self, x):
* return x + 2
*
* def C(..):
* def __init__(..):
* self.A0 = A()
* self.B0 = B()
* def forward(self, x, y):
* return self.A0.forward(x, y) + self.B0.forward(x)
*
* std::vector<IValue> inputs;
* inputs.emplace_back(torch::rand({2, 4}));
* inputs.emplace_back(torch::rand({13, 9}));
* Run with inputs and expect exception
* Erro stack trace will look like this:
* Module hierarchy:top(C).A0(backend_with_compiler_demoLoweredModule).AA0(AA)
* Traceback of TorchScript (most recent call last):
* File "<string>", line 3, in FunctionName_UNKNOWN
*
* def forward(self, x, y):
* return self.A0.forward(x, y) + self.B0.forward(x)
* ~~~~~~~~~~~~~~~ <--- HERE
*
* File "<string>", line 5, in FunctionName_UNKNOWN
* typed_inputs: List[Any] = [x, y, ]
* if self.__backend.is_available() :
* _0, = self.__backend.execute(self.__handles["forward"],
* typed_inputs)
* ~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
* assert isinstance(_0, Tensor)
* return _0
* File "<string>", line 3, in FunctionName_UNKNOWN
*
* def forward(self, x, y):
* return self.AA0.forward(x, y) + 3
* ~~~~~~~~~~~~~~~~ <--- HERE
*
* File "<string>", line 3, in FunctionName_UNKNOWN
*
* def forward(self, x, y):
* return x + y
* ~~~~~ <--- HERE
*
*
*/
testModelFile.append("delegated_submodule_with_debug_info.ptl");
auto mlm = _load_for_mobile(testModelFile);
std::vector<IValue> inputs;
inputs.emplace_back(torch::rand({2, 4}));
inputs.emplace_back(torch::rand({13, 9}));
std::string error_pattern = R"(
Module hierarchy:top(C)::<unknown>.A0(backend_with_compiler_demoLoweredModule)::forward.AA0(AA)::forward.aten::add
Traceback of TorchScript (most recent call last):
File "<string>", line 3, in <unknown>
def forward(self, x, y):
return self.A0.forward(x, y) + self.B0.forward(x)
~~~~~~~~~~~~~~~ <--- HERE
File "<string>", line 5, in forward
typed_inputs: List[Any] = [x, y, ]
if self.__backend.is_available() :
_0, = self.__backend.execute(self.__handles["forward"], typed_inputs)
~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
assert isinstance(_0, Tensor)
return _0
File "<string>", line 3, in <unknown>
def forward(self, x, y):
return self.AA0.forward(x, y) + 3
~~~~~~~~~~~~~~~~ <--- HERE
File "<string>", line 3, in forward
def forward(self, x, y):
return x + y
~~~~~ <--- HERE
)";
ASSERT_THROWS_WITH_MESSAGE(mlm.forward(inputs), error_pattern);
}
} // namespace mobile
} // namespace jit
} // namespace torch
|
