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
199
|
// Copyright 2016 Yahoo Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include <vespa/vespalib/testkit/test_kit.h>
#include <vespa/vespalib/eval/simple_tensor.h>
#include <vespa/vespalib/eval/simple_tensor_engine.h>
#include <vespa/vespalib/eval/operation.h>
#include <vespa/vespalib/util/stash.h>
#include <iostream>
using namespace vespalib::eval;
using Cell = SimpleTensor::Cell;
using Cells = SimpleTensor::Cells;
using Address = SimpleTensor::Address;
using Stash = vespalib::Stash;
// need to specify numbers explicitly as size_t to avoid ambiguous behavior for 0
constexpr size_t operator "" _z (unsigned long long int n) { return n; }
void dump(const Cells &cells, std::ostream &out) {
out << std::endl;
for (const auto &cell: cells) {
size_t n = 0;
out << " [";
for (const auto &label: cell.address) {
if (n++) {
out << ",";
}
if (label.is_mapped()) {
out << label.name;
} else {
out << label.index;
}
}
out << "]: " << cell.value << std::endl;
}
}
struct Check {
Cells cells;
Check() : cells() {}
explicit Check(const SimpleTensor &tensor) : cells() {
for (const auto &cell: tensor.cells()) {
add(cell.address, cell.value);
}
}
explicit Check(const TensorSpec &spec)
: Check(*SimpleTensor::create(spec)) {}
Check &add(const Address &address, double value) {
cells.emplace_back(address, value);
std::sort(cells.begin(), cells.end(),
[](const auto &a, const auto &b){ return (a.address < b.address); });
return *this;
}
bool operator==(const Check &rhs) const {
if (cells.size() != rhs.cells.size()) {
return false;
}
for (size_t i = 0; i < cells.size(); ++i) {
if ((cells[i].address != rhs.cells[i].address) ||
(cells[i].value != rhs.cells[i].value))
{
return false;
}
}
return true;
}
};
std::ostream &operator<<(std::ostream &out, const Check &value) {
dump(value.cells, out);
return out;
}
const SimpleTensor &unwrap(const Tensor &tensor) {
ASSERT_EQUAL(&tensor.engine(), &SimpleTensorEngine::ref());
return static_cast<const SimpleTensor &>(tensor);
}
const SimpleTensor &unwrap(const Value &value) {
ASSERT_TRUE(value.is_tensor());
return unwrap(*value.as_tensor());
}
TEST("require that simple tensors can be built using tensor spec") {
TensorSpec spec("tensor(w{},x[2],y{},z[2])");
spec.add({{"w", "xxx"}, {"x", 0}, {"y", "xxx"}, {"z", 0}}, 1.0)
.add({{"w", "xxx"}, {"x", 0}, {"y", "yyy"}, {"z", 1}}, 2.0)
.add({{"w", "yyy"}, {"x", 1}, {"y", "xxx"}, {"z", 0}}, 3.0)
.add({{"w", "yyy"}, {"x", 1}, {"y", "yyy"}, {"z", 1}}, 4.0);
std::unique_ptr<SimpleTensor> tensor = SimpleTensor::create(spec);
Check expect = Check()
.add({{"xxx"}, {0_z}, {"xxx"}, {0_z}}, 1.0)
.add({{"xxx"}, {0_z}, {"xxx"}, {1_z}}, 0.0)
.add({{"xxx"}, {1_z}, {"xxx"}, {0_z}}, 0.0)
.add({{"xxx"}, {1_z}, {"xxx"}, {1_z}}, 0.0)
//-----------------------------------------
.add({{"xxx"}, {0_z}, {"yyy"}, {0_z}}, 0.0)
.add({{"xxx"}, {0_z}, {"yyy"}, {1_z}}, 2.0)
.add({{"xxx"}, {1_z}, {"yyy"}, {0_z}}, 0.0)
.add({{"xxx"}, {1_z}, {"yyy"}, {1_z}}, 0.0)
//-----------------------------------------
.add({{"yyy"}, {0_z}, {"xxx"}, {0_z}}, 0.0)
.add({{"yyy"}, {0_z}, {"xxx"}, {1_z}}, 0.0)
.add({{"yyy"}, {1_z}, {"xxx"}, {0_z}}, 3.0)
.add({{"yyy"}, {1_z}, {"xxx"}, {1_z}}, 0.0)
//-----------------------------------------
.add({{"yyy"}, {0_z}, {"yyy"}, {0_z}}, 0.0)
.add({{"yyy"}, {0_z}, {"yyy"}, {1_z}}, 0.0)
.add({{"yyy"}, {1_z}, {"yyy"}, {0_z}}, 0.0)
.add({{"yyy"}, {1_z}, {"yyy"}, {1_z}}, 4.0);
EXPECT_EQUAL(expect, Check(*tensor));
std::unique_ptr<Tensor> tensor2 = SimpleTensorEngine::ref().create(spec);
EXPECT_EQUAL(expect, Check(unwrap(*tensor2)));
};
TEST("require that simple tensors can have their values negated") {
auto tensor = SimpleTensor::create(
TensorSpec("tensor(x{},y{})")
.add({{"x","1"},{"y","1"}}, 1)
.add({{"x","2"},{"y","1"}}, -3)
.add({{"x","1"},{"y","2"}}, 5));
auto expect = SimpleTensor::create(
TensorSpec("tensor(x{},y{})")
.add({{"x","1"},{"y","1"}}, -1)
.add({{"x","2"},{"y","1"}}, 3)
.add({{"x","1"},{"y","2"}}, -5));
auto result = SimpleTensor::perform(operation::Neg(), *tensor);
EXPECT_EQUAL(Check(*expect), Check(*result));
Stash stash;
const Value &result2 = SimpleTensorEngine::ref().map(operation::Neg(), *tensor, stash);
EXPECT_EQUAL(Check(*expect), Check(unwrap(result2)));
}
TEST("require that simple tensors can be multiplied with each other") {
auto lhs = SimpleTensor::create(
TensorSpec("tensor(x{},y{})")
.add({{"x","1"},{"y","1"}}, 1)
.add({{"x","2"},{"y","1"}}, 3)
.add({{"x","1"},{"y","2"}}, 5));
auto rhs = SimpleTensor::create(
TensorSpec("tensor(y{},z{})")
.add({{"y","1"},{"z","1"}}, 7)
.add({{"y","2"},{"z","1"}}, 11)
.add({{"y","1"},{"z","2"}}, 13));
auto expect = SimpleTensor::create(
TensorSpec("tensor(x{},y{},z{})")
.add({{"x","1"},{"y","1"},{"z","1"}}, 7)
.add({{"x","1"},{"y","1"},{"z","2"}}, 13)
.add({{"x","2"},{"y","1"},{"z","1"}}, 21)
.add({{"x","2"},{"y","1"},{"z","2"}}, 39)
.add({{"x","1"},{"y","2"},{"z","1"}}, 55));
auto result = SimpleTensor::perform(operation::Mul(), *lhs, *rhs);
EXPECT_EQUAL(Check(*expect), Check(*result));
Stash stash;
const Value &result2 = SimpleTensorEngine::ref().apply(operation::Mul(), *lhs, *rhs, stash);
EXPECT_EQUAL(Check(*expect), Check(unwrap(result2)));
}
TEST("require that simple tensors support dimension reduction") {
auto tensor = SimpleTensor::create(
TensorSpec("tensor(x[3],y[2])")
.add({{"x",0},{"y",0}}, 1)
.add({{"x",1},{"y",0}}, 2)
.add({{"x",2},{"y",0}}, 3)
.add({{"x",0},{"y",1}}, 4)
.add({{"x",1},{"y",1}}, 5)
.add({{"x",2},{"y",1}}, 6));
auto expect_sum_y = SimpleTensor::create(
TensorSpec("tensor(x[3])")
.add({{"x",0}}, 5)
.add({{"x",1}}, 7)
.add({{"x",2}}, 9));
auto expect_sum_x = SimpleTensor::create(
TensorSpec("tensor(y[2])")
.add({{"y",0}}, 6)
.add({{"y",1}}, 15));
auto expect_sum_all = SimpleTensor::create(TensorSpec("double").add({}, 21));
auto result_sum_y = tensor->reduce(operation::Add(), {"y"});
auto result_sum_x = tensor->reduce(operation::Add(), {"x"});
auto result_sum_all = tensor->reduce(operation::Add(), {"x", "y"});
EXPECT_EQUAL(Check(*expect_sum_y), Check(*result_sum_y));
EXPECT_EQUAL(Check(*expect_sum_x), Check(*result_sum_x));
EXPECT_EQUAL(Check(*expect_sum_all), Check(*result_sum_all));
Stash stash;
const Value &result_sum_y_2 = SimpleTensorEngine::ref().reduce(*tensor, operation::Add(), {"y"}, stash);
const Value &result_sum_x_2 = SimpleTensorEngine::ref().reduce(*tensor, operation::Add(), {"x"}, stash);
const Value &result_sum_all_2 = SimpleTensorEngine::ref().reduce(*tensor, operation::Add(), {"x", "y"}, stash);
const Value &result_sum_all_3 = SimpleTensorEngine::ref().reduce(*tensor, operation::Add(), {}, stash);
EXPECT_EQUAL(Check(*expect_sum_y), Check(unwrap(result_sum_y_2)));
EXPECT_EQUAL(Check(*expect_sum_x), Check(unwrap(result_sum_x_2)));
EXPECT_TRUE(result_sum_all_2.is_double());
EXPECT_TRUE(result_sum_all_3.is_double());
EXPECT_EQUAL(21, result_sum_all_2.as_double());
EXPECT_EQUAL(21, result_sum_all_3.as_double());
EXPECT_TRUE(SimpleTensorEngine::ref().equal(*result_sum_y, *result_sum_y));
EXPECT_TRUE(!SimpleTensorEngine::ref().equal(*result_sum_y, *result_sum_x));
}
TEST_MAIN() { TEST_RUN_ALL(); }
|
