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
|
// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include <vespa/eval/eval/simple_value.h>
#include <vespa/eval/eval/fast_value.h>
#include <vespa/eval/eval/value_codec.h>
#include <vespa/eval/instruction/generic_reduce.h>
#include <vespa/eval/eval/interpreted_function.h>
#include <vespa/eval/eval/test/tensor_model.hpp>
#include <vespa/vespalib/util/stringfmt.h>
#include <vespa/vespalib/gtest/gtest.h>
#include <optional>
using namespace vespalib;
using namespace vespalib::eval;
using namespace vespalib::eval::instruction;
using namespace vespalib::eval::test;
using vespalib::make_string_short::fmt;
std::vector<Layout> layouts = {
{},
{x(3)},
{x(3),y(5)},
{x(3),y(5),z(7)},
float_cells({x(3),y(5),z(7)}),
{x({"a","b","c"})},
{x({})},
{x({}),y(10)},
{x({"a","b","c"}),y({"foo","bar"})},
{x({"a","b","c"}),y({"foo","bar"}),z({"i","j","k","l"})},
float_cells({x({"a","b","c"}),y({"foo","bar"}),z({"i","j","k","l"})}),
{x(3),y({"foo", "bar"}),z(7)},
{x({"a","b","c"}),y(5),z({"i","j","k","l"})},
float_cells({x({"a","b","c"}),y(5),z({"i","j","k","l"})})
};
TensorSpec reference_reduce(const TensorSpec &a, const std::vector<vespalib::string> &dims, Aggr aggr) {
Stash stash;
ValueType res_type = ValueType::from_spec(a.type()).reduce(dims);
EXPECT_FALSE(res_type.is_error());
std::map<TensorSpec::Address,std::optional<Aggregator*>> my_map;
for (const auto &cell: a.cells()) {
TensorSpec::Address addr;
for (const auto &dim: cell.first) {
if (res_type.dimension_index(dim.first) != ValueType::Dimension::npos) {
addr.insert_or_assign(dim.first, dim.second);
}
}
auto [pos, is_empty] = my_map.emplace(addr, std::nullopt);
if (is_empty) {
pos->second = &Aggregator::create(aggr, stash);
pos->second.value()->first(cell.second);
} else {
pos->second.value()->next(cell.second);
}
}
TensorSpec result(res_type.to_spec());
for (const auto &my_entry: my_map) {
result.add(my_entry.first, my_entry.second.value()->result());
}
// use SimpleValue to add implicit cells with default value
const auto &factory = SimpleValueBuilderFactory::get();
return spec_from_value(*value_from_spec(result, factory));
}
TensorSpec perform_generic_reduce(const TensorSpec &a, const std::vector<vespalib::string> &dims,
Aggr aggr, const ValueBuilderFactory &factory)
{
Stash stash;
auto lhs = value_from_spec(a, factory);
auto my_op = GenericReduce::make_instruction(lhs->type(), aggr, dims, factory, stash);
InterpretedFunction::EvalSingle single(factory, my_op);
return spec_from_value(single.eval(std::vector<Value::CREF>({*lhs})));
}
TEST(GenericReduceTest, dense_reduce_plan_can_be_created) {
auto type = ValueType::from_spec("tensor(a[2],aa{},b[2],bb[1],c[2],cc{},d[2],dd[1],e[2],ee{},f[2])");
auto plan = DenseReducePlan(type, type.reduce({"a", "d", "e"}));
std::vector<size_t> expect_keep_loop = {4,2};
std::vector<size_t> expect_keep_stride = {8,1};
std::vector<size_t> expect_reduce_loop = {2,4};
std::vector<size_t> expect_reduce_stride = {32,2};
EXPECT_EQ(plan.in_size, 64);
EXPECT_EQ(plan.out_size, 8);
EXPECT_EQ(plan.keep_loop, expect_keep_loop);
EXPECT_EQ(plan.keep_stride, expect_keep_stride);
EXPECT_EQ(plan.reduce_loop, expect_reduce_loop);
EXPECT_EQ(plan.reduce_stride, expect_reduce_stride);
}
TEST(GenericReduceTest, sparse_reduce_plan_can_be_created) {
auto type = ValueType::from_spec("tensor(a{},aa[10],b{},c{},cc[5],d{},e{},ee[1],f{})");
auto plan = SparseReducePlan(type, type.reduce({"a", "d", "e"}));
std::vector<size_t> expect_keep_dims = {1,2,5};
EXPECT_EQ(plan.num_reduce_dims, 3);
EXPECT_EQ(plan.keep_dims, expect_keep_dims);
}
void test_generic_reduce_with(const ValueBuilderFactory &factory) {
for (const Layout &layout: layouts) {
TensorSpec input = spec(layout, Div16(N()));
for (Aggr aggr: {Aggr::SUM, Aggr::AVG, Aggr::MIN, Aggr::MAX}) {
for (const Domain &domain: layout) {
auto expect = reference_reduce(input, {domain.dimension}, aggr);
auto actual = perform_generic_reduce(input, {domain.dimension}, aggr, factory);
EXPECT_EQ(actual, expect);
}
auto expect = reference_reduce(input, {}, aggr);
auto actual = perform_generic_reduce(input, {}, aggr, factory);
EXPECT_EQ(actual, expect);
}
}
}
TEST(GenericReduceTest, generic_reduce_works_for_simple_values) {
test_generic_reduce_with(SimpleValueBuilderFactory::get());
}
TEST(GenericReduceTest, generic_reduce_works_for_fast_values) {
test_generic_reduce_with(FastValueBuilderFactory::get());
}
TensorSpec immediate_generic_reduce(const TensorSpec &a, const std::vector<vespalib::string> &dims, Aggr aggr) {
const auto &factory = SimpleValueBuilderFactory::get();
auto lhs = value_from_spec(a, factory);
auto up = GenericReduce::perform_reduce(*lhs, aggr, dims, factory);
return spec_from_value(*up);
}
TEST(GenericReduceTest, immediate_generic_reduce_works) {
for (const Layout &layout: layouts) {
TensorSpec input = spec(layout, Div16(N()));
for (Aggr aggr: {Aggr::SUM, Aggr::AVG, Aggr::MIN, Aggr::MAX}) {
for (const Domain &domain: layout) {
auto expect = reference_reduce(input, {domain.dimension}, aggr);
auto actual = immediate_generic_reduce(input, {domain.dimension}, aggr);
EXPECT_EQ(actual, expect);
}
auto expect = reference_reduce(input, {}, aggr);
auto actual = immediate_generic_reduce(input, {}, aggr);
EXPECT_EQ(actual, expect);
}
}
}
GTEST_MAIN_RUN_ALL_TESTS()
|
