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
|
/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <functional>
#include <memory>
#include <vector>
#include "tensorflow/cc/ops/array_ops.h"
#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/fake_input.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/kernels/ops_testutil.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test_benchmark.h"
namespace tensorflow {
namespace {
class DequantizeOpTest : public OpsTestBase {
protected:
template <typename T>
void ComputeDequantizeMinCombinedUsingEigen(const Tensor& input,
float min_range, float max_range,
Tensor* output) {
float half_range =
!std::is_signed<T>::value
? 0.0f
: (static_cast<float>(std::numeric_limits<T>::max()) -
std::numeric_limits<T>::min() + 1) /
2.0f;
const float scale_factor =
(max_range - min_range) /
(static_cast<float>(std::numeric_limits<T>::max()) -
std::numeric_limits<T>::min());
output->flat<float>() =
((input.flat<T>().template cast<int>().template cast<float>() +
half_range) *
scale_factor) +
min_range;
}
// Compares dequantize min vs the same using eigen. This tests that a change
// to not use eigen gives equivalent results to using eigen.
template <typename T>
void RunDequantizeMinCombinedTest(float min_range, float max_range) {
TF_ASSERT_OK(NodeDefBuilder("dequantize_op", "Dequantize")
.Input(FakeInput(DataTypeToEnum<T>::v()))
.Input(FakeInput(DT_FLOAT))
.Input(FakeInput(DT_FLOAT))
.Attr("T", DataTypeToEnum<T>::v())
.Attr("mode", "MIN_COMBINED")
.Finalize(node_def()));
TF_ASSERT_OK(InitOp());
std::vector<T> input;
for (int64 i = std::numeric_limits<T>::min();
i < std::numeric_limits<T>::max(); ++i) {
input.push_back(static_cast<T>(i));
}
TensorShape shape({static_cast<int64>(input.size())});
AddInputFromArray<T>(shape, input);
AddInputFromArray<float>(TensorShape({}), {min_range});
AddInputFromArray<float>(TensorShape({}), {max_range});
TF_ASSERT_OK(RunOpKernel());
Tensor expected(allocator(), DT_FLOAT, shape);
ComputeDequantizeMinCombinedUsingEigen<T>(GetInput(0), min_range, max_range,
&expected);
test::ExpectTensorEqual<float>(expected, *GetOutput(0));
}
template <typename T>
void RunDequantizeScaledTest(float min_range, float max_range, int input_int,
float expected_output) {
TF_ASSERT_OK(NodeDefBuilder("dequantize_op", "Dequantize")
.Input(FakeInput(DataTypeToEnum<T>::v()))
.Input(FakeInput(DT_FLOAT))
.Input(FakeInput(DT_FLOAT))
.Attr("T", DataTypeToEnum<T>::v())
.Attr("mode", "SCALED")
.Finalize(node_def()));
TF_ASSERT_OK(InitOp());
std::vector<T> input;
input.push_back(static_cast<T>(input_int));
TensorShape shape({static_cast<int64>(input.size())});
AddInputFromArray<T>(shape, input);
AddInputFromArray<float>(TensorShape({}), {min_range});
AddInputFromArray<float>(TensorShape({}), {max_range});
TF_ASSERT_OK(RunOpKernel());
Tensor expected(allocator(), DT_FLOAT, shape);
test::FillValues<float>(&expected, {expected_output});
test::ExpectClose(expected, *GetOutput(0));
}
};
TEST_F(DequantizeOpTest, DequantizeMinCombinedQuint8) {
RunDequantizeMinCombinedTest<quint8>(0, 255.0f);
}
TEST_F(DequantizeOpTest, DequantizeMinCombinedQint8) {
RunDequantizeMinCombinedTest<qint8>(0, 255.0f);
}
TEST_F(DequantizeOpTest, DequantizeMinCombinedQint16) {
RunDequantizeMinCombinedTest<qint16>(0, 255.0f);
}
TEST_F(DequantizeOpTest, DequantizeMinCombinedQuint16) {
RunDequantizeMinCombinedTest<quint16>(0, 255.0f);
}
TEST_F(DequantizeOpTest, DequantizeScaledQuint8Zero) {
RunDequantizeScaledTest<quint8>(-255.0f, 127.0f, 0, 0.0);
}
TEST_F(DequantizeOpTest, DequantizeScaledQuint8CheckIgnoresNegative) {
RunDequantizeScaledTest<quint8>(-512.0f, 255.0f, 255, 255.0);
}
TEST_F(DequantizeOpTest, DequantizeScaledQuint8ScaleDown) {
RunDequantizeScaledTest<quint8>(-1.0f, 2.0f, 255, 2.0);
}
TEST_F(DequantizeOpTest, DequantizeScaledQuint8ScaleUp) {
RunDequantizeScaledTest<quint8>(200.0f, 400.0f, 255, 400.0);
}
TEST_F(DequantizeOpTest, DequantizeScaledQint8Zero) {
RunDequantizeScaledTest<qint8>(-255.0f, 127.0f, 0, 0.0);
}
TEST_F(DequantizeOpTest, DequantizeScaledQint8ScaleIdentity) {
RunDequantizeScaledTest<qint8>(-10.0f, 127.0f, -127, -127.0);
}
TEST_F(DequantizeOpTest, DequantizeScaledQint8ScaleDown) {
RunDequantizeScaledTest<qint8>(-2.0f, 1.0f, -128, -2.0);
}
TEST_F(DequantizeOpTest, DequantizeScaledQint8ScaleUp) {
RunDequantizeScaledTest<qint8>(-1.0f, 300.0f, 42, 99.212601);
}
template <typename T>
static void BM_DequantizeMinCombinedCpu(int iters) {
auto root = Scope::NewRootScope().ExitOnError();
const int64 num_values = 1500 * 250;
std::vector<T> inputs;
inputs.reserve(num_values);
for (int i = 0; i < num_values; ++i) inputs.push_back(i);
ops::Dequantize(root, test::AsTensor<T>(inputs), test::AsScalar<float>(-1.5f),
test::AsScalar<float>(20.5f),
ops::Dequantize::Attrs().Mode("MIN_COMBINED"));
TF_CHECK_OK(root.status());
Graph* g = new Graph(OpRegistry::Global());
TF_CHECK_OK(root.ToGraph(g));
test::Benchmark("cpu", g).Run(iters);
testing::BytesProcessed(iters * num_values * (sizeof(float) + sizeof(T)));
testing::ItemsProcessed(iters);
}
static void BM_DequantizeMinCombinedCpuQuint16(int iters) {
BM_DequantizeMinCombinedCpu<quint16>(iters);
}
static void BM_DequantizeMinCombinedCpuQint16(int iters) {
BM_DequantizeMinCombinedCpu<qint16>(iters);
}
static void BM_DequantizeMinCombinedCpuQuint8(int iters) {
BM_DequantizeMinCombinedCpu<quint8>(iters);
}
static void BM_DequantizeMinCombinedCpuQint8(int iters) {
BM_DequantizeMinCombinedCpu<qint8>(iters);
}
BENCHMARK(BM_DequantizeMinCombinedCpuQuint16);
BENCHMARK(BM_DequantizeMinCombinedCpuQint16);
BENCHMARK(BM_DequantizeMinCombinedCpuQuint8);
BENCHMARK(BM_DequantizeMinCombinedCpuQint8);
} // namespace
} // namespace tensorflow
|
