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
|
/* Copyright 2015 Google Inc. 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 "tensorflow/core/public/tensor.h"
#include <gtest/gtest.h>
#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/platform/test_benchmark.h"
namespace tensorflow {
// Creates a Graph which applies a unary "func" on a 3D float tensor
// of "num" elements.
static Graph* Unary(const string& func, int num) {
RequireDefaultOps();
Graph* g = new Graph(OpRegistry::Global());
Tensor data(DT_FLOAT, TensorShape({64, 64, num / (64 * 64)}));
CHECK_GT(data.NumElements(), 0);
data.flat<float>().setRandom();
test::graph::Unary(g, func, test::graph::Constant(g, data), 0);
return g;
}
static int kRows = 100000;
static int RowsAndColsArg(int r, int c) { return r * kRows + c; }
static int RowsFromArg(int arg) { return (arg / kRows); }
static int ColsFromArg(int arg) { return (arg % kRows); }
#define BM_UNARY(DEVICE, FUNC) \
static void BM_##DEVICE##_##FUNC(int iters, int num) { \
const int64 tot = static_cast<int64>(iters) * num; \
testing::ItemsProcessed(tot); \
testing::BytesProcessed(tot * sizeof(float)); \
test::Benchmark(#DEVICE, Unary(#FUNC, num)).Run(iters); \
} \
BENCHMARK(BM_##DEVICE##_##FUNC)->Range(4 << 10, 1 << 20);
BM_UNARY(cpu, Floor);
BM_UNARY(gpu, Floor);
// data func scalar.
static Graph* BinaryScalar(int num, const string& func) {
RequireDefaultOps();
Graph* g = new Graph(OpRegistry::Global());
Tensor lhs(DT_FLOAT, TensorShape({64, 64, num / (64 * 64)}));
lhs.flat<float>().setRandom();
Tensor rhs(DT_FLOAT, TensorShape({}));
rhs.flat<float>().setRandom();
test::graph::Binary(g, func, test::graph::Constant(g, lhs),
test::graph::Constant(g, rhs));
return g;
}
#define BM_BINARY_SCALAR(DEVICE, FUNC) \
static void BM_##DEVICE##_##FUNC##_scalar(int iters, int num) { \
const int64 tot = static_cast<int64>(iters) * num; \
testing::ItemsProcessed(tot); \
testing::BytesProcessed(tot * sizeof(float)); \
test::Benchmark(#DEVICE, BinaryScalar(num, #FUNC)).Run(iters); \
} \
BENCHMARK(BM_##DEVICE##_##FUNC##_scalar) \
->Arg(4096) /* must >= 4096 */ \
->Arg(32768) \
->Arg(131072) \
->Arg(1048576);
BM_BINARY_SCALAR(cpu, Less);
BM_BINARY_SCALAR(gpu, Less);
BM_BINARY_SCALAR(cpu, Add);
BM_BINARY_SCALAR(gpu, Add);
#undef BM_BINARY_SCALAR
static Graph* BiasAdd(int rows, int cols) {
RequireDefaultOps();
Graph* g = new Graph(OpRegistry::Global());
Tensor lhs(DT_FLOAT, TensorShape({rows, cols}));
lhs.flat<float>().setRandom();
TensorShape rhs_shape;
rhs_shape = TensorShape({cols});
Tensor rhs(DT_FLOAT, rhs_shape);
rhs.flat<float>().setRandom();
test::graph::Binary(g, "BiasAdd", test::graph::Constant(g, lhs),
test::graph::Constant(g, rhs));
return g;
}
#define BM_BIAS_ADD(DEVICE, R, C) \
static void BM_##DEVICE##_BiasAdd_R##R##_C##C(int iters, int arg) { \
const int rows = RowsFromArg(arg); \
const int cols = ColsFromArg(arg); \
const int64 tot = static_cast<int64>(iters) * rows * cols; \
testing::ItemsProcessed(tot); \
testing::BytesProcessed(tot * sizeof(float)); \
test::Benchmark(#DEVICE, BiasAdd(rows, cols)).Run(iters); \
} \
BENCHMARK(BM_##DEVICE##_BiasAdd_R##R##_C##C)->Arg(RowsAndColsArg(R, C));
#define BM_BIAS_ADD_ALL(DEVICE) \
BM_BIAS_ADD(DEVICE, 512, 2048); \
BM_BIAS_ADD(DEVICE, 512, 4096); \
BM_BIAS_ADD(DEVICE, 2048, 512); \
BM_BIAS_ADD(DEVICE, 4096, 512);
BM_BIAS_ADD_ALL(cpu);
BM_BIAS_ADD_ALL(gpu);
#undef BM_BIAS_ADD_ALL
#undef BM_BIAS_ADD
static Graph* BcastAdd(int rows, int cols, int dim) {
RequireDefaultOps();
Graph* g = new Graph(OpRegistry::Global());
Tensor lhs(DT_FLOAT, TensorShape({rows, cols}));
lhs.flat<float>().setRandom();
TensorShape rhs_shape;
if (dim == 0) {
rhs_shape = TensorShape({rows, 1});
} else {
rhs_shape = TensorShape({cols});
}
Tensor rhs(DT_FLOAT, rhs_shape);
rhs.flat<float>().setRandom();
test::graph::Binary(g, "Add", test::graph::Constant(g, lhs),
test::graph::Constant(g, rhs));
return g;
}
#define BM_BCAST_ADD_ROW(DEVICE, R, C) \
static void BM_##DEVICE##_BcastAddRow_R##R##_C##C(int iters, int arg) { \
const int rows = RowsFromArg(arg); \
const int cols = ColsFromArg(arg); \
const int64 tot = static_cast<int64>(iters) * rows * cols; \
testing::ItemsProcessed(tot); \
testing::BytesProcessed(tot * sizeof(float)); \
test::Benchmark(#DEVICE, BcastAdd(rows, cols, 0)).Run(iters); \
} \
BENCHMARK(BM_##DEVICE##_BcastAddRow_R##R##_C##C)->Arg(RowsAndColsArg(R, C));
#define BM_BCAST_ADD_ROW_ALL(DEVICE) \
BM_BCAST_ADD_ROW(DEVICE, 512, 2048); \
BM_BCAST_ADD_ROW(DEVICE, 512, 4096); \
BM_BCAST_ADD_ROW(DEVICE, 2048, 512); \
BM_BCAST_ADD_ROW(DEVICE, 4096, 512);
BM_BCAST_ADD_ROW_ALL(cpu);
BM_BCAST_ADD_ROW_ALL(gpu);
#undef BM_BCAST_ADD_ROW_ALL
#undef BM_BCAST_ADD_ROW
#define BM_BCAST_ADD_COL(DEVICE, R, C) \
static void BM_##DEVICE##_BcastAddCol_R##R##_C##C(int iters, int arg) { \
const int rows = RowsFromArg(arg); \
const int cols = ColsFromArg(arg); \
const int64 tot = static_cast<int64>(iters) * rows * cols; \
testing::ItemsProcessed(tot); \
testing::BytesProcessed(tot * sizeof(float)); \
test::Benchmark(#DEVICE, BcastAdd(rows, cols, 1)).Run(iters); \
} \
BENCHMARK(BM_##DEVICE##_BcastAddCol_R##R##_C##C)->Arg(RowsAndColsArg(R, C));
#define BM_BCAST_ADD_COL_ALL(DEVICE) \
BM_BCAST_ADD_COL(DEVICE, 512, 2048); \
BM_BCAST_ADD_COL(DEVICE, 512, 4096); \
BM_BCAST_ADD_COL(DEVICE, 2048, 512); \
BM_BCAST_ADD_COL(DEVICE, 4096, 512);
BM_BCAST_ADD_COL_ALL(cpu);
BM_BCAST_ADD_COL_ALL(gpu);
#undef BM_BCAST_ADD_COL_ALL
#undef BM_BCAST_ADD_COL
} // end namespace tensorflow
|
