-
Notifications
You must be signed in to change notification settings - Fork 74k
/
conv_grad_filter_ops_benchmark_test.cc
185 lines (155 loc) · 8.1 KB
/
conv_grad_filter_ops_benchmark_test.cc
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
/* 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 <string>
#include <vector>
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/kernels/conv_ops.h"
#include "tensorflow/core/kernels/ops_testutil.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"
namespace tensorflow {
template <typename T>
static Tensor MakeRandomTensor(const TensorShape& shape) {
Tensor tensor(DataTypeToEnum<T>::value, TensorShape(shape));
tensor.flat<T>() = tensor.flat<T>().setRandom();
return tensor;
}
template <typename T>
static Graph* Conv2DBackpropFilter(int batch, int height, int width,
int in_depth, int filter_h, int filter_w,
int out_depth, int stride_h, int stride_w,
Padding padding, TensorFormat data_format) {
auto* graph = new Graph(OpRegistry::Global());
Tensor input_t = data_format == FORMAT_NHWC
? MakeRandomTensor<T>({batch, height, width, in_depth})
: MakeRandomTensor<T>({batch, in_depth, height, width});
Tensor filter_t =
MakeRandomTensor<T>({filter_h, filter_w, in_depth, out_depth});
// Compute dimensions for the `out_backprop` tensor.
Conv2DParameters params;
params.dilations = {1, 1, 1, 1};
params.strides = {1, stride_h, stride_w, 1};
params.padding = padding;
params.data_format = data_format;
Conv2DDimensions conv2d_dims;
TF_CHECK_OK(ComputeConv2DDimension(params, input_t, filter_t, &conv2d_dims));
Tensor out_backprop_t =
data_format == FORMAT_NHWC
? MakeRandomTensor<T>(
{batch, conv2d_dims.out_rows, conv2d_dims.out_cols, out_depth})
: MakeRandomTensor<T>(
{batch, out_depth, conv2d_dims.out_rows, conv2d_dims.out_cols});
Tensor filter_dims_t(DT_INT32, TensorShape({4}));
filter_dims_t.flat<int32>()(0) = filter_h;
filter_dims_t.flat<int32>()(1) = filter_w;
filter_dims_t.flat<int32>()(2) = in_depth;
filter_dims_t.flat<int32>()(3) = out_depth;
Node* input = test::graph::Constant(graph, input_t, "input");
Node* filter_dims =
test::graph::HostConstant(graph, filter_dims_t, "filter_dims");
Node* backprop = test::graph::Constant(graph, out_backprop_t, "backprop");
Node* conv2d;
TF_CHECK_OK(
NodeBuilder(graph->NewName("conv_bwd_filter"), "Conv2DBackpropFilter")
.Input(input)
.Input(filter_dims)
.Input(backprop)
.Attr("T", DataTypeToEnum<T>::value)
.Attr("strides", {1, stride_h, stride_w, 1})
.Attr("padding", padding == Padding::SAME
? "SAME"
: padding == Padding::VALID ? "VALID" : "N/A")
.Attr("data_format", ToString(data_format))
.Finalize(graph, &conv2d));
return graph;
}
// Macro arguments names: --------------------------------------------------- //
// T: data type
// FMT: data format (NHWC or NCHW)
// N: batch size
// H: height
// W: width
// C: channels
// FC: filter count
// FH: filter height
// FW: filter width
// SH: stride height
// SW: stride width
#define BM_CONCAT(a, b) a##_##b
#define BM_NAME(name, type, T, FMT, N, H, W, C, FH, FW, FC, SH, SW, PADDING) \
BM_CONCAT(name##_##T##_##FMT##_##type##_in##N##x##H##x##W##x##C, \
f##FH##x##FW##x##FC##_##s##SH##x##SW##_##PADDING)
#define BM_Conv2DBwdFilter(T, FMT, N, H, W, C, FH, FW, FC, SH, SW, PADDING, \
type) \
static void BM_NAME(BM_Conv2DBackpropFilter, type, T, FMT, N, H, W, C, FH, \
FW, FC, SH, SW, \
PADDING)(::testing::benchmark::State & state) { \
test::Benchmark(#type, \
Conv2DBackpropFilter<T>(N, H, W, C, FH, FW, FC, SH, SW, \
PADDING, FORMAT_##FMT), \
/*old_benchmark_api*/ false) \
.Run(state); \
state.SetItemsProcessed(static_cast<int64_t>(state.iterations()) * (N) * \
(H) * (W) * (C)); \
} \
BENCHMARK(BM_NAME(BM_Conv2DBackpropFilter, type, T, FMT, N, H, W, C, FH, FW, \
FC, SH, SW, PADDING));
// ResNet50-ish convolutions.
#define BENCHMARK_DTYPE(FMT, BATCH, T, D) \
BM_Conv2DBwdFilter(T, FMT, BATCH, 112, 112, 64, 2, 2, 64, 2, 2, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 56, 56, 128, 2, 2, 128, 2, 2, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 28, 28, 256, 2, 2, 256, 2, 2, SAME, D); \
\
BM_Conv2DBwdFilter(T, FMT, BATCH, 112, 112, 64, 2, 2, 64, 2, 2, VALID, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 56, 56, 128, 2, 2, 128, 2, 2, VALID, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 28, 28, 256, 2, 2, 256, 2, 2, VALID, D); \
\
BM_Conv2DBwdFilter(T, FMT, BATCH, 56, 56, 64, 1, 1, 64, 1, 1, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 56, 56, 64, 1, 1, 256, 1, 1, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 56, 56, 256, 1, 1, 64, 1, 1, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 56, 56, 64, 3, 3, 64, 1, 1, SAME, D); \
\
BM_Conv2DBwdFilter(T, FMT, BATCH, 28, 28, 128, 1, 1, 128, 1, 1, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 28, 28, 128, 1, 1, 512, 1, 1, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 28, 28, 512, 1, 1, 128, 1, 1, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 28, 28, 512, 3, 3, 128, 1, 1, SAME, D); \
\
BM_Conv2DBwdFilter(T, FMT, BATCH, 14, 14, 256, 1, 1, 256, 1, 1, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 14, 14, 256, 1, 1, 1024, 1, 1, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 14, 14, 1024, 1, 1, 256, 1, 1, SAME, D); \
BM_Conv2DBwdFilter(T, FMT, BATCH, 14, 14, 256, 3, 3, 256, 1, 1, SAME, D);
using fp32 = float;
using fp16 = Eigen::half;
BENCHMARK_DTYPE(NHWC, 8, fp32, cpu);
BENCHMARK_DTYPE(NHWC, 16, fp32, cpu);
BENCHMARK_DTYPE(NHWC, 32, fp32, cpu);
#if GOOGLE_CUDA
// -------------------------------------------------------------------------- //
// The following benchmarks are used to compare different data format
// performance for different data types. They make sense only when CUDA enabled,
// because on CPU we only support data in NHWC.
// -------------------------------------------------------------------------- //
BENCHMARK_DTYPE(NHWC, 32, fp32, gpu);
BENCHMARK_DTYPE(NCHW, 32, fp32, gpu);
BENCHMARK_DTYPE(NHWC, 32, fp16, gpu);
BENCHMARK_DTYPE(NCHW, 32, fp16, gpu);
BENCHMARK_DTYPE(NHWC, 64, fp32, gpu);
BENCHMARK_DTYPE(NCHW, 64, fp32, gpu);
BENCHMARK_DTYPE(NHWC, 64, fp16, gpu);
BENCHMARK_DTYPE(NCHW, 64, fp16, gpu);
#endif // GOOGLE_CUDA
} // namespace tensorflow