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
|
##Profile Time
* [Times in TensorFlow and tfprof](#times-in-tensorflow-and-tfprof)
* [Profile by Python Code](#profile-by-python-code)
* [Profile by Operation Type](#profile-by-operation-type)
* [Profile by Graph](#profile-by-graph)
* [Profile by Name Scope](#profile-by-name-scope)
###Times in TensorFlow and tfprof
When we run a model, Tensorflow schedules and runs the nodes (operations)
in the graph. An operation can be placed on an accelerator or on CPU.
#### On Accelerator
When an operation is placed on accelerator, it will first be scheduled
by TensorFlow on CPU. Normally, it's the code in OpKernel::Compute.
OpKernel::Compute can decide to dispatch some of the computations on the
accelerator. While some computation (e.g. pre-processing) is still done
in CPU. OpKernel::Compute can dispatch computation on accelerator
and return, or it can also wait for the accelerator to finish.
tfprof reports 3 execution times:
* <b>accelerator_micros</b>, which is the part of computation time spent on accelerator.
* <b>cpu_micros</b>, which is the part of computation time spent on cpu, including
any wait times that might happen if OpKernel::Compute decides to wait.
* <b>exec_micros</b>, which is the sum of accelerator_micros and cpu_micros.
Since accelerator, such as GPU, usually runs operation asynchronously, you
might notice an operation finishes on cpu before it starts running on
accelerator.
#### On CPU
When an operation is placed on CPU, it will completely run on CPU. Hence,
<b>exec_micros</b> is equal to <b>cpu_micros</b> and <b>accelerator_micros</b>
should be 0.
###Profile by Python Code
```python
# In code view, the time of each line of Python code is the aggregated
# times of all operations created by that line.
# In command line, it requires --graph_path --op_log_path and --run_meta_path.
# --op_log_path provides the code traces information.
# --run_meta_path provides the time information.
tfprof> code -show_name_regexes seq2seq_attention.* -max_depth 10 -select micros -order_by micros
node name | execution time
_TFProfRoot (--/3.74sec)
seq2seq_attention.py'>:168:run_filename_from...:none (0us/3.74sec)
seq2seq_attention.py'>:33:_run_code_in_main:none (0us/3.74sec)
seq2seq_attention.py:316:<module>:app.run() (0us/3.74sec)
seq2seq_attention.py:270:main:_Train(model, bat... (0us/3.74sec)
seq2seq_attention.py:128:_Train:model.build_graph() (0us/3.74sec)
seq2seq_attention_model.py:360:build_graph:self._add_seq2seq() (0us/2.79sec)
seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ... (0us/2.46sec)
seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a... (0us/265.31ms)
seq2seq_attention_model.py:253:_add_seq2seq:initial_state_att... (0us/50.35ms)
seq2seq_attention_model.py:173:_add_seq2seq:for x in encoder_... (0us/8.72ms)
seq2seq_attention_model.py:218:_add_seq2seq:w_t = tf.transpos... (0us/2.39ms)
...
seq2seq_attention_model.py:363:build_graph:self._add_train_o... (0us/949.10ms)
seq2seq_attention_model.py:307:_add_train_op:tf.gradients(self... (0us/641.44ms)
seq2seq_attention_model.py:322:_add_train_op:zip(grads, tvars)... (0us/307.56ms)
...
seq2seq_attention_model.py:364:build_graph:self._summaries =... (0us/13us)
seq2seq_attention_model.py:361:build_graph:self.global_step ... (0us/12us)
...
seq2seq_attention.py:129:_Train:saver = tf.train.... (0us/0us)
seq2seq_attention.py:140:_Train:global_step=model... (0us/0us)
# Sometimes you want to explore a specific function. You can do that
# with -start_name_regexes.
tfprof> code -start_name_regexes .*_add_seq2seq.* -show_name_regexes seq2seq_attention.* -max_depth 10 -select micros -order_by micros
node name | execution time
_TFProfRoot (--/3.74sec)
seq2seq_attention_model.py:360:build_graph:self._add_seq2seq() (0us/2.79sec)
seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ... (0us/2.46sec)
seq2seq_attention_model.py:289:sampled_loss_func:num_classes=vsize) (0us/2.46sec)
seq2seq_attention_model.py:282:sampled_loss_func:labels = tf.resha... (0us/164us)
# You can also dive deeper into tensorflow's libraries.
tfprof> code -max_depth 5 -select micros -order_by micros -start_name_regexes .*_add_seq2seq.* -min_micros 100000
_TFProfRoot (--/3.74sec)
seq2seq_attention_model.py:360:build_graph:self._add_seq2seq() (0us/2.79sec)
seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ... (0us/2.46sec)
seq2seq_lib.py:181:sampled_sequence_...:average_across_ti... (0us/2.46sec)
seq2seq_lib.py:147:sequence_loss_by_...:crossent = loss_f... (0us/2.46sec)
seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a... (0us/265.31ms)
seq2seq_lib.py:104:bidirectional_rnn:sequence_length, ... (0us/127.27ms)
core_rnn.py:195:static_rnn:state_size=cell.s... (0us/127.20ms)
seq2seq_lib.py:110:bidirectional_rnn:initial_state_bw,... (0us/125.96ms)
core_rnn.py:195:static_rnn:state_size=cell.s... (0us/125.86ms)
# It can also be done in Python API
opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
opts['account_type_regexes'] = ['.*']
opts['show_name_regexes'] = ['.*model_analyzer_testlib.py.*']
opts['account_displayed_op_only'] = False
opts['select'] = ['micros']
tfprof_node = model_analyzer.print_model_analysis(
sess.graph, run_meta, cmd='code', options=opts)
```
You can generate some visualization in code view:
Set ```-output timeline:outfile=<filename>``` to generate timeline instead of stdout.
<left>
</left>
###Profile by Operation Type
```python
# In op view, you can view the aggregated time of each operation type.
tfprof> op -select micros,occurrence -order_by micros
node name | execution time | op occurrence
SoftmaxCrossEntropyWithLogits 1.37sec (100.00%, 36.44%), 30
MatMul 618.97ms (63.56%, 16.51%), 3450
Add 273.76ms (47.06%, 7.30%), 2180
Sub 215.41ms (39.76%, 5.74%), 4372
ConcatV2 203.88ms (34.01%, 5.44%), 6098
Mul 134.32ms (28.58%, 3.58%), 9427
ApplyAdam 92.66ms (25.00%, 2.47%), 27
Switch 72.43ms (22.53%, 1.93%), 30654
LogUniformCandidateSampler 69.01ms (20.59%, 1.84%), 30
Unique 53.50ms (18.75%, 1.43%), 2
AddN 50.10ms (17.33%, 1.34%), 5481
# You might be surprised to see that SoftmaxCrossEntropyWithLogits is
# that expensive. As shown below, it is placed on cpu.
tfprof> op -select micros,device -order_by micros
node name | execution time | assigned devices
SoftmaxCrossEntropyWithLogits 1.37sec (100.00%, 36.44%), /job:worker/replica:0/task:0/cpu:0
MatMul 618.97ms (63.56%, 16.51%), |/job:worker/replica:0/task:0/cpu:0|/job:worker/replica:0/task:0/device:GPU:0|/job:worker/replica:0/task:0/device:GPU:1|/job:worker/replica:0/task:0/device:GPU:2|/job:worker/replica:0/task:0/device:GPU:3
```
###Profile by Graph
Usually, use graph view to generate a timeline to visualize the result.
In the chrome://tracing UI, click "Flow Event" in "View Options" of upper
right corner to see the flow of tensors.
<left>
TODO(xpan): Show the image correctly in github.
</left>
tfprof options allow users to generate timeline in some advanced ways.
```python
# Only generate timeline for gpu3 and cpu on workers.
graph -max_depth 10000000 -step 0 -account_type_regexes .*gpu:3.*,.*worker.*cpu:0.* -output timeline:outfile=<filename.json>
generating trace file.
******************************************************
Timeline file is written to <filename.json>.
Open a Chrome browser, enter URL chrome://tracing and load the timeline file.
******************************************************
```
###Profile by Name Scope
Usually scope view allows you to pin point the problematic places if you
have properly named your operations with tf.name_scope or tf.variable_scope.
```python
tfprof> scope -max_depth 30 -select micros -min_micros 100000 -order_by micros
node name | execution time
_TFProfRoot (--/8.12sec)
tower_3/gradients/tower_3/Conv2d_1a_3x3/convolution_grad/Conv2DBackpropFilter (126.34ms/126.34ms)
tower_1/gradients/tower_1/Conv2d_1a_3x3/convolution_grad/Conv2DBackpropFilter (125.44ms/125.44ms)
tower_2/gradients/tower_2/Conv2d_1a_3x3/convolution_grad/Conv2DBackpropFilter (124.85ms/124.85ms)
tower_0/gradients/tower_0/Conv2d_1a_3x3/convolution_grad/Conv2DBackpropFilter (124.45ms/124.45ms)
```
|
