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profiler.py
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profiler.py
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# Copyright (c) 2018 PaddlePaddle 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.
from __future__ import print_function
from . import core
from .wrapped_decorator import signature_safe_contextmanager
import os
import six
__all__ = [
'cuda_profiler', 'reset_profiler', 'profiler', 'start_profiler',
'stop_profiler'
]
NVPROF_CONFIG = [
"gpustarttimestamp",
"gpuendtimestamp",
"gridsize3d",
"threadblocksize",
"streamid",
"enableonstart 0",
"conckerneltrace",
]
@signature_safe_contextmanager
def cuda_profiler(output_file, output_mode=None, config=None):
"""
The CUDA profiler.
This fuctions is used to profile CUDA program by CUDA runtime application
programming interface. The profiling result will be written into
`output_file`. The users can set the output mode by `output_mode` argument
and set the nvidia profiling config by `config` argument.
After getting the profiling result file, users can use
`NVIDIA Visual Profiler <https://developer.nvidia.com/nvidia-visual-profiler>`_
to load this output file to visualize results.
Args:
output_file (str) : The output file name, the result will be
written into this file.
output_mode (str, optional) : The output mode has Key-Value pair format ('kvp')
and Comma separated values format ('csv', default).
config (list<str>, optional) : Nvidia profile config. Default config is
['gpustarttimestamp', 'gpuendtimestamp', 'gridsize3d', 'threadblocksize',
'streamid', 'enableonstart 0', 'conckerneltrace']. For more details, please
refer to `Compute Command Line Profiler User Guide <https://developer.download.nvidia.cn/compute/DevZone/docs/html/C/doc/Compute_Command_Line_Profiler_User_Guide.pdf>`_ .
Raises:
ValueError: If `output_mode` is not in ['kvp', 'csv'].
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
import numpy as np
epoc = 8
dshape = [4, 3, 28, 28]
data = fluid.data(name='data', shape=[None, 3, 28, 28], dtype='float32')
conv = fluid.layers.conv2d(data, 20, 3, stride=[1, 1], padding=[1, 1])
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
output_file = 'cuda_profiler.txt'
with profiler.cuda_profiler(output_file, 'csv') as nvprof:
for i in range(epoc):
input = np.random.random(dshape).astype('float32')
exe.run(fluid.default_main_program(), feed={'data': input})
# then use NVIDIA Visual Profiler (nvvp) to load this output file
# to visualize results.
"""
if output_mode is None:
output_mode = 'csv'
if output_mode not in ['kvp', 'csv']:
raise ValueError("The output mode must be 'kvp' or 'csv'.")
config = NVPROF_CONFIG if config is None else config
config_file = 'nvprof_config_file'
with open(config_file, 'wb') as fp:
fp.writelines([six.b("%s\n" % item) for item in config])
core.nvprof_init(output_file, output_mode, config_file)
# Enables profiler collection by the active CUDA profiling tool.
core.nvprof_start()
try:
yield
# Disables profiler collection.
finally:
core.nvprof_stop()
os.remove(config_file)
def reset_profiler():
"""
Clear the previous time record. This interface does not work for
`fluid.profiler.cuda_profiler`, it only works for
`fluid.profiler.start_profiler`, `fluid.profiler.stop_profiler`,
and `fluid.profiler.profiler`.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
with profiler.profiler('CPU', 'total', '/tmp/profile'):
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# ...
"""
core.reset_profiler()
def start_profiler(state, tracer_option='Default'):
"""
Enable the profiler. Uers can use `fluid.profiler.start_profiler` and
`fluid.profiler.stop_profiler` to profile, which is equal to the usage
of `fluid.profiler.profiler` interface.
Args:
state (str) : The profiling state, which should be one of 'CPU', 'GPU'
or 'All'. 'CPU' means only profiling CPU; 'GPU' means profiling
both CPU and GPU; 'All' means profiling both CPU and GPU, and
generates timeline as well.
tracer_option (str, optional) : tracer_option can be one of ['Default', 'OpDetail', 'AllOpDetail'], it
can control the profile level and print the different level profile result. `Default` option print
the different Op type profiling result and the `OpDetail` option print the detail profiling
result of different op types such as compute and data transform, `AllOpDetail` option
print the detail profiling result of different op name same as `OpDetail`.
Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All'] or `tracer_option`
is not in ['Default', 'OpDetail', 'AllOpDetail'].
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
profiler.start_profiler('GPU')
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# except each iteration
profiler.stop_profiler('total', '/tmp/profile')
profiler.start_profiler('GPU', "OpDetail")
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# except each iteration
profiler.stop_profiler('total', '/tmp/profile')
"""
if core.is_profiler_enabled():
return
if state not in ['CPU', 'GPU', "All"]:
raise ValueError("The state must be 'CPU' or 'GPU' or 'All'.")
if state == "GPU":
prof_state = core.ProfilerState.kCUDA
elif state == "CPU":
prof_state = core.ProfilerState.kCPU
else:
prof_state = core.ProfilerState.kAll
if tracer_option not in ['Default', 'OpDetail', 'AllOpDetail']:
raise ValueError(
"tracer option must be 'Default', 'OpDetail', 'AllOpDetail'.")
if tracer_option == "Default":
prof_tracer_option = core.TracerOption.kDefault
elif tracer_option == "OpDetail":
prof_tracer_option = core.TracerOption.kOpDetail
else:
prof_tracer_option = core.TracerOption.kAllOpDetail
core.set_tracer_option(prof_tracer_option)
core.enable_profiler(prof_state)
def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
"""
Stop the profiler. Uers can use `fluid.profiler.start_profiler` and
`fluid.profiler.stop_profiler` to profile, which is equal to the usage
of `fluid.profiler.profiler` interface.
Args:
sorted_key (str, optional) : The order of profiling results, which
should be one of None, 'calls', 'total', 'max', 'min' or 'ave'.
Default is None, means the profiling results will be printed
in the order of first end time of events.
The `calls` means sorting by the number of calls.
The `total` means sorting by the total execution time.
The `max` means sorting by the maximum execution time.
The `min` means sorting by the minimum execution time.
The `ave` means sorting by the average execution time.
and write it into `profile_path`. The default profile_path is `/tmp/profile`.
profile_path (str, optional) : If state == 'All', it will generate timeline,
Raises:
ValueError: If `sorted_key` is not in
['calls', 'total', 'max', 'min', 'ave'].
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
profiler.start_profiler('GPU')
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# except each iteration
profiler.stop_profiler('total', '/tmp/profile')
"""
if not core.is_profiler_enabled():
return
sorted_key = 'default' if sorted_key is None else sorted_key
if sorted_key not in ['default', 'calls', 'total', 'max', 'min', 'ave']:
raise ValueError("The sorted_key must be None or in 'calls', 'total', "
"'max', 'min' and 'ave'")
key_map = {
'default': core.EventSortingKey.kDefault,
'calls': core.EventSortingKey.kCalls,
'total': core.EventSortingKey.kTotal,
'max': core.EventSortingKey.kMax,
'min': core.EventSortingKey.kMin,
'ave': core.EventSortingKey.kAve,
}
# TODO(qingqing) : redirect C++ ostream to Python stream.
# with core.ostream_redirect(stdout=True, stderr=True):
core.disable_profiler(key_map[sorted_key], profile_path)
@signature_safe_contextmanager
def profiler(state,
sorted_key=None,
profile_path='/tmp/profile',
tracer_option='Default'):
"""
The profiler interface. Different from `fluid.profiler.cuda_profiler`,
this profiler can be used to profile both CPU and GPU program.
Args:
state (str) : The profiling state, which should be one of 'CPU', 'GPU'
or 'All'. 'CPU' means only profiling CPU; 'GPU' means profiling
both CPU and GPU; 'All' means profiling both CPU and GPU, and
generates timeline as well.
sorted_key (str, optional) : The order of profiling results, which
should be one of None, 'calls', 'total', 'max', 'min' or 'ave'.
Default is None, means the profiling results will be printed
in the order of first end time of events.
The `calls` means sorting by the number of calls.
The `total` means sorting by the total execution time.
The `max` means sorting by the maximum execution time.
The `min` means sorting by the minimum execution time.
The `ave` means sorting by the average execution time.
profile_path (str, optional) : If state == 'All', it will generate timeline,
and write it into `profile_path`. The default profile_path is `/tmp/profile`.
tracer_option (str, optional) : tracer_option can be one of ['Default', 'OpDetail', 'AllOpDetail'], it
can control the profile level and print the different level profile result. `Default` option print
the different Op type profiling result and the `OpDetail` option print the detail profiling
result of different op types such as compute and data transform, `AllOpDetail` option
print the detail profiling result of different op name same as `OpDetail`.
Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All']. If `sorted_key` is
not in ['calls', 'total', 'max', 'min', 'ave'].
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
import numpy as np
epoc = 8
dshape = [4, 3, 28, 28]
data = fluid.data(name='data', shape=[None, 3, 28, 28], dtype='float32')
conv = fluid.layers.conv2d(data, 20, 3, stride=[1, 1], padding=[1, 1])
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
with profiler.profiler('CPU', 'total', '/tmp/profile', 'Default') as prof:
for i in range(epoc):
input = np.random.random(dshape).astype('float32')
exe.run(fluid.default_main_program(), feed={'data': input})
Examples Results:
.. code-block:: text
#### Examples Results ####
#### 1) sorted_key = 'total', 'calls', 'max', 'min', 'ave' ####
# The only difference in 5 sorted_key results is the following sentence:
# "Sorted by number of xxx in descending order in the same thread."
# The reason is that in this example, above 5 columns are already sorted.
-------------------------> Profiling Report <-------------------------
Place: CPU
Time unit: ms
Sorted by total time in descending order in the same thread
#Sorted by number of calls in descending order in the same thread
#Sorted by number of max in descending order in the same thread
#Sorted by number of min in descending order in the same thread
#Sorted by number of avg in descending order in the same thread
Event Calls Total Min. Max. Ave. Ratio.
thread0::conv2d 8 129.406 0.304303 127.076 16.1758 0.983319
thread0::elementwise_add 8 2.11865 0.193486 0.525592 0.264832 0.016099
thread0::feed 8 0.076649 0.006834 0.024616 0.00958112 0.000582432
#### 2) sorted_key = None ####
# Since the profiling results are printed in the order of first end time of Ops,
# the printed order is feed->conv2d->elementwise_add
-------------------------> Profiling Report <-------------------------
Place: CPU
Time unit: ms
Sorted by event first end time in descending order in the same thread
Event Calls Total Min. Max. Ave. Ratio.
thread0::feed 8 0.077419 0.006608 0.023349 0.00967738 0.00775934
thread0::conv2d 8 7.93456 0.291385 5.63342 0.99182 0.795243
thread0::elementwise_add 8 1.96555 0.191884 0.518004 0.245693 0.196998
"""
start_profiler(state, tracer_option)
try:
yield
finally:
stop_profiler(sorted_key, profile_path)