未验证 提交 9bffbb7d 编写于 作者: T Tao Luo 提交者: GitHub

refine profiler document (#20326)

* refine profiler document

test=develop test=document_fix

* update profiler document

test=develop test=document_fix
上级 ade9bc04
...@@ -1125,11 +1125,11 @@ paddle.fluid.dygraph_grad_clip.GradClipByNorm ('paddle.fluid.dygraph_grad_clip.G ...@@ -1125,11 +1125,11 @@ paddle.fluid.dygraph_grad_clip.GradClipByNorm ('paddle.fluid.dygraph_grad_clip.G
paddle.fluid.dygraph_grad_clip.GradClipByNorm.__init__ (ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph_grad_clip.GradClipByNorm.__init__ (ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph_grad_clip.GradClipByGlobalNorm ('paddle.fluid.dygraph_grad_clip.GradClipByGlobalNorm', ('document', 'd1872377e7d7a5fe0dd2e8c42e4c9656')) paddle.fluid.dygraph_grad_clip.GradClipByGlobalNorm ('paddle.fluid.dygraph_grad_clip.GradClipByGlobalNorm', ('document', 'd1872377e7d7a5fe0dd2e8c42e4c9656'))
paddle.fluid.dygraph_grad_clip.GradClipByGlobalNorm.__init__ (ArgSpec(args=['self', 'max_global_norm'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph_grad_clip.GradClipByGlobalNorm.__init__ (ArgSpec(args=['self', 'max_global_norm'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.profiler.cuda_profiler (ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None)), ('document', '4053b45953807a24e28027dc86829d6c')) paddle.fluid.profiler.cuda_profiler (ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6ae5833bd2490c6a3bdcae0d31ce5ec5'))
paddle.fluid.profiler.reset_profiler (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'fd1f25a7a06516ca9a1f4ab0783a4d70')) paddle.fluid.profiler.reset_profiler (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'fd1f25a7a06516ca9a1f4ab0783a4d70'))
paddle.fluid.profiler.profiler (ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'a2be24e028dffa06ab28cc55a27c59e4')) paddle.fluid.profiler.profiler (ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', '8e8d777eb0127876d7bdb6c421db7f5c'))
paddle.fluid.profiler.start_profiler (ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None), ('document', '4c192ea399e6e80b1ab47a8265b022a5')) paddle.fluid.profiler.start_profiler (ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None), ('document', '9494b48e79a0e07b49017ba5a97800b6'))
paddle.fluid.profiler.stop_profiler (ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'bc8628b859b04242200e48a458c971c4')) paddle.fluid.profiler.stop_profiler (ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', '10406b144bd8b5e01ea44301219f7fef'))
paddle.fluid.unique_name.generate (ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None), ('document', '4d68cde4c4df8f1b8018620b4dc19b42')) paddle.fluid.unique_name.generate (ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None), ('document', '4d68cde4c4df8f1b8018620b4dc19b42'))
paddle.fluid.unique_name.switch (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', '695a6e91afbcdbafac69a069038811be')) paddle.fluid.unique_name.switch (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', '695a6e91afbcdbafac69a069038811be'))
paddle.fluid.unique_name.guard (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ead717d6d440a1eb11971695cd1727f4')) paddle.fluid.unique_name.guard (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ead717d6d440a1eb11971695cd1727f4'))
......
...@@ -37,25 +37,27 @@ NVPROF_CONFIG = [ ...@@ -37,25 +37,27 @@ NVPROF_CONFIG = [
@signature_safe_contextmanager @signature_safe_contextmanager
def cuda_profiler(output_file, output_mode=None, config=None): def cuda_profiler(output_file, output_mode=None, config=None):
"""The CUDA profiler. """
The CUDA profiler.
This fuctions is used to profile CUDA program by CUDA runtime application This fuctions is used to profile CUDA program by CUDA runtime application
programming interface. The profiling result will be written into programming interface. The profiling result will be written into
`output_file` with Key-Value pair format or Comma separated values format. `output_file`. The users can set the output mode by `output_mode` argument
The user can set the output mode by `output_mode` argument and set the and set the nvidia profiling config by `config` argument.
counters/options for profiling by `config` argument. The default config
is ['gpustarttimestamp', 'gpuendtimestamp', 'gridsize3d', After getting the profiling result file, users can use
'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace']. `NVIDIA Visual Profiler <https://developer.nvidia.com/nvidia-visual-profiler>`_
Then users can use NVIDIA Visual Profiler to load this output file to visualize results.
(https://developer.nvidia.com/nvidia-visual-profiler) tools to load this
this output file to visualize results.
Args: Args:
output_file (string) : The output file name, the result will be output_file (str) : The output file name, the result will be
written into this file. written into this file.
output_mode (string) : The output mode has Key-Value pair format and output_mode (str, optional) : The output mode has Key-Value pair format ('kvp')
Comma separated values format. It should be 'kvp' or 'csv'. and Comma separated values format ('csv', default).
config (list of string) : The profiler options and counters can refer config (list<str>, optional) : Nvidia profile config. Default config is
to "Compute Command Line Profiler User Guide". ['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: Raises:
ValueError: If `output_mode` is not in ['kvp', 'csv']. ValueError: If `output_mode` is not in ['kvp', 'csv'].
...@@ -70,7 +72,7 @@ def cuda_profiler(output_file, output_mode=None, config=None): ...@@ -70,7 +72,7 @@ def cuda_profiler(output_file, output_mode=None, config=None):
epoc = 8 epoc = 8
dshape = [4, 3, 28, 28] dshape = [4, 3, 28, 28]
data = fluid.layers.data(name='data', shape=[3, 28, 28], dtype='float32') 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]) conv = fluid.layers.conv2d(data, 20, 3, stride=[1, 1], padding=[1, 1])
place = fluid.CUDAPlace(0) place = fluid.CUDAPlace(0)
...@@ -127,13 +129,14 @@ def reset_profiler(): ...@@ -127,13 +129,14 @@ def reset_profiler():
def start_profiler(state): def start_profiler(state):
""" """
Enable the profiler. Uers can use `fluid.profiler.start_profiler` and Enable the profiler. Uers can use `fluid.profiler.start_profiler` and
`fluid.profiler.stop_profiler` to insert the code, except the usage of `fluid.profiler.stop_profiler` to profile, which is equal to the usage
`fluid.profiler.profiler` interface. of `fluid.profiler.profiler` interface.
Args: Args:
state (string) : The profiling state, which should be 'CPU', 'GPU' state (str) : The profiling state, which should be one of 'CPU', 'GPU'
or 'All'. 'CPU' means only profile CPU. 'GPU' means profiling or 'All'. 'CPU' means only profiling CPU; 'GPU' means profiling
GPU as well. 'All' also generates timeline. both CPU and GPU; 'All' means profiling both CPU and GPU, and
generates timeline as well.
Raises: Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All']. ValueError: If `state` is not in ['CPU', 'GPU', 'All'].
...@@ -168,21 +171,21 @@ def start_profiler(state): ...@@ -168,21 +171,21 @@ def start_profiler(state):
def stop_profiler(sorted_key=None, profile_path='/tmp/profile'): def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
""" """
Stop the profiler. Uers can use `fluid.profiler.start_profiler` and Stop the profiler. Uers can use `fluid.profiler.start_profiler` and
`fluid.profiler.stop_profiler` to insert the code, except the usage of `fluid.profiler.stop_profiler` to profile, which is equal to the usage
`fluid.profiler.profiler` interface. of `fluid.profiler.profiler` interface.
Args: Args:
sorted_key (string) : If None, the profiling results will be printed sorted_key (str, optional) : The order of profiling results, which
in the order of first end time of events. Otherwise, the profiling should be one of None, 'calls', 'total', 'max', 'min' or 'ave'.
results will be sorted by the this flag. This flag should be one Default is None, means the profiling results will be printed
of 'calls', 'total', 'max', 'min' or 'ave'. in the order of first end time of events.
The `calls` means sorting by the number of calls. The `calls` means sorting by the number of calls.
The `total` means sorting by the total execution time. The `total` means sorting by the total execution time.
The `max` means sorting by the maximum execution time. The `max` means sorting by the maximum execution time.
The `min` means sorting by the minimum execution time. The `min` means sorting by the minimum execution time.
The `ave` means sorting by the average execution time. The `ave` means sorting by the average execution time.
profile_path (string) : If state == 'All', it will write a profile profile_path (str, optional) : If state == 'All', it will generate timeline,
proto output file. and write it into `profile_path`. The default profile_path is `/tmp/profile`.
Raises: Raises:
ValueError: If `sorted_key` is not in ValueError: If `sorted_key` is not in
...@@ -223,34 +226,26 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'): ...@@ -223,34 +226,26 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
@signature_safe_contextmanager @signature_safe_contextmanager
def profiler(state, sorted_key=None, profile_path='/tmp/profile'): def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
"""The profiler interface. """
Different from cuda_profiler, this profiler can be used to profile both CPU The profiler interface. Different from `fluid.profiler.cuda_profiler`,
and GPU program. By default, it records the CPU and GPU operator kernels, this profiler can be used to profile both CPU and GPU program.
if you want to profile other program, you can refer the profiling tutorial
to add more records in C++ code.
If the state == 'All', a profile proto file will be written to
`profile_path`. This file records timeline information during the execution.
Then users can visualize this file to see the timeline, please refer
https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/howto/optimization/timeline.md
Args: Args:
state (string) : The profiling state, which should be 'CPU' or 'GPU', state (str) : The profiling state, which should be one of 'CPU', 'GPU'
telling the profiler to use CPU timer or GPU timer for profiling. or 'All'. 'CPU' means only profiling CPU; 'GPU' means profiling
Although users may have already specified the execution place both CPU and GPU; 'All' means profiling both CPU and GPU, and
(CPUPlace/CUDAPlace) in the beginning, for flexibility the profiler generates timeline as well.
would not inherit this place. sorted_key (str, optional) : The order of profiling results, which
sorted_key (string) : If None, the profiling results will be printed should be one of None, 'calls', 'total', 'max', 'min' or 'ave'.
in the order of first end time of events. Otherwise, the profiling Default is None, means the profiling results will be printed
results will be sorted by the this flag. This flag should be one in the order of first end time of events.
of 'calls', 'total', 'max', 'min' or 'ave'.
The `calls` means sorting by the number of calls. The `calls` means sorting by the number of calls.
The `total` means sorting by the total execution time. The `total` means sorting by the total execution time.
The `max` means sorting by the maximum execution time. The `max` means sorting by the maximum execution time.
The `min` means sorting by the minimum execution time. The `min` means sorting by the minimum execution time.
The `ave` means sorting by the average execution time. The `ave` means sorting by the average execution time.
profile_path (string) : If state == 'All', it will write a profile profile_path (str, optional) : If state == 'All', it will generate timeline,
proto output file. and write it into `profile_path`. The default profile_path is `/tmp/profile`.
Raises: Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All']. If `sorted_key` is ValueError: If `state` is not in ['CPU', 'GPU', 'All']. If `sorted_key` is
...@@ -266,7 +261,7 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'): ...@@ -266,7 +261,7 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
epoc = 8 epoc = 8
dshape = [4, 3, 28, 28] dshape = [4, 3, 28, 28]
data = fluid.layers.data(name='data', shape=[3, 28, 28], dtype='float32') 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]) conv = fluid.layers.conv2d(data, 20, 3, stride=[1, 1], padding=[1, 1])
place = fluid.CPUPlace() place = fluid.CPUPlace()
...@@ -277,6 +272,44 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'): ...@@ -277,6 +272,44 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
for i in range(epoc): for i in range(epoc):
input = np.random.random(dshape).astype('float32') input = np.random.random(dshape).astype('float32')
exe.run(fluid.default_main_program(), feed={'data': input}) 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 sentense:
# "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) start_profiler(state)
yield yield
......
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