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Paddle
未验证 提交 234b530d 编写于 作者: T Tao Luo 提交者： GitHub
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refine profiler, name_scope document (#20431) 

* refine profiler document (#20326)

* refine profiler document

test=develop test=document_fix

* update profiler document

test=develop test=document_fix

* refine profiler, name_scope document

test=develop test=document_fix
上级 dfde0eaa
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Showing with 118 addition and 66 deletion
paddle/fluid/API.spec
浏览文件 @ 234b530d
......@@ -10,7 +10,7 @@ paddle.fluid.Program.to_string (ArgSpec(args=['self', 'throw_on_error', 'with_de
paddle.fluid.default_startup_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'f53890b2fb8c0642b6047e4fee2d6d58'))
paddle.fluid.default_main_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '853718df675e59aea7104f3d61bbf11d'))
paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)), ('document', '78fb5c7f70ef76bcf4a1862c3f6b8191'))
paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)), ('document', '917d313881ff990de5fb18d98a9c7b42'))
paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)), ('document', '907a5f877206079d8e67ae69b06bb3ba'))
paddle.fluid.cuda_places (ArgSpec(args=['device_ids'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ab9bd2079536114aa7c1488a489ee87f'))
paddle.fluid.cpu_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a7352a3dd39308fde4fbbf6421a4193d'))
paddle.fluid.cuda_pinned_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', '567ac29567716fd8e7432b533337d529'))
......@@ -1088,11 +1088,11 @@ 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.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.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.profiler (ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'a2be24e028dffa06ab28cc55a27c59e4'))
paddle.fluid.profiler.start_profiler (ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None), ('document', '4c192ea399e6e80b1ab47a8265b022a5'))
paddle.fluid.profiler.stop_profiler (ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'bc8628b859b04242200e48a458c971c4'))
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', '9494b48e79a0e07b49017ba5a97800b6'))
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.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'))
......
python/paddle/fluid/framework.py
浏览文件 @ 234b530d
......@@ -285,27 +285,46 @@ def name_scope(prefix=None):
"""
Generate hierarchical name prefix for the operators.
Note: This should only used for debugging and visualization purpose.
Don't use it for serious analysis such as graph/program transformations.
Note:
This should only used for debugging and visualization purpose.
Don't use it for serious analysis such as graph/program transformations.
Args:
prefix(str): prefix.
prefix(str, optional): prefix. Default is none.
Examples:
.. code-block:: python
import paddle.fluid as fluid
with fluid.name_scope("s1"):
a = fluid.layers.data(name='data', shape=[1], dtype='int32')
b = a + 1
with fluid.name_scope("s2"):
c = b * 1
with fluid.name_scope("s3"):
d = c / 1
a = fluid.data(name='data', shape=[None, 1], dtype='int32')
b = a + 1
with fluid.name_scope("s2"):
c = b * 1
with fluid.name_scope("s3"):
d = c / 1
with fluid.name_scope("s1"):
f = fluid.layers.pow(d, 2.0)
f = fluid.layers.pow(d, 2.0)
with fluid.name_scope("s4"):
g = f - 1
g = f - 1
# Op are created in the default main program.
for op in fluid.default_main_program().block(0).ops:
# elementwise_add is created in /s1/
if op.type == 'elementwise_add':
assert op.desc.attr("op_namescope") == '/s1/'
# elementwise_mul is created in '/s1/s2'
elif op.type == 'elementwise_mul':
assert op.desc.attr("op_namescope") == '/s1/s2/'
# elementwise_div is created in '/s1/s3'
elif op.type == 'elementwise_div':
assert op.desc.attr("op_namescope") == '/s1/s3/'
# elementwise_sum is created in '/s4'
elif op.type == 'elementwise_sub':
assert op.desc.attr("op_namescope") == '/s4/'
# pow is created in /s1_1/
elif op.type == 'pow':
assert op.desc.attr("op_namescope") == '/s1_1/'
"""
# TODO(panyx0718): Only [0-9a-z].
# in dygraph we don't need namescope since it will cause mem leak
......
python/paddle/fluid/profiler.py
浏览文件 @ 234b530d
......@@ -37,25 +37,27 @@ NVPROF_CONFIG = [
@signature_safe_contextmanager
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
programming interface. The profiling result will be written into
`output_file` with Key-Value pair format or Comma separated values format.
The user can set the output mode by `output_mode` argument and set the
counters/options for profiling by `config` argument. The default config
is ['gpustarttimestamp', 'gpuendtimestamp', 'gridsize3d',
'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace'].
Then users can use NVIDIA Visual Profiler
(https://developer.nvidia.com/nvidia-visual-profiler) tools to load this
this output file to visualize results.
`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 (string) : The output file name, the result will be
output_file (str) : The output file name, the result will be
written into this file.
output_mode (string) : The output mode has Key-Value pair format and
Comma separated values format. It should be 'kvp' or 'csv'.
config (list of string) : The profiler options and counters can refer
to "Compute Command Line Profiler User Guide".
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'].
......@@ -70,7 +72,7 @@ def cuda_profiler(output_file, output_mode=None, config=None):
epoc = 8
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])
place = fluid.CUDAPlace(0)
......@@ -127,13 +129,14 @@ def reset_profiler():
def start_profiler(state):
"""
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.profiler` interface.
`fluid.profiler.stop_profiler` to profile, which is equal to the usage
of `fluid.profiler.profiler` interface.
Args:
state (string) : The profiling state, which should be 'CPU', 'GPU'
or 'All'. 'CPU' means only profile CPU. 'GPU' means profiling
GPU as well. 'All' also generates timeline.
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.
Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All'].
......@@ -168,21 +171,21 @@ def start_profiler(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 insert the code, except the usage of
`fluid.profiler.profiler` interface.
`fluid.profiler.stop_profiler` to profile, which is equal to the usage
of `fluid.profiler.profiler` interface.
Args:
sorted_key (string) : If None, the profiling results will be printed
in the order of first end time of events. Otherwise, the profiling
results will be sorted by the this flag. This flag should be one
of 'calls', 'total', 'max', 'min' or 'ave'.
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 (string) : If state == 'All', it will write a profile
proto output file.
profile_path (str, optional) : If state == 'All', it will generate timeline,
and write it into `profile_path`. The default profile_path is `/tmp/profile`.
Raises:
ValueError: If `sorted_key` is not in
......@@ -223,34 +226,26 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
@signature_safe_contextmanager
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
and GPU program. By default, it records the CPU and GPU operator kernels,
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
"""
The profiler interface. Different from `fluid.profiler.cuda_profiler`,
this profiler can be used to profile both CPU and GPU program.
Args:
state (string) : The profiling state, which should be 'CPU' or 'GPU',
telling the profiler to use CPU timer or GPU timer for profiling.
Although users may have already specified the execution place
(CPUPlace/CUDAPlace) in the beginning, for flexibility the profiler
would not inherit this place.
sorted_key (string) : If None, the profiling results will be printed
in the order of first end time of events. Otherwise, the profiling
results will be sorted by the this flag. This flag should be one
of 'calls', 'total', 'max', 'min' or 'ave'.
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 (string) : If state == 'All', it will write a profile
proto output file.
profile_path (str, optional) : If state == 'All', it will generate timeline,
and write it into `profile_path`. The default profile_path is `/tmp/profile`.
Raises:
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'):
epoc = 8
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])
place = fluid.CPUPlace()
......@@ -277,6 +272,44 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
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 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)
yield
......
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