未验证 提交 9988f8ec 编写于 作者: Y Yu Yang 提交者: GitHub

Merge pull request #11559 from qingqing01/fluid_doc_profiler

Polish profiler Python API.
...@@ -42,6 +42,9 @@ def cuda_profiler(output_file, output_mode=None, config=None): ...@@ -42,6 +42,9 @@ def cuda_profiler(output_file, output_mode=None, config=None):
counters/options for profiling by `config` argument. The default config counters/options for profiling by `config` argument. The default config
is ['gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d', is ['gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d',
'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace']. '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.
Args: Args:
output_file (string) : The output file name, the result will be output_file (string) : The output file name, the result will be
...@@ -50,6 +53,33 @@ def cuda_profiler(output_file, output_mode=None, config=None): ...@@ -50,6 +53,33 @@ def cuda_profiler(output_file, output_mode=None, config=None):
Comma separated values format. It should be 'kvp' or 'csv'. Comma separated values format. It should be 'kvp' or 'csv'.
config (list of string) : The profiler options and counters can refer config (list of string) : The profiler options and counters can refer
to "Compute Command Line Profiler User Guide". to "Compute Command Line Profiler User Guide".
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
epoc = 8
dshape = [4, 3, 28, 28]
data = fluid.layers.data(name='data', shape=[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: if output_mode is None:
output_mode = 'csv' output_mode = 'csv'
...@@ -69,19 +99,52 @@ def cuda_profiler(output_file, output_mode=None, config=None): ...@@ -69,19 +99,52 @@ def cuda_profiler(output_file, output_mode=None, config=None):
def reset_profiler(): def reset_profiler():
"""The profiler clear interface. """
reset_profiler will clear the previous time record. 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.profiler as profiler
with profiler.profiler(state, 'total', '/tmp/profile'):
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# ...
""" """
core.reset_profiler() core.reset_profiler()
def start_profiler(state): def start_profiler(state):
"""Enable the profiler. """
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.
Args: Args:
state (string) : The profiling state, which should be 'CPU', 'GPU' state (string) : The profiling state, which should be 'CPU', 'GPU'
or 'All'. 'CPU' means only profile CPU. 'GPU' means profiling or 'All'. 'CPU' means only profile CPU. 'GPU' means profiling
GPU as well. 'All' also generates timeline. GPU as well. 'All' also generates timeline.
Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All'].
Examples:
.. code-block:: python
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 core.is_profiler_enabled(): if core.is_profiler_enabled():
return return
...@@ -97,7 +160,10 @@ def start_profiler(state): ...@@ -97,7 +160,10 @@ 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. """
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.
Args: Args:
sorted_key (string) : If None, the profiling results will be printed sorted_key (string) : If None, the profiling results will be printed
...@@ -111,6 +177,23 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'): ...@@ -111,6 +177,23 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
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 (string) : If state == 'All', it will write a profile
proto output file. proto output file.
Raises:
ValueError: If `sorted_key` is not in
['calls', 'total', 'max', 'min', 'ave'].
Examples:
.. code-block:: python
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(): if not core.is_profiler_enabled():
return return
...@@ -137,7 +220,12 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'): ...@@ -137,7 +220,12 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
Different from cuda_profiler, this profiler can be used to profile both CPU Different from cuda_profiler, this profiler can be used to profile both CPU
and GPU program. By defalut, it records the CPU and GPU operator kernels, and GPU program. By defalut, it records the CPU and GPU operator kernels,
if you want to profile other program, you can refer the profiling tutorial if you want to profile other program, you can refer the profiling tutorial
to add more records. 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 (string) : The profiling state, which should be 'CPU' or 'GPU',
...@@ -156,6 +244,25 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'): ...@@ -156,6 +244,25 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
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 (string) : If state == 'All', it will write a profile
proto output file. proto output file.
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.profiler as profiler
with profiler.profiler('All', 'total', '/tmp/profile') as prof:
for pass_id in range(pass_num):
for batch_id, data in enumerate(train_reader()):
exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[],
use_program_cache=True)
# ...
""" """
start_profiler(state) start_profiler(state)
yield yield
......
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