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BuildStrategy api comment (#17348)

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Python examples of fluid.layers.io.double_buffer and some BuildStrategy's methods.
fix_ema
flame 6 years ago committed by GitHub
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3 changed files with 90 additions and 30 deletions
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2
paddle/fluid/API.spec
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@ -242,7 +242,7 @@ paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels
paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', '32181f6037e387fb6e68a5beaafe33b6'))
paddle.fluid.layers.shuffle (ArgSpec(args=['reader', 'buffer_size'], varargs=None, keywords=None, defaults=None), ('document', 'f967a73426db26f970bc70bfb03cffca'))
paddle.fluid.layers.batch (ArgSpec(args=['reader', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', 'fcb24383c6eef2ca040ee824c26e22fd'))
paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '07e5b796674796eb1ef3fee9c10d24e3'))
paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'c13b8a8521bea5f8123b925ae2a5d5db'))
paddle.fluid.layers.random_data_generator (ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,)), ('document', '9b7f0f86ec24bbc97643cadcb6499cff'))
paddle.fluid.layers.py_reader (ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '5c54493d96c7e0760dc6758af1c8dd72'))
paddle.fluid.layers.create_py_reader_by_data (ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True)), ('document', 'b42332b894e1e0962c6a43f0151c2640'))

115
paddle/fluid/pybind/pybind.cc
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@ -1334,14 +1334,9 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
train_exe = fluid.ParallelExecutor(use_cuda=True,
loss_name=loss.name,
build_strategy=build_strategy)
train_loss, = train_exe.run([loss.name], feed=feed_dict)
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
)DOC");
py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
@ -1363,11 +1358,19 @@ All parameter, weight, gradient are variables in Paddle.
self.reduce_ = strategy;
},
R"DOC(The type is STR, there are two reduce strategies in ParallelExecutor,
'AllReduce' and 'Reduce'. If you want that all the parameters'
optimization are done on all devices independently, you should choose 'AllReduce';
if you choose 'Reduce', all the parameters' optimization will be evenly distributed
to different devices, and then broadcast the optimized parameter to other devices.
In some models, `Reduce` is faster. Default 'AllReduce'. )DOC")
'AllReduce' and 'Reduce'. If you want that all the parameters'
optimization are done on all devices independently, you should choose 'AllReduce';
if you choose 'Reduce', all the parameters' optimization will be evenly distributed
to different devices, and then broadcast the optimized parameter to other devices.
In some models, `Reduce` is faster. Default 'AllReduce'.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
)DOC")
.def_property(
"gradient_scale_strategy",
[](const BuildStrategy &self) { return self.gradient_scale_; },
@ -1377,10 +1380,18 @@ All parameter, weight, gradient are variables in Paddle.
self.gradient_scale_ = strategy;
},
R"DOC(The type is STR, there are three ways of defining :math:`loss@grad` in
ParallelExecutor, 'CoeffNumDevice', 'One' and 'Customized'. By default,
ParallelExecutor sets the :math:`loss@grad` according to the number of devices.
If you want to customize :math:`loss@grad`, you can choose 'Customized'.
Default 'CoeffNumDevice'.)DOC")
ParallelExecutor, 'CoeffNumDevice', 'One' and 'Customized'. By default,
ParallelExecutor sets the :math:`loss@grad` according to the number of devices.
If you want to customize :math:`loss@grad`, you can choose 'Customized'.
Default 'CoeffNumDevice'.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.gradient_scale_strategy = True
)DOC")
.def_property(
"debug_graphviz_path",
[](const BuildStrategy &self) { return self.debug_graphviz_path_; },
@ -1389,8 +1400,16 @@ All parameter, weight, gradient are variables in Paddle.
self.debug_graphviz_path_ = path;
},
R"DOC(The type is STR, debug_graphviz_path indicate the path that
writing the SSA Graph to file in the form of graphviz, you.
It is useful for debugging. Default "")DOC")
writing the SSA Graph to file in the form of graphviz.
It is useful for debugging. Default ""
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.debug_graphviz_path = ""
)DOC")
.def_property(
"enable_sequential_execution",
[](const BuildStrategy &self) {
@ -1400,7 +1419,15 @@ All parameter, weight, gradient are variables in Paddle.
PADDLE_ENFORCE(!self.IsFinalized(), "BuildStrategy is finlaized.");
self.enable_sequential_execution_ = b;
},
R"DOC(The type is BOOL. If set True, the execution order of ops would be the same as what is in the program. Default False.)DOC")
R"DOC(The type is BOOL. If set True, the execution order of ops would be the same as what is in the program. Default False.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.enable_sequential_execution = True
)DOC")
.def_property(
"remove_unnecessary_lock",
[](const BuildStrategy &self) {
@ -1410,7 +1437,15 @@ All parameter, weight, gradient are variables in Paddle.
PADDLE_ENFORCE(!self.IsFinalized(), "BuildStrategy is finlaized.");
self.remove_unnecessary_lock_ = b;
},
R"DOC(The type is BOOL. If set True, some locks in GPU ops would be released and ParallelExecutor would run faster. Default True.)DOC")
R"DOC(The type is BOOL. If set True, some locks in GPU ops would be released and ParallelExecutor would run faster. Default True.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.remove_unnecessary_lock = True
)DOC")
.def_property(
"num_trainers",
[](const BuildStrategy &self) { return self.num_trainers_; },
@ -1439,8 +1474,16 @@ All parameter, weight, gradient are variables in Paddle.
self.fuse_elewise_add_act_ops_ = b;
},
R"DOC(The type is BOOL, fuse_elewise_add_act_ops indicate whether
to fuse elementwise_add_op and activation_op,
it may make the execution faster. Default False)DOC")
to fuse elementwise_add_op and activation_op,
it may make the execution faster. Default False
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_elewise_add_act_ops = True
)DOC")
.def_property(
"fuse_relu_depthwise_conv",
[](const BuildStrategy &self) {
@ -1451,10 +1494,18 @@ All parameter, weight, gradient are variables in Paddle.
self.fuse_relu_depthwise_conv_ = b;
},
R"DOC(The type is BOOL, fuse_relu_depthwise_conv indicate whether
to fuse relu and depthwise_conv2d,
it will save GPU memory and may make the execution faster.
This options is only available in GPU devices.
Default False.)DOC")
to fuse relu and depthwise_conv2d,
it will save GPU memory and may make the execution faster.
This options is only available in GPU devices.
Default False.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_relu_depthwise_conv = True
)DOC")
.def_property(
"fuse_broadcast_ops",
[](const BuildStrategy &self) { return self.fuse_broadcast_ops_; },
@ -1491,7 +1542,15 @@ All parameter, weight, gradient are variables in Paddle.
Current implementation doesn't support FP16 training and CPU.
And only synchronous on one machine, not all machines.
Default False)DOC")
Default False
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.sync_batch_norm = True
)DOC")
.def_property(
"memory_optimize",
[](const BuildStrategy &self) { return self.memory_optimize_; },

3
python/paddle/fluid/layers/io.py
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@ -1055,7 +1055,8 @@ def double_buffer(reader, place=None, name=None):
Examples:
>>> reader = fluid.layers.open_files(filenames=['somefile'],
>>> import paddle.fluid as fluid
>>> reader = fluid.layers.open_files(filenames=['mnist.recordio'],
>>> shapes=[[-1, 784], [-1, 1]],
>>> dtypes=['float32', 'int64'])
>>> reader = fluid.layers.double_buffer(reader)

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