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未验证 提交 b14ecb86 编写于 作者: C Chen Weihang 提交者： GitHub
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Polish api BuildStrategy/ExecutionStrategy doc & code example (#27662) 

* polish BuildStrategy api doc & example

* polish ExecutionStrategy api doc & example

* polish details
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Showing with 180 addition and 78 deletion
paddle/fluid/pybind/pybind.cc
浏览文件 @ b14ecb86
......@@ -1980,27 +1980,34 @@ All parameter, weight, gradient are variables in Paddle.
ExecutionStrategy allows the user to more preciously control how to run
the program in ParallelExecutor by setting the property.
Returns:
ExecutionStrategy: An ExecutionStrategy object.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
import paddle
import paddle.static as static
import paddle.nn.functional as F
paddle.enable_static()
x = static.data(name='x', shape=[None, 13], dtype='float32')
y = static.data(name='y', shape=[None, 1], dtype='float32')
y_predict = static.nn.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(cost)
cost = F.square_error_cost(input=y_predict, label=y)
avg_loss = paddle.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer = paddle.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_loss)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy = static.ExecutionStrategy()
exec_strategy.num_threads = 4
train_exe = fluid.ParallelExecutor(use_cuda=False,
loss_name=avg_loss.name,
exec_strategy=exec_strategy)
train_exe = static.ParallelExecutor(use_cuda=False,
loss_name=avg_loss.name,
exec_strategy=exec_strategy)
)DOC");
exec_strategy.def(py::init())
......@@ -2010,7 +2017,8 @@ All parameter, weight, gradient are variables in Paddle.
[](ExecutionStrategy &self, size_t num_threads) {
self.num_threads_ = num_threads;
},
R"DOC(The type is INT, num_threads represents the size of thread pool that
R"DOC(
The type is INT, num_threads represents the size of thread pool that
used to run the operators of the current program in ParallelExecutor.
If :math:`num\_threads=1`, all the operators will execute one by one,
but the order maybe difference between iterations.
......@@ -2018,7 +2026,19 @@ All parameter, weight, gradient are variables in Paddle.
device type and device count, for GPU, :math:`num\_threads=device\_count*4`, for CPU,
:math:`num\_threads=CPU\_NUM*4`, the explanation of:math:`CPU\_NUM` is in ParallelExecutor.
if it is not set, ParallelExecutor will get the cpu count by calling
`multiprocessing.cpu_count()`. Default 0.)DOC")
`multiprocessing.cpu_count()`. Default 0.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
exec_strategy = static.ExecutionStrategy()
exec_strategy.num_threads = 4
)DOC")
.def_property(
"use_cuda",
[](const ExecutionStrategy &self) { return self.use_cuda_; },
......@@ -2050,13 +2070,24 @@ All parameter, weight, gradient are variables in Paddle.
many iterations to clean up the temp variables which
is generated during execution. It may make the execution faster,
because the temp variable's shape maybe the same between two iterations.
Default 1.
Default 100.
.. note::
1. If you fetch data when calling the 'run', the ParallelExecutor
will clean up the temp variables at the end of the current iteration.
2. In some NLP model, it may cause the GPU memory is insufficient,
in this case, you should reduce `num_iteration_per_drop_scope`.
Examples:
.. code-block:: python
NOTES:
1. If you fetch data when calling the 'run', the ParallelExecutor
will clean up the temp variables at the end of the current iteration.
2. In some NLP model, it may cause the GPU memory is insufficient,
in this case, you should reduce `num_iteration_per_drop_scope`.
import paddle
import paddle.static as static
paddle.enable_static()
exec_strategy = static.ExecutionStrategy()
exec_strategy.num_iteration_per_drop_scope = 10
)DOC")
.def_property(
"num_iteration_per_run",
......@@ -2067,7 +2098,18 @@ All parameter, weight, gradient are variables in Paddle.
self.num_iteration_per_run_ = num_iteration_per_run;
},
R"DOC(This config that how many iteration the executor will run when
user call exe.run() in python
user call exe.run() in python。Default: 1.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
exec_strategy = static.ExecutionStrategy()
exec_strategy.num_iteration_per_run = 10
)DOC")
.def_property(
"use_thread_barrier",
......@@ -2097,29 +2139,34 @@ All parameter, weight, gradient are variables in Paddle.
BuildStrategy allows the user to more preciously control how to
build the SSA Graph in ParallelExecutor by setting the property.
Returns:
BuildStrategy: An BuildStrategy object.
Examples:
.. code-block:: python
import os
import numpy as np
import paddle.fluid as fluid
import paddle
import paddle.static as static
paddle.enable_static()
os.environ["CPU_NUM"] = '2'
places = fluid.cpu_places()
os.environ['CPU_NUM'] = str(2)
places = static.cpu_places()
data = fluid.layers.data(name="x", shape=[1], dtype="float32")
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
fluid.optimizer.SGD(learning_rate=0.01).minimize(loss)
data = static.data(name="x", shape=[None, 1], dtype="float32")
hidden = static.nn.fc(input=data, size=10)
loss = paddle.mean(hidden)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
build_strategy = fluid.BuildStrategy()
build_strategy = static.BuildStrategy()
build_strategy.enable_inplace = True
build_strategy.memory_optimize = True
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
program = fluid.compiler.CompiledProgram(fluid.default_main_program())
build_strategy.reduce_strategy = static.BuildStrategy.ReduceStrategy.Reduce
program = static.CompiledProgram(static.default_main_program())
program = program.with_data_parallel(loss_name=loss.name,
build_strategy=build_strategy,
places=places)
build_strategy=build_strategy,
places=places)
)DOC");
py::enum_<BuildStrategy::ReduceStrategy>(build_strategy, "ReduceStrategy")
......@@ -2154,9 +2201,13 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.reduce_strategy = static.BuildStrategy.ReduceStrategy.Reduce
)DOC")
.def_property(
"gradient_scale_strategy",
......@@ -2178,50 +2229,51 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.compiler as compiler
import numpy
import os
import paddle
import paddle.static as static
paddle.enable_static()
use_cuda = True
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
exe = static.Executor(place)
# NOTE: If you use CPU to run the program, you need
# to specify the CPU_NUM, otherwise, fluid will use
# to specify the CPU_NUM, otherwise, paddle will use
# all the number of the logic core as the CPU_NUM,
# in that case, the batch size of the input should be
# greater than CPU_NUM, if not, the process will be
# failed by an exception.
if not use_cuda:
os.environ['CPU_NUM'] = str(2)
places = fluid.cpu_places()
places = static.cpu_places()
else:
places = places = fluid.cuda_places()
places = static.cuda_places()
data = fluid.layers.data(name='X', shape=[1], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
loss = fluid.layers.mean(hidden)
fluid.optimizer.SGD(learning_rate=0.01).minimize(loss)
data = static.data(name='X', shape=[None, 1], dtype='float32')
hidden = static.nn.fc(input=data, size=10)
loss = paddle.mean(hidden)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
fluid.default_startup_program().random_seed=1
exe.run(fluid.default_startup_program())
exe.run(static.default_startup_program())
build_strategy = fluid.BuildStrategy()
build_strategy = static.BuildStrategy()
build_strategy.gradient_scale_strategy = \
fluid.BuildStrategy.GradientScaleStrategy.Customized
compiled_prog = compiler.CompiledProgram(
fluid.default_main_program()).with_data_parallel(
static.BuildStrategy.GradientScaleStrategy.Customized
compiled_prog = static.CompiledProgram(
static.default_main_program()).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy,
places = places)
places=places)
dev_count = len(places)
x = numpy.random.random(size=(10, 1)).astype('float32')
loss_grad = numpy.ones((dev_count)).astype("float32") * 0.01
loss_grad_name = loss.name+"@GRAD"
loss_data = exe.run(compiled_prog,
feed={"X": x, loss_grad_name : loss_grad},
fetch_list=[loss.name, loss_grad_name])
feed={"X": x, loss_grad_name : loss_grad},
fetch_list=[loss.name, loss_grad_name])
)DOC")
.def_property(
"debug_graphviz_path",
......@@ -2240,10 +2292,13 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
build_strategy.debug_graphviz_path = "./graph"
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.debug_graphviz_path = "./graph"
)DOC")
.def_property(
"enable_sequential_execution",
......@@ -2263,8 +2318,12 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.enable_sequential_execution = True
)DOC")
.def_property(
......@@ -2285,8 +2344,12 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.remove_unnecessary_lock = True
)DOC")
.def_property(
......@@ -2351,8 +2414,12 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.fuse_elewise_add_act_ops = True
)DOC")
.def_property(
......@@ -2372,8 +2439,12 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.fuse_bn_act_ops = True
)DOC")
.def_property(
......@@ -2394,8 +2465,12 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.enable_auto_fusion = True
)DOC")
.def_property(
......@@ -2419,8 +2494,12 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.fuse_relu_depthwise_conv = True
)DOC")
.def_property("fuse_broadcast_ops",
......@@ -2445,8 +2524,12 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.fuse_broadcast_ops = True
)DOC")
.def_property("fuse_all_optimizer_ops",
......@@ -2481,8 +2564,12 @@ All parameter, weight, gradient are variables in Paddle.
Examples:
.. code-block:: python
import paddle.fluid as fluid
build_strategy = fluid.BuildStrategy()
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.sync_batch_norm = True
)DOC")
.def_property(
......@@ -2512,7 +2599,20 @@ All parameter, weight, gradient are variables in Paddle.
Default None. None means framework would choose to use or not use
this strategy automatically. Currently, None means that it is
enabled when GC is disabled, and disabled when GC is enabled.
True means enabling and False means disabling. Default is None.)DOC")
True means enabling and False means disabling. Default is None.
Examples:
.. code-block:: python
import paddle
import paddle.static as static
paddle.enable_static()
build_strategy = static.BuildStrategy()
build_strategy.memory_optimize = True
)DOC")
.def_property(
"is_distribution",
[](const BuildStrategy &self) { return self.is_distribution_; },
......
python/paddle/static/__init__.py
浏览文件 @ b14ecb86
......@@ -19,7 +19,7 @@ __all__ = [
'name_scope', 'ParallelExecutor', 'program_guard', 'WeightNormParamAttr',
'default_main_program', 'default_startup_program', 'Program', 'data',
'InputSpec', 'save', 'load', 'save_inference_model', 'load_inference_model',
'load_program_state', 'set_program_state'
'load_program_state', 'set_program_state', 'cpu_places', 'cuda_places'
]
from . import nn
......@@ -38,6 +38,8 @@ from ..fluid.framework import default_startup_program #DEFINE_ALIAS
from ..fluid.framework import Program #DEFINE_ALIAS
from ..fluid.framework import name_scope #DEFINE_ALIAS
from ..fluid.framework import program_guard #DEFINE_ALIAS
from ..fluid.framework import cpu_places #DEFINE_ALIAS
from ..fluid.framework import cuda_places #DEFINE_ALIAS
from ..fluid.layers.control_flow import Print #DEFINE_ALIAS
from ..fluid.layers.nn import py_func #DEFINE_ALIAS
from ..fluid.parallel_executor import ParallelExecutor #DEFINE_ALIAS
......
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