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未验证 提交 8d05c00c 编写于 作者: D danleifeng 提交者： GitHub
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fix paddle.fleet en-doc for apis in dynamic mode (#27354) 

* fix fleet dynamic-mode en-doc;test=develop
上级 746a8ded
隐藏空白更改
内联 并排
Showing with 121 addition and 114 deletion
python/paddle/distributed/fleet/base/fleet_base.py
浏览文件 @ 8d05c00c
......@@ -608,25 +608,31 @@ class Fleet(object):
@dygraph_only
def distributed_model(self, model):
"""
Return dygraph distributed data parallel model (Layer)
Only work in dygraph mode
Return distributed data parallel model (Only work in dygraph mode)
Args:
model (Layer): the user-defind model which inherits Layer.
Returns:
distributed data parallel model which inherits Layer.
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
def forward(self, x):
return self._linear2(self._linear1(x))
def train():
# 1. enable dynamic mode
paddle.disable_static()
......@@ -658,8 +664,7 @@ class Fleet(object):
adam.step()
adam.clear_grad()
if __name__ == '__main__':
paddle.distributed.spawn(train)
"""
assert model is not None
self.model = paddle.DataParallel(model)
......@@ -669,29 +674,30 @@ class Fleet(object):
def state_dict(self):
"""
Get state dict information from optimizer.
Only work in dygraph mode
(Only work in dygraph mode)
Returns:
state_dict(dict) : dict contains all the Tensor used by optimizer
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.state_dict()
......@@ -700,34 +706,36 @@ class Fleet(object):
def set_state_dict(self, state_dict):
"""
Load optimizer state dict.
Only work in dygraph mode
(Only work in dygraph mode)
Args:
state_dict(dict) : Dict contains all the Tensor needed by optimizer
Returns: None
Returns:
None
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
paddle.framework.save(state_dict, "paddle_dy")
para_state_dict, opti_state_dict = paddle.framework.load( "paddle_dy")
adam.set_state_dict(opti_state_dict)
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
paddle.framework.save(state_dict, "paddle_dy")
para_state_dict, opti_state_dict = paddle.framework.load( "paddle_dy")
adam.set_state_dict(opti_state_dict)
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.set_state_dict(state_dict)
......@@ -736,42 +744,44 @@ class Fleet(object):
def set_lr(self, value):
"""
Set the value of the learning rate manually in the optimizer.
Only work in dygraph mode
(Only work in dygraph mode)
Args:
value (float|Tensor): the value of learning rate
Returns: None
Returns:
None
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
import numpy as np
import paddle
from paddle.distributed import fleet
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
paddle.disable_static()
fleet.init(is_collective=True)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
for i in range(5):
adam.set_lr(lr_list[i])
lr = adam.get_lr()
print("current lr is {}".format(lr))
# Print:
# current lr is 0.2
# current lr is 0.3
# current lr is 0.4
# current lr is 0.5
# current lr is 0.6
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
for i in range(5):
adam.set_lr(lr_list[i])
lr = adam.get_lr()
print("current lr is {}".format(lr))
# Print:
# current lr is 0.2
# current lr is 0.3
# current lr is 0.4
# current lr is 0.5
# current lr is 0.6
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.set_lr(value)
......@@ -780,31 +790,32 @@ class Fleet(object):
def get_lr(self):
"""
Get current step learning rate.
Only work in dygraph mode
(Only work in dygraph mode)
Returns:
float: The learning rate of the current step.
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
lr = adam.get_lr()
print(lr) # 0.01
lr = adam.get_lr()
print(lr) # 0.01
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.get_lr()
......@@ -813,27 +824,27 @@ class Fleet(object):
def step(self):
"""
Execute the optimizer once.
Only work in dygraph mode
(Only work in dygraph mode)
Returns: None
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
def forward(self, x):
return self._linear2(self._linear1(x))
def train():
# 1. enable dynamic mode
paddle.disable_static()
......@@ -865,8 +876,6 @@ class Fleet(object):
adam.step()
adam.clear_grad()
if __name__ == '__main__':
paddle.distributed.spawn(train)
"""
# imitate target optimizer retrieval
......@@ -875,28 +884,28 @@ class Fleet(object):
@dygraph_only
def clear_grad(self):
"""
Execute the optimizer once.
Only work in dygraph mode
Clear the gradients of all optimized parameters for model.
(Only work in dygraph mode)
Returns: None
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
def forward(self, x):
return self._linear2(self._linear1(x))
def train():
# 1. enable dynamic mode
paddle.disable_static()
......@@ -928,8 +937,6 @@ class Fleet(object):
adam.step()
adam.clear_grad()
if __name__ == '__main__':
paddle.distributed.spawn(train)
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.clear_grad()
......
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