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Fix parameter list iterator bug (#25089)

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* fix optimizer parameter is a iterator; test=develop

* fix parameter list None bug; test=develop

* use is not None; test=develop

* change list to iterable; test=develop
fix_copy_if_different
hong 5 years ago committed by GitHub
parent 91f7b4e0e3
commit fed0588571
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 43 additions and 16 deletions
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33
python/paddle/fluid/optimizer.py
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@ -67,7 +67,8 @@ class Optimizer(object):
regularization=None,
grad_clip=None,
name=None):
self._parameter_list = parameter_list
self._parameter_list = list(
parameter_list) if parameter_list is not None else None
self._name = name
if framework.in_dygraph_mode():
if not isinstance(learning_rate, float) and \
@ -705,7 +706,7 @@ class Optimizer(object):
startup_program (Program, optional): :ref:`api_fluid_Program` for
initializing parameters in ``parameter_list``. The default value
is None, at this time :ref:`api_fluid_default_startup_program` will be used.
parameter_list (list, optional): List of ``Variable`` or ``Variable.name`` to update
parameter_list (Iterable, optional): Iterable of ``Variable`` or ``Variable.name`` to update
to minimize ``loss``. The default value is None, at this time all parameters
will be updated.
no_grad_set (set, optional): Set of ``Variable`` or ``Variable.name`` that don't need
@ -882,7 +883,7 @@ class Optimizer(object):
startup_program (Program, optional): :ref:`api_fluid_Program` for
initializing parameters in ``parameter_list``. The default value
is None, at this time :ref:`api_fluid_default_startup_program` will be used.
parameter_list (list, optional): List of ``Variable`` or ``Variable.name`` to update
parameter_list (Iterable, optional): Iterable of ``Variable`` or ``Variable.name`` to update
to minimize ``loss``. The default value is None, at this time all parameters
will be updated.
no_grad_set (set, optional): Set of ``Variable`` or ``Variable.name`` that don't need
@ -926,7 +927,7 @@ class SGDOptimizer(Optimizer):
Parameters:
learning_rate (float|Variable): The learning rate used to update parameters. \
Can be a float value or a Variable with one float value as data element.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
@ -1034,7 +1035,7 @@ class MomentumOptimizer(Optimizer):
learning_rate (float|Variable): The learning rate used to update parameters. \
Can be a float value or a Variable with one float value as data element.
momentum (float): Momentum factor
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
use_nesterov (bool, optional): Enables Nesterov momentum, default is false.
@ -1182,7 +1183,7 @@ class DGCMomentumOptimizer(Optimizer):
sparsity (list[float]): Get top important element from gradient tensor, the ratio is (1 - current sparsity). \
Default is [0.999]. For example, if the sparsity is [0.99, 0.999], \
the top [1%, 0.1%] important element will be transmitted.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
use_nesterov (bool): Enables Nesterov momentum. True means use Nesterov. Default is False.
@ -1580,7 +1581,7 @@ class LarsMomentumOptimizer(Optimizer):
momentum (float): momentum factor
lars_coeff (float): Defines how much we trust the layer to change its weights.
lars_weight_decay (float): Weight decay coefficient for decaying using LARS.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
@ -1699,7 +1700,7 @@ class AdagradOptimizer(Optimizer):
It can be a float value or a ``Variable`` with a float type.
epsilon (float, optional): A small float value for numerical stability.
The default value is 1e-06.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
@ -1824,7 +1825,7 @@ class AdamOptimizer(Optimizer):
The default value is 0.999.
epsilon (float, optional): A small float value for numerical stability.
The default value is 1e-08.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
@ -2090,7 +2091,7 @@ class AdamaxOptimizer(Optimizer):
The default value is 0.999.
epsilon (float, optional): A small float value for numerical stability.
The default value is 1e-08.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
@ -2265,7 +2266,7 @@ class DpsgdOptimizer(Optimizer):
clip (float): clipping threshold
batch_size (float): batch size.
sigma (float): for gaussian noise.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
Notes:
@ -2348,7 +2349,7 @@ class DecayedAdagradOptimizer(Optimizer):
decay (float, optional): The decay rate. The default value is 0.95.
epsilon (float, optional): A small float value for numerical stability.
The default value is 1e-06.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
@ -2453,7 +2454,7 @@ class AdadeltaOptimizer(Optimizer):
learning_rate (float|Variable): global learning rate.
epsilon (float): a small float number for numeric stability. Default 1.0e-6.
rho (float): a floating point value indicating the decay rate. Default 0.95.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
@ -2610,7 +2611,7 @@ class RMSPropOptimizer(Optimizer):
the gradient; if False, by the uncentered second moment. Setting this to
True may help with training, but is slightly more expensive in terms of
computation and memory. Defaults to False.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
@ -2784,7 +2785,7 @@ class FtrlOptimizer(Optimizer):
l1 (float): L1 regularization strength, default is 0.0.
l2 (float): L2 regularization strength, default is 0.0.
lr_power (float): Learning Rate Power, default is -0.5.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
@ -2932,7 +2933,7 @@ class LambOptimizer(AdamOptimizer):
beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.
Default 0.999.
epsilon (float, optional): A small float value for numerical stability. Default 1e-6.
parameter_list (list, optional): List of ``Variable`` names to update to minimize ``loss``. \
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \

26
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
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@ -18,6 +18,7 @@ import contextlib
import unittest
import numpy as np
import six
import itertools
import paddle
import paddle.fluid as fluid
@ -699,5 +700,30 @@ class TestImperativeRecomputeOptimizer(TestImperativeOptimizerBase):
self._check_exception(exception_message)
class TestImperativeOptimizerList(unittest.TestCase):
def test_parameter_list(self):
with fluid.dygraph.guard():
linear_1 = Linear(10, 10)
linear_2 = Linear(10, 10)
sgd = SGDOptimizer(
1.0,
parameter_list=itertools.chain(linear_1.parameters(),
linear_2.parameters()))
in_np = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
in_data = fluid.dygraph.to_variable(in_np)
y = linear_1(in_data)
y = linear_2(y)
loss = fluid.layers.reduce_mean(y)
loss.backward()
sgd.minimize(loss)
self.assertTrue(
len(sgd._parameter_list) ==
len(linear_1.parameters() + linear_2.parameters()))
if __name__ == '__main__':
unittest.main()

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