未验证 提交 5cc2f0bd 编写于 作者: Q Qiao Longfei 提交者: GitHub

Add polynomial_decay and piecewise_decay (#8013)

* init polynomial_decay

* test polynomial_decay

* complete polynomial_decay

* fix conditional block op

* init scalar-switch-case-op

* switch op can compile

* complete forward switch_op

* add GetMatchCaseIndex

* add switch_grad_op

* init switch Python API

* add test_switch

* support set block list in python

* fix scope problem

* complete test

* optimize test

* optimize test

* rm backward part

* clear grad op

* polynomial_decay use switch op

* revert conditional_block_op and reshape_op

* add piecewise_decay and test

* fix piecewise_decay

* try to use condition op for switch

* can work

* clean old code

* revert

* rm switch_op.cc

* optimize code

* add attr is_scalar_condition for condition_block_op

* fix comment

* fix comment

* add export
上级 6612068e
......@@ -38,6 +38,7 @@ __all__ = [
'array_write',
'create_array',
'less_than',
'equal',
'array_read',
'shrink_memory',
'array_length',
......@@ -975,6 +976,36 @@ def less_than(x, y, cond=None, **ignored):
return cond
def equal(x, y, cond=None, **ignored):
"""
**equal**
This layer returns the truth value of :math:`x == y` elementwise.
Args:
x(Variable): First operand of *equal*
y(Variable): Second operand of *equal*
cond(Variable|None): Optional output variable to store the result of *equal*
Returns:
Variable: The tensor variable storing the output of *equal*.
Examples:
.. code-block:: python
less = fluid.layers.equal(x=label, y=limit)
"""
helper = LayerHelper("equal", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond.stop_gradient = True
helper.append_op(
type='equal', inputs={'X': [x],
'Y': [y]}, outputs={'Out': [cond]})
return cond
def array_read(array, i):
"""This function performs the operation to read the data in as an
LOD_TENSOR_ARRAY.
......
......@@ -15,7 +15,10 @@
import layers
from framework import Variable
__all__ = ['exponential_decay', 'natural_exp_decay', 'inverse_time_decay']
__all__ = [
'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
'polynomial_decay', 'piecewise_decay'
]
"""
When training a model, it's often useful to decay the
learning rate during training process, this is called
......@@ -101,7 +104,7 @@ def inverse_time_decay(learning_rate,
```python
if staircase:
decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
else
else:
decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
```
Args:
......@@ -123,3 +126,98 @@ def inverse_time_decay(learning_rate,
div_res = layers.floor(x=div_res)
return learning_rate / (1 + decay_rate * div_res)
def polynomial_decay(learning_rate,
global_step,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False):
"""Applies polynomial decay to the initial learning rate.
```python
if cycle:
decay_steps = decay_steps * ceil(global_step / decay_steps)
else:
global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ power +
end_learning_rate
```
Args:
learning_rate: A scalar float32 value or a Variable. This
will be the initial learning rate during training
global_step: A Variable that record the training step.
decay_steps: A Python `int32` number.
end_learning_rate: A Python `float` number.
power: A Python `float` number
cycle: Boolean. If set true, decay the learning rate every decay_steps.
Returns:
The decayed learning rate
"""
if not isinstance(global_step, Variable):
raise ValueError("global_step is required for inverse_time_decay.")
if cycle:
div_res = layers.ceil(x=(global_step / decay_steps))
zero_var = layers.fill_constant(shape=[1], dtype='float32', value=0.0)
one_var = layers.fill_constant(shape=[1], dtype='float32', value=1.0)
with layers.Switch() as switch:
with switch.case(layers.equal(x=global_step, y=zero_var)):
layers.assign(input=one_var, output=div_res)
decay_steps = decay_steps * div_res
else:
decay_steps_var = layers.fill_constant(
shape=[1], dtype='float32', value=float(decay_steps))
global_step = layers.elementwise_min(x=global_step, y=decay_steps_var)
return (learning_rate - end_learning_rate) * \
((1 - global_step / decay_steps) ** power) + end_learning_rate
def piecewise_decay(global_step, boundaries, values):
"""Applies piecewise decay to the initial learning rate.
```python
boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1]
if step < 10000:
learning_rate = 1.0
elif step >= 10000 and step < 20000:
learning_rate = 0.5
else:
learning_rate = 0.1
```
"""
if len(values) - len(boundaries) != 1:
raise ValueError("len(values) - len(boundaries) should be 1")
if not isinstance(global_step, Variable):
raise ValueError("global_step is required for piecewise_decay.")
lr = layers.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
with layers.Switch() as switch:
for i in range(len(boundaries)):
boundary_val = layers.fill_constant(
shape=[1], dtype='float32', value=float(boundaries[i]))
value_var = layers.fill_constant(
shape=[1], dtype='float32', value=float(values[i]))
with switch.case(layers.less_than(global_step, boundary_val)):
layers.assign(value_var, lr)
last_value_var = layers.fill_constant(
shape=[1], dtype='float32', value=float(values[len(values) - 1]))
with switch.default():
layers.assign(last_value_var, lr)
return lr
......@@ -15,6 +15,8 @@
import unittest
import math
import copy
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid as fluid
import paddle.v2.fluid.layers as layers
......@@ -54,21 +56,37 @@ def inverse_time_decay(learning_rate,
return learning_rate / (1 + decay_rate * temp)
class TestLearningRateDecay(unittest.TestCase):
def check_decay(self, python_decay_fn, fluid_decay_fn, staircase):
init_lr = 1.0
decay_steps = 5
decay_rate = 0.5
def polynomial_decay(learning_rate,
global_step,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False):
if cycle:
div = math.ceil(global_step / float(decay_steps))
if div == 0:
div = 1
decay_steps = decay_steps * div
else:
global_step = min(global_step, decay_steps)
return (learning_rate - end_learning_rate) * \
((1 - float(global_step) / float(decay_steps)) ** power) + end_learning_rate
def piecewise_decay(global_step, boundaries, values):
assert len(boundaries) + 1 == len(values)
for i in range(len(boundaries)):
if global_step < boundaries[i]:
return values[i]
return values[len(values) - 1]
class TestLearningRateDecay(unittest.TestCase):
def check_decay(self, python_decay_fn, fluid_decay_fn, kwargs):
global_step = layers.create_global_var(
shape=[1], value=0.0, dtype='float32', persistable=True)
decayed_lr = fluid_decay_fn(
learning_rate=init_lr,
global_step=global_step,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=staircase)
decayed_lr = fluid_decay_fn(global_step=global_step, **kwargs)
layers.increment(global_step, 1.0)
place = fluid.CPUPlace()
......@@ -79,31 +97,52 @@ class TestLearningRateDecay(unittest.TestCase):
step_val, lr_val = exe.run(fluid.default_main_program(),
feed=[],
fetch_list=[global_step, decayed_lr])
python_decayed_lr = python_decay_fn(
learning_rate=init_lr,
global_step=step,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=staircase)
python_decayed_lr = python_decay_fn(global_step=step, **kwargs)
self.assertAlmostEqual(python_decayed_lr, lr_val[0])
def test_decay(self):
common_kwargs_true = {
"learning_rate": 1.0,
"decay_steps": 5,
"decay_rate": 0.5,
"staircase": True
}
common_kwargs_false = copy.deepcopy(common_kwargs_true)
common_kwargs_false["staircase"] = False
decay_fns = [
(exponential_decay, lr_decay.exponential_decay, True),
(exponential_decay, lr_decay.exponential_decay, False),
(natural_exp_decay, lr_decay.natural_exp_decay, True),
(natural_exp_decay, lr_decay.natural_exp_decay, False),
(inverse_time_decay, lr_decay.inverse_time_decay, True),
(inverse_time_decay, lr_decay.inverse_time_decay, False),
(exponential_decay, lr_decay.exponential_decay, common_kwargs_true),
(exponential_decay, lr_decay.exponential_decay,
common_kwargs_false),
(natural_exp_decay, lr_decay.natural_exp_decay, common_kwargs_true),
(natural_exp_decay, lr_decay.natural_exp_decay,
common_kwargs_false),
(inverse_time_decay, lr_decay.inverse_time_decay,
common_kwargs_true),
(inverse_time_decay, lr_decay.inverse_time_decay,
common_kwargs_false),
(polynomial_decay, lr_decay.polynomial_decay, {
"learning_rate": 1.0,
"decay_steps": 5,
"cycle": True
}),
(polynomial_decay, lr_decay.polynomial_decay, {
"learning_rate": 1.0,
"decay_steps": 5,
"cycle": False
}),
(piecewise_decay, lr_decay.piecewise_decay, {
"boundaries": [3, 6, 9],
"values": [0.1, 0.2, 0.3, 0.4]
}),
]
for py_decay_fn, fluid_decay_fn, staircase in decay_fns:
print("decay_fn=" + str(py_decay_fn) + " staircase=" + str(
staircase))
for py_decay_fn, fluid_decay_fn, kwargs in decay_fns:
print("decay_fn=" + py_decay_fn.__name__ + " kwargs=" + str(kwargs))
main_program = framework.Program()
startup_program = framework.Program()
with framework.program_guard(main_program, startup_program):
self.check_decay(py_decay_fn, fluid_decay_fn, staircase)
self.check_decay(py_decay_fn, fluid_decay_fn, kwargs)
if __name__ == '__main__':
......
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