add lamb optimizer and unittest (#28772) TODO：FIX BUGS LATER
* add lamb optimizer and unittest * fix lamb * fix lamb v2 op * fix sampling id * fix lamb sample code * Update lamb.py * fix doc * fix doc * Update lamb.pymusl/disable_test_yolov3_temporarily
Jiawei Wang
4 years ago
committed by
GitHub
parent
3815d7aa40
commit
f21513307a
@ -0,0 +1,53 @@
|
||||
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import unittest
|
||||
import numpy as np
|
||||
from op_test import OpTest
|
||||
from paddle.fluid import core
|
||||
from paddle.fluid.op import Operator
|
||||
import paddle.fluid as fluid
|
||||
import paddle
|
||||
|
||||
|
||||
class TestLambOpV2(unittest.TestCase):
|
||||
def test_lamb_op(self):
|
||||
paddle.enable_static()
|
||||
place = fluid.CPUPlace()
|
||||
shape = [2, 3, 8, 8]
|
||||
exe = fluid.Executor(place)
|
||||
train_prog = fluid.Program()
|
||||
startup = fluid.Program()
|
||||
with fluid.program_guard(train_prog, startup):
|
||||
with fluid.unique_name.guard():
|
||||
data = fluid.data(name="data", shape=shape)
|
||||
conv = fluid.layers.conv2d(data, 8, 3)
|
||||
loss = fluid.layers.reduce_mean(conv)
|
||||
beta1 = 0.85
|
||||
beta2 = 0.95
|
||||
betas = [beta1, beta2]
|
||||
opt = paddle.optimizer.Lamb(
|
||||
learning_rate=1e-5, beta1=beta1, beta2=beta2, epsilon=1e-8)
|
||||
opt.minimize(loss)
|
||||
|
||||
exe.run(startup)
|
||||
data_np = np.random.random(shape).astype('float32')
|
||||
rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss])
|
||||
assert rets[0] is not None
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@ -0,0 +1,177 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from .optimizer import Optimizer
|
||||
from ..fluid import core
|
||||
from ..fluid import framework
|
||||
from ..fluid.framework import Variable
|
||||
|
||||
__all__ = ["Lamb"]
|
||||
|
||||
|
||||
class Lamb(Optimizer):
|
||||
"""
|
||||
LAMB (Layer-wise Adaptive Moments optimizer for Batching training) Optimizer.
|
||||
|
||||
LAMB Optimizer is designed to scale up the batch size of training without losing
|
||||
accuracy, which supports adaptive element-wise updating and accurate layer-wise
|
||||
correction. For more information, please refer to `Large Batch Optimization for
|
||||
Deep Learning: Training BERT in 76 minutes <https://arxiv.org/abs/1904.00962>`_ .
|
||||
|
||||
The updating of parameters follows:
|
||||
|
||||
.. math::
|
||||
|
||||
m_t &= \\beta_1 m_{t - 1}+ (1 - \\beta_1)g_t
|
||||
|
||||
v_t &= \\beta_2 v_{t - 1} + (1 - \\beta_2)g_t^2
|
||||
|
||||
r_t &= \\frac{m_t}{\\sqrt{v_t}+\\epsilon}
|
||||
|
||||
w_t &= w_{t-1} -\\eta_t \\frac{\\left \| w_{t-1}\\right \|}{\\left \| r_t + \\lambda w_{t-1}\\right \|} (r_t + \\lambda w_{t-1})
|
||||
|
||||
|
||||
where :math:`m` is the 1st moment, and :math:`v` the 2nd moment, :math:`\\eta` the
|
||||
learning rate, :math:`\\lambda` the LAMB weight decay rate.
|
||||
|
||||
Args:
|
||||
learning_rate (float|Variable, optional): the learning rate used to update parameters. \
|
||||
Can be a float value or a Variable with data type float32. Default 0.001.
|
||||
lamb_weight_decay (float, optional): The LAMB weight decay rate. Default 0.01. Remind that weight_decay should be None.
|
||||
beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
|
||||
Default 0.9.
|
||||
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.
|
||||
parameters (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.
|
||||
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
|
||||
some derived class of ``GradientClipBase`` . There are three cliping strategies
|
||||
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
|
||||
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
|
||||
name(str|None): For detailed information, please refer to
|
||||
:ref:`api_guide_Name` . Usually name is no need to set and None by default.
|
||||
Examples:
|
||||
.. code-block:: python
|
||||
import paddle
|
||||
import numpy as np
|
||||
inp = paddle.uniform(min=-0.1, max=0.1, shape=[10, 10], dtype='float32')
|
||||
linear = paddle.nn.Linear(10, 10)
|
||||
out = linear(inp)
|
||||
loss = paddle.mean(out)
|
||||
beta1 = paddle.to_tensor([0.9], dtype="float32")
|
||||
beta2 = paddle.to_tensor([0.85], dtype="float32")
|
||||
lamb = paddle.optimizer.Lamb(learning_rate=0.002, parameters=linear.parameters(), lamb_weight_decay=0.01)
|
||||
back = out.backward()
|
||||
lamb.step()
|
||||
lamb.clear_grad()
|
||||
"""
|
||||
_moment1_acc_str = "moment1"
|
||||
_moment2_acc_str = "moment2"
|
||||
# these two not used in op temporarily
|
||||
_beta1_pow_acc_str = "beta1_pow_acc"
|
||||
_beta2_pow_acc_str = "beta2_pow_acc"
|
||||
|
||||
def __init__(self,
|
||||
learning_rate=0.001,
|
||||
lamb_weight_decay=0.01,
|
||||
beta1=0.9,
|
||||
beta2=0.999,
|
||||
epsilon=1e-6,
|
||||
parameters=None,
|
||||
grad_clip=None,
|
||||
name=None):
|
||||
assert learning_rate is not None
|
||||
assert beta1 is not None
|
||||
assert beta2 is not None
|
||||
assert epsilon is not None
|
||||
super(Lamb, self).__init__(
|
||||
learning_rate=learning_rate,
|
||||
parameters=parameters,
|
||||
weight_decay=None,
|
||||
grad_clip=grad_clip,
|
||||
name=name)
|
||||
self.type = "lamb"
|
||||
self._beta1 = beta1
|
||||
self._beta2 = beta2
|
||||
self._epsilon = epsilon
|
||||
self._lamb_weight_decay = lamb_weight_decay
|
||||
|
||||
def _create_accumulators(self, block, parameters):
|
||||
assert isinstance(block, framework.Block)
|
||||
|
||||
# Create accumulator tensors for first and second moments
|
||||
for p in parameters:
|
||||
self._add_accumulator(self._moment1_acc_str, p)
|
||||
self._add_accumulator(self._moment2_acc_str, p)
|
||||
self._add_accumulator(
|
||||
name=self._beta1_pow_acc_str,
|
||||
param=p,
|
||||
fill_value=0.9 if isinstance(self._beta1, Variable) \
|
||||
else self._beta1,
|
||||
shape=[1],
|
||||
type=core.VarDesc.VarType.LOD_TENSOR, device='cpu')
|
||||
self._add_accumulator(
|
||||
name=self._beta2_pow_acc_str,
|
||||
param=p,
|
||||
fill_value=0.999 if isinstance(self._beta2, Variable) \
|
||||
else self._beta2,
|
||||
shape=[1],
|
||||
type=core.VarDesc.VarType.LOD_TENSOR, device='cpu')
|
||||
|
||||
def _append_optimize_op(self, block, param_and_grad):
|
||||
assert isinstance(block, framework.Block)
|
||||
block.program._use_lamb = True
|
||||
|
||||
moment1 = self._get_accumulator(self._moment1_acc_str,
|
||||
param_and_grad[0])
|
||||
moment2 = self._get_accumulator(self._moment2_acc_str,
|
||||
param_and_grad[0])
|
||||
beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
|
||||
param_and_grad[0])
|
||||
beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
|
||||
param_and_grad[0])
|
||||
|
||||
if param_and_grad[0].need_clip:
|
||||
weight_decay = 0.0
|
||||
else:
|
||||
weight_decay = self._lamb_weight_decay
|
||||
|
||||
# create the lamb optimize op
|
||||
lamb_op = block.append_op(
|
||||
type=self.type,
|
||||
inputs={
|
||||
"Param": param_and_grad[0],
|
||||
"Grad": param_and_grad[1],
|
||||
"LearningRate": self._create_param_lr(param_and_grad),
|
||||
"Moment1": moment1,
|
||||
"Moment2": moment2,
|
||||
"Beta1Pow": beta1_pow_acc,
|
||||
"Beta2Pow": beta2_pow_acc
|
||||
},
|
||||
outputs={
|
||||
"ParamOut": param_and_grad[0],
|
||||
"Moment1Out": moment1,
|
||||
"Moment2Out": moment2
|
||||
},
|
||||
attrs={
|
||||
"beta1": self._beta1,
|
||||
"beta2": self._beta2,
|
||||
"epsilon": self._epsilon,
|
||||
"weight_decay": weight_decay
|
||||
},
|
||||
stop_gradient=True)
|
||||
|
||||
return lamb_op
|
||||
Loading…
Reference in new issue