[WIP] update optimizer for 2.0 (#26288)
refine Optimizer/Adam/Admax/RMSProp && add Admw * buf fix * update comment * unify arguments place; notest * fix ut, test=develop * bug fix * fix conflicts, test=develop * add examples code * bug fix * fix comments * fix sample code * add sample code for Optimizer * add adamax ut, test=develop * fix rmsprop ut, test=develop * add ut for optimizer.py and adamw.py * remove TestAdamOptimizerBetaVariable * update api && add ut * update doc && fix ut * add ut Co-authored-by: mapingshuo <mps2012@yeah.net>test_feature_precision_test_c
MRXLT
5 years ago
committed by
GitHub
parent
e2b82e0439
commit
eeda90d674
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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class TestAdamaxAPI(unittest.TestCase):
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def test_adamax_api_dygraph(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_variable(value)
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linear = paddle.nn.Linear(13, 5, dtype="float32")
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adam = paddle.optimizer.Adamax(
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learning_rate=0.01,
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parameters=linear.parameters(),
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weight_decay=0.01)
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out = linear(a)
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out.backward()
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adam.step()
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adam.clear_gradients()
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def test_adamax_api(self):
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place = fluid.CPUPlace()
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shape = [2, 3, 8, 8]
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exe = fluid.Executor(place)
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train_prog = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(train_prog, startup):
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with fluid.unique_name.guard():
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data = fluid.data(name="data", shape=shape)
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conv = fluid.layers.conv2d(data, 8, 3)
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loss = paddle.mean(conv)
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beta1 = 0.85
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beta2 = 0.95
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opt = paddle.optimizer.Adamax(
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learning_rate=1e-5,
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beta1=beta1,
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beta2=beta2,
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weight_decay=0.01,
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epsilon=1e-8)
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opt.minimize(loss)
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exe.run(startup)
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data_np = np.random.random(shape).astype('float32')
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rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss])
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assert rets[0] is not None
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if __name__ == "__main__":
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unittest.main()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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import numpy as np
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import paddle.fluid as fluid
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class TestAdamWOp(unittest.TestCase):
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def test_adamw_op_dygraph(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_variable(value)
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linear = paddle.nn.Linear(13, 5, dtype="float32")
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adam = paddle.optimizer.AdamW(
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learning_rate=0.01,
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parameters=linear.parameters(),
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apply_decay_param_fun=lambda name: True,
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weight_decay=0.01)
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out = linear(a)
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out.backward()
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adam.step()
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adam.clear_gradients()
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def test_adamw_op_coverage(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_variable(value)
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linear = paddle.nn.Linear(13, 5, dtype="float32")
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adam = paddle.optimizer.AdamW(
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learning_rate=0.0,
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parameters=linear.parameters(),
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apply_decay_param_fun=lambda name: True,
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weight_decay=0.01)
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assert (adam.__str__() is not None)
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def test_adamw_op(self):
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place = fluid.CPUPlace()
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shape = [2, 3, 8, 8]
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exe = fluid.Executor(place)
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train_prog = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(train_prog, startup):
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with fluid.unique_name.guard():
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data = fluid.data(name="data", shape=shape)
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conv = fluid.layers.conv2d(data, 8, 3)
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loss = paddle.mean(conv)
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beta1 = fluid.layers.create_global_var(
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shape=[1], value=0.85, dtype='float32', persistable=True)
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beta2 = fluid.layers.create_global_var(
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shape=[1], value=0.95, dtype='float32', persistable=True)
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betas = [beta1, beta2]
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opt = paddle.optimizer.AdamW(
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learning_rate=1e-5,
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beta1=beta1,
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beta2=beta2,
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weight_decay=0.01,
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epsilon=1e-8)
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opt.minimize(loss)
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exe.run(startup)
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data_np = np.random.random(shape).astype('float32')
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rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss])
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assert rets[0] is not None
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if __name__ == "__main__":
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unittest.main()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .optimizer import Optimizer
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from ..fluid import core
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from ..fluid import framework
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from ..fluid.framework import Variable
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__all__ = ["Adam"]
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class Adam(Optimizer):
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"""
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The Adam optimizer uses an optimization described at the end
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of section 2 of `Adam paper <https://arxiv.org/abs/1412.6980>`_ ,
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it can dynamically adjusts the learning rate of each parameter using
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the 1st moment estimates and the 2nd moment estimates of the gradient.
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The parameter ``param_out`` update rule with gradient ``grad``:
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.. math::
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t & = t + 1
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moment\_1\_out & = {\\beta}_1 * moment\_1 + (1 - {\\beta}_1) * grad
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moment\_2\_out & = {\\beta}_2 * moment\_2 + (1 - {\\beta}_2) * grad * grad
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learning\_rate & = learning\_rate * \\
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\\frac{\sqrt{1 - {\\beta}_2^t}}{1 - {\\beta}_1^t}
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param\_out & = param - learning\_rate * \\frac{moment\_1}{\sqrt{moment\_2} + \epsilon}
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Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
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Args:
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learning_rate (float|LearningRateDecay, optional): The learning rate used to update ``Parameter``.
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It can be a float value or a LearningRateDecay. The default value is 0.001.
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beta1 (float|Tensor, optional): The exponential decay rate for the 1st moment estimates.
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It should be a float number or a Tensor with shape [1] and data type as float32.
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The default value is 0.9.
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beta2 (float|Tensor, optional): The exponential decay rate for the 2nd moment estimates.
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It should be a float number or a Tensor with shape [1] and data type as float32.
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The default value is 0.999.
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epsilon (float, optional): A small float value for numerical stability.
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The default value is 1e-08.
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parameters (list, optional): List of ``Tensor`` names to update to minimize ``loss``. \
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This parameter is required in dygraph mode. \
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The default value is None in static mode, at this time all parameters will be updated.
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weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
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It canbe a float value as coeff of L2 regularization or \
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:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
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If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
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the regularization setting here in optimizer will be ignored for this parameter. \
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Otherwise, the regularization setting here in optimizer will take effect. \
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Default None, meaning there is no regularization.
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grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
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some derived class of ``GradientClipBase`` . There are three cliping strategies
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( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
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:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
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name (str, optional): Normally there is no need for user to set this property.
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For more information, please refer to :ref:`api_guide_Name`.
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The default value is None.
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lazy_mode (bool, optional): The official Adam algorithm has two moving-average accumulators.
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The accumulators are updated at every step. Every element of the two moving-average
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is updated in both dense mode and sparse mode. If the size of parameter is very large,
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then the update may be very slow. The lazy mode only update the element that has
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gradient in current mini-batch, so it will be much more faster. But this mode has
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different semantics with the original Adam algorithm and may lead to different result.
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The default value is False.
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Examples:
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.. code-block:: python
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import paddle
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import numpy as np
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paddle.disable_static()
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inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
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linear = paddle.nn.Linear(10, 10)
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inp = paddle.to_tensor(inp)
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out = linear(inp)
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loss = paddle.mean(out)
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adam = paddle.optimizer.Adam(learning_rate=0.1,
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parameters=linear.parameters())
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out.backward()
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adam.step()
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adam.clear_grad()
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.. code-block:: python
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# Adam with beta1/beta2 as Tensor and weight_decay as float
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import paddle
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import numpy as np
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paddle.disable_static()
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inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
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linear = paddle.nn.Linear(10, 10)
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inp = paddle.to_tensor(inp)
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out = linear(inp)
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loss = paddle.mean(out)
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beta1 = paddle.to_tensor([0.9], dtype="float32")
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beta2 = paddle.to_tensor([0.99], dtype="float32")
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adam = paddle.optimizer.Adam(learning_rate=0.1,
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parameters=linear.parameters(),
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beta1=beta1,
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beta2=beta2,
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weight_decay=0.01)
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out.backward()
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adam.step()
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adam.clear_grad()
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"""
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_moment1_acc_str = "moment1"
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_moment2_acc_str = "moment2"
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_beta1_pow_acc_str = "beta1_pow_acc"
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_beta2_pow_acc_str = "beta2_pow_acc"
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def __init__(self,
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8,
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parameters=None,
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weight_decay=None,
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grad_clip=None,
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name=None,
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lazy_mode=False):
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assert learning_rate is not None
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assert beta1 is not None
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assert beta2 is not None
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assert epsilon is not None
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super(Adam, self).__init__(
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learning_rate=learning_rate,
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parameters=parameters,
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weight_decay=weight_decay,
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grad_clip=grad_clip,
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name=name)
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self.type = "adam"
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self._beta1 = beta1
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self._beta2 = beta2
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self._epsilon = epsilon
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self._lazy_mode = lazy_mode
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def _create_accumulators(self, block, parameters):
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assert isinstance(block, framework.Block)
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# Create accumulator tensors for first and second moments
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for p in parameters:
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self._add_accumulator(self._moment1_acc_str, p)
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self._add_accumulator(self._moment2_acc_str, p)
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self._add_accumulator(
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name=self._beta1_pow_acc_str,
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param=p,
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fill_value=0.9 if isinstance(self._beta1, Variable) \
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else self._beta1,
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shape=[1],
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type=core.VarDesc.VarType.LOD_TENSOR, device='cpu')
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self._add_accumulator(
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name=self._beta2_pow_acc_str,
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param=p,
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fill_value=0.999 if isinstance(self._beta2, Variable) \
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else self._beta2,
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shape=[1],
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type=core.VarDesc.VarType.LOD_TENSOR, device='cpu')
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def _append_optimize_op(self, block, param_and_grad):
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assert isinstance(block, framework.Block)
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moment1 = self._get_accumulator(self._moment1_acc_str,
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param_and_grad[0])
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moment2 = self._get_accumulator(self._moment2_acc_str,
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param_and_grad[0])
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beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
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param_and_grad[0])
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beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
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param_and_grad[0])
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lr = self._create_param_lr(param_and_grad)
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# create the adam optimize op
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if framework.in_dygraph_mode():
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_beta1 = self._beta1 if not isinstance(
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self._beta1, Variable) else self._beta1.numpy().item(0)
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_beta2 = self._beta2 if not isinstance(
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self._beta2, Variable) else self._beta2.numpy().item(0)
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_, _, _, _, _ = core.ops.adam(
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param_and_grad[0], param_and_grad[1], lr, moment1, moment2,
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beta1_pow_acc, beta2_pow_acc, param_and_grad[0], moment1,
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moment2, beta1_pow_acc, beta2_pow_acc, 'epsilon', self._epsilon,
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'lazy_mode', self._lazy_mode, 'min_row_size_to_use_multithread',
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1000, 'beta1', _beta1, 'beta2', _beta2)
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return None
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inputs = {
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"Param": [param_and_grad[0]],
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"Grad": [param_and_grad[1]],
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"LearningRate": [lr],
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"Moment1": [moment1],
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"Moment2": [moment2],
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"Beta1Pow": [beta1_pow_acc],
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"Beta2Pow": [beta2_pow_acc]
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}
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outputs = {
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"ParamOut": [param_and_grad[0]],
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"Moment1Out": [moment1],
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"Moment2Out": [moment2],
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"Beta1PowOut": [beta1_pow_acc],
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"Beta2PowOut": [beta2_pow_acc],
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}
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attrs = {
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"epsilon": self._epsilon,
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"lazy_mode": self._lazy_mode,
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"min_row_size_to_use_multithread": 1000
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}
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if isinstance(self._beta1, Variable):
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inputs['Beta1Tensor'] = self._beta1
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else:
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attrs['beta1'] = self._beta1
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if isinstance(self._beta2, Variable):
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inputs['Beta2Tensor'] = self._beta2
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else:
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attrs['beta2'] = self._beta2
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adam_op = block.append_op(
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type=self.type,
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inputs=inputs,
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outputs=outputs,
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attrs=attrs,
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stop_gradient=True)
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return adam_op
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@ -0,0 +1,192 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
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# limitations under the License.
|
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from .optimizer import Optimizer
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from ..fluid import core
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from ..fluid import framework
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from ..fluid.framework import Variable, name_scope
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__all__ = ["Adamax"]
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class Adamax(Optimizer):
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"""
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The Adamax optimizer is implemented based on the Adamax Optimization
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in Section 7 of `Adam paper <https://arxiv.org/abs/1412.6980>`_.
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The Adamax algorithm is a variant of the Adam algorithm based on the infinite norm,
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which makes the learning rate update algorithm more stable and simple.
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The parameter ``param_out`` update rule with gradient ``grad``:
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.. math::
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t & = t + 1
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moment\_out & = {\\beta}_1 * moment + (1 - {\\beta}_1) * grad
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inf\_norm\_out & = max({\\beta}_2 * inf\_norm + \epsilon, |grad|)
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learning\_rate & = \\frac{learning\_rate}{1 - {\\beta}_1^t}
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|
||||
param\_out & = param - learning\_rate * \\frac{moment\_out}{inf\_norm\_out}
|
||||
|
||||
Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
|
||||
|
||||
The original paper does not have an ``epsilon`` attribute,
|
||||
it is added here for numerical stability to prevent the division by 0 error.
|
||||
|
||||
Args:
|
||||
learning_rate (float|LearningRateDecay, optional): The learning rate used to update ``Parameter``.
|
||||
It can be a float value or a LearningRateDecay. The default value is 0.001.
|
||||
beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
|
||||
The default value is 0.9.
|
||||
beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.
|
||||
The default value is 0.999.
|
||||
epsilon (float, optional): A small float value for numerical stability.
|
||||
The default value is 1e-08.
|
||||
parameters (list, optional): List of ``Tensor`` 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.
|
||||
weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
|
||||
It canbe a float value as coeff of L2 regularization or \
|
||||
:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
|
||||
If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
|
||||
the regularization setting here in optimizer will be ignored for this parameter. \
|
||||
Otherwise, the regularization setting here in optimizer will take effect. \
|
||||
Default None, meaning there is no regularization.
|
||||
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, optional): Normally there is no need for user to set this property.
|
||||
For more information, please refer to :ref:`api_guide_Name`.
|
||||
The default value is None.
|
||||
|
||||
**Notes**:
|
||||
**Currently, Adamax doesn't support sparse parameter optimization.**
|
||||
|
||||
Examples:
|
||||
.. code-block:: python
|
||||
import paddle
|
||||
import numpy as np
|
||||
|
||||
paddle.disable_static()
|
||||
inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
|
||||
linear = paddle.nn.Linear(10, 10)
|
||||
inp = paddle.to_tensor(inp)
|
||||
out = linear(inp)
|
||||
loss = paddle.mean(out)
|
||||
|
||||
beta1 = paddle.to_tensor([0.9], dtype="float32")
|
||||
beta2 = paddle.to_tensor([0.99], dtype="float32")
|
||||
|
||||
adam = paddle.optimizer.Adamax(learning_rate=0.1,
|
||||
parameters=linear.parameters(),
|
||||
beta1=beta1,
|
||||
beta2=beta2,
|
||||
weight_decay=0.01)
|
||||
out.backward()
|
||||
adam.step()
|
||||
adam.clear_grad()
|
||||
|
||||
"""
|
||||
_moment_acc_str = "moment"
|
||||
_inf_norm_acc_str = "inf_norm"
|
||||
_beta1_pow_acc_str = "beta1_pow_acc"
|
||||
|
||||
def __init__(self,
|
||||
learning_rate=0.001,
|
||||
beta1=0.9,
|
||||
beta2=0.999,
|
||||
epsilon=1e-8,
|
||||
parameters=None,
|
||||
weight_decay=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(Adamax, self).__init__(
|
||||
learning_rate=learning_rate,
|
||||
parameters=parameters,
|
||||
weight_decay=weight_decay,
|
||||
grad_clip=grad_clip,
|
||||
name=name)
|
||||
self.type = "adamax"
|
||||
self._beta1 = beta1
|
||||
self._beta2 = beta2
|
||||
self._epsilon = epsilon
|
||||
|
||||
def _create_accumulators(self, block, parameters):
|
||||
# Create accumulator tensors for first moment and infinity norm
|
||||
for p in parameters:
|
||||
self._add_accumulator(self._moment_acc_str, p)
|
||||
self._add_accumulator(self._inf_norm_acc_str, p)
|
||||
self._add_accumulator(
|
||||
name=self._beta1_pow_acc_str,
|
||||
param=p,
|
||||
fill_value=self._beta1,
|
||||
shape=[1])
|
||||
|
||||
def _append_optimize_op(self, block, param_and_grad):
|
||||
assert isinstance(block, framework.Block)
|
||||
|
||||
moment = self._get_accumulator(self._moment_acc_str, param_and_grad[0])
|
||||
inf_norm = self._get_accumulator(self._inf_norm_acc_str,
|
||||
param_and_grad[0])
|
||||
beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
|
||||
param_and_grad[0])
|
||||
# create the adamax optimize op
|
||||
adamax_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),
|
||||
"Moment": moment,
|
||||
"InfNorm": inf_norm,
|
||||
"Beta1Pow": beta1_pow_acc
|
||||
},
|
||||
outputs={
|
||||
"ParamOut": param_and_grad[0],
|
||||
"MomentOut": moment,
|
||||
"InfNormOut": inf_norm
|
||||
},
|
||||
attrs={
|
||||
"beta1": self._beta1,
|
||||
"beta2": self._beta2,
|
||||
"epsilon": self._epsilon
|
||||
},
|
||||
stop_gradient=True)
|
||||
|
||||
return adamax_op
|
||||
|
||||
def _finish_update(self, block, parameters_and_grads):
|
||||
"""Update Beta1 Power accumulator
|
||||
"""
|
||||
assert isinstance(block, framework.Block)
|
||||
for param, grad in parameters_and_grads:
|
||||
if grad is None or param.trainable is False:
|
||||
continue
|
||||
with param.block.program._optimized_guard(
|
||||
[param, grad]), name_scope('adamax'):
|
||||
beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
|
||||
param)
|
||||
block.append_op(
|
||||
type="scale",
|
||||
inputs={"X": beta1_pow_acc},
|
||||
outputs={"Out": beta1_pow_acc},
|
||||
attrs={"scale": self._beta1},
|
||||
stop_gradient=True)
|
||||
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Reference in new issue