Add DecoupledWeightDecay (#16427)
* Add DecoupledWeightDecayrevert-16555-model_data_cryption_link_all_lib
chengduo
6 years ago
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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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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from . import extend_optimizer_with_weight_decay
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from .extend_optimizer_with_weight_decay import *
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__all__ = []
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__all__ += extend_optimizer_with_weight_decay.__all__
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# Copyright (c) 2019 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 paddle.fluid
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from paddle.fluid import framework as framework
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__all__ = ["extend_with_decoupled_weight_decay"]
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class DecoupledWeightDecay(object):
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def __init__(self, coeff=0.0, apply_decay_param_fun=None, **kwargs):
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if not isinstance(coeff, float) and \
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not isinstance(coeff, framework.Variable):
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raise TypeError("coeff should be float or Variable.")
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self._params_name = set()
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self._apply_decay_param_fun = apply_decay_param_fun
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self._coeff = coeff
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super(DecoupledWeightDecay, self).__init__(**kwargs)
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def _scale_parameters(self, params_and_grads):
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"""
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Adds weight decay ops.
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scaled_parameter = parameter * coeff
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Args:
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params_and_grads: A list of (parameters, gradients) pairs,
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the parameters need to decay.
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Raises:
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Exception: The type of coeff and parameter is not consistent.
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"""
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if isinstance(self._coeff, float) and self._coeff == 0.0:
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return
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scaled_params = []
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for param, grad in params_and_grads:
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# If no gradient then we don't need to do anything
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if grad is None:
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continue
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if self._apply_decay_param_fun is not None \
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and not self._apply_decay_param_fun(param.name):
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continue
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if isinstance(self._coeff, float):
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assert param.dtype is not paddle.fluid.core.VarDesc.VarType.FP32, \
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"the type of coeff(float) and parameter(%s) is not consistent."%(self._coeff.dtype)
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else:
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assert self._coeff.dtype == param.dtype, \
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"the type of coeff(%s) and parameter(%s) is not consistent."%(self._coeff.dtype, param.dtype)
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with param.block.program._optimized_guard(
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[param, grad]), framework.name_scope('weight decay'):
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assert param.name not in self._params_name
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scaled_params.append((param, grad, param * self._coeff))
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self._params_name.add(param.name)
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return scaled_params
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def backward(self, **kargs):
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return super(DecoupledWeightDecay, self).backward(**kargs)
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def apply_optimize(self, **kargs):
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return super(DecoupledWeightDecay, self).apply_optimize(**kargs)
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def minimize(self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None):
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params_grads = self.backward(
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loss=loss,
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startup_program=startup_program,
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parameter_list=parameter_list,
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no_grad_set=no_grad_set)
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scaled_params = self._scale_parameters(params_grads)
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for p_grad_sgrad in scaled_params:
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param, grad, scaled_param = p_grad_sgrad
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with param.block.program._optimized_guard(
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[param, grad]), framework.name_scope('weight decay'):
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updated_param = paddle.fluid.layers.elementwise_sub(
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x=param, y=scaled_param)
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paddle.fluid.layers.assign(input=updated_param, output=param)
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optimize_ops = self.apply_optimize(
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loss=loss,
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params_grads=params_grads,
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startup_program=startup_program)
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return optimize_ops, params_grads
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def __str__(self):
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return " ".join(["Weight Decay, params:", ",".join(self._params_name)])
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def extend_with_decoupled_weight_decay(base_optimizer):
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"""
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extend_with_decoupled_weight_decay is a decorator function, it returns an
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optimizer class with decoupled weight decay. The returned optimizer will
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apply weight decay on the optimized parameters with the parameters before
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optimization, i.e: new_parameter = optimized_parameter - parameter * coeff.
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The details of decoupled weight decay yplease refer to this
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`DECOUPLED WEIGHT DECAY REGULARIZATION <https://arxiv.org/pdf/1711.05101.pdf>`_.
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Args:
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base_optimizer (Optimizer): The base_optimizer should be a derived class of Optimizer.
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Returns:
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OptimizerWithDecoupledWeightDecay: the optimizer with decouple weight decay.
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Examples:
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.. code-block:: python
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AdamW = fluid.contrib.extend_with_decoupled_weight_decay(
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fluid.optimizer.Adam)
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optimizer = AdamW(learning_rate=0.1,
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weight_decay=0.01)
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optimizer.minimize(cost)
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"""
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if not issubclass(base_optimizer, paddle.fluid.optimizer.Optimizer):
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raise TypeError(
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"The input(base_optimizer) should be a derived class of Optimizer.")
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class OptimizerWithDecoupledWeightDecay(DecoupledWeightDecay,
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base_optimizer):
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"""
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OptimizerWithDecoupledWeightDecay is used to update the optimized parameters
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with the parameters before optimization. For more information, please refer:
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https://arxiv.org/pdf/1711.05101.pdf.
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Args:
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weight_decay (float|Variable): The weight decay coefficient, it can be
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float or Variable.
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apply_decay_param_fun (function|None): If it is not None,
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only variables that makes apply_decay_param_fun(variable)==True
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will be updated. It only works when we want to specify variables.
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Default: None.
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"""
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def __init__(self, weight_decay, apply_decay_param_fun=None, **kwargs):
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super(OptimizerWithDecoupledWeightDecay, self).__init__(
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weight_decay, apply_decay_param_fun, **kwargs)
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return OptimizerWithDecoupledWeightDecay
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@ -0,0 +1,151 @@
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# Copyright (c) 2019 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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from functools import partial
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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import contextlib
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def get_places():
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places = [fluid.CPUPlace()]
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if fluid.core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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return places
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@contextlib.contextmanager
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def prog_scope_guard(main_prog, startup_prog):
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scope = fluid.core.Scope()
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with fluid.unique_name.guard():
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with fluid.scope_guard(scope):
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with fluid.program_guard(main_prog, startup_prog):
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yield
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def bow_net(data,
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label,
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dict_dim,
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is_sparse=False,
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emb_dim=128,
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hid_dim=128,
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hid_dim2=96,
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class_dim=2):
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"""
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BOW net
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This model is from https://github.com/PaddlePaddle/models:
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fluid/PaddleNLP/text_classification/nets.py
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"""
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emb = fluid.layers.embedding(
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input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim])
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bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
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bow_tanh = fluid.layers.tanh(bow)
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fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
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fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
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prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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return avg_cost
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class TestWeightDecay(unittest.TestCase):
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def setUp(self):
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self.word_dict = paddle.dataset.imdb.word_dict()
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reader = paddle.batch(
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paddle.dataset.imdb.train(self.word_dict), batch_size=2)()
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self.train_data = [next(reader) for _ in range(5)]
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self.learning_rate = .5
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def run_program(self, place, feed_list):
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exe = fluid.Executor(place)
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feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
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exe.run(fluid.default_startup_program())
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main_prog = fluid.default_main_program()
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param_list = [var.name for var in main_prog.block(0).all_parameters()]
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param_sum = []
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for data in self.train_data:
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out = exe.run(main_prog,
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feed=feeder.feed(data),
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fetch_list=param_list)
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p_sum = 0
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for v in out:
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p_sum += np.sum(np.abs(v))
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param_sum.append(p_sum)
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return param_sum
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def check_weight_decay(self, place, model):
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main_prog = fluid.framework.Program()
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startup_prog = fluid.framework.Program()
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startup_prog.random_seed = 1
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with prog_scope_guard(main_prog=main_prog, startup_prog=startup_prog):
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data = fluid.layers.data(
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name="words", shape=[1], dtype="int64", lod_level=1)
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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avg_cost = model(data, label, len(self.word_dict))
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AdamW = fluid.contrib.extend_with_decoupled_weight_decay(
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fluid.optimizer.Adam)
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optimizer = AdamW(
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learning_rate=self.learning_rate,
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weight_decay=self.learning_rate)
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optimizer.minimize(avg_cost)
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param_sum = self.run_program(place, [data, label])
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return param_sum
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def check_weight_decay2(self, place, model):
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main_prog = fluid.framework.Program()
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startup_prog = fluid.framework.Program()
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startup_prog.random_seed = 1
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with prog_scope_guard(main_prog=main_prog, startup_prog=startup_prog):
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data = fluid.layers.data(
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name="words", shape=[1], dtype="int64", lod_level=1)
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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avg_cost = model(data, label, len(self.word_dict))
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param_list = [(var, var * self.learning_rate)
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for var in main_prog.block(0).all_parameters()]
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optimizer = fluid.optimizer.Adam(learning_rate=self.learning_rate)
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optimizer.minimize(avg_cost)
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for params in param_list:
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updated_p = fluid.layers.elementwise_sub(
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x=params[0], y=params[1])
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fluid.layers.assign(input=updated_p, output=params[0])
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param_sum = self.run_program(place, [data, label])
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return param_sum
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def test_weight_decay(self):
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for place in get_places():
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model = partial(bow_net, is_sparse=False)
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param_sum1 = self.check_weight_decay(place, model)
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param_sum2 = self.check_weight_decay2(place, model)
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for i in range(len(param_sum1)):
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assert np.isclose(a=param_sum1[i], b=param_sum2[i], rtol=5e-5)
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if __name__ == '__main__':
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unittest.main()
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