You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
74 lines
2.0 KiB
74 lines
2.0 KiB
# 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
|
|
|
|
|
|
class TestDpsgdOp(OpTest):
|
|
def setUp(self):
|
|
'''Test Dpsgd Operator with supplied attributes
|
|
'''
|
|
self.op_type = "dpsgd"
|
|
param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
|
|
learning_rate = 0.001
|
|
clip = 10000.0
|
|
batch_size = 16.0
|
|
sigma = 0.0
|
|
|
|
self.inputs = {
|
|
'Param': param,
|
|
'Grad': grad,
|
|
'LearningRate': np.array([learning_rate]).astype("float32")
|
|
}
|
|
|
|
self.attrs = {'clip': clip, 'batch_size': batch_size, 'sigma': sigma}
|
|
|
|
param_out = dpsgd_step(self.inputs, self.attrs)
|
|
|
|
self.outputs = {'ParamOut': param_out}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
def dpsgd_step(inputs, attributes):
|
|
'''
|
|
Simulate one step of the dpsgd optimizer
|
|
:param inputs: dict of inputs
|
|
:param attributes: dict of attributes
|
|
:return tuple: tuple of output param, moment, inf_norm and
|
|
beta1 power accumulator
|
|
'''
|
|
param = inputs['Param']
|
|
grad = inputs['Grad']
|
|
lr = inputs['LearningRate']
|
|
|
|
clip = attributes['clip']
|
|
batch_size = attributes['batch_size']
|
|
sigma = attributes['sigma']
|
|
|
|
param_out = param - lr * grad
|
|
|
|
return param_out
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|