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.
316 lines
10 KiB
316 lines
10 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.
|
|
|
|
import unittest
|
|
import numpy as np
|
|
from op_test import OpTest
|
|
from paddle.fluid import core
|
|
from paddle.fluid.op import Operator
|
|
|
|
|
|
class TestAdamOp1(OpTest):
|
|
def setUp(self):
|
|
'''Test Adam Op with supplied attributes
|
|
'''
|
|
self.op_type = "adam"
|
|
param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
# The second moment is positive
|
|
moment2 = np.random.random((102, 105)).astype("float32")
|
|
|
|
learning_rate = 0.004
|
|
beta1 = 0.78
|
|
beta2 = 0.836
|
|
epsilon = 1e-4
|
|
beta1_pow = beta1**10
|
|
beta2_pow = beta2**10
|
|
|
|
self.inputs = {
|
|
'Param': param,
|
|
'Grad': grad,
|
|
'Moment1': moment1,
|
|
'Moment2': moment2,
|
|
'LearningRate': np.array([learning_rate]).astype("float32"),
|
|
'Beta1Pow': np.array([beta1_pow]).astype("float32"),
|
|
'Beta2Pow': np.array([beta2_pow]).astype("float32")
|
|
}
|
|
|
|
self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
|
|
|
|
param_out, moment1_out, \
|
|
moment2_out = adam_step(self.inputs, self.attrs)
|
|
|
|
self.outputs = {
|
|
'Moment1Out': moment1_out,
|
|
'Moment2Out': moment2_out,
|
|
'ParamOut': param_out
|
|
}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
class TestAdamOp2(OpTest):
|
|
def setUp(self):
|
|
'''Test Adam Op with supplied attributes
|
|
'''
|
|
self.op_type = "adam"
|
|
param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
# The second moment is positive
|
|
moment2 = np.random.random((102, 105)).astype("float32")
|
|
|
|
learning_rate = 0.001
|
|
beta1 = 0.9
|
|
beta2 = 0.999
|
|
epsilon = 1e-8
|
|
beta1_pow = beta1**10
|
|
beta2_pow = beta2**10
|
|
|
|
self.inputs = {
|
|
'Param': param,
|
|
'Grad': grad,
|
|
'Moment1': moment1,
|
|
'Moment2': moment2,
|
|
'LearningRate': np.array([learning_rate]).astype("float32"),
|
|
'Beta1Pow': np.array([beta1_pow]).astype("float32"),
|
|
'Beta2Pow': np.array([beta2_pow]).astype("float32")
|
|
}
|
|
|
|
attributes = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
|
|
|
|
param_out, moment1_out, \
|
|
moment2_out = adam_step(self.inputs, attributes)
|
|
|
|
self.outputs = {
|
|
'Moment1Out': moment1_out,
|
|
'Moment2Out': moment2_out,
|
|
'ParamOut': param_out
|
|
}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
class TestAdamOpMultipleSteps(OpTest):
|
|
def setUp(self):
|
|
'''Test Adam Operator with supplied attributes
|
|
'''
|
|
self.op_type = "adam"
|
|
self.num_steps = 10
|
|
|
|
param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
|
|
# The second moment is positive
|
|
moment2 = np.random.random((102, 105)).astype("float32")
|
|
|
|
learning_rate = 0.001
|
|
beta1 = 0.9
|
|
beta2 = 0.999
|
|
epsilon = 1e-8
|
|
beta1_pow = beta1**10
|
|
beta2_pow = beta2**10
|
|
|
|
self.inputs = {
|
|
'Param': param,
|
|
'Grad': grad,
|
|
'Moment1': moment1,
|
|
'Moment2': moment2,
|
|
'LearningRate': np.array([learning_rate]).astype("float32"),
|
|
'Beta1Pow': np.array([beta1_pow]).astype("float32"),
|
|
'Beta2Pow': np.array([beta2_pow]).astype("float32")
|
|
}
|
|
|
|
self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
|
|
|
|
def test_check_output(self):
|
|
for _ in range(self.num_steps):
|
|
param_out, moment1_out, \
|
|
moment2_out = adam_step(self.inputs, self.attrs)
|
|
|
|
self.outputs = {
|
|
'Moment1Out': moment1_out,
|
|
'Moment2Out': moment2_out,
|
|
'ParamOut': param_out
|
|
}
|
|
|
|
# Verify output for this step
|
|
self.check_output()
|
|
|
|
# Output of this step becomes input for next step
|
|
self.inputs['Param'] = param_out
|
|
self.inputs['Moment1'] = moment1_out
|
|
self.inputs['Moment2'] = moment2_out
|
|
|
|
# Update powers of Beta1 and Beta2 for next time step
|
|
self.inputs['Beta1Pow'] *= self.attrs['beta1']
|
|
self.inputs['Beta2Pow'] *= self.attrs['beta1']
|
|
|
|
# Randomize gradient for next step
|
|
self.inputs['Grad'] = np.random.uniform(
|
|
-1, 1, (102, 105)).astype("float32")
|
|
|
|
|
|
def adam_step(inputs, attributes):
|
|
'''
|
|
Simulate one step of the adam optimizer
|
|
:param inputs: dict of inputs
|
|
:param attributes: dict of attributes
|
|
:return tuple: tuple of output param, moment1, moment2,
|
|
beta1 power accumulator and beta2 power accumulator
|
|
'''
|
|
param = inputs['Param']
|
|
grad = inputs['Grad']
|
|
moment1 = inputs['Moment1']
|
|
moment2 = inputs['Moment2']
|
|
lr = inputs['LearningRate']
|
|
beta1_pow = inputs['Beta1Pow']
|
|
beta2_pow = inputs['Beta2Pow']
|
|
|
|
beta1 = attributes['beta1']
|
|
beta2 = attributes['beta2']
|
|
epsilon = attributes['epsilon']
|
|
|
|
moment1_out = beta1 * moment1 + (1 - beta1) * grad
|
|
moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
|
|
lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
|
|
param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon))
|
|
return param_out, moment1_out, moment2_out
|
|
|
|
|
|
def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
|
|
'''
|
|
Simulate one step of the adam optimizer
|
|
:param inputs: dict of inputs
|
|
:param attributes: dict of attributes
|
|
:return tuple: tuple of output param, moment1, moment2,
|
|
beta1 power accumulator and beta2 power accumulator
|
|
'''
|
|
param = inputs['Param']
|
|
# grad = inputs['Grad']
|
|
moment1 = inputs['Moment1']
|
|
moment2 = inputs['Moment2']
|
|
lr = inputs['LearningRate']
|
|
beta1_pow = inputs['Beta1Pow']
|
|
beta2_pow = inputs['Beta2Pow']
|
|
|
|
beta1 = attributes['beta1']
|
|
beta2 = attributes['beta2']
|
|
epsilon = attributes['epsilon']
|
|
|
|
moment1_out = np.zeros(shape=[height, row_numel])
|
|
moment2_out = np.zeros(shape=[height, row_numel])
|
|
param_out = np.zeros(shape=[height, row_numel])
|
|
|
|
for idx, row_id in enumerate(rows):
|
|
moment1_out[row_id] = beta1 * moment1[row_id] + (1 - beta1
|
|
) * np_grad[idx]
|
|
moment2_out[row_id] = beta2 * moment2[row_id] + (
|
|
1 - beta2) * np.square(np_grad[idx])
|
|
lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
|
|
param_out[row_id] = param[row_id] - lr_t * (moment1_out[row_id] / (
|
|
np.sqrt(moment2_out[row_id]) + epsilon))
|
|
return param_out, moment1_out, moment2_out
|
|
|
|
|
|
class TestSparseAdamOp(unittest.TestCase):
|
|
def setup(self, scope, place):
|
|
beta1 = 0.78
|
|
beta2 = 0.836
|
|
epsilon = 1e-4
|
|
|
|
height = 10
|
|
rows = [0, 4, 7]
|
|
self.rows = rows
|
|
row_numel = 12
|
|
self.row_numel = row_numel
|
|
self.dense_inputs = {
|
|
"Param": np.full((height, row_numel), 5.0).astype("float32"),
|
|
"Moment1": np.full((height, row_numel), 5.0).astype("float32"),
|
|
"Moment2": np.full((height, row_numel), 5.0).astype("float32"),
|
|
'Beta1Pow': np.array([beta1**10]).astype("float32"),
|
|
'Beta2Pow': np.array([beta2**10]).astype("float32"),
|
|
"LearningRate": np.full((1), 2.0).astype("float32")
|
|
}
|
|
self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
|
|
|
|
grad_selected_rows = scope.var('Grad').get_selected_rows()
|
|
grad_selected_rows.set_height(height)
|
|
grad_selected_rows.set_rows(rows)
|
|
np_array = np.ones((len(rows), row_numel)).astype("float32")
|
|
np_array[0, 0] = 2.0
|
|
np_array[2, 8] = 4.0
|
|
|
|
grad_tensor = grad_selected_rows.get_tensor()
|
|
grad_tensor.set(np_array, place)
|
|
|
|
self.sparse_inputs = ["Grad"]
|
|
|
|
param_out, mom1, mom2 = adam_step_sparse(
|
|
self.dense_inputs, self.attrs, height, rows, row_numel, np_array)
|
|
self.outputs = {
|
|
"ParamOut": param_out,
|
|
"Moment1Out": mom1,
|
|
"Moment2Out": mom2
|
|
}
|
|
|
|
def check_with_place(self, place):
|
|
scope = core.Scope()
|
|
self.setup(scope, place)
|
|
|
|
op_args = dict()
|
|
for key, np_array in self.dense_inputs.iteritems():
|
|
var = scope.var(key).get_tensor()
|
|
var.set(np_array, place)
|
|
op_args[key] = key
|
|
for s in self.sparse_inputs:
|
|
op_args[s] = s
|
|
for s in self.outputs:
|
|
var = scope.var(s).get_tensor()
|
|
var.set(self.outputs[s], place)
|
|
op_args[s] = s
|
|
for k in self.attrs:
|
|
op_args[k] = self.attrs[k]
|
|
|
|
# create and run sgd operator
|
|
adam_op = Operator("adam", **op_args)
|
|
adam_op.run(scope, place)
|
|
|
|
for key, np_array in self.outputs.iteritems():
|
|
out_var = scope.var(key).get_tensor()
|
|
actual = np.array(out_var)
|
|
actual = actual.reshape([actual.size])
|
|
np_array = np_array.reshape([np_array.size])
|
|
for idx, row_id in enumerate(self.rows):
|
|
j = 0
|
|
while j < self.row_numel:
|
|
pos = row_id * self.row_numel + j
|
|
self.assertLess((actual[pos] - np_array[pos]) / actual[pos],
|
|
0.00001)
|
|
j += 1
|
|
|
|
def test_sparse_sgd(self):
|
|
places = [core.CPUPlace()]
|
|
if core.is_compiled_with_cuda():
|
|
places.append(core.CUDAPlace(0))
|
|
for place in places:
|
|
self.check_with_place(place)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|