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paddlepaddle--paddle/test/legacy_test/test_lbfgs_class.py
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wehub-resource-sync e25996e7db chore: import upstream snapshot with attribution
2026-07-13 12:40:42 +08:00

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# Copyright (c) 2022 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
import paddle
from paddle.incubate.optimizer import (
line_search_dygraph,
)
from paddle.optimizer import lbfgs
np.random.seed(123)
# func()should be func(w, x)where w is parameter to be optimize ,x is input of optimizer func
# np_w is the init parameter of w
class Net(paddle.nn.Layer):
def __init__(self, np_w, func):
super().__init__()
self.func = func
w = paddle.to_tensor(np_w)
self.w = paddle.create_parameter(
shape=w.shape,
dtype=w.dtype,
default_initializer=paddle.nn.initializer.Assign(w),
)
def forward(self, x):
return self.func(self.w, x)
def train_step(inputs, targets, net, opt):
def closure():
outputs = net(inputs)
loss = paddle.nn.functional.mse_loss(outputs, targets)
opt.clear_grad()
loss.backward()
return loss
loss = opt.step(closure)
return loss
class TestLbfgs(unittest.TestCase):
def test_function_fix_incubate(self):
paddle.disable_static()
np_w = np.random.rand(1).astype(np.float32)
input = np.random.rand(1).astype(np.float32)
weights = [np.random.rand(1).astype(np.float32) for i in range(5)]
targets = [weights[i] * input for i in range(5)]
def func(w, x):
return w * x
net = Net(np_w, func)
opt = lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=5,
line_search_fn='strong_wolfe',
parameters=net.parameters(),
)
for weight, target in zip(weights, targets):
input = paddle.to_tensor(input)
target = paddle.to_tensor(target)
loss = 1
while loss > 1e-4:
loss = train_step(input, target, net, opt)
np.testing.assert_allclose(net.w, weight, rtol=1e-05)
def test_inf_minima_incubate(self):
# not converge
input = np.random.rand(1).astype(np.float32)
def outputs1(x):
# weight[0] = 1.01 weight[1] = 0.99
return x * x * x - 3 * x * x + 3 * 1.01 * 0.99 * x
def outputs2(x):
# weight[0] = 4 weight[1] = 2
return pow(x, 4) + 5 * pow(x, 2)
targets = [outputs1(input), outputs2(input)]
input = paddle.to_tensor(input)
def func1(extreme_point, x):
return (
x * x * x
- 3 * x * x
+ 3 * extreme_point[0] * extreme_point[1] * x
)
def func2(extreme_point, x):
return pow(x, extreme_point[0]) + 5 * pow(x, extreme_point[1])
extreme_point = np.array([-2.34, 1.45]).astype('float32')
net1 = Net(extreme_point, func1)
# converge of old_sk.pop()
opt1 = lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=1,
line_search_fn='strong_wolfe',
parameters=net1.parameters(),
)
net2 = Net(extreme_point, func2)
# converge of line_search = None
opt2 = lbfgs.LBFGS(
learning_rate=1,
max_iter=50,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=10,
line_search_fn=None,
parameters=net2.parameters(),
)
n_iter = 0
while n_iter < 20:
loss = train_step(input, paddle.to_tensor(targets[0]), net1, opt1)
n_iter = opt1.state_dict()["state"]["func_evals"]
n_iter = 0
while n_iter < 10:
loss = train_step(input, paddle.to_tensor(targets[1]), net2, opt2)
n_iter = opt1.state_dict()["state"]["func_evals"]
def test_error_incubate(self):
# test parameter is not Paddle Tensor
def error_func1():
extreme_point = np.array([-1, 2]).astype('float32')
extreme_point = paddle.to_tensor(extreme_point)
return lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=3,
line_search_fn='strong_wolfe',
parameters=extreme_point,
)
self.assertRaises(TypeError, error_func1)
def test_error2_incubate(self):
# not converge
input = np.random.rand(1).astype(np.float32)
def outputs2(x):
# weight[0] = 4 weight[1] = 2
return pow(x, 4) + 5 * pow(x, 2)
targets = [outputs2(input)]
input = paddle.to_tensor(input)
def func2(extreme_point, x):
return pow(x, extreme_point[0]) + 5 * pow(x, extreme_point[1])
extreme_point = np.array([-2.34, 1.45]).astype('float32')
net2 = Net(extreme_point, func2)
# converge of line_search = None
opt2 = lbfgs.LBFGS(
learning_rate=1,
max_iter=50,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=10,
line_search_fn='None',
parameters=net2.parameters(),
)
def error_func():
n_iter = 0
while n_iter < 10:
loss = train_step(
input, paddle.to_tensor(targets[0]), net2, opt2
)
n_iter = opt2.state_dict()["state"]["func_evals"]
self.assertRaises(RuntimeError, error_func)
def test_line_search_incubate(self):
def func1(x, alpha, d):
return paddle.to_tensor(x + alpha * d), paddle.to_tensor([0.0])
def func2(x, alpha, d):
return paddle.to_tensor(x + alpha * d), paddle.to_tensor([1.0])
def func3(x, alpha, d):
return paddle.to_tensor(x + alpha * d), paddle.to_tensor([-1.0])
line_search_dygraph._strong_wolfe(
func1,
paddle.to_tensor([1.0]),
paddle.to_tensor([0.001]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([0.0]),
paddle.to_tensor([0.0]),
max_ls=1,
)
line_search_dygraph._strong_wolfe(
func1,
paddle.to_tensor([1.0]),
paddle.to_tensor([0.001]),
paddle.to_tensor([0.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([0.0]),
paddle.to_tensor([0.0]),
max_ls=0,
)
line_search_dygraph._strong_wolfe(
func2,
paddle.to_tensor([1.0]),
paddle.to_tensor([-0.001]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
max_ls=1,
)
line_search_dygraph._strong_wolfe(
func3,
paddle.to_tensor([1.0]),
paddle.to_tensor([-0.001]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
max_ls=1,
)
line_search_dygraph._cubic_interpolate(
paddle.to_tensor([2.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([0.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([2.0]),
paddle.to_tensor([0.0]),
[0.1, 0.5],
)
line_search_dygraph._cubic_interpolate(
paddle.to_tensor([2.0]),
paddle.to_tensor([0.0]),
paddle.to_tensor([-3.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([-0.1]),
[0.1, 0.5],
)
def test_error3_incubate(self):
# test parameter shape size <= 0
def error_func3():
extreme_point = np.array([-1, 2]).astype('float32')
extreme_point = paddle.to_tensor(extreme_point)
def func(w, x):
return w * x
net = Net(extreme_point, func)
net.w = paddle.create_parameter(
shape=[-1, 2],
dtype=net.w.dtype,
)
opt = lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=5,
line_search_fn='strong_wolfe',
parameters=net.parameters(),
)
self.assertRaises(AssertionError, error_func3)
def test_function_fix(self):
paddle.disable_static()
np_w = np.random.rand(1).astype(np.float32)
input = np.random.rand(1).astype(np.float32)
weights = [np.random.rand(1).astype(np.float32) for i in range(5)]
targets = [weights[i] * input for i in range(5)]
def func(w, x):
return w * x
net = Net(np_w, func)
opt = lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=5,
line_search_fn='strong_wolfe',
parameters=net.parameters(),
)
for weight, target in zip(weights, targets):
input = paddle.to_tensor(input)
target = paddle.to_tensor(target)
loss = 1
while loss > 1e-4:
loss = train_step(input, target, net, opt)
np.testing.assert_allclose(net.w, weight, rtol=1e-05)
def test_inf_minima(self):
# not converge
input = np.random.rand(1).astype(np.float32)
def outputs1(x):
# weight[0] = 1.01 weight[1] = 0.99
return x * x * x - 3 * x * x + 3 * 1.01 * 0.99 * x
def outputs2(x):
# weight[0] = 4 weight[1] = 2
return pow(x, 4) + 5 * pow(x, 2)
targets = [outputs1(input), outputs2(input)]
input = paddle.to_tensor(input)
def func1(extreme_point, x):
return (
x * x * x
- 3 * x * x
+ 3 * extreme_point[0] * extreme_point[1] * x
)
def func2(extreme_point, x):
return pow(x, extreme_point[0]) + 5 * pow(x, extreme_point[1])
extreme_point = np.array([-2.34, 1.45]).astype('float32')
net1 = Net(extreme_point, func1)
# converge of old_sk.pop()
opt1 = lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=1,
line_search_fn='strong_wolfe',
parameters=net1.parameters(),
)
net2 = Net(extreme_point, func2)
# converge of line_search = None
opt2 = lbfgs.LBFGS(
learning_rate=1,
max_iter=50,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=10,
line_search_fn=None,
parameters=net2.parameters(),
)
n_iter = 0
while n_iter < 20:
loss = train_step(input, paddle.to_tensor(targets[0]), net1, opt1)
n_iter = opt1.state_dict()["state"]["func_evals"]
n_iter = 0
while n_iter < 10:
loss = train_step(input, paddle.to_tensor(targets[1]), net2, opt2)
n_iter = opt1.state_dict()["state"]["func_evals"]
def test_error(self):
# test parameter is not Paddle Tensor
def error_func1():
extreme_point = np.array([-1, 2]).astype('float32')
extreme_point = paddle.to_tensor(extreme_point)
return lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=3,
line_search_fn='strong_wolfe',
parameters=extreme_point,
)
self.assertRaises(TypeError, error_func1)
def test_error2(self):
# not converge
input = np.random.rand(1).astype(np.float32)
def outputs2(x):
# weight[0] = 4 weight[1] = 2
return pow(x, 4) + 5 * pow(x, 2)
targets = [outputs2(input)]
input = paddle.to_tensor(input)
def func2(extreme_point, x):
return pow(x, extreme_point[0]) + 5 * pow(x, extreme_point[1])
extreme_point = np.array([-2.34, 1.45]).astype('float32')
net2 = Net(extreme_point, func2)
# converge of line_search = None
opt2 = lbfgs.LBFGS(
learning_rate=1,
max_iter=50,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=10,
line_search_fn='None',
parameters=net2.parameters(),
)
def error_func():
n_iter = 0
while n_iter < 10:
loss = train_step(
input, paddle.to_tensor(targets[0]), net2, opt2
)
n_iter = opt2.state_dict()["state"]["func_evals"]
self.assertRaises(RuntimeError, error_func)
def test_line_search(self):
def func1(x, alpha, d):
return paddle.to_tensor(x + alpha * d), paddle.to_tensor([0.0])
def func2(x, alpha, d):
return paddle.to_tensor(x + alpha * d), paddle.to_tensor([1.0])
def func3(x, alpha, d):
return paddle.to_tensor(x + alpha * d), paddle.to_tensor([-1.0])
lbfgs._strong_wolfe(
func1,
paddle.to_tensor([1.0]),
paddle.to_tensor([0.001]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([0.0]),
paddle.to_tensor([0.0]),
max_ls=1,
)
lbfgs._strong_wolfe(
func1,
paddle.to_tensor([1.0]),
paddle.to_tensor([0.001]),
paddle.to_tensor([0.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([0.0]),
paddle.to_tensor([0.0]),
max_ls=0,
)
lbfgs._strong_wolfe(
func2,
paddle.to_tensor([1.0]),
paddle.to_tensor([-0.001]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
max_ls=1,
)
lbfgs._strong_wolfe(
func2,
paddle.to_tensor([1.0]),
-0.001,
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
max_ls=1,
)
lbfgs._strong_wolfe(
func3,
paddle.to_tensor([1.0]),
paddle.to_tensor([-0.001]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
max_ls=1,
)
lbfgs._cubic_interpolate(
paddle.to_tensor([2.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([0.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([2.0]),
paddle.to_tensor([0.0]),
[0.1, 0.5],
)
lbfgs._cubic_interpolate(
paddle.to_tensor([2.0]),
paddle.to_tensor([0.0]),
paddle.to_tensor([-3.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([1.0]),
paddle.to_tensor([-0.1]),
[0.1, 0.5],
)
def test_error3(self):
# test parameter shape size <= 0
def error_func3():
extreme_point = np.array([-1, 2]).astype('float32')
extreme_point = paddle.to_tensor(extreme_point)
def func(w, x):
return w * x
net = Net(extreme_point, func)
net.w = paddle.create_parameter(
shape=[-1, 2],
dtype=net.w.dtype,
)
opt = lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=5,
line_search_fn='strong_wolfe',
parameters=net.parameters(),
)
self.assertRaises(AssertionError, error_func3)
def test_error4(self):
# test call minimize(loss)
paddle.disable_static()
def error_func4():
inputs = np.random.rand(1).astype(np.float32)
targets = paddle.to_tensor([inputs * 2])
inputs = paddle.to_tensor(inputs)
extreme_point = np.array([-1, 1]).astype('float32')
def func(extreme_point, x):
return x * extreme_point[0] + 5 * x * extreme_point[1]
net = Net(extreme_point, func)
opt = lbfgs.LBFGS(
learning_rate=1,
max_iter=10,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=5,
line_search_fn='strong_wolfe',
parameters=net.parameters(),
)
loss = train_step(inputs, targets, net, opt)
opt.minimize(loss)
self.assertRaises(NotImplementedError, error_func4)
if __name__ == '__main__':
unittest.main()
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