wehub-resource-sync
186 lines
6.3 KiB
Python
186 lines
6.3 KiB
Python
# 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
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.base.backward import gradients
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestCalcGradient(unittest.TestCase):
|
|
def test_calc_gradient(self):
|
|
main = base.Program()
|
|
startup = base.Program()
|
|
with base.program_guard(main, startup):
|
|
x = paddle.create_parameter(dtype="float32", shape=[5, 10])
|
|
y = paddle.create_parameter(dtype="float32", shape=[10, 8])
|
|
mul_out = paddle.matmul(x=x, y=y)
|
|
mean_out = paddle.mean(mul_out)
|
|
a = gradients(mean_out, mul_out)
|
|
b = gradients(mean_out, x)
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
exe.run(startup)
|
|
exe.run(main, feed={}, fetch_list=[a, b])
|
|
|
|
|
|
class TestDoubleGrad(unittest.TestCase):
|
|
def test1(self):
|
|
main = base.Program()
|
|
startup = base.Program()
|
|
with base.program_guard(main, startup):
|
|
net = lambda x: x * x
|
|
x = paddle.create_parameter(
|
|
name='x',
|
|
shape=[1],
|
|
dtype='float32',
|
|
default_initializer=paddle.nn.initializer.Constant(3),
|
|
)
|
|
(grad1,) = base.gradients(net(x), x) # 2x = 6
|
|
z = net(x - grad1)
|
|
(grad2,) = base.gradients(z, x) # gradients( (x - 2x)^2) = 2x = 6
|
|
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
exe.run(startup)
|
|
out = exe.run(main, fetch_list=[grad1, grad2])
|
|
self.assertEqual(6, out[0][0])
|
|
self.assertEqual(6, out[1][0])
|
|
|
|
def test2(self):
|
|
main = base.Program()
|
|
startup = base.Program()
|
|
with base.program_guard(main, startup):
|
|
x = paddle.create_parameter(
|
|
name='x',
|
|
shape=[1],
|
|
dtype='float32',
|
|
default_initializer=paddle.nn.initializer.Constant(1),
|
|
)
|
|
y = x * x
|
|
(dx1,) = base.gradients(y, x)
|
|
z = dx1 * dx1 + y * y
|
|
(dx2,) = base.gradients(z, x)
|
|
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
exe.run(startup)
|
|
(out,) = exe.run(main, fetch_list=[dx2])
|
|
self.assertEqual(12, out[0])
|
|
|
|
|
|
class TestGradientWithPrune(unittest.TestCase):
|
|
def test_prune(self):
|
|
with paddle.base.scope_guard(paddle.static.Scope()):
|
|
x = paddle.static.data(name='x', shape=[3], dtype='float32')
|
|
x.stop_gradient = False
|
|
x1, x2, x3 = paddle.split(x, axis=0, num_or_sections=3)
|
|
y = x1 * 2
|
|
x1_grad = base.gradients(y, x)
|
|
|
|
exe = base.Executor(base.CPUPlace())
|
|
main = paddle.static.default_main_program()
|
|
exe.run(paddle.static.default_startup_program())
|
|
out = exe.run(
|
|
main,
|
|
feed={'x': np.ones([3]).astype('float32')},
|
|
fetch_list=[x1_grad],
|
|
)
|
|
np.testing.assert_array_equal(out[0], [2.0, 0.0, 0.0])
|
|
|
|
|
|
class TestDoubleGradient(unittest.TestCase):
|
|
def build_program(self):
|
|
start_prog = paddle.static.Program()
|
|
main_prog = paddle.static.Program()
|
|
|
|
with paddle.static.program_guard(main_prog, start_prog):
|
|
x = paddle.static.data('x', shape=[2, 2])
|
|
x.stop_gradient = False
|
|
y = x * x
|
|
|
|
v = paddle.ones([2, 2])
|
|
v.stop_gradient = False
|
|
|
|
grad_y = paddle.zeros_like(y)
|
|
grad_y.stop_gradient = False
|
|
grad_x = paddle.static.gradients(y, x, grad_y)
|
|
# test with single targets
|
|
jvp = paddle.static.gradients(grad_x, grad_y, v)
|
|
|
|
return start_prog, main_prog, [grad_x, jvp]
|
|
|
|
def test_calc_gradient(self):
|
|
with paddle.base.scope_guard(paddle.static.Scope()):
|
|
start_prog, main_prog, fetch_list = self.build_program()
|
|
exe = paddle.static.Executor()
|
|
exe.run(start_prog)
|
|
ans = exe.run(
|
|
main_prog,
|
|
feed={'x': np.ones([2, 2]).astype(np.float32)},
|
|
fetch_list=fetch_list,
|
|
)
|
|
self.assertEqual(len(ans), 2)
|
|
self.assertListEqual(ans[0].tolist(), [[0.0, 0.0], [0.0, 0.0]])
|
|
self.assertListEqual(ans[1].tolist(), [[2.0, 2.0], [2.0, 2.0]])
|
|
|
|
|
|
class TestDoubleGradient2(unittest.TestCase):
|
|
def build_program(self):
|
|
start_prog = paddle.static.Program()
|
|
main_prog = paddle.static.Program()
|
|
|
|
with paddle.static.program_guard(main_prog, start_prog):
|
|
x = paddle.static.data('x', shape=[2, 2])
|
|
x.stop_gradient = False
|
|
y = x * x
|
|
y2 = y + x
|
|
|
|
v = paddle.ones([2, 2])
|
|
v.stop_gradient = False
|
|
|
|
grad_y = paddle.zeros_like(y)
|
|
grad_y.stop_gradient = False
|
|
grad_x = paddle.static.gradients(y, x, grad_y)
|
|
grad_x2 = paddle.static.gradients(y2, x, grad_y)
|
|
# test with multi targets
|
|
jvp = paddle.static.gradients(
|
|
[grad_x[0], grad_x2[0]], grad_y, [v, v]
|
|
)
|
|
|
|
return start_prog, main_prog, [grad_x, jvp]
|
|
|
|
def test_calc_gradient(self):
|
|
with paddle.base.scope_guard(paddle.static.Scope()):
|
|
start_prog, main_prog, fetch_list = self.build_program()
|
|
exe = paddle.static.Executor()
|
|
exe.run(start_prog)
|
|
ans = exe.run(
|
|
main_prog,
|
|
feed={'x': np.ones([2, 2]).astype(np.float32)},
|
|
fetch_list=fetch_list,
|
|
)
|
|
self.assertEqual(len(ans), 2)
|
|
self.assertListEqual(ans[0].tolist(), [[0.0, 0.0], [0.0, 0.0]])
|
|
self.assertListEqual(ans[1].tolist(), [[5.0, 5.0], [5.0, 5.0]])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|