wehub-resource-sync
116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
# 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 parameterized as param
|
|
|
|
import paddle
|
|
from paddle.base import core, framework
|
|
|
|
|
|
@param.parameterized_class(
|
|
('name', 'primals', 'stop_gradients', 'cotangents', 'dtype'),
|
|
(
|
|
(
|
|
'test_normal_case',
|
|
(np.random.rand(2, 3, 4), np.random.rand(2, 3, 4)),
|
|
(False, False),
|
|
(np.random.rand(2, 3, 4),),
|
|
np.float32,
|
|
),
|
|
(
|
|
'test_broadcast_diff_rank',
|
|
(np.random.rand(2, 3, 1, 4), np.random.rand(3, 3, 4)),
|
|
(False, False),
|
|
(np.random.rand(2, 3, 3, 4),),
|
|
np.float32,
|
|
),
|
|
(
|
|
'test_broadcast_same_rank',
|
|
(np.random.rand(2, 3, 1, 4), np.random.rand(2, 1, 3, 4)),
|
|
(False, False),
|
|
(np.random.rand(2, 3, 3, 4),),
|
|
np.float32,
|
|
),
|
|
(
|
|
'test_stop_gradient',
|
|
(np.random.rand(2, 3, 1, 4), np.random.rand(2, 1, 3, 4)),
|
|
(False, True),
|
|
(np.random.rand(2, 3, 3, 4),),
|
|
np.float32,
|
|
),
|
|
),
|
|
)
|
|
class TestMultiplyGradComp(unittest.TestCase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.primals = tuple(primal.astype(cls.dtype) for primal in cls.primals)
|
|
cls.cotangents = tuple(co.astype(cls.dtype) for co in cls.cotangents)
|
|
|
|
def setUp(self):
|
|
paddle.enable_static()
|
|
|
|
def tearDown(self):
|
|
paddle.disable_static()
|
|
|
|
def as_tuple(self, x):
|
|
return (x,) if isinstance(x, framework.Variable) else x
|
|
|
|
def net(self):
|
|
primals, cotangents = self.primals, self.cotangents
|
|
mp, sp = paddle.static.Program(), paddle.static.Program()
|
|
with paddle.static.program_guard(mp, sp):
|
|
primals = tuple(
|
|
paddle.static.data(f'primal{i}', primal.shape, primal.dtype)
|
|
for i, primal in enumerate(primals)
|
|
)
|
|
for primal, flag in zip(primals, self.stop_gradients):
|
|
primal.stop_gradient = flag
|
|
cotangents = tuple(
|
|
paddle.static.data(f'cotangent{i}', co.shape, co.dtype)
|
|
for i, co in enumerate(cotangents)
|
|
)
|
|
out = self.as_tuple(paddle.tanh(paddle.multiply(*primals)))
|
|
grads = paddle.static.gradients(out, primals)
|
|
exe = paddle.static.Executor()
|
|
exe.run(sp)
|
|
return exe.run(
|
|
program=mp,
|
|
feed={f'primal{i}': primal for i, primal in enumerate(self.primals)}
|
|
| {f'cotangent{i}': co for i, co in enumerate(self.cotangents)},
|
|
fetch_list=[g for g in grads if g is not None],
|
|
)
|
|
|
|
def test_comp(self):
|
|
core._set_prim_backward_enabled(True)
|
|
actual = self.net()
|
|
|
|
core._set_prim_backward_enabled(False)
|
|
desired = self.net()
|
|
|
|
self.assertEqual(len(actual), len(desired))
|
|
for i, j in zip(actual, desired):
|
|
np.testing.assert_allclose(
|
|
i,
|
|
j,
|
|
rtol=1e-6,
|
|
atol=0,
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|