wehub-resource-sync
150 lines
5.0 KiB
Python
150 lines
5.0 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 os
|
|
|
|
os.environ['CPU_NUM'] = '2'
|
|
|
|
import unittest
|
|
|
|
import numpy
|
|
from op_test import (
|
|
get_device_class,
|
|
get_places,
|
|
is_custom_device,
|
|
)
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.base import core, in_pir_mode
|
|
from paddle.base.executor import Executor
|
|
|
|
paddle.enable_static()
|
|
base.core._set_eager_deletion_mode(0.0, 1.0, True)
|
|
|
|
|
|
class TestEagerDeletionWhileOpBase(unittest.TestCase):
|
|
def test_main(self):
|
|
for p in get_places():
|
|
with (
|
|
base.program_guard(base.Program(), base.Program()),
|
|
base.scope_guard(base.Scope()),
|
|
):
|
|
self.run_main(p)
|
|
|
|
def run_main(self, place):
|
|
self.place = place
|
|
|
|
if not (
|
|
core.is_compiled_with_cuda() or is_custom_device()
|
|
) and isinstance(self.place, get_device_class()):
|
|
return
|
|
|
|
device_cnt = 1
|
|
|
|
d0 = paddle.static.data("d0", shape=[-1, 10], dtype='float32')
|
|
d1 = paddle.static.data("d1", shape=[-1, 10], dtype='float32')
|
|
d2 = paddle.static.data("d2", shape=[-1, 10], dtype='float32')
|
|
|
|
i = paddle.zeros(shape=[1], dtype='int64')
|
|
i.stop_gradient = True
|
|
|
|
init = paddle.zeros(shape=[10], dtype='float32')
|
|
mem_array = paddle.tensor.array_write(x=init, i=i)
|
|
data_array = paddle.tensor.array_write(x=d0, i=i)
|
|
|
|
i = paddle.increment(i)
|
|
paddle.tensor.array_write(d1, i, array=data_array)
|
|
|
|
i = paddle.increment(i)
|
|
paddle.tensor.array_write(d2, i, array=data_array)
|
|
|
|
i = paddle.zeros(shape=[1], dtype='int64')
|
|
i.stop_gradient = True
|
|
|
|
array_len = paddle.tensor.fill_constant(
|
|
shape=[1], dtype='int64', value=1
|
|
)
|
|
array_len.stop_gradient = True
|
|
cond = paddle.less_than(x=i, y=array_len)
|
|
|
|
j = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=1)
|
|
j.stop_gradient = True
|
|
|
|
array_len2 = paddle.tensor.fill_constant(
|
|
shape=[1], dtype='int64', value=3
|
|
)
|
|
array_len2.stop_gradient = True
|
|
cond2 = paddle.less_than(x=j, y=array_len2)
|
|
|
|
while_op = paddle.static.nn.control_flow.While(cond=cond)
|
|
while_op2 = paddle.static.nn.control_flow.While(cond=cond2)
|
|
with while_op.block():
|
|
d = paddle.tensor.array_read(array=data_array, i=i)
|
|
prev = paddle.tensor.array_read(array=mem_array, i=i)
|
|
d = paddle.reshape(d, shape=[10])
|
|
prev = paddle.reshape(prev, shape=[10])
|
|
result = paddle.add_n([d, prev])
|
|
|
|
i = paddle.increment(x=i)
|
|
paddle.tensor.array_write(result, i=i, array=mem_array)
|
|
paddle.assign(paddle.less_than(x=i, y=array_len), cond)
|
|
with while_op2.block():
|
|
d2 = paddle.tensor.array_read(array=data_array, i=j)
|
|
prev2 = paddle.tensor.array_read(array=mem_array, i=j)
|
|
d2 = paddle.reshape(d2, shape=[10])
|
|
prev2 = paddle.reshape(prev2, shape=[10])
|
|
result2 = paddle.add_n([d2, prev2])
|
|
|
|
j = paddle.increment(x=j)
|
|
paddle.tensor.array_write(result2, i=j, array=mem_array)
|
|
paddle.assign(paddle.less_than(x=j, y=array_len2), cond2)
|
|
|
|
sum_result = paddle.tensor.array_read(array=mem_array, i=j)
|
|
sum_result.persistable = True
|
|
tmp = paddle.unsqueeze(sum_result, axis=[0])
|
|
tmp = paddle.expand(tmp, [10, -1])
|
|
loss = paddle.mean(sum_result)
|
|
|
|
optim = paddle.optimizer.Adam(learning_rate=1e-3)
|
|
optim.minimize(loss)
|
|
|
|
if not in_pir_mode():
|
|
gc_vars = core._get_eager_deletion_vars(
|
|
base.default_main_program().desc, [loss.name]
|
|
)
|
|
self.assertEqual(len(gc_vars), 3)
|
|
|
|
exe = Executor(self.place)
|
|
exe.run(paddle.static.default_startup_program())
|
|
|
|
prog = paddle.static.default_main_program()
|
|
|
|
for _ in range(5):
|
|
d = []
|
|
for i in range(3):
|
|
tmp = numpy.random.random(size=[10]).astype('float32')
|
|
d.append(numpy.array([tmp] * device_cnt))
|
|
|
|
outs = exe.run(
|
|
program=prog,
|
|
feed={'d0': d[0], 'd1': d[1], 'd2': d[2]},
|
|
fetch_list=[sum_result],
|
|
)
|
|
self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|