Unmerged fetch list (#22635)
* update ScopeBufferedSSAGraphExecutor&AsyncSSAGraphExecutor&ThreadedSSAGraphExecutor&FastThreadedSSAGraphExecutor&ParallelSSAGraphExecutor&ParallelExecutor for fetching unmerged results. * add the unit test for fetch_unmerged. * update ut for multi-card and multi-cpu. * add the error message and the user suggestion in FetchOpHandle. test=developrevert-22710-feature/integrated_ps_api
Zhen Wang
6 years ago
committed by
GitHub
parent
f05c213f98
commit
89cfa49156
@ -0,0 +1,121 @@
|
||||
# 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
|
||||
import unittest
|
||||
import random
|
||||
import numpy as np
|
||||
import paddle.fluid as fluid
|
||||
import six
|
||||
import paddle
|
||||
|
||||
os.environ["CPU_NUM"] = "2"
|
||||
|
||||
|
||||
class TestFetchUnmerged(unittest.TestCase):
|
||||
def conv_net(self, img, label):
|
||||
conv_pool_1 = fluid.nets.simple_img_conv_pool(
|
||||
input=img,
|
||||
filter_size=5,
|
||||
num_filters=20,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
pool_type='max',
|
||||
act="relu")
|
||||
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
|
||||
conv_pool_2 = fluid.nets.simple_img_conv_pool(
|
||||
input=conv_pool_1,
|
||||
filter_size=5,
|
||||
num_filters=50,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
pool_type='avg',
|
||||
act="relu")
|
||||
hidden = fluid.layers.fc(input=conv_pool_2, size=100, act='relu')
|
||||
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
|
||||
loss = fluid.layers.cross_entropy(input=prediction, label=label)
|
||||
avg_loss = fluid.layers.mean(loss)
|
||||
return avg_loss, prediction
|
||||
|
||||
def build_program(self, main, startup, is_test):
|
||||
with fluid.unique_name.guard():
|
||||
with fluid.program_guard(main, startup):
|
||||
img = fluid.layers.data(
|
||||
name='image', shape=[1, 28, 28], dtype='float32')
|
||||
label = fluid.layers.data(
|
||||
name='label', shape=[1], dtype='int64')
|
||||
loss, prediction = self.conv_net(img, label)
|
||||
if not is_test:
|
||||
opt = fluid.optimizer.Adam(learning_rate=0.001)
|
||||
opt.minimize(loss)
|
||||
return [img, label], loss, prediction
|
||||
|
||||
def fetch_unmerged(self, use_cuda=True):
|
||||
main_program = fluid.Program()
|
||||
startup_program = fluid.Program()
|
||||
feeds, loss, prediction = self.build_program(main_program,
|
||||
startup_program, False)
|
||||
|
||||
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
|
||||
exe = fluid.Executor(place)
|
||||
exe.run(startup_program)
|
||||
|
||||
build_strategy = fluid.BuildStrategy()
|
||||
binary = fluid.CompiledProgram(main_program).with_data_parallel(
|
||||
loss_name=loss.name, build_strategy=build_strategy)
|
||||
|
||||
iters = 3
|
||||
batch_size = 64
|
||||
train_reader = paddle.batch(
|
||||
paddle.reader.shuffle(
|
||||
paddle.dataset.mnist.train(), buf_size=500),
|
||||
batch_size=batch_size)
|
||||
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
|
||||
|
||||
device_num = fluid.core.get_cuda_device_count() if use_cuda else 2
|
||||
for _ in range(iters):
|
||||
data = next(train_reader())
|
||||
loss_v, prediction_v = exe.run(binary,
|
||||
feed=feeder.feed(data),
|
||||
fetch_list=[loss, prediction],
|
||||
return_merged=False)
|
||||
self.assertEqual(np.array(loss_v).shape, (device_num, 1))
|
||||
self.assertEqual(
|
||||
np.array(prediction_v).shape,
|
||||
(device_num, batch_size / device_num, 10))
|
||||
|
||||
for _ in range(iters):
|
||||
data = next(train_reader())
|
||||
loss_v, prediction_v = exe.run(binary,
|
||||
feed=feeder.feed(data),
|
||||
fetch_list=[loss, prediction],
|
||||
return_merged=True)
|
||||
self.assertEqual(np.array(loss_v).shape, (device_num, ))
|
||||
self.assertEqual(np.array(prediction_v).shape, (batch_size, 10))
|
||||
|
||||
def test_fetch_unmerged(self):
|
||||
if fluid.core.is_compiled_with_cuda():
|
||||
self.fetch_unmerged(use_cuda=True)
|
||||
self.fetch_unmerged(use_cuda=False)
|
||||
|
||||
def test_fetch_unmerged_parallel_graph(self):
|
||||
fluid.core.globals()['FLAGS_enable_parallel_graph'] = True
|
||||
if fluid.core.is_compiled_with_cuda():
|
||||
self.fetch_unmerged(use_cuda=True)
|
||||
self.fetch_unmerged(use_cuda=False)
|
||||
fluid.core.globals()['FLAGS_enable_parallel_graph'] = False
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue