提交 9c8383cf 编写于 作者: Y yuyang18

Parallel Executor revised feeder

上级 28de0ea4
......@@ -16,6 +16,7 @@ from __future__ import print_function
import core
import numpy
import six.moves as six
import multiprocessing
from framework import Variable, default_main_program
......@@ -116,3 +117,60 @@ class DataFeeder(object):
for each_name, each_converter in six.zip(self.feed_names, converter):
ret_dict[each_name] = each_converter.done()
return ret_dict
def feed_parallel(self, iterable, num_places=None):
if isinstance(self.place, core.CUDAPlace):
places = [
core.CUDAPlace(i)
for i in six.xrange(self._get_number_of_places_(num_places))
]
else:
places = [
core.CPUPlace()
for _ in six.xrange(self._get_number_of_places_(num_places))
]
if len(iterable) != len(places):
raise ValueError("feed_parallel takes multiple mini-batches. Each "
"mini-batch will be feed on each device. The "
"number of devices and number of mini-batches "
"must be same.")
place = self.place
for p, batch in six.zip(places, iterable):
self.place = p
yield self.feed(batch)
self.place = place
def _get_number_of_places_(self, num_places):
if num_places is not None:
return int(num_places)
elif isinstance(self.place, core.CUDAPlace):
return core.get_cuda_device_count()
else:
return multiprocessing.cpu_count()
def decorate_reader(self,
reader,
multi_devices,
num_places=None,
drop_last=True):
def __reader_creator__():
if not multi_devices:
for item in reader():
yield self.feed(item)
else:
num = self._get_number_of_places_(num_places)
item = []
for batch in reader():
item.append(batch)
if len(item) == num:
yield list(self.feed_parallel(item, num))
item = []
if not drop_last and len(item) != 0:
raise ValueError(
"The data batch which cannot fit for devices will be "
"dropped is not implementation. Other strategies are "
"not implemented")
return __reader_creator__
......@@ -796,5 +796,42 @@ class TestFetchOp(unittest.TestCase):
self.parallel_exe(train_inputs, seed=1)
class TestFeedParallel(unittest.TestCase):
def test_main(self):
main = fluid.Program()
startup = fluid.Program()
startup.random_seed = 1
with fluid.scope_guard(fluid.core.Scope()):
with fluid.program_guard(main, startup):
data = fluid.layers.data(
name='image', shape=[3, 224, 224], dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
out = Lenet(data, class_dim=102)
loss = fluid.layers.cross_entropy(input=out, label=label)
loss = fluid.layers.mean(loss)
opt = fluid.optimizer.Momentum(
learning_rate=0.1,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
opt.minimize(loss)
place = fluid.CUDAPlace(0)
feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
reader = feeder.decorate_reader(
paddle.batch(
flowers.train(), batch_size=16), multi_devices=True)
exe = fluid.Executor(place)
exe.run(startup)
pe = fluid.ParallelExecutor(
use_cuda=True, loss_name=loss.name, main_program=main)
for batch_id, data in enumerate(reader()):
loss_np = np.array(pe.run(feed=data, fetch_list=[loss.name])[0])
print batch_id, loss_np
if batch_id == 2:
break
if __name__ == '__main__':
unittest.main()
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