未验证 提交 a9dbdab5 编写于 作者: Y Yu Yang 提交者: GitHub

Merge pull request #7396 from reyoung/feature/parallel_for_unittest

Feature/parallel for unittest
......@@ -11,6 +11,7 @@ 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. */
#include <string.h> // for strdup
#include <algorithm>
#include <string>
......@@ -60,7 +61,9 @@ void InitDevices() {
}
void InitGLOG(const std::string &prog_name) {
google::InitGoogleLogging(prog_name.c_str());
// glog will not hold the ARGV[0] inside.
// Use strdup to alloc a new string.
google::InitGoogleLogging(strdup(prog_name.c_str()));
google::InstallFailureSignalHandler();
}
......
import unittest
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid as fluid
from paddle.v2.fluid.framework import Program
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.backward import append_backward
import numpy as np
import paddle.v2.fluid.core as core
class ParallelOpTest(unittest.TestCase):
def setUp(self):
x = layers.data(
shape=[-1, 30, 40],
dtype='float32',
name='x',
append_batch_size=False,
stop_gradient=False)
places = layers.get_places(device_count=4)
pd = layers.ParallelDo(places=places)
with pd.do():
data = pd.read_input(x)
hidden = layers.fc(input=data, size=7)
pd.write_output(hidden)
data = pd()
loss = layers.mean(x=data)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(loss)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
exe.run(fluid.default_main_program(),
feed={
x.name: np.random.uniform(0.1, 0.6,
(20, 30, 40)).astype("float32")
})
def test_forward(self):
pass
import numpy
class BaseParallelForTest(unittest.TestCase):
def run_test(self, callback, feed, fetch):
"""
Run the unittest for parallel.for
Args:
callback(callable): A callable function returns a generator. There
are two yields in the generator function. The first yield
returns the data layers, and the second yield returns the loss.
The modified data variables will be sent back during the first
yield.
feed(dict): The executor feeding dictionary.
fetch(list|basestr): The fetch name lists.
Returns:
None
Raises:
AssertionError when the computation of cpu, parallel.for in cpu,
gpu, parallel.for in gpu are different.
"""
cpu = fluid.CPUPlace()
result_cpu = self._run_test_impl_(
callback=callback,
feed=feed,
fetch=fetch,
place=cpu,
use_parallel=False)
result_cpu_parallel = self._run_test_impl_(
callback=callback,
feed=feed,
fetch=fetch,
place=cpu,
use_parallel=True)
if fluid.core.is_compile_gpu():
gpu = fluid.CUDAPlace(0)
result_gpu = self._run_test_impl_(
callback=callback,
feed=feed,
fetch=fetch,
place=gpu,
use_parallel=False)
result_gpu_parallel = self._run_test_impl_(
callback=callback,
feed=feed,
fetch=fetch,
place=gpu,
use_parallel=True)
self._assert_same_(fetch, result_cpu, result_cpu_parallel,
result_gpu, result_gpu_parallel)
else:
self._assert_same_(fetch, result_cpu, result_cpu_parallel)
def _run_test_impl_(self, callback, feed, fetch, place, use_parallel=False):
"""
Run a single test, returns the fetch values
Args:
place(Place): the computation place.
use_parallel(bool): Whether use parallel.for or not.
Returns:
Fetched numpy arrays.
"""
if isinstance(fetch, basestring):
fetch = [fetch]
main = fluid.Program()
startup = fluid.Program()
# Fix seed
main.random_seed = 10
startup.random_seed = 10
with fluid.program_guard(main, startup):
generator = callback()
# Automatically insert parallel do if use_parallel = True
if use_parallel:
places = fluid.layers.get_places()
pd = fluid.layers.ParallelDo(places)
data = next(generator)
if isinstance(data, fluid.Variable):
data = [data]
with pd.do():
ins = map(pd.read_input, data)
if len(ins) == 1:
ins = ins[0]
loss = generator.send(ins) # patch input
pd.write_output(loss)
loss = pd()
else:
data = next(generator)
loss = generator.send(data)
self.assertIsNotNone(loss)
avg_loss = fluid.layers.mean(x=loss)
fluid.backward.append_backward(loss=avg_loss)
exe = fluid.Executor(place)
exe.run(startup)
return exe.run(main, feed=feed, fetch_list=fetch)
def _assert_same_(self, fetch, *args):
"""
Assert the return values of `run_test` are same.
Args:
fetch: Fetch list. Used for print error message
*args: The fetch result lists of each situations.
Returns:
None
Raises:
AssertionError
"""
def _impl_(a, b, fetch_id, item_id):
item_str = ['CPU', 'ParallelCPU', 'GPU', 'ParallelGPU']
flag = numpy.allclose(a, b, rtol=0.1)
self.assertTrue(flag, "The {0} are different in {1}".format(
fetch[fetch_id], item_str[item_id]))
for i, items in enumerate(zip(*args)):
self.assertGreater(len(items), 0)
for j in range(1, len(items)):
_impl_(items[0], items[j], fetch_id=i, item_id=j)
class ParallelOpTest(BaseParallelForTest):
def test_simple_fc(self):
def __network__():
x = fluid.layers.data(shape=[784], dtype='float32', name='img')
# FIXME: This is a bug of parallel.do
x.stop_gradient = False
x = yield x
hidden = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
loss = fluid.layers.mean(x=hidden)
yield loss
self.run_test(
callback=__network__,
feed={
'img':
numpy.random.random(size=(128 * 3, 784)).astype('float32')
},
fetch='fc1.w@GRAD')
if __name__ == '__main__':
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册