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Paddle/python/paddle/fluid/parallel_executor.py

73 lines
2.4 KiB

# 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 core
import multiprocessing
import framework
import executor
__all__ = ['ParallelExecutor']
class ParallelExecutor(object):
def __init__(self,
loss_name,
use_cuda,
num_threads=None,
allow_op_delay=False):
places = []
if use_cuda:
for i in xrange(core.get_cuda_device_count()):
p = core.Place()
p.set_place(core.CUDAPlace(i))
places.append(p)
else:
for i in xrange(multiprocessing.cpu_count()):
p = core.Place()
p.set_place(core.CPUPlace())
places.append(p)
if num_threads is None:
if use_cuda:
# Experiments on se-resnext shows that too many threads hurt
# performance. Worth tunning for other models in the future.
num_threads = len(places)
else:
min(len(places) * 2, multiprocessing.cpu_count())
startup = framework.default_startup_program()
main = framework.default_main_program()
scope = executor.global_scope()
self.executor = core.ParallelExecutor(
num_threads,
True if use_cuda else False, # use_event
places,
set([
p.name for p in main.global_block().iter_parameters()
if not p.stop_gradient
]),
startup.desc,
main.desc,
loss_name,
scope,
allow_op_delay)
self.scope = scope
def run(self, fetch_list):
fetch_var_name = '@FETCHED_VAR_NAME@'
self.executor.run(fetch_list, fetch_var_name)
arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
return [arr[i] for i in range(len(arr))]