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Paddle/python/paddle/fluid/parallel_executor.py

94 lines
3.4 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):
self._places = []
self._act_places = []
if use_cuda:
for i in xrange(core.get_cuda_device_count()):
p = core.Place()
self._act_places.append(core.CUDAPlace(i))
p.set_place(self._act_places[-1])
self._places.append(p)
else:
for i in xrange(multiprocessing.cpu_count()):
p = core.Place()
self._act_places.append(core.CPUPlace(i))
p.set_place(self._act_places[-1])
self._places.append(p)
assert self._places, "no place for execution"
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(self._places)
else:
min(len(self._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
self._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, feed_dict={}):
"""
:param fetch_list: A list of variable names that will be fetched.
:param feed_dict: A dict mapping for feed variable name to LoDTensor
or numpy array.
:return: fetched value list.
"""
if not isinstance(feed_dict, dict):
raise TypeError("feed_dict should be a dict")
feed_tensor_dict = {}
for i, feed_name in enumerate(feed_dict):
feed_tensor = feed_dict[feed_name]
if not isinstance(feed_tensor, core.LoDTensor):
feed_tensor = core.LoDTensor()
feed_tensor.set(feed_dict[feed_name], self._act_places[0])
feed_tensor_dict[feed_name] = feed_tensor
fetch_var_name = '@FETCHED_VAR_NAME@'
self.executor.run(fetch_list, fetch_var_name, feed_tensor_dict)
arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
return [arr[i] for i in range(len(arr))]