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94 lines
3.4 KiB
94 lines
3.4 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import core
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import multiprocessing
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import framework
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import executor
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__all__ = ['ParallelExecutor']
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class ParallelExecutor(object):
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def __init__(self,
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loss_name,
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use_cuda,
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num_threads=None,
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allow_op_delay=False):
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self._places = []
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self._act_places = []
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if use_cuda:
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for i in xrange(core.get_cuda_device_count()):
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p = core.Place()
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self._act_places.append(core.CUDAPlace(i))
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p.set_place(self._act_places[-1])
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self._places.append(p)
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else:
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for i in xrange(multiprocessing.cpu_count()):
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p = core.Place()
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self._act_places.append(core.CPUPlace(i))
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p.set_place(self._act_places[-1])
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self._places.append(p)
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assert self._places, "no place for execution"
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if num_threads is None:
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if use_cuda:
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# Experiments on se-resnext shows that too many threads hurt
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# performance. Worth tunning for other models in the future.
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num_threads = len(self._places)
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else:
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min(len(self._places) * 2, multiprocessing.cpu_count())
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startup = framework.default_startup_program()
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main = framework.default_main_program()
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scope = executor.global_scope()
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self.executor = core.ParallelExecutor(
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num_threads,
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True if use_cuda else False, # use_event
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self._places,
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set([
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p.name for p in main.global_block().iter_parameters()
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if not p.stop_gradient
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]),
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startup.desc,
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main.desc,
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loss_name,
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scope,
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allow_op_delay)
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self.scope = scope
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def run(self, fetch_list, feed_dict={}):
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"""
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:param fetch_list: A list of variable names that will be fetched.
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:param feed_dict: A dict mapping for feed variable name to LoDTensor
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or numpy array.
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:return: fetched value list.
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"""
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if not isinstance(feed_dict, dict):
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raise TypeError("feed_dict should be a dict")
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feed_tensor_dict = {}
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for i, feed_name in enumerate(feed_dict):
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feed_tensor = feed_dict[feed_name]
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if not isinstance(feed_tensor, core.LoDTensor):
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feed_tensor = core.LoDTensor()
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feed_tensor.set(feed_dict[feed_name], self._act_places[0])
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feed_tensor_dict[feed_name] = feed_tensor
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fetch_var_name = '@FETCHED_VAR_NAME@'
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self.executor.run(fetch_list, fetch_var_name, feed_tensor_dict)
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arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
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return [arr[i] for i in range(len(arr))]
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