# 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 import warnings import sys __all__ = ['ParallelExecutor', 'ExecutionStrategy'] ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy class ParallelExecutor(object): def __init__(self, use_cuda, loss_name=None, main_program=None, share_vars_from=None, use_default_grad_scale=True, balance_parameter_opt_between_cards=False, exec_strategy=None, **kwargs): """ ParallelExecutor can run program in parallel. Args: use_cuda(bool): Whether to use CUDA or not. loss_name(str, default None): The loss name must set in training. main_program(Program, default None): The program that need to run, if not provided, then default_main_program will be used. share_vars_from(ParallelExecutor, default None): If provied, it will share variables from the specified ParallelExecutor. use_default_grad_scale(bool, default True): If set True, a default scale value equal to `1./device_count` would be multiplied to gradients of each device and scaled gradients would be aggregated. Otherwise, a customized scale value should be fed to the network. balance_parameter_opt_between_cards(bool, default True): Whether updating different gradients on different cards. Currently, it is not recommended. Returns: A ParallelExecutor object. Raises: TypeError: If share_vars_from is provided, but not ParallelExecutor object. Examples: .. code-block:: python train_exe = fluid.ParallelExecutor( use_cuda=True, loss_name=loss.name) test_exe = fluid.ParallelExecutor( use_cuda=True, main_program=test_program, share_vars_from=train_exe) train_loss, = train_exe.run([loss.name], feed=feed_dict) test_loss, = test_exe.run([loss.name], feed=feed_dict) """ if len(kwargs) != 0: err_msg = "" for key in kwargs: if key in dir(ExecutionStrategy): err_msg += \ "Setting {0} by constructor is deprecated. Use " \ "strategy=ExecutionStrategy(); strategy.{0}=xxx; " \ "pe=ParallelExecutor(exec_strategy=strategy) " \ "instead.\n " raise ValueError(err_msg) 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()) p.set_place(self._act_places[-1]) self._places.append(p) assert self._places, "no place for execution" if exec_strategy is None: exec_strategy = ExecutionStrategy() if use_cuda: exec_strategy.use_event = True else: exec_strategy.use_event = False if exec_strategy.num_threads == 0: if use_cuda: # Experiments on se-resnext shows that too many threads hurt # performance. Worth tunning for other models in the future. exec_strategy.num_threads = len(self._places) * 2 else: exec_strategy.num_threads = min( len(self._places) * 2, multiprocessing.cpu_count()) main = main_program main = main if main else framework.default_main_program() scope = executor.global_scope() if share_vars_from and not isinstance(share_vars_from, ParallelExecutor): raise TypeError("share_vars_from must be ParallelExecutor.") local_scopes = share_vars_from.executor.local_scopes( ) if share_vars_from else [] self.persistable_vars = [ v.name for v in filter( lambda var: var.persistable and var.type != core.VarDesc.VarType.RAW, main.list_vars()) ] self.executor = core.ParallelExecutor( self._places, set([ p.name for p in main.global_block().iter_parameters() if not p.stop_gradient ]), set(self.persistable_vars), main.desc, loss_name if loss_name else '', scope, local_scopes, use_default_grad_scale, balance_parameter_opt_between_cards, exec_strategy) self.scope = scope def run(self, fetch_list, feed=None, feed_dict=None): """ Run a parallel executor with fetch_list. The feed parameter can be a dict or a list. If feed is a dict, the feed data will be split into multiple devices. If feed is a list, we assume the data has been splitted into multiple devices, the each element in the list will be copied to each device directly. For example, if the feed is a dict: >>> exe = ParallelExecutor() >>> # the image will be splitted into devices. If there is two devices >>> # each device will process an image with shape (24, 1, 28, 28) >>> exe.run(feed={'image': numpy.random.random(size=(48, 1, 28, 28))}) For example, if the feed is a list: >>> exe = ParallelExecutor() >>> # each device will process each element in the list. >>> # the 1st device will process an image with shape (48, 1, 28, 28) >>> # the 2nd device will process an image with shape (32, 1, 28, 28) >>> # >>> # you can use exe.device_count to get the device number. >>> exe.run(feed=[{"image": numpy.random.random(size=(48, 1, 28, 28))}, >>> {"image": numpy.random.random(size=(32, 1, 28, 28))}, >>> ]) Args: fetch_list(list): The fetched variable names feed(list|dict|None): The feed variables. If the feed is a dict, tensors in that dict will be splitted into each devices. If the feed is a list, each element of the list will be copied to each device. feed_dict: Alias for feed parameter, for backward compatibility. This parameter is deprecated. Returns: fetched result list. """ if feed is None and feed_dict is not None: feed = feed_dict print >> sys.stderr, "`feed_dict` is deprecated. Please use `feed=`" if isinstance(feed, dict): feed_tensor_dict = dict() for feed_name in feed: feed_tensor = feed[feed_name] if not isinstance(feed_tensor, core.LoDTensor): feed_tensor = core.LoDTensor() # always set to CPU place, since the tensor need to be splitted # it is fast in CPU feed_tensor.set(feed[feed_name], core.CPUPlace()) feed_tensor_dict[feed_name] = feed_tensor self.executor.feed_and_split_tensor_into_local_scopes( feed_tensor_dict) elif isinstance(feed, list) or isinstance(feed, tuple): if len(feed) != len(self._act_places): raise ValueError( "Feed a list of tensor, the list should be the same size as places" ) res = list() for i, each in enumerate(feed): if not isinstance(each, dict): raise TypeError( "Each element of feed list should be a dict") res_dict = dict() for feed_name in each: tensor = each[feed_name] if not isinstance(tensor, core.LoDTensor): tmp = core.LoDTensor() tmp.set(tensor, self._act_places[i]) tensor = tmp res_dict[feed_name] = tensor res.append(res_dict) self.executor.feed_tensors_into_local_scopes(res) 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))] def bcast_params(self): self.executor.bcast_params(set(self.persistable_vars)) @property def device_count(self): return len(self._act_places)