Source code for paddle.fluid.parallel_executor

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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from __future__ import print_function
from . import core
from . import framework
from . import executor
from . import compiler
import sys

__all__ = ['ParallelExecutor']

ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy


[docs]class ParallelExecutor(object): """ ParallelExecutor is designed for data parallelism, which focuses on distributing the data across different nodes and every node operates on the data in parallel. If you use ParallelExecutor to run the current program on GPU, the node means GPU device, and ParallelExecutor will get the available GPU device automatically on the current machine. If you use ParallelExecutor to run the current program on CPU, the node means the CPU device, and you can specify the CPU device number by adding 'CPU_NUM' environment variable, for example 'CPU_NUM=4', if the environment variable is not found, ParallelExecutor will call `multiprocessing.cpu_count` to get the number of CPUs in the system. Examples: .. code-block:: python import paddle.fluid as fluid import numpy import os use_cuda = True place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() # NOTE: If you use CPU to run the program, you need # to specify the CPU_NUM, otherwise, fluid will use # all the number of the logic core as the CPU_NUM, # in that case, the batch size of the input should be # greater than CPU_NUM, if not, the process will be # failed by an exception. if not use_cuda: os.environ['CPU_NUM'] = str(2) exe = fluid.Executor(place) train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): data = fluid.layers.data(name='X', shape=[1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) test_program = fluid.default_main_program().clone(for_test=True) fluid.optimizer.SGD(learning_rate=0.01).minimize(loss) startup_program.random_seed=1 exe.run(startup_program) train_exe = fluid.ParallelExecutor(use_cuda=use_cuda, main_program=train_program, loss_name=loss.name) test_exe = fluid.ParallelExecutor(use_cuda=use_cuda, main_program=test_program, share_vars_from=train_exe) x = numpy.random.random(size=(10, 1)).astype('float32') loss_data, = train_exe.run(feed={"X": x}, fetch_list=[loss.name]) loss_data, = test_exe.run(feed={"X": x}, fetch_list=[loss.name]) Args: use_cuda (bool): Whether to use CUDA or not. loss_name (str): The loss name must set in training. Default None. main_program (Program): The program that need to run, if not provided, then default_main_program will be used. Default None. share_vars_from(ParallelExecutor): If provide, it will share variables from the specified ParallelExecutor. Default None. exec_strategy(ExecutionStrategy): exec_strategy is used to control how to run the program in ParallelExecutor, for example how many threads are used to execute the program, how many iterations to clean up the temp variables which is generated during execution. For more information, please refer to fluid.ExecutionStrategy. Default None. build_strategy(BuildStrategy): build_strategy is used to control how to build the SSA Graph in ParallelExecutor by setting the property, for example reduce_strategy, gradient_scale_strategy. For more information, please refer to fluid.BuildStrategy. Default None. num_trainers(int): If greater than 1, NCCL will be initialized with multiple rank of nodes, each node should have same number of GPUs. Distributed training will be enabled then. Default 1. trainer_id(int): Must use together with num_trainers. trainer_id is the "rank" of current node starts from 0. Default 0. scope(Scope): scope to run with, default use fluid.global_scope(). Returns: ParallelExecutor: The initialized ParallelExecutor object. Raises: TypeError: If share_vars_from is provided, but not ParallelExecutor object. """ def __init__(self, use_cuda, loss_name=None, main_program=None, share_vars_from=None, exec_strategy=None, build_strategy=None, num_trainers=1, trainer_id=0, scope=None): if build_strategy is None: build_strategy = BuildStrategy() # TODO(paddle-dev): trainer_id and num_trainers should be removed from parameter list. if num_trainers != 1 and build_strategy.num_trainers != num_trainers: sys.stderr.write( 'The value of build_strategy.num_trainers[%d] is overwritten ' 'by the passed num_trainers[%d].\n' % (build_strategy.num_trainers, num_trainers)) build_strategy.num_trainers = num_trainers if trainer_id != 0 and build_strategy.trainer_id != trainer_id: sys.stderr.write( 'The value of build_strategy.trainer_id[%d] is overwritten ' 'by the passed trainer_id[%d].\n' % (build_strategy.trainer_id, trainer_id)) build_strategy.trainer_id = trainer_id self._places = framework.cuda_places( ) if use_cuda else framework.cpu_places() self._scope = scope if scope is not None else executor.global_scope() if main_program is not None and main_program._enable_dgc: assert build_strategy.num_trainers > 1, "dgc is not useful when num_trainers <= 1" assert build_strategy.reduce_strategy == BuildStrategy.ReduceStrategy.AllReduce, "dgc \ only used for allreduce" assert build_strategy.num_trainers * len( self._places) > 1, "dgc is not useful for single card training" assert use_cuda, "dgc only used under cuda" main_program = main_program if main_program is not None \ else framework.default_main_program() self._compiled_program = compiler.CompiledProgram(main_program) if share_vars_from: assert isinstance( share_vars_from, ParallelExecutor ), "The share_vars_from should be ParallelExecutor." self._compiled_program.with_data_parallel( loss_name=loss_name, build_strategy=build_strategy, exec_strategy=exec_strategy, share_vars_from=share_vars_from._compiled_program if share_vars_from else None) self._place = core.CUDAPlace(0) if use_cuda else core.CPUPlace() self._exe = executor.Executor(self._place) self._compiled_program._compile(place=self._place, scope=self._scope)
[docs] def run(self, fetch_list, feed=None, feed_dict=None, return_numpy=True): """ 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. Examples: .. code-block:: python import paddle.fluid as fluid import numpy import os use_cuda = True place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() # NOTE: If you use CPU to run the program, you need # to specify the CPU_NUM, otherwise, fluid will use # all the number of the logic core as the CPU_NUM, # in that case, the batch size of the input should be # greater than CPU_NUM, if not, the process will be # failed by an exception. if not use_cuda: os.environ['CPU_NUM'] = str(2) exe = fluid.Executor(place) train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): data = fluid.layers.data(name='X', shape=[1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) fluid.optimizer.SGD(learning_rate=0.01).minimize(loss) startup_program.random_seed=1 exe.run(startup_program) train_exe = fluid.ParallelExecutor(use_cuda=use_cuda, main_program=train_program, loss_name=loss.name) # If the feed is a dict: # the image will be splitted into devices. If there is two devices # each device will process an image with shape (5, 1) x = numpy.random.random(size=(10, 1)).astype('float32') loss_data, = train_exe.run(feed={"X": x}, fetch_list=[loss.name]) # If the feed is a list: # each device will process each element in the list. # the 1st device will process an image with shape (10, 1) # the 2nd device will process an image with shape (9, 1) # # you can use exe.device_count to get the device number. x2 = numpy.random.random(size=(9, 1)).astype('float32') loss_data, = train_exe.run(feed=[{"X": x}, {"X": x2}], fetch_list=[loss.name]) 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. Default None. feed_dict: Alias for feed parameter, for backward compatibility. This parameter has been deprecated. Default None. return_numpy(bool): Whether converts the fetched tensor to numpy. Default: True. Returns: List: The fetched result list. Raises: ValueError: If the feed is a list, but its length is not equal the length of active places, or its element's is not dict. NOTES: 1. If the feed's type is dict, the number of data that feeds to ParallelExecutor must be bigger than active places. Otherwise, it will throw exception from C++ side. Special attention should be paid to check whether the last batch of the dataset is bigger than active places. 2. If active places are more than one, the fetch results for each variable is a list, and each element of this list is the variable of respective active place. Examples: .. code-block:: python pe = fluid.ParallelExecutor(use_cuda=use_cuda, loss_name=avg_cost.name, main_program=fluid.default_main_program()) loss = pe.run(feed=feeder.feed(cur_batch), fetch_list=[avg_cost.name])) """ return self._exe.run(program=self._compiled_program, scope=self._scope, feed=feed, fetch_list=fetch_list, return_numpy=return_numpy)
@property def device_count(self): return len(self._places)
[docs] def drop_local_exe_scopes(self): """ Drop the local execution scope immediately. During the execution of the Program, the generate intermediate results are placed in local execution scope, in some model the creation and deletion of those intermediate results are time-consuming. To resolve that problem, ParallelExecutor provides an option in ExecutionStrategy, i.g. num_iteration_per_drop_scope, this option indicates how many iterations to run before dropping the local execution scope. But in some situation, each iteration generates different intermediate results, it will lead to the result that the memory which is needed by local execution scope gradually increase. And if you want to run another program at this time, there may be insufficient storage, At this point you should drop the local execution scope of other Programs. Examples: .. code-block:: python import paddle.fluid as fluid import numpy import os use_cuda = True # NOTE: If you use CPU to run the program, you need # to specify the CPU_NUM, otherwise, fluid will use # all the number of the logic core as the CPU_NUM, # in that case, the batch size of the input should be # greater than CPU_NUM, if not, the process will be # failed by an exception. if not use_cuda: os.environ['CPU_NUM'] = str(2) train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): data = fluid.layers.data(name='X', shape=[1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_program) parallel_exe = fluid.ParallelExecutor(use_cuda=use_cuda, main_program=train_program, loss_name=loss.name) x = numpy.random.random(size=(10, 1)).astype('float32') loss_data, = parallel_exe.run(feed={"X": x}, fetch_list=[loss.name]) parallel_exe.drop_local_exe_scopes() """ assert isinstance( self._compiled_program._executor, core.ParallelExecutor), "The Executor should be ParallelExecutor." self._compiled_program._executor.drop_local_exe_scopes()
# This API is used to check whether DropLocalExeScopes can work. def _need_create_local_exe_scopes(self): assert isinstance( self._compiled_program._executor, core.ParallelExecutor), "The Executor should be ParallelExecutor." return self._compiled_program._executor._need_create_local_exe_scopes()