Source code for paddle.fluid.parallel_executor

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#     http://www.apache.org/licenses/LICENSE-2.0
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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. 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. 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) """ 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): sys.stderr.write( 'ParallelExecutor is deprecated. ' 'Please use CompiledProgram and Executor. CompiledProgram ' 'is a central place for optimization and Executor is the ' 'unified executor. Example can be found in compiler.py.\n') if build_strategy is None: build_strategy = BuildStrategy() build_strategy.num_trainers = num_trainers 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 num_trainers > 1, "dgc is not useful for single trainer training." assert build_strategy.reduce_strategy == BuildStrategy.ReduceStrategy.AllReduce assert num_trainers * len( self._places) > 1, "dgc is not useful for single card training." assert use_cuda, "dgc only used when cuda is used." 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) # FIXME(gongwb): I will move dgc from dist mode to allreduce mode in next pr. if main_program._enable_dgc: self._compiled_program._build_strategy.is_distribution = True 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. 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. 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)