parallel_executor.py

#   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.

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


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)

    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)

    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()