dist_utils.py

#  Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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 absolute_import
from __future__ import division
from __future__ import print_function
import os
import paddle.fluid as fluid

def nccl2_prepare(trainer_id, startup_prog, main_prog):
   config = fluid.DistributeTranspilerConfig()
   config.mode = "nccl2"
   t = fluid.DistributeTranspiler(config=config)
   t.transpile(trainer_id,
      trainers=os.environ.get('PADDLE_TRAINER_ENDPOINTS'),
      current_endpoint=os.environ.get('PADDLE_CURRENT_ENDPOINT'),
      startup_program=startup_prog,
      program=main_prog)

def prepare_for_multi_process(exe, build_strategy, train_prog):
   # prepare for multi-process
   trainer_id = int(os.environ.get('PADDLE_TRAINER_ID', 0))
   num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
   if num_trainers < 2: return
   print("PADDLE_TRAINERS_NUM", num_trainers)
   print("PADDLE_TRAINER_ID", trainer_id)
   build_strategy.num_trainers =  num_trainers
   build_strategy.trainer_id = trainer_id
   # NOTE(zcd): use multi processes to train the model,
   # and each process use one GPU card.
   startup_prog = fluid.Program()
   nccl2_prepare(trainer_id, startup_prog, train_prog)
   # the startup_prog are run two times, but it doesn't matter.
   exe.run(startup_prog)