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255 lines
8.6 KiB
255 lines
8.6 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import six
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import time
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import math
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import socket
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import contextlib
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import numpy as np
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from paddle import fluid
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from paddle.fluid.layers import collective
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from paddle.fluid.dygraph.parallel import ParallelEnv, ParallelStrategy
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from paddle.io import BatchSampler
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_parallel_context_initialized = False
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__all__ = ['DistributedBatchSampler']
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class DistributedBatchSampler(BatchSampler):
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"""Sampler that restricts data loading to a subset of the dataset.
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In such case, each process can pass a DistributedBatchSampler instance
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as a DataLoader sampler, and load a subset of the original dataset that
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is exclusive to it.
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.. note::
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Dataset is assumed to be of constant size.
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Args:
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dataset(paddle.io.Dataset): this could be a `paddle.io.Dataset` implement
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or other python object which implemented
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`__len__` for BatchSampler to get sample
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number of data source.
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batch_size(int): sample indice number in a mini-batch indices.
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shuffle(bool): whther to shuffle indices order before genrating
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batch indices. Default False.
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drop_last(bool): whether drop the last incomplete batch dataset size
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is not divisible by the batch size. Default False
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Examples:
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.. code-block:: python
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import numpy as np
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from paddle.incubate.hapi.datasets import MNIST
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from paddle.incubate.hapi.distributed import DistributedBatchSampler
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class MnistDataset(MNIST):
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def __init__(self, mode, return_label=True):
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super(MnistDataset, self).__init__(mode=mode)
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self.return_label = return_label
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def __getitem__(self, idx):
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img = np.reshape(self.images[idx], [1, 28, 28])
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if self.return_label:
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return img, np.array(self.labels[idx]).astype('int64')
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return img,
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def __len__(self):
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return len(self.images)
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train_dataset = MnistDataset(mode='train')
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dist_train_dataloader = DistributedBatchSampler(train_dataset, batch_size=64)
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for data in dist_train_dataloader:
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# do something
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break
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"""
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def __init__(self, dataset, batch_size, shuffle=False, drop_last=False):
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self.dataset = dataset
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assert isinstance(batch_size, int) and batch_size > 0, \
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"batch_size should be a positive integer"
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self.batch_size = batch_size
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assert isinstance(shuffle, bool), \
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"shuffle should be a boolean value"
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self.shuffle = shuffle
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assert isinstance(drop_last, bool), \
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"drop_last should be a boolean number"
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self.drop_last = drop_last
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self.nranks = ParallelEnv().nranks
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self.local_rank = ParallelEnv().local_rank
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self.epoch = 0
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self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.nranks))
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self.total_size = self.num_samples * self.nranks
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def __iter__(self):
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num_samples = len(self.dataset)
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indices = np.arange(num_samples).tolist()
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indices += indices[:(self.total_size - len(indices))]
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assert len(indices) == self.total_size
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if self.shuffle:
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np.random.RandomState(self.epoch).shuffle(indices)
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self.epoch += 1
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# subsample
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def _get_indices_by_batch_size(indices):
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subsampled_indices = []
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last_batch_size = self.total_size % (self.batch_size * self.nranks)
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assert last_batch_size % self.nranks == 0
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last_local_batch_size = last_batch_size // self.nranks
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for i in range(self.local_rank * self.batch_size,
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len(indices) - last_batch_size,
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self.batch_size * self.nranks):
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subsampled_indices.extend(indices[i:i + self.batch_size])
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indices = indices[len(indices) - last_batch_size:]
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subsampled_indices.extend(indices[
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self.local_rank * last_local_batch_size:(
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self.local_rank + 1) * last_local_batch_size])
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return subsampled_indices
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if self.nranks > 1:
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indices = _get_indices_by_batch_size(indices)
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assert len(indices) == self.num_samples
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_sample_iter = iter(indices)
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batch_indices = []
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for idx in _sample_iter:
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batch_indices.append(idx)
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if len(batch_indices) == self.batch_size:
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yield batch_indices
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batch_indices = []
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if not self.drop_last and len(batch_indices) > 0:
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yield batch_indices
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def __len__(self):
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num_samples = self.num_samples
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num_samples += int(not self.drop_last) * (self.batch_size - 1)
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return num_samples // self.batch_size
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def set_epoch(self, epoch):
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self.epoch = epoch
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def _all_gather(x, nranks, ring_id=0, use_calc_stream=True):
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return collective._c_allgather(
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x, nranks, ring_id=ring_id, use_calc_stream=use_calc_stream)
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def wait_server_ready(endpoints):
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assert not isinstance(endpoints, six.string_types)
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while True:
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all_ok = True
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not_ready_endpoints = []
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for ep in endpoints:
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ip_port = ep.split(":")
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with contextlib.closing(
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socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
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sock.settimeout(2)
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result = sock.connect_ex((ip_port[0], int(ip_port[1])))
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if result != 0:
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all_ok = False
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not_ready_endpoints.append(ep)
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if not all_ok:
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time.sleep(3)
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else:
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break
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def init_communicator(program, rank, nranks, wait_port, current_endpoint,
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endpoints):
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if nranks < 2:
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return
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other_endpoints = endpoints[:]
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other_endpoints.remove(current_endpoint)
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if rank == 0 and wait_port:
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wait_server_ready(other_endpoints)
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block = program.global_block()
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nccl_id_var = block.create_var(
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name=fluid.unique_name.generate('nccl_id'),
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persistable=True,
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type=fluid.core.VarDesc.VarType.RAW)
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block.append_op(
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type='c_gen_nccl_id',
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inputs={},
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outputs={'Out': nccl_id_var},
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attrs={
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'rank': rank,
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'endpoint': current_endpoint,
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'other_endpoints': other_endpoints
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})
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block.append_op(
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type='c_comm_init',
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inputs={'X': nccl_id_var},
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outputs={},
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attrs={
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'nranks': nranks,
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'rank': rank,
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'ring_id': 0,
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})
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def prepare_distributed_context(place=None):
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if place is None:
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place = fluid.CUDAPlace(ParallelEnv().dev_id) if ParallelEnv().nranks > 1 \
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else fluid.CUDAPlace(0)
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strategy = ParallelStrategy()
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strategy.nranks = ParallelEnv().nranks
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strategy.local_rank = ParallelEnv().local_rank
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strategy.trainer_endpoints = ParallelEnv().trainer_endpoints
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strategy.current_endpoint = ParallelEnv().current_endpoint
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if strategy.nranks < 2:
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return
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global _parallel_context_initialized
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if not _parallel_context_initialized and isinstance(place, fluid.CUDAPlace):
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def _init_context():
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communicator_prog = fluid.Program()
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init_communicator(communicator_prog, strategy.local_rank,
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strategy.nranks, True, strategy.current_endpoint,
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strategy.trainer_endpoints)
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exe = fluid.Executor(place)
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exe.run(communicator_prog)
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if fluid.in_dygraph_mode():
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fluid.disable_dygraph()
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_init_context()
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fluid.enable_dygraph(place)
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else:
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_init_context()
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else:
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assert ("Only support CUDAPlace for now.")
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_parallel_context_initialized = True
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return strategy
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