code: # Copyright (c) 2022 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.
  
  import unittest
  import os
  import json
  
  import paddle
  import numpy as np
  import paddle.nn as nn
  import paddle.utils as utils
  import paddle.static as static
  import paddle.nn.functional as F
  
  from paddle.distributed import fleet
  import paddle.distributed.auto_parallel as auto
  from paddle.distributed.auto_parallel.dist_context import DistributedContext
  from paddle.distributed.auto_parallel.utils import print_program_with_dist_attr
  
  paddle.enable_static()
  
  batch_size = 4
  hidden_size = 1024
  sequence_len = 512
  _g_process_mesh = [[0, 1], [2, 3]]
  
  
  def get_random_inputs_and_labels(input_shape, label_shape):
      input = np.random.random(size=input_shape).astype('float32')
      label = np.random.random(size=label_shape).astype('float32')
      return input, label
  
  
  def batch_generator_creator():
      def __reader__():
          for _ in range(batch_size):
              batch_input, batch_label = get_random_inputs_and_labels(
                  [batch_size, sequence_len, hidden_size],
                  [batch_size, sequence_len, 1])
              yield batch_input, batch_label
  
      return __reader__
  
  
  class MLPLayer(nn.Layer):
      def __init__(self,
                   hidden_size=1024,
                   intermediate_size=4 * 1024,
                   dropout_ratio=0.1,
                   initializer_range=0.02):
          super(MLPLayer, self).__init__()
          d_model = hidden_size
          dim_feedforward = intermediate_size
          param_initializer = nn.initializer.Normal(
              mean=0.0, std=initializer_range)
  
          self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
          self.linear0 = nn.Linear(
              d_model,
              dim_feedforward,
              weight_attr=paddle.ParamAttr(initializer=param_initializer),
              bias_attr=None)
          self.linear1 = nn.Linear(
              dim_feedforward,
              d_model,
              weight_attr=paddle.ParamAttr(initializer=param_initializer),
              bias_attr=None)
  
      def forward(self, input):
          out = self.norm(input)
          auto.shard_tensor(
              self.linear0.weight,
              dist_attr={
                  "process_mesh": _g_process_mesh[0],
                  "dims_mapping": [-1, 0]
              })
          out = self.linear0(out)
          out = F.gelu(out, approximate=True)
          auto.shard_tensor(
              self.linear1.weight,
              dist_attr={
                  "process_mesh": _g_process_mesh[1],
                  "dims_mapping": [0, -1]
              })
          out = self.linear1(out)
  
          return out
  
  
  def get_program():
      dist_strategy = fleet.DistributedStrategy()
      dist_strategy.semi_auto = True
      # fleet.init(is_collective=True, strategy=dist_strategy)
  
      train_program = static.Program()
      start_program = static.Program()
      with static.program_guard(train_program, start_program):
          # input
          input = static.data(
              name="input",
              shape=[batch_size, sequence_len, hidden_size],
              dtype='float32')
          label = static.data(
              name="label", shape=[batch_size, sequence_len, 1], dtype='float32')
          data_holder = [input, label]
          # dataloader
          dataloader = paddle.io.DataLoader.from_generator(
              feed_list=data_holder, capacity=4 * batch_size, iterable=False)
          dataloader.set_batch_generator(
              batch_generator_creator(), places=paddle.static.cuda_places())
          # data dist_attr
          auto.shard_tensor(
              input,
              dist_attr={
                  "process_mesh": _g_process_mesh[0],
                  "dims_mapping": [0, -1, -1]
              })
          auto.shard_tensor(
              label,
              dist_attr={
                  "process_mesh": _g_process_mesh[0],
                  "dims_mapping": [0, -1, -1]
              })
  
          mlp_start = MLPLayer(
              hidden_size=hidden_size,
              intermediate_size=4 * hidden_size,
              dropout_ratio=0.1,
              initializer_range=0.02)
          pred = mlp_start(input)
  
          mlp_mid = MLPLayer(
              hidden_size=hidden_size,
              intermediate_size=4 * hidden_size,
              dropout_ratio=0.1,
              initializer_range=0.02)
          pred = mlp_mid(pred)
  
          mlp_end = MLPLayer(
              hidden_size=hidden_size,
              intermediate_size=4 * hidden_size,
              dropout_ratio=0.1,
              initializer_range=0.02)
          pred = mlp_end(pred)
  
          error_cost = paddle.nn.functional.square_error_cost(pred, label)
          loss = paddle.mean(error_cost)
  
          optimizer = paddle.optimizer.Adam(
              learning_rate=0.00001,
              beta1=0.9,
              beta2=0.999,
              epsilon=1e-08,
              grad_clip=None)
  
          feed_vars = {"inputs": [input], "labels": [label]}
          fetch_vars = {"loss": [loss]}
  
      return train_program, start_program, dataloader, loss, optimizer, feed_vars, fetch_vars
  
  
  class TestDistributedContext(unittest.TestCase):
      def test_backup_restore(self):
          train_program, start_program, dataloader, loss, optimizer, feed_vars, fetch_vars = get_program(
          )
          dist_context = DistributedContext(train_program, start_program,
                                            optimizer, loss, feed_vars,
                                            fetch_vars)
          dist_context.initialize()
  
          dist_context._backup(serial=True, dist=True)
          dist_context._restore(
              serial=True,
              serial_mode="to_backup",
              dist=True,
              dist_mode="to_backup")
  
          dist_context._backup(serial=True, dist=True)
          dist_context._restore(
              serial=True,
              serial_mode="to_original",
              dist=True,
              dist_mode="to_original")
  
          dist_context._backup(serial=True, dist=True)
          dist_context._restore(serial=True, dist=True, dist_mode="to_default")
  
          dist_context._backup(serial=True, dist=True)
          dist_context._restore(serial=True, dist=True, dist_mode="to_nothing")
  
  
  if __name__ == "__main__":
      unittest.main()
  
apis: numpy.random.random