text: # Copyright (c) 2021 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 division
  from __future__ import print_function
  
  import unittest
  import paddle
  import numpy as np
  import random
  import paddle
  import paddle.distributed as dist
  import paddle.distributed.fleet as fleet
  from paddle.fluid.dygraph.container import Sequential
  from paddle.distributed.fleet.meta_parallel import PipelineLayer
  from paddle.fluid.dygraph.layers import Layer
  import paddle.nn as nn
  import paddle.fluid as fluid
  
  
  def set_random_seed(seed, dp_id, rank_id):
      """Set random seed for reproducability."""
      random.seed(seed)
      np.random.seed(seed + dp_id)
      paddle.seed(seed + dp_id)
  
  
  batch_size = 16
  micro_batch_size = 4
  vocab_size = 128
  hidden_size = 8
  
  
  class SimpleNet(Layer):
      def __init__(self):
          super(SimpleNet, self).__init__()
          self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
  
          self.softmax_weight = self.create_parameter(
              shape=[hidden_size, vocab_size])
          self.softmax_bias = self.create_parameter(
              shape=[vocab_size], is_bias=False)
  
      def forward(self, x1, x2, y1):
          x_emb = self.word_embeddings(x1)
          fc = fluid.layers.matmul(x_emb, self.softmax_weight)
          fc = fluid.layers.elementwise_add(fc, self.softmax_bias)
          projection = fluid.layers.reshape(fc, shape=[-1, vocab_size])
          loss = fluid.layers.softmax_with_cross_entropy(
              logits=projection, label=y1, soft_label=False)
          return loss.mean()
  
  
  class EmbeddingNet(Layer):
      def __init__(self):
          super(EmbeddingNet, self).__init__()
          self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
  
      def forward(self, args):
          x1, x2 = args
          x_emb = self.word_embeddings(x1)
          return x_emb, x2
  
  
  class MatmulNet(Layer):
      def __init__(self):
          super(MatmulNet, self).__init__()
          self.softmax_weight = self.create_parameter(
              shape=[hidden_size, vocab_size])
  
      def forward(self, args):
          x1, x2 = args
          fc = fluid.layers.matmul(x1, self.softmax_weight)
  
          return fc, x2
  
  
  class BiasNet(Layer):
      def __init__(self):
          super(BiasNet, self).__init__()
          self.softmax_bias = self.create_parameter(shape=[vocab_size])
  
      def forward(self, args):
          fc, x2 = args
          fc = fluid.layers.elementwise_add(fc, self.softmax_bias)
          projection = fluid.layers.reshape(fc, shape=[-1, vocab_size])
          return projection, x2
  
  
  class LossNet(Layer):
      def __init__(self):
          super(LossNet, self).__init__()
  
      def forward(self, args, y1):
          projection, x2 = args
          loss = fluid.layers.softmax_with_cross_entropy(
              logits=projection, label=y1[0], soft_label=False)
          return loss.mean()
  
  
  class SimpleNetPipe(Layer):
      def __init__(self):
          super(SimpleNetPipe, self).__init__()
          self.features = Sequential(EmbeddingNet(), MatmulNet(), BiasNet())
  
      def to_layers(self):
          feat = [self.features[i] for i in range(len(self.features))]
          return feat
  
  
  class TestDistEmbeddingTraning(unittest.TestCase):
      def setUp(self):
          strategy = fleet.DistributedStrategy()
          self.model_parallel_size = 1
          self.data_parallel_size = 1
          self.pipeline_parallel_size = 2
          strategy.hybrid_configs = {
              "dp_degree": self.data_parallel_size,
              "mp_degree": self.model_parallel_size,
              "pp_degree": self.pipeline_parallel_size,
          }
          strategy.pipeline_configs = {
              "accumulate_steps": batch_size // micro_batch_size,
              "micro_batch_size": micro_batch_size
          }
          fleet.init(is_collective=True, strategy=strategy)
  
      def test_pp_model(self):
          hcg = fleet.get_hybrid_communicate_group()
          word_size = hcg.get_model_parallel_world_size()
          dp_id = hcg.get_data_parallel_rank()
          pp_id = hcg.get_stage_id()
          rank_id = dist.get_rank()
          set_random_seed(1024, dp_id, rank_id)
  
          #construct model a
          model_a = SimpleNet()
          scheduler_a = paddle.optimizer.lr.PiecewiseDecay(
              boundaries=[2, 3, 4], values=[0.01, 0.02, 0.03, 0.04], verbose=True)
          optimizer_a = paddle.optimizer.SGD(learning_rate=scheduler_a,
                                             parameters=model_a.parameters())
  
          init_net = SimpleNetPipe()
          model_b = PipelineLayer(
              layers=init_net.to_layers(),
              num_stages=self.pipeline_parallel_size,
              loss_fn=LossNet())
  
          scheduler_b = paddle.optimizer.lr.PiecewiseDecay(
              boundaries=[2, 3, 4], values=[0.01, 0.02, 0.03, 0.04], verbose=True)
          optimizer_b = paddle.optimizer.SGD(learning_rate=scheduler_b,
                                             parameters=model_b.parameters())
          model_b = fleet.distributed_model(model_b)
          optimizer_b = fleet.distributed_optimizer(optimizer_b)
  
          param_len = len(model_a.parameters())
  
          parameters = []
          for param in model_a.parameters():
              print(param.name, param.shape)
              parameters.append(param.numpy())
  
          model_b_params = model_b.parameters()
          if pp_id == 0:
              model_b_params[0].set_value(parameters[2])
          else:
              model_b_params[0].set_value(parameters[0])
              model_b_params[1].set_value(parameters[1])
  
          for step in range(5):
              x1_data = np.random.randint(0, vocab_size, size=[batch_size, 1])
              x2_data = np.random.randint(0, vocab_size, size=[batch_size, 1])
              y1_data = np.random.randint(0, 10, size=[batch_size, 1])
  
              x1 = paddle.to_tensor(x1_data)
              x2 = paddle.to_tensor(x2_data)
              y1 = paddle.to_tensor(y1_data)
  
              x1.stop_gradient = True
              x2.stop_gradient = True
              y1.stop_gradient = True
  
              loss_a = model_a(x1, x2, y1)
              loss_a.backward()
              optimizer_a.step()
              optimizer_a.clear_grad()
              scheduler_a.step()
  
              loss_b = model_b.train_batch([(x1, x2), (y1, )], optimizer_b,
                                           scheduler_b)
  
              print("loss", loss_a.numpy(), loss_b.numpy())
              np.testing.assert_allclose(loss_a.numpy(), loss_b.numpy())
  
  
  if __name__ == "__main__":
      unittest.main()
  