text: # Copyright (c) 2020 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 os
  import io
  import random
  import time
  import argparse
  from functools import partial
  
  import numpy as np
  import yaml
  import paddle
  import pgl
  from easydict import EasyDict as edict
  from paddlenlp.transformers import ErnieTokenizer, ErnieTinyTokenizer
  from paddlenlp.utils.log import logger
  
  from models import ErnieSageForLinkPrediction
  from data import TrainData, PredictData, GraphDataLoader, batch_fn
  
  MODEL_CLASSES = {
      "ernie-tiny": (ErnieSageForLinkPrediction, ErnieTinyTokenizer),
      "ernie-1.0": (ErnieSageForLinkPrediction, ErnieTokenizer),
  }
  
  
  def set_seed(config):
      random.seed(config.seed)
      np.random.seed(config.seed)
      paddle.seed(config.seed)
  
  
  def load_data(graph_data_path):
      base_graph = pgl.Graph.load(graph_data_path)
      term_ids = np.load(
          os.path.join(graph_data_path, "term_ids.npy"), mmap_mode="r")
      return base_graph, term_ids
  
  
  def do_train(config):
      paddle.set_device(config.device)
      if paddle.distributed.get_world_size() > 1:
          paddle.distributed.init_parallel_env()
      set_seed(config)
  
      base_graph, term_ids = load_data(config.graph_work_path)
      collate_fn = partial(
          batch_fn,
          samples=config.samples,
          base_graph=base_graph,
          term_ids=term_ids)
  
      mode = 'train'
      train_ds = TrainData(config.graph_work_path)
  
      model_class, tokenizer_class = MODEL_CLASSES[config.model_name_or_path]
      tokenizer = tokenizer_class.from_pretrained(config.model_name_or_path)
      config.cls_token_id = tokenizer.cls_token_id
  
      model = model_class.from_pretrained(
          config.model_name_or_path, config=config)
      model = paddle.DataParallel(model)
  
      train_loader = GraphDataLoader(
          train_ds,
          batch_size=config.batch_size,
          shuffle=True,
          num_workers=config.sample_workers,
          collate_fn=collate_fn)
  
      optimizer = paddle.optimizer.Adam(
          learning_rate=config.lr, parameters=model.parameters())
  
      rank = paddle.distributed.get_rank()
      global_step = 0
      tic_train = time.time()
      for epoch in range(config.epoch):
          for step, (graphs, datas) in enumerate(train_loader):
              global_step += 1
              loss, outputs = model(graphs, datas)
              if global_step % config.log_per_step == 0:
                  logger.info(
                      "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
                      % (global_step, epoch, step, loss,
                         config.log_per_step / (time.time() - tic_train)))
                  tic_train = time.time()
              loss.backward()
              optimizer.step()
              optimizer.clear_grad()
              if global_step % config.save_per_step == 0:
                  if rank == 0:
                      output_dir = os.path.join(config.output_path,
                                                "model_%d" % global_step)
                      if not os.path.exists(output_dir):
                          os.makedirs(output_dir)
                      model._layers.save_pretrained(output_dir)
      if rank == 0:
          output_dir = os.path.join(config.output_path, "last")
          if not os.path.exists(output_dir):
              os.makedirs(output_dir)
          model._layers.save_pretrained(output_dir)
  
  
  def tostr(data_array):
      return " ".join(["%.5lf" % d for d in data_array])
  
  
  @paddle.no_grad()
  def do_predict(config):
      paddle.set_device(config.device)
      if paddle.distributed.get_world_size() > 1:
          paddle.distributed.init_parallel_env()
      set_seed(config)
  
      mode = 'predict'
      num_nodes = int(
          np.load(os.path.join(config.graph_work_path, "num_nodes.npy")))
  
      base_graph, term_ids = load_data(config.graph_work_path)
      collate_fn = partial(
          batch_fn,
          samples=config.samples,
          base_graph=base_graph,
          term_ids=term_ids)
  
      model_class, tokenizer_class = MODEL_CLASSES[config.model_name_or_path]
      tokenizer = tokenizer_class.from_pretrained(config.model_name_or_path)
      config.cls_token_id = tokenizer.cls_token_id
  
      model = model_class.from_pretrained(config.infer_model, config=config)
  
      model = paddle.DataParallel(model)
      predict_ds = PredictData(num_nodes)
  
      predict_loader = GraphDataLoader(
          predict_ds,
          batch_size=config.infer_batch_size,
          shuffle=True,
          num_workers=config.sample_workers,
          collate_fn=collate_fn)
  
      trainer_id = paddle.distributed.get_rank()
      id2str = io.open(
          os.path.join(config.graph_work_path, "terms.txt"),
          encoding=config.encoding).readlines()
      if not os.path.exists(config.output_path):
          os.mkdir(config.output_path)
      fout = io.open(
          "%s/part-%s" % (config.output_path, trainer_id), "w", encoding="utf8")
  
      global_step = 0
      epoch = 0
      tic_train = time.time()
      model.eval()
      for step, (graphs, datas) in enumerate(predict_loader):
          global_step += 1
          loss, outputs = model(graphs, datas)
          for user_feat, user_real_index in zip(outputs[0].numpy(),
                                                outputs[3].numpy()):
              sri = id2str[int(user_real_index)].strip("\n")
              line = "{}\t{}\n".format(sri, tostr(user_feat))
              fout.write(line)
          if global_step % config.log_per_step == 0:
              logger.info(
                  "predict step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
                  % (global_step, epoch, step, loss,
                     config.log_per_step / (time.time() - tic_train)))
              tic_train = time.time()
      fout.close()
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(description='main')
      parser.add_argument("--conf", type=str, default="./config.yaml")
      parser.add_argument("--do_predict", action='store_true', default=False)
      args = parser.parse_args()
      config = edict(yaml.load(open(args.conf), Loader=yaml.FullLoader))
  
      assert config.device in [
          "gpu", "cpu"
      ], "Device should be gpu/cpu, but got %s." % config.device
      logger.info(config)
      if args.do_predict:
          do_predict(config)
      else:
          do_train(config)
  