code: # 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 argparse
  import logging
  import os
  import sys
  import random
  import time
  import math
  import distutils.util
  from functools import partial
  
  import numpy as np
  import paddle
  from paddle.io import DataLoader
  from paddle.metric import Metric, Accuracy, Precision, Recall
  
  from paddlenlp.datasets import GlueCoLA, GlueSST2, GlueMRPC, GlueSTSB, GlueQQP, GlueMNLI, GlueQNLI, GlueRTE
  from paddlenlp.data import Stack, Tuple, Pad
  from paddlenlp.transformers import BertForSequenceClassification, BertTokenizer
  from paddlenlp.transformers import ElectraForSequenceClassification, ElectraTokenizer
  from paddlenlp.transformers import ErnieForSequenceClassification, ErnieTokenizer
  from paddlenlp.transformers import LinearDecayWithWarmup
  from paddlenlp.metrics import AccuracyAndF1, Mcc, PearsonAndSpearman
  
  FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
  logging.basicConfig(level=logging.INFO, format=FORMAT)
  logger = logging.getLogger(__name__)
  
  TASK_CLASSES = {
      "cola": (GlueCoLA, Mcc),
      "sst-2": (GlueSST2, Accuracy),
      "mrpc": (GlueMRPC, AccuracyAndF1),
      "sts-b": (GlueSTSB, PearsonAndSpearman),
      "qqp": (GlueQQP, AccuracyAndF1),
      "mnli": (GlueMNLI, Accuracy),
      "qnli": (GlueQNLI, Accuracy),
      "rte": (GlueRTE, Accuracy),
  }
  
  MODEL_CLASSES = {
      "bert": (BertForSequenceClassification, BertTokenizer),
      "ernie": (ErnieForSequenceClassification, ErnieTokenizer)
  }
  
  
  def parse_args():
      parser = argparse.ArgumentParser()
  
      # Required parameters
      parser.add_argument(
          "--task_name",
          default=None,
          type=str,
          required=True,
          help="The name of the task to train selected in the list: " +
          ", ".join(TASK_CLASSES.keys()), )
      parser.add_argument(
          "--model_type",
          default=None,
          type=str,
          required=True,
          help="Model type selected in the list: " +
          ", ".join(MODEL_CLASSES.keys()), )
      parser.add_argument(
          "--model_name_or_path",
          default=None,
          type=str,
          required=True,
          help="Path to pre-trained model or shortcut name selected in the list: "
          + ", ".join(
              sum([
                  list(classes[-1].pretrained_init_configuration.keys())
                  for classes in MODEL_CLASSES.values()
              ], [])), )
      parser.add_argument(
          "--output_dir",
          default=None,
          type=str,
          required=True,
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      parser.add_argument(
          "--max_seq_length",
          default=128,
          type=int,
          help="The maximum total input sequence length after tokenization. Sequences longer "
          "than this will be truncated, sequences shorter will be padded.", )
      parser.add_argument(
          "--learning_rate",
          default=1e-4,
          type=float,
          help="The initial learning rate for Adam.")
      parser.add_argument(
          "--num_train_epochs",
          default=3,
          type=int,
          help="Total number of training epochs to perform.", )
      parser.add_argument(
          "--logging_steps",
          type=int,
          default=100,
          help="Log every X updates steps.")
      parser.add_argument(
          "--save_steps",
          type=int,
          default=100,
          help="Save checkpoint every X updates steps.")
      parser.add_argument(
          "--batch_size",
          default=32,
          type=int,
          help="Batch size per GPU/CPU for training.", )
      parser.add_argument(
          "--weight_decay",
          default=0.0,
          type=float,
          help="Weight decay if we apply some.")
      parser.add_argument(
          "--warmup_steps",
          default=0,
          type=int,
          help="Linear warmup over warmup_steps. If > 0: Override warmup_proportion"
      )
      parser.add_argument(
          "--warmup_proportion",
          default=0.,
          type=float,
          help="Linear warmup proportion over total steps.")
      parser.add_argument(
          "--adam_epsilon",
          default=1e-6,
          type=float,
          help="Epsilon for Adam optimizer.")
      parser.add_argument(
          "--max_steps",
          default=-1,
          type=int,
          help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
      )
      parser.add_argument(
          "--seed", default=42, type=int, help="random seed for initialization")
      parser.add_argument(
          "--n_cards",
          default=1,
          type=int,
          help="Number cards for the training, only support multi cards in the gpu."
      )
      parser.add_argument(
          "--select_device",
          type=str,
          default="gpu",
          help="Device for selecting for the training.")
      parser.add_argument(
          "--use_amp",
          type=distutils.util.strtobool,
          default=False,
          help="Enable mixed precision training.")
      parser.add_argument(
          "--scale_loss",
          type=float,
          default=2**15,
          help="The value of scale_loss for fp16.")
      args = parser.parse_args()
      return args
  
  
  def set_seed(args):
      # Use the same data seed(for data shuffle) for all procs to guarantee data
      # consistency after sharding.
      random.seed(args.seed)
      np.random.seed(args.seed)
      # Maybe different op seeds(for dropout) for different procs is better. By:
      # `paddle.seed(args.seed + paddle.distributed.get_rank())`
      paddle.seed(args.seed)
  
  
  def evaluate(model, loss_fct, metric, data_loader):
      model.eval()
      metric.reset()
      for batch in data_loader:
          input_ids, segment_ids, labels = batch
          logits = model(input_ids, segment_ids)
          loss = loss_fct(logits, labels)
          correct = metric.compute(logits, labels)
          metric.update(correct)
      res = metric.accumulate()
      if isinstance(metric, AccuracyAndF1):
          logger.info(
              "eval loss: %f, acc: %s, precision: %s, recall: %s, f1: %s, acc and f1: %s."
              % (loss.numpy(), res[0], res[1], res[2], res[3], res[4]))
      elif isinstance(metric, Mcc):
          logger.info("eval loss: %f, mcc: %s." % (loss.numpy(), res[0]))
      elif isinstance(metric, PearsonAndSpearman):
          logger.info(
              "eval loss: %f, pearson: %s, spearman: %s, pearson and spearman: %s."
              % (loss.numpy(), res[0], res[1], res[2]))
      else:
          logger.info("eval loss: %f, acc: %s." % (loss.numpy(), res))
      model.train()
  
  
  def convert_example(example,
                      tokenizer,
                      label_list,
                      max_seq_length=512,
                      is_test=False):
      """convert a glue example into necessary features"""
  
      def _truncate_seqs(seqs, max_seq_length):
          if len(seqs) == 1:  # single sentence
              # Account for [CLS] and [SEP] with "- 2"
              seqs[0] = seqs[0][0:(max_seq_length - 2)]
          else:  # Sentence pair
              # Account for [CLS], [SEP], [SEP] with "- 3"
              tokens_a, tokens_b = seqs
              max_seq_length -= 3
              while True:  # Truncate with longest_first strategy
                  total_length = len(tokens_a) + len(tokens_b)
                  if total_length <= max_seq_length:
                      break
                  if len(tokens_a) > len(tokens_b):
                      tokens_a.pop()
                  else:
                      tokens_b.pop()
          return seqs
  
      def _concat_seqs(seqs, separators, seq_mask=0, separator_mask=1):
          concat = sum((seq + sep for sep, seq in zip(separators, seqs)), [])
          segment_ids = sum(
              ([i] * (len(seq) + len(sep))
               for i, (sep, seq) in enumerate(zip(separators, seqs))), [])
          if isinstance(seq_mask, int):
              seq_mask = [[seq_mask] * len(seq) for seq in seqs]
          if isinstance(separator_mask, int):
              separator_mask = [[separator_mask] * len(sep) for sep in separators]
          p_mask = sum((s_mask + mask
                        for sep, seq, s_mask, mask in zip(
                            separators, seqs, seq_mask, separator_mask)), [])
          return concat, segment_ids, p_mask
  
      if not is_test:
          # `label_list == None` is for regression task
          label_dtype = "int64" if label_list else "float32"
          # Get the label
          label = example[-1]
          example = example[:-1]
          # Create label maps if classification task
          if label_list:
              label_map = {}
              for (i, l) in enumerate(label_list):
                  label_map[l] = i
              label = label_map[label]
          label = np.array([label], dtype=label_dtype)
  
      # Tokenize raw text
      tokens_raw = [tokenizer(l) for l in example]
      # Truncate to the truncate_length,
      tokens_trun = _truncate_seqs(tokens_raw, max_seq_length)
      # Concate the sequences with special tokens
      tokens_trun[0] = [tokenizer.cls_token] + tokens_trun[0]
      tokens, segment_ids, _ = _concat_seqs(tokens_trun, [[tokenizer.sep_token]] *
                                            len(tokens_trun))
      # Convert the token to ids
      input_ids = tokenizer.convert_tokens_to_ids(tokens)
      valid_length = len(input_ids)
      # The mask has 1 for real tokens and 0 for padding tokens. Only real
      # tokens are attended to.
      # input_mask = [1] * len(input_ids)
      if not is_test:
          return input_ids, segment_ids, valid_length, label
      else:
          return input_ids, segment_ids, valid_length
  
  
  def do_train(args):
      paddle.set_device(args.select_device)
      if paddle.distributed.get_world_size() > 1:
          paddle.distributed.init_parallel_env()
  
      set_seed(args)
  
      args.task_name = args.task_name.lower()
      dataset_class, metric_class = TASK_CLASSES[args.task_name]
      args.model_type = args.model_type.lower()
      model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
  
      train_dataset = dataset_class.get_datasets(["train"])
      tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
  
      trans_func = partial(
          convert_example,
          tokenizer=tokenizer,
          label_list=train_dataset.get_labels(),
          max_seq_length=args.max_seq_length)
      train_dataset = train_dataset.apply(trans_func, lazy=True)
      train_batch_sampler = paddle.io.DistributedBatchSampler(
          train_dataset, batch_size=args.batch_size, shuffle=True)
      batchify_fn = lambda samples, fn=Tuple(
          Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input
          Pad(axis=0, pad_val=tokenizer.pad_token_id),  # segment
          Stack(),  # length
          Stack(dtype="int64" if train_dataset.get_labels() else "float32")  # label
      ): [data for i, data in enumerate(fn(samples)) if i != 2]
      train_data_loader = DataLoader(
          dataset=train_dataset,
          batch_sampler=train_batch_sampler,
          collate_fn=batchify_fn,
          num_workers=0,
          return_list=True)
      if args.task_name == "mnli":
          dev_dataset_matched, dev_dataset_mismatched = dataset_class.get_datasets(
              ["dev_matched", "dev_mismatched"])
          dev_dataset_matched = dev_dataset_matched.apply(trans_func, lazy=True)
          dev_dataset_mismatched = dev_dataset_mismatched.apply(
              trans_func, lazy=True)
          dev_batch_sampler_matched = paddle.io.BatchSampler(
              dev_dataset_matched, batch_size=args.batch_size, shuffle=False)
          dev_data_loader_matched = DataLoader(
              dataset=dev_dataset_matched,
              batch_sampler=dev_batch_sampler_matched,
              collate_fn=batchify_fn,
              num_workers=0,
              return_list=True)
          dev_batch_sampler_mismatched = paddle.io.BatchSampler(
              dev_dataset_mismatched, batch_size=args.batch_size, shuffle=False)
          dev_data_loader_mismatched = DataLoader(
              dataset=dev_dataset_mismatched,
              batch_sampler=dev_batch_sampler_mismatched,
              collate_fn=batchify_fn,
              num_workers=0,
              return_list=True)
      else:
          dev_dataset = dataset_class.get_datasets(["dev"])
          dev_dataset = dev_dataset.apply(trans_func, lazy=True)
          dev_batch_sampler = paddle.io.BatchSampler(
              dev_dataset, batch_size=args.batch_size, shuffle=False)
          dev_data_loader = DataLoader(
              dataset=dev_dataset,
              batch_sampler=dev_batch_sampler,
              collate_fn=batchify_fn,
              num_workers=0,
              return_list=True)
  
      num_classes = 1 if train_dataset.get_labels() == None else len(
          train_dataset.get_labels())
      model = model_class.from_pretrained(
          args.model_name_or_path, num_classes=num_classes)
      if paddle.distributed.get_world_size() > 1:
          model = paddle.DataParallel(model)
  
      num_training_steps = args.max_steps if args.max_steps > 0 else (
          len(train_data_loader) * args.num_train_epochs)
      warmup = args.warmup_steps if args.warmup_steps > 0 else args.warmup_proportion
  
      lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps,
                                           warmup)
  
      optimizer = paddle.optimizer.AdamW(
          learning_rate=lr_scheduler,
          beta1=0.9,
          beta2=0.999,
          epsilon=args.adam_epsilon,
          parameters=model.parameters(),
          weight_decay=args.weight_decay,
          apply_decay_param_fun=lambda x: x in [
              p.name for n, p in model.named_parameters()
              if not any(nd in n for nd in ["bias", "norm"])
          ])
  
      loss_fct = paddle.nn.loss.CrossEntropyLoss() if train_dataset.get_labels(
      ) else paddle.nn.loss.MSELoss()
  
      metric = metric_class()
  
      if args.use_amp:
          scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)
  
      global_step = 0
      tic_train = time.time()
      for epoch in range(args.num_train_epochs):
          for step, batch in enumerate(train_data_loader):
              global_step += 1
              input_ids, segment_ids, labels = batch
              with paddle.amp.auto_cast(
                      args.use_amp,
                      custom_white_list=["layer_norm", "softmax", "gelu"]):
                  logits = model(input_ids, segment_ids)
                  loss = loss_fct(logits, labels)
              if args.use_amp:
                  scaler.scale(loss).backward()
                  scaler.minimize(optimizer, loss)
              else:
                  loss.backward()
                  optimizer.step()
              lr_scheduler.step()
              optimizer.clear_gradients()
              if global_step % args.logging_steps == 0:
                  logger.info(
                      "global step %d/%d, epoch: %d, batch: %d, rank_id: %s, loss: %f, lr: %.10f, speed: %.4f step/s"
                      % (global_step, num_training_steps, epoch, step,
                         paddle.distributed.get_rank(), loss, optimizer.get_lr(),
                         args.logging_steps / (time.time() - tic_train)))
                  tic_train = time.time()
              if global_step % args.save_steps == 0:
                  tic_eval = time.time()
                  if args.task_name == "mnli":
                      evaluate(model, loss_fct, metric, dev_data_loader_matched)
                      evaluate(model, loss_fct, metric,
                               dev_data_loader_mismatched)
                      logger.info("eval done total : %s s" %
                                  (time.time() - tic_eval))
                  else:
                      evaluate(model, loss_fct, metric, dev_data_loader)
                      logger.info("eval done total : %s s" %
                                  (time.time() - tic_eval))
                  if (not args.n_cards > 1) or paddle.distributed.get_rank() == 0:
                      output_dir = os.path.join(args.output_dir,
                                                "%s_ft_model_%d.pdparams" %
                                                (args.task_name, global_step))
                      if not os.path.exists(output_dir):
                          os.makedirs(output_dir)
                      # Need better way to get inner model of DataParallel
                      model_to_save = model._layers if isinstance(
                          model, paddle.DataParallel) else model
                      model_to_save.save_pretrained(output_dir)
                      tokenizer.save_pretrained(output_dir)
  
  
  def print_arguments(args):
      """print arguments"""
      print('-----------  Configuration Arguments -----------')
      for arg, value in sorted(vars(args).items()):
          print('%s: %s' % (arg, value))
      print('------------------------------------------------')
  
  
  if __name__ == "__main__":
      args = parse_args()
      print_arguments(args)
      if args.n_cards > 1 and args.select_device == "gpu":
          paddle.distributed.spawn(do_train, args=(args, ), nprocs=args.n_cards)
      else:
          do_train(args)
  
apis: numpy.array numpy.random.seed