code: # coding=utf-8
  # Copyright (c) 2019, NVIDIA CORPORATION.  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.
  
  """argparser configuration"""
  
  import argparse
  import os
  import torch
  import deepspeed
  
  
  def add_model_config_args(parser):
      """Model arguments"""
  
      group = parser.add_argument_group('model', 'model configuration')
  
      group.add_argument('--pretrained-bert', action='store_true',
                         help='use a pretrained bert-large-uncased model instead'
                         'of initializing from scratch. See '
                         '--tokenizer-model-type to specify which pretrained '
                         'BERT model to use')
      group.add_argument('--attention-dropout', type=float, default=0.1,
                         help='dropout probability for attention weights')
      group.add_argument('--num-attention-heads', type=int, default=16,
                         help='num of transformer attention heads')
      group.add_argument('--hidden-size', type=int, default=1024,
                         help='tansformer hidden size')
      group.add_argument('--intermediate-size', type=int, default=None,
                         help='transformer embedding dimension for FFN'
                         'set to 4*`--hidden-size` if it is None')
      group.add_argument('--num-layers', type=int, default=24,
                         help='num decoder layers')
      group.add_argument('--layernorm-epsilon', type=float, default=1e-5,
                         help='layer norm epsilon')
      group.add_argument('--hidden-dropout', type=float, default=0.1,
                         help='dropout probability for hidden state transformer')
      group.add_argument('--max-position-embeddings', type=int, default=512,
                         help='maximum number of position embeddings to use')
      group.add_argument('--vocab-size', type=int, default=30522,
                         help='vocab size to use for non-character-level '
                         'tokenization. This value will only be used when '
                         'creating a tokenizer')
      group.add_argument('--deep-init', action='store_true',
                         help='initialize bert model similar to gpt2 model.'
                         'scales initialization of projection layers by a '
                         'factor of 1/sqrt(2N). Necessary to train bert '
                         'models larger than BERT-Large.')
      group.add_argument('--make-vocab-size-divisible-by', type=int, default=128,
                         help='Pad the vocab size to be divisible by this value.'
                         'This is added for computational efficieny reasons.')
      group.add_argument('--cpu-optimizer', action='store_true',
                                     help='Run optimizer on CPU')
      group.add_argument('--cpu_torch_adam', action='store_true',
                                     help='Use Torch Adam as optimizer on CPU.')
  
      return parser
  
  
  def add_fp16_config_args(parser):
      """Mixed precision arguments."""
  
      group = parser.add_argument_group('fp16', 'fp16 configurations')
  
      group.add_argument('--fp16', action='store_true',
                         help='Run model in fp16 mode')
      group.add_argument('--fp32-embedding', action='store_true',
                         help='embedding in fp32')
      group.add_argument('--fp32-layernorm', action='store_true',
                         help='layer norm in fp32')
      group.add_argument('--fp32-tokentypes', action='store_true',
                         help='embedding token types in fp32')
      group.add_argument('--fp32-allreduce', action='store_true',
                         help='all-reduce in fp32')
      group.add_argument('--hysteresis', type=int, default=2,
                         help='hysteresis for dynamic loss scaling')
      group.add_argument('--loss-scale', type=float, default=None,
                         help='Static loss scaling, positive power of 2 '
                         'values can improve fp16 convergence. If None, dynamic'
                         'loss scaling is used.')
      group.add_argument('--loss-scale-window', type=float, default=1000,
                         help='Window over which to raise/lower dynamic scale')
      group.add_argument('--min-scale', type=float, default=1,
                         help='Minimum loss scale for dynamic loss scale')
  
      return parser
  
  
  def add_training_args(parser):
      """Training arguments."""
  
      group = parser.add_argument_group('train', 'training configurations')
  
      group.add_argument('--do_train', action='store_true',
                         help="Do training")
      group.add_argument('--do_eval', action='store_true',
                         help="Do evaluation")
      group.add_argument('--zero_shot', action="store_true",
                          help="do zero-shot")
      group.add_argument('--batch-size', type=int, default=4,
                         help='Data Loader batch size')
      group.add_argument('--weight-decay', type=float, default=0.01,
                         help='weight decay coefficient for L2 regularization')
      group.add_argument('--checkpoint-activations', action='store_true',
                         help='checkpoint activation to allow for training '
                         'with larger models and sequences')
      group.add_argument('--checkpoint-num-layers', type=int, default=1,
                         help='chunk size (number of layers) for checkpointing')
      group.add_argument('--deepspeed-activation-checkpointing', action='store_true',
                         help='uses activation checkpointing from deepspeed')
      group.add_argument('--clip-grad', type=float, default=1.0,
                         help='gradient clipping')
      group.add_argument('--epoch', type=int, default=10,
                         help='total number of iterations to train over all training runs')
      group.add_argument('--log-interval', type=int, default=100,
                         help='report interval')
      group.add_argument('--exit-interval', type=int, default=None,
                         help='Exit the program after this many new iterations.')
  
      group.add_argument('--seed', type=int, default=1234,
                         help='random seed')
      # Batch prodecuer arguments
      group.add_argument('--reset-position-ids', action='store_true',
                         help='Reset posistion ids after end-of-document token.')
      group.add_argument('--reset-attention-mask', action='store_true',
                         help='Reset self attention maske after '
                         'end-of-document token.')
      
      # Learning rate.
      group.add_argument('--lr-decay-iters', type=int, default=None,
                         help='number of iterations to decay LR over,'
                         ' If None defaults to `--train-iters`*`--epochs`')
      group.add_argument('--lr-decay-style', type=str, default='linear',
                         choices=['constant', 'linear', 'cosine', 'exponential'],
                         help='learning rate decay function')
      group.add_argument('--lr', type=float, default=1.0e-4,
                         help='initial learning rate')
      group.add_argument('--warmup', type=float, default=0.01,
                         help='percentage of data to warmup on (.01 = 1% of all '
                         'training iters). Default 0.01')
      # model checkpointing
      group.add_argument('--save', type=str, default=None,
                         help='Output directory to save checkpoints to.')
      group.add_argument('--save-interval', type=int, default=5000,
                         help='number of iterations between saves')
      group.add_argument('--no-save-optim', action='store_true',
                         help='Do not save current optimizer.')
      group.add_argument('--no-save-rng', action='store_true',
                         help='Do not save current rng state.')
      group.add_argument('--load', type=str, default=None,
                         help='Path to a directory containing a model checkpoint.')
      group.add_argument('--no-load-optim', action='store_true',
                         help='Do not load optimizer when loading checkpoint.')
      group.add_argument('--no-load-rng', action='store_true',
                         help='Do not load rng state when loading checkpoint.')
      group.add_argument('--finetune', action='store_true',
                         help='Load model for finetuning. Do not load optimizer '
                         'or rng state from checkpoint and set iteration to 0. '
                         'Assumed when loading a release checkpoint.')
      # distributed training args
      group.add_argument('--distributed-backend', default='nccl',
                         help='which backend to use for distributed '
                         'training. One of [gloo, nccl]')
  
      group.add_argument('--local_rank', type=int, default=None,
                         help='local rank passed from distributed launcher.')
  
      group.add_argument('--results_dir', type=str, default=None,
                         help='The dir to save the model.')
      group.add_argument('--model_name', type=str, default="test",
                         help="The name you give to the model.")
  
      # eval
      group.add_argument('--eval_ckpt_path', type=str, default=None,
                         help='The checkpoint path used for evaluation')
  
      return parser
  
  
  def add_evaluation_args(parser):
      """Evaluation arguments."""
  
      group = parser.add_argument_group('validation', 'validation configurations')
  
      group.add_argument('--eval-batch-size', type=int, default=None,
                         help='Data Loader batch size for evaluation datasets.'
                         'Defaults to `--batch-size`')
      group.add_argument('--eval-iters', type=int, default=100,
                         help='number of iterations to run for evaluation'
                         'validation/test for')
      group.add_argument('--eval-interval', type=int, default=1000,
                         help='interval between running evaluation on validation set')
      group.add_argument('--eval-seq-length', type=int, default=None,
                         help='Maximum sequence length to process for '
                         'evaluation. Defaults to `--seq-length`')
      group.add_argument('--eval-max-preds-per-seq', type=int, default=None,
                         help='Maximum number of predictions to use for '
                         'evaluation. Defaults to '
                         'math.ceil(`--eval-seq-length`*.15/10)*10')
      group.add_argument('--overlapping-eval', type=int, default=32,
                         help='sliding window for overlapping eval ')
      group.add_argument('--cloze-eval', action='store_true',
                         help='Evaluation dataset from `--valid-data` is a cloze task')
      group.add_argument('--eval-hf', action='store_true',
                         help='perform evaluation with huggingface openai model.'
                         'use `--load` to specify weights path to be loaded')
      group.add_argument('--load-openai', action='store_true',
                         help='load openai weights into our model. Use `--load` '
                         'to specify weights path to be loaded')
  
      return parser
  
  def add_text_generate_args(parser):
      """Text generate arguments."""
  
      group = parser.add_argument_group('Text generation', 'configurations')
      group.add_argument("--temperature", type=float, default=1.0)
      group.add_argument("--top_p", type=float, default=0.0)
      group.add_argument("--top_k", type=int, default=0)
      group.add_argument("--out-seq-length", type=int, default=256)
      return parser
  
  
  def add_data_args(parser):
      """Train/valid/test data arguments."""
  
      group = parser.add_argument_group('data', 'data configurations')
      group.add_argument('--data_dir', type=str, required=True,
                         help="Training data dir")
      group.add_argument('--mmap-warmup', action='store_true',
                         help='Warm up mmap files.')
      group.add_argument('--model-parallel-size', type=int, default=1,
                         help='size of the model parallel.')
      group.add_argument('--shuffle', action='store_true',
                         help='Shuffle data. Shuffling is deterministic '
                         'based on seed and current epoch.')
      group.add_argument('--use-npy-data-loader', action='store_true',
                         help='Use the numpy data loader. If set, then'
                         'train-data-path, val-data-path, and test-data-path'
                         'should also be provided.')
      group.add_argument('--num-workers', type=int, default=2,
                         help="""Number of workers to use for dataloading""")
      group.add_argument('--tokenizer-model-type', type=str,
                         default='bert-large-uncased',
                         help="Model type to use for sentencepiece tokenization \
                         (one of ['bpe', 'char', 'unigram', 'word']) or \
                         bert vocab to use for BertWordPieceTokenizer (one of \
                         ['bert-large-uncased', 'bert-large-cased', etc.])")
      group.add_argument('--tokenizer-path', type=str, default='tokenizer.model',
                         help='path used to save/load sentencepiece tokenization '
                         'models')
      group.add_argument('--tokenizer-type', type=str,
                         default='BertWordPieceTokenizer',
                         choices=['CharacterLevelTokenizer',
                                  'SentencePieceTokenizer',
                                  'BertWordPieceTokenizer',
                                  'GPT2BPETokenizer'],
                         help='what type of tokenizer to use')
      group.add_argument("--cache-dir", default=None, type=str,
                         help="Where to store pre-trained BERT downloads")
      group.add_argument('--use-tfrecords', action='store_true',
                         help='load `--train-data`, `--valid-data`, '
                         '`--test-data` from BERT tf records instead of '
                         'normal data pipeline')
      group.add_argument('--seq-length', type=int, default=512,
                         help="Maximum sequence length to process")
      group.add_argument('--max-preds-per-seq', type=int, default=None,
                         help='Maximum number of predictions to use per sequence.'
                         'Defaults to math.ceil(`--seq-length`*.15/10)*10.'
                         'MUST BE SPECIFIED IF `--use-tfrecords` is True.')
  
      return parser
  
  def get_args():
      """Parse all the args."""
  
      parser = argparse.ArgumentParser(description='PyTorch BERT Model')
      parser = add_model_config_args(parser)
      parser = add_fp16_config_args(parser)
      parser = add_training_args(parser)
      parser = add_evaluation_args(parser)
      parser = add_text_generate_args(parser)
      parser = add_data_args(parser)
  
      # Include DeepSpeed configuration arguments
      parser = deepspeed.add_config_arguments(parser)
  
      args = parser.parse_args()
  
      if not args.data_dir:
          print('WARNING: No data specified')
  
      args.cuda = torch.cuda.is_available()
  
      args.rank = int(os.getenv('RANK', '0'))
      args.world_size = int(os.getenv("WORLD_SIZE", '1'))
  
      if os.getenv('OMPI_COMM_WORLD_LOCAL_RANK'):
          # We are using (OpenMPI) mpirun for launching distributed data parallel processes
          local_rank = int(os.getenv('OMPI_COMM_WORLD_LOCAL_RANK'))
          local_size = int(os.getenv('OMPI_COMM_WORLD_LOCAL_SIZE'))
  
          # Possibly running with Slurm
          num_nodes = int(os.getenv('SLURM_JOB_NUM_NODES', '1'))
          nodeid = int(os.getenv('SLURM_NODEID', '0'))
  
          args.local_rank = local_rank
          args.rank = nodeid*local_size + local_rank
          args.world_size = num_nodes*local_size
  
      args.model_parallel_size = min(args.model_parallel_size, args.world_size)
      if args.rank == 0:
          print('using world size: {} and model-parallel size: {} '.format(
              args.world_size, args.model_parallel_size))
  
      args.dynamic_loss_scale = False
      if args.loss_scale is None:
          args.dynamic_loss_scale = True
          if args.rank == 0:
              print(' > using dynamic loss scaling')
  
      # The args fp32_* or fp16_* meant to be active when the
      # args fp16 is set. So the default behaviour should all
      # be false.
      if not args.fp16:
          args.fp32_embedding = False
          args.fp32_tokentypes = False
          args.fp32_layernorm = False
  
      return args
  
apis: torch.cuda.is_available