arguments.py

# 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
import json
from utils import get_hostname
import random
import socket
from datetime import datetime


def add_model_config_args(parser):
    """Model arguments"""

    group = parser.add_argument_group('model', 'model configuration')

    group.add_argument('--transformer-xl', action='store_true', help='use transformer-xl for training')
    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('--encoder-decoder', action='store_true',
                       help="use the encoder-decoder architecture for blocklm")
    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('--output-dropout', type=float, default=0.1,
                       help='dropout probability for pooled output')
    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')
    group.add_argument('--attention-scale', type=float, default=1.0)
    return parser


def add_training_args(parser):
    """Training arguments."""

    group = parser.add_argument_group('train', 'training configurations')

    group.add_argument('--experiment-name', type=str, default="gpt-345M",
                       help="The experiment name for summary and checkpoint")
    group.add_argument('--batch-size', type=int, default=4,
                       help='Data Loader batch size')
    group.add_argument('--gradient-accumulation-steps', type=int, default=1,
                       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('--epochs', type=int, default=None,
                       help='Number of finetunning epochs. Zero results in evaluation only.')
    group.add_argument('--clip-grad', type=float, default=1.0,
                       help='gradient clipping')
    group.add_argument('--train-iters', type=int, default=0,
                       help='total number of iterations to train over all training runs')
    group.add_argument('--label-smoothing', type=float, default=0.0)
    group.add_argument('--log-interval', type=int, default=100,
                       help='report interval')
    group.add_argument('--summary-dir', type=str, default="", help="The directory to store the summary")
    group.add_argument('--seed', type=int, default=1234, help='random seed')
    # Batch producer 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-decay-ratio', type=float, default=0.1)
    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')
    group.add_argument('--switch-linear', action='store_true', help="Switch to linear decay for cosine decay")
    # model checkpointing
    group.add_argument('--save', type=str, default=None,
                       help='Output directory to save checkpoints to.')
    group.add_argument('--new-save-directory', action='store_true')
    group.add_argument('--save-epoch', type=int, default=1,
                       help='number of epochs between saves')
    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('--no-load-lr-scheduler', action='store_true',
                       help='Do not load lr scheduler when loading checkpoint.')
    group.add_argument('--no-deepspeed-load', action='store_true', help='Not use deepspeed 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.')
    group.add_argument('--resume-dataloader', action='store_true',
                       help='Resume the dataloader when resuming training. '
                            'Does not apply to tfrecords dataloader, try resuming'
                            'with a different seed in this case.')
    # distributed training args
    group.add_argument('--distributed-backend', default='nccl',
                       help='which backend to use for distributed training. One of [gloo, nccl]',
                       choices=['nccl', 'gloo'])
    group.add_argument('--DDP-impl', default='torch', choices=['local', 'torch', 'none'],
                       help='which DistributedDataParallel implementation to use.')

    group.add_argument('--local_rank', type=int, default=None,
                       help='local rank passed from distributed launcher')
    # BlockLM training args
    group.add_argument('--block-lm', action='store_true', help="whether use the BlockLM pre-training")
    group.add_argument('--masked-lm', action='store_true', help='whether to use the mlm objective')
    group.add_argument('--bert-prob', type=float, default=0.5)
    group.add_argument('--gpt-infill-prob', type=float, default=0.5)
    group.add_argument('--gpt-min-ratio', type=float, default=0.5)
    group.add_argument('--gap-sentence-prob', type=float, default=0.0)
    group.add_argument('--gap-sentence-ratio', type=float, default=0.15)
    group.add_argument('--avg-block-length', type=int, default=3)
    group.add_argument('--short-seq-prob', type=float, default=0.0)
    group.add_argument('--single-span-prob', type=float, default=0.0)
    group.add_argument('--task-mask', action='store_true', help="Use different mask for generation and blank filling")
    group.add_argument('--no-shuffle-block', action='store_true', help="not shuffle the blocks when filling the blank")
    group.add_argument('--no-block-position', action='store_true',
                       help='Use (rough) absolute positions instead of block positions')
    group.add_argument('--sentinel-token', action='store_true',
                       help="Use sentinel (mask) tokens to replace 2d position encoding")
    group.add_argument('--block-mask-prob', type=float, default=0.0)
    group.add_argument('--context-mask-ratio', type=float, default=0.0)
    group.add_argument('--random-position', action='store_true',
                       help="Use random start position to cover all the position embeddings")
    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-epoch', type=int, default=1,
                       help='epoch 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)

    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)
    group.add_argument("--num-beams", type=int, default=1)
    group.add_argument("--length-penalty", type=float, default=0.0)
    group.add_argument("--no-repeat-ngram-size", type=int, default=0)
    group.add_argument("--min-tgt-length", type=int, default=0)
    group.add_argument("--select-topk", action='store_true')
    group.add_argument("--blank-maskratio", type=float, default=0.1)
    return parser


def add_data_args(parser):
    """Train/valid/test data arguments."""

    group = parser.add_argument_group('data', 'data configurations')

    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('--filter-english', action='store_true')
    group.add_argument('--train-data', nargs='+', default=None,
                       help='Whitespace separated filenames or corpora names '
                            'for training.')
    group.add_argument('--valid-data', nargs='*', default=None,
                       help="""Filename for validation data.""")
    group.add_argument('--test-data', nargs='*', default=None,
                       help="""Filename for testing""")
    group.add_argument('--data-dir', type=str, default=None, help="The data path to all the data files")
    group.add_argument('--input-data-sizes-file', type=str, default='sizes.txt',
                       help='the filename containing all the shards sizes')

    group.add_argument('--delim', default=',',
                       help='delimiter used to parse csv data files')
    group.add_argument('--text-key', default='sentence',
                       help='key to use to extract text from json/csv')
    group.add_argument('--eval-text-key', default=None,
                       help='key to use to extract text from '
                            'json/csv evaluation datasets')
    group.add_argument('--split', default='1000,1,1',
                       help='comma-separated list of proportions for training,'
                            ' validation, and test split')

    group.add_argument('--no-lazy-loader', action='store_true',
                       help='whether to lazy read the data set')
    group.add_argument('--half-lazy-loader', action='store_true')
    group.add_argument('--loader-scatter', type=int, default=None, help='Number of scatters to use for dataloaders')
    group.add_argument('--loose-json', action='store_true',
                       help='Use loose json (one json-formatted string per '
                            'newline), instead of tight json (data file is one '
                            'json string)')
    group.add_argument('--presplit-sentences', action='store_true',
                       help='Dataset content consists of documents where '
                            'each document consists of newline separated sentences')
    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=None,
                       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',
                                'ChineseSPTokenizer'],
                       help='what type of tokenizer to use')
    group.add_argument('--fix-command-token', action='store_true')
    group.add_argument('--no-pre-tokenize', action='store_true')
    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('--mem-length', type=int, default=0,
                       help="The memory length to preserve")
    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.')
    group.add_argument('--non-sentence-start', type=float, default=0.0)
    group.add_argument('--sample-one-document', action='store_true', help='only sample one document in one sample')
    group.add_argument('--load-splits', type=str, default=None, help="The path to load split indices from")
    group.add_argument('--save-splits', type=str, default=None, help="The path to save split indices to")
    group.add_argument('--save-test-data', type=str, default=None, help="The path to save the test data")
    group.add_argument('--multi-task-data', nargs='*', default=None,
                       help="Downsteam task names for multi-task pre-training")
    group.add_argument('--multi-task-ratio', type=float, default=0.0, help="Ratio for multi-task pre-training")
    group.add_argument('--multi-seq-length', type=int, default=None)
    group.add_argument('--multi-batch-size', type=int, default=None)
    return parser


def add_finetune_config_args(parser):
    group = parser.add_argument_group('finetune', 'finetune configurations')
    group.add_argument('--task', type=str, help='Task name.')
    group.add_argument('--load-pretrained', type=str, help="Load pretrained model", default=None)
    group.add_argument('--pool-token', type=str, choices=['start', 'pad', 'cls'],
                       help='The token to pool the sequence representation', default='cls')
    group.add_argument('--cloze-eval', action='store_true', help='Evaluation dataset with cloze task')
    group.add_argument('--multi-token', action='store_true', help='Use multi token for cloze evaluation')
    group.add_argument('--segment-length', type=int, default=0, help="The maximum segment length for cloze evaluation")
    group.add_argument('--loss-func', type=str, choices=["cross_entropy", "hinge", "generative", "mix"],
                       default="cross_entropy")
    group.add_argument('--block-lm-ratio', type=float, default=0.0)
    group.add_argument('--adapet', action='store_true', help="Use the decoupled cross entropy loss in AdaPET")
    group.add_argument('--pattern-id', type=int, default=0)
    group.add_argument('--fast-decode', action='store_true',
                       help="Fast decode for multi-token cloze. Can only be used without checkpoint activation.")
    group.add_argument('--few-superglue', action='store_true')
    group.add_argument('--eval-valid', action='store_true', help="Whether evaluate on the valid set")
    group.add_argument('--validation-metric', type=str, default=None)
    group.add_argument('--unidirectional', action='store_true', help="Use the left to right language model")
    group.add_argument('--src-seq-length', type=int, default=None)
    group.add_argument('--tgt-seq-length', type=int, default=None)
    group.add_argument('--adam-beta1', type=float, default=0.9)
    group.add_argument('--adam-beta2', type=float, default=0.999)
    group.add_argument('--adam-eps', type=float, default=1e-8)
    group.add_argument('--optimizer', type=str, choices=['adam', 'adafactor'], default='adam')
    group.add_argument('--wsc-negative', action='store_true')
    group.add_argument('--overwrite', action='store_true')
    group.add_argument('--no-validation', action='store_true')
    # Continuous prompt arguments
    group.add_argument('--continuous-prompt', action='store_true', help="Use continuous prompt for PET")
    group.add_argument('--num-prompt-tokens', type=int, default=0)
    group.add_argument('--prompt-func', default='lstm', choices=["lstm", "mlp", "none"])
    group.add_argument('--freeze-transformer', action='store_true', default=False)
    group.add_argument('--tune-prefix-layers', type=int, default=None)
    group.add_argument('--prefix-prompt', type=int, default=0)
    group.add_argument('--prompt-init', action='store_true', default=False)
    return parser


def get_random_port():
    def net_is_used(port,ip='127.0.0.1'):
        s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
        try:
            s.connect((ip,port))
            s.shutdown(2)
            return True
        except:
            return False
    while True:
        port = random.randint(10000,65535)
        if net_is_used(port):
            continue
        else:
            break
    return port


def add_custom_args(parser: argparse.ArgumentParser):
    group = parser.add_argument_group('custom', 'custom configurations')
    group.add_argument('--spare_port', type=int, default=get_random_port())
    group.add_argument('--custom_tmp_result', type=str, default=None)
    group.add_argument('--custom_first_eval', action='store_true')
    group.add_argument('--custom_logits_paralle', action='store_true', help='forward_step中得到的logits是否是并行的,根据tasks自动设置,可用于蒸馏模型判断')
    group.add_argument('--custom_current_epoch', type=int, default=0, help='当前的epoch,一般微调才有')
    group.add_argument('--custom_no_save_checkpoint', action='store_true', help='不保存检查点和预测值,意味着目录只读取')
    group.add_argument('--custom_no_summary_writer', action='store_true', help='不保tensorboard,意味着目录只读取')
    group.add_argument('--forward_repeat_num', type=int, default=0, help='forward_step对相同数据多重复运行几次,1就代表数据会过2遍.运行loss结果累加,其他非loss值取第0次结果.evaluate的时候不会重复')
    group.add_argument('--forward_repeat_current_n', type=int, default=0, help='当前是重复的第几次,自动分配不用设置,0表示无重复,1表示重复的第一次')
    group.add_argument('--ignore_first_backward_gard', action='store_true', help='当forward_repeat_num大于0的时候是否忽略第1次反向传播的梯度,目前和梯度累积不兼容.可用于LRC_BERT的gradient perturbation等.evaluate时不考虑')
    group.add_argument('--ft_final_save', action='store_true', help='是否在微调的最后一轮保存模型,将覆盖best模型latest_checkpointed_iteration')
    group.add_argument('--save_interval_time', type=float, default=0, help='隔多少小时保存一次模型,大于0有效.可以和其他save参数一起使用')
    group.add_argument('--zero_stage', type=int, default=0, help='zero_optimization.stage')
    group.add_argument('--cpu_offload', action='store_true', help='zero_optimization.cpu_offload, zero_stage=1/2/3')
    group.add_argument('--args_to_ds_config', action='store_true', help='args里面的 fp16/batch-size/gradient-accumulation-steps/zero_stage/cpu_offload 参数是否反向写入deepspeed配置文件,生成临时配置文件目录')
    group.add_argument('--fix_variable_num_choices', action='store_true', help='对于一些多分类样本的nlu任务,使用这个保证每次样本的num_choices都保持一致.不够的0填充,多于max_candidates_per_question的则切掉尾部候选(尾部有标签的把标签放置在第0个)')
    group.add_argument('--current_gradient_accumulation_steps', type=int, default=0, help='记录当前的gas,自动分配不用设置')
    group.add_argument('--single_step', action='store_true', help='记录当前train_step中的single_step,自动分配不用设置,用于处理gradient_accumulation_steps问题')
    # 注意: 以下可能和教师模型共享参数
    group.add_argument('--map_vocab_size', type=float, default=0, help='映射的词数量,蒸馏或读取模型文件时使用.0-1表示占vacab-size的比例')
    group.add_argument('--unmap_vocab_output', action='store_true', help='在使用map_vocab_size时GLM是否不返回映射后的结果,使用这个需要下游所有任务配合乱序且不全的词表,但可以减少时空消耗.可能导致一些使用logits的蒸馏(例如一些自定义的pre_loss)产生兼容性问题,同时因为维度变化可能导致一些任务的loss产生一点变化')
    group.add_argument('--inverted_bottleneck_mode', action='store_true', help='可用于MobileBERT方法')
    group.add_argument('--ib_hidden_size', type=int, default=128, help='IB结构中的隐层维度,线性层依然和--hidden-size一致')
    group.add_argument('--ib_ffn_num', type=int, default=4, help='IB结构中的FFN重复次数')
    group.add_argument('--ib_word_emb', type=int, default=0, help='IB结构中的词向量维度,None或0表示不使用.可以独立于inverted_bottleneck_mode使用,不会改变weight维度')
    group.add_argument('--compress_word_emb', type=int, default=0, help='ALBERT词表压缩方法的词向量维度,None或0表示不使用.会改变weight维度')
    group.add_argument('--cross_layer_parameter_sharing', action='store_true', help='共享所有transformer层参数')
    return parser


def generate_unique():
    return datetime.now().strftime('%y%m%d_%H%M%S.%f') + '_' + str(random.random())[2:]


def restructure_ds_config(args):
    if not (hasattr(args, "deepspeed") and args.deepspeed and args.deepspeed_config is not None):
        return False
    with open(args.deepspeed_config) as file:
        deepspeed_config = json.load(file)
    has_change = False
    if args.batch_size != deepspeed_config["train_micro_batch_size_per_gpu"]:
        has_change = True
        deepspeed_config["train_micro_batch_size_per_gpu"] = args.batch_size
        print(f'>> ds.train_micro_batch_size_per_gpu -> {args.batch_size}')
    if args.gradient_accumulation_steps != deepspeed_config["gradient_accumulation_steps"]:
        has_change = True
        deepspeed_config["gradient_accumulation_steps"] = args.gradient_accumulation_steps
        print(f'>> ds.gradient_accumulation_steps -> {args.gradient_accumulation_steps}')
    if args.fp16 != deepspeed_config.get('fp16', {}).get('enabled', {}):
        has_change = True
        deepspeed_config.setdefault('fp16', {})['enabled'] = args.fp16
        print(f'>> ds.fp16.enabled -> {args.fp16}')
    zero_stage = deepspeed_config.get('zero_optimization', {}).get('stage', {})
    zero_stage = zero_stage if isinstance(zero_stage, int) else 0
    if args.zero_stage != zero_stage:
        has_change = True
        if 'zero_optimization' not in deepspeed_config:
            deepspeed_config['zero_optimization'] = {
                'stage': args.zero_stage,
                'contiguous_gradients': False,
                'overlap_comm': True,
                'reduce_scatter': True,
                'reduce_bucket_size': 5e7,
                'allgather_bucket_size': 5e7,
                'cpu_offload': False,
            }
            deepspeed_config['zero_allow_untested_optimizer'] = True
            print(f'>> ds.zero_optimization.zero_allow_untested_optimizer -> True')
        else:
            deepspeed_config['zero_optimization']['stage'] = args.zero_stage
        zero_stage = args.zero_stage
        print(f'>> ds.zero_optimization.stage -> {args.zero_stage}')
    if args.cpu_offload != deepspeed_config.get('zero_optimization', {}).get('cpu_offload', {}) and zero_stage > 0:
        has_change = True
        deepspeed_config.setdefault('zero_optimization', {})['cpu_offload'] = args.cpu_offload
        print(f'>> ds.zero_optimization.cpu_offload -> {args.cpu_offload}')
    if not has_change:
        return False
    args.deepspeed_config = os.path.join('tmp_deepspeed_config', generate_unique() + '.json')
    print(f'>> args.deepspeed_config -> {args.deepspeed_config}')
    if not os.path.exists('tmp_deepspeed_config'):
        os.mkdir('tmp_deepspeed_config')
    with open(args.deepspeed_config, 'w', encoding='utf8') as w:
        json.dump(deepspeed_config, w, ensure_ascii=False, indent=2, sort_keys=True)
    return True


def get_args(arg_list=None):
    """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)
    parser = add_finetune_config_args(parser)
    parser = add_custom_args(parser)

    # Include DeepSpeed configuration arguments
    parser = deepspeed.add_config_arguments(parser)

    args = parser.parse_args(arg_list)
    if not args.train_data and not args.data_dir:
        print('WARNING: No training 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 hasattr(args, 'deepspeed_mpi') and args.deepspeed_mpi:
        mpi_define_env(args)
    elif 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

    if args.args_to_ds_config:
        restructure_ds_config(args)
    if hasattr(args, "deepspeed") and args.deepspeed and args.deepspeed_config is not None:
        with open(args.deepspeed_config) as file:
            deepspeed_config = json.load(file)
        if "train_micro_batch_size_per_gpu" in deepspeed_config:
            args.batch_size = deepspeed_config["train_micro_batch_size_per_gpu"]
        if "gradient_accumulation_steps" in deepspeed_config:
            args.gradient_accumulation_steps = deepspeed_config["gradient_accumulation_steps"]
        else:
            args.gradient_accumulation_steps = 1
        if "optimizer" in deepspeed_config:
            optimizer_params_config = deepspeed_config["optimizer"].get("params", {})
            args.lr = optimizer_params_config.get("lr", args.lr)
            args.weight_decay = optimizer_params_config.get("weight_decay", args.weight_decay)
    return args


def mpi_define_env(args):
    from mpi4py import MPI
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    world_size = comm.Get_size()

    master_addr = None
    if rank == 0:
        master_addr = get_hostname()
    master_addr = comm.bcast(master_addr, root=0)

    # Determine local rank by assuming hostnames are unique
    proc_name = MPI.Get_processor_name()
    all_procs = comm.allgather(proc_name)
    local_rank = sum([i == proc_name for i in all_procs[:rank]])

    os.environ['RANK'] = str(rank)
    os.environ['WORLD_SIZE'] = str(world_size)
    args.local_rank = local_rank
    args.world_size = world_size
    args.rank = rank
    os.environ['MASTER_ADDR'] = master_addr
    os.environ['MASTER_PORT'] = "29500"  # TORCH_DISTRIBUTED_DEFAULT_PORT = 29500

    print(
        "Discovered MPI settings of world_rank={}, local_rank={}, world_size={}, master_addr={}, master_port={}"
            .format(os.environ['RANK'],
                    args.local_rank,
                    os.environ['WORLD_SIZE'],
                    os.environ['MASTER_ADDR'],
                    os.environ['MASTER_PORT']))