arguments.py

# coding=utf-8
# Copyright (c) 2020, 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.

"""Megatron arguments."""

import argparse
import os

import torch

def parse_args(extra_args_provider=None, defaults={},
               ignore_unknown_args=False, validate=True):
    """Parse all arguments."""
    parser = argparse.ArgumentParser(description='Megatron-LM Arguments',
                                     allow_abbrev=False)

    # Standard arguments.
    parser = _add_network_size_args(parser)
    parser = _add_regularization_args(parser)
    parser = _add_training_args(parser)
    parser = _add_initialization_args(parser)
    parser = _add_learning_rate_args(parser)
    parser = _add_checkpointing_args(parser)
    parser = _add_mixed_precision_args(parser)
    parser = _add_distributed_args(parser)
    parser = _add_validation_args(parser)
    parser = _add_data_args(parser)
    parser = _add_autoresume_args(parser)
    parser = _add_biencoder_args(parser)
    parser = _add_vit_args(parser)
    parser = _add_logging_args(parser)
    parser = _add_inference_args(parser)

    # Custom arguments.
    if extra_args_provider is not None:
        parser = extra_args_provider(parser)

    # Parse.
    if ignore_unknown_args:
        args, _ = parser.parse_known_args()
    else:
        args = parser.parse_args()

    if validate:
        return validate_args(args, defaults)
    return args

def validate_args(args, defaults={}):
    # Distributed args.
    args.rank = int(os.getenv('RANK', '0'))
    args.world_size = int(os.getenv("WORLD_SIZE", '1'))
    # Tensor model parallel size.
    args.tensor_model_parallel_size = min(
        args.tensor_model_parallel_size, args.world_size)
    assert args.world_size % args.tensor_model_parallel_size == 0, 'world size'\
        ' ({}) is not divisible by tensor model parallel size ({})'.format(
            args.world_size, args.tensor_model_parallel_size)
    # Pipeline model parallel size.
    args.pipeline_model_parallel_size = min(
        args.pipeline_model_parallel_size,
        (args.world_size // args.tensor_model_parallel_size))
    # Checks.
    model_parallel_size = args.pipeline_model_parallel_size * \
                          args.tensor_model_parallel_size
    assert args.world_size % model_parallel_size == 0, 'world size is not'\
        ' divisible by tensor parallel size ({}) times pipeline parallel ' \
        'size ({})'.format(args.world_size, args.tensor_model_parallel_size,
                           args.pipeline_model_parallel_size)
    args.data_parallel_size = args.world_size // model_parallel_size
    if args.rank == 0:
        print('using world size: {}, data-parallel-size: {}, '
              'tensor-model-parallel size: {}, '
              'pipeline-model-parallel size: {} '.format(
                  args.world_size, args.data_parallel_size,
                  args.tensor_model_parallel_size,
                  args.pipeline_model_parallel_size), flush=True)
    if args.pipeline_model_parallel_size > 1:
        if args.pipeline_model_parallel_split_rank is not None:
            assert args.pipeline_model_parallel_split_rank < \
                    args.pipeline_model_parallel_size, 'split rank needs'\
                    ' to be less than pipeline model parallel size ({})'.format(
                            args.pipeline_model_parallel_size)

    # Deprecated arguments
    assert args.batch_size is None, '--batch-size argument is no longer ' \
        'valid, use --micro-batch-size instead'
    del args.batch_size
    assert args.warmup is None, '--warmup argument is no longer valid, use ' \
        '--lr-warmup-fraction instead'
    del args.warmup
    assert args.model_parallel_size is None, '--model-parallel-size is no ' \
        'longer valid, use --tensor-model-parallel-size instead'
    del args.model_parallel_size
    if args.checkpoint_activations:
        args.activations_checkpoint_method = 'uniform'
        if args.rank == 0:
            print('--checkpoint-activations is no longer valid, '
                  'use --activation-checkpoint-method instead. '
                  'Defaulting to activation-checkpoint-method=uniform.')
    del args.checkpoint_activations

    # Set input defaults.
    for key in defaults:
        # For default to be valid, it should not be provided in the
        # arguments that are passed to the program. We check this by
        # ensuring the arg is set to None.
        if getattr(args, key) is not None:
            if args.rank == 0:
                print('WARNING: overriding default arguments for {key}:{v} \
                       with {key}:{v2}'.format(key=key, v=defaults[key],
                                               v2=getattr(args, key)),
                                               flush=True)
        else:
            setattr(args, key, defaults[key])

    # Batch size.
    assert args.micro_batch_size is not None
    assert args.micro_batch_size > 0
    if args.global_batch_size is None:
        args.global_batch_size = args.micro_batch_size * args.data_parallel_size
        if args.rank == 0:
            print('setting global batch size to {}'.format(
                args.global_batch_size), flush=True)
    assert args.global_batch_size > 0
    if args.num_layers_per_virtual_pipeline_stage is not None:
        assert args.pipeline_model_parallel_size > 2, \
            'pipeline-model-parallel size should be greater than 2 with ' \
            'interleaved schedule'
        assert args.num_layers % args.num_layers_per_virtual_pipeline_stage == 0, \
            'number of layers is not divisible by number of layers per virtual ' \
            'pipeline stage'
        args.virtual_pipeline_model_parallel_size = \
            (args.num_layers // args.pipeline_model_parallel_size) // \
            args.num_layers_per_virtual_pipeline_stage
    else:
        args.virtual_pipeline_model_parallel_size = None

    # Parameters dtype.
    args.params_dtype = torch.float
    if args.fp16:
        assert not args.bf16
        args.params_dtype = torch.half
    if args.bf16:
        assert not args.fp16
        args.params_dtype = torch.bfloat16
        # bfloat16 requires gradient accumulation and all-reduce to
        # be done in fp32.
        if not args.accumulate_allreduce_grads_in_fp32:
            args.accumulate_allreduce_grads_in_fp32 = True
            if args.rank == 0:
                print('accumulate and all-reduce gradients in fp32 for '
                      'bfloat16 data type.', flush=True)

    if args.rank == 0:
        print('using {} for parameters ...'.format(args.params_dtype),
              flush=True)

    # If we do accumulation and all-reduces in fp32, we need to have local DDP
    # and we should make sure use-contiguous-buffers-in-local-ddp is not off.
    if args.accumulate_allreduce_grads_in_fp32:
        assert args.DDP_impl == 'local'
        assert args.use_contiguous_buffers_in_local_ddp

    # For torch DDP, we do not use contiguous buffer
    if args.DDP_impl == 'torch':
        args.use_contiguous_buffers_in_local_ddp = False

    if args.dataloader_type is None:
        args.dataloader_type = 'single'

    # Consumed tokens.
    args.consumed_train_samples = 0
    args.consumed_valid_samples = 0

    # Iteration-based training.
    if args.train_iters:
        # If we use iteration-based training, make sure the
        # sample-based options are off.
        assert args.train_samples is None, \
            'expected iteration-based training'
        assert args.lr_decay_samples is None, \
            'expected iteration-based learning rate decay'
        assert args.lr_warmup_samples == 0, \
            'expected iteration-based learning rate warmup'
        assert args.rampup_batch_size is None, \
            'expected no batch-size rampup for iteration-based training'
        if args.lr_warmup_fraction is not None:
            assert args.lr_warmup_iters == 0, \
                'can only specify one of lr-warmup-fraction and lr-warmup-iters'

    # Sample-based training.
    if args.train_samples:
        # If we use sample-based training, make sure the
        # iteration-based options are off.
        assert args.train_iters is None, \
            'expected sample-based training'
        assert args.lr_decay_iters is None, \
            'expected sample-based learning rate decay'
        assert args.lr_warmup_iters == 0, \
            'expected sample-based learnig rate warmup'
        if args.lr_warmup_fraction is not None:
            assert args.lr_warmup_samples == 0, \
                'can only specify one of lr-warmup-fraction ' \
                'and lr-warmup-samples'

    # Check required arguments.
    required_args = ['num_layers', 'hidden_size', 'num_attention_heads',
                     'max_position_embeddings']
    for req_arg in required_args:
        _check_arg_is_not_none(args, req_arg)

    # Checks.
    if args.ffn_hidden_size is None:
        args.ffn_hidden_size = 4 * args.hidden_size

    if args.kv_channels is None:
        assert args.hidden_size % args.num_attention_heads == 0
        args.kv_channels = args.hidden_size // args.num_attention_heads

    if args.seq_length is not None:
        assert args.encoder_seq_length is None
        args.encoder_seq_length = args.seq_length
    else:
        assert args.encoder_seq_length is not None
        args.seq_length = args.encoder_seq_length

    if args.seq_length is not None:
        assert args.max_position_embeddings >= args.seq_length
    if args.decoder_seq_length is not None:
        assert args.max_position_embeddings >= args.decoder_seq_length
    if args.lr is not None:
        assert args.min_lr <= args.lr
    if args.save is not None:
        assert args.save_interval is not None
    # Mixed precision checks.
    if args.fp16_lm_cross_entropy:
        assert args.fp16, 'lm cross entropy in fp16 only support in fp16 mode.'
    if args.fp32_residual_connection:
        assert args.fp16 or args.bf16, \
            'residual connection in fp32 only supported when using fp16 or bf16.'
    # Activation checkpointing.
    if args.distribute_checkpointed_activations:
        assert args.tensor_model_parallel_size > 1, 'can distribute ' \
            'checkpointed activations only across tensor model ' \
            'parallel groups'
        assert args.activations_checkpoint_method is not None, \
            'for distribute-checkpointed-activations to work you '\
            'need to use a activation-checkpoint method '
        assert args.num_layers_per_virtual_pipeline_stage is None, \
            'currently distrobuted checkpoint activations only supported for ' \
            'nointerleaved pipeline parallelism'

    _print_args(args)
    return args


def _print_args(args):
    """Print arguments."""
    if args.rank == 0:
        print('------------------------ arguments ------------------------',
              flush=True)
        str_list = []
        for arg in vars(args):
            dots = '.' * (48 - len(arg))
            str_list.append('  {} {} {}'.format(arg, dots, getattr(args, arg)))
        for arg in sorted(str_list, key=lambda x: x.lower()):
            print(arg, flush=True)
        print('-------------------- end of arguments ---------------------',
              flush=True)


def _check_arg_is_not_none(args, arg):
    assert getattr(args, arg) is not None, '{} argument is None'.format(arg)


def _add_inference_args(parser):
    group = parser.add_argument_group(title='inference')

    group.add_argument('--inference-batch-times-seqlen-threshold',
                       type=int, default=512,
                       help='During inference, if batch-size times '
                       'sequence-length is smaller than this threshold '
                       'then we will not use pipelining, otherwise we will.')

    return parser

    
def _add_network_size_args(parser):
    group = parser.add_argument_group(title='network size')

    group.add_argument('--num-layers', type=int, default=None,
                       help='Number of transformer layers.')
    group.add_argument('--hidden-size', type=int, default=None,
                       help='Tansformer hidden size.')
    group.add_argument('--ffn-hidden-size', type=int, default=None,
                       help='Transformer Feed-Forward Network hidden size. '
                       'This is set to 4*hidden-size if not provided')
    group.add_argument('--num-attention-heads', type=int, default=None,
                       help='Number of transformer attention heads.')
    group.add_argument('--kv-channels', type=int, default=None,
                       help='Projection weights dimension in multi-head '
                       'attention. This is set to '
                       '   args.hidden_size // args.num_attention_heads '
                       'if not provided.')
    group.add_argument('--max-position-embeddings', type=int, default=None,
                       help='Maximum number of position embeddings to use. '
                       'This is the size of position embedding.')
    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('--layernorm-epsilon', type=float, default=1e-5,
                       help='Layer norm epsilon.')
    group.add_argument('--apply-residual-connection-post-layernorm',
                       action='store_true',
                       help='If set, use original BERT residula connection '
                       'ordering.')
    group.add_argument('--openai-gelu', action='store_true',
                       help='Use OpenAIs GeLU implementation. This option'
                       'should not be used unless for backward compatibility'
                       'reasons.')
    group.add_argument('--onnx-safe', type=bool, required=False,
                       help='Use workarounds for known problems with '
                       'Torch ONNX exporter')
    group.add_argument('--bert-no-binary-head', action='store_false',
                       help='Disable BERT binary head.',
                       dest='bert_binary_head')

    return parser


def _add_logging_args(parser):
    group = parser.add_argument_group(title='logging')

    group.add_argument('--log-params-norm', action='store_true',
                       help='If set, calculate and log parameters norm.')
    group.add_argument('--log-num-zeros-in-grad', action='store_true',
                       help='If set, calculate and log the number of zeros in gradient.')
    group.add_argument('--tensorboard-log-interval', type=int, default=1,
                       help='Report to tensorboard interval.')
    group.add_argument('--tensorboard-queue-size', type=int, default=1000,
                       help='Size of the tensorboard queue for pending events '
                       'and summaries before one of the ‘add’ calls forces a '
                       'flush to disk.')
    group.add_argument('--log-timers-to-tensorboard', action='store_true',
                       help='If set, write timers to tensorboard.')
    group.add_argument('--log-batch-size-to-tensorboard', action='store_true',
                       help='If set, write batch-size to tensorboard.')
    group.add_argument('--no-log-learnig-rate-to-tensorboard',
                       action='store_false',
                       help='Disable learning rate logging to tensorboard.',
                       dest='log_learning_rate_to_tensorboard')
    group.add_argument('--no-log-loss-scale-to-tensorboard',
                       action='store_false',
                       help='Disable loss-scale logging to tensorboard.',
                       dest='log_loss_scale_to_tensorboard')
    group.add_argument('--log-validation-ppl-to-tensorboard',
                       action='store_true',
                       help='If set, write validation perplexity to '
                       'tensorboard.')
    group.add_argument('--log-memory-to-tensorboard',
                       action='store_true',
                       help='Enable memory logging to tensorboard.')

    return parser


def _add_regularization_args(parser):
    group = parser.add_argument_group(title='regularization')

    group.add_argument('--attention-dropout', type=float, default=0.1,
                       help='Post attention dropout probability.')
    group.add_argument('--hidden-dropout', type=float, default=0.1,
                       help='Dropout probability for hidden state transformer.')
    group.add_argument('--weight-decay', type=float, default=0.01,
                       help='Weight decay coefficient for L2 regularization.')
    group.add_argument('--clip-grad', type=float, default=1.0,
                       help='Gradient clipping based on global L2 norm.')
    group.add_argument('--adam-beta1', type=float, default=0.9,
                       help='First coefficient for computing running averages '
                       'of gradient and its square')
    group.add_argument('--adam-beta2', type=float, default=0.999,
                       help='Second coefficient for computing running averages '
                       'of gradient and its square')
    group.add_argument('--adam-eps', type=float, default=1e-08,
                       help='Term added to the denominator to improve'
                       'numerical stability')
    group.add_argument('--sgd-momentum', type=float, default=0.9,
                       help='Momentum factor for sgd')

    return parser


def _add_training_args(parser):
    group = parser.add_argument_group(title='training')

    group.add_argument('--micro-batch-size', type=int, default=None,
                       help='Batch size per model instance (local batch size). '
                       'Global batch size is local batch size times data '
                       'parallel size times number of micro batches.')
    group.add_argument('--batch-size', type=int, default=None,
                       help='Old batch size parameter, do not use. '
                       'Use --micro-batch-size instead')
    group.add_argument('--global-batch-size', type=int, default=None,
                       help='Training batch size. If set, it should be a '
                       'multiple of micro-batch-size times data-parallel-size. '
                       'If this value is None, then '
                       'use micro-batch-size * data-parallel-size as the '
                       'global batch size. This choice will result in 1 for '
                       'number of micro-batches.')
    group.add_argument('--rampup-batch-size', nargs='*', default=None,
                       help='Batch size ramp up with the following values:'
                       '  --rampup-batch-size <start batch size> '
                       '                      <batch size incerement> '
                       '                      <ramp-up samples> '
                       'For example:'
                       '   --rampup-batch-size 16 8 300000 \ '
                       '   --global-batch-size 1024'
                       'will start with global batch size 16 and over '
                       ' (1024 - 16) / 8 = 126 intervals will increase'
                       'the batch size linearly to 1024. In each interval'
                       'we will use approximately 300000 / 126 = 2380 samples.')
    group.add_argument('--checkpoint-activations', action='store_true',
                       help='Checkpoint activation to allow for training '
                       'with larger models, sequences, and batch sizes.')
    group.add_argument('--distribute-checkpointed-activations',
                       action='store_true',
                       help='If set, distribute checkpointed activations '
                       'across model parallel group.')
    group.add_argument('--activations-checkpoint-method', type=str, default=None,
                       choices=['uniform', 'block'],
                       help='1) uniform: uniformly divide the total number of '
                       'Transformer layers and checkpoint the input activation of '
                       'each divided chunk, '
                       '2) checkpoint the input activations of only a set number of '
                       'individual Transformer layers per pipeline stage and do the '
                       'rest without any checkpointing'
                       'default) do not apply activations checkpoint to any layers')
    group.add_argument('--activations-checkpoint-num-layers', type=int, default=1,
                       help='1) uniform: the number of Transformer layers in each '
                       'uniformly divided checkpoint unit, '
                       '2) block: the number of individual Transformer layers '
                       'to checkpoint within each pipeline stage.')
    group.add_argument('--train-iters', type=int, default=None,
                       help='Total number of iterations to train over all '
                       'training runs. Note that either train-iters or '
                       'train-samples should be provided.')
    group.add_argument('--train-samples', type=int, default=None,
                       help='Total number of samples to train over all '
                       'training runs. Note that either train-iters or '
                       'train-samples should be provided.')
    group.add_argument('--log-interval', type=int, default=100,
                       help='Report loss and timing interval.')
    group.add_argument('--exit-interval', type=int, default=None,
                       help='Exit the program after the iteration is divisible '
                       'by this value.')
    group.add_argument('--exit-duration-in-mins', type=int, default=None,
                       help='Exit the program after this many minutes.')
    group.add_argument('--tensorboard-dir', type=str, default=None,
                       help='Write TensorBoard logs to this directory.')
    group.add_argument('--no-masked-softmax-fusion',
                       action='store_false',
                       help='Disable fusion of query_key_value scaling, '
                       'masking, and softmax.',
                       dest='masked_softmax_fusion')
    group.add_argument('--no-bias-gelu-fusion', action='store_false',
                       help='Disable bias and gelu fusion.',
                       dest='bias_gelu_fusion')
    group.add_argument('--no-bias-dropout-fusion', action='store_false',
                       help='Disable bias and dropout fusion.',
                       dest='bias_dropout_fusion')
    group.add_argument('--optimizer', type=str, default='adam',
                       choices=['adam', 'sgd'],
                       help='Optimizer function')
    group.add_argument('--dataloader-type', type=str, default=None,
                       choices=['single', 'cyclic'],
                       help='Single pass vs multiple pass data loader')
    group.add_argument('--no-async-tensor-model-parallel-allreduce',
                       action='store_true',
                       help='Disable asynchronous execution of '
                       'tensor-model-parallel all-reduce with weight '
                       'gradient compuation of a column-linear layer.')
    return parser


def _add_initialization_args(parser):
    group = parser.add_argument_group(title='initialization')

    group.add_argument('--seed', type=int, default=1234,
                       help='Random seed used for python, numpy, '
                       'pytorch, and cuda.')
    group.add_argument('--init-method-std', type=float, default=0.02,
                       help='Standard deviation of the zero mean normal '
                       'distribution used for weight initialization.')
    group.add_argument('--init-method-xavier-uniform', action='store_true',
                       help='Enable Xavier uniform parameter initialization')

    return parser


def _add_learning_rate_args(parser):
    group = parser.add_argument_group(title='learning rate')

    group.add_argument('--lr', type=float, default=None,
                       help='Initial learning rate. Depending on decay style '
                       'and initial warmup, the learing rate at each '
                       'iteration would be different.')
    group.add_argument('--lr-decay-style', type=str, default='linear',
                       choices=['constant', 'linear', 'cosine'],
                       help='Learning rate decay function.')
    group.add_argument('--lr-decay-iters', type=int, default=None,
                       help='number of iterations to decay learning rate over,'
                       ' If None defaults to `--train-iters`')
    group.add_argument('--lr-decay-samples', type=int, default=None,
                       help='number of samples to decay learning rate over,'
                       ' If None defaults to `--train-samples`')
    group.add_argument('--lr-warmup-fraction', type=float, default=None,
                       help='fraction of lr-warmup-(iters/samples) to use '
                       'for warmup (as a float)')
    group.add_argument('--lr-warmup-iters', type=int, default=0,
                       help='number of iterations to linearly warmup '
                       'learning rate over.')
    group.add_argument('--lr-warmup-samples', type=int, default=0,
                       help='number of samples to linearly warmup '
                       'learning rate over.')
    group.add_argument('--warmup', type=int, default=None,
                       help='Old lr warmup argument, do not use. Use one of the'
                       '--lr-warmup-* arguments above')
    group.add_argument('--min-lr', type=float, default=0.0,
                       help='Minumum value for learning rate. The scheduler'
                       'clip values below this threshold.')
    group.add_argument('--override-lr-scheduler', action='store_true',
                       help='Reset the values of the scheduler (learning rate,'
                       'warmup iterations, minimum learning rate, maximum '
                       'number of iterations, and decay style from input '
                       'arguments and ignore values from checkpoints. Note'
                       'that all the above values will be reset.')
    group.add_argument('--use-checkpoint-lr-scheduler', action='store_true',
                       help='Use checkpoint to set the values of the scheduler '
                       '(learning rate, warmup iterations, minimum learning '
                       'rate, maximum number of iterations, and decay style '
                       'from checkpoint and ignore input arguments.')

    return parser


def _add_checkpointing_args(parser):
    group = parser.add_argument_group(title='checkpointing')

    group.add_argument('--save', type=str, default=None,
                       help='Output directory to save checkpoints to.')
    group.add_argument('--save-interval', type=int, default=None,
                       help='Number of iterations between checkpoint saves.')
    group.add_argument('--no-save-optim', action='store_true', default=None,
                       help='Do not save current optimizer.')
    group.add_argument('--no-save-rng', action='store_true', default=None,
                       help='Do not save current rng state.')
    group.add_argument('--load', type=str, default=None,
                       help='Directory containing a model checkpoint.')
    group.add_argument('--no-load-optim', action='store_true', default=None,
                       help='Do not load optimizer when loading checkpoint.')
    group.add_argument('--no-load-rng', action='store_true', default=None,
                       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.')
    group.add_argument('--no-initialization', action='store_false',
                       help='Do not perform initialization when building model, '
                       'can reduce startup time when definitely loading from a '
                       'checkpoint',
                       dest='perform_initialization')

    return parser


def _add_mixed_precision_args(parser):
    group = parser.add_argument_group(title='mixed precision')

    group.add_argument('--fp16', action='store_true',
                       help='Run model in fp16 mode.')
    group.add_argument('--bf16', action='store_true',
                       help='Run model in bfloat16 mode.')
    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('--initial-loss-scale', type=float, default=2**32,
                       help='Initial loss-scale for dynamic loss scaling.')
    group.add_argument('--min-loss-scale', type=float, default=1.0,
                       help='Minimum loss scale for dynamic loss scale.')
    group.add_argument('--loss-scale-window', type=float, default=1000,
                       help='Window over which to raise/lower dynamic scale.')
    group.add_argument('--hysteresis', type=int, default=2,
                       help='hysteresis for dynamic loss scaling')
    group.add_argument('--fp32-residual-connection', action='store_true',
                       help='Move residual connections to fp32.')
    group.add_argument('--no-query-key-layer-scaling', action='store_false',
                       help='Do not scale Q * K^T by 1 / layer-number.',
                       dest='apply_query_key_layer_scaling')
    group.add_argument('--attention-softmax-in-fp32', action='store_true',
                       help='Run attention masking and softmax in fp32. '
                       'This flag is ignored unless '
                       '--no-query-key-layer-scaling is specified.')
    group.add_argument('--accumulate-allreduce-grads-in-fp32',
                       action='store_true',
                       help='Gradient accumulation and all-reduce in fp32.')
    group.add_argument('--fp16-lm-cross-entropy', action='store_true',
                       help='Move the cross entropy unreduced loss calculation'
                       'for lm head to fp16.')

    return parser


def _add_distributed_args(parser):
    group = parser.add_argument_group(title='distributed')

    group.add_argument('--tensor-model-parallel-size', type=int, default=1,
                       help='Degree of tensor model parallelism.')
    group.add_argument('--pipeline-model-parallel-size', type=int, default=1,
                       help='Degree of pipeline model parallelism.')
    group.add_argument('--pipeline-model-parallel-split-rank',
                       type=int, default=None,
                       help='Rank where encoder and decoder should be split.')
    group.add_argument('--model-parallel-size', type=int, default=None,
                       help='Old model parallel argument, do not use. Use '
                       '--tensor-model-parallel-size instead.')
    group.add_argument('--num-layers-per-virtual-pipeline-stage', type=int, default=None,
                       help='Number of layers per virtual pipeline stage')
    group.add_argument('--distributed-backend', default='nccl',
                       choices=['nccl', 'gloo'],
                       help='Which backend to use for distributed training.')
    group.add_argument('--DDP-impl', default='local',
                       choices=['local', 'torch'],
                       help='which DistributedDataParallel implementation '
                       'to use.')
    group.add_argument('--no-contiguous-buffers-in-local-ddp',
                       action='store_false', help='If set, dont use '
                       'contiguous buffer in local DDP.',
                       dest='use_contiguous_buffers_in_local_ddp')
    group.add_argument('--no-scatter-gather-tensors-in-pipeline', action='store_false',
                       help='Use scatter/gather to optimize communication of tensors in pipeline',
                       dest='scatter_gather_tensors_in_pipeline')
    group.add_argument('--local_rank', type=int, default=None,
                       help='local rank passed from distributed launcher.')
    group.add_argument('--lazy-mpu-init', type=bool, required=False,
                       help='If set to True, initialize_megatron() '
                       'skips DDP initialization and returns function to '
                       'complete it instead.Also turns on '
                       '--use-cpu-initialization flag. This is for '
                       'external DDP manager.' )
    group.add_argument('--use-cpu-initialization', action='store_true',
                       default=None, help='If set, affine parallel weights '
                       'initialization uses CPU' )
    group.add_argument('--empty-unused-memory-level', default=0, type=int,
                       choices=[0, 1, 2],
                       help='Call torch.cuda.empty_cache() each iteration '
                       '(training and eval), to reduce fragmentation.'
                       '0=off, 1=moderate, 2=aggressive.')
    return parser


def _add_validation_args(parser):
    group = parser.add_argument_group(title='validation')

    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.')

    return parser


def _add_data_args(parser):
    group = parser.add_argument_group(title='data and dataloader')

    group.add_argument('--data-path', nargs='*', default=None,
                       help='Path to the training dataset. Accepted format:'
                       '1) a single data path, 2) multiple datasets in the'
                       'form: dataset1-weight dataset1-path dataset2-weight '
                       'dataset2-path ...')
    group.add_argument('--split', type=str, default='969, 30, 1',
                       help='Comma-separated list of proportions for training,'
                       ' validation, and test split. For example the split '
                       '`90,5,5` will use 90%% of data for training, 5%% for '
                       'validation and 5%% for test.')
    group.add_argument('--vocab-file', type=str, default=None,
                       help='Path to the vocab file.')
    group.add_argument('--merge-file', type=str, default=None,
                       help='Path to the BPE merge file.')
    group.add_argument('--vocab-extra-ids', type=int, default=0,
                       help='Number of additional vocabulary tokens. '
                            'They are used for span masking in the T5 model')
    group.add_argument('--seq-length', type=int, default=None,
                       help='Maximum sequence length to process.')
    group.add_argument('--encoder-seq-length', type=int, default=None,
                       help='Maximum encoder sequence length to process.'
                       'This should be exclusive of --seq-length')
    group.add_argument('--decoder-seq-length', type=int, default=None,
                       help="Maximum decoder sequence length to process.")
    group.add_argument('--retriever-seq-length', type=int, default=256,
                       help='Maximum sequence length for the biencoder model '
                        ' for retriever')
    group.add_argument('--sample-rate', type=float, default=1.0,
                       help='sample rate for training data. Supposed to be 0 '
                            ' < sample_rate < 1')
    group.add_argument('--mask-prob', type=float, default=0.15,
                       help='Probability of replacing a token with mask.')
    group.add_argument('--short-seq-prob', type=float, default=0.1,
                       help='Probability of producing a short sequence.')
    group.add_argument('--mmap-warmup', action='store_true',
                       help='Warm up mmap files.')
    group.add_argument('--num-workers', type=int, default=2,
                       help="Dataloader number of workers.")
    group.add_argument('--tokenizer-type', type=str,
                       default=None,
                       choices=['BertWordPieceLowerCase',
                                'BertWordPieceCase',
                                'GPT2BPETokenizer'],
                       help='What type of tokenizer to use.')
    group.add_argument('--data-impl', type=str, default='infer',
                       choices=['lazy', 'cached', 'mmap', 'infer'],
                       help='Implementation of indexed datasets.')
    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.')
    group.add_argument('--eod-mask-loss', action='store_true',
                       help='Mask loss for the end of document tokens.')

    return parser


def _add_autoresume_args(parser):
    group = parser.add_argument_group(title='autoresume')

    group.add_argument('--adlr-autoresume', action='store_true',
                       help='Enable autoresume on adlr cluster.')
    group.add_argument('--adlr-autoresume-interval', type=int, default=1000,
                       help='Intervals over which check for autoresume'
                       'termination signal')

    return parser


def _add_biencoder_args(parser):
    group = parser.add_argument_group(title='biencoder')

    # network size
    group.add_argument('--ict-head-size', type=int, default=None,
                       help='Size of block embeddings to be used in ICT and '
                        'REALM (paper default: 128)')
    group.add_argument('--biencoder-projection-dim', type=int, default=0,
                       help='Size of projection head used in biencoder (paper'
                        ' default: 128)')
    group.add_argument('--biencoder-shared-query-context-model', action='store_true',
                        help='Whether to share the parameters of the query '
                        'and context models or not')

    # checkpointing
    group.add_argument('--ict-load', type=str, default=None,
                       help='Directory containing an ICTBertModel checkpoint')
    group.add_argument('--bert-load', type=str, default=None,
                       help='Directory containing an BertModel checkpoint '
                       '(needed to start ICT and REALM)')

    # data
    group.add_argument('--titles-data-path', type=str, default=None,
                       help='Path to titles dataset used for ICT')
    group.add_argument('--query-in-block-prob', type=float, default=0.1,
                       help='Probability of keeping query in block for '
                       'ICT dataset')
    group.add_argument('--use-one-sent-docs', action='store_true',
                       help='Whether to use one sentence documents in ICT')
    group.add_argument('--evidence-data-path', type=str, default=None,
                       help='Path to Wikipedia Evidence frm DPR paper')

    # training
    group.add_argument('--retriever-report-topk-accuracies', nargs='+', type=int,
                        default=[], help="Which top-k accuracies to report "
                        "(e.g. '1 5 20')")
    group.add_argument('--retriever-score-scaling', action='store_true',
                       help='Whether to scale retriever scores by inverse '
                        'square root of hidden size')

    # faiss index
    group.add_argument('--block-data-path', type=str, default=None,
                       help='Where to save/load BlockData to/from')
    group.add_argument('--embedding-path', type=str, default=None,
                       help='Where to save/load Open-Retrieval Embedding'
                        ' data to/from')

    # indexer
    group.add_argument('--indexer-batch-size', type=int, default=128,
                       help='How large of batches to use when doing indexing '
                       'jobs')
    group.add_argument('--indexer-log-interval', type=int, default=1000,
                       help='After how many batches should the indexer '
                       'report progress')
    return parser


def _add_vit_args(parser):
    group = parser.add_argument_group(title="vit")

    group.add_argument('--num-classes', type=int, default=1000,
                       help='num of classes in vision classificaiton task')
    group.add_argument('--img-dim', type=int, default=224,
                       help='Image size for vision classification task')
    group.add_argument('--num-channels', type=int, default=3,
                       help='Number of channels in input image data')
    group.add_argument('--patch-dim', type=int, default=16,
                       help='patch dimension used in vit')

    return parser