                                     op=torch.distributed.ReduceOp.SUM,
                                     group=mpu.get_tensor_and_expert_parallel_group())
        torch.distributed.all_reduce(norm_2,
                                     op=torch.distributed.ReduceOp.SUM,
                                     group=mpu.get_pipeline_model_parallel_group())
    return norm_2.item() ** 0.5

def average_losses_across_data_parallel_group(losses):
    """Reduce a tensor of losses across all GPUs."""
    averaged_losses = torch.cat(
        [loss.clone().detach().view(1) for loss in losses])
    torch.distributed.all_reduce(averaged_losses,
                                 group=mpu.get_data_parallel_group())
    averaged_losses = averaged_losses / \
        torch.distributed.get_world_size(group=mpu.get_data_parallel_group())

    return averaged_losses

def report_memory(name):
    """Simple GPU memory report."""
    mega_bytes = 1024.0 * 1024.0
    string = name + ' memory (MB)'
    string += ' | allocated: {}'.format(
        torch.cuda.memory_allocated() / mega_bytes)
    string += ' | max allocated: {}'.format(
        torch.cuda.max_memory_allocated() / mega_bytes)
    string += ' | reserved: {}'.format(
        torch.cuda.memory_reserved() / mega_bytes)
    string += ' | max reserved: {}'.format(
        torch.cuda.max_memory_reserved() / mega_bytes)
    if mpu.get_data_parallel_rank() == 0:
        print("[Rank {}] {}".format(torch.distributed.get_rank(), string),
              flush=True)

def print_params_min_max_norm(optimizer, iteration):
    """Print min, max, and norm of all parameters."""
    index = 0
    rank = torch.distributed.get_rank()
    string = 'iteration, rank, index, tensor-model-parallel, min, max, norm\n'
    optimizer_ = optimizer.optimizer
    for param_group in optimizer_.param_groups:
        for param in param_group['params']:
            index += 1
            min_ = param.data.min()
            max_ = param.data.max()
            norm = torch.linalg.norm(param.data)
            string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format(
                iteration, rank, index, int(param.tensor_model_parallel))
            string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm)
    print(string, flush=True)


    return averaged_losses

def report_memory(name):
    """Simple GPU memory report."""
    mega_bytes = 1024.0 * 1024.0
    string = name + ' memory (MB)'
    string += ' | allocated: {}'.format(
        torch.cuda.memory_allocated() / mega_bytes)
    string += ' | max allocated: {}'.format(
        torch.cuda.max_memory_allocated() / mega_bytes)
    string += ' | reserved: {}'.format(
        torch.cuda.memory_reserved() / mega_bytes)
    string += ' | max reserved: {}'.format(
        torch.cuda.max_memory_reserved() / mega_bytes)
    if mpu.get_data_parallel_rank() == 0:
        print("[Rank {}] {}".format(torch.distributed.get_rank(), string),
              flush=True)

def print_params_min_max_norm(optimizer, iteration):
    """Print min, max, and norm of all parameters."""
    index = 0
    rank = torch.distributed.get_rank()
    string = 'iteration, rank, index, tensor-model-parallel, min, max, norm\n'
    optimizer_ = optimizer.optimizer
    for param_group in optimizer_.param_groups:
        for param in param_group['params']:
            index += 1
            min_ = param.data.min()
            max_ = param.data.max()
            norm = torch.linalg.norm(param.data)
            string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format(
                iteration, rank, index, int(param.tensor_model_parallel))
            string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm)
    print(string, flush=True)

def check_adlr_autoresume_termination(iteration, model,
                                      optimizer, opt_param_scheduler):
    """Check for autoresume signal and exit if it is received."""
    from megatron.checkpointing import save_checkpoint

    args = get_args()
    autoresume = get_adlr_autoresume()
    # Add barrier to ensure consistnecy.
    torch.distributed.barrier()
    if autoresume.termination_requested():
        if args.save:
            save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
        print_rank_0(">>> autoresume termination request found!")
        if torch.distributed.get_rank() == 0:
            autoresume.request_resume()
        print_rank_0(">>> training terminated. Returning")
        sys.exit(0)


def print_params_min_max_norm(optimizer, iteration):
    """Print min, max, and norm of all parameters."""
    index = 0
    rank = torch.distributed.get_rank()
    string = 'iteration, rank, index, tensor-model-parallel, min, max, norm\n'
    optimizer_ = optimizer.optimizer
    for param_group in optimizer_.param_groups:
        for param in param_group['params']:
            index += 1
            min_ = param.data.min()
            max_ = param.data.max()
            norm = torch.linalg.norm(param.data)
            string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format(
                iteration, rank, index, int(param.tensor_model_parallel))
            string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm)
    print(string, flush=True)

def check_adlr_autoresume_termination(iteration, model,
                                      optimizer, opt_param_scheduler):
    """Check for autoresume signal and exit if it is received."""
    from megatron.checkpointing import save_checkpoint

    args = get_args()
    autoresume = get_adlr_autoresume()
    # Add barrier to ensure consistnecy.
    torch.distributed.barrier()
    if autoresume.termination_requested():
        if args.save:
            save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
        print_rank_0(">>> autoresume termination request found!")
        if torch.distributed.get_rank() == 0:
            autoresume.request_resume()
        print_rank_0(">>> training terminated. Returning")
        sys.exit(0)

def get_ltor_masks_and_position_ids(data,
                                    eod_token,
                                    reset_position_ids,
                                    reset_attention_mask,
                                    eod_mask_loss):
    """Build masks and position id for left to right model."""

    # Extract batch size and sequence length.
    micro_batch_size, seq_length = data.size()

    # Attention mask (lower triangular).
    if reset_attention_mask:
        att_mask_batch = micro_batch_size
    else:
        att_mask_batch = 1
    attention_mask = torch.tril(torch.ones(
        (att_mask_batch, seq_length, seq_length), device=data.device)).view(
            att_mask_batch, 1, seq_length, seq_length)


def check_adlr_autoresume_termination(iteration, model,
                                      optimizer, opt_param_scheduler):
    """Check for autoresume signal and exit if it is received."""
    from megatron.checkpointing import save_checkpoint

    args = get_args()
    autoresume = get_adlr_autoresume()
    # Add barrier to ensure consistnecy.
    torch.distributed.barrier()
    if autoresume.termination_requested():
        if args.save:
            save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
        print_rank_0(">>> autoresume termination request found!")
        if torch.distributed.get_rank() == 0:
            autoresume.request_resume()
        print_rank_0(">>> training terminated. Returning")
        sys.exit(0)

def get_ltor_masks_and_position_ids(data,
                                    eod_token,
                                    reset_position_ids,
                                    reset_attention_mask,
                                    eod_mask_loss):
    """Build masks and position id for left to right model."""

    # Extract batch size and sequence length.
    micro_batch_size, seq_length = data.size()

    # Attention mask (lower triangular).
    if reset_attention_mask:
        att_mask_batch = micro_batch_size
    else:
        att_mask_batch = 1
    attention_mask = torch.tril(torch.ones(
        (att_mask_batch, seq_length, seq_length), device=data.device)).view(
            att_mask_batch, 1, seq_length, seq_length)

    # Loss mask.
    loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
    if eod_mask_loss:
        loss_mask[data == eod_token] = 0.0

    # Position ids.
    position_ids = torch.arange(seq_length, dtype=torch.long,
                                device=data.device)
    position_ids = position_ids.unsqueeze(0).expand_as(data)
    # We need to clone as the ids will be modifed based on batch index.
    if reset_position_ids:
        position_ids = position_ids.clone()

    if reset_position_ids or reset_attention_mask:
        # Loop through the batches:
        for b in range(micro_batch_size):

            # Find indecies where EOD token is.
            eod_index = position_ids[b, data[b] == eod_token]
            # Detach indecies from positions if going to modify positions.
            if reset_position_ids:
                eod_index = eod_index.clone()

            # Loop through EOD indecies:
            prev_index = 0
            for j in range(eod_index.size()[0]):
                i = eod_index[j]
                # Mask attention loss.
                if reset_attention_mask:
                    attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
                # Reset positions.
                if reset_position_ids:
                    position_ids[b, (i + 1):] -= (i + 1 - prev_index)
                    prev_index = i + 1

    # Convert attention mask to binary:
    attention_mask = (attention_mask < 0.5)

    return attention_mask, loss_mask, position_ids

def print_rank_0(message):
    """If distributed is initialized, print only on rank 0."""
    if torch.distributed.is_initialized():
        if torch.distributed.get_rank() == 0:
            print(message, flush=True)
    else:
        print(message, flush=True)

def is_last_rank():
    return torch.distributed.get_rank() == (
        torch.distributed.get_world_size() - 1)

def print_rank_last(message):
    """If distributed is initialized, print only on last rank."""
    if torch.distributed.is_initialized():
        if is_last_rank():
            print(message, flush=True)
    else:


                eod_index = eod_index.clone()

            # Loop through EOD indecies:
            prev_index = 0
            for j in range(eod_index.size()[0]):
                i = eod_index[j]
                # Mask attention loss.
                if reset_attention_mask:
                    attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
                # Reset positions.
                if reset_position_ids:
                    position_ids[b, (i + 1):] -= (i + 1 - prev_index)
                    prev_index = i + 1

    # Convert attention mask to binary:
    attention_mask = (attention_mask < 0.5)

    return attention_mask, loss_mask, position_ids

def print_rank_0(message):
    """If distributed is initialized, print only on rank 0."""
    if torch.distributed.is_initialized():
        if torch.distributed.get_rank() == 0:
            print(message, flush=True)
    else:
        print(message, flush=True)

def is_last_rank():
    return torch.distributed.get_rank() == (
        torch.distributed.get_world_size() - 1)

def print_rank_last(message):
    """If distributed is initialized, print only on last rank."""
    if torch.distributed.is_initialized():
        if is_last_rank():
            print(message, flush=True)
    else:
        print(message, flush=True)


                    attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
                # Reset positions.
                if reset_position_ids:
                    position_ids[b, (i + 1):] -= (i + 1 - prev_index)
                    prev_index = i + 1

    # Convert attention mask to binary:
    attention_mask = (attention_mask < 0.5)

    return attention_mask, loss_mask, position_ids

def print_rank_0(message):
    """If distributed is initialized, print only on rank 0."""
    if torch.distributed.is_initialized():
        if torch.distributed.get_rank() == 0:
            print(message, flush=True)
    else:
        print(message, flush=True)

def is_last_rank():
    return torch.distributed.get_rank() == (
        torch.distributed.get_world_size() - 1)

def print_rank_last(message):
    """If distributed is initialized, print only on last rank."""
    if torch.distributed.is_initialized():
        if is_last_rank():
            print(message, flush=True)
    else:
        print(message, flush=True)


                    prev_index = i + 1

    # Convert attention mask to binary:
    attention_mask = (attention_mask < 0.5)

    return attention_mask, loss_mask, position_ids

def print_rank_0(message):
    """If distributed is initialized, print only on rank 0."""
    if torch.distributed.is_initialized():
        if torch.distributed.get_rank() == 0:
            print(message, flush=True)
    else:
        print(message, flush=True)

def is_last_rank():
    return torch.distributed.get_rank() == (
        torch.distributed.get_world_size() - 1)

def print_rank_last(message):
    """If distributed is initialized, print only on last rank."""
    if torch.distributed.is_initialized():
        if is_last_rank():
            print(message, flush=True)
    else:
        print(message, flush=True)


megatron.arguments

# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.

"""Megatron arguments."""

import argparse
import dataclasses
import json
import os
import torch
import types
import math

import torch.nn.functional as F
from megatron.global_vars import set_retro_args, get_retro_args
from tools.retro.utils import get_args_path as get_retro_args_path

from megatron.core.transformer import TransformerConfig

def parse_args(extra_args_provider=None, ignore_unknown_args=False):
    """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_vision_args(parser)
    parser = _add_logging_args(parser)
    parser = _add_inference_args(parser)
    parser = _add_transformer_engine_args(parser)
    parser = _add_retro_args(parser)
    parser = _add_experimental_args(parser)
    parser = _add_early_exit_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()

    # Args from environment
    args.rank = int(os.getenv('RANK', '0'))
    args.world_size = int(os.getenv("WORLD_SIZE", '1'))

    return args

def validate_args(args, defaults={}):
    # 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.
    if not args.tune_exit:
        args.pipeline_model_parallel_size = min(
            args.pipeline_model_parallel_size,
            (args.world_size // args.tensor_model_parallel_size))
    args.transformer_pipeline_model_parallel_size = (
        args.pipeline_model_parallel_size - 1
        if args.standalone_embedding_stage else
        args.pipeline_model_parallel_size
    )
    # Checks.
    model_parallel_size = args.pipeline_model_parallel_size * \
                          args.tensor_model_parallel_size
    if not args.tune_exit:
        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
    else:
        args.data_parallel_size = args.world_size // (args.tensor_model_parallel_size * args.tune_exit_pipeline_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:
        if args.rank == 0:
            print('--checkpoint-activations is no longer valid, use --recompute-activations, '
                  'or, for more control, --recompute-granularity and --recompute-method.')
        exit()
    del args.checkpoint_activations

    if args.recompute_activations:
        args.recompute_granularity = 'selective'
    del args.recompute_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, None) 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.transformer_pipeline_model_parallel_size == 0, \
            'number of layers should be divisible by the pipeline parallel size'
        num_layers_per_pipeline_stage = args.num_layers // args.transformer_pipeline_model_parallel_size
        assert num_layers_per_pipeline_stage % args.num_layers_per_virtual_pipeline_stage == 0, \
            'number of layers per pipeline stage must be divisible number of layers per virtual pipeline stage'
        args.virtual_pipeline_model_parallel_size = num_layers_per_pipeline_stage // \
            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)

    # Overlapping grad reduce not supported with interleaved PP right now.
    if args.overlap_grad_reduce:
        assert args.virtual_pipeline_model_parallel_size is None

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

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

    # Support for variable sequence lengths across batches/microbatches.
    # set it if the dataloader supports generation of variable sequence lengths
    # across batches/microbatches. Due to additional communication overhead
    # during pipeline parallelism, it should not be set if sequence length
    # is constant during training.
    args.variable_seq_lengths = False

    # 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'

    if args.num_layers is not None:
        assert args.encoder_num_layers is None, \
            'cannot have both num-layers and encoder-num-layers specified'
        args.encoder_num_layers = args.num_layers
    else:
        assert args.encoder_num_layers is not None, \
            'either num-layers or encoder-num-layers should be specified'
        args.num_layers = args.encoder_num_layers

    # 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:
        if args.swiglu:
            # reduce the dimnesion for MLP since projections happens on
            # two linear layers. this keeps the number of paramters in
            # the same ballpark as the counterpart with 4*h size
            # we keep it a multiple of 64, which means the actual tensor size
            # will be a multiple of 64 / tp_size
            args.ffn_hidden_size = int((4 * args.hidden_size * 2 / 3) / 64) * 64
        else:
            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.'

    if args.weight_decay_incr_style == 'constant':
        assert args.start_weight_decay is None
        assert args.end_weight_decay is None
        args.start_weight_decay = args.weight_decay
        args.end_weight_decay = args.weight_decay
    else:
        assert args.start_weight_decay is not None
        assert args.end_weight_decay is not None

    TORCH_MAJOR = int(torch.__version__.split('.')[0])
    TORCH_MINOR = int(torch.__version__.split('.')[1])
    # Persistent fused layer norm.
    if TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 11):
        args.no_persist_layer_norm = True
        if args.rank == 0:
            print('Persistent fused layer norm kernel is supported from '
                  'pytorch v1.11 (nvidia pytorch container paired with v1.11). '
                  'Defaulting to no_persist_layer_norm=True')

    # Activation recomputing.
    if args.distribute_saved_activations:
        assert args.tensor_model_parallel_size > 1, 'can distribute ' \
            'recomputed activations only across tensor model ' \
            'parallel groups'
        assert args.recompute_granularity == 'full', \
            'distributed recompute activations is only '\
            'application to full recompute granularity'
        assert args.recompute_method is not None, \
            'for distributed recompute activations to work you '\
            'need to use a recompute method '
        assert (TORCH_MAJOR, TORCH_MINOR) >= (1, 10), \
            'distributed recompute activations are supported for pytorch ' \
            'v1.10 and above (Nvidia Pytorch container >= 21.07). Current ' \
            'pytorch version is v%s.%s.' % (TORCH_MAJOR, TORCH_MINOR)

    if args.recompute_granularity == 'selective':
        assert args.recompute_method is None, \
            'recompute method is not yet supported for ' \
            'selective recomputing granularity'

    # disable sequence parallelism when tp=1
    # to avoid change in numerics when
    # sequence_parallelism is enabled.
    if args.tensor_model_parallel_size == 1:
        args.sequence_parallel = False

    # disable async_tensor_model_parallel_allreduce when
    # model parallel memory optimization is enabled
    if args.sequence_parallel:
        args.async_tensor_model_parallel_allreduce = False

    if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
        if args.sequence_parallel:
            raise RuntimeError(
                "Using sequence parallelism requires setting the environment variable "
                "CUDA_DEVICE_MAX_CONNECTIONS to 1")
        if args.async_tensor_model_parallel_allreduce:
            raise RuntimeError(
                "Using async gradient all reduce requires setting the environment "
                "variable CUDA_DEVICE_MAX_CONNECTIONS to 1")

    # Disable bias gelu fusion if we are disabling bias altogether
    if not args.add_bias_linear:
        args.bias_gelu_fusion = False

    # Retro checks.
    if args.retro_add_retriever:

        # Sequence parallelism unsupported.
        assert not args.sequence_parallel, \
            "retro currently does not support sequence parallelism."

        # Pipeline parallelism unsupported.
        assert args.pipeline_model_parallel_size == 1, \
            "retro currently does not support pipeline parallelism."

        # Load retro args.
        retro_args_path = get_retro_args_path(args.retro_workdir)
        assert os.path.exists(retro_args_path), "retro workdir missing args.json"
        with open(retro_args_path) as f:
            retro_args = types.SimpleNamespace(**json.load(f))
            retro_args.retro_return_doc_ids = args.retro_return_doc_ids
            retro_args.retro_gpt_retrieved_length = \
                args.retro_num_retrieved_chunks * \
                retro_args.retro_gpt_chunk_length
            set_retro_args(retro_args)

    # check early exit
    if len(args.exit_layer_nums) > 0:
        assert not args.standalone_embedding_stage, "early exit not support standalone embedding stage"
        assert args.num_layers_per_virtual_pipeline_stage is None, "early exit not support virtual pipeline"
        assert args.retro_add_retriever is False, "early exit not support retro_add_retriever"
        assert args.exit_layer_weight_warmup_iters >= 0, '--exit-layer-weight-warmup-iters should be non-negative'

    # check bubble filling
    if args.fill_explicit_bubbles:
        assert args.pipeline_model_parallel_size > 1, "--fill-explicit-bubbles requires pipeline parallel size > 1"
        # calculate for warmup
        opt_num_fill_warmup_microbatches = int((args.pipeline_model_parallel_size - 1) * args.backward_forward_ratio / (1.0 + args.backward_forward_ratio))
        if args.num_fill_warmup_microbatches is None:
            args.num_fill_warmup_microbatches = opt_num_fill_warmup_microbatches
        elif args.num_fill_warmup_microbatches > opt_num_fill_warmup_microbatches:
            if args.rank == 0:
                print(f"WARNING: num_fill_warmup_microbatches is larger than optimal value {opt_num_fill_warmup_microbatches}, set to {opt_num_fill_warmup_microbatches}.")
            args.num_fill_warmup_microbatches = opt_num_fill_warmup_microbatches
        opt_num_fill_cooldown_microbatches = int((args.pipeline_model_parallel_size - 1) * args.backward_forward_ratio / (1.0 + args.backward_forward_ratio))
        if args.num_fill_cooldown_microbatches is None:
            args.num_fill_cooldown_microbatches = opt_num_fill_cooldown_microbatches
        elif args.num_fill_cooldown_microbatches > opt_num_fill_cooldown_microbatches:
            if args.rank == 0:
                print(f"WARNING: num_fill_cooldown_microbatches is larger than optimal value {opt_num_fill_cooldown_microbatches}, set to {opt_num_fill_cooldown_microbatches}.")
            args.num_fill_cooldown_microbatches = opt_num_fill_cooldown_microbatches

    # Legacy RoPE arguments
    if args.use_rotary_position_embeddings:
        args.position_embedding_type = 'rope'

    # Would just need to add 'NoPE' as a position_embedding_type to support this, but for now
    # don't allow it to keep things simple
    if not args.add_position_embedding and args.position_embedding_type != 'rope':
        raise RuntimeError('--no-position-embedding is deprecated, use --position-embedding-type')

    if args.position_embedding_type == 'rope':
        args.add_position_embedding = False

    # MoE Spec check
    if args.num_experts is not None:
        assert args.model_spec is None, "Model Spec must be None when using MoEs"

    # Expert parallelism check
    if args.expert_model_parallel_size  > 1:
        assert args.num_experts is not None, "num_experts must be non None to use expert model parallelism"
        assert args.num_experts % args.expert_model_parallel_size == 0, \
            "Number of experts should be a multiple of expert model parallel_size."
        if args.tensor_model_parallel_size > 1:
            assert args.sequence_parallel, \
                "When using expert parallelism and tensor parallelism, sequence parallelism must be used."
    # multi exit checks.
    if len(args.exit_layer_weight) == 0:
        args.exit_layer_weight = [1.0 for _ in args.exit_layer_nums]
    if len(args.exit_layer_weight_init) == 0:
        args.exit_layer_weight_init = [0.0 for _ in args.exit_layer_nums]
    if len(args.exit_layer_temperature) == 0:
        args.exit_layer_temperature = [1.0 for _ in args.exit_layer_nums]
    if len(args.exit_layer_nums) != len(args.exit_layer_weight):
        raise RuntimeError("--exit-layer-nums and --exit-layer-weight must correspond one to one")
    if len(args.exit_layer_nums) != len(args.exit_layer_weight_init):
        raise RuntimeError("--exit-layer-nums and --exit-layer-weight-init must correspond one to one")
# ... truncated ...


# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.

"""Megatron arguments."""

import argparse
import dataclasses
import json
import os
import torch
import types
import math

import torch.nn.functional as F
from megatron.global_vars import set_retro_args, get_retro_args
from tools.retro.utils import get_args_path as get_retro_args_path

from megatron.core.transformer import TransformerConfig

def parse_args(extra_args_provider=None, ignore_unknown_args=False):
    """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_vision_args(parser)
    parser = _add_logging_args(parser)
    parser = _add_inference_args(parser)
    parser = _add_transformer_engine_args(parser)
    parser = _add_retro_args(parser)
    parser = _add_experimental_args(parser)
    parser = _add_early_exit_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()

    # Args from environment
    args.rank = int(os.getenv('RANK', '0'))
    args.world_size = int(os.getenv("WORLD_SIZE", '1'))

    return args

def validate_args(args, defaults={}):
    # 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.
    if not args.tune_exit:
        args.pipeline_model_parallel_size = min(
            args.pipeline_model_parallel_size,
            (args.world_size // args.tensor_model_parallel_size))
    args.transformer_pipeline_model_parallel_size = (
        args.pipeline_model_parallel_size - 1
        if args.standalone_embedding_stage else
        args.pipeline_model_parallel_size
    )
    # Checks.
    model_parallel_size = args.pipeline_model_parallel_size * \


    parser = _add_retro_args(parser)
    parser = _add_experimental_args(parser)
    parser = _add_early_exit_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()

    # Args from environment
    args.rank = int(os.getenv('RANK', '0'))
    args.world_size = int(os.getenv("WORLD_SIZE", '1'))

    return args

def validate_args(args, defaults={}):
    # 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.
    if not args.tune_exit:
        args.pipeline_model_parallel_size = min(
            args.pipeline_model_parallel_size,
            (args.world_size // args.tensor_model_parallel_size))
    args.transformer_pipeline_model_parallel_size = (
        args.pipeline_model_parallel_size - 1
        if args.standalone_embedding_stage else
        args.pipeline_model_parallel_size
    )
    # Checks.
    model_parallel_size = args.pipeline_model_parallel_size * \
                          args.tensor_model_parallel_size
    if not args.tune_exit:
        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
    else:
        args.data_parallel_size = args.world_size // (args.tensor_model_parallel_size * args.tune_exit_pipeline_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:
        if args.rank == 0:
            print('--checkpoint-activations is no longer valid, use --recompute-activations, '
                  'or, for more control, --recompute-granularity and --recompute-method.')
        exit()
    del args.checkpoint_activations

    if args.recompute_activations:
        args.recompute_granularity = 'selective'
    del args.recompute_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, None) 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.transformer_pipeline_model_parallel_size == 0, \
            'number of layers should be divisible by the pipeline parallel size'
        num_layers_per_pipeline_stage = args.num_layers // args.transformer_pipeline_model_parallel_size
        assert num_layers_per_pipeline_stage % args.num_layers_per_virtual_pipeline_stage == 0, \
            'number of layers per pipeline stage must be divisible number of layers per virtual pipeline stage'
        args.virtual_pipeline_model_parallel_size = num_layers_per_pipeline_stage // \
            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)

    # Overlapping grad reduce not supported with interleaved PP right now.
    if args.overlap_grad_reduce:
        assert args.virtual_pipeline_model_parallel_size is None

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

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

    # Support for variable sequence lengths across batches/microbatches.
    # set it if the dataloader supports generation of variable sequence lengths
    # across batches/microbatches. Due to additional communication overhead
    # during pipeline parallelism, it should not be set if sequence length
    # is constant during training.
    args.variable_seq_lengths = False

    # 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'

    if args.num_layers is not None:
        assert args.encoder_num_layers is None, \
            'cannot have both num-layers and encoder-num-layers specified'
        args.encoder_num_layers = args.num_layers
    else:
        assert args.encoder_num_layers is not None, \
            'either num-layers or encoder-num-layers should be specified'
        args.num_layers = args.encoder_num_layers

    # 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:
        if args.swiglu:
            # reduce the dimnesion for MLP since projections happens on
            # two linear layers. this keeps the number of paramters in
            # the same ballpark as the counterpart with 4*h size
            # we keep it a multiple of 64, which means the actual tensor size
            # will be a multiple of 64 / tp_size
            args.ffn_hidden_size = int((4 * args.hidden_size * 2 / 3) / 64) * 64
        else:
            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.'

    if args.weight_decay_incr_style == 'constant':
        assert args.start_weight_decay is None
        assert args.end_weight_decay is None
        args.start_weight_decay = args.weight_decay
        args.end_weight_decay = args.weight_decay
    else:
        assert args.start_weight_decay is not None
        assert args.end_weight_decay is not None

    TORCH_MAJOR = int(torch.__version__.split('.')[0])
    TORCH_MINOR = int(torch.__version__.split('.')[1])
    # Persistent fused layer norm.
    if TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 11):
        args.no_persist_layer_norm = True
        if args.rank == 0:
            print('Persistent fused layer norm kernel is supported from '
                  'pytorch v1.11 (nvidia pytorch container paired with v1.11). '
                  'Defaulting to no_persist_layer_norm=True')

    # Activation recomputing.
    if args.distribute_saved_activations:
        assert args.tensor_model_parallel_size > 1, 'can distribute ' \
            'recomputed activations only across tensor model ' \
            'parallel groups'
        assert args.recompute_granularity == 'full', \
            'distributed recompute activations is only '\
            'application to full recompute granularity'
        assert args.recompute_method is not None, \
            'for distributed recompute activations to work you '\
            'need to use a recompute method '
        assert (TORCH_MAJOR, TORCH_MINOR) >= (1, 10), \
            'distributed recompute activations are supported for pytorch ' \
            'v1.10 and above (Nvidia Pytorch container >= 21.07). Current ' \
            'pytorch version is v%s.%s.' % (TORCH_MAJOR, TORCH_MINOR)

    if args.recompute_granularity == 'selective':
        assert args.recompute_method is None, \
            'recompute method is not yet supported for ' \
            'selective recomputing granularity'

    # disable sequence parallelism when tp=1
    # to avoid change in numerics when
    # sequence_parallelism is enabled.
    if args.tensor_model_parallel_size == 1:
        args.sequence_parallel = False

    # disable async_tensor_model_parallel_allreduce when
    # model parallel memory optimization is enabled
    if args.sequence_parallel:
        args.async_tensor_model_parallel_allreduce = False

    if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
        if args.sequence_parallel:
            raise RuntimeError(
                "Using sequence parallelism requires setting the environment variable "
                "CUDA_DEVICE_MAX_CONNECTIONS to 1")
        if args.async_tensor_model_parallel_allreduce:
            raise RuntimeError(
                "Using async gradient all reduce requires setting the environment "
                "variable CUDA_DEVICE_MAX_CONNECTIONS to 1")

    # Disable bias gelu fusion if we are disabling bias altogether
    if not args.add_bias_linear:
        args.bias_gelu_fusion = False

    # Retro checks.
    if args.retro_add_retriever:

        # Sequence parallelism unsupported.
        assert not args.sequence_parallel, \
            "retro currently does not support sequence parallelism."

        # Pipeline parallelism unsupported.
        assert args.pipeline_model_parallel_size == 1, \
            "retro currently does not support pipeline parallelism."

        # Load retro args.
        retro_args_path = get_retro_args_path(args.retro_workdir)
        assert os.path.exists(retro_args_path), "retro workdir missing args.json"
        with open(retro_args_path) as f:
            retro_args = types.SimpleNamespace(**json.load(f))
            retro_args.retro_return_doc_ids = args.retro_return_doc_ids
            retro_args.retro_gpt_retrieved_length = \
                args.retro_num_retrieved_chunks * \
                retro_args.retro_gpt_chunk_length
            set_retro_args(retro_args)

    # check early exit
    if len(args.exit_layer_nums) > 0:
        assert not args.standalone_embedding_stage, "early exit not support standalone embedding stage"
        assert args.num_layers_per_virtual_pipeline_stage is None, "early exit not support virtual pipeline"
        assert args.retro_add_retriever is False, "early exit not support retro_add_retriever"
        assert args.exit_layer_weight_warmup_iters >= 0, '--exit-layer-weight-warmup-iters should be non-negative'

    # check bubble filling
    if args.fill_explicit_bubbles:
        assert args.pipeline_model_parallel_size > 1, "--fill-explicit-bubbles requires pipeline parallel size > 1"
        # calculate for warmup
        opt_num_fill_warmup_microbatches = int((args.pipeline_model_parallel_size - 1) * args.backward_forward_ratio / (1.0 + args.backward_forward_ratio))
        if args.num_fill_warmup_microbatches is None:
            args.num_fill_warmup_microbatches = opt_num_fill_warmup_microbatches
        elif args.num_fill_warmup_microbatches > opt_num_fill_warmup_microbatches:
            if args.rank == 0:
                print(f"WARNING: num_fill_warmup_microbatches is larger than optimal value {opt_num_fill_warmup_microbatches}, set to {opt_num_fill_warmup_microbatches}.")
            args.num_fill_warmup_microbatches = opt_num_fill_warmup_microbatches
        opt_num_fill_cooldown_microbatches = int((args.pipeline_model_parallel_size - 1) * args.backward_forward_ratio / (1.0 + args.backward_forward_ratio))
        if args.num_fill_cooldown_microbatches is None:
            args.num_fill_cooldown_microbatches = opt_num_fill_cooldown_microbatches
        elif args.num_fill_cooldown_microbatches > opt_num_fill_cooldown_microbatches:
            if args.rank == 0:
                print(f"WARNING: num_fill_cooldown_microbatches is larger than optimal value {opt_num_fill_cooldown_microbatches}, set to {opt_num_fill_cooldown_microbatches}.")
            args.num_fill_cooldown_microbatches = opt_num_fill_cooldown_microbatches

    # Legacy RoPE arguments
    if args.use_rotary_position_embeddings:
        args.position_embedding_type = 'rope'

    # Would just need to add 'NoPE' as a position_embedding_type to support this, but for now
    # don't allow it to keep things simple
    if not args.add_position_embedding and args.position_embedding_type != 'rope':
        raise RuntimeError('--no-position-embedding is deprecated, use --position-embedding-type')

    if args.position_embedding_type == 'rope':
        args.add_position_embedding = False

    # MoE Spec check
    if args.num_experts is not None:
        assert args.model_spec is None, "Model Spec must be None when using MoEs"

    # Expert parallelism check
    if args.expert_model_parallel_size  > 1:
        assert args.num_experts is not None, "num_experts must be non None to use expert model parallelism"
        assert args.num_experts % args.expert_model_parallel_size == 0, \
            "Number of experts should be a multiple of expert model parallel_size."
        if args.tensor_model_parallel_size > 1:
            assert args.sequence_parallel, \
                "When using expert parallelism and tensor parallelism, sequence parallelism must be used."
    # multi exit checks.
    if len(args.exit_layer_weight) == 0:
        args.exit_layer_weight = [1.0 for _ in args.exit_layer_nums]
    if len(args.exit_layer_weight_init) == 0:
        args.exit_layer_weight_init = [0.0 for _ in args.exit_layer_nums]
    if len(args.exit_layer_temperature) == 0:
        args.exit_layer_temperature = [1.0 for _ in args.exit_layer_nums]
    if len(args.exit_layer_nums) != len(args.exit_layer_weight):
        raise RuntimeError("--exit-layer-nums and --exit-layer-weight must correspond one to one")
    if len(args.exit_layer_nums) != len(args.exit_layer_weight_init):
        raise RuntimeError("--exit-layer-nums and --exit-layer-weight-init must correspond one to one")
    if len(args.exit_layer_nums) != len(args.exit_layer_temperature):
        raise RuntimeError("--exit-layer-nums and --exit-layer-temperature must correspond one to one")
    if args.use_exit_mlp:
        assert len(args.exit_layer_nums) > 0, "--use-exit-mlp requires at least one early exit layer"
        assert not args.pre_exit, "--use-exit-mlp not supports pre_exit"

    # Print arguments.
    _print_args("arguments", args)
    retro_args = get_retro_args()
    if retro_args and args != retro_args:
        _print_args("retro arguments", types.SimpleNamespace(**{k:v for k,v in vars(retro_args).items() if k.startswith("retro")}, rank=args.rank))

    return args

def _print_args(title, args):
    """Print arguments."""
    if args.rank == 0:
        print(f'------------------------ {title} ------------------------',
              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(f'-------------------- end of {title} ---------------------',
              flush=True)

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


        args.exit_layer_temperature = [1.0 for _ in args.exit_layer_nums]
    if len(args.exit_layer_nums) != len(args.exit_layer_weight):
        raise RuntimeError("--exit-layer-nums and --exit-layer-weight must correspond one to one")
    if len(args.exit_layer_nums) != len(args.exit_layer_weight_init):
        raise RuntimeError("--exit-layer-nums and --exit-layer-weight-init must correspond one to one")
    if len(args.exit_layer_nums) != len(args.exit_layer_temperature):
        raise RuntimeError("--exit-layer-nums and --exit-layer-temperature must correspond one to one")
    if args.use_exit_mlp:
        assert len(args.exit_layer_nums) > 0, "--use-exit-mlp requires at least one early exit layer"
        assert not args.pre_exit, "--use-exit-mlp not supports pre_exit"

    # Print arguments.
    _print_args("arguments", args)
    retro_args = get_retro_args()
    if retro_args and args != retro_args:
        _print_args("retro arguments", types.SimpleNamespace(**{k:v for k,v in vars(retro_args).items() if k.startswith("retro")}, rank=args.rank))

    return args

def _print_args(title, args):
    """Print arguments."""
    if args.rank == 0:
        print(f'------------------------ {title} ------------------------',
              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(f'-------------------- end of {title} ---------------------',
              flush=True)

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

def core_transformer_config_from_args(args):

    # Translate args to core transformer configuration
    kw_args = {}
    for f in dataclasses.fields(TransformerConfig):
        if hasattr(args, f.name):
            kw_args[f.name] = getattr(args, f.name)
    kw_args['persist_layer_norm'] = not args.no_persist_layer_norm
    kw_args['layernorm_zero_centered_gamma'] = args.apply_layernorm_1p
    kw_args['layernorm_epsilon'] = args.norm_epsilon
    kw_args['deallocate_pipeline_outputs'] = True
    kw_args['pipeline_dtype'] = args.params_dtype
    kw_args['batch_p2p_comm'] = not args.overlap_p2p_comm
    kw_args['num_moe_experts'] = args.num_experts
    if args.swiglu:


        _print_args("retro arguments", types.SimpleNamespace(**{k:v for k,v in vars(retro_args).items() if k.startswith("retro")}, rank=args.rank))

    return args

def _print_args(title, args):
    """Print arguments."""
    if args.rank == 0:
        print(f'------------------------ {title} ------------------------',
              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(f'-------------------- end of {title} ---------------------',
              flush=True)

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

def core_transformer_config_from_args(args):

    # Translate args to core transformer configuration
    kw_args = {}
    for f in dataclasses.fields(TransformerConfig):
        if hasattr(args, f.name):
            kw_args[f.name] = getattr(args, f.name)
    kw_args['persist_layer_norm'] = not args.no_persist_layer_norm
    kw_args['layernorm_zero_centered_gamma'] = args.apply_layernorm_1p
    kw_args['layernorm_epsilon'] = args.norm_epsilon
    kw_args['deallocate_pipeline_outputs'] = True
    kw_args['pipeline_dtype'] = args.params_dtype
    kw_args['batch_p2p_comm'] = not args.overlap_p2p_comm
    kw_args['num_moe_experts'] = args.num_experts
    if args.swiglu:
        kw_args['activation_func'] = F.silu
        kw_args['gated_linear_unit'] = True
        kw_args['bias_gelu_fusion'] = False
    if args.init_method_xavier_uniform:


def _print_args(title, args):
    """Print arguments."""
    if args.rank == 0:
        print(f'------------------------ {title} ------------------------',
              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(f'-------------------- end of {title} ---------------------',
              flush=True)

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

def core_transformer_config_from_args(args):

    # Translate args to core transformer configuration
    kw_args = {}
    for f in dataclasses.fields(TransformerConfig):
        if hasattr(args, f.name):
            kw_args[f.name] = getattr(args, f.name)
    kw_args['persist_layer_norm'] = not args.no_persist_layer_norm
    kw_args['layernorm_zero_centered_gamma'] = args.apply_layernorm_1p
    kw_args['layernorm_epsilon'] = args.norm_epsilon
    kw_args['deallocate_pipeline_outputs'] = True
    kw_args['pipeline_dtype'] = args.params_dtype
    kw_args['batch_p2p_comm'] = not args.overlap_p2p_comm
    kw_args['num_moe_experts'] = args.num_experts
    if args.swiglu:
        kw_args['activation_func'] = F.silu
        kw_args['gated_linear_unit'] = True
        kw_args['bias_gelu_fusion'] = False
    if args.init_method_xavier_uniform:
        kw_args['init_method'] = torch.nn.init.xavier_uniform_
        kw_args['scaled_init_method'] = torch.nn.init.xavier_uniform_
    if args.group_query_attention:
        kw_args['num_query_groups'] = args.num_query_groups
    else:
        kw_args['num_query_groups'] = None

    return TransformerConfig(**kw_args)

def _add_transformer_engine_args(parser):
    group = parser.add_argument_group(title='Transformer-Engine')

    group.add_argument('--fp8-format', default=None,
                       choices=['e4m3', 'hybrid'],
                       help='Which fp8 format scheme to use for FP8 tensors in the forward and backward pass',
                       dest='fp8')
    group.add_argument('--fp8-margin', type=int, default=0,
                       help='Scaling margin for fp8',
                       dest='fp8_margin')
    group.add_argument('--fp8-interval', type=int, default=1,
                       help='Scaling update interval for fp8',
                       dest='fp8_interval')
    group.add_argument('--fp8-amax-history-len', type=int, default=1,
                       help='Number of steps for which amax history is recorded per tensor',
                       dest='fp8_amax_history_len')
    group.add_argument('--fp8-amax-compute-algo', default='most_recent',
                       choices=['most_recent', 'max'],
                       help='Algorithm for computing amax from history',


    kw_args['layernorm_zero_centered_gamma'] = args.apply_layernorm_1p
    kw_args['layernorm_epsilon'] = args.norm_epsilon
    kw_args['deallocate_pipeline_outputs'] = True
    kw_args['pipeline_dtype'] = args.params_dtype
    kw_args['batch_p2p_comm'] = not args.overlap_p2p_comm
    kw_args['num_moe_experts'] = args.num_experts
    if args.swiglu:
        kw_args['activation_func'] = F.silu
        kw_args['gated_linear_unit'] = True
        kw_args['bias_gelu_fusion'] = False
    if args.init_method_xavier_uniform:
        kw_args['init_method'] = torch.nn.init.xavier_uniform_
        kw_args['scaled_init_method'] = torch.nn.init.xavier_uniform_
    if args.group_query_attention:
        kw_args['num_query_groups'] = args.num_query_groups
    else:
        kw_args['num_query_groups'] = None

    return TransformerConfig(**kw_args)

def _add_transformer_engine_args(parser):
    group = parser.add_argument_group(title='Transformer-Engine')

    group.add_argument('--fp8-format', default=None,
                       choices=['e4m3', 'hybrid'],
                       help='Which fp8 format scheme to use for FP8 tensors in the forward and backward pass',
                       dest='fp8')
    group.add_argument('--fp8-margin', type=int, default=0,
                       help='Scaling margin for fp8',
                       dest='fp8_margin')
    group.add_argument('--fp8-interval', type=int, default=1,
                       help='Scaling update interval for fp8',
                       dest='fp8_interval')
    group.add_argument('--fp8-amax-history-len', type=int, default=1,
                       help='Number of steps for which amax history is recorded per tensor',
                       dest='fp8_amax_history_len')
    group.add_argument('--fp8-amax-compute-algo', default='most_recent',
                       choices=['most_recent', 'max'],
                       help='Algorithm for computing amax from history',
                       dest='fp8_amax_compute_algo')
    group.add_argument('--no-fp8-wgrad', action='store_false',
                       help='Execute wgrad in higher precision even for FP8 runs',
                       dest='fp8_wgrad')
    group.add_argument('--transformer-impl', default='local',
                       choices=['local', 'transformer_engine'],
                       help='Which Transformer implementation to use.')

    return parser

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.')
    group.add_argument('--max-tokens-to-oom',
                       type=int, default=12000,
                       help='Maximum number of tokens during inference'
                       'tokens here is # in prompt + # to generate'
                       'Allows us to throw an error before OOM crashes server')
    group.add_argument('--output-bert-embeddings', action='store_true',
                       help='Output Bert embeddings (via mean pooling) from '
                       'model, rather than its binary head output or entire '
                       'hidden batch.')
    group.add_argument('--bert-embedder-type', default="megatron",
                       choices=["megatron", "huggingface"],


                       dest='fp8_margin')
    group.add_argument('--fp8-interval', type=int, default=1,
                       help='Scaling update interval for fp8',
                       dest='fp8_interval')
    group.add_argument('--fp8-amax-history-len', type=int, default=1,
                       help='Number of steps for which amax history is recorded per tensor',
                       dest='fp8_amax_history_len')
    group.add_argument('--fp8-amax-compute-algo', default='most_recent',
                       choices=['most_recent', 'max'],
                       help='Algorithm for computing amax from history',
                       dest='fp8_amax_compute_algo')
    group.add_argument('--no-fp8-wgrad', action='store_false',
                       help='Execute wgrad in higher precision even for FP8 runs',
                       dest='fp8_wgrad')
    group.add_argument('--transformer-impl', default='local',
                       choices=['local', 'transformer_engine'],
                       help='Which Transformer implementation to use.')

    return parser

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.')
    group.add_argument('--max-tokens-to-oom',
                       type=int, default=12000,
                       help='Maximum number of tokens during inference'
                       'tokens here is # in prompt + # to generate'
                       'Allows us to throw an error before OOM crashes server')
    group.add_argument('--output-bert-embeddings', action='store_true',
                       help='Output Bert embeddings (via mean pooling) from '
                       'model, rather than its binary head output or entire '
                       'hidden batch.')
    group.add_argument('--bert-embedder-type', default="megatron",
                       choices=["megatron", "huggingface"],
                       help='Select either Megatron or Huggingface as the '
                       'Bert embedder.')

    return parser

def _add_retro_args(parser):
    group = parser.add_argument_group(title='retro')

    group.add_argument('--retro-workdir', default=None,
                       help='Retro working directory, which contains the '
                       'preprocessed data for for pretraining. This directory '
                       'is built during preprocessing (see '
                       'tools/retro/README.md), and contains subdirectories '
                       'for the chunk database and pretraining neighbors.')
    group.add_argument('--retro-add-retriever',
                       action='store_true', default=False,
                       help='Add a retriever to the transformer, for use in '
                       'pretraining a Retro model.')
    group.add_argument('--retro-cyclic-train-iters', type=int, default=None,
                       help='Set number of training iterations for cyclic '
                       'Retro training.')
    group.add_argument('--retro-encoder-layers', type=int, default=2,
                       help='Number of layers to use for the retrieval '


                       help='During inference, if batch-size times '
                       'sequence-length is smaller than this threshold '
                       'then we will not use pipelining, otherwise we will.')
    group.add_argument('--max-tokens-to-oom',
                       type=int, default=12000,
                       help='Maximum number of tokens during inference'
                       'tokens here is # in prompt + # to generate'
                       'Allows us to throw an error before OOM crashes server')
    group.add_argument('--output-bert-embeddings', action='store_true',
                       help='Output Bert embeddings (via mean pooling) from '
                       'model, rather than its binary head output or entire '
                       'hidden batch.')
    group.add_argument('--bert-embedder-type', default="megatron",
                       choices=["megatron", "huggingface"],
                       help='Select either Megatron or Huggingface as the '
                       'Bert embedder.')

    return parser

def _add_retro_args(parser):
    group = parser.add_argument_group(title='retro')

    group.add_argument('--retro-workdir', default=None,
                       help='Retro working directory, which contains the '
                       'preprocessed data for for pretraining. This directory '
                       'is built during preprocessing (see '
                       'tools/retro/README.md), and contains subdirectories '
                       'for the chunk database and pretraining neighbors.')
    group.add_argument('--retro-add-retriever',
                       action='store_true', default=False,
                       help='Add a retriever to the transformer, for use in '
                       'pretraining a Retro model.')
    group.add_argument('--retro-cyclic-train-iters', type=int, default=None,
                       help='Set number of training iterations for cyclic '
                       'Retro training.')
    group.add_argument('--retro-encoder-layers', type=int, default=2,
                       help='Number of layers to use for the retrieval '
                       'encoder.')
    group.add_argument('--retro-encoder-hidden-dropout',
                       type=float, default=0.1, help='Hidden dropout for '
                       'retrieval encoder.')
    group.add_argument('--retro-encoder-attention-dropout',
                       type=float, default=0.1, help='Attention dropout for '
                       'retrieval encoder.')
    group.add_argument("--retro-num-neighbors", type=int, default=2,
                       help='Number of neighbors to retrieve during '
                       'pretraining.')
    group.add_argument("--retro-num-retrieved-chunks", type=int, default=2,
                       help='Number of chunks to retrieve from the retrieval '
                       'database.')
    group.add_argument("--retro-return-doc-ids", action="store_true",
                       help="Turn this on when preprocessing retro data.")

    # Enforce argument naming convention.
    for action in group._group_actions:
        prefix = action.dest.split("_")[0]
        assert prefix == "retro", \
            "Retro args must be prefixed with '--retro-*', for consistent " \
            "styling. Please fix '%s'." % ", ".join(action.option_strings)

    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('--encoder-num-layers', type=int, default=None,
                       help='Number of encoder transformer layers.')
    group.add_argument('--decoder-num-layers', type=int, default=None,
                       help='Number of decoder 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 '


                       'retrieval encoder.')
    group.add_argument("--retro-num-neighbors", type=int, default=2,
                       help='Number of neighbors to retrieve during '
                       'pretraining.')
    group.add_argument("--retro-num-retrieved-chunks", type=int, default=2,
                       help='Number of chunks to retrieve from the retrieval '
                       'database.')
    group.add_argument("--retro-return-doc-ids", action="store_true",
                       help="Turn this on when preprocessing retro data.")

    # Enforce argument naming convention.
    for action in group._group_actions:
        prefix = action.dest.split("_")[0]
        assert prefix == "retro", \
            "Retro args must be prefixed with '--retro-*', for consistent " \
            "styling. Please fix '%s'." % ", ".join(action.option_strings)

    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('--encoder-num-layers', type=int, default=None,
                       help='Number of encoder transformer layers.')
    group.add_argument('--decoder-num-layers', type=int, default=None,
                       help='Number of decoder 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('--group-query-attention', action='store_true',
                          help='Use group-query attention.')
    group.add_argument('--num-query-groups', type=int, default=1)

    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('--position-embedding-type', type=str, default='learned_absolute',
                       choices=['learned_absolute', 'rope'],
                       help='Position embedding type.')
    group.add_argument('--use-rotary-position-embeddings', action='store_true',
                       help='Use rotary positional embeddings or not. '
                       'Deprecated: use --position-embedding-type')
    group.add_argument('--rotary-percent', type=float, default=1.0,
                       help='Percent of rotary dimension to use, default 100%%')
    group.add_argument('--rotary-seq-len-interpolation-factor', type=int, default=None,
                       help='Sequence length interpolation factor for rotary embeddings.')
    group.add_argument('--no-position-embedding',
                       action='store_false',
                       help='Disable position embedding. Deprecated: use --position-embedding-type',
                       dest='add_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('--normalization', default='LayerNorm',
                       choices=['LayerNorm', 'RMSNorm'],
                       help='Which normalization technique to use.')
    group.add_argument('--norm-epsilon', type=float, default=1e-5,
                       help='Epsilon for layer norm and RMS norm.')
    group.add_argument('--apply-layernorm-1p', action='store_true',
                       help='Adjust LayerNorm weights such that they are centered '
                       'around zero. This improves numerical stability.')
    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('--squared-relu', action='store_true',
                       help='Use squared relu activation instead of default gelu')
    group.add_argument('--swiglu', action='store_true',
                       help='Use gated linear units and SiLU activation instead of default gelu')
    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')
    group.add_argument('--num-experts', type=int, default=None,
                       help='Number of Experts in Switch Transformer (None means no Switch)')
    group.add_argument('--untie-embeddings-and-output-weights', action='store_true',
                       help='Untie embeddings and output weights.'),

    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('--timing-log-level', type=int,
                       default=0, choices=range(0,3),
                       help='Granularity level to measure and report timing. '
                       '   0: report only iteration time and make sure timing '
                       '      does not introduce extra overhead.'
                       '   1: report timing for operations that are executed '
                       '      very limited times (basically once) during '
                       '      each iteration (such as gradient all-reduce) '
                       '   2: report timing for operations that migh be '
                       '      executed numerous times during each iteration. '
                       'Note that setting the level to 1 or 2 might '


                       'should not be used unless for backward compatibility'
                       'reasons.')
    group.add_argument('--squared-relu', action='store_true',
                       help='Use squared relu activation instead of default gelu')
    group.add_argument('--swiglu', action='store_true',
                       help='Use gated linear units and SiLU activation instead of default gelu')
    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')
    group.add_argument('--num-experts', type=int, default=None,
                       help='Number of Experts in Switch Transformer (None means no Switch)')
    group.add_argument('--untie-embeddings-and-output-weights', action='store_true',
                       help='Untie embeddings and output weights.'),

    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('--timing-log-level', type=int,
                       default=0, choices=range(0,3),
                       help='Granularity level to measure and report timing. '
                       '   0: report only iteration time and make sure timing '
                       '      does not introduce extra overhead.'
                       '   1: report timing for operations that are executed '
                       '      very limited times (basically once) during '
                       '      each iteration (such as gradient all-reduce) '
                       '   2: report timing for operations that migh be '
                       '      executed numerous times during each iteration. '
                       'Note that setting the level to 1 or 2 might '
                       'cause increase in iteration time.')
    group.add_argument('--no-barrier-with-level-1-timing', action='store_false',
                       help='If not set, use barrier with level 1 time '
                       'measurements. Note that this is up to the user '
                       'to make sure calling barrier with their timers '
                       'will not result in hangs. This can happen if for '
                       'example the user adds a level 1 timer that is not '
                       'called by all ranks.',
                       dest='barrier_with_L1_time')
    group.add_argument('--timing-log-option', type=str, default='minmax',
                       choices=['max', 'minmax', 'all'],
                       help='Options for logging timing:'
                       '  max: report the max timing across all ranks'
                       '  minmax: report min and max timings across all ranks'
                       '  all: report timings of all ranks.')
    group.add_argument('--tracker-log-interval', type=int, default=1,
                       help='Report to trackers 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-tracker', action='store_true',
                       help='If set, write timers to trackers.')
    group.add_argument('--log-batch-size-to-tracker', action='store_true',
                       help='If set, write batch-size to trackers.')
    group.add_argument('--no-log-learnig-rate-to-tracker',
                       action='store_false',
                       help='Disable learning rate logging to trackers.',
                       dest='log_learning_rate_to_tracker')
    group.add_argument('--no-log-loss-scale-to-tracker',
                       action='store_false',
                       help='Disable loss-scale logging to trackers.',
                       dest='log_loss_scale_to_tracker')
    group.add_argument('--log-validation-ppl-to-tracker',
                       action='store_true',
                       help='If set, write validation perplexity to '
                       'trackers.')
    group.add_argument('--log-memory-to-tracker',
                       action='store_true',
                       help='Enable memory logging to trackers.')
    group.add_argument('--log-world-size-to-tracker',
                       action='store_true',
                       help='Enable world size logging to trackers.')
    group.add_argument('--wandb-project', type=str, default=None,
                       help='The wandb project name. Ignore wandb by default.')
    group.add_argument('--wandb-group', type=str, default=None,
                       help='The wandb group name.')
    group.add_argument('--wandb-exp-name', type=str, default='default',
                       help='The wandb experiment name.')
    group.add_argument('--wandb-save-dir', type=str, default='',
                       help='Path to save the wandb results locally.')
    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('--start-weight-decay', type=float,
                       help='Initial weight decay coefficient for L2 regularization.')
    group.add_argument('--end-weight-decay', type=float,
                       help='End of run weight decay coefficient for L2 regularization.')
    group.add_argument('--weight-decay-incr-style', type=str, default='constant',
                       choices=['constant', 'linear', 'cosine'],
                       help='Weight decay increment function.')
    group.add_argument('--clip-grad', type=float, default=1.0,
                       help='Gradient clipping based on global L2 norm.')


                       action='store_true',
                       help='If set, write validation perplexity to '
                       'trackers.')
    group.add_argument('--log-memory-to-tracker',
                       action='store_true',
                       help='Enable memory logging to trackers.')
    group.add_argument('--log-world-size-to-tracker',
                       action='store_true',
                       help='Enable world size logging to trackers.')
    group.add_argument('--wandb-project', type=str, default=None,
                       help='The wandb project name. Ignore wandb by default.')
    group.add_argument('--wandb-group', type=str, default=None,
                       help='The wandb group name.')
    group.add_argument('--wandb-exp-name', type=str, default='default',
                       help='The wandb experiment name.')
    group.add_argument('--wandb-save-dir', type=str, default='',
                       help='Path to save the wandb results locally.')
    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('--start-weight-decay', type=float,
                       help='Initial weight decay coefficient for L2 regularization.')
    group.add_argument('--end-weight-decay', type=float,
                       help='End of run weight decay coefficient for L2 regularization.')
    group.add_argument('--weight-decay-incr-style', type=str, default='constant',
                       choices=['constant', 'linear', 'cosine'],
                       help='Weight decay increment function.')
    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=1,
                       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,