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arguments.py
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arguments.py
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# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Megatron arguments."""
import argparse
import os
import torch
import deepspeed
def parse_args(extra_args_provider=None, defaults={},
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_vit_args(parser)
parser = _add_logging_args(parser)
parser = _add_zero_args(parser)
parser = _add_memoryopt_args(parser)
parser = _add_activation_checkpoint_args(parser)
parser = _add_distillation_args(parser)
# Custom arguments.
if extra_args_provider is not None:
parser = extra_args_provider(parser)
parser = deepspeed.add_config_arguments(parser)
# Parse.
if ignore_unknown_args:
args, _ = parser.parse_known_args()
else:
args = parser.parse_args()
# helper argument to set deepspeed pipeline parallel or not
args.ds_pipeline_enabled = not args.no_pipeline_parallel
# Distributed args.
args.rank = int(os.getenv('RANK', '0'))
args.world_size = int(os.getenv("WORLD_SIZE", '1'))
# Tensor model parallel size.
args.tensor_model_parallel_size = min(
args.tensor_model_parallel_size, args.world_size)
assert args.world_size % args.tensor_model_parallel_size == 0, 'world size'\
' ({}) is not divisible by tensor model parallel size ({})'.format(
args.world_size, args.tensor_model_parallel_size)
# Pipeline model parallel size.
args.pipeline_model_parallel_size = min(
args.pipeline_model_parallel_size,
(args.world_size // args.tensor_model_parallel_size))
# Checks.
if args.no_pipeline_parallel:
assert args.pipeline_model_parallel_size == 1, \
"pipeline_model_parallel_size must be 1 if pipeline parallel is disabled"
model_parallel_size = args.pipeline_model_parallel_size * \
args.tensor_model_parallel_size
assert args.world_size % model_parallel_size == 0, 'world size is not'\
' divisible by tensor parallel size ({}) times pipeline parallel ' \
'size ({})'.format(args.world_size, args.tensor_model_parallel_size,
args.pipeline_model_parallel_size)
args.data_parallel_size = args.world_size // model_parallel_size
if args.rank == 0:
print('using world size: {}, data-parallel-size: {}, '
'tensor-model-parallel size: {}, '
'pipeline-model-parallel size: {} '.format(
args.world_size, args.data_parallel_size,
args.tensor_model_parallel_size,
args.pipeline_model_parallel_size), flush=True)
# 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
# Set input defaults.
for key in defaults:
# For default to be valid, it should not be provided in the
# arguments that are passed to the program. We check this by
# ensuring the arg is set to None.
if getattr(args, key) is not None:
if args.rank == 0:
print('WARNING: overriding default arguments for {key}:{v} \
with {key}:{v2}'.format(key=key, v=defaults[key],
v2=getattr(args, key)),
flush=True)
else:
setattr(args, key, defaults[key])
# Batch size.
assert args.micro_batch_size is not None
assert args.micro_batch_size > 0
if args.global_batch_size is None:
args.global_batch_size = args.micro_batch_size * args.data_parallel_size
if args.rank == 0:
print('setting global batch size to {}'.format(
args.global_batch_size), flush=True)
assert args.global_batch_size > 0
if args.num_layers_per_virtual_pipeline_stage is not None:
assert args.pipeline_model_parallel_size > 2, \
'pipeline-model-parallel size should be greater than 2 with ' \
'interleaved schedule'
assert args.num_layers % args.num_layers_per_virtual_pipeline_stage == 0, \
'number of layers is not divisible by number of layers per virtual ' \
'pipeline stage'
args.virtual_pipeline_model_parallel_size = \
(args.num_layers // args.pipeline_model_parallel_size) // \
args.num_layers_per_virtual_pipeline_stage
else:
args.virtual_pipeline_model_parallel_size = None
# Parameters dtype.
args.params_dtype = torch.float
if args.fp16:
assert not args.bf16
args.params_dtype = torch.half
if args.bf16:
assert not args.fp16
args.params_dtype = torch.bfloat16
# bfloat16 requires gradient accumulation and all-reduce to
# be done in fp32.
if not args.accumulate_allreduce_grads_in_fp32:
args.accumulate_allreduce_grads_in_fp32 = True
if args.rank == 0:
print('accumulate and all-reduce gradients in fp32 for '
'bfloat16 data type.', flush=True)
if args.rank == 0:
print('using {} for parameters ...'.format(args.params_dtype),
flush=True)
# If we do accumulation and all-reduces in fp32, we need to have
# local DDP and we should set the use-contiguous-buffers-in-ddp.
if args.accumulate_allreduce_grads_in_fp32:
assert args.DDP_impl == 'local'
args.use_contiguous_buffers_in_ddp = True
if args.dataloader_type is None:
args.dataloader_type = 'single'
# Consumed tokens.
args.consumed_train_samples = 0
args.consumed_valid_samples = 0
args.consumed_train_tokens = 0
args.custom_token_counting = 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'
# Check required arguments.
required_args = ['num_layers', 'hidden_size', 'num_attention_heads',
'max_position_embeddings']
for req_arg in required_args:
_check_arg_is_not_none(args, req_arg)
# Checks.
if args.ffn_hidden_size is None:
args.ffn_hidden_size = 4 * args.hidden_size
if args.kv_channels is None:
assert args.hidden_size % args.num_attention_heads == 0
args.kv_channels = args.hidden_size // args.num_attention_heads
if args.seq_length is not None:
assert args.encoder_seq_length is None
args.encoder_seq_length = args.seq_length
else:
assert args.encoder_seq_length is not None
args.seq_length = args.encoder_seq_length
if args.seq_length is not None:
assert args.max_position_embeddings >= args.seq_length
if args.decoder_seq_length is not None:
assert args.max_position_embeddings >= args.decoder_seq_length
if args.lr is not None:
assert args.min_lr <= args.lr
if args.save is not None:
assert args.save_interval is not None
# Mixed precision checks.
if args.fp16_lm_cross_entropy:
assert args.fp16, 'lm cross entropy in fp16 only support in fp16 mode.'
if args.fp32_residual_connection:
assert args.fp16 or args.bf16, \
'residual connection in fp32 only supported when using fp16 or bf16.'
# Activation checkpointing.
if args.distribute_checkpointed_activations:
assert args.checkpoint_activations, \
'for distribute-checkpointed-activations to work you '\
'need to enable checkpoint-activations'
args.curriculum_learning_legacy = False
args.compression_training = False
# AML
if args.aml_data_download_path is not None:
data_paths = []
for path in args.data_path:
data_paths.append(f"{args.aml_data_download_path}/{path}")
args.data_path = data_paths
_print_args(args)
return args
def _print_args(args):
"""Print arguments."""
if args.rank == 0:
print('------------------------ arguments ------------------------',
flush=True)
str_list = []
for arg in vars(args):
dots = '.' * (48 - len(arg))
str_list.append(' {} {} {}'.format(arg, dots, getattr(args, arg)))
for arg in sorted(str_list, key=lambda x: x.lower()):
print(arg, flush=True)
print('-------------------- end of arguments ---------------------',
flush=True)
def _check_arg_is_not_none(args, arg):
assert getattr(args, arg) is not None, '{} argument is None'.format(arg)
def _add_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('--num-experts', type=int, nargs='+', default=[1,],
help='number of experts list, MoE related.')
group.add_argument('--mlp-type', type=str, default='standard',
help='Only applicable when num-experts > 1, accepts [standard, residual]')
group.add_argument('--topk', type=int, default=1,
help='Sets the k in TopK gating for MoE layers')
group.add_argument('--expert-interval', type=int, default=2,
help='Use experts in every "expert-interval" layers')
group.add_argument('--hidden-size', type=int, default=None,
help='Tansformer hidden size.')
group.add_argument('--ffn-hidden-size', type=int, default=None,
help='Transformer Feed-Forward Network hidden size. '
'This is set to 4*hidden-size if not provided')
group.add_argument('--num-attention-heads', type=int, default=None,
help='Number of transformer attention heads.')
group.add_argument('--kv-channels', type=int, default=None,
help='Projection weights dimension in multi-head '
'attention. This is set to '
' args.hidden_size // args.num_attention_heads '
'if not provided.')
group.add_argument('--max-position-embeddings', type=int, default=None,
help='Maximum number of position embeddings to use. '
'This is the size of position embedding.')
group.add_argument('--make-vocab-size-divisible-by', type=int, default=128,
help='Pad the vocab size to be divisible by this value.'
'This is added for computational efficieny reasons.')
group.add_argument('--layernorm-epsilon', type=float, default=1e-5,
help='Layer norm epsilon.')
group.add_argument('--apply-residual-connection-post-layernorm',
action='store_true',
help='If set, use original BERT residula connection '
'ordering.')
group.add_argument('--openai-gelu', action='store_true',
help='Use OpenAIs GeLU implementation. This option'
'should not be used unless for backward compatibility'
'reasons.')
group.add_argument('--onnx-safe', type=bool, required=False,
help='Use workarounds for known problems with '
'Torch ONNX exporter')
group.add_argument('--bert-no-binary-head', action='store_false',
help='Disable BERT binary head.',
dest='bert_binary_head')
return parser
def _add_logging_args(parser):
group = parser.add_argument_group(title='logging')
group.add_argument('--log-params-norm', action='store_true',
help='If set, calculate and log parameters norm.')
group.add_argument('--log-num-zeros-in-grad', action='store_true',
help='If set, calculate and log the number of zeros in gradient.')
group.add_argument('--tensorboard-log-interval', type=int, default=1,
help='Report to tensorboard interval.')
group.add_argument('--tensorboard-queue-size', type=int, default=1000,
help='Size of the tensorboard queue for pending events '
'and summaries before one of the ‘add’ calls forces a '
'flush to disk.')
group.add_argument('--log-timers-to-tensorboard', action='store_true',
help='If set, write timers to tensorboard.')
group.add_argument('--log-batch-size-to-tensorboard', action='store_true',
help='If set, write batch-size to tensorboard.')
group.add_argument('--no-log-learnig-rate-to-tensorboard',
action='store_false',
help='Disable learning rate logging to tensorboard.',
dest='log_learning_rate_to_tensorboard')
group.add_argument('--no-log-loss-scale-to-tensorboard',
action='store_false',
help='Disable loss-scale logging to tensorboard.',
dest='log_loss_scale_to_tensorboard')
group.add_argument('--log-validation-ppl-to-tensorboard',
action='store_true',
help='If set, write validation perplexity to '
'tensorboard.')
group.add_argument('--log-optimizer-states-to-tensorboard',
action='store_true',
help='If set, write various optimizer states to '
'tensorboard. This feature may consume extra GPU memory.')
return parser
def _add_regularization_args(parser):
group = parser.add_argument_group(title='regularization')
group.add_argument('--attention-dropout', type=float, default=0.1,
help='Post attention dropout probability.')
group.add_argument('--hidden-dropout', type=float, default=0.1,
help='Dropout probability for hidden state transformer.')
group.add_argument('--weight-decay', type=float, default=0.01,
help='Weight decay coefficient for L2 regularization.')
group.add_argument('--clip-grad', type=float, default=1.0,
help='Gradient clipping based on global L2 norm.')
group.add_argument('--adam-beta1', type=float, default=0.9,
help='First coefficient for computing running averages '
'of gradient and its square')
group.add_argument('--adam-beta2', type=float, default=0.999,
help='Second coefficient for computing running averages '
'of gradient and its square')
group.add_argument('--adam-eps', type=float, default=1e-08,
help='Term added to the denominator to improve'
'numerical stability')
group.add_argument('--sgd-momentum', type=float, default=0.9,
help='Momentum factor for sgd')
return parser
def _add_training_args(parser):
group = parser.add_argument_group(title='training')
group.add_argument('--micro-batch-size', type=int, default=None,
help='Batch size per model instance (local batch size). '
'Global batch size is local batch size times data '
'parallel size times number of micro batches.')
group.add_argument('--batch-size', type=int, default=None,
help='Old batch size parameter, do not use. '
'Use --micro-batch-size instead')
group.add_argument('--global-batch-size', type=int, default=None,
help='Training batch size. If set, it should be a '
'multiple of micro-batch-size times data-parallel-size. '
'If this value is None, then '
'use micro-batch-size * data-parallel-size as the '
'global batch size. This choice will result in 1 for '
'number of micro-batches.')
group.add_argument('--rampup-batch-size', nargs='*', default=None,
help='Batch size ramp up with the following values:'
' --rampup-batch-size <start batch size> '
' <batch size incerement> '
' <ramp-up samples> '
'For example:'
' --rampup-batch-size 16 8 300000 \ '
' --global-batch-size 1024'
'will start with global batch size 16 and over '
' (1024 - 16) / 8 = 126 intervals will increase'
'the batch size linearly to 1024. In each interval'
'we will use approximately 300000 / 126 = 2380 samples.')
group.add_argument('--checkpoint-activations', action='store_true',
help='Checkpoint activation to allow for training '
'with larger models, sequences, and batch sizes.')
group.add_argument('--distribute-checkpointed-activations',
action='store_true',
help='If set, distribute checkpointed activations '
'across model parallel group.')
group.add_argument('--checkpoint-num-layers', type=int, default=1,
help='chunk size (number of layers) for checkpointing.')
group.add_argument('--train-iters', type=int, default=None,
help='Total number of iterations to train over all '
'training runs. Note that either train-iters or '
'train-samples should be provided.')
group.add_argument('--train-samples', type=int, default=None,
help='Total number of samples to train over all '
'training runs. Note that either train-iters or '
'train-samples should be provided.')
group.add_argument('--train-tokens', type=int, default=None,
help='Total number of tokens to train over all '
'training runs.')
group.add_argument('--random-ltd',
action='store_true',
help='enable random layer token drop')
group.add_argument('--log-interval', type=int, default=100,
help='Report loss and timing interval.')
group.add_argument('--exit-interval', type=int, default=None,
help='Exit the program after the iteration is divisible '
'by this value.')
group.add_argument('--exit-duration-in-mins', type=int, default=None,
help='Exit the program after this many minutes.')
group.add_argument('--tensorboard-dir', type=str, default=None,
help='Write TensorBoard logs to this directory.')
group.add_argument('--no-masked-softmax-fusion',
action='store_false',
help='Disable fusion of query_key_value scaling, '
'masking, and softmax.',
dest='masked_softmax_fusion')
group.add_argument('--no-bias-gelu-fusion', action='store_false',
help='Disable bias and gelu fusion.',
dest='bias_gelu_fusion')
group.add_argument('--no-bias-dropout-fusion', action='store_false',
help='Disable bias and dropout fusion.',
dest='bias_dropout_fusion')
group.add_argument('--disable-moe-token-dropping', action='store_false',
help='Disable MoE expert token dropping.',
dest='moe_token_dropping')
group.add_argument('--moe-train-capacity-factor', type=float, default=1.0,
help='The capacity of the MoE expert at training time')
group.add_argument('--moe-eval-capacity-factor', type=float, default=1.0,
help='The capacity of the MoE expert at eval time.')
group.add_argument('--moe-min-capacity', type=int, default=4,
help='The minimum capacity per MoE expert regardless of the capacity_factor.')
group.add_argument('--moe-loss-coeff', type=float, default=0.1,
help='Scaling coefficient for adding MoE loss to model loss')
group.add_argument('--create-moe-param-group', action='store_true',
help='Create separate groups for MoE params.'
'This is necessary for techniques like ZeRO.')
group.add_argument('--optimizer', type=str, default='adam',
choices=['adam', 'sgd'],
help='Optimizer function')
group.add_argument('--dataloader-type', type=str, default=None,
choices=['single', 'cyclic'],
help='Single pass vs multiple pass data loader')
group.add_argument('--ds-inference', action='store_true',
help='DeepSpeed inference engine being used')
group.add_argument('--cpu-optimizer', action='store_true',
help='Run optimizer on CPU')
group.add_argument('--cpu_torch_adam', action='store_true',
help='Use Torch Adam as optimizer on CPU.')
group.add_argument('--no-pipeline-parallel', action='store_true',
help='Disable pipeline parallelism')
group.add_argument('--use-tutel', action='store_true',
help='Use Tutel optimization for MoE')
group.add_argument('--inference', action='store_true',
help='Very basic inference mode: not allocating optim/lr - requires ZERO_STAGE=0')
return parser
def _add_initialization_args(parser):
group = parser.add_argument_group(title='initialization')
group.add_argument('--seed', type=int, default=1234,
help='Random seed used for python, numpy, '
'pytorch, and cuda.')
group.add_argument('--init-method-std', type=float, default=0.02,
help='Standard deviation of the zero mean normal '
'distribution used for weight initialization.')
group.add_argument('--init-method-xavier-uniform', action='store_true',
help='Enable Xavier uniform parameter initialization')
return parser
def _add_learning_rate_args(parser):
group = parser.add_argument_group(title='learning rate')
group.add_argument('--lr', type=float, default=None,
help='Initial learning rate. Depending on decay style '
'and initial warmup, the learing rate at each '
'iteration would be different.')
group.add_argument('--lr-decay-style', type=str, default='linear',
choices=['constant', 'linear', 'cosine'],
help='Learning rate decay function.')
group.add_argument('--lr-decay-iters', type=int, default=None,
help='number of iterations to decay learning rate over,'
' If None defaults to `--train-iters`')
group.add_argument('--lr-decay-samples', type=int, default=None,
help='number of samples to decay learning rate over,'
' If None defaults to `--train-samples`')
group.add_argument('--lr-decay-tokens', type=int, default=None,
help='number of tokens to decay learning rate over,'
' If not None will override iter/sample-based decay')
group.add_argument('--lr-warmup-fraction', type=float, default=None,
help='fraction of lr-warmup-(iters/samples) to use '
'for warmup (as a float)')
group.add_argument('--lr-warmup-iters', type=int, default=0,
help='number of iterations to linearly warmup '
'learning rate over.')
group.add_argument('--lr-warmup-samples', type=int, default=0,
help='number of samples to linearly warmup '
'learning rate over.')
group.add_argument('--lr-warmup-tokens', type=int, default=None,
help='number of tokens to linearly warmup '
'learning rate over.')
group.add_argument('--warmup', type=int, default=None,
help='Old lr warmup argument, do not use. Use one of the'
'--lr-warmup-* arguments above')
group.add_argument('--min-lr', type=float, default=0.0,
help='Minumum value for learning rate. The scheduler'
'clip values below this threshold.')
group.add_argument('--override-lr-scheduler', action='store_true',
help='Reset the values of the scheduler (learning rate,'
'warmup iterations, minimum learning rate, maximum '
'number of iterations, and decay style from input '
'arguments and ignore values from checkpoints. Note'
'that all the above values will be reset.')
group.add_argument('--use-checkpoint-lr-scheduler', action='store_true',
help='Use checkpoint to set the values of the scheduler '
'(learning rate, warmup iterations, minimum learning '
'rate, maximum number of iterations, and decay style '
'from checkpoint and ignore input arguments.')
return parser
def _add_checkpointing_args(parser):
group = parser.add_argument_group(title='checkpointing')
group.add_argument('--save', type=str, default=None,
help='Output directory to save checkpoints to.')
group.add_argument('--save-interval', type=int, default=None,
help='Number of iterations between checkpoint saves.')
group.add_argument('--no-save-optim', action='store_true', default=None,
help='Do not save current optimizer.')
group.add_argument('--no-save-rng', action='store_true', default=None,
help='Do not save current rng state.')
group.add_argument('--load', type=str, default=None,
help='Directory containing a model checkpoint.')
group.add_argument('--no-load-optim', action='store_true', default=None,
help='Do not load optimizer when loading checkpoint.')
group.add_argument('--no-load-rng', action='store_true', default=None,
help='Do not load rng state when loading checkpoint.')
group.add_argument('--no-load-lr-state', action='store_true',
help='Do not load lr state when loading checkpoint.')
group.add_argument('--finetune', action='store_true',
help='Load model for finetuning. Do not load optimizer '
'or rng state from checkpoint and set iteration to 0. '
'Assumed when loading a release checkpoint.')
return parser
def _add_mixed_precision_args(parser):
group = parser.add_argument_group(title='mixed precision')
group.add_argument('--fp16', action='store_true',
help='Run model in fp16 mode.')
group.add_argument('--bf16', action='store_true',
help='Run model in bfloat16 mode.')
group.add_argument('--loss-scale', type=float, default=None,
help='Static loss scaling, positive power of 2 '
'values can improve fp16 convergence. If None, dynamic'
'loss scaling is used.')
group.add_argument('--initial-loss-scale', type=float, default=2**32,
help='Initial loss-scale for dynamic loss scaling.')
group.add_argument('--min-loss-scale', type=float, default=1.0,
help='Minimum loss scale for dynamic loss scale.')
group.add_argument('--loss-scale-window', type=float, default=1000,
help='Window over which to raise/lower dynamic scale.')
group.add_argument('--hysteresis', type=int, default=2,
help='hysteresis for dynamic loss scaling')
group.add_argument('--fp32-residual-connection', action='store_true',
help='Move residual connections to fp32.')
group.add_argument('--no-query-key-layer-scaling', action='store_false',
help='Do not scale Q * K^T by 1 / layer-number.',
dest='apply_query_key_layer_scaling')
group.add_argument('--attention-softmax-in-fp32', action='store_true',
help='Run attention masking and softmax in fp32. '
'This flag is ignored unless '
'--no-query-key-layer-scaling is specified.')
group.add_argument('--accumulate-allreduce-grads-in-fp32',
action='store_true',
help='Gradient accumulation and all-reduce in fp32.')
group.add_argument('--fp16-lm-cross-entropy', action='store_true',
help='Move the cross entropy unreduced loss calculation'
'for lm head to fp16.')
return parser
def _add_distributed_args(parser):
group = parser.add_argument_group(title='distributed')
group.add_argument('--tensor-model-parallel-size', type=int, default=1,
help='Degree of tensor model parallelism.')
group.add_argument('--enable-expert-tensor-parallelism', action='store_true',
default=False,
help="use tensor parallelism for expert layers in MoE")
group.add_argument('--pipeline-model-parallel-size', type=int, default=1,
help='Degree of pipeline model parallelism.')
group.add_argument('--moe-expert-parallel-size', type=int, default=1,
help='Degree of the MoE expert parallelism.')
group.add_argument('--model-parallel-size', type=int, default=None,
help='Old model parallel argument, do not use. Use '
'--tensor-model-parallel-size instead.')
group.add_argument('--num-layers-per-virtual-pipeline-stage', type=int, default=None,
help='Number of layers per virtual pipeline stage')
group.add_argument('--distributed-backend', default='nccl',
choices=['nccl', 'gloo', 'ccl'],
help='Which backend to use for distributed training.')
group.add_argument('--DDP-impl', default='local',
choices=['local', 'torch'],
help='which DistributedDataParallel implementation '
'to use.')
group.add_argument('--use-contiguous-buffers-in-ddp', action='store_true',
help='If set, use contiguous buffer in DDP. Note that '
'this option only works woth local DDP.' )
group.add_argument('--no-scatter-gather-tensors-in-pipeline', action='store_false',
help='Use scatter/gather to optimize communication of tensors in pipeline',
dest='scatter_gather_tensors_in_pipeline')
group.add_argument('--local_rank', type=int, default=None,
help='local rank passed from distributed launcher.')
group.add_argument('--lazy-mpu-init', type=bool, required=False,
help='If set to True, initialize_megatron() '
'skips DDP initialization and returns function to '
'complete it instead.Also turns on '
'--use-cpu-initialization flag. This is for '
'external DDP manager.' )
group.add_argument('--use-cpu-initialization', action='store_true',
default=None, help='If set, affine parallel weights '
'initialization uses CPU' )
return parser
def _add_validation_args(parser):
group = parser.add_argument_group(title='validation')
group.add_argument('--eval-iters', type=int, default=100,
help='Number of iterations to run for evaluation'
'validation/test for.')
group.add_argument('--eval-interval', type=int, default=1000,
help='Interval between running evaluation on '
'validation set.')
return parser
def _add_data_args(parser):
group = parser.add_argument_group(title='data and dataloader')
group.add_argument('--aml-data-download-path', type=str, default=None,
help='Path to mounted input dataset')
group.add_argument('--data-path', nargs='*', default=None,
help='Path to the training dataset. Accepted format:'
'1) a single data path, 2) multiple datasets in the'
'form: dataset1-weight dataset1-path dataset2-weight '
'dataset2-path ...')
group.add_argument('--split', type=str, default='969, 30, 1',
help='Comma-separated list of proportions for training,'
' validation, and test split. For example the split '
'`90,5,5` will use 90%% of data for training, 5%% for '
'validation and 5%% for test.')
group.add_argument('--vocab-file', type=str, default=None,
help='Path to the vocab file.')
group.add_argument('--merge-file', type=str, default=None,
help='Path to the BPE merge file.')
group.add_argument('--vocab-extra-ids', type=int, default=0,
help='Number of additional vocabulary tokens. '
'They are used for span masking in the T5 model')
group.add_argument('--seq-length', type=int, default=None,
help='Maximum sequence length to process.')
group.add_argument('--encoder-seq-length', type=int, default=None,
help='Maximum encoder sequence length to process.'
'This should be exclusive of --seq-length')
group.add_argument('--decoder-seq-length', type=int, default=None,
help="Maximum decoder sequence length to process.")
group.add_argument('--retriever-seq-length', type=int, default=256,
help='Maximum sequence length for the biencoder model '
' for retriever')
group.add_argument('--sample-rate', type=float, default=1.0,
help='sample rate for training data. Supposed to be 0 '
' < sample_rate < 1')
group.add_argument('--mask-prob', type=float, default=0.15,
help='Probability of replacing a token with mask.')
group.add_argument('--short-seq-prob', type=float, default=0.1,
help='Probability of producing a short sequence.')
group.add_argument('--mmap-warmup', action='store_true',
help='Warm up mmap files.')
group.add_argument('--num-workers', type=int, default=2,
help="Dataloader number of workers.")
group.add_argument('--tokenizer-type', type=str,
default=None,
choices=['BertWordPieceLowerCase',
'BertWordPieceCase',
'GPT2BPETokenizer'],
help='What type of tokenizer to use.')
group.add_argument('--data-impl', type=str, default='infer',
choices=['lazy', 'cached', 'mmap', 'infer'],
help='Implementation of indexed datasets.')
group.add_argument('--reset-position-ids', action='store_true',
help='Reset posistion ids after end-of-document token.')
group.add_argument('--reset-attention-mask', action='store_true',
help='Reset self attention maske after '
'end-of-document token.')
group.add_argument('--eod-mask-loss', action='store_true',
help='Mask loss for the end of document tokens.')
group.add_argument('--train-data-exact-num-epochs', type=int, default=None,
help='When building the train dataset, force it to be '
'an exact number of epochs of the raw data')
group.add_argument('--return-data-index', action='store_true',
help='Return the index of data sample.')
group.add_argument('--data-efficiency-curriculum-learning', action='store_true',
help='Use DeepSpeed data efficiency library curriculum learning feature.')
group.add_argument('--train-idx-path', type=str, default=None,
help='Force to use certain index file.')
group.add_argument('--train-doc-idx-path', type=str, default=None,
help='Force to use certain index file.')
group.add_argument('--train-sample-idx-path', type=str, default=None,
help='Force to use certain index file.')
group.add_argument('--train-shuffle-idx-path', type=str, default=None,
help='Force to use certain index file.')
return parser
def _add_autoresume_args(parser):
group = parser.add_argument_group(title='autoresume')
group.add_argument('--adlr-autoresume', action='store_true',
help='Enable autoresume on adlr cluster.')
group.add_argument('--adlr-autoresume-interval', type=int, default=1000,
help='Intervals over which check for autoresume'
'termination signal')
return parser
def _add_biencoder_args(parser):
group = parser.add_argument_group(title='biencoder')
# network size
group.add_argument('--ict-head-size', type=int, default=None,
help='Size of block embeddings to be used in ICT and '
'REALM (paper default: 128)')
group.add_argument('--biencoder-projection-dim', type=int, default=0,
help='Size of projection head used in biencoder (paper'
' default: 128)')
group.add_argument('--biencoder-shared-query-context-model', action='store_true',
help='Whether to share the parameters of the query '
'and context models or not')
# checkpointing
group.add_argument('--ict-load', type=str, default=None,
help='Directory containing an ICTBertModel checkpoint')
group.add_argument('--bert-load', type=str, default=None,
help='Directory containing an BertModel checkpoint '
'(needed to start ICT and REALM)')
# data
group.add_argument('--titles-data-path', type=str, default=None,
help='Path to titles dataset used for ICT')
group.add_argument('--query-in-block-prob', type=float, default=0.1,
help='Probability of keeping query in block for '
'ICT dataset')
group.add_argument('--use-one-sent-docs', action='store_true',
help='Whether to use one sentence documents in ICT')
group.add_argument('--evidence-data-path', type=str, default=None,
help='Path to Wikipedia Evidence frm DPR paper')
# training
group.add_argument('--retriever-report-topk-accuracies', nargs='+', type=int,
default=[], help="Which top-k accuracies to report "
"(e.g. '1 5 20')")
group.add_argument('--retriever-score-scaling', action='store_true',
help='Whether to scale retriever scores by inverse '
'square root of hidden size')
# faiss index
group.add_argument('--block-data-path', type=str, default=None,
help='Where to save/load BlockData to/from')
group.add_argument('--embedding-path', type=str, default=None,
help='Where to save/load Open-Retrieval Embedding'
' data to/from')
# indexer
group.add_argument('--indexer-batch-size', type=int, default=128,
help='How large of batches to use when doing indexing '
'jobs')
group.add_argument('--indexer-log-interval', type=int, default=1000,
help='After how many batches should the indexer '
'report progress')
return parser
def _add_vit_args(parser):
group = parser.add_argument_group(title="vit")
group.add_argument('--num-classes', type=int, default=1000,
help='num of classes in vision classificaiton task')
group.add_argument('--img-dim', type=int, default=224,
help='Image size for vision classification task')
group.add_argument('--num-channels', type=int, default=3,
help='Number of channels in input image data')
group.add_argument('--patch-dim', type=int, default=16,
help='patch dimension used in vit')
return parser
def _add_zero_args(parser):
"""Text generate arguments."""
group = parser.add_argument_group('ZeRO configurations', 'configurations')
group.add_argument("--zero-stage", type=int, default=1.0)
group.add_argument('--zero-reduce-scatter', action='store_true',
help='Use reduce scatter if specified')
group.add_argument('--zero-contigious-gradients', action='store_true',
help='Use contigious memory optimizaiton if specified')
group.add_argument("--zero-reduce-bucket-size", type=int, default=0.0)
group.add_argument("--zero-allgather-bucket-size", type=int, default=0.0)
group.add_argument('--remote-device', type=str, default='none', choices=['none', 'cpu', 'nvme'],
help='Remote device for ZeRO-3 initialized parameters.')
group.add_argument('--use-pin-memory', action='store_true',
help='Use pinned CPU memory for ZeRO-3 initialized model parameters.')
return parser
def _add_memoryopt_args(parser):
"""Memory optimization arguments."""
group = parser.add_argument_group('Memory optimizations', 'configurations')
group.add_argument("--scattered-embeddings", action='store_true',
help='Save memory by scattering embedding activations. '
'Introduces dropout differences across MP configurations.')
group.add_argument("--split-transformers", action='store_true',
help='Save memory by splitting transformer layers into two parts, '
'allowing for more frequent activation checkpoint savings.')
group.add_argument("--memory-centric-tiled-linear", action="store_true",
help='Save memory by tiling with deepspeed.zero.TiledLinear.')
group.add_argument("--tile-factor", type=int, default=1,
help='Make all linear layers the same size of [hidden/tile_factor, hidden/tile_factor]. '
'Must be enabled with --memory-centric-tiled-linear. '
'Example A: if tile_factor=1, the qkv layer [hidden, 3* hidden] would be converted into [1,3] tiles of size [hidden,hidden]. '
'Example B: if tile_factor=2, the intermediate layer [4*hidden, hidden] will be converted into [8, 2] tiles of size [hidden/2, hidden/2]. '
'Default is 1.')
return parser
def _add_activation_checkpoint_args(parser):
group = parser.add_argument_group('Activation Checkpointing',
'Checkpointing Configurations')
group.add_argument('--deepspeed-activation-checkpointing', action='store_true',
help='uses activation checkpointing from deepspeed')
group.add_argument('--partition-activations', action='store_true',
help='partition Activations across GPUs before checkpointing.')
group.add_argument('--contigious-checkpointing', action='store_true',
help='Contigious memory checkpointing for activatoins.')
group.add_argument('--checkpoint-in-cpu', action='store_true',
help='Move the activation checkpoints to CPU.')
group.add_argument('--synchronize-each-layer', action='store_true',
help='does a synchronize at the beginning and end of each checkpointed layer.')
group.add_argument('--profile-backward', action='store_true',
help='Enables backward pass profiling for checkpointed layers.')
return parser
def _add_distillation_args(parser):
group = parser.add_argument_group('Knowledge distillation',
'Distillation Configurations')
group.add_argument('--num-layers-teacher', type=int, default=None,
help='Number of the teacher transformer layers.')
group.add_argument('--num-experts-teacher', type=int, nargs='+', default=[1,],
help='number of teacher experts list, MoE related.')
group.add_argument('--hidden-size-teacher', type=int, default=None,
help='Tansformer teacher hidden size.')
group.add_argument('--num-attention-heads-teacher', type=int, default=None,
help='Number of teacher transformer attention heads.')
group.add_argument('--mos', action='store_true',
help='Enable Mixture-of-Students via knolwedge distillation.')
group.add_argument('--kd', action='store_true',
help='Enable knolwedge distillation.')
group.add_argument('--kd-alpha-ce', default=1, type=float)
group.add_argument('--kd-beta-ce', default=1, type=float)
group.add_argument('--kd-temp', default=1.0, type=float)
group.add_argument('--reset-iteration', action='store_true',
help='Reset the iteration count.')
group.add_argument('--load-teacher', type=str, default=None,
help='Directory containing a teacher model checkpoint.')
return parser