# coding=utf-8 # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Megatron arguments.""" import argparse import os def parse_args(extra_args_provider=None, defaults={}): """Parse all arguments.""" parser = argparse.ArgumentParser(description='Megatron-LM Arguments') # 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) # TODO: Refactor parser = _add_gpt2_args(parser) # Custom arguments. if extra_args_provider is not None: parser = extra_args_provider(parser) # Parse. args = parser.parse_args() # 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. assert getattr(args, key) is None, \ 'defaults can only be overwritten for args with None values.' setattr(args, key, defaults[key]) # Distributed args. args.rank = int(os.getenv('RANK', '0')) args.world_size = int(os.getenv("WORLD_SIZE", '1')) args.model_parallel_size = min(args.model_parallel_size, args.world_size) if args.rank == 0: print('using world size: {} and model-parallel size: {} '.format( args.world_size, args.model_parallel_size)) # Fp16 loss scaling. args.dynamic_loss_scale = False if args.loss_scale is None: args.dynamic_loss_scale = True # Checks. assert args.hidden_size % args.num_attention_heads == 0 assert args.max_position_embeddings >= args.seq_length assert args.min_lr <= args.lr if args.save is not None: assert args.save_interval is not None _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 = '.' * (32 - 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 _add_network_size_args(parser): group = parser.add_argument_group(title='network size') group.add_argument('--num-layers', type=int, required=True, help='Number of transformer layers.') group.add_argument('--hidden-size', type=int, required=True, help='Tansformer hidden size.') group.add_argument('--num-attention-heads', type=int, required=True, help='Number of transformer attention heads.') group.add_argument('--max-position-embeddings', type=int, required=True, 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.') 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 ptobability.') 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.') return parser def _add_training_args(parser): group = parser.add_argument_group(title='training') group.add_argument('--batch-size', type=int, required=True, help='Batch size per model instance (local batch size). ' 'Global batch size is local batch size times data ' 'parallel size.') 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('--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.') 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('--tensorboard-dir', type=str, default=None, help='Write TensorBoard logs to this directory.') 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.') 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', 'exponential'], 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('--min-lr', type=float, default=0.0, help='Minumum value for learning rate. The scheduler' 'clip values below this threshold.') group.add_argument('--warmup', type=float, default=0.01, help='Percentage of total iterations to warmup on ' '(.01 = 1 percent of all training iters).') 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', help='Do not save current optimizer.') group.add_argument('--no-save-rng', action='store_true', help='Do not save current rng state.') group.add_argument('--load', type=str, default=None, help='Directory containing a model checkpoint.') group.add_argument('--no-load-optim', action='store_true', help='Do not load optimizer when loading checkpoint.') group.add_argument('--no-load-rng', action='store_true', help='Do not load rng state when loading checkpoint.') group.add_argument('--finetune', action='store_true', help='Load model for finetuning. Do not load optimizer ' 'or rng state from checkpoint and set iteration to 0. ' 'Assumed when loading a release checkpoint.') 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('--apply-query-key-layer-scaling', action='store_true', help='Scale Q * K^T by 1 / layer-number. If this flag ' 'is set, then it will automatically set ' 'attention-softmax-in-fp32 to true') group.add_argument('--attention-softmax-in-fp32', action='store_true', help='Run attention masking and softmax in fp32.') group.add_argument('--fp32-allreduce', action='store_true', help='All-reduce in fp32') group.add_argument('--hysteresis', type=int, default=2, help='hysteresis for dynamic loss scaling') group.add_argument('--loss-scale', type=float, default=None, help='Static loss scaling, positive power of 2 ' 'values can improve fp16 convergence. If None, dynamic' 'loss scaling is used.') group.add_argument('--loss-scale-window', type=float, default=1000, help='Window over which to raise/lower dynamic scale.') group.add_argument('--min-scale', type=float, default=1, help='Minimum loss scale for dynamic loss scale.') return parser def _add_distributed_args(parser): group = parser.add_argument_group(title='mixed precision') group.add_argument('--model-parallel-size', type=int, default=1, help='Size of the model parallel.') group.add_argument('--distributed-backend', default='nccl', choices=['nccl', 'gloo'], help='Which backend to use for distributed training.') group.add_argument('--DDP-impl', default='local', choices=['local', 'torch'], help='which DistributedDataParallel implementation ' 'to use.') group.add_argument('--local_rank', type=int, default=None, help='local rank passed from distributed launcher.') return parser def _add_validation_args(parser): group = parser.add_argument_group(title='validation') group.add_argument('--eval-iters', type=int, default=100, help='Number of iterations to run for evaluation' 'validation/test for.') group.add_argument('--eval-interval', type=int, default=1000, help='Interval between running evaluation on ' 'validation set.') return parser def _add_data_args(parser): group = parser.add_argument_group(title='data and dataloader') group.add_argument('--data-path', type=str, default=None, help='Path to combined dataset to split.') 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('--seq-length', type=int, required=True, help="Maximum sequence length to process.") 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', 'GPT2BPETokenizer'], help='What type of tokenizer to use.') group.add_argument('--data-impl', type=str, default='infer', choices=['lazy', 'cached', 'mmap', 'infer'], help='Implementation of indexed datasets.') group.add_argument('--reset-position-ids', action='store_true', help='Reset posistion ids after end-of-document token.') group.add_argument('--reset-attention-mask', action='store_true', help='Reset self attention maske after ' 'end-of-document token.') group.add_argument('--eod-mask-loss', action='store_true', help='Mask loss for the end of document tokens.') return parser def _add_autoresume_args(parser): group = parser.add_argument_group(title='autoresume') group.add_argument('--adlr-autoresume', action='store_true', help='Enable autoresume on adlr cluster.') group.add_argument('--adlr-autoresume-interval', type=int, default=1000, help='Intervals over which check for autoresume' 'termination signal') return parser ######################################################################## def _add_gpt2_args(parser): group = parser.add_argument_group(title='gpt2') group.add_argument('--input-data-sizes-file', type=str, default='sizes.txt', help='The filename containing all the shards ' 'sizes for numpy data loader') return parser def add_data_args_(parser): """Train/valid/test data arguments.""" group = parser.add_argument_group('data', 'data configurations') group.add_argument('--data-loader', type=str, default=None, choices=['raw', 'lazy', 'tfrecords', 'numpy', 'binary'], help='Which data loader to use. Default varies by model.') return parser