# coding=utf-8 # Copyright (c) 2023 Habana Labs, Ltd. an Intel Company. # 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 from megatron.enums import PositionEmbeddingType 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) parser = _add_tensor_logger_args(parser) parser = _add_profiler_args(parser) parser = _add_deterministic_args(parser) parser = _add_hpu_optimizations_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')) if not args.local_rank and 'LOCAL_RANK' in os.environ: args.local_rank = int(os.getenv("LOCAL_RANK", -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.eval_micro_batch_size is None: args.eval_micro_batch_size = args.micro_batch_size 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 # 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'] 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 = int(args.ffn_hidden_coeff * 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.position_embedding_type == PositionEmbeddingType.absolute \ or args.position_embedding_type == PositionEmbeddingType.alibi \ or args.position_embedding_type == PositionEmbeddingType.learnable: assert args.max_position_embeddings is not None, "Must specify position embedding size" if args.seq_length is not None: assert args.max_position_embeddings >= args.seq_length, \ "Number of position embeddings can't be less than sequence length" if args.decoder_seq_length is not None: assert args.max_position_embeddings >= args.decoder_seq_length, \ "Number of position embeddings can't be less than decoder sequence length" else: assert args.max_position_embeddings is None, \ "Rotary method doesn't hold position embedding matrix, but a rotation matrix, \ which its size depends only on word embedding dimension" if args.activation_func_type != 'gelu': assert not args.openai_gelu, 'openai gelu cannot be used when \ specified activation function is not gelu' assert not args.onnx_safe, 'workarounds for onnx-safe cannot be \ used when specified activation function is not gelu' 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' assert args.checkpoint_activations_granularity == 'full', \ 'distributed recompute activations is only '\ 'applicable to full checkpoint activations granularity' args.curriculum_learning = 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 # 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 _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 ffn-hidden-coeff*hidden-size if not provided') group.add_argument('--ffn-hidden-coeff', type=float, default=4, help='Transformer Feed-Forward Network hidden size coeff. ' 'ffn-hidden-size is set to ffn-hidden-coeff*hidden-size if --ffn-hidden-size is not provided. ' 'This coefficient is set to 4 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-layernorm-weight-plus-one', action='store_true', help='If set, use layernorm weight as plus one (initialize layernorm weight to 0 instead of 1 and add 1 after loading the weight' 'This is used for achieving better BF16 accuracy in the layernorm weight.') group.add_argument('--apply-residual-connection-post-layernorm', action='store_true', help='If set, use original BERT residula connection ' 'ordering.') group.add_argument('--embed-layernorm', action='store_true', help='use layernorm for embedding') group.add_argument('--layernorm-type', type=str, default='layernorm', choices=['layernorm', 'rmsnorm'], help='What kind of layernorm to use in the model. Supported types are LayerNorm and RMSNorm.' 'If not specified, each model is used with its default') 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('--activation-func-type', type=str, default='gelu', choices=['gelu', 'swiglu'], help='What kind of activation func to use in the model. Supported types are Gelu and SwiGLU.' 'If not specified, each model is used with its default') group.add_argument('--no-bias', action='store_true', help='Do not use bias in linear layers of attention and MLP') group.add_argument('--bert-no-binary-head', action='store_false', help='Disable BERT binary head.', dest='bert_binary_head') group.add_argument('--position-embedding-type', type=lambda x: PositionEmbeddingType[x], choices=list(PositionEmbeddingType), default=PositionEmbeddingType.learnable, help='Define position embedding type ' '("rotary" | "absolute" | "alibi" | "learnable"). "learnable" by default.') group.add_argument('--fix-position-emb-redundant-alloc', action='store_true', help='If true, will not allocate position embeddings at ' 'the embed object that is used to generate logits.') group.add_argument('--kill-switch-path', type=str, default=None, help='Path to look for a kill switch. ' 'If found will automatically exit the program.') 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.') group.add_argument('--mllog-output-path', type=str, default="/tmp/result_0.txt", help='Path to mllog output file. Defaults to /tmp/result_0.txt.') 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') group.add_argument('--do-layernorm-bias-weight-decay', action='store_true', help='Enable Weight Decay for LayerNorm (weight and bias) and non Bias Parameters') 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('--eval-micro-batch-size', type=int, default=None, help='Batch size per model instance (local batch size) for evaluation. ' 'If not defined, using --micro-batch-size value instead') 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 ' ' ' ' ' '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('--checkpoint-activations-granularity', type=str, default='full', choices=['full', 'selective'], help='Checkpoint activations to allow for training ' 'with larger models, sequences, and batch sizes. ' 'It is supported at two granularities 1) full: ' 'whole transformer layer is recomputed, ' '2) selective: core attention part of the transformer ' 'layer is recomputed.') 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('--skip-train', action='store_true', help='If set, skips training.') 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('--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='adamw', choices=['adam', 'sgd', 'adamw', 'fusedadamw'], 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') group.add_argument('--sequence-parallel', action='store_true', help='Enable sequence parallel optimization.') group.add_argument("--device-warmup", action='store_true', help="Enable device warmup, which executes given number of iterations of training and evaluation in order to create graphs before timer starts") group.add_argument("--device-warmup-iterations", type=int, default=5, help="Number of warmup iterations before training.") 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') group.add_argument('--no-scaled-init', action='store_true', help='No scaled initialization with number of ' 'layers and have same init method for all the model ' 'parameters') 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.') group.add_argument('--universal-checkpoint', action='store_true', help='Loading a universal format checkpoint.') 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.') group.add_argument('--verify-checkpoint', action='store_true', help='run verification on saved checkpoint.') group.add_argument("--verify-checkpoint-model-type", default='GPT', type=str, help='Type of Model',choices=['GPT', 'BLOOM', 'LLAMA']) group.add_argument('--ext-lr-steps', type=int, default=0, help='Use External LR steps.') 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', 'hccl'], 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.') group.add_argument('--do-pretrain-validation', action='store_true', help="run validation before starting the training") group.add_argument('--eval-loss-exit-value', type=float, default=None, help='Eval loss value below which the training will exit') 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('--train-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('--valid-data-path', nargs='*', default=None, help='Path to the validation 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('--test-data-path', nargs='*', default=None, help='Path to the test 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('--warmup-dataset-path', type=str, default="/tmp/synthetic_text_document", help='Path to a preexisting synthetic warmup dataset. \ Needs to point to a file name without an extension. Defaults to /tmp/synthetic_text_document.') 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('--mask-tensor-adding', action='store_true', help='Perform attention masking by adding tensor instead of doing fill') 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', 'SentencePieceTokenizer', 'LlamaTokenizer'], 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 mask 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('--no-seq-len-plus-one-tokens', action='store_false', help='If set, dont get ' 'sequence length plus one tokens for training', dest='use_seq_len_plus_one_tokens') group.add_argument('--tokenizer-model-file', type=str, default=None, help='Path to tokenizer model file, where applicable (e.g. SentencePiece)') group.add_argument('--tokenizer-eod-id', type=int, default=None, help='End of document token id, where applicable (e.g. SentencePiece)') 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') group.add_argument('--no-data-sharding', action='store_false', help='Disable data sharding.', dest='data_sharding') 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 def _add_tensor_logger_args(parser): group = parser.add_argument_group(title='tensor-logger logging configuration') group.add_argument("--log-model-inputs", action="store_true", help="If set, log model\'s inputs for configured iterations") group.add_argument("--log-fwd-activations", action="store_true", help="If set, log model\'s nn.Module forward activations for configured iterations") group.add_argument("--log-bwd-grads", action="store_true", help="If set, log model\'s nn.Module backward gradients for configured iterations") group.add_argument("--tensor-logger-max-iter", type=int, default=0, help="Sets the maximum number of iterations to capture. If 0, disable tensor logger") group.add_argument("--tensor-logger-path", type=str, default=None, help="Path for saving tensor logger captured tensors file") group.add_argument("--clearml-config-path", type=str, default=None, help="Path for clearml config file") group.add_argument("--clearml-exp-name", type=str, default=None, help="Experiment name for clearml") group.add_argument("--clearml-continue-exp", action='store_true', help="if indicated, will try to continue the previous experiment with the same name. \ If fail, create new experiment") return parser def _add_profiler_args(parser): group = parser.add_argument_group(title='profiling configuration') group.add_argument("--profile", type=str, default=None, choices=['pt', 'pt-full', 'hltv'], help="Enable profiling") group.add_argument("--profile-steps", type=str, default='2,3', help="Which steps to profile. Format: ,") return parser def _add_deterministic_args(parser): group = parser.add_argument_group(title='deterministic configuration') group.add_argument("--hpu-deterministic", action='store_true', help="sets deterministic flag run for hpu") return parser def _add_hpu_optimizations_args(parser): group = parser.add_argument_group(title='fp8 configuration') group.add_argument('--use-hpu-fp8-transformer-engine', default=False, action='store_true', help='Enable FP8 layers') group.add_argument('--use-fused-sdpa', type=lambda x: x.lower() in ['true', '1'], default=False, help='Enable Fused Scaled Dot Product Attention.') group.add_argument('--use-fused-sdpa-with-recompute', type=lambda x: x.lower() in ['true', '1'], default=False, help='Enable Fused Scaled Dot Product Attention with recompute feature.') group.add_argument('--use-hpu-graphs', type=lambda x: x.lower() in ['true', '1'], default=False, help='Enable hpu graphs') group.add_argument('--cache-fp8-weight', default=False, action='store_true', help='Cache fp8 weight from forward to backward. \ This will increase memory usage, but improve performance.') group.add_argument('--cache-fp8-weight-fwd', type=lambda x: x.lower() in ['true', '1'], default=True, help='In forward, calculate fp8 weight only once for the entire batch.') group.add_argument('--hpu-fp8-measure-interval', type=int, default=10, help='Amax measurement interval for transformer engine') return parser