examples/nlp/language_modeling/conf/megatron_bert_config.yaml

name: megatron_bert
restore_from_path: null # used when starting from a .nemo file

trainer:
  devices: 2
  num_nodes: 1
  accelerator: gpu
  precision: 16
  logger: False # logger provided by exp_manager
  enable_checkpointing: False
  replace_sampler_ddp: False
  max_epochs: -1 # PTL default. In practice we don't usually train for more than 1 epoch.
  max_steps: 100000 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches
  log_every_n_steps: 10
  val_check_interval: 100
  limit_val_batches: 50
  limit_test_batches: 500
  accumulate_grad_batches: 1
  gradient_clip_val: 1.0
  benchmark: False

exp_manager:
  explicit_log_dir: null
  exp_dir: null
  name: megatron_bert
  create_wandb_logger: False
  wandb_logger_kwargs:
    project: null
    name: null
  resume_if_exists: True
  resume_ignore_no_checkpoint: True
  create_checkpoint_callback: True
  checkpoint_callback_params:
    monitor: val_loss
    save_top_k: 10
    mode: min
    always_save_nemo: False # saves nemo file during validation, not implemented for model parallel
    filename: 'megatron_bert--{val_loss:.2f}-{step}-{consumed_samples}'
    model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}


model:
  # model parallelism 
  micro_batch_size: 4
  global_batch_size: 8
  tensor_model_parallel_size: 1
  pipeline_model_parallel_size: 1
  virtual_pipeline_model_parallel_size: null

  # model architecture
  encoder_seq_length: 512
  max_position_embeddings: ${.encoder_seq_length}
  num_layers: 12
  hidden_size: 768
  ffn_hidden_size: 3072 # Transformer FFN hidden size. Usually 4 * hidden_size.
  num_attention_heads: 12
  init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.')
  hidden_dropout: 0.1 # Dropout probability for hidden state transformer.
  kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null
  apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number.
  layernorm_epsilon: 1e-5
  make_vocab_size_divisible_by: 128 # Pad the vocab size to be divisible by this value for computation efficiency.
  pre_process: True # add embedding
  post_process: True # add pooler
  bert_binary_head: True # BERT binary head

  tokenizer:
    library: 'megatron'
    type: 'BertWordPieceLowerCase'
    model: null
    vocab_file: null
    merge_file: null 

  # precision
  native_amp_init_scale: 4294967296 # 2 ** 32
  native_amp_growth_interval: 1000
  fp32_residual_connection: False # Move residual connections to fp32
  fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16

  # Megatron O2-style half-precision
  megatron_amp_O2: False # Enable O2-level automatic mixed precision using main parameters
  grad_allreduce_chunk_size_mb: 125
  grad_div_ar_fusion: False 

  # miscellaneous
  seed: 1234
  use_cpu_initialization: False # Init weights on the CPU (slow for large models)
  onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter.
  gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory)
  
  ## Activation Checkpointing
  # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed.
  # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+).
  # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
  # 'full' will checkpoint the entire transformer layer.
  activations_checkpoint_granularity: null # 'selective' or 'full' 
  activations_checkpoint_method: null # 'uniform', 'block'
  # 'uniform' divides the total number of transformer layers and checkpoints the input activation
  # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model.
  # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity
  activations_checkpoint_num_layers: null
  # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory.
  # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage.
  num_micro_batches_with_partial_activation_checkpoints: null
  # This feature is valid only when used with pipeline-model-parallelism.
  # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed
  # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is
  # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint
  # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'.
  # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage.
  activations_checkpoint_layers_per_pipeline: null
  # This feature is valid only when used with pipeline-model-parallelism.
  # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later
  # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than
  # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage
  # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints',
  # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path.
  sequence_parallel: False
  
  data:
    # Path to data must be specified by the user.
    # can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]",
    # Or see example below: 
    # data_prefix: 
    #   - .5
    #   - /raid/data/pile/my-gpt3_00_text_document
    #   - .5
    #   - /raid/data/pile/my-gpt3_01_text_document
    data_prefix: ???
    index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix
    data_impl: mmap
    splits_string: 900,50,50
    seq_length: ${model.encoder_seq_length}
    skip_warmup: True
    num_workers: 0
    dataloader_type: single # cyclic
    reset_position_ids: False # Reset position ids after end-of-document token
    reset_attention_mask: False # Reset attention mask after end-of-document token
    eod_mask_loss: False # Mask loss for the end of document tokens
    masked_lm_prob: 0.15 # Probability of replacing a token with mask.
    short_seq_prob: 0.1 # Probability of producing a short sequence.
  
  optim:
    name: fused_adam
    lr: 2e-4
    weight_decay: 0.01 
    betas: 
    - 0.9
    - 0.98
    sched:
      name: CosineAnnealing
      warmup_steps: 500
      constant_steps: 50000
      min_lr: 2e-5