neox_args.py

import subprocess
from dataclasses import dataclass

try:
    from .template import NeoXArgsTemplate
except ImportError:
    from template import NeoXArgsTemplate

try:
    from typing import Literal
except ImportError:
    from typing_extensions import Literal

ATTENTION_TYPE_CHOICES = [
    "global",
    "local",
    "sparse_fixed",
    "sparse_variable",
    "bigbird",
    "bslongformer",
    "gmlp",
    "amlp",
]


def get_git_commit_hash():
    """Gets the git commit hash of your current repo (if it exists)"""
    try:
        git_hash = subprocess.check_output(["git", "describe", "--always"]).strip()
        git_hash = git_hash.decode()
    except subprocess.CalledProcessError:
        git_hash = None
    return git_hash


@dataclass
class NeoXArgsParallelism(NeoXArgsTemplate):
    """
    Parallelism Arguments
    """

    pipe_parallel_size: int = 0
    """
    Number of pipeline parallel stages. Disable with 0.
    """

    model_parallel_size: int = 1
    """
    Size of the model parallelism.
    """

    pipe_partition_method: str = "type:transformer|mlp"
    """
    method used to distribute model layers across pipeline stages. Choose from "parameters", which balances the number
    of parameters on each pipeline stage, "uniform", which naively balances the number of layers per stage, or
    "type:[regex]", which balances layers whose class names match [regex]
    """

    world_size: int = None
    """
    Total world size (i.e number of gpus in cluster). Configured post-launch using distributed launcher
    """

    is_pipe_parallel: bool = False
    """
    flag to determine whether pipeline parallelism is on - shouldn't be set by user, is automatically determined
    according to pipeline parallel size.
    """


@dataclass
class NeoXArgsModel(NeoXArgsTemplate):
    """
    Model Arguments
    """

    precision: Literal["fp16", "fp32", "bfloat16"] = None
    """
    description of the used precision, either one of fp16 or fp32 (and in the future bf16).
    """

    num_layers: int = None
    """
    Number of transformer layers.
    """

    hidden_size: int = None
    """
    Transformer hidden size.
    """

    num_attention_heads: int = None
    """
    Number of transformer attention heads.
    """

    seq_length: int = None
    """
    Maximum sequence length to process.
    """

    max_position_embeddings: int = None
    """
    Maximum number of position embeddings to use. This is the size of position embedding.
    """

    norm: Literal["layernorm", "rmsnorm", "scalenorm"] = "layernorm"
    """
    Normalization layer to use. Choose from "layernorm", "rmsnorm", "scalenorm".
    """

    layernorm_epsilon: float = 1.0e-5
    """
    Layer norm epsilon.
    """

    rms_norm_epsilon: float = 1.0e-8
    """
    Root mean squared norm epsilon
    """

    scalenorm_epsilon: float = 1.0e-8
    """
    Scalenorm epsilon
    """

    pos_emb: Literal[
        "learned", "rotary", "sinusoidal", "rpe", "alibi", "none"
    ] = "learned"
    """
    Type of positional embedding to use - choose from 'learned', 'rotary', 'sinusoidal', 'rpe', 'none'
    """

    rpe_num_buckets: int = 32
    """
    T5 relative positional encoding number of buckets, default 32.
    """

    rpe_max_distance: int = 128
    """
    T5 relative positional encoding max distance, default 128.
    """

    opt_pos_emb_offset: int = 0
    """
    Learned position embedding offset (only used by OPT, where it should be set to 2).
    """

    no_weight_tying: bool = False
    """
    Disables weight tying between embedding weights and final Linear layer
    """

    attention_config: list = None

    """
    Attention configuration for gpt-neox

    The first item in the list specifies the attention type(s), and should be a list of strings. The second item
    specifies the number of times to repeat those attention types in the full list.

    attention type choices:  [global, local, sparse_fixed, sparse_variable, bslongformer, bigbird]

    So a 12 layer network with only global attention could be specified like:
        [[[`global`], 12]]

    or a 12 layer network with alternating global / local like:
        [[[`global`, `local`], 6]]

    If none is specified, this defaults to
        [[[`global`], n_layers]]
    """

    sparsity_config: dict = None

    """
    Sparsity configuration dict as defined in https://www.deepspeed.ai/docs/config-json/#sparse-attention

    Note that since neox is autoregressive, attention is always "unidirectional" and `horizontal_global_attention` is
    always false.

    The main difference between our sparsity config and deepspeed's is that `mode` is ignored - since it is instead
    specified in attention_config defining each layer.

    An example config is given below:
          "sparse_attention": {
            "block": 16,
            "different_layout_per_head": true,
            "num_local_blocks": 4,
            "num_global_blocks": 1,
            "num_different_global_patterns": 4,
            "num_random_blocks": 0,
            "local_window_blocks": [4],
            "global_block_indices": [0],
            "global_block_end_indices": None,
            "num_sliding_window_blocks": 3
          }
    """

    num_unique_layers: int = None
    """
    Number of unique transformer layers. num-layers should be divisible by this value. Currently only has an effect when pipe_parallel_size=0.
    """

    param_sharing_style: str = "grouped"
    """
    Ordering of the shared parameters. For example, for a num-layers=4 and --num-unique-layers=2, we will have the following ordering for two unique layers 1 and 2-: grouped: [1, 2, 1, 2] and spaced: [1, 1, 2, 2].
    """

    make_vocab_size_divisible_by: int = 128
    """
    Pad the vocab size to be divisible by this value. This is added for computational efficiency reasons.
    """

    activation: Literal["gelu", "geglu", "relu", "softsign", "swish", "mish"] = "gelu"
    """
    Activation function to use - choose from ["gelu", "geglu", "relu", "softsign", "swish", "mish"]
    """

    scaled_upper_triang_masked_softmax_fusion: bool = False
    """
    Enable fusion of query_key_value_scaling time (upper diagonal) masking and softmax.
    """

    scaled_masked_softmax_fusion: bool = False
    """
    Enable fusion of query_key_value_scaling general masking and softmax.
    """

    bias_gelu_fusion: bool = False
    """
    Enable bias and gelu fusion.
    """

    bias_dropout_fusion: bool = False
    """
    Enable bias and dropout fusion.
    """

    fp16_lm_cross_entropy: bool = False
    """
    Move the cross entropy unreduced loss calculation for lm head to fp16.
    """

    init_method_std: float = 0.02
    """
    Standard deviation of the zero mean normal distribution used for weight initialization.
    """

    apply_query_key_layer_scaling: bool = False
    """
    Scale Q * K^T by 1 / layer-number. If this flag is set, then it will automatically set attention-softmax-in-fp32 to true
    """

    use_cpu_initialization: bool = False
    """
    If set, affine parallel weights initialization uses CPU
    """

    attention_softmax_in_fp32: bool = False
    """
    Run attention masking and softmax in fp32.
    """

    rotary_pct: float = 1.0
    """
    pct of hidden dims to apply rotary positional embedding to
    """

    rotary_emb_base: int = 10000
    """
    Base for rotary positional embedding
    """

    init_method: Literal[
        "normal",
        "scaled_normal",
        "orthogonal",
        "scaled_orthogonal",
        "xavier_uniform",
        "xavier_normal",
        "wang_init",
        "small_init",
    ] = "normal"
    """
    Init function used on all layers except ff residual outputs - choose from
    ["normal", "scaled_normal", "orthogonal", "scaled_orthogonal", "xavier_uniform", "xavier_normal", "wang_init", "small_init"]
    """

    output_layer_init_method: Literal[
        "normal",
        "scaled_normal",
        "orthogonal",
        "scaled_orthogonal",
        "xavier_uniform",
        "xavier_normal",
        "wang_init",
        "small_init",
    ] = "scaled_normal"
    """
    Init function used for ff residual outputs - choose from
    ["normal", "scaled_normal", "orthogonal", "scaled_orthogonal", "xavier_uniform", "xavier_normal", "wang_init", "small_init"]
    """

    gmlp_attn_dim: int = 64
    """
    the dimension of the single head self attention in gmlp model (not used in gpt models).
    If None - gmlp model doesn't use attention.
    """

    gpt_j_residual: bool = False
    """
    If false, we use the conventional residual path:
      x = x + attn(ln1(x))
      x = x + mlp(ln2(x))
    Otherwise, we use the residual path from GPT-J, which offers a slight speedup:
      x = ln(x)
      x = x + attn(x) + mlp(x)
    """

    soft_prompt_tuning: dict = None
    """
    Dictionary configuring the soft prompt tuning parameters.
    If enabled, will train *only* the soft prompt, and freezes the rest of the model.
    parameters in the dict are:
        'enabled': bool = True # enables soft prompting
        'num_tokens': int = 10 # length of the soft prompt in tokens
        'init_string': str = '' # if provided, initialize the soft prompt with the word embeddings of this string
        'init_range': float = 0.5 # if no init string is provided, initialize the soft prompt with a uniform distribution between -init_range and init_rang
    """

    output_layer_parallelism: Literal["row", "column"] = "row"

    """
    Parameter controlling whether the output layer is parallelized over the hidden dim (row) or the vocab dim (column)
    """


@dataclass
class NeoXArgsOptimizer(NeoXArgsTemplate):
    """
    Optimizer Arguments
    """

    optimizer_type: Literal[
        "adam", "onebitadam", "cpu_adam", "cpu_torch_adam", "sm3", "madgrad_wd"
    ] = "adam"
    """
    Type of optimizer to use. Choose from ['adam', 'onebitadam', 'cpu_adam', 'cpu_torch_adam', 'sm3', 'madgrad_wd]
    """

    use_bnb_optimizer: bool = False
    """
    Whether to enable the bitsandbytes optimizers
    """

    zero_stage: int = None
    """
    Zero Optimizer stage
    """

    zero_reduce_scatter: bool = None
    """
    Zero: Uses reduce or reduce scatter instead of allreduce to average gradients
    """

    zero_contiguous_gradients: bool = None
    """
    Zero: Copies the gradients to a contiguous buffer as they are produced. Avoids memory fragmentation during backward pass. Only useful when running very large models.
    """

    zero_reduce_bucket_size: int = None
    """
    Zero: Number of elements reduced/allreduced at a time. Limits the memory required for the allgather for large model sizes
    """

    zero_allgather_bucket_size: int = None
    """
    Zero: Number of elements allgathered at a time. Limits the memory required for the allgather for large model sizes
    """

    lr: float = None
    """
    Max Learning rate during training
    """


@dataclass
class NeoXArgsLRScheduler(NeoXArgsTemplate):
    """
    LR Scheduler Arguments
    """

    lr_decay_style: Literal["constant", "linear", "cosine", "exponential"] = "linear"
    """
    Learning rate decay function. Choose from 'constant', 'linear', 'cosine', 'exponential'.
    """

    lr_decay_iters: int = None
    """
    Number of iterations to decay learning rate over, If None defaults to --train-iters
    """

    min_lr: float = 0.0
    """
    Minimum value for learning rate. The scheduler clips values below this threshold.
    """

    warmup: float = 0.01
    """
    Percentage of total iterations to warmup on (.01 = 1 percent of all training iters).
    """

    override_lr_scheduler: bool = False
    """
    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.
    """

    use_checkpoint_lr_scheduler: bool = False
    """
    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.
    """


@dataclass
class NeoXArgsLogging(NeoXArgsTemplate):
    """
    Logging Arguments
    """

    use_wandb: bool = None
    """Flag indicating if wandb is to be used."""

    wandb_group: str = None
    """Weights and Biases group name - used to group together "runs"."""

    wandb_team: str = None
    """Team name for Weights and Biases."""

    wandb_project: str = "neox"
    """wandb project name"""

    wandb_host: str = "https://api.wandb.ai"
    """url of the wandb host"""

    wandb_init_all_ranks: bool = False
    """Initialize wandb on all ranks."""

    git_hash: str = get_git_commit_hash()
    """current git hash of repository"""

    log_dir: str = None
    """
    Directory to save logs to.
    """

    tensorboard_writer = None
    """
    initialized tensorboard writer
    """

    tensorboard_dir: str = None
    """
    Write TensorBoard logs to this directory.
    """

    log_interval: int = None
    """
    Interval between logging.
    """

    log_grad_pct_zeros: bool = False
    """
    Log the percentage of zeros for the gradient of each parameter to wandb / tensorboard (useful for debugging). Needs wandb_init_all_ranks set to True if using pipeline parallelism to log all ranks.
    """

    log_param_norm: bool = False
    """
    Log the frob norm of the parameters to wandb / tensorboard (useful for debugging). Needs wandb_init_all_ranks set to True if using pipeline parallelism to log all ranks.
    """

    log_grad_norm: bool = False
    """
    Log the frob norm of the gradients to wandb / tensorboard (useful for debugging).
    (N.B - this will only work with pp = 0 for now, as we don't have access to the gradients of the model because
    deepspeed.)
    """

    log_optimizer_states: bool = False
    """
    Log the frob norm of the optimizer states to wandb / tensorboard (useful for debugging).
    """

    log_gradient_noise_scale: bool = False
    """
    Whether to log the gradient noise scale when training (cf. https://arxiv.org/abs/1812.06162 for explanation)
    """

    gradient_noise_scale_n_batches: int = 5
    """
    Number of batches to accumulate gradients for in the gradient noise scale logger.
    """

    gradient_noise_scale_cpu_offload: bool = False
    """
    Whether to offload the buffered gradients to cpu when measuring gradient noise scale.
    """


@dataclass
class NeoXArgsOther(NeoXArgsTemplate):
    """
    Misc. Arguments
    """

    distributed_backend: str = "nccl"
    """
    Which backend to use for distributed training.
    """

    local_rank: int = None
    """
    local rank passed from distributed launcher.
    """

    rank: int = None
    """
    global rank of process being run (passed in via distributed launcher)
    """

    lazy_mpu_init: bool = False
    """
    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.
    """

    short_seq_prob: float = 0.1
    """
    Probability of producing a short sequence.
    """

    eod_mask_loss: bool = False
    """
    Mask loss for the end of document tokens.
    """

    adlr_autoresume: bool = False
    """
    Enable auto-resume on adlr cluster.
    """

    adlr_autoresume_object = None
    """
    imported autoresume
    """

    adlr_autoresume_interval: int = 1000
    """
    Intervals over which check for auto-resume termination signal
    """

    seed: int = 1234
    """
    Random seed used for python, numpy, pytorch, and cuda.
    """

    onnx_safe: bool = False
    """
    Use workarounds for known problems with Torch ONNX exporter
    """

    deepscale: bool = False
    """
    (Deprecated) enable DeepSpeed (helper flag for user code, no impact on DeepSpeed backend)'
    """

    deepscale_config: str = None
    """(Deprecated) deepscale json configuration file."""

    deepspeed_mpi: bool = False
    """
    Run via MPI, this will attempt to discover the necessary variables to initialize torch distributed from the MPI environment
    """

    user_script: str = None
    """
    user script to be run
    """

    iteration: int = None
    """
    Set during training
    """

    do_train: int = None
    """
    Set during training
    """

    do_valid: int = None
    """
    Set during training
    """

    do_test: int = None
    """
    Set during training
    """

    global_num_gpus: int = None
    """
    Set during launching
    """


@dataclass
class NeoXArgsTokenizer(NeoXArgsTemplate):
    """
    Tokenizer Arguments
    """

    tokenizer_type: Literal[
        "GPT2BPETokenizer",
        "HFTokenizer",
        "HFGPT2Tokenizer",
        "SPMTokenizer",
        "CharLevelTokenizer",
    ] = "GPT2BPETokenizer"
    """
    Type of tokenizer to use - should be one of ["GPT2BPETokenizer", "HFTokenizer", "HFGPT2Tokenizer", "SPMTokenizer", "CharLevelTokenizer"]
    """

    padded_vocab_size: int = None
    """
    Total (padded) vocabulary size of tokenizer. Configured after launching of training,
    as it's dependent on the parallelism size.
    """

    tokenizer = None
    """
    tokenizer object loaded into memory and accessible by other functions
    """


@dataclass
class NeoXArgsTraining(NeoXArgsTemplate):
    """
    Training Arguments
    """

    data_path: str = None
    """
    Path to combined dataset to split.
    """

    train_data_paths: list = None
    """
    List of paths to train datasets.
    """

    test_data_paths: list = None
    """
    List of paths to test datasets.
    """

    valid_data_paths: list = None
    """
    List of paths to validation datasets.
    """

    train_data_weights: list = None
    """
    List of 'weights' that decide how often to sample from each training dataset when blending datasets. If None, defaults to equal weighting.
    Should be a list the same length as `train_data_paths`
    """

    valid_data_weights: list = None
    """
    List of 'weights' that decide how often to sample from each validation dataset when blending datasets. If None, defaults to equal weighting.
    Should be a list the same length as `valid_data_paths`
    """

    test_data_weights: list = None
    """
    List of 'weights' that decide how often to sample from each test dataset when blending datasets. If None, defaults to equal weighting.
    Should be a list the same length as `test_data_paths`
    """

    weight_by_num_documents: bool = False
    """
    If True, Builds dataset weights from a multinomial distribution over groups of data according to the number of
    documents in each group.

    WARNING: setting this to True will override any user provided weights

    We sample from a group according to the probability p(L) ∝ |L| ** α,
    where p(L) is the probability of sampling from a given group,
          |L| is the number of examples in that datapoint,
          and α is a coefficient that acts to upsample data from underrepresented groups

    Hence α (`alpha`) allows us to control how much to 'boost' the probability of training on low-resource groups.

    See https://arxiv.org/abs/1911.02116 for more details
    """

    weighted_sampler_alpha: float = 0.3
    """
    Alpha value for `weight_by_num_documents`. Only has an effect if `weight_by_num_documents` = True.

    when alpha = 1, the probability of sampling from a given group = n_samples / total_samples
    as alpha -> 0, the probability of sampling from all groups becomes equal, and number of documents has no effect
    as alpha -> inf, the probability of sampling from the groups with *the most samples* -> 1
    """

    data_impl: str = "infer"
    """
    Implementation of indexed datasets.
    """

    mmap_warmup: bool = False
    """
    Warm up mmap files.
    """

    save: str = None
    """
    Output directory to save checkpoints to.
    """

    config_files: dict = None
    """
    Store of original config files mapping config filename to file contents
    """

    load: str = None
    """
    Directory containing a model checkpoint.
    """

    checkpoint_validation_with_forward_pass: bool = False
    """
    save input and output of a forward pass with the checkpoint and validate after load
    """

    save_interval: int = None
    """
    Number of iterations between checkpoint saves.
    """

    no_save_optim: bool = False
    """
    Do not save current optimizer.
    """

    no_save_rng: bool = False
    """
    Do not save current rng state.
    """

    no_load_optim: bool = False
    """
    Do not load optimizer when loading checkpoint.
    """

    no_load_rng: bool = False
    """
    Do not load rng state when loading checkpoint.
    """

    finetune: bool = False
    """
    Load model for finetuning. Do not load optimizer or rng state from checkpoint and set iteration to 0. Assumed when loading a release checkpoint.
    """

    batch_size: int = None
    """
    training microbatch size per gpu
    """

    train_iters: int = None
    """
    Number of iterations to run for training.
    """

    eval_iters: int = 100
    """
    Number of iterations to run for evaluation validation/test for.
    """

    keep_last_n_checkpoints: int = None
    """
    Number of last checkpoints to keep
    """

    eval_interval: int = 1000
    """
    Interval between running evaluation on validation set.
    """

    split: str = "969, 30, 1"
    """
    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.
    """

    vocab_file: str = None
    """
    Path to the vocab file.
    """

    merge_file: str = None
    """
    Path to the BPE merge file.
    """

    num_workers: int = 2
    """
    Dataloader number of workers.
    """

    exit_interval: int = None
    """
    Exit the program after the iteration is divisible by this value.
    """

    attention_dropout: float = 0.1
    """
    Post attention dropout probability.
    """

    hidden_dropout: float = 0.1
    """
    Dropout probability for hidden state transformer.
    """

    weight_decay: float = 0.01
    """
    Weight decay coefficient for L2 regularization.
    """

    checkpoint_activations: bool = False
    """
    Checkpoint activation to allow for training with larger models, sequences, and batch sizes.
    """

    checkpoint_num_layers: int = 1
    """
    Chunk size (number of layers) for checkpointing.
    """

    deepspeed_activation_checkpointing: bool = True
    """
    DEPRECATED - TODO: remove
    Uses activation checkpointing from deepspeed
    """

    contiguous_checkpointing: bool = False
    """
    Contiguous memory checkpointing for activations.
    """

    checkpoint_in_cpu: bool = False
    """
    Move the activation checkpoints to CPU.
    """

    synchronize_each_layer: bool = False
    """
    does a synchronize at the beginning and end of each checkpointed layer.
    """

    profile_backward: bool = False
    """
    Enables backward pass profiling for checkpointed layers.
    """

    partition_activations: bool = False
    """
    Partition Activations across GPUs before checkpointing.
    """

    gas: int = None
    """gradient_accumulation_steps"""  # TODO this is a duplicate, remove?

    clip_grad: float = None
    """
    Gradient clipping based on global L2 norm.
    """

    hysteresis: int = 2
    """
    hysteresis for dynamic loss scaling
    """

    dynamic_loss_scale: bool = None
    """
    flag indicating whether dynamic loss scale is used
    """

    loss_scale: float = None
    """
    Static loss scaling, positive power of 2
    values can improve fp16 convergence. If None, dynamic loss scaling is used.
    """

    loss_scale_window: float = 1000.0
    """
    Window over which to raise/lower dynamic scale.
    """

    min_scale: float = 1.0
    """
    Minimum loss scale for dynamic loss scale.
    """

    char_level_ppl: bool = False
    """
    Whether to calculate character level perplexity as well as token level perplexity. (may incur a time cost)
    """


@dataclass
class NeoXArgsTextgen(NeoXArgsTemplate):
    """
    Text Generation arguments
    """

    text_gen_type: str = None
    """
    How to generate text/sample the model.
    Options: `unconditional`, `input-file`, `interactive`
    """

    temperature: float = 0.0
    """
    exponential scaling output distribution ("higher == more risk")
    """

    top_p: float = 0.0
    """
    Top-p (nucleus) sampling chooses from the smallest possible set of tokens whose cumulative probability exceeds the probability top_p.
    """

    top_k: int = 0
    """
    integer between 0 and the models vocab size. Filters out any logits with a probability less than that of the top_kth token.
    """

    maximum_tokens: int = 64
    """
    maximum number of tokens to be generated
    """

    sample_input_file: str = None
    """
    Get input from file instead of interactive mode, each line is an input.
    """

    sample_output_file: str = "samples.txt"
    """
    Output file
    """

    num_samples: int = 1
    """
    Number of samples to generate unconditionally, defaults to 1 and interactive conditional sampling
    """

    recompute: bool = False
    """
    During generation recompute all attention instead of using previously computed keys/values.
    Should be set to true for sparse attention models
    """

    eval_results_prefix: str = ""
    """
    prefix to which to save evaluation results - final fp will be {eval_results_prefix}_eval_results_yy-mm-dd-HH-MM.json
    """

    eval_tasks: list = None
    """
    Tasks to evaluate on using lm_eval_harness
    """