InternLM/configs/7B_sft.py

JOB_NAME = "7b_train"
DO_ALERT = False

SEQ_LEN = 2048
HIDDEN_SIZE = 4096
NUM_ATTENTION_HEAD = 32
MLP_RATIO = 8 / 3
NUM_LAYER = 32
VOCAB_SIZE = 103168

MODEL_ONLY_FOLDER = "local:llm_ckpts/xxxx"
# Ckpt folder format:
# fs: 'local:/mnt/nfs/XXX'
SAVE_CKPT_FOLDER = "local:llm_ckpts"
LOAD_CKPT_FOLDER = "local:llm_ckpts/49"

# boto3 Ckpt folder format:
# import os
# BOTO3_IP = os.environ["BOTO3_IP"] # boto3 bucket endpoint
# SAVE_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm"
# LOAD_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm/snapshot/1/"
CHECKPOINT_EVERY = 50
ckpt = dict(
    enable_save_ckpt=False,  # enable ckpt save.
    save_ckpt_folder=SAVE_CKPT_FOLDER,  # Path to save training ckpt.
    # load_ckpt_folder= dict(path=MODEL_ONLY_FOLDER, content=["model"], ckpt_type="normal"),
    load_ckpt_folder="local:llm_ckpts/",
    # 'load_ckpt_info' setting guide:
    # 1. the 'path' indicate ckpt path,
    # 2. the 'content‘ means what states will be loaded, support: "model", "sampler", "optimizer", "scheduler", "all"
    # 3. the ’ckpt_type‘ means the type of checkpoint to be loaded, now only 'normal' type is supported.
    load_ckpt_info=dict(path=MODEL_ONLY_FOLDER, content=("model",), ckpt_type="internlm"),
    checkpoint_every=CHECKPOINT_EVERY,
    async_upload=True,  # async ckpt upload. (only work for boto3 ckpt)
    async_upload_tmp_folder="/dev/shm/internlm_tmp_ckpt/",  # path for temporarily files during asynchronous upload.
    oss_snapshot_freq=int(CHECKPOINT_EVERY / 2),  # snapshot ckpt save frequency.
)

TRAIN_FOLDER = "/path/to/dataset"
VALID_FOLDER = "/path/to/dataset"
data = dict(
    seq_len=SEQ_LEN,
    # micro_num means the number of micro_batch contained in one gradient update
    micro_num=4,
    # packed_length = micro_bsz * SEQ_LEN
    micro_bsz=2,
    # defaults to the value of micro_num
    valid_micro_num=4,
    # defaults to 0, means disable evaluate
    valid_every=50,
    pack_sample_into_one=False,
    total_steps=50000,
    skip_batches="",
    rampup_batch_size="",
    # Datasets with less than 50 rows will be discarded
    min_length=50,
    # train_folder=TRAIN_FOLDER,
    # valid_folder=VALID_FOLDER,
    empty_cache_and_diag_interval=10,
    diag_outlier_ratio=1.1,
)

grad_scaler = dict(
    fp16=dict(
        # the initial loss scale, defaults to 2**16
        initial_scale=2**16,
        # the minimum loss scale, defaults to None
        min_scale=1,
        # the number of steps to increase loss scale when no overflow occurs
        growth_interval=1000,
    ),
    # the multiplication factor for increasing loss scale, defaults to 2
    growth_factor=2,
    # the multiplication factor for decreasing loss scale, defaults to 0.5
    backoff_factor=0.5,
    # the maximum loss scale, defaults to None
    max_scale=2**24,
    # the number of overflows before decreasing loss scale, defaults to 2
    hysteresis=2,
)

hybrid_zero_optimizer = dict(
    # Enable low_level_optimzer overlap_communication
    overlap_sync_grad=True,
    overlap_sync_param=True,
    # bucket size for nccl communication params
    reduce_bucket_size=512 * 1024 * 1024,
    # grad clipping
    clip_grad_norm=1.0,
)

loss = dict(
    label_smoothing=0,
)

adam = dict(
    lr=1e-4,
    adam_beta1=0.9,
    adam_beta2=0.95,
    adam_beta2_c=0,
    adam_eps=1e-8,
    weight_decay=0.01,
)

lr_scheduler = dict(
    total_steps=data["total_steps"],
    init_steps=0,  # optimizer_warmup_step
    warmup_ratio=0.01,
    eta_min=1e-5,
    last_epoch=-1,
)

beta2_scheduler = dict(
    init_beta2=adam["adam_beta2"],
    c=adam["adam_beta2_c"],
    cur_iter=-1,
)

model = dict(
    checkpoint=False,  # The proportion of layers for activation aheckpointing, the optional value are True/False/[0-1]
    num_attention_heads=NUM_ATTENTION_HEAD,
    embed_split_hidden=True,
    vocab_size=VOCAB_SIZE,
    embed_grad_scale=1,
    parallel_output=True,
    hidden_size=HIDDEN_SIZE,
    num_layers=NUM_LAYER,
    mlp_ratio=MLP_RATIO,
    apply_post_layer_norm=False,
    dtype="torch.bfloat16",  # Support: "torch.float16", "torch.half", "torch.bfloat16", "torch.float32", "torch.tf32"
    norm_type="rmsnorm",
    layer_norm_epsilon=1e-5,
    use_flash_attn=True,
    num_chunks=1,  # if num_chunks > 1, interleaved pipeline scheduler is used.
)
"""
zero1 parallel:
    1. if zero1 <= 0, The size of the zero process group is equal to the size of the dp process group,
        so parameters will be divided within the range of dp.
    2. if zero1 == 1, zero is not used, and all dp groups retain the full amount of model parameters.
    3. zero1 > 1 and zero1 <= dp world size, the world size of zero is a subset of dp world size.
        For smaller models, it is usually a better choice to split the parameters within nodes with a setting <= 8.
pipeline parallel (dict):
    1. size: int, the size of pipeline parallel.
    2. interleaved_overlap: bool, enable/disable communication overlap when using interleaved pipeline scheduler.
tensor parallel: tensor parallel size, usually the number of GPUs per node.
"""
parallel = dict(
    zero1=8,
    pipeline=dict(size=1, interleaved_overlap=True),
    sequence_parallel=False,
)

cudnn_deterministic = False
cudnn_benchmark = False

monitor = dict(
    # feishu alert configs
    alert=dict(
        enable_feishu_alert=DO_ALERT,
        feishu_alert_address=None,  # feishu webhook to send alert message
        light_monitor_address=None,  # light_monitor address to send heartbeat
    ),
)