LLaMA-Factory-Mirror/scripts/cal_lr.py

# coding=utf-8
# Calculates the optimal learning rate for 7B/13B models using LLaMA's hyper-parameters.
# Usage: python cal_lr.py --model_name_or_path path_to_model --dataset alpaca_en --cutoff_len 1024 --batch_size 16
# Inspired by: https://github.com/imoneoi/openchat/blob/master/ochat/training_deepspeed/train.py

import math
from typing import Optional

import fire
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq

from llmtuner.data import get_dataset
from llmtuner.extras.constants import IGNORE_INDEX
from llmtuner.hparams import get_train_args
from llmtuner.model import load_model_and_tokenizer


BASE_LR = 3e-4  # 1.5e-4 for 30B-70B models
BASE_BS = 4_000_000  # from llama paper


def calculate_lr(
    model_name_or_path: str,
    batch_size: int,  # total batch size, namely (batch size * gradient accumulation * world size)
    stage: Optional[str] = "sft",
    dataset: Optional[str] = "alpaca_en",
    dataset_dir: Optional[str] = "data",
    template: Optional[str] = "default",
    cutoff_len: Optional[int] = 1024,  # i.e. maximum input length during training
    is_mistral: Optional[bool] = False,  # mistral model uses a smaller learning rate,
):
    model_args, data_args, training_args, finetuning_args, _ = get_train_args(
        dict(
            stage=stage,
            model_name_or_path=model_name_or_path,
            dataset=dataset,
            dataset_dir=dataset_dir,
            template=template,
            cutoff_len=cutoff_len,
            output_dir="dummy_dir",
            overwrite_cache=True,
        )
    )
    _, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, add_valuehead=False)
    trainset = get_dataset(tokenizer, model_args, data_args, training_args, stage=stage)
    if stage == "pt":
        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
    elif stage == "sft":
        data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX)
    else:
        raise NotImplementedError

    dataloader = DataLoader(
        dataset=trainset, batch_size=batch_size, shuffle=True, collate_fn=data_collator, pin_memory=True
    )
    valid_tokens, total_tokens = 0, 0
    for batch in tqdm(dataloader):
        valid_tokens += torch.sum(batch["labels"] != IGNORE_INDEX).item()
        total_tokens += torch.numel(batch["labels"])

    batch_max_len = cutoff_len * batch_size  # max tokens in a batch
    valid_ratio = valid_tokens / total_tokens
    batch_valid_len = batch_max_len * valid_ratio
    lr = BASE_LR * math.sqrt(batch_valid_len / BASE_BS)  # lr ~ sqrt(batch_size)
    lr = lr / 6.0 if is_mistral else lr
    print(
        "Optimal learning rate is {:.2e} for valid ratio% {:.2f} and effective batch size {:.2f}".format(
            lr, valid_ratio * 100, batch_valid_len
        )
    )


if __name__ == "__main__":
    fire.Fire(calculate_lr)