cal_lr.py

# Copyright 2024 imoneoi and the LlamaFactory team.
#
# This code is inspired by the imoneoi's OpenChat library.
# https://github.com/imoneoi/openchat/blob/3.6.0/ochat/training_deepspeed/train.py
#
# 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.

import math
from typing import Literal

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

from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.hparams import get_train_args
from llamafactory.model import load_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: Literal["pt", "sft"] = "sft",
    dataset: str = "alpaca_en_demo",
    dataset_dir: str = "data",
    template: str = "default",
    cutoff_len: int = 1024,  # i.e. maximum input length during training
    is_mistral_or_gemma: bool = False,  # mistral and gemma models opt for a smaller learning rate,
    packing: bool = False,
):
    r"""
    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_demo --cutoff_len 1024 --batch_size 16
    """
    model_args, data_args, training_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,
            packing=packing,
            output_dir="dummy_dir",
            overwrite_cache=True,
            do_train=True,
        )
    )
    tokenizer_module = load_tokenizer(model_args)
    tokenizer = tokenizer_module["tokenizer"]
    template = get_template_and_fix_tokenizer(tokenizer, data_args)
    trainset = get_dataset(template, model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
    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(f"Stage does not supported: {stage}.")

    dataloader = DataLoader(trainset, batch_size, shuffle=False, 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_or_gemma 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)