78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
# coding=utf-8
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# Calculates the optimal learning rate for 7B/13B models using LLaMA's hyper-parameters.
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# Usage: python cal_lr.py --model_name_or_path path_to_model --dataset alpaca_en --cutoff_len 1024 --batch_size 16
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# Inspired by: https://github.com/imoneoi/openchat/blob/master/ochat/training_deepspeed/train.py
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import math
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from typing import Optional
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import fire
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import torch
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
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from llmtuner.data import get_dataset
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from llmtuner.extras.constants import IGNORE_INDEX
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from llmtuner.hparams import get_train_args
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from llmtuner.model import load_model_and_tokenizer
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BASE_LR = 3e-4 # 1.5e-4 for 30B-70B models
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BASE_BS = 4_000_000 # from llama paper
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def calculate_lr(
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model_name_or_path: str,
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batch_size: int, # total batch size, namely (batch size * gradient accumulation * world size)
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stage: Optional[str] = "sft",
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dataset: Optional[str] = "alpaca_en",
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dataset_dir: Optional[str] = "data",
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template: Optional[str] = "default",
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cutoff_len: Optional[int] = 1024, # i.e. maximum input length during training
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is_mistral: Optional[bool] = False, # mistral model uses a smaller learning rate,
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):
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model_args, data_args, training_args, finetuning_args, _ = get_train_args(
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dict(
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stage=stage,
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model_name_or_path=model_name_or_path,
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dataset=dataset,
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dataset_dir=dataset_dir,
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template=template,
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cutoff_len=cutoff_len,
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output_dir="dummy_dir",
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overwrite_cache=True,
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)
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)
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_, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, add_valuehead=False)
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trainset = get_dataset(tokenizer, model_args, data_args, training_args, stage=stage)
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if stage == "pt":
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data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
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elif stage == "sft":
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data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX)
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else:
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raise NotImplementedError
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dataloader = DataLoader(
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dataset=trainset, batch_size=batch_size, shuffle=True, collate_fn=data_collator, pin_memory=True
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)
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valid_tokens, total_tokens = 0, 0
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for batch in tqdm(dataloader):
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valid_tokens += torch.sum(batch["labels"] != IGNORE_INDEX).item()
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total_tokens += torch.numel(batch["labels"])
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batch_max_len = cutoff_len * batch_size # max tokens in a batch
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valid_ratio = valid_tokens / total_tokens
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batch_valid_len = batch_max_len * valid_ratio
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lr = BASE_LR * math.sqrt(batch_valid_len / BASE_BS) # lr ~ sqrt(batch_size)
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lr = lr / 6.0 if is_mistral else lr
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print(
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"Optimal learning rate is {:.2e} for valid ratio% {:.2f} and effective batch size {:.2f}".format(
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lr, valid_ratio * 100, batch_valid_len
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)
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)
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if __name__ == "__main__":
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fire.Fire(calculate_lr)
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