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https://github.com/hiyouga/LLaMA-Factory.git
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97 lines
3.8 KiB
Python
97 lines
3.8 KiB
Python
# Copyright 2024 imoneoi and the LlamaFactory team.
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#
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# This code is inspired by the imoneoi's OpenChat library.
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# https://github.com/imoneoi/openchat/blob/3.6.0/ochat/training_deepspeed/train.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from typing import Literal
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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
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from llamafactory.data import get_dataset, get_template_and_fix_tokenizer, MultiModalDataCollatorForSeq2Seq
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from llamafactory.extras.constants import IGNORE_INDEX
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from llamafactory.hparams import get_train_args
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from llamafactory.model import load_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: Literal["pt", "sft"] = "sft",
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dataset: str = "alpaca_en_demo",
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dataset_dir: str = "data",
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template: str = "default",
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cutoff_len: int = 1024, # i.e. maximum input length during training
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is_mistral_or_gemma: bool = False, # mistral and gemma models opt for a smaller learning rate,
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packing: bool = False,
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):
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r"""
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Calculates the optimal learning rate for 7B/13B models using LLaMA's hyper-parameters.
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Usage:
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python cal_lr.py --model_name_or_path path_to_model --dataset alpaca_en_demo --cutoff_len 1024 --batch_size 16
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"""
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model_args, data_args, training_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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packing=packing,
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output_dir="dummy_dir",
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overwrite_cache=True,
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do_train=True,
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)
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)
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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trainset = get_dataset(template, model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
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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 = MultiModalDataCollatorForSeq2Seq(template=template, tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX)
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else:
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raise NotImplementedError(f"Stage does not supported: {stage}.")
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dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
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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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valid_ratio = valid_tokens / total_tokens
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token_batch_size = cutoff_len * batch_size * valid_ratio
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lr = BASE_LR * math.sqrt(token_batch_size / BASE_BS) # lr ~ sqrt(batch_size)
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lr = lr / 6.0 if is_mistral_or_gemma else lr
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print(
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"Optimal learning rate is {:.2e} for valid ratio% {:.2f} and effective token batch size {:.2f}".format(
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lr, valid_ratio * 100, token_batch_size
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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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