Google Colab を使って Llama 3 のファインチューニングを試してみました／大塚

# flake8: noqa
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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.
"""
# regular:
python examples/scripts/sft.py \
    --model_name_or_path="facebook/opt-350m" \
    --report_to="wandb" \
    --learning_rate=1.41e-5 \
    --per_device_train_batch_size=64 \
    --gradient_accumulation_steps=16 \
    --output_dir="sft_openassistant-guanaco" \
    --logging_steps=1 \
    --num_train_epochs=3 \
    --max_steps=-1 \
    --push_to_hub \
    --gradient_checkpointing \

# peft:
python examples/scripts/sft.py \
    --model_name_or_path="facebook/opt-350m" \
    --report_to="wandb" \
    --learning_rate=1.41e-5 \
    --per_device_train_batch_size=64 \
    --gradient_accumulation_steps=16 \
    --output_dir="sft_openassistant-guanaco" \
    --logging_steps=1 \
    --num_train_epochs=3 \
    --max_steps=-1 \
    --push_to_hub \
    --gradient_checkpointing \
    --use_peft \
    --lora_r=64 \
    --lora_alpha=16
"""
import logging
import os
from contextlib import nullcontext

TRL_USE_RICH = os.environ.get("TRL_USE_RICH", False)

from trl.commands.cli_utils import init_zero_verbose, SftScriptArguments, TrlParser

if TRL_USE_RICH:
    init_zero_verbose()
    FORMAT = "%(message)s"

    from rich.console import Console
    from rich.logging import RichHandler

import torch
from datasets import load_dataset

from tqdm.rich import tqdm
from transformers import AutoTokenizer, TrainingArguments

from trl import (
    ModelConfig,
    RichProgressCallback,
    SFTTrainer,
    get_peft_config,
    get_quantization_config,
    get_kbit_device_map,
)

tqdm.pandas()

if TRL_USE_RICH:
    logging.basicConfig(format=FORMAT, datefmt="[%X]", handlers=[RichHandler()], level=logging.INFO)


if __name__ == "__main__":
    parser = TrlParser((SftScriptArguments, TrainingArguments, ModelConfig))
    args, training_args, model_config = parser.parse_args_and_config()

    # Force use our print callback
    if TRL_USE_RICH:
        training_args.disable_tqdm = True
        console = Console()

    ################
    # Model & Tokenizer
    ################
    torch_dtype = (
        model_config.torch_dtype
        if model_config.torch_dtype in ["auto", None]
        else getattr(torch, model_config.torch_dtype)
    )
    quantization_config = get_quantization_config(model_config)
    model_kwargs = dict(
        revision=model_config.model_revision,
        trust_remote_code=model_config.trust_remote_code,
        attn_implementation=model_config.attn_implementation,
        torch_dtype=torch_dtype,
        use_cache=False if training_args.gradient_checkpointing else True,
        device_map=get_kbit_device_map() if quantization_config is not None else None,
        quantization_config=quantization_config,
    )
    tokenizer = AutoTokenizer.from_pretrained(model_config.model_name_or_path, use_fast=True)
    tokenizer.pad_token = tokenizer.eos_token

    ################
    # Dataset
    ################
    raw_datasets = load_dataset(args.dataset_name)

    train_dataset = raw_datasets[args.dataset_train_name]
    eval_dataset = raw_datasets[args.dataset_test_name]

    ################
    # Optional rich context managers
    ###############
    init_context = nullcontext() if not TRL_USE_RICH else console.status("[bold green]Initializing the SFTTrainer...")
    save_context = (
        nullcontext()
        if not TRL_USE_RICH
        else console.status(f"[bold green]Training completed! Saving the model to {training_args.output_dir}")
    )

    ################
    # Training
    ################
    with init_context:
        trainer = SFTTrainer(
            model=model_config.model_name_or_path,
            model_init_kwargs=model_kwargs,
            args=training_args,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            dataset_text_field=args.dataset_text_field,
            max_seq_length=args.max_seq_length,
            tokenizer=tokenizer,
            packing=args.packing,
            peft_config=get_peft_config(model_config),
            callbacks=[RichProgressCallback] if TRL_USE_RICH else None,
        )

    trainer.train()

    with save_context:
        trainer.save_model(training_args.output_dir)

    # データセットの読み込み
    dataset = load_dataset("bbz662bbz/databricks-dolly-15k-ja-gozarinnemon", split="train")
    dataset = dataset.filter(lambda example: example["category"] == "open_qa")

    # プロンプトの生成
    def generate_prompt(example):
        messages = [
            {
                'role': "system",
                'content': "あなたは日本語で回答するAIアシスタントです。"
            },
            {
                'role': "user",
                'content': example["instruction"]
            },
            {
                'role': "assistant",
                'content': example["output"]
            }
        ]
        return tokenizer.apply_chat_template(messages, tokenize=False)

    # textカラムの追加
    def add_text(example):
        example["text"] = generate_prompt(example)
        return example

    dataset = dataset.map(add_text)
    dataset = dataset.remove_columns(["input", "category", "output", "index", "instruction"])

    # データセットの分割
    train_test_split = dataset.train_test_split(test_size=0.1)
    train_dataset = train_test_split["train"]
    eval_dataset = train_test_split["test"]

# 学習
!python examples/scripts/sft.py \
    --model_name meta-llama/Meta-Llama-3-8B-Instruct \
    --dataset_name bbz662bbz/databricks-dolly-15k-ja-gozaru \
    --dataset_text_field text \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 1 \
    --learning_rate 2e-4 \
    --optim adamw_torch \
    --save_steps 50 \
    --logging_steps 50 \
    --max_steps 500 \
    --use_peft \
    --lora_r 64 \
    --lora_alpha 16 \
    --lora_dropout 0.1 \
    --load_in_4bit \
    --report_to wandb \
    --output_dir Llama-3-Gozaru-8B-Instruct