253 lines
7.6 KiB
Plaintext
253 lines
7.6 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"gpuType": "T4"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"このnotebookは`stockmark/gpt-neox-japanese-1.4b`のモデルを`kunishou/databricks-dolly-15k-ja`のデータセットを用いてLoRA tuningするためのコードの例です。以下の例では、学習を1 epochを行います。T4 GPUで実行すると30分ほどかかります。\n",
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"\n",
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"- モデル：https://huggingface.co/stockmark/gpt-neox-japanese-1.4b\n",
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"- データ：https://github.com/kunishou/databricks-dolly-15k-ja\n",
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"\n",
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"\n",
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"また、ここで用いている設定は暫定的なもので、必要に応じて調整してください。"
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],
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"metadata": {
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"id": "BPGgCZtMdMsv"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# ライブラリのインストール"
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],
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"metadata": {
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"id": "hCZH9e6EcZyj"
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "cmn52bx3v5Ha"
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},
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"outputs": [],
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"source": [
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"!python3 -m pip install -U pip\n",
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"!python3 -m pip install transformers accelerate datasets peft"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 準備"
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],
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"metadata": {
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"id": "4t3Cqs9_ce3J"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import torch\n",
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"import datasets\n",
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"from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments\n",
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"from peft import get_peft_model, LoraConfig, TaskType, PeftModel, PeftConfig\n",
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"\n",
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"model_name = \"stockmark/gpt-neox-japanese-1.4b\"\n",
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"peft_model_name = \"peft_model\"\n",
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"\n",
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"prompt_template = \"\"\"### Instruction:\n",
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"{instruction}\n",
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"\n",
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"### Input:\n",
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"{input}\n",
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"\n",
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"### Response:\n",
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"\"\"\"\n",
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"\n",
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"def encode(sample):\n",
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" prompt = prompt_template.format(instruction=sample[\"instruction\"], input=sample[\"input\"])\n",
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" target = sample[\"output\"] + tokenizer.eos_token\n",
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" input_ids_prompt, input_ids_target = tokenizer([prompt, target]).input_ids\n",
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" input_ids = input_ids_prompt + input_ids_target\n",
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" labels = input_ids.copy()\n",
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" labels[:len(input_ids_prompt)] = [-100] * len(input_ids_prompt)\n",
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" return {\"input_ids\": input_ids, \"labels\": labels}\n",
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"\n",
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"def get_collator(tokenizer, max_length):\n",
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" def collator(batch):\n",
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" batch = [{ key: value[:max_length] for key, value in sample.items() } for sample in batch ]\n",
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" batch = tokenizer.pad(batch, padding=True)\n",
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" batch[\"labels\"] = [ e + [-100] * (len(batch[\"input_ids\"][0]) - len(e)) for e in batch[\"labels\"] ]\n",
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" batch = { key: torch.tensor(value) for key, value in batch.items() }\n",
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" return batch\n",
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"\n",
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" return collator\n"
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],
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"metadata": {
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"id": "hNdYMGMRzAVn"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# データセットとモデルの準備\n"
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],
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"metadata": {
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"id": "UqXxPjJ_cliu"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# prepare dataset\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
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"\n",
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"dataset_name = \"kunishou/databricks-dolly-15k-ja\"\n",
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"dataset = datasets.load_dataset(dataset_name)\n",
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"dataset = dataset.map(encode)\n",
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"dataset = dataset[\"train\"].train_test_split(0.2)\n",
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"train_dataset = dataset[\"train\"]\n",
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"val_dataset = dataset[\"test\"]\n",
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"\n",
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"# load model\n",
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"model = AutoModelForCausalLM.from_pretrained(model_name, device_map={\"\": 0}, torch_dtype=torch.float16)\n",
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"\n",
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"peft_config = LoraConfig(\n",
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" task_type=TaskType.CAUSAL_LM,\n",
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" inference_mode=False,\n",
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" target_modules=[\"query_key_value\"],\n",
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" r=16,\n",
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" lora_alpha=32,\n",
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" lora_dropout=0.05\n",
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")\n",
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"\n",
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"model = get_peft_model(model, peft_config)\n",
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"model.print_trainable_parameters()"
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],
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"metadata": {
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"id": "ZWdN-p7t0Grk"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# LoRA tuning"
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],
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"metadata": {
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"id": "XCrdVAJYc88c"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"training_args = TrainingArguments(\n",
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" output_dir=\"./train_results\",\n",
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" learning_rate=2e-4,\n",
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" per_device_train_batch_size=4,\n",
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" gradient_accumulation_steps=4,\n",
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" per_device_eval_batch_size=16,\n",
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" num_train_epochs=1,\n",
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" logging_strategy='steps',\n",
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" logging_steps=10,\n",
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" save_strategy='epoch',\n",
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" evaluation_strategy='epoch',\n",
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" load_best_model_at_end=True,\n",
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" metric_for_best_model=\"eval_loss\",\n",
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" greater_is_better=False,\n",
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" save_total_limit=2\n",
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")\n",
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"\n",
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"trainer = Trainer(\n",
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" model=model,\n",
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" args=training_args,\n",
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" train_dataset=train_dataset,\n",
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" eval_dataset=val_dataset,\n",
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" data_collator=get_collator(tokenizer, 512)\n",
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")\n",
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"\n",
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"trainer.train()\n",
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"model = trainer.model\n",
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"model.save_pretrained(peft_model_name)"
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],
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"metadata": {
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"id": "4LH9tOCTJVk1"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 学習したモデルのロード"
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],
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"metadata": {
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"id": "ORgzOPAqdEZR"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
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"model = AutoModelForCausalLM.from_pretrained(model_name, device_map={\"\": 0}, torch_dtype=torch.float16)\n",
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"model = PeftModel.from_pretrained(model, peft_model_name)"
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],
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"metadata": {
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"id": "yrExyO9EOvzR"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 推論"
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],
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"metadata": {
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"id": "-dttR6tkdG0k"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"prompt = prompt_template.format(instruction=\"日本で人気のスポーツは？\", input=\"\")\n",
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"\n",
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"inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
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"with torch.no_grad():\n",
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" tokens = model.generate(\n",
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" **inputs,\n",
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" max_new_tokens=128,\n",
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" repetition_penalty=1.1\n",
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" )\n",
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"\n",
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"output = tokenizer.decode(tokens[0], skip_special_tokens=True)\n",
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"print(output)"
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],
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"metadata": {
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"id": "pC5t9F1GJuFN"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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} |