Delete notebooks/QLoRA.ipynb

notebooks/QLoRA.ipynb

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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1a884871-7a65-4501-9063-c85ad260d0da",
-   "metadata": {},
-   "source": [
-    "このnotebookはstockmark/stockmark-13bのモデルをkunishou/databricks-dolly-15k-jaのデータセットを用いてQLoRA tuningするためのコードの例です。ここでは主に、T4やV100などのGPUメモリの少ないGPUを用いることを想定しています。A100またはH100のGPUを用いている方は、本レポジトリのLoRA tuningのサンプルをお試しください。\n",
-    "\n",
-    "- モデル：https://huggingface.co/stockmark/stockmark-13b\n",
-    "- データ：https://github.com/kunishou/databricks-dolly-15k-ja\n",
-    "\n",
-    "以下の例では、学習から推論までの一連の流れを効率よく回すため、学習を0.1 epochのみ行います。T4 GPUで実行すると30分ほどかかります。\n",
-    "\n",
-    "また、ここで用いられているハイパーパラメータは最適化されたものではありませんので、必要に応じて調整してください。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93b3f4b5-2825-4ef3-a0ee-7a60155aee5d",
-   "metadata": {},
-   "source": [
-    "# 準備\n",
-    "\n",
-    "Google Colaboratoryで実行される方は、以下のライブラリをインストールしてください。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7e05d465-4851-46fe-984d-942cab5e09a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python3 -m pip install transformers accelerate datasets peft bitsandbytes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6a694ba9-a0fa-4f14-81cf-f35f683ba889",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import datasets\n",
-    "from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, BitsAndBytesConfig\n",
-    "from peft import get_peft_model, LoraConfig, PeftModel, PeftConfig, prepare_model_for_kbit_training\n",
-    "\n",
-    "model_name = \"stockmark/stockmark-13b\"\n",
-    "peft_model_name = \"stockmark-13b-adapter\"\n",
-    "\n",
-    "prompt_template = \"\"\"### Instruction:\n",
-    "{instruction}\n",
-    "\n",
-    "### Input:\n",
-    "{input}\n",
-    "\n",
-    "### Response:\n",
-    "\"\"\"\n",
-    "\n",
-    "def encode(sample):\n",
-    "    prompt = prompt_template.format(instruction=sample[\"instruction\"], input=sample[\"input\"])\n",
-    "    target = sample[\"output\"]\n",
-    "    input_ids_prompt, input_ids_target = tokenizer([prompt, target]).input_ids\n",
-    "    input_ids_target = input_ids_target + [ tokenizer.eos_token_id ]\n",
-    "    input_ids = input_ids_prompt + input_ids_target\n",
-    "    labels = input_ids.copy()\n",
-    "    labels[:len(input_ids_prompt)] = [-100] * len(input_ids_prompt) # ignore label tokens in a prompt for loss calculation\n",
-    "    return {\"input_ids\": input_ids, \"labels\": labels}\n",
-    "\n",
-    "def get_collator(tokenizer, max_length):\n",
-    "    def collator(batch):\n",
-    "        batch = [{ key: value[:max_length] for key, value in sample.items() } for sample in batch ]\n",
-    "        batch = tokenizer.pad(batch)\n",
-    "        batch[\"labels\"] = [ e + [-100] * (len(batch[\"input_ids\"][0]) - len(e)) for e in batch[\"labels\"] ]\n",
-    "        batch = { key: torch.tensor(value) for key, value in batch.items() }\n",
-    "        return batch\n",
-    "\n",
-    "    return collator\n",
-    "\n",
-    "bnb_config = BitsAndBytesConfig(\n",
-    "    load_in_4bit=True,\n",
-    "    bnb_4bit_use_double_quant=True,\n",
-    "    bnb_4bit_quant_type=\"nf4\",\n",
-    "    bnb_4bit_compute_dtype=torch.float16\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51e6cfcf-1ac1-400e-a4bc-ea64375d0f9e",
-   "metadata": {},
-   "source": [
-    "# データセットとモデルのロード"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3ac80067-4e60-46c4-90da-05647cf96ccd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load_tokenizer\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "\n",
-    "# prepare dataset\n",
-    "dataset_name = \"kunishou/databricks-dolly-15k-ja\"\n",
-    "dataset = datasets.load_dataset(dataset_name)\n",
-    "dataset = dataset.map(encode)\n",
-    "dataset = dataset[\"train\"].train_test_split(0.1)\n",
-    "train_dataset = dataset[\"train\"]\n",
-    "eval_dataset = dataset[\"test\"]\n",
-    "\n",
-    "# load model\n",
-    "model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\", quantization_config=bnb_config, torch_dtype=torch.float16)\n",
-    "\n",
-    "peft_config = LoraConfig(\n",
-    "    task_type=\"CAUSAL_LM\",\n",
-    "    inference_mode=False,\n",
-    "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],\n",
-    "    r=16,\n",
-    "    lora_alpha=32,\n",
-    "    lora_dropout=0.05\n",
-    ")\n",
-    "\n",
-    "model = get_peft_model(model, peft_config)\n",
-    "model.print_trainable_parameters()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9b471da0-7fba-4127-8b07-22da4cbee6a9",
-   "metadata": {},
-   "source": [
-    "# QLoRA Tuning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b9bafa12-538c-4abb-b8b3-bffeb0990b46",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\n",
-    "    output_dir=\"./log_stockmark_13b\",\n",
-    "    learning_rate=2e-4,\n",
-    "    per_device_train_batch_size=1,\n",
-    "    gradient_accumulation_steps=16,\n",
-    "    per_device_eval_batch_size=1,\n",
-    "    num_train_epochs=0.1,\n",
-    "    logging_strategy='steps',\n",
-    "    logging_steps=10,\n",
-    "    save_strategy='epoch',\n",
-    "    evaluation_strategy='epoch',\n",
-    "    load_best_model_at_end=True,\n",
-    "    metric_for_best_model=\"eval_loss\",\n",
-    "    greater_is_better=False,\n",
-    "    save_total_limit=2\n",
-    ")\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=training_args,\n",
-    "    train_dataset=train_dataset,\n",
-    "    eval_dataset=eval_dataset,\n",
-    "    data_collator=get_collator(tokenizer, 256)\n",
-    ")\n",
-    "\n",
-    "# QLoRA tuning\n",
-    "trainer.train()\n",
-    "\n",
-    "# save model\n",
-    "model = trainer.model\n",
-    "model.save_pretrained(peft_model_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3f80a8e-1ac2-4bdc-8232-fe0ee18ffff5",
-   "metadata": {},
-   "source": [
-    "# 学習したモデルのロード（Optional）\n",
-    "異なるセッションでモデルを読み込む場合、まず最初の準備のセクションのコードを実行して、このコードを実行してください。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "43241395-3035-4cb9-8c1c-45ffe8cd48be",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\", quantization_config=bnb_config, torch_dtype=torch.float16)\n",
-    "model = PeftModel.from_pretrained(model, peft_model_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ce4db1f-9bad-4c8e-9c04-d1102b299f24",
-   "metadata": {},
-   "source": [
-    "# 推論"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d7d6359b-e0ac-49df-a178-39bb9f79ca93",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = prompt_template.format(instruction=\"自然言語処理とは？\", input=\"\")\n",
-    "\n",
-    "inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
-    "with torch.no_grad():\n",
-    "    tokens = model.generate(\n",
-    "        **inputs,\n",
-    "        max_new_tokens=128,\n",
-    "        do_sample=True,\n",
-    "        temperature=0.7\n",
-    "    )\n",
-    "\n",
-    "output = tokenizer.decode(tokens[0], skip_special_tokens=True)\n",
-    "print(output)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}