Create notebooks/LoRA.ipynb

notebooks/LoRA.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LoRA tuningするためのコードの例です。A100またはH100のGPUを用いることを想定しています。T4やV100などのGPUメモリの少ないGPUを用いている場合には、本レポジトリのQLoRA 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、学習を1 epochを行います。A100 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"
+   ]
+  },
+  {
+   "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\n",
+    "from peft import get_peft_model, LoraConfig, PeftModel, PeftConfig\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"
+   ]
+  },
+  {
+   "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",
+    "val_dataset = dataset[\"test\"]\n",
+    "\n",
+    "# load model\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\", torch_dtype=torch.bfloat16)\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": [
+    "# LoRA 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=2,\n",
+    "    gradient_accumulation_steps=8,\n",
+    "    per_device_eval_batch_size=16,\n",
+    "    num_train_epochs=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=val_dataset,\n",
+    "    data_collator=get_collator(tokenizer, 320)\n",
+    ")\n",
+    "\n",
+    "# LoRA 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\", torch_dtype=torch.bfloat16)\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
+}