tasks/generative-ai/text-to-text/fine-tuning/instruction-tuning/Transformers/OpenCALM_LoRA_jaqket.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b262721-9ba7-40c2-acc4-96c41cf9230a",
   "metadata": {},
   "source": [
    "# OpenCALM SageMaker Finetuning by JAQKET dataset\n",
    "\n",
    "[OpenCALM](https://huggingface.co/spaces/kyo-takano/OpenCALM-7B) を SageMaker の Training Job を使用し Fine Tuning し、学習済みモデルを推論エンドポイントに Hosting する Notebook です。[JAQKET](https://www.nlp.ecei.tohoku.ac.jp/projects/jaqket/) データセットから、 Fine Tuning 用に学習データ、 推論用に評価データセットを使用しています。\n",
    "\n",
    "以下の環境で Training Job / Hosting を行い動作確認を行ってます。\n",
    "\n",
    "* `ml.g5.2xlarge(NVIDIA A10G Tensor Core GPU 搭載 VRAM 24GB, RAM 32GB, vCPU 8)` : `PyTorch 1.13 Python 3.9 GPU Optimized`\n",
    " \n",
    "[各インスタンスの料金についてはこちら](https://aws.amazon.com/jp/sagemaker/pricing/)をご確認ください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32904a97-bedb-451a-b2cc-ac691f7708de",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -U \"sagemaker>=2.143.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34747940-eb9c-4428-9646-fa0fc9f169cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2edc8a17-9a6b-4dd6-823e-878f6b950ea2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker, boto3, json\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.pytorch.model import PyTorchModel\n",
    "from sagemaker.huggingface import HuggingFace\n",
    "\n",
    "role = get_execution_role()\n",
    "region = boto3.Session().region_name\n",
    "sess = sagemaker.Session()\n",
    "bucket = sess.default_bucket()\n",
    "\n",
    "sagemaker.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1b5fe7-9ed9-4d24-b344-a40a781a24a1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Upload Data\n",
    "\n",
    "Fine Tuning 用の日本語データをフォルダに配置してアップロードする。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae9977e-6be2-44a2-b1be-3b5d8e66df92",
   "metadata": {},
   "source": [
    "### Prepare JAQKET dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "951e92f0-acce-4c68-bc43-4fc5dafdae93",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!wget -P data https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_02/aio_02_train.jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd856b83-5a09-4db4-b33d-790f1b9ecffe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!head -n 2 data/aio_02_train.jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65efe8ce-09ed-4cc8-94e3-4198c49762ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convet .jsonl to .json\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_json(\"data/aio_02_train.jsonl\", orient=\"records\", lines=True)\n",
    "df = df.rename(columns={\"question\": \"instruction\", \"answers\": \"output\"})\n",
    "df = df[[\"instruction\", \"output\"]]\n",
    "df[\"output\"] = df[\"output\"].apply(lambda x: f\"{x[0]}」\")\n",
    "df[\"input\"] = \"\"\n",
    "print(df.shape)\n",
    "df.to_json(\n",
    "    \"data/aio_02_train_formatted.jsonl\", orient=\"records\", force_ascii=False, lines=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afbbbac7-e783-4a22-a220-3a6e5c268340",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfa10b80-b583-499b-ae1f-d03cd7dd1435",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "input_train = sess.upload_data(\n",
    "    path=\"./data/aio_02_train_formatted.jsonl\", key_prefix=\"OpenCALM\"\n",
    ")\n",
    "input_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b5098b-1ea9-4b85-88ee-9297d5e00c09",
   "metadata": {},
   "source": [
    "## Fine-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f58c68a-704d-42fa-b2a4-5befb1377fe9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_name = \"cyberagent/open-calm-7b\"\n",
    "model_name_base = model_name.split(\"/\")[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cb98557-3a3f-40ef-baf3-538c4ee4aa8a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hyperparameters = {\n",
    "    \"base_model\": model_name,\n",
    "    # 'load_in_8bit': True,\n",
    "    # 'load_in_4bit': True,\n",
    "    \"pad_token_id\": 1,\n",
    "    \"data_path\": \"/opt/ml/input/data/train/aio_02_train_formatted.jsonl\",\n",
    "    \"num_epochs\": 2,  # default 3\n",
    "    \"cutoff_len\": 256,\n",
    "    \"group_by_length\": False,\n",
    "    \"output_dir\": \"/opt/ml/model\",\n",
    "    # 'resume_from_checkpoint': '/opt/ml/checkpoints',\n",
    "    \"lora_target_modules\": \"[query_key_value]\",\n",
    "    \"lora_r\": 16,\n",
    "    \"batch_size\": 32,\n",
    "    \"micro_batch_size\": 4,\n",
    "    # 'val_set_size': 200,\n",
    "    \"prompt_template_name\": \"simple_qa_ja\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4a8d836-d44f-4bdc-8d87-cff90f9f6ea7",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "huggingface_estimator = HuggingFace(\n",
    "    base_job_name=model_name_base,\n",
    "    role=role,\n",
    "    entry_point=\"finetune.py\",\n",
    "    source_dir=\"./scripts/code\",\n",
    "    instance_type=\"ml.g5.2xlarge\",\n",
    "    instance_count=1,\n",
    "    volume_size=200,\n",
    "    transformers_version=\"4.26\",\n",
    "    pytorch_version=\"1.13\",\n",
    "    py_version=\"py39\",\n",
    "    use_spot_instances=True,\n",
    "    max_wait=86400,\n",
    "    hyperparameters=hyperparameters,\n",
    "    metric_definitions=[\n",
    "        {\"Name\": \"eval_loss\", \"Regex\": \"'eval_loss': (\\d\\.\\d+)\"},\n",
    "        {\"Name\": \"train_loss\", \"Regex\": \"'loss': (\\d\\.\\d+)\"},\n",
    "    ],\n",
    "    # checkpoint_s3_uri=f\"s3://{bucket}/{base_job_name}/checkpoint/\",\n",
    ")\n",
    "huggingface_estimator.fit({\"train\": input_train})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8df141b-86f8-4eff-8ba5-129b78d3efd4",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Download and Extract Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a02eb6aa-b4c3-4d60-a3fc-94f394df7ac1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "\n",
    "\n",
    "def get_latest_training_job_artifact(base_job_name):\n",
    "    sagemaker_client = boto3.client(\"sagemaker\")\n",
    "    response = sagemaker_client.list_training_jobs(\n",
    "        NameContains=base_job_name, SortBy=\"CreationTime\", SortOrder=\"Descending\"\n",
    "    )\n",
    "    training_job_arn = response[\"TrainingJobSummaries\"][0][\"TrainingJobArn\"]\n",
    "    training_job_description = sagemaker_client.describe_training_job(\n",
    "        TrainingJobName=training_job_arn.split(\"/\")[-1]\n",
    "    )\n",
    "    return training_job_description[\"ModelArtifacts\"][\"S3ModelArtifacts\"]\n",
    "\n",
    "\n",
    "try:\n",
    "    model_data = huggingface_estimator.model_data\n",
    "except:\n",
    "    # Retrieve artifact url when kernel is restarted\n",
    "    model_data = get_latest_training_job_artifact(model_name_base)\n",
    "\n",
    "print(model_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21529d11-eb97-4e50-bf07-980a2e26ae24",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!aws s3 cp {model_data} {model_name_base}.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e23bbc7-6038-4dfd-a632-21b38be59e78",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf scripts/{model_name_base} && mkdir scripts/{model_name_base}\n",
    "!tar -xvf {model_name_base}.tar.gz -C scripts/{model_name_base} --no-same-owner --wildcards adapter_*\n",
    "!ls -l scripts/{model_name_base}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44bf1cde-57d4-43f6-be6b-b09bd942219f",
   "metadata": {},
   "source": [
    "## Package and Upload Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "916cee87-d530-4e67-8fa6-18550ff92532",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "%cd scripts\n",
    "!tar -czvf ../{model_name_base}.tar.gz *\n",
    "%cd -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e252d1ba-c402-4352-a0f5-c59dc51d6fc9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_path = sess.upload_data(\n",
    "    f\"{model_name_base}.tar.gz\", bucket=bucket, key_prefix=f\"OpenCALM\"\n",
    ")\n",
    "model_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee0a96e-b185-4e2f-a8dd-75cbb086abfa",
   "metadata": {},
   "source": [
    "## Deploy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8232665-f10e-4a74-84d6-d5c87640d1f2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.async_inference import AsyncInferenceConfig\n",
    "from sagemaker.serializers import JSONSerializer\n",
    "\n",
    "\n",
    "huggingface_model = PyTorchModel(\n",
    "    model_data=model_path,\n",
    "    framework_version=\"1.13\",\n",
    "    py_version=\"py39\",\n",
    "    role=role,\n",
    "    name=model_name_base,\n",
    "    env={\n",
    "        \"model_params\": json.dumps(\n",
    "            {\n",
    "                \"base_model\": model_name,\n",
    "                \"lora_weights\": model_name_base,  # path relative to model package\n",
    "                \"peft\": True,\n",
    "                \"load_8bit\": False,\n",
    "                \"prompt_template\": \"simple_qa_ja\",\n",
    "            }\n",
    "        ),\n",
    "        \"SAGEMAKER_MODEL_SERVER_TIMEOUT\": \"3600\",\n",
    "    },\n",
    ")\n",
    "\n",
    "# deploy model to SageMaker Inference\n",
    "predictor = huggingface_model.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type=\"ml.g5.2xlarge\",\n",
    "    endpoint_name=model_name_base,\n",
    "    serializer=JSONSerializer(),\n",
    "    # async_inference_config=AsyncInferenceConfig()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4f6d9f-666d-40d6-b2c3-93ec553dded1",
   "metadata": {},
   "source": [
    "## Run Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be982e42-166c-47fb-bc35-cc20ee6a5c4e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# With SageMaker SDK\n",
    "\n",
    "from sagemaker.predictor import Predictor\n",
    "from sagemaker.predictor_async import AsyncPredictor\n",
    "from sagemaker.serializers import JSONSerializer\n",
    "from sagemaker.deserializers import JSONDeserializer\n",
    "\n",
    "predictor_client = Predictor(\n",
    "    endpoint_name=model_name_base,\n",
    "    sagemaker_session=sess,\n",
    "    serializer=JSONSerializer(),\n",
    "    deserializer=JSONDeserializer(),\n",
    ")\n",
    "# predictor_client = AsyncPredictor(\n",
    "#     predictor=predictor_client,\n",
    "#     name=endpoint_name\n",
    "# )\n",
    "data = {\n",
    "    \"instruction\": \"映画『ウエスト・サイド物語』に登場する2つの少年グループといえば、シャーク団と何団?\",\n",
    "    \"max_new_tokens\": 64,\n",
    "    \"temperature\": 0.1,\n",
    "    \"do_sample\": True,\n",
    "    \"pad_token_id\": 1,\n",
    "    \"bos_token_id\": 0,\n",
    "    \"eos_token_is\": 0,\n",
    "    # \"repetition_penalty\": 1.05,\n",
    "    # \"top_p\": 0.75,\n",
    "    # \"top_k\": 40,\n",
    "    # \"no_repeat_ngram_size\": 2,\n",
    "    \"stop_ids\": [1, 0],\n",
    "}\n",
    "response = predictor_client.predict(data=data)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4290a244-a43a-41c0-ab72-6a778da58ce0",
   "metadata": {},
   "source": [
    "### Inference for dev data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3f78389-f7aa-4655-820c-05bdda306710",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!wget -P data https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_02/aio_02_dev_v1.0.jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a40e3b01-726d-4137-b78f-e5de5cb5f313",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "def inference(instruction):\n",
    "    data = {\n",
    "        \"instruction\": instruction,\n",
    "        \"input\": \"\",\n",
    "        \"max_new_tokens\": 64,\n",
    "        \"temperature\": 0.1,\n",
    "        \"do_sample\": False,\n",
    "        \"num_beams\": 5,\n",
    "        \"pad_token_id\": 1,\n",
    "        \"bos_token_id\": 0,\n",
    "        \"eos_token_is\": 0,\n",
    "        # \"repetition_penalty\": 1.05,\n",
    "        \"stop_ids\": [1, 0],\n",
    "    }\n",
    "    response = predictor_client.predict(data=data)\n",
    "    answer = \"\"\n",
    "    try:\n",
    "        answer = re.findall(\"「(.*?)」\", f\"「{response}\")[-1]\n",
    "    except IndexError:\n",
    "        answer = response\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a19f8c0-1877-423b-b79f-e38181c97451",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "df = pd.read_json(\"data/aio_02_dev_v1.0.jsonl\", orient=\"records\", lines=True)\n",
    "\n",
    "llm_answers = []\n",
    "matches = []\n",
    "for idx, row in tqdm(df.iterrows()):\n",
    "    llm_answer = inference(row[\"question\"])\n",
    "    llm_answers += [llm_answer]\n",
    "    matches += [llm_answer in row[\"answers\"]]\n",
    "\n",
    "\n",
    "df[\"llm_answers\"] = pd.Series(llm_answers)\n",
    "df[\"match\"] = pd.Series(matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b2ebd61-9365-4780-9b82-81764003e714",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.match.sum(), \"/\", len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21fc487b-fd12-4730-8242-144026c31ada",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.to_csv(f\"data/{model_name_base}.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae16c211-5f6a-4dc4-bbc3-b8f9b07666a0",
   "metadata": {},
   "source": [
    "## Benchmark Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1c9ddd3-76ee-4f71-8888-4ae51069a967",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%timeit response = predictor_client.predict(data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39737593-7053-4dbb-b39b-112e27d228a1",
   "metadata": {},
   "source": [
    "## Delete Endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82559f16-10f8-4573-91d7-e0b85cb7d0de",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictor.delete_model()\n",
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c75d2062-a88e-4c2f-97e9-8778ee231d29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "availableInstances": [
   {
    "_defaultOrder": 0,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.t3.medium",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 1,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.t3.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 2,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.t3.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 3,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.t3.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 4,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 5,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 6,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 7,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
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