{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f24a09e9-262f-4943-84a2-b60b6c24c389",
   "metadata": {},
   "source": [
    "# OpenCALM SageMaker Inference for JAQKET dataset\n",
    "\n",
    "[OpenCALM](https://huggingface.co/spaces/kyo-takano/OpenCALM-7B) を SageMaker の推論エンドポイントに Hosting し、[JAQKET](https://www.nlp.ecei.tohoku.ac.jp/projects/jaqket/) の評価データセットについて回答を得る Notebook です。\n",
    "\n",
    "以下の環境で Hosting し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": "0428530b-8f42-4ab7-83ce-c3bdfa96a51b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install \"sagemaker>=2.143.0\" -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d91b104b-51f1-4f2a-b77e-7afac73de372",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f94c4e8-de95-4657-97e0-dc213415bee5",
   "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": "9882e8b3-4b8a-4700-8233-8f7e504c6b8d",
   "metadata": {},
   "source": [
    "## Package and Upload Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a08d3c5f-2537-433b-a7c9-e2da3480a5f4",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf scripts/model\n",
    "%cd scripts\n",
    "!tar -czvf ../package.tar.gz *\n",
    "%cd -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11d0ae99-8cfe-43f5-8677-863e91bce7a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_path = sess.upload_data(\"package.tar.gz\", bucket=bucket, key_prefix=f\"OpenCALM\")\n",
    "model_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6432b637-a883-4d94-ba28-54fc1be73408",
   "metadata": {},
   "source": [
    "## Deploy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30357ace-1d7e-4947-ab7b-6d716d6a8c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"cyberagent/open-calm-7b\"\n",
    "model_name_base = model_name.split(\"/\")[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a20f1592-7ff0-45a2-bbb7-3f9528a11a98",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.serializers import JSONSerializer\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",
    "                \"peft\": False,\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",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07b0901-2675-49e5-8ccd-968f3ca2820e",
   "metadata": {},
   "source": [
    "## Run Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6eb4904-4b77-4cd0-bbe9-4d4685bc018e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.predictor import Predictor\n",
    "from sagemaker.predictor_async import AsyncPredictor\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",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5526e52c-2ae8-40e6-aaa0-136ec8d9caf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "def inference(instruction):\n",
    "    data = {\n",
    "        \"instruction\": instruction,\n",
    "        \"input\": \"\",\n",
    "        \"max_new_tokens\": 32,\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": "66890ae1-091d-4750-b205-fb90438bc732",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(inference(\"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?\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd0dcb0-4a6a-4e34-9bd0-9c81e19ebcf4",
   "metadata": {
    "tags": []
   },
   "source": [
    "JAQKET データセットをダウンロード。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69eeadb0-5c50-4d67-bd8c-f55dc1755a86",
   "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": "1810485f-6e2e-4b29-9375-54fe018e0d5d",
   "metadata": {},
   "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": "380186a7-593a-4e5a-90c8-699ab81781de",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.match.sum(), \"/\", len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89011735-b7b6-417d-a24b-07b452a87d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(f\"data/{model_name_base}_inference.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219ebbfd-3b41-4305-95af-8d35c013969b",
   "metadata": {},
   "source": [
    "## Delete Endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cd9be37-f988-4394-b920-61d1cfeaf066",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictor.delete_model()\n",
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b8974c6-3f58-40c8-b849-ebe7968e0310",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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