{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f07cbcc-de13-4889-8c08-662cd12f1f36",
   "metadata": {},
   "source": [
    "# RWKV SageMaker LoRA Finetuning\n",
    "\n",
    "RWKV を LoRA Finetuning し、SageMaker でデプロイするサンプルコード。\n",
    "\n",
    "例として、日本語が 10% 学習に使用されている RWKV Raven 7B を利用します。必要に応じてモデルを変更してご利用ください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9cfaff7-f9ef-4244-b5e8-0e49581e7c20",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8230fde4-8b19-43bf-be9d-23c83a0f556d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker, boto3, json, os\n",
    "import pandas as pd\n",
    "import numpy as np\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": "579df76e-e2f1-45f5-bcd6-fddb5567a682",
   "metadata": {},
   "source": [
    "## Download and Tokenize Dataset\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します。\n",
    "\n",
    "ここでは例として [Databricks Dolly 15k](https://github.com/databrickslabs/dolly/tree/master/data) データセットを日本語に翻訳したものを利用します。(License: [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "537e465c-8dae-46bb-8ea3-26e621c5bbc4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Download Databricks Dolly 15k dataset\n",
    "!curl -L https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja/resolve/main/databricks-dolly-15k-ja.json --create-dirs -o ./data/databricks-dolly-15k-ja.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "806fd846-a86c-4bfb-870a-1cbd3503ef61",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!head ./data/databricks-dolly-15k-ja.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9165409e-e395-46e4-945d-0844f76f7af3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Download Tokenizer\n",
    "!curl https://raw.githubusercontent.com/BlinkDL/RWKV-LM/main/RWKV-v4/20B_tokenizer.json --create-dirs -o data/20B_tokenizer.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0ca3a42-73a0-4591-a206-d4419a719373",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Tokenize Input\n",
    "from transformers import PreTrainedTokenizerFast\n",
    "\n",
    "def generate_prompt(instruction, input, output):\n",
    "    if input != \"\":\n",
    "        return f\"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "# Instruction:\n",
    "{instruction}\n",
    "# Input:\n",
    "{input}\n",
    "# Response:\n",
    "{output}\n",
    "<|endoftext|>\n",
    "\"\"\"\n",
    "    else:\n",
    "        return f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "# Instruction:\n",
    "{instruction}\n",
    "# Response:\n",
    "{output}\n",
    "<|endoftext|>\n",
    "\"\"\"\n",
    "\n",
    "def tokenize_and_save(input_file):\n",
    "    dataset = pd.read_json(input_file)\n",
    "    dataset = dataset.rename(columns={\"context\": \"input\", \"response\": \"output\"})\n",
    "    dataset = dataset.to_dict(orient='records')\n",
    "\n",
    "    output = \"\"\n",
    "    for item in dataset:\n",
    "        if len(item.keys()) > 0:\n",
    "            output += generate_prompt(\n",
    "                item[\"instruction\"].strip(),\n",
    "                item[\"input\"].strip(),\n",
    "                item[\"output\"].strip()\n",
    "            )\n",
    "\n",
    "    np.set_printoptions(precision=4, suppress=True, linewidth=200)\n",
    "\n",
    "    tokenizer = PreTrainedTokenizerFast(tokenizer_file='data/20B_tokenizer.json')\n",
    "    output_file = 'data/train.npy'\n",
    "    data_raw = output\n",
    "    data_code = tokenizer.encode(data_raw)\n",
    "    print(f'Tokenized length = {len(data_code)}')\n",
    "\n",
    "    out = np.array(data_code, dtype='uint16')\n",
    "    np.save(output_file, out, allow_pickle=False)\n",
    "\n",
    "tokenize_and_save('data/databricks-dolly-15k-ja.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3b306cf-fdaa-421b-89db-78de9c0b139e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "input_train = sess.upload_data(\n",
    "    path=\"./data/train.npy\",\n",
    "    key_prefix=\"Dolly\"\n",
    ")\n",
    "input_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f63b73ac-e0e9-498d-b002-f3e9b53e2fcf",
   "metadata": {},
   "source": [
    "## Prepare Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47778c00-5fd2-4d87-89ef-b9ea4642ec78",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "base_model_name = \"rwkv-4-raven\"\n",
    "model_size = \"3B\"\n",
    "\n",
    "# List of models trained on Japanese. Feel free to change here.\n",
    "if model_size == \"3B\":\n",
    "    base_model_file = \"RWKV-4-Raven-3B-v11-Eng49%25-Chn49%25-Jpn1%25-Other1%25-20230429-ctx4096.pth\"\n",
    "    n_layer = 32\n",
    "    n_embd = 2560\n",
    "elif model_size == \"7B\":\n",
    "    base_model_file = \"RWKV-4-Raven-7B-v10-Eng89%25-Jpn10%25-Other1%25-20230420-ctx4096.pth\"\n",
    "    n_layer = 32\n",
    "    n_embd = 4096\n",
    "\n",
    "base_model_url = f\"https://huggingface.co/BlinkDL/{base_model_name.lower()}/resolve/main/{base_model_file}\"\n",
    "base_model_path = f\"scripts/code/base_models/{base_model_file}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00fd9e7c-b7fc-4107-a21d-9d1b3fd23162",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "346d75b0-b761-4b69-b106-bc7d66ddacc4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hyperparameters={\n",
    "    'load_model': f\"/tmp/{base_model_file}\",\n",
    "    'model_url': base_model_url,\n",
    "    'proj_dir': '/opt/ml/model',\n",
    "    'data_file': '/opt/ml/input/data/train/train.npy',\n",
    "    'data_type': 'numpy',\n",
    "    'vocab_size': 50277,\n",
    "    'ctx_len': 384,\n",
    "    'epoch_save': 5,\n",
    "    'epoch_count': 3,\n",
    "    'max_epochs': 3, # Added to stop training\n",
    "    'n_layer': n_layer,\n",
    "    'n_embd': n_embd,\n",
    "    'epoch_steps': 1000,\n",
    "    'micro_bsz': 8,\n",
    "    'accelerator': \"gpu\",\n",
    "    'devices': 1,\n",
    "    'precision': 'bf16',\n",
    "    'lora': True,\n",
    "    'lora_r': 8,\n",
    "    'lora_alpha': 32,\n",
    "    'lora_dropout': 0.01,\n",
    "    'strategy': \"deepspeed_stage_2\",\n",
    "    # 'enable_progress_bar': False, # Added to Reduce Log\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35e5e800-0b62-4af7-a69b-c767db354e6d",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "huggingface_estimator = HuggingFace(\n",
    "    base_job_name=\"RWKV\",\n",
    "    role=role,\n",
    "    entry_point='train.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",
    ")\n",
    "huggingface_estimator.fit({'train': input_train})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523768e4-cde1-4d32-8239-41fb34d25079",
   "metadata": {},
   "source": [
    "## Download and Extract Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3596166b-514e-44fb-adbd-050dd26abdfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "\n",
    "def get_latest_training_job_artifact(base_job_name):\n",
    "    sagemaker_client = boto3.client('sagemaker')\n",
    "    response = sagemaker_client.list_training_jobs(NameContains=base_job_name, SortBy='CreationTime', SortOrder='Descending')\n",
    "    training_job_arn = response['TrainingJobSummaries'][0]['TrainingJobArn']\n",
    "    training_job_description = sagemaker_client.describe_training_job(TrainingJobName=training_job_arn.split('/')[-1])\n",
    "    return training_job_description['ModelArtifacts']['S3ModelArtifacts']\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('RWKV')\n",
    "    \n",
    "!aws s3 cp {model_data} rwkv.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6169defe-d8c0-472d-a8bc-c1b245b13db8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tarfile\n",
    "tarf = tarfile.open('rwkv.tar.gz', 'r:gz')\n",
    "files = sorted(tarf.getnames())\n",
    "target_file = files[-2]\n",
    "print(files, target_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c49cbf18-0a74-42a7-bfbd-37188a4f4ef1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf scripts/model && mkdir scripts/model\n",
    "!tar -xvf rwkv.tar.gz -C scripts/model --no-same-owner {target_file}\n",
    "!cp data/20B_tokenizer.json scripts/model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e619a3-b7a6-49e7-8c3b-207aed37e61a",
   "metadata": {},
   "source": [
    "## Package and Upload Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00277a09-e838-44d1-88a0-93cd8d5b7fb2",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "%cd scripts\n",
    "!tar --exclude base_models -czvf ../package.tar.gz *\n",
    "%cd -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76e1da20-e6dc-434b-8f11-a80a33a4daac",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_path = sess.upload_data('package.tar.gz', bucket=bucket, key_prefix=f\"RWKV\")\n",
    "model_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf44a77-115e-4c7c-96d0-1ed4065cf5a9",
   "metadata": {},
   "source": [
    "## Deploy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f333ef49-769a-4200-91f4-1564c9d4ead3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.async_inference import AsyncInferenceConfig\n",
    "from sagemaker.serializers import JSONSerializer\n",
    "\n",
    "endpoint_name = \"RWKV\"\n",
    "\n",
    "huggingface_model = PyTorchModel(\n",
    "    model_data=model_path,\n",
    "    framework_version=\"1.13\",\n",
    "    py_version='py39',\n",
    "    role=role,\n",
    "    name=endpoint_name,\n",
    "    env={\n",
    "        \"model_params\": json.dumps({\n",
    "            \"model_url\": base_model_url,\n",
    "            \"model_path\": f\"/tmp/{base_model_file}\",\n",
    "            \"tokenizer_path\": \"model/20B_tokenizer.json\", # path relative to model package\n",
    "            \"strategy\": \"cuda bf16\",\n",
    "            \"lora\": \"model/\" + target_file,\n",
    "            \"lora_alpha\": hyperparameters[\"lora_alpha\"]\n",
    "        }),\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=endpoint_name,\n",
    "    serializer=JSONSerializer(),\n",
    "    async_inference_config=AsyncInferenceConfig()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c99cbe-2ec2-4019-864e-c01f733d2a12",
   "metadata": {},
   "source": [
    "## Run Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa15a167-9d83-4332-95ce-7564d361b2ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "from sagemaker.predictor import Predictor\n",
    "from sagemaker.predictor_async import AsyncPredictor\n",
    "from sagemaker.serializers import JSONSerializer\n",
    "from sagemaker.deserializers import NumpyDeserializer, JSONDeserializer\n",
    "\n",
    "endpoint_name = \"RWKV\"\n",
    "\n",
    "predictor_client = AsyncPredictor(\n",
    "    predictor=Predictor(\n",
    "        endpoint_name=endpoint_name,\n",
    "        sagemaker_session=sagemaker.Session(),\n",
    "        serializer=JSONSerializer(),\n",
    "        deserializer=NumpyDeserializer()\n",
    "    ),\n",
    "    name=endpoint_name\n",
    ")\n",
    "data = {\n",
    "    \"instruction\": \"ヴァージン・オーストラリア航空はいつから運航を開始したのですか？\",\n",
    "    \"input\": \"ヴァージン・オーストラリア航空（Virgin Australia Airlines Pty Ltd）の商号で、オーストラリアを拠点とする航空会社です。ヴァージン・グループを使用する航空会社の中で、保有機材数では最大の航空会社である。2000年8月31日にヴァージン・ブルー航空として、2機の航空機で単一路線で運航を開始した[3]。2001年9月のアンセット・オーストラリア航空の破綻後、突然オーストラリア国内市場の大手航空会社としての地位を確立した。その後、ブリスベン、メルボルン、シドニーをハブとして、オーストラリア国内の32都市に直接乗り入れるまでに成長した[4]。\",\n",
    "}\n",
    "response = predictor_client.predict(\n",
    "    data=data\n",
    ")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0ee0ab-e30d-49a7-845b-aa303ada0afa",
   "metadata": {},
   "source": [
    "## Delete Endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fce6b8bf-4a0a-4305-9e13-a5735fd288f0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictor.delete_model()\n",
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2c77c9b-3bc8-46c2-9788-c47c6c61ad00",
   "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,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 8,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 9,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.m5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 10,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.m5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 11,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.m5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 12,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5d.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 13,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5d.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 14,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5d.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 15,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5d.4xlarge",
    "vcpuNum": 16
   },
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