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

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b262721-9ba7-40c2-acc4-96c41cf9230a",
   "metadata": {},
   "source": [
    "# Rinna SageMaker Finetuning\n",
    "\n",
    "This is a sample code to finetune and deploy [Rinna NeoX](https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft) with LoRA on SageMaker."
   ]
  },
  {
   "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": "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",
    "from sagemaker.djl_inference.model import DJLModel\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": {},
   "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。\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": "2ef68da3-a2d6-4f18-9803-d87f4687c9dd",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "!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": "0f432997-71eb-4fb2-9aa2-ba51ffd243c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!head ./data/databricks-dolly-15k-ja.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "014e57f3-9435-490c-b9d9-0fa5a6a8a6a9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "input_train = sess.upload_data(\n",
    "    path=\"./data/databricks-dolly-15k-ja.json\",\n",
    "    key_prefix=\"Dolly\"\n",
    ")\n",
    "input_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b5098b-1ea9-4b85-88ee-9297d5e00c09",
   "metadata": {},
   "source": [
    "## Fine-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cb98557-3a3f-40ef-baf3-538c4ee4aa8a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "base_job_name=\"Rinna\"\n",
    "hyperparameters={\n",
    "    'base_model':'rinna/japanese-gpt-neox-3.6b',\n",
    "    'load_in_8bit': True,\n",
    "    # 'load_in_4bit': True,\n",
    "    'data_path': '/opt/ml/input/data/train/databricks-dolly-15k-ja.json',\n",
    "    'num_epochs': 3, # default 3\n",
    "    'cutoff_len': 512,\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': 8,\n",
    "    'micro_batch_size': 8,\n",
    "    'prompt_template_name': 'alpaca',\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=base_job_name,\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=[{'Name': 'eval_loss', 'Regex': \"'eval_loss': (\\d\\.\\d+)\"},\n",
    "                        {'Name': 'train_loss', 'Regex': \"'loss': (\\d\\.\\d+)\"}],\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",
    "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('Rinna')\n",
    "    \n",
    "!aws s3 cp {model_data} rinna.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e23bbc7-6038-4dfd-a632-21b38be59e78",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf djl_scripts/model && mkdir djl_scripts/model\n",
    "!tar -xvf rinna.tar.gz -C djl_scripts/model --no-same-owner --wildcards adapter_*\n",
    "!ls -l djl_scripts/model"
   ]
  },
  {
   "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": [
    "%%writefile ./djl_scripts/serving.properties\n",
    "engine=deepspeed\n",
    "option.entryPoint=inference.py\n",
    "option.model_id=rinna/japanese-gpt-neox-3.6b\n",
    "option.tensor_parallel_degree=1\n",
    "option.task=text-generation\n",
    "option.dtype=bfloat16\n",
    "option.model_loading_timeout=600\n",
    "option.prompt_input=Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: \n",
    "option.prompt_no_input=Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: \n",
    "option.lora_weights=model"
   ]
  },
  {
   "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": [
    "endpoint_name = \"Rinna-LoRA-DJL\"\n",
    "\n",
    "model = DJLModel(\n",
    "    model_id=\"rinna/japanese-gpt-neox-3.6b\", # This can also be a HuggingFace Hub model id\n",
    "    role=role,\n",
    "    source_dir=\"djl_scripts\",\n",
    "    entry_point=\"inference.py\"\n",
    ")\n",
    "\n",
    "# deploy model to SageMaker Inference\n",
    "predictor = model.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type='ml.g5.2xlarge',\n",
    "    endpoint_name=endpoint_name\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=endpoint_name,\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\": \"ヴァージン・オーストラリア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・ブランド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",
    "    \"properties\": {\n",
    "        \"max_new_tokens\": 128,\n",
    "        \"temperature\": 0.7,\n",
    "        \"do_sample\": True,\n",
    "        \"pad_token_id\": 0,\n",
    "        \"bos_token_id\": 2,\n",
    "        \"eos_token_id\": 3,\n",
    "    },\n",
    "}\n",
    "response = predictor_client.predict(\n",
    "    data=data\n",
    ")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94610911-2fa9-46c2-a619-dcf80b4f63da",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# With Boto3\n",
    "\n",
    "import boto3\n",
    "import json\n",
    "\n",
    "endpoint_name = \"Rinna-LoRA-DJL\"\n",
    "sagemaker_client = boto3.client('sagemaker-runtime')\n",
    "\n",
    "data = {\n",
    "    \"instruction\": \"ヴァージン・オーストラリア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・ブランド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",
    "    \"properties\": {\n",
    "        \"max_new_tokens\": 128,\n",
    "        \"temperature\": 0.7,\n",
    "        \"do_sample\": True,\n",
    "        \"pad_token_id\": 0,\n",
    "        \"bos_token_id\": 2,\n",
    "        \"eos_token_id\": 3,\n",
    "    },\n",
    "}\n",
    "\n",
    "response = sagemaker_client.invoke_endpoint(\n",
    "    EndpointName=endpoint_name,\n",
    "    ContentType='application/json',\n",
    "    Accept='application/json',\n",
    "    Body=json.dumps(data)\n",
    ")\n",
    "\n",
    "result = json.loads(response['Body'].read())\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae16c211-5f6a-4dc4-bbc3-b8f9b07666a0",
   "metadata": {},
   "source": [
    "## Benchmark Speed\n",
    "\n",
    "2.28 s ± 1.91 s per loop (mean ± std. dev. of 7 runs, 1 loop each)"
   ]
  },
  {
   "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": []
  }
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