{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b262721-9ba7-40c2-acc4-96c41cf9230a",
   "metadata": {},
   "source": [
    "# Dolly v2 SageMaker Finetuning\n",
    "\n",
    "This is a sample code to finetune and deploy Dolly v2 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",
    "\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",
    "We will use Databricks-dolly-15k as sample dataset to finetune the model. (License: [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode))\n",
    "\n",
    "You may also choose to use custom dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ef68da3-a2d6-4f18-9803-d87f4687c9dd",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "!curl -L https://huggingface.co/datasets/databricks/databricks-dolly-15k/resolve/main/databricks-dolly-15k.jsonl --create-dirs -o data/databricks-dolly-15k.jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab6171f8-8e4b-4711-9c19-0681a6b953fb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!head -n 2 data/databricks-dolly-15k.jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ace3ac7d-d1c2-4872-90c9-34f844f70f6e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convet .jsonl to .json\n",
    "import pandas as pd\n",
    "df = pd.read_json('data/databricks-dolly-15k.jsonl', orient='records', lines=True)\n",
    "df = df.rename(columns={\"context\": \"input\", \"response\": \"output\"})\n",
    "df.to_json(\"data/databricks-dolly-15k.json\", orient='records')"
   ]
  },
  {
   "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.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": [
    "hyperparameters={\n",
    "    'base_model':'databricks/dolly-v2-3b',\n",
    "    'load_in_8bit': False,\n",
    "    'data_path': '/opt/ml/input/data/train/databricks-dolly-15k.json',\n",
    "    'num_epochs': 3, # default 3\n",
    "    'cutoff_len': 512,\n",
    "    'group_by_length': True,\n",
    "    'output_dir': '/opt/ml/model',\n",
    "    'lora_target_modules': '[query_key_value]',\n",
    "    'lora_r': 16,\n",
    "    'batch_size': 32,\n",
    "    'micro_batch_size': 4,\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=\"Dolly-v2\",\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",
    ")\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('Dolly-v2')\n",
    "    \n",
    "!aws s3 cp {model_data} dolly-v2.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e23bbc7-6038-4dfd-a632-21b38be59e78",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf scripts/model && mkdir scripts/model\n",
    "!tar -xvf dolly-v2.tar.gz -C scripts/model --no-same-owner --wildcards adapter_*"
   ]
  },
  {
   "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 ../package.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('package.tar.gz', bucket=bucket, key_prefix=f\"Dolly-v2\")\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",
    "endpoint_name = \"Dolly-v2\"\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",
    "            \"base_model\": \"databricks/dolly-v2-3b\",\n",
    "            \"lora_weights\": \"model\", # path relative to model package\n",
    "            \"peft\": True,\n",
    "            \"load_8bit\": True,\n",
    "            \"prompt_template\": \"alpaca\",\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": "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": [
    "from sagemaker.predictor import Predictor\n",
    "from sagemaker.predictor_async import AsyncPredictor\n",
    "from sagemaker.serializers import JSONSerializer\n",
    "from sagemaker.deserializers import NumpyDeserializer\n",
    "\n",
    "predictor_client = AsyncPredictor(\n",
    "    predictor=Predictor(\n",
    "        endpoint_name=endpoint_name,\n",
    "        sagemaker_session=sess,\n",
    "        serializer=JSONSerializer(),\n",
    "        deserializer=NumpyDeserializer()\n",
    "    ),\n",
    "    name=endpoint_name\n",
    ")\n",
    "data = {\n",
    "    \"instruction\": \"When was George Washington president?\",\n",
    "    \"input\": \"\"\"George Washington (February 22, 1732[b] – December 14, 1799) was an American military officer, statesman,\n",
    "and Founding Father who served as the first president of the United States from 1789 to 1797.\n",
    "\"\"\",\n",
    "    \"max_new_tokens\": 64,\n",
    "    \"temperature\": 0.7,\n",
    "    \"do_sample\": True,\n",
    "    \"stop_ids\": [50278, 50279, 50277, 1, 0],\n",
    "}\n",
    "response = predictor_client.predict(\n",
    "    data=data\n",
    ")\n",
    "print(response)"
   ]
  },
  {
   "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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