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

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b262721-9ba7-40c2-acc4-96c41cf9230a",
   "metadata": {},
   "source": [
    "# Cerebras SageMaker Finetuning\n",
    "\n",
    "This is a sample code to finetune and deploy Cerebras 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": "c1eb410e-54f2-4d31-b768-9afe8420b5b2",
   "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\n",
    "\n",
    "Fine-tuning took approximately 4 hours for 1 epoch on p3.2xlarge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cb98557-3a3f-40ef-baf3-538c4ee4aa8a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hyperparameters={\n",
    "    'base_model':'cerebras/Cerebras-GPT-6.7B',\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': '[c_attn]',\n",
    "    'lora_r': 8,\n",
    "    'micro_batch_size': 8,\n",
    "    \"prompt_template_name\": \"alpaca_short\",\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=\"Cerebras\",\n",
    "    role=role,\n",
    "    entry_point='finetune.py',\n",
    "    source_dir='./scripts/code',\n",
    "    instance_type='ml.p3.2xlarge',\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('Cerebras')\n",
    "    \n",
    "!aws s3 cp {model_data} cerebras.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 -xf cerebras.tar.gz -C scripts/model --no-same-owner"
   ]
  },
  {
   "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\"Cerebras\")\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 = \"Cerebras\"\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\": \"cerebras/Cerebras-GPT-6.7B\",\n",
    "            \"lora_weights\": \"model\", # path relative to model package\n",
    "            \"load_8bit\": \"True\",\n",
    "            \"prompt_template\": \"alpaca_short\",\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",
    "endpoint_name = \"Cerebras\"\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 did Virgin Australia start operating?\",\n",
    "    \"input\": \"Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route.[3] It suddenly found itself as a major airline in Australia's domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.[4]\",\n",
    "    \"max_new_tokens\": 512,\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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