{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b262721-9ba7-40c2-acc4-96c41cf9230a",
   "metadata": {},
   "source": [
    "# MPT SageMaker Inference\n",
    "\n",
    "This is a sample code to deploy MPT 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": "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": [
    "!rm -rf scripts/model && mkdir scripts/model\n",
    "%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\"MPT\")\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 = \"MPT-Inference\"\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\": \"mosaicml/mpt-7b-Instruct\",\n",
    "            \"lora_weights\": \"model\", # path relative to model package\n",
    "            \"peft\": False,\n",
    "            \"load_8bit\": True,\n",
    "            \"trust_remote_code\": True,\n",
    "            \"prompt_template\": \"alpaca\",\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=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": [
    "# 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\": \"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",
    "    \"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": "code",
   "execution_count": null,
   "id": "04c6e83b-e1c3-43f0-ab10-335a7a66ebd0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# With Boto3\n",
    "\n",
    "import boto3\n",
    "import json\n",
    "\n",
    "endpoint_name = \"MPT-Inference\"\n",
    "sagemaker_client = boto3.client('sagemaker-runtime')\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",
    "    \"max_new_tokens\": 64,\n",
    "    \"temperature\": 0.7,\n",
    "    \"do_sample\": True,\n",
    "    \"stop_ids\": [50278, 50279, 50277, 1, 0],\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": "8b2c563e-c996-419e-a250-439ba79fdfa5",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Benchmark Speed\n",
    "\n",
    "1.39 s ± 538 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0774860-2b17-42ca-80fc-7caf7fc6f14d",
   "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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