aoai/aoai_finetune.ipynb

{
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   "source": [
    "# Azure OpenAI Fine-Tuning w/ Wandb\n",
    "\n",
    "Reference: https://colab.research.google.com/github/wandb/examples/blob/master/colabs/openai/Fine_tune_Azure_OpenAI_with_Weights_and_Biases.ipynb#scrollTo=J18CDzt49swB\n",
    "\n",
    "What's different from the official guide and OpenAI cookbook is that the jsonl validation logic is specified considering real-world situations. It counts the number of tokens and checks whether there is an appropriate key, taking into account even the function call.\n",
    "\n",
    "## 1. Connect to Azure\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from openai import AzureOpenAI\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "load_dotenv()\n",
    "\n",
    "aoai_api_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n",
    "aoai_api_key = os.getenv(\"AZURE_OPENAI_API_KEY\")\n",
    "aoai_api_version = os.getenv(\"AZURE_OPENAI_API_VERSION\")\n",
    "aoai_deployment_name = os.getenv(\"AZURE_OPENAI_DEPLOYMENT_NAME\")\n",
    "\n",
    "if not aoai_api_version:\n",
    "    aoai_api_version = os.getenv(\"OPENAI_API_VERSION\")\n",
    "if not aoai_deployment_name:\n",
    "    aoai_deployment_name = os.getenv(\"DEPLOYMENT_NAME\")\n",
    "    \n",
    "print(f\"aoai_api_endpoint: {aoai_api_endpoint}\")\n",
    "print(f\"aoai_api_key: {aoai_api_key}\")\n",
    "print(f\"aoai_api_version: {aoai_api_version}\")\n",
    "print(f\"aoai_deployment_name: {aoai_deployment_name}\")\n",
    "\n",
    "try:\n",
    "    client = AzureOpenAI(\n",
    "        azure_endpoint = aoai_api_endpoint,\n",
    "        api_key        = aoai_api_key,\n",
    "        api_version    = aoai_api_version\n",
    "    )\n",
    "    deployment_name = aoai_deployment_name\n",
    "except (ValueError, TypeError) as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data preparation\n",
    "\n",
    "---\n",
    "\n",
    "### Create sample dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile dataset/training_set.jsonl\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"Who discovered Antarctica?\"}, {\"role\": \"assistant\", \"content\": \"Some chaps named Fabian Gottlieb von Bellingshausen and Mikhail Lazarev, as if they don't teach that in every school!\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What is the biggest ocean?\"}, {\"role\": \"assistant\", \"content\": \"The Pacific Ocean. It's not like it's a small pond or anything.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What is the largest planet?\"}, {\"role\": \"assistant\", \"content\": \"It's called Jupiter, you may have heard of it...or not.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"Who painted the Mona Lisa?\"}, {\"role\": \"assistant\", \"content\": \"Oh, some obscure artist named Leonardo da Vinci. Not that he is famous or anything.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What's the boiling point of water?\"}, {\"role\": \"assistant\", \"content\": \"Exciting fact of the day: it's 100 degrees Celsius. Life-changing, isn't it?\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"Who wrote 'The Odyssey'?\"}, {\"role\": \"assistant\", \"content\": \"Oh, just some poet named Homer. Nothing special, really.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What is the largest mammal?\"}, {\"role\": \"assistant\", \"content\": \"Blue Whale. Quite a big fish isn't it? Oh wait, it's not a fish!\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What's the highest mountain peak?\"}, {\"role\": \"assistant\", \"content\": \"Everest. Brace yourself, it's not a walk in the park.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"Who invented the telephone?\"}, {\"role\": \"assistant\", \"content\": \"A guy named Alexander Graham Bell. Bet you didn't guess that one!\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What is the distance from Earth to the Sun?\"}, {\"role\": \"assistant\", \"content\": \"About 93 million miles. Just a quick drive, really.\"}]}"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "%%writefile dataset/validation_set.jsonl\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What's the capital of Australia?\"}, {\"role\": \"assistant\", \"content\": \"It's Canberra, not Sydney. Shocking, I know!\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"Who wrote 'The Great Gatsby'?\"}, {\"role\": \"assistant\", \"content\": \"Some guy named F. Scott Fitzgerald. Ever heard of him?\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What's the deepest part of the ocean?\"}, {\"role\": \"assistant\", \"content\": \"Mariana Trench, makes your swimming pool seem a bit shallow, doesn't it?\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"Who composed the 'Moonlight Sonata'?\"}, {\"role\": \"assistant\", \"content\": \"Just a little-known guy named Beethoven. No big deal.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What are the colors of the rainbow?\"}, {\"role\": \"assistant\", \"content\": \"ROYGBIV. Red, orange, yellow, green, blue, indigo, violet. Not that we learned this in preschool or anything!\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What is the smallest planet?\"}, {\"role\": \"assistant\", \"content\": \"Meet Mercury, the runt of the solar system family.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"Who wrote 'Pride and Prejudice'?\"}, {\"role\": \"assistant\", \"content\": \"Do the words Jane Austen ring a bell? No? Well, she wrote it.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What's the largest desert?\"}, {\"role\": \"assistant\", \"content\": \"Antarctica. Surprise, surprise! Deserts aren't just full of sand, you know.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What is the longest river?\"}, {\"role\": \"assistant\", \"content\": \"The Nile River. It's not like it's famous or anything.\"}]}\n",
    "{\"messages\": [{\"role\": \"system\", \"content\": \"Clippy is a factual chatbot that is also sarcastic.\"}, {\"role\": \"user\", \"content\": \"What's the capital of Germany?\"}, {\"role\": \"assistant\", \"content\": \"Berlin. Shocking news, right?\"}]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validate datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from token_count_utils import validate_jsonl, print_stats_tokens\n",
    "\n",
    "MODEL = \"gpt-4o-mini\"\n",
    "files = [\"dataset/training_set.jsonl\", \"dataset/validation_set.jsonl\"]\n",
    "print_stats_tokens(files, model=MODEL)\n",
    "validate_jsonl(files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upload the Validated datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_file_name = 'dataset/training_set.jsonl'\n",
    "validation_file_name = 'dataset/validation_set.jsonl'\n",
    "\n",
    "# Upload the training and validation dataset files to Azure OpenAI with the SDK.\n",
    "\n",
    "training_response = client.files.create(\n",
    "    file=open(training_file_name, \"rb\"), purpose=\"fine-tune\"\n",
    ")\n",
    "training_file_id = training_response.id\n",
    "\n",
    "validation_response = client.files.create(\n",
    "    file=open(validation_file_name, \"rb\"), purpose=\"fine-tune\"\n",
    ")\n",
    "validation_file_id = validation_response.id\n",
    "\n",
    "print(\"Training file ID:\", training_file_id)\n",
    "print(\"Validation file ID:\", validation_file_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Fine-tuning\n",
    "\n",
    "---\n",
    "\n",
    "### Start fine-tuning job\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_EPOCHS = 1\n",
    "BATCH_SIZE = 2\n",
    "LEARNING_RATE_MULTIPLIER = 0.05\n",
    "\n",
    "response = client.fine_tuning.jobs.create(\n",
    "    training_file=training_file_id,\n",
    "    validation_file=validation_file_id,\n",
    "    model=MODEL, # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters.\n",
    "    hyperparameters={\"n_epochs\": N_EPOCHS, \"batch_size\": BATCH_SIZE, \"learning_rate_multiplier\": LEARNING_RATE_MULTIPLIER},\n",
    ")\n",
    "\n",
    "job_id = response.id\n",
    "\n",
    "# You can use the job ID to monitor the status of the fine-tuning job.\n",
    "# The fine-tuning job will take some time to start and complete.\n",
    "print(\"Job ID:\", job_id)\n",
    "print(response.model_dump_json(indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sync metrics to Wandb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_id = response.id\n",
    "wandb_project = \"Azure_Openai_Finetuning\"\n",
    "from wandb.integration.openai.fine_tuning import WandbLogger\n",
    "WandbLogger.sync(fine_tune_job_id=job_id, openai_client=client, project=wandb_project, wait_for_job_success=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # List 5 fine-tuning jobs\n",
    "# l = client.fine_tuning.jobs.list(limit=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wait for the fine-tuning job to complete (this may take a while)\n",
    "\n",
    "You do not need to run this cell if you have set `wait_for_job_success=False` in the `WandbLogger.sync` call above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "import time\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# Get the status of our fine-tuning job.\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "\n",
    "status = response.status\n",
    "\n",
    "# If the job isn't done yet, poll it every 10 seconds.\n",
    "while status not in [\"succeeded\", \"failed\"]:\n",
    "    time.sleep(10)\n",
    "\n",
    "    response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "    print(response.model_dump_json(indent=2))\n",
    "    print(\"Elapsed time: {} minutes {} seconds\".format(int((time.time() - start_time) // 60), int((time.time() - start_time) % 60)))\n",
    "    status = response.status\n",
    "    print(f'Status: {status}')\n",
    "    clear_output(wait=True)\n",
    "\n",
    "print(f'Fine-tuning job {job_id} finished with status: {status}')\n",
    "\n",
    "# List all fine-tuning jobs for this resource.\n",
    "print('Checking other fine-tune jobs for this resource.')\n",
    "response = client.fine_tuning.jobs.list()\n",
    "print(f'Found {len(response.data)} fine-tune jobs.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve fine-tuned model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "print(response.model_dump_json(indent=2))\n",
    "fine_tuned_model = response.fine_tuned_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.identity import DefaultAzureCredential\n",
    "from azure.mgmt.resource import ResourceManagementClient\n",
    "\n",
    "credential = DefaultAzureCredential()\n",
    "\n",
    "subscription_id = os.getenv(\"AZURE_SUBSCRIPTION_ID\")\n",
    "resource_client = ResourceManagementClient(credential, subscription_id)\n",
    "\n",
    "# get access token\n",
    "token = (credential.get_token(\"https://management.azure.com/.default\")).token\n",
    "print(\"Access Token:\", token)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Deploy fine-tuned model\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "from azure.identity import DefaultAzureCredential\n",
    "from azure.mgmt.resource import ResourceManagementClient\n",
    "subscription = os.getenv(\"AZURE_SUBSCRIPTION_ID\")\n",
    "resource_group = os.getenv(\"AZURE_RESOURCE_GROUP_NAME\")\n",
    "\n",
    "credential = DefaultAzureCredential()\n",
    "resource_client = ResourceManagementClient(credential, subscription_id)\n",
    "\n",
    "# get access token\n",
    "token = (credential.get_token(\"https://management.azure.com/.default\")).token\n",
    "print(\"Access Token:\", token)\n",
    "\n",
    "\n",
    "resource_name = aoai_api_endpoint.split(\"https://\")[1].split(\".\")[0]\n",
    "model_deployment_name = \"gpt-4o-mini-ft-daekeun\" # YOUR-DEPLOYMENT-NAME\n",
    "\n",
    "deploy_params = {'api-version': \"2023-05-01\"}\n",
    "deploy_headers = {'Authorization': 'Bearer {}'.format(token), 'Content-Type': 'application/json'}\n",
    "\n",
    "deploy_data = {\n",
    "    \"sku\": {\"name\": \"standard\", \"capacity\": 50},\n",
    "    \"properties\": {\n",
    "        \"model\": {\n",
    "            \"format\": \"OpenAI\",\n",
    "            \"name\": fine_tuned_model, #retrieve this value from the previous call, it will look like gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83\n",
    "            \"version\": \"1\"\n",
    "        }\n",
    "    }\n",
    "}\n",
    "deploy_data = json.dumps(deploy_data)\n",
    "\n",
    "request_url = f'https://management.azure.com/subscriptions/{subscription}/resourceGroups/{resource_group}/providers/Microsoft.CognitiveServices/accounts/{resource_name}/deployments/{model_deployment_name}'\n",
    "\n",
    "print('Creating a new deployment...')\n",
    "\n",
    "r = requests.put(request_url, params=deploy_params, headers=deploy_headers, data=deploy_data)\n",
    "\n",
    "print(r)\n",
    "print(r.reason)\n",
    "print(r.json())"
   ]
  }
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