{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uG0PEEBEV1P1"
   },
   "source": [
    "# Evaluation of LLMs\n",
    "\n",
    "![](runtime.png)\n",
    "\n",
    "Okay, so we've made our sweet new LLM - but how can we confirm that it's working as intended?\n",
    "\n",
    "In this notebook, we'll walk through a few popular methods of evaluating LLMs on various tasks:\n",
    "\n",
    "- Metric evaluation, like [Perplexity](https://thegradient.pub/understanding-evaluation-metrics-for-language-models/)\n",
    "- Human or AI Evaluation\n",
    "- Eleuther AI's [Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) - [Notebook Here](https://colab.research.google.com/drive/1CsaPpqsB21QgQxhJpV22SgwryFFapDBP?usp=sharing)\n",
    "- Stanford's [HELM](https://github.com/stanford-crfm/helm) - [Notebook here]()\n",
    "\n",
    "There's nothing left to do but get started - and we'll start with the most familiar method: Metrics!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wfyIOQr9mL-E"
   },
   "source": [
    "If you run into CUDA memory issues - please restart the notebook at start from the next session."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3E997GhwY80E"
   },
   "source": [
    "### Base Model\n",
    "\n",
    "For this exercise, we'll be using bigscience's `bloom-1b7` as our base model.\n",
    "\n",
    "This is to ensure we stay consistent across all tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "62YhJ9tlZkkX"
   },
   "outputs": [],
   "source": [
    "model_id = \"bigscience/bloom-1b1\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BjYz9DvaXgi9"
   },
   "source": [
    "### Perplexity\n",
    "\n",
    "First things first, perplexity is limited to autoregressive (CausalLM) models. That does restrict its usefulness, but not tremendously!\n",
    "\n",
    "Secondly, Perplexity has a number of pros and cons associated with it:\n",
    "\n",
    "Pros:\n",
    "- Time-efficient, since perplexity can be calculated in a single-pass - it's fairly quick to obtain\n",
    "- Can be used as signal for over/under-fitting, if perplexity scales proportionally with training data size - it could indicate your model is overfitting\n",
    "\n",
    "Cons:\n",
    "- Doesn't indicate model's performance on the final task\n",
    "- Because the perplexity score depends heavily on what text was used to train the model - the scores are not comparable between models or datasets\n",
    "\n",
    "That con is a big one, and is one of the reasons that - while perplexity is useful to calculate - it isn't great signal on how well your model will perform on its desired task.\n",
    "\n",
    "Let's get started by getting the `evaluate` library and some other dependencies we'll use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WzLeJ_1BVyE5",
    "outputId": "701d1847-d5fe-4df5-9d04-055e781227ba"
   },
   "outputs": [],
   "source": [
    "!pip install -q evaluate datasets #transformers torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ry8sXkzdZ8Qj"
   },
   "source": [
    "Now, let's get a small test set of strings we wish to use!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241,
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      "c7750bae0e3c46b899ddaeee60cf4e73"
     ]
    },
    "id": "stgWCOqWZ_1a",
    "outputId": "859770bf-b346-4403-ee83-86bbc2c768fa"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "input_data = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JYzzAv2AaZk4",
    "outputId": "9e65fb75-40b7-4c4b-8e3d-8034bf0d4826"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    test: Dataset({\n",
       "        features: ['text'],\n",
       "        num_rows: 4358\n",
       "    })\n",
       "    train: Dataset({\n",
       "        features: ['text'],\n",
       "        num_rows: 36718\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['text'],\n",
       "        num_rows: 3760\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MneQ7sHrabA1"
   },
   "source": [
    "We'll use some of the data present in the `test` split to ensure we're not usings something the model was trained on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "9WYwMCqmaWfP"
   },
   "outputs": [],
   "source": [
    "test_text = input_data[\"test\"][:50][\"text\"]\n",
    "\n",
    "test_text = [text for text in test_text if text != \"\"]"
   ]
  },
  {
   "cell_type": "code",
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     "base_uri": "https://localhost:8080/",
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    },
    "id": "08Jd0w6RZqKT",
    "outputId": "5f745b5a-e1ee-413a-d807-69a5b3377c5f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "670087ec112343fa837317ab1547e812",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/693 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c693ab9a546d40b2a84681334f058dbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading model.safetensors:   0%|          | 0.00/2.13G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91318bcfef3a41f6bdbff8ce3a74ce92",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/222 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab58a41f34744060a549708ae4efd577",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading tokenizer.json:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84908d1954a14ed09dda632a59384050",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "477c3cd90bff4708a360d49ead3deb2f",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from evaluate import load\n",
    "\n",
    "perplexity = load(\"perplexity\", module_type=\"metric\")\n",
    "results = perplexity.compute(predictions=test_text, model_id=model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Fn1mz19kbvam",
    "outputId": "8bc99a9c-cb9c-415c-f35d-7c67a6b34cd7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264.3619388297752"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"mean_perplexity\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OTKzyjQVcCx8"
   },
   "source": [
    "Perplexity is measured as a score between 0 and `inf`, so a lower score is better.\n",
    "\n",
    "In this case, the results are absolutely fine - though unimpressive.\n",
    "\n",
    "bloom-1b7 was not trained on Wikitext."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bnQd9q75bbfj"
   },
   "source": [
    "### Human or AI Evaluation\n",
    "\n",
    "Now, let's get into how we could compare the actual final production of the model - with human or AI supervision!\n",
    "\n",
    "The idea here is that we ask the model to perform a task - and then get some kind of results from a human being.\n",
    "\n",
    "This method similarly comes with some pros and cons:\n",
    "\n",
    "Pros:\n",
    "- Should provide excellent feedback on wether or not your model is performing as expected\n",
    "\n",
    "Cons:\n",
    "- Extremely expensive\n",
    "\n",
    "Since we're going to be leveraging AI in this example, you will need an OpenAI API key!\n",
    "\n",
    "Also, we're going to use an instruct-tuned version of the Bloom (`bigscience/bloomz-1b7`) base-model to guage how well it's doing on following instructions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "sDx8XDFzgzN9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    }
   ],
   "source": [
    "!pip install -q openai accelerate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
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    },
    "id": "xdNbq8LAi5D-",
    "outputId": "d7fd4889-ce3a-4c0b-e94d-db2260bf4cd3"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "\n",
    "model_name = \"bigscience/bloom-1b1\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype= torch.float16)\n",
    "\n",
    "prompt_template = \"Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\n{instruction}\\n\\n### Response:\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "sLRo2RUDg09X"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"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\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uDqoOmrhhsQA"
   },
   "source": [
    "We're going to be using a short list of instructions to score the models - the idea can be extended as far as you'd like!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "i69qimUwhg-v"
   },
   "outputs": [],
   "source": [
    "list_of_instructions = [\n",
    "    \"Give three tips for staying healthy.\",\n",
    "    \"What are the three primary colors?\",\n",
    "    \"Describe a time when you had to make a difficult decision.\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "DFw3Rlsjix9_"
   },
   "outputs": [],
   "source": [
    "def get_model_response(text):\n",
    "  input = prompt_template.format(instruction= text)\n",
    "  input_ids = tokenizer(input, return_tensors=\"pt\").input_ids.to(\"cpu\")\n",
    "\n",
    "  model.to(\"cpu\")  # Moving model to CPU\n",
    "  model.float()  # Ensuring model is in full precision mode\n",
    "\n",
    "  output1 = model.generate(input_ids, max_length=512)\n",
    "  input_length = input_ids.shape[1]\n",
    "  output1 = output1[:, input_length:]\n",
    "  output = tokenizer.decode(output1[0])\n",
    "\n",
    "  return output\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mRJaj1qFkvvJ",
    "outputId": "6e126350-b818-4d57-a99a-6685f08255cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Give three tips for staying healthy.\n",
      "Yes, this is a good response to the instruction. It provides three clear and concise tips for staying healthy: exercise regularly, eat a balanced diet, and avoid smoking.\n",
      "-----------------\n",
      "What are the three primary colors?\n",
      "primary colors?\n",
      "\n",
      "### Response:\n",
      "The three primary colors are red, blue, and yellow.\n",
      "-----------------\n",
      "Describe a time when you had to make a difficult decision.\n",
      "No, this response is not appropriate. It does not provide any information or insight about a difficult decision.\n",
      "-----------------\n"
     ]
    }
   ],
   "source": [
    "import openai\n",
    "\n",
    "for prompt in list_of_instructions:\n",
    "  gpt_35_turbo_prompt = [\n",
    "      {\"role\" : \"system\",\n",
    "      \"content\" : f\"Is the following a good response to this instruction: {prompt}\"}\n",
    "  ]\n",
    "  output_to_test = get_model_response(prompt)\n",
    "  gpt_35_turbo_prompt.append(\n",
    "      {\"role\" : \"user\",\n",
    "       \"content\" : f\"{output_to_test}\"}\n",
    "  )\n",
    "\n",
    "  print(prompt)\n",
    "\n",
    "  print(openai.ChatCompletion.create(\n",
    "      model=\"gpt-3.5-turbo\",\n",
    "      messages=gpt_35_turbo_prompt\n",
    "  )[\"choices\"][0][\"message\"][\"content\"])\n",
    "\n",
    "  print(\"-----------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ku_ONi7ZrFJp"
   },
   "source": [
    "Try it out yourself!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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