2023/FinetuningLargeLanguageModels/06_Evaluation_lab_student.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p-4WU7pH4jpo"
   },
   "source": [
    "### Technically, there are very few steps to run it on GPUs, elsewhere (ie. on Lamini).\n",
    "```\n",
    "finetuned_model = BasicModelRunner(\n",
    "    \"lamini/lamini_docs_finetuned\"\n",
    ")\n",
    "finetuned_output = finetuned_model(\n",
    "    test_dataset_list # batched!\n",
    ") \n",
    "```\n",
    "\n",
    "### Let's look again under the hood! This is the open core code of Lamini's `llama` library :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets\n",
    "import tempfile\n",
    "import logging\n",
    "import random\n",
    "import config\n",
    "import os\n",
    "import yaml\n",
    "import logging\n",
    "import difflib\n",
    "import pandas as pd\n",
    "\n",
    "import transformers\n",
    "import datasets\n",
    "import torch\n",
    "\n",
    "from tqdm import tqdm\n",
    "from utilities import *\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "\n",
    "logger = logging.getLogger(__name__)\n",
    "global_config = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = datasets.load_dataset(\"lamini/lamini_docs\")\n",
    "\n",
    "test_dataset = dataset[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(test_dataset[0][\"question\"])\n",
    "print(test_dataset[0][\"answer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"lamini/lamini_docs_finetuned\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup a really basic evaluation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_exact_match(a, b):\n",
    "    return a.strip() == b.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=100):\n",
    "  # Tokenize\n",
    "  tokenizer.pad_token = tokenizer.eos_token\n",
    "  input_ids = tokenizer.encode(\n",
    "      text,\n",
    "      return_tensors=\"pt\",\n",
    "      truncation=True,\n",
    "      max_length=max_input_tokens\n",
    "  )\n",
    "\n",
    "  # Generate\n",
    "  device = model.device\n",
    "  generated_tokens_with_prompt = model.generate(\n",
    "    input_ids=input_ids.to(device),\n",
    "    max_length=max_output_tokens\n",
    "  )\n",
    "\n",
    "  # Decode\n",
    "  generated_text_with_prompt = tokenizer.batch_decode(generated_tokens_with_prompt, skip_special_tokens=True)\n",
    "\n",
    "  # Strip the prompt\n",
    "  generated_text_answer = generated_text_with_prompt[0][len(text):]\n",
    "\n",
    "  return generated_text_answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run model and compare to expected answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_question = test_dataset[0][\"question\"]\n",
    "generated_answer = inference(test_question, model, tokenizer)\n",
    "print(test_question)\n",
    "print(generated_answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer = test_dataset[0][\"answer\"]\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exact_match = is_exact_match(generated_answer, answer)\n",
    "print(exact_match)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run over entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10\n",
    "metrics = {'exact_matches': []}\n",
    "predictions = []\n",
    "for i, item in tqdm(enumerate(test_dataset)):\n",
    "    print(\"i Evaluating: \" + str(item))\n",
    "    question = item['question']\n",
    "    answer = item['answer']\n",
    "\n",
    "    try:\n",
    "      predicted_answer = inference(question, model, tokenizer)\n",
    "    except:\n",
    "      continue\n",
    "    predictions.append([predicted_answer, answer])\n",
    "\n",
    "    exact_match = is_exact_match(generated_answer, answer)\n",
    "    metrics['exact_matches'].append(exact_match)\n",
    "\n",
    "    if i > n and n != -1:\n",
    "      break\n",
    "print('Number of exact matches: ', sum(metrics['exact_matches']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(predictions, columns=[\"predicted_answer\", \"target_answer\"])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate all the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_dataset_path = \"lamini/lamini_docs_evaluation\"\n",
    "evaluation_dataset = datasets.load_dataset(evaluation_dataset_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(evaluation_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try the ARC benchmark\n",
    "This can take several minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python lm-evaluation-harness/main.py --model hf-causal --model_args pretrained=lamini/lamini_docs_finetuned --tasks arc_easy --device cpu"
   ]
  }
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