{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qAkXdLL2D25p"
   },
   "source": [
    "## Peft model evaluation using [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness)\n",
    "\n",
    "In this notebook, we are going to learn how to evaluate the finetuned lora model on the hellaswag task using lm-eval-harness toolkit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o52TJHcYD25q",
    "outputId": "c5482c79-ff56-4ffa-d20c-46c3d30d2cd5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33m  DEPRECATION: Building 'rouge-score' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'rouge-score'. Discussion can be found at https://github.com/pypa/pip/issues/6334\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  DEPRECATION: Building 'sqlitedict' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'sqlitedict'. Discussion can be found at https://github.com/pypa/pip/issues/6334\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  DEPRECATION: Building 'word2number' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'word2number'. Discussion can be found at https://github.com/pypa/pip/issues/6334\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Install LM-Eval\n",
    "!pip install -q datasets evaluate lm_eval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uhUflrJXD25q"
   },
   "source": [
    "### First we will check the accuracy score on the hellaswag task for the base bert without finetuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hwJIYD5KD25q",
    "outputId": "51e69f81-d048-46b2-9699-658d3ffc5f08"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b1ea8948a0747bc98795d6459270044",
       "version_major": 2,
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      "text/plain": [
       "README.md: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "4ec51e06812446899b66826c41697f8d",
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c887d06a56a410eae563e11a3080a52",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "adb9c60c23f74f7d9af72ea2e34bc22c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/39905 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3418259aeaf4d459d5ed4fe9b8434fc",
       "version_major": 2,
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      },
      "text/plain": [
       "Generating test split:   0%|          | 0/10003 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da448b2a7a534fec9045c086fbda5d0d",
       "version_major": 2,
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      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/10042 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "83f04bb57ab94be58080e8c67675a4e5",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f08ebbc81eaa45818c85e51169ac48cf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/10042 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████| 10042/10042 [00:02<00:00, 4111.19it/s]\n",
      "Running loglikelihood requests:   0%|                                                                           | 0/40168 [00:00<?, ?it/s]We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n",
      "Running loglikelihood requests: 100%|██████████████████████████████████████████████████████████████| 40168/40168 [02:40<00:00, 250.28it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'hellaswag': {'alias': 'hellaswag',\n",
       "  'acc,none': 0.24915355506871142,\n",
       "  'acc_stderr,none': 0.004316389476434537,\n",
       "  'acc_norm,none': 0.244672376020713,\n",
       "  'acc_norm_stderr,none': 0.004290142029921662}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import lm_eval\n",
    "\n",
    "\n",
    "device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
    "output = lm_eval.simple_evaluate(model = 'hf',\n",
    "                        model_args = {\n",
    "                            'pretrained' : 'bert-base-cased',\n",
    "                            'dtype' : 'bfloat16'},\n",
    "                        tasks = 'hellaswag',\n",
    "                        device = device,\n",
    "                        batch_size = 128,\n",
    "                        log_samples = False)\n",
    "output[\"results\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EZk-1JT7D25r"
   },
   "source": [
    "### Now lets try to finetune the bert on the imdb dataset (this is for demonstration and finetuning on imdb may not increase the scores on hellaswag task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "FmtVeh7QD25r"
   },
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import evaluate\n",
    "import numpy as np\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoTokenizer, BertForSequenceClassification, Trainer, TrainingArguments\n",
    "\n",
    "from peft import LoraConfig, TaskType, get_peft_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rHF7tzN9D25r",
    "outputId": "352ad9ab-2efc-41f8-c3d5-a7da05d9529b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "The 8-bit optimizer is not available on your device, only available on CUDA for now.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 296,450 || all params: 108,608,260 || trainable%: 0.2730\n"
     ]
    }
   ],
   "source": [
    "# Configure LoRA for Sequence Classification\n",
    "lora_config = LoraConfig(\n",
    "    task_type=TaskType.SEQ_CLS,        # Set task type to sequence classification\n",
    "    target_modules=[\"query\", \"key\"]    # Specify target modules for LoRA tuning\n",
    ")\n",
    "\n",
    "# Initialize the BERT model for sequence classification\n",
    "model = BertForSequenceClassification.from_pretrained(\n",
    "    'bert-base-cased',\n",
    "    num_labels = 2\n",
    ")\n",
    "\n",
    "# Wrap the model with LoRA configuration\n",
    "model = get_peft_model(model, lora_config)\n",
    "\n",
    "model.print_trainable_parameters()\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
   ]
  },
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   "source": [
    "# load the dataset\n",
    "dataset = load_dataset(\"imdb\")\n",
    "\n",
    "def tokenize_function(row):\n",
    "    return tokenizer(row[\"text\"], padding=\"max_length\", truncation = True)\n",
    "\n",
    "tokenized_datasets = dataset.map(tokenize_function, batched = True)\n",
    "\n",
    "train_dataset = tokenized_datasets[\"train\"]\n",
    "eval_dataset = tokenized_datasets[\"test\"]"
   ]
  },
  {
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   "execution_count": 7,
   "metadata": {
    "id": "bA3k0iVED25r"
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   "source": [
    "# Define a function to compute evaluation metrics\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    logits, labels = eval_pred\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    metric = evaluate.load(\"accuracy\")\n",
    "    return metric.compute(predictions = predictions, references = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "DFG74c3kD25s",
    "outputId": "5dd9f988-95db-4efb-e632-5f741801910a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No label_names provided for model class `PeftModelForSequenceClassification`. Since `PeftModel` hides base models input arguments, if label_names is not given, label_names can't be set automatically within `Trainer`. Note that empty label_names list will be used instead.\n"
     ]
    },
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='3910' max='3910' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [3910/3910 40:13, Epoch 5/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.353800</td>\n",
       "      <td>0.261258</td>\n",
       "      <td>0.901160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.277400</td>\n",
       "      <td>0.221651</td>\n",
       "      <td>0.912480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.244500</td>\n",
       "      <td>0.216107</td>\n",
       "      <td>0.918200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.197000</td>\n",
       "      <td>0.215257</td>\n",
       "      <td>0.920040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.157700</td>\n",
       "      <td>0.215050</td>\n",
       "      <td>0.923240</td>\n",
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      "text/plain": [
       "TrainOutput(global_step=3910, training_loss=0.24082870385835847, metrics={'train_runtime': 2416.0772, 'train_samples_per_second': 51.737, 'train_steps_per_second': 1.618, 'total_flos': 3.300271872e+16, 'train_loss': 0.24082870385835847, 'epoch': 5.0})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Configure training arguments\n",
    "training_args = TrainingArguments(\"bert-lora-imdb\",\n",
    "    eval_strategy=\"epoch\",\n",
    "    per_device_train_batch_size=32, # decrease this for OOM error\n",
    "    per_device_eval_batch_size=64,\n",
    "    save_strategy=\"epoch\",\n",
    "    learning_rate=2e-3,\n",
    "    num_train_epochs=5,\n",
    "    weight_decay=0.01,\n",
    "    load_best_model_at_end=True,\n",
    "    do_eval=True,\n",
    "    do_predict=True,\n",
    "    metric_for_best_model=\"accuracy\",\n",
    "    report_to=\"none\")\n",
    "\n",
    "# Initialize the Trainer for the model training loop\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=eval_dataset,\n",
    "    compute_metrics=compute_metrics,\n",
    ")\n",
    "\n",
    "#start training\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "34h3g_eED25s"
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   "source": [
    "### Now take the finetuned lora checkpoint and check the accuracy score on hellaswag task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7tgAq7nLD25s"
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   "source": [
    "# use the path of your checkpoint here\n",
    "output = lm_eval.simple_evaluate(model = 'hf',\n",
    "                        model_args = {\n",
    "                          'pretrained' : 'bert-base-cased',\n",
    "                          'peft' : './bert-lora-imdb/checkpoint-3910',\n",
    "                          'dtype' : 'bfloat16'},\n",
    "                        tasks = 'hellaswag',\n",
    "                        device = device,\n",
    "                        batch_size = 128,\n",
    "                        log_samples = False)\n",
    "\n",
    "output[\"results\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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