PEFT.ipynb

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     "text": [
      "Requirement already satisfied: pip in /opt/conda/lib/python3.10/site-packages (24.0)\n",
      "^C\n",
      "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install --upgrade pip\n",
    "%pip install --disable-pip-version-check torch==1.13.1 torchdata==0.5.1 --quiet "
   ]
  },
  {
   "cell_type": "code",
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   "id": "9c79ba64-ef2a-4b66-8eef-8ac07f0a56e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install transformers==4.27.2 datasets==2.11.0 \\\n",
    "    evaluate==0.4.0 \\\n",
    "    rouge_score==0.1.2\\\n",
    "    loralib==0.1.1 \\\n",
    "    peft==0.3.0 --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4342fc39-ac8a-43b8-8ae2-b8ba06df3b14",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f45ec75-9666-4132-98fd-efee90407d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "import time \n",
    "import evaluate  ## for calculating rouge score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from peft import LoraConfig, get_peft_model, TaskType\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig, TrainingArguments, Trainer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3309559b-e4cd-4670-9dd3-08265e2c122c",
   "metadata": {},
   "source": [
    "### Load Dataset and LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb21dcbb-9d7b-439e-82f0-9e6de1e5e855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset csv (/home/sagemaker-user/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"
     ]
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       "  0%|          | 0/3 [00:00<?, ?it/s]"
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     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 12460\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 1500\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 500\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huggingface_dataset_name = \"knkarthick/dialogsum\"\n",
    "\n",
    "dataset = load_dataset(huggingface_dataset_name)\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dbb95e15-6cd4-405e-973d-3c28f80e1c44",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_name = 'google/flan-t5-base'\n",
    "\n",
    "# bfloat16 mean we are using the small version of flan-t5\n",
    "original_model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81626c3-87d6-4091-aac5-538c336e69b8",
   "metadata": {},
   "source": [
    "It is possible to pull out the number of parameters from the model and find out how many of them are trainable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "116ed4e2-8f18-47c9-a3b2-31a46277397c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def print_number_of_trainable_model_parameters(model):\n",
    "    trainable_model_params = 0\n",
    "    all_model_params = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_model_params += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_model_params += param.numel()\n",
    "    return f'trainable model parameters: {trainable_model_params}\\n \\\n",
    "            all model parameters: {all_model_params} \\n \\\n",
    "            percentage of trainable model parameters: {(trainable_model_params / all_model_params) * 100} %'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11549678-e6d0-45d9-9f9f-c31ec81048d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable model parameters: 247577856\n",
      "             all model parameters: 247577856 \n",
      "             percentage of trainable model parameters: 100.0 %\n"
     ]
    }
   ],
   "source": [
    "print(print_number_of_trainable_model_parameters(original_model))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda90b8c-854b-406d-8220-1f4c55cd57ba",
   "metadata": {},
   "source": [
    "# 1. Preprocess the Dialog-Summary Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4b76a5-52ac-4a75-b401-1a8ebacc486f",
   "metadata": {},
   "source": [
    "We need to convert dialog-summary into explicit instruction for LLM. Prepend an instruction to the start of the dialog with Summarize the following conversation and to the start of the summary with summary as follows:\n",
    "<br>\n",
    "Training prompt (dialogue):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ab806b-aae6-4404-ac9c-b5fe65dbbad4",
   "metadata": {},
   "source": [
    "```\n",
    "Summarize the following conversation.\n",
    "\n",
    "    Chris: This is his part of conversation\n",
    "    Antje: This is her part of conversation\n",
    "    \n",
    "Summary:\n",
    "```\n",
    "\n",
    "Training response (summary):\n",
    "\n",
    "```\n",
    "Both Chrish and Antje participated in the conversation.\n",
    "```\n",
    "\n",
    "Then preprocess the prompt-response dataset into token and pull out their input_ids (1 per token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da709638-1b41-4b38-96df-d5093a9ca10a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/sagemaker-user/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-1ac4b8d3502cfcf7.arrow\n",
      "Loading cached processed dataset at /home/sagemaker-user/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-7b58069a688c8eb7.arrow\n"
     ]
    },
    {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/500 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def tokeninze_function(example):\n",
    "    start_prompt = 'Summarize the following conversation. \\n\\n'\n",
    "    end_prompt = '\\n\\nSummary: '\n",
    "    prompt = [start_prompt + dialogue + end_prompt for dialogue in example[\"dialogue\"]]\n",
    "    example['input_ids'] = tokenizer(prompt, padding='max_length', truncation=True, \n",
    "                                     return_tensors='pt').input_ids\n",
    "    example['labels'] = tokenizer(example['summary'], padding='max_length', truncation=True, \n",
    "                                 return_tensors='pt').input_ids\n",
    "    \n",
    "    return example\n",
    "\n",
    "# The Dataseta ctually contains 3 diff splits: train, validation, and test.\n",
    "# The tokenize_function code is handling all data across all splits in batches\n",
    "tokenize_datasets = dataset.map(tokeninze_function, batched=True)\n",
    "tokenize_datasets = tokenize_datasets.remove_columns(['id', 'topic', 'dialogue',\n",
    "                                                     'summary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a5622c-68a9-401f-9787-55da313489c8",
   "metadata": {},
   "source": [
    "To save some time in the lab, you will subsample the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6750a739-582e-4b89-913e-cc0047f17c45",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/sagemaker-user/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-ac3cd98dc41c44d5.arrow\n",
      "Loading cached processed dataset at /home/sagemaker-user/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-00ac8b56c956cb88.arrow\n"
     ]
    },
    {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filter:   0%|          | 0/500 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenize_datasets = tokenize_datasets.filter(lambda exmaple, index: index % 100 == 0, \n",
    "                                            with_indices=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b514091-1f54-4d11-bce7-560b6bac59f1",
   "metadata": {},
   "source": [
    "Check the shape of all three dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9877ceb2-83bf-4e90-bb9e-19d1d9a55b05",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: (125, 2)\n",
      "Valdiation: (5, 2)\n",
      "Test: (15, 2)\n",
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['input_ids', 'labels'],\n",
      "        num_rows: 125\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['input_ids', 'labels'],\n",
      "        num_rows: 15\n",
      "    })\n",
      "    validation: Dataset({\n",
      "        features: ['input_ids', 'labels'],\n",
      "        num_rows: 5\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "print(f'Training: {tokenize_datasets[\"train\"].shape}')\n",
    "print(f'Valdiation: {tokenize_datasets[\"validation\"].shape}')\n",
    "print(f'Test: {tokenize_datasets[\"test\"].shape}')\n",
    "print(tokenize_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007a7a0e-74c6-497b-80fd-08fab6a7dcaf",
   "metadata": {},
   "source": [
    "Use the Hugging face builtin `Trainer` library. Pass the preprocessed dataset with reference to original model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed9a4117-6c98-4838-a38d-a388f9b5b155",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir = f'./dialogue-summary-training-{str(int(time.time()))}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a79b0b62-9a31-4284-b77d-7ad28d6b8822",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.environ['WANDB_DISABLED'] = 'true'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a16d61-64e4-42c2-be09-9feea04078b7",
   "metadata": {},
   "source": [
    "## 2. Parameter Efficient Fine Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cb817f4-f4b1-43ff-9832-76efdfcebeaa",
   "metadata": {},
   "source": [
    "### 2.1. PEFT using LoRA technique\n",
    "LoRA required setting up a new layer of adapter. PEFT freezes the underlying LLM parameters and train only adapter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3028efbe-3e33-4538-bdb7-5430d3251c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "lora_config = LoraConfig(r=32, #rank 32,\n",
    "                         lora_alpha=32, ## LoRA Scaling factor \n",
    "                         target_modules=['q', 'v'], ## The modules(for example, attention blocks) to apply the LoRA update matrices.\n",
    "                         lora_dropout = 0.05,\n",
    "                         bias='none',\n",
    "                         task_type=TaskType.SEQ_2_SEQ_LM ## flan-t5\n",
    ")\n",
    "\n",
    "## target_modules='q', This represents the value projection layer in the transformer model. The value projection layer transforms input tokens into value vectors,\n",
    "# which are the actual values that are attended to based on the attention scores computed from query and key vectors.\n",
    "\n",
    "## target_modules='v',This typically refers to the query projection layer in a transformer-based model. The query projection layer is responsible for transforming \n",
    "# input tokens into query vectors, which are used to attend to other tokens in the sequence during self-attention mechanism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11768344-aa99-4c8b-8997-455d7fa738b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "58cc7143-0957-4935-98bf-71686446f320",
   "metadata": {},
   "source": [
    "Add LoRA parameter to original model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30c553f4-4d25-44ef-84b4-7096a41a47ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable model parameters: 3538944\n",
      "             all model parameters: 251116800 \n",
      "             percentage of trainable model parameters: 1.4092820552029972 %\n"
     ]
    }
   ],
   "source": [
    "peft_model = get_peft_model(original_model, lora_config)\n",
    "\n",
    "print(print_number_of_trainable_model_parameters(peft_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b051d8e-00b1-4bef-b628-9edf338d8d2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "30631901-543a-479c-85c5-bd7e642d3d64",
   "metadata": {},
   "source": [
    "### 2.2. Train PEFT Adapter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0f9d252e-560a-4802-a308-d1c10f578a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir = f'./dialogue-summary-training-{str(int(time.time()))}'\n",
    "## this is we are again back to the hugging face trainer module\n",
    "peft_training_args = TrainingArguments(output_dir=output_dir,\n",
    "                                       auto_find_batch_size=True,\n",
    "                                       learning_rate=1e-3,\n",
    "                                       num_train_epochs=1,\n",
    "                                       logging_steps=1,\n",
    "                                       max_steps=1,\n",
    "                                        report_to='none' ## can be wandb, but we are reporint to noe\n",
    "                )\n",
    "\n",
    "## this is same except we are using PEFT model instead of regular\n",
    "peft_trainer = Trainer(model=peft_model, \n",
    "                      args=peft_training_args,\n",
    "                      train_dataset=tokenize_datasets['train']\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "758a7010-c42a-4562-bd8a-3d728add6d82",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1/1 00:00, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>51.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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      ]
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    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1, training_loss=51.25, metrics={'train_runtime': 51.2322, 'train_samples_per_second': 0.156, 'train_steps_per_second': 0.02, 'total_flos': 5565031907328.0, 'train_loss': 51.25, 'epoch': 0.06})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peft_trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2408f5a8-9e01-4e9f-a39a-47495d6cb2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('./peft-dialogue-summary-checkpoint-local/tokenizer_config.json',\n",
       " './peft-dialogue-summary-checkpoint-local/special_tokens_map.json',\n",
       " './peft-dialogue-summary-checkpoint-local/spiece.model',\n",
       " './peft-dialogue-summary-checkpoint-local/added_tokens.json',\n",
       " './peft-dialogue-summary-checkpoint-local/tokenizer.json')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peft_model_path = './peft-dialogue-summary-checkpoint-local'\n",
    "\n",
    "peft_trainer.model.save_pretrained(peft_model_path)\n",
    "tokenizer.save_pretrained(peft_model_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a1e3880d-4546-40b4-a182-d880ed36171d",
   "metadata": {},
   "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",
      "-rw-r--r-- 1 sagemaker-user users 14M Mar 21 17:27 ./peft-dialogue-summary-checkpoint-local/adapter_model.bin\n"
     ]
    }
   ],
   "source": [
    "!ls -alh ./peft-dialogue-summary-checkpoint-local/adapter_model.bin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07fa8cde-c4c0-4fd1-9c6f-e5a7112c244c",
   "metadata": {},
   "source": [
    "This is just a 14MB model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35999fd1-9f24-4f9e-9a4f-b0aca0b4f122",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2a3f4033-5149-4abb-9f15-5064d9b6b785",
   "metadata": {},
   "source": [
    "Inferencing from PEFT fine tuned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a6e94124-6ab0-41e5-95c2-1e820d5b1685",
   "metadata": {},
   "outputs": [],
   "source": [
    "peft_model_base = AutoModelForSeq2SeqLM.from_pretrained('google/flan-t5-base', torch_dtype=torch.bfloat16)\n",
    "tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-base')\n",
    "\n",
    "peft_model = PeftModel.from_pretrained(peft_model_base, \n",
    "                                      './peft-dialogue-summary-checkpoint-local',\n",
    "                                      torch_dtype=torch.bfloat16,\n",
    "                                      is_trainable=False) ## is_trainable mean just a forward pass jsut to get a sumamry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d7921355-5f78-4548-9b06-bfb2661bffd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Human Baseline summary: \n",
      "#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n",
      "\n",
      "Original Model Output \n",
      "#Person1#: I'm thinking about upgrading my computer.\n",
      "\n",
      "Peft Model Output \n",
      "#Person1#: I'm thinking of upgrading my computer.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "index = 200 ## randomly pick index\n",
    "dialogue = dataset['test'][index]['dialogue']\n",
    "human_baseline_summary = dataset['test'][index]['summary']\n",
    "\n",
    "prompt = f\"\"\"\n",
    "Summarize the following conversation.\n",
    "\n",
    "{dialogue}\n",
    "\n",
    "Summary:\n",
    "\"\"\"\n",
    "\n",
    "input_ids = tokenizer(prompt, return_tensors='pt').input_ids\n",
    "\n",
    "original_model_outputs = original_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200, num_beams=1))\n",
    "original_model_text_output = tokenizer.decode(original_model_outputs[0], skip_special_tokens=True)\n",
    "\n",
    "\n",
    "peft_model_outputs = peft_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200, num_beams=1))\n",
    "peft_model_text_output = tokenizer.decode(peft_model_outputs[0], skip_special_tokens=True)\n",
    "\n",
    "print(f'Human Baseline summary: \\n{human_baseline_summary}\\n')\n",
    "print(f'Original Model Output \\n{original_model_text_output}\\n')\n",
    "print(f'Peft Model Output \\n{peft_model_text_output}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfd7df36-a5ae-4918-9965-86d59e2036df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73067ff1-b2ef-4053-8780-e0756d19c62f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "65f94efe-702b-4772-bd95-5c1e97f7fb26",
   "metadata": {},
   "source": [
    "### 2.3. Evaluate model quantitavely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6aa703f2-8ded-41f2-b0d3-afde8c83a845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>human_baseline_summaries</th>\n",
       "      <th>original_model_summaries</th>\n",
       "      <th>peft_model_summaries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ms. Dawson helps #Person1# to write a memo to ...</td>\n",
       "      <td>This memo should be typed up and distributed t...</td>\n",
       "      <td>#Person1#: I need to take a dictation for you....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>In order to prevent employees from wasting tim...</td>\n",
       "      <td>This memo will be distributed to all employees...</td>\n",
       "      <td>#Person1#: I need to take a dictation for you....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ms. Dawson takes a dictation for #Person1# abo...</td>\n",
       "      <td>#Person1: I need to take a dictamenum for ever...</td>\n",
       "      <td>#Person1#: I need to take a dictation for you....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#Person2# arrives late because of traffic jam....</td>\n",
       "      <td>The traffic in this city is terrible.</td>\n",
       "      <td>The traffic jam at the Carrefour intersection ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#Person2# decides to follow #Person1#'s sugges...</td>\n",
       "      <td>The traffic jam near the Carrefour intersectio...</td>\n",
       "      <td>The traffic jam at the Carrefour intersection ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>#Person2# complains to #Person1# about the tra...</td>\n",
       "      <td>#Person1#: I'm finally here!</td>\n",
       "      <td>The traffic jam at the Carrefour intersection ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>#Person1# tells Kate that Masha and Hero get d...</td>\n",
       "      <td>Masha and Hero are having a separation for 2 m...</td>\n",
       "      <td>Masha and Hero are getting divorced.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>#Person1# tells Kate that Masha and Hero are g...</td>\n",
       "      <td>Masha and Hero are divorced.</td>\n",
       "      <td>Masha and Hero are getting divorced.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>#Person1# and Kate talk about the divorce betw...</td>\n",
       "      <td>Masha and Hero are divorced.</td>\n",
       "      <td>Masha and Hero are getting divorced.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>#Person1# and Brian are at the birthday party ...</td>\n",
       "      <td>Brian is having a party with his friends.</td>\n",
       "      <td>Brian's birthday is coming up.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            human_baseline_summaries  \\\n",
       "0  Ms. Dawson helps #Person1# to write a memo to ...   \n",
       "1  In order to prevent employees from wasting tim...   \n",
       "2  Ms. Dawson takes a dictation for #Person1# abo...   \n",
       "3  #Person2# arrives late because of traffic jam....   \n",
       "4  #Person2# decides to follow #Person1#'s sugges...   \n",
       "5  #Person2# complains to #Person1# about the tra...   \n",
       "6  #Person1# tells Kate that Masha and Hero get d...   \n",
       "7  #Person1# tells Kate that Masha and Hero are g...   \n",
       "8  #Person1# and Kate talk about the divorce betw...   \n",
       "9  #Person1# and Brian are at the birthday party ...   \n",
       "\n",
       "                            original_model_summaries  \\\n",
       "0  This memo should be typed up and distributed t...   \n",
       "1  This memo will be distributed to all employees...   \n",
       "2  #Person1: I need to take a dictamenum for ever...   \n",
       "3              The traffic in this city is terrible.   \n",
       "4  The traffic jam near the Carrefour intersectio...   \n",
       "5                       #Person1#: I'm finally here!   \n",
       "6  Masha and Hero are having a separation for 2 m...   \n",
       "7                       Masha and Hero are divorced.   \n",
       "8                       Masha and Hero are divorced.   \n",
       "9          Brian is having a party with his friends.   \n",
       "\n",
       "                                peft_model_summaries  \n",
       "0  #Person1#: I need to take a dictation for you....  \n",
       "1  #Person1#: I need to take a dictation for you....  \n",
       "2  #Person1#: I need to take a dictation for you....  \n",
       "3  The traffic jam at the Carrefour intersection ...  \n",
       "4  The traffic jam at the Carrefour intersection ...  \n",
       "5  The traffic jam at the Carrefour intersection ...  \n",
       "6               Masha and Hero are getting divorced.  \n",
       "7               Masha and Hero are getting divorced.  \n",
       "8               Masha and Hero are getting divorced.  \n",
       "9                     Brian's birthday is coming up.  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dialogue = dataset['test'][0:10]['dialogue']\n",
    "human_baseline_summaries = dataset['test'][0:10]['summary']\n",
    "\n",
    "original_model_summaries = []\n",
    "peft_model_summaries = []\n",
    "\n",
    "for _, dialogue in enumerate(dialogue):\n",
    "    prompt = f\"\"\"\n",
    "    Summarize the following conversations. \n",
    "\n",
    "    {dialogue}\n",
    "\n",
    "    Summary: \"\"\"\n",
    "\n",
    "    input_ids = tokenizer(prompt, return_tensors='pt').input_ids\n",
    "\n",
    "    original_model_outputs = original_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200, num_beams=1))\n",
    "    original_model_text_output = tokenizer.decode(original_model_outputs[0], skip_special_tokens=True)\n",
    "    original_model_summaries.append(original_model_text_output)\n",
    "    \n",
    "\n",
    "    peft_model_outputs = peft_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200, num_beams=1))\n",
    "    peft_model_text_output = tokenizer.decode(peft_model_outputs[0], skip_special_tokens=True)\n",
    "    peft_model_summaries.append(peft_model_text_output)\n",
    "\n",
    "\n",
    "zipped_summaries = list(zip(human_baseline_summaries, original_model_summaries,\n",
    "                           peft_model_summaries))\n",
    "\n",
    "df = pd.DataFrame(zipped_summaries, columns=['human_baseline_summaries', 'original_model_summaries', 'peft_model_summaries'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40e50fcb-12ef-4315-a466-2009538b4648",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "87ebd48c-debd-4c99-8459-ffd898411ddd",
   "metadata": {},
   "source": [
    "Calculate rouge Metrics score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9942e3fc-31ac-4803-972b-98e75db8404c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rouge = evaluate.load('rouge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "05c126db-9060-405b-a17b-4c313cfd0676",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Model: \n",
      "{'rouge1': 0.20830532190588022, 'rouge2': 0.07178140096618357, 'rougeL': 0.17268616035826098, 'rougeLsum': 0.17717816419240226}\n",
      "\n",
      "PEFT Model: \n",
      "{'rouge1': 0.2350105883395357, 'rouge2': 0.10417391304347826, 'rougeL': 0.2156123822571191, 'rougeLsum': 0.22044670728881255}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "original_model_results = rouge.compute(predictions=original_model_summaries, \n",
    "                                       references=human_baseline_summaries[0: len(original_model_summaries)],\n",
    "                                      use_aggregator=True,\n",
    "                                      use_stemmer=True)\n",
    "\n",
    "peft_model_results = rouge.compute(predictions=peft_model_summaries, \n",
    "                                    references=human_baseline_summaries[0: len(peft_model_summaries)],\n",
    "                                    use_aggregator=True,\n",
    "                                    use_stemmer=True)\n",
    "\n",
    "print(f'Original Model: \\n{original_model_results}\\n') \n",
    "print(f'PEFT Model: \\n{peft_model_results}\\n') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f5d16c-c438-4d04-a882-1bb31eebb2cc",
   "metadata": {},
   "source": [
    "PEFT rouge score is better than the original model. It uses the less resources and improves in comparison to original model. <br> \n",
    "These are just a few examples but you can  imagine its impact at scale and how much it saves in terms of resources, and times. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61ad4720-3045-4942-9216-c415659e1cb3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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