Sentiment_Analysis_with_Parameter_Efficient_Finetuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alturkim/nlp-notebooks/blob/main/Sentiment_Analysis_with_Parameter_Efficient_Finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PEFT: Parameter Efficient Finetuning by HuggingFace.\n",
        "\n",
        "This notebook finetune xlm-roberta-base using PEFT to train a sentiment classifier for Arabic. <br>\n",
        "\n",
        "\n",
        "Author: <br>\n",
        "Mustafa Alturki <br>\n",
        "https://github.com/alturkim"
      ],
      "metadata": {
        "id": "dOxQKVXrbL7O"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "9unczTpUG8w7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c79ff492-bb7a-4b8d-8699-0e8ea7e51052"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 KB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets evaluate transformers[sentencepiece]\n",
        "!pip install -q accelerate loralib tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkKeaVZKhsQ5",
        "outputId": "db1dafbe-9b5a-4e11-cc2b-a28a692fdf23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "UWwQeV3yiZyW"
      },
      "outputs": [],
      "source": [
        "!pip install -q bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GlhvMrFGsyE",
        "outputId": "68abaaa4-1490-4fe8-dbf9-d1b9c4ce5b0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "from transformers import AutoModel, AdamW, get_scheduler\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from datasets import load_dataset, arrow_dataset\n",
        "from datasets.arrow_dataset import Dataset\n",
        "\n",
        "import evaluate\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.optim import AdamW\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "from typing import Tuple, Dict\n",
        "\n",
        "\n",
        "import argparse\n",
        "import os\n",
        "\n",
        "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, \\\n",
        "set_peft_model_state_dict, LoraConfig, PeftType, \\\n",
        "PrefixTuningConfig, PromptEncoderConfig\n",
        "\n",
        "from transformers import AutoModelForSequenceClassification, \\\n",
        "get_linear_schedule_with_warmup, set_seed\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "G1YK34Hti-3W"
      },
      "outputs": [],
      "source": [
        "peft_type = PeftType.LORA\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "    task_type=\"SEQ_CLS\",\n",
        "    inference_mode=False,\n",
        "    r=8,\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0.1\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "1xwjDAilKk1T"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_preds):\n",
        "    # eval_preds is an EvalPrediction object which is a named tuple\n",
        "    logits, labels = eval_preds\n",
        "    predictions = torch.argmax(logits, axis=-1)\n",
        "\n",
        "    metric_names = [\"precision\", \"recall\", \"f1\", \"accuracy\"]\n",
        "    results = dict()\n",
        "    for m in metric_names:\n",
        "        metric = evaluate.load(m)\n",
        "        results = {**results, **metric.compute(predictions=predictions, references=labels)}\n",
        "    return results\n",
        "\n",
        "class Trainer:\n",
        "    def __init__(self, model, \n",
        "                 train_dataloader: DataLoader, eval_dataloader: DataLoader,\n",
        "                 num_train_epochs: int, lr: float, device: torch.device):\n",
        "        self.model = model\n",
        "        self.train_dataloader = train_dataloader\n",
        "        self.eval_dataloader = eval_dataloader\n",
        "        self.num_train_epochs = num_train_epochs\n",
        "        self.lr = lr\n",
        "        self.device = device\n",
        "        self.optimizer = AdamW(self.model.parameters(), lr=self.lr)\n",
        "\n",
        "        self.model.to(self.device)\n",
        "\n",
        "    def train(self) -> None:\n",
        "        num_training_steps = self.num_train_epochs * len(self.train_dataloader)\n",
        "        progress_bar = tqdm(range(num_training_steps))\n",
        "        lr_scheduler = get_linear_schedule_with_warmup(\n",
        "            optimizer=self.optimizer,\n",
        "            num_warmup_steps=0.06*(len(self.train_dataloader) * self.num_train_epochs),\n",
        "            num_training_steps=num_training_steps,\n",
        "        )\n",
        "\n",
        "        for epoch in range(self.num_train_epochs):\n",
        "            self.model.train()\n",
        "            for batch in self.train_dataloader:\n",
        "                batch = {k: v.to(device) for k, v in batch.items()}\n",
        "                output = self.model(**batch)\n",
        "                loss = output.loss\n",
        "                loss.backward()\n",
        "                self.optimizer.step()\n",
        "                lr_scheduler.step()\n",
        "                self.optimizer.zero_grad()\n",
        "                progress_bar.update(1)\n",
        "\n",
        "            # evaluate after each epoch\n",
        "            print(f\"evaluate ... epoch:{epoch}\")\n",
        "            results = self.evaluation()\n",
        "            print(results)\n",
        "\n",
        "    def evaluation(self) -> dict:\n",
        "        self.model.eval()\n",
        "        for batch in self.eval_dataloader:\n",
        "            batch = {k: v.to(device) for k, v in batch.items()}\n",
        "            with torch.no_grad():\n",
        "                output = self.model(**batch)\n",
        "                logits = output.logits\n",
        "            \n",
        "            labels = batch[\"labels\"]\n",
        "            return compute_metrics((logits, labels))\n",
        "        \n",
        "    def predict(self, dataset) -> dict:\n",
        "        self.model.eval()\n",
        "        dataloader = DataLoader(\n",
        "            dataset,\n",
        "            shuffle=False,\n",
        "            collate_fn=collate_fn,\n",
        "            batch_size=8)\n",
        "        for batch in dataloader:\n",
        "            batch = {k: v.to(device) for k, v in batch.items()}\n",
        "            with torch.no_grad():\n",
        "                output = self.model(**batch)\n",
        "                logits = output.logits\n",
        "            \n",
        "            labels = batch[\"labels\"]\n",
        "            return compute_metrics((logits, labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "kFE1Iw4o3pyM"
      },
      "outputs": [],
      "source": [
        "def tokenize(batch: arrow_dataset.Dataset):\n",
        "    processed = tokenizer(batch[\"text\"], truncation=True,\n",
        "                          padding=\"max_length\", return_tensors=\"pt\")\n",
        "    # processed.pop(\"input_ids\")\n",
        "    # processed.pop(\"attention_mask\")\n",
        "    return processed\n",
        "    \n",
        "def collate_fn(examples):\n",
        "    return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "hIW4nTyFqDan"
      },
      "outputs": [],
      "source": [
        "def train_eval_test_split(dataset : Dataset) -> Dict[str, Dataset]:\n",
        "    split_datasets = dict()\n",
        "    train_eval_test = dataset.train_test_split(test_size=0.4, stratify_by_column=\"labels\", seed=10)\n",
        "    split_datasets[\"train\"] = train_eval_test[\"train\"]\n",
        "    eval_test = train_eval_test[\"test\"].train_test_split(test_size=0.5, stratify_by_column=\"labels\", seed=10)\n",
        "    split_datasets[\"eval\"] = eval_test[\"train\"]\n",
        "    split_datasets[\"test\"] = eval_test[\"test\"]\n",
        "    return split_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "cgisFOcq0dM2"
      },
      "outputs": [],
      "source": [
        "def get_stat(dataset : Dataset) -> None:\n",
        "    labels = dataset[\"labels\"]\n",
        "    pos_count = sum([1 for i in labels if i==1])\n",
        "    neg_count = sum([1 for i in labels if i==0])\n",
        "\n",
        "    pos_pct = pos_count/(pos_count + neg_count)\n",
        "    neg_pct = neg_count/(pos_count + neg_count)\n",
        "    print(f\"There are: \\n{pos_count} positive reviews.\\n{neg_count} negative reviews.\")\n",
        "    print(f\"Percentage of positive reviews: {pos_pct*100:.2f}%\")\n",
        "    print(f\"Percentage of negative reviews: {neg_pct*100:.2f}%\")"
      ]
    },
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            "Downloading and preparing dataset ar_res_reviews/default to /root/.cache/huggingface/datasets/ar_res_reviews/default/0.0.0/f303714dc96c8056d45dca8950e5b7fe6ad59b88d0c095e07724e0484824031c...\n"
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          "data": {
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          "output_type": "stream",
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          "text": [
            "Dataset ar_res_reviews downloaded and prepared to /root/.cache/huggingface/datasets/ar_res_reviews/default/0.0.0/f303714dc96c8056d45dca8950e5b7fe6ad59b88d0c095e07724e0484824031c. Subsequent calls will reuse this data.\n",
            "train data stats\n",
            "There are: \n",
            "3567 positive reviews.\n",
            "1451 negative reviews.\n",
            "Percentage of positive reviews: 71.08%\n",
            "Percentage of negative reviews: 28.92%\n",
            "\n",
            "eval data stats\n",
            "There are: \n",
            "1190 positive reviews.\n",
            "483 negative reviews.\n",
            "Percentage of positive reviews: 71.13%\n",
            "Percentage of negative reviews: 28.87%\n",
            "\n",
            "test data stats\n",
            "There are: \n",
            "1189 positive reviews.\n",
            "484 negative reviews.\n",
            "Percentage of positive reviews: 71.07%\n",
            "Percentage of negative reviews: 28.93%\n",
            "\n"
          ]
        },
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          "text": [
            "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForSequenceClassification: ['lm_head.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.bias', 'roberta.pooler.dense.weight', 'lm_head.layer_norm.weight']\n",
            "- This IS expected if you are initializing XLMRobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing XLMRobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 887042 || all params: 278340098 || trainable%: 0.31868997904858104\n"
          ]
        },
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          "metadata": {}
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      "source": [
        "if __name__ == \"__main__\":\n",
        "    lm_checkpoint = \"xlm-roberta-base\"\n",
        "    device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "    raw_dataset = load_dataset(\"ar_res_reviews\", split=\"train\").rename_column(\"polarity\", \"labels\")\n",
        "\n",
        "    split_datasets = train_eval_test_split(raw_dataset)\n",
        "\n",
        "    for split, data in split_datasets.items():\n",
        "        print(f\"{split} data stats\")\n",
        "        get_stat(data)\n",
        "        print()\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(lm_checkpoint, padding_side=\"right\")\n",
        "    if getattr(tokenizer, \"pad_token_id\") is None:\n",
        "        tokenizer.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "    model = AutoModelForSequenceClassification.from_pretrained(lm_checkpoint, return_dict=True)\n",
        "    model = get_peft_model(model, peft_config)\n",
        "    model.print_trainable_parameters()\n",
        "\n",
        "    tokenized_datasets = dict()\n",
        "    for split in [\"train\", \"eval\", \"test\"]:\n",
        "        tokenized_datasets[split] = split_datasets[split].map(tokenize, batched=True, batch_size=8)\n",
        "        tokenized_datasets[split] = tokenized_datasets[split].remove_columns([\"text\", \"restaurant_id\", \"user_id\"])\n",
        "        tokenized_datasets[split] = tokenized_datasets[split].with_format(\"torch\")\n",
        "\n",
        "    train_dataloader = DataLoader(\n",
        "        tokenized_datasets[\"train\"],\n",
        "        # collate_fn=collate_fn,\n",
        "        shuffle=True,\n",
        "        batch_size=8)\n",
        "    \n",
        "    eval_dataloader = DataLoader(\n",
        "        tokenized_datasets[\"eval\"],\n",
        "        # collate_fn=collate_fn,\n",
        "        shuffle=False, \n",
        "        batch_size=8)\n"
      ]
    },
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          "text": [
            "training ... \n"
          ]
        },
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          "text": [
            "evaluate ... epoch:0\n"
          ]
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          "text": [
            "{'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'accuracy': 0.75}\n",
            "evaluate ... epoch:1\n",
            "{'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'accuracy': 0.875}\n",
            "evaluate ... epoch:2\n",
            "{'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'accuracy': 0.875}\n",
            "evaluate ... epoch:3\n"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'accuracy': 0.875}\n",
            "Evaluating after training ... \n",
            "{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'accuracy': 1.0}\n"
          ]
        }
      ],
      "source": [
        "if __name__ == \"__main__\":\n",
        "    trainer = Trainer(model=model, \n",
        "                      train_dataloader=train_dataloader, \n",
        "                      eval_dataloader=eval_dataloader,\n",
        "                      num_train_epochs=4, \n",
        "                      lr=3e-4,\n",
        "                      device=device)\n",
        "    # print(\"Evaluating before training ... \")\n",
        "    # result = trainer.predict(tokenized_datasets[\"test\"])\n",
        "    # print(result)\n",
        "    \n",
        "    print(\"training ... \")\n",
        "    trainer.train()\n",
        "\n",
        "    print(\"Evaluating after training ... \")\n",
        "    result = trainer.predict(tokenized_datasets[\"test\"])\n",
        "    print(result)\n",
        "    \n",
        "\n"
      ]
    }
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